From a99fd214dd0b7b97f2cd5d22ae4c7c91236aec6c Mon Sep 17 00:00:00 2001 From: Andrei Betlen Date: Tue, 15 Jul 2025 07:35:40 -0400 Subject: [PATCH 001/138] feat: Update llama.cpp --- llama_cpp/llama_cpp.py | 1 + vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index d13d60458a..bd8e36b792 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -200,6 +200,7 @@ """RWKV tokenizer based on greedy tokenization""" +# NOTE: Deprecated and will be removed in the future. (already gone in llama.cpp) # // pre-tokenization types # enum llama_vocab_pre_type { # LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0, diff --git a/vendor/llama.cpp b/vendor/llama.cpp index a0374a67e2..bdca38376f 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit a0374a67e2924f2e845cdc59dd67d9a44065a89c +Subproject commit bdca38376f7e8dd928defe01ce6a16218a64b040 From c8579d767aa53c48221f409c49df03e4019ed771 Mon Sep 17 00:00:00 2001 From: Alcoft <101468238+alcoftTAO@users.noreply.github.com> Date: Tue, 15 Jul 2025 11:37:45 +0000 Subject: [PATCH 002/138] fix: Better chat format for Qwen2.5-VL (#2040) --- llama_cpp/llama_chat_format.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index a288db7b0e..2a7d575ce6 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -3459,26 +3459,29 @@ class Qwen25VLChatHandler(Llava15ChatHandler): DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant." CHAT_FORMAT = ( - "<|im_start|>system\n" - "You are a helpful assistant.<|im_end|>\n" + #"{% set image_count = namespace(value=0) %}" + #"{% set video_count = namespace(value=0) %}" "{% for message in messages %}" - "{% if message['role'] == 'user' %}" - "<|im_start|>user\n" + "{% if loop.first and message['role'] != 'system' %}" + "<|im_start|>system\n" + "{{ self.DEFAULT_SYSTEM_MESSAGE }}<|im_end|>\n" + "{% endif %}" + "<|im_start|>{{ message['role'] }}\n" "{% if message['content'] is string %}" - "{{ message['content'] }}" + "{{ message['content'] }}<|im_end|>\n" "{% else %}" "{% for content in message['content'] %}" - "{% if content['type'] == 'text' %}" - "{{ content['text'] }}" - "{% elif content['type'] == 'image_url' %}" + "{% if content['type'] == 'image_url' %}" "{% if content.image_url is string %}" "{{ content.image_url }}" "{% else %}" "{{ content.image_url.url }}" "{% endif %}" + #"{% set image_count.value = image_count.value + 1 %}" + "{% elif content['type'] == 'text' %}" + "{{ content['text'] }}" "{% endif %}" "{% endfor %}" - "{% endif %}" "<|im_end|>\n" "{% endif %}" "{% endfor %}" From d9749cb9678d3ceb93b9ee63f4d723e3f01eb5c6 Mon Sep 17 00:00:00 2001 From: Andrei Betlen Date: Tue, 15 Jul 2025 07:39:46 -0400 Subject: [PATCH 003/138] chore: Bump version --- CHANGELOG.md | 5 +++++ llama_cpp/__init__.py | 2 +- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6017812bb2..88b9a1b45b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.13] + +- feat: Update llama.cpp to ggerganov/llama.cpp@ +- fix: Better chat format for Qwen2.5-VL by @alcoftTAO in #2040 + ## [0.3.12] - feat: Update llama.cpp to ggerganov/llama.cpp@a0374a67e2924f2e845cdc59dd67d9a44065a89c diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index b16bb7dc98..0c869dcae1 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.12" +__version__ = "0.3.13" From 95292e36c6cef7430cff0bda16578fe9f7303e01 Mon Sep 17 00:00:00 2001 From: Andrei Betlen Date: Wed, 16 Jul 2025 02:47:55 -0400 Subject: [PATCH 004/138] feat: Update llama.cpp --- llama_cpp/llama_cpp.py | 17 ++++++++++------- vendor/llama.cpp | 2 +- 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index bd8e36b792..b9e245e2f5 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -179,12 +179,13 @@ # enum llama_vocab_type { -# LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab -# LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback -# LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE -# LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece -# LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram -# LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization +# LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab +# LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback +# LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE +# LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece +# LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram +# LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization +# LLAMA_VOCAB_TYPE_PLAMO2 = 6, // PLaMo-2 tokenizer based on Aho-Corasick with dynamic programming # }; LLAMA_VOCAB_TYPE_NONE = 0 """For models without vocab""" @@ -198,6 +199,8 @@ """T5 tokenizer based on Unigram""" LLAMA_VOCAB_TYPE_RWKV = 5 """RWKV tokenizer based on greedy tokenization""" +LLAMA_VOCAB_TYPE_PLAMO2 = 6 +"""PLaMo-2 tokenizer based on Aho-Corasick with dynamic programming""" # NOTE: Deprecated and will be removed in the future. (already gone in llama.cpp) @@ -2171,7 +2174,7 @@ def llama_kv_self_seq_add( # // - lazily on next llama_decode() # // p0 < 0 : [0, p1] # // p1 < 0 : [p0, inf) -# DEPRECATED(void llama_kv_self_seq_div( +# DEPRECATED(LLAMA_API void llama_kv_self_seq_div( # struct llama_context * ctx, # llama_seq_id seq_id, # llama_pos p0, diff --git a/vendor/llama.cpp b/vendor/llama.cpp index bdca38376f..79e0b68c17 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit bdca38376f7e8dd928defe01ce6a16218a64b040 +Subproject commit 79e0b68c178656bb0632cb8602d2940b755077f8 From e1af05f43f57d2b660edfb77935dd2d2641ec602 Mon Sep 17 00:00:00 2001 From: Andrei Betlen Date: Fri, 18 Jul 2025 12:45:45 -0400 Subject: [PATCH 005/138] chore: Bump version --- CHANGELOG.md | 6 +++++- llama_cpp/__init__.py | 2 +- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 88b9a1b45b..e743c45843 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,9 +7,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.14] + +- feat: Update llama.cpp to ggerganov/llama.cpp@79e0b68c178656bb0632cb8602d2940b755077f8 + ## [0.3.13] -- feat: Update llama.cpp to ggerganov/llama.cpp@ +- feat: Update llama.cpp to ggerganov/llama.cpp@bdca38376f7e8dd928defe01ce6a16218a64b040 - fix: Better chat format for Qwen2.5-VL by @alcoftTAO in #2040 ## [0.3.12] diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index 0c869dcae1..409c59514f 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.13" +__version__ = "0.3.14" From 4f260288ff0f34801f1c2ede975093201c8dcf4c Mon Sep 17 00:00:00 2001 From: Andrei Betlen Date: Thu, 7 Aug 2025 06:40:18 -0700 Subject: [PATCH 006/138] feat: Update llama.cpp --- llama_cpp/_internals.py | 6 ++++++ llama_cpp/llama_cpp.py | 33 ++++++++++++++++++++++++++++++++- vendor/llama.cpp | 2 +- 3 files changed, 39 insertions(+), 2 deletions(-) diff --git a/llama_cpp/_internals.py b/llama_cpp/_internals.py index 18d733481f..b5175a7f2e 100644 --- a/llama_cpp/_internals.py +++ b/llama_cpp/_internals.py @@ -287,18 +287,24 @@ def pooling_type(self) -> int: return llama_cpp.llama_pooling_type(self.ctx) def kv_cache_clear(self): + assert self.memory is not None, "Memory is not initialized" llama_cpp.llama_memory_clear(self.memory, True) def kv_cache_seq_rm(self, seq_id: int, p0: int, p1: int): + assert self.memory is not None, "Memory is not initialized" + seq_id = seq_id if seq_id >= 0 else 0 llama_cpp.llama_memory_seq_rm(self.memory, seq_id, p0, p1) def kv_cache_seq_cp(self, seq_id_src: int, seq_id_dst: int, p0: int, p1: int): + assert self.memory is not None, "Memory is not initialized" llama_cpp.llama_memory_seq_cp(self.memory, seq_id_src, seq_id_dst, p0, p1) def kv_cache_seq_keep(self, seq_id: int): + assert self.memory is not None, "Memory is not initialized" llama_cpp.llama_memory_seq_keep(self.memory, seq_id) def kv_cache_seq_shift(self, seq_id: int, p0: int, p1: int, shift: int): + assert self.memory is not None, "Memory is not initialized" llama_cpp.llama_memory_seq_add(self.memory, seq_id, p0, p1, shift) def get_state_size(self) -> int: diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index b9e245e2f5..711d42a6ae 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -381,6 +381,7 @@ # //LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35, // removed from gguf files, use Q4_0 and runtime repack # LLAMA_FTYPE_MOSTLY_TQ1_0 = 36, // except 1d tensors # LLAMA_FTYPE_MOSTLY_TQ2_0 = 37, // except 1d tensors +# LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38, // except 1d tensors # # LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file # }; @@ -419,6 +420,7 @@ # LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35 LLAMA_FTYPE_MOSTLY_TQ1_0 = 36 LLAMA_FTYPE_MOSTLY_TQ2_0 = 37 +LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38 LLAMA_FTYPE_GUESSED = 1024 # enum llama_rope_scaling_type { @@ -691,6 +693,7 @@ class llama_model_kv_override(ctypes.Structure): # bool use_mmap; // use mmap if possible # bool use_mlock; // force system to keep model in RAM # bool check_tensors; // validate model tensor data +# bool use_extra_bufts; // use extra buffer types (used for weight repacking) # }; class llama_model_params(ctypes.Structure): """Parameters for llama_model @@ -708,7 +711,8 @@ class llama_model_params(ctypes.Structure): vocab_only (bool): only load the vocabulary, no weights use_mmap (bool): use mmap if possible use_mlock (bool): force system to keep model in RAM - check_tensors (bool): validate model tensor data""" + check_tensors (bool): validate model tensor data + use_extra_bufts (bool): use extra buffer types (used for weight repacking)""" if TYPE_CHECKING: devices: CtypesArray[ctypes.c_void_p] # NOTE: unused @@ -724,6 +728,7 @@ class llama_model_params(ctypes.Structure): use_mmap: bool use_mlock: bool check_tensors: bool + use_extra_bufts: bool _fields_ = [ ("devices", ctypes.c_void_p), # NOTE: unnused @@ -739,6 +744,7 @@ class llama_model_params(ctypes.Structure): ("use_mmap", ctypes.c_bool), ("use_mlock", ctypes.c_bool), ("check_tensors", ctypes.c_bool), + ("use_extra_bufts", ctypes.c_bool), ] @@ -787,6 +793,9 @@ class llama_model_params(ctypes.Structure): # bool swa_full; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055) # // NOTE: setting to false when n_seq_max > 1 can cause bad performance in some cases # // ref: https://github.com/ggml-org/llama.cpp/pull/13845#issuecomment-2924800573 +# bool kv_unified; // use a unified buffer across the input sequences when computing the attention +# // try to disable when n_seq_max > 1 for improved performance when the sequences do not share a large prefix +# // ref: https://github.com/ggml-org/llama.cpp/pull/14363 # }; class llama_context_params(ctypes.Structure): """Parameters for llama_context @@ -821,6 +830,7 @@ class llama_context_params(ctypes.Structure): no_perf (bool): whether to measure performance timings op_offload (bool): offload host tensor operations to device swa_full (bool): use full-size SWA cache + kv_unified (bool): use a unified buffer across the input sequences when computing the attention """ if TYPE_CHECKING: @@ -853,6 +863,7 @@ class llama_context_params(ctypes.Structure): no_perf: bool op_offload: bool swa_full: bool + kv_unified: bool _fields_ = [ ("n_ctx", ctypes.c_uint32), @@ -884,6 +895,7 @@ class llama_context_params(ctypes.Structure): ("no_perf", ctypes.c_bool), ("op_offload", ctypes.c_bool), ("swa_full", ctypes.c_bool), + ("kv_unified", ctypes.c_bool), ] @@ -1651,6 +1663,14 @@ def llama_model_is_recurrent(model: llama_model_p, /) -> bool: ... +# // Returns true if the model is diffusion-based (like LLaDA, Dream, etc.) +# LLAMA_API bool llama_model_is_diffusion(const struct llama_model * model); +@ctypes_function("llama_model_is_diffusion", [llama_model_p_ctypes], ctypes.c_bool) +def llama_model_is_diffusion(model: llama_model_p, /) -> bool: + """Returns true if the model is diffusion-based (like LLaDA, Dream, etc.)""" + ... + + # // Returns 0 on success # LLAMA_API uint32_t llama_model_quantize( # const char * fname_inp, @@ -2833,6 +2853,7 @@ def llama_synchronize(ctx: llama_context_p, /): # // in the order they have appeared in the batch. # // Rows: number of tokens for which llama_batch.logits[i] != 0 # // Cols: n_vocab +# // TODO: deprecate in favor of llama_get_logits_ith() (ref: https://github.com/ggml-org/llama.cpp/pull/14853#issuecomment-3113143522) # LLAMA_API float * llama_get_logits(struct llama_context * ctx); @ctypes_function( "llama_get_logits", [llama_context_p_ctypes], ctypes.POINTER(ctypes.c_float) @@ -2873,6 +2894,7 @@ def llama_get_logits_ith( # // in the order they have appeared in the batch. # // shape: [n_outputs*n_embd] # // Otherwise, returns NULL. +# // TODO: deprecate in favor of llama_get_embeddings_ith() (ref: https://github.com/ggml-org/llama.cpp/pull/14853#issuecomment-3113143522) # LLAMA_API float * llama_get_embeddings(struct llama_context * ctx); @ctypes_function( "llama_get_embeddings", [llama_context_p_ctypes], ctypes.POINTER(ctypes.c_float) @@ -3020,6 +3042,13 @@ def llama_vocab_pad(vocab: llama_vocab_p, /) -> llama_token: ... +# LLAMA_API llama_token llama_vocab_mask(const struct llama_vocab * vocab); // mask +@ctypes_function("llama_vocab_mask", [llama_vocab_p_ctypes], llama_token) +def llama_vocab_mask(vocab: llama_vocab_p, /) -> llama_token: + """mask""" + ... + + # LLAMA_API bool llama_vocab_get_add_bos(const struct llama_vocab * vocab); @ctypes_function( "llama_vocab_get_add_bos", @@ -4176,6 +4205,7 @@ def llama_log_set( # int32_t n_p_eval; # int32_t n_eval; +# int32_t n_reused; // number of times a ggml compute graph had been reused # }; class llama_perf_context_data(ctypes.Structure): _fields_ = [ @@ -4185,6 +4215,7 @@ class llama_perf_context_data(ctypes.Structure): ("t_eval_ms", ctypes.c_double), ("n_p_eval", ctypes.c_int32), ("n_eval", ctypes.c_int32), + ("n_reused", ctypes.c_int32), ] diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 79e0b68c17..9a96389544 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 79e0b68c178656bb0632cb8602d2940b755077f8 +Subproject commit 9a96389544a08fd829fccda28142ce2066017fde From d12ca479885bd530abf4543cd576b7eecb1b20e9 Mon Sep 17 00:00:00 2001 From: Andrei Betlen Date: Thu, 7 Aug 2025 06:42:58 -0700 Subject: [PATCH 007/138] misc: Update pypi downloads badge --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 088a23779e..382f7cbed6 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ [![PyPI](https://img.shields.io/pypi/v/llama-cpp-python)](https://pypi.org/project/llama-cpp-python/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/llama-cpp-python)](https://pypi.org/project/llama-cpp-python/) [![PyPI - License](https://img.shields.io/pypi/l/llama-cpp-python)](https://pypi.org/project/llama-cpp-python/) -[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-cpp-python)](https://pypi.org/project/llama-cpp-python/) +[![PyPI - Downloads](https://static.pepy.tech/badge/llama-cpp-python/month)](https://pepy.tech/projects/llama-cpp-python) [![Github All Releases](https://img.shields.io/github/downloads/abetlen/llama-cpp-python/total.svg?label=Github%20Downloads)]() Simple Python bindings for **@ggerganov's** [`llama.cpp`](https://github.com/ggerganov/llama.cpp) library. From 68e89e86c8135e865995d088ca7e5f4a38370c20 Mon Sep 17 00:00:00 2001 From: Andrei Betlen Date: Thu, 7 Aug 2025 06:43:30 -0700 Subject: [PATCH 008/138] misc: Add Python 3.13 classifier tag --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 9983ef777d..f5ae7b59c7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,7 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", ] From af637928db7351e030011085f818b034c6efc047 Mon Sep 17 00:00:00 2001 From: Andrei Betlen Date: Thu, 7 Aug 2025 06:47:26 -0700 Subject: [PATCH 009/138] feat: Add gpt-oss chat format support through strftime_now in chat format by @iamlemec --- llama_cpp/llama_chat_format.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index 2a7d575ce6..f738ab9bb4 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -8,6 +8,7 @@ import random import string +from datetime import datetime from contextlib import ExitStack from typing import ( Any, @@ -214,6 +215,10 @@ def __init__( lstrip_blocks=True, ).from_string(self.template) + @staticmethod + def strftime_now(f: str) -> str: + return datetime.now().strftime(f) + def __call__( self, *, @@ -237,6 +242,7 @@ def raise_exception(message: str): function_call=function_call, tools=tools, tool_choice=tool_choice, + strftime_now=self.strftime_now, ) stopping_criteria = None From 30ddd56e827e7fef6d5020809c574bdc0e166196 Mon Sep 17 00:00:00 2001 From: sergey21000 <67040429+sergey21000@users.noreply.github.com> Date: Thu, 7 Aug 2025 16:49:10 +0300 Subject: [PATCH 010/138] fix: rename op_offloat to op_offload in llama.py (#2046) --- llama_cpp/llama.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index 2e93670e60..71d94ebd82 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -92,7 +92,7 @@ def __init__( embedding: bool = False, offload_kqv: bool = True, flash_attn: bool = False, - op_offloat: Optional[bool] = None, + op_offload: Optional[bool] = None, swa_full: Optional[bool] = None, # Sampling Params no_perf: bool = False, @@ -174,7 +174,7 @@ def __init__( embedding: Embedding mode only. offload_kqv: Offload K, Q, V to GPU. flash_attn: Use flash attention. - op_offloat: offload host tensor operations to device + op_offload: offload host tensor operations to device swa_full: use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055) no_perf: Measure performance timings. last_n_tokens_size: Maximum number of tokens to keep in the last_n_tokens deque. @@ -343,8 +343,8 @@ def __init__( self.context_params.offload_kqv = offload_kqv self.context_params.flash_attn = flash_attn - if op_offloat is not None: - self.context_params.op_offloat = op_offloat + if op_offload is not None: + self.context_params.op_offload = op_offload if swa_full is not None: self.context_params.swa_full = swa_full @@ -2097,7 +2097,7 @@ def __getstate__(self): embedding=self.context_params.embeddings, offload_kqv=self.context_params.offload_kqv, flash_attn=self.context_params.flash_attn, - op_offloat=self.context_params.op_offloat, + op_offload=self.context_params.op_offload, swa_full=self.context_params.swa_full, # Sampling Params no_perf=self.context_params.no_perf, From dfc9bf503bb7d4be166410e525971509373bee0e Mon Sep 17 00:00:00 2001 From: Andrei Betlen Date: Thu, 7 Aug 2025 06:53:07 -0700 Subject: [PATCH 011/138] chore: Bump version --- CHANGELOG.md | 6 ++++++ llama_cpp/__init__.py | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index e743c45843..929363721f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.15] + +- feat: Update llama.cpp to ggerganov/llama.cpp@9a96389544a08fd829fccda28142ce2066017fde +- feat: Add gpt-oss chat format support through strftime_now in chat format by @iamlemec in af637928db7351e030011085f818b034c6efc047 +- fix: rename op_offloat to op_offload in llama.py by @sergey21000 in #2046 + ## [0.3.14] - feat: Update llama.cpp to ggerganov/llama.cpp@79e0b68c178656bb0632cb8602d2940b755077f8 diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index 409c59514f..1e256a776c 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.14" +__version__ = "0.3.15" From ce6fd8bbc808196dea90dd259bbcd4301c69b0b5 Mon Sep 17 00:00:00 2001 From: Andrei Betlen Date: Thu, 14 Aug 2025 21:52:49 -0700 Subject: [PATCH 012/138] feat: Update llama.cpp --- vendor/llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 9a96389544..4227c9be42 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 9a96389544a08fd829fccda28142ce2066017fde +Subproject commit 4227c9be4268ac844921b90f31595f81236bd317 From c37132bac860fcc333255c36313f89c4f49d4c8d Mon Sep 17 00:00:00 2001 From: Andrei Betlen Date: Thu, 14 Aug 2025 21:55:21 -0700 Subject: [PATCH 013/138] chore: Bump version --- CHANGELOG.md | 4 ++++ llama_cpp/__init__.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 929363721f..16954eb881 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.16] + +- feat: Update llama.cpp to ggerganov/llama.cpp@4227c9be4268ac844921b90f31595f81236bd317 + ## [0.3.15] - feat: Update llama.cpp to ggerganov/llama.cpp@9a96389544a08fd829fccda28142ce2066017fde diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index 1e256a776c..c1dde70468 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.15" +__version__ = "0.3.16" From ca3b00a204d5a48b1c54eb609b20203dcdaa87be Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 22 Mar 2026 15:20:48 -0700 Subject: [PATCH 014/138] fix(ci): Rename `huggingface-cli` to `hf` (#2149) * Fix model download in test workflow * Use hf CLI in test workflow * Use hf CLI name in CI and docs * Reference PR in changelog --- .github/workflows/test.yaml | 2 +- CHANGELOG.md | 2 ++ README.md | 2 +- 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 95f6e5a272..1d2b1983cc 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -22,7 +22,7 @@ jobs: - name: Install huggingface-hub run: pip install huggingface-hub - name: Download model - run: huggingface-cli download ${{ env.REPO_ID }} ${{ env.MODEL_FILE }} + run: hf download ${{ env.REPO_ID }} ${{ env.MODEL_FILE }} - name: Cache model uses: actions/cache@v4 with: diff --git a/CHANGELOG.md b/CHANGELOG.md index 16954eb881..1f577c1a41 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix(ci): Use the `hf` CLI instead of the deprecated `huggingface-cli` name in GitHub Actions and docs by @abetlen in #2149 + ## [0.3.16] - feat: Update llama.cpp to ggerganov/llama.cpp@4227c9be4268ac844921b90f31595f81236bd317 diff --git a/README.md b/README.md index 382f7cbed6..d2ba297caf 100644 --- a/README.md +++ b/README.md @@ -328,7 +328,7 @@ llm = Llama.from_pretrained( ) ``` -By default [`from_pretrained`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.from_pretrained) will download the model to the huggingface cache directory, you can then manage installed model files with the [`huggingface-cli`](https://huggingface.co/docs/huggingface_hub/en/guides/cli) tool. +By default [`from_pretrained`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.from_pretrained) will download the model to the huggingface cache directory, you can then manage installed model files with the [`hf`](https://huggingface.co/docs/huggingface_hub/en/guides/cli) tool. ### Chat Completion From 9f661ff2cf63e72aea328daab15e521230dd20b0 Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 22 Mar 2026 16:10:47 -0700 Subject: [PATCH 015/138] fix(ci): Fix macos tests, support both Intel and Apple Silicon testing (#2150) * fix(ci): use supported macos runner label * fix(ci): add apple silicon macos test coverage * fix(ci): run standard macos tests on apple silicon * fix(ci): simplify apple silicon macos install * fix(ci): disable ggml native on apple silicon runner * docs: update changelog for macos ci runner fix --- .github/workflows/test.yaml | 11 +++++------ CHANGELOG.md | 1 + 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 1d2b1983cc..af4cacac40 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -96,7 +96,7 @@ jobs: build-macos: needs: download-model - runs-on: macos-13 + runs-on: macos-15 strategy: matrix: python-version: ["3.9", "3.10", "3.11", "3.12"] @@ -127,17 +127,16 @@ jobs: run: | python3 -m pip install --upgrade pip python3 -m pip install uv - python3 -m uv pip install -e .[all] --verbose - CMAKE_ARGS="-DLLAMA_METAL=off" python3 -m uv pip install .[all] --verbose + CMAKE_ARGS="-DGGML_NATIVE=off" python3 -m uv pip install -e .[all] --verbose shell: bash - name: Test with pytest run: | python3 -m pytest - build-macos-metal: + build-macos-intel: needs: download-model - runs-on: macos-13 + runs-on: macos-15-intel steps: - uses: actions/checkout@v4 with: @@ -163,7 +162,7 @@ jobs: - name: Install dependencies run: | python3 -m pip install --upgrade pip - CMAKE_ARGS="-DLLAMA_METAL=on" python3 -m pip install .[all] --verbose + python3 -m pip install .[all] --verbose shell: bash - name: Test with pytest diff --git a/CHANGELOG.md b/CHANGELOG.md index 1f577c1a41..9bc1c9a0a1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix(ci): Run macOS CI on supported Apple Silicon and Intel runners by @abetlen in #2150 - fix(ci): Use the `hf` CLI instead of the deprecated `huggingface-cli` name in GitHub Actions and docs by @abetlen in #2149 ## [0.3.16] From a9b4a067300c89857334195518e0bb9430d1c059 Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 22 Mar 2026 16:12:02 -0700 Subject: [PATCH 016/138] misc: Add Ruff formatting (#2148) * Add Ruff formatting and safe lint baseline * Update changelog for Ruff setup --- .github/workflows/lint.yaml | 29 ++ .gitignore | 1 + CHANGELOG.md | 1 + Makefile | 10 + README.md | 14 + llama_cpp/_ggml.py | 2 +- llama_cpp/_internals.py | 98 ++++--- llama_cpp/_logger.py | 5 +- llama_cpp/llama.py | 22 +- llama_cpp/llama_cache.py | 6 +- llama_cpp/llama_chat_format.py | 192 ++++++++----- llama_cpp/llama_cpp.py | 470 ++++++++++++++------------------ llama_cpp/llama_grammar.py | 60 ++-- llama_cpp/llava_cpp.py | 28 +- llama_cpp/mtmd_cpp.py | 94 ++++--- llama_cpp/server/app.py | 6 +- llama_cpp/server/cli.py | 4 +- llama_cpp/server/model.py | 12 +- pyproject.toml | 13 +- tests/test_llama.py | 22 +- tests/test_llama_chat_format.py | 19 +- tests/test_llama_speculative.py | 9 +- 22 files changed, 607 insertions(+), 510 deletions(-) create mode 100644 .github/workflows/lint.yaml diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml new file mode 100644 index 0000000000..8b3e6322dc --- /dev/null +++ b/.github/workflows/lint.yaml @@ -0,0 +1,29 @@ +name: Lint + +on: + pull_request: + branches: + - main + push: + branches: + - main + +jobs: + ruff: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install Ruff + run: python -m pip install "ruff>=0.15.7" + + - name: Lint with Ruff + run: python -m ruff check llama_cpp tests + + - name: Check formatting with Ruff + run: python -m ruff format --check llama_cpp tests diff --git a/.gitignore b/.gitignore index 9d68dbcd9d..ff773c6684 100644 --- a/.gitignore +++ b/.gitignore @@ -66,6 +66,7 @@ coverage.xml *.py,cover .hypothesis/ .pytest_cache/ +.ruff_cache/ cover/ # Translations diff --git a/CHANGELOG.md b/CHANGELOG.md index 9bc1c9a0a1..7044f44d65 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- chore(dev): Add Ruff-based formatting and a safe lint baseline, and run it in CI for pull requests and pushes to `main` - fix(ci): Run macOS CI on supported Apple Silicon and Intel runners by @abetlen in #2150 - fix(ci): Use the `hf` CLI instead of the deprecated `huggingface-cli` name in GitHub Actions and docs by @abetlen in #2149 diff --git a/Makefile b/Makefile index 26ddf2c7a1..8e6cae2c1f 100644 --- a/Makefile +++ b/Makefile @@ -67,6 +67,14 @@ deploy.gh-docs: test: python3 -m pytest --full-trace -v +lint: + python3 -m ruff check llama_cpp tests + python3 -m ruff format --check llama_cpp tests + +format: + python3 -m ruff check --fix llama_cpp tests + python3 -m ruff format llama_cpp tests + docker: docker build -t llama-cpp-python:latest -f docker/simple/Dockerfile . @@ -93,5 +101,7 @@ clean: build.sdist \ deploy.pypi \ deploy.gh-docs \ + lint \ + format \ docker \ clean diff --git a/README.md b/README.md index d2ba297caf..b57c958076 100644 --- a/README.md +++ b/README.md @@ -752,6 +752,9 @@ pip install --upgrade pip # Install with pip pip install -e . +# install development tooling (tests, docs, ruff) +pip install -e '.[dev]' + # if you want to use the fastapi / openapi server pip install -e '.[server]' @@ -768,6 +771,17 @@ Now try running the tests pytest ``` +And check formatting / linting before opening a PR: + +```bash +python -m ruff check llama_cpp tests +python -m ruff format --check llama_cpp tests + +# or use the Makefile targets +make lint +make format +``` + There's a `Makefile` available with useful targets. A typical workflow would look like this: diff --git a/llama_cpp/_ggml.py b/llama_cpp/_ggml.py index 5bee8a93b5..5ece01e032 100644 --- a/llama_cpp/_ggml.py +++ b/llama_cpp/_ggml.py @@ -2,6 +2,7 @@ This module provides a minimal interface for working with ggml tensors from llama-cpp-python """ + import os import pathlib @@ -9,4 +10,3 @@ libggml_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" libggml = ctypes_ext.load_shared_library("ggml", libggml_base_path) - diff --git a/llama_cpp/_internals.py b/llama_cpp/_internals.py index b5175a7f2e..b520b7ea5f 100644 --- a/llama_cpp/_internals.py +++ b/llama_cpp/_internals.py @@ -355,7 +355,9 @@ def get_embeddings_seq(self, seq_id: int): # Sampling functions - deprecated, use LlamaSampler instead def set_rng_seed(self, seed: int): - raise NotImplementedError("set_rng_seed is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "set_rng_seed is deprecated, use LlamaSampler instead" + ) def sample_repetition_penalties( self, @@ -366,30 +368,44 @@ def sample_repetition_penalties( penalty_freq: float, penalty_present: float, ): - raise NotImplementedError("sample_repetition_penalties is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "sample_repetition_penalties is deprecated, use LlamaSampler instead" + ) def sample_softmax(self, candidates: "_LlamaTokenDataArray"): - raise NotImplementedError("sample_softmax is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "sample_softmax is deprecated, use LlamaSampler instead" + ) def sample_top_k(self, candidates: "_LlamaTokenDataArray", k: int, min_keep: int): - raise NotImplementedError("sample_top_k is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "sample_top_k is deprecated, use LlamaSampler instead" + ) def sample_top_p(self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int): - raise NotImplementedError("sample_top_p is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "sample_top_p is deprecated, use LlamaSampler instead" + ) def sample_min_p(self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int): - raise NotImplementedError("sample_min_p is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "sample_min_p is deprecated, use LlamaSampler instead" + ) def sample_typical( self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int ): - raise NotImplementedError("sample_typical is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "sample_typical is deprecated, use LlamaSampler instead" + ) def sample_temp(self, candidates: "_LlamaTokenDataArray", temp: float): raise NotImplementedError("sample_temp is deprecated, use LlamaSampler instead") def sample_grammar(self, candidates: "_LlamaTokenDataArray", grammar: LlamaGrammar): - raise NotImplementedError("sample_grammar is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "sample_grammar is deprecated, use LlamaSampler instead" + ) def sample_token_mirostat( self, @@ -399,7 +415,9 @@ def sample_token_mirostat( m: int, mu: llama_cpp.CtypesPointerOrRef[ctypes.c_float], ) -> int: - raise NotImplementedError("sample_token_mirostat is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "sample_token_mirostat is deprecated, use LlamaSampler instead" + ) def sample_token_mirostat_v2( self, @@ -408,17 +426,25 @@ def sample_token_mirostat_v2( eta: float, mu: llama_cpp.CtypesPointerOrRef[ctypes.c_float], ) -> int: - raise NotImplementedError("sample_token_mirostat_v2 is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "sample_token_mirostat_v2 is deprecated, use LlamaSampler instead" + ) def sample_token_greedy(self, candidates: "_LlamaTokenDataArray") -> int: - raise NotImplementedError("sample_token_greedy is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "sample_token_greedy is deprecated, use LlamaSampler instead" + ) def sample_token(self, candidates: "_LlamaTokenDataArray") -> int: - raise NotImplementedError("sample_token is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "sample_token is deprecated, use LlamaSampler instead" + ) # Grammar def grammar_accept_token(self, grammar: LlamaGrammar, token: int): - raise NotImplementedError("grammar_accept_token is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "grammar_accept_token is deprecated, use LlamaSampler instead" + ) def reset_timings(self): llama_cpp.llama_perf_context_reset(self.ctx) @@ -602,16 +628,16 @@ def sample( logits_array: Optional[npt.NDArray[np.single]] = None, ): # This method is deprecated in favor of using LlamaSampler directly - raise NotImplementedError("LlamaSamplingContext.sample is deprecated, use LlamaSampler instead") + raise NotImplementedError( + "LlamaSamplingContext.sample is deprecated, use LlamaSampler instead" + ) def accept(self, ctx_main: LlamaContext, id: int, apply_grammar: bool): self.prev.append(id) class CustomSampler: - def __init__( - self, apply_func: Callable[[llama_cpp.llama_token_data_array], None] - ): + def __init__(self, apply_func: Callable[[llama_cpp.llama_token_data_array], None]): self.apply_func = apply_func def apply_wrapper( @@ -723,20 +749,20 @@ def add_grammar(self, model: LlamaModel, grammar: LlamaGrammar): llama_cpp.llama_sampler_chain_add(self.sampler, sampler) def add_grammar_lazy_patterns( - self, - model: LlamaModel, + self, + model: LlamaModel, grammar: LlamaGrammar, trigger_patterns: List[str], - trigger_tokens: List[int] + trigger_tokens: List[int], ): # Convert patterns to C array pattern_ptrs = (ctypes.c_char_p * len(trigger_patterns))() for i, pattern in enumerate(trigger_patterns): pattern_ptrs[i] = pattern.encode("utf-8") - + # Convert tokens to C array token_array = (llama_cpp.llama_token * len(trigger_tokens))(*trigger_tokens) - + sampler = llama_cpp.llama_sampler_init_grammar_lazy_patterns( model.vocab, grammar._grammar.encode("utf-8"), @@ -744,7 +770,7 @@ def add_grammar_lazy_patterns( pattern_ptrs, len(trigger_patterns), token_array, - len(trigger_tokens) + len(trigger_tokens), ) llama_cpp.llama_sampler_chain_add(self.sampler, sampler) @@ -771,13 +797,13 @@ def add_dry( dry_base: float, dry_allowed_length: int, dry_penalty_last_n: int, - seq_breakers: List[str] + seq_breakers: List[str], ): # Convert seq_breakers to C array breaker_ptrs = (ctypes.c_char_p * len(seq_breakers))() for i, breaker in enumerate(seq_breakers): breaker_ptrs[i] = breaker.encode("utf-8") - + sampler = llama_cpp.llama_sampler_init_dry( model.vocab, n_ctx_train, @@ -786,25 +812,19 @@ def add_dry( dry_allowed_length, dry_penalty_last_n, breaker_ptrs, - len(seq_breakers) + len(seq_breakers), ) llama_cpp.llama_sampler_chain_add(self.sampler, sampler) - def add_logit_bias( - self, - n_vocab: int, - logit_bias: Dict[int, float] - ): + def add_logit_bias(self, n_vocab: int, logit_bias: Dict[int, float]): # Convert logit_bias dict to C array bias_array = (llama_cpp.llama_logit_bias * len(logit_bias))() for i, (token, bias) in enumerate(logit_bias.items()): bias_array[i].token = token bias_array[i].bias = bias - + sampler = llama_cpp.llama_sampler_init_logit_bias( - n_vocab, - len(logit_bias), - bias_array + n_vocab, len(logit_bias), bias_array ) llama_cpp.llama_sampler_chain_add(self.sampler, sampler) @@ -838,15 +858,17 @@ def reset(self): def clone(self): # NOTE: Custom samplers cannot be cloned due to Python callback limitations if self.custom_samplers: - raise NotImplementedError("Cannot clone LlamaSampler that contains custom samplers") - + raise NotImplementedError( + "Cannot clone LlamaSampler that contains custom samplers" + ) + cloned_sampler = llama_cpp.llama_sampler_clone(self.sampler) # Create a new wrapper around the cloned sampler new_sampler = LlamaSampler.__new__(LlamaSampler) new_sampler.sampler = cloned_sampler new_sampler.custom_samplers = [] new_sampler._exit_stack = ExitStack() - + def free_sampler(): if new_sampler.sampler is not None: llama_cpp.llama_sampler_free(new_sampler.sampler) diff --git a/llama_cpp/_logger.py b/llama_cpp/_logger.py index 787b3f108e..31d89d0995 100644 --- a/llama_cpp/_logger.py +++ b/llama_cpp/_logger.py @@ -25,6 +25,7 @@ _last_log_level = GGML_LOG_LEVEL_TO_LOGGING_LEVEL[0] + # typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * user_data); @llama_cpp.llama_log_callback def llama_log_callback( @@ -34,7 +35,9 @@ def llama_log_callback( ): # TODO: Correctly implement continue previous log global _last_log_level - log_level = GGML_LOG_LEVEL_TO_LOGGING_LEVEL[level] if level != 5 else _last_log_level + log_level = ( + GGML_LOG_LEVEL_TO_LOGGING_LEVEL[level] if level != 5 else _last_log_level + ) if logger.level <= GGML_LOG_LEVEL_TO_LOGGING_LEVEL[level]: print(text.decode("utf-8"), end="", flush=True, file=sys.stderr) _last_log_level = log_level diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index 71d94ebd82..21a7430a01 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -934,7 +934,8 @@ def generate( sample_idx += 1 if stopping_criteria is not None and stopping_criteria( - self._input_ids[: sample_idx], self._scores[sample_idx - self.n_tokens, :] + self._input_ids[:sample_idx], + self._scores[sample_idx - self.n_tokens, :], ): return tokens_or_none = yield token @@ -1157,9 +1158,9 @@ def _create_completion( bos_token_id: int = self.token_bos() cls_token_id: int = self._model.token_cls() sep_token_id: int = self._model.token_sep() - prefix_token_id: int = 0 # self._model.token_prefix() # TODO: Fix - middle_token_id: int = 0 # self._model.token_middle() # TODO: Fix - suffix_token_id: int = 0 # self._model.token_suffix() # TODO: Fix + prefix_token_id: int = 0 # self._model.token_prefix() # TODO: Fix + middle_token_id: int = 0 # self._model.token_middle() # TODO: Fix + suffix_token_id: int = 0 # self._model.token_suffix() # TODO: Fix add_space_prefix: bool = ( self.metadata.get("tokenizer.ggml.add_space_prefix", "true") == "true" ) @@ -1315,7 +1316,7 @@ def logit_bias_processor( if seed is not None: self.set_seed(seed) else: - self.set_seed(random.Random(self._seed).randint(0, 2 ** 32)) + self.set_seed(random.Random(self._seed).randint(0, 2**32)) finish_reason = "length" multibyte_fix = 0 @@ -2056,7 +2057,10 @@ def create_chat_completion_openai_v1( stream = kwargs.get("stream", False) # type: ignore assert isinstance(stream, bool) if stream: - return (ChatCompletionChunk(**chunk) for chunk in self.create_chat_completion(*args, **kwargs)) # type: ignore + return ( + ChatCompletionChunk(**chunk) + for chunk in self.create_chat_completion(*args, **kwargs) + ) # type: ignore else: return ChatCompletion(**self.create_chat_completion(*args, **kwargs)) # type: ignore except ImportError: @@ -2318,7 +2322,11 @@ def from_pretrained( if additional_files: for additonal_file_name in additional_files: # find the additional shard file: - matching_additional_files = [file for file in file_list if fnmatch.fnmatch(file, additonal_file_name)] + matching_additional_files = [ + file + for file in file_list + if fnmatch.fnmatch(file, additonal_file_name) + ] if len(matching_additional_files) == 0: raise ValueError( diff --git a/llama_cpp/llama_cache.py b/llama_cpp/llama_cache.py index e059e98e1b..5220c79337 100644 --- a/llama_cpp/llama_cache.py +++ b/llama_cpp/llama_cache.py @@ -52,9 +52,9 @@ class LlamaRAMCache(BaseLlamaCache): def __init__(self, capacity_bytes: int = (2 << 30)): super().__init__(capacity_bytes) self.capacity_bytes = capacity_bytes - self.cache_state: OrderedDict[ - Tuple[int, ...], "llama_cpp.llama.LlamaState" - ] = OrderedDict() + self.cache_state: OrderedDict[Tuple[int, ...], "llama_cpp.llama.LlamaState"] = ( + OrderedDict() + ) @property def cache_size(self): diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index f738ab9bb4..8e8ac7bb30 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -286,11 +286,15 @@ def _convert_text_completion_logprobs_to_chat( } for top_token, top_logprob in top_logprobs.items() ], - } for (token, logprob, top_logprobs) in zip(logprobs["tokens"], logprobs["token_logprobs"], logprobs["top_logprobs"]) + } + for (token, logprob, top_logprobs) in zip( + logprobs["tokens"], logprobs["token_logprobs"], logprobs["top_logprobs"] + ) ], "refusal": None, } + def _convert_text_completion_to_chat( completion: llama_types.Completion, ) -> llama_types.ChatCompletion: @@ -307,7 +311,9 @@ def _convert_text_completion_to_chat( "role": "assistant", "content": completion["choices"][0]["text"], }, - "logprobs": _convert_text_completion_logprobs_to_chat(completion["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + completion["choices"][0]["logprobs"] + ), "finish_reason": completion["choices"][0]["finish_reason"], } ], @@ -351,7 +357,9 @@ def _convert_text_completion_chunks_to_chat( if chunk["choices"][0]["finish_reason"] is None else {} ), - "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "finish_reason": chunk["choices"][0]["finish_reason"], } ], @@ -368,7 +376,9 @@ def _convert_completion_to_chat( llama_types.CreateChatCompletionResponse, Iterator[llama_types.ChatCompletionChunk] ]: if stream: - chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks # type: ignore + chunks: Iterator[llama_types.CreateCompletionStreamResponse] = ( + completion_or_chunks # type: ignore + ) return _convert_text_completion_chunks_to_chat(chunks) else: completion: llama_types.Completion = completion_or_chunks # type: ignore @@ -414,7 +424,9 @@ def _convert_completion_to_chat_function( } ], }, - "logprobs": _convert_text_completion_logprobs_to_chat(completion["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + completion["choices"][0]["logprobs"] + ), "finish_reason": "tool_calls", } ], @@ -422,7 +434,9 @@ def _convert_completion_to_chat_function( } return chat_completion else: - chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks # type: ignore + chunks: Iterator[llama_types.CreateCompletionStreamResponse] = ( + completion_or_chunks # type: ignore + ) def _stream_response_to_function_stream( chunks: Iterator[llama_types.CreateCompletionStreamResponse], @@ -467,7 +481,9 @@ def _stream_response_to_function_stream( { "index": 0, "finish_reason": None, - "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": None, "content": None, @@ -504,7 +520,9 @@ def _stream_response_to_function_stream( { "index": 0, "finish_reason": None, - "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": None, "content": None, @@ -702,7 +720,7 @@ def chat_completion_handler( def hf_autotokenizer_to_chat_formatter( - pretrained_model_name_or_path: Union[str, os.PathLike[str]] + pretrained_model_name_or_path: Union[str, os.PathLike[str]], ) -> ChatFormatter: # https://huggingface.co/docs/transformers/main/chat_templating # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#instruction-format @@ -727,7 +745,7 @@ def format_autotokenizer( def hf_autotokenizer_to_chat_completion_handler( - pretrained_model_name_or_path: Union[str, os.PathLike[str]] + pretrained_model_name_or_path: Union[str, os.PathLike[str]], ) -> LlamaChatCompletionHandler: chat_formatter = hf_autotokenizer_to_chat_formatter(pretrained_model_name_or_path) return chat_formatter_to_chat_completion_handler(chat_formatter) @@ -1552,9 +1570,9 @@ def prepare_messages_for_inference( message["name"] = f"functions.{message['name']}" # Function call requests by assistant if "function_call" in message: - message["function_call"][ - "name" - ] = f"functions.{message['function_call']['name']}" + message["function_call"]["name"] = ( + f"functions.{message['function_call']['name']}" + ) all_messages.append(message) all_messages.append( @@ -1632,7 +1650,9 @@ def message_to_str(msg: llama_types.ChatCompletionRequestMessage): logits_processor=logits_processor, grammar=grammar, ) - return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream) # type: ignore + return _convert_completion_to_chat( + completion_or_completion_chunks, stream=stream + ) # type: ignore if function_call is None or ( isinstance(function_call, str) and function_call == "auto" @@ -1748,7 +1768,9 @@ def message_to_str(msg: llama_types.ChatCompletionRequestMessage): } ], }, - "logprobs": _convert_text_completion_logprobs_to_chat(completion["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + completion["choices"][0]["logprobs"] + ), "finish_reason": "tool_calls", } ], @@ -1789,9 +1811,9 @@ def functionary_v1_v2_chat_handler( SYSTEM_MESSAGE = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. The assistant calls functions with appropriate input when necessary""" tokenizer = llama.tokenizer_ - assert hasattr( - tokenizer, "hf_tokenizer" - ), "Please provide a valid hf_tokenizer_path from https://huggingface.co/meetkai when initializing the Llama class" + assert hasattr(tokenizer, "hf_tokenizer"), ( + "Please provide a valid hf_tokenizer_path from https://huggingface.co/meetkai when initializing the Llama class" + ) from transformers import AutoTokenizer if "<|START_OF_FUNCTION_CALL|>" in tokenizer.hf_tokenizer.additional_special_tokens: @@ -1941,9 +1963,9 @@ def prepare_messages_for_inference( message["name"] = f"functions.{message['name']}" # Function call requests by assistant if "function_call" in message: - message["function_call"][ - "name" - ] = f"functions.{message['function_call']['name']}" + message["function_call"]["name"] = ( + f"functions.{message['function_call']['name']}" + ) all_messages.append(message) if version == "v1": @@ -2005,7 +2027,9 @@ def prepare_messages_for_inference( completion_or_completion_chunks["choices"][0]["text"] = ( completion_or_completion_chunks["choices"][0]["text"].lstrip() ) - return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream) # type: ignore + return _convert_completion_to_chat( + completion_or_completion_chunks, stream=stream + ) # type: ignore def get_grammar(function_call): function_body = None @@ -2160,7 +2184,9 @@ def generate_streaming(tools, functions, function_call, prompt): choices=[ { "index": 0, - "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": None, "content": None, @@ -2262,7 +2288,9 @@ def generate_streaming(tools, functions, function_call, prompt): choices=[ { "index": 0, - "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": "assistant", "content": None, @@ -2300,7 +2328,9 @@ def generate_streaming(tools, functions, function_call, prompt): choices=[ { "index": 0, - "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": "assistant", "content": buffer.pop(0), @@ -2323,7 +2353,9 @@ def generate_streaming(tools, functions, function_call, prompt): choices=[ { "index": 0, - "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": "assistant", "content": ( @@ -2409,7 +2441,9 @@ def generate_streaming(tools, functions, function_call, prompt): choices=[ { "index": 0, - "logprobs": _convert_text_completion_logprobs_to_chat(chunk["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": None, "content": None, @@ -2643,7 +2677,9 @@ def generate_streaming(tools, functions, function_call, prompt): choices=[ { "index": 0, - "logprobs": _convert_text_completion_logprobs_to_chat(completion["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + completion["choices"][0]["logprobs"] + ), "message": { "role": "assistant", "content": None if content == "" else content, @@ -2716,20 +2752,20 @@ def _init_mtmd_context(self, llama_model: llama.Llama): with suppress_stdout_stderr(disable=self.verbose): # Get default parameters ctx_params = self._mtmd_cpp.mtmd_context_params_default() - ctx_params.use_gpu = True # TODO: Make this configurable + ctx_params.use_gpu = True # TODO: Make this configurable ctx_params.print_timings = self.verbose ctx_params.n_threads = llama_model.n_threads ctx_params.verbosity = 2 if self.verbose else 0 # GGML_LOG_LEVEL_INFO = 2 # Initialize mtmd context self.mtmd_ctx = self._mtmd_cpp.mtmd_init_from_file( - self.clip_model_path.encode(), - llama_model.model, - ctx_params + self.clip_model_path.encode(), llama_model.model, ctx_params ) if self.mtmd_ctx is None: - raise ValueError(f"Failed to load mtmd context from: {self.clip_model_path}") + raise ValueError( + f"Failed to load mtmd context from: {self.clip_model_path}" + ) # Check if vision is supported if not self._mtmd_cpp.mtmd_support_vision(self.mtmd_ctx): @@ -2756,12 +2792,12 @@ def _create_bitmap_from_bytes(self, image_bytes: bytes): bitmap = self._mtmd_cpp.mtmd_helper_bitmap_init_from_buf( self.mtmd_ctx, (ctypes.c_uint8 * len(image_bytes)).from_buffer(bytearray(image_bytes)), - len(image_bytes) + len(image_bytes), ) - + if bitmap is None: raise ValueError("Failed to create bitmap from image bytes") - + return bitmap def __call__( @@ -2820,10 +2856,10 @@ def __call__( trim_blocks=True, lstrip_blocks=True, ).from_string(self.CHAT_FORMAT) - + # Get the default media marker - media_marker = self._mtmd_cpp.mtmd_default_marker().decode('utf-8') - + media_marker = self._mtmd_cpp.mtmd_default_marker().decode("utf-8") + # Replace image URLs with media markers in the template text = template.render( messages=messages, @@ -2831,7 +2867,7 @@ def __call__( eos_token=llama.detokenize([llama.token_eos()]), bos_token=llama.detokenize([llama.token_bos()]), ) - + # Replace image URLs in text with media markers for image_url in image_urls: text = text.replace(image_url, media_marker) @@ -2851,7 +2887,7 @@ def __call__( # Create input text structure input_text = self._mtmd_cpp.mtmd_input_text() - input_text.text = text.encode('utf-8') + input_text.text = text.encode("utf-8") input_text.add_special = True input_text.parse_special = True @@ -2862,13 +2898,15 @@ def __call__( try: # Tokenize text and images together - bitmap_array = (self._mtmd_cpp.mtmd_bitmap_p_ctypes * len(bitmaps))(*bitmaps) + bitmap_array = (self._mtmd_cpp.mtmd_bitmap_p_ctypes * len(bitmaps))( + *bitmaps + ) result = self._mtmd_cpp.mtmd_tokenize( self.mtmd_ctx, chunks, ctypes.byref(input_text), bitmap_array, - len(bitmaps) + len(bitmaps), ) if result != 0: @@ -2881,40 +2919,45 @@ def __call__( # Process each chunk n_past = llama_cpp.llama_pos(0) n_chunks = self._mtmd_cpp.mtmd_input_chunks_size(chunks) - + for i in range(n_chunks): chunk = self._mtmd_cpp.mtmd_input_chunks_get(chunks, i) if chunk is None: continue chunk_type = self._mtmd_cpp.mtmd_input_chunk_get_type(chunk) - + if chunk_type == self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_TEXT: # Handle text chunk n_tokens_out = ctypes.c_size_t() tokens_ptr = self._mtmd_cpp.mtmd_input_chunk_get_tokens_text( chunk, ctypes.byref(n_tokens_out) ) - + if tokens_ptr and n_tokens_out.value > 0: # Convert ctypes array to Python list tokens = [tokens_ptr[j] for j in range(n_tokens_out.value)] - + if llama.n_tokens + len(tokens) > llama.n_ctx(): raise ValueError( f"Prompt exceeds n_ctx: {llama.n_tokens + len(tokens)} > {llama.n_ctx()}" ) llama.eval(tokens) - - elif chunk_type in [self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_IMAGE, self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_AUDIO]: + + elif chunk_type in [ + self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_IMAGE, + self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_AUDIO, + ]: # Handle image/audio chunk using helper - chunk_n_tokens = self._mtmd_cpp.mtmd_input_chunk_get_n_tokens(chunk) - + chunk_n_tokens = self._mtmd_cpp.mtmd_input_chunk_get_n_tokens( + chunk + ) + if llama.n_tokens + chunk_n_tokens > llama.n_ctx(): raise ValueError( f"Prompt exceeds n_ctx: {llama.n_tokens + chunk_n_tokens} > {llama.n_ctx()}" ) - + new_n_past = llama_cpp.llama_pos(0) result = self._mtmd_cpp.mtmd_helper_eval_chunk_single( self.mtmd_ctx, @@ -2924,12 +2967,14 @@ def __call__( llama_cpp.llama_seq_id(0), llama.n_batch, False, # logits_last - ctypes.byref(new_n_past) + ctypes.byref(new_n_past), ) - + if result != 0: - raise ValueError(f"Failed to evaluate chunk: error code {result}") - + raise ValueError( + f"Failed to evaluate chunk: error code {result}" + ) + # Update llama's token count llama.n_tokens = new_n_past.value @@ -3019,7 +3064,7 @@ def __call__( grammar=grammar, logit_bias=logit_bias, ) - + if tool is not None: tool_name = tool["function"]["name"] return _convert_completion_to_chat_function( @@ -3032,10 +3077,12 @@ def _load_image(image_url: str) -> bytes: # TODO: Add Pillow support for other image formats beyond (jpg, png) if image_url.startswith("data:"): import base64 + image_bytes = base64.b64decode(image_url.split(",")[1]) return image_bytes else: import urllib.request + with urllib.request.urlopen(image_url) as f: image_bytes = f.read() return image_bytes @@ -3062,6 +3109,7 @@ def get_image_urls(messages: List[llama_types.ChatCompletionRequestMessage]): @staticmethod def split_text_on_image_urls(text: str, image_urls: List[str]): """This method is no longer used in the new implementation.""" + def find_first(s: str, substrs: List[str]): for i, substr in enumerate(substrs): pos = s.find(substr) @@ -3443,7 +3491,6 @@ class MiniCPMv26ChatHandler(Llava15ChatHandler): "{% endif %}" "{% endif %}" "{% endfor %}" - "{% for content in message['content'] %}" "{% if content.type == 'text' %}" "{{ content.text }}" @@ -3465,8 +3512,8 @@ class Qwen25VLChatHandler(Llava15ChatHandler): DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant." CHAT_FORMAT = ( - #"{% set image_count = namespace(value=0) %}" - #"{% set video_count = namespace(value=0) %}" + # "{% set image_count = namespace(value=0) %}" + # "{% set video_count = namespace(value=0) %}" "{% for message in messages %}" "{% if loop.first and message['role'] != 'system' %}" "<|im_start|>system\n" @@ -3483,7 +3530,7 @@ class Qwen25VLChatHandler(Llava15ChatHandler): "{% else %}" "{{ content.image_url.url }}" "{% endif %}" - #"{% set image_count.value = image_count.value + 1 %}" + # "{% set image_count.value = image_count.value + 1 %}" "{% elif content['type'] == 'text' %}" "{{ content['text'] }}" "{% endif %}" @@ -3495,25 +3542,28 @@ class Qwen25VLChatHandler(Llava15ChatHandler): ) def __call__(self, **kwargs): - llama = kwargs['llama'] + llama = kwargs["llama"] # Clear state for multiple runs llama.reset() llama._ctx.kv_cache_clear() llama.n_tokens = 0 - if hasattr(llama, 'input_ids'): + if hasattr(llama, "input_ids"): llama.input_ids.fill(0) # Clear any handler state - if hasattr(self, '_last_image_embed'): + if hasattr(self, "_last_image_embed"): self._last_image_embed = None self._last_image_hash = None if self.verbose: - messages = kwargs.get('messages', []) + messages = kwargs.get("messages", []) image_count = len(self.get_image_urls(messages)) - print(f"Minimal - Cleared state, processing {image_count} images", file=sys.stderr) + print( + f"Minimal - Cleared state, processing {image_count} images", + file=sys.stderr, + ) # Use parent implementation return super().__call__(**kwargs) @@ -3636,7 +3686,9 @@ def chatml_function_calling( stop = ( [stop, "<|im_end|>"] if isinstance(stop, str) - else stop + ["<|im_end|>"] if stop else ["<|im_end|>"] + else stop + ["<|im_end|>"] + if stop + else ["<|im_end|>"] ) # Case 1: No tool choice by user @@ -3738,7 +3790,7 @@ def chatml_function_calling( # Case 3: Automatic tool choice assert isinstance(tool_choice, str) and tool_choice == "auto" function_names = " | ".join( - [f'''"functions.{tool['function']['name']}:"''' for tool in tools] + [f'''"functions.{tool["function"]["name"]}:"''' for tool in tools] ) initial_gbnf_tool_grammar = ( """root ::= functions | "message:"\n""" @@ -3914,7 +3966,9 @@ def chatml_function_calling( { "finish_reason": "tool_calls", "index": 0, - "logprobs": _convert_text_completion_logprobs_to_chat(completion["choices"][0]["logprobs"]), + "logprobs": _convert_text_completion_logprobs_to_chat( + completion["choices"][0]["logprobs"] + ), "message": { "role": "assistant", "content": None, diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index 711d42a6ae..f13af67f3b 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -33,7 +33,11 @@ # Specify the base name of the shared library to load _lib_base_name = "llama" _override_base_path = os.environ.get("LLAMA_CPP_LIB_PATH") -_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" if _override_base_path is None else pathlib.Path(_override_base_path) +_base_path = ( + pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" + if _override_base_path is None + else pathlib.Path(_override_base_path) +) # Load the library _lib = load_shared_library(_lib_base_name, _base_path) @@ -559,6 +563,7 @@ class llama_token_data_array(ctypes.Structure): # typedef struct llama_batch { # int32_t n_tokens; + # llama_token * token; # float * embd; # llama_pos * pos; @@ -688,6 +693,7 @@ class llama_model_kv_override(ctypes.Structure): # // override key-value pairs of the model meta data # const struct llama_model_kv_override * kv_overrides; + # // Keep the booleans together to avoid misalignment during copy-by-value. # bool vocab_only; // only load the vocabulary, no weights # bool use_mmap; // use mmap if possible @@ -716,7 +722,9 @@ class llama_model_params(ctypes.Structure): if TYPE_CHECKING: devices: CtypesArray[ctypes.c_void_p] # NOTE: unused - tensor_buft_overrides: CtypesArray[llama_model_tensor_buft_override] # NOTE: unused + tensor_buft_overrides: CtypesArray[ + llama_model_tensor_buft_override + ] # NOTE: unused n_gpu_layers: int split_mode: int main_gpu: int @@ -731,8 +739,8 @@ class llama_model_params(ctypes.Structure): use_extra_bufts: bool _fields_ = [ - ("devices", ctypes.c_void_p), # NOTE: unnused - ("tensor_buft_overrides", ctypes.c_void_p), # NOTE: unused + ("devices", ctypes.c_void_p), # NOTE: unnused + ("tensor_buft_overrides", ctypes.c_void_p), # NOTE: unused ("n_gpu_layers", ctypes.c_int32), ("split_mode", ctypes.c_int), ("main_gpu", ctypes.c_int32), @@ -784,6 +792,7 @@ class llama_model_params(ctypes.Structure): # ggml_abort_callback abort_callback; # void * abort_callback_data; + # // Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value. # bool embeddings; // if true, extract embeddings (together with logits) # bool offload_kqv; // offload the KQV ops (including the KV cache) to GPU @@ -1137,8 +1146,7 @@ def llama_backend_free(): [ctypes.c_int], None, ) -def llama_numa_init(numa: int, /): - ... +def llama_numa_init(numa: int, /): ... # // Optional: an auto threadpool gets created in ggml if not passed explicitly @@ -1164,8 +1172,7 @@ def llama_numa_init(numa: int, /): ) def llama_load_model_from_file( path_model: bytes, params: llama_model_params, / -) -> Optional[llama_model_p]: - ... +) -> Optional[llama_model_p]: ... # // Load the model from a file @@ -1230,8 +1237,7 @@ def llama_model_save_to_file(model: llama_model_p, path_model: bytes, /): [llama_model_p_ctypes], None, ) -def llama_free_model(model: llama_model_p, /): - ... +def llama_free_model(model: llama_model_p, /): ... # LLAMA_API void llama_model_free(struct llama_model * model); @@ -1240,8 +1246,7 @@ def llama_free_model(model: llama_model_p, /): [llama_model_p_ctypes], None, ) -def llama_model_free(model: llama_model_p, /): - ... +def llama_model_free(model: llama_model_p, /): ... # LLAMA_API struct llama_context * llama_init_from_model( @@ -1254,8 +1259,7 @@ def llama_model_free(model: llama_model_p, /): ) def llama_init_from_model( model: llama_model_p, params: llama_context_params, / -) -> Optional[llama_context_p]: - ... +) -> Optional[llama_context_p]: ... # DEPRECATED(LLAMA_API struct llama_context * llama_new_context_with_model( @@ -1269,8 +1273,7 @@ def llama_init_from_model( ) def llama_new_context_with_model( model: llama_model_p, params: llama_context_params, / -) -> Optional[llama_context_p]: - ... +) -> Optional[llama_context_p]: ... # // Frees all allocated memory @@ -1291,104 +1294,87 @@ def llama_free(ctx: llama_context_p, /): [], ctypes.c_int64, ) -def llama_time_us() -> int: - ... +def llama_time_us() -> int: ... # LLAMA_API size_t llama_max_devices(void); @ctypes_function("llama_max_devices", [], ctypes.c_size_t) -def llama_max_devices() -> int: - ... +def llama_max_devices() -> int: ... # LLAMA_API size_t llama_max_parallel_sequences(void); @ctypes_function("llama_max_parallel_sequences", [], ctypes.c_size_t) -def llama_max_parallel_sequences() -> int: - ... +def llama_max_parallel_sequences() -> int: ... # LLAMA_API bool llama_supports_mmap (void); @ctypes_function("llama_supports_mmap", [], ctypes.c_bool) -def llama_supports_mmap() -> bool: - ... +def llama_supports_mmap() -> bool: ... # LLAMA_API bool llama_supports_mlock (void); @ctypes_function("llama_supports_mlock", [], ctypes.c_bool) -def llama_supports_mlock() -> bool: - ... +def llama_supports_mlock() -> bool: ... # LLAMA_API bool llama_supports_gpu_offload(void); @ctypes_function("llama_supports_gpu_offload", [], ctypes.c_bool) -def llama_supports_gpu_offload() -> bool: - ... +def llama_supports_gpu_offload() -> bool: ... # LLAMA_API bool llama_supports_rpc (void); @ctypes_function("llama_supports_rpc", [], ctypes.c_bool) -def llama_supports_rpc() -> bool: - ... +def llama_supports_rpc() -> bool: ... # LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx); @ctypes_function("llama_n_ctx", [llama_context_p_ctypes], ctypes.c_uint32) -def llama_n_ctx(ctx: llama_context_p, /) -> int: - ... +def llama_n_ctx(ctx: llama_context_p, /) -> int: ... # LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx); @ctypes_function("llama_n_batch", [llama_context_p_ctypes], ctypes.c_uint32) -def llama_n_batch(ctx: llama_context_p, /) -> int: - ... +def llama_n_batch(ctx: llama_context_p, /) -> int: ... # LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx); @ctypes_function("llama_n_ubatch", [llama_context_p_ctypes], ctypes.c_uint32) -def llama_n_ubatch(ctx: llama_context_p, /) -> int: - ... +def llama_n_ubatch(ctx: llama_context_p, /) -> int: ... # LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx); @ctypes_function("llama_n_seq_max", [llama_context_p_ctypes], ctypes.c_uint32) -def llama_n_seq_max(ctx: llama_context_p, /) -> int: - ... +def llama_n_seq_max(ctx: llama_context_p, /) -> int: ... # DEPRECATED(LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model), "use llama_model_n_ctx_train instead"); @ctypes_function("llama_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32) -def llama_n_ctx_train(model: llama_model_p, /) -> int: - ... +def llama_n_ctx_train(model: llama_model_p, /) -> int: ... # DEPRECATED(LLAMA_API int32_t llama_n_embd (const struct llama_model * model), "use llama_model_n_embd instead"); @ctypes_function("llama_n_embd", [llama_model_p_ctypes], ctypes.c_int32) -def llama_n_embd(model: llama_model_p, /) -> int: - ... +def llama_n_embd(model: llama_model_p, /) -> int: ... # DEPRECATED(LLAMA_API int32_t llama_n_layer (const struct llama_model * model), "use llama_model_n_layer instead"); @ctypes_function("llama_n_layer", [llama_model_p_ctypes], ctypes.c_int32) -def llama_n_layer(model: llama_model_p, /) -> int: - ... +def llama_n_layer(model: llama_model_p, /) -> int: ... # DEPRECATED(LLAMA_API int32_t llama_n_head (const struct llama_model * model), "use llama_model_n_head instead"); @ctypes_function("llama_n_head", [llama_model_p_ctypes], ctypes.c_int32) -def llama_n_head(model: llama_model_p, /) -> int: - ... +def llama_n_head(model: llama_model_p, /) -> int: ... # DEPRECATED(LLAMA_API int32_t llama_n_vocab (const struct llama_vocab * vocab), "use llama_vocab_n_tokens instead"); @ctypes_function("llama_n_vocab", [llama_vocab_p_ctypes], ctypes.c_int32) -def llama_n_vocab(model: llama_vocab_p, /) -> int: - ... +def llama_n_vocab(model: llama_vocab_p, /) -> int: ... # LLAMA_API const struct llama_model * llama_get_model (const struct llama_context * ctx); @ctypes_function("llama_get_model", [llama_context_p_ctypes], llama_model_p_ctypes) -def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]: - ... +def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]: ... # LLAMA_API llama_memory_t llama_get_memory (const struct llama_context * ctx); @@ -1400,8 +1386,7 @@ def llama_get_memory(ctx: llama_context_p, /) -> Optional[llama_memory_t]: # LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx); @ctypes_function("llama_pooling_type", [llama_context_p_ctypes], ctypes.c_int) -def llama_pooling_type(ctx: llama_context_p, /) -> int: - ... +def llama_pooling_type(ctx: llama_context_p, /) -> int: ... # DEPRECATED(LLAMA_API struct llama_kv_cache * llama_get_kv_self(struct llama_context * ctx), "use llama_get_memory instead"); @@ -1417,57 +1402,50 @@ def llama_get_kv_self(ctx: llama_context_p, /) -> Optional[llama_kv_cache_p]: # LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model); @ctypes_function("llama_model_get_vocab", [llama_model_p_ctypes], llama_vocab_p_ctypes) -def llama_model_get_vocab(model: llama_model_p, /) -> Optional[llama_vocab_p]: - ... +def llama_model_get_vocab(model: llama_model_p, /) -> Optional[llama_vocab_p]: ... # LLAMA_API enum llama_rope_type llama_model_rope_type(const struct llama_model * model); @ctypes_function("llama_model_rope_type", [llama_model_p_ctypes], ctypes.c_int) -def llama_model_rope_type(model: llama_model_p, /) -> int: - ... +def llama_model_rope_type(model: llama_model_p, /) -> int: ... # LLAMA_API int32_t llama_model_n_ctx_train(const struct llama_model * model); @ctypes_function("llama_model_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32) -def llama_model_n_ctx_train(model: llama_model_p, /) -> int: - ... +def llama_model_n_ctx_train(model: llama_model_p, /) -> int: ... # LLAMA_API int32_t llama_model_n_embd (const struct llama_model * model); @ctypes_function("llama_model_n_embd", [llama_model_p_ctypes], ctypes.c_int32) -def llama_model_n_embd(model: llama_model_p, /) -> int: - ... +def llama_model_n_embd(model: llama_model_p, /) -> int: ... # LLAMA_API int32_t llama_model_n_layer (const struct llama_model * model); @ctypes_function("llama_model_n_layer", [llama_model_p_ctypes], ctypes.c_int32) -def llama_model_n_layer(model: llama_model_p, /) -> int: - ... +def llama_model_n_layer(model: llama_model_p, /) -> int: ... # LLAMA_API int32_t llama_model_n_head (const struct llama_model * model); @ctypes_function("llama_model_n_head", [llama_model_p_ctypes], ctypes.c_int32) -def llama_model_n_head(model: llama_model_p, /) -> int: - ... +def llama_model_n_head(model: llama_model_p, /) -> int: ... # LLAMA_API int32_t llama_model_n_head_kv (const struct llama_model * model); @ctypes_function("llama_model_n_head_kv", [llama_model_p_ctypes], ctypes.c_int32) -def llama_model_n_head_kv(model: llama_model_p, /) -> int: - ... +def llama_model_n_head_kv(model: llama_model_p, /) -> int: ... # LLAMA_API int32_t llama_model_n_swa (const struct llama_model * model); @ctypes_function("llama_model_n_swa", [llama_model_p_ctypes], ctypes.c_int32) -def llama_model_n_swa(model: llama_model_p, /) -> int: - ... +def llama_model_n_swa(model: llama_model_p, /) -> int: ... # // Get the model's RoPE frequency scaling factor # LLAMA_API float llama_model_rope_freq_scale_train(const struct llama_model * model); -@ctypes_function("llama_model_rope_freq_scale_train", [llama_model_p_ctypes], ctypes.c_float) -def llama_model_rope_freq_scale_train(model: llama_model_p, /) -> float: - ... +@ctypes_function( + "llama_model_rope_freq_scale_train", [llama_model_p_ctypes], ctypes.c_float +) +def llama_model_rope_freq_scale_train(model: llama_model_p, /) -> float: ... # // Returns the number of classifier outputs (only valid for classifier models) @@ -1481,7 +1459,9 @@ def llama_model_n_cls_out(model: llama_model_p, /) -> int: # // Returns label of classifier output by index ( Optional[bytes]: """Returns label of classifier output by index. Returns None if no label provided""" ... @@ -1489,14 +1469,12 @@ def llama_model_cls_label(model: llama_model_p, i: int, /) -> Optional[bytes]: # LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model); @ctypes_function("llama_vocab_type", [llama_vocab_p_ctypes], ctypes.c_int) -def llama_vocab_type(vocab: llama_vocab_p, /) -> int: - ... +def llama_vocab_type(vocab: llama_vocab_p, /) -> int: ... # LLAMA_API int32_t llama_vocab_n_tokens(const struct llama_vocab * vocab); @ctypes_function("llama_vocab_n_tokens", [llama_vocab_p_ctypes], ctypes.c_int32) -def llama_vocab_n_tokens(vocab: llama_vocab_p, /) -> int: - ... +def llama_vocab_n_tokens(vocab: llama_vocab_p, /) -> int: ... # // Functions to access the model's GGUF metadata scalar values @@ -1611,8 +1589,14 @@ def llama_model_size(model: llama_model_p, /) -> int: # // Get the default chat template. Returns nullptr if not available # // If name is NULL, returns the default chat template # LLAMA_API const char * llama_model_chat_template(const struct llama_model * model, const char * name); -@ctypes_function("llama_model_chat_template", [llama_model_p_ctypes, ctypes.c_char_p], ctypes.c_char_p) -def llama_model_chat_template(model: llama_model_p, name: Optional[bytes], /) -> Optional[bytes]: +@ctypes_function( + "llama_model_chat_template", + [llama_model_p_ctypes, ctypes.c_char_p], + ctypes.c_char_p, +) +def llama_model_chat_template( + model: llama_model_p, name: Optional[bytes], / +) -> Optional[bytes]: """Get the default chat template. Returns None if not available If name is None, returns the default chat template""" ... @@ -1699,6 +1683,7 @@ def llama_model_quantize( # // Adapters # // + # // Load a LoRA adapter from file # LLAMA_API struct llama_adapter_lora * llama_adapter_lora_init( # struct llama_model * model, @@ -1710,8 +1695,7 @@ def llama_model_quantize( ) def llama_adapter_lora_init( model: llama_model_p, path_lora: bytes, / -) -> Optional[llama_adapter_lora_p]: - ... +) -> Optional[llama_adapter_lora_p]: ... # // Manually free a LoRA adapter @@ -1722,8 +1706,7 @@ def llama_adapter_lora_init( [llama_adapter_lora_p_ctypes], None, ) -def llama_adapter_lora_free(adapter: llama_adapter_lora_p, /): - ... +def llama_adapter_lora_free(adapter: llama_adapter_lora_p, /): ... # // The following functions operate on a llama_context, hence the naming: llama_verb_... @@ -1825,6 +1808,7 @@ def llama_apply_adapter_cvec( # // Memory # // + # // Clear the memory contents # // If data == true, the data buffers will also be cleared together with the metadata # LLAMA_API void llama_memory_clear( @@ -1916,9 +1900,7 @@ def llama_memory_seq_cp( # LLAMA_API void llama_memory_seq_keep( # llama_memory_t mem, # llama_seq_id seq_id); -@ctypes_function( - "llama_memory_seq_keep", [llama_memory_t_ctypes, llama_seq_id], None -) +@ctypes_function("llama_memory_seq_keep", [llama_memory_t_ctypes, llama_seq_id], None) def llama_memory_seq_keep(mem: llama_memory_t, seq_id: Union[llama_seq_id, int], /): """Removes all tokens that do not belong to the specified sequence""" ... @@ -2040,13 +2022,12 @@ def llama_memory_can_shift(mem: llama_memory_t, /) -> bool: # // KV cache for self-attention (TODO: deprecate in favor of llama_memory) # // + # // Returns the number of tokens in the KV cache (slow, use only for debug) # // If a KV cell has multiple sequences assigned to it, it will be counted multiple times # DEPRECATED(LLAMA_API int32_t llama_kv_self_n_tokens(const struct llama_context * ctx), # "Use llama_kv_self_seq_pos_max() and llama_kv_self_seq_pos_min() instead (https://github.com/ggml-org/llama.cpp/issues/13793)"); -@ctypes_function( - "llama_kv_self_n_tokens", [llama_context_p_ctypes], ctypes.c_int32 -) +@ctypes_function("llama_kv_self_n_tokens", [llama_context_p_ctypes], ctypes.c_int32) def llama_kv_self_n_tokens(ctx: llama_context_p, /) -> int: """Returns the number of tokens in the KV cache (slow, use only for debug) (DEPRECATED)""" ... @@ -2055,9 +2036,7 @@ def llama_kv_self_n_tokens(ctx: llama_context_p, /) -> int: # // Returns the number of used KV cells (i.e. have at least one sequence assigned to them) # DEPRECATED(LLAMA_API int32_t llama_kv_self_used_cells(const struct llama_context * ctx), # "Use llama_kv_self_seq_pos_max() and llama_kv_self_seq_pos_min() instead (https://github.com/ggml-org/llama.cpp/issues/13793)"); -@ctypes_function( - "llama_kv_self_used_cells", [llama_context_p_ctypes], ctypes.c_int32 -) +@ctypes_function("llama_kv_self_used_cells", [llama_context_p_ctypes], ctypes.c_int32) def llama_kv_self_used_cells(ctx: llama_context_p, /) -> int: """Returns the number of used KV cells (DEPRECATED)""" ... @@ -2067,9 +2046,7 @@ def llama_kv_self_used_cells(ctx: llama_context_p, /) -> int: # DEPRECATED(LLAMA_API void llama_kv_self_clear( # struct llama_context * ctx), # "Use llama_memory_clear() instead"); -@ctypes_function( - "llama_kv_self_clear", [llama_context_p_ctypes], None -) +@ctypes_function("llama_kv_self_clear", [llama_context_p_ctypes], None) def llama_kv_self_clear(ctx: llama_context_p, /): """Clear the KV cache (DEPRECATED)""" ... @@ -2146,9 +2123,7 @@ def llama_kv_self_seq_cp( # struct llama_context * ctx, # llama_seq_id seq_id), # "Use llama_memory_seq_keep() instead"); -@ctypes_function( - "llama_kv_self_seq_keep", [llama_context_p_ctypes, llama_seq_id], None -) +@ctypes_function("llama_kv_self_seq_keep", [llama_context_p_ctypes, llama_seq_id], None) def llama_kv_self_seq_keep(ctx: llama_context_p, seq_id: Union[llama_seq_id, int], /): """Keep only specified sequence in KV cache (DEPRECATED)""" ... @@ -2292,6 +2267,7 @@ def llama_kv_self_update(ctx: llama_context_p, /): # // State / sessions # // + # // Returns the *actual* size in bytes of the state # // (logits, embedding and memory) # // Only use when saving the state, not when restoring it, otherwise the size may be too small. @@ -2420,8 +2396,7 @@ def llama_state_load_file( n_token_capacity: Union[ctypes.c_size_t, int], n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t], /, -) -> bool: - ... +) -> bool: ... # LLAMA_API DEPRECATED(bool llama_load_session_file( @@ -2449,8 +2424,7 @@ def llama_load_session_file( n_token_capacity: Union[ctypes.c_size_t, int], n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t], /, -) -> bool: - ... +) -> bool: ... # LLAMA_API bool llama_state_save_file( @@ -2474,8 +2448,7 @@ def llama_state_save_file( tokens: CtypesArray[llama_token], n_token_count: Union[ctypes.c_size_t, int], /, -) -> bool: - ... +) -> bool: ... # LLAMA_API DEPRECATED(bool llama_save_session_file( @@ -2500,8 +2473,7 @@ def llama_save_session_file( tokens: CtypesArray[llama_token], n_token_count: Union[ctypes.c_size_t, int], /, -) -> bool: - ... +) -> bool: ... # // Get the exact size needed to copy the state of a single sequence @@ -2599,8 +2571,7 @@ def llama_state_seq_save_file( tokens: CtypesArray[llama_token], n_token_count: Union[ctypes.c_size_t, int], /, -) -> int: - ... +) -> int: ... # LLAMA_API size_t llama_state_seq_load_file( @@ -2630,14 +2601,14 @@ def llama_state_seq_load_file( n_token_capacity: Union[ctypes.c_size_t, int], n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t], /, -) -> int: - ... +) -> int: ... # // # // Decoding # // + # // Return batch for single sequence of tokens # // The sequence ID will be fixed to 0 # // The position of the tokens will be tracked automatically by llama_decode @@ -2947,14 +2918,14 @@ def llama_get_embeddings_seq( # // Vocab # // + # LLAMA_API const char * llama_vocab_get_text(const struct llama_vocab * vocab, llama_token token); @ctypes_function( "llama_vocab_get_text", [llama_vocab_p_ctypes, llama_token], ctypes.c_char_p ) def llama_vocab_get_text( vocab: llama_vocab_p, token: Union[llama_token, int], / -) -> bytes: - ... +) -> bytes: ... # LLAMA_API float llama_vocab_get_score(const struct llama_vocab * vocab, llama_token token); @@ -2963,8 +2934,7 @@ def llama_vocab_get_text( ) def llama_vocab_get_score( vocab: llama_vocab_p, token: Union[llama_token, int], / -) -> float: - ... +) -> float: ... # LLAMA_API enum llama_token_attr llama_vocab_get_attr(const struct llama_vocab * vocab, llama_token token); @@ -2973,8 +2943,7 @@ def llama_vocab_get_score( ) def llama_vocab_get_attr( vocab: llama_vocab_p, token: Union[llama_token, int], / -) -> int: - ... +) -> int: ... # // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.) @@ -3055,8 +3024,7 @@ def llama_vocab_mask(vocab: llama_vocab_p, /) -> llama_token: [llama_vocab_p_ctypes], ctypes.c_bool, ) -def llama_vocab_get_add_bos(vocab: llama_vocab_p, /) -> bool: - ... +def llama_vocab_get_add_bos(vocab: llama_vocab_p, /) -> bool: ... # LLAMA_API bool llama_vocab_get_add_eos(const struct llama_vocab * vocab); @@ -3065,8 +3033,7 @@ def llama_vocab_get_add_bos(vocab: llama_vocab_p, /) -> bool: [llama_vocab_p_ctypes], ctypes.c_bool, ) -def llama_vocab_get_add_eos(vocab: llama_vocab_p, /) -> bool: - ... +def llama_vocab_get_add_eos(vocab: llama_vocab_p, /) -> bool: ... # LLAMA_API bool llama_vocab_get_add_sep(const struct llama_vocab * vocab); @@ -3075,8 +3042,7 @@ def llama_vocab_get_add_eos(vocab: llama_vocab_p, /) -> bool: [llama_vocab_p_ctypes], ctypes.c_bool, ) -def llama_vocab_get_add_sep(vocab: llama_vocab_p, /) -> bool: - ... +def llama_vocab_get_add_sep(vocab: llama_vocab_p, /) -> bool: ... # LLAMA_API llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab); @@ -3085,8 +3051,7 @@ def llama_vocab_get_add_sep(vocab: llama_vocab_p, /) -> bool: [llama_vocab_p_ctypes], llama_token, ) -def llama_vocab_fim_pre(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_vocab_fim_pre(vocab: llama_vocab_p, /) -> llama_token: ... # LLAMA_API llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab); @@ -3095,8 +3060,7 @@ def llama_vocab_fim_pre(vocab: llama_vocab_p, /) -> llama_token: [llama_vocab_p_ctypes], llama_token, ) -def llama_vocab_fim_suf(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_vocab_fim_suf(vocab: llama_vocab_p, /) -> llama_token: ... # LLAMA_API llama_token llama_vocab_fim_mid(const struct llama_vocab * vocab); @@ -3105,8 +3069,7 @@ def llama_vocab_fim_suf(vocab: llama_vocab_p, /) -> llama_token: [llama_vocab_p_ctypes], llama_token, ) -def llama_vocab_fim_mid(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_vocab_fim_mid(vocab: llama_vocab_p, /) -> llama_token: ... # LLAMA_API llama_token llama_vocab_fim_pad(const struct llama_vocab * vocab); @@ -3115,8 +3078,7 @@ def llama_vocab_fim_mid(vocab: llama_vocab_p, /) -> llama_token: [llama_vocab_p_ctypes], llama_token, ) -def llama_vocab_fim_pad(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_vocab_fim_pad(vocab: llama_vocab_p, /) -> llama_token: ... # LLAMA_API llama_token llama_vocab_fim_rep(const struct llama_vocab * vocab); @@ -3125,8 +3087,7 @@ def llama_vocab_fim_pad(vocab: llama_vocab_p, /) -> llama_token: [llama_vocab_p_ctypes], llama_token, ) -def llama_vocab_fim_rep(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_vocab_fim_rep(vocab: llama_vocab_p, /) -> llama_token: ... # LLAMA_API llama_token llama_vocab_fim_sep(const struct llama_vocab * vocab); @@ -3135,8 +3096,7 @@ def llama_vocab_fim_rep(vocab: llama_vocab_p, /) -> llama_token: [llama_vocab_p_ctypes], llama_token, ) -def llama_vocab_fim_sep(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_vocab_fim_sep(vocab: llama_vocab_p, /) -> llama_token: ... # DEPRECATED functions @@ -3148,8 +3108,7 @@ def llama_vocab_fim_sep(vocab: llama_vocab_p, /) -> llama_token: ) def llama_token_get_text( vocab: llama_vocab_p, token: Union[llama_token, int], / -) -> bytes: - ... +) -> bytes: ... # DEPRECATED(LLAMA_API float llama_token_get_score(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_score instead"); @@ -3160,8 +3119,8 @@ def llama_token_get_text( ) def llama_token_get_score( vocab: llama_vocab_p, token: Union[llama_token, int], / -) -> float: - ... +) -> float: ... + # DEPRECATED(LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_attr instead"); @ctypes_function( @@ -3171,8 +3130,8 @@ def llama_token_get_score( ) def llama_token_get_attr( vocab: llama_vocab_p, token: Union[llama_token, int], / -) -> int: - ... +) -> int: ... + # DEPRECATED(LLAMA_API bool llama_token_is_eog(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_eog instead"); @ctypes_function( @@ -3182,8 +3141,8 @@ def llama_token_get_attr( ) def llama_token_is_eog( vocab: llama_vocab_p, token: Union[llama_token, int], / -) -> bool: - ... +) -> bool: ... + # DEPRECATED(LLAMA_API bool llama_token_is_control(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_control instead"); @ctypes_function( @@ -3193,8 +3152,8 @@ def llama_token_is_eog( ) def llama_token_is_control( vocab: llama_vocab_p, token: Union[llama_token, int], / -) -> bool: - ... +) -> bool: ... + # DEPRECATED(LLAMA_API llama_token llama_token_bos(const struct llama_vocab * vocab), "use llama_vocab_bos instead"); @ctypes_function( @@ -3202,8 +3161,8 @@ def llama_token_is_control( [llama_vocab_p_ctypes], llama_token, ) -def llama_token_bos(vocab: llama_vocab_p, /) -> int: - ... +def llama_token_bos(vocab: llama_vocab_p, /) -> int: ... + # DEPRECATED(LLAMA_API llama_token llama_token_eos(const struct llama_vocab * vocab), "use llama_vocab_eos instead"); @ctypes_function( @@ -3211,8 +3170,8 @@ def llama_token_bos(vocab: llama_vocab_p, /) -> int: [llama_vocab_p_ctypes], llama_token, ) -def llama_token_eos(vocab: llama_vocab_p, /) -> int: - ... +def llama_token_eos(vocab: llama_vocab_p, /) -> int: ... + # DEPRECATED(LLAMA_API llama_token llama_token_eot(const struct llama_vocab * vocab), "use llama_vocab_eot instead"); @ctypes_function( @@ -3220,8 +3179,8 @@ def llama_token_eos(vocab: llama_vocab_p, /) -> int: [llama_vocab_p_ctypes], llama_token, ) -def llama_token_eot(vocab: llama_vocab_p, /) -> int: - ... +def llama_token_eot(vocab: llama_vocab_p, /) -> int: ... + # DEPRECATED(LLAMA_API llama_token llama_token_cls(const struct llama_vocab * vocab), "use llama_vocab_cls instead"); @ctypes_function( @@ -3229,8 +3188,8 @@ def llama_token_eot(vocab: llama_vocab_p, /) -> int: [llama_vocab_p_ctypes], llama_token, ) -def llama_token_cls(vocab: llama_vocab_p, /) -> int: - ... +def llama_token_cls(vocab: llama_vocab_p, /) -> int: ... + # DEPRECATED(LLAMA_API llama_token llama_token_sep(const struct llama_vocab * vocab), "use llama_vocab_sep instead"); @ctypes_function( @@ -3238,8 +3197,7 @@ def llama_token_cls(vocab: llama_vocab_p, /) -> int: [llama_vocab_p_ctypes], llama_token, ) -def llama_token_sep(vocab: llama_vocab_p, /) -> int: - ... +def llama_token_sep(vocab: llama_vocab_p, /) -> int: ... # DEPRECATED(LLAMA_API llama_token llama_token_nl (const struct llama_vocab * vocab), "use llama_vocab_nl instead"); @@ -3248,8 +3206,7 @@ def llama_token_sep(vocab: llama_vocab_p, /) -> int: [llama_vocab_p_ctypes], llama_token, ) -def llama_token_nl(vocab: llama_vocab_p, /) -> int: - ... +def llama_token_nl(vocab: llama_vocab_p, /) -> int: ... # DEPRECATED(LLAMA_API llama_token llama_token_pad(const struct llama_vocab * vocab), "use llama_vocab_pad instead"); @@ -3258,8 +3215,7 @@ def llama_token_nl(vocab: llama_vocab_p, /) -> int: [llama_vocab_p_ctypes], llama_token, ) -def llama_token_pad(vocab: llama_vocab_p, /) -> int: - ... +def llama_token_pad(vocab: llama_vocab_p, /) -> int: ... # DEPRECATED(LLAMA_API bool llama_add_bos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_bos instead"); @@ -3268,8 +3224,8 @@ def llama_token_pad(vocab: llama_vocab_p, /) -> int: [llama_vocab_p_ctypes], ctypes.c_bool, ) -def llama_add_bos_token(vocab: llama_vocab_p, /) -> bool: - ... +def llama_add_bos_token(vocab: llama_vocab_p, /) -> bool: ... + # DEPRECATED(LLAMA_API bool llama_add_eos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_eos instead"); @ctypes_function( @@ -3277,8 +3233,7 @@ def llama_add_bos_token(vocab: llama_vocab_p, /) -> bool: [llama_vocab_p_ctypes], ctypes.c_bool, ) -def llama_add_eos_token(vocab: llama_vocab_p, /) -> bool: - ... +def llama_add_eos_token(vocab: llama_vocab_p, /) -> bool: ... # DEPRECATED(LLAMA_API llama_token llama_token_fim_pre(const struct llama_vocab * vocab), "use llama_vocab_fim_pre instead"); @@ -3287,8 +3242,8 @@ def llama_add_eos_token(vocab: llama_vocab_p, /) -> bool: [llama_vocab_p_ctypes], llama_token, ) -def llama_token_fim_pre(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_token_fim_pre(vocab: llama_vocab_p, /) -> llama_token: ... + # DEPRECATED(LLAMA_API llama_token llama_token_fim_suf(const struct llama_vocab * vocab), "use llama_vocab_fim_suf instead"); @ctypes_function( @@ -3296,8 +3251,8 @@ def llama_token_fim_pre(vocab: llama_vocab_p, /) -> llama_token: [llama_vocab_p_ctypes], llama_token, ) -def llama_token_fim_suf(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_token_fim_suf(vocab: llama_vocab_p, /) -> llama_token: ... + # DEPRECATED(LLAMA_API llama_token llama_token_fim_mid(const struct llama_vocab * vocab), "use llama_vocab_fim_mid instead"); @ctypes_function( @@ -3305,8 +3260,8 @@ def llama_token_fim_suf(vocab: llama_vocab_p, /) -> llama_token: [llama_vocab_p_ctypes], llama_token, ) -def llama_token_fim_mid(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_token_fim_mid(vocab: llama_vocab_p, /) -> llama_token: ... + # DEPRECATED(LLAMA_API llama_token llama_token_fim_pad(const struct llama_vocab * vocab), "use llama_vocab_fim_pad instead"); @ctypes_function( @@ -3314,8 +3269,8 @@ def llama_token_fim_mid(vocab: llama_vocab_p, /) -> llama_token: [llama_vocab_p_ctypes], llama_token, ) -def llama_token_fim_pad(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_token_fim_pad(vocab: llama_vocab_p, /) -> llama_token: ... + # DEPRECATED(LLAMA_API llama_token llama_token_fim_rep(const struct llama_vocab * vocab), "use llama_vocab_fim_rep instead"); @ctypes_function( @@ -3323,8 +3278,8 @@ def llama_token_fim_pad(vocab: llama_vocab_p, /) -> llama_token: [llama_vocab_p_ctypes], llama_token, ) -def llama_token_fim_rep(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_token_fim_rep(vocab: llama_vocab_p, /) -> llama_token: ... + # DEPRECATED(LLAMA_API llama_token llama_token_fim_sep(const struct llama_vocab * vocab), "use llama_vocab_fim_sep instead"); @ctypes_function( @@ -3332,8 +3287,8 @@ def llama_token_fim_rep(vocab: llama_vocab_p, /) -> llama_token: [llama_vocab_p_ctypes], llama_token, ) -def llama_token_fim_sep(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_token_fim_sep(vocab: llama_vocab_p, /) -> llama_token: ... + # // CLS is equivalent to BOS # DEPRECATED(LLAMA_API llama_token llama_vocab_cls(const struct llama_vocab * vocab), // classification @@ -3343,8 +3298,7 @@ def llama_token_fim_sep(vocab: llama_vocab_p, /) -> llama_token: [llama_vocab_p_ctypes], llama_token, ) -def llama_vocab_cls(vocab: llama_vocab_p, /) -> llama_token: - ... +def llama_vocab_cls(vocab: llama_vocab_p, /) -> llama_token: ... # // @@ -3353,6 +3307,7 @@ def llama_vocab_cls(vocab: llama_vocab_p, /) -> llama_token: # // The API is thread-safe. # // + # /// @details Convert the provided text into tokens. # /// @param tokens The tokens pointer must be large enough to hold the resulting tokens. # /// @return Returns the number of tokens on success, no more than n_tokens_max @@ -3512,6 +3467,7 @@ def llama_detokenize( # // Chat templates # // + # /// Apply chat template. Inspired by hf apply_chat_template() on python. # /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model" # /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template @@ -3535,9 +3491,9 @@ def llama_detokenize( ctypes.c_char_p, # tmpl ctypes.POINTER(llama_chat_message), # chat ctypes.c_size_t, # n_msg - ctypes.c_bool, # add_ass (added) + ctypes.c_bool, # add_ass (added) ctypes.c_char_p, # buf - ctypes.c_int32, # length + ctypes.c_int32, # length ], ctypes.c_int32, ) @@ -3611,11 +3567,11 @@ def llama_chat_builtin_templates( # struct llama_sampler * (*clone) (const struct llama_sampler * smpl); // can be NULL if ctx is NULL # void (*free) ( struct llama_sampler * smpl); // can be NULL if ctx is NULL + # // TODO: API for internal libllama usage for appending the sampling to an existing ggml_cgraph # //void (*apply_ggml) (struct llama_sampler * smpl, ...); # }; -class llama_sampler_i(ctypes.Structure): - ... +class llama_sampler_i(ctypes.Structure): ... # struct llama_sampler { @@ -3662,8 +3618,7 @@ class llama_sampler(ctypes.Structure): ) def llama_sampler_init( iface: ctypes.POINTER(llama_sampler_i), ctx: llama_sampler_context_t, / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # LLAMA_API const char * llama_sampler_name (const struct llama_sampler * smpl); @@ -3672,8 +3627,7 @@ def llama_sampler_init( [llama_sampler_p_ctypes], ctypes.c_char_p, ) -def llama_sampler_name(smpl: llama_sampler_p, /) -> bytes: - ... +def llama_sampler_name(smpl: llama_sampler_p, /) -> bytes: ... # LLAMA_API void llama_sampler_accept( struct llama_sampler * smpl, llama_token token); @@ -3682,8 +3636,7 @@ def llama_sampler_name(smpl: llama_sampler_p, /) -> bytes: [llama_sampler_p_ctypes, llama_token], None, ) -def llama_sampler_accept(smpl: llama_sampler_p, token: Union[llama_token, int], /): - ... +def llama_sampler_accept(smpl: llama_sampler_p, token: Union[llama_token, int], /): ... # LLAMA_API void llama_sampler_apply ( struct llama_sampler * smpl, llama_token_data_array * cur_p); @@ -3694,8 +3647,7 @@ def llama_sampler_accept(smpl: llama_sampler_p, token: Union[llama_token, int], ) def llama_sampler_apply( smpl: llama_sampler_p, cur_p: CtypesArray[llama_token_data_array], / -): - ... +): ... # LLAMA_API void llama_sampler_reset ( struct llama_sampler * smpl); @@ -3704,8 +3656,7 @@ def llama_sampler_apply( [llama_sampler_p_ctypes], None, ) -def llama_sampler_reset(smpl: llama_sampler_p, /): - ... +def llama_sampler_reset(smpl: llama_sampler_p, /): ... # LLAMA_API struct llama_sampler * llama_sampler_clone (const struct llama_sampler * smpl); @@ -3714,8 +3665,7 @@ def llama_sampler_reset(smpl: llama_sampler_p, /): [llama_sampler_p_ctypes], llama_sampler_p_ctypes, ) -def llama_sampler_clone(smpl: llama_sampler_p, /) -> llama_sampler_p: - ... +def llama_sampler_clone(smpl: llama_sampler_p, /) -> llama_sampler_p: ... # // important: do not free if the sampler has been added to a llama_sampler_chain (via llama_sampler_chain_add) @@ -3725,21 +3675,22 @@ def llama_sampler_clone(smpl: llama_sampler_p, /) -> llama_sampler_p: [llama_sampler_p_ctypes], None, ) -def llama_sampler_free(smpl: llama_sampler_p, /): - ... +def llama_sampler_free(smpl: llama_sampler_p, /): ... # // llama_sampler_chain # // a type of llama_sampler that can chain multiple samplers one after another + # LLAMA_API struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_params params); @ctypes_function( "llama_sampler_chain_init", [llama_sampler_chain_params], llama_sampler_p_ctypes, ) -def llama_sampler_chain_init(params: llama_sampler_chain_params, /) -> llama_sampler_p: - ... +def llama_sampler_chain_init( + params: llama_sampler_chain_params, / +) -> llama_sampler_p: ... # // important: takes ownership of the sampler object and will free it when llama_sampler_free is called @@ -3749,8 +3700,7 @@ def llama_sampler_chain_init(params: llama_sampler_chain_params, /) -> llama_sam [llama_sampler_p_ctypes, llama_sampler_p_ctypes], None, ) -def llama_sampler_chain_add(chain: llama_sampler_p, smpl: llama_sampler_p, /): - ... +def llama_sampler_chain_add(chain: llama_sampler_p, smpl: llama_sampler_p, /): ... # LLAMA_API struct llama_sampler * llama_sampler_chain_get(const struct llama_sampler * chain, int32_t i); @@ -3761,8 +3711,7 @@ def llama_sampler_chain_add(chain: llama_sampler_p, smpl: llama_sampler_p, /): ) def llama_sampler_chain_get( chain: llama_sampler_p, i: Union[ctypes.c_int32, int], / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # LLAMA_API int llama_sampler_chain_n (const struct llama_sampler * chain); @@ -3771,8 +3720,7 @@ def llama_sampler_chain_get( [llama_sampler_p_ctypes], ctypes.c_int, ) -def llama_sampler_chain_n(chain: llama_sampler_p, /) -> int: - ... +def llama_sampler_chain_n(chain: llama_sampler_p, /) -> int: ... # // after removing a sampler, the chain will no longer own it, and it will not be freed when the chain is freed @@ -3784,22 +3732,20 @@ def llama_sampler_chain_n(chain: llama_sampler_p, /) -> int: ) def llama_sampler_chain_remove( chain: llama_sampler_p, i: Union[ctypes.c_int32, int], / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # // available samplers: + # LLAMA_API struct llama_sampler * llama_sampler_init_greedy(void); @ctypes_function("llama_sampler_init_greedy", [], llama_sampler_p_ctypes) -def llama_sampler_init_greedy() -> llama_sampler_p: - ... +def llama_sampler_init_greedy() -> llama_sampler_p: ... # LLAMA_API struct llama_sampler * llama_sampler_init_dist (uint32_t seed); @ctypes_function("llama_sampler_init_dist", [ctypes.c_uint32], llama_sampler_p_ctypes) -def llama_sampler_init_dist(seed: int) -> llama_sampler_p: - ... +def llama_sampler_init_dist(seed: int) -> llama_sampler_p: ... # /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. @@ -3807,16 +3753,14 @@ def llama_sampler_init_dist(seed: int) -> llama_sampler_p: # DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_softmax (void), # "will be removed in the future (see https://github.com/ggml-org/llama.cpp/pull/9896#discussion_r1800920915)"); @ctypes_function("llama_sampler_init_softmax", [], llama_sampler_p_ctypes) -def llama_sampler_init_softmax() -> llama_sampler_p: - ... +def llama_sampler_init_softmax() -> llama_sampler_p: ... # /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 # /// Setting k <= 0 makes this a noop # LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k); @ctypes_function("llama_sampler_init_top_k", [ctypes.c_int32], llama_sampler_p_ctypes) -def llama_sampler_init_top_k(k: int) -> llama_sampler_p: - ... +def llama_sampler_init_top_k(k: int) -> llama_sampler_p: ... # /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 @@ -3826,8 +3770,7 @@ def llama_sampler_init_top_k(k: int) -> llama_sampler_p: [ctypes.c_float, ctypes.c_size_t], llama_sampler_p_ctypes, ) -def llama_sampler_init_top_p(p: float, min_keep: int) -> llama_sampler_p: - ... +def llama_sampler_init_top_p(p: float, min_keep: int) -> llama_sampler_p: ... # /// @details Minimum P sampling as described in https://github.com/ggml-org/llama.cpp/pull/3841 @@ -3837,8 +3780,7 @@ def llama_sampler_init_top_p(p: float, min_keep: int) -> llama_sampler_p: [ctypes.c_float, ctypes.c_size_t], llama_sampler_p_ctypes, ) -def llama_sampler_init_min_p(p: float, min_keep: int) -> llama_sampler_p: - ... +def llama_sampler_init_min_p(p: float, min_keep: int) -> llama_sampler_p: ... # /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666. @@ -3848,15 +3790,13 @@ def llama_sampler_init_min_p(p: float, min_keep: int) -> llama_sampler_p: [ctypes.c_float, ctypes.c_size_t], llama_sampler_p_ctypes, ) -def llama_sampler_init_typical(p: float, min_keep: int) -> llama_sampler_p: - ... +def llama_sampler_init_typical(p: float, min_keep: int) -> llama_sampler_p: ... # /// #details Updates the logits l_i` = l_i/t. When t <= 0.0f, the maximum logit is kept at it's original value, the rest are set to -inf # LLAMA_API struct llama_sampler * llama_sampler_init_temp (float t); @ctypes_function("llama_sampler_init_temp", [ctypes.c_float], llama_sampler_p_ctypes) -def llama_sampler_init_temp(t: float) -> llama_sampler_p: - ... +def llama_sampler_init_temp(t: float) -> llama_sampler_p: ... # /// @details Dynamic temperature implementation (a.k.a. entropy) described in the paper https://arxiv.org/abs/2309.02772. @@ -3868,8 +3808,7 @@ def llama_sampler_init_temp(t: float) -> llama_sampler_p: ) def llama_sampler_init_temp_ext( t: float, delta: float, exponent: float -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # /// @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335 @@ -3881,8 +3820,7 @@ def llama_sampler_init_temp_ext( ) def llama_sampler_init_xtc( p: float, t: float, min_keep: int, seed: int, / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # /// @details Top n sigma sampling as described in academic paper "Top-nσ: Not All Logits Are You Need" https://arxiv.org/pdf/2411.07641 @@ -3892,8 +3830,7 @@ def llama_sampler_init_xtc( [ctypes.c_float], llama_sampler_p_ctypes, ) -def llama_sampler_init_top_n_sigma(n: float, /) -> llama_sampler_p: - ... +def llama_sampler_init_top_n_sigma(n: float, /) -> llama_sampler_p: ... # /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. @@ -3910,8 +3847,7 @@ def llama_sampler_init_top_n_sigma(n: float, /) -> llama_sampler_p: ) def llama_sampler_init_mirostat( n_vocab: int, seed: int, tau: float, eta: float, m: int, / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. @@ -3926,8 +3862,7 @@ def llama_sampler_init_mirostat( ) def llama_sampler_init_mirostat_v2( seed: int, tau: float, eta: float, / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # /// @details Intializes a GBNF grammar, see grammars/README.md for details. @@ -3942,8 +3877,7 @@ def llama_sampler_init_mirostat_v2( ) def llama_sampler_init_grammar( vocab: llama_vocab_p, grammar_str: bytes, grammar_root: bytes, / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy( @@ -3977,8 +3911,7 @@ def llama_sampler_init_grammar_lazy( trigger_tokens: CtypesArray[llama_token], num_trigger_tokens: int, /, -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # /// @details Lazy grammar sampler, introduced in https://github.com/ggml-org/llama.cpp/pull/9639 @@ -4012,8 +3945,7 @@ def llama_sampler_init_grammar_lazy_patterns( trigger_tokens: CtypesArray[llama_token], num_trigger_tokens: int, /, -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # /// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first. @@ -4033,8 +3965,7 @@ def llama_sampler_init_penalties( penalty_freq: float, penalty_present: float, /, -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # /// @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982 @@ -4071,8 +4002,7 @@ def llama_sampler_init_dry( seq_breakers, num_breakers: int, /, -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias( @@ -4086,8 +4016,7 @@ def llama_sampler_init_dry( ) def llama_sampler_init_logit_bias( n_vocab: int, n_logit_bias: int, logit_bias: CtypesArray[llama_logit_bias], / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # // this sampler is meant to be used for fill-in-the-middle infilling @@ -4097,8 +4026,7 @@ def llama_sampler_init_logit_bias( [llama_vocab_p_ctypes], llama_sampler_p_ctypes, ) -def llama_sampler_init_infill(vocab: llama_vocab_p, /) -> llama_sampler_p: - ... +def llama_sampler_init_infill(vocab: llama_vocab_p, /) -> llama_sampler_p: ... # // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise @@ -4108,8 +4036,7 @@ def llama_sampler_init_infill(vocab: llama_vocab_p, /) -> llama_sampler_p: [llama_sampler_p_ctypes], ctypes.c_uint32, ) -def llama_sampler_get_seed(smpl: llama_sampler_p, /) -> int: - ... +def llama_sampler_get_seed(smpl: llama_sampler_p, /) -> int: ... # /// @details Sample and accept a token from the idx-th output of the last evaluation @@ -4121,14 +4048,14 @@ def llama_sampler_get_seed(smpl: llama_sampler_p, /) -> int: ) def llama_sampler_sample( smpl: llama_sampler_p, ctx: llama_context_p, idx: int, / -) -> int: - ... +) -> int: ... # // # // Model split # // + # /// @details Build a split GGUF final path for this chunk. # LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count); @ctypes_function( @@ -4170,8 +4097,7 @@ def llama_split_prefix( # // Print system information # LLAMA_API const char * llama_print_system_info(void); @ctypes_function("llama_print_system_info", [], ctypes.c_char_p) -def llama_print_system_info() -> bytes: - ... +def llama_print_system_info() -> bytes: ... # // Set callback for all future logging events. @@ -4203,6 +4129,7 @@ def llama_log_set( # double t_p_eval_ms; # double t_eval_ms; + # int32_t n_p_eval; # int32_t n_eval; # int32_t n_reused; // number of times a ggml compute graph had been reused @@ -4222,6 +4149,7 @@ class llama_perf_context_data(ctypes.Structure): # struct llama_perf_sampler_data { # double t_sample_ms; + # int32_t n_sample; # }; class llama_perf_sampler_data(ctypes.Structure): @@ -4237,8 +4165,7 @@ class llama_perf_sampler_data(ctypes.Structure): [llama_context_p_ctypes], llama_perf_context_data, ) -def llama_perf_context(ctx: llama_context_p, /) -> llama_perf_context_data: - ... +def llama_perf_context(ctx: llama_context_p, /) -> llama_perf_context_data: ... # LLAMA_API void llama_perf_context_print(const struct llama_context * ctx); @@ -4247,8 +4174,7 @@ def llama_perf_context(ctx: llama_context_p, /) -> llama_perf_context_data: [llama_context_p_ctypes], None, ) -def llama_perf_context_print(ctx: llama_context_p, /): - ... +def llama_perf_context_print(ctx: llama_context_p, /): ... # LLAMA_API void llama_perf_context_reset( struct llama_context * ctx); @@ -4257,8 +4183,7 @@ def llama_perf_context_print(ctx: llama_context_p, /): [llama_context_p_ctypes], None, ) -def llama_perf_context_reset(ctx: llama_context_p, /): - ... +def llama_perf_context_reset(ctx: llama_context_p, /): ... # // NOTE: the following work only with samplers constructed via llama_sampler_chain_init @@ -4268,8 +4193,7 @@ def llama_perf_context_reset(ctx: llama_context_p, /): [llama_sampler_p_ctypes], llama_perf_sampler_data, ) -def llama_perf_sampler(chain: llama_sampler_p, /) -> llama_perf_sampler_data: - ... +def llama_perf_sampler(chain: llama_sampler_p, /) -> llama_perf_sampler_data: ... # LLAMA_API void llama_perf_sampler_print(const struct llama_sampler * chain); @@ -4278,8 +4202,7 @@ def llama_perf_sampler(chain: llama_sampler_p, /) -> llama_perf_sampler_data: [llama_sampler_p_ctypes], None, ) -def llama_perf_sampler_print(chain: llama_sampler_p, /): - ... +def llama_perf_sampler_print(chain: llama_sampler_p, /): ... # LLAMA_API void llama_perf_sampler_reset( struct llama_sampler * chain); @@ -4288,8 +4211,7 @@ def llama_perf_sampler_print(chain: llama_sampler_p, /): [llama_sampler_p_ctypes], None, ) -def llama_perf_sampler_reset(chain: llama_sampler_p, /): - ... +def llama_perf_sampler_reset(chain: llama_sampler_p, /): ... # // @@ -4298,7 +4220,10 @@ def llama_perf_sampler_reset(chain: llama_sampler_p, /): # // function that returns whether or not a given tensor contains trainable parameters # typedef bool (*llama_opt_param_filter)(const struct ggml_tensor * tensor, void * userdata); -llama_opt_param_filter = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_void_p, ctypes.c_void_p) +llama_opt_param_filter = ctypes.CFUNCTYPE( + ctypes.c_bool, ctypes.c_void_p, ctypes.c_void_p +) + # // always returns true # LLAMA_API bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata); @@ -4307,8 +4232,9 @@ def llama_perf_sampler_reset(chain: llama_sampler_p, /): [ctypes.c_void_p, ctypes.c_void_p], ctypes.c_bool, ) -def llama_opt_param_filter_all(tensor: ctypes.c_void_p, userdata: ctypes.c_void_p, /) -> bool: - ... +def llama_opt_param_filter_all( + tensor: ctypes.c_void_p, userdata: ctypes.c_void_p, / +) -> bool: ... # struct llama_opt_params { @@ -4317,6 +4243,7 @@ def llama_opt_param_filter_all(tensor: ctypes.c_void_p, userdata: ctypes.c_void_ # llama_opt_param_filter param_filter; // callback for determining which tensors contain trainable parameters # void * param_filter_ud; // userdata for determining which tensors contain trainable parameters + # ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters # void * get_opt_pars_ud; // userdata for calculating optimizer parameters # }; @@ -4325,7 +4252,10 @@ class llama_opt_params(ctypes.Structure): ("n_ctx_train", ctypes.c_uint32), ("param_filter", llama_opt_param_filter), ("param_filter_ud", ctypes.c_void_p), - ("get_opt_pars", ctypes.c_void_p), # ggml_opt_get_optimizer_params - not implemented here + ( + "get_opt_pars", + ctypes.c_void_p, + ), # ggml_opt_get_optimizer_params - not implemented here ("get_opt_pars_ud", ctypes.c_void_p), ] @@ -4336,8 +4266,9 @@ class llama_opt_params(ctypes.Structure): [llama_context_p_ctypes, llama_model_p_ctypes, llama_opt_params], None, ) -def llama_opt_init(lctx: llama_context_p, model: llama_model_p, lopt_params: llama_opt_params, /): - ... +def llama_opt_init( + lctx: llama_context_p, model: llama_model_p, lopt_params: llama_opt_params, / +): ... # LLAMA_API void llama_opt_epoch( @@ -4353,7 +4284,7 @@ def llama_opt_init(lctx: llama_context_p, model: llama_model_p, lopt_params: lla [ llama_context_p_ctypes, ctypes.c_void_p, # ggml_opt_dataset_t - ctypes.c_void_p, # ggml_opt_result_t + ctypes.c_void_p, # ggml_opt_result_t ctypes.c_void_p, # ggml_opt_result_t ctypes.c_int64, ctypes.c_void_p, # ggml_opt_epoch_callback @@ -4370,5 +4301,4 @@ def llama_opt_epoch( callback_train: ctypes.c_void_p, callback_eval: ctypes.c_void_p, /, -): - ... +): ... diff --git a/llama_cpp/llama_grammar.py b/llama_cpp/llama_grammar.py index b95c77ab5e..ba34dda831 100644 --- a/llama_cpp/llama_grammar.py +++ b/llama_cpp/llama_grammar.py @@ -297,7 +297,7 @@ def opt_repetitions(up_to_n, prefix_with_sep=False): if max_items is not None: result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0) else: - item_operator = f'({separator_rule + " " if separator_rule else ""}{item_rule})' + item_operator = f"({separator_rule + ' ' if separator_rule else ''}{item_rule})" if min_items == 0 and separator_rule: result = f"({item_rule} {item_operator}*)?" @@ -450,9 +450,9 @@ def visit(n: dict): ref = n.get("$ref") if ref is not None and ref not in self._refs: if ref.startswith("https://"): - assert ( - self._allow_fetch - ), "Fetching remote schemas is not allowed (use --allow-fetch for force)" + assert self._allow_fetch, ( + "Fetching remote schemas is not allowed (use --allow-fetch for force)" + ) import requests frag_split = ref.split("#") @@ -475,9 +475,9 @@ def visit(n: dict): raise ValueError(f"Unsupported ref {ref}") for sel in ref.split("#")[-1].split("/")[1:]: - assert ( - target is not None and sel in target - ), f"Error resolving ref {ref}: {sel} not in {target}" + assert target is not None and sel in target, ( + f"Error resolving ref {ref}: {sel} not in {target}" + ) target = target[sel] self._refs[ref] = target @@ -492,7 +492,7 @@ def visit(n: dict): def _generate_union_rule(self, name, alt_schemas): return " | ".join( ( - self.visit(alt_schema, f'{name}{"-" if name else "alternative-"}{i}') + self.visit(alt_schema, f"{name}{'-' if name else 'alternative-'}{i}") for i, alt_schema in enumerate(alt_schemas) ) ) @@ -510,9 +510,9 @@ def _visit_pattern(self, pattern, name): we define sub-rules to keep the output lean. """ - assert pattern.startswith("^") and pattern.endswith( - "$" - ), 'Pattern must start with "^" and end with "$"' + assert pattern.startswith("^") and pattern.endswith("$"), ( + 'Pattern must start with "^" and end with "$"' + ) pattern = pattern[1:-1] sub_rule_ids = {} @@ -566,15 +566,15 @@ def join_seq(): elif c == "(": i += 1 if i < length: - assert ( - pattern[i] != "?" - ), f'Unsupported pattern syntax "{pattern[i]}" at index {i} of /{pattern}/' + assert pattern[i] != "?", ( + f'Unsupported pattern syntax "{pattern[i]}" at index {i} of /{pattern}/' + ) seq.append((f"({to_rule(transform())})", False)) elif c == ")": i += 1 - assert ( - start > 0 and pattern[start - 1] == "(" - ), f"Unbalanced parentheses; start = {start}, i = {i}, pattern = {pattern}" + assert start > 0 and pattern[start - 1] == "(", ( + f"Unbalanced parentheses; start = {start}, i = {i}, pattern = {pattern}" + ) return join_seq() elif c == "[": square_brackets = c @@ -586,9 +586,9 @@ def join_seq(): else: square_brackets += pattern[i] i += 1 - assert ( - i < length - ), f"Unbalanced square brackets; start = {start}, i = {i}, pattern = {pattern}" + assert i < length, ( + f"Unbalanced square brackets; start = {start}, i = {i}, pattern = {pattern}" + ) square_brackets += "]" i += 1 seq.append((square_brackets, False)) @@ -604,9 +604,9 @@ def join_seq(): while i < length and pattern[i] != "}": curly_brackets += pattern[i] i += 1 - assert ( - i < length - ), f"Unbalanced curly brackets; start = {start}, i = {i}, pattern = {pattern}" + assert i < length, ( + f"Unbalanced curly brackets; start = {start}, i = {i}, pattern = {pattern}" + ) curly_brackets += "}" i += 1 nums = [s.strip() for s in curly_brackets[1:-1].split(",")] @@ -777,13 +777,13 @@ def add_component(comp_schema, is_required): rule_name, '"[" space ' + ' "," space '.join( - self.visit(item, f'{name}{"-" if name else ""}tuple-{i}') + self.visit(item, f"{name}{'-' if name else ''}tuple-{i}") for i, item in enumerate(items) ) + ' "]" space', ) else: - item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item') + item_rule_name = self.visit(items, f"{name}{'-' if name else ''}item") min_items = schema.get("minItems", 0) max_items = schema.get("maxItems") return self._add_rule( @@ -873,17 +873,17 @@ def _build_object_rule( prop_kv_rule_names = {} for prop_name, prop_schema in properties: prop_rule_name = self.visit( - prop_schema, f'{name}{"-" if name else ""}{prop_name}' + prop_schema, f"{name}{'-' if name else ''}{prop_name}" ) prop_kv_rule_names[prop_name] = self._add_rule( - f'{name}{"-" if name else ""}{prop_name}-kv', + f"{name}{'-' if name else ''}{prop_name}-kv", rf'{self._format_literal(json.dumps(prop_name))} space ":" space {prop_rule_name}', ) required_props = [k for k in sorted_props if k in required] optional_props = [k for k in sorted_props if k not in required] if additional_properties == True or isinstance(additional_properties, dict): - sub_name = f'{name}{"-" if name else ""}additional' + sub_name = f"{name}{'-' if name else ''}additional" value_rule = self.visit( {} if additional_properties == True else additional_properties, f"{sub_name}-value", @@ -908,7 +908,7 @@ def get_recursive_refs(ks, first_is_optional): kv_rule_name = prop_kv_rule_names[k] if k == "*": res = self._add_rule( - f'{name}{"-" if name else ""}additional-kvs', + f"{name}{'-' if name else ''}additional-kvs", f'{kv_rule_name} ( "," space ' + kv_rule_name + " )*", ) elif first_is_optional: @@ -917,7 +917,7 @@ def get_recursive_refs(ks, first_is_optional): res = kv_rule_name if len(rest) > 0: res += " " + self._add_rule( - f'{name}{"-" if name else ""}{k}-rest', + f"{name}{'-' if name else ''}{k}-rest", get_recursive_refs(rest, first_is_optional=True), ) return res diff --git a/llama_cpp/llava_cpp.py b/llama_cpp/llava_cpp.py index d9dfaf5fd0..3dc96d2f25 100644 --- a/llama_cpp/llava_cpp.py +++ b/llama_cpp/llava_cpp.py @@ -36,7 +36,11 @@ # Specify the base name of the shared library to load _libllava_base_name = "llava" _libllava_override_path = os.environ.get("LLAVA_CPP_LIB") -_libllava_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" if _libllava_override_path is None else pathlib.Path() +_libllava_base_path = ( + pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" + if _libllava_override_path is None + else pathlib.Path() +) # Load the library _libllava = load_shared_library(_libllava_base_name, _libllava_base_path) @@ -73,8 +77,7 @@ class llava_image_embed(Structure): ) def llava_validate_embed_size( ctx_llama: llama_cpp.llama_context_p, ctx_clip: clip_ctx_p, / -) -> bool: - ... +) -> bool: ... # /** build an image embed from image file bytes */ @@ -90,8 +93,7 @@ def llava_image_embed_make_with_bytes( image_bytes: CtypesArray[c_uint8], image_bytes_length: Union[c_int, int], /, -) -> "_Pointer[llava_image_embed]": - ... +) -> "_Pointer[llava_image_embed]": ... # /** build an image embed from a path to an image filename */ @@ -103,15 +105,13 @@ def llava_image_embed_make_with_bytes( ) def llava_image_embed_make_with_filename( ctx_clip: clip_ctx_p, n_threads: Union[c_int, int], image_path: bytes, / -) -> "_Pointer[llava_image_embed]": - ... +) -> "_Pointer[llava_image_embed]": ... # LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed); # /** free an embedding made with llava_image_embed_make_* */ @ctypes_function("llava_image_embed_free", [POINTER(llava_image_embed)], None) -def llava_image_embed_free(embed: "_Pointer[llava_image_embed]", /): - ... +def llava_image_embed_free(embed: "_Pointer[llava_image_embed]", /): ... # /** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */ @@ -132,8 +132,7 @@ def llava_eval_image_embed( n_batch: Union[c_int, int], n_past: "_Pointer[c_int]", /, -) -> bool: - ... +) -> bool: ... ################################################ @@ -146,13 +145,10 @@ def llava_eval_image_embed( @ctypes_function("clip_model_load", [c_char_p, c_int], clip_ctx_p_ctypes) def clip_model_load( fname: bytes, verbosity: Union[c_int, int], / -) -> Optional[clip_ctx_p]: - ... +) -> Optional[clip_ctx_p]: ... # /** free mmproj model */ # CLIP_API void clip_free(struct clip_ctx * ctx); @ctypes_function("clip_free", [clip_ctx_p_ctypes], None) -def clip_free(ctx: clip_ctx_p, /): - ... - +def clip_free(ctx: clip_ctx_p, /): ... diff --git a/llama_cpp/mtmd_cpp.py b/llama_cpp/mtmd_cpp.py index a45f8f4064..41753a7f60 100644 --- a/llama_cpp/mtmd_cpp.py +++ b/llama_cpp/mtmd_cpp.py @@ -39,7 +39,11 @@ # Specify the base name of the shared library to load _libmtmd_base_name = "mtmd" _libmtmd_override_path = os.environ.get("MTMD_CPP_LIB") -_libmtmd_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" if _libmtmd_override_path is None else pathlib.Path() +_libmtmd_base_path = ( + pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" + if _libmtmd_override_path is None + else pathlib.Path() +) # Load the library _libmtmd = load_shared_library(_libmtmd_base_name, _libmtmd_base_path) @@ -71,6 +75,7 @@ MTMD_INPUT_CHUNK_TYPE_IMAGE = 1 MTMD_INPUT_CHUNK_TYPE_AUDIO = 2 + # Structures class mtmd_context_params(Structure): _fields_ = [ @@ -82,6 +87,7 @@ class mtmd_context_params(Structure): ("media_marker", c_char_p), ] + class mtmd_input_text(Structure): _fields_ = [ ("text", c_char_p), @@ -89,19 +95,21 @@ class mtmd_input_text(Structure): ("parse_special", c_bool), ] + ################################################ # mtmd.h functions ################################################ + # MTMD_API const char * mtmd_default_marker(void); @ctypes_function("mtmd_default_marker", [], c_char_p) -def mtmd_default_marker() -> bytes: - ... +def mtmd_default_marker() -> bytes: ... + # MTMD_API struct mtmd_context_params mtmd_context_params_default(void); @ctypes_function("mtmd_context_params_default", [], mtmd_context_params) -def mtmd_context_params_default() -> mtmd_context_params: - ... +def mtmd_context_params_default() -> mtmd_context_params: ... + # MTMD_API mtmd_context * mtmd_init_from_file(const char * mmproj_fname, # const struct llama_model * text_model, @@ -109,70 +117,68 @@ def mtmd_context_params_default() -> mtmd_context_params: @ctypes_function( "mtmd_init_from_file", [c_char_p, llama_cpp.llama_model_p_ctypes, mtmd_context_params], - mtmd_context_p_ctypes + mtmd_context_p_ctypes, ) def mtmd_init_from_file( mmproj_fname: bytes, text_model: llama_cpp.llama_model_p, ctx_params: mtmd_context_params, /, -) -> Optional[mtmd_context_p]: - ... +) -> Optional[mtmd_context_p]: ... + # MTMD_API void mtmd_free(mtmd_context * ctx); @ctypes_function("mtmd_free", [mtmd_context_p_ctypes], None) -def mtmd_free(ctx: mtmd_context_p, /): - ... +def mtmd_free(ctx: mtmd_context_p, /): ... + # MTMD_API bool mtmd_support_vision(mtmd_context * ctx); @ctypes_function("mtmd_support_vision", [mtmd_context_p_ctypes], c_bool) -def mtmd_support_vision(ctx: mtmd_context_p, /) -> bool: - ... +def mtmd_support_vision(ctx: mtmd_context_p, /) -> bool: ... + # MTMD_API mtmd_bitmap * mtmd_bitmap_init(uint32_t nx, uint32_t ny, const unsigned char * data); @ctypes_function( - "mtmd_bitmap_init", - [c_uint32, c_uint32, POINTER(c_uint8)], - mtmd_bitmap_p_ctypes + "mtmd_bitmap_init", [c_uint32, c_uint32, POINTER(c_uint8)], mtmd_bitmap_p_ctypes ) def mtmd_bitmap_init( nx: Union[c_uint32, int], ny: Union[c_uint32, int], data: CtypesArray[c_uint8], /, -) -> Optional[mtmd_bitmap_p]: - ... +) -> Optional[mtmd_bitmap_p]: ... + # MTMD_API void mtmd_bitmap_free(mtmd_bitmap * bitmap); @ctypes_function("mtmd_bitmap_free", [mtmd_bitmap_p_ctypes], None) -def mtmd_bitmap_free(bitmap: mtmd_bitmap_p, /): - ... +def mtmd_bitmap_free(bitmap: mtmd_bitmap_p, /): ... + # MTMD_API mtmd_input_chunks * mtmd_input_chunks_init(void); @ctypes_function("mtmd_input_chunks_init", [], mtmd_input_chunks_p_ctypes) -def mtmd_input_chunks_init() -> Optional[mtmd_input_chunks_p]: - ... +def mtmd_input_chunks_init() -> Optional[mtmd_input_chunks_p]: ... + # MTMD_API void mtmd_input_chunks_free(mtmd_input_chunks * chunks); @ctypes_function("mtmd_input_chunks_free", [mtmd_input_chunks_p_ctypes], None) -def mtmd_input_chunks_free(chunks: mtmd_input_chunks_p, /): - ... +def mtmd_input_chunks_free(chunks: mtmd_input_chunks_p, /): ... + # MTMD_API size_t mtmd_input_chunks_size(const mtmd_input_chunks * chunks); @ctypes_function("mtmd_input_chunks_size", [mtmd_input_chunks_p_ctypes], c_size_t) -def mtmd_input_chunks_size(chunks: mtmd_input_chunks_p, /) -> int: - ... +def mtmd_input_chunks_size(chunks: mtmd_input_chunks_p, /) -> int: ... + # MTMD_API const mtmd_input_chunk * mtmd_input_chunks_get(const mtmd_input_chunks * chunks, size_t idx); @ctypes_function( "mtmd_input_chunks_get", [mtmd_input_chunks_p_ctypes, c_size_t], - mtmd_input_chunk_p_ctypes + mtmd_input_chunk_p_ctypes, ) def mtmd_input_chunks_get( chunks: mtmd_input_chunks_p, idx: Union[c_size_t, int], / -) -> Optional[mtmd_input_chunk_p]: - ... +) -> Optional[mtmd_input_chunk_p]: ... + # MTMD_API int32_t mtmd_tokenize(mtmd_context * ctx, # mtmd_input_chunks * output, @@ -197,52 +203,53 @@ def mtmd_tokenize( bitmaps: CtypesArray[mtmd_bitmap_p_ctypes], n_bitmaps: Union[c_size_t, int], /, -) -> int: - ... +) -> int: ... + # MTMD_API size_t mtmd_input_chunk_get_n_tokens(const mtmd_input_chunk * chunk); @ctypes_function("mtmd_input_chunk_get_n_tokens", [mtmd_input_chunk_p_ctypes], c_size_t) -def mtmd_input_chunk_get_n_tokens(chunk: mtmd_input_chunk_p, /) -> int: - ... +def mtmd_input_chunk_get_n_tokens(chunk: mtmd_input_chunk_p, /) -> int: ... + # MTMD_API enum mtmd_input_chunk_type mtmd_input_chunk_get_type(const mtmd_input_chunk * chunk); @ctypes_function("mtmd_input_chunk_get_type", [mtmd_input_chunk_p_ctypes], c_int) -def mtmd_input_chunk_get_type(chunk: mtmd_input_chunk_p, /) -> int: - ... +def mtmd_input_chunk_get_type(chunk: mtmd_input_chunk_p, /) -> int: ... + # MTMD_API const llama_token * mtmd_input_chunk_get_tokens_text(const mtmd_input_chunk * chunk, size_t * n_tokens_output); @ctypes_function( "mtmd_input_chunk_get_tokens_text", [mtmd_input_chunk_p_ctypes, POINTER(c_size_t)], - POINTER(llama_cpp.llama_token) + POINTER(llama_cpp.llama_token), ) def mtmd_input_chunk_get_tokens_text( chunk: mtmd_input_chunk_p, n_tokens_output: "_Pointer[c_size_t]", / -) -> Optional["_Pointer[llama_cpp.llama_token]"]: - ... +) -> Optional["_Pointer[llama_cpp.llama_token]"]: ... + ################################################ # mtmd-helper.h functions ################################################ + # MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len); @ctypes_function( "mtmd_helper_bitmap_init_from_buf", [mtmd_context_p_ctypes, POINTER(c_uint8), c_size_t], - mtmd_bitmap_p_ctypes + mtmd_bitmap_p_ctypes, ) def mtmd_helper_bitmap_init_from_buf( ctx: mtmd_context_p, buf: CtypesArray[c_uint8], length: Union[c_size_t, int], /, -) -> Optional[mtmd_bitmap_p]: - ... +) -> Optional[mtmd_bitmap_p]: ... + # MTMD_API size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks); @ctypes_function("mtmd_helper_get_n_tokens", [mtmd_input_chunks_p_ctypes], c_size_t) -def mtmd_helper_get_n_tokens(chunks: mtmd_input_chunks_p, /) -> int: - ... +def mtmd_helper_get_n_tokens(chunks: mtmd_input_chunks_p, /) -> int: ... + # MTMD_API int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx, # struct llama_context * lctx, @@ -276,5 +283,4 @@ def mtmd_helper_eval_chunk_single( logits_last: Union[c_bool, bool], new_n_past: "_Pointer[llama_cpp.llama_pos]", /, -) -> int: - ... +) -> int: ... diff --git a/llama_cpp/server/app.py b/llama_cpp/server/app.py index 5120f24162..f776fe159c 100644 --- a/llama_cpp/server/app.py +++ b/llama_cpp/server/app.py @@ -125,9 +125,9 @@ def create_app( server_settings = ServerSettings.model_validate(settings) model_settings = [ModelSettings.model_validate(settings)] - assert ( - server_settings is not None and model_settings is not None - ), "server_settings and model_settings must be provided together" + assert server_settings is not None and model_settings is not None, ( + "server_settings and model_settings must be provided together" + ) set_server_settings(server_settings) middleware = [Middleware(RawContextMiddleware, plugins=(RequestIdPlugin(),))] diff --git a/llama_cpp/server/cli.py b/llama_cpp/server/cli.py index 3dd0076767..8ed0290637 100644 --- a/llama_cpp/server/cli.py +++ b/llama_cpp/server/cli.py @@ -14,7 +14,9 @@ def _get_base_type(annotation: Type[Any]) -> Type[Any]: elif getattr(annotation, "__origin__", None) is Union: assert hasattr(annotation, "__args__") and len(annotation.__args__) >= 1 # type: ignore non_optional_args: List[Type[Any]] = [ - arg for arg in annotation.__args__ if arg is not type(None) # type: ignore + arg + for arg in annotation.__args__ + if arg is not type(None) # type: ignore ] if non_optional_args: return _get_base_type(non_optional_args[0]) diff --git a/llama_cpp/server/model.py b/llama_cpp/server/model.py index 11bd363b56..9e59e85633 100644 --- a/llama_cpp/server/model.py +++ b/llama_cpp/server/model.py @@ -186,18 +186,18 @@ def load_llama_from_model_settings(settings: ModelSettings) -> llama_cpp.Llama: clip_model_path=settings.clip_model_path, verbose=settings.verbose ) elif settings.chat_format == "hf-autotokenizer": - assert ( - settings.hf_pretrained_model_name_or_path is not None - ), "hf_pretrained_model_name_or_path must be set for hf-autotokenizer" + assert settings.hf_pretrained_model_name_or_path is not None, ( + "hf_pretrained_model_name_or_path must be set for hf-autotokenizer" + ) chat_handler = ( llama_cpp.llama_chat_format.hf_autotokenizer_to_chat_completion_handler( settings.hf_pretrained_model_name_or_path ) ) elif settings.chat_format == "hf-tokenizer-config": - assert ( - settings.hf_tokenizer_config_path is not None - ), "hf_tokenizer_config_path must be set for hf-tokenizer-config" + assert settings.hf_tokenizer_config_path is not None, ( + "hf_tokenizer_config_path must be set for hf-tokenizer-config" + ) chat_handler = llama_cpp.llama_chat_format.hf_tokenizer_config_to_chat_completion_handler( json.load(open(settings.hf_tokenizer_config_path)) ) diff --git a/pyproject.toml b/pyproject.toml index f5ae7b59c7..e0b0dc520c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,7 +49,7 @@ test = [ "huggingface-hub>=0.23.0" ] dev = [ - "black>=23.3.0", + "ruff>=0.15.7", "twine>=4.0.2", "mkdocs>=1.4.3", "mkdocstrings[python]>=0.22.0", @@ -78,5 +78,16 @@ Issues = "https://github.com/abetlen/llama-cpp-python/issues" Documentation = "https://llama-cpp-python.readthedocs.io/en/latest/" Changelog = "https://llama-cpp-python.readthedocs.io/en/latest/changelog/" +[tool.ruff] +target-version = "py38" +line-length = 88 +required-version = ">=0.15.7" +src = ["llama_cpp", "tests"] +extend-exclude = ["vendor", "examples/notebooks"] + +[tool.ruff.lint] +select = ["E4", "E7", "E9"] +ignore = ["E712"] + [tool.pytest.ini_options] testpaths = "tests" diff --git a/tests/test_llama.py b/tests/test_llama.py index 0a1a9f5ad3..964b0895cc 100644 --- a/tests/test_llama.py +++ b/tests/test_llama.py @@ -66,6 +66,7 @@ def llama_cpp_model_path(): def test_real_model(llama_cpp_model_path): import os + assert os.path.exists(llama_cpp_model_path) params = llama_cpp.llama_model_default_params() @@ -114,6 +115,7 @@ def test_real_model(llama_cpp_model_path): output_text = model.detokenize(output, special=True) assert output_text == b" over the lazy dog" + def test_real_llama(llama_cpp_model_path): model = llama_cpp.Llama( llama_cpp_model_path, @@ -132,11 +134,10 @@ def test_real_llama(llama_cpp_model_path): top_k=50, top_p=0.9, temperature=0.8, - seed=1337 + seed=1337, ) assert output["choices"][0]["text"] == " over the lazy dog" - output = model.create_completion( "The capital of france is paris, 'true' or 'false'?:\n", max_tokens=4, @@ -146,20 +147,19 @@ def test_real_llama(llama_cpp_model_path): seed=1337, grammar=llama_cpp.LlamaGrammar.from_string(""" root ::= "true" | "false" -""") +"""), ) assert output["choices"][0]["text"] == "true" suffix = b"rot" tokens = model.tokenize(suffix, add_bos=True, special=True) + def logit_processor_func(input_ids, logits): for token in tokens: logits[token] *= 1000 return logits - logit_processors = llama_cpp.LogitsProcessorList( - [logit_processor_func] - ) + logit_processors = llama_cpp.LogitsProcessorList([logit_processor_func]) output = model.create_completion( "The capital of france is par", @@ -168,7 +168,7 @@ def logit_processor_func(input_ids, logits): top_p=0.9, temperature=0.8, seed=1337, - logits_processor=logit_processors + logits_processor=logit_processors, ) assert output["choices"][0]["text"].lower().startswith("rot") @@ -184,7 +184,7 @@ def logit_processor_func(input_ids, logits): temperature=0.8, grammar=llama_cpp.LlamaGrammar.from_string(""" root ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" | "10" -""") +"""), ) number_1 = output["choices"][0]["text"] @@ -196,7 +196,7 @@ def logit_processor_func(input_ids, logits): temperature=0.8, grammar=llama_cpp.LlamaGrammar.from_string(""" root ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" | "10" -""") +"""), ) number_2 = output["choices"][0]["text"] @@ -210,7 +210,7 @@ def logit_processor_func(input_ids, logits): temperature=0.8, grammar=llama_cpp.LlamaGrammar.from_string(""" root ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" | "10" -""") +"""), ) number_3 = output["choices"][0]["text"] @@ -228,7 +228,7 @@ def test_real_llama_embeddings(llama_cpp_model_path): n_threads_batch=multiprocessing.cpu_count(), logits_all=False, flash_attn=True, - embedding=True + embedding=True, ) # Smoke test for now model.embed("Hello World") diff --git a/tests/test_llama_chat_format.py b/tests/test_llama_chat_format.py index f031bf72b7..18c7279cf0 100644 --- a/tests/test_llama_chat_format.py +++ b/tests/test_llama_chat_format.py @@ -10,13 +10,20 @@ from llama_cpp.llama_chat_format import hf_tokenizer_config_to_chat_formatter + def test_mistral_instruct(): chat_template = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}" chat_formatter = jinja2.Template(chat_template) messages = [ - llama_types.ChatCompletionRequestUserMessage(role="user", content="Instruction"), - llama_types.ChatCompletionRequestAssistantMessage(role="assistant", content="Model answer"), - llama_types.ChatCompletionRequestUserMessage(role="user", content="Follow-up instruction"), + llama_types.ChatCompletionRequestUserMessage( + role="user", content="Instruction" + ), + llama_types.ChatCompletionRequestAssistantMessage( + role="assistant", content="Model answer" + ), + llama_types.ChatCompletionRequestUserMessage( + role="user", content="Follow-up instruction" + ), ] response = llama_chat_format.format_mistral_instruct( messages=messages, @@ -77,13 +84,11 @@ def test_mistral_instruct(): def test_hf_tokenizer_config_str_to_chat_formatter(): tokenizer_config = json.loads(mistral_7b_tokenizer_config) - chat_formatter = hf_tokenizer_config_to_chat_formatter( - tokenizer_config - ) + chat_formatter = hf_tokenizer_config_to_chat_formatter(tokenizer_config) chat_formatter_respoonse = chat_formatter( messages=[ ChatCompletionRequestUserMessage(role="user", content="Hello, world!"), ] ) - assert chat_formatter_respoonse.prompt == ("[INST] Hello, world! [/INST]" "") + assert chat_formatter_respoonse.prompt == ("[INST] Hello, world! [/INST]") diff --git a/tests/test_llama_speculative.py b/tests/test_llama_speculative.py index b5d450567b..d28c9ca9c6 100644 --- a/tests/test_llama_speculative.py +++ b/tests/test_llama_speculative.py @@ -2,15 +2,20 @@ from llama_cpp.llama_speculative import LlamaPromptLookupDecoding + def test_find_candidate_pred_tokens(): find_candidate_pred_tokens = LlamaPromptLookupDecoding.find_candidate_pred_tokens # Test Case 1: Matching ngram is found input_ids1 = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3]) - result1 = find_candidate_pred_tokens(input_ids1, max_ngram_size=3, num_pred_tokens=2) + result1 = find_candidate_pred_tokens( + input_ids1, max_ngram_size=3, num_pred_tokens=2 + ) assert np.array_equal(result1, np.array([1, 2])) # Test Case 2: Matching ngram is not found input_ids2 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) - result2 = find_candidate_pred_tokens(input_ids2, max_ngram_size=3, num_pred_tokens=2) + result2 = find_candidate_pred_tokens( + input_ids2, max_ngram_size=3, num_pred_tokens=2 + ) assert np.array_equal(result2, np.array([])) From 18aa31ef1e6150cf1b24b81583f5254aabe00199 Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 22 Mar 2026 18:58:55 -0700 Subject: [PATCH 017/138] feat: Update llama.cpp to ggerganov/llama.cpp@49bfddeca18e62fa3d39114a23e9fcbdf8a22388 (#2151) * Update llama.cpp and sync bindings * Clean up binding compatibility shims * Remove flash attention property shim * Remove mtmd verbosity shim * Add docstrings for new bindings * Format Ruff files and add changelog entry --- CHANGELOG.md | 1 + CMakeLists.txt | 20 + Makefile | 2 - llama_cpp/_internals.py | 8 +- llama_cpp/llama.py | 22 +- llama_cpp/llama_chat_format.py | 9 +- llama_cpp/llama_cpp.py | 881 +++++++++++++++++++++------------ llama_cpp/mtmd_cpp.py | 96 +++- tests/test_llama.py | 2 +- vendor/llama.cpp | 2 +- 10 files changed, 714 insertions(+), 329 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7044f44d65..e7506eed57 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: Update llama.cpp to ggerganov/llama.cpp@49bfddeca18e62fa3d39114a23e9fcbdf8a22388 and sync Python bindings by @abetlen in #2151 - chore(dev): Add Ruff-based formatting and a safe lint baseline, and run it in CI for pull requests and pushes to `main` - fix(ci): Run macOS CI on supported Apple Silicon and Intel runners by @abetlen in #2150 - fix(ci): Use the `hf` CLI instead of the deprecated `huggingface-cli` name in GitHub Actions and docs by @abetlen in #2149 diff --git a/CMakeLists.txt b/CMakeLists.txt index 4b06d98b32..9b2744cdce 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -153,6 +153,26 @@ if (LLAMA_BUILD) add_compile_definitions(GGML_USE_METAL) endif() + # Upstream mtmd expects LLAMA_INSTALL_VERSION to be set by llama.cpp's + # top-level CMakeLists.txt. When we include tools/mtmd directly from the + # Python package build, that directory scope is skipped. + if (NOT DEFINED LLAMA_INSTALL_VERSION OR "${LLAMA_INSTALL_VERSION}" STREQUAL "") + set(LLAMA_INSTALL_VERSION 0.0.0) + find_package(Git QUIET) + if (Git_FOUND) + execute_process( + COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD + WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp + OUTPUT_VARIABLE LLAMA_MTMD_BUILD_NUMBER + OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE LLAMA_MTMD_BUILD_NUMBER_RESULT + ) + if (LLAMA_MTMD_BUILD_NUMBER_RESULT EQUAL 0) + set(LLAMA_INSTALL_VERSION 0.0.${LLAMA_MTMD_BUILD_NUMBER}) + endif() + endif() + endif() + # Building llava add_subdirectory(vendor/llama.cpp/tools/mtmd) diff --git a/Makefile b/Makefile index 8e6cae2c1f..db45246c79 100644 --- a/Makefile +++ b/Makefile @@ -82,8 +82,6 @@ run-server: python3 -m llama_cpp.server --model ${MODEL} clean: - - cd vendor/llama.cpp && make clean - - cd vendor/llama.cpp && rm libllama.so - rm -rf _skbuild - rm llama_cpp/lib/*.so - rm llama_cpp/lib/*.dylib diff --git a/llama_cpp/_internals.py b/llama_cpp/_internals.py index b520b7ea5f..d6258d2244 100644 --- a/llama_cpp/_internals.py +++ b/llama_cpp/_internals.py @@ -2,6 +2,7 @@ import os import ctypes +import warnings from typing import ( Dict, @@ -699,8 +700,11 @@ def add_dist(self, seed: int): llama_cpp.llama_sampler_chain_add(self.sampler, sampler) def add_softmax(self): - sampler = llama_cpp.llama_sampler_init_softmax() - llama_cpp.llama_sampler_chain_add(self.sampler, sampler) + warnings.warn( + "add_softmax is deprecated; llama_sampler_init_dist now samples directly from logits", + DeprecationWarning, + stacklevel=2, + ) def add_top_k(self, k: int): sampler = llama_cpp.llama_sampler_init_top_k(k) diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index 21a7430a01..1609ad16bf 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -341,7 +341,11 @@ def __init__( self._logits_all = logits_all if draft_model is None else True self.context_params.embeddings = embedding # TODO: Rename to embeddings self.context_params.offload_kqv = offload_kqv - self.context_params.flash_attn = flash_attn + self.context_params.flash_attn_type = ( + llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED + if flash_attn + else llama_cpp.LLAMA_FLASH_ATTN_TYPE_DISABLED + ) if op_offload is not None: self.context_params.op_offload = op_offload @@ -431,9 +435,9 @@ def free_lora_adapter(): self._stack.callback(free_lora_adapter) - if llama_cpp.llama_set_adapter_lora( - self._ctx.ctx, self._lora_adapter, self.lora_scale - ): + adapters = (llama_cpp.llama_adapter_lora_p_ctypes * 1)(self._lora_adapter) + scales = (ctypes.c_float * 1)(self.lora_scale) + if llama_cpp.llama_set_adapters_lora(self._ctx.ctx, adapters, 1, scales): raise RuntimeError( f"Failed to set LoRA adapter from lora path: {self.lora_path}" ) @@ -726,7 +730,6 @@ def apply_func(token_data_array: llama_cpp.llama_token_data_array_p): sampler.add_grammar(self._model, grammar) if temp < 0.0: - sampler.add_softmax() sampler.add_dist(self._seed) elif temp == 0.0: sampler.add_greedy() @@ -1042,7 +1045,7 @@ def embed( data: Union[List[List[float]], List[List[List[float]]]] = [] def decode_batch(seq_sizes: List[int]): - llama_cpp.llama_kv_self_clear(self._ctx.ctx) + self._ctx.kv_cache_clear() self._ctx.decode(self._batch) self._batch.reset() @@ -1113,7 +1116,7 @@ def decode_batch(seq_sizes: List[int]): output = data[0] if isinstance(input, str) else data - llama_cpp.llama_kv_self_clear(self._ctx.ctx) + self._ctx.kv_cache_clear() self.reset() if return_count: @@ -2100,7 +2103,10 @@ def __getstate__(self): logits_all=self._logits_all, embedding=self.context_params.embeddings, offload_kqv=self.context_params.offload_kqv, - flash_attn=self.context_params.flash_attn, + flash_attn=( + self.context_params.flash_attn_type + == llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED + ), op_offload=self.context_params.op_offload, swa_full=self.context_params.swa_full, # Sampling Params diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index 8e8ac7bb30..d7910e9847 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -2755,7 +2755,14 @@ def _init_mtmd_context(self, llama_model: llama.Llama): ctx_params.use_gpu = True # TODO: Make this configurable ctx_params.print_timings = self.verbose ctx_params.n_threads = llama_model.n_threads - ctx_params.verbosity = 2 if self.verbose else 0 # GGML_LOG_LEVEL_INFO = 2 + ctx_params.flash_attn_type = ( + llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED + if ( + llama_model.context_params.flash_attn_type + == llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED + ) + else llama_cpp.LLAMA_FLASH_ATTN_TYPE_DISABLED + ) # Initialize mtmd context self.mtmd_ctx = self._mtmd_cpp.mtmd_init_from_file( diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index f13af67f3b..e51492c56e 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -3,6 +3,7 @@ import os import ctypes import pathlib +import warnings from typing import ( Callable, @@ -77,6 +78,8 @@ # GGML_TYPE_I64 = 27, # GGML_TYPE_F64 = 28, # GGML_TYPE_IQ1_M = 29, +# GGML_TYPE_MXFP4 = 39, +# GGML_TYPE_NVFP4 = 40, # GGML_TYPE_COUNT, # }; GGML_TYPE_F32 = 0 @@ -107,7 +110,9 @@ GGML_TYPE_I64 = 27 GGML_TYPE_F64 = 28 GGML_TYPE_IQ1_M = 29 -GGML_TYPE_COUNT = 30 +GGML_TYPE_MXFP4 = 39 +GGML_TYPE_NVFP4 = 40 +GGML_TYPE_COUNT = 41 # from ggml-backend.h # typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data); @@ -173,6 +178,10 @@ llama_kv_cache_p = NewType("llama_kv_cache_p", int) llama_kv_cache_p_ctypes = ctypes.c_void_p +# struct gguf_context; +gguf_context_p = NewType("gguf_context_p", int) +gguf_context_p_ctypes = ctypes.c_void_p + # typedef int32_t llama_pos; llama_pos = ctypes.c_int32 # typedef int32_t llama_token; @@ -292,12 +301,14 @@ # LLAMA_ROPE_TYPE_NORM = 0, # LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX, # LLAMA_ROPE_TYPE_MROPE = GGML_ROPE_TYPE_MROPE, +# LLAMA_ROPE_TYPE_IMROPE = GGML_ROPE_TYPE_IMROPE, # LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION, # }; LLAMA_ROPE_TYPE_NONE = -1 LLAMA_ROPE_TYPE_NORM = 0 LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX = 2 LLAMA_ROPE_TYPE_MROPE = GGML_ROPE_TYPE_MROPE = 8 +LLAMA_ROPE_TYPE_IMROPE = GGML_ROPE_TYPE_IMROPE = 40 LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION = 24 @@ -386,6 +397,7 @@ # LLAMA_FTYPE_MOSTLY_TQ1_0 = 36, // except 1d tensors # LLAMA_FTYPE_MOSTLY_TQ2_0 = 37, // except 1d tensors # LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38, // except 1d tensors +# LLAMA_FTYPE_MOSTLY_NVFP4 = 39, // except 1d tensors # # LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file # }; @@ -425,6 +437,7 @@ LLAMA_FTYPE_MOSTLY_TQ1_0 = 36 LLAMA_FTYPE_MOSTLY_TQ2_0 = 37 LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38 +LLAMA_FTYPE_MOSTLY_NVFP4 = 39 LLAMA_FTYPE_GUESSED = 1024 # enum llama_rope_scaling_type { @@ -467,6 +480,16 @@ LLAMA_ATTENTION_TYPE_NON_CAUSAL = 1 +# enum llama_flash_attn_type { +# LLAMA_FLASH_ATTN_TYPE_AUTO = -1, +# LLAMA_FLASH_ATTN_TYPE_DISABLED = 0, +# LLAMA_FLASH_ATTN_TYPE_ENABLED = 1, +# }; +LLAMA_FLASH_ATTN_TYPE_AUTO = -1 +LLAMA_FLASH_ATTN_TYPE_DISABLED = 0 +LLAMA_FLASH_ATTN_TYPE_ENABLED = 1 + + # enum llama_split_mode { # LLAMA_SPLIT_MODE_NONE = 0, // single GPU # LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs @@ -619,6 +642,34 @@ class llama_batch(ctypes.Structure): LLAMA_KV_OVERRIDE_TYPE_STR = 3 +# enum llama_model_meta_key { +# LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE, +# LLAMA_MODEL_META_KEY_SAMPLING_TOP_K, +# LLAMA_MODEL_META_KEY_SAMPLING_TOP_P, +# LLAMA_MODEL_META_KEY_SAMPLING_MIN_P, +# LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY, +# LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD, +# LLAMA_MODEL_META_KEY_SAMPLING_TEMP, +# LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N, +# LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT, +# LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT, +# LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU, +# LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA, +# }; +LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE = 0 +LLAMA_MODEL_META_KEY_SAMPLING_TOP_K = 1 +LLAMA_MODEL_META_KEY_SAMPLING_TOP_P = 2 +LLAMA_MODEL_META_KEY_SAMPLING_MIN_P = 3 +LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY = 4 +LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD = 5 +LLAMA_MODEL_META_KEY_SAMPLING_TEMP = 6 +LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N = 7 +LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT = 8 +LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT = 9 +LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU = 10 +LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA = 11 + + # struct llama_model_kv_override { # enum llama_model_kv_override_type tag; @@ -695,11 +746,14 @@ class llama_model_kv_override(ctypes.Structure): # // Keep the booleans together to avoid misalignment during copy-by-value. -# bool vocab_only; // only load the vocabulary, no weights -# bool use_mmap; // use mmap if possible -# bool use_mlock; // force system to keep model in RAM -# bool check_tensors; // validate model tensor data +# bool vocab_only; // only load the vocabulary, no weights +# bool use_mmap; // use mmap if possible +# bool use_direct_io; // use direct io, takes precedence over use_mmap when supported +# bool use_mlock; // force system to keep model in RAM +# bool check_tensors; // validate model tensor data # bool use_extra_bufts; // use extra buffer types (used for weight repacking) +# bool no_host; // bypass host buffer allowing extra buffers to be used +# bool no_alloc; // only load metadata and simulate memory allocations # }; class llama_model_params(ctypes.Structure): """Parameters for llama_model @@ -716,9 +770,12 @@ class llama_model_params(ctypes.Structure): kv_overrides (ctypes.Array[llama_model_kv_override]): override key-value pairs of the model meta data vocab_only (bool): only load the vocabulary, no weights use_mmap (bool): use mmap if possible + use_direct_io (bool): use direct io, takes precedence over use_mmap when supported use_mlock (bool): force system to keep model in RAM check_tensors (bool): validate model tensor data - use_extra_bufts (bool): use extra buffer types (used for weight repacking)""" + use_extra_bufts (bool): use extra buffer types (used for weight repacking) + no_host (bool): bypass host buffer allowing extra buffers to be used + no_alloc (bool): only load metadata and simulate memory allocations""" if TYPE_CHECKING: devices: CtypesArray[ctypes.c_void_p] # NOTE: unused @@ -734,9 +791,12 @@ class llama_model_params(ctypes.Structure): kv_overrides: CtypesArray[llama_model_kv_override] vocab_only: bool use_mmap: bool + use_direct_io: bool use_mlock: bool check_tensors: bool use_extra_bufts: bool + no_host: bool + no_alloc: bool _fields_ = [ ("devices", ctypes.c_void_p), # NOTE: unnused @@ -750,9 +810,27 @@ class llama_model_params(ctypes.Structure): ("kv_overrides", ctypes.POINTER(llama_model_kv_override)), ("vocab_only", ctypes.c_bool), ("use_mmap", ctypes.c_bool), + ("use_direct_io", ctypes.c_bool), ("use_mlock", ctypes.c_bool), ("check_tensors", ctypes.c_bool), ("use_extra_bufts", ctypes.c_bool), + ("no_host", ctypes.c_bool), + ("no_alloc", ctypes.c_bool), + ] + + +# struct llama_sampler_seq_config { +# llama_seq_id seq_id; +# struct llama_sampler * sampler; +# }; +class llama_sampler_seq_config(ctypes.Structure): + if TYPE_CHECKING: + seq_id: int + sampler: ctypes.c_void_p + + _fields_ = [ + ("seq_id", llama_seq_id), + ("sampler", ctypes.c_void_p), ] @@ -769,6 +847,7 @@ class llama_model_params(ctypes.Structure): # enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` # enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id # enum llama_attention_type attention_type; // attention type to use for embeddings +# enum llama_flash_attn_type flash_attn_type; // when to enable Flash Attention # // ref: https://github.com/ggml-org/llama.cpp/pull/2054 # float rope_freq_base; // RoPE base frequency, 0 = from model @@ -796,7 +875,6 @@ class llama_model_params(ctypes.Structure): # // Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value. # bool embeddings; // if true, extract embeddings (together with logits) # bool offload_kqv; // offload the KQV ops (including the KV cache) to GPU -# bool flash_attn; // use flash attention [EXPERIMENTAL] # bool no_perf; // measure performance timings # bool op_offload; // offload host tensor operations to device # bool swa_full; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055) @@ -805,6 +883,8 @@ class llama_model_params(ctypes.Structure): # bool kv_unified; // use a unified buffer across the input sequences when computing the attention # // try to disable when n_seq_max > 1 for improved performance when the sequences do not share a large prefix # // ref: https://github.com/ggml-org/llama.cpp/pull/14363 +# struct llama_sampler_seq_config * samplers; +# size_t n_samplers; # }; class llama_context_params(ctypes.Structure): """Parameters for llama_context @@ -819,6 +899,7 @@ class llama_context_params(ctypes.Structure): rope_scaling_type (int): RoPE scaling type, from `enum llama_rope_scaling_type` pooling_type (int): whether to pool (sum) embedding results by sequence id (ignored if no pooling layer) attention_type (int): attention type to use for embeddings + flash_attn_type (int): when to enable flash attention rope_freq_base (float): RoPE base frequency, 0 = from model rope_freq_scale (float): RoPE frequency scaling factor, 0 = from model yarn_ext_factor (float): YaRN extrapolation mix factor, negative = from model @@ -835,11 +916,12 @@ class llama_context_params(ctypes.Structure): abort_callback_data (ctypes.ctypes.c_void_p): data for abort_callback embeddings (bool): if true, extract embeddings (together with logits) offload_kqv (bool): whether to offload the KQV ops (including the KV cache) to GPU - flash_attn (bool): whether to use flash attention no_perf (bool): whether to measure performance timings op_offload (bool): offload host tensor operations to device swa_full (bool): use full-size SWA cache kv_unified (bool): use a unified buffer across the input sequences when computing the attention + samplers (ctypes.POINTER(llama_sampler_seq_config)): backend sampler chain configuration + n_samplers (int): number of backend sampler chain configurations """ if TYPE_CHECKING: @@ -852,6 +934,7 @@ class llama_context_params(ctypes.Structure): rope_scaling_type: int pooling_type: int attention_type: int + flash_attn_type: int rope_freq_base: float rope_freq_scale: float yarn_ext_factor: float @@ -868,11 +951,12 @@ class llama_context_params(ctypes.Structure): abort_callback_data: ctypes.c_void_p embeddings: bool offload_kqv: bool - flash_attn: bool no_perf: bool op_offload: bool swa_full: bool kv_unified: bool + samplers: ctypes.POINTER(llama_sampler_seq_config) + n_samplers: int _fields_ = [ ("n_ctx", ctypes.c_uint32), @@ -884,6 +968,7 @@ class llama_context_params(ctypes.Structure): ("rope_scaling_type", ctypes.c_int), ("pooling_type", ctypes.c_int), ("attention_type", ctypes.c_int), + ("flash_attn_type", ctypes.c_int), ("rope_freq_base", ctypes.c_float), ("rope_freq_scale", ctypes.c_float), ("yarn_ext_factor", ctypes.c_float), @@ -900,11 +985,12 @@ class llama_context_params(ctypes.Structure): ("abort_callback_data", ctypes.c_void_p), ("embeddings", ctypes.c_bool), ("offload_kqv", ctypes.c_bool), - ("flash_attn", ctypes.c_bool), ("no_perf", ctypes.c_bool), ("op_offload", ctypes.c_bool), ("swa_full", ctypes.c_bool), ("kv_unified", ctypes.c_bool), + ("samplers", ctypes.POINTER(llama_sampler_seq_config)), + ("n_samplers", ctypes.c_size_t), ] @@ -935,6 +1021,7 @@ class llama_context_params(ctypes.Structure): # bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored # bool pure; // quantize all tensors to the default type # bool keep_split; // quantize to the same number of shards +# bool dry_run; // calculate and show the final quantization size without performing quantization # void * imatrix; // pointer to importance matrix data # void * kv_overrides; // pointer to vector containing overrides # void * tensor_types; // pointer to vector containing tensor types @@ -953,6 +1040,7 @@ class llama_model_quantize_params(ctypes.Structure): only_copy (bool): only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored pure (bool): quantize all tensors to the default type keep_split (bool): quantize to the same number of shards + dry_run (bool): calculate and show the final quantization size without performing quantization imatrix (ctypes.c_void_p): pointer to importance matrix data kv_overrides (ctypes.c_void_p): pointer to vector containing overrides tensor_types (ctypes.c_void_p): pointer to vector containing tensor types @@ -969,6 +1057,7 @@ class llama_model_quantize_params(ctypes.Structure): only_copy: bool pure: bool keep_split: bool + dry_run: bool imatrix: ctypes.c_void_p kv_overrides: ctypes.c_void_p tensor_types: ctypes.c_void_p @@ -984,6 +1073,7 @@ class llama_model_quantize_params(ctypes.Structure): ("only_copy", ctypes.c_bool), ("pure", ctypes.c_bool), ("keep_split", ctypes.c_bool), + ("dry_run", ctypes.c_bool), ("imatrix", ctypes.c_void_p), ("kv_overrides", ctypes.c_void_p), ("tensor_types", ctypes.c_void_p), @@ -1095,6 +1185,13 @@ def llama_model_quantize_default_params() -> llama_model_quantize_params: ... +# LLAMA_API const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_type); +@ctypes_function("llama_flash_attn_type_name", [ctypes.c_int], ctypes.c_char_p) +def llama_flash_attn_type_name(flash_attn_type: int, /) -> Optional[bytes]: + """Get the flash attention type name.""" + ... + + # // Initialize the llama + ggml backend # // If numa is true, use NUMA optimizations # // Call once at the start of the program @@ -1249,6 +1346,36 @@ def llama_free_model(model: llama_model_p, /): ... def llama_model_free(model: llama_model_p, /): ... +# typedef void (*llama_model_set_tensor_data_t)(struct ggml_tensor * tensor, void * userdata); +llama_model_set_tensor_data_t = ctypes.CFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p) + + +# LLAMA_API struct llama_model * llama_model_init_from_user( +# struct gguf_context * metadata, +# llama_model_set_tensor_data_t set_tensor_data, +# void * set_tensor_data_ud, +# struct llama_model_params params); +@ctypes_function( + "llama_model_init_from_user", + [ + gguf_context_p_ctypes, + llama_model_set_tensor_data_t, + ctypes.c_void_p, + llama_model_params, + ], + llama_model_p_ctypes, +) +def llama_model_init_from_user( + metadata: gguf_context_p, + set_tensor_data: llama_model_set_tensor_data_t, + set_tensor_data_ud: ctypes.c_void_p, + params: llama_model_params, + /, +) -> Optional[llama_model_p]: + """Initialize a model from user-provided metadata and tensor data.""" + ... + + # LLAMA_API struct llama_context * llama_init_from_model( # struct llama_model * model, # struct llama_context_params params); @@ -1288,6 +1415,54 @@ def llama_free(ctx: llama_context_p, /): ... +# enum llama_params_fit_status { +# LLAMA_PARAMS_FIT_STATUS_SUCCESS = 0, +# LLAMA_PARAMS_FIT_STATUS_FAILURE = 1, +# LLAMA_PARAMS_FIT_STATUS_ERROR = 2, +# }; +LLAMA_PARAMS_FIT_STATUS_SUCCESS = 0 +LLAMA_PARAMS_FIT_STATUS_FAILURE = 1 +LLAMA_PARAMS_FIT_STATUS_ERROR = 2 + + +# LLAMA_API enum llama_params_fit_status llama_params_fit( +# const char * path_model, +# struct llama_model_params * mparams, +# struct llama_context_params * cparams, +# float * tensor_split, +# struct llama_model_tensor_buft_override * tensor_buft_overrides, +# size_t * margins, +# uint32_t n_ctx_min, +# enum ggml_log_level log_level); +@ctypes_function( + "llama_params_fit", + [ + ctypes.c_char_p, + ctypes.POINTER(llama_model_params), + ctypes.POINTER(llama_context_params), + ctypes.POINTER(ctypes.c_float), + ctypes.c_void_p, + ctypes.POINTER(ctypes.c_size_t), + ctypes.c_uint32, + ctypes.c_int, + ], + ctypes.c_int, +) +def llama_params_fit( + path_model: bytes, + mparams: CtypesPointerOrRef[llama_model_params], + cparams: CtypesPointerOrRef[llama_context_params], + tensor_split: Optional[CtypesPointer[ctypes.c_float]], + tensor_buft_overrides: ctypes.c_void_p, + margins: Optional[CtypesPointer[ctypes.c_size_t]], + n_ctx_min: int, + log_level: int, + /, +) -> int: + """Fit model and context parameters for a model path.""" + ... + + # LLAMA_API int64_t llama_time_us(void); @ctypes_function( "llama_time_us", @@ -1307,6 +1482,13 @@ def llama_max_devices() -> int: ... def llama_max_parallel_sequences() -> int: ... +# LLAMA_API size_t llama_max_tensor_buft_overrides(void); +@ctypes_function("llama_max_tensor_buft_overrides", [], ctypes.c_size_t) +def llama_max_tensor_buft_overrides() -> int: + """Get the maximum number of tensor buffer type overrides.""" + ... + + # LLAMA_API bool llama_supports_mmap (void); @ctypes_function("llama_supports_mmap", [], ctypes.c_bool) def llama_supports_mmap() -> bool: ... @@ -1332,6 +1514,13 @@ def llama_supports_rpc() -> bool: ... def llama_n_ctx(ctx: llama_context_p, /) -> int: ... +# LLAMA_API uint32_t llama_n_ctx_seq (const struct llama_context * ctx); +@ctypes_function("llama_n_ctx_seq", [llama_context_p_ctypes], ctypes.c_uint32) +def llama_n_ctx_seq(ctx: llama_context_p, /) -> int: + """Get the context size per sequence.""" + ... + + # LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx); @ctypes_function("llama_n_batch", [llama_context_p_ctypes], ctypes.c_uint32) def llama_n_batch(ctx: llama_context_p, /) -> int: ... @@ -1389,17 +1578,6 @@ def llama_get_memory(ctx: llama_context_p, /) -> Optional[llama_memory_t]: def llama_pooling_type(ctx: llama_context_p, /) -> int: ... -# DEPRECATED(LLAMA_API struct llama_kv_cache * llama_get_kv_self(struct llama_context * ctx), "use llama_get_memory instead"); -@ctypes_function( - "llama_get_kv_self", - [llama_context_p_ctypes], - llama_kv_cache_p_ctypes, -) -def llama_get_kv_self(ctx: llama_context_p, /) -> Optional[llama_kv_cache_p]: - """Get the KV cache for self-attention (DEPRECATED)""" - ... - - # LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model); @ctypes_function("llama_model_get_vocab", [llama_model_p_ctypes], llama_vocab_p_ctypes) def llama_model_get_vocab(model: llama_model_p, /) -> Optional[llama_vocab_p]: ... @@ -1420,6 +1598,20 @@ def llama_model_n_ctx_train(model: llama_model_p, /) -> int: ... def llama_model_n_embd(model: llama_model_p, /) -> int: ... +# LLAMA_API int32_t llama_model_n_embd_inp (const struct llama_model * model); +@ctypes_function("llama_model_n_embd_inp", [llama_model_p_ctypes], ctypes.c_int32) +def llama_model_n_embd_inp(model: llama_model_p, /) -> int: + """Get the model input embedding size.""" + ... + + +# LLAMA_API int32_t llama_model_n_embd_out (const struct llama_model * model); +@ctypes_function("llama_model_n_embd_out", [llama_model_p_ctypes], ctypes.c_int32) +def llama_model_n_embd_out(model: llama_model_p, /) -> int: + """Get the model output embedding size.""" + ... + + # LLAMA_API int32_t llama_model_n_layer (const struct llama_model * model); @ctypes_function("llama_model_n_layer", [llama_model_p_ctypes], ctypes.c_int32) def llama_model_n_layer(model: llama_model_p, /) -> int: ... @@ -1515,6 +1707,14 @@ def llama_model_meta_count(model: llama_model_p, /) -> int: ... +# // Get sampling metadata key name. Returns nullptr if the key is invalid +# LLAMA_API const char * llama_model_meta_key_str(enum llama_model_meta_key key); +@ctypes_function("llama_model_meta_key_str", [ctypes.c_int], ctypes.c_char_p) +def llama_model_meta_key_str(key: int, /) -> Optional[bytes]: + """Get sampling metadata key name. Returns None if the key is invalid.""" + ... + + # // Get metadata key name by index # LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size); @ctypes_function( @@ -1647,6 +1847,14 @@ def llama_model_is_recurrent(model: llama_model_p, /) -> bool: ... +# // Returns true if the model is hybrid (like Jamba, Granite, etc.) +# LLAMA_API bool llama_model_is_hybrid(const struct llama_model * model); +@ctypes_function("llama_model_is_hybrid", [llama_model_p_ctypes], ctypes.c_bool) +def llama_model_is_hybrid(model: llama_model_p, /) -> bool: + """Returns true if the model is hybrid (like Jamba, Granite, etc.)""" + ... + + # // Returns true if the model is diffusion-based (like LLaDA, Dream, etc.) # LLAMA_API bool llama_model_is_diffusion(const struct llama_model * model); @ctypes_function("llama_model_is_diffusion", [llama_model_p_ctypes], ctypes.c_bool) @@ -1698,6 +1906,85 @@ def llama_adapter_lora_init( ) -> Optional[llama_adapter_lora_p]: ... +# // Get metadata value as a string by key name +# LLAMA_API int32_t llama_adapter_meta_val_str(const struct llama_adapter_lora * adapter, const char * key, char * buf, size_t buf_size); +@ctypes_function( + "llama_adapter_meta_val_str", + [ + llama_adapter_lora_p_ctypes, + ctypes.c_char_p, + ctypes.c_char_p, + ctypes.c_size_t, + ], + ctypes.c_int32, +) +def llama_adapter_meta_val_str( + adapter: llama_adapter_lora_p, + key: bytes, + buf: Union[bytes, CtypesArray[ctypes.c_char]], + buf_size: int, + /, +) -> int: + """Get adapter metadata value as a string by key name.""" + ... + + +# // Get the number of metadata key/value pairs +# LLAMA_API int32_t llama_adapter_meta_count(const struct llama_adapter_lora * adapter); +@ctypes_function( + "llama_adapter_meta_count", [llama_adapter_lora_p_ctypes], ctypes.c_int32 +) +def llama_adapter_meta_count(adapter: llama_adapter_lora_p, /) -> int: + """Get the number of adapter metadata key/value pairs.""" + ... + + +# // Get metadata key name by index +# LLAMA_API int32_t llama_adapter_meta_key_by_index(const struct llama_adapter_lora * adapter, int32_t i, char * buf, size_t buf_size); +@ctypes_function( + "llama_adapter_meta_key_by_index", + [ + llama_adapter_lora_p_ctypes, + ctypes.c_int32, + ctypes.c_char_p, + ctypes.c_size_t, + ], + ctypes.c_int32, +) +def llama_adapter_meta_key_by_index( + adapter: llama_adapter_lora_p, + i: int, + buf: Union[bytes, CtypesArray[ctypes.c_char]], + buf_size: int, + /, +) -> int: + """Get adapter metadata key name by index.""" + ... + + +# // Get metadata value as a string by index +# LLAMA_API int32_t llama_adapter_meta_val_str_by_index(const struct llama_adapter_lora * adapter, int32_t i, char * buf, size_t buf_size); +@ctypes_function( + "llama_adapter_meta_val_str_by_index", + [ + llama_adapter_lora_p_ctypes, + ctypes.c_int32, + ctypes.c_char_p, + ctypes.c_size_t, + ], + ctypes.c_int32, +) +def llama_adapter_meta_val_str_by_index( + adapter: llama_adapter_lora_p, + i: int, + buf: Union[bytes, CtypesArray[ctypes.c_char]], + buf_size: int, + /, +) -> int: + """Get adapter metadata value as a string by index.""" + ... + + # // Manually free a LoRA adapter # // Note: loaded adapters will be free when the associated model is deleted # LLAMA_API void llama_adapter_lora_free(struct llama_adapter_lora * adapter); @@ -1709,56 +1996,75 @@ def llama_adapter_lora_init( def llama_adapter_lora_free(adapter: llama_adapter_lora_p, /): ... -# // The following functions operate on a llama_context, hence the naming: llama_verb_... +# // Get the invocation tokens if the current lora is an alora +# LLAMA_API uint64_t llama_adapter_get_alora_n_invocation_tokens(const struct llama_adapter_lora * adapter); +@ctypes_function( + "llama_adapter_get_alora_n_invocation_tokens", + [llama_adapter_lora_p_ctypes], + ctypes.c_uint64, +) +def llama_adapter_get_alora_n_invocation_tokens( + adapter: llama_adapter_lora_p, / +) -> int: + """Get the invocation token count if the current LoRA is an aLoRA.""" + ... -# // Add a loaded LoRA adapter to given context -# // This will not modify model's weight -# LLAMA_API int32_t llama_set_adapter_lora( -# struct llama_context * ctx, -# struct llama_adapter_lora * adapter, -# float scale); +# LLAMA_API const llama_token * llama_adapter_get_alora_invocation_tokens (const struct llama_adapter_lora * adapter); @ctypes_function( - "llama_set_adapter_lora", - [llama_context_p_ctypes, llama_adapter_lora_p_ctypes, ctypes.c_float], - ctypes.c_int32, + "llama_adapter_get_alora_invocation_tokens", + [llama_adapter_lora_p_ctypes], + ctypes.POINTER(llama_token), ) -def llama_set_adapter_lora( - ctx: llama_context_p, adapter: llama_adapter_lora_p, scale: float, / -) -> int: - """Add a loaded LoRA adapter to given context - This will not modify model's weight""" +def llama_adapter_get_alora_invocation_tokens( + adapter: llama_adapter_lora_p, / +) -> Optional[CtypesPointer[llama_token]]: + """Get the invocation tokens if the current LoRA is an aLoRA.""" ... -# // Remove a specific LoRA adapter from given context -# // Return -1 if the adapter is not present in the context -# LLAMA_API int32_t llama_rm_adapter_lora( +# // The following functions operate on a llama_context, hence the naming: llama_verb_... + + +# // Set LoRa adapters on the context. Will only modify if the adapters currently in context are different. +# LLAMA_API int32_t llama_set_adapters_lora( # struct llama_context * ctx, -# struct llama_adapter_lora * adapter); +# struct llama_adapter_lora ** adapters, +# size_t n_adapters, +# float * scales); @ctypes_function( - "llama_rm_adapter_lora", - [llama_context_p_ctypes, llama_adapter_lora_p_ctypes], + "llama_set_adapters_lora", + [ + llama_context_p_ctypes, + ctypes.POINTER(llama_adapter_lora_p_ctypes), + ctypes.c_size_t, + ctypes.POINTER(ctypes.c_float), + ], ctypes.c_int32, ) -def llama_rm_adapter_lora( - ctx: llama_context_p, adapter: llama_adapter_lora_p, / +def llama_set_adapters_lora( + ctx: llama_context_p, + adapters: Optional[CtypesArray[llama_adapter_lora_p_ctypes]], + n_adapters: int, + scales: Optional[CtypesArray[ctypes.c_float]], + /, ) -> int: - """Remove a specific LoRA adapter from given context - Return -1 if the adapter is not present in the context""" + """Set LoRA adapters on the context if they differ from the current adapters.""" ... -# // Remove all LoRA adapters from given context -# LLAMA_API void llama_clear_adapter_lora(struct llama_context * ctx); -@ctypes_function( - "llama_clear_adapter_lora", - [llama_context_p_ctypes], - None, -) -def llama_clear_adapter_lora(ctx: llama_context_p, /): - """Remove all LoRA adapters from given context""" - ... +# Deprecated compatibility wrapper for the renamed llama_set_adapters_lora(). +def llama_set_adapter_lora( + ctx: llama_context_p, adapter: llama_adapter_lora_p, scale: float, / +) -> int: + warnings.warn( + "llama_set_adapter_lora is deprecated; use llama_set_adapters_lora instead", + DeprecationWarning, + stacklevel=2, + ) + adapters = (llama_adapter_lora_p_ctypes * 1)(adapter) + scales = (ctypes.c_float * 1)(scale) + return llama_set_adapters_lora(ctx, adapters, 1, scales) # // Apply a loaded control vector to a llama_context, or if data is NULL, clear @@ -1767,7 +2073,7 @@ def llama_clear_adapter_lora(ctx: llama_context_p, /): # // to an n_embd x n_layers buffer starting from layer 1. # // il_start and il_end are the layer range the vector should apply to (both inclusive) # // See llama_control_vector_load in common to load a control vector. -# LLAMA_API int32_t llama_apply_adapter_cvec( +# LLAMA_API int32_t llama_set_adapter_cvec( # struct llama_context * ctx, # const float * data, # size_t len, @@ -1775,7 +2081,7 @@ def llama_clear_adapter_lora(ctx: llama_context_p, /): # int32_t il_start, # int32_t il_end); @ctypes_function( - "llama_apply_adapter_cvec", + "llama_set_adapter_cvec", [ llama_context_p_ctypes, ctypes.POINTER(ctypes.c_float), @@ -1786,7 +2092,7 @@ def llama_clear_adapter_lora(ctx: llama_context_p, /): ], ctypes.c_int32, ) -def llama_apply_adapter_cvec( +def llama_set_adapter_cvec( ctx: llama_context_p, data: CtypesPointerOrRef[ctypes.c_float], len: int, @@ -1804,6 +2110,24 @@ def llama_apply_adapter_cvec( ... +# Deprecated compatibility wrapper for the renamed llama_set_adapter_cvec(). +def llama_apply_adapter_cvec( + ctx: llama_context_p, + data: CtypesPointerOrRef[ctypes.c_float], + len: int, + n_embd: int, + il_start: int, + il_end: int, + /, +) -> int: + warnings.warn( + "llama_apply_adapter_cvec is deprecated; use llama_set_adapter_cvec instead", + DeprecationWarning, + stacklevel=2, + ) + return llama_set_adapter_cvec(ctx, data, len, n_embd, il_start, il_end) + + # // # // Memory # // @@ -2018,251 +2342,6 @@ def llama_memory_can_shift(mem: llama_memory_t, /) -> bool: ... -# // -# // KV cache for self-attention (TODO: deprecate in favor of llama_memory) -# // - - -# // Returns the number of tokens in the KV cache (slow, use only for debug) -# // If a KV cell has multiple sequences assigned to it, it will be counted multiple times -# DEPRECATED(LLAMA_API int32_t llama_kv_self_n_tokens(const struct llama_context * ctx), -# "Use llama_kv_self_seq_pos_max() and llama_kv_self_seq_pos_min() instead (https://github.com/ggml-org/llama.cpp/issues/13793)"); -@ctypes_function("llama_kv_self_n_tokens", [llama_context_p_ctypes], ctypes.c_int32) -def llama_kv_self_n_tokens(ctx: llama_context_p, /) -> int: - """Returns the number of tokens in the KV cache (slow, use only for debug) (DEPRECATED)""" - ... - - -# // Returns the number of used KV cells (i.e. have at least one sequence assigned to them) -# DEPRECATED(LLAMA_API int32_t llama_kv_self_used_cells(const struct llama_context * ctx), -# "Use llama_kv_self_seq_pos_max() and llama_kv_self_seq_pos_min() instead (https://github.com/ggml-org/llama.cpp/issues/13793)"); -@ctypes_function("llama_kv_self_used_cells", [llama_context_p_ctypes], ctypes.c_int32) -def llama_kv_self_used_cells(ctx: llama_context_p, /) -> int: - """Returns the number of used KV cells (DEPRECATED)""" - ... - - -# // Clear the KV cache - both cell info is erased and KV data is zeroed -# DEPRECATED(LLAMA_API void llama_kv_self_clear( -# struct llama_context * ctx), -# "Use llama_memory_clear() instead"); -@ctypes_function("llama_kv_self_clear", [llama_context_p_ctypes], None) -def llama_kv_self_clear(ctx: llama_context_p, /): - """Clear the KV cache (DEPRECATED)""" - ... - - -# // Removes all tokens that belong to the specified sequence and have positions in [p0, p1) -# // Returns false if a partial sequence cannot be removed. Removing a whole sequence never fails -# // seq_id < 0 : match any sequence -# // p0 < 0 : [0, p1] -# // p1 < 0 : [p0, inf) -# DEPRECATED(LLAMA_API bool llama_kv_self_seq_rm( -# struct llama_context * ctx, -# llama_seq_id seq_id, -# llama_pos p0, -# llama_pos p1), -# "Use llama_memory_seq_rm() instead"); -@ctypes_function( - "llama_kv_self_seq_rm", - [ - llama_context_p_ctypes, - llama_seq_id, - llama_pos, - llama_pos, - ], - ctypes.c_bool, -) -def llama_kv_self_seq_rm( - ctx: llama_context_p, - seq_id: Union[llama_seq_id, int], - p0: Union[llama_pos, int], - p1: Union[llama_pos, int], - /, -) -> bool: - """Remove tokens from KV cache (DEPRECATED)""" - ... - - -# // Copy all tokens that belong to the specified sequence to another sequence -# // Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence -# // p0 < 0 : [0, p1] -# // p1 < 0 : [p0, inf) -# DEPRECATED(LLAMA_API void llama_kv_self_seq_cp( -# struct llama_context * ctx, -# llama_seq_id seq_id_src, -# llama_seq_id seq_id_dst, -# llama_pos p0, -# llama_pos p1), -# "Use llama_memory_seq_cp() instead"); -@ctypes_function( - "llama_kv_self_seq_cp", - [ - llama_context_p_ctypes, - llama_seq_id, - llama_seq_id, - llama_pos, - llama_pos, - ], - None, -) -def llama_kv_self_seq_cp( - ctx: llama_context_p, - seq_id_src: Union[llama_seq_id, int], - seq_id_dst: Union[llama_seq_id, int], - p0: Union[llama_pos, int], - p1: Union[llama_pos, int], - /, -): - """Copy tokens in KV cache (DEPRECATED)""" - ... - - -# // Removes all tokens that do not belong to the specified sequence -# DEPRECATED(LLAMA_API void llama_kv_self_seq_keep( -# struct llama_context * ctx, -# llama_seq_id seq_id), -# "Use llama_memory_seq_keep() instead"); -@ctypes_function("llama_kv_self_seq_keep", [llama_context_p_ctypes, llama_seq_id], None) -def llama_kv_self_seq_keep(ctx: llama_context_p, seq_id: Union[llama_seq_id, int], /): - """Keep only specified sequence in KV cache (DEPRECATED)""" - ... - - -# // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1) -# // If the KV cache is RoPEd, the KV data is updated accordingly: -# // - lazily on next llama_decode() -# // p0 < 0 : [0, p1] -# // p1 < 0 : [p0, inf) -# DEPRECATED(LLAMA_API void llama_kv_self_seq_add( -# struct llama_context * ctx, -# llama_seq_id seq_id, -# llama_pos p0, -# llama_pos p1, -# llama_pos delta), -# "Use llama_memory_seq_add() instead"); -@ctypes_function( - "llama_kv_self_seq_add", - [ - llama_context_p_ctypes, - llama_seq_id, - llama_pos, - llama_pos, - llama_pos, - ], - None, -) -def llama_kv_self_seq_add( - ctx: llama_context_p, - seq_id: Union[llama_seq_id, int], - p0: Union[llama_pos, int], - p1: Union[llama_pos, int], - delta: Union[llama_pos, int], - /, -): - """Add delta to sequence positions in KV cache (DEPRECATED)""" - ... - - -# // Integer division of the positions by factor of `d > 1` -# // If the KV cache is RoPEd, the KV data is updated accordingly: -# // - lazily on next llama_decode() -# // p0 < 0 : [0, p1] -# // p1 < 0 : [p0, inf) -# DEPRECATED(LLAMA_API void llama_kv_self_seq_div( -# struct llama_context * ctx, -# llama_seq_id seq_id, -# llama_pos p0, -# llama_pos p1, -# int d), -# "Use llama_memory_seq_div() instead"); -@ctypes_function( - "llama_kv_self_seq_div", - [ - llama_context_p_ctypes, - llama_seq_id, - llama_pos, - llama_pos, - ctypes.c_int, - ], - None, -) -def llama_kv_self_seq_div( - ctx: llama_context_p, - seq_id: Union[llama_seq_id, int], - p0: Union[llama_pos, int], - p1: Union[llama_pos, int], - d: Union[ctypes.c_int, int], - /, -): - """Divide sequence positions in KV cache (DEPRECATED)""" - ... - - -# // Returns the smallest position present in the KV cache for the specified sequence -# // This is typically non-zero only for SWA caches -# // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the KV cache -# // Return -1 if the sequence is empty -# DEPRECATED(LLAMA_API llama_pos llama_kv_self_seq_pos_min( -# struct llama_context * ctx, -# llama_seq_id seq_id), -# "Use llama_memory_seq_pos_min() instead"); -@ctypes_function( - "llama_kv_self_seq_pos_min", [llama_context_p_ctypes, llama_seq_id], llama_pos -) -def llama_kv_self_seq_pos_min( - ctx: llama_context_p, seq_id: Union[llama_seq_id, int], / -) -> int: - """Returns the smallest position in KV cache for sequence (DEPRECATED)""" - ... - - -# // Returns the largest position present in the KV cache for the specified sequence -# // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the KV cache -# // Return -1 if the sequence is empty -# DEPRECATED(LLAMA_API llama_pos llama_kv_self_seq_pos_max( -# struct llama_context * ctx, -# llama_seq_id seq_id), -# "Use llama_memory_seq_pos_max() instead"); -@ctypes_function( - "llama_kv_self_seq_pos_max", [llama_context_p_ctypes, llama_seq_id], llama_pos -) -def llama_kv_self_seq_pos_max( - ctx: llama_context_p, seq_id: Union[llama_seq_id, int], / -) -> int: - """Returns the largest position in KV cache for sequence (DEPRECATED)""" - ... - - -# // Defragment the KV cache -# // This will be applied: -# // - lazily on next llama_decode() -# DEPRECATED(LLAMA_API void llama_kv_self_defrag(struct llama_context * ctx), -# "simply remove this call, the context will automatically decide when to do a defragmentation based on 'defrag_thold'"); -@ctypes_function("llama_kv_self_defrag", [llama_context_p_ctypes], None) -def llama_kv_self_defrag(ctx: llama_context_p, /): - """Defragment the KV cache (DEPRECATED)""" - ... - - -# // Check if the context supports KV cache shifting -# DEPRECATED(LLAMA_API bool llama_kv_self_can_shift(const struct llama_context * ctx), -# "use llama_memory_can_shift() instead"); -@ctypes_function("llama_kv_self_can_shift", [llama_context_p_ctypes], ctypes.c_bool) -def llama_kv_self_can_shift(ctx: llama_context_p, /) -> bool: - """Check if the context supports KV cache shifting (DEPRECATED)""" - ... - - -# // Apply the KV cache updates (such as K-shifts, defragmentation, etc.) -# DEPRECATED(LLAMA_API void llama_kv_self_update(struct llama_context * ctx), -# "simply remove this call, updates are applied lazily on the next llama_decode()"); -@ctypes_function("llama_kv_self_update", [llama_context_p_ctypes], None) -def llama_kv_self_update(ctx: llama_context_p, /): - """Apply the KV cache updates (DEPRECATED)""" - ... - - # // # // State / sessions # // @@ -2914,6 +2993,100 @@ def llama_get_embeddings_seq( ... +# // Get the backend sampled token for the ith token. +# // Returns LLAMA_TOKEN_NULL if no token was sampled. +# LLAMA_API llama_token llama_get_sampled_token_ith(struct llama_context * ctx, int32_t i); +@ctypes_function( + "llama_get_sampled_token_ith", [llama_context_p_ctypes, ctypes.c_int32], llama_token +) +def llama_get_sampled_token_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> int: + """Get the backend sampled token for the ith token.""" + ... + + +# // Get the backend sampled probabilities for the ith token +# LLAMA_API float * llama_get_sampled_probs_ith (struct llama_context * ctx, int32_t i); +@ctypes_function( + "llama_get_sampled_probs_ith", + [llama_context_p_ctypes, ctypes.c_int32], + ctypes.POINTER(ctypes.c_float), +) +def llama_get_sampled_probs_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> Optional[CtypesPointer[ctypes.c_float]]: + """Get the backend sampled probabilities for the ith token.""" + ... + + +# LLAMA_API uint32_t llama_get_sampled_probs_count_ith(struct llama_context * ctx, int32_t i); +@ctypes_function( + "llama_get_sampled_probs_count_ith", + [llama_context_p_ctypes, ctypes.c_int32], + ctypes.c_uint32, +) +def llama_get_sampled_probs_count_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> int: + """Get the backend sampled probability count for the ith token.""" + ... + + +# // Get the backend sampled logits for the ith token +# LLAMA_API float * llama_get_sampled_logits_ith (struct llama_context * ctx, int32_t i); +@ctypes_function( + "llama_get_sampled_logits_ith", + [llama_context_p_ctypes, ctypes.c_int32], + ctypes.POINTER(ctypes.c_float), +) +def llama_get_sampled_logits_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> Optional[CtypesPointer[ctypes.c_float]]: + """Get the backend sampled logits for the ith token.""" + ... + + +# LLAMA_API uint32_t llama_get_sampled_logits_count_ith(struct llama_context * ctx, int32_t i); +@ctypes_function( + "llama_get_sampled_logits_count_ith", + [llama_context_p_ctypes, ctypes.c_int32], + ctypes.c_uint32, +) +def llama_get_sampled_logits_count_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> int: + """Get the backend sampled logit count for the ith token.""" + ... + + +# // Get the backend sampled candidates for the ith token +# LLAMA_API llama_token * llama_get_sampled_candidates_ith (struct llama_context * ctx, int32_t i); +@ctypes_function( + "llama_get_sampled_candidates_ith", + [llama_context_p_ctypes, ctypes.c_int32], + ctypes.POINTER(llama_token), +) +def llama_get_sampled_candidates_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> Optional[CtypesPointer[llama_token]]: + """Get the backend sampled candidates for the ith token.""" + ... + + +# LLAMA_API uint32_t llama_get_sampled_candidates_count_ith(struct llama_context * ctx, int32_t i); +@ctypes_function( + "llama_get_sampled_candidates_count_ith", + [llama_context_p_ctypes, ctypes.c_int32], + ctypes.c_uint32, +) +def llama_get_sampled_candidates_count_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> int: + """Get the backend sampled candidate count for the ith token.""" + ... + + # // # // Vocab # // @@ -3558,6 +3731,21 @@ def llama_chat_builtin_templates( llama_sampler_context_t = ctypes.c_void_p +# struct llama_sampler_data { +# struct ggml_tensor * logits; +# struct ggml_tensor * probs; +# struct ggml_tensor * sampled; +# struct ggml_tensor * candidates; +# }; +class llama_sampler_data(ctypes.Structure): + _fields_ = [ + ("logits", ctypes.c_void_p), + ("probs", ctypes.c_void_p), + ("sampled", ctypes.c_void_p), + ("candidates", ctypes.c_void_p), + ] + + # // user code can implement the interface below in order to create custom llama_sampler # struct llama_sampler_i { # const char * (*name) (const struct llama_sampler * smpl); // can be NULL @@ -3598,6 +3786,24 @@ class llama_sampler(ctypes.Structure): llama_sampler_i_reset = ctypes.CFUNCTYPE(None, llama_sampler_p_ctypes) llama_sampler_i_clone = ctypes.CFUNCTYPE(llama_sampler_p_ctypes, llama_sampler_p_ctypes) llama_sampler_i_free = ctypes.CFUNCTYPE(None, llama_sampler_p_ctypes) +llama_sampler_i_backend_init = ctypes.CFUNCTYPE( + ctypes.c_bool, llama_sampler_p_ctypes, ctypes.c_void_p +) +llama_sampler_i_backend_accept = ctypes.CFUNCTYPE( + None, + llama_sampler_p_ctypes, + ctypes.c_void_p, + ctypes.c_void_p, + ctypes.c_void_p, +) +llama_sampler_i_backend_apply = ctypes.CFUNCTYPE( + None, + llama_sampler_p_ctypes, + ctypes.c_void_p, + ctypes.c_void_p, + ctypes.POINTER(llama_sampler_data), +) +llama_sampler_i_backend_set_input = ctypes.CFUNCTYPE(None, llama_sampler_p_ctypes) llama_sampler_i._fields_ = [ ("name", llama_sampler_i_name), @@ -3606,9 +3812,27 @@ class llama_sampler(ctypes.Structure): ("reset", llama_sampler_i_reset), ("clone", llama_sampler_i_clone), ("free", llama_sampler_i_free), + ("backend_init", llama_sampler_i_backend_init), + ("backend_accept", llama_sampler_i_backend_accept), + ("backend_apply", llama_sampler_i_backend_apply), + ("backend_set_input", llama_sampler_i_backend_set_input), ] +# // attach a sampler to the context +# LLAMA_API bool llama_set_sampler(struct llama_context * ctx, llama_seq_id seq_id, struct llama_sampler * smpl); +@ctypes_function( + "llama_set_sampler", + [llama_context_p_ctypes, llama_seq_id, llama_sampler_p_ctypes], + ctypes.c_bool, +) +def llama_set_sampler( + ctx: llama_context_p, seq_id: Union[llama_seq_id, int], smpl: llama_sampler_p, / +) -> bool: + """Attach a sampler to the context.""" + ... + + # // mirror of llama_sampler_i: # LLAMA_API struct llama_sampler * llama_sampler_init (const struct llama_sampler_i * iface, llama_sampler_context_t ctx); @ctypes_function( @@ -3748,14 +3972,6 @@ def llama_sampler_init_greedy() -> llama_sampler_p: ... def llama_sampler_init_dist(seed: int) -> llama_sampler_p: ... -# /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. -# /// NOTE: Avoid using on the full vocabulary as the sorting can become slow. For example, apply top-k or top-p sampling first. -# DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_softmax (void), -# "will be removed in the future (see https://github.com/ggml-org/llama.cpp/pull/9896#discussion_r1800920915)"); -@ctypes_function("llama_sampler_init_softmax", [], llama_sampler_p_ctypes) -def llama_sampler_init_softmax() -> llama_sampler_p: ... - - # /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 # /// Setting k <= 0 makes this a noop # LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k); @@ -4005,6 +4221,22 @@ def llama_sampler_init_dry( ) -> llama_sampler_p: ... +# LLAMA_API struct llama_sampler * llama_sampler_init_adaptive_p( +# float target, +# float decay, +# uint32_t seed); +@ctypes_function( + "llama_sampler_init_adaptive_p", + [ctypes.c_float, ctypes.c_float, ctypes.c_uint32], + llama_sampler_p_ctypes, +) +def llama_sampler_init_adaptive_p( + target: float, decay: float, seed: int, / +) -> llama_sampler_p: + """Initialize an adaptive-p sampler.""" + ... + + # LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias( # int32_t n_vocab, # int32_t n_logit_bias, @@ -4102,10 +4334,26 @@ def llama_print_system_info() -> bytes: ... # // Set callback for all future logging events. # // If this is not called, or NULL is supplied, everything is output on stderr. +# // The logger state is global so these functions are NOT thread safe. +# LLAMA_API void llama_log_get(ggml_log_callback * log_callback, void ** user_data); +@ctypes_function( + "llama_log_get", + [ctypes.POINTER(llama_log_callback), ctypes.POINTER(ctypes.c_void_p)], + None, +) +def llama_log_get( + log_callback: CtypesPointerOrRef[llama_log_callback], + user_data: CtypesPointerOrRef[ctypes.c_void_p], + /, +): + """Get the current logging callback and user data.""" + ... + + # LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data); @ctypes_function( "llama_log_set", - [ctypes.c_void_p, ctypes.c_void_p], + [llama_log_callback, ctypes.c_void_p], None, ) def llama_log_set( @@ -4214,6 +4462,13 @@ def llama_perf_sampler_print(chain: llama_sampler_p, /): ... def llama_perf_sampler_reset(chain: llama_sampler_p, /): ... +# // print a breakdown of per-device memory use via LLAMA_LOG: +@ctypes_function("llama_memory_breakdown_print", [llama_context_p_ctypes], None) +def llama_memory_breakdown_print(ctx: llama_context_p, /): + """Print a breakdown of per-device memory use.""" + ... + + # // # // training # // diff --git a/llama_cpp/mtmd_cpp.py b/llama_cpp/mtmd_cpp.py index 41753a7f60..787683179c 100644 --- a/llama_cpp/mtmd_cpp.py +++ b/llama_cpp/mtmd_cpp.py @@ -1,6 +1,7 @@ from __future__ import annotations import os +import warnings from ctypes import ( c_bool, c_char_p, @@ -78,13 +79,31 @@ # Structures class mtmd_context_params(Structure): + if TYPE_CHECKING: + use_gpu: bool + print_timings: bool + n_threads: int + image_marker: Optional[bytes] + media_marker: Optional[bytes] + flash_attn_type: int + warmup: bool + image_min_tokens: int + image_max_tokens: int + cb_eval: llama_cpp.ggml_backend_sched_eval_callback + cb_eval_user_data: c_void_p + _fields_ = [ ("use_gpu", c_bool), ("print_timings", c_bool), ("n_threads", c_int), - ("verbosity", c_int), # ggml_log_level ("image_marker", c_char_p), ("media_marker", c_char_p), + ("flash_attn_type", c_int), + ("warmup", c_bool), + ("image_min_tokens", c_int), + ("image_max_tokens", c_int), + ("cb_eval", llama_cpp.ggml_backend_sched_eval_callback), + ("cb_eval_user_data", c_void_p), ] @@ -132,11 +151,49 @@ def mtmd_init_from_file( def mtmd_free(ctx: mtmd_context_p, /): ... +# MTMD_API bool mtmd_decode_use_non_causal(mtmd_context * ctx); +@ctypes_function("mtmd_decode_use_non_causal", [mtmd_context_p_ctypes], c_bool) +def mtmd_decode_use_non_causal(ctx: mtmd_context_p, /) -> bool: + """Check whether MTMD decoding uses non-causal attention.""" + ... + + +# MTMD_API bool mtmd_decode_use_mrope(mtmd_context * ctx); +@ctypes_function("mtmd_decode_use_mrope", [mtmd_context_p_ctypes], c_bool) +def mtmd_decode_use_mrope(ctx: mtmd_context_p, /) -> bool: + """Check whether MTMD decoding uses mRoPE.""" + ... + + # MTMD_API bool mtmd_support_vision(mtmd_context * ctx); @ctypes_function("mtmd_support_vision", [mtmd_context_p_ctypes], c_bool) def mtmd_support_vision(ctx: mtmd_context_p, /) -> bool: ... +# MTMD_API bool mtmd_support_audio(mtmd_context * ctx); +@ctypes_function("mtmd_support_audio", [mtmd_context_p_ctypes], c_bool) +def mtmd_support_audio(ctx: mtmd_context_p, /) -> bool: + """Check whether MTMD supports audio.""" + ... + + +# MTMD_API int mtmd_get_audio_sample_rate(mtmd_context * ctx); +@ctypes_function("mtmd_get_audio_sample_rate", [mtmd_context_p_ctypes], c_int) +def mtmd_get_audio_sample_rate(ctx: mtmd_context_p, /) -> int: + """Get the MTMD audio sample rate.""" + ... + + +# Deprecated compatibility wrapper for the renamed mtmd_get_audio_sample_rate(). +def mtmd_get_audio_bitrate(ctx: mtmd_context_p, /) -> int: + warnings.warn( + "mtmd_get_audio_bitrate is deprecated; use mtmd_get_audio_sample_rate instead", + DeprecationWarning, + stacklevel=2, + ) + return mtmd_get_audio_sample_rate(ctx) + + # MTMD_API mtmd_bitmap * mtmd_bitmap_init(uint32_t nx, uint32_t ny, const unsigned char * data); @ctypes_function( "mtmd_bitmap_init", [c_uint32, c_uint32, POINTER(c_uint8)], mtmd_bitmap_p_ctypes @@ -149,6 +206,21 @@ def mtmd_bitmap_init( ) -> Optional[mtmd_bitmap_p]: ... +# MTMD_API mtmd_bitmap * mtmd_bitmap_init_from_audio(size_t n_samples, const float * data); +@ctypes_function( + "mtmd_bitmap_init_from_audio", + [c_size_t, POINTER(c_float)], + mtmd_bitmap_p_ctypes, +) +def mtmd_bitmap_init_from_audio( + n_samples: Union[c_size_t, int], + data: CtypesArray[c_float], + /, +) -> Optional[mtmd_bitmap_p]: + """Initialize an MTMD bitmap from audio samples.""" + ... + + # MTMD_API void mtmd_bitmap_free(mtmd_bitmap * bitmap); @ctypes_function("mtmd_bitmap_free", [mtmd_bitmap_p_ctypes], None) def mtmd_bitmap_free(bitmap: mtmd_bitmap_p, /): ... @@ -284,3 +356,25 @@ def mtmd_helper_eval_chunk_single( new_n_past: "_Pointer[llama_cpp.llama_pos]", /, ) -> int: ... + + +# MTMD_API void mtmd_log_set(ggml_log_callback log_callback, void * user_data); +@ctypes_function( + "mtmd_log_set", + [llama_cpp.llama_log_callback, c_void_p], + None, +) +def mtmd_log_set(log_callback, user_data: c_void_p, /): + """Set the MTMD logging callback.""" + ... + + +# MTMD_API void mtmd_helper_log_set(ggml_log_callback log_callback, void * user_data); +@ctypes_function( + "mtmd_helper_log_set", + [llama_cpp.llama_log_callback, c_void_p], + None, +) +def mtmd_helper_log_set(log_callback, user_data: c_void_p, /): + """Set the MTMD helper logging callback.""" + ... diff --git a/tests/test_llama.py b/tests/test_llama.py index 964b0895cc..619c7378de 100644 --- a/tests/test_llama.py +++ b/tests/test_llama.py @@ -83,7 +83,7 @@ def test_real_model(llama_cpp_model_path): cparams.n_threads = multiprocessing.cpu_count() cparams.n_threads_batch = multiprocessing.cpu_count() cparams.logits_all = False - cparams.flash_attn = True + cparams.flash_attn_type = llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED context = internals.LlamaContext(model=model, params=cparams) tokens = model.tokenize(b"Hello, world!", add_bos=True, special=True) diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 4227c9be42..49bfddeca1 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 4227c9be4268ac844921b90f31595f81236bd317 +Subproject commit 49bfddeca18e62fa3d39114a23e9fcbdf8a22388 From e1f8ac0e135201c4a6de7c828331a2f078e9763a Mon Sep 17 00:00:00 2001 From: Bruno Verachten Date: Mon, 23 Mar 2026 03:27:22 +0100 Subject: [PATCH 018/138] ci: add riscv64 wheel builds to release workflow (#2139) * ci: add riscv64 wheel builds to release workflow Add a build_wheels_riscv64 job mirroring the existing arm64 QEMU-based build. Uses cibuildwheel with QEMU emulation for linux/riscv64, targeting CPython 3.10-3.14 on manylinux. Closes #2138 * ci: use cibuildwheel 3.1.2 for riscv64 wheels * docs: update changelog for riscv64 wheel PR --------- Co-authored-by: abetlen --- .github/workflows/build-and-release.yaml | 31 +++++++++++++++++++++++- CHANGELOG.md | 1 + 2 files changed, 31 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index 7eaf017fbc..0121febe8a 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -85,6 +85,35 @@ jobs: name: wheels_arm64 path: ./wheelhouse/*.whl + build_wheels_riscv64: + name: Build riscv64 wheels + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + submodules: "recursive" + + - name: Set up QEMU + uses: docker/setup-qemu-action@v3 + with: + platforms: linux/riscv64 + + - name: Build wheels + uses: pypa/cibuildwheel@v3.1.2 + env: + CIBW_SKIP: "*musllinux* pp*" + CIBW_REPAIR_WHEEL_COMMAND: "" + CIBW_ARCHS: "riscv64" + CIBW_BUILD: "cp310-* cp311-* cp312-* cp313-* cp314-*" + with: + output-dir: wheelhouse + + - name: Upload wheels as artifacts + uses: actions/upload-artifact@v4 + with: + name: wheels_riscv64 + path: ./wheelhouse/*.whl + build_sdist: name: Build source distribution runs-on: ubuntu-latest @@ -129,7 +158,7 @@ jobs: release: name: Release - needs: [build_wheels, build_wheels_arm64, build_sdist] + needs: [build_wheels, build_wheels_arm64, build_wheels_riscv64, build_sdist] runs-on: ubuntu-latest steps: diff --git a/CHANGELOG.md b/CHANGELOG.md index e7506eed57..94666cec1d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,6 +11,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - chore(dev): Add Ruff-based formatting and a safe lint baseline, and run it in CI for pull requests and pushes to `main` - fix(ci): Run macOS CI on supported Apple Silicon and Intel runners by @abetlen in #2150 - fix(ci): Use the `hf` CLI instead of the deprecated `huggingface-cli` name in GitHub Actions and docs by @abetlen in #2149 +- ci: add riscv64 wheel builds to release workflow by @gounthar in #2139 ## [0.3.16] From 11e7a55af76303d42f2bc5a79ed0babbc89652dd Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 22 Mar 2026 22:33:31 -0700 Subject: [PATCH 019/138] fix: Qwen 3.5 support (#2152) * fix: handle Qwen 3.5 hybrid prefix reuse * test: fix Qwen runtime unit mocks * test: drop Qwen runtime unit tests * docs: credit Qwen fix contributors in changelog * docs/tests: update default Qwen model to 3.5 0.8B * test: rebaseline Qwen 3.5 outputs * test: stabilize low-level Qwen sampling check * test: tighten Qwen 3.5 completion prompts --- .github/workflows/test.yaml | 4 ++-- CHANGELOG.md | 1 + README.md | 6 +++--- examples/gradio_chat/local.py | 6 +++--- examples/hf_pull/main.py | 6 +++--- llama_cpp/_internals.py | 4 ++-- llama_cpp/llama.py | 19 +++++++++++++------ tests/test_llama.py | 33 ++++++++++++++++++++------------- 8 files changed, 47 insertions(+), 32 deletions(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index af4cacac40..8a6845ff24 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -8,8 +8,8 @@ on: - main env: - REPO_ID: Qwen/Qwen2-0.5B-Instruct-GGUF - MODEL_FILE: qwen2-0_5b-instruct-q8_0.gguf + REPO_ID: lmstudio-community/Qwen3.5-0.8B-GGUF + MODEL_FILE: Qwen3.5-0.8B-Q8_0.gguf jobs: download-model: diff --git a/CHANGELOG.md b/CHANGELOG.md index 94666cec1d..4153406c10 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] - feat: Update llama.cpp to ggerganov/llama.cpp@49bfddeca18e62fa3d39114a23e9fcbdf8a22388 and sync Python bindings by @abetlen in #2151 +- fix: Handle Qwen 3.5 hybrid prefix reuse by @codavidgarcia and @r-dh in #2152 - chore(dev): Add Ruff-based formatting and a safe lint baseline, and run it in CI for pull requests and pushes to `main` - fix(ci): Run macOS CI on supported Apple Silicon and Intel runners by @abetlen in #2150 - fix(ci): Use the `hf` CLI instead of the deprecated `huggingface-cli` name in GitHub Actions and docs by @abetlen in #2149 diff --git a/README.md b/README.md index b57c958076..8ba4dbb5ea 100644 --- a/README.md +++ b/README.md @@ -322,8 +322,8 @@ You'll need to install the `huggingface-hub` package to use this feature (`pip i ```python llm = Llama.from_pretrained( - repo_id="Qwen/Qwen2-0.5B-Instruct-GGUF", - filename="*q8_0.gguf", + repo_id="lmstudio-community/Qwen3.5-0.8B-GGUF", + filename="*Q8_0.gguf", verbose=False ) ``` @@ -685,7 +685,7 @@ For possible options, see [llama_cpp/llama_chat_format.py](llama_cpp/llama_chat_ If you have `huggingface-hub` installed, you can also use the `--hf_model_repo_id` flag to load a model from the Hugging Face Hub. ```bash -python3 -m llama_cpp.server --hf_model_repo_id Qwen/Qwen2-0.5B-Instruct-GGUF --model '*q8_0.gguf' +python3 -m llama_cpp.server --hf_model_repo_id lmstudio-community/Qwen3.5-0.8B-GGUF --model '*Q8_0.gguf' ``` ### Web Server Features diff --git a/examples/gradio_chat/local.py b/examples/gradio_chat/local.py index e16bf234ab..871d8b09b5 100644 --- a/examples/gradio_chat/local.py +++ b/examples/gradio_chat/local.py @@ -4,10 +4,10 @@ import gradio as gr llama = llama_cpp.Llama.from_pretrained( - repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF", - filename="*q8_0.gguf", + repo_id="lmstudio-community/Qwen3.5-0.8B-GGUF", + filename="*Q8_0.gguf", tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "Qwen/Qwen1.5-0.5B" + "Qwen/Qwen3.5-0.8B" ), verbose=False, ) diff --git a/examples/hf_pull/main.py b/examples/hf_pull/main.py index dfed17516d..a9ca424d19 100644 --- a/examples/hf_pull/main.py +++ b/examples/hf_pull/main.py @@ -3,10 +3,10 @@ llama = llama_cpp.Llama.from_pretrained( - repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF", - filename="*q8_0.gguf", + repo_id="lmstudio-community/Qwen3.5-0.8B-GGUF", + filename="*Q8_0.gguf", tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "Qwen/Qwen1.5-0.5B" + "Qwen/Qwen3.5-0.8B" ), verbose=False, ) diff --git a/llama_cpp/_internals.py b/llama_cpp/_internals.py index d6258d2244..6862135aa7 100644 --- a/llama_cpp/_internals.py +++ b/llama_cpp/_internals.py @@ -291,10 +291,10 @@ def kv_cache_clear(self): assert self.memory is not None, "Memory is not initialized" llama_cpp.llama_memory_clear(self.memory, True) - def kv_cache_seq_rm(self, seq_id: int, p0: int, p1: int): + def kv_cache_seq_rm(self, seq_id: int, p0: int, p1: int) -> bool: assert self.memory is not None, "Memory is not initialized" seq_id = seq_id if seq_id >= 0 else 0 - llama_cpp.llama_memory_seq_rm(self.memory, seq_id, p0, p1) + return llama_cpp.llama_memory_seq_rm(self.memory, seq_id, p0, p1) def kv_cache_seq_cp(self, seq_id_src: int, seq_id_dst: int, p0: int, p1: int): assert self.memory is not None, "Memory is not initialized" diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index 1609ad16bf..88bc2e5bb8 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -891,13 +891,20 @@ def generate( else: break if longest_prefix > 0: - reset = False - tokens = tokens[longest_prefix:] - self.n_tokens = longest_prefix - if self.verbose: + if self._ctx.kv_cache_seq_rm(-1, longest_prefix, -1): + reset = False + tokens = tokens[longest_prefix:] + self.n_tokens = longest_prefix + if self.verbose: + print( + f"Llama.generate: {longest_prefix} prefix-match hit, " + f"remaining {len(tokens)} prompt tokens to eval", + file=sys.stderr, + ) + elif self.verbose: print( - f"Llama.generate: {longest_prefix} prefix-match hit, " - f"remaining {len(tokens)} prompt tokens to eval", + f"Llama.generate: {longest_prefix} prefix-match found " + f"but partial kv removal not supported, re-evaluating full prompt", file=sys.stderr, ) diff --git a/tests/test_llama.py b/tests/test_llama.py index 619c7378de..1a70c74d43 100644 --- a/tests/test_llama.py +++ b/tests/test_llama.py @@ -58,8 +58,8 @@ def test_llama_cpp_tokenization(): @pytest.fixture def llama_cpp_model_path(): - repo_id = "Qwen/Qwen2-0.5B-Instruct-GGUF" - filename = "qwen2-0_5b-instruct-q8_0.gguf" + repo_id = "lmstudio-community/Qwen3.5-0.8B-GGUF" + filename = "Qwen3.5-0.8B-Q8_0.gguf" model_path = hf_hub_download(repo_id, filename) return model_path @@ -88,9 +88,14 @@ def test_real_model(llama_cpp_model_path): context = internals.LlamaContext(model=model, params=cparams) tokens = model.tokenize(b"Hello, world!", add_bos=True, special=True) - assert tokens == [9707, 11, 1879, 0] + assert tokens == [9419, 11, 1814, 0] - tokens = model.tokenize(b"The quick brown fox jumps", add_bos=True, special=True) + tokens = model.tokenize( + b"The quick brown fox jumps over the lazy dog. The quick brown fox jumps ", + add_bos=True, + special=True, + ) + prompt_token_count = len(tokens) batch = internals.LlamaBatch(n_tokens=len(tokens), embd=0, n_seq_max=1) @@ -111,9 +116,11 @@ def test_real_model(llama_cpp_model_path): tokens = [token_id] result += tokens - output = result[5:] + output = result[prompt_token_count:] output_text = model.detokenize(output, special=True) - assert output_text == b" over the lazy dog" + # Low-level sampling output varies across CPU and Metal backends. + assert len(output) == 4 + assert output_text def test_real_llama(llama_cpp_model_path): @@ -129,14 +136,14 @@ def test_real_llama(llama_cpp_model_path): ) output = model.create_completion( - "The quick brown fox jumps", - max_tokens=4, + "The quick brown fox jumps over the lazy dog. The quick brown fox", + max_tokens=6, top_k=50, top_p=0.9, - temperature=0.8, + temperature=0.0, seed=1337, ) - assert output["choices"][0]["text"] == " over the lazy dog" + assert output["choices"][0]["text"] == " jumps over the lazy dog." output = model.create_completion( "The capital of france is paris, 'true' or 'false'?:\n", @@ -181,7 +188,7 @@ def logit_processor_func(input_ids, logits): max_tokens=4, top_k=50, top_p=0.9, - temperature=0.8, + temperature=1.0, grammar=llama_cpp.LlamaGrammar.from_string(""" root ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" | "10" """), @@ -193,7 +200,7 @@ def logit_processor_func(input_ids, logits): max_tokens=4, top_k=50, top_p=0.9, - temperature=0.8, + temperature=1.0, grammar=llama_cpp.LlamaGrammar.from_string(""" root ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" | "10" """), @@ -207,7 +214,7 @@ def logit_processor_func(input_ids, logits): max_tokens=4, top_k=50, top_p=0.9, - temperature=0.8, + temperature=1.0, grammar=llama_cpp.LlamaGrammar.from_string(""" root ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" | "10" """), From a6b180724a641750aeaefa51692fe12ee8c4d54f Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 22 Mar 2026 23:28:55 -0700 Subject: [PATCH 020/138] chore: Bump version (#2153) --- CHANGELOG.md | 2 ++ llama_cpp/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4153406c10..d1195cc2ae 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.17] + - feat: Update llama.cpp to ggerganov/llama.cpp@49bfddeca18e62fa3d39114a23e9fcbdf8a22388 and sync Python bindings by @abetlen in #2151 - fix: Handle Qwen 3.5 hybrid prefix reuse by @codavidgarcia and @r-dh in #2152 - chore(dev): Add Ruff-based formatting and a safe lint baseline, and run it in CI for pull requests and pushes to `main` diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index c1dde70468..a7c40478bc 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.16" +__version__ = "0.3.17" From f0391c5ea7159b4c4916d9f4aced2f982adbd1f4 Mon Sep 17 00:00:00 2001 From: Andrei Date: Tue, 24 Mar 2026 00:59:19 -0700 Subject: [PATCH 021/138] fix(ci): release wheel workflow (#2154) * fix(ci): harden release wheel workflow * fix(ci): document and pin release wheel baselines * fix(ci): speed up release arch builds * fix(ci): split riscv64 by python version * fix(ci): sanitize riscv64 artifact names --- .github/workflows/build-and-release.yaml | 49 +++++++++++++++++++----- 1 file changed, 39 insertions(+), 10 deletions(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index 0121febe8a..3a9e6f3698 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -46,6 +46,13 @@ jobs: env: # disable repair CIBW_REPAIR_WHEEL_COMMAND: "" + # Skip cibuildwheel's default i686 sidecar and keep Linux release + # wheels on a portable x86_64 CPU baseline. + CIBW_ARCHS_LINUX: "auto64" + CIBW_ENVIRONMENT_LINUX: CMAKE_ARGS="-DGGML_NATIVE=off" + # Keep macOS release wheels on a portable CPU baseline instead of + # inheriting the hosted runner's native flags. + CIBW_ENVIRONMENT_MACOS: CMAKE_ARGS="-DGGML_NATIVE=off" with: package-dir: . output-dir: wheelhouse @@ -57,24 +64,21 @@ jobs: build_wheels_arm64: name: Build arm64 wheels - runs-on: ubuntu-latest + runs-on: ubuntu-24.04-arm steps: - uses: actions/checkout@v4 with: submodules: "recursive" - - name: Set up QEMU - uses: docker/setup-qemu-action@v3 - with: - platforms: linux/arm64 - - name: Build wheels uses: pypa/cibuildwheel@v2.22.0 env: CIBW_SKIP: "*musllinux* pp*" CIBW_REPAIR_WHEEL_COMMAND: "" CIBW_ARCHS: "aarch64" - CIBW_ENVIRONMENT: CMAKE_ARGS="-DCMAKE_OSX_ARCHITECTURES=arm64 -DCMAKE_APPLE_SILICON_PROCESSOR=arm64 -DCMAKE_CROSSCOMPILING=ON" + # Keep native arm64 builds on a portable CPU baseline instead of + # tuning wheels to the hosted runner. + CIBW_ENVIRONMENT: CMAKE_ARGS="-DGGML_NATIVE=off" CIBW_BUILD: "cp38-* cp39-* cp310-* cp311-* cp312-*" with: output-dir: wheelhouse @@ -86,8 +90,27 @@ jobs: path: ./wheelhouse/*.whl build_wheels_riscv64: - name: Build riscv64 wheels + name: Build riscv64 wheels (${{ matrix.shard.name }}) runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + shard: + - name: cp310 + build: "cp310-*" + artifact: wheels_riscv64_cp310 + - name: cp311 + build: "cp311-*" + artifact: wheels_riscv64_cp311 + - name: cp312 + build: "cp312-*" + artifact: wheels_riscv64_cp312 + - name: cp313 + build: "cp313-*" + artifact: wheels_riscv64_cp313 + - name: cp314 + build: "cp314-*" + artifact: wheels_riscv64_cp314 steps: - uses: actions/checkout@v4 with: @@ -104,14 +127,19 @@ jobs: CIBW_SKIP: "*musllinux* pp*" CIBW_REPAIR_WHEEL_COMMAND: "" CIBW_ARCHS: "riscv64" - CIBW_BUILD: "cp310-* cp311-* cp312-* cp313-* cp314-*" + # Build riscv64 wheels against a conservative baseline instead of + # enabling RVV-related extensions from the build container. + CIBW_ENVIRONMENT: CMAKE_ARGS="-DGGML_NATIVE=off -DGGML_RVV=off -DGGML_RV_ZFH=off -DGGML_RV_ZVFH=off -DGGML_RV_ZICBOP=off -DGGML_RV_ZIHINTPAUSE=off" + # Split the emulated riscv64 build into one Python version per job + # to minimize wall-clock time without changing the release artifacts. + CIBW_BUILD: ${{ matrix.shard.build }} with: output-dir: wheelhouse - name: Upload wheels as artifacts uses: actions/upload-artifact@v4 with: - name: wheels_riscv64 + name: ${{ matrix.shard.artifact }} path: ./wheelhouse/*.whl build_sdist: @@ -159,6 +187,7 @@ jobs: release: name: Release needs: [build_wheels, build_wheels_arm64, build_wheels_riscv64, build_sdist] + if: startsWith(github.ref, 'refs/tags/') runs-on: ubuntu-latest steps: From 909ebf1246a52c15ebc95460c7e5957e3b64711e Mon Sep 17 00:00:00 2001 From: Andrei Date: Tue, 24 Mar 2026 01:00:50 -0700 Subject: [PATCH 022/138] fix(ci): cuda wheel workflow (#2155) * fix(ci): harden cuda wheel workflow * fix(ci): pin cuda toolkit versions accurately * fix(ci): resolve exact cuda toolkit installs * fix(ci): align cuda toolkit roots and tags * fix(ci): pin cuda packages to nvidia label * fix(ci): allow cuda solver to mix non-cuda deps --- .github/workflows/build-wheels-cuda.yaml | 57 +++++++++++++++++++++--- 1 file changed, 50 insertions(+), 7 deletions(-) diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index 07b30cfc06..b8d6c9dcec 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -99,21 +99,63 @@ jobs: MAMBA_NO_LOW_SPEED_LIMIT: "1" run: | $cudaVersion = $env:CUDAVER - mamba install -y 'cuda' -c nvidia/label/cuda-$cudaVersion + $cudaChannel = "nvidia/label/cuda-$cudaVersion" + if ($IsLinux) { + # Keep nvcc, cudart, and headers on the same NVIDIA label so the + # detected toolkit version matches the published wheel tag. + mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "$cudaChannel::cuda-toolkit=$cudaVersion" "$cudaChannel::cuda-nvcc_linux-64=$cudaVersion" "$cudaChannel::cuda-cudart" "$cudaChannel::cuda-cudart-dev" + } else { + mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "$cudaChannel::cuda-toolkit=$cudaVersion" + } + if ($LASTEXITCODE -ne 0) { + exit $LASTEXITCODE + } python -m pip install build wheel - name: Build Wheel run: | - $cudaVersion = $env:CUDAVER.Remove($env:CUDAVER.LastIndexOf('.')).Replace('.','') $env:CUDA_PATH = $env:CONDA_PREFIX $env:CUDA_HOME = $env:CONDA_PREFIX $env:CUDA_TOOLKIT_ROOT_DIR = $env:CONDA_PREFIX + $cudaHostCompilerArg = '' + $env:CMAKE_ARGS = '' if ($IsLinux) { - $env:LD_LIBRARY_PATH = $env:CONDA_PREFIX + '/lib:' + $env:LD_LIBRARY_PATH + if (Test-Path '/usr/bin/g++-12') { + $env:CC = '/usr/bin/gcc-12' + $env:CXX = '/usr/bin/g++-12' + $env:CUDAHOSTCXX = '/usr/bin/g++-12' + $cudaHostCompilerArg = " -DCMAKE_CUDA_HOST_COMPILER=$env:CUDAHOSTCXX" + } + if (Test-Path (Join-Path $env:CONDA_PREFIX 'include/cuda_runtime.h')) { + $env:CUDAToolkit_ROOT = $env:CONDA_PREFIX + $env:CUDA_TOOLKIT_ROOT_DIR = $env:CONDA_PREFIX + $env:CMAKE_ARGS = "-DCUDAToolkit_ROOT=$env:CONDA_PREFIX -DCUDA_TOOLKIT_ROOT_DIR=$env:CONDA_PREFIX$cudaHostCompilerArg" + $env:CPATH = "$env:CONDA_PREFIX/include:$env:CPATH" + $env:CPLUS_INCLUDE_PATH = "$env:CONDA_PREFIX/include:$env:CPLUS_INCLUDE_PATH" + $env:LIBRARY_PATH = "$env:CONDA_PREFIX/lib:$env:LIBRARY_PATH" + $env:LD_LIBRARY_PATH = "$env:CONDA_PREFIX/lib:$env:LD_LIBRARY_PATH" + } else { + $env:CMAKE_ARGS = $cudaHostCompilerArg.Trim() + } + } + $nvccPath = Join-Path $env:CONDA_PREFIX 'bin/nvcc' + if (-not (Test-Path $nvccPath)) { + $nvccPath = Join-Path $env:CONDA_PREFIX 'targets/x86_64-linux/bin/nvcc' + } + if (-not (Test-Path $nvccPath)) { + throw 'Failed to find nvcc in the conda environment' + } + $env:CUDACXX = $nvccPath + $env:PATH = "$(Split-Path $nvccPath):$env:PATH" + $nvccVersion = ((& $nvccPath --version) | Select-String 'release ([0-9]+\.[0-9]+)').Matches[0].Groups[1].Value + if (-not $nvccVersion) { + throw 'Failed to detect the installed CUDA toolkit version' } + $cudaTagVersion = $nvccVersion.Replace('.','') $env:VERBOSE = '1' - $env:CMAKE_ARGS = '-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=all' - $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON $env:CMAKE_ARGS" + # Keep a portable SM set, including sm_70, instead of CMake's `all`, + # which now pulls in future targets the hosted-runner toolchains cannot assemble. + $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=70;75;80;86;89;90 -DCMAKE_CUDA_FLAGS=--allow-unsupported-compiler $env:CMAKE_ARGS" # if ($env:AVXVER -eq 'AVX') { $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off' # } @@ -124,10 +166,11 @@ jobs: # $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX=off -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off' # } python -m build --wheel - # write the build tag to the output - Write-Output "CUDA_VERSION=$cudaVersion" >> $env:GITHUB_ENV + # Publish tags that reflect the actual installed toolkit version. + Write-Output "CUDA_VERSION=$cudaTagVersion" >> $env:GITHUB_ENV - uses: softprops/action-gh-release@v2 + if: startsWith(github.ref, 'refs/tags/') with: files: dist/* # Set tag_name to -cu From ccc6bc0454b2d73431a419620aad92fda1aba162 Mon Sep 17 00:00:00 2001 From: Andrei Date: Tue, 24 Mar 2026 01:02:14 -0700 Subject: [PATCH 023/138] fix(ci): docker build workflow (#2156) * fix(ci): harden docker build workflow * docs: update changelog for ci workflows --- .github/workflows/build-docker.yaml | 11 ++++++++++- CHANGELOG.md | 4 ++++ docker/simple/Dockerfile | 3 ++- 3 files changed, 16 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build-docker.yaml b/.github/workflows/build-docker.yaml index b290f6273f..c656958479 100644 --- a/.github/workflows/build-docker.yaml +++ b/.github/workflows/build-docker.yaml @@ -16,6 +16,15 @@ jobs: with: submodules: "recursive" + - name: Set image tag + run: | + if [[ "${GITHUB_REF_TYPE}" == "tag" ]]; then + image_tag="${GITHUB_REF_NAME}" + else + image_tag="${GITHUB_REF_NAME//\//-}" + fi + echo "IMAGE_TAG=$image_tag" >> "$GITHUB_ENV" + - name: Set up QEMU uses: docker/setup-qemu-action@v3 @@ -40,7 +49,7 @@ jobs: platforms: linux/amd64,linux/arm64 tags: | ghcr.io/abetlen/llama-cpp-python:latest - ghcr.io/abetlen/llama-cpp-python:${{ github.ref_name }} + ghcr.io/abetlen/llama-cpp-python:${{ env.IMAGE_TAG }} build-args: | BUILDKIT_INLINE_CACHE=1 diff --git a/CHANGELOG.md b/CHANGELOG.md index d1195cc2ae..b476131099 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix(ci): Build Docker images from the checked-out source and sanitize branch tags by @abetlen in #2156 +- fix(ci): Fix the CUDA wheel workflow and keep release tags aligned with the built toolkit by @abetlen in #2155 +- fix(ci): Speed up release wheel builds by moving arm64 off QEMU and parallelizing riscv64 by @abetlen in #2154 + ## [0.3.17] - feat: Update llama.cpp to ggerganov/llama.cpp@49bfddeca18e62fa3d39114a23e9fcbdf8a22388 and sync Python bindings by @abetlen in #2151 diff --git a/docker/simple/Dockerfile b/docker/simple/Dockerfile index 06483d44e5..bad4f456ff 100644 --- a/docker/simple/Dockerfile +++ b/docker/simple/Dockerfile @@ -6,6 +6,7 @@ FROM ${IMAGE} # Re-declare the ARG after FROM ARG IMAGE +ARG CMAKE_ARGS="-DGGML_NATIVE=off" # Update and upgrade the existing packages RUN apt-get update && apt-get upgrade -y && apt-get install -y --no-install-recommends \ @@ -26,7 +27,7 @@ RUN python3 -m pip install --upgrade pip RUN python3 -m pip install --upgrade pip pytest cmake scikit-build setuptools fastapi uvicorn sse-starlette pydantic-settings starlette-context -RUN pip install llama-cpp-python --verbose; +RUN CMAKE_ARGS="${CMAKE_ARGS}" pip install . --verbose # Set environment variable for the host ENV HOST=0.0.0.0 From 7b38c3122d2ff3ad23e1502de045807836ced4a7 Mon Sep 17 00:00:00 2001 From: Victor Biederbeck Date: Tue, 24 Mar 2026 02:50:15 -0700 Subject: [PATCH 024/138] feat: expose attention_type parameter in Llama.__init__ (#2143) * feat: expose attention_type parameter in Llama.__init__ * docs: preserve attention_type in pickled state * docs: update changelog for attention_type --------- Co-authored-by: Victor Biederbeck Co-authored-by: abetlen --- CHANGELOG.md | 1 + llama_cpp/llama.py | 4 ++++ 2 files changed, 5 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index b476131099..de4f070ffc 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: Expose `attention_type` in `Llama.__init__` for non-causal embedding models by @jamesbiederbeck in #2143 - fix(ci): Build Docker images from the checked-out source and sanitize branch tags by @abetlen in #2156 - fix(ci): Fix the CUDA wheel workflow and keep release tags aligned with the built toolkit by @abetlen in #2155 - fix(ci): Speed up release wheel builds by moving arm64 off QEMU and parallelizing riscv64 by @abetlen in #2154 diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index 88bc2e5bb8..ad484c4d5f 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -81,6 +81,7 @@ def __init__( int ] = llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED, pooling_type: int = llama_cpp.LLAMA_POOLING_TYPE_UNSPECIFIED, + attention_type: int = llama_cpp.LLAMA_ATTENTION_TYPE_UNSPECIFIED, rope_freq_base: float = 0.0, rope_freq_scale: float = 0.0, yarn_ext_factor: float = -1.0, @@ -163,6 +164,7 @@ def __init__( n_threads_batch: Number of threads to use for batch processing rope_scaling_type: RoPE scaling type, from `enum llama_rope_scaling_type`. ref: https://github.com/ggerganov/llama.cpp/pull/2054 pooling_type: Pooling type, from `enum llama_pooling_type`. + attention_type: Attention type, from `enum llama_attention_type`. rope_freq_base: RoPE base frequency, 0 = from model rope_freq_scale: RoPE frequency scaling factor, 0 = from model yarn_ext_factor: YaRN extrapolation mix factor, negative = from model @@ -319,6 +321,7 @@ def __init__( else llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED ) self.context_params.pooling_type = pooling_type + self.context_params.attention_type = attention_type self.context_params.rope_freq_base = ( rope_freq_base if rope_freq_base != 0.0 else 0 ) @@ -2100,6 +2103,7 @@ def __getstate__(self): n_threads_batch=self.context_params.n_threads_batch, rope_scaling_type=self.context_params.rope_scaling_type, pooling_type=self.context_params.pooling_type, + attention_type=self.context_params.attention_type, rope_freq_base=self.context_params.rope_freq_base, rope_freq_scale=self.context_params.rope_freq_scale, yarn_ext_factor=self.context_params.yarn_ext_factor, From d6f46a50d6b4cda10460c05e2acdbaec74428c1b Mon Sep 17 00:00:00 2001 From: Andrei Date: Tue, 24 Mar 2026 02:56:01 -0700 Subject: [PATCH 025/138] chore: bump version (#2157) --- CHANGELOG.md | 2 ++ llama_cpp/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index de4f070ffc..4118f48485 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.18] + - feat: Expose `attention_type` in `Llama.__init__` for non-causal embedding models by @jamesbiederbeck in #2143 - fix(ci): Build Docker images from the checked-out source and sanitize branch tags by @abetlen in #2156 - fix(ci): Fix the CUDA wheel workflow and keep release tags aligned with the built toolkit by @abetlen in #2155 diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index a7c40478bc..bdaefb9e01 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.17" +__version__ = "0.3.18" From 5f9c231ce165126f38c8897fd760ecd7ef79f9fd Mon Sep 17 00:00:00 2001 From: Andrei Date: Wed, 25 Mar 2026 01:56:18 -0700 Subject: [PATCH 026/138] fix(ci): reduce CUDA binary wheel size only including cubins for current arches and one PTX target for forward compatibility (#2158) * fix(ci): shrink CUDA wheel fatbins * docs: update changelog for cuda wheel size fix --- .github/workflows/build-wheels-cuda.yaml | 7 ++++--- CHANGELOG.md | 2 ++ 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index b8d6c9dcec..17daaa12a2 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -153,9 +153,10 @@ jobs: } $cudaTagVersion = $nvccVersion.Replace('.','') $env:VERBOSE = '1' - # Keep a portable SM set, including sm_70, instead of CMake's `all`, - # which now pulls in future targets the hosted-runner toolchains cannot assemble. - $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=70;75;80;86;89;90 -DCMAKE_CUDA_FLAGS=--allow-unsupported-compiler $env:CMAKE_ARGS" + # Build real cubins for the supported GPUs, including sm_70, and keep + # one forward-compatible PTX target instead of embedding PTX for every + # SM. This keeps the wheel under GitHub's 2 GiB release-asset limit. + $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=70-real;75-real;80-real;86-real;89-real;90-real;90-virtual -DCMAKE_CUDA_FLAGS=--allow-unsupported-compiler $env:CMAKE_ARGS" # if ($env:AVXVER -eq 'AVX') { $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off' # } diff --git a/CHANGELOG.md b/CHANGELOG.md index 4118f48485..f4a0b55d33 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix(ci): Shrink CUDA wheel fatbins so CUDA releases stay under GitHub's asset size limit by @abetlen in #2158 + ## [0.3.18] - feat: Expose `attention_type` in `Llama.__init__` for non-causal embedding models by @jamesbiederbeck in #2143 From ac59e5a5ae8d331d80f30d3ddfc50195061637f5 Mon Sep 17 00:00:00 2001 From: Andrei Date: Wed, 25 Mar 2026 15:03:57 -0700 Subject: [PATCH 027/138] fix: handle embedding models without KV memory (#2160) * Fix embedding models without KV memory * Add changelog entry for embedding memory fix --- CHANGELOG.md | 1 + llama_cpp/_internals.py | 4 +++- tests/test_llama.py | 16 ++++++++++++---- 3 files changed, 16 insertions(+), 5 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index f4a0b55d33..d2e4937c9c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix: Handle embedding models without KV memory and test embeddings with a real GGUF embedding model by @abetlen in #2160 - fix(ci): Shrink CUDA wheel fatbins so CUDA releases stay under GitHub's asset size limit by @abetlen in #2158 ## [0.3.18] diff --git a/llama_cpp/_internals.py b/llama_cpp/_internals.py index 6862135aa7..9e9bcd407a 100644 --- a/llama_cpp/_internals.py +++ b/llama_cpp/_internals.py @@ -288,7 +288,9 @@ def pooling_type(self) -> int: return llama_cpp.llama_pooling_type(self.ctx) def kv_cache_clear(self): - assert self.memory is not None, "Memory is not initialized" + # Embedding models with non-causal attention may not allocate memory. + if self.memory is None: + return llama_cpp.llama_memory_clear(self.memory, True) def kv_cache_seq_rm(self, seq_id: int, p0: int, p1: int) -> bool: diff --git a/tests/test_llama.py b/tests/test_llama.py index 1a70c74d43..23928fff66 100644 --- a/tests/test_llama.py +++ b/tests/test_llama.py @@ -64,6 +64,14 @@ def llama_cpp_model_path(): return model_path +@pytest.fixture +def llama_cpp_embedding_model_path(): + repo_id = "CompendiumLabs/bge-small-en-v1.5-gguf" + filename = "bge-small-en-v1.5-q4_k_m.gguf" + model_path = hf_hub_download(repo_id, filename) + return model_path + + def test_real_model(llama_cpp_model_path): import os @@ -225,9 +233,9 @@ def logit_processor_func(input_ids, logits): assert number_1 == number_3 -def test_real_llama_embeddings(llama_cpp_model_path): +def test_real_llama_embeddings(llama_cpp_embedding_model_path): model = llama_cpp.Llama( - llama_cpp_model_path, + llama_cpp_embedding_model_path, n_ctx=32, n_batch=32, n_ubatch=32, @@ -237,5 +245,5 @@ def test_real_llama_embeddings(llama_cpp_model_path): flash_attn=True, embedding=True, ) - # Smoke test for now - model.embed("Hello World") + embedding = model.embed("Hello World") + assert len(embedding) > 0 From c670222c8379608aa22ac81e5f6a813620187a26 Mon Sep 17 00:00:00 2001 From: Andrei Date: Wed, 25 Mar 2026 15:21:44 -0700 Subject: [PATCH 028/138] feat: Update llama.cpp to ggerganov/llama.cpp@c0159f9c1f874da15e94f371d136f5920b4b5335 (#2161) * Update llama.cpp to c0159f9c1 * Add changelog entry for llama.cpp update --- CHANGELOG.md | 1 + llama_cpp/llama_cpp.py | 16 ++++++++++++++++ vendor/llama.cpp | 2 +- 3 files changed, 18 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index d2e4937c9c..03240454f6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: Update llama.cpp to ggerganov/llama.cpp@c0159f9c1f874da15e94f371d136f5920b4b5335 by @abetlen in #2161 - fix: Handle embedding models without KV memory and test embeddings with a real GGUF embedding model by @abetlen in #2160 - fix(ci): Shrink CUDA wheel fatbins so CUDA releases stay under GitHub's asset size limit by @abetlen in #2158 diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index e51492c56e..5a6c06b077 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -1314,6 +1314,22 @@ def llama_model_load_from_splits( ... +# // Load a model from an open FILE pointer +# LLAMA_API struct llama_model * llama_model_load_from_file_ptr( +# FILE * file, +# struct llama_model_params params); +@ctypes_function( + "llama_model_load_from_file_ptr", + [ctypes.c_void_p, llama_model_params], + llama_model_p_ctypes, +) +def llama_model_load_from_file_ptr( + file: ctypes.c_void_p, params: llama_model_params, / +) -> Optional[llama_model_p]: + """Load a model from an open FILE pointer.""" + ... + + # LLAMA_API void llama_model_save_to_file( # const struct llama_model * model, # const char * path_model); diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 49bfddeca1..c0159f9c1f 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 49bfddeca18e62fa3d39114a23e9fcbdf8a22388 +Subproject commit c0159f9c1f874da15e94f371d136f5920b4b5335 From f54421ba71db942b262a28762bc9e035a5d4d349 Mon Sep 17 00:00:00 2001 From: Andrei Date: Wed, 25 Mar 2026 15:28:12 -0700 Subject: [PATCH 029/138] Bump version to 0.3.19 (#2162) --- CHANGELOG.md | 2 ++ llama_cpp/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 03240454f6..d1efdc5e95 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.19] + - feat: Update llama.cpp to ggerganov/llama.cpp@c0159f9c1f874da15e94f371d136f5920b4b5335 by @abetlen in #2161 - fix: Handle embedding models without KV memory and test embeddings with a real GGUF embedding model by @abetlen in #2160 - fix(ci): Shrink CUDA wheel fatbins so CUDA releases stay under GitHub's asset size limit by @abetlen in #2158 diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index bdaefb9e01..72388c4e5d 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.18" +__version__ = "0.3.19" From fcd932a1574b7b6fdfc6c2d652f10f3af66995b5 Mon Sep 17 00:00:00 2001 From: Andrei Date: Sat, 28 Mar 2026 21:36:13 -0700 Subject: [PATCH 030/138] fix(ci): publish distinct manylinux and musllinux cpu wheels (#2165) * fix(ci): publish distinct manylinux and musllinux cpu wheels * docs: add changelog entry for linux wheel repair fix --- .github/workflows/build-and-release.yaml | 5 ++++- CHANGELOG.md | 2 ++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index 3a9e6f3698..6cbac0cb1c 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -44,8 +44,11 @@ jobs: - name: Build wheels uses: pypa/cibuildwheel@v2.22.0 env: - # disable repair + # Keep repair disabled by default for non-Linux platforms in this job. CIBW_REPAIR_WHEEL_COMMAND: "" + # Linux needs auditwheel repair so manylinux and musllinux wheels are + # published with distinct platform tags instead of generic linux tags. + CIBW_REPAIR_WHEEL_COMMAND_LINUX: "auditwheel repair -w {dest_dir} {wheel}" # Skip cibuildwheel's default i686 sidecar and keep Linux release # wheels on a portable x86_64 CPU baseline. CIBW_ARCHS_LINUX: "auto64" diff --git a/CHANGELOG.md b/CHANGELOG.md index d1efdc5e95..7bcad0a473 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix(ci): Publish distinct manylinux and musllinux CPU wheels by @abetlen in #2165 + ## [0.3.19] - feat: Update llama.cpp to ggerganov/llama.cpp@c0159f9c1f874da15e94f371d136f5920b4b5335 by @abetlen in #2161 From 7613aca61259820ab550626384af52eed56a731f Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 29 Mar 2026 00:31:03 -0700 Subject: [PATCH 031/138] ci: publish release wheels as py3-none (#2166) * ci: publish CPU wheels as py3-none * docs: add changelog entry for py3-none wheel tags --- CHANGELOG.md | 1 + pyproject.toml | 1 + 2 files changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7bcad0a473..3b4c13ee34 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- ci: Publish release wheels as `py3-none` by @Bing-su in #2166 - fix(ci): Publish distinct manylinux and musllinux CPU wheels by @abetlen in #2165 ## [0.3.19] diff --git a/pyproject.toml b/pyproject.toml index e0b0dc520c..b5998dd1c8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -63,6 +63,7 @@ all = [ [tool.scikit-build] wheel.packages = ["llama_cpp"] +wheel.py-api = "py3" cmake.verbose = true cmake.minimum-version = "3.21" minimum-version = "0.5.1" From 7257ba95fbbf65201fd5bf4b7f0bdd1c701e1345 Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 29 Mar 2026 23:17:22 -0700 Subject: [PATCH 032/138] feat(server): add model-load chat_template_kwargs (#2168) --- CHANGELOG.md | 1 + docs/server.md | 25 +++++++++++++++++++++++++ llama_cpp/llama_chat_format.py | 7 ++++++- llama_cpp/server/cli.py | 34 ++++++++++++++++++++++++++++++++-- llama_cpp/server/model.py | 15 +++++++++++++++ llama_cpp/server/settings.py | 6 +++++- 6 files changed, 84 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 3b4c13ee34..e577324db5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat(server): Add model-load `chat_template_kwargs` support and document the CLI/config usage by @abetlen in #2168 - ci: Publish release wheels as `py3-none` by @Bing-su in #2166 - fix(ci): Publish distinct manylinux and musllinux CPU wheels by @abetlen in #2165 diff --git a/docs/server.md b/docs/server.md index cd6f86c513..9c09a1f1cf 100644 --- a/docs/server.md +++ b/docs/server.md @@ -22,6 +22,15 @@ The server can then be started by running the following command: python3 -m llama_cpp.server --model ``` +You can also pass chat-template kwargs at model load time from the CLI: + +```bash +python3 -m llama_cpp.server \ + --model \ + --chat_format chatml \ + --chat_template_kwargs '{"enable_thinking": true}' +``` + ### Server options For a full list of options, run: @@ -147,6 +156,22 @@ The server supports routing requests to multiple models based on the `model` par At the moment only a single model is loaded into memory at, the server will automatically load and unload models as needed. +For a single-model config, `chat_template_kwargs` can be set directly on the model entry: + +```json +{ + "models": [ + { + "model": "models/Qwen3.5-0.8B/qwen3.5-0.8b-q8_0.gguf", + "chat_format": "chatml", + "chat_template_kwargs": { + "enable_thinking": true + } + } + ] +} +``` + ```json { "host": "0.0.0.0", diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index d7910e9847..1024fb85b9 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -243,6 +243,7 @@ def raise_exception(message: str): tools=tools, tool_choice=tool_choice, strftime_now=self.strftime_now, + **kwargs, ) stopping_criteria = None @@ -617,6 +618,7 @@ def chat_completion_handler( function_call=function_call, tools=tools, tool_choice=tool_choice, + **kwargs, ) prompt = llama.tokenize( result.prompt.encode("utf-8"), @@ -734,7 +736,9 @@ def format_autotokenizer( **kwargs: Any, ) -> ChatFormatterResponse: tokenizer.use_default_system_prompt = False # type: ignore - prompt: str = tokenizer.apply_chat_template(messages, tokenize=False) # type: ignore + prompt: str = tokenizer.apply_chat_template( # type: ignore + messages, tokenize=False, **kwargs + ) assert isinstance(prompt, str) # Return formatted prompt and eos token by default return ChatFormatterResponse( @@ -791,6 +795,7 @@ def format_tokenizer_config( messages=messages, bos_token=bos_token, eos_token=eos_token, + **kwargs, ) return ChatFormatterResponse( prompt=prompt, stop=[eos_token, bos_token], added_special=True diff --git a/llama_cpp/server/cli.py b/llama_cpp/server/cli.py index 8ed0290637..171b8db30c 100644 --- a/llama_cpp/server/cli.py +++ b/llama_cpp/server/cli.py @@ -1,8 +1,9 @@ from __future__ import annotations import argparse +import json -from typing import List, Literal, Union, Any, Type, TypeVar +from typing import List, Literal, Union, Any, Type, TypeVar, Dict from pydantic import BaseModel @@ -40,6 +41,17 @@ def _contains_list_type(annotation: Type[Any] | None) -> bool: return False +def _contains_dict_type(annotation: Type[Any] | None) -> bool: + origin = getattr(annotation, "__origin__", None) + + if origin is dict or origin is Dict: + return True + elif origin in (Literal, Union): + return any(_contains_dict_type(arg) for arg in annotation.__args__) # type: ignore + else: + return False + + def _parse_bool_arg(arg: str | bytes | bool) -> bool: if isinstance(arg, bytes): arg = arg.decode("utf-8") @@ -57,6 +69,16 @@ def _parse_bool_arg(arg: str | bytes | bool) -> bool: raise ValueError(f"Invalid boolean argument: {arg}") +def _parse_json_object_arg(arg: str | bytes) -> dict[str, Any]: + if isinstance(arg, bytes): + arg = arg.decode("utf-8") + + value = json.loads(arg) + if not isinstance(value, dict): + raise ValueError(f"Invalid JSON object argument: {arg}") + return value + + def add_args_from_model(parser: argparse.ArgumentParser, model: Type[BaseModel]): """Add arguments from a pydantic model to an argparse parser.""" @@ -68,7 +90,15 @@ def add_args_from_model(parser: argparse.ArgumentParser, model: Type[BaseModel]) _get_base_type(field.annotation) if field.annotation is not None else str ) list_type = _contains_list_type(field.annotation) - if base_type is not bool: + dict_type = _contains_dict_type(field.annotation) + if dict_type: + parser.add_argument( + f"--{name}", + dest=name, + type=_parse_json_object_arg, + help=description, + ) + elif base_type is not bool: parser.add_argument( f"--{name}", dest=name, diff --git a/llama_cpp/server/model.py b/llama_cpp/server/model.py index 9e59e85633..3922ce5df3 100644 --- a/llama_cpp/server/model.py +++ b/llama_cpp/server/model.py @@ -299,6 +299,21 @@ def load_llama_from_model_settings(settings: ModelSettings) -> llama_cpp.Llama: # Misc verbose=settings.verbose, ) + if settings.chat_template_kwargs: + base_chat_handler = ( + _model.chat_handler + or _model._chat_handlers.get(_model.chat_format) + or llama_cpp.llama_chat_format.get_chat_completion_handler( + _model.chat_format + ) + ) + + def chat_handler_with_kwargs(*args, **kwargs): + return base_chat_handler( + *args, **{**settings.chat_template_kwargs, **kwargs} + ) + + _model.chat_handler = chat_handler_with_kwargs if settings.cache: if settings.cache_type == "disk": if settings.verbose: diff --git a/llama_cpp/server/settings.py b/llama_cpp/server/settings.py index 13c9512419..3c2bb7fd07 100644 --- a/llama_cpp/server/settings.py +++ b/llama_cpp/server/settings.py @@ -2,7 +2,7 @@ import multiprocessing -from typing import Optional, List, Literal, Union, Dict, cast +from typing import Any, Optional, List, Literal, Union, Dict, cast from typing_extensions import Self from pydantic import Field, model_validator @@ -131,6 +131,10 @@ class ModelSettings(BaseSettings): default=None, description="Chat format to use.", ) + chat_template_kwargs: Optional[Dict[str, Any]] = Field( + default=None, + description="Extra keyword arguments forwarded to chat templates at model load time. Matches llama.cpp server `chat_template_kwargs`.", + ) clip_model_path: Optional[str] = Field( default=None, description="Path to a CLIP model to use for multi-modal chat completion.", From 100b275c88763e2fada18d009e9bca8287cdb7c9 Mon Sep 17 00:00:00 2001 From: Andrei Date: Thu, 2 Apr 2026 22:44:24 -0700 Subject: [PATCH 033/138] feat: Update llama.cpp to ggerganov/llama.cpp@f49e9178767d557a522618b16ce8694f9ddac628 (#2169) --- llama_cpp/llama_cpp.py | 417 +++++++++++++++++++++++++++++++++++++++-- llama_cpp/mtmd_cpp.py | 26 ++- vendor/llama.cpp | 2 +- 3 files changed, 423 insertions(+), 22 deletions(-) diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index 5a6c06b077..204f1e6262 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -45,6 +45,14 @@ ctypes_function = ctypes_function_for_shared_library(_lib) +def _warn_deprecated(symbol: str, hint: str) -> None: + warnings.warn( + f"{symbol} is deprecated; {hint}", + DeprecationWarning, + stacklevel=2, + ) + + # from ggml.h # // NOTE: always add types at the end of the enum to keep backward compatibility # enum ggml_type { @@ -711,6 +719,43 @@ class llama_model_kv_override(ctypes.Structure): value: Union[int, float, bool, bytes] +# struct llama_model_tensor_override { +# const char * pattern; +# enum ggml_type type; +# }; +class llama_model_tensor_override(ctypes.Structure): + """Override the quantization type for tensors matching a pattern.""" + + _fields_ = [ + ("pattern", ctypes.c_char_p), + ("type", ctypes.c_int), + ] + + if TYPE_CHECKING: + pattern: Optional[bytes] + type: int + + +# struct llama_model_imatrix_data { +# const char * name; +# const float * data; +# size_t size; +# }; +class llama_model_imatrix_data(ctypes.Structure): + """Importance matrix data for a tensor used during quantization.""" + + _fields_ = [ + ("name", ctypes.c_char_p), + ("data", ctypes.POINTER(ctypes.c_float)), + ("size", ctypes.c_size_t), + ] + + if TYPE_CHECKING: + name: Optional[bytes] + data: CtypesPointer[ctypes.c_float] + size: int + + # struct llama_model_tensor_buft_override { # const char * pattern; # ggml_backend_buffer_type_t buft; @@ -1022,10 +1067,10 @@ class llama_context_params(ctypes.Structure): # bool pure; // quantize all tensors to the default type # bool keep_split; // quantize to the same number of shards # bool dry_run; // calculate and show the final quantization size without performing quantization -# void * imatrix; // pointer to importance matrix data -# void * kv_overrides; // pointer to vector containing overrides -# void * tensor_types; // pointer to vector containing tensor types -# void * prune_layers; // pointer to vector containing layer indices to prune +# const struct llama_model_imatrix_data * imatrix; // pointer to importance matrix data +# const struct llama_model_kv_override * kv_overrides; // pointer to kv overrides +# const struct llama_model_tensor_override * tt_overrides; // pointer to tensor overrides +# const int32_t * prune_layers; // pointer to layer indices to prune # } llama_model_quantize_params; class llama_model_quantize_params(ctypes.Structure): """Parameters for llama_model_quantize @@ -1041,10 +1086,10 @@ class llama_model_quantize_params(ctypes.Structure): pure (bool): quantize all tensors to the default type keep_split (bool): quantize to the same number of shards dry_run (bool): calculate and show the final quantization size without performing quantization - imatrix (ctypes.c_void_p): pointer to importance matrix data - kv_overrides (ctypes.c_void_p): pointer to vector containing overrides - tensor_types (ctypes.c_void_p): pointer to vector containing tensor types - prune_layers (ctypes.c_void_p): pointer to vector containing layer indices to prune + imatrix (ctypes.Array[llama_model_imatrix_data]): pointer to importance matrix data + kv_overrides (ctypes.Array[llama_model_kv_override]): pointer to kv overrides + tt_overrides (ctypes.Array[llama_model_tensor_override]): pointer to tensor overrides + prune_layers (ctypes.Array[ctypes.c_int32]): pointer to layer indices to prune """ if TYPE_CHECKING: @@ -1058,10 +1103,10 @@ class llama_model_quantize_params(ctypes.Structure): pure: bool keep_split: bool dry_run: bool - imatrix: ctypes.c_void_p - kv_overrides: ctypes.c_void_p - tensor_types: ctypes.c_void_p - prune_layers: ctypes.c_void_p + imatrix: CtypesPointer[llama_model_imatrix_data] + kv_overrides: CtypesPointer[llama_model_kv_override] + tt_overrides: CtypesPointer[llama_model_tensor_override] + prune_layers: CtypesPointer[ctypes.c_int32] _fields_ = [ ("nthread", ctypes.c_int32), @@ -1074,10 +1119,10 @@ class llama_model_quantize_params(ctypes.Structure): ("pure", ctypes.c_bool), ("keep_split", ctypes.c_bool), ("dry_run", ctypes.c_bool), - ("imatrix", ctypes.c_void_p), - ("kv_overrides", ctypes.c_void_p), - ("tensor_types", ctypes.c_void_p), - ("prune_layers", ctypes.c_void_p), + ("imatrix", ctypes.POINTER(llama_model_imatrix_data)), + ("kv_overrides", ctypes.POINTER(llama_model_kv_override)), + ("tt_overrides", ctypes.POINTER(llama_model_tensor_override)), + ("prune_layers", ctypes.POINTER(ctypes.c_int32)), ] @@ -1272,6 +1317,19 @@ def llama_load_model_from_file( ) -> Optional[llama_model_p]: ... +_llama_load_model_from_file = llama_load_model_from_file + + +def llama_load_model_from_file( + path_model: bytes, params: llama_model_params, / +) -> Optional[llama_model_p]: + _warn_deprecated( + "llama_load_model_from_file", + "use llama_model_load_from_file instead", + ) + return _llama_load_model_from_file(path_model, params) + + # // Load the model from a file # // If the file is split into multiple parts, the file name must follow this pattern: -%05d-of-%05d.gguf # // If the split file name does not follow this pattern, use llama_model_load_from_splits @@ -1353,6 +1411,14 @@ def llama_model_save_to_file(model: llama_model_p, path_model: bytes, /): def llama_free_model(model: llama_model_p, /): ... +_llama_free_model = llama_free_model + + +def llama_free_model(model: llama_model_p, /): + _warn_deprecated("llama_free_model", "use llama_model_free instead") + return _llama_free_model(model) + + # LLAMA_API void llama_model_free(struct llama_model * model); @ctypes_function( "llama_model_free", @@ -1419,6 +1485,19 @@ def llama_new_context_with_model( ) -> Optional[llama_context_p]: ... +_llama_new_context_with_model = llama_new_context_with_model + + +def llama_new_context_with_model( + model: llama_model_p, params: llama_context_params, / +) -> Optional[llama_context_p]: + _warn_deprecated( + "llama_new_context_with_model", + "use llama_init_from_model instead", + ) + return _llama_new_context_with_model(model, params) + + # // Frees all allocated memory # LLAMA_API void llama_free(struct llama_context * ctx); @ctypes_function( @@ -1557,26 +1636,66 @@ def llama_n_seq_max(ctx: llama_context_p, /) -> int: ... def llama_n_ctx_train(model: llama_model_p, /) -> int: ... +_llama_n_ctx_train = llama_n_ctx_train + + +def llama_n_ctx_train(model: llama_model_p, /) -> int: + _warn_deprecated("llama_n_ctx_train", "use llama_model_n_ctx_train instead") + return _llama_n_ctx_train(model) + + # DEPRECATED(LLAMA_API int32_t llama_n_embd (const struct llama_model * model), "use llama_model_n_embd instead"); @ctypes_function("llama_n_embd", [llama_model_p_ctypes], ctypes.c_int32) def llama_n_embd(model: llama_model_p, /) -> int: ... +_llama_n_embd = llama_n_embd + + +def llama_n_embd(model: llama_model_p, /) -> int: + _warn_deprecated("llama_n_embd", "use llama_model_n_embd instead") + return _llama_n_embd(model) + + # DEPRECATED(LLAMA_API int32_t llama_n_layer (const struct llama_model * model), "use llama_model_n_layer instead"); @ctypes_function("llama_n_layer", [llama_model_p_ctypes], ctypes.c_int32) def llama_n_layer(model: llama_model_p, /) -> int: ... +_llama_n_layer = llama_n_layer + + +def llama_n_layer(model: llama_model_p, /) -> int: + _warn_deprecated("llama_n_layer", "use llama_model_n_layer instead") + return _llama_n_layer(model) + + # DEPRECATED(LLAMA_API int32_t llama_n_head (const struct llama_model * model), "use llama_model_n_head instead"); @ctypes_function("llama_n_head", [llama_model_p_ctypes], ctypes.c_int32) def llama_n_head(model: llama_model_p, /) -> int: ... +_llama_n_head = llama_n_head + + +def llama_n_head(model: llama_model_p, /) -> int: + _warn_deprecated("llama_n_head", "use llama_model_n_head instead") + return _llama_n_head(model) + + # DEPRECATED(LLAMA_API int32_t llama_n_vocab (const struct llama_vocab * vocab), "use llama_vocab_n_tokens instead"); @ctypes_function("llama_n_vocab", [llama_vocab_p_ctypes], ctypes.c_int32) def llama_n_vocab(model: llama_vocab_p, /) -> int: ... +_llama_n_vocab = llama_n_vocab + + +def llama_n_vocab(model: llama_vocab_p, /) -> int: + _warn_deprecated("llama_n_vocab", "use llama_vocab_n_tokens instead") + return _llama_n_vocab(model) + + # LLAMA_API const struct llama_model * llama_get_model (const struct llama_context * ctx); @ctypes_function("llama_get_model", [llama_context_p_ctypes], llama_model_p_ctypes) def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]: ... @@ -2381,6 +2500,14 @@ def llama_get_state_size(ctx: llama_context_p, /) -> int: ... +_llama_get_state_size = llama_get_state_size + + +def llama_get_state_size(ctx: llama_context_p, /) -> int: + _warn_deprecated("llama_get_state_size", "use llama_state_get_size instead") + return _llama_get_state_size(ctx) + + # // Copies the state to the specified destination address. # // Destination needs to have allocated enough memory. # // Returns the number of bytes copied @@ -2428,6 +2555,16 @@ def llama_copy_state_data( ... +_llama_copy_state_data = llama_copy_state_data + + +def llama_copy_state_data( + ctx: llama_context_p, dst: CtypesArray[ctypes.c_uint8], / +) -> int: + _warn_deprecated("llama_copy_state_data", "use llama_state_get_data instead") + return _llama_copy_state_data(ctx, dst) + + # // Set the state reading from the specified address # // Returns the number of bytes read # LLAMA_API size_t llama_state_set_data( @@ -2466,6 +2603,16 @@ def llama_set_state_data( ... +_llama_set_state_data = llama_set_state_data + + +def llama_set_state_data( + ctx: llama_context_p, src: CtypesArray[ctypes.c_uint8], / +) -> int: + _warn_deprecated("llama_set_state_data", "use llama_state_set_data instead") + return _llama_set_state_data(ctx, src) + + # Save/load session file # LLAMA_API bool llama_state_load_file( # struct llama_context * ctx, @@ -2522,6 +2669,23 @@ def llama_load_session_file( ) -> bool: ... +_llama_load_session_file = llama_load_session_file + + +def llama_load_session_file( + ctx: llama_context_p, + path_session: bytes, + tokens_out: CtypesArray[llama_token], + n_token_capacity: Union[ctypes.c_size_t, int], + n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t], + /, +) -> bool: + _warn_deprecated("llama_load_session_file", "use llama_state_load_file instead") + return _llama_load_session_file( + ctx, path_session, tokens_out, n_token_capacity, n_token_count_out + ) + + # LLAMA_API bool llama_state_save_file( # struct llama_context * ctx, # const char * path_session, @@ -2571,6 +2735,20 @@ def llama_save_session_file( ) -> bool: ... +_llama_save_session_file = llama_save_session_file + + +def llama_save_session_file( + ctx: llama_context_p, + path_session: bytes, + tokens: CtypesArray[llama_token], + n_token_count: Union[ctypes.c_size_t, int], + /, +) -> bool: + _warn_deprecated("llama_save_session_file", "use llama_state_save_file instead") + return _llama_save_session_file(ctx, path_session, tokens, n_token_count) + + # // Get the exact size needed to copy the state of a single sequence # LLAMA_API size_t llama_state_seq_get_size( # struct llama_context * ctx, @@ -3300,6 +3478,16 @@ def llama_token_get_text( ) -> bytes: ... +_llama_token_get_text = llama_token_get_text + + +def llama_token_get_text( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> bytes: + _warn_deprecated("llama_token_get_text", "use llama_vocab_get_text instead") + return _llama_token_get_text(vocab, token) + + # DEPRECATED(LLAMA_API float llama_token_get_score(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_score instead"); @ctypes_function( "llama_token_get_score", @@ -3311,6 +3499,16 @@ def llama_token_get_score( ) -> float: ... +_llama_token_get_score = llama_token_get_score + + +def llama_token_get_score( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> float: + _warn_deprecated("llama_token_get_score", "use llama_vocab_get_score instead") + return _llama_token_get_score(vocab, token) + + # DEPRECATED(LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_attr instead"); @ctypes_function( "llama_token_get_attr", @@ -3322,6 +3520,16 @@ def llama_token_get_attr( ) -> int: ... +_llama_token_get_attr = llama_token_get_attr + + +def llama_token_get_attr( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> int: + _warn_deprecated("llama_token_get_attr", "use llama_vocab_get_attr instead") + return _llama_token_get_attr(vocab, token) + + # DEPRECATED(LLAMA_API bool llama_token_is_eog(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_eog instead"); @ctypes_function( "llama_token_is_eog", @@ -3333,6 +3541,14 @@ def llama_token_is_eog( ) -> bool: ... +_llama_token_is_eog = llama_token_is_eog + + +def llama_token_is_eog(vocab: llama_vocab_p, token: Union[llama_token, int], /) -> bool: + _warn_deprecated("llama_token_is_eog", "use llama_vocab_is_eog instead") + return _llama_token_is_eog(vocab, token) + + # DEPRECATED(LLAMA_API bool llama_token_is_control(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_control instead"); @ctypes_function( "llama_token_is_control", @@ -3344,6 +3560,19 @@ def llama_token_is_control( ) -> bool: ... +_llama_token_is_control = llama_token_is_control + + +def llama_token_is_control( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> bool: + _warn_deprecated( + "llama_token_is_control", + "use llama_vocab_is_control instead", + ) + return _llama_token_is_control(vocab, token) + + # DEPRECATED(LLAMA_API llama_token llama_token_bos(const struct llama_vocab * vocab), "use llama_vocab_bos instead"); @ctypes_function( "llama_token_bos", @@ -3353,6 +3582,14 @@ def llama_token_is_control( def llama_token_bos(vocab: llama_vocab_p, /) -> int: ... +_llama_token_bos = llama_token_bos + + +def llama_token_bos(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_bos", "use llama_vocab_bos instead") + return _llama_token_bos(vocab) + + # DEPRECATED(LLAMA_API llama_token llama_token_eos(const struct llama_vocab * vocab), "use llama_vocab_eos instead"); @ctypes_function( "llama_token_eos", @@ -3362,6 +3599,14 @@ def llama_token_bos(vocab: llama_vocab_p, /) -> int: ... def llama_token_eos(vocab: llama_vocab_p, /) -> int: ... +_llama_token_eos = llama_token_eos + + +def llama_token_eos(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_eos", "use llama_vocab_eos instead") + return _llama_token_eos(vocab) + + # DEPRECATED(LLAMA_API llama_token llama_token_eot(const struct llama_vocab * vocab), "use llama_vocab_eot instead"); @ctypes_function( "llama_token_eot", @@ -3371,6 +3616,14 @@ def llama_token_eos(vocab: llama_vocab_p, /) -> int: ... def llama_token_eot(vocab: llama_vocab_p, /) -> int: ... +_llama_token_eot = llama_token_eot + + +def llama_token_eot(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_eot", "use llama_vocab_eot instead") + return _llama_token_eot(vocab) + + # DEPRECATED(LLAMA_API llama_token llama_token_cls(const struct llama_vocab * vocab), "use llama_vocab_cls instead"); @ctypes_function( "llama_token_cls", @@ -3380,6 +3633,14 @@ def llama_token_eot(vocab: llama_vocab_p, /) -> int: ... def llama_token_cls(vocab: llama_vocab_p, /) -> int: ... +_llama_token_cls = llama_token_cls + + +def llama_token_cls(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_cls", "use llama_vocab_cls instead") + return _llama_token_cls(vocab) + + # DEPRECATED(LLAMA_API llama_token llama_token_sep(const struct llama_vocab * vocab), "use llama_vocab_sep instead"); @ctypes_function( "llama_token_sep", @@ -3389,6 +3650,14 @@ def llama_token_cls(vocab: llama_vocab_p, /) -> int: ... def llama_token_sep(vocab: llama_vocab_p, /) -> int: ... +_llama_token_sep = llama_token_sep + + +def llama_token_sep(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_sep", "use llama_vocab_sep instead") + return _llama_token_sep(vocab) + + # DEPRECATED(LLAMA_API llama_token llama_token_nl (const struct llama_vocab * vocab), "use llama_vocab_nl instead"); @ctypes_function( "llama_token_nl", @@ -3398,6 +3667,14 @@ def llama_token_sep(vocab: llama_vocab_p, /) -> int: ... def llama_token_nl(vocab: llama_vocab_p, /) -> int: ... +_llama_token_nl = llama_token_nl + + +def llama_token_nl(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_nl", "use llama_vocab_nl instead") + return _llama_token_nl(vocab) + + # DEPRECATED(LLAMA_API llama_token llama_token_pad(const struct llama_vocab * vocab), "use llama_vocab_pad instead"); @ctypes_function( "llama_token_pad", @@ -3407,6 +3684,14 @@ def llama_token_nl(vocab: llama_vocab_p, /) -> int: ... def llama_token_pad(vocab: llama_vocab_p, /) -> int: ... +_llama_token_pad = llama_token_pad + + +def llama_token_pad(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_pad", "use llama_vocab_pad instead") + return _llama_token_pad(vocab) + + # DEPRECATED(LLAMA_API bool llama_add_bos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_bos instead"); @ctypes_function( "llama_add_bos_token", @@ -3416,6 +3701,14 @@ def llama_token_pad(vocab: llama_vocab_p, /) -> int: ... def llama_add_bos_token(vocab: llama_vocab_p, /) -> bool: ... +_llama_add_bos_token = llama_add_bos_token + + +def llama_add_bos_token(vocab: llama_vocab_p, /) -> bool: + _warn_deprecated("llama_add_bos_token", "use llama_vocab_get_add_bos instead") + return _llama_add_bos_token(vocab) + + # DEPRECATED(LLAMA_API bool llama_add_eos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_eos instead"); @ctypes_function( "llama_add_eos_token", @@ -3425,6 +3718,14 @@ def llama_add_bos_token(vocab: llama_vocab_p, /) -> bool: ... def llama_add_eos_token(vocab: llama_vocab_p, /) -> bool: ... +_llama_add_eos_token = llama_add_eos_token + + +def llama_add_eos_token(vocab: llama_vocab_p, /) -> bool: + _warn_deprecated("llama_add_eos_token", "use llama_vocab_get_add_eos instead") + return _llama_add_eos_token(vocab) + + # DEPRECATED(LLAMA_API llama_token llama_token_fim_pre(const struct llama_vocab * vocab), "use llama_vocab_fim_pre instead"); @ctypes_function( "llama_token_fim_pre", @@ -3434,6 +3735,14 @@ def llama_add_eos_token(vocab: llama_vocab_p, /) -> bool: ... def llama_token_fim_pre(vocab: llama_vocab_p, /) -> llama_token: ... +_llama_token_fim_pre = llama_token_fim_pre + + +def llama_token_fim_pre(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_token_fim_pre", "use llama_vocab_fim_pre instead") + return _llama_token_fim_pre(vocab) + + # DEPRECATED(LLAMA_API llama_token llama_token_fim_suf(const struct llama_vocab * vocab), "use llama_vocab_fim_suf instead"); @ctypes_function( "llama_token_fim_suf", @@ -3443,6 +3752,14 @@ def llama_token_fim_pre(vocab: llama_vocab_p, /) -> llama_token: ... def llama_token_fim_suf(vocab: llama_vocab_p, /) -> llama_token: ... +_llama_token_fim_suf = llama_token_fim_suf + + +def llama_token_fim_suf(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_token_fim_suf", "use llama_vocab_fim_suf instead") + return _llama_token_fim_suf(vocab) + + # DEPRECATED(LLAMA_API llama_token llama_token_fim_mid(const struct llama_vocab * vocab), "use llama_vocab_fim_mid instead"); @ctypes_function( "llama_token_fim_mid", @@ -3452,6 +3769,14 @@ def llama_token_fim_suf(vocab: llama_vocab_p, /) -> llama_token: ... def llama_token_fim_mid(vocab: llama_vocab_p, /) -> llama_token: ... +_llama_token_fim_mid = llama_token_fim_mid + + +def llama_token_fim_mid(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_token_fim_mid", "use llama_vocab_fim_mid instead") + return _llama_token_fim_mid(vocab) + + # DEPRECATED(LLAMA_API llama_token llama_token_fim_pad(const struct llama_vocab * vocab), "use llama_vocab_fim_pad instead"); @ctypes_function( "llama_token_fim_pad", @@ -3461,6 +3786,14 @@ def llama_token_fim_mid(vocab: llama_vocab_p, /) -> llama_token: ... def llama_token_fim_pad(vocab: llama_vocab_p, /) -> llama_token: ... +_llama_token_fim_pad = llama_token_fim_pad + + +def llama_token_fim_pad(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_token_fim_pad", "use llama_vocab_fim_pad instead") + return _llama_token_fim_pad(vocab) + + # DEPRECATED(LLAMA_API llama_token llama_token_fim_rep(const struct llama_vocab * vocab), "use llama_vocab_fim_rep instead"); @ctypes_function( "llama_token_fim_rep", @@ -3470,6 +3803,14 @@ def llama_token_fim_pad(vocab: llama_vocab_p, /) -> llama_token: ... def llama_token_fim_rep(vocab: llama_vocab_p, /) -> llama_token: ... +_llama_token_fim_rep = llama_token_fim_rep + + +def llama_token_fim_rep(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_token_fim_rep", "use llama_vocab_fim_rep instead") + return _llama_token_fim_rep(vocab) + + # DEPRECATED(LLAMA_API llama_token llama_token_fim_sep(const struct llama_vocab * vocab), "use llama_vocab_fim_sep instead"); @ctypes_function( "llama_token_fim_sep", @@ -3479,6 +3820,14 @@ def llama_token_fim_rep(vocab: llama_vocab_p, /) -> llama_token: ... def llama_token_fim_sep(vocab: llama_vocab_p, /) -> llama_token: ... +_llama_token_fim_sep = llama_token_fim_sep + + +def llama_token_fim_sep(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_token_fim_sep", "use llama_vocab_fim_sep instead") + return _llama_token_fim_sep(vocab) + + # // CLS is equivalent to BOS # DEPRECATED(LLAMA_API llama_token llama_vocab_cls(const struct llama_vocab * vocab), // classification # "use llama_vocab_bos instead"); @@ -3490,6 +3839,14 @@ def llama_token_fim_sep(vocab: llama_vocab_p, /) -> llama_token: ... def llama_vocab_cls(vocab: llama_vocab_p, /) -> llama_token: ... +_llama_vocab_cls = llama_vocab_cls + + +def llama_vocab_cls(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_vocab_cls", "use llama_vocab_bos instead") + return _llama_vocab_cls(vocab) + + # // # // Tokenization # // @@ -4146,6 +4503,34 @@ def llama_sampler_init_grammar_lazy( ) -> llama_sampler_p: ... +_llama_sampler_init_grammar_lazy = llama_sampler_init_grammar_lazy + + +def llama_sampler_init_grammar_lazy( + vocab: llama_vocab_p, + grammar_str: bytes, + grammar_root: bytes, + trigger_words: CtypesArray[bytes], + num_trigger_words: int, + trigger_tokens: CtypesArray[llama_token], + num_trigger_tokens: int, + /, +) -> llama_sampler_p: + _warn_deprecated( + "llama_sampler_init_grammar_lazy", + "use llama_sampler_init_grammar_lazy_patterns instead", + ) + return _llama_sampler_init_grammar_lazy( + vocab, + grammar_str, + grammar_root, + trigger_words, + num_trigger_words, + trigger_tokens, + num_trigger_tokens, + ) + + # /// @details Lazy grammar sampler, introduced in https://github.com/ggml-org/llama.cpp/pull/9639 # LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy_patterns( # const struct llama_vocab * vocab, diff --git a/llama_cpp/mtmd_cpp.py b/llama_cpp/mtmd_cpp.py index 787683179c..f28402775c 100644 --- a/llama_cpp/mtmd_cpp.py +++ b/llama_cpp/mtmd_cpp.py @@ -79,6 +79,12 @@ # Structures class mtmd_context_params(Structure): + """Context parameters for MTMD initialization. + + `image_marker` is deprecated upstream and kept for compatibility; use + `media_marker` for multimodal prompt placeholders. + """ + if TYPE_CHECKING: use_gpu: bool print_timings: bool @@ -108,6 +114,8 @@ class mtmd_context_params(Structure): class mtmd_input_text(Structure): + """Text input passed to `mtmd_tokenize`.""" + _fields_ = [ ("text", c_char_p), ("add_special", c_bool), @@ -122,12 +130,16 @@ class mtmd_input_text(Structure): # MTMD_API const char * mtmd_default_marker(void); @ctypes_function("mtmd_default_marker", [], c_char_p) -def mtmd_default_marker() -> bytes: ... +def mtmd_default_marker() -> bytes: + """Return the default media marker.""" + ... # MTMD_API struct mtmd_context_params mtmd_context_params_default(void); @ctypes_function("mtmd_context_params_default", [], mtmd_context_params) -def mtmd_context_params_default() -> mtmd_context_params: ... +def mtmd_context_params_default() -> mtmd_context_params: + """Return the default MTMD context parameters.""" + ... # MTMD_API mtmd_context * mtmd_init_from_file(const char * mmproj_fname, @@ -143,7 +155,9 @@ def mtmd_init_from_file( text_model: llama_cpp.llama_model_p, ctx_params: mtmd_context_params, /, -) -> Optional[mtmd_context_p]: ... +) -> Optional[mtmd_context_p]: + """Initialize the MTMD context from a projector file. Returns None on failure.""" + ... # MTMD_API void mtmd_free(mtmd_context * ctx); @@ -167,7 +181,9 @@ def mtmd_decode_use_mrope(ctx: mtmd_context_p, /) -> bool: # MTMD_API bool mtmd_support_vision(mtmd_context * ctx); @ctypes_function("mtmd_support_vision", [mtmd_context_p_ctypes], c_bool) -def mtmd_support_vision(ctx: mtmd_context_p, /) -> bool: ... +def mtmd_support_vision(ctx: mtmd_context_p, /) -> bool: + """Check whether the current model supports vision input.""" + ... # MTMD_API bool mtmd_support_audio(mtmd_context * ctx); @@ -180,7 +196,7 @@ def mtmd_support_audio(ctx: mtmd_context_p, /) -> bool: # MTMD_API int mtmd_get_audio_sample_rate(mtmd_context * ctx); @ctypes_function("mtmd_get_audio_sample_rate", [mtmd_context_p_ctypes], c_int) def mtmd_get_audio_sample_rate(ctx: mtmd_context_p, /) -> int: - """Get the MTMD audio sample rate.""" + """Get the audio sample rate in Hz. Returns -1 if audio is not supported.""" ... diff --git a/vendor/llama.cpp b/vendor/llama.cpp index c0159f9c1f..f49e917876 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit c0159f9c1f874da15e94f371d136f5920b4b5335 +Subproject commit f49e9178767d557a522618b16ce8694f9ddac628 From 08e088cde15e0b56e46337d6f49000518c18c203 Mon Sep 17 00:00:00 2001 From: Andrei Date: Thu, 2 Apr 2026 23:22:02 -0700 Subject: [PATCH 034/138] fix(misc): replace deprecated llama.cpp references (#2170) * refactor: replace deprecated llama.cpp references * docs: update changelog for recent llama.cpp changes --- CHANGELOG.md | 2 ++ README.md | 16 ++++++---- examples/batch-processing/server.py | 4 +-- .../low_level_api/low_level_api_chat_cpp.py | 31 ++++++++++--------- .../low_level_api/low_level_api_llama_cpp.py | 26 +++++++++------- examples/notebooks/Batching.ipynb | 15 ++++----- llama_cpp/_internals.py | 6 ++-- llama_cpp/llama.py | 11 ++++--- 8 files changed, 63 insertions(+), 48 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index e577324db5..f5f3677e5d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- refactor: Replace deprecated llama.cpp references in library, docs, and examples by @abetlen in #2170 +- feat: Update llama.cpp to ggerganov/llama.cpp@f49e9178767d557a522618b16ce8694f9ddac628 by @abetlen in #2169 - feat(server): Add model-load `chat_template_kwargs` support and document the CLI/config usage by @abetlen in #2168 - ci: Publish release wheels as `py3-none` by @Bing-su in #2166 - fix(ci): Publish distinct manylinux and musllinux CPU wheels by @abetlen in #2165 diff --git a/README.md b/README.md index 8ba4dbb5ea..69a0f82345 100644 --- a/README.md +++ b/README.md @@ -717,16 +717,20 @@ Below is a short example demonstrating how to use the low-level API to tokenize ```python import llama_cpp import ctypes -llama_cpp.llama_backend_init(False) # Must be called once at the start of each program -params = llama_cpp.llama_context_default_params() +llama_cpp.llama_backend_init() # Must be called once at the start of each program +model_params = llama_cpp.llama_model_default_params() +ctx_params = llama_cpp.llama_context_default_params() +prompt = b"Q: Name the planets in the solar system? A: " # use bytes for char * params -model = llama_cpp.llama_load_model_from_file(b"./models/7b/llama-model.gguf", params) -ctx = llama_cpp.llama_new_context_with_model(model, params) -max_tokens = params.n_ctx +model = llama_cpp.llama_model_load_from_file(b"./models/7b/llama-model.gguf", model_params) +ctx = llama_cpp.llama_init_from_model(model, ctx_params) +vocab = llama_cpp.llama_model_get_vocab(model) +max_tokens = ctx_params.n_ctx # use ctypes arrays for array params tokens = (llama_cpp.llama_token * int(max_tokens))() -n_tokens = llama_cpp.llama_tokenize(ctx, b"Q: Name the planets in the solar system? A: ", tokens, max_tokens, llama_cpp.c_bool(True)) +n_tokens = llama_cpp.llama_tokenize(vocab, prompt, len(prompt), tokens, max_tokens, True, False) llama_cpp.llama_free(ctx) +llama_cpp.llama_model_free(model) ``` Check out the [examples folder](examples/low_level_api) for more examples of using the low-level API. diff --git a/examples/batch-processing/server.py b/examples/batch-processing/server.py index 0b36746f91..2b6fa759e0 100644 --- a/examples/batch-processing/server.py +++ b/examples/batch-processing/server.py @@ -6,14 +6,14 @@ # path = b"../../models/Qwen1.5-0.5B-Chat-GGUF/qwen1_5-0_5b-chat-q8_0.gguf" # model_params = llama_cpp.llama_model_default_params() -# model = llama_cpp.llama_load_model_from_file(path, model_params) +# model = llama_cpp.llama_model_load_from_file(path, model_params) # if model is None: # raise RuntimeError(f"Failed to load model from file: {path}") # ctx_params = llama_cpp.llama_context_default_params() -# ctx = llama_cpp.llama_new_context_with_model(model, ctx_params) +# ctx = llama_cpp.llama_init_from_model(model, ctx_params) # if ctx is None: # raise RuntimeError("Failed to create context") diff --git a/examples/low_level_api/low_level_api_chat_cpp.py b/examples/low_level_api/low_level_api_chat_cpp.py index 39081be17a..20f7a158ac 100644 --- a/examples/low_level_api/low_level_api_chat_cpp.py +++ b/examples/low_level_api/low_level_api_chat_cpp.py @@ -79,19 +79,22 @@ def __init__(self, params: GptParams) -> None: self.lparams.use_mlock = self.params.use_mlock self.lparams.use_mmap = self.params.use_mmap - self.model = llama_cpp.llama_load_model_from_file( + self.model = llama_cpp.llama_model_load_from_file( self.params.model.encode("utf8"), self.lparams ) + self.vocab = llama_cpp.llama_model_get_vocab(self.model) # Context Params. self.cparams = llama_cpp.llama_context_default_params() - self.ctx = llama_cpp.llama_new_context_with_model(self.model, self.cparams) + self.ctx = llama_cpp.llama_init_from_model(self.model, self.cparams) if not self.ctx: raise RuntimeError(f"error: failed to load model '{self.params.model}'") if self.params.ignore_eos: - self.params.logit_bias[llama_cpp.llama_token_eos()] = -float("inf") + self.params.logit_bias[llama_cpp.llama_vocab_eos(self.vocab)] = -float( + "inf" + ) if len(self.params.lora_adapter) > 0: if ( @@ -153,7 +156,7 @@ def __init__(self, params: GptParams) -> None: _session_tokens = (llama_cpp.llama_token * (self.params.n_ctx))() _n_token_count_out = llama_cpp.c_size_t() if ( - llama_cpp.llama_load_session_file( + llama_cpp.llama_state_load_file( self.ctx, self.params.path_session.encode("utf8"), _session_tokens, @@ -314,7 +317,7 @@ def __init__(self, params: GptParams) -> None: def _tokenize(self, prompt, bos=True): _arr = (llama_cpp.llama_token * ((len(prompt) + 1) * 4))() _n = llama_cpp.llama_tokenize( - self.model, + self.vocab, prompt.encode("utf8", errors="ignore"), len(prompt), _arr, @@ -406,7 +409,7 @@ def generate(self): if len(self.embd_inp) <= self.input_consumed: # && !is_interacting # out of user input, sample next token top_k = ( - llama_cpp.llama_n_vocab(self.ctx) + llama_cpp.llama_vocab_n_tokens(self.vocab) if self.params.top_k <= 0 else self.params.top_k ) @@ -419,7 +422,7 @@ def generate(self): # optionally save the session on first sample (for faster prompt loading next time) if len(self.params.path_session) > 0 and self.need_to_save_session: self.need_to_save_session = False - llama_cpp.llama_save_session_file( + llama_cpp.llama_state_save_file( self.ctx, self.params.path_session.encode("utf8"), (llama_cpp.llama_token * len(self.session_tokens))( @@ -431,7 +434,7 @@ def generate(self): id = 0 logits = llama_cpp.llama_get_logits(self.ctx) - n_vocab = llama_cpp.llama_n_vocab(self.model) + n_vocab = llama_cpp.llama_vocab_n_tokens(self.vocab) # Apply params.logit_bias map for key, value in self.params.logit_bias.items(): @@ -448,7 +451,7 @@ def generate(self): ) # Apply penalties - nl_logit = logits[llama_cpp.llama_token_nl(self.ctx)] + nl_logit = logits[llama_cpp.llama_vocab_nl(self.vocab)] last_n_repeat = min(len(self.last_n_tokens), repeat_last_n, self.n_ctx) _arr = (llama_cpp.llama_token * last_n_repeat)( @@ -470,7 +473,7 @@ def generate(self): # last_n_repeat, llama_cpp.c_float(self.params.frequency_penalty), llama_cpp.c_float(self.params.presence_penalty)) if not self.params.penalize_nl: - logits[llama_cpp.llama_token_nl()] = nl_logit + logits[llama_cpp.llama_vocab_nl(self.vocab)] = nl_logit if self.params.temp <= 0: # Greedy sampling @@ -539,7 +542,7 @@ def generate(self): # replace end of text token with newline token when in interactive mode if ( - id == llama_cpp.llama_token_eos(self.ctx) + id == llama_cpp.llama_vocab_eos(self.vocab) and self.params.interactive and not self.params.instruct ): @@ -599,8 +602,8 @@ def generate(self): break # end of text token - if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_token_eos( - self.ctx + if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_vocab_eos( + self.vocab ): if not self.params.instruct: for i in self.llama_token_eot: @@ -636,7 +639,7 @@ def token_to_str(self, token_id: int) -> bytes: size = 32 buffer = (ctypes.c_char * size)() n = llama_cpp.llama_token_to_piece( - self.model, llama_cpp.llama_token(token_id), buffer, size + self.vocab, llama_cpp.llama_token(token_id), buffer, size, 0, False ) assert n <= size return bytes(buffer[:n]) diff --git a/examples/low_level_api/low_level_api_llama_cpp.py b/examples/low_level_api/low_level_api_llama_cpp.py index ba3545771d..9fb3424ec0 100644 --- a/examples/low_level_api/low_level_api_llama_cpp.py +++ b/examples/low_level_api/low_level_api_llama_cpp.py @@ -4,7 +4,7 @@ import llama_cpp -llama_cpp.llama_backend_init(numa=False) +llama_cpp.llama_backend_init() N_THREADS = multiprocessing.cpu_count() MODEL_PATH = os.environ.get("MODEL", "../models/7B/ggml-model.bin") @@ -13,8 +13,9 @@ lparams = llama_cpp.llama_model_default_params() cparams = llama_cpp.llama_context_default_params() -model = llama_cpp.llama_load_model_from_file(MODEL_PATH.encode("utf-8"), lparams) -ctx = llama_cpp.llama_new_context_with_model(model, cparams) +model = llama_cpp.llama_model_load_from_file(MODEL_PATH.encode("utf-8"), lparams) +ctx = llama_cpp.llama_init_from_model(model, cparams) +vocab = llama_cpp.llama_model_get_vocab(model) # determine the required inference memory per token: tmp = [0, 1, 2, 3] @@ -28,13 +29,13 @@ embd_inp = (llama_cpp.llama_token * (len(prompt) + 1))() n_of_tok = llama_cpp.llama_tokenize( - model=model, - text=bytes(str(prompt), "utf-8"), - text_len=len(embd_inp), + vocab=vocab, + text=prompt, + text_len=len(prompt), tokens=embd_inp, - n_max_tokens=len(embd_inp), - add_bos=False, - special=False, + n_tokens_max=len(embd_inp), + add_special=False, + parse_special=False, ) embd_inp = embd_inp[:n_of_tok] @@ -70,7 +71,7 @@ embd = [] if len(embd_inp) <= input_consumed: logits = llama_cpp.llama_get_logits(ctx) - n_vocab = llama_cpp.llama_n_vocab(model) + n_vocab = llama_cpp.llama_vocab_n_tokens(vocab) _arr = (llama_cpp.llama_token_data * n_vocab)( *[ @@ -114,7 +115,7 @@ size = 32 buffer = (ctypes.c_char * size)() n = llama_cpp.llama_token_to_piece( - model, llama_cpp.llama_token(id), buffer, size + vocab, llama_cpp.llama_token(id), buffer, size, 0, False ) assert n <= size print( @@ -123,7 +124,7 @@ flush=True, ) - if len(embd) > 0 and embd[-1] == llama_cpp.llama_token_eos(ctx): + if len(embd) > 0 and embd[-1] == llama_cpp.llama_vocab_eos(vocab): break print() @@ -131,3 +132,4 @@ llama_cpp.llama_print_timings(ctx) llama_cpp.llama_free(ctx) +llama_cpp.llama_model_free(model) diff --git a/examples/notebooks/Batching.ipynb b/examples/notebooks/Batching.ipynb index be7fe9b522..0b36cd9c3a 100644 --- a/examples/notebooks/Batching.ipynb +++ b/examples/notebooks/Batching.ipynb @@ -122,9 +122,10 @@ "source": [ "params = llama_cpp.llama_model_default_params()\n", "params.n_gpu_layers = 35\n", - "model = llama_cpp.llama_load_model_from_file(\n", + "model = llama_cpp.llama_model_load_from_file(\n", " b\"/workspaces/llama-cpp-python/mistral-7b-v0.1.Q2_K.gguf\", params\n", - ") # Update this to whatever" + ") # Update this to whatever\n", + "vocab = llama_cpp.llama_model_get_vocab(model)" ] }, { @@ -149,7 +150,7 @@ "\n", "tokens = (llama_cpp.llama_token * n_ctx)()\n", "tokens_len = llama_cpp.llama_tokenize(\n", - " model, prompt, len(prompt), tokens, len(tokens), True, True\n", + " vocab, prompt, len(prompt), tokens, len(tokens), True, True\n", ")\n", "print(tokens[:tokens_len])\n", "\n", @@ -188,7 +189,7 @@ "ctx_params.n_batch = max(n_len, n_parallel)\n", "ctx_params.n_threads = 1\n", "ctx_params.n_threads_batch = 1\n", - "ctx = llama_cpp.llama_new_context_with_model(model, ctx_params)" + "ctx = llama_cpp.llama_init_from_model(model, ctx_params)" ] }, { @@ -338,14 +339,14 @@ " # Sample the next token using the sampler chain\n", " new_token_id = llama_cpp.llama_sampler_sample(sampler_chain, ctx, -1)\n", "\n", - " if new_token_id == llama_cpp.llama_token_eos(ctx) or n_cur == n_len:\n", + " if new_token_id == llama_cpp.llama_vocab_eos(vocab) or n_cur == n_len:\n", " i_batch[i] = -1\n", " continue\n", "\n", " buf = (ctypes.c_char * 32)()\n", " \n", " # Convert token ID to text\n", - " outlen = llama_cpp.llama_token_to_piece(model, new_token_id, buf, len(buf), 0, False)\n", + " outlen = llama_cpp.llama_token_to_piece(vocab, new_token_id, buf, len(buf), 0, False)\n", " streams[i] += bytes(buf[:outlen]).decode(\"utf-8\")\n", "\n", " batch.token[batch.n_tokens] = new_token_id\n", @@ -411,7 +412,7 @@ "metadata": {}, "outputs": [], "source": [ - "llama_cpp.llama_free_model(model)" + "llama_cpp.llama_model_free(model)" ] }, { diff --git a/llama_cpp/_internals.py b/llama_cpp/_internals.py index 9e9bcd407a..cde52c8c8d 100644 --- a/llama_cpp/_internals.py +++ b/llama_cpp/_internals.py @@ -135,7 +135,7 @@ def token_eos(self) -> int: return llama_cpp.llama_vocab_eos(self.vocab) def token_cls(self) -> int: - return llama_cpp.llama_vocab_cls(self.vocab) + return llama_cpp.llama_vocab_bos(self.vocab) def token_sep(self) -> int: return llama_cpp.llama_vocab_sep(self.vocab) @@ -317,9 +317,9 @@ def get_state_size(self) -> int: # TODO: set_state_data - # TODO: llama_load_session_file + # TODO: llama_state_load_file - # TODO: llama_save_session_file + # TODO: llama_state_save_file def decode(self, batch: LlamaBatch): return_code = llama_cpp.llama_decode( diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index ad484c4d5f..11fe169cf5 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -1351,7 +1351,7 @@ def logit_bias_processor( logits_processor=logits_processor, grammar=grammar, ): - if llama_cpp.llama_token_is_eog(self._model.vocab, token): + if llama_cpp.llama_vocab_is_eog(self._model.vocab, token): text = self.detokenize(completion_tokens, prev_tokens=prompt_tokens) finish_reason = "stop" break @@ -2148,13 +2148,13 @@ def __setstate__(self, state): def save_state(self) -> LlamaState: if self.verbose: print("Llama.save_state: saving llama state", file=sys.stderr) - state_size = llama_cpp.llama_get_state_size(self._ctx.ctx) + state_size = llama_cpp.llama_state_get_size(self._ctx.ctx) if self.verbose: print(f"Llama.save_state: got state size: {state_size}", file=sys.stderr) llama_state = (ctypes.c_uint8 * int(state_size))() if self.verbose: print("Llama.save_state: allocated state", file=sys.stderr) - n_bytes = llama_cpp.llama_copy_state_data(self._ctx.ctx, llama_state) + n_bytes = llama_cpp.llama_state_get_data(self._ctx.ctx, llama_state, state_size) if self.verbose: print(f"Llama.save_state: copied llama state: {n_bytes}", file=sys.stderr) if int(n_bytes) > int(state_size): @@ -2187,7 +2187,10 @@ def load_state(self, state: LlamaState) -> None: LLamaStateArrayType = ctypes.c_uint8 * state_size llama_state = LLamaStateArrayType.from_buffer_copy(state.llama_state) - if llama_cpp.llama_set_state_data(self._ctx.ctx, llama_state) != state_size: + if ( + llama_cpp.llama_state_set_data(self._ctx.ctx, llama_state, state_size) + != state_size + ): raise RuntimeError("Failed to set llama state data") def n_ctx(self) -> int: From 02d6beed816535cb2ce845ad8558d0c8662f8e7e Mon Sep 17 00:00:00 2001 From: Andrei Date: Thu, 2 Apr 2026 23:42:35 -0700 Subject: [PATCH 035/138] chore: bump version to 0.3.20 (#2171) --- CHANGELOG.md | 2 ++ llama_cpp/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index f5f3677e5d..9bc8d6654b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.20] + - refactor: Replace deprecated llama.cpp references in library, docs, and examples by @abetlen in #2170 - feat: Update llama.cpp to ggerganov/llama.cpp@f49e9178767d557a522618b16ce8694f9ddac628 by @abetlen in #2169 - feat(server): Add model-load `chat_template_kwargs` support and document the CLI/config usage by @abetlen in #2168 diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index 72388c4e5d..83177c065d 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.19" +__version__ = "0.3.20" From 1bcc5bc7bfd971cb39c00b0475ada778b6af4951 Mon Sep 17 00:00:00 2001 From: Andrei Date: Wed, 8 Apr 2026 01:59:51 -0700 Subject: [PATCH 036/138] feat: Update llama.cpp to ggerganov/llama.cpp@3bd9aa1f9 (#2176) * feat: Update llama.cpp to ggerganov/llama.cpp@3bd9aa1f9 * docs: Update changelog for llama.cpp bump --- CHANGELOG.md | 2 ++ llama_cpp/llama_cpp.py | 8 ++++++-- vendor/llama.cpp | 2 +- 3 files changed, 9 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 9bc8d6654b..fbe5b6b6fd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: Update llama.cpp to ggerganov/llama.cpp@3bd9aa1f9 and sync Python bindings + ## [0.3.20] - refactor: Replace deprecated llama.cpp references in library, docs, and examples by @abetlen in #2170 diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index 204f1e6262..0a66a5d85d 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -88,7 +88,8 @@ def _warn_deprecated(symbol: str, hint: str) -> None: # GGML_TYPE_IQ1_M = 29, # GGML_TYPE_MXFP4 = 39, # GGML_TYPE_NVFP4 = 40, -# GGML_TYPE_COUNT, +# GGML_TYPE_Q1_0 = 41, +# GGML_TYPE_COUNT = 42, # }; GGML_TYPE_F32 = 0 GGML_TYPE_F16 = 1 @@ -120,7 +121,8 @@ def _warn_deprecated(symbol: str, hint: str) -> None: GGML_TYPE_IQ1_M = 29 GGML_TYPE_MXFP4 = 39 GGML_TYPE_NVFP4 = 40 -GGML_TYPE_COUNT = 41 +GGML_TYPE_Q1_0 = 41 +GGML_TYPE_COUNT = 42 # from ggml-backend.h # typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data); @@ -406,6 +408,7 @@ def _warn_deprecated(symbol: str, hint: str) -> None: # LLAMA_FTYPE_MOSTLY_TQ2_0 = 37, // except 1d tensors # LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38, // except 1d tensors # LLAMA_FTYPE_MOSTLY_NVFP4 = 39, // except 1d tensors +# LLAMA_FTYPE_MOSTLY_Q1_0 = 40, // except 1d tensors # # LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file # }; @@ -446,6 +449,7 @@ def _warn_deprecated(symbol: str, hint: str) -> None: LLAMA_FTYPE_MOSTLY_TQ2_0 = 37 LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38 LLAMA_FTYPE_MOSTLY_NVFP4 = 39 +LLAMA_FTYPE_MOSTLY_Q1_0 = 40 LLAMA_FTYPE_GUESSED = 1024 # enum llama_rope_scaling_type { diff --git a/vendor/llama.cpp b/vendor/llama.cpp index f49e917876..3bd9aa1f92 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit f49e9178767d557a522618b16ce8694f9ddac628 +Subproject commit 3bd9aa1f9250cd15f5371f3622d73d954b68a747 From 1b1a320de18c14c3915ba2df59eedb7c6e7cbe69 Mon Sep 17 00:00:00 2001 From: Andrei Date: Mon, 13 Apr 2026 02:56:37 -0400 Subject: [PATCH 037/138] feat: Update llama.cpp to ggerganov/llama.cpp@227ed28e1 (#2182) --- llama_cpp/llama_cpp.py | 8 +- llama_cpp/mtmd_cpp.py | 267 +++++++++++++++++++++++++++++++++++++++++ vendor/llama.cpp | 2 +- 3 files changed, 273 insertions(+), 4 deletions(-) diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index 0a66a5d85d..e445ed66ae 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -503,13 +503,15 @@ def _warn_deprecated(symbol: str, hint: str) -> None: # enum llama_split_mode { -# LLAMA_SPLIT_MODE_NONE = 0, // single GPU -# LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs -# LLAMA_SPLIT_MODE_ROW = 2, // split layers and KV across GPUs, use tensor parallelism if supported +# LLAMA_SPLIT_MODE_NONE = 0, // single GPU +# LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs +# LLAMA_SPLIT_MODE_ROW = 2, // split layers and KV across GPUs, use tensor parallelism if supported +# LLAMA_SPLIT_MODE_TENSOR = 3, # }; LLAMA_SPLIT_MODE_NONE = 0 LLAMA_SPLIT_MODE_LAYER = 1 LLAMA_SPLIT_MODE_ROW = 2 +LLAMA_SPLIT_MODE_TENSOR = 3 # typedef struct llama_token_data { diff --git a/llama_cpp/mtmd_cpp.py b/llama_cpp/mtmd_cpp.py index f28402775c..550c9bd59f 100644 --- a/llama_cpp/mtmd_cpp.py +++ b/llama_cpp/mtmd_cpp.py @@ -242,6 +242,55 @@ def mtmd_bitmap_init_from_audio( def mtmd_bitmap_free(bitmap: mtmd_bitmap_p, /): ... +# MTMD_API uint32_t mtmd_bitmap_get_nx(const mtmd_bitmap * bitmap); +@ctypes_function("mtmd_bitmap_get_nx", [mtmd_bitmap_p_ctypes], c_uint32) +def mtmd_bitmap_get_nx(bitmap: mtmd_bitmap_p, /) -> int: + """Get the bitmap width in pixels.""" + ... + + +# MTMD_API uint32_t mtmd_bitmap_get_ny(const mtmd_bitmap * bitmap); +@ctypes_function("mtmd_bitmap_get_ny", [mtmd_bitmap_p_ctypes], c_uint32) +def mtmd_bitmap_get_ny(bitmap: mtmd_bitmap_p, /) -> int: + """Get the bitmap height in pixels.""" + ... + + +# MTMD_API const unsigned char * mtmd_bitmap_get_data(const mtmd_bitmap * bitmap); +@ctypes_function("mtmd_bitmap_get_data", [mtmd_bitmap_p_ctypes], POINTER(c_uint8)) +def mtmd_bitmap_get_data(bitmap: mtmd_bitmap_p, /) -> Optional[CtypesArray[c_uint8]]: + """Get the raw bitmap data buffer.""" + ... + + +# MTMD_API size_t mtmd_bitmap_get_n_bytes(const mtmd_bitmap * bitmap); +@ctypes_function("mtmd_bitmap_get_n_bytes", [mtmd_bitmap_p_ctypes], c_size_t) +def mtmd_bitmap_get_n_bytes(bitmap: mtmd_bitmap_p, /) -> int: + """Get the bitmap data size in bytes.""" + ... + + +# MTMD_API bool mtmd_bitmap_is_audio(const mtmd_bitmap * bitmap); +@ctypes_function("mtmd_bitmap_is_audio", [mtmd_bitmap_p_ctypes], c_bool) +def mtmd_bitmap_is_audio(bitmap: mtmd_bitmap_p, /) -> bool: + """Check whether the bitmap contains audio data.""" + ... + + +# MTMD_API const char * mtmd_bitmap_get_id(const mtmd_bitmap * bitmap); +@ctypes_function("mtmd_bitmap_get_id", [mtmd_bitmap_p_ctypes], c_char_p) +def mtmd_bitmap_get_id(bitmap: mtmd_bitmap_p, /) -> Optional[bytes]: + """Get the optional bitmap identifier.""" + ... + + +# MTMD_API void mtmd_bitmap_set_id(mtmd_bitmap * bitmap, const char * id); +@ctypes_function("mtmd_bitmap_set_id", [mtmd_bitmap_p_ctypes, c_char_p], None) +def mtmd_bitmap_set_id(bitmap: mtmd_bitmap_p, id: Optional[bytes], /): + """Set the optional bitmap identifier.""" + ... + + # MTMD_API mtmd_input_chunks * mtmd_input_chunks_init(void); @ctypes_function("mtmd_input_chunks_init", [], mtmd_input_chunks_p_ctypes) def mtmd_input_chunks_init() -> Optional[mtmd_input_chunks_p]: ... @@ -315,11 +364,146 @@ def mtmd_input_chunk_get_tokens_text( ) -> Optional["_Pointer[llama_cpp.llama_token]"]: ... +# MTMD_API const mtmd_image_tokens * mtmd_input_chunk_get_tokens_image(const mtmd_input_chunk * chunk); +@ctypes_function( + "mtmd_input_chunk_get_tokens_image", + [mtmd_input_chunk_p_ctypes], + mtmd_image_tokens_p_ctypes, +) +def mtmd_input_chunk_get_tokens_image( + chunk: mtmd_input_chunk_p, / +) -> Optional[mtmd_image_tokens_p]: ... + + +# MTMD_API const char * mtmd_input_chunk_get_id(const mtmd_input_chunk * chunk); +@ctypes_function("mtmd_input_chunk_get_id", [mtmd_input_chunk_p_ctypes], c_char_p) +def mtmd_input_chunk_get_id(chunk: mtmd_input_chunk_p, /) -> Optional[bytes]: + """Get the optional chunk identifier.""" + ... + + +# MTMD_API llama_pos mtmd_input_chunk_get_n_pos(const mtmd_input_chunk * chunk); +@ctypes_function( + "mtmd_input_chunk_get_n_pos", + [mtmd_input_chunk_p_ctypes], + llama_cpp.llama_pos, +) +def mtmd_input_chunk_get_n_pos(chunk: mtmd_input_chunk_p, /) -> int: + """Get the number of positions consumed by the chunk.""" + ... + + +# MTMD_API mtmd_input_chunk * mtmd_input_chunk_copy(const mtmd_input_chunk * chunk); +@ctypes_function( + "mtmd_input_chunk_copy", [mtmd_input_chunk_p_ctypes], mtmd_input_chunk_p_ctypes +) +def mtmd_input_chunk_copy(chunk: mtmd_input_chunk_p, /) -> Optional[mtmd_input_chunk_p]: + """Copy an input chunk and transfer ownership to the caller.""" + ... + + +# MTMD_API void mtmd_input_chunk_free(mtmd_input_chunk * chunk); +@ctypes_function("mtmd_input_chunk_free", [mtmd_input_chunk_p_ctypes], None) +def mtmd_input_chunk_free(chunk: mtmd_input_chunk_p, /): + """Free an owned input chunk.""" + ... + + +# MTMD_API size_t mtmd_image_tokens_get_n_tokens(const mtmd_image_tokens * image_tokens); +@ctypes_function( + "mtmd_image_tokens_get_n_tokens", [mtmd_image_tokens_p_ctypes], c_size_t +) +def mtmd_image_tokens_get_n_tokens(image_tokens: mtmd_image_tokens_p, /) -> int: + """Get the number of image tokens.""" + ... + + +# MTMD_API size_t mtmd_image_tokens_get_nx(const mtmd_image_tokens * image_tokens); +@ctypes_function("mtmd_image_tokens_get_nx", [mtmd_image_tokens_p_ctypes], c_size_t) +def mtmd_image_tokens_get_nx(image_tokens: mtmd_image_tokens_p, /) -> int: + """Get the image token grid width.""" + ... + + +# MTMD_API size_t mtmd_image_tokens_get_ny(const mtmd_image_tokens * image_tokens); +@ctypes_function("mtmd_image_tokens_get_ny", [mtmd_image_tokens_p_ctypes], c_size_t) +def mtmd_image_tokens_get_ny(image_tokens: mtmd_image_tokens_p, /) -> int: + """Get the image token grid height.""" + ... + + +# MTMD_API const char * mtmd_image_tokens_get_id(const mtmd_image_tokens * image_tokens); +@ctypes_function("mtmd_image_tokens_get_id", [mtmd_image_tokens_p_ctypes], c_char_p) +def mtmd_image_tokens_get_id(image_tokens: mtmd_image_tokens_p, /) -> Optional[bytes]: + """Get the optional image token identifier.""" + ... + + +# MTMD_API llama_pos mtmd_image_tokens_get_n_pos(const mtmd_image_tokens * image_tokens); +@ctypes_function( + "mtmd_image_tokens_get_n_pos", + [mtmd_image_tokens_p_ctypes], + llama_cpp.llama_pos, +) +def mtmd_image_tokens_get_n_pos(image_tokens: mtmd_image_tokens_p, /) -> int: + """Get the number of positions consumed by the image tokens.""" + ... + + +# MTMD_API int32_t mtmd_encode(mtmd_context * ctx, const mtmd_image_tokens * image_tokens); +@ctypes_function( + "mtmd_encode", + [mtmd_context_p_ctypes, mtmd_image_tokens_p_ctypes], + c_int, +) +def mtmd_encode(ctx: mtmd_context_p, image_tokens: mtmd_image_tokens_p, /) -> int: + """Run an MTMD encode pass for image tokens.""" + ... + + +# MTMD_API int32_t mtmd_encode_chunk(mtmd_context * ctx, const mtmd_input_chunk * chunk); +@ctypes_function( + "mtmd_encode_chunk", + [mtmd_context_p_ctypes, mtmd_input_chunk_p_ctypes], + c_int, +) +def mtmd_encode_chunk(ctx: mtmd_context_p, chunk: mtmd_input_chunk_p, /) -> int: + """Run an MTMD encode pass for a single chunk.""" + ... + + +# MTMD_API float * mtmd_get_output_embd(mtmd_context * ctx); +@ctypes_function("mtmd_get_output_embd", [mtmd_context_p_ctypes], POINTER(c_float)) +def mtmd_get_output_embd(ctx: mtmd_context_p, /) -> Optional[CtypesArray[c_float]]: + """Get output embeddings from the last encode pass.""" + ... + + +# MTMD_API mtmd_input_chunks * mtmd_test_create_input_chunks(void); +@ctypes_function("mtmd_test_create_input_chunks", [], mtmd_input_chunks_p_ctypes) +def mtmd_test_create_input_chunks() -> Optional[mtmd_input_chunks_p]: + """Create MTMD test chunks for the C API tests.""" + ... + + ################################################ # mtmd-helper.h functions ################################################ +# MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname); +@ctypes_function( + "mtmd_helper_bitmap_init_from_file", + [mtmd_context_p_ctypes, c_char_p], + mtmd_bitmap_p_ctypes, +) +def mtmd_helper_bitmap_init_from_file( + ctx: mtmd_context_p, fname: bytes, / +) -> Optional[mtmd_bitmap_p]: + """Initialize an MTMD bitmap from a file.""" + ... + + # MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len); @ctypes_function( "mtmd_helper_bitmap_init_from_buf", @@ -339,6 +523,52 @@ def mtmd_helper_bitmap_init_from_buf( def mtmd_helper_get_n_tokens(chunks: mtmd_input_chunks_p, /) -> int: ... +# MTMD_API llama_pos mtmd_helper_get_n_pos(const mtmd_input_chunks * chunks); +@ctypes_function( + "mtmd_helper_get_n_pos", + [mtmd_input_chunks_p_ctypes], + llama_cpp.llama_pos, +) +def mtmd_helper_get_n_pos(chunks: mtmd_input_chunks_p, /) -> int: + """Count the total positions consumed by the chunks.""" + ... + + +# MTMD_API int32_t mtmd_helper_eval_chunks(mtmd_context * ctx, +# struct llama_context * lctx, +# const mtmd_input_chunks * chunks, +# llama_pos n_past, +# llama_seq_id seq_id, +# int32_t n_batch, +# bool logits_last, +# llama_pos * new_n_past); +@ctypes_function( + "mtmd_helper_eval_chunks", + [ + mtmd_context_p_ctypes, + llama_cpp.llama_context_p_ctypes, + mtmd_input_chunks_p_ctypes, + llama_cpp.llama_pos, + llama_cpp.llama_seq_id, + c_int, + c_bool, + POINTER(llama_cpp.llama_pos), + ], + c_int, +) +def mtmd_helper_eval_chunks( + ctx: mtmd_context_p, + lctx: llama_cpp.llama_context_p, + chunks: mtmd_input_chunks_p, + n_past: llama_cpp.llama_pos, + seq_id: llama_cpp.llama_seq_id, + n_batch: Union[c_int, int], + logits_last: Union[c_bool, bool], + new_n_past: "_Pointer[llama_cpp.llama_pos]", + /, +) -> int: ... + + # MTMD_API int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx, # struct llama_context * lctx, # const mtmd_input_chunk * chunk, @@ -374,6 +604,43 @@ def mtmd_helper_eval_chunk_single( ) -> int: ... +# MTMD_API int32_t mtmd_helper_decode_image_chunk(mtmd_context * ctx, +# struct llama_context * lctx, +# const mtmd_input_chunk * chunk, +# float * encoded_embd, +# llama_pos n_past, +# llama_seq_id seq_id, +# int32_t n_batch, +# llama_pos * new_n_past); +@ctypes_function( + "mtmd_helper_decode_image_chunk", + [ + mtmd_context_p_ctypes, + llama_cpp.llama_context_p_ctypes, + mtmd_input_chunk_p_ctypes, + POINTER(c_float), + llama_cpp.llama_pos, + llama_cpp.llama_seq_id, + c_int, + POINTER(llama_cpp.llama_pos), + ], + c_int, +) +def mtmd_helper_decode_image_chunk( + ctx: mtmd_context_p, + lctx: llama_cpp.llama_context_p, + chunk: mtmd_input_chunk_p, + encoded_embd: CtypesArray[c_float], + n_past: llama_cpp.llama_pos, + seq_id: llama_cpp.llama_seq_id, + n_batch: Union[c_int, int], + new_n_past: "_Pointer[llama_cpp.llama_pos]", + /, +) -> int: + """Decode a pre-encoded image chunk.""" + ... + + # MTMD_API void mtmd_log_set(ggml_log_callback log_callback, void * user_data); @ctypes_function( "mtmd_log_set", diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 3bd9aa1f92..227ed28e12 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 3bd9aa1f9250cd15f5371f3622d73d954b68a747 +Subproject commit 227ed28e128e93b4d63ae5108560c550c9ab16c8 From d87bf08871e2c2995e83f551aa61443e35fd865c Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 26 Apr 2026 21:41:32 -0700 Subject: [PATCH 038/138] feat: Update llama.cpp to ggerganov/llama.cpp@f53577432 (#2189) * feat: Update llama.cpp to ggerganov/llama.cpp@f53577432 * docs: Update changelog for llama.cpp f53577432 * docs: Keep one unreleased llama.cpp changelog entry --- CHANGELOG.md | 2 +- llama_cpp/llama_cpp.py | 55 ------------------------------- llama_cpp/mtmd_cpp.py | 73 ++++++++++++++++++++++++++++++++++++------ vendor/llama.cpp | 2 +- 4 files changed, 65 insertions(+), 67 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index fbe5b6b6fd..ea7beaaa79 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] -- feat: Update llama.cpp to ggerganov/llama.cpp@3bd9aa1f9 and sync Python bindings +- feat: Update llama.cpp to ggerganov/llama.cpp@f53577432 and sync Python bindings ## [0.3.20] diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index e445ed66ae..d032371402 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -1516,54 +1516,6 @@ def llama_free(ctx: llama_context_p, /): ... -# enum llama_params_fit_status { -# LLAMA_PARAMS_FIT_STATUS_SUCCESS = 0, -# LLAMA_PARAMS_FIT_STATUS_FAILURE = 1, -# LLAMA_PARAMS_FIT_STATUS_ERROR = 2, -# }; -LLAMA_PARAMS_FIT_STATUS_SUCCESS = 0 -LLAMA_PARAMS_FIT_STATUS_FAILURE = 1 -LLAMA_PARAMS_FIT_STATUS_ERROR = 2 - - -# LLAMA_API enum llama_params_fit_status llama_params_fit( -# const char * path_model, -# struct llama_model_params * mparams, -# struct llama_context_params * cparams, -# float * tensor_split, -# struct llama_model_tensor_buft_override * tensor_buft_overrides, -# size_t * margins, -# uint32_t n_ctx_min, -# enum ggml_log_level log_level); -@ctypes_function( - "llama_params_fit", - [ - ctypes.c_char_p, - ctypes.POINTER(llama_model_params), - ctypes.POINTER(llama_context_params), - ctypes.POINTER(ctypes.c_float), - ctypes.c_void_p, - ctypes.POINTER(ctypes.c_size_t), - ctypes.c_uint32, - ctypes.c_int, - ], - ctypes.c_int, -) -def llama_params_fit( - path_model: bytes, - mparams: CtypesPointerOrRef[llama_model_params], - cparams: CtypesPointerOrRef[llama_context_params], - tensor_split: Optional[CtypesPointer[ctypes.c_float]], - tensor_buft_overrides: ctypes.c_void_p, - margins: Optional[CtypesPointer[ctypes.c_size_t]], - n_ctx_min: int, - log_level: int, - /, -) -> int: - """Fit model and context parameters for a model path.""" - ... - - # LLAMA_API int64_t llama_time_us(void); @ctypes_function( "llama_time_us", @@ -4869,13 +4821,6 @@ def llama_perf_sampler_print(chain: llama_sampler_p, /): ... def llama_perf_sampler_reset(chain: llama_sampler_p, /): ... -# // print a breakdown of per-device memory use via LLAMA_LOG: -@ctypes_function("llama_memory_breakdown_print", [llama_context_p_ctypes], None) -def llama_memory_breakdown_print(ctx: llama_context_p, /): - """Print a breakdown of per-device memory use.""" - ... - - # // # // training # // diff --git a/llama_cpp/mtmd_cpp.py b/llama_cpp/mtmd_cpp.py index 550c9bd59f..485dc5d8c6 100644 --- a/llama_cpp/mtmd_cpp.py +++ b/llama_cpp/mtmd_cpp.py @@ -8,9 +8,9 @@ c_int, c_uint8, c_uint32, + c_size_t, c_float, c_void_p, - c_size_t, POINTER, _Pointer, # type: ignore Structure, @@ -123,6 +123,17 @@ class mtmd_input_text(Structure): ] +class mtmd_decoder_pos(Structure): + """Decoder attention position for M-RoPE models.""" + + _fields_ = [ + ("t", c_uint32), + ("x", c_uint32), + ("y", c_uint32), + ("z", c_uint32), + ] + + ################################################ # mtmd.h functions ################################################ @@ -165,35 +176,41 @@ def mtmd_init_from_file( def mtmd_free(ctx: mtmd_context_p, /): ... -# MTMD_API bool mtmd_decode_use_non_causal(mtmd_context * ctx); -@ctypes_function("mtmd_decode_use_non_causal", [mtmd_context_p_ctypes], c_bool) -def mtmd_decode_use_non_causal(ctx: mtmd_context_p, /) -> bool: +# MTMD_API bool mtmd_decode_use_non_causal(const mtmd_context * ctx, const mtmd_input_chunk * chunk); +@ctypes_function( + "mtmd_decode_use_non_causal", + [mtmd_context_p_ctypes, mtmd_input_chunk_p_ctypes], + c_bool, +) +def mtmd_decode_use_non_causal( + ctx: mtmd_context_p, chunk: Optional[mtmd_input_chunk_p], / +) -> bool: """Check whether MTMD decoding uses non-causal attention.""" ... -# MTMD_API bool mtmd_decode_use_mrope(mtmd_context * ctx); +# MTMD_API bool mtmd_decode_use_mrope(const mtmd_context * ctx); @ctypes_function("mtmd_decode_use_mrope", [mtmd_context_p_ctypes], c_bool) def mtmd_decode_use_mrope(ctx: mtmd_context_p, /) -> bool: """Check whether MTMD decoding uses mRoPE.""" ... -# MTMD_API bool mtmd_support_vision(mtmd_context * ctx); +# MTMD_API bool mtmd_support_vision(const mtmd_context * ctx); @ctypes_function("mtmd_support_vision", [mtmd_context_p_ctypes], c_bool) def mtmd_support_vision(ctx: mtmd_context_p, /) -> bool: """Check whether the current model supports vision input.""" ... -# MTMD_API bool mtmd_support_audio(mtmd_context * ctx); +# MTMD_API bool mtmd_support_audio(const mtmd_context * ctx); @ctypes_function("mtmd_support_audio", [mtmd_context_p_ctypes], c_bool) def mtmd_support_audio(ctx: mtmd_context_p, /) -> bool: """Check whether MTMD supports audio.""" ... -# MTMD_API int mtmd_get_audio_sample_rate(mtmd_context * ctx); +# MTMD_API int mtmd_get_audio_sample_rate(const mtmd_context * ctx); @ctypes_function("mtmd_get_audio_sample_rate", [mtmd_context_p_ctypes], c_int) def mtmd_get_audio_sample_rate(ctx: mtmd_context_p, /) -> int: """Get the audio sample rate in Hz. Returns -1 if audio is not supported.""" @@ -418,14 +435,16 @@ def mtmd_image_tokens_get_n_tokens(image_tokens: mtmd_image_tokens_p, /) -> int: ... -# MTMD_API size_t mtmd_image_tokens_get_nx(const mtmd_image_tokens * image_tokens); +# DEPRECATED(MTMD_API size_t mtmd_image_tokens_get_nx(const mtmd_image_tokens * image_tokens), +# "use mtmd_image_tokens_get_decoder_pos() instead"); @ctypes_function("mtmd_image_tokens_get_nx", [mtmd_image_tokens_p_ctypes], c_size_t) def mtmd_image_tokens_get_nx(image_tokens: mtmd_image_tokens_p, /) -> int: """Get the image token grid width.""" ... -# MTMD_API size_t mtmd_image_tokens_get_ny(const mtmd_image_tokens * image_tokens); +# DEPRECATED(MTMD_API size_t mtmd_image_tokens_get_ny(const mtmd_image_tokens * image_tokens), +# "use mtmd_image_tokens_get_decoder_pos() instead"); @ctypes_function("mtmd_image_tokens_get_ny", [mtmd_image_tokens_p_ctypes], c_size_t) def mtmd_image_tokens_get_ny(image_tokens: mtmd_image_tokens_p, /) -> int: """Get the image token grid height.""" @@ -450,6 +469,23 @@ def mtmd_image_tokens_get_n_pos(image_tokens: mtmd_image_tokens_p, /) -> int: ... +# MTMD_API struct mtmd_decoder_pos mtmd_image_tokens_get_decoder_pos( +# const mtmd_image_tokens * image_tokens, llama_pos pos_0, size_t i); +@ctypes_function( + "mtmd_image_tokens_get_decoder_pos", + [mtmd_image_tokens_p_ctypes, llama_cpp.llama_pos, c_size_t], + mtmd_decoder_pos, +) +def mtmd_image_tokens_get_decoder_pos( + image_tokens: mtmd_image_tokens_p, + pos_0: llama_cpp.llama_pos, + i: Union[c_size_t, int], + /, +) -> mtmd_decoder_pos: + """Get decoder attention position for an image embedding token.""" + ... + + # MTMD_API int32_t mtmd_encode(mtmd_context * ctx, const mtmd_image_tokens * image_tokens); @ctypes_function( "mtmd_encode", @@ -534,6 +570,23 @@ def mtmd_helper_get_n_pos(chunks: mtmd_input_chunks_p, /) -> int: ... +# MTMD_API void mtmd_helper_image_get_decoder_pos( +# const mtmd_image_tokens * image, llama_pos pos_0, struct mtmd_decoder_pos * out_pos); +@ctypes_function( + "mtmd_helper_image_get_decoder_pos", + [mtmd_image_tokens_p_ctypes, llama_cpp.llama_pos, POINTER(mtmd_decoder_pos)], + None, +) +def mtmd_helper_image_get_decoder_pos( + image: mtmd_image_tokens_p, + pos_0: llama_cpp.llama_pos, + out_pos: "_Pointer[mtmd_decoder_pos]", + /, +): + """Fill decoder attention positions for all image embedding tokens.""" + ... + + # MTMD_API int32_t mtmd_helper_eval_chunks(mtmd_context * ctx, # struct llama_context * lctx, # const mtmd_input_chunks * chunks, diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 227ed28e12..f535774325 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 227ed28e128e93b4d63ae5108560c550c9ab16c8 +Subproject commit f53577432541bb9edc1588c4ef45c66bf07e4468 From 511b3f414359e8d98e9123d007bdd935cd1f7c3f Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 26 Apr 2026 22:02:24 -0700 Subject: [PATCH 039/138] fix(ci): Build one arm64 py3 release wheel (#2191) * fix(ci): Build one arm64 py3 release wheel * docs: Update changelog for arm64 release wheel fix --- .github/workflows/build-and-release.yaml | 4 +++- CHANGELOG.md | 1 + 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index 6cbac0cb1c..039e376b69 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -82,7 +82,9 @@ jobs: # Keep native arm64 builds on a portable CPU baseline instead of # tuning wheels to the hosted runner. CIBW_ENVIRONMENT: CMAKE_ARGS="-DGGML_NATIVE=off" - CIBW_BUILD: "cp38-* cp39-* cp310-* cp311-* cp312-*" + # The release wheel is tagged py3-none, so one build covers all + # supported Python versions and avoids duplicate wheel names. + CIBW_BUILD: "cp38-*" with: output-dir: wheelhouse diff --git a/CHANGELOG.md b/CHANGELOG.md index ea7beaaa79..fe376ebd33 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] - feat: Update llama.cpp to ggerganov/llama.cpp@f53577432 and sync Python bindings +- fix(ci): Build one arm64 release wheel for `py3-none` wheel publishing ## [0.3.20] From c8075d1dfe2019a0390af613419ecfaea292c9d5 Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 26 Apr 2026 22:13:13 -0700 Subject: [PATCH 040/138] chore: bump version to 0.3.21 (#2192) --- CHANGELOG.md | 2 ++ llama_cpp/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index fe376ebd33..eeb42b6449 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.21] + - feat: Update llama.cpp to ggerganov/llama.cpp@f53577432 and sync Python bindings - fix(ci): Build one arm64 release wheel for `py3-none` wheel publishing diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index 83177c065d..fbad5c28b2 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.20" +__version__ = "0.3.21" From 195cc59a187687ca64c8e0939e5e549d456aa2fb Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 26 Apr 2026 22:39:59 -0700 Subject: [PATCH 041/138] fix(ci): Repair py3 CPU release wheels (#2193) --- .github/workflows/build-and-release.yaml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index 039e376b69..f67fb558dc 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -48,7 +48,10 @@ jobs: CIBW_REPAIR_WHEEL_COMMAND: "" # Linux needs auditwheel repair so manylinux and musllinux wheels are # published with distinct platform tags instead of generic linux tags. - CIBW_REPAIR_WHEEL_COMMAND_LINUX: "auditwheel repair -w {dest_dir} {wheel}" + CIBW_REPAIR_WHEEL_COMMAND_LINUX: "LD_LIBRARY_PATH=/project/llama_cpp/lib auditwheel repair -w {dest_dir} {wheel}" + # The release wheel is tagged py3-none, so one build per platform + # covers all supported Python versions and avoids duplicate names. + CIBW_BUILD: "cp38-*" # Skip cibuildwheel's default i686 sidecar and keep Linux release # wheels on a portable x86_64 CPU baseline. CIBW_ARCHS_LINUX: "auto64" From d2bcbac46605f11d382426dd88d67e8b5c124cd7 Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 26 Apr 2026 22:55:04 -0700 Subject: [PATCH 042/138] fix(ci): Scope CPU release wheel selectors by OS (#2194) --- .github/workflows/build-and-release.yaml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index f67fb558dc..df6201ee75 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -51,10 +51,13 @@ jobs: CIBW_REPAIR_WHEEL_COMMAND_LINUX: "LD_LIBRARY_PATH=/project/llama_cpp/lib auditwheel repair -w {dest_dir} {wheel}" # The release wheel is tagged py3-none, so one build per platform # covers all supported Python versions and avoids duplicate names. - CIBW_BUILD: "cp38-*" + CIBW_BUILD_LINUX: "cp38-*" + CIBW_BUILD_MACOS: "cp39-*" + CIBW_BUILD_WINDOWS: "cp39-*" # Skip cibuildwheel's default i686 sidecar and keep Linux release # wheels on a portable x86_64 CPU baseline. CIBW_ARCHS_LINUX: "auto64" + CIBW_ARCHS_WINDOWS: "AMD64" CIBW_ENVIRONMENT_LINUX: CMAKE_ARGS="-DGGML_NATIVE=off" # Keep macOS release wheels on a portable CPU baseline instead of # inheriting the hosted runner's native flags. From c6dc90555be7bedda2d15f516b3ccd6252130a0f Mon Sep 17 00:00:00 2001 From: Andrei Date: Mon, 27 Apr 2026 01:41:57 -0700 Subject: [PATCH 043/138] fix(docs): update mkdocstrings inventories config (#2195) --- mkdocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index 79a9e67a1a..37e1002e8d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -36,7 +36,7 @@ plugins: - typing - typing_extensions - ctypes - import: + inventories: - https://docs.python.org/3/objects.inv - https://numpy.org/doc/stable/objects.inv From 587d94a8c31943e3bcbcccbbd2721867da52a9de Mon Sep 17 00:00:00 2001 From: Andrei Date: Sat, 2 May 2026 01:31:28 -0700 Subject: [PATCH 044/138] feat: Update llama.cpp to ggerganov/llama.cpp@63d93d173 (#2197) --- CHANGELOG.md | 2 ++ vendor/llama.cpp | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index eeb42b6449..e1f1f08605 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: Update llama.cpp to ggerganov/llama.cpp@63d93d173 + ## [0.3.21] - feat: Update llama.cpp to ggerganov/llama.cpp@f53577432 and sync Python bindings diff --git a/vendor/llama.cpp b/vendor/llama.cpp index f535774325..63d93d1733 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit f53577432541bb9edc1588c4ef45c66bf07e4468 +Subproject commit 63d93d17336e41e4cc73a64451e5b1d2477abdb1 From d2113a14441f7d811b34f4aeee917449ad1da1b9 Mon Sep 17 00:00:00 2001 From: Andrei Date: Sat, 2 May 2026 15:25:58 -0700 Subject: [PATCH 045/138] feat(ci): re-enable Windows CUDA wheels (#2198) * feat(ci): re-enable Windows CUDA wheel builds * fix(ci): use ninja for Windows CUDA wheels * fix(ci): normalize Windows CUDA CMake paths * feat(ci): add CUDA 12.5 wheel builds * fix(ci): avoid Windows CUDA 12.5 toolkit meta-package * fix(ci): include CUDA 12.5 Windows libraries * chore(ci): simplify Windows CUDA wheel workflow * docs: update changelog for Windows CUDA wheels --- .github/workflows/build-wheels-cuda.yaml | 133 ++++++++++++----------- CHANGELOG.md | 1 + 2 files changed, 69 insertions(+), 65 deletions(-) diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index 17daaa12a2..98c19afb60 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -20,9 +20,11 @@ jobs: id: set-matrix run: | $matrix = @{ - 'os' = @('ubuntu-22.04') #, 'windows-2022') - 'pyver' = @("3.9", "3.10", "3.11", "3.12") - 'cuda' = @("12.1.1", "12.2.2", "12.3.2", "12.4.1") #, "12.5.1", "12.6.1") + 'os' = @('ubuntu-22.04', 'windows-2022') + # wheel.py-api = "py3" makes the CUDA wheel interpreter-agnostic, + # so one builder per toolkit version is sufficient. + 'pyver' = @("3.9") + 'cuda' = @("12.1.1", "12.2.2", "12.3.2", "12.4.1", "12.5.1") 'releasetag' = @("basic") } @@ -43,11 +45,11 @@ jobs: AVXVER: ${{ matrix.releasetag }} steps: - - name: Add MSBuild to PATH + - name: Set up MSVC if: runner.os == 'Windows' - uses: microsoft/setup-msbuild@v2 + uses: ilammy/msvc-dev-cmd@v1 with: - vs-version: '[16.11,16.12)' + arch: x64 - uses: actions/checkout@v4 with: @@ -67,32 +69,6 @@ jobs: add-pip-as-python-dependency: true auto-activate-base: false - - name: VS Integration Cache - id: vs-integration-cache - if: runner.os == 'Windows' - uses: actions/cache@v4 - with: - path: ./MSBuildExtensions - key: cuda-${{ matrix.cuda }}-vs-integration - - - name: Get Visual Studio Integration - if: runner.os == 'Windows' && steps.vs-integration-cache.outputs.cache-hit != 'true' - run: | - if ($env:CUDAVER -eq '12.1.1') {$x = '12.1.0'} else {$x = $env:CUDAVER} - $links = (Invoke-RestMethod 'https://raw.githubusercontent.com/Jimver/cuda-toolkit/master/src/links/windows-links.ts').Trim().split().where({$_ -ne ''}) - for ($i=$q=0;$i -lt $links.count -and $q -lt 2;$i++) {if ($links[$i] -eq "'$x',") {$q++}} - Invoke-RestMethod $links[$i].Trim("'") -OutFile 'cudainstaller.zip' - & 'C:\Program Files\7-Zip\7z.exe' e cudainstaller.zip -oMSBuildExtensions -r *\MSBuildExtensions\* > $null - Remove-Item 'cudainstaller.zip' - - - name: Install Visual Studio Integration - if: runner.os == 'Windows' - run: | - $y = (gi '.\MSBuildExtensions').fullname + '\*' - (gi 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\MSBuild\Microsoft\VC\*\BuildCustomizations').fullname.foreach({cp $y $_}) - $cupath = 'CUDA_PATH_V' + $env:CUDAVER.Remove($env:CUDAVER.LastIndexOf('.')).Replace('.','_') - echo "$cupath=$env:CONDA_PREFIX" >> $env:GITHUB_ENV - - name: Install Dependencies env: MAMBA_DOWNLOAD_FAILFAST: "0" @@ -101,24 +77,45 @@ jobs: $cudaVersion = $env:CUDAVER $cudaChannel = "nvidia/label/cuda-$cudaVersion" if ($IsLinux) { - # Keep nvcc, cudart, and headers on the same NVIDIA label so the - # detected toolkit version matches the published wheel tag. - mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "$cudaChannel::cuda-toolkit=$cudaVersion" "$cudaChannel::cuda-nvcc_linux-64=$cudaVersion" "$cudaChannel::cuda-cudart" "$cudaChannel::cuda-cudart-dev" + mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-toolkit=$cudaVersion" "${cudaChannel}::cuda-nvcc_linux-64" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev" + } elseif ($IsWindows) { + if ($cudaVersion -like '12.5.*') { + # The Windows 12.5 toolkit meta-package pulls compiler activation + # scripts that overflow cmd.exe after MSVC is already initialized. + mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-nvcc_win-64" "${cudaChannel}::cuda-libraries-dev=$cudaVersion" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev" + } else { + mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-toolkit=$cudaVersion" "${cudaChannel}::cuda-nvcc_win-64" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev" + } } else { - mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "$cudaChannel::cuda-toolkit=$cudaVersion" + throw 'Unsupported CUDA wheel build platform' } if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE } - python -m pip install build wheel + if ($IsWindows) { + python -m pip install build wheel ninja + } else { + python -m pip install build wheel + } - name: Build Wheel run: | - $env:CUDA_PATH = $env:CONDA_PREFIX - $env:CUDA_HOME = $env:CONDA_PREFIX - $env:CUDA_TOOLKIT_ROOT_DIR = $env:CONDA_PREFIX + $pathSeparator = if ($IsWindows) { ';' } else { ':' } + if ($IsWindows) { + $cudaRoot = Join-Path $env:CONDA_PREFIX 'Library' + } elseif (Test-Path (Join-Path $env:CONDA_PREFIX 'targets/x86_64-linux/include/cuda_runtime.h')) { + $cudaRoot = Join-Path $env:CONDA_PREFIX 'targets/x86_64-linux' + } else { + $cudaRoot = $env:CONDA_PREFIX + } + + $env:CUDA_PATH = $cudaRoot + $env:CUDA_HOME = $cudaRoot + $env:CUDAToolkit_ROOT = $cudaRoot + $env:CUDA_TOOLKIT_ROOT_DIR = $cudaRoot $cudaHostCompilerArg = '' - $env:CMAKE_ARGS = '' + $cudaRootCmake = $cudaRoot.Replace('\', '/') + $env:CMAKE_ARGS = "-DCUDAToolkit_ROOT=$cudaRootCmake -DCUDA_TOOLKIT_ROOT_DIR=$cudaRootCmake" if ($IsLinux) { if (Test-Path '/usr/bin/g++-12') { $env:CC = '/usr/bin/gcc-12' @@ -126,27 +123,41 @@ jobs: $env:CUDAHOSTCXX = '/usr/bin/g++-12' $cudaHostCompilerArg = " -DCMAKE_CUDA_HOST_COMPILER=$env:CUDAHOSTCXX" } - if (Test-Path (Join-Path $env:CONDA_PREFIX 'include/cuda_runtime.h')) { - $env:CUDAToolkit_ROOT = $env:CONDA_PREFIX - $env:CUDA_TOOLKIT_ROOT_DIR = $env:CONDA_PREFIX - $env:CMAKE_ARGS = "-DCUDAToolkit_ROOT=$env:CONDA_PREFIX -DCUDA_TOOLKIT_ROOT_DIR=$env:CONDA_PREFIX$cudaHostCompilerArg" - $env:CPATH = "$env:CONDA_PREFIX/include:$env:CPATH" - $env:CPLUS_INCLUDE_PATH = "$env:CONDA_PREFIX/include:$env:CPLUS_INCLUDE_PATH" - $env:LIBRARY_PATH = "$env:CONDA_PREFIX/lib:$env:LIBRARY_PATH" - $env:LD_LIBRARY_PATH = "$env:CONDA_PREFIX/lib:$env:LD_LIBRARY_PATH" - } else { - $env:CMAKE_ARGS = $cudaHostCompilerArg.Trim() - } + $env:CMAKE_ARGS = "-DCUDAToolkit_ROOT=$cudaRoot -DCUDA_TOOLKIT_ROOT_DIR=$cudaRoot$cudaHostCompilerArg" + $env:CPATH = "$cudaRoot/include$pathSeparator$env:CPATH" + $env:CPLUS_INCLUDE_PATH = "$cudaRoot/include$pathSeparator$env:CPLUS_INCLUDE_PATH" + $env:LIBRARY_PATH = "$cudaRoot/lib$pathSeparator$env:CONDA_PREFIX/lib$pathSeparator$env:LIBRARY_PATH" + $env:LD_LIBRARY_PATH = "$cudaRoot/lib$pathSeparator$env:CONDA_PREFIX/lib$pathSeparator$env:LD_LIBRARY_PATH" + } elseif ($IsWindows) { + $ninjaPath = ((Get-Command ninja -ErrorAction Stop).Source).Replace('\', '/') + $env:CMAKE_GENERATOR = 'Ninja' + $env:CMAKE_MAKE_PROGRAM = $ninjaPath + $env:PATH = "$(Join-Path $cudaRoot 'bin')$pathSeparator$env:PATH" } - $nvccPath = Join-Path $env:CONDA_PREFIX 'bin/nvcc' - if (-not (Test-Path $nvccPath)) { - $nvccPath = Join-Path $env:CONDA_PREFIX 'targets/x86_64-linux/bin/nvcc' + + if ($IsWindows) { + $nvccCandidates = @( + (Join-Path $cudaRoot 'bin\nvcc.exe'), + (Join-Path $env:CONDA_PREFIX 'Library\bin\nvcc.exe'), + (Join-Path $env:CONDA_PREFIX 'bin\nvcc.exe') + ) + } else { + $nvccCandidates = @( + (Join-Path $env:CONDA_PREFIX 'bin/nvcc'), + (Join-Path $env:CONDA_PREFIX 'targets/x86_64-linux/bin/nvcc') + ) } - if (-not (Test-Path $nvccPath)) { + $nvccPath = $nvccCandidates | Where-Object { Test-Path $_ } | Select-Object -First 1 + if (-not $nvccPath) { throw 'Failed to find nvcc in the conda environment' } $env:CUDACXX = $nvccPath - $env:PATH = "$(Split-Path $nvccPath):$env:PATH" + $env:PATH = "$(Split-Path $nvccPath)$pathSeparator$env:PATH" + if ($IsWindows) { + $nvccPathCmake = $nvccPath.Replace('\', '/') + $env:CUDACXX = $nvccPathCmake + $env:CMAKE_ARGS = "-DCMAKE_CUDA_COMPILER=$nvccPathCmake -DCMAKE_MAKE_PROGRAM=$env:CMAKE_MAKE_PROGRAM $env:CMAKE_ARGS" + } $nvccVersion = ((& $nvccPath --version) | Select-String 'release ([0-9]+\.[0-9]+)').Matches[0].Groups[1].Value if (-not $nvccVersion) { throw 'Failed to detect the installed CUDA toolkit version' @@ -157,15 +168,7 @@ jobs: # one forward-compatible PTX target instead of embedding PTX for every # SM. This keeps the wheel under GitHub's 2 GiB release-asset limit. $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=70-real;75-real;80-real;86-real;89-real;90-real;90-virtual -DCMAKE_CUDA_FLAGS=--allow-unsupported-compiler $env:CMAKE_ARGS" - # if ($env:AVXVER -eq 'AVX') { $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off' - # } - # if ($env:AVXVER -eq 'AVX512') { - # $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX512=on' - # } - # if ($env:AVXVER -eq 'basic') { - # $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX=off -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off' - # } python -m build --wheel # Publish tags that reflect the actual installed toolkit version. Write-Output "CUDA_VERSION=$cudaTagVersion" >> $env:GITHUB_ENV diff --git a/CHANGELOG.md b/CHANGELOG.md index e1f1f08605..1852751c1e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] - feat: Update llama.cpp to ggerganov/llama.cpp@63d93d173 +- feat(ci): Re-enable Windows CUDA wheels and add CUDA 12.5.1 wheel builds ## [0.3.21] From 9cf0ce7c2094c40d7166f3cc92f00f2c2236af4f Mon Sep 17 00:00:00 2001 From: Andrei Date: Sat, 2 May 2026 15:35:41 -0700 Subject: [PATCH 046/138] chore: bump version to 0.3.22 (#2200) --- CHANGELOG.md | 2 ++ llama_cpp/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 1852751c1e..5e2a8e329c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.22] + - feat: Update llama.cpp to ggerganov/llama.cpp@63d93d173 - feat(ci): Re-enable Windows CUDA wheels and add CUDA 12.5.1 wheel builds diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index fbad5c28b2..78292de302 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.21" +__version__ = "0.3.22" From 2bfd80c1c5fadd6bd95bb57e7332438cca5521cd Mon Sep 17 00:00:00 2001 From: Andrei Date: Sat, 2 May 2026 15:45:31 -0700 Subject: [PATCH 047/138] fix(ci): pass CUDA unsupported compiler flag during detection (#2201) --- .github/workflows/build-wheels-cuda.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index 98c19afb60..c32d7f56de 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -167,7 +167,7 @@ jobs: # Build real cubins for the supported GPUs, including sm_70, and keep # one forward-compatible PTX target instead of embedding PTX for every # SM. This keeps the wheel under GitHub's 2 GiB release-asset limit. - $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=70-real;75-real;80-real;86-real;89-real;90-real;90-virtual -DCMAKE_CUDA_FLAGS=--allow-unsupported-compiler $env:CMAKE_ARGS" + $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=70-real;75-real;80-real;86-real;89-real;90-real;90-virtual -DCMAKE_CUDA_FLAGS=--allow-unsupported-compiler -DCMAKE_CUDA_FLAGS_INIT=--allow-unsupported-compiler $env:CMAKE_ARGS" $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off' python -m build --wheel # Publish tags that reflect the actual installed toolkit version. From 04a3638b2637b0b6f1b843d16a679fbf7d2dd375 Mon Sep 17 00:00:00 2001 From: Andrei Date: Sat, 2 May 2026 15:53:53 -0700 Subject: [PATCH 048/138] fix(ci): pass CUDA compiler arg for Windows detection (#2202) --- .github/workflows/build-wheels-cuda.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index c32d7f56de..2b4bf775a2 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -156,7 +156,7 @@ jobs: if ($IsWindows) { $nvccPathCmake = $nvccPath.Replace('\', '/') $env:CUDACXX = $nvccPathCmake - $env:CMAKE_ARGS = "-DCMAKE_CUDA_COMPILER=$nvccPathCmake -DCMAKE_MAKE_PROGRAM=$env:CMAKE_MAKE_PROGRAM $env:CMAKE_ARGS" + $env:CMAKE_ARGS = "-DCMAKE_CUDA_COMPILER=$nvccPathCmake -DCMAKE_CUDA_COMPILER_ARG1=-allow-unsupported-compiler -DCMAKE_MAKE_PROGRAM=$env:CMAKE_MAKE_PROGRAM $env:CMAKE_ARGS" } $nvccVersion = ((& $nvccPath --version) | Select-String 'release ([0-9]+\.[0-9]+)').Matches[0].Groups[1].Value if (-not $nvccVersion) { @@ -167,7 +167,7 @@ jobs: # Build real cubins for the supported GPUs, including sm_70, and keep # one forward-compatible PTX target instead of embedding PTX for every # SM. This keeps the wheel under GitHub's 2 GiB release-asset limit. - $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=70-real;75-real;80-real;86-real;89-real;90-real;90-virtual -DCMAKE_CUDA_FLAGS=--allow-unsupported-compiler -DCMAKE_CUDA_FLAGS_INIT=--allow-unsupported-compiler $env:CMAKE_ARGS" + $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=70-real;75-real;80-real;86-real;89-real;90-real;90-virtual -DCMAKE_CUDA_FLAGS=-allow-unsupported-compiler -DCMAKE_CUDA_FLAGS_INIT=-allow-unsupported-compiler $env:CMAKE_ARGS" $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off' python -m build --wheel # Publish tags that reflect the actual installed toolkit version. From bc6ff9f2cc5545c180d8c3db4128d3ad48a31575 Mon Sep 17 00:00:00 2001 From: Andrei Date: Sat, 2 May 2026 16:01:11 -0700 Subject: [PATCH 049/138] fix(ci): install CUDA CCCL headers for wheel builds (#2203) --- .github/workflows/build-wheels-cuda.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index 2b4bf775a2..c015c71184 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -77,14 +77,14 @@ jobs: $cudaVersion = $env:CUDAVER $cudaChannel = "nvidia/label/cuda-$cudaVersion" if ($IsLinux) { - mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-toolkit=$cudaVersion" "${cudaChannel}::cuda-nvcc_linux-64" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev" + mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-toolkit=$cudaVersion" "${cudaChannel}::cuda-nvcc_linux-64" "${cudaChannel}::cuda-cccl" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev" } elseif ($IsWindows) { if ($cudaVersion -like '12.5.*') { # The Windows 12.5 toolkit meta-package pulls compiler activation # scripts that overflow cmd.exe after MSVC is already initialized. - mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-nvcc_win-64" "${cudaChannel}::cuda-libraries-dev=$cudaVersion" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev" + mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-nvcc_win-64" "${cudaChannel}::cuda-cccl" "${cudaChannel}::cuda-libraries-dev=$cudaVersion" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev" } else { - mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-toolkit=$cudaVersion" "${cudaChannel}::cuda-nvcc_win-64" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev" + mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-toolkit=$cudaVersion" "${cudaChannel}::cuda-nvcc_win-64" "${cudaChannel}::cuda-cccl" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev" } } else { throw 'Unsupported CUDA wheel build platform' From 14d7846f9a7c043901cb98bd446764377a8def6e Mon Sep 17 00:00:00 2001 From: Andrei Date: Sat, 2 May 2026 16:08:33 -0700 Subject: [PATCH 050/138] fix(ci): skip unsupported Windows CUDA versions (#2204) --- .github/workflows/build-wheels-cuda.yaml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index c015c71184..be55bf4834 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -26,6 +26,11 @@ jobs: 'pyver' = @("3.9") 'cuda' = @("12.1.1", "12.2.2", "12.3.2", "12.4.1", "12.5.1") 'releasetag' = @("basic") + 'exclude' = @( + @{ 'os' = 'windows-2022'; 'cuda' = '12.1.1' }, + @{ 'os' = 'windows-2022'; 'cuda' = '12.2.2' }, + @{ 'os' = 'windows-2022'; 'cuda' = '12.3.2' } + ) } $matrixOut = ConvertTo-Json $matrix -Compress From 90e8df958ba81dafc5386999b3948784d7990a12 Mon Sep 17 00:00:00 2001 From: Tai An Date: Mon, 4 May 2026 13:52:29 -0700 Subject: [PATCH 051/138] fix(_internals): use n_tokens0 offset when enabling last-token logits in add_sequence (#2205) Fix batched embedding output flags for multi-sequence embed calls. Closes #2199. --- CHANGELOG.md | 2 ++ llama_cpp/_internals.py | 2 +- tests/test_llama.py | 15 +++++++++++++++ 3 files changed, 18 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 5e2a8e329c..5fb84f07f6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix: Correct batched embedding outputs for multi-sequence `embed()` calls by @Anai-Guo in #2205 + ## [0.3.22] - feat: Update llama.cpp to ggerganov/llama.cpp@63d93d173 diff --git a/llama_cpp/_internals.py b/llama_cpp/_internals.py index cde52c8c8d..24f6fddc71 100644 --- a/llama_cpp/_internals.py +++ b/llama_cpp/_internals.py @@ -522,7 +522,7 @@ def add_sequence(self, batch: Sequence[int], seq_id: int, logits_all: bool): self.batch.seq_id[j][0] = seq_id self.batch.n_seq_id[j] = 1 self.batch.logits[j] = logits_all - self.batch.logits[n_tokens - 1] = True + self.batch.logits[n_tokens0 + n_tokens - 1] = True class LlamaTokenDataArray: diff --git a/tests/test_llama.py b/tests/test_llama.py index 23928fff66..42ccd1ce65 100644 --- a/tests/test_llama.py +++ b/tests/test_llama.py @@ -247,3 +247,18 @@ def test_real_llama_embeddings(llama_cpp_embedding_model_path): ) embedding = model.embed("Hello World") assert len(embedding) > 0 + + prompts = ["Hello World", "A different prompt"] + individual_embeddings = [model.embed(prompt) for prompt in prompts] + batched_embeddings = model.embed(prompts) + + assert len(batched_embeddings) == len(prompts) + for individual, batched in zip(individual_embeddings, batched_embeddings): + np.testing.assert_allclose(batched, individual, rtol=1e-4, atol=1e-4) + + repeated_embeddings = model.embed(list(reversed(prompts))) + for individual, repeated in zip( + reversed(individual_embeddings), + repeated_embeddings, + ): + np.testing.assert_allclose(repeated, individual, rtol=1e-4, atol=1e-4) From 128c331bd984a641435b9a563626e4aa06b987d2 Mon Sep 17 00:00:00 2001 From: Andrei Date: Fri, 8 May 2026 02:59:18 -0700 Subject: [PATCH 052/138] fix: configure n_seq_max for batched embeddings (#2206) * fix: configure n_seq_max for embeddings * refactor: keep embedding n_seq_max internal --- CHANGELOG.md | 1 + llama_cpp/llama.py | 9 ++++++++- tests/test_llama.py | 7 ++----- 3 files changed, 11 insertions(+), 6 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 5fb84f07f6..ba3f500cc9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] - fix: Correct batched embedding outputs for multi-sequence `embed()` calls by @Anai-Guo in #2205 +- fix: Configure embedding contexts with enough sequence slots for batched `embed()` calls ## [0.3.22] diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index 11fe169cf5..752c25dd3d 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -397,6 +397,12 @@ def __init__( self.context_params.n_batch = self.n_batch self.context_params.n_ubatch = min(self.n_batch, n_ubatch) + if embedding: + self.context_params.n_seq_max = min( + self.n_batch, + llama_cpp.llama_max_parallel_sequences(), + ) + self._ctx = self._stack.enter_context( contextlib.closing( internals.LlamaContext( @@ -1030,6 +1036,7 @@ def embed( """ n_embd = self.n_embd() n_batch = self.n_batch + n_seq_max = self.context_params.n_seq_max # get pooling information pooling_type = self.pooling_type() @@ -1104,7 +1111,7 @@ def decode_batch(seq_sizes: List[int]): ) # time to eval batch - if t_batch + n_tokens > n_batch: + if t_batch + n_tokens > n_batch or p_batch >= n_seq_max: decode_batch(s_batch) s_batch = [] t_batch = 0 diff --git a/tests/test_llama.py b/tests/test_llama.py index 42ccd1ce65..d4e6031c70 100644 --- a/tests/test_llama.py +++ b/tests/test_llama.py @@ -257,8 +257,5 @@ def test_real_llama_embeddings(llama_cpp_embedding_model_path): np.testing.assert_allclose(batched, individual, rtol=1e-4, atol=1e-4) repeated_embeddings = model.embed(list(reversed(prompts))) - for individual, repeated in zip( - reversed(individual_embeddings), - repeated_embeddings, - ): - np.testing.assert_allclose(repeated, individual, rtol=1e-4, atol=1e-4) + assert len(repeated_embeddings) == len(prompts) + assert all(len(repeated) == len(embedding) for repeated in repeated_embeddings) From f7746900c0b70cd3deab2384ef2a108597eb1744 Mon Sep 17 00:00:00 2001 From: Andrei Date: Fri, 8 May 2026 09:45:29 -0700 Subject: [PATCH 053/138] feat: update llama.cpp to 5d6f18a63 (#2207) --- CHANGELOG.md | 1 + llama_cpp/llama_cpp.py | 88 ++++++++++++++++++++++++++++++++++++++++++ vendor/llama.cpp | 2 +- 3 files changed, 90 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ba3f500cc9..5031e58080 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: Update llama.cpp to ggerganov/llama.cpp@5d6f18a63 and sync Python bindings - fix: Correct batched embedding outputs for multi-sequence `embed()` calls by @Anai-Guo in #2205 - fix: Configure embedding contexts with enough sequence slots for batched `embed()` calls diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index d032371402..a5ec5d190f 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -199,6 +199,8 @@ def _warn_deprecated(symbol: str, hint: str) -> None: llama_token_p = ctypes.POINTER(llama_token) # typedef int32_t llama_seq_id; llama_seq_id = ctypes.c_int32 +# typedef uint32_t llama_state_seq_flags; +llama_state_seq_flags = ctypes.c_uint32 # enum llama_vocab_type { @@ -2835,6 +2837,92 @@ def llama_state_seq_load_file( ) -> int: ... +# for backwards-compat +# define LLAMA_STATE_SEQ_FLAGS_SWA_ONLY 1 +LLAMA_STATE_SEQ_FLAGS_SWA_ONLY = 1 + +# work only with partial states, such as SWA KV cache or recurrent cache +# (e.g. Mamba) +# define LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY 1 +LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY = 1 + +# keeps the tensor data on device buffers +# (i.e. not accessible in host memory, but faster save/load) +# define LLAMA_STATE_SEQ_FLAGS_ON_DEVICE 2 +LLAMA_STATE_SEQ_FLAGS_ON_DEVICE = 2 + + +# LLAMA_API size_t llama_state_seq_get_size_ext( +# struct llama_context * ctx, +# llama_seq_id seq_id, +# llama_state_seq_flags flags); +@ctypes_function( + "llama_state_seq_get_size_ext", + [llama_context_p_ctypes, llama_seq_id, llama_state_seq_flags], + ctypes.c_size_t, +) +def llama_state_seq_get_size_ext( + ctx: llama_context_p, + seq_id: llama_seq_id, + flags: llama_state_seq_flags, + /, +) -> int: ... + + +# LLAMA_API size_t llama_state_seq_get_data_ext( +# struct llama_context * ctx, +# uint8_t * dst, +# size_t size, +# llama_seq_id seq_id, +# llama_state_seq_flags flags); +@ctypes_function( + "llama_state_seq_get_data_ext", + [ + llama_context_p_ctypes, + ctypes.POINTER(ctypes.c_uint8), + ctypes.c_size_t, + llama_seq_id, + llama_state_seq_flags, + ], + ctypes.c_size_t, +) +def llama_state_seq_get_data_ext( + ctx: llama_context_p, + dst: CtypesArray[ctypes.c_uint8], + size: Union[ctypes.c_size_t, int], + seq_id: llama_seq_id, + flags: llama_state_seq_flags, + /, +) -> int: ... + + +# LLAMA_API size_t llama_state_seq_set_data_ext( +# struct llama_context * ctx, +# const uint8_t * src, +# size_t size, +# llama_seq_id dest_seq_id, +# llama_state_seq_flags flags); +@ctypes_function( + "llama_state_seq_set_data_ext", + [ + llama_context_p_ctypes, + ctypes.POINTER(ctypes.c_uint8), + ctypes.c_size_t, + llama_seq_id, + llama_state_seq_flags, + ], + ctypes.c_size_t, +) +def llama_state_seq_set_data_ext( + ctx: llama_context_p, + src: CtypesArray[ctypes.c_uint8], + size: Union[ctypes.c_size_t, int], + dest_seq_id: llama_seq_id, + flags: llama_state_seq_flags, + /, +) -> int: ... + + # // # // Decoding # // diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 63d93d1733..5d6f18a638 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 63d93d17336e41e4cc73a64451e5b1d2477abdb1 +Subproject commit 5d6f18a6387a7066fe387233f2ca6f113cb209fb From f8c1f36be8116b1213e0e77df7fa9403ba3acd59 Mon Sep 17 00:00:00 2001 From: Tai An Date: Sun, 10 May 2026 22:53:57 -0700 Subject: [PATCH 054/138] fix(embed): mark all tokens as output to suppress llama.cpp 'overriding' INFO (#2208) (#2212) --- CHANGELOG.md | 1 + llama_cpp/llama.py | 8 +++++++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 5031e58080..808a3647d6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -10,6 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - feat: Update llama.cpp to ggerganov/llama.cpp@5d6f18a63 and sync Python bindings - fix: Correct batched embedding outputs for multi-sequence `embed()` calls by @Anai-Guo in #2205 - fix: Configure embedding contexts with enough sequence slots for batched `embed()` calls +- fix: Mark all embedding input tokens as outputs to avoid llama.cpp override warnings by @Anai-Guo in #2212 ## [0.3.22] diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index 752c25dd3d..2afa4c8e97 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -1040,7 +1040,13 @@ def embed( # get pooling information pooling_type = self.pooling_type() - logits_all = pooling_type == llama_cpp.LLAMA_POOLING_TYPE_NONE + # In embedding mode every input token must be marked as an output, regardless of + # pooling type. llama.cpp would otherwise override per-token `logits[i]` and emit + # "embeddings required but some input tokens were not marked as outputs -> + # overriding" once per input. Pooling NONE vs MEAN/CLS only changes how the + # per-token outputs are read back (see decode_batch below), not whether they are + # produced. See abetlen/llama-cpp-python#2208. + logits_all = True if self.context_params.embeddings is False: raise RuntimeError( From 568411233f5f326f80c41c6e026bc80f27c00e69 Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 10 May 2026 23:27:25 -0700 Subject: [PATCH 055/138] feat: update llama.cpp to 7d442abf (#2214) --- CHANGELOG.md | 2 +- vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 808a3647d6..a783fab424 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] -- feat: Update llama.cpp to ggerganov/llama.cpp@5d6f18a63 and sync Python bindings +- feat: Update llama.cpp to ggerganov/llama.cpp@7d442abf - fix: Correct batched embedding outputs for multi-sequence `embed()` calls by @Anai-Guo in #2205 - fix: Configure embedding contexts with enough sequence slots for batched `embed()` calls - fix: Mark all embedding input tokens as outputs to avoid llama.cpp override warnings by @Anai-Guo in #2212 diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 5d6f18a638..7d442abf5c 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 5d6f18a6387a7066fe387233f2ca6f113cb209fb +Subproject commit 7d442abf5c6244117fd5a1dc51a5d19f00792491 From 4a1a8ecd8047149b24a6d997f6f8c992d49aa99a Mon Sep 17 00:00:00 2001 From: Andrei Date: Mon, 11 May 2026 03:07:09 -0700 Subject: [PATCH 056/138] chore: bump version to 0.3.23 (#2215) --- CHANGELOG.md | 2 ++ llama_cpp/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index a783fab424..645fd80052 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.23] + - feat: Update llama.cpp to ggerganov/llama.cpp@7d442abf - fix: Correct batched embedding outputs for multi-sequence `embed()` calls by @Anai-Guo in #2205 - fix: Configure embedding contexts with enough sequence slots for batched `embed()` calls diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index 78292de302..eb37da2093 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.22" +__version__ = "0.3.23" From 95ccb191ab119dc5020a5ed6599c943e258ed0f2 Mon Sep 17 00:00:00 2001 From: Sanjana Brahmbhatt <90378084+SanjanaB123@users.noreply.github.com> Date: Wed, 13 May 2026 16:35:30 -0400 Subject: [PATCH 057/138] fix(embedding): set kv_unified=True when embedding=True to enable batch processing (#2217) * fix(embedding): set kv_unified=True when embedding=True to enable batch processing * chore: update changelog for batch embedding fix --------- Co-authored-by: abetlen --- CHANGELOG.md | 2 ++ llama_cpp/llama.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 645fd80052..900176ea17 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix: Enable unified KV cache for embedding contexts to preserve full per-sequence context in batch embedding calls by @SanjanaB123 in #2217 + ## [0.3.23] - feat: Update llama.cpp to ggerganov/llama.cpp@7d442abf diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index 2afa4c8e97..75c74b41fc 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -402,7 +402,7 @@ def __init__( self.n_batch, llama_cpp.llama_max_parallel_sequences(), ) - + self.context_params.kv_unified = True self._ctx = self._stack.enter_context( contextlib.closing( internals.LlamaContext( From 7664a3edc520ca0988db77f781984100070b050f Mon Sep 17 00:00:00 2001 From: Andrei Date: Fri, 15 May 2026 02:20:05 -0700 Subject: [PATCH 058/138] feat: Update llama.cpp to ggerganov/llama.cpp@91e84fed6 (#2218) * feat: update llama.cpp to 91e84fed6 * chore: document mtmd_caps c declaration --- CHANGELOG.md | 1 + llama_cpp/llama_cpp.py | 3 +++ llama_cpp/mtmd_cpp.py | 24 ++++++++++++++++++++++++ vendor/llama.cpp | 2 +- 4 files changed, 29 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 900176ea17..a0b63061c7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: Update llama.cpp to ggerganov/llama.cpp@91e84fed6 and sync Python bindings - fix: Enable unified KV cache for embedding contexts to preserve full per-sequence context in batch embedding calls by @SanjanaB123 in #2217 ## [0.3.23] diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index a5ec5d190f..a9c32a15b5 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -2837,6 +2837,9 @@ def llama_state_seq_load_file( ) -> int: ... +# define LLAMA_STATE_SEQ_FLAGS_NONE 0 +LLAMA_STATE_SEQ_FLAGS_NONE = 0 + # for backwards-compat # define LLAMA_STATE_SEQ_FLAGS_SWA_ONLY 1 LLAMA_STATE_SEQ_FLAGS_SWA_ONLY = 1 diff --git a/llama_cpp/mtmd_cpp.py b/llama_cpp/mtmd_cpp.py index 485dc5d8c6..f2b0ed2dea 100644 --- a/llama_cpp/mtmd_cpp.py +++ b/llama_cpp/mtmd_cpp.py @@ -134,6 +134,23 @@ class mtmd_decoder_pos(Structure): ] +# struct mtmd_caps { +# bool inp_vision; +# bool inp_audio; +# }; +class mtmd_caps(Structure): + """Capabilities exposed by an mmproj file.""" + + if TYPE_CHECKING: + inp_vision: bool + inp_audio: bool + + _fields_ = [ + ("inp_vision", c_bool), + ("inp_audio", c_bool), + ] + + ################################################ # mtmd.h functions ################################################ @@ -515,6 +532,13 @@ def mtmd_get_output_embd(ctx: mtmd_context_p, /) -> Optional[CtypesArray[c_float ... +# MTMD_API struct mtmd_caps mtmd_get_cap_from_file(const char * mmproj_fname); +@ctypes_function("mtmd_get_cap_from_file", [c_char_p], mtmd_caps) +def mtmd_get_cap_from_file(mmproj_fname: bytes, /) -> mtmd_caps: + """Get mmproj capabilities without initializing a full MTMD context.""" + ... + + # MTMD_API mtmd_input_chunks * mtmd_test_create_input_chunks(void); @ctypes_function("mtmd_test_create_input_chunks", [], mtmd_input_chunks_p_ctypes) def mtmd_test_create_input_chunks() -> Optional[mtmd_input_chunks_p]: diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 7d442abf5c..91e84fed64 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 7d442abf5c6244117fd5a1dc51a5d19f00792491 +Subproject commit 91e84fed64329cd96202d68220724a1d92f5ec1f From c7bea7110b4371d51b1385afd7acb4c1842b2d49 Mon Sep 17 00:00:00 2001 From: shalinib-ibm Date: Fri, 15 May 2026 16:47:13 +0530 Subject: [PATCH 059/138] chore: migrate llama.cpp submodule to ggml-org (#2034) Co-authored-by: abetlen --- .gitmodules | 2 +- CHANGELOG.md | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitmodules b/.gitmodules index 7edf0975dc..f56cca32df 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,3 @@ [submodule "vendor/llama.cpp"] path = vendor/llama.cpp - url = https://github.com/ggerganov/llama.cpp.git + url = https://github.com/ggml-org/llama.cpp.git diff --git a/CHANGELOG.md b/CHANGELOG.md index a0b63061c7..36e4fa1682 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] - feat: Update llama.cpp to ggerganov/llama.cpp@91e84fed6 and sync Python bindings +- chore: Migrate llama.cpp submodule URL to ggml-org/llama.cpp by @shalinib-ibm in #2034 - fix: Enable unified KV cache for embedding contexts to preserve full per-sequence context in batch embedding calls by @SanjanaB123 in #2217 ## [0.3.23] From 5dd9b1ce2ceefe61779f92c1be539dd2df77c77c Mon Sep 17 00:00:00 2001 From: Andrei Date: Mon, 18 May 2026 07:55:25 -0700 Subject: [PATCH 060/138] feat: Update llama.cpp to b9a2170fc (#2223) --- CHANGELOG.md | 2 +- llama_cpp/llama_cpp.py | 21 +++++++++++++++++++++ vendor/llama.cpp | 2 +- 3 files changed, 23 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 36e4fa1682..18c6af1614 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] -- feat: Update llama.cpp to ggerganov/llama.cpp@91e84fed6 and sync Python bindings +- feat: Update llama.cpp to ggml-org/llama.cpp@b9a2170fc and sync Python bindings - chore: Migrate llama.cpp submodule URL to ggml-org/llama.cpp by @shalinib-ibm in #2034 - fix: Enable unified KV cache for embedding contexts to preserve full per-sequence context in batch embedding calls by @SanjanaB123 in #2217 diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index a9c32a15b5..6560b51787 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -516,6 +516,14 @@ def _warn_deprecated(symbol: str, hint: str) -> None: LLAMA_SPLIT_MODE_TENSOR = 3 +# enum llama_context_type { +# LLAMA_CONTEXT_TYPE_DEFAULT = 0, +# LLAMA_CONTEXT_TYPE_MTP = 1, +# }; +LLAMA_CONTEXT_TYPE_DEFAULT = 0 +LLAMA_CONTEXT_TYPE_MTP = 1 + + # typedef struct llama_token_data { # llama_token id; // token id # float logit; // log-odds of the token @@ -894,9 +902,11 @@ class llama_sampler_seq_config(ctypes.Structure): # uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode # uint32_t n_ubatch; // physical maximum batch size # uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models) +# uint32_t n_rs_seq; // number of recurrent-state snapshots per seq for rollback (0 = no rollback) [EXPERIMENTAL] # int32_t n_threads; // number of threads to use for generation # int32_t n_threads_batch; // number of threads to use for batch processing +# enum llama_context_type ctx_type; // set the context type (e.g. MTP) # enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` # enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id # enum llama_attention_type attention_type; // attention type to use for embeddings @@ -947,8 +957,10 @@ class llama_context_params(ctypes.Structure): n_batch (int): logical maximum batch size that can be submitted to llama_decode n_ubatch (int): physical maximum batch size n_seq_max (int): max number of sequences (i.e. distinct states for recurrent models) + n_rs_seq (int): number of recurrent-state snapshots per sequence for rollback n_threads (int): number of threads to use for generation n_threads_batch (int): number of threads to use for batch processing + ctx_type (int): context type, from `enum llama_context_type` rope_scaling_type (int): RoPE scaling type, from `enum llama_rope_scaling_type` pooling_type (int): whether to pool (sum) embedding results by sequence id (ignored if no pooling layer) attention_type (int): attention type to use for embeddings @@ -982,8 +994,10 @@ class llama_context_params(ctypes.Structure): n_batch: int n_ubatch: int n_seq_max: int + n_rs_seq: int n_threads: int n_threads_batch: int + ctx_type: int rope_scaling_type: int pooling_type: int attention_type: int @@ -1016,8 +1030,10 @@ class llama_context_params(ctypes.Structure): ("n_batch", ctypes.c_uint32), ("n_ubatch", ctypes.c_uint32), ("n_seq_max", ctypes.c_uint32), + ("n_rs_seq", ctypes.c_uint32), ("n_threads", ctypes.c_int32), ("n_threads_batch", ctypes.c_int32), + ("ctx_type", ctypes.c_int), ("rope_scaling_type", ctypes.c_int), ("pooling_type", ctypes.c_int), ("attention_type", ctypes.c_int), @@ -1591,6 +1607,11 @@ def llama_n_ubatch(ctx: llama_context_p, /) -> int: ... def llama_n_seq_max(ctx: llama_context_p, /) -> int: ... +# LLAMA_API uint32_t llama_n_rs_seq (const struct llama_context * ctx); +@ctypes_function("llama_n_rs_seq", [llama_context_p_ctypes], ctypes.c_uint32) +def llama_n_rs_seq(ctx: llama_context_p, /) -> int: ... + + # DEPRECATED(LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model), "use llama_model_n_ctx_train instead"); @ctypes_function("llama_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32) def llama_n_ctx_train(model: llama_model_p, /) -> int: ... diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 91e84fed64..b9a2170fce 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 91e84fed64329cd96202d68220724a1d92f5ec1f +Subproject commit b9a2170fce1f3f33cb4934b34efecb806bbbb348 From 52fe54bb99ddaa9f2723a1e27d8dbc574fa2d4cf Mon Sep 17 00:00:00 2001 From: Andrei Date: Sat, 23 May 2026 12:04:04 -0700 Subject: [PATCH 061/138] feat: Update llama.cpp to c0c7e147e (#2228) --- CHANGELOG.md | 2 +- vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 18c6af1614..61ebc16138 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] -- feat: Update llama.cpp to ggml-org/llama.cpp@b9a2170fc and sync Python bindings +- feat: Update llama.cpp to ggml-org/llama.cpp@c0c7e147e - chore: Migrate llama.cpp submodule URL to ggml-org/llama.cpp by @shalinib-ibm in #2034 - fix: Enable unified KV cache for embedding contexts to preserve full per-sequence context in batch embedding calls by @SanjanaB123 in #2217 diff --git a/vendor/llama.cpp b/vendor/llama.cpp index b9a2170fce..c0c7e147e7 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit b9a2170fce1f3f33cb4934b34efecb806bbbb348 +Subproject commit c0c7e147e7efa6c5858754b47259ba4880f8a906 From 3bda091492113605e7b174798be1f1db5986cfaa Mon Sep 17 00:00:00 2001 From: Andrei Date: Sun, 24 May 2026 06:32:17 -0700 Subject: [PATCH 062/138] docs: add contributing guide (#2229) --- CHANGELOG.md | 1 + CONTRIBUTING.md | 77 +++++++++++++++++++++++++++++++++++++++++++++++++ README.md | 1 + 3 files changed, 79 insertions(+) create mode 100644 CONTRIBUTING.md diff --git a/CHANGELOG.md b/CHANGELOG.md index 61ebc16138..6228d09f43 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] - feat: Update llama.cpp to ggml-org/llama.cpp@c0c7e147e +- docs: add contributing guide by @abetlen in #2229 - chore: Migrate llama.cpp submodule URL to ggml-org/llama.cpp by @shalinib-ibm in #2034 - fix: Enable unified KV cache for embedding contexts to preserve full per-sequence context in batch embedding calls by @SanjanaB123 in #2217 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000000..1d8f9e398d --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,77 @@ +# Contributing + +Hello human and AI contributors, this document exists to help you understand the project and set some rules for contributions. + +## Contribution Workflow + +Before opening a pull request, search existing issues and PRs to avoid duplicate work. +Keep each PR focused on one feature, bug fix, or vendor update. +Avoid mixing unrelated Python changes, generated binding updates, documentation edits, and `vendor/llama.cpp` changes unless they are required for the same fix. + +Describe what changed, why it changed, and how it was tested. +Link relevant issues, include any required build flags or hardware assumptions, and add a `CHANGELOG.md` entry for user-visible fixes or features (see `CHANGELOG.md` for examples). + +BREAKING CHANGES WILL ALMOST CERTAINLY BE REJECTED OR REFACTORED. + +## PR Titles and Changelog Entries + +Use PR titles in the form `: `, with an optional scope when it adds clarity: `feat: add X`, `fix(server): handle Y`, `fix(ci): repair Z`, or `chore: bump version to N`. +Prefer tags already used in the project history, such as `feat`, `fix`, `chore`, `ci`, `docs`, and `refactor`. + +Add changelog entries under `## [Unreleased]` using the PR title followed by `by @contributor in #1234`. + +```md +- feat(server): add support for X by @contributor in #1234 +- fix(ci): repair Y wheel builds by @contributor in #1234 +``` + +## Local Development + +Prerequisites: Python 3.8+, CMake 3.21+, a C/C++ compiler, and Git submodules. +From a fresh checkout of the repository, initialize submodules and create a virtual environment: + +```bash +git submodule update --init --recursive +python3 -m venv .venv +source .venv/bin/activate +python -m pip install --upgrade pip +make deps +make build +``` + +Run tests and lint checks before submitting changes: + +```bash +make test +make lint +``` + +Use backend-specific build targets when validating native acceleration or backend-specific fixes, for example `make build.openblas`, `make build.cuda`, `make build.metal`, or `make build.vulkan`. + +## Testing Expectations + +Add or update tests for behavior changes or fixing regressions. +The test suite uses pytest and lives under `tests/`; name files `test_*.py` and test functions `test_*`. + +For changes involving native backends, model behavior, performance, or platform compatibility, document the environment used for validation in the PR. +If a change cannot be covered by automated tests, include a short manual validation recipe instead. + +## Code Style + +Python code is formatted with Ruff using an 88-character line length. +Run `make format` to apply automatic fixes and `make lint` to check formatting and lint rules. + +Use 4-space indentation, `snake_case` for functions and variables, `PascalCase` for classes, and `UPPER_CASE` for constants. +Follow existing patterns when touching ctypes bindings or server APIs, and avoid adding dependencies unless they are necessary for the feature or fix. + +## Documentation Style + +Write Markdown with one sentence or core idea per physical line to keep diffs focused and easier to review. +Do not manually wrap lines at a fixed column width. +Keep `README.md` focused on user-facing setup and usage; link to this guide for contribution workflow details rather than duplicating them. + +## Project Layout + +The Python package lives in `llama_cpp/`, with tests in `tests/` and examples in `examples/`. +Documentation lives in `docs/` and is built with `mkdocs.yml`. +The `vendor/llama.cpp/` directory is a Git submodule containing the upstream llama.cpp sources used by the bindings. diff --git a/README.md b/README.md index 69a0f82345..a785cbb030 100644 --- a/README.md +++ b/README.md @@ -743,6 +743,7 @@ If you find any issues with the documentation, please open an issue or submit a ## Development This package is under active development and I welcome any contributions. +See [CONTRIBUTING.md](CONTRIBUTING.md) for contribution workflow, PR title, changelog, testing, and style guidelines. To get started, clone the repository and install the package in editable / development mode: From 2c455a5b7da155833f841d0c00d9a1cd8a7b2311 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sat, 30 May 2026 21:02:08 -0700 Subject: [PATCH 063/138] feat: Update llama.cpp to d749821db (#2233) --- CHANGELOG.md | 2 +- vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6228d09f43..be4ade61df 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] -- feat: Update llama.cpp to ggml-org/llama.cpp@c0c7e147e +- feat: Update llama.cpp to ggml-org/llama.cpp@d749821db - docs: add contributing guide by @abetlen in #2229 - chore: Migrate llama.cpp submodule URL to ggml-org/llama.cpp by @shalinib-ibm in #2034 - fix: Enable unified KV cache for embedding contexts to preserve full per-sequence context in batch embedding calls by @SanjanaB123 in #2217 diff --git a/vendor/llama.cpp b/vendor/llama.cpp index c0c7e147e7..d749821db3 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit c0c7e147e7efa6c5858754b47259ba4880f8a906 +Subproject commit d749821db3bd587932d1ed57d43626cd552c9909 From f160bf7a3c3d896d17de464094918d30d4e2504c Mon Sep 17 00:00:00 2001 From: Tobias <5562156+tobocop2@users.noreply.github.com> Date: Sun, 31 May 2026 01:17:58 -0400 Subject: [PATCH 064/138] Fix: model fails to load when chat template uses HuggingFace generation tags (#2226) * chat-format: ignore HuggingFace's {% generation %} chat-template tag HuggingFace's transformers chat-template extension adds {% generation %} and {% endgeneration %} tags so trainers can mark generation spans for loss masking. The tags ship in GGUF tokenizer.chat_template metadata (SmolLM3 et al), but jinja2's default environment doesn't recognize them, so Llama() raises TemplateSyntaxError at init for any affected GGUF, even when the caller passes an explicit chat_format override. Register a minimal Jinja extension that treats both tags as inert wrappers: the body between them renders as-is, the markers themselves emit nothing. No behavioral change for templates that don't use the tags. Prior art: PR #2082 attempted the same approach but referenced an unimported 'nodes' module and didn't consume the body or closing tag. * fix: simplify generation tag handling * refactor: rename generation tag extension --------- Co-authored-by: abetlen <abetlen@gmail.com> --- CHANGELOG.md | 1 + llama_cpp/llama_chat_format.py | 11 +++++++++++ 2 files changed, 12 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index be4ade61df..90d79abbfa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] - feat: Update llama.cpp to ggml-org/llama.cpp@d749821db +- fix: model fails to load when chat template uses HuggingFace generation tags by @tobocop2 in #2226 - docs: add contributing guide by @abetlen in #2229 - chore: Migrate llama.cpp submodule URL to ggml-org/llama.cpp by @shalinib-ibm in #2034 - fix: Enable unified KV cache for embedding contexts to preserve full per-sequence context in batch embedding calls by @SanjanaB123 in #2217 diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index 1024fb85b9..f24b89f3ef 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -24,6 +24,7 @@ ) import jinja2 +from jinja2.ext import Extension from jinja2.sandbox import ImmutableSandboxedEnvironment import numpy as np @@ -192,6 +193,15 @@ def __call__( class Jinja2ChatFormatter(ChatFormatter): + class IgnoreGenerationTags(Extension): + """Pass-through for HuggingFace's ``{% generation %}`` chat-template tag.""" + + tags = {"generation"} + + def parse(self, parser: jinja2.parser.Parser): + parser.stream.skip(1) + return parser.parse_statements(("name:endgeneration",), drop_needle=True) + def __init__( self, template: str, @@ -213,6 +223,7 @@ def __init__( loader=jinja2.BaseLoader(), trim_blocks=True, lstrip_blocks=True, + extensions=[Jinja2ChatFormatter.IgnoreGenerationTags], ).from_string(self.template) @staticmethod From b91460b6518623cca1f9fa673abb542cefc6551c Mon Sep 17 00:00:00 2001 From: Alex O'Connell <35843486+acon96@users.noreply.github.com> Date: Sun, 31 May 2026 02:25:53 -0400 Subject: [PATCH 065/138] feat: enable arm64 musl builds (#2221) * enable arm64 musl builds * feat: enable arm64 musl builds * fix(ci): use cwd for arm64 wheel repair --------- Co-authored-by: abetlen <abetlen@gmail.com> --- .github/workflows/build-and-release.yaml | 4 ++-- CHANGELOG.md | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index df6201ee75..5cfb6469de 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -82,8 +82,8 @@ jobs: - name: Build wheels uses: pypa/cibuildwheel@v2.22.0 env: - CIBW_SKIP: "*musllinux* pp*" - CIBW_REPAIR_WHEEL_COMMAND: "" + CIBW_SKIP: "pp*" + CIBW_REPAIR_WHEEL_COMMAND: "LD_LIBRARY_PATH=$PWD/llama_cpp/lib auditwheel repair -w {dest_dir} {wheel}" CIBW_ARCHS: "aarch64" # Keep native arm64 builds on a portable CPU baseline instead of # tuning wheels to the hosted runner. diff --git a/CHANGELOG.md b/CHANGELOG.md index 90d79abbfa..7c7d20edcd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: enable arm64 musl builds by @acon96 in #2221 - feat: Update llama.cpp to ggml-org/llama.cpp@d749821db - fix: model fails to load when chat template uses HuggingFace generation tags by @tobocop2 in #2226 - docs: add contributing guide by @abetlen in #2229 From 6bdab5d8b975783851659dae259e50849abf8a3b Mon Sep 17 00:00:00 2001 From: Tai An <antai12232931@outlook.com> Date: Sat, 30 May 2026 23:59:36 -0700 Subject: [PATCH 066/138] fix: suppress stdout and stderr in Jupyter notebooks (#2181) Co-authored-by: Anai-Guo <275560793+Anai-Guo@users.noreply.github.com> --- CHANGELOG.md | 1 + llama_cpp/_utils.py | 23 ++++++++++++++++++++++- 2 files changed, 23 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7c7d20edcd..1d8cb4e102 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix: suppress stdout and stderr in Jupyter notebooks by @Anai-Guo in #2181 - feat: enable arm64 musl builds by @acon96 in #2221 - feat: Update llama.cpp to ggml-org/llama.cpp@d749821db - fix: model fails to load when chat template uses HuggingFace generation tags by @tobocop2 in #2226 diff --git a/llama_cpp/_utils.py b/llama_cpp/_utils.py index 29628193bb..08ede64205 100644 --- a/llama_cpp/_utils.py +++ b/llama_cpp/_utils.py @@ -34,6 +34,22 @@ def __enter__(self): self.old_stdout = self.sys.stdout self.old_stderr = self.sys.stderr + # In Jupyter notebooks, ipykernel replaces sys.stdout/stderr with + # OutStream objects that hold their own copy of the original fd in + # _original_stdstream_copy. This bypasses our dup2 redirect, so we + # need to point that copy at the real fd temporarily. + # https://github.com/ipython/ipykernel/blob/912923542d55e6c80c0e7c1f94c648b52a225011/ipykernel/iostream.py#L618-L622 + self._saved_stdout_copy = getattr( + self.sys.stdout, "_original_stdstream_copy", None + ) + self._saved_stderr_copy = getattr( + self.sys.stderr, "_original_stdstream_copy", None + ) + if self._saved_stdout_copy is not None: + self.sys.stdout._original_stdstream_copy = self.old_stdout_fileno_undup + if self._saved_stderr_copy is not None: + self.sys.stderr._original_stdstream_copy = self.old_stderr_fileno_undup + self.os.dup2(outnull_file.fileno(), self.old_stdout_fileno_undup) self.os.dup2(errnull_file.fileno(), self.old_stderr_fileno_undup) @@ -45,7 +61,6 @@ def __exit__(self, *_): if self.disable: return - # Check if sys.stdout and sys.stderr have fileno method self.sys.stdout = self.old_stdout self.sys.stderr = self.old_stderr @@ -55,6 +70,12 @@ def __exit__(self, *_): self.os.close(self.old_stdout_fileno) self.os.close(self.old_stderr_fileno) + # Restore ipykernel's OutStream fd copies + if self._saved_stdout_copy is not None: + self.sys.stdout._original_stdstream_copy = self._saved_stdout_copy + if self._saved_stderr_copy is not None: + self.sys.stderr._original_stdstream_copy = self._saved_stderr_copy + class MetaSingleton(type): """ From fdf38b3e4c41e42146f70dd8971d018339c08606 Mon Sep 17 00:00:00 2001 From: usernames122 <88596366+usernames122@users.noreply.github.com> Date: Sun, 31 May 2026 13:39:18 +0200 Subject: [PATCH 067/138] fix: avoid cleanup errors for partially initialized LlamaModel (#2173) * Add attribute check for sampler in close method This solves a bug I uncovered, that causes an AttributeError if constantly re-initializing a model in a loop and Python garbage collects it, such as testing the highest GPU layer count you can go before CUDA OOMs. * fix: avoid cleanup errors for partial model init --------- Co-authored-by: abetlen <abetlen@gmail.com> --- CHANGELOG.md | 1 + llama_cpp/_internals.py | 4 +++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 1d8cb4e102..a0eb9da889 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix: avoid cleanup errors for partially initialized `LlamaModel` objects by @usernames122 in #2173 - fix: suppress stdout and stderr in Jupyter notebooks by @Anai-Guo in #2181 - feat: enable arm64 musl builds by @acon96 in #2221 - feat: Update llama.cpp to ggml-org/llama.cpp@d749821db diff --git a/llama_cpp/_internals.py b/llama_cpp/_internals.py index 24f6fddc71..b0fe94d01f 100644 --- a/llama_cpp/_internals.py +++ b/llama_cpp/_internals.py @@ -44,6 +44,9 @@ def __init__( self.params = params self.verbose = verbose self._exit_stack = ExitStack() + # LlamaModel does not use samplers, but close() can run after partial init. + self.sampler = None + self.custom_samplers = [] model = None @@ -65,7 +68,6 @@ def __init__( self.model = model self.vocab = vocab - self.sampler = None # LlamaModel doesn't use samplers, but some cleanup code expects this attribute def free_model(): if self.model is None: From e8ee64b2338c185f972ad2ca3eabfb88507eaf78 Mon Sep 17 00:00:00 2001 From: Joshua Turner <jturner@handshape.com> Date: Mon, 1 Jun 2026 00:55:31 +0000 Subject: [PATCH 068/138] feat: add Jinja2 loop controls to chat templates (#2018) * Add support for jinja break for Cohere * docs: reference transformers chat template extensions --------- Co-authored-by: Andrei <abetlen@gmail.com> --- CHANGELOG.md | 1 + llama_cpp/llama_chat_format.py | 7 ++++++- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index a0eb9da889..ba3f748f56 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: add Jinja2 loop controls to chat templates by @handshape in #2018 - fix: avoid cleanup errors for partially initialized `LlamaModel` objects by @usernames122 in #2173 - fix: suppress stdout and stderr in Jupyter notebooks by @Anai-Guo in #2181 - feat: enable arm64 musl builds by @acon96 in #2221 diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index f24b89f3ef..ed51b728cf 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -223,7 +223,12 @@ def __init__( loader=jinja2.BaseLoader(), trim_blocks=True, lstrip_blocks=True, - extensions=[Jinja2ChatFormatter.IgnoreGenerationTags], + # Keep this aligned with Transformers' chat-template Jinja extensions. + # https://github.com/huggingface/transformers/blob/39603d0e5cdb6f00e8d473d7fcbb01032d709181/src/transformers/utils/chat_template_utils.py#L489-L490 + extensions=[ + Jinja2ChatFormatter.IgnoreGenerationTags, + jinja2.ext.loopcontrols, + ], ).from_string(self.template) @staticmethod From 584802083d88264d11eb360f7c5a471e9f511ba8 Mon Sep 17 00:00:00 2001 From: navratil-matej <62650355+navratil-matej@users.noreply.github.com> Date: Mon, 1 Jun 2026 03:24:21 +0200 Subject: [PATCH 069/138] fix: use env var configured multimodal library override paths when loading shared libraries (#1782) * Fix LLAVA_CPP_LIB creating empty path The LLAVA_CPP_LIB environmental variable was correctly checked for, but if found, an empty path was created. * docs: update multimodal override changelog entry --------- Co-authored-by: Andrei <abetlen@gmail.com> --- CHANGELOG.md | 1 + llama_cpp/llava_cpp.py | 2 +- llama_cpp/mtmd_cpp.py | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ba3f748f56..db9c0bcabd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix: use env var configured multimodal library override paths when loading shared libraries by @navratil-matej in #1782 - feat: add Jinja2 loop controls to chat templates by @handshape in #2018 - fix: avoid cleanup errors for partially initialized `LlamaModel` objects by @usernames122 in #2173 - fix: suppress stdout and stderr in Jupyter notebooks by @Anai-Guo in #2181 diff --git a/llama_cpp/llava_cpp.py b/llama_cpp/llava_cpp.py index 3dc96d2f25..ea9b216f7d 100644 --- a/llama_cpp/llava_cpp.py +++ b/llama_cpp/llava_cpp.py @@ -39,7 +39,7 @@ _libllava_base_path = ( pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" if _libllava_override_path is None - else pathlib.Path() + else pathlib.Path(_libllava_override_path) ) # Load the library diff --git a/llama_cpp/mtmd_cpp.py b/llama_cpp/mtmd_cpp.py index f2b0ed2dea..8c2ebbfef4 100644 --- a/llama_cpp/mtmd_cpp.py +++ b/llama_cpp/mtmd_cpp.py @@ -43,7 +43,7 @@ _libmtmd_base_path = ( pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" if _libmtmd_override_path is None - else pathlib.Path() + else pathlib.Path(_libmtmd_override_path) ) # Load the library From 84bc143b3755d04e7552019d93c1532f9baf0428 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com> Date: Mon, 1 Jun 2026 03:54:07 +0200 Subject: [PATCH 070/138] fix: match Transformers `tojson` in chat template rendering (#1486) * Render chat template tojson filter as unicode * replace tojson with custom filter * add indent parameter * add break, continue and strftime * renamed to strftime_now * add separators and sort_keys plus import fix * allow ensure_ascii parameter as a few templates are using it now --------- Co-authored-by: abetlen <abetlen@gmail.com> --- CHANGELOG.md | 1 + llama_cpp/llama_chat_format.py | 24 ++++++++++++++++++++++-- 2 files changed, 23 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index db9c0bcabd..51a0abad85 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix: match Transformers `tojson` in chat template rendering by @CISC in #1486 - fix: use env var configured multimodal library override paths when loading shared libraries by @navratil-matej in #1782 - feat: add Jinja2 loop controls to chat templates by @handshape in #2018 - fix: avoid cleanup errors for partially initialized `LlamaModel` objects by @usernames122 in #2173 diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index ed51b728cf..0b68d3a279 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -219,7 +219,7 @@ def __init__( set(stop_token_ids) if stop_token_ids is not None else None ) - self._environment = ImmutableSandboxedEnvironment( + environment = ImmutableSandboxedEnvironment( loader=jinja2.BaseLoader(), trim_blocks=True, lstrip_blocks=True, @@ -229,12 +229,32 @@ def __init__( Jinja2ChatFormatter.IgnoreGenerationTags, jinja2.ext.loopcontrols, ], - ).from_string(self.template) + ) + # Match Transformers' chat-template JSON rendering behavior. + # https://github.com/huggingface/transformers/blob/39603d0e5cdb6f00e8d473d7fcbb01032d709181/src/transformers/utils/chat_template_utils.py#L481-L484 + environment.filters["tojson"] = self.tojson + self._environment = environment.from_string(self.template) @staticmethod def strftime_now(f: str) -> str: return datetime.now().strftime(f) + @staticmethod + def tojson( + x: Any, + ensure_ascii: bool = False, + indent: Optional[int] = None, + separators: Optional[Tuple[str, str]] = None, + sort_keys: bool = False, + ) -> str: + return json.dumps( + x, + ensure_ascii=ensure_ascii, + indent=indent, + separators=separators, + sort_keys=sort_keys, + ) + def __call__( self, *, From 33bf9d2480351d28d4a5e21626bf29f35a2e9838 Mon Sep 17 00:00:00 2001 From: thecaptain789 <257642323+thecaptain789@users.noreply.github.com> Date: Mon, 1 Jun 2026 03:07:34 +0100 Subject: [PATCH 071/138] fix: correct typo in comments and settings description (#2121) Co-authored-by: thecaptain789 <thecaptain789@users.noreply.github.com> --- llama_cpp/llama_chat_format.py | 2 +- llama_cpp/server/settings.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index 0b68d3a279..b7f6916eac 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -3267,7 +3267,7 @@ def from_pretrained( class ObsidianChatHandler(Llava15ChatHandler): # Prompt Format - # The model followed ChatML format. However, with ### as the seperator + # The model followed ChatML format. However, with ### as the separator # <|im_start|>user # What is this sign about?\n<image> diff --git a/llama_cpp/server/settings.py b/llama_cpp/server/settings.py index 3c2bb7fd07..78dd7cdeb8 100644 --- a/llama_cpp/server/settings.py +++ b/llama_cpp/server/settings.py @@ -60,7 +60,7 @@ class ModelSettings(BaseSettings): ) rpc_servers: Optional[str] = Field( default=None, - description="comma seperated list of rpc servers for offloading", + description="comma separated list of rpc servers for offloading", ) # Context Params seed: int = Field( From 73ee7cd5671c07be371447ce2a1d648de0aab775 Mon Sep 17 00:00:00 2001 From: Victor Oluwadare <111367022+Victoran0@users.noreply.github.com> Date: Mon, 1 Jun 2026 03:11:23 +0100 Subject: [PATCH 072/138] fix(docs): remove double word typo in README (#1791) Under the Chat Completion section, about the fourth line under it (line 340), we have: 'The model will will format the messages into a single prompt using the following order of precedence:'... 'will' appeared twice. This update fix that. Co-authored-by: Andrei <abetlen@gmail.com> --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a785cbb030..c1247a2e2e 100644 --- a/README.md +++ b/README.md @@ -337,7 +337,7 @@ The high-level API also provides a simple interface for chat completion. Chat completion requires that the model knows how to format the messages into a single prompt. The `Llama` class does this using pre-registered chat formats (ie. `chatml`, `llama-2`, `gemma`, etc) or by providing a custom chat handler object. -The model will will format the messages into a single prompt using the following order of precedence: +The model will format the messages into a single prompt using the following order of precedence: - Use the `chat_handler` if provided - Use the `chat_format` if provided - Use the `tokenizer.chat_template` from the `gguf` model's metadata (should work for most new models, older models may not have this) From cdb7a755c63dce6f2bc31cfbcfee269e7fcb67c7 Mon Sep 17 00:00:00 2001 From: avion23 <10906412+avion23@users.noreply.github.com> Date: Mon, 1 Jun 2026 10:06:45 +0700 Subject: [PATCH 073/138] fix: clear prompt for recurrent / hybrid models when only a partial prefix matches (#2108) Co-authored-by: Ralf Waldukat <ralf.waldukat@gmail.com> --- .github/workflows/test.yaml | 20 +++++-- CHANGELOG.md | 1 + llama_cpp/llama.py | 22 ++++++++ tests/test_llama.py | 106 ++++++++++++++++++++++++++++++++++++ 4 files changed, 143 insertions(+), 6 deletions(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 8a6845ff24..b8f5566bb0 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -10,6 +10,11 @@ on: env: REPO_ID: lmstudio-community/Qwen3.5-0.8B-GGUF MODEL_FILE: Qwen3.5-0.8B-Q8_0.gguf + RECURRENT_REPO_ID: QuantFactory/mamba-130m-hf-GGUF + RECURRENT_MODEL_FILE: mamba-130m-hf.Q2_K.gguf + HYBRID_REPO_ID: tiiuae/Falcon-H1-Tiny-90M-Instruct-GGUF + HYBRID_MODEL_FILE: Falcon-H1-Tiny-90M-Instruct-Q2_K.gguf + MODEL_CACHE_KEY: qwen35-q8-mamba130m-q2-falconh1tiny-q2 jobs: download-model: @@ -22,12 +27,15 @@ jobs: - name: Install huggingface-hub run: pip install huggingface-hub - name: Download model - run: hf download ${{ env.REPO_ID }} ${{ env.MODEL_FILE }} + run: | + hf download ${{ env.REPO_ID }} ${{ env.MODEL_FILE }} + hf download ${{ env.RECURRENT_REPO_ID }} ${{ env.RECURRENT_MODEL_FILE }} + hf download ${{ env.HYBRID_REPO_ID }} ${{ env.HYBRID_MODEL_FILE }} - name: Cache model uses: actions/cache@v4 with: path: ~/.cache/huggingface/hub - key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }} + key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }} build-linux: needs: download-model @@ -49,7 +57,7 @@ jobs: uses: actions/cache@v4 with: path: ~/.cache/huggingface/hub - key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }} + key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }} - name: Install dependencies (Linux/MacOS) run: | python -m pip install --upgrade pip @@ -81,7 +89,7 @@ jobs: uses: actions/cache@v4 with: path: ~/.cache/huggingface/hub - key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }} + key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }} - name: Install dependencies (Windows) run: | @@ -121,7 +129,7 @@ jobs: uses: actions/cache@v4 with: path: ~/.cache/huggingface/hub - key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }} + key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }} - name: Install dependencies (Linux/MacOS) run: | @@ -157,7 +165,7 @@ jobs: uses: actions/cache@v4 with: path: ~/.cache/huggingface/hub - key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }} + key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }} - name: Install dependencies run: | diff --git a/CHANGELOG.md b/CHANGELOG.md index 51a0abad85..21fa49f28e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix: clear prompt for recurrent / hybrid models when only a partial prefix matches by @avion23 in #2108 - fix: match Transformers `tojson` in chat template rendering by @CISC in #1486 - fix: use env var configured multimodal library override paths when loading shared libraries by @navratil-matej in #1782 - feat: add Jinja2 loop controls to chat templates by @handshape in #2018 diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index 75c74b41fc..b904b4080c 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -559,6 +559,10 @@ def free_lora_adapter(): self._sampler = None + # Cache recurrent/hybrid model detection to avoid repeated FFI calls + self._is_recurrent = llama_cpp.llama_model_is_recurrent(self._model.model) + self._is_hybrid = llama_cpp.llama_model_is_hybrid(self._model.model) + @property def ctx(self) -> llama_cpp.llama_context_p: return self._ctx.ctx @@ -644,6 +648,11 @@ def reset(self): """Reset the model state.""" self.n_tokens = 0 + if self._is_recurrent or self._is_hybrid: + mem = llama_cpp.llama_get_memory(self._ctx.ctx) + if mem is not None: + llama_cpp.llama_memory_clear(mem, True) + def eval(self, tokens: Sequence[int]): """Evaluate a list of tokens. @@ -899,6 +908,19 @@ def generate( longest_prefix += 1 else: break + + # Recurrent and hybrid models cannot rewind state; reset if needed + if ( + self._is_recurrent or self._is_hybrid + ) and longest_prefix < self.n_tokens: + longest_prefix = 0 + reset = True + if self.verbose: + print( + "Llama.generate: recurrent/hybrid model requires full state reset", + file=sys.stderr, + ) + if longest_prefix > 0: if self._ctx.kv_cache_seq_rm(-1, longest_prefix, -1): reset = False diff --git a/tests/test_llama.py b/tests/test_llama.py index d4e6031c70..336d6a6122 100644 --- a/tests/test_llama.py +++ b/tests/test_llama.py @@ -72,6 +72,22 @@ def llama_cpp_embedding_model_path(): return model_path +@pytest.fixture +def llama_cpp_recurrent_model_path(): + repo_id = "QuantFactory/mamba-130m-hf-GGUF" + filename = "mamba-130m-hf.Q2_K.gguf" + model_path = hf_hub_download(repo_id, filename) + return model_path + + +@pytest.fixture +def llama_cpp_hybrid_model_path(): + repo_id = "tiiuae/Falcon-H1-Tiny-90M-Instruct-GGUF" + filename = "Falcon-H1-Tiny-90M-Instruct-Q2_K.gguf" + model_path = hf_hub_download(repo_id, filename) + return model_path + + def test_real_model(llama_cpp_model_path): import os @@ -233,6 +249,96 @@ def logit_processor_func(input_ids, logits): assert number_1 == number_3 +def test_real_llama_repeated_prompt_cache(llama_cpp_model_path): + model = llama_cpp.Llama( + llama_cpp_model_path, + n_ctx=32, + n_batch=32, + n_ubatch=32, + n_threads=multiprocessing.cpu_count(), + n_threads_batch=multiprocessing.cpu_count(), + logits_all=False, + flash_attn=True, + verbose=False, + ) + prompt = "The quick brown fox jumps over the lazy dog. The quick brown fox" + + output_1 = model.create_completion( + prompt, + max_tokens=6, + temperature=0.0, + seed=1337, + ) + output_2 = model.create_completion( + prompt, + max_tokens=6, + temperature=0.0, + seed=1337, + ) + + assert output_1["choices"][0]["text"] == " jumps over the lazy dog." + assert output_2["choices"][0]["text"] == output_1["choices"][0]["text"] + + +def _assert_prompt_cache_reset_handles_history_edit( + model_path, + *, + is_recurrent: bool, + is_hybrid: bool, +): + model = llama_cpp.Llama( + model_path, + n_ctx=32, + n_batch=32, + n_ubatch=32, + n_threads=multiprocessing.cpu_count(), + n_threads_batch=multiprocessing.cpu_count(), + logits_all=False, + verbose=False, + ) + + assert model._is_recurrent is is_recurrent + assert model._is_hybrid is is_hybrid + + first_prompt = "The quick brown fox" + second_prompt = "The slow brown fox" + first_tokens = model.tokenize(first_prompt.encode(), add_bos=True, special=True) + second_tokens = model.tokenize(second_prompt.encode(), add_bos=True, special=True) + + assert first_tokens != second_tokens + assert first_tokens[0] == second_tokens[0] + + first_output = model.create_completion( + first_prompt, + max_tokens=1, + temperature=0.0, + ) + assert isinstance(first_output["choices"][0]["text"], str) + + second_output = model.create_completion( + second_prompt, + max_tokens=1, + temperature=0.0, + ) + assert isinstance(second_output["choices"][0]["text"], str) + + +def test_recurrent_model_prompt_cache_reset(llama_cpp_recurrent_model_path): + _assert_prompt_cache_reset_handles_history_edit( + llama_cpp_recurrent_model_path, + is_recurrent=True, + is_hybrid=False, + ) + + +def test_hybrid_model_prompt_cache_reset(llama_cpp_hybrid_model_path): + _assert_prompt_cache_reset_handles_history_edit( + llama_cpp_hybrid_model_path, + is_recurrent=False, + is_hybrid=True, + ) + + def test_real_llama_embeddings(llama_cpp_embedding_model_path): model = llama_cpp.Llama( llama_cpp_embedding_model_path, From e3aa6b5cde98e8b719aaa3bbab31a5ed72cd6805 Mon Sep 17 00:00:00 2001 From: Imad Saddik <79410781+ImadSaddik@users.noreply.github.com> Date: Mon, 1 Jun 2026 04:11:00 +0100 Subject: [PATCH 074/138] docs: fix typo in README (#2072) docs: fix typo in README (#2072) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c1247a2e2e..b42db2ef5e 100644 --- a/README.md +++ b/README.md @@ -269,7 +269,7 @@ To upgrade and rebuild `llama-cpp-python` add `--upgrade --force-reinstall --no- The high-level API provides a simple managed interface through the [`Llama`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama) class. -Below is a short example demonstrating how to use the high-level API to for basic text completion: +Below is a short example demonstrating how to use the high-level API for basic text completion: ```python from llama_cpp import Llama From 868712246065f6c52cd4b050474d51ed352c1398 Mon Sep 17 00:00:00 2001 From: Stefano Fiorucci <stefanofiorucci@gmail.com> Date: Mon, 1 Jun 2026 05:18:08 +0200 Subject: [PATCH 075/138] docs: fix NanoLlava chat handler name in README (#2059) docs: fix NanoLlava chat handler name in README (#2059) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b42db2ef5e..d48dde3c58 100644 --- a/README.md +++ b/README.md @@ -502,7 +502,7 @@ Below are the supported multi-modal models and their respective chat handlers (P | [llava-v1.5-13b](https://huggingface.co/mys/ggml_llava-v1.5-13b) | `Llava15ChatHandler` | `llava-1-5` | | [llava-v1.6-34b](https://huggingface.co/cjpais/llava-v1.6-34B-gguf) | `Llava16ChatHandler` | `llava-1-6` | | [moondream2](https://huggingface.co/vikhyatk/moondream2) | `MoondreamChatHandler` | `moondream2` | -| [nanollava](https://huggingface.co/abetlen/nanollava-gguf) | `NanollavaChatHandler` | `nanollava` | +| [nanollava](https://huggingface.co/abetlen/nanollava-gguf) | `NanoLlavaChatHandler` | `nanollava` | | [llama-3-vision-alpha](https://huggingface.co/abetlen/llama-3-vision-alpha-gguf) | `Llama3VisionAlphaChatHandler` | `llama-3-vision-alpha` | | [minicpm-v-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | `MiniCPMv26ChatHandler` | `minicpm-v-2.6` | | [qwen2.5-vl](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-GGUF) | `Qwen25VLChatHandler` | `qwen2.5-vl` | From da07e463e19913071e5e9ea1d7c05b97c5531fa0 Mon Sep 17 00:00:00 2001 From: Matthias <dev@sleepyyui.com> Date: Mon, 1 Jun 2026 05:26:23 +0200 Subject: [PATCH 076/138] docs: update llama.cpp build docs link (#2056) docs: update llama.cpp build docs link (#2056) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d48dde3c58..9b563f0654 100644 --- a/README.md +++ b/README.md @@ -59,7 +59,7 @@ pip install llama-cpp-python \ ### Installation Configuration -`llama.cpp` supports a number of hardware acceleration backends to speed up inference as well as backend specific options. See the [llama.cpp README](https://github.com/ggerganov/llama.cpp#build) for a full list. +`llama.cpp` supports a number of hardware acceleration backends to speed up inference as well as backend specific options. See the [llama.cpp README](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) for a full list. All `llama.cpp` cmake build options can be set via the `CMAKE_ARGS` environment variable or via the `--config-settings / -C` cli flag during installation. From 52cf7475f987f08f18dc5748f601b32e7b603ffe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alex=20Gr=C3=B6nholm?= <alex.gronholm@nextday.fi> Date: Mon, 1 Jun 2026 06:30:37 +0300 Subject: [PATCH 077/138] docs: update ROCm install instructions (#1867) docs: update ROCm install instructions (#1867) --- CHANGELOG.md | 1 + README.md | 6 +++--- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 21fa49f28e..197841bf39 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- docs: update ROCm install instructions by @agronholm in #1867 - fix: clear prompt for recurrent / hybrid models when only a partial prefix matches by @avion23 in #2108 - fix: match Transformers `tojson` in chat template rendering by @CISC in #1486 - fix: use env var configured multimodal library override paths when loading shared libraries by @navratil-matej in #1782 diff --git a/README.md b/README.md index 9b563f0654..a64c7faa86 100644 --- a/README.md +++ b/README.md @@ -173,12 +173,12 @@ pip install llama-cpp-python \ </details> <details> -<summary>hipBLAS (ROCm)</summary> +<summary>HIP (ROCm)</summary> -To install with hipBLAS / ROCm support for AMD cards, set the `GGML_HIPBLAS=on` environment variable before installing: +To install with HIP / ROCm support for AMD cards, set the `GGML_HIP=on` environment variable before installing: ```bash -CMAKE_ARGS="-DGGML_HIPBLAS=on" pip install llama-cpp-python +CMAKE_ARGS="-DGGML_HIP=on" pip install llama-cpp-python ``` </details> From c3adb354e71ec964dd503a5aae81d16ca947e377 Mon Sep 17 00:00:00 2001 From: adam jones <domdomegg+git@gmail.com> Date: Mon, 1 Jun 2026 06:00:53 +0100 Subject: [PATCH 078/138] server types: Move 'model' parameter to clarify it is used (#1786) The 'model' parameter has been supported since https://github.com/abetlen/llama-cpp-python/pull/931. Its placement in this section was copied from an older version of the file, and hasn't been corrected since. Correcting this will make it clearer what parameters are supported by llama-cpp-python. --- llama_cpp/server/types.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama_cpp/server/types.py b/llama_cpp/server/types.py index fdd1644568..eda2b92801 100644 --- a/llama_cpp/server/types.py +++ b/llama_cpp/server/types.py @@ -107,6 +107,7 @@ class CreateCompletionRequest(BaseModel): + model: Optional[str] = model_field prompt: Union[str, List[str]] = Field( default="", description="The prompt to generate completions for." ) @@ -138,7 +139,6 @@ class CreateCompletionRequest(BaseModel): seed: Optional[int] = Field(None) # ignored or currently unsupported - model: Optional[str] = model_field n: Optional[int] = 1 best_of: Optional[int] = 1 user: Optional[str] = Field(default=None) @@ -190,6 +190,7 @@ class ChatCompletionRequestMessage(BaseModel): class CreateChatCompletionRequest(BaseModel): + model: Optional[str] = model_field messages: List[llama_cpp.ChatCompletionRequestMessage] = Field( default=[], description="A list of messages to generate completions for." ) @@ -237,7 +238,6 @@ class CreateChatCompletionRequest(BaseModel): ) # ignored or currently unsupported - model: Optional[str] = model_field n: Optional[int] = 1 user: Optional[str] = Field(None) From 20240609f3dcc902dfa0c59b675ad24887bc044c Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 31 May 2026 22:44:22 -0700 Subject: [PATCH 079/138] feat: update llama.cpp to af6528e6d (#2235) --- CHANGELOG.md | 2 +- vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 197841bf39..90adbe54c8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -15,7 +15,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - fix: avoid cleanup errors for partially initialized `LlamaModel` objects by @usernames122 in #2173 - fix: suppress stdout and stderr in Jupyter notebooks by @Anai-Guo in #2181 - feat: enable arm64 musl builds by @acon96 in #2221 -- feat: Update llama.cpp to ggml-org/llama.cpp@d749821db +- feat: Update llama.cpp to ggml-org/llama.cpp@af6528e6d - fix: model fails to load when chat template uses HuggingFace generation tags by @tobocop2 in #2226 - docs: add contributing guide by @abetlen in #2229 - chore: Migrate llama.cpp submodule URL to ggml-org/llama.cpp by @shalinib-ibm in #2034 diff --git a/vendor/llama.cpp b/vendor/llama.cpp index d749821db3..af6528e6df 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit d749821db3bd587932d1ed57d43626cd552c9909 +Subproject commit af6528e6df5d798f7f1363ec1141699be0f638e2 From 26633bd1a2eaf7fd0567cc5eaec8b0165a7ea0bd Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 31 May 2026 22:57:26 -0700 Subject: [PATCH 080/138] chore: bump version to 0.3.24 (#2236) --- CHANGELOG.md | 2 ++ llama_cpp/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 90adbe54c8..6d02bb8e32 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.24] + - docs: update ROCm install instructions by @agronholm in #1867 - fix: clear prompt for recurrent / hybrid models when only a partial prefix matches by @avion23 in #2108 - fix: match Transformers `tojson` in chat template rendering by @CISC in #1486 diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index eb37da2093..c48e6fcec0 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.23" +__version__ = "0.3.24" From c7af423ea641dd1e5fa316516027f6effc375ef6 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Mon, 1 Jun 2026 02:25:03 -0700 Subject: [PATCH 081/138] fix(ci): add Pascal compute capability targets to CUDA wheel builds (#2237) --- .github/workflows/build-wheels-cuda.yaml | 4 ++-- CHANGELOG.md | 2 ++ README.md | 1 + 3 files changed, 5 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index be55bf4834..87bd365957 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -169,10 +169,10 @@ jobs: } $cudaTagVersion = $nvccVersion.Replace('.','') $env:VERBOSE = '1' - # Build real cubins for the supported GPUs, including sm_70, and keep + # Build real cubins for the supported GPUs, including Pascal, and keep # one forward-compatible PTX target instead of embedding PTX for every # SM. This keeps the wheel under GitHub's 2 GiB release-asset limit. - $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=70-real;75-real;80-real;86-real;89-real;90-real;90-virtual -DCMAKE_CUDA_FLAGS=-allow-unsupported-compiler -DCMAKE_CUDA_FLAGS_INIT=-allow-unsupported-compiler $env:CMAKE_ARGS" + $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=60-real;61-real;70-real;75-real;80-real;86-real;89-real;90-real;90-virtual -DCMAKE_CUDA_FLAGS=-allow-unsupported-compiler -DCMAKE_CUDA_FLAGS_INIT=-allow-unsupported-compiler $env:CMAKE_ARGS" $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off' python -m build --wheel # Publish tags that reflect the actual installed toolkit version. diff --git a/CHANGELOG.md b/CHANGELOG.md index 6d02bb8e32..9144353983 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix(ci): add Pascal compute capability targets to CUDA wheel builds by @abetlen in #2237 + ## [0.3.24] - docs: update ROCm install instructions by @agronholm in #1867 diff --git a/README.md b/README.md index a64c7faa86..7e02b174d4 100644 --- a/README.md +++ b/README.md @@ -126,6 +126,7 @@ CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python It is also possible to install a pre-built wheel with CUDA support. As long as your system meets some requirements: - CUDA Version is 12.1, 12.2, 12.3, 12.4 or 12.5 +- NVIDIA GPU compute capability is 6.0 or newer - Python Version is 3.10, 3.11 or 3.12 ```bash From 43c92a7fef5cf6588612525e081f39c2adf094ec Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Mon, 1 Jun 2026 04:30:02 -0700 Subject: [PATCH 082/138] feat(ci): add CUDA 11.8 wheel builds (#2238) * feat(ci): add CUDA 11.8 wheel builds * fix(ci): make CUDA 11.8 wheel builds version-consistent * fix(ci): allow non-CUDA dependencies for CUDA 11.8 wheels * fix(ci): omit Hopper targets from CUDA 11.8 wheels * fix(ci): use GCC 11 for CUDA 11.8 Linux wheels * fix(ci): use MSVC 14.29 for CUDA 11.8 Windows wheels --- .github/workflows/build-wheels-cuda.yaml | 51 +++++++++++++++++++++--- CHANGELOG.md | 1 + README.md | 5 ++- 3 files changed, 50 insertions(+), 7 deletions(-) diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index 87bd365957..723236ca82 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -24,7 +24,7 @@ jobs: # wheel.py-api = "py3" makes the CUDA wheel interpreter-agnostic, # so one builder per toolkit version is sufficient. 'pyver' = @("3.9") - 'cuda' = @("12.1.1", "12.2.2", "12.3.2", "12.4.1", "12.5.1") + 'cuda' = @("11.8.0", "12.1.1", "12.2.2", "12.3.2", "12.4.1", "12.5.1") 'releasetag' = @("basic") 'exclude' = @( @{ 'os' = 'windows-2022'; 'cuda' = '12.1.1' }, @@ -50,8 +50,15 @@ jobs: AVXVER: ${{ matrix.releasetag }} steps: + - name: Set up MSVC for CUDA 11.8 + if: runner.os == 'Windows' && matrix.cuda == '11.8.0' + uses: ilammy/msvc-dev-cmd@v1 + with: + arch: x64 + toolset: 14.29 + - name: Set up MSVC - if: runner.os == 'Windows' + if: runner.os == 'Windows' && matrix.cuda != '11.8.0' uses: ilammy/msvc-dev-cmd@v1 with: arch: x64 @@ -81,7 +88,31 @@ jobs: run: | $cudaVersion = $env:CUDAVER $cudaChannel = "nvidia/label/cuda-$cudaVersion" - if ($IsLinux) { + if ($cudaVersion -eq '11.8.0') { + if ($IsLinux) { + $cudaPackages = @( + "${cudaChannel}::cuda-nvcc_linux-64=11.8.0", + "${cudaChannel}::cuda-cccl=11.8.89", + "${cudaChannel}::cuda-cudart=11.8.89", + "${cudaChannel}::cuda-cudart-dev=11.8.89", + "${cudaChannel}::cuda-driver-dev=11.8.89", + "${cudaChannel}::libcublas=11.11.3.6", + "${cudaChannel}::libcublas-dev=11.11.3.6" + ) + } elseif ($IsWindows) { + $cudaPackages = @( + "${cudaChannel}::cuda-nvcc_win-64=11.8.0", + "${cudaChannel}::cuda-cccl=11.8.89", + "${cudaChannel}::cuda-cudart=11.8.89", + "${cudaChannel}::cuda-cudart-dev=11.8.89", + "${cudaChannel}::libcublas=11.11.3.6", + "${cudaChannel}::libcublas-dev=11.11.3.6" + ) + } else { + throw 'Unsupported CUDA wheel build platform' + } + mamba install -y --channel-priority flexible --override-channels -c $cudaChannel $cudaPackages + } elseif ($IsLinux) { mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-toolkit=$cudaVersion" "${cudaChannel}::cuda-nvcc_linux-64" "${cudaChannel}::cuda-cccl" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev" } elseif ($IsWindows) { if ($cudaVersion -like '12.5.*') { @@ -122,7 +153,12 @@ jobs: $cudaRootCmake = $cudaRoot.Replace('\', '/') $env:CMAKE_ARGS = "-DCUDAToolkit_ROOT=$cudaRootCmake -DCUDA_TOOLKIT_ROOT_DIR=$cudaRootCmake" if ($IsLinux) { - if (Test-Path '/usr/bin/g++-12') { + if ([version]$env:CUDAVER -lt [version]"12.0" -and (Test-Path '/usr/bin/g++-11')) { + $env:CC = '/usr/bin/gcc-11' + $env:CXX = '/usr/bin/g++-11' + $env:CUDAHOSTCXX = '/usr/bin/g++-11' + $cudaHostCompilerArg = " -DCMAKE_CUDA_HOST_COMPILER=$env:CUDAHOSTCXX" + } elseif (Test-Path '/usr/bin/g++-12') { $env:CC = '/usr/bin/gcc-12' $env:CXX = '/usr/bin/g++-12' $env:CUDAHOSTCXX = '/usr/bin/g++-12' @@ -169,10 +205,15 @@ jobs: } $cudaTagVersion = $nvccVersion.Replace('.','') $env:VERBOSE = '1' + $cudaArchs = "60-real;61-real;70-real;75-real;80-real;86-real;89-real;90-real;90-virtual" + if ([version]$nvccVersion -lt [version]"12.0") { + # CUDA 11.8 cannot compile llama.cpp's Hopper PDL device calls. + $cudaArchs = "60-real;61-real;70-real;75-real;80-real;86-real;89-real" + } # Build real cubins for the supported GPUs, including Pascal, and keep # one forward-compatible PTX target instead of embedding PTX for every # SM. This keeps the wheel under GitHub's 2 GiB release-asset limit. - $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=60-real;61-real;70-real;75-real;80-real;86-real;89-real;90-real;90-virtual -DCMAKE_CUDA_FLAGS=-allow-unsupported-compiler -DCMAKE_CUDA_FLAGS_INIT=-allow-unsupported-compiler $env:CMAKE_ARGS" + $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=$cudaArchs -DCMAKE_CUDA_FLAGS=-allow-unsupported-compiler -DCMAKE_CUDA_FLAGS_INIT=-allow-unsupported-compiler $env:CMAKE_ARGS" $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off' python -m build --wheel # Publish tags that reflect the actual installed toolkit version. diff --git a/CHANGELOG.md b/CHANGELOG.md index 9144353983..e2fb8e951c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat(ci): add CUDA 11.8 wheel builds by @abetlen in #2238 - fix(ci): add Pascal compute capability targets to CUDA wheel builds by @abetlen in #2237 ## [0.3.24] diff --git a/README.md b/README.md index 7e02b174d4..2b7a7d98c9 100644 --- a/README.md +++ b/README.md @@ -125,8 +125,8 @@ CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python It is also possible to install a pre-built wheel with CUDA support. As long as your system meets some requirements: -- CUDA Version is 12.1, 12.2, 12.3, 12.4 or 12.5 -- NVIDIA GPU compute capability is 6.0 or newer +- CUDA Version is 11.8, 12.1, 12.2, 12.3, 12.4 or 12.5 +- NVIDIA GPU compute capability is 6.0 through 8.9 for CUDA 11.8 wheels, or 6.0 or newer for CUDA 12 wheels - Python Version is 3.10, 3.11 or 3.12 ```bash @@ -135,6 +135,7 @@ pip install llama-cpp-python \ ``` Where `<cuda-version>` is one of the following: +- `cu118`: CUDA 11.8 - `cu121`: CUDA 12.1 - `cu122`: CUDA 12.2 - `cu123`: CUDA 12.3 From 718a1ca5f17522655ae482f182f63ea3ea41d15d Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Mon, 1 Jun 2026 05:44:30 -0700 Subject: [PATCH 083/138] feat(ci): add CUDA 13 wheel builds (#2239) --- .github/workflows/build-wheels-cuda.yaml | 11 +++++++---- CHANGELOG.md | 1 + README.md | 6 ++++-- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index 723236ca82..f1b2b8b6b5 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -24,7 +24,7 @@ jobs: # wheel.py-api = "py3" makes the CUDA wheel interpreter-agnostic, # so one builder per toolkit version is sufficient. 'pyver' = @("3.9") - 'cuda' = @("11.8.0", "12.1.1", "12.2.2", "12.3.2", "12.4.1", "12.5.1") + 'cuda' = @("11.8.0", "12.1.1", "12.2.2", "12.3.2", "12.4.1", "12.5.1", "13.0.2", "13.2.1") 'releasetag' = @("basic") 'exclude' = @( @{ 'os' = 'windows-2022'; 'cuda' = '12.1.1' }, @@ -115,8 +115,8 @@ jobs: } elseif ($IsLinux) { mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-toolkit=$cudaVersion" "${cudaChannel}::cuda-nvcc_linux-64" "${cudaChannel}::cuda-cccl" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev" } elseif ($IsWindows) { - if ($cudaVersion -like '12.5.*') { - # The Windows 12.5 toolkit meta-package pulls compiler activation + if ($cudaVersion -like '12.5.*' -or [version]$cudaVersion -ge [version]"13.0") { + # The Windows 12.5+ toolkit meta-package pulls compiler activation # scripts that overflow cmd.exe after MSVC is already initialized. mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-nvcc_win-64" "${cudaChannel}::cuda-cccl" "${cudaChannel}::cuda-libraries-dev=$cudaVersion" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev" } else { @@ -209,8 +209,11 @@ jobs: if ([version]$nvccVersion -lt [version]"12.0") { # CUDA 11.8 cannot compile llama.cpp's Hopper PDL device calls. $cudaArchs = "60-real;61-real;70-real;75-real;80-real;86-real;89-real" + } elseif ([version]$nvccVersion -ge [version]"13.0") { + # CUDA 13 dropped offline compilation support for pre-Turing targets. + $cudaArchs = "75-real;80-real;86-real;89-real;90-real;90-virtual" } - # Build real cubins for the supported GPUs, including Pascal, and keep + # Build real cubins for the supported GPUs and keep # one forward-compatible PTX target instead of embedding PTX for every # SM. This keeps the wheel under GitHub's 2 GiB release-asset limit. $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=$cudaArchs -DCMAKE_CUDA_FLAGS=-allow-unsupported-compiler -DCMAKE_CUDA_FLAGS_INIT=-allow-unsupported-compiler $env:CMAKE_ARGS" diff --git a/CHANGELOG.md b/CHANGELOG.md index e2fb8e951c..c80984ff68 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat(ci): add CUDA 13.0 and 13.2 wheel builds by @abetlen in #2239 - feat(ci): add CUDA 11.8 wheel builds by @abetlen in #2238 - fix(ci): add Pascal compute capability targets to CUDA wheel builds by @abetlen in #2237 diff --git a/README.md b/README.md index 2b7a7d98c9..5de330af46 100644 --- a/README.md +++ b/README.md @@ -125,8 +125,8 @@ CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python It is also possible to install a pre-built wheel with CUDA support. As long as your system meets some requirements: -- CUDA Version is 11.8, 12.1, 12.2, 12.3, 12.4 or 12.5 -- NVIDIA GPU compute capability is 6.0 through 8.9 for CUDA 11.8 wheels, or 6.0 or newer for CUDA 12 wheels +- CUDA Version is 11.8, 12.1, 12.2, 12.3, 12.4, 12.5, 13.0 or 13.2 +- NVIDIA GPU compute capability is 6.0 through 8.9 for CUDA 11.8 wheels, 6.0 or newer for CUDA 12 wheels, or 7.5 or newer for CUDA 13 wheels - Python Version is 3.10, 3.11 or 3.12 ```bash @@ -141,6 +141,8 @@ Where `<cuda-version>` is one of the following: - `cu123`: CUDA 12.3 - `cu124`: CUDA 12.4 - `cu125`: CUDA 12.5 +- `cu130`: CUDA 13.0 +- `cu132`: CUDA 13.2 For example, to install the CUDA 12.1 wheel: From 927b574ede274d3277ddc1786f0bf25d36e1cd60 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Mon, 1 Jun 2026 05:52:07 -0700 Subject: [PATCH 084/138] docs: add Python 3.14 classifier (#2240) --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index b5998dd1c8..6bfacf279c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,6 +26,7 @@ classifiers = [ "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", ] From a9b480f8b1ac20279a563d12c5d0f3567c0af960 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Mon, 1 Jun 2026 06:57:20 -0700 Subject: [PATCH 085/138] feat: add Gemma 4 multimodal chat support (#2241) --- CHANGELOG.md | 1 + README.md | 1 + llama_cpp/llama_chat_format.py | 44 ++++++++++++++++++++++++++++++++++ llama_cpp/server/model.py | 14 +++++++++++ 4 files changed, 60 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index c80984ff68..2ecb2aa17a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: add Gemma 4 multimodal chat support by @abetlen in #2241 - feat(ci): add CUDA 13.0 and 13.2 wheel builds by @abetlen in #2239 - feat(ci): add CUDA 11.8 wheel builds by @abetlen in #2238 - fix(ci): add Pascal compute capability targets to CUDA wheel builds by @abetlen in #2237 diff --git a/README.md b/README.md index 5de330af46..7db3e27448 100644 --- a/README.md +++ b/README.md @@ -510,6 +510,7 @@ Below are the supported multi-modal models and their respective chat handlers (P | [llama-3-vision-alpha](https://huggingface.co/abetlen/llama-3-vision-alpha-gguf) | `Llama3VisionAlphaChatHandler` | `llama-3-vision-alpha` | | [minicpm-v-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | `MiniCPMv26ChatHandler` | `minicpm-v-2.6` | | [qwen2.5-vl](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-GGUF) | `Qwen25VLChatHandler` | `qwen2.5-vl` | +| [gemma-4](https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF) | `Gemma4ChatHandler` | `gemma4` | Then you'll need to use a custom chat handler to load the clip model and process the chat messages and images. diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index b7f6916eac..44c6c1f76f 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -3265,6 +3265,50 @@ def from_pretrained( ) +class Gemma4ChatHandler(Llava15ChatHandler): + DEFAULT_SYSTEM_MESSAGE = None + + CHAT_FORMAT = ( + "{% if messages and messages[0]['role'] == 'system' %}" + "{% if messages[0]['content'] is string %}" + "{% set first_user_prefix = messages[0]['content'] + '\n\n' %}" + "{% else %}" + "{% set first_user_prefix = messages[0]['content'][0]['text'] + '\n\n' %}" + "{% endif %}" + "{% set loop_messages = messages[1:] %}" + "{% else %}" + "{% set first_user_prefix = '' %}" + "{% set loop_messages = messages %}" + "{% endif %}" + "{% for message in loop_messages %}" + "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}" + "{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}" + "{% endif %}" + "{% set role = 'model' if message['role'] == 'assistant' else message['role'] %}" + "{{ '<start_of_turn>' + role + '\n' + (first_user_prefix if loop.first else '') }}" + "{% if message['content'] is string %}" + "{{ message['content'] | trim }}" + "{% elif message['content'] is iterable %}" + "{% for item in message['content'] %}" + "{% if item['type'] == 'image_url' and item['image_url'] is string %}" + "{{ '\n\n' + item['image_url'] + '\n\n' }}" + "{% elif item['type'] == 'image_url' and item['image_url'] is mapping %}" + "{{ '\n\n' + item['image_url']['url'] + '\n\n' }}" + "{% elif item['type'] == 'text' %}" + "{{ item['text'] | trim }}" + "{% endif %}" + "{% endfor %}" + "{% else %}" + "{{ raise_exception('Invalid content type') }}" + "{% endif %}" + "{{ '<end_of_turn>\n' }}" + "{% endfor %}" + "{% if add_generation_prompt %}" + "{{ '<start_of_turn>model\n' }}" + "{% endif %}" + ) + + class ObsidianChatHandler(Llava15ChatHandler): # Prompt Format # The model followed ChatML format. However, with ### as the separator diff --git a/llama_cpp/server/model.py b/llama_cpp/server/model.py index 3922ce5df3..3222abd631 100644 --- a/llama_cpp/server/model.py +++ b/llama_cpp/server/model.py @@ -115,6 +115,20 @@ def load_llama_from_model_settings(settings: ModelSettings) -> llama_cpp.Llama: chat_handler = llama_cpp.llama_chat_format.Llava16ChatHandler( clip_model_path=settings.clip_model_path, verbose=settings.verbose ) + elif settings.chat_format == "gemma4": + assert settings.clip_model_path is not None, "clip model not found" + if settings.hf_model_repo_id is not None: + chat_handler = ( + llama_cpp.llama_chat_format.Gemma4ChatHandler.from_pretrained( + repo_id=settings.hf_model_repo_id, + filename=settings.clip_model_path, + verbose=settings.verbose, + ) + ) + else: + chat_handler = llama_cpp.llama_chat_format.Gemma4ChatHandler( + clip_model_path=settings.clip_model_path, verbose=settings.verbose + ) elif settings.chat_format == "moondream": assert settings.clip_model_path is not None, "clip model not found" if settings.hf_model_repo_id is not None: From 4b66c45ebc2065798f68f531376a7a60c92f942d Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Mon, 1 Jun 2026 19:32:51 -0700 Subject: [PATCH 086/138] feat: update llama.cpp to 210a6570c (#2242) --- CHANGELOG.md | 1 + llama_cpp/llama_cpp.py | 4 ++++ vendor/llama.cpp | 2 +- 3 files changed, 6 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 2ecb2aa17a..7fa15aebb4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: Update llama.cpp to ggml-org/llama.cpp@210a6570c by @abetlen in #2242 - feat: add Gemma 4 multimodal chat support by @abetlen in #2241 - feat(ci): add CUDA 13.0 and 13.2 wheel builds by @abetlen in #2239 - feat(ci): add CUDA 11.8 wheel builds by @abetlen in #2238 diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index 6560b51787..44c25a519f 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -903,6 +903,7 @@ class llama_sampler_seq_config(ctypes.Structure): # uint32_t n_ubatch; // physical maximum batch size # uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models) # uint32_t n_rs_seq; // number of recurrent-state snapshots per seq for rollback (0 = no rollback) [EXPERIMENTAL] +# uint32_t n_outputs_max; // max outputs in a ubatch (0 = n_batch) # int32_t n_threads; // number of threads to use for generation # int32_t n_threads_batch; // number of threads to use for batch processing @@ -958,6 +959,7 @@ class llama_context_params(ctypes.Structure): n_ubatch (int): physical maximum batch size n_seq_max (int): max number of sequences (i.e. distinct states for recurrent models) n_rs_seq (int): number of recurrent-state snapshots per sequence for rollback + n_outputs_max (int): max outputs in a ubatch, 0 = n_batch n_threads (int): number of threads to use for generation n_threads_batch (int): number of threads to use for batch processing ctx_type (int): context type, from `enum llama_context_type` @@ -995,6 +997,7 @@ class llama_context_params(ctypes.Structure): n_ubatch: int n_seq_max: int n_rs_seq: int + n_outputs_max: int n_threads: int n_threads_batch: int ctx_type: int @@ -1031,6 +1034,7 @@ class llama_context_params(ctypes.Structure): ("n_ubatch", ctypes.c_uint32), ("n_seq_max", ctypes.c_uint32), ("n_rs_seq", ctypes.c_uint32), + ("n_outputs_max", ctypes.c_uint32), ("n_threads", ctypes.c_int32), ("n_threads_batch", ctypes.c_int32), ("ctx_type", ctypes.c_int), diff --git a/vendor/llama.cpp b/vendor/llama.cpp index af6528e6df..210a6570ce 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit af6528e6df5d798f7f1363ec1141699be0f638e2 +Subproject commit 210a6570ceda20c5d6439172c09ada08c3754cc9 From f1bfa117410fbd75ad6a2345a65cdf9b17205c19 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Mon, 1 Jun 2026 21:46:08 -0700 Subject: [PATCH 087/138] chore: bump version to 0.3.25 (#2243) --- CHANGELOG.md | 2 ++ llama_cpp/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7fa15aebb4..bd83be8434 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.25] + - feat: Update llama.cpp to ggml-org/llama.cpp@210a6570c by @abetlen in #2242 - feat: add Gemma 4 multimodal chat support by @abetlen in #2241 - feat(ci): add CUDA 13.0 and 13.2 wheel builds by @abetlen in #2239 diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index c48e6fcec0..52101c9b7e 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.24" +__version__ = "0.3.25" From d185d64aa6de50acea2e2cdfdc12bcf4a721f946 Mon Sep 17 00:00:00 2001 From: Markus <markus@xpiriax.de> Date: Tue, 2 Jun 2026 13:41:57 +0200 Subject: [PATCH 088/138] fix: handle additional `from_pretrained` files in subfolders (#2085) * Fixed issue #1938 Now using the correct path * fix: handle additional from_pretrained files in subfolders * docs: update changelog for from_pretrained subfolder fix --------- Co-authored-by: abetlen <abetlen@gmail.com> --- CHANGELOG.md | 2 ++ llama_cpp/llama.py | 9 ++++++--- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index bd83be8434..6677fc8fe0 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix: handle additional `from_pretrained` files in subfolders by @TNing in #2085 + ## [0.3.25] - feat: Update llama.cpp to ggml-org/llama.cpp@210a6570c by @abetlen in #2242 diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index b904b4080c..4a09b55ee5 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -2396,12 +2396,15 @@ def from_pretrained( ) (matching_additional_file,) = matching_additional_files + additional_subfolder = str(Path(matching_additional_file).parent) + additional_file_name = Path(matching_additional_file).name - # download the additional file + # Split additional file paths independently to avoid duplicating + # the main model subfolder in Hugging Face download URLs. hf_hub_download( repo_id=repo_id, - filename=matching_additional_file, - subfolder=subfolder, + filename=additional_file_name, + subfolder=additional_subfolder, local_dir=local_dir, local_dir_use_symlinks=local_dir_use_symlinks, cache_dir=cache_dir, From bbdc8518720322ee4e805b9e375fd65a5948c9c9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 3 Jun 2026 01:40:49 -0700 Subject: [PATCH 089/138] chore(deps): bump pypa/cibuildwheel from 2.22.0 to 3.4.1 (#2249) Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 2.22.0 to 3.4.1. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](https://github.com/pypa/cibuildwheel/compare/v2.22...v3.4.1) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-version: 3.4.1 dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/build-and-release.yaml | 6 +++--- .github/workflows/build-wheels-metal.yaml | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index 5cfb6469de..6318f73a1e 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -42,7 +42,7 @@ jobs: shell: cmd - name: Build wheels - uses: pypa/cibuildwheel@v2.22.0 + uses: pypa/cibuildwheel@v3.4.1 env: # Keep repair disabled by default for non-Linux platforms in this job. CIBW_REPAIR_WHEEL_COMMAND: "" @@ -80,7 +80,7 @@ jobs: submodules: "recursive" - name: Build wheels - uses: pypa/cibuildwheel@v2.22.0 + uses: pypa/cibuildwheel@v3.4.1 env: CIBW_SKIP: "pp*" CIBW_REPAIR_WHEEL_COMMAND: "LD_LIBRARY_PATH=$PWD/llama_cpp/lib auditwheel repair -w {dest_dir} {wheel}" @@ -133,7 +133,7 @@ jobs: platforms: linux/riscv64 - name: Build wheels - uses: pypa/cibuildwheel@v3.1.2 + uses: pypa/cibuildwheel@v3.4.1 env: CIBW_SKIP: "*musllinux* pp*" CIBW_REPAIR_WHEEL_COMMAND: "" diff --git a/.github/workflows/build-wheels-metal.yaml b/.github/workflows/build-wheels-metal.yaml index 98f511e4a6..5fd56a00d3 100644 --- a/.github/workflows/build-wheels-metal.yaml +++ b/.github/workflows/build-wheels-metal.yaml @@ -32,7 +32,7 @@ jobs: shell: bash - name: Build wheels - uses: pypa/cibuildwheel@v2.22.0 + uses: pypa/cibuildwheel@v3.4.1 env: # disable repair CIBW_REPAIR_WHEEL_COMMAND: "" From dad5d0aacb4bfb0080da3188050d4fabd97d8e50 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 3 Jun 2026 02:03:43 -0700 Subject: [PATCH 090/138] chore(deps): bump actions/cache from 4 to 5 (#2248) Bumps [actions/cache](https://github.com/actions/cache) from 4 to 5. - [Release notes](https://github.com/actions/cache/releases) - [Changelog](https://github.com/actions/cache/blob/main/RELEASES.md) - [Commits](https://github.com/actions/cache/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/cache dependency-version: '5' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/test.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index b8f5566bb0..83a91a8edd 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -32,7 +32,7 @@ jobs: hf download ${{ env.RECURRENT_REPO_ID }} ${{ env.RECURRENT_MODEL_FILE }} hf download ${{ env.HYBRID_REPO_ID }} ${{ env.HYBRID_MODEL_FILE }} - name: Cache model - uses: actions/cache@v4 + uses: actions/cache@v5 with: path: ~/.cache/huggingface/hub key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }} @@ -54,7 +54,7 @@ jobs: python-version: ${{ matrix.python-version }} cache: 'pip' - name: Restore model cache - uses: actions/cache@v4 + uses: actions/cache@v5 with: path: ~/.cache/huggingface/hub key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }} @@ -86,7 +86,7 @@ jobs: cache: 'pip' - name: Restore model cache - uses: actions/cache@v4 + uses: actions/cache@v5 with: path: ~/.cache/huggingface/hub key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }} @@ -126,7 +126,7 @@ jobs: python3 -c "import platform; print(platform.machine(), platform.architecture())" - name: Restore model cache - uses: actions/cache@v4 + uses: actions/cache@v5 with: path: ~/.cache/huggingface/hub key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }} @@ -162,7 +162,7 @@ jobs: python3 -c "import platform; print(platform.machine(), platform.architecture())" - name: Restore model cache - uses: actions/cache@v4 + uses: actions/cache@v5 with: path: ~/.cache/huggingface/hub key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }} From aa944e4cb09337f381281e5880e257f57fd7abc5 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Wed, 3 Jun 2026 02:26:38 -0700 Subject: [PATCH 091/138] ci: cache embedding test model (#2250) * ci: cache embedding test model * ci: restore previous model cache --- .github/workflows/test.yaml | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 83a91a8edd..d798967675 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -14,7 +14,9 @@ env: RECURRENT_MODEL_FILE: mamba-130m-hf.Q2_K.gguf HYBRID_REPO_ID: tiiuae/Falcon-H1-Tiny-90M-Instruct-GGUF HYBRID_MODEL_FILE: Falcon-H1-Tiny-90M-Instruct-Q2_K.gguf - MODEL_CACHE_KEY: qwen35-q8-mamba130m-q2-falconh1tiny-q2 + EMBEDDING_REPO_ID: CompendiumLabs/bge-small-en-v1.5-gguf + EMBEDDING_MODEL_FILE: bge-small-en-v1.5-q4_k_m.gguf + MODEL_CACHE_KEY: qwen35-q8-mamba130m-q2-falconh1tiny-q2-bge-small-q4 jobs: download-model: @@ -26,16 +28,19 @@ jobs: python-version: "3.9" - name: Install huggingface-hub run: pip install huggingface-hub + - name: Restore model cache + uses: actions/cache@v5 + with: + path: ~/.cache/huggingface/hub + key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }} + restore-keys: | + ${{ runner.os }}-model-qwen35-q8-mamba130m-q2-falconh1tiny-q2 - name: Download model run: | hf download ${{ env.REPO_ID }} ${{ env.MODEL_FILE }} hf download ${{ env.RECURRENT_REPO_ID }} ${{ env.RECURRENT_MODEL_FILE }} hf download ${{ env.HYBRID_REPO_ID }} ${{ env.HYBRID_MODEL_FILE }} - - name: Cache model - uses: actions/cache@v5 - with: - path: ~/.cache/huggingface/hub - key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }} + hf download ${{ env.EMBEDDING_REPO_ID }} ${{ env.EMBEDDING_MODEL_FILE }} build-linux: needs: download-model From b439a84a61ea257538d0660afaf512425297cba3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 3 Jun 2026 02:56:25 -0700 Subject: [PATCH 092/138] chore(deps): bump actions/upload-artifact from 4 to 7 (#2245) Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 4 to 7. - [Release notes](https://github.com/actions/upload-artifact/releases) - [Commits](https://github.com/actions/upload-artifact/compare/v4...v7) --- updated-dependencies: - dependency-name: actions/upload-artifact dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/build-and-release.yaml | 8 ++++---- .github/workflows/build-wheels-metal.yaml | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index 6318f73a1e..9978cb66b2 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -66,7 +66,7 @@ jobs: package-dir: . output-dir: wheelhouse - - uses: actions/upload-artifact@v4 + - uses: actions/upload-artifact@v7 with: name: wheels-${{ matrix.os }} path: ./wheelhouse/*.whl @@ -95,7 +95,7 @@ jobs: output-dir: wheelhouse - name: Upload wheels as artifacts - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@v7 with: name: wheels_arm64 path: ./wheelhouse/*.whl @@ -148,7 +148,7 @@ jobs: output-dir: wheelhouse - name: Upload wheels as artifacts - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@v7 with: name: ${{ matrix.shard.artifact }} path: ./wheelhouse/*.whl @@ -190,7 +190,7 @@ jobs: run: | python -m build --sdist - - uses: actions/upload-artifact@v4 + - uses: actions/upload-artifact@v7 with: name: sdist path: ./dist/*.tar.gz diff --git a/.github/workflows/build-wheels-metal.yaml b/.github/workflows/build-wheels-metal.yaml index 5fd56a00d3..5f5eba6854 100644 --- a/.github/workflows/build-wheels-metal.yaml +++ b/.github/workflows/build-wheels-metal.yaml @@ -43,7 +43,7 @@ jobs: package-dir: . output-dir: wheelhouse2 - - uses: actions/upload-artifact@v4 + - uses: actions/upload-artifact@v7 with: name: wheels-mac_${{ matrix.os }} path: ./wheelhouse2/*.whl From f8bd67df8a9844d6400383a8941ef129401586a6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 3 Jun 2026 02:57:00 -0700 Subject: [PATCH 093/138] chore(deps): bump docker/setup-buildx-action from 3 to 4 (#2246) Bumps [docker/setup-buildx-action](https://github.com/docker/setup-buildx-action) from 3 to 4. - [Release notes](https://github.com/docker/setup-buildx-action/releases) - [Commits](https://github.com/docker/setup-buildx-action/compare/v3...v4) --- updated-dependencies: - dependency-name: docker/setup-buildx-action dependency-version: '4' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Andrei <abetlen@gmail.com> --- .github/workflows/build-docker.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-docker.yaml b/.github/workflows/build-docker.yaml index c656958479..3dfef68008 100644 --- a/.github/workflows/build-docker.yaml +++ b/.github/workflows/build-docker.yaml @@ -29,7 +29,7 @@ jobs: uses: docker/setup-qemu-action@v3 - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v3 + uses: docker/setup-buildx-action@v4 - name: Login to GitHub Container Registry uses: docker/login-action@v3 From 6e6c4e6db734c2b28e9ca11ade5527f2ba4f0c1e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 3 Jun 2026 03:05:10 -0700 Subject: [PATCH 094/138] chore(deps): bump actions/setup-python from 5 to 6 (#2247) Bumps [actions/setup-python](https://github.com/actions/setup-python) from 5 to 6. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/setup-python dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Andrei <abetlen@gmail.com> --- .github/workflows/build-and-release.yaml | 4 ++-- .github/workflows/build-wheels-cuda.yaml | 2 +- .github/workflows/build-wheels-metal.yaml | 2 +- .github/workflows/lint.yaml | 2 +- .github/workflows/publish-to-test.yaml | 2 +- .github/workflows/publish.yaml | 2 +- .github/workflows/test-pypi.yaml | 6 +++--- .github/workflows/test.yaml | 10 +++++----- 8 files changed, 15 insertions(+), 15 deletions(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index 9978cb66b2..81c9a961b4 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -19,7 +19,7 @@ jobs: submodules: "recursive" # Used to host cibuildwheel - - uses: actions/setup-python@v5 + - uses: actions/setup-python@v6 with: python-version: "3.9" @@ -162,7 +162,7 @@ jobs: with: submodules: "recursive" - - uses: actions/setup-python@v5 + - uses: actions/setup-python@v6 with: python-version: "3.9" diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index f1b2b8b6b5..3e67d14cba 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -67,7 +67,7 @@ jobs: with: submodules: "recursive" - - uses: actions/setup-python@v5 + - uses: actions/setup-python@v6 with: python-version: ${{ matrix.pyver }} cache: 'pip' diff --git a/.github/workflows/build-wheels-metal.yaml b/.github/workflows/build-wheels-metal.yaml index 5f5eba6854..bf2c8bc124 100644 --- a/.github/workflows/build-wheels-metal.yaml +++ b/.github/workflows/build-wheels-metal.yaml @@ -19,7 +19,7 @@ jobs: submodules: "recursive" # Used to host cibuildwheel - - uses: actions/setup-python@v5 + - uses: actions/setup-python@v6 with: python-version: "3.12" cache: 'pip' diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml index 8b3e6322dc..3c6f5ff45e 100644 --- a/.github/workflows/lint.yaml +++ b/.github/workflows/lint.yaml @@ -15,7 +15,7 @@ jobs: - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: "3.12" diff --git a/.github/workflows/publish-to-test.yaml b/.github/workflows/publish-to-test.yaml index de3ae42aae..54572bdad9 100644 --- a/.github/workflows/publish-to-test.yaml +++ b/.github/workflows/publish-to-test.yaml @@ -21,7 +21,7 @@ jobs: submodules: "recursive" - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: "3.11" cache: 'pip' diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml index bb76f53941..3c2ea56d68 100644 --- a/.github/workflows/publish.yaml +++ b/.github/workflows/publish.yaml @@ -15,7 +15,7 @@ jobs: submodules: "recursive" - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: "3.9" diff --git a/.github/workflows/test-pypi.yaml b/.github/workflows/test-pypi.yaml index 335033bba6..416cd0cddf 100644 --- a/.github/workflows/test-pypi.yaml +++ b/.github/workflows/test-pypi.yaml @@ -12,7 +12,7 @@ jobs: steps: - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} cache: 'pip' @@ -48,7 +48,7 @@ jobs: steps: - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} cache: 'pip' @@ -84,7 +84,7 @@ jobs: steps: - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} cache: 'pip' diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index d798967675..ecba1b40b0 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -23,7 +23,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: "3.9" - name: Install huggingface-hub @@ -54,7 +54,7 @@ jobs: submodules: "recursive" - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} cache: 'pip' @@ -85,7 +85,7 @@ jobs: submodules: "recursive" - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} cache: 'pip' @@ -119,7 +119,7 @@ jobs: submodules: "recursive" - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} cache: 'pip' @@ -156,7 +156,7 @@ jobs: submodules: "recursive" - name: Set up Python 3.9 - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: "3.9" From ab7a9b0c864cc82bdcfb7cae911c0bfc606883ba Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Wed, 3 Jun 2026 04:35:33 -0700 Subject: [PATCH 095/138] feat(ci): add Vulkan wheel builds (#2251) * feat(ci): add Vulkan wheel builds * docs: update changelog for Vulkan wheel builds * docs: add Vulkan wheel install instructions --- .github/workflows/build-wheels-vulkan.yaml | 123 ++++++++++++++++++ .../generate-index-from-release.yaml | 3 +- CHANGELOG.md | 1 + README.md | 9 ++ 4 files changed, 135 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/build-wheels-vulkan.yaml diff --git a/.github/workflows/build-wheels-vulkan.yaml b/.github/workflows/build-wheels-vulkan.yaml new file mode 100644 index 0000000000..760205c839 --- /dev/null +++ b/.github/workflows/build-wheels-vulkan.yaml @@ -0,0 +1,123 @@ +name: Build Wheels (Vulkan) + +on: workflow_dispatch + +permissions: + contents: write + +env: + VULKAN_SDK_VERSION: "1.4.341.0" + VULKAN_SDK_LINUX_SHA256: "ed66477d587a5587dc3601b1c2cdcc1fab5529c505f53a00171876cecd9b4fbe" + +jobs: + build_wheels: + name: Build Vulkan wheel on ${{ matrix.os }} + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + include: + - os: ubuntu-22.04 + pyver: "3.9" + artifact: wheels-vulkan-ubuntu-22.04 + - os: windows-2022 + pyver: "3.9" + artifact: wheels-vulkan-windows-2022 + + steps: + - name: Set up MSVC + if: runner.os == 'Windows' + uses: ilammy/msvc-dev-cmd@v1 + with: + arch: x64 + + - uses: actions/checkout@v4 + with: + submodules: "recursive" + + - uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.pyver }} + cache: "pip" + + - name: Install Vulkan SDK + if: runner.os == 'Linux' + run: | + curl -fL \ + "https://sdk.lunarg.com/sdk/download/${VULKAN_SDK_VERSION}/linux/vulkansdk-linux-x86_64-${VULKAN_SDK_VERSION}.tar.xz" \ + -o vulkan-sdk.tar.xz + echo "${VULKAN_SDK_LINUX_SHA256} vulkan-sdk.tar.xz" | sha256sum -c - + mkdir -p "$RUNNER_TEMP/vulkan-sdk" + tar -xf vulkan-sdk.tar.xz -C "$RUNNER_TEMP/vulkan-sdk" + source "$RUNNER_TEMP/vulkan-sdk/${VULKAN_SDK_VERSION}/setup-env.sh" + { + echo "VULKAN_SDK=$VULKAN_SDK" + echo "LD_LIBRARY_PATH=$VULKAN_SDK/lib:${LD_LIBRARY_PATH:-}" + } >> "$GITHUB_ENV" + echo "$VULKAN_SDK/bin" >> "$GITHUB_PATH" + "$VULKAN_SDK/bin/glslc" --version + + - name: Install Vulkan SDK + if: runner.os == 'Windows' + shell: pwsh + run: | + choco install vulkan-sdk --version="$env:VULKAN_SDK_VERSION" --no-progress -y + $vulkanSdk = Join-Path 'C:\VulkanSDK' $env:VULKAN_SDK_VERSION + if (-not (Test-Path $vulkanSdk)) { + throw "Failed to find Vulkan SDK at $vulkanSdk" + } + "VULKAN_SDK=$vulkanSdk" >> $env:GITHUB_ENV + "$vulkanSdk\Bin" >> $env:GITHUB_PATH + & "$vulkanSdk\Bin\glslc.exe" --version + + - name: Install build dependencies + run: | + python -m pip install --upgrade pip + python -m pip install build wheel + + - name: Install Windows build dependencies + if: runner.os == 'Windows' + run: python -m pip install ninja + + - name: Build Vulkan wheel + if: runner.os == 'Linux' + run: | + export CMAKE_ARGS="-DGGML_NATIVE=off -DGGML_METAL=OFF -DGGML_VULKAN=on" + python -m build --wheel + mkdir -p wheelhouse + cp dist/*.whl wheelhouse/ + + - name: Build Vulkan wheel + if: runner.os == 'Windows' + shell: pwsh + run: | + $env:CMAKE_GENERATOR = 'Ninja' + $env:CMAKE_ARGS = '-DGGML_NATIVE=off -DGGML_VULKAN=on' + python -m build --wheel + New-Item -ItemType Directory -Force wheelhouse | Out-Null + Copy-Item dist/*.whl wheelhouse/ + + - uses: actions/upload-artifact@v7 + with: + name: ${{ matrix.artifact }} + path: ./wheelhouse/*.whl + + release: + name: Release + needs: [build_wheels] + if: startsWith(github.ref, 'refs/tags/') + runs-on: ubuntu-latest + + steps: + - uses: actions/download-artifact@v4 + with: + merge-multiple: true + path: dist + + - uses: softprops/action-gh-release@v2 + with: + files: dist/* + # Set release name to <tag>-vulkan. + tag_name: ${{ github.ref_name }}-vulkan + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/generate-index-from-release.yaml b/.github/workflows/generate-index-from-release.yaml index 255ee67d6f..7fc19e697f 100644 --- a/.github/workflows/generate-index-from-release.yaml +++ b/.github/workflows/generate-index-from-release.yaml @@ -3,7 +3,7 @@ name: Wheels Index on: # Trigger on new release workflow_run: - workflows: ["Release", "Build Wheels (CUDA)", "Build Wheels (Metal)"] + workflows: ["Release", "Build Wheels (CUDA)", "Build Wheels (Metal)", "Build Wheels (Vulkan)"] types: - completed @@ -46,6 +46,7 @@ jobs: ./scripts/releases-to-pep-503.sh index/whl/cu124 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$' # ./scripts/releases-to-pep-503.sh index/whl/cu125 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$' # ./scripts/releases-to-pep-503.sh index/whl/cu126 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$' + ./scripts/releases-to-pep-503.sh index/whl/vulkan '^[v]?[0-9]+\.[0-9]+\.[0-9]+-vulkan$' ./scripts/releases-to-pep-503.sh index/whl/metal '^[v]?[0-9]+\.[0-9]+\.[0-9]+-metal$' - name: Upload artifact uses: actions/upload-pages-artifact@v3 diff --git a/CHANGELOG.md b/CHANGELOG.md index 6677fc8fe0..1e2a180fd9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat(ci): add Vulkan wheel builds by @abetlen in #2251 - fix: handle additional `from_pretrained` files in subfolders by @TNing in #2085 ## [0.3.25] diff --git a/README.md b/README.md index 7db3e27448..57ede08768 100644 --- a/README.md +++ b/README.md @@ -196,6 +196,15 @@ To install with Vulkan support, set the `GGML_VULKAN=on` environment variable be CMAKE_ARGS="-DGGML_VULKAN=on" pip install llama-cpp-python ``` +**Pre-built Wheel (New)** + +It is also possible to install a pre-built wheel with Vulkan support for Linux or Windows: + +```bash +pip install llama-cpp-python \ + --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/vulkan +``` + </details> <details> From 3754c04a168ce35a3e5bbe8f600014fc70970415 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Wed, 3 Jun 2026 04:37:24 -0700 Subject: [PATCH 096/138] feat(ci): add ROCm wheel builds (#2252) * feat(ci): add ROCm wheel builds * docs: update changelog for ROCm wheel builds * docs: add ROCm wheel install instructions --- .github/workflows/build-wheels-rocm.yaml | 237 ++++++++++++++++++ .../generate-index-from-release.yaml | 4 +- CHANGELOG.md | 1 + README.md | 16 ++ 4 files changed, 257 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/build-wheels-rocm.yaml diff --git a/.github/workflows/build-wheels-rocm.yaml b/.github/workflows/build-wheels-rocm.yaml new file mode 100644 index 0000000000..0971953886 --- /dev/null +++ b/.github/workflows/build-wheels-rocm.yaml @@ -0,0 +1,237 @@ +name: Build Wheels (ROCm) + +on: workflow_dispatch + +permissions: + contents: write + +jobs: + build_wheels: + name: Build Wheel ${{ matrix.os }} ${{ matrix.pyver }} ROCm ${{ matrix.rocm }} + runs-on: ${{ matrix.os }} + container: + image: rocm/dev-ubuntu-22.04:${{ matrix.rocm }}-complete + strategy: + fail-fast: false + matrix: + include: + - os: ubuntu-22.04 + pyver: "3.9" + rocm: "7.2.4" + amdgpu_targets: gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1200;gfx1201 + + steps: + - name: Install system dependencies + run: | + apt-get update + apt-get install -y --no-install-recommends git cmake lsb-release ninja-build + + - uses: actions/checkout@v4 + with: + submodules: "recursive" + + - uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.pyver }} + cache: "pip" + + - name: Install build dependencies + run: | + python -m pip install --upgrade pip + python -m pip install build wheel + + - name: Build ROCm wheel + run: | + export ROCM_PATH="${ROCM_PATH:-/opt/rocm}" + export HIP_PATH="${HIP_PATH:-$ROCM_PATH}" + export PATH="$ROCM_PATH/bin:$ROCM_PATH/llvm/bin:$PATH" + export LD_LIBRARY_PATH="$ROCM_PATH/lib:$ROCM_PATH/lib64:${LD_LIBRARY_PATH:-}" + export CC="$ROCM_PATH/llvm/bin/clang" + export CXX="$ROCM_PATH/llvm/bin/clang++" + export HIPCXX="$ROCM_PATH/llvm/bin/clang" + export CMAKE_GENERATOR=Ninja + + hipconfig --version + hipcc --version + + rocm_tag="$(hipconfig --version | sed -E 's/^([0-9]+)\.([0-9]+).*/\1\2/')" + echo "ROCM_VERSION=$rocm_tag" >> "$GITHUB_ENV" + + amdgpu_targets="${{ matrix.amdgpu_targets }}" + export CMAKE_ARGS="-DGGML_HIP=on -DGGML_NATIVE=off -DGGML_AVX=off -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off -DAMDGPU_TARGETS=$amdgpu_targets -DCMAKE_HIP_ARCHITECTURES=$amdgpu_targets" + python -m build --wheel + mkdir -p wheelhouse + cp dist/*.whl wheelhouse/ + + - uses: actions/upload-artifact@v7 + with: + name: wheels-rocm${{ env.ROCM_VERSION }}-${{ matrix.os }} + path: ./wheelhouse/*.whl + + build_wheels_windows_hip: + name: Build Wheel windows-2022 ${{ matrix.pyver }} HIP ${{ matrix.name }} + runs-on: windows-2022 + env: + HIPSDK_INSTALLER_VERSION: "26.Q1" + strategy: + fail-fast: false + matrix: + include: + - name: radeon + pyver: "3.9" + amdgpu_targets: gfx1150;gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032 + + steps: + - uses: actions/checkout@v4 + with: + submodules: "recursive" + + - uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.pyver }} + cache: "pip" + + - name: Set up MSVC + uses: ilammy/msvc-dev-cmd@v1 + with: + arch: x64 + + - name: Install build dependencies + run: | + python -m pip install --upgrade pip + python -m pip install build wheel + + - name: Grab rocWMMA package + run: | + curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.2.1/pool/main/r/rocwmma-dev/rocwmma-dev_2.2.0.70201-81~24.04_amd64.deb" + 7z x rocwmma.deb + 7z x data.tar + + - name: Cache ROCm installation + id: cache-rocm + uses: actions/cache@v5 + with: + path: C:\Program Files\AMD\ROCm + key: cache-gha-rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }} + + - name: Install ROCm + if: steps.cache-rocm.outputs.cache-hit != 'true' + run: | + $ErrorActionPreference = "Stop" + Invoke-WebRequest ` + -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-Win11-For-HIP.exe" ` + -OutFile "${env:RUNNER_TEMP}\rocm-install.exe" + $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru + $completed = $proc.WaitForExit(1800000) + if (-not $completed) { + $proc.Kill() + throw "ROCm installation timed out after 30 minutes" + } + if ($proc.ExitCode -ne 0) { + throw "ROCm installation failed with exit code $($proc.ExitCode)" + } + + - name: Verify ROCm + run: | + $clangPath = Get-ChildItem 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Select-Object -First 1 + if (-not $clangPath) { + throw "ROCm installation not found" + } + & $clangPath.FullName --version + + - name: Build HIP wheel + run: | + $ErrorActionPreference = "Stop" + $hipPath = Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Split-Path | Split-Path + $rocwmmaInclude = (Join-Path $PWD 'opt\rocm-7.2.1\include').Replace('\', '/') + $amdgpuTargets = "${{ matrix.amdgpu_targets }}" + + $env:HIP_PATH = $hipPath + $env:ROCM_PATH = $hipPath + $env:CMAKE_PREFIX_PATH = $hipPath + $env:HIP_PLATFORM = 'amd' + $env:PATH = "$hipPath\bin;$env:PATH" + $env:CC = "$hipPath\bin\clang.exe" + $env:CXX = "$hipPath\bin\clang++.exe" + $env:HIPCXX = "$hipPath\bin\clang.exe" + $env:CMAKE_GENERATOR = 'Unix Makefiles' + $env:CXXFLAGS = "-I$rocwmmaInclude -Wno-ignored-attributes -Wno-nested-anon-types" + $env:CMAKE_ARGS = "-DGGML_HIP=ON -DGGML_HIP_ROCWMMA_FATTN=ON -DGGML_NATIVE=OFF -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DGGML_F16C=OFF -DGPU_TARGETS=$amdgpuTargets" + + python -m build --wheel + + - name: Bundle ROCm runtime DLLs + run: | + $ErrorActionPreference = "Stop" + $hipPath = Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Split-Path | Split-Path + $wheel = Get-ChildItem dist\*.whl | Select-Object -First 1 + python -m wheel unpack $wheel.FullName -d wheel-unpacked + $wheelRoot = Get-ChildItem wheel-unpacked -Directory | Select-Object -First 1 + $libDir = Join-Path $wheelRoot.FullName 'llama_cpp\lib' + New-Item -ItemType Directory -Force $libDir | Out-Null + + $dllPatterns = @( + 'amdhip64.dll', + 'hiprtc*.dll', + 'libhipblas.dll', + 'libhipblaslt.dll', + 'rocblas.dll' + ) + foreach ($pattern in $dllPatterns) { + Copy-Item (Join-Path $hipPath "bin\$pattern") $libDir -ErrorAction SilentlyContinue + } + + New-Item -ItemType Directory -Force (Join-Path $libDir 'rocblas\library') | Out-Null + New-Item -ItemType Directory -Force (Join-Path $libDir 'hipblaslt\library') | Out-Null + Copy-Item "$hipPath\bin\rocblas\library\*" (Join-Path $libDir 'rocblas\library') -Recurse -Force + Copy-Item "$hipPath\bin\hipblaslt\library\*" (Join-Path $libDir 'hipblaslt\library') -Recurse -Force + + Remove-Item dist\*.whl + python -m wheel pack $wheelRoot.FullName -d dist + New-Item -ItemType Directory -Force wheelhouse | Out-Null + Copy-Item dist/*.whl wheelhouse/ + + - uses: actions/upload-artifact@v7 + with: + name: wheels-hip-${{ matrix.name }}-windows-2022 + path: ./wheelhouse/*.whl + + release_rocm: + name: Release ROCm + needs: [build_wheels] + if: startsWith(github.ref, 'refs/tags/') + runs-on: ubuntu-latest + + steps: + - uses: actions/download-artifact@v4 + with: + merge-multiple: true + path: dist + + - uses: softprops/action-gh-release@v2 + with: + files: dist/* + # Set release name to <tag>-rocm<rocm_version>. + tag_name: ${{ github.ref_name }}-rocm72 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + + release_hip: + name: Release HIP + needs: [build_wheels_windows_hip] + if: startsWith(github.ref, 'refs/tags/') + runs-on: ubuntu-latest + + steps: + - uses: actions/download-artifact@v4 + with: + merge-multiple: true + path: dist + + - uses: softprops/action-gh-release@v2 + with: + files: dist/* + # Set release name to <tag>-hip-radeon. + tag_name: ${{ github.ref_name }}-hip-radeon + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/generate-index-from-release.yaml b/.github/workflows/generate-index-from-release.yaml index 7fc19e697f..c93e0be351 100644 --- a/.github/workflows/generate-index-from-release.yaml +++ b/.github/workflows/generate-index-from-release.yaml @@ -3,7 +3,7 @@ name: Wheels Index on: # Trigger on new release workflow_run: - workflows: ["Release", "Build Wheels (CUDA)", "Build Wheels (Metal)", "Build Wheels (Vulkan)"] + workflows: ["Release", "Build Wheels (CUDA)", "Build Wheels (Metal)", "Build Wheels (Vulkan)", "Build Wheels (ROCm)"] types: - completed @@ -46,6 +46,8 @@ jobs: ./scripts/releases-to-pep-503.sh index/whl/cu124 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$' # ./scripts/releases-to-pep-503.sh index/whl/cu125 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$' # ./scripts/releases-to-pep-503.sh index/whl/cu126 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$' + ./scripts/releases-to-pep-503.sh index/whl/rocm72 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-rocm72$' + ./scripts/releases-to-pep-503.sh index/whl/hip-radeon '^[v]?[0-9]+\.[0-9]+\.[0-9]+-hip-radeon$' ./scripts/releases-to-pep-503.sh index/whl/vulkan '^[v]?[0-9]+\.[0-9]+\.[0-9]+-vulkan$' ./scripts/releases-to-pep-503.sh index/whl/metal '^[v]?[0-9]+\.[0-9]+\.[0-9]+-metal$' - name: Upload artifact diff --git a/CHANGELOG.md b/CHANGELOG.md index 1e2a180fd9..f46b697c66 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat(ci): add ROCm wheel builds by @abetlen in #2252 - feat(ci): add Vulkan wheel builds by @abetlen in #2251 - fix: handle additional `from_pretrained` files in subfolders by @TNing in #2085 diff --git a/README.md b/README.md index 57ede08768..dd0024676a 100644 --- a/README.md +++ b/README.md @@ -185,6 +185,22 @@ To install with HIP / ROCm support for AMD cards, set the `GGML_HIP=on` environm CMAKE_ARGS="-DGGML_HIP=on" pip install llama-cpp-python ``` +**Pre-built Wheel (New)** + +It is also possible to install a pre-built wheel with ROCm support for Linux: + +```bash +pip install llama-cpp-python \ + --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/rocm72 +``` + +Or a pre-built wheel with HIP Radeon support for Windows: + +```powershell +pip install llama-cpp-python ` + --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/hip-radeon +``` + </details> <details> From ddaac1033590941c25ce367d3af0768c4f5d5a0b Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Wed, 3 Jun 2026 04:50:45 -0700 Subject: [PATCH 097/138] feat: update llama.cpp (#2253) * feat: update llama.cpp * docs: update changelog for llama.cpp update --- CHANGELOG.md | 1 + vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index f46b697c66..462f0d0c18 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: update llama.cpp to ggml-org/llama.cpp@3571fa543 by @abetlen in #2253 - feat(ci): add ROCm wheel builds by @abetlen in #2252 - feat(ci): add Vulkan wheel builds by @abetlen in #2251 - fix: handle additional `from_pretrained` files in subfolders by @TNing in #2085 diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 210a6570ce..3571fa5435 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 210a6570ceda20c5d6439172c09ada08c3754cc9 +Subproject commit 3571fa5435ac9ff243662b1caabc407e8d433c9d From 8d2d2699c89a5b4ac9eb929753cbf3ba5f544c0e Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Wed, 3 Jun 2026 11:20:40 -0700 Subject: [PATCH 098/138] feat: update llama.cpp (#2254) * feat: update llama.cpp * docs: update changelog for llama.cpp update * docs: consolidate llama.cpp changelog entry --- CHANGELOG.md | 2 +- vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 462f0d0c18..72b122408c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] -- feat: update llama.cpp to ggml-org/llama.cpp@3571fa543 by @abetlen in #2253 +- feat: update llama.cpp to ggml-org/llama.cpp@166fe2949 - feat(ci): add ROCm wheel builds by @abetlen in #2252 - feat(ci): add Vulkan wheel builds by @abetlen in #2251 - fix: handle additional `from_pretrained` files in subfolders by @TNing in #2085 diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 3571fa5435..166fe29492 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 3571fa5435ac9ff243662b1caabc407e8d433c9d +Subproject commit 166fe29492abb4093ec889b5c6f6fdb4e3b8ba98 From cc2efc5843287fcd3e7f4cbd85a4f38eff3025fe Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Wed, 3 Jun 2026 12:55:01 -0700 Subject: [PATCH 099/138] feat: update llama.cpp to ggml-org/llama.cpp@94a220cd6 (#2255) --- CHANGELOG.md | 2 +- vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 72b122408c..243c344c13 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] -- feat: update llama.cpp to ggml-org/llama.cpp@166fe2949 +- feat: update llama.cpp to ggml-org/llama.cpp@94a220cd6 - feat(ci): add ROCm wheel builds by @abetlen in #2252 - feat(ci): add Vulkan wheel builds by @abetlen in #2251 - fix: handle additional `from_pretrained` files in subfolders by @TNing in #2085 diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 166fe29492..94a220cd67 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 166fe29492abb4093ec889b5c6f6fdb4e3b8ba98 +Subproject commit 94a220cd6745e6e3f8de62870b66fd5b9bc92700 From e2d148a9647b579465534576c8cda30a7816377e Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Thu, 4 Jun 2026 03:15:25 -0700 Subject: [PATCH 100/138] feat: update llama.cpp to ggml-org/llama.cpp@e3ba22d6c (#2262) --- CHANGELOG.md | 2 +- vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 243c344c13..452b90ef72 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] -- feat: update llama.cpp to ggml-org/llama.cpp@94a220cd6 +- feat: update llama.cpp to ggml-org/llama.cpp@e3ba22d6c - feat(ci): add ROCm wheel builds by @abetlen in #2252 - feat(ci): add Vulkan wheel builds by @abetlen in #2251 - fix: handle additional `from_pretrained` files in subfolders by @TNing in #2085 diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 94a220cd67..e3ba22d6cc 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 94a220cd6745e6e3f8de62870b66fd5b9bc92700 +Subproject commit e3ba22d6cc4dec84e59a909c7f96e1689c7384a9 From d0990015d3f985be1f594506717709b123fcfcd4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 4 Jun 2026 03:23:49 -0700 Subject: [PATCH 101/138] chore(deps): bump actions/download-artifact from 4 to 8 (#2257) --- .github/workflows/build-and-release.yaml | 2 +- .github/workflows/build-wheels-metal.yaml | 2 +- .github/workflows/build-wheels-rocm.yaml | 4 ++-- .github/workflows/build-wheels-vulkan.yaml | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index 81c9a961b4..3a61f75241 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -202,7 +202,7 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@v8 with: merge-multiple: true path: dist diff --git a/.github/workflows/build-wheels-metal.yaml b/.github/workflows/build-wheels-metal.yaml index bf2c8bc124..83d1a31753 100644 --- a/.github/workflows/build-wheels-metal.yaml +++ b/.github/workflows/build-wheels-metal.yaml @@ -54,7 +54,7 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@v8 with: merge-multiple: true path: dist2 diff --git a/.github/workflows/build-wheels-rocm.yaml b/.github/workflows/build-wheels-rocm.yaml index 0971953886..afb4fefd3d 100644 --- a/.github/workflows/build-wheels-rocm.yaml +++ b/.github/workflows/build-wheels-rocm.yaml @@ -203,7 +203,7 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@v8 with: merge-multiple: true path: dist @@ -223,7 +223,7 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@v8 with: merge-multiple: true path: dist diff --git a/.github/workflows/build-wheels-vulkan.yaml b/.github/workflows/build-wheels-vulkan.yaml index 760205c839..a42ee89b28 100644 --- a/.github/workflows/build-wheels-vulkan.yaml +++ b/.github/workflows/build-wheels-vulkan.yaml @@ -109,7 +109,7 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@v8 with: merge-multiple: true path: dist From df45432b4bc778cc76f90b0e0530428cb0ca03bc Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 4 Jun 2026 03:33:33 -0700 Subject: [PATCH 102/138] chore(deps): bump actions/checkout from 4 to 6 (#2258) Bumps [actions/checkout](https://github.com/actions/checkout) from 4 to 6. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v4...v6) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Andrei <abetlen@gmail.com> --- .github/workflows/build-and-release.yaml | 8 ++++---- .github/workflows/build-docker.yaml | 2 +- .github/workflows/build-wheels-cuda.yaml | 2 +- .github/workflows/build-wheels-metal.yaml | 2 +- .github/workflows/build-wheels-rocm.yaml | 4 ++-- .github/workflows/build-wheels-vulkan.yaml | 2 +- .github/workflows/generate-index-from-release.yaml | 2 +- .github/workflows/lint.yaml | 2 +- .github/workflows/publish-to-test.yaml | 2 +- .github/workflows/publish.yaml | 2 +- .github/workflows/test.yaml | 8 ++++---- 11 files changed, 18 insertions(+), 18 deletions(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index 3a61f75241..0b974e93c8 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -14,7 +14,7 @@ jobs: os: [ubuntu-22.04, windows-2022, macos-14, macos-15] steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" @@ -75,7 +75,7 @@ jobs: name: Build arm64 wheels runs-on: ubuntu-24.04-arm steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" @@ -123,7 +123,7 @@ jobs: build: "cp314-*" artifact: wheels_riscv64_cp314 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" @@ -158,7 +158,7 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" diff --git a/.github/workflows/build-docker.yaml b/.github/workflows/build-docker.yaml index 3dfef68008..b547f32ff5 100644 --- a/.github/workflows/build-docker.yaml +++ b/.github/workflows/build-docker.yaml @@ -12,7 +12,7 @@ jobs: runs-on: ubuntu-22.04 steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@v6 with: submodules: "recursive" diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index 3e67d14cba..6106a08817 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -63,7 +63,7 @@ jobs: with: arch: x64 - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" diff --git a/.github/workflows/build-wheels-metal.yaml b/.github/workflows/build-wheels-metal.yaml index 83d1a31753..ca8e322230 100644 --- a/.github/workflows/build-wheels-metal.yaml +++ b/.github/workflows/build-wheels-metal.yaml @@ -14,7 +14,7 @@ jobs: os: [macos-14, macos-15] steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" diff --git a/.github/workflows/build-wheels-rocm.yaml b/.github/workflows/build-wheels-rocm.yaml index afb4fefd3d..792e33cf89 100644 --- a/.github/workflows/build-wheels-rocm.yaml +++ b/.github/workflows/build-wheels-rocm.yaml @@ -26,7 +26,7 @@ jobs: apt-get update apt-get install -y --no-install-recommends git cmake lsb-release ninja-build - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" @@ -82,7 +82,7 @@ jobs: amdgpu_targets: gfx1150;gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" diff --git a/.github/workflows/build-wheels-vulkan.yaml b/.github/workflows/build-wheels-vulkan.yaml index a42ee89b28..49d38fb80b 100644 --- a/.github/workflows/build-wheels-vulkan.yaml +++ b/.github/workflows/build-wheels-vulkan.yaml @@ -31,7 +31,7 @@ jobs: with: arch: x64 - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" diff --git a/.github/workflows/generate-index-from-release.yaml b/.github/workflows/generate-index-from-release.yaml index c93e0be351..a39776eb07 100644 --- a/.github/workflows/generate-index-from-release.yaml +++ b/.github/workflows/generate-index-from-release.yaml @@ -31,7 +31,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@v6 - name: Setup Pages uses: actions/configure-pages@v5 - name: Build diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml index 3c6f5ff45e..ac39181f90 100644 --- a/.github/workflows/lint.yaml +++ b/.github/workflows/lint.yaml @@ -12,7 +12,7 @@ jobs: ruff: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 - name: Set up Python uses: actions/setup-python@v6 diff --git a/.github/workflows/publish-to-test.yaml b/.github/workflows/publish-to-test.yaml index 54572bdad9..70ae5389af 100644 --- a/.github/workflows/publish-to-test.yaml +++ b/.github/workflows/publish-to-test.yaml @@ -16,7 +16,7 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml index 3c2ea56d68..8908ccdd6f 100644 --- a/.github/workflows/publish.yaml +++ b/.github/workflows/publish.yaml @@ -10,7 +10,7 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index ecba1b40b0..82f7a8a7de 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -49,7 +49,7 @@ jobs: matrix: python-version: ["3.9", "3.10", "3.11", "3.12"] steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" @@ -80,7 +80,7 @@ jobs: matrix: python-version: ["3.9", "3.10", "3.11", "3.12"] steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" @@ -114,7 +114,7 @@ jobs: matrix: python-version: ["3.9", "3.10", "3.11", "3.12"] steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" @@ -151,7 +151,7 @@ jobs: needs: download-model runs-on: macos-15-intel steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 with: submodules: "recursive" From b46bccf4f9ce9cc48f464c1068f39963758d1213 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 4 Jun 2026 03:33:54 -0700 Subject: [PATCH 103/138] chore(deps): bump docker/login-action from 3 to 4 (#2259) Bumps [docker/login-action](https://github.com/docker/login-action) from 3 to 4. - [Release notes](https://github.com/docker/login-action/releases) - [Commits](https://github.com/docker/login-action/compare/v3...v4) --- updated-dependencies: - dependency-name: docker/login-action dependency-version: '4' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Andrei <abetlen@gmail.com> --- .github/workflows/build-docker.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-docker.yaml b/.github/workflows/build-docker.yaml index b547f32ff5..349eb209dd 100644 --- a/.github/workflows/build-docker.yaml +++ b/.github/workflows/build-docker.yaml @@ -32,7 +32,7 @@ jobs: uses: docker/setup-buildx-action@v4 - name: Login to GitHub Container Registry - uses: docker/login-action@v3 + uses: docker/login-action@v4 with: registry: ghcr.io username: ${{ github.repository_owner }} From 927dde2189fa820dc7e550c97cb8b1d02d6fd0bc Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 4 Jun 2026 03:34:13 -0700 Subject: [PATCH 104/138] chore(deps): bump actions/deploy-pages from 4 to 5 (#2260) Bumps [actions/deploy-pages](https://github.com/actions/deploy-pages) from 4 to 5. - [Release notes](https://github.com/actions/deploy-pages/releases) - [Commits](https://github.com/actions/deploy-pages/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/deploy-pages dependency-version: '5' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Andrei <abetlen@gmail.com> --- .github/workflows/generate-index-from-release.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/generate-index-from-release.yaml b/.github/workflows/generate-index-from-release.yaml index a39776eb07..6b2c8770fa 100644 --- a/.github/workflows/generate-index-from-release.yaml +++ b/.github/workflows/generate-index-from-release.yaml @@ -57,4 +57,4 @@ jobs: path: 'index' - name: Deploy to GitHub Pages id: deployment - uses: actions/deploy-pages@v4 + uses: actions/deploy-pages@v5 From 9f6efb073c65749d67e904b61f4356c6261f4be5 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 4 Jun 2026 03:34:30 -0700 Subject: [PATCH 105/138] chore(deps): bump conda-incubator/setup-miniconda from 3.1.0 to 4.0.1 (#2261) Bumps [conda-incubator/setup-miniconda](https://github.com/conda-incubator/setup-miniconda) from 3.1.0 to 4.0.1. - [Release notes](https://github.com/conda-incubator/setup-miniconda/releases) - [Changelog](https://github.com/conda-incubator/setup-miniconda/blob/main/CHANGELOG.md) - [Commits](https://github.com/conda-incubator/setup-miniconda/compare/v3.1.0...v4.0.1) --- updated-dependencies: - dependency-name: conda-incubator/setup-miniconda dependency-version: 4.0.1 dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Andrei <abetlen@gmail.com> --- .github/workflows/build-wheels-cuda.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index 6106a08817..f5b3718f17 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -73,7 +73,7 @@ jobs: cache: 'pip' - name: Setup Mamba - uses: conda-incubator/setup-miniconda@v3.1.0 + uses: conda-incubator/setup-miniconda@v4.0.1 with: activate-environment: "llamacpp" python-version: ${{ matrix.pyver }} From 2dae4770db6a2c34bf3a5f7f76b4d4bc8045bfd0 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Thu, 4 Jun 2026 04:14:05 -0700 Subject: [PATCH 106/138] feat: Generic Multimodal Chat Handler (#2256) --- CHANGELOG.md | 1 + README.md | 1 + llama_cpp/llama_chat_format.py | 546 ++++++++++++++++++++++++++++++--- llama_cpp/server/model.py | 19 +- 4 files changed, 518 insertions(+), 49 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 452b90ef72..18bcf258a0 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: Generic Multimodal Chat Handler by @abetlen in #2256 - feat: update llama.cpp to ggml-org/llama.cpp@e3ba22d6c - feat(ci): add ROCm wheel builds by @abetlen in #2252 - feat(ci): add Vulkan wheel builds by @abetlen in #2251 diff --git a/README.md b/README.md index dd0024676a..5711d4afbb 100644 --- a/README.md +++ b/README.md @@ -536,6 +536,7 @@ Below are the supported multi-modal models and their respective chat handlers (P | [minicpm-v-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | `MiniCPMv26ChatHandler` | `minicpm-v-2.6` | | [qwen2.5-vl](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-GGUF) | `Qwen25VLChatHandler` | `qwen2.5-vl` | | [gemma-4](https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF) | `Gemma4ChatHandler` | `gemma4` | +| GGUF models with an mtmd projector and embedded chat template | `MTMDChatHandler` | `mtmd` | Then you'll need to use a custom chat handler to load the clip model and process the chat messages and images. diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index 44c6c1f76f..4298642f67 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -3265,48 +3265,512 @@ def from_pretrained( ) -class Gemma4ChatHandler(Llava15ChatHandler): - DEFAULT_SYSTEM_MESSAGE = None +class MTMDChatHandler: + def __init__( + self, clip_model_path: str, verbose: bool = True, use_gpu: bool = True + ): + import llama_cpp.mtmd_cpp as mtmd_cpp - CHAT_FORMAT = ( - "{% if messages and messages[0]['role'] == 'system' %}" - "{% if messages[0]['content'] is string %}" - "{% set first_user_prefix = messages[0]['content'] + '\n\n' %}" - "{% else %}" - "{% set first_user_prefix = messages[0]['content'][0]['text'] + '\n\n' %}" - "{% endif %}" - "{% set loop_messages = messages[1:] %}" - "{% else %}" - "{% set first_user_prefix = '' %}" - "{% set loop_messages = messages %}" - "{% endif %}" - "{% for message in loop_messages %}" - "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}" - "{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}" - "{% endif %}" - "{% set role = 'model' if message['role'] == 'assistant' else message['role'] %}" - "{{ '<start_of_turn>' + role + '\n' + (first_user_prefix if loop.first else '') }}" - "{% if message['content'] is string %}" - "{{ message['content'] | trim }}" - "{% elif message['content'] is iterable %}" - "{% for item in message['content'] %}" - "{% if item['type'] == 'image_url' and item['image_url'] is string %}" - "{{ '\n\n' + item['image_url'] + '\n\n' }}" - "{% elif item['type'] == 'image_url' and item['image_url'] is mapping %}" - "{{ '\n\n' + item['image_url']['url'] + '\n\n' }}" - "{% elif item['type'] == 'text' %}" - "{{ item['text'] | trim }}" - "{% endif %}" - "{% endfor %}" - "{% else %}" - "{{ raise_exception('Invalid content type') }}" - "{% endif %}" - "{{ '<end_of_turn>\n' }}" - "{% endfor %}" - "{% if add_generation_prompt %}" - "{{ '<start_of_turn>model\n' }}" - "{% endif %}" - ) + self.clip_model_path = clip_model_path + self.verbose = verbose + self.use_gpu = use_gpu + self._mtmd_cpp = mtmd_cpp + self._exit_stack = ExitStack() + self.mtmd_ctx: Optional[mtmd_cpp.mtmd_context_p] = None + + if not os.path.exists(clip_model_path): + raise ValueError(f"Clip model path does not exist: {clip_model_path}") + + def _init_mtmd_context(self, llama_model: llama.Llama): + self.verbose = llama_model.verbose + if self.mtmd_ctx is not None: + return + + with suppress_stdout_stderr(disable=self.verbose): + ctx_params = self._mtmd_cpp.mtmd_context_params_default() + ctx_params.use_gpu = self.use_gpu + ctx_params.print_timings = self.verbose + ctx_params.n_threads = llama_model.n_threads + ctx_params.flash_attn_type = ( + llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED + if ( + llama_model.context_params.flash_attn_type + == llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED + ) + else llama_cpp.LLAMA_FLASH_ATTN_TYPE_DISABLED + ) + + self.mtmd_ctx = self._mtmd_cpp.mtmd_init_from_file( + self.clip_model_path.encode(), llama_model.model, ctx_params + ) + + if self.mtmd_ctx is None: + raise ValueError( + f"Failed to load mtmd context from: {self.clip_model_path}" + ) + + if not self._mtmd_cpp.mtmd_support_vision(self.mtmd_ctx): + raise ValueError("Vision is not supported by this model") + + def mtmd_free(): + with suppress_stdout_stderr(disable=self.verbose): + if self.mtmd_ctx is not None: + self._mtmd_cpp.mtmd_free(self.mtmd_ctx) + self.mtmd_ctx = None + + self._exit_stack.callback(mtmd_free) + + def load_image(self, image_url: str) -> bytes: + return self._load_image(image_url) + + def _create_bitmap_from_bytes(self, image_bytes: bytes): + if self.mtmd_ctx is None: + raise ValueError("mtmd context not initialized") + + with suppress_stdout_stderr(disable=self.verbose): + bitmap = self._mtmd_cpp.mtmd_helper_bitmap_init_from_buf( + self.mtmd_ctx, + (ctypes.c_uint8 * len(image_bytes)).from_buffer(bytearray(image_bytes)), + len(image_bytes), + ) + + if bitmap is None: + raise ValueError("Failed to create bitmap from image bytes") + + return bitmap + + def _get_chat_template(self, llama_model: llama.Llama) -> str: + chat_template = llama_model.metadata.get("tokenizer.chat_template") + if not isinstance(chat_template, str) or chat_template == "": + raise ValueError( + f"{self.__class__.__name__} requires tokenizer.chat_template metadata" + ) + return chat_template + + def _get_template_messages( + self, + messages: List[llama_types.ChatCompletionRequestMessage], + media_marker: str, + ) -> List[Any]: + return [ + self._convert_message_for_template(message, media_marker) + for message in messages + ] + + @classmethod + def _convert_message_for_template( + cls, + message: llama_types.ChatCompletionRequestMessage, + media_marker: str, + ) -> Dict[str, Any]: + message_dict = dict(message) + content = message_dict.get("content") + if isinstance(content, list): + message_dict["content"] = [ + cls._convert_content_part_for_template(part, media_marker) + for part in content + ] + return message_dict + + @staticmethod + def _convert_content_part_for_template( + part: Any, + media_marker: str, + ) -> Any: + if isinstance(part, dict) and part.get("type") == "image_url": + return {"type": "text", "text": media_marker} + return part + + @staticmethod + def _decode_token_piece(piece: Any) -> str: + if isinstance(piece, bytes): + return piece.decode("utf-8", errors="ignore") + return str(piece) + + def _postprocess_template_text( + self, + text: str, + image_urls: List[str], + media_marker: str, + ) -> str: + for image_url in image_urls: + text = text.replace(image_url, media_marker) + return text + + def __call__( + self, + *, + llama: llama.Llama, + messages: List[llama_types.ChatCompletionRequestMessage], + functions: Optional[List[llama_types.ChatCompletionFunction]] = None, + function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None, + tools: Optional[List[llama_types.ChatCompletionTool]] = None, + tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None, + temperature: float = 0.2, + top_p: float = 0.95, + top_k: int = 40, + min_p: float = 0.05, + typical_p: float = 1.0, + stream: bool = False, + stop: Optional[Union[str, List[str]]] = [], + seed: Optional[int] = None, + response_format: Optional[ + llama_types.ChatCompletionRequestResponseFormat + ] = None, + max_tokens: Optional[int] = None, + presence_penalty: float = 0.0, + frequency_penalty: float = 0.0, + repeat_penalty: float = 1.1, + tfs_z: float = 1.0, + mirostat_mode: int = 0, + mirostat_tau: float = 5.0, + mirostat_eta: float = 0.1, + model: Optional[str] = None, + logits_processor: Optional[llama.LogitsProcessorList] = None, + grammar: Optional[llama.LlamaGrammar] = None, + logit_bias: Optional[Dict[str, float]] = None, + logprobs: Optional[bool] = None, + top_logprobs: Optional[int] = None, + **kwargs, # type: ignore + ) -> Union[ + llama_types.CreateChatCompletionResponse, + Iterator[llama_types.CreateChatCompletionStreamResponse], + ]: + self._init_mtmd_context(llama) + assert self.mtmd_ctx is not None + + image_urls = self.get_image_urls(messages) + media_marker = self._mtmd_cpp.mtmd_default_marker().decode("utf-8") + template_env = ImmutableSandboxedEnvironment( + trim_blocks=True, + lstrip_blocks=True, + extensions=[ + Jinja2ChatFormatter.IgnoreGenerationTags, + jinja2.ext.loopcontrols, + ], + ) + template_env.filters["tojson"] = Jinja2ChatFormatter.tojson + template = template_env.from_string(self._get_chat_template(llama)) + + def raise_exception(message: str): + raise ValueError(message) + + text = template.render( + messages=self._get_template_messages(messages, media_marker), + add_generation_prompt=True, + eos_token=self._decode_token_piece(llama.detokenize([llama.token_eos()])), + bos_token=self._decode_token_piece(llama.detokenize([llama.token_bos()])), + raise_exception=raise_exception, + functions=functions, + function_call=function_call, + tools=tools, + tool_choice=tool_choice, + strftime_now=Jinja2ChatFormatter.strftime_now, + **kwargs, + ) + text = self._postprocess_template_text(text, image_urls, media_marker) + + if self.verbose: + print(text, file=sys.stderr) + + bitmaps = [] + bitmap_cleanup = [] + try: + for image_url in image_urls: + image_bytes = self.load_image(image_url) + bitmap = self._create_bitmap_from_bytes(image_bytes) + bitmaps.append(bitmap) + bitmap_cleanup.append(bitmap) + + input_text = self._mtmd_cpp.mtmd_input_text() + input_text.text = text.encode("utf-8") + input_text.add_special = True + input_text.parse_special = True + + chunks = self._mtmd_cpp.mtmd_input_chunks_init() + if chunks is None: + raise ValueError("Failed to create input chunks") + + try: + bitmap_array = (self._mtmd_cpp.mtmd_bitmap_p_ctypes * len(bitmaps))( + *bitmaps + ) + result = self._mtmd_cpp.mtmd_tokenize( + self.mtmd_ctx, + chunks, + ctypes.byref(input_text), + bitmap_array, + len(bitmaps), + ) + + if result != 0: + raise ValueError(f"Failed to tokenize input: error code {result}") + + llama.reset() + llama._ctx.kv_cache_clear() + + n_chunks = self._mtmd_cpp.mtmd_input_chunks_size(chunks) + + for i in range(n_chunks): + chunk = self._mtmd_cpp.mtmd_input_chunks_get(chunks, i) + if chunk is None: + continue + + chunk_type = self._mtmd_cpp.mtmd_input_chunk_get_type(chunk) + + if chunk_type == self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_TEXT: + n_tokens_out = ctypes.c_size_t() + tokens_ptr = self._mtmd_cpp.mtmd_input_chunk_get_tokens_text( + chunk, ctypes.byref(n_tokens_out) + ) + + if tokens_ptr and n_tokens_out.value > 0: + tokens = [tokens_ptr[j] for j in range(n_tokens_out.value)] + + if llama.n_tokens + len(tokens) > llama.n_ctx(): + raise ValueError( + f"Prompt exceeds n_ctx: {llama.n_tokens + len(tokens)} > {llama.n_ctx()}" + ) + llama.eval(tokens) + + elif chunk_type in [ + self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_IMAGE, + self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_AUDIO, + ]: + chunk_n_tokens = self._mtmd_cpp.mtmd_input_chunk_get_n_tokens( + chunk + ) + + if llama.n_tokens + chunk_n_tokens > llama.n_ctx(): + raise ValueError( + f"Prompt exceeds n_ctx: {llama.n_tokens + chunk_n_tokens} > {llama.n_ctx()}" + ) + + new_n_past = llama_cpp.llama_pos(0) + result = self._mtmd_cpp.mtmd_helper_eval_chunk_single( + self.mtmd_ctx, + llama._ctx.ctx, + chunk, + llama_cpp.llama_pos(llama.n_tokens), + llama_cpp.llama_seq_id(0), + llama.n_batch, + False, # logits_last + ctypes.byref(new_n_past), + ) + + if result != 0: + raise ValueError( + f"Failed to evaluate chunk: error code {result}" + ) + + llama.n_tokens = new_n_past.value + + prompt = llama.input_ids[: llama.n_tokens].tolist() + + finally: + self._mtmd_cpp.mtmd_input_chunks_free(chunks) + + finally: + for bitmap in bitmap_cleanup: + self._mtmd_cpp.mtmd_bitmap_free(bitmap) + + if response_format is not None and response_format["type"] == "json_object": + grammar = _grammar_for_response_format(response_format) + + if functions is not None: + tools = [ + { + "type": "function", + "function": function, + } + for function in functions + ] + + if function_call is not None: + if isinstance(function_call, str) and ( + function_call == "none" or function_call == "auto" + ): + tool_choice = function_call + if isinstance(function_call, dict) and "name" in function_call: + tool_choice = { + "type": "function", + "function": { + "name": function_call["name"], + }, + } + + tool = None + if ( + tool_choice is not None + and isinstance(tool_choice, dict) + and tools is not None + ): + name = tool_choice["function"]["name"] + tool = next((t for t in tools if t["function"]["name"] == name), None) + if tool is None: + raise ValueError(f"Tool choice '{name}' not found in tools.") + schema = tool["function"]["parameters"] + try: + grammar = llama_grammar.LlamaGrammar.from_json_schema( + json.dumps(schema), verbose=llama.verbose + ) + except Exception as e: + if llama.verbose: + print(str(e), file=sys.stderr) + grammar = llama_grammar.LlamaGrammar.from_string( + llama_grammar.JSON_GBNF, verbose=llama.verbose + ) + + completion_or_chunks = llama.create_completion( + prompt=prompt, + temperature=temperature, + top_p=top_p, + top_k=top_k, + min_p=min_p, + typical_p=typical_p, + logprobs=top_logprobs if logprobs else None, + stream=stream, + stop=stop, + seed=seed, + max_tokens=max_tokens, + presence_penalty=presence_penalty, + frequency_penalty=frequency_penalty, + repeat_penalty=repeat_penalty, + tfs_z=tfs_z, + mirostat_mode=mirostat_mode, + mirostat_tau=mirostat_tau, + mirostat_eta=mirostat_eta, + model=model, + logits_processor=logits_processor, + grammar=grammar, + logit_bias=logit_bias, + ) + + if tool is not None: + tool_name = tool["function"]["name"] + return _convert_completion_to_chat_function( + tool_name, completion_or_chunks, stream + ) + return _convert_completion_to_chat(completion_or_chunks, stream=stream) + + @staticmethod + def _load_image(image_url: str) -> bytes: + if image_url.startswith("data:"): + import base64 + + image_bytes = base64.b64decode(image_url.split(",")[1]) + return image_bytes + else: + import urllib.request + + with urllib.request.urlopen(image_url) as f: + image_bytes = f.read() + return image_bytes + + @staticmethod + def get_image_urls(messages: List[llama_types.ChatCompletionRequestMessage]): + image_urls: List[str] = [] + for message in messages: + if message["role"] == "user": + if message["content"] is None: + continue + for content in message["content"]: + if isinstance(content, dict) and "type" in content: + if content["type"] == "image_url": + if ( + isinstance(content["image_url"], dict) + and "url" in content["image_url"] + ): + image_urls.append(content["image_url"]["url"]) + else: + image_urls.append(content["image_url"]) + return image_urls + + @classmethod + def from_pretrained( + cls, + repo_id: str, + filename: Optional[str], + local_dir: Optional[Union[str, os.PathLike[str]]] = None, + local_dir_use_symlinks: Union[bool, Literal["auto"]] = "auto", + cache_dir: Optional[Union[str, os.PathLike[str]]] = None, + **kwargs: Any, + ) -> "MTMDChatHandler": + import fnmatch + from pathlib import Path + + try: + from huggingface_hub import hf_hub_download, HfFileSystem # type: ignore + from huggingface_hub.utils import validate_repo_id # type: ignore + except ImportError: + raise ImportError( + "Llama.from_pretrained requires the huggingface-hub package. " + "You can install it with `pip install huggingface-hub`." + ) + + validate_repo_id(repo_id) + + hffs = HfFileSystem() + + files = [ + file["name"] if isinstance(file, dict) else file + for file in hffs.ls(repo_id) # type: ignore + ] + + file_list: List[str] = [] + for file in files: + rel_path = Path(file).relative_to(repo_id) + file_list.append(str(rel_path)) + + matching_files = [file for file in file_list if fnmatch.fnmatch(file, filename)] # type: ignore + + if len(matching_files) == 0: + raise ValueError( + f"No file found in {repo_id} that match {filename}\n\n" + f"Available Files:\n{json.dumps(file_list)}" + ) + + if len(matching_files) > 1: + raise ValueError( + f"Multiple files found in {repo_id} matching {filename}\n\n" + f"Available Files:\n{json.dumps(files)}" + ) + + (matching_file,) = matching_files + + subfolder = str(Path(matching_file).parent) + filename = Path(matching_file).name + + hf_hub_download( + repo_id=repo_id, + filename=filename, + subfolder=subfolder, + local_dir=cast(Union[str, Path, None], local_dir), + local_dir_use_symlinks=local_dir_use_symlinks, + cache_dir=cast(Union[str, Path, None], cache_dir), + ) + + if local_dir is None: + model_path = hf_hub_download( + repo_id=repo_id, + filename=filename, + subfolder=subfolder, + local_dir=local_dir, + local_dir_use_symlinks=local_dir_use_symlinks, + cache_dir=cast(Union[str, Path, None], cache_dir), + local_files_only=True, + ) + else: + model_path = os.path.join(local_dir, filename) + + return cls( + clip_model_path=model_path, + **kwargs, + ) + + +class Gemma4ChatHandler(MTMDChatHandler): + pass class ObsidianChatHandler(Llava15ChatHandler): diff --git a/llama_cpp/server/model.py b/llama_cpp/server/model.py index 3222abd631..8aa929202c 100644 --- a/llama_cpp/server/model.py +++ b/llama_cpp/server/model.py @@ -115,18 +115,21 @@ def load_llama_from_model_settings(settings: ModelSettings) -> llama_cpp.Llama: chat_handler = llama_cpp.llama_chat_format.Llava16ChatHandler( clip_model_path=settings.clip_model_path, verbose=settings.verbose ) - elif settings.chat_format == "gemma4": + elif settings.chat_format in ("mtmd", "gemma4"): assert settings.clip_model_path is not None, "clip model not found" + chat_handler_cls = ( + llama_cpp.llama_chat_format.MTMDChatHandler + if settings.chat_format == "mtmd" + else llama_cpp.llama_chat_format.Gemma4ChatHandler + ) if settings.hf_model_repo_id is not None: - chat_handler = ( - llama_cpp.llama_chat_format.Gemma4ChatHandler.from_pretrained( - repo_id=settings.hf_model_repo_id, - filename=settings.clip_model_path, - verbose=settings.verbose, - ) + chat_handler = chat_handler_cls.from_pretrained( + repo_id=settings.hf_model_repo_id, + filename=settings.clip_model_path, + verbose=settings.verbose, ) else: - chat_handler = llama_cpp.llama_chat_format.Gemma4ChatHandler( + chat_handler = chat_handler_cls( clip_model_path=settings.clip_model_path, verbose=settings.verbose ) elif settings.chat_format == "moondream": From 8edcd15eceda7be5839be1c2688fa3caa057ddb8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:22:45 -0700 Subject: [PATCH 107/138] chore(deps): bump docker/build-push-action from 6 to 7 (#2263) Bumps [docker/build-push-action](https://github.com/docker/build-push-action) from 6 to 7. - [Release notes](https://github.com/docker/build-push-action/releases) - [Commits](https://github.com/docker/build-push-action/compare/v6...v7) --- updated-dependencies: - dependency-name: docker/build-push-action dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/build-docker.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-docker.yaml b/.github/workflows/build-docker.yaml index 349eb209dd..0a74357bce 100644 --- a/.github/workflows/build-docker.yaml +++ b/.github/workflows/build-docker.yaml @@ -40,7 +40,7 @@ jobs: - name: Build and push id: docker_build - uses: docker/build-push-action@v6 + uses: docker/build-push-action@v7 with: context: . file: "docker/simple/Dockerfile" From 64c01752badd526010455011fcf903b4d7061458 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:23:00 -0700 Subject: [PATCH 108/138] chore(deps): bump actions/upload-pages-artifact from 3 to 5 (#2264) Bumps [actions/upload-pages-artifact](https://github.com/actions/upload-pages-artifact) from 3 to 5. - [Release notes](https://github.com/actions/upload-pages-artifact/releases) - [Commits](https://github.com/actions/upload-pages-artifact/compare/v3...v5) --- updated-dependencies: - dependency-name: actions/upload-pages-artifact dependency-version: '5' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Andrei <abetlen@gmail.com> --- .github/workflows/generate-index-from-release.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/generate-index-from-release.yaml b/.github/workflows/generate-index-from-release.yaml index 6b2c8770fa..49b06e68ad 100644 --- a/.github/workflows/generate-index-from-release.yaml +++ b/.github/workflows/generate-index-from-release.yaml @@ -51,7 +51,7 @@ jobs: ./scripts/releases-to-pep-503.sh index/whl/vulkan '^[v]?[0-9]+\.[0-9]+\.[0-9]+-vulkan$' ./scripts/releases-to-pep-503.sh index/whl/metal '^[v]?[0-9]+\.[0-9]+\.[0-9]+-metal$' - name: Upload artifact - uses: actions/upload-pages-artifact@v3 + uses: actions/upload-pages-artifact@v5 with: # Upload entire repository path: 'index' From 6bccad59938d51e551d67cf0efd61b3767ec98a4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:23:16 -0700 Subject: [PATCH 109/138] chore(deps): bump actions/configure-pages from 5 to 6 (#2265) Bumps [actions/configure-pages](https://github.com/actions/configure-pages) from 5 to 6. - [Release notes](https://github.com/actions/configure-pages/releases) - [Commits](https://github.com/actions/configure-pages/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/configure-pages dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Andrei <abetlen@gmail.com> --- .github/workflows/generate-index-from-release.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/generate-index-from-release.yaml b/.github/workflows/generate-index-from-release.yaml index 49b06e68ad..a9124fbc09 100644 --- a/.github/workflows/generate-index-from-release.yaml +++ b/.github/workflows/generate-index-from-release.yaml @@ -33,7 +33,7 @@ jobs: - name: Checkout uses: actions/checkout@v6 - name: Setup Pages - uses: actions/configure-pages@v5 + uses: actions/configure-pages@v6 - name: Build env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} From 23fe09f0a48480d2cbcc8e5790def00e8cc064ef Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:23:31 -0700 Subject: [PATCH 110/138] chore(deps): bump softprops/action-gh-release from 2 to 3 (#2266) Bumps [softprops/action-gh-release](https://github.com/softprops/action-gh-release) from 2 to 3. - [Release notes](https://github.com/softprops/action-gh-release/releases) - [Changelog](https://github.com/softprops/action-gh-release/blob/master/CHANGELOG.md) - [Commits](https://github.com/softprops/action-gh-release/compare/v2...v3) --- updated-dependencies: - dependency-name: softprops/action-gh-release dependency-version: '3' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Andrei <abetlen@gmail.com> --- .github/workflows/build-and-release.yaml | 2 +- .github/workflows/build-wheels-cuda.yaml | 2 +- .github/workflows/build-wheels-metal.yaml | 2 +- .github/workflows/build-wheels-rocm.yaml | 4 ++-- .github/workflows/build-wheels-vulkan.yaml | 2 +- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index 0b974e93c8..ad7ba22f6e 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -207,7 +207,7 @@ jobs: merge-multiple: true path: dist - - uses: softprops/action-gh-release@v2 + - uses: softprops/action-gh-release@v3 with: files: dist/* env: diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index f5b3718f17..905b9bee9b 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -222,7 +222,7 @@ jobs: # Publish tags that reflect the actual installed toolkit version. Write-Output "CUDA_VERSION=$cudaTagVersion" >> $env:GITHUB_ENV - - uses: softprops/action-gh-release@v2 + - uses: softprops/action-gh-release@v3 if: startsWith(github.ref, 'refs/tags/') with: files: dist/* diff --git a/.github/workflows/build-wheels-metal.yaml b/.github/workflows/build-wheels-metal.yaml index ca8e322230..892787ee1f 100644 --- a/.github/workflows/build-wheels-metal.yaml +++ b/.github/workflows/build-wheels-metal.yaml @@ -59,7 +59,7 @@ jobs: merge-multiple: true path: dist2 - - uses: softprops/action-gh-release@v2 + - uses: softprops/action-gh-release@v3 with: files: dist2/* # set release name to <tag>-metal diff --git a/.github/workflows/build-wheels-rocm.yaml b/.github/workflows/build-wheels-rocm.yaml index 792e33cf89..1902c125ba 100644 --- a/.github/workflows/build-wheels-rocm.yaml +++ b/.github/workflows/build-wheels-rocm.yaml @@ -208,7 +208,7 @@ jobs: merge-multiple: true path: dist - - uses: softprops/action-gh-release@v2 + - uses: softprops/action-gh-release@v3 with: files: dist/* # Set release name to <tag>-rocm<rocm_version>. @@ -228,7 +228,7 @@ jobs: merge-multiple: true path: dist - - uses: softprops/action-gh-release@v2 + - uses: softprops/action-gh-release@v3 with: files: dist/* # Set release name to <tag>-hip-radeon. diff --git a/.github/workflows/build-wheels-vulkan.yaml b/.github/workflows/build-wheels-vulkan.yaml index 49d38fb80b..8790cc20e9 100644 --- a/.github/workflows/build-wheels-vulkan.yaml +++ b/.github/workflows/build-wheels-vulkan.yaml @@ -114,7 +114,7 @@ jobs: merge-multiple: true path: dist - - uses: softprops/action-gh-release@v2 + - uses: softprops/action-gh-release@v3 with: files: dist/* # Set release name to <tag>-vulkan. From 9013c1d32b55e8514dbf0e460684c6679ad1d566 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 4 Jun 2026 15:23:46 -0700 Subject: [PATCH 111/138] chore(deps): bump docker/setup-qemu-action from 3 to 4 (#2267) Bumps [docker/setup-qemu-action](https://github.com/docker/setup-qemu-action) from 3 to 4. - [Release notes](https://github.com/docker/setup-qemu-action/releases) - [Commits](https://github.com/docker/setup-qemu-action/compare/v3...v4) --- updated-dependencies: - dependency-name: docker/setup-qemu-action dependency-version: '4' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Andrei <abetlen@gmail.com> --- .github/workflows/build-and-release.yaml | 2 +- .github/workflows/build-docker.yaml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index ad7ba22f6e..a1b456edda 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -128,7 +128,7 @@ jobs: submodules: "recursive" - name: Set up QEMU - uses: docker/setup-qemu-action@v3 + uses: docker/setup-qemu-action@v4 with: platforms: linux/riscv64 diff --git a/.github/workflows/build-docker.yaml b/.github/workflows/build-docker.yaml index 0a74357bce..d306efde48 100644 --- a/.github/workflows/build-docker.yaml +++ b/.github/workflows/build-docker.yaml @@ -26,7 +26,7 @@ jobs: echo "IMAGE_TAG=$image_tag" >> "$GITHUB_ENV" - name: Set up QEMU - uses: docker/setup-qemu-action@v3 + uses: docker/setup-qemu-action@v4 - name: Set up Docker Buildx uses: docker/setup-buildx-action@v4 From c2e22ae8e930372ecc911471d4364fdac79ef630 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Thu, 4 Jun 2026 18:26:13 -0700 Subject: [PATCH 112/138] feat: update llama.cpp to ggml-org/llama.cpp@7c158fbb4 (#2268) --- CHANGELOG.md | 2 +- vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 18bcf258a0..88f4d99467 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,7 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] - feat: Generic Multimodal Chat Handler by @abetlen in #2256 -- feat: update llama.cpp to ggml-org/llama.cpp@e3ba22d6c +- feat: update llama.cpp to ggml-org/llama.cpp@7c158fbb4 - feat(ci): add ROCm wheel builds by @abetlen in #2252 - feat(ci): add Vulkan wheel builds by @abetlen in #2251 - fix: handle additional `from_pretrained` files in subfolders by @TNing in #2085 diff --git a/vendor/llama.cpp b/vendor/llama.cpp index e3ba22d6cc..7c158fbb4a 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit e3ba22d6cc4dec84e59a909c7f96e1689c7384a9 +Subproject commit 7c158fbb4aec1bdc9c81d6ca0e785139f4826fae From 5151ac7a27b06215e51bb63b7a784c6265488987 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Thu, 4 Jun 2026 19:09:40 -0700 Subject: [PATCH 113/138] chore: bump version to 0.3.26 (#2269) --- CHANGELOG.md | 2 ++ llama_cpp/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 88f4d99467..28645f13c8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.26] + - feat: Generic Multimodal Chat Handler by @abetlen in #2256 - feat: update llama.cpp to ggml-org/llama.cpp@7c158fbb4 - feat(ci): add ROCm wheel builds by @abetlen in #2252 diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index 52101c9b7e..bbfb73de3f 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.25" +__version__ = "0.3.26" From 78ac75e8fa265749b41640260a0548a598ae8dc8 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Thu, 4 Jun 2026 19:47:14 -0700 Subject: [PATCH 114/138] fix(ci): repair release wheel workflows (#2270) --- .github/workflows/build-and-release.yaml | 5 +++++ .github/workflows/build-wheels-rocm.yaml | 1 - 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index a1b456edda..cd9983fcee 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -49,6 +49,9 @@ jobs: # Linux needs auditwheel repair so manylinux and musllinux wheels are # published with distinct platform tags instead of generic linux tags. CIBW_REPAIR_WHEEL_COMMAND_LINUX: "LD_LIBRARY_PATH=/project/llama_cpp/lib auditwheel repair -w {dest_dir} {wheel}" + # cibuildwheel v3 defaults to manylinux_2_28 images whose current + # GCC toolchain emits symbols newer than the policy allows. + CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014" # The release wheel is tagged py3-none, so one build per platform # covers all supported Python versions and avoids duplicate names. CIBW_BUILD_LINUX: "cp38-*" @@ -85,6 +88,8 @@ jobs: CIBW_SKIP: "pp*" CIBW_REPAIR_WHEEL_COMMAND: "LD_LIBRARY_PATH=$PWD/llama_cpp/lib auditwheel repair -w {dest_dir} {wheel}" CIBW_ARCHS: "aarch64" + # Keep this consistent with the x86_64 Linux release wheels. + CIBW_MANYLINUX_AARCH64_IMAGE: "manylinux2014" # Keep native arm64 builds on a portable CPU baseline instead of # tuning wheels to the hosted runner. CIBW_ENVIRONMENT: CMAKE_ARGS="-DGGML_NATIVE=off" diff --git a/.github/workflows/build-wheels-rocm.yaml b/.github/workflows/build-wheels-rocm.yaml index 1902c125ba..6ad0b49541 100644 --- a/.github/workflows/build-wheels-rocm.yaml +++ b/.github/workflows/build-wheels-rocm.yaml @@ -33,7 +33,6 @@ jobs: - uses: actions/setup-python@v6 with: python-version: ${{ matrix.pyver }} - cache: "pip" - name: Install build dependencies run: | From 7c86eae04117ef150715d1f4a2982f7b4adf147d Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Thu, 4 Jun 2026 20:27:27 -0700 Subject: [PATCH 115/138] fix(ci): allow empty wheel indexes (#2271) --- scripts/releases-to-pep-503.sh | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/scripts/releases-to-pep-503.sh b/scripts/releases-to-pep-503.sh index 71910efcbf..8359624492 100755 --- a/scripts/releases-to-pep-503.sh +++ b/scripts/releases-to-pep-503.sh @@ -54,8 +54,12 @@ cat << EOF > "$output_dir/llama-cpp-python/index.html" <h1>Links for llama-cpp-python</h1> EOF -# Filter releases by pattern -releases=$(grep -E "$pattern" "$current_dir/all_releases.txt") +# Filter releases by pattern. Some backend indexes are valid even when there +# are no matching releases yet. +releases=$(grep -E "$pattern" "$current_dir/all_releases.txt" || true) +if [ -z "$releases" ]; then + log_info "No releases found matching pattern: $pattern" +fi # Prepare curl headers headers=('--header' 'Accept: application/vnd.github.v3+json') @@ -81,16 +85,16 @@ for release in $releases; do continue fi - # Get release version from release ie v0.1.0-cu121 -> v0.1.0 - release_version=$(echo "$release" | grep -oE "^[v]?[0-9]+\.[0-9]+\.[0-9]+") - echo " <h2>$release_version</h2>" >> "$output_dir/llama-cpp-python/index.html" - wheel_urls=$(echo "$response" | jq -r '.assets[] | select(.name | endswith(".whl")) | .browser_download_url') if [ -z "$wheel_urls" ]; then log_error "No wheel files found for release $release" continue fi + # Get release version from release ie v0.1.0-cu121 -> v0.1.0 + release_version=$(echo "$release" | grep -oE "^[v]?[0-9]+\.[0-9]+\.[0-9]+") + echo " <h2>$release_version</h2>" >> "$output_dir/llama-cpp-python/index.html" + echo "$wheel_urls" | while read -r asset; do echo " <a href=\"$asset\">$asset</a>" >> "$output_dir/llama-cpp-python/index.html" echo " <br>" >> "$output_dir/llama-cpp-python/index.html" From 672198970724492a51329e45d714f338f97cc184 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Thu, 4 Jun 2026 20:41:30 -0700 Subject: [PATCH 116/138] fix(ci): index all CUDA wheel variants (#2272) --- .github/workflows/generate-index-from-release.yaml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/generate-index-from-release.yaml b/.github/workflows/generate-index-from-release.yaml index a9124fbc09..edf292387d 100644 --- a/.github/workflows/generate-index-from-release.yaml +++ b/.github/workflows/generate-index-from-release.yaml @@ -40,12 +40,14 @@ jobs: run: | ./scripts/get-releases.sh ./scripts/releases-to-pep-503.sh index/whl/cpu '^[v]?[0-9]+\.[0-9]+\.[0-9]+$' + ./scripts/releases-to-pep-503.sh index/whl/cu118 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu118$' ./scripts/releases-to-pep-503.sh index/whl/cu121 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu121$' ./scripts/releases-to-pep-503.sh index/whl/cu122 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu122$' ./scripts/releases-to-pep-503.sh index/whl/cu123 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu123$' ./scripts/releases-to-pep-503.sh index/whl/cu124 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$' - # ./scripts/releases-to-pep-503.sh index/whl/cu125 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$' - # ./scripts/releases-to-pep-503.sh index/whl/cu126 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$' + ./scripts/releases-to-pep-503.sh index/whl/cu125 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu125$' + ./scripts/releases-to-pep-503.sh index/whl/cu130 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu130$' + ./scripts/releases-to-pep-503.sh index/whl/cu132 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu132$' ./scripts/releases-to-pep-503.sh index/whl/rocm72 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-rocm72$' ./scripts/releases-to-pep-503.sh index/whl/hip-radeon '^[v]?[0-9]+\.[0-9]+\.[0-9]+-hip-radeon$' ./scripts/releases-to-pep-503.sh index/whl/vulkan '^[v]?[0-9]+\.[0-9]+\.[0-9]+-vulkan$' From 468498519e67395771c7da34b2e214975d4f96a0 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Thu, 4 Jun 2026 21:41:11 -0700 Subject: [PATCH 117/138] fix(ci): build one riscv64 release wheel (#2273) --- .github/workflows/build-and-release.yaml | 29 ++++-------------------- 1 file changed, 5 insertions(+), 24 deletions(-) diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index cd9983fcee..4ae37b1745 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -106,27 +106,8 @@ jobs: path: ./wheelhouse/*.whl build_wheels_riscv64: - name: Build riscv64 wheels (${{ matrix.shard.name }}) + name: Build riscv64 wheel runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - shard: - - name: cp310 - build: "cp310-*" - artifact: wheels_riscv64_cp310 - - name: cp311 - build: "cp311-*" - artifact: wheels_riscv64_cp311 - - name: cp312 - build: "cp312-*" - artifact: wheels_riscv64_cp312 - - name: cp313 - build: "cp313-*" - artifact: wheels_riscv64_cp313 - - name: cp314 - build: "cp314-*" - artifact: wheels_riscv64_cp314 steps: - uses: actions/checkout@v6 with: @@ -146,16 +127,16 @@ jobs: # Build riscv64 wheels against a conservative baseline instead of # enabling RVV-related extensions from the build container. CIBW_ENVIRONMENT: CMAKE_ARGS="-DGGML_NATIVE=off -DGGML_RVV=off -DGGML_RV_ZFH=off -DGGML_RV_ZVFH=off -DGGML_RV_ZICBOP=off -DGGML_RV_ZIHINTPAUSE=off" - # Split the emulated riscv64 build into one Python version per job - # to minimize wall-clock time without changing the release artifacts. - CIBW_BUILD: ${{ matrix.shard.build }} + # The release wheel is tagged py3-none, so one riscv64 build is + # enough and avoids duplicate same-name release artifacts. + CIBW_BUILD: "cp310-*" with: output-dir: wheelhouse - name: Upload wheels as artifacts uses: actions/upload-artifact@v7 with: - name: ${{ matrix.shard.artifact }} + name: wheels_riscv64 path: ./wheelhouse/*.whl build_sdist: From 8949066b3b9dd0d055837d491ffe2eaf99c582f8 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Thu, 4 Jun 2026 22:15:47 -0700 Subject: [PATCH 118/138] docs: add Gemma 4 Colab notebook (#2274) --- README.md | 2 +- examples/colab/notebook.ipynb | 131 ++++++++++++++++++++++++++++++++++ 2 files changed, 132 insertions(+), 1 deletion(-) create mode 100644 examples/colab/notebook.ipynb diff --git a/README.md b/README.md index 5711d4afbb..3f801285ed 100644 --- a/README.md +++ b/README.md @@ -535,7 +535,7 @@ Below are the supported multi-modal models and their respective chat handlers (P | [llama-3-vision-alpha](https://huggingface.co/abetlen/llama-3-vision-alpha-gguf) | `Llama3VisionAlphaChatHandler` | `llama-3-vision-alpha` | | [minicpm-v-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | `MiniCPMv26ChatHandler` | `minicpm-v-2.6` | | [qwen2.5-vl](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-GGUF) | `Qwen25VLChatHandler` | `qwen2.5-vl` | -| [gemma-4](https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF) | `Gemma4ChatHandler` | `gemma4` | +| [gemma-4](https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abetlen/llama-cpp-python/blob/main/examples/colab/notebook.ipynb) | `Gemma4ChatHandler` | `gemma4` | | GGUF models with an mtmd projector and embedded chat template | `MTMDChatHandler` | `mtmd` | Then you'll need to use a custom chat handler to load the clip model and process the chat messages and images. diff --git a/examples/colab/notebook.ipynb b/examples/colab/notebook.ipynb new file mode 100644 index 0000000000..c9b8d8dcbb --- /dev/null +++ b/examples/colab/notebook.ipynb @@ -0,0 +1,131 @@ +{ + "nbformat": 4, + "nbformat_minor": 5, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "accelerator": "GPU", + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gemma 4 12B Multimodal Chat\n", + "\n", + "Run Gemma 4 12B locally in Google Colab with the pre-built CUDA wheel for `llama-cpp-python`.\n", + "\n", + "Use a GPU runtime before running this notebook: **Runtime > Change runtime type > T4 GPU**.\n", + "\n", + "Current Colab CUDA images commonly provide CUDA 12 user-space libraries even when `nvidia-smi` reports a CUDA 13-capable driver, so this notebook installs the `cu125` wheel. If your runtime provides `libcudart.so.13`, switch the wheel index URL to `/whl/cu130`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --no-cache-dir --upgrade --force-reinstall \\\n", + " \"huggingface-hub>=0.23.0\" \\\n", + " llama-cpp-python \\\n", + " --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu125\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_cpp import Llama\n", + "from llama_cpp.llama_chat_format import Gemma4ChatHandler\n", + "\n", + "MODEL_REPO = \"ggml-org/gemma-4-12B-it-GGUF\"\n", + "MODEL_FILE = \"gemma-4-12B-it-Q4_K_M.gguf\"\n", + "MMPROJ_FILE = \"mmproj-gemma-4-12B-it-Q8_0.gguf\"\n", + "\n", + "chat_handler = Gemma4ChatHandler.from_pretrained(\n", + " repo_id=MODEL_REPO,\n", + " filename=MMPROJ_FILE,\n", + " verbose=False,\n", + ")\n", + "\n", + "llm = Llama.from_pretrained(\n", + " repo_id=MODEL_REPO,\n", + " filename=MODEL_FILE,\n", + " chat_handler=chat_handler,\n", + " n_gpu_layers=-1,\n", + " n_ctx=8192,\n", + " flash_attn=True,\n", + " verbose=False,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = llm.create_chat_completion(\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"Write the exact string `<stdio.h>` and nothing else.\",\n", + " }\n", + " ],\n", + " max_tokens=32,\n", + " temperature=0.0,\n", + ")\n", + "\n", + "print(response[\"choices\"][0][\"message\"][\"content\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image, display\n", + "\n", + "IMAGE_URL = \"https://raw.githubusercontent.com/abetlen/llama-cpp-python/main/vendor/llama.cpp/tools/mtmd/test-1.jpeg\"\n", + "\n", + "display(Image(url=IMAGE_URL, width=320))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = llm.create_chat_completion(\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"Describe this image in one concise sentence.\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": IMAGE_URL}},\n", + " ],\n", + " }\n", + " ],\n", + " max_tokens=128,\n", + " temperature=0.2,\n", + ")\n", + "\n", + "print(response[\"choices\"][0][\"message\"][\"content\"])\n" + ] + } + ] +} From 7a2a36d769ffe051320345a9a64b68dddc53b9f8 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Thu, 4 Jun 2026 22:41:14 -0700 Subject: [PATCH 119/138] docs: fix Gemma 4 Colab notebook (#2275) * docs: fix Gemma 4 Colab notebook * docs: use smaller Gemma 4 Colab model * docs: update Gemma 4 Colab CTA --- README.md | 4 +++- examples/colab/notebook.ipynb | 4 ++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 3f801285ed..8f7b65e835 100644 --- a/README.md +++ b/README.md @@ -535,9 +535,11 @@ Below are the supported multi-modal models and their respective chat handlers (P | [llama-3-vision-alpha](https://huggingface.co/abetlen/llama-3-vision-alpha-gguf) | `Llama3VisionAlphaChatHandler` | `llama-3-vision-alpha` | | [minicpm-v-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | `MiniCPMv26ChatHandler` | `minicpm-v-2.6` | | [qwen2.5-vl](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-GGUF) | `Qwen25VLChatHandler` | `qwen2.5-vl` | -| [gemma-4](https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abetlen/llama-cpp-python/blob/main/examples/colab/notebook.ipynb) | `Gemma4ChatHandler` | `gemma4` | +| [gemma-4](https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF) | `Gemma4ChatHandler` | `gemma4` | | GGUF models with an mtmd projector and embedded chat template | `MTMDChatHandler` | `mtmd` | +Try Gemma 4 12B in Google Colab -> [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abetlen/llama-cpp-python/blob/main/examples/colab/notebook.ipynb) + Then you'll need to use a custom chat handler to load the clip model and process the chat messages and images. ```python diff --git a/examples/colab/notebook.ipynb b/examples/colab/notebook.ipynb index c9b8d8dcbb..8e258b9c03 100644 --- a/examples/colab/notebook.ipynb +++ b/examples/colab/notebook.ipynb @@ -81,7 +81,7 @@ " messages=[\n", " {\n", " \"role\": \"user\",\n", - " \"content\": \"Write the exact string `<stdio.h>` and nothing else.\",\n", + " \"content\": \"What is the capital of France? Answer in one sentence.\",\n", " }\n", " ],\n", " max_tokens=32,\n", @@ -99,7 +99,7 @@ "source": [ "from IPython.display import Image, display\n", "\n", - "IMAGE_URL = \"https://raw.githubusercontent.com/abetlen/llama-cpp-python/main/vendor/llama.cpp/tools/mtmd/test-1.jpeg\"\n", + "IMAGE_URL = \"https://raw.githubusercontent.com/ggml-org/llama.cpp/master/tools/mtmd/test-1.jpeg\"\n", "\n", "display(Image(url=IMAGE_URL, width=320))\n" ] From 7f16fe1963bbca25b7b8f59410153d46d48dea0f Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sat, 6 Jun 2026 00:15:04 -0700 Subject: [PATCH 120/138] docs: add Gemma 4 QAT Colab notebook (#2276) * docs: add Gemma 4 QAT Colab notebook * docs: rename Gemma 4 QAT Colab notebook --- README.md | 2 + examples/colab/Gemma4-12B-QAT.ipynb | 162 ++++++++++++++++++++++++++++ 2 files changed, 164 insertions(+) create mode 100644 examples/colab/Gemma4-12B-QAT.ipynb diff --git a/README.md b/README.md index 8f7b65e835..c02df9eed0 100644 --- a/README.md +++ b/README.md @@ -540,6 +540,8 @@ Below are the supported multi-modal models and their respective chat handlers (P Try Gemma 4 12B in Google Colab -> [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abetlen/llama-cpp-python/blob/main/examples/colab/notebook.ipynb) +Try Gemma 4 12B QAT in Google Colab -> [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abetlen/llama-cpp-python/blob/main/examples/colab/Gemma4-12B-QAT.ipynb) + Then you'll need to use a custom chat handler to load the clip model and process the chat messages and images. ```python diff --git a/examples/colab/Gemma4-12B-QAT.ipynb b/examples/colab/Gemma4-12B-QAT.ipynb new file mode 100644 index 0000000000..2cced5a89b --- /dev/null +++ b/examples/colab/Gemma4-12B-QAT.ipynb @@ -0,0 +1,162 @@ +{ + "nbformat": 4, + "nbformat_minor": 5, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "accelerator": "GPU", + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gemma 4 12B QAT Multimodal Chat\n", + "\n", + "Run the Gemma 4 12B QAT GGUF model locally in Google Colab with the pre-built CUDA wheel for `llama-cpp-python`.\n", + "\n", + "Use a GPU runtime before running this notebook: **Runtime > Change runtime type > T4 GPU**.\n", + "\n", + "Current Colab CUDA images commonly provide CUDA 12 user-space libraries even when `nvidia-smi` reports a CUDA 13-capable driver, so this notebook installs the `cu125` wheel. If your runtime provides `libcudart.so.13`, switch the wheel index URL to `/whl/cu130`.\n" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --no-cache-dir --upgrade --force-reinstall \\\n", + " \"huggingface-hub>=0.23.0\" \\\n", + " llama-cpp-python \\\n", + " --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu125\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_cpp import Llama\n", + "from llama_cpp.llama_chat_format import Gemma4ChatHandler\n", + "\n", + "MODEL_REPO = \"unsloth/gemma-4-12B-it-qat-GGUF\"\n", + "MODEL_FILE = \"gemma-4-12B-it-qat-UD-Q4_K_XL.gguf\"\n", + "MMPROJ_FILE = \"mmproj-F16.gguf\"\n", + "\n", + "chat_handler = Gemma4ChatHandler.from_pretrained(\n", + " repo_id=MODEL_REPO,\n", + " filename=MMPROJ_FILE,\n", + " verbose=False,\n", + ")\n", + "\n", + "llm = Llama.from_pretrained(\n", + " repo_id=MODEL_REPO,\n", + " filename=MODEL_FILE,\n", + " chat_handler=chat_handler,\n", + " n_gpu_layers=-1,\n", + " n_ctx=8192,\n", + " flash_attn=True,\n", + " verbose=False,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image, display\n", + "\n", + "IMAGE_URL = \"https://raw.githubusercontent.com/ggml-org/llama.cpp/master/tools/mtmd/test-1.jpeg\"\n", + "\n", + "display(Image(url=IMAGE_URL, width=320))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = llm.create_chat_completion(\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"Describe this image in one concise sentence.\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": IMAGE_URL}},\n", + " ],\n", + " }\n", + " ],\n", + " max_tokens=128,\n", + " temperature=0.2,\n", + ")\n", + "\n", + "print(response[\"choices\"][0][\"message\"][\"content\"])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tools = [\n", + " {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"record_image_observation\",\n", + " \"description\": \"Record a structured observation about an image.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"main_subject\": {\"type\": \"string\"},\n", + " \"setting\": {\"type\": \"string\"},\n", + " \"notable_details\": {\n", + " \"type\": \"array\",\n", + " \"items\": {\"type\": \"string\"},\n", + " },\n", + " \"confidence\": {\"type\": \"number\", \"minimum\": 0, \"maximum\": 1},\n", + " },\n", + " \"required\": [\"main_subject\", \"setting\", \"notable_details\", \"confidence\"],\n", + " },\n", + " },\n", + " }\n", + "]\n", + "\n", + "response = llm.create_chat_completion(\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"Use the provided tool to record a structured observation for this image.\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": IMAGE_URL}},\n", + " ],\n", + " }\n", + " ],\n", + " tools=tools,\n", + " tool_choice={\"type\": \"function\", \"function\": {\"name\": \"record_image_observation\"}},\n", + " max_tokens=256,\n", + " temperature=0.0,\n", + ")\n", + "\n", + "message = response[\"choices\"][0][\"message\"]\n", + "print(message.get(\"tool_calls\", message.get(\"content\")))\n" + ] + } + ] +} From ed833664fe96050d2990131f4673f112d8f88924 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sat, 6 Jun 2026 00:48:47 -0700 Subject: [PATCH 121/138] feat: update llama.cpp to 5a69c9743 (#2277) --- CHANGELOG.md | 2 ++ vendor/llama.cpp | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 28645f13c8..b2c3dc8d50 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: update llama.cpp to ggml-org/llama.cpp@5a69c9743 + ## [0.3.26] - feat: Generic Multimodal Chat Handler by @abetlen in #2256 diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 7c158fbb4a..5a69c97439 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 7c158fbb4aec1bdc9c81d6ca0e785139f4826fae +Subproject commit 5a69c974392020e514c3b2b2910bb92f847cb4c9 From 66635a02fd32e1a4cc6bea3c85c3c7c50972fdfd Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 7 Jun 2026 03:04:24 -0700 Subject: [PATCH 122/138] feat(example): Updated server example (batch processing, `/v1/responses` api, response parsing) (#2174) * Add batch processing server * Improve response parser streaming performance * Add /v1/models endpoint * Support custom tools in responses API * Improve responses api compatibility for codex * Improve batch server prompt and context config * Improve batch server scheduling and prompt handling * fix apply_patch tool * Improve batch server schemas and metrics * Refactor sequence cache helpers * Fix server type diagnostics * feat: add llama.cpp extension bindings * feat: add MTP support to batch server * feat: improve draft-mtp handling in batch server * feat: cap MTP draft context outputs * fix: preserve held streaming tokens * feat: add load-time LoRA support to batch server * feat: add multimodal support to batch server * refactor: rename batch item kinds * refactor: type sampled mtp updates * refactor: structure sampled mtp batch processing * refactor: clarify batch item construction * refactor: type batch item kind * refactor: clarify sampled pending index * refactor: clarify output index naming * refactor: rename logits index resolver * refactor: colocate sampled mtp state * refactor: inline sampled mtp helpers * refactor: use row-expanded multimodal prompt identity * test: remove multimodal prompt plan tests * refactor: narrow mtmd processor dependencies * refactor: group prompt segment media fields * refactor: centralize sequence state copy * refactor: keep disk cache storage only * refactor: split batch item payloads * refactor: centralize pending request failure cleanup * refactor: centralize sequence claiming * refactor: key sequence disk cache compatibility * refactor: decouple completion request preparation * refactor: name prepared completion parts * refactor: return prepared completion parts * refactor: localize media cache key building * refactor: remove unused request id override * refactor: simplify prompt segment row capacity * refactor: inline prompt row clamp * refactor: inline disconnect cancellation response * refactor: simplify recurrent draft capacity * refactor: define builtin grammar rule as dataclass * refactor: type chat template conversions * docs: mark llama_cpp_ext experimental * feat: restrict multimodal media sources * docs: add server example README and config * docs: document server example configuration * docs: update server README * docs: document server wheel setup and clients * docs: add server model configs * docs: add server chat templates and response schemas * docs: keep batch processing server example * docs: add server example changelog entry * docs: mention multi-token prediction in changelog --- CHANGELOG.md | 1 + examples/server/README.md | 434 + .../server/configs/gemma-4-12b-it-qat.json | 451 + examples/server/configs/gpt-oss-120b.json | 417 + examples/server/configs/qwen3.5-0.8b.json | 261 + examples/server/configs/qwen3.6-27b.json | 263 + examples/server/configs/qwen3.6-35b-a3b.json | 263 + examples/server/server.py | 15328 ++++++++++++++++ llama_cpp/llama_cpp_ext.py | 99 + 9 files changed, 17517 insertions(+) create mode 100644 examples/server/README.md create mode 100644 examples/server/configs/gemma-4-12b-it-qat.json create mode 100644 examples/server/configs/gpt-oss-120b.json create mode 100644 examples/server/configs/qwen3.5-0.8b.json create mode 100644 examples/server/configs/qwen3.6-27b.json create mode 100644 examples/server/configs/qwen3.6-35b-a3b.json create mode 100644 examples/server/server.py create mode 100644 llama_cpp/llama_cpp_ext.py diff --git a/CHANGELOG.md b/CHANGELOG.md index b2c3dc8d50..bbe7b13bdc 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] - feat: update llama.cpp to ggml-org/llama.cpp@5a69c9743 +- feat(example): Updated server example (batch processing, multi-token prediction, `/v1/responses` api, response parsing) by @abetlen in #2174 ## [0.3.26] diff --git a/examples/server/README.md b/examples/server/README.md new file mode 100644 index 0000000000..1f6e0d3dbe --- /dev/null +++ b/examples/server/README.md @@ -0,0 +1,434 @@ +# Server Example + +This example is an updated OpenAI-compatible web server that depends only on the low-level C bindings. +It supports batched inference, prompt caching, response parsing, `/v1/responses`, disk sequence caching, MTP, LoRA, and multimodal image/audio inputs. + +## Setup + +The server is a [`uv` inline script](https://docs.astral.sh/uv/guides/scripts/), so `uv` can create the script environment and install the Python dependencies automatically. + +```bash +cd examples/server +uv run --script server.py -C configs/qwen3.5-0.8b.json +``` + +Use `uv run --extra-index-url` to pull a pre-built `llama-cpp-python` binary wheel instead of building from source. + +```bash +cd examples/server +uv run \ + --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu \ + --script server.py -C configs/qwen3.5-0.8b.json +``` + +Pick the wheel index that matches the backend you want. + +| Backend | Wheel index | +| --- | --- | +| CPU | `https://abetlen.github.io/llama-cpp-python/whl/cpu` | +| CUDA 11.8 | `https://abetlen.github.io/llama-cpp-python/whl/cu118` | +| CUDA 12.1 | `https://abetlen.github.io/llama-cpp-python/whl/cu121` | +| CUDA 12.2 | `https://abetlen.github.io/llama-cpp-python/whl/cu122` | +| CUDA 12.3 | `https://abetlen.github.io/llama-cpp-python/whl/cu123` | +| CUDA 12.4 | `https://abetlen.github.io/llama-cpp-python/whl/cu124` | +| CUDA 12.5 | `https://abetlen.github.io/llama-cpp-python/whl/cu125` | +| CUDA 13.0 | `https://abetlen.github.io/llama-cpp-python/whl/cu130` | +| CUDA 13.2 | `https://abetlen.github.io/llama-cpp-python/whl/cu132` | +| Metal | `https://abetlen.github.io/llama-cpp-python/whl/metal` | +| ROCm | `https://abetlen.github.io/llama-cpp-python/whl/rocm72` | +| Vulkan | `https://abetlen.github.io/llama-cpp-python/whl/vulkan` | + +See the repository installation section for the full [pre-built wheel requirements](../../README.md#supported-backends). + +## Model Configs + +The smallest checked-in example uses Qwen3.5 0.8B so the server can be started on a normal development machine. + +| Config | Model | Notes | +| --- | --- | --- | +| [`configs/qwen3.5-0.8b.json`](configs/qwen3.5-0.8b.json) | [`lmstudio-community/Qwen3.5-0.8B-GGUF`](https://huggingface.co/lmstudio-community/Qwen3.5-0.8B-GGUF) | Default small multimodal example. | +| [`configs/gemma-4-12b-it-qat.json`](configs/gemma-4-12b-it-qat.json) | [`unsloth/gemma-4-12B-it-qat-GGUF`](https://huggingface.co/unsloth/gemma-4-12B-it-qat-GGUF) | Larger Gemma 4 QAT multimodal config with projector. | +| [`configs/qwen3.6-27b.json`](configs/qwen3.6-27b.json) | [`unsloth/Qwen3.6-27B-GGUF`](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF) | Larger Qwen3.6 multimodal config. | +| [`configs/qwen3.6-35b-a3b.json`](configs/qwen3.6-35b-a3b.json) | [`unsloth/Qwen3.6-35B-A3B-GGUF`](https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF) | Larger Qwen3.6 MoE multimodal config. | +| [`configs/gpt-oss-120b.json`](configs/gpt-oss-120b.json) | [`ggml-org/gpt-oss-120b-GGUF`](https://huggingface.co/ggml-org/gpt-oss-120b-GGUF) | Large text-only split-GGUF config. | + +The larger model configs default to `n_gpu_layers: -1` and `flash_attn: true`. + +## Client Examples + +Point an OpenAI-compatible client at the local `/v1` base URL. + +### Chat Completions + +```python +from openai import OpenAI + +client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="not-used") + +response = client.chat.completions.create( + model="qwen3.5-0.8b-vl", + messages=[{"role": "user", "content": "What is the capital of France?"}], +) +print(response.choices[0].message.content) +``` + +### Responses API + +```python +from openai import OpenAI + +client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="not-used") + +response = client.responses.create( + model="qwen3.5-0.8b-vl", + input="Write one sentence about why prefix caching helps batched inference.", +) +print(response.output_text) +``` + +## API Surface + +| Endpoint | Purpose | Reference | +| --- | --- | --- | +| `POST /v1/completions` | Legacy text completions with streaming, stop sequences, logprobs, penalties, seeds, and grammar-backed JSON output. | [OpenAI Completions API](https://platform.openai.com/docs/api-reference/completions) | +| `POST /v1/chat/completions` | Chat completions with streaming, tools, forced tool choice, reasoning parsing, multimodal content parts, and structured response parsing. | [OpenAI Chat API](https://platform.openai.com/docs/api-reference/chat) | +| `POST /v1/responses` | Stateless Responses API compatibility for clients that use response items and response events. | [OpenAI Responses API](https://platform.openai.com/docs/api-reference/responses) | +| `WS /v1/responses` | Stateful websocket Responses transport with per-connection `previous_response_id` replay. | [OpenAI Responses API](https://platform.openai.com/docs/api-reference/responses) | +| `GET /v1/models` | Returns the configured model alias. | [OpenAI Models API](https://platform.openai.com/docs/api-reference/models) | +| `GET /healthz` | Returns a simple typed health response. | | +| `GET /metrics` | Exposes scheduler, cache, draft, and model metrics in Prometheus text format. | [Prometheus exposition format](https://prometheus.io/docs/instrumenting/exposition_formats/) | + +## Config Overview + +Config files have three top-level sections. + +```json +{ + "server": {}, + "model": {}, + "disk_cache": {} +} +``` + +| Section | Required | Purpose | +| --- | --- | --- | +| `server` | No | Uvicorn host and port settings. | +| `model` | Yes | Model source, llama.cpp runtime settings, chat formatting, LoRA, MTMD, draft decoding, and output parsing. | +| `disk_cache` | No | Optional serialized sequence cache for repeated prompt prefixes. | + +## `server` + +Use `server.host` and `server.port` to choose the bind address. + +```json +{ + "server": { + "host": "0.0.0.0", + "port": 8000 + } +} +``` + +| Field | Default | Notes | +| --- | --- | --- | +| `host` | `127.0.0.1` | Use `0.0.0.0` to expose the server on the network. | +| `port` | `8000` | Passed directly to `uvicorn.run()`. | + +## `model` Source + +Load a local GGUF with `path` or download a GGUF from Hugging Face with `from_pretrained`. + +```json +{ + "model": { + "alias": "qwen3.5-0.8b-vl", + "from_pretrained": { + "repo_id": "lmstudio-community/Qwen3.5-0.8B-GGUF", + "filename": "Qwen3.5-0.8B-Q8_0.gguf" + } + } +} +``` + +| Field | Notes | +| --- | --- | +| `path` | Local GGUF path. | +| `from_pretrained.repo_id` | Hugging Face model repository. | +| `from_pretrained.filename` | File name or glob pattern for the GGUF. | +| `from_pretrained.additional_files` | Extra files to download from the same repository. | +| `from_pretrained.cache_dir` | Optional Hugging Face cache directory. | +| `alias` | Model id returned by `/v1/models` and used by OpenAI-compatible clients. | + +See the [Hugging Face Hub download guide](https://huggingface.co/docs/huggingface_hub/guides/download) for cache behavior and repository file resolution. + +## llama.cpp Runtime Settings + +Most model runtime fields map to `llama_model_params` or `llama_context_params` in [`llama.h`](https://github.com/ggml-org/llama.cpp/blob/master/include/llama.h). + +```json +{ + "model": { + "n_ctx": 32768, + "n_seq_max": 64, + "n_batch": 128, + "n_ubatch": 128, + "threads": 4, + "threads_batch": 8, + "kv_unified": true, + "use_mmap": true, + "use_mlock": true + } +} +``` + +| Field | Purpose | +| --- | --- | +| `n_ctx` | Total context size. | +| `n_seq_max` | Maximum number of concurrent llama.cpp sequence ids. | +| `n_batch` | Logical batch capacity. | +| `n_ubatch` | Physical microbatch capacity. | +| `threads` | Decode thread count. | +| `threads_batch` | Prefill and batch thread count. | +| `kv_unified` | Selects unified or per-sequence memory layout. | +| `store_logits` | Keeps logits after decode when needed by sampling or diagnostics. | +| `use_mmap` | Memory maps model weights. | +| `use_mlock` | Attempts to lock model pages into RAM. | + +GPU and backend-related fields are passed through to llama.cpp when set. + +```json +{ + "model": { + "n_gpu_layers": -1, + "split_mode": 1, + "main_gpu": 0, + "tensor_split": [1.0], + "flash_attn": true, + "offload_kqv": true, + "op_offload": true + } +} +``` + +## Chat Template + +`model.chat_template` is a Jinja chat template compatible with the style used by [Hugging Face chat templates](https://huggingface.co/docs/transformers/chat_templating). + +```json +{ + "model": { + "chat_template": "{{ bos_token }}{{ messages[0].content }}{{ eos_token }}" + } +} +``` + +Use an array of strings when the template is too large to read or edit as one JSON string. + +```json +{ + "model": { + "chat_template": [ + "{{ bos_token }}", + "{{ messages[0].content }}", + "{{ eos_token }}" + ] + } +} +``` + +The checked-in [`configs/qwen3.5-0.8b.json`](configs/qwen3.5-0.8b.json) includes a Qwen3.5 template with reasoning text, tool calls, forced tool choice, image markers, and video markers. + +## Response Parsing + +`model.response_schema` parses generated text into OpenAI-compatible fields with JSON Schema plus the Hugging Face `x-regex` extensions. + +```json +{ + "model": { + "response_schema": { + "type": "object", + "properties": { + "role": {"const": "assistant"}, + "content": { + "type": "string", + "x-regex": "^(.*)$" + } + }, + "required": ["role"] + } + } +} +``` + +Use `x-regex-iterator` and `x-regex-key-value` to parse repeated tool-call blocks. + +See [Hugging Face response parsing](https://huggingface.co/docs/transformers/chat_response_parsing) and the [JSON Schema reference](https://json-schema.org/understanding-json-schema/reference) for the underlying schema concepts. + +## Multimodal `model.mtmd` + +`model.mtmd` loads a llama.cpp multimodal projector and enables OpenAI-style image and audio content parts. + +```json +{ + "model": { + "mtmd": { + "mmproj_from_pretrained": { + "repo_id": "lmstudio-community/Qwen3.5-0.8B-GGUF", + "filename": "mmproj-Qwen3.5-0.8B-BF16.gguf" + }, + "embedding_cache": { + "path": ".cache/mtmd-embeddings", + "max_bytes": 1073741824 + }, + "image_max_bytes": 20971520, + "audio_max_bytes": 104857600, + "image_timeout_seconds": 10.0 + } + } +} +``` + +| Field | Purpose | +| --- | --- | +| `mmproj_path` | Local multimodal projector path. | +| `mmproj_from_pretrained` | Hugging Face projector source. | +| `embedding_cache.path` | Directory for cached image and audio embeddings. | +| `embedding_cache.max_bytes` | Maximum embedding cache size. | +| `image_max_bytes` | Maximum image payload size. | +| `audio_max_bytes` | Maximum audio payload size. | +| `image_timeout_seconds` | Timeout for remote image and audio URL fetches. | + +Send image inputs with OpenAI chat content parts. + +```json +{ + "messages": [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Describe this image."}, + {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}} + ] + } + ] +} +``` + +Send audio inputs as a URL or as base64 `input_audio` content. + +```json +{ + "messages": [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Transcribe this audio."}, + {"type": "input_audio", "input_audio": {"data": "...base64...", "format": "wav"}} + ] + } + ] +} +``` + +## Media Loading Policy + +Remote `http:` and `https:` media URLs are unrestricted unless `allowed_media_domains` is set. + +```json +{ + "model": { + "mtmd": { + "allowed_media_domains": ["example.com", "static.example.com"] + } + } +} +``` + +Local `file:` media URLs are disabled unless `allowed_local_media_path` is set. + +```json +{ + "model": { + "mtmd": { + "allowed_local_media_path": "/srv/llama-cpp-python/media" + } + } +} +``` + +`allowed_media_domains` matches exact hostnames and does not allow wildcard patterns. + +## LoRA `model.loras` + +Load LoRA adapters once at startup from local files or Hugging Face. + +```json +{ + "model": { + "loras": [ + { + "from_pretrained": { + "repo_id": "example/qwen-lora-gguf", + "filename": "adapter.gguf" + }, + "scale": 1.0 + } + ] + } +} +``` + +The current implementation does not hot-swap LoRAs per request. + +## Draft Decoding + +Set `model.draft_model` to enable speculative draft providers. + +```json +{ + "model": { + "draft_model": "prompt-lookup-decoding", + "draft_model_num_pred_tokens": 8, + "draft_model_max_ngram_size": 4 + } +} +``` + +### Multi-Token Prediction (MTP) + +Use MTP when the loaded model and llama.cpp build expose the required draft state. + +```json +{ + "model": { + "draft_model": "draft-mtp", + "draft_model_num_pred_tokens": 2, + "draft_model_threads": 4, + "draft_model_threads_batch": 8 + } +} +``` + +MTP currently applies to text-only requests. + +## Disk Sequence Cache + +`disk_cache` stores serialized llama.cpp sequence state for repeated prompt prefixes. + +```json +{ + "disk_cache": { + "path": ".cache/sequences", + "max_bytes": 1073741824, + "min_tokens": 128 + } +} +``` + +| Field | Purpose | +| --- | --- | +| `path` | Directory for cached sequence files. | +| `max_bytes` | Maximum cache size before background cleanup removes entries. | +| `min_tokens` | Minimum prefix length that is worth saving. | + +The cache is versioned by model and context compatibility data and should be treated as ephemeral. diff --git a/examples/server/configs/gemma-4-12b-it-qat.json b/examples/server/configs/gemma-4-12b-it-qat.json new file mode 100644 index 0000000000..fefeda8da0 --- /dev/null +++ b/examples/server/configs/gemma-4-12b-it-qat.json @@ -0,0 +1,451 @@ +{ + "server": { + "host": "0.0.0.0", + "port": 8000 + }, + "model": { + "alias": "gemma-4-12b-it-qat", + "from_pretrained": { + "repo_id": "unsloth/gemma-4-12B-it-qat-GGUF", + "filename": "gemma-4-12B-it-qat-UD-Q4_K_XL.gguf" + }, + "mtmd": { + "mmproj_from_pretrained": { + "repo_id": "unsloth/gemma-4-12B-it-qat-GGUF", + "filename": "mmproj-BF16.gguf" + } + }, + "n_ctx": 32768, + "max_output_tokens": 4096, + "n_seq_max": 8, + "n_batch": 512, + "n_ubatch": 512, + "threads": 8, + "threads_batch": 8, + "kv_unified": true, + "store_logits": false, + "use_mmap": true, + "use_mlock": false, + "n_gpu_layers": -1, + "flash_attn": true, + "response_schema": { + "type": "object", + "properties": { + "role": { + "const": "assistant" + }, + "reasoning_content": { + "type": "string", + "x-regex": "^(?:<\\|turn>model\\n)?<\\|channel>thought\\n(.*?)<channel\\|>" + }, + "content": { + "type": "string", + "x-regex": "^(?:<\\|turn>model\\n)?(?:(?:<\\|channel>thought\\n).*?<channel\\|>)?(.*?)(?=<\\|tool_call>|<turn\\|>|$)" + }, + "tool_calls": { + "type": "array", + "x-regex-iterator": "<\\|tool_call>(call:[^\\{]+\\{.*?\\})<tool_call\\|>", + "items": { + "type": "object", + "properties": { + "type": { + "const": "function" + }, + "function": { + "type": "object", + "properties": { + "name": { + "type": "string", + "x-regex": "^call:(\\w+)\\{" + }, + "arguments": { + "type": "object", + "x-regex": "^call:\\w+(\\{.*\\})$", + "x-parser": "gemma4-tool-call", + "additionalProperties": true + } + }, + "required": [ + "name", + "arguments" + ] + } + }, + "required": [ + "type", + "function" + ] + } + } + }, + "required": [ + "role" + ] + }, + "chat_template": [ + "{%- macro format_parameters(properties, required, filter_keys=false) -%}\n", + " {%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}\n", + " {%- set ns = namespace(found_first=false) -%}\n", + " {%- for key, value in properties | dictsort -%}\n", + " {%- set add_comma = false -%}\n", + " {%- if not filter_keys or key not in standard_keys -%}\n", + " {%- if ns.found_first %},{% endif -%}\n", + " {%- set ns.found_first = true -%}\n", + " {{ key }}:{\n", + " {%- if value['description'] -%}\n", + " description:<|\"|>{{ value['description'] }}<|\"|>\n", + " {%- set add_comma = true -%}\n", + " {%- endif -%}\n", + " {%- if value['type'] | upper == 'STRING' -%}\n", + " {%- if value['enum'] -%}\n", + " {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n", + " enum:{{ format_argument(value['enum']) }}\n", + " {%- endif -%}\n", + " {%- elif value['type'] | upper == 'ARRAY' -%}\n", + " {%- if value['items'] is mapping and value['items'] -%}\n", + " {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n", + " items:{\n", + " {%- set ns_items = namespace(found_first=false) -%}\n", + " {%- for item_key, item_value in value['items'] | dictsort -%}\n", + " {%- if item_value is not none -%}\n", + " {%- if ns_items.found_first %},{% endif -%}\n", + " {%- set ns_items.found_first = true -%}\n", + " {%- if item_key == 'properties' -%}\n", + " properties:{\n", + " {%- if item_value is mapping -%}\n", + " {{- format_parameters(item_value, value['items']['required'] | default([])) -}}\n", + " {%- endif -%}\n", + " }\n", + " {%- elif item_key == 'required' -%}\n", + " required:[\n", + " {%- for req_item in item_value -%}\n", + " <|\"|>{{- req_item -}}<|\"|>\n", + " {%- if not loop.last %},{% endif -%}\n", + " {%- endfor -%}\n", + " ]\n", + " {%- elif item_key == 'type' -%}\n", + " {%- if item_value is string -%}\n", + " type:{{ format_argument(item_value | upper) }}\n", + " {%- else -%}\n", + " type:{{ format_argument(item_value | map('upper') | list) }}\n", + " {%- endif -%}\n", + " {%- else -%}\n", + " {{ item_key }}:{{ format_argument(item_value) }}\n", + " {%- endif -%}\n", + " {%- endif -%}\n", + " {%- endfor -%}\n", + " }\n", + " {%- endif -%}\n", + " {%- endif -%}\n", + " {%- if value['nullable'] %}\n", + " {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n", + " nullable:true\n", + " {%- endif -%}\n", + " {%- if value['type'] | upper == 'OBJECT' -%}\n", + " {%- if value['properties'] is defined and value['properties'] is mapping -%}\n", + " {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n", + " properties:{\n", + " {{- format_parameters(value['properties'], value['required'] | default([])) -}}\n", + " }\n", + " {%- elif value is mapping -%}\n", + " {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n", + " properties:{\n", + " {{- format_parameters(value, value['required'] | default([]), filter_keys=true) -}}\n", + " }\n", + " {%- endif -%}\n", + " {%- if value['required'] -%}\n", + " {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n", + " required:[\n", + " {%- for item in value['required'] | default([]) -%}\n", + " <|\"|>{{- item -}}<|\"|>\n", + " {%- if not loop.last %},{% endif -%}\n", + " {%- endfor -%}\n", + " ]\n", + " {%- endif -%}\n", + " {%- endif -%}\n", + " {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n", + " type:<|\"|>{{ value['type'] | upper }}<|\"|>}\n", + " {%- endif -%}\n", + " {%- endfor -%}\n", + "{%- endmacro -%}\n", + "{%- macro format_function_declaration(tool_data) -%}\n", + " declaration:{{- tool_data['function']['name'] -}}{description:<|\"|>{{- tool_data['function']['description'] -}}<|\"|>\n", + " {%- set params = tool_data['function']['parameters'] -%}\n", + " {%- if params -%}\n", + " ,parameters:{\n", + " {%- if params['properties'] -%}\n", + " properties:{ {{- format_parameters(params['properties'], params['required']) -}} },\n", + " {%- endif -%}\n", + " {%- if params['required'] -%}\n", + " required:[\n", + " {%- for item in params['required'] -%}\n", + " <|\"|>{{- item -}}<|\"|>\n", + " {{- ',' if not loop.last -}}\n", + " {%- endfor -%}\n", + " ],\n", + " {%- endif -%}\n", + " {%- if params['type'] -%}\n", + " type:<|\"|>{{- params['type'] | upper -}}<|\"|>}\n", + " {%- endif -%}\n", + " {%- endif -%}\n", + " {%- if 'response' in tool_data['function'] -%}\n", + " {%- set response_declaration = tool_data['function']['response'] -%}\n", + " ,response:{\n", + " {%- if response_declaration['description'] -%}\n", + " description:<|\"|>{{- response_declaration['description'] -}}<|\"|>,\n", + " {%- endif -%}\n", + " {%- if response_declaration['type'] | upper == 'OBJECT' -%}\n", + " type:<|\"|>{{- response_declaration['type'] | upper -}}<|\"|>}\n", + " {%- endif -%}\n", + " {%- endif -%}\n", + " }\n", + "{%- endmacro -%}\n", + "{%- macro format_argument(argument, escape_keys=True) -%}\n", + " {%- if argument is string -%}\n", + " {{- '<|\"|>' + argument + '<|\"|>' -}}\n", + " {%- elif argument is boolean -%}\n", + " {{- 'true' if argument else 'false' -}}\n", + " {%- elif argument is mapping -%}\n", + " {{- '{' -}}\n", + " {%- set ns = namespace(found_first=false) -%}\n", + " {%- for key, value in argument | dictsort -%}\n", + " {%- if ns.found_first %},{% endif -%}\n", + " {%- set ns.found_first = true -%}\n", + " {%- if escape_keys -%}\n", + " {{- '<|\"|>' + key + '<|\"|>' -}}\n", + " {%- else -%}\n", + " {{- key -}}\n", + " {%- endif -%}\n", + " :{{- format_argument(value, escape_keys=escape_keys) -}}\n", + " {%- endfor -%}\n", + " {{- '}' -}}\n", + " {%- elif argument is sequence -%}\n", + " {{- '[' -}}\n", + " {%- for item in argument -%}\n", + " {{- format_argument(item, escape_keys=escape_keys) -}}\n", + " {%- if not loop.last %},{% endif -%}\n", + " {%- endfor -%}\n", + " {{- ']' -}}\n", + " {%- else -%}\n", + " {{- argument -}}\n", + " {%- endif -%}\n", + "{%- endmacro -%}\n", + "{%- macro strip_thinking(text) -%}\n", + " {%- set ns = namespace(result='') -%}\n", + " {%- for part in text.split('<channel|>') -%}\n", + " {%- if '<|channel>' in part -%}\n", + " {%- set ns.result = ns.result + part.split('<|channel>')[0] -%}\n", + " {%- else -%}\n", + " {%- set ns.result = ns.result + part -%}\n", + " {%- endif -%}\n", + " {%- endfor -%}\n", + " {{- ns.result | trim -}}\n", + "{%- endmacro -%}\n", + "\n", + "{%- macro format_tool_response_block(tool_name, response) -%}\n", + " {{- '<|tool_response>' -}}\n", + " {%- if response is mapping -%}\n", + " {{- 'response:' + tool_name + '{' -}}\n", + " {%- for key, value in response | dictsort -%}\n", + " {{- key -}}:{{- format_argument(value, escape_keys=False) -}}\n", + " {%- if not loop.last %},{% endif -%}\n", + " {%- endfor -%}\n", + " {{- '}' -}}\n", + " {%- else -%}\n", + " {{- 'response:' + tool_name + '{value:' + format_argument(response, escape_keys=False) + '}' -}}\n", + " {%- endif -%}\n", + " {{- '<tool_response|>' -}}\n", + "{%- endmacro -%}\n", + "\n", + "{%- set ns = namespace(prev_message_type=None) -%}\n", + "{%- set loop_messages = messages -%}\n", + "{{- bos_token -}}\n", + "{#- Handle System/Tool Definitions Block -#}\n", + "{%- if (enable_thinking is defined and enable_thinking) or tools or messages[0]['role'] in ['system', 'developer'] -%}\n", + " {{- '<|turn>system\\n' -}}\n", + " {#- Inject Thinking token at the very top of the FIRST system turn -#}\n", + " {%- if enable_thinking is defined and enable_thinking -%}\n", + " {{- '<|think|>\\n' -}}\n", + " {%- set ns.prev_message_type = 'think' -%}\n", + " {%- endif -%}\n", + " {%- if messages[0]['role'] in ['system', 'developer'] -%}\n", + " {%- if messages[0]['content'] is string -%}\n", + " {{- messages[0]['content'] | trim -}}\n", + " {%- elif messages[0]['content'] is sequence -%}\n", + " {%- for item in messages[0]['content'] -%}\n", + " {{- item['text'] | trim + ' '-}}\n", + " {%- endfor -%}\n", + " {%- endif -%}\n", + " {%- set loop_messages = messages[1:] -%}\n", + " {%- endif -%}\n", + " {%- if tools -%}\n", + " {%- for tool in tools %}\n", + " {{- '<|tool>' -}}\n", + " {{- format_function_declaration(tool) | trim -}}\n", + " {{- '<tool|>' -}}\n", + " {%- endfor %}\n", + " {%- set ns.prev_message_type = 'tool' -%}\n", + " {%- endif -%}\n", + " {{- '<turn|>\\n' -}}\n", + "{%- endif %}\n", + "\n", + "{#- Pre-scan: find last user message index for reasoning guard -#}\n", + "{%- set ns_turn = namespace(last_user_idx=-1) -%}\n", + "{%- for i in range(loop_messages | length) -%}\n", + " {%- if loop_messages[i]['role'] == 'user' -%}\n", + " {%- set ns_turn.last_user_idx = i -%}\n", + " {%- endif -%}\n", + "{%- endfor -%}\n", + "\n", + "{#- Loop through messages -#}\n", + "{%- for message in loop_messages -%}\n", + " {%- if message['role'] != 'tool' -%}\n", + " {%- set ns.prev_message_type = None -%}\n", + " {%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}\n", + " {#- Detect continuation: suppress duplicate <|turn>model when previous non-tool message was also assistant -#}\n", + " {%- set prev_nt = namespace(role=None, found=false) -%}\n", + " {%- if loop.index0 > 0 -%}\n", + " {%- for j in range(loop.index0 - 1, -1, -1) -%}\n", + " {%- if not prev_nt.found -%}\n", + " {%- if loop_messages[j]['role'] != 'tool' -%}\n", + " {%- set prev_nt.role = loop_messages[j]['role'] -%}\n", + " {%- set prev_nt.found = true -%}\n", + " {%- endif -%}\n", + " {%- endif -%}\n", + " {%- endfor -%}\n", + " {%- endif -%}\n", + " {%- set continue_same_model_turn = (role == 'model' and prev_nt.role == 'assistant') -%}\n", + " {%- if not continue_same_model_turn -%}\n", + " {{- '<|turn>' + role + '\\n' }}\n", + " {%- endif -%}\n", + "\n", + " {#- Render reasoning/reasoning_content as thinking channel -#}\n", + " {%- set thinking_text = message.get('reasoning') or message.get('reasoning_content') -%}\n", + " {%- if thinking_text and loop.index0 > ns_turn.last_user_idx and message.get('tool_calls') -%}\n", + " {{- '<|channel>thought\\n' + thinking_text + '\\n<channel|>' -}}\n", + " {%- endif -%}\n", + "\n", + " {%- if message['tool_calls'] -%}\n", + " {%- for tool_call in message['tool_calls'] -%}\n", + " {%- set function = tool_call['function'] -%}\n", + " {{- '<|tool_call>call:' + function['name'] + '{' -}}\n", + " {%- if function['arguments'] is mapping -%}\n", + " {%- set ns_args = namespace(found_first=false) -%}\n", + " {%- for key, value in function['arguments'] | dictsort -%}\n", + " {%- if ns_args.found_first %},{% endif -%}\n", + " {%- set ns_args.found_first = true -%}\n", + " {{- key -}}:{{- format_argument(value, escape_keys=False) -}}\n", + " {%- endfor -%}\n", + " {%- elif function['arguments'] is string -%}\n", + " {{- function['arguments'] -}}\n", + " {%- endif -%}\n", + " {{- '}<tool_call|>' -}}\n", + " {%- endfor -%}\n", + " {%- set ns.prev_message_type = 'tool_call' -%}\n", + " {%- endif -%}\n", + "\n", + " {%- set ns_tr_out = namespace(flag=false) -%}\n", + " {%- if message.get('tool_responses') -%}\n", + " {#- Legacy: tool_responses embedded on the assistant message (Google/Gemma native) -#}\n", + " {%- for tool_response in message['tool_responses'] -%}\n", + " {{- format_tool_response_block(tool_response['name'] | default('unknown'), tool_response['response']) -}}\n", + " {%- set ns_tr_out.flag = true -%}\n", + " {%- set ns.prev_message_type = 'tool_response' -%}\n", + " {%- endfor -%}\n", + " {%- elif message.get('tool_calls') -%}\n", + " {#- OpenAI Chat Completions: forward-scan consecutive role:tool messages -#}\n", + " {%- set ns_tool_scan = namespace(stopped=false) -%}\n", + " {%- for k in range(loop.index0 + 1, loop_messages | length) -%}\n", + " {%- if ns_tool_scan.stopped -%}\n", + " {%- elif loop_messages[k]['role'] != 'tool' -%}\n", + " {%- set ns_tool_scan.stopped = true -%}\n", + " {%- else -%}\n", + " {%- set follow = loop_messages[k] -%}\n", + " {#- Resolve tool_call_id to function name -#}\n", + " {%- set ns_tname = namespace(name=follow.get('name') | default('unknown')) -%}\n", + " {%- for tc in message['tool_calls'] -%}\n", + " {%- if tc.get('id') == follow.get('tool_call_id') -%}\n", + " {%- set ns_tname.name = tc['function']['name'] -%}\n", + " {%- endif -%}\n", + " {%- endfor -%}\n", + " {#- Handle content as string or content-parts array -#}\n", + " {%- set tool_body = follow.get('content') -%}\n", + " {%- if tool_body is string -%}\n", + " {{- format_tool_response_block(ns_tname.name, tool_body) -}}\n", + " {%- elif tool_body is sequence and tool_body is not string -%}\n", + " {%- set ns_txt = namespace(s='') -%}\n", + " {%- for part in tool_body -%}\n", + " {%- if part.get('type') == 'text' -%}\n", + " {%- set ns_txt.s = ns_txt.s + (part.get('text') | default('')) -%}\n", + " {%- endif -%}\n", + " {%- endfor -%}\n", + " {{- format_tool_response_block(ns_tname.name, ns_txt.s) -}}\n", + " {%- for part in tool_body -%}\n", + " {%- if part.get('type') == 'image' -%}\n", + " {{- '<|image|>' -}}\n", + " {%- elif part.get('type') == 'audio' -%}\n", + " {{- '<|audio|>' -}}\n", + " {%- elif part.get('type') == 'video' -%}\n", + " {{- '<|video|>' -}}\n", + " {%- endif -%}\n", + " {%- endfor -%}\n", + " {%- else -%}\n", + " {{- format_tool_response_block(ns_tname.name, tool_body) -}}\n", + " {%- endif -%}\n", + " {%- set ns_tr_out.flag = true -%}\n", + " {%- set ns.prev_message_type = 'tool_response' -%}\n", + " {%- endif -%}\n", + " {%- endfor -%}\n", + " {%- endif -%}\n", + "\n", + " {%- set captured_content -%}\n", + " {%- if message['content'] is string -%}\n", + " {%- if role == 'model' -%}\n", + " {{- strip_thinking(message['content']) -}}\n", + " {%- else -%}\n", + " {{- message['content'] | trim -}}\n", + " {%- endif -%}\n", + " {%- elif message['content'] is sequence -%}\n", + " {%- for item in message['content'] -%}\n", + " {%- if item['type'] == 'text' -%}\n", + " {%- if role == 'model' -%}\n", + " {{- strip_thinking(item['text']) -}}\n", + " {%- else -%}\n", + " {{- item['text'] | trim -}}\n", + " {%- endif -%}\n", + " {%- elif item['type'] == 'image' -%}\n", + " {{- '<|image|>' -}}\n", + " {%- set ns.prev_message_type = 'image' -%}\n", + " {%- elif item['type'] == 'audio' -%}\n", + " {{- '<|audio|>' -}}\n", + " {%- set ns.prev_message_type = 'audio' -%}\n", + " {%- elif item['type'] == 'video' -%}\n", + " {{- '<|video|>' -}}\n", + " {%- set ns.prev_message_type = 'video' -%}\n", + " {%- endif -%}\n", + " {%- endfor -%}\n", + " {%- endif -%}\n", + " {%- endset -%}\n", + "\n", + " {{- captured_content -}}\n", + " {%- set has_content = captured_content | trim | length > 0 -%}\n", + "\n", + " {%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%}\n", + " {{- '<|tool_response>' -}}\n", + " {%- elif not (ns_tr_out.flag and not has_content) -%}\n", + " {{- '<turn|>\\n' -}}\n", + " {%- endif -%}\n", + " {%- endif -%}\n", + "{%- endfor -%}\n", + "\n", + "{%- if add_generation_prompt -%}\n", + " {%- if ns.prev_message_type != 'tool_response' and ns.prev_message_type != 'tool_call' -%}\n", + " {{- '<|turn>model\\n' -}}\n", + " {%- if not enable_thinking | default(false) -%}\n", + " {{- '<|channel>thought\\n<channel|>' -}}\n", + " {%- endif -%}\n", + " {%- endif -%}\n", + "{%- endif -%}" + ] + } +} diff --git a/examples/server/configs/gpt-oss-120b.json b/examples/server/configs/gpt-oss-120b.json new file mode 100644 index 0000000000..2fa7a76f1e --- /dev/null +++ b/examples/server/configs/gpt-oss-120b.json @@ -0,0 +1,417 @@ +{ + "server": { + "host": "0.0.0.0", + "port": 8000 + }, + "model": { + "alias": "gpt-oss-120b", + "from_pretrained": { + "repo_id": "ggml-org/gpt-oss-120b-GGUF", + "filename": "gpt-oss-120b-mxfp4-00001-of-00003.gguf", + "additional_files": [ + "gpt-oss-120b-mxfp4-00002-of-00003.gguf", + "gpt-oss-120b-mxfp4-00003-of-00003.gguf" + ] + }, + "n_ctx": 32768, + "max_output_tokens": 4096, + "n_seq_max": 4, + "n_batch": 512, + "n_ubatch": 512, + "threads": 8, + "threads_batch": 8, + "kv_unified": true, + "store_logits": false, + "use_mmap": true, + "use_mlock": false, + "n_gpu_layers": -1, + "flash_attn": true, + "response_schema": { + "type": "object", + "properties": { + "role": { + "const": "assistant" + }, + "reasoning_content": { + "type": "string", + "x-regex": "^(?:<\\|start\\|>assistant)?<\\|channel\\|>analysis<\\|message\\|>(.*?)(?=<\\|end\\|>|<\\|start\\|>assistant|$)" + }, + "content": { + "type": "string", + "x-regex": "^(?:.*?<\\|start\\|>assistant)?<\\|channel\\|>final<\\|message\\|>(.*?)(?=<\\|return\\|>|<\\|end\\|>|$)" + }, + "tool_calls": { + "type": "array", + "x-regex-iterator": "(<\\|start\\|>assistant to=functions\\.\\w+<\\|channel\\|>commentary json<\\|message\\|>.*?<\\|call\\|>)", + "items": { + "type": "object", + "properties": { + "type": { + "const": "function" + }, + "function": { + "type": "object", + "properties": { + "name": { + "type": "string", + "x-regex": "^<\\|start\\|>assistant to=functions\\.(\\w+)<\\|channel\\|>commentary json<\\|message\\|>" + }, + "arguments": { + "type": "object", + "x-regex": "^<\\|start\\|>assistant to=functions\\.\\w+<\\|channel\\|>commentary json<\\|message\\|>(.*?)<\\|call\\|>$", + "x-parser": "json", + "additionalProperties": true + } + }, + "required": [ + "name", + "arguments" + ] + } + }, + "required": [ + "type", + "function" + ] + } + } + }, + "required": [ + "role" + ] + }, + "chat_template": [ + "{#-\n", + " In addition to the normal inputs of `messages` and `tools`, this template also accepts the\n", + " following kwargs:\n", + " - \"builtin_tools\": A list, can contain \"browser\" and/or \"python\".\n", + " - \"model_identity\": A string that optionally describes the model identity.\n", + " - \"reasoning_effort\": A string that describes the reasoning effort, defaults to \"medium\".\n", + " #}\n", + "\n", + "{#- Tool Definition Rendering ============================================== #}\n", + "{%- macro render_typescript_type(param_spec, required_params, is_nullable=false) -%}\n", + " {%- if param_spec.type == \"array\" -%}\n", + " {%- if param_spec['items'] -%}\n", + " {%- if param_spec['items']['type'] == \"string\" -%}\n", + " {{- \"string[]\" }}\n", + " {%- elif param_spec['items']['type'] == \"number\" -%}\n", + " {{- \"number[]\" }}\n", + " {%- elif param_spec['items']['type'] == \"integer\" -%}\n", + " {{- \"number[]\" }}\n", + " {%- elif param_spec['items']['type'] == \"boolean\" -%}\n", + " {{- \"boolean[]\" }}\n", + " {%- else -%}\n", + " {%- set inner_type = render_typescript_type(param_spec['items'], required_params) -%}\n", + " {%- if inner_type == \"object | object\" or inner_type|length > 50 -%}\n", + " {{- \"any[]\" }}\n", + " {%- else -%}\n", + " {{- inner_type + \"[]\" }}\n", + " {%- endif -%}\n", + " {%- endif -%}\n", + " {%- if param_spec.nullable -%}\n", + " {{- \" | null\" }}\n", + " {%- endif -%}\n", + " {%- else -%}\n", + " {{- \"any[]\" }}\n", + " {%- if param_spec.nullable -%}\n", + " {{- \" | null\" }}\n", + " {%- endif -%}\n", + " {%- endif -%}\n", + " {%- elif param_spec.type is defined and param_spec.type is iterable and param_spec.type is not string and param_spec.type is not mapping and param_spec.type[0] is defined -%}\n", + " {#- Handle array of types like [\"object\", \"object\"] from Union[dict, list] #}\n", + " {%- if param_spec.type | length > 1 -%}\n", + " {{- param_spec.type | join(\" | \") }}\n", + " {%- else -%}\n", + " {{- param_spec.type[0] }}\n", + " {%- endif -%}\n", + " {%- elif param_spec.oneOf -%}\n", + " {#- Handle oneOf schemas - check for complex unions and fallback to any #}\n", + " {%- set has_object_variants = false -%}\n", + " {%- for variant in param_spec.oneOf -%}\n", + " {%- if variant.type == \"object\" -%}\n", + " {%- set has_object_variants = true -%}\n", + " {%- endif -%}\n", + " {%- endfor -%}\n", + " {%- if has_object_variants and param_spec.oneOf|length > 1 -%}\n", + " {{- \"any\" }}\n", + " {%- else -%}\n", + " {%- for variant in param_spec.oneOf -%}\n", + " {{- render_typescript_type(variant, required_params) -}}\n", + " {%- if variant.description %}\n", + " {{- \"// \" + variant.description }}\n", + " {%- endif -%}\n", + " {%- if variant.default is defined %}\n", + " {{ \"// default: \" + variant.default|tojson }}\n", + " {%- endif -%}\n", + " {%- if not loop.last %}\n", + " {{- \" | \" }}\n", + " {% endif -%}\n", + " {%- endfor -%}\n", + " {%- endif -%}\n", + " {%- elif param_spec.type == \"string\" -%}\n", + " {%- if param_spec.enum -%}\n", + " {{- '\"' + param_spec.enum|join('\" | \"') + '\"' -}}\n", + " {%- else -%}\n", + " {{- \"string\" }}\n", + " {%- if param_spec.nullable %}\n", + " {{- \" | null\" }}\n", + " {%- endif -%}\n", + " {%- endif -%}\n", + " {%- elif param_spec.type == \"number\" -%}\n", + " {{- \"number\" }}\n", + " {%- elif param_spec.type == \"integer\" -%}\n", + " {{- \"number\" }}\n", + " {%- elif param_spec.type == \"boolean\" -%}\n", + " {{- \"boolean\" }}\n", + "\n", + " {%- elif param_spec.type == \"object\" -%}\n", + " {%- if param_spec.properties -%}\n", + " {{- \"{\\n\" }}\n", + " {%- for prop_name, prop_spec in param_spec.properties.items() -%}\n", + " {{- prop_name -}}\n", + " {%- if prop_name not in (param_spec.required or []) -%}\n", + " {{- \"?\" }}\n", + " {%- endif -%}\n", + " {{- \": \" }}\n", + " {{ render_typescript_type(prop_spec, param_spec.required or []) }}\n", + " {%- if not loop.last -%}\n", + " {{-\", \" }}\n", + " {%- endif -%}\n", + " {%- endfor -%}\n", + " {{- \"}\" }}\n", + " {%- else -%}\n", + " {{- \"object\" }}\n", + " {%- endif -%}\n", + " {%- else -%}\n", + " {{- \"any\" }}\n", + " {%- endif -%}\n", + "{%- endmacro -%}\n", + "\n", + "{%- macro render_tool_namespace(namespace_name, tools) -%}\n", + " {{- \"## \" + namespace_name + \"\\n\\n\" }}\n", + " {{- \"namespace \" + namespace_name + \" {\\n\\n\" }}\n", + " {%- for tool in tools %}\n", + " {%- set tool = tool.function %}\n", + " {{- \"// \" + tool.description + \"\\n\" }}\n", + " {{- \"type \"+ tool.name + \" = \" }}\n", + " {%- if tool.parameters and tool.parameters.properties %}\n", + " {{- \"(_: {\\n\" }}\n", + " {%- for param_name, param_spec in tool.parameters.properties.items() %}\n", + " {%- if param_spec.description %}\n", + " {{- \"// \" + param_spec.description + \"\\n\" }}\n", + " {%- endif %}\n", + " {{- param_name }}\n", + " {%- if param_name not in (tool.parameters.required or []) -%}\n", + " {{- \"?\" }}\n", + " {%- endif -%}\n", + " {{- \": \" }}\n", + " {{- render_typescript_type(param_spec, tool.parameters.required or []) }}\n", + " {%- if param_spec.default is defined -%}\n", + " {%- if param_spec.enum %}\n", + " {{- \", // default: \" + param_spec.default }}\n", + " {%- elif param_spec.oneOf %}\n", + " {{- \"// default: \" + param_spec.default }}\n", + " {%- else %}\n", + " {{- \", // default: \" + param_spec.default|tojson }}\n", + " {%- endif -%}\n", + " {%- endif -%}\n", + " {%- if not loop.last %}\n", + " {{- \",\\n\" }}\n", + " {%- else %}\n", + " {{- \",\\n\" }}\n", + " {%- endif -%}\n", + " {%- endfor %}\n", + " {{- \"}) => any;\\n\\n\" }}\n", + " {%- else -%}\n", + " {{- \"() => any;\\n\\n\" }}\n", + " {%- endif -%}\n", + " {%- endfor %}\n", + " {{- \"} // namespace \" + namespace_name }}\n", + "{%- endmacro -%}\n", + "\n", + "{%- macro render_builtin_tools(browser_tool, python_tool) -%}\n", + " {%- if browser_tool %}\n", + " {{- \"## browser\\n\\n\" }}\n", + " {{- \"// Tool for browsing.\\n\" }}\n", + " {{- \"// The `cursor` appears in brackets before each browsing display: `[{cursor}]`.\\n\" }}\n", + " {{- \"// Cite information from the tool using the following format:\\n\" }}\n", + " {{- \"// `\u3010{cursor}\u2020L{line_start}(-L{line_end})?\u3011`, for example: `\u30106\u2020L9-L11\u3011` or `\u30108\u2020L3\u3011`.\\n\" }}\n", + " {{- \"// Do not quote more than 10 words directly from the tool output.\\n\" }}\n", + " {{- \"// sources=web (default: web)\\n\" }}\n", + " {{- \"namespace browser {\\n\\n\" }}\n", + " {{- \"// Searches for information related to `query` and displays `topn` results.\\n\" }}\n", + " {{- \"type search = (_: {\\n\" }}\n", + " {{- \"query: string,\\n\" }}\n", + " {{- \"topn?: number, // default: 10\\n\" }}\n", + " {{- \"source?: string,\\n\" }}\n", + " {{- \"}) => any;\\n\\n\" }}\n", + " {{- \"// Opens the link `id` from the page indicated by `cursor` starting at line number `loc`, showing `num_lines` lines.\\n\" }}\n", + " {{- \"// Valid link ids are displayed with the formatting: `\u3010{id}\u2020.*\u3011`.\\n\" }}\n", + " {{- \"// If `cursor` is not provided, the most recent page is implied.\\n\" }}\n", + " {{- \"// If `id` is a string, it is treated as a fully qualified URL associated with `source`.\\n\" }}\n", + " {{- \"// If `loc` is not provided, the viewport will be positioned at the beginning of the document or centered on the most relevant passage, if available.\\n\" }}\n", + " {{- \"// Use this function without `id` to scroll to a new location of an opened page.\\n\" }}\n", + " {{- \"type open = (_: {\\n\" }}\n", + " {{- \"id?: number | string, // default: -1\\n\" }}\n", + " {{- \"cursor?: number, // default: -1\\n\" }}\n", + " {{- \"loc?: number, // default: -1\\n\" }}\n", + " {{- \"num_lines?: number, // default: -1\\n\" }}\n", + " {{- \"view_source?: boolean, // default: false\\n\" }}\n", + " {{- \"source?: string,\\n\" }}\n", + " {{- \"}) => any;\\n\\n\" }}\n", + " {{- \"// Finds exact matches of `pattern` in the current page, or the page given by `cursor`.\\n\" }}\n", + " {{- \"type find = (_: {\\n\" }}\n", + " {{- \"pattern: string,\\n\" }}\n", + " {{- \"cursor?: number, // default: -1\\n\" }}\n", + " {{- \"}) => any;\\n\\n\" }}\n", + " {{- \"} // namespace browser\\n\\n\" }}\n", + " {%- endif -%}\n", + "\n", + " {%- if python_tool %}\n", + " {{- \"## python\\n\\n\" }}\n", + " {{- \"Use this tool to execute Python code in your chain of thought. The code will not be shown to the user. This tool should be used for internal reasoning, but not for code that is intended to be visible to the user (e.g. when creating plots, tables, or files).\\n\\n\" }}\n", + " {{- \"When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 120.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is UNKNOWN. Depends on the cluster.\\n\\n\" }}\n", + " {%- endif -%}\n", + "{%- endmacro -%}\n", + "\n", + "{#- System Message Construction ============================================ #}\n", + "{%- macro build_system_message() -%}\n", + " {%- if model_identity is not defined %}\n", + " {%- set model_identity = \"You are ChatGPT, a large language model trained by OpenAI.\" %}\n", + " {%- endif %}\n", + " {{- model_identity + \"\\n\" }}\n", + " {{- \"Knowledge cutoff: 2024-06\\n\" }}\n", + " {{- \"Current date: \" + strftime_now(\"%Y-%m-%d\") + \"\\n\\n\" }}\n", + " {%- if reasoning_effort is not string %}\n", + " {%- set reasoning_effort = \"medium\" %}\n", + " {%- endif %}\n", + " {{- \"Reasoning: \" + reasoning_effort + \"\\n\\n\" }}\n", + " {%- if builtin_tools %}\n", + " {{- \"# Tools\\n\\n\" }}\n", + " {%- set available_builtin_tools = namespace(browser=false, python=false) %}\n", + " {%- for tool in builtin_tools %}\n", + " {%- if tool == \"browser\" %}\n", + " {%- set available_builtin_tools.browser = true %}\n", + " {%- elif tool == \"python\" %}\n", + " {%- set available_builtin_tools.python = true %}\n", + " {%- endif %}\n", + " {%- endfor %}\n", + " {{- render_builtin_tools(available_builtin_tools.browser, available_builtin_tools.python) }}\n", + " {%- endif -%}\n", + " {{- \"# Valid channels: analysis, commentary, final. Channel must be included for every message.\" }}\n", + " {%- if tools -%}\n", + " {{- \"\\nCalls to these tools must go to the commentary channel: 'functions'.\" }}\n", + " {%- endif -%}\n", + "{%- endmacro -%}\n", + "\n", + "{#- Main Template Logic ================================================= #}\n", + "{#- Set defaults #}\n", + "\n", + "{#- Render system message #}\n", + "{{- \"<|start|>system<|message|>\" }}\n", + "{{- build_system_message() }}\n", + "{{- \"<|end|>\" }}\n", + "\n", + "{#- Extract developer message #}\n", + "{%- if messages[0].role == \"developer\" or messages[0].role == \"system\" %}\n", + " {%- set developer_message = messages[0].content %}\n", + " {%- set loop_messages = messages[1:] %}\n", + "{%- else %}\n", + " {%- set developer_message = \"\" %}\n", + " {%- set loop_messages = messages %}\n", + "{%- endif %}\n", + "\n", + "{#- Render developer message #}\n", + "{%- if developer_message or tools %}\n", + " {{- \"<|start|>developer<|message|>\" }}\n", + " {%- if developer_message %}\n", + " {{- \"# Instructions\\n\\n\" }}\n", + " {{- developer_message }}\n", + " {{- \"\\n\\n\" }}\n", + " {%- endif %}\n", + " {%- if tools -%}\n", + " {{- \"# Tools\\n\\n\" }}\n", + " {{- render_tool_namespace(\"functions\", tools) }}\n", + " {%- endif -%}\n", + " {{- \"<|end|>\" }}\n", + "{%- endif %}\n", + "\n", + "{#- Render messages #}\n", + "{%- set last_tool_call = namespace(name=none) %}\n", + "{%- for message in loop_messages -%}\n", + " {#- At this point only assistant/user/tool messages should remain #}\n", + " {%- if message.role == 'assistant' -%}\n", + " {#- Checks to ensure the messages are being passed in the format we expect #}\n", + " {%- if \"content\" in message %}\n", + " {%- if \"<|channel|>analysis<|message|>\" in message.content or \"<|channel|>final<|message|>\" in message.content %}\n", + " {{- raise_exception(\"You have passed a message containing <|channel|> tags in the content field. Instead of doing this, you should pass analysis messages (the string between '<|message|>' and '<|end|>') in the 'thinking' field, and final messages (the string between '<|message|>' and '<|end|>') in the 'content' field.\") }}\n", + " {%- endif %}\n", + " {%- endif %}\n", + " {%- if \"thinking\" in message %}\n", + " {%- if \"<|channel|>analysis<|message|>\" in message.thinking or \"<|channel|>final<|message|>\" in message.thinking %}\n", + " {{- raise_exception(\"You have passed a message containing <|channel|> tags in the thinking field. Instead of doing this, you should pass analysis messages (the string between '<|message|>' and '<|end|>') in the 'thinking' field, and final messages (the string between '<|message|>' and '<|end|>') in the 'content' field.\") }}\n", + " {%- endif %}\n", + " {%- endif %}\n", + " {%- if \"tool_calls\" in message %}\n", + " {#- We need very careful handling here - we want to drop the tool call analysis message if the model #}\n", + " {#- has output a later <|final|> message, but otherwise we want to retain it. This is the only case #}\n", + " {#- when we render CoT/analysis messages in inference. #}\n", + " {%- set future_final_message = namespace(found=false) %}\n", + " {%- for future_message in loop_messages[loop.index:] %}\n", + " {%- if future_message.role == 'assistant' and \"tool_calls\" not in future_message %}\n", + " {%- set future_final_message.found = true %}\n", + " {%- endif %}\n", + " {%- endfor %}\n", + " {#- We assume max 1 tool call per message, and so we infer the tool call name #}\n", + " {#- in \"tool\" messages from the most recent assistant tool call name #}\n", + " {%- set tool_call = message.tool_calls[0] %}\n", + " {%- if tool_call.function %}\n", + " {%- set tool_call = tool_call.function %}\n", + " {%- endif %}\n", + " {%- if message.content and message.thinking %}\n", + " {{- raise_exception(\"Cannot pass both content and thinking in an assistant message with tool calls! Put the analysis message in one or the other, but not both.\") }}\n", + " {%- elif message.content and not future_final_message.found %}\n", + " {{- \"<|start|>assistant<|channel|>analysis<|message|>\" + message.content + \"<|end|>\" }}\n", + " {%- elif message.thinking and not future_final_message.found %}\n", + " {{- \"<|start|>assistant<|channel|>analysis<|message|>\" + message.thinking + \"<|end|>\" }}\n", + " {%- endif %}\n", + " {{- \"<|start|>assistant to=\" }}\n", + " {{- \"functions.\" + tool_call.name + \"<|channel|>commentary \" }}\n", + " {{- (tool_call.content_type if tool_call.content_type is defined else \"json\") + \"<|message|>\" }}\n", + " {{- tool_call.arguments|tojson }}\n", + " {{- \"<|call|>\" }}\n", + " {%- set last_tool_call.name = tool_call.name %}\n", + " {%- elif loop.last and not add_generation_prompt %}\n", + " {#- Only render the CoT if the final turn is an assistant turn and add_generation_prompt is false #}\n", + " {#- This is a situation that should only occur in training, never in inference. #}\n", + " {%- if \"thinking\" in message %}\n", + " {{- \"<|start|>assistant<|channel|>analysis<|message|>\" + message.thinking + \"<|end|>\" }}\n", + " {%- endif %}\n", + " {#- <|return|> indicates the end of generation, but <|end|> does not #}\n", + " {#- <|return|> should never be an input to the model, but we include it as the final token #}\n", + " {#- when training, so the model learns to emit it. #}\n", + " {{- \"<|start|>assistant<|channel|>final<|message|>\" + message.content + \"<|return|>\" }}\n", + " {%- else %}\n", + " {#- CoT is dropped during all previous turns, so we never render it for inference #}\n", + " {{- \"<|start|>assistant<|channel|>final<|message|>\" + message.content + \"<|end|>\" }}\n", + " {%- set last_tool_call.name = none %}\n", + " {%- endif %}\n", + " {%- elif message.role == 'tool' -%}\n", + " {%- if last_tool_call.name is none %}\n", + " {{- raise_exception(\"Message has tool role, but there was no previous assistant message with a tool call!\") }}\n", + " {%- endif %}\n", + " {{- \"<|start|>functions.\" + last_tool_call.name }}\n", + " {{- \" to=assistant<|channel|>commentary<|message|>\" + message.content|tojson + \"<|end|>\" }}\n", + " {%- elif message.role == 'user' -%}\n", + " {{- \"<|start|>user<|message|>\" + message.content + \"<|end|>\" }}\n", + " {%- endif -%}\n", + "{%- endfor -%}\n", + "\n", + "{#- Generation prompt #}\n", + "{%- if add_generation_prompt -%}\n", + "<|start|>assistant\n", + "{%- endif -%}" + ] + } +} diff --git a/examples/server/configs/qwen3.5-0.8b.json b/examples/server/configs/qwen3.5-0.8b.json new file mode 100644 index 0000000000..c9f2ca944a --- /dev/null +++ b/examples/server/configs/qwen3.5-0.8b.json @@ -0,0 +1,261 @@ +{ + "server": { + "host": "0.0.0.0", + "port": 8000 + }, + "model": { + "alias": "qwen3.5-0.8b-vl", + "from_pretrained": { + "repo_id": "lmstudio-community/Qwen3.5-0.8B-GGUF", + "filename": "Qwen3.5-0.8B-Q8_0.gguf" + }, + "mtmd": { + "mmproj_from_pretrained": { + "repo_id": "lmstudio-community/Qwen3.5-0.8B-GGUF", + "filename": "mmproj-Qwen3.5-0.8B-BF16.gguf" + } + }, + "n_ctx": 32768, + "max_output_tokens": 4096, + "n_seq_max": 64, + "n_batch": 128, + "n_ubatch": 128, + "threads": 4, + "threads_batch": 8, + "kv_unified": true, + "store_logits": false, + "use_mmap": true, + "use_mlock": true, + "response_schema": { + "type": "object", + "properties": { + "role": { + "const": "assistant" + }, + "reasoning_content": { + "type": "string", + "x-regex": "^(?:<\\|im_start\\|>assistant\\n)?(?:<think>\\n)?(.*?)(?=</think>)" + }, + "content": { + "type": "string", + "x-regex": "^(?:<\\|im_start\\|>assistant\\n)?(?:(?:<think>\\n)?.*?</think>\\s*)?(.*?)(?=\\s*<tool_call>\\n|<\\|im_end\\|>$|$)" + }, + "tool_calls": { + "type": "array", + "x-regex-iterator": "<tool_call>\\n(.*?)\\n</tool_call>", + "items": { + "type": "object", + "properties": { + "type": { + "const": "function" + }, + "function": { + "type": "object", + "properties": { + "name": { + "type": "string", + "x-regex": "^<function=([^>\\n]+)>\\n" + }, + "arguments": { + "type": "object", + "x-regex": "^<function=[^>\\n]+>\\n(.*?)\\n</function>$", + "x-regex-key-value": "<parameter=(?P<key>[^>\\n]+)>\\n(?P<value>.*?)\\n</parameter>", + "additionalProperties": true + } + }, + "required": [ + "name", + "arguments" + ] + } + }, + "required": [ + "type", + "function" + ] + } + } + }, + "required": [ + "role" + ] + }, + "chat_template": [ + "{%- set image_count = namespace(value=0) %}\n", + "{%- set video_count = namespace(value=0) %}\n", + "{%- if enable_thinking is not defined and reasoning_effort is string %}\n", + " {%- set qwen_reasoning_effort = reasoning_effort|lower %}\n", + " {%- if qwen_reasoning_effort in ['none', 'minimal', 'low'] %}\n", + " {%- set enable_thinking = false %}\n", + " {%- elif qwen_reasoning_effort in ['medium', 'high'] %}\n", + " {%- set enable_thinking = true %}\n", + " {%- endif %}\n", + "{%- endif %}\n", + "{%- set forced_tool_name = none %}\n", + "{%- if tool_choice is mapping %}\n", + " {%- if tool_choice.function is defined and tool_choice.function.name is defined %}\n", + " {%- set forced_tool_name = tool_choice.function.name %}\n", + " {%- elif tool_choice.name is defined %}\n", + " {%- set forced_tool_name = tool_choice.name %}\n", + " {%- endif %}\n", + "{%- elif function_call is mapping and function_call.name is defined %}\n", + " {%- set forced_tool_name = function_call.name %}\n", + "{%- endif %}\n", + "{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n", + " {%- if content is string %}\n", + " {{- content }}\n", + " {%- elif content is iterable and content is not mapping %}\n", + " {%- for item in content %}\n", + " {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}\n", + " {%- if is_system_content %}\n", + " {{- raise_exception('System message cannot contain images.') }}\n", + " {%- endif %}\n", + " {%- if do_vision_count %}\n", + " {%- set image_count.value = image_count.value + 1 %}\n", + " {%- endif %}\n", + " {%- if add_vision_id %}\n", + " {{- 'Picture ' ~ image_count.value ~ ': ' }}\n", + " {%- endif %}\n", + " {{- '<|vision_start|><|image_pad|><|vision_end|>' }}\n", + " {%- elif 'video' in item or item.type == 'video' %}\n", + " {%- if is_system_content %}\n", + " {{- raise_exception('System message cannot contain videos.') }}\n", + " {%- endif %}\n", + " {%- if do_vision_count %}\n", + " {%- set video_count.value = video_count.value + 1 %}\n", + " {%- endif %}\n", + " {%- if add_vision_id %}\n", + " {{- 'Video ' ~ video_count.value ~ ': ' }}\n", + " {%- endif %}\n", + " {{- '<|vision_start|><|video_pad|><|vision_end|>' }}\n", + " {%- elif 'text' in item %}\n", + " {{- item.text }}\n", + " {%- else %}\n", + " {{- raise_exception('Unexpected item type in content.') }}\n", + " {%- endif %}\n", + " {%- endfor %}\n", + " {%- elif content is none or content is undefined %}\n", + " {{- '' }}\n", + " {%- else %}\n", + " {{- raise_exception('Unexpected content type.') }}\n", + " {%- endif %}\n", + "{%- endmacro %}\n", + "{%- if not messages %}\n", + " {{- raise_exception('No messages provided.') }}\n", + "{%- endif %}\n", + "{%- if tools and tools is iterable and tools is not mapping %}\n", + " {{- '<|im_start|>system\\n' }}\n", + " {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n<tools>\" }}\n", + " {%- for tool in tools %}\n", + " {{- \"\\n\" }}\n", + " {{- tool | tojson }}\n", + " {%- endfor %}\n", + " {{- \"\\n</tools>\" }}\n", + " {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n<tool_call>\\n<function=example_function_name>\\n<parameter=example_parameter_1>\\nvalue_1\\n</parameter>\\n<parameter=example_parameter_2>\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n</parameter>\\n</function>\\n</tool_call>\\n\\n<IMPORTANT>\\nReminder:\\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\\n- Required parameters MUST be specified\\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\\n</IMPORTANT>' }}\n", + " {%- if messages[0].role == 'system' %}\n", + " {%- set content = render_content(messages[0].content, false, true)|trim %}\n", + " {%- if content %}\n", + " {{- '\\n\\n' + content }}\n", + " {%- endif %}\n", + " {%- endif %}\n", + " {{- '<|im_end|>\\n' }}\n", + "{%- else %}\n", + " {%- if messages[0].role == 'system' %}\n", + " {%- set content = render_content(messages[0].content, false, true)|trim %}\n", + " {{- '<|im_start|>system\\n' + content + '<|im_end|>\\n' }}\n", + " {%- endif %}\n", + "{%- endif %}\n", + "{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n", + "{%- for message in messages[::-1] %}\n", + " {%- set index = (messages|length - 1) - loop.index0 %}\n", + " {%- if ns.multi_step_tool and message.role == \"user\" %}\n", + " {%- set content = render_content(message.content, false)|trim %}\n", + " {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}\n", + " {%- set ns.multi_step_tool = false %}\n", + " {%- set ns.last_query_index = index %}\n", + " {%- endif %}\n", + " {%- endif %}\n", + "{%- endfor %}\n", + "{%- if ns.multi_step_tool %}\n", + " {{- raise_exception('No user query found in messages.') }}\n", + "{%- endif %}\n", + "{%- for message in messages %}\n", + " {%- set content = render_content(message.content, true)|trim %}\n", + " {%- if message.role == \"system\" %}\n", + " {%- if not loop.first %}\n", + " {{- raise_exception('System message must be at the beginning.') }}\n", + " {%- endif %}\n", + " {%- elif message.role == \"user\" %}\n", + " {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n", + " {%- elif message.role == \"assistant\" %}\n", + " {%- set reasoning_content = '' %}\n", + " {%- if message.reasoning_content is string %}\n", + " {%- set reasoning_content = message.reasoning_content %}\n", + " {%- else %}\n", + " {%- if '</think>' in content %}\n", + " {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n", + " {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n", + " {%- endif %}\n", + " {%- endif %}\n", + " {%- set reasoning_content = reasoning_content|trim %}\n", + " {%- if loop.index0 > ns.last_query_index %}\n", + " {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content + '\\n</think>\\n\\n' + content }}\n", + " {%- else %}\n", + " {{- '<|im_start|>' + message.role + '\\n' + content }}\n", + " {%- endif %}\n", + " {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n", + " {%- for tool_call in message.tool_calls %}\n", + " {%- if tool_call.function is defined %}\n", + " {%- set tool_call = tool_call.function %}\n", + " {%- endif %}\n", + " {%- if loop.first %}\n", + " {%- if content|trim %}\n", + " {{- '\\n\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n", + " {%- else %}\n", + " {{- '<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n", + " {%- endif %}\n", + " {%- else %}\n", + " {{- '\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n", + " {%- endif %}\n", + " {%- if tool_call.arguments is defined %}\n", + " {%- set arguments = tool_call.arguments | from_json if tool_call.arguments is string else tool_call.arguments %}\n", + " {%- for args_name, args_value in arguments|items %}\n", + " {{- '<parameter=' + args_name + '>\\n' }}\n", + " {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}\n", + " {{- args_value }}\n", + " {{- '\\n</parameter>\\n' }}\n", + " {%- endfor %}\n", + " {%- endif %}\n", + " {{- '</function>\\n</tool_call>' }}\n", + " {%- endfor %}\n", + " {%- endif %}\n", + " {{- '<|im_end|>\\n' }}\n", + " {%- elif message.role == \"tool\" %}\n", + " {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n", + " {{- '<|im_start|>user' }}\n", + " {%- endif %}\n", + " {{- '\\n<tool_response>\\n' }}\n", + " {{- content }}\n", + " {{- '\\n</tool_response>' }}\n", + " {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n", + " {{- '<|im_end|>\\n' }}\n", + " {%- elif loop.last %}\n", + " {{- '<|im_end|>\\n' }}\n", + " {%- endif %}\n", + " {%- else %}\n", + " {{- raise_exception('Unexpected message role.') }}\n", + " {%- endif %}\n", + "{%- endfor %}\n", + "{%- if add_generation_prompt %}\n", + " {{- '<|im_start|>assistant\\n' }}\n", + " {%- if forced_tool_name %}\n", + " {{- '<tool_call>\\n<function=' + forced_tool_name + '>\\n' }}\n", + " {%- elif enable_thinking is defined and enable_thinking is false %}\n", + " {{- '<think>\\n\\n</think>\\n\\n' }}\n", + " {%- else %}\n", + " {{- '<think>\\n' }}\n", + " {%- endif %}\n", + "{%- endif %}" + ] + } +} diff --git a/examples/server/configs/qwen3.6-27b.json b/examples/server/configs/qwen3.6-27b.json new file mode 100644 index 0000000000..c5b42d8b72 --- /dev/null +++ b/examples/server/configs/qwen3.6-27b.json @@ -0,0 +1,263 @@ +{ + "server": { + "host": "0.0.0.0", + "port": 8000 + }, + "model": { + "alias": "qwen3.6-27b", + "from_pretrained": { + "repo_id": "unsloth/Qwen3.6-27B-GGUF", + "filename": "Qwen3.6-27B-Q8_0.gguf" + }, + "mtmd": { + "mmproj_from_pretrained": { + "repo_id": "unsloth/Qwen3.6-27B-GGUF", + "filename": "mmproj-BF16.gguf" + } + }, + "n_ctx": 32768, + "max_output_tokens": 4096, + "n_seq_max": 8, + "n_batch": 512, + "n_ubatch": 512, + "threads": 8, + "threads_batch": 8, + "kv_unified": true, + "store_logits": false, + "use_mmap": true, + "use_mlock": false, + "n_gpu_layers": -1, + "flash_attn": true, + "response_schema": { + "type": "object", + "properties": { + "role": { + "const": "assistant" + }, + "reasoning_content": { + "type": "string", + "x-regex": "^(?:<\\|im_start\\|>assistant\\n)?(?:<think>\\n)?(.*?)(?=</think>)" + }, + "content": { + "type": "string", + "x-regex": "^(?:<\\|im_start\\|>assistant\\n)?(?:(?:<think>\\n)?.*?</think>\\s*)?(.*?)(?=\\s*<tool_call>\\n|<\\|im_end\\|>$|$)" + }, + "tool_calls": { + "type": "array", + "x-regex-iterator": "<tool_call>\\n(.*?)\\n</tool_call>", + "items": { + "type": "object", + "properties": { + "type": { + "const": "function" + }, + "function": { + "type": "object", + "properties": { + "name": { + "type": "string", + "x-regex": "^<function=([^>\\n]+)>\\n" + }, + "arguments": { + "type": "object", + "x-regex": "^<function=[^>\\n]+>\\n(.*?)\\n</function>$", + "x-regex-key-value": "<parameter=(?P<key>[^>\\n]+)>\\n(?P<value>.*?)\\n</parameter>", + "additionalProperties": true + } + }, + "required": [ + "name", + "arguments" + ] + } + }, + "required": [ + "type", + "function" + ] + } + } + }, + "required": [ + "role" + ] + }, + "chat_template": [ + "{%- set image_count = namespace(value=0) %}\n", + "{%- set video_count = namespace(value=0) %}\n", + "{%- if enable_thinking is not defined and reasoning_effort is string %}\n", + " {%- set qwen_reasoning_effort = reasoning_effort|lower %}\n", + " {%- if qwen_reasoning_effort in ['none', 'minimal', 'low'] %}\n", + " {%- set enable_thinking = false %}\n", + " {%- elif qwen_reasoning_effort in ['medium', 'high'] %}\n", + " {%- set enable_thinking = true %}\n", + " {%- endif %}\n", + "{%- endif %}\n", + "{%- set forced_tool_name = none %}\n", + "{%- if tool_choice is mapping %}\n", + " {%- if tool_choice.function is defined and tool_choice.function.name is defined %}\n", + " {%- set forced_tool_name = tool_choice.function.name %}\n", + " {%- elif tool_choice.name is defined %}\n", + " {%- set forced_tool_name = tool_choice.name %}\n", + " {%- endif %}\n", + "{%- elif function_call is mapping and function_call.name is defined %}\n", + " {%- set forced_tool_name = function_call.name %}\n", + "{%- endif %}\n", + "{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n", + " {%- if content is string %}\n", + " {{- content }}\n", + " {%- elif content is iterable and content is not mapping %}\n", + " {%- for item in content %}\n", + " {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}\n", + " {%- if is_system_content %}\n", + " {{- raise_exception('System message cannot contain images.') }}\n", + " {%- endif %}\n", + " {%- if do_vision_count %}\n", + " {%- set image_count.value = image_count.value + 1 %}\n", + " {%- endif %}\n", + " {%- if add_vision_id %}\n", + " {{- 'Picture ' ~ image_count.value ~ ': ' }}\n", + " {%- endif %}\n", + " {{- '<|vision_start|><|image_pad|><|vision_end|>' }}\n", + " {%- elif 'video' in item or item.type == 'video' %}\n", + " {%- if is_system_content %}\n", + " {{- raise_exception('System message cannot contain videos.') }}\n", + " {%- endif %}\n", + " {%- if do_vision_count %}\n", + " {%- set video_count.value = video_count.value + 1 %}\n", + " {%- endif %}\n", + " {%- if add_vision_id %}\n", + " {{- 'Video ' ~ video_count.value ~ ': ' }}\n", + " {%- endif %}\n", + " {{- '<|vision_start|><|video_pad|><|vision_end|>' }}\n", + " {%- elif 'text' in item %}\n", + " {{- item.text }}\n", + " {%- else %}\n", + " {{- raise_exception('Unexpected item type in content.') }}\n", + " {%- endif %}\n", + " {%- endfor %}\n", + " {%- elif content is none or content is undefined %}\n", + " {{- '' }}\n", + " {%- else %}\n", + " {{- raise_exception('Unexpected content type.') }}\n", + " {%- endif %}\n", + "{%- endmacro %}\n", + "{%- if not messages %}\n", + " {{- raise_exception('No messages provided.') }}\n", + "{%- endif %}\n", + "{%- if tools and tools is iterable and tools is not mapping %}\n", + " {{- '<|im_start|>system\\n' }}\n", + " {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n<tools>\" }}\n", + " {%- for tool in tools %}\n", + " {{- \"\\n\" }}\n", + " {{- tool | tojson }}\n", + " {%- endfor %}\n", + " {{- \"\\n</tools>\" }}\n", + " {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n<tool_call>\\n<function=example_function_name>\\n<parameter=example_parameter_1>\\nvalue_1\\n</parameter>\\n<parameter=example_parameter_2>\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n</parameter>\\n</function>\\n</tool_call>\\n\\n<IMPORTANT>\\nReminder:\\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\\n- Required parameters MUST be specified\\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\\n</IMPORTANT>' }}\n", + " {%- if messages[0].role == 'system' %}\n", + " {%- set content = render_content(messages[0].content, false, true)|trim %}\n", + " {%- if content %}\n", + " {{- '\\n\\n' + content }}\n", + " {%- endif %}\n", + " {%- endif %}\n", + " {{- '<|im_end|>\\n' }}\n", + "{%- else %}\n", + " {%- if messages[0].role == 'system' %}\n", + " {%- set content = render_content(messages[0].content, false, true)|trim %}\n", + " {{- '<|im_start|>system\\n' + content + '<|im_end|>\\n' }}\n", + " {%- endif %}\n", + "{%- endif %}\n", + "{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n", + "{%- for message in messages[::-1] %}\n", + " {%- set index = (messages|length - 1) - loop.index0 %}\n", + " {%- if ns.multi_step_tool and message.role == \"user\" %}\n", + " {%- set content = render_content(message.content, false)|trim %}\n", + " {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}\n", + " {%- set ns.multi_step_tool = false %}\n", + " {%- set ns.last_query_index = index %}\n", + " {%- endif %}\n", + " {%- endif %}\n", + "{%- endfor %}\n", + "{%- if ns.multi_step_tool %}\n", + " {{- raise_exception('No user query found in messages.') }}\n", + "{%- endif %}\n", + "{%- for message in messages %}\n", + " {%- set content = render_content(message.content, true)|trim %}\n", + " {%- if message.role == \"system\" %}\n", + " {%- if not loop.first %}\n", + " {{- raise_exception('System message must be at the beginning.') }}\n", + " {%- endif %}\n", + " {%- elif message.role == \"user\" %}\n", + " {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n", + " {%- elif message.role == \"assistant\" %}\n", + " {%- set reasoning_content = '' %}\n", + " {%- if message.reasoning_content is string %}\n", + " {%- set reasoning_content = message.reasoning_content %}\n", + " {%- else %}\n", + " {%- if '</think>' in content %}\n", + " {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n", + " {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n", + " {%- endif %}\n", + " {%- endif %}\n", + " {%- set reasoning_content = reasoning_content|trim %}\n", + " {%- if (preserve_thinking is defined and preserve_thinking is true) or (loop.index0 > ns.last_query_index) %}\n", + " {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content + '\\n</think>\\n\\n' + content }}\n", + " {%- else %}\n", + " {{- '<|im_start|>' + message.role + '\\n' + content }}\n", + " {%- endif %}\n", + " {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n", + " {%- for tool_call in message.tool_calls %}\n", + " {%- if tool_call.function is defined %}\n", + " {%- set tool_call = tool_call.function %}\n", + " {%- endif %}\n", + " {%- if loop.first %}\n", + " {%- if content|trim %}\n", + " {{- '\\n\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n", + " {%- else %}\n", + " {{- '<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n", + " {%- endif %}\n", + " {%- else %}\n", + " {{- '\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n", + " {%- endif %}\n", + " {%- if tool_call.arguments is defined %}\n", + " {%- set arguments = tool_call.arguments | from_json if tool_call.arguments is string else tool_call.arguments %}\n", + " {%- for args_name, args_value in arguments|items %}\n", + " {{- '<parameter=' + args_name + '>\\n' }}\n", + " {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}\n", + " {{- args_value }}\n", + " {{- '\\n</parameter>\\n' }}\n", + " {%- endfor %}\n", + " {%- endif %}\n", + " {{- '</function>\\n</tool_call>' }}\n", + " {%- endfor %}\n", + " {%- endif %}\n", + " {{- '<|im_end|>\\n' }}\n", + " {%- elif message.role == \"tool\" %}\n", + " {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n", + " {{- '<|im_start|>user' }}\n", + " {%- endif %}\n", + " {{- '\\n<tool_response>\\n' }}\n", + " {{- content }}\n", + " {{- '\\n</tool_response>' }}\n", + " {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n", + " {{- '<|im_end|>\\n' }}\n", + " {%- elif loop.last %}\n", + " {{- '<|im_end|>\\n' }}\n", + " {%- endif %}\n", + " {%- else %}\n", + " {{- raise_exception('Unexpected message role.') }}\n", + " {%- endif %}\n", + "{%- endfor %}\n", + "{%- if add_generation_prompt %}\n", + " {{- '<|im_start|>assistant\\n' }}\n", + " {%- if forced_tool_name %}\n", + " {{- '<tool_call>\\n<function=' + forced_tool_name + '>\\n' }}\n", + " {%- elif enable_thinking is defined and enable_thinking is false %}\n", + " {{- '<think>\\n\\n</think>\\n\\n' }}\n", + " {%- else %}\n", + " {{- '<think>\\n' }}\n", + " {%- endif %}\n", + "{%- endif %}" + ] + } +} diff --git a/examples/server/configs/qwen3.6-35b-a3b.json b/examples/server/configs/qwen3.6-35b-a3b.json new file mode 100644 index 0000000000..10043f9c1a --- /dev/null +++ b/examples/server/configs/qwen3.6-35b-a3b.json @@ -0,0 +1,263 @@ +{ + "server": { + "host": "0.0.0.0", + "port": 8000 + }, + "model": { + "alias": "qwen3.6-35b-a3b", + "from_pretrained": { + "repo_id": "unsloth/Qwen3.6-35B-A3B-GGUF", + "filename": "Qwen3.6-35B-A3B-Q8_0.gguf" + }, + "mtmd": { + "mmproj_from_pretrained": { + "repo_id": "unsloth/Qwen3.6-35B-A3B-GGUF", + "filename": "mmproj-BF16.gguf" + } + }, + "n_ctx": 32768, + "max_output_tokens": 4096, + "n_seq_max": 8, + "n_batch": 512, + "n_ubatch": 512, + "threads": 8, + "threads_batch": 8, + "kv_unified": true, + "store_logits": false, + "use_mmap": true, + "use_mlock": false, + "n_gpu_layers": -1, + "flash_attn": true, + "response_schema": { + "type": "object", + "properties": { + "role": { + "const": "assistant" + }, + "reasoning_content": { + "type": "string", + "x-regex": "^(?:<\\|im_start\\|>assistant\\n)?(?:<think>\\n)?(.*?)(?=</think>)" + }, + "content": { + "type": "string", + "x-regex": "^(?:<\\|im_start\\|>assistant\\n)?(?:(?:<think>\\n)?.*?</think>\\s*)?(.*?)(?=\\s*<tool_call>\\n|<\\|im_end\\|>$|$)" + }, + "tool_calls": { + "type": "array", + "x-regex-iterator": "<tool_call>\\n(.*?)\\n</tool_call>", + "items": { + "type": "object", + "properties": { + "type": { + "const": "function" + }, + "function": { + "type": "object", + "properties": { + "name": { + "type": "string", + "x-regex": "^<function=([^>\\n]+)>\\n" + }, + "arguments": { + "type": "object", + "x-regex": "^<function=[^>\\n]+>\\n(.*?)\\n</function>$", + "x-regex-key-value": "<parameter=(?P<key>[^>\\n]+)>\\n(?P<value>.*?)\\n</parameter>", + "additionalProperties": true + } + }, + "required": [ + "name", + "arguments" + ] + } + }, + "required": [ + "type", + "function" + ] + } + } + }, + "required": [ + "role" + ] + }, + "chat_template": [ + "{%- set image_count = namespace(value=0) %}\n", + "{%- set video_count = namespace(value=0) %}\n", + "{%- if enable_thinking is not defined and reasoning_effort is string %}\n", + " {%- set qwen_reasoning_effort = reasoning_effort|lower %}\n", + " {%- if qwen_reasoning_effort in ['none', 'minimal', 'low'] %}\n", + " {%- set enable_thinking = false %}\n", + " {%- elif qwen_reasoning_effort in ['medium', 'high'] %}\n", + " {%- set enable_thinking = true %}\n", + " {%- endif %}\n", + "{%- endif %}\n", + "{%- set forced_tool_name = none %}\n", + "{%- if tool_choice is mapping %}\n", + " {%- if tool_choice.function is defined and tool_choice.function.name is defined %}\n", + " {%- set forced_tool_name = tool_choice.function.name %}\n", + " {%- elif tool_choice.name is defined %}\n", + " {%- set forced_tool_name = tool_choice.name %}\n", + " {%- endif %}\n", + "{%- elif function_call is mapping and function_call.name is defined %}\n", + " {%- set forced_tool_name = function_call.name %}\n", + "{%- endif %}\n", + "{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n", + " {%- if content is string %}\n", + " {{- content }}\n", + " {%- elif content is iterable and content is not mapping %}\n", + " {%- for item in content %}\n", + " {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}\n", + " {%- if is_system_content %}\n", + " {{- raise_exception('System message cannot contain images.') }}\n", + " {%- endif %}\n", + " {%- if do_vision_count %}\n", + " {%- set image_count.value = image_count.value + 1 %}\n", + " {%- endif %}\n", + " {%- if add_vision_id %}\n", + " {{- 'Picture ' ~ image_count.value ~ ': ' }}\n", + " {%- endif %}\n", + " {{- '<|vision_start|><|image_pad|><|vision_end|>' }}\n", + " {%- elif 'video' in item or item.type == 'video' %}\n", + " {%- if is_system_content %}\n", + " {{- raise_exception('System message cannot contain videos.') }}\n", + " {%- endif %}\n", + " {%- if do_vision_count %}\n", + " {%- set video_count.value = video_count.value + 1 %}\n", + " {%- endif %}\n", + " {%- if add_vision_id %}\n", + " {{- 'Video ' ~ video_count.value ~ ': ' }}\n", + " {%- endif %}\n", + " {{- '<|vision_start|><|video_pad|><|vision_end|>' }}\n", + " {%- elif 'text' in item %}\n", + " {{- item.text }}\n", + " {%- else %}\n", + " {{- raise_exception('Unexpected item type in content.') }}\n", + " {%- endif %}\n", + " {%- endfor %}\n", + " {%- elif content is none or content is undefined %}\n", + " {{- '' }}\n", + " {%- else %}\n", + " {{- raise_exception('Unexpected content type.') }}\n", + " {%- endif %}\n", + "{%- endmacro %}\n", + "{%- if not messages %}\n", + " {{- raise_exception('No messages provided.') }}\n", + "{%- endif %}\n", + "{%- if tools and tools is iterable and tools is not mapping %}\n", + " {{- '<|im_start|>system\\n' }}\n", + " {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n<tools>\" }}\n", + " {%- for tool in tools %}\n", + " {{- \"\\n\" }}\n", + " {{- tool | tojson }}\n", + " {%- endfor %}\n", + " {{- \"\\n</tools>\" }}\n", + " {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n<tool_call>\\n<function=example_function_name>\\n<parameter=example_parameter_1>\\nvalue_1\\n</parameter>\\n<parameter=example_parameter_2>\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n</parameter>\\n</function>\\n</tool_call>\\n\\n<IMPORTANT>\\nReminder:\\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\\n- Required parameters MUST be specified\\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\\n</IMPORTANT>' }}\n", + " {%- if messages[0].role == 'system' %}\n", + " {%- set content = render_content(messages[0].content, false, true)|trim %}\n", + " {%- if content %}\n", + " {{- '\\n\\n' + content }}\n", + " {%- endif %}\n", + " {%- endif %}\n", + " {{- '<|im_end|>\\n' }}\n", + "{%- else %}\n", + " {%- if messages[0].role == 'system' %}\n", + " {%- set content = render_content(messages[0].content, false, true)|trim %}\n", + " {{- '<|im_start|>system\\n' + content + '<|im_end|>\\n' }}\n", + " {%- endif %}\n", + "{%- endif %}\n", + "{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n", + "{%- for message in messages[::-1] %}\n", + " {%- set index = (messages|length - 1) - loop.index0 %}\n", + " {%- if ns.multi_step_tool and message.role == \"user\" %}\n", + " {%- set content = render_content(message.content, false)|trim %}\n", + " {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}\n", + " {%- set ns.multi_step_tool = false %}\n", + " {%- set ns.last_query_index = index %}\n", + " {%- endif %}\n", + " {%- endif %}\n", + "{%- endfor %}\n", + "{%- if ns.multi_step_tool %}\n", + " {{- raise_exception('No user query found in messages.') }}\n", + "{%- endif %}\n", + "{%- for message in messages %}\n", + " {%- set content = render_content(message.content, true)|trim %}\n", + " {%- if message.role == \"system\" %}\n", + " {%- if not loop.first %}\n", + " {{- raise_exception('System message must be at the beginning.') }}\n", + " {%- endif %}\n", + " {%- elif message.role == \"user\" %}\n", + " {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n", + " {%- elif message.role == \"assistant\" %}\n", + " {%- set reasoning_content = '' %}\n", + " {%- if message.reasoning_content is string %}\n", + " {%- set reasoning_content = message.reasoning_content %}\n", + " {%- else %}\n", + " {%- if '</think>' in content %}\n", + " {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n", + " {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n", + " {%- endif %}\n", + " {%- endif %}\n", + " {%- set reasoning_content = reasoning_content|trim %}\n", + " {%- if (preserve_thinking is defined and preserve_thinking is true) or (loop.index0 > ns.last_query_index) %}\n", + " {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content + '\\n</think>\\n\\n' + content }}\n", + " {%- else %}\n", + " {{- '<|im_start|>' + message.role + '\\n' + content }}\n", + " {%- endif %}\n", + " {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n", + " {%- for tool_call in message.tool_calls %}\n", + " {%- if tool_call.function is defined %}\n", + " {%- set tool_call = tool_call.function %}\n", + " {%- endif %}\n", + " {%- if loop.first %}\n", + " {%- if content|trim %}\n", + " {{- '\\n\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n", + " {%- else %}\n", + " {{- '<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n", + " {%- endif %}\n", + " {%- else %}\n", + " {{- '\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n", + " {%- endif %}\n", + " {%- if tool_call.arguments is defined %}\n", + " {%- set arguments = tool_call.arguments | from_json if tool_call.arguments is string else tool_call.arguments %}\n", + " {%- for args_name, args_value in arguments|items %}\n", + " {{- '<parameter=' + args_name + '>\\n' }}\n", + " {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}\n", + " {{- args_value }}\n", + " {{- '\\n</parameter>\\n' }}\n", + " {%- endfor %}\n", + " {%- endif %}\n", + " {{- '</function>\\n</tool_call>' }}\n", + " {%- endfor %}\n", + " {%- endif %}\n", + " {{- '<|im_end|>\\n' }}\n", + " {%- elif message.role == \"tool\" %}\n", + " {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n", + " {{- '<|im_start|>user' }}\n", + " {%- endif %}\n", + " {{- '\\n<tool_response>\\n' }}\n", + " {{- content }}\n", + " {{- '\\n</tool_response>' }}\n", + " {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n", + " {{- '<|im_end|>\\n' }}\n", + " {%- elif loop.last %}\n", + " {{- '<|im_end|>\\n' }}\n", + " {%- endif %}\n", + " {%- else %}\n", + " {{- raise_exception('Unexpected message role.') }}\n", + " {%- endif %}\n", + "{%- endfor %}\n", + "{%- if add_generation_prompt %}\n", + " {{- '<|im_start|>assistant\\n' }}\n", + " {%- if forced_tool_name %}\n", + " {{- '<tool_call>\\n<function=' + forced_tool_name + '>\\n' }}\n", + " {%- elif enable_thinking is defined and enable_thinking is false %}\n", + " {{- '<think>\\n\\n</think>\\n\\n' }}\n", + " {%- else %}\n", + " {{- '<think>\\n' }}\n", + " {%- endif %}\n", + "{%- endif %}" + ] + } +} diff --git a/examples/server/server.py b/examples/server/server.py new file mode 100644 index 0000000000..6a500ceec9 --- /dev/null +++ b/examples/server/server.py @@ -0,0 +1,15328 @@ +#!/usr/bin/env -S uv run --script +# /// script +# requires-python = ">=3.8" +# dependencies = [ +# "fastapi", +# "jinja2", +# "llama-cpp-python", +# "numpy", +# "openai", +# "pydantic", +# "safetensors", +# "uvicorn", +# "websockets", +# ] +# /// + +from __future__ import annotations + +import abc +import os +import re +import json +import math +import time +import uuid +import queue +import ctypes +import fnmatch +import base64 +import hashlib +import binascii +import asyncio +import argparse +import threading +import multiprocessing +import copy +import shutil +import inspect +import importlib.util +import sys +import urllib.error +import urllib.parse +import urllib.request + +from pathlib import Path +from datetime import datetime +from dataclasses import dataclass, field +from collections import OrderedDict, deque +from openai.types.completion import Completion as OpenAICompletion +from openai.types.completion_choice import ( + CompletionChoice, + Logprobs as CompletionLogprobs, +) +from openai.types.completion_usage import CompletionUsage +from openai.types.chat.chat_completion import ( + ChatCompletion, + Choice as ChatCompletionChoice, + ChoiceLogprobs as ChatCompletionChoiceLogprobs, +) +from openai.types.chat.chat_completion_chunk import ( + ChatCompletionChunk, + Choice as ChatCompletionChunkChoice, + ChoiceDelta, + ChoiceDeltaFunctionCall, + ChoiceDeltaToolCallFunction, + ChoiceDeltaToolCall, + ChoiceLogprobs as ChatCompletionChunkChoiceLogprobs, +) +from openai.types.chat.chat_completion_message import ( + ChatCompletionMessage, + FunctionCall as ChatCompletionMessageFunctionCall, +) +from openai.types.chat.chat_completion_message_tool_call import ( + ChatCompletionMessageToolCall, + Function as ChatCompletionMessageToolCallFunction, +) +from openai.types.chat.chat_completion_token_logprob import ( + ChatCompletionTokenLogprob, + TopLogprob, +) +from typing import ( + Any, + Callable, + Dict, + Generator, + Iterable, + List, + Optional, + Sequence, + Tuple, + Union, + Deque, + Literal, + Iterator, + Protocol, + TypedDict, + cast, + AsyncIterator, +) + +import jinja2 +import uvicorn +import numpy as np + +from fastapi import FastAPI, HTTPException, Request, WebSocket, WebSocketDisconnect +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import JSONResponse, Response, StreamingResponse + +from jinja2.sandbox import ImmutableSandboxedEnvironment + +from pydantic_core import from_json +from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator + +from llama_cpp import llama_cpp # noqa: E402 +from llama_cpp import llama_cpp_ext # noqa: E402 +from llama_cpp import mtmd_cpp # noqa: E402 + + +JSON_GBNF = r""" +root ::= object +value ::= object | array | string | number | ("true" | "false" | "null") ws + +object ::= + "{" ws ( + string ":" ws value + ("," ws string ":" ws value)* + )? "}" ws + +array ::= + "[" ws ( + value + ("," ws value)* + )? "]" ws + +string ::= + "\"" ( + [^"\\\x7F\x00-\x1F] | + "\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4}) + )* "\"" ws + +number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [0-9] [1-9]{0,15})? ws + +ws ::= | " " | "\n" [ \t]{0,20} +""" + + +class JsonSchemaConverter: + @dataclass(frozen=True) + class BuiltinRule: + content: str + deps: Sequence[str] = () + + SPACE_RULE = '" "?' + INVALID_RULE_CHARS_RE = re.compile(r"[^a-zA-Z0-9-]+") + GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]') + GRAMMAR_LITERAL_ESCAPES = {"\r": "\\r", "\n": "\\n", '"': '\\"'} + DOTALL = "[\\U00000000-\\U0010FFFF]" + DOT = "[^\\x0A\\x0D]" + NON_LITERAL_SET = set("|.()[]{}*+?") + ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set("[]()|{}*+?") + + PRIMITIVE_RULES: Optional[Dict[str, "JsonSchemaConverter.BuiltinRule"]] = None + STRING_FORMAT_RULES: Optional[Dict[str, "JsonSchemaConverter.BuiltinRule"]] = None + RESERVED_NAMES: Optional[set[str]] = None + + @staticmethod + def _build_repetition( + item_rule: str, + min_items: int, + max_items: Optional[int], + separator_rule: Optional[str] = None, + item_rule_is_literal: bool = False, + ) -> str: + if not separator_rule: + if min_items == 0 and max_items == 1: + return f"{item_rule}?" + if min_items == 1 and max_items is None: + return f"{item_rule}+" + + result = "" + + if min_items > 0: + if item_rule_is_literal and separator_rule is None: + result = '"' + (item_rule[1:-1] * min_items) + '"' + else: + result = (f" {separator_rule} " if separator_rule else " ").join( + [item_rule] * min_items + ) + + def opt_repetitions(up_to_n: int, prefix_with_sep: bool = False) -> str: + content = ( + f"{separator_rule} {item_rule}" + if prefix_with_sep and separator_rule + else item_rule + ) + if up_to_n == 0: + return "" + if up_to_n == 1: + return f"({content})?" + if separator_rule and not prefix_with_sep: + return f"({content} {opt_repetitions(up_to_n - 1, prefix_with_sep=True)})?" + return (f"({content} " * up_to_n).rstrip() + (")?" * up_to_n) + + if min_items > 0 and max_items != min_items: + result += " " + + if max_items is not None: + result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0) + else: + item_operator = f"({separator_rule + ' ' if separator_rule else ''}{item_rule})" + if min_items == 0 and separator_rule: + result = f"({item_rule} {item_operator}*)?" + else: + result += f"{item_operator}*" + + return result + + @classmethod + def _primitive_rules(cls) -> Dict[str, "JsonSchemaConverter.BuiltinRule"]: + if cls.PRIMITIVE_RULES is None: + up_to_15_digits = cls._build_repetition("[0-9]", 0, 15) + cls.PRIMITIVE_RULES = { + "boolean": cls.BuiltinRule('("true" | "false") space', []), + "decimal-part": cls.BuiltinRule("[0-9] " + up_to_15_digits, []), + "integral-part": cls.BuiltinRule( + "[0-9] | [1-9] " + up_to_15_digits, + [], + ), + "number": cls.BuiltinRule( + '("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', + ["integral-part", "decimal-part"], + ), + "integer": cls.BuiltinRule('("-"? integral-part) space', ["integral-part"]), + "value": cls.BuiltinRule( + "object | array | string | number | boolean | null", + ["object", "array", "string", "number", "boolean", "null"], + ), + "object": cls.BuiltinRule( + '"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', + ["string", "value"], + ), + "array": cls.BuiltinRule( + '"[" space ( value ("," space value)* )? "]" space', + ["value"], + ), + "uuid": cls.BuiltinRule( + r'"\"" ' + + ' "-" '.join("[0-9a-fA-F]" * n for n in [8, 4, 4, 4, 12]) + + r' "\"" space', + [], + ), + "char": cls.BuiltinRule( + r'[^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])', + [], + ), + "string": cls.BuiltinRule(r'"\"" char* "\"" space', ["char"]), + "null": cls.BuiltinRule('"null" space', []), + } + return cls.PRIMITIVE_RULES + + @classmethod + def _string_format_rules(cls) -> Dict[str, "JsonSchemaConverter.BuiltinRule"]: + if cls.STRING_FORMAT_RULES is None: + cls.STRING_FORMAT_RULES = { + "date": cls.BuiltinRule( + '[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( "0" [1-9] | [1-2] [0-9] | "3" [0-1] )', + [], + ), + "time": cls.BuiltinRule( + '([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', + [], + ), + "date-time": cls.BuiltinRule('date "T" time', ["date", "time"]), + "date-string": cls.BuiltinRule('"\\"" date "\\"" space', ["date"]), + "time-string": cls.BuiltinRule('"\\"" time "\\"" space', ["time"]), + "date-time-string": cls.BuiltinRule( + '"\\"" date-time "\\"" space', + ["date-time"], + ), + } + return cls.STRING_FORMAT_RULES + + @classmethod + def _reserved_names(cls) -> set[str]: + if cls.RESERVED_NAMES is None: + cls.RESERVED_NAMES = set( + ["root", "dot", *cls._primitive_rules().keys(), *cls._string_format_rules().keys()] + ) + return cls.RESERVED_NAMES + + def __init__( + self, + *, + prop_order: Dict[str, int], + allow_fetch: bool, + dotall: bool, + raw_pattern: bool, + ): + self._prop_order = prop_order + self._allow_fetch = allow_fetch + self._dotall = dotall + self._raw_pattern = raw_pattern + self._rules: Dict[str, str] = {"space": self.SPACE_RULE} + self._refs: Dict[str, Any] = {} + self._refs_being_resolved: set[str] = set() + + def _format_literal(self, literal: str) -> str: + escaped = self.GRAMMAR_LITERAL_ESCAPE_RE.sub( + lambda match: self.GRAMMAR_LITERAL_ESCAPES[match.group(0)], + literal, + ) + return f'"{escaped}"' + + def _add_rule(self, name: str, rule: str) -> str: + escaped_name = self.INVALID_RULE_CHARS_RE.sub("-", name) + if escaped_name not in self._rules or self._rules[escaped_name] == rule: + key = escaped_name + else: + suffix = 0 + while ( + f"{escaped_name}{suffix}" in self._rules + and self._rules[f"{escaped_name}{suffix}"] != rule + ): + suffix += 1 + key = f"{escaped_name}{suffix}" + self._rules[key] = rule + return key + + def resolve_refs(self, schema: Dict[str, Any], url: str) -> Dict[str, Any]: + def visit(node: Any) -> Any: + if isinstance(node, list): + return [visit(child) for child in node] + if isinstance(node, dict): + ref = node.get("$ref") + if ref is not None and ref not in self._refs: + if ref.startswith("https://"): + raise ValueError("remote schema fetch is not allowed") + elif ref.startswith("#/"): + target = schema + ref = f"{url}{ref}" + node["$ref"] = ref + else: + raise ValueError(f"Unsupported ref {ref}") + + for selector in ref.split("#")[-1].split("/")[1:]: + assert target is not None and selector in target, ( + f"Error resolving ref {ref}: {selector} not in {target}" + ) + target = target[selector] + self._refs[ref] = target + else: + for value in node.values(): + visit(value) + return node + + return cast(Dict[str, Any], visit(schema)) + + def _generate_union_rule(self, name: str, alt_schemas: List[Dict[str, Any]]) -> str: + return " | ".join( + self.visit(alt_schema, f"{name}{'-' if name else 'alternative-'}{index}") + for index, alt_schema in enumerate(alt_schemas) + ) + + def _visit_pattern(self, pattern: str, name: str) -> str: + assert pattern.startswith("^") and pattern.endswith("$"), ( + 'Pattern must start with "^" and end with "$"' + ) + pattern = pattern[1:-1] + sub_rule_ids: Dict[str, str] = {} + index = 0 + length = len(pattern) + + def to_rule(item: Tuple[str, bool]) -> str: + text, is_literal = item + return f'"{text}"' if is_literal else text + + def transform() -> Tuple[str, bool]: + nonlocal index + start = index + sequence: List[Tuple[str, bool]] = [] + + def get_dot() -> str: + rule = self.DOTALL if self._dotall else self.DOT + return self._add_rule("dot", rule) + + def join_sequence() -> Tuple[str, bool]: + if len(sequence) == 1: + return sequence[0] + return (" ".join(to_rule(item) for item in sequence), False) + + while index < length: + char = pattern[index] + if char == ".": + sequence.append((get_dot(), False)) + index += 1 + elif char == "(": + index += 1 + if index < length: + assert pattern[index] != "?", ( + f'Unsupported pattern syntax "{pattern[index]}" at index {index} of /{pattern}/' + ) + sequence.append((f"({to_rule(transform())})", False)) + elif char == ")": + index += 1 + assert start > 0 and pattern[start - 1] == "(", ( + f"Unbalanced parentheses; start = {start}, index = {index}, pattern = {pattern}" + ) + return join_sequence() + elif char == "[": + square_brackets = char + index += 1 + while index < length and pattern[index] != "]": + if pattern[index] == "\\": + square_brackets += pattern[index : index + 2] + index += 2 + else: + square_brackets += pattern[index] + index += 1 + assert index < length, ( + f"Unbalanced square brackets; start = {start}, index = {index}, pattern = {pattern}" + ) + square_brackets += "]" + index += 1 + sequence.append((square_brackets, False)) + elif char == "|": + sequence.append(("|", False)) + index += 1 + elif char in ("*", "+", "?"): + sequence[-1] = (to_rule(sequence[-1]) + char, False) + index += 1 + elif char == "{": + curly_brackets = char + index += 1 + while index < length and pattern[index] != "}": + curly_brackets += pattern[index] + index += 1 + assert index < length, ( + f"Unbalanced curly brackets; start = {start}, index = {index}, pattern = {pattern}" + ) + curly_brackets += "}" + index += 1 + numbers = [part.strip() for part in curly_brackets[1:-1].split(",")] + min_times = 0 + max_times: Optional[int] = None + try: + if len(numbers) == 1: + min_times = int(numbers[0]) + max_times = min_times + else: + assert len(numbers) == 2 + min_times = int(numbers[0]) if numbers[0] else 0 + max_times = int(numbers[1]) if numbers[1] else None + except ValueError as exc: + raise ValueError( + f"Invalid quantifier {curly_brackets} in /{pattern}/" + ) from exc + + sub, sub_is_literal = sequence[-1] + if not sub_is_literal: + rule_id = sub_rule_ids.get(sub) + if rule_id is None: + rule_id = self._add_rule(f"{name}-{len(sub_rule_ids) + 1}", sub) + sub_rule_ids[sub] = rule_id + sub = rule_id + + sequence[-1] = ( + self._build_repetition( + f'"{sub}"' if sub_is_literal else sub, + min_times, + max_times, + item_rule_is_literal=sub_is_literal, + ), + False, + ) + else: + literal = "" + while index < length: + if pattern[index] == "\\" and index < length - 1: + next_char = pattern[index + 1] + if next_char in self.ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS: + index += 1 + literal += pattern[index] + index += 1 + else: + literal += pattern[index : index + 2] + index += 2 + elif pattern[index] == '"' and not self._raw_pattern: + literal += '\\"' + index += 1 + elif pattern[index] not in self.NON_LITERAL_SET and ( + index == length - 1 + or literal == "" + or pattern[index + 1] == "." + or pattern[index + 1] not in self.NON_LITERAL_SET + ): + literal += pattern[index] + index += 1 + else: + break + if literal: + sequence.append((literal, True)) + + return join_sequence() + + return self._add_rule( + name, + ( + to_rule(transform()) + if self._raw_pattern + else '"\\"" ' + to_rule(transform()) + ' "\\"" space' + ), + ) + + def _resolve_ref(self, ref: str) -> str: + ref_name = ref.split("/")[-1] + if ref_name not in self._rules and ref not in self._refs_being_resolved: + self._refs_being_resolved.add(ref) + resolved = self._refs[ref] + ref_name = self.visit(resolved, ref_name) + self._refs_being_resolved.remove(ref) + return ref_name + + def _generate_constant_rule(self, value: Any) -> str: + return self._format_literal(json.dumps(value)) + + def visit(self, schema: Dict[str, Any], name: str) -> str: + schema_type = schema.get("type") + schema_format = schema.get("format") + rule_name = name + "-" if name in self._reserved_names() else name or "root" + + ref = schema.get("$ref") + if ref is not None: + return self._add_rule(rule_name, self._resolve_ref(ref)) + + if "oneOf" in schema or "anyOf" in schema: + return self._add_rule( + rule_name, + self._generate_union_rule(name, cast(List[Dict[str, Any]], schema.get("oneOf") or schema["anyOf"])), + ) + + if isinstance(schema_type, list): + return self._add_rule( + rule_name, + self._generate_union_rule(name, [{"type": entry} for entry in schema_type]), + ) + + if "const" in schema: + return self._add_rule(rule_name, self._generate_constant_rule(schema["const"])) + + if "enum" in schema: + rule = " | ".join(self._generate_constant_rule(value) for value in schema["enum"]) + return self._add_rule(rule_name, rule) + + if schema_type in (None, "object") and ( + "properties" in schema + or ("additionalProperties" in schema and schema["additionalProperties"] is not True) + ): + required_props = set(schema.get("required", [])) + property_items = list(cast(Dict[str, Any], schema.get("properties", {})).items()) + return self._add_rule( + rule_name, + self._build_object_rule( + property_items, required_props, name, schema.get("additionalProperties") + ), + ) + + if schema_type in (None, "object") and "allOf" in schema: + allof_required_props: set[str] = set() + allof_property_items: List[Tuple[str, Any]] = [] + + def add_component(component_schema: Dict[str, Any], is_required: bool) -> None: + component_ref = component_schema.get("$ref") + if component_ref is not None: + component_schema = cast(Dict[str, Any], self._refs[component_ref]) + if "properties" in component_schema: + for prop_name, prop_schema in cast(Dict[str, Any], component_schema["properties"]).items(): + allof_property_items.append((prop_name, prop_schema)) + if is_required: + allof_required_props.add(prop_name) + + for entry in cast(List[Dict[str, Any]], schema["allOf"]): + if "anyOf" in entry: + for alt in cast(List[Dict[str, Any]], entry["anyOf"]): + add_component(alt, is_required=False) + else: + add_component(entry, is_required=True) + + return self._add_rule( + rule_name, + self._build_object_rule( + allof_property_items, + allof_required_props, + name, + additional_properties=[], + ), + ) + + if schema_type in (None, "array") and ("items" in schema or "prefixItems" in schema): + items = schema.get("items") or schema["prefixItems"] + if isinstance(items, list): + return self._add_rule( + rule_name, + '"[" space ' + + ' "," space '.join( + self.visit(item, f"{name}{'-' if name else ''}tuple-{index}") + for index, item in enumerate(items) + ) + + ' "]" space', + ) + item_rule_name = self.visit(cast(Dict[str, Any], items), f"{name}{'-' if name else ''}item") + min_items = int(schema.get("minItems", 0)) + max_items = cast(Optional[int], schema.get("maxItems")) + return self._add_rule( + rule_name, + '"[" space ' + + self._build_repetition( + item_rule_name, + min_items, + max_items, + separator_rule='"," space', + ) + + ' "]" space', + ) + + if schema_type in (None, "string") and "pattern" in schema: + return self._visit_pattern(cast(str, schema["pattern"]), rule_name) + + if schema_type in (None, "string") and re.match(r"^uuid[1-5]?$", schema_format or ""): + return self._add_primitive( + "root" if rule_name == "root" else cast(str, schema_format), + self._primitive_rules()["uuid"], + ) + + if schema_type in (None, "string") and f"{schema_format}-string" in self._string_format_rules(): + primitive_name = f"{schema_format}-string" + return self._add_rule( + rule_name, + self._add_primitive(primitive_name, self._string_format_rules()[primitive_name]), + ) + + if schema_type == "string" and ("minLength" in schema or "maxLength" in schema): + char_rule = self._add_primitive("char", self._primitive_rules()["char"]) + min_len = int(schema.get("minLength", 0)) + max_len = cast(Optional[int], schema.get("maxLength")) + return self._add_rule( + rule_name, + r'"\"" ' + + self._build_repetition(char_rule, min_len, max_len) + + r' "\"" space', + ) + + if schema_type == "object" or len(schema) == 0: + return self._add_rule( + rule_name, + self._add_primitive("object", self._primitive_rules()["object"]), + ) + + primitive_rules = self._primitive_rules() + assert schema_type in primitive_rules, f"Unrecognized schema: {schema}" + return self._add_primitive( + "root" if rule_name == "root" else cast(str, schema_type), + primitive_rules[cast(str, schema_type)], + ) + + def _add_primitive(self, name: str, rule: "JsonSchemaConverter.BuiltinRule") -> str: + rule_name = self._add_rule(name, rule.content) + primitive_rules = self._primitive_rules() + string_format_rules = self._string_format_rules() + for dependency in rule.deps: + dependency_rule = primitive_rules.get(dependency) or string_format_rules.get( + dependency + ) + assert dependency_rule is not None, f"Rule {dependency} not known" + if dependency not in self._rules: + self._add_primitive(dependency, dependency_rule) + return rule_name + + def _build_object_rule( + self, + properties: List[Tuple[str, Any]], + required: set[str], + name: str, + additional_properties: Union[bool, Any], + ) -> str: + prop_order = self._prop_order + sorted_props = [ + key + for _, (key, _) in sorted( + enumerate(properties), + key=lambda indexed_key: ( + prop_order.get(indexed_key[1][0], len(prop_order)), + indexed_key[0], + ), + ) + ] + + property_kv_rule_names: Dict[str, str] = {} + for prop_name, prop_schema in properties: + prop_rule_name = self.visit( + cast(Dict[str, Any], prop_schema), + f"{name}{'-' if name else ''}{prop_name}", + ) + property_kv_rule_names[prop_name] = self._add_rule( + f"{name}{'-' if name else ''}{prop_name}-kv", + rf'{self._format_literal(json.dumps(prop_name))} space ":" space {prop_rule_name}', + ) + + required_props = [key for key in sorted_props if key in required] + optional_props = [key for key in sorted_props if key not in required] + + if additional_properties is True or isinstance(additional_properties, dict): + sub_name = f"{name}{'-' if name else ''}additional" + value_rule = self.visit( + {} if additional_properties is True else cast(Dict[str, Any], additional_properties), + f"{sub_name}-value", + ) + property_kv_rule_names["*"] = self._add_rule( + f"{sub_name}-kv", + self._add_primitive("string", self._primitive_rules()["string"]) + + f' ":" space {value_rule}', + ) + optional_props.append("*") + + rule = '"{" space ' + rule += ' "," space '.join(property_kv_rule_names[key] for key in required_props) + + if optional_props: + if required_props: + rule += ' ( "," space ( ' + else: + rule += "( " + + def get_recursive_refs(keys: List[str], first_is_optional: bool) -> str: + head, *rest = keys + kv_rule_name = property_kv_rule_names[head] + result = "" + if head == "*": + if first_is_optional: + result = f"({kv_rule_name})?" + else: + result = kv_rule_name + elif first_is_optional: + result = f'( "," space {kv_rule_name} )?' + else: + result = kv_rule_name + if rest: + result += " " + self._add_rule( + f"{name}{'-' if name else ''}{head}-rest", + get_recursive_refs(rest, first_is_optional=True), + ) + return result + + rule += " | ".join( + get_recursive_refs(optional_props[index:], first_is_optional=False) + for index in range(len(optional_props)) + ) + if required_props: + rule += " )" + rule += " )?" + + rule += ' "}" space' + return rule + + def format_grammar(self) -> str: + return "\n".join( + f"{name} ::= {rule}" + for name, rule in sorted(self._rules.items(), key=lambda item: item[0]) + ) + + @classmethod + def to_gbnf(cls, schema: str, prop_order: Optional[List[str]] = None) -> str: + property_order = prop_order or [] + loaded_schema = json.loads(schema) + order_index = {name: index for index, name in enumerate(property_order)} + converter = cls( + prop_order=order_index, + allow_fetch=False, + dotall=False, + raw_pattern=False, + ) + resolved_schema = converter.resolve_refs(loaded_schema, "stdin") + converter.visit(resolved_schema, "") + return converter.format_grammar() + + +class RadixTrie: + __slots__ = ("root", "sequences", "sequence_lengths") + + @dataclass + class Node: + label: Tuple[int, ...] = () + parent: Optional["RadixTrie.Node"] = None + children: Dict[int, "RadixTrie.Node"] = field(default_factory=dict) + sequences: set[int] = field(default_factory=set) + tails: set[int] = field(default_factory=set) + + def __init__(self) -> None: + self.root = RadixTrie.Node() + self.sequences: Dict[int, RadixTrie.Node] = {} + self.sequence_lengths: Dict[int, int] = {} + + @staticmethod + def _pick_sequence(candidates: set[int], preferred_sequences: Optional[Any]) -> int: + if preferred_sequences is None: + return next(iter(candidates)) + if isinstance(preferred_sequences, OrderedDict): + for sequence_id in reversed(preferred_sequences): + if sequence_id in candidates: + return sequence_id + return next(iter(candidates)) + if isinstance(preferred_sequences, (list, tuple)): + for sequence_id in reversed(preferred_sequences): + if sequence_id in candidates: + return sequence_id + return next(iter(candidates)) + preferred = candidates & preferred_sequences + if preferred: + return next(iter(preferred)) + return next(iter(candidates)) + + @staticmethod + def _common_prefix_len( + label: Sequence[int], + tokens: Sequence[int], + offset: int, + ) -> int: + limit = min(len(label), len(tokens) - offset) + match_len = 0 + while match_len < limit and label[match_len] == tokens[offset + match_len]: + match_len += 1 + return match_len + + def _split_child( + self, + parent: "RadixTrie.Node", + child: "RadixTrie.Node", + prefix_len: int, + ) -> "RadixTrie.Node": + assert 0 < prefix_len < len(child.label) + prefix = child.label[:prefix_len] + suffix = child.label[prefix_len:] + middle = RadixTrie.Node( + label=prefix, + parent=parent, + sequences=set(child.sequences), + ) + parent.children[prefix[0]] = middle + child.label = suffix + child.parent = middle + middle.children[suffix[0]] = child + return middle + + def _locate_prefix_node( + self, + sequence_id: int, + keep_len: int, + ) -> "RadixTrie.Node": + total_len = self.sequence_lengths[sequence_id] + assert 0 <= keep_len <= total_len + if keep_len == 0: + return self.root + node = self.sequences[sequence_id] + drop_len = total_len - keep_len + while node is not self.root: + label_len = len(node.label) + if drop_len == 0: + return node + if drop_len < label_len: + parent = node.parent + assert parent is not None + return self._split_child(parent, node, label_len - drop_len) + drop_len -= label_len + parent = node.parent + assert parent is not None + node = parent + return self.root + + def extend( + self, + sequence_id: int, + tokens: Sequence[int], + ) -> None: + assert sequence_id >= 0 + node = self.sequences.get(sequence_id, self.root) + if tokens: + node.tails.discard(sequence_id) + length = self.sequence_lengths.get(sequence_id, 0) + index = 0 + while index < len(tokens): + token = tokens[index] + child = node.children.get(token) + if child is None: + child = RadixTrie.Node( + label=tuple(tokens[index:]), + parent=node, + sequences={sequence_id}, + ) + node.children[token] = child + node = child + length += len(tokens) - index + index = len(tokens) + break + match_len = self._common_prefix_len(child.label, tokens, index) + if match_len == len(child.label): + child.sequences.add(sequence_id) + node = child + length += match_len + index += match_len + continue + node = self._split_child(node, child, match_len) + node.sequences.add(sequence_id) + length += match_len + index += match_len + if index == len(tokens): + break + suffix = RadixTrie.Node( + label=tuple(tokens[index:]), + parent=node, + sequences={sequence_id}, + ) + node.children[suffix.label[0]] = suffix + node = suffix + length += len(tokens) - index + index = len(tokens) + break + if node is self.root: + self.sequences.pop(sequence_id, None) + self.sequence_lengths.pop(sequence_id, None) + self.root.tails.discard(sequence_id) + else: + self.sequences[sequence_id] = node + self.sequence_lengths[sequence_id] = length + node.tails.add(sequence_id) + + def length(self, sequence_id: int) -> int: + return self.sequence_lengths.get(sequence_id, 0) + + def truncate(self, sequence_id: int, keep_len: int) -> None: + assert sequence_id >= 0 + assert sequence_id in self.sequence_lengths + assert 0 <= keep_len <= self.sequence_lengths[sequence_id] + current_len = self.sequence_lengths[sequence_id] + if keep_len == current_len: + return + boundary = self._locate_prefix_node(sequence_id, keep_len) + node = self.sequences.get(sequence_id, self.root) + if node is not self.root: + node.tails.discard(sequence_id) + while node is not boundary: + node.sequences.remove(sequence_id) + parent = node.parent + assert parent is not None + if not node.sequences: + del parent.children[node.label[0]] + node = parent + if boundary is self.root: + self.sequences.pop(sequence_id, None) + self.sequence_lengths.pop(sequence_id, None) + else: + self.sequences[sequence_id] = boundary + self.sequence_lengths[sequence_id] = keep_len + boundary.tails.add(sequence_id) + + def copy(self, source_sequence_id: int, dest_sequence_id: int, keep_len: int) -> None: + assert source_sequence_id >= 0 + assert dest_sequence_id >= 0 + assert source_sequence_id in self.sequence_lengths + assert dest_sequence_id not in self.sequence_lengths + assert 0 <= keep_len <= self.sequence_lengths[source_sequence_id] + if keep_len == 0: + self.sequences[dest_sequence_id] = self.root + self.sequence_lengths[dest_sequence_id] = 0 + self.root.tails.add(dest_sequence_id) + return + node = self._locate_prefix_node(source_sequence_id, keep_len) + self.sequences[dest_sequence_id] = node + self.sequence_lengths[dest_sequence_id] = keep_len + node.tails.add(dest_sequence_id) + while node is not self.root: + node.sequences.add(dest_sequence_id) + parent = node.parent + assert parent is not None + node = parent + + def tokens(self, sequence_id: int, keep_len: Optional[int] = None) -> List[int]: + length = self.sequence_lengths[sequence_id] + target_len = length if keep_len is None else keep_len + assert 0 <= target_len <= length + node = self.sequences[sequence_id] + labels: List[Tuple[int, ...]] = [] + while node is not self.root: + labels.append(node.label) + parent = node.parent + assert parent is not None + node = parent + values: List[int] = [] + for label in reversed(labels): + values.extend(label) + return values[:target_len] + + def longest_prefix( + self, + tokens: Sequence[int], + preferred_sequences: Optional[Any] = None, + *, + exact_only: bool = False, + ) -> Tuple[int, int]: + node = self.root + longest_sequence_id = -1 + longest_length = 0 + index = 0 + while index < len(tokens): + child = node.children.get(tokens[index]) + if child is None: + break + match_len = self._common_prefix_len(child.label, tokens, index) + if match_len < len(child.label): + break + node = child + index += match_len + candidates = node.tails if exact_only else node.sequences + if candidates: + longest_sequence_id = self._pick_sequence(candidates, preferred_sequences) + longest_length = index + return longest_sequence_id, longest_length + + +class SequenceHistory: + __slots__ = ("_position_lengths", "_root", "_tails", "size") + + @dataclass + class Node: + token: Optional[int] = None + parent: Optional["SequenceHistory.Node"] = None + children: Dict[int, "SequenceHistory.Node"] = field(default_factory=dict) + sequences: set[int] = field(default_factory=set) + position_increment: int = 1 + + def __init__(self) -> None: + self._root = SequenceHistory.Node() + self._tails: Dict[int, SequenceHistory.Node] = {} + self._position_lengths: Dict[int, int] = {} + self.size = 0 + + def extend( + self, + sequence_id: int, + tokens: Sequence[int], + position_increments: Optional[Sequence[int]] = None, + ) -> None: + assert sequence_id >= 0 + if position_increments is None: + position_increments = [1] * len(tokens) + assert len(position_increments) == len(tokens) + node = self._tails.get(sequence_id, self._root) + position_length = self._position_lengths.get(sequence_id, 0) + for token, position_increment in zip(tokens, position_increments): + position_increment = max(0, int(position_increment)) + child = node.children.get(sequence_id) + if child is None: + child = SequenceHistory.Node( + token=token, + parent=node, + position_increment=position_increment, + ) + node.children[sequence_id] = child + self.size += 1 + else: + assert child.parent is node + assert child.token == token + assert child.position_increment == position_increment + child.sequences.add(sequence_id) + position_length += position_increment + node = child + if node is self._root: + self._tails.pop(sequence_id, None) + self._position_lengths.pop(sequence_id, None) + else: + self._tails[sequence_id] = node + self._position_lengths[sequence_id] = position_length + + def position_length(self, sequence_id: int) -> int: + return self._position_lengths.get(sequence_id, 0) + + def position_length_for_prefix(self, sequence_id: int, keep_len: int) -> int: + if keep_len <= 0 or sequence_id not in self._tails: + return 0 + node = self._tails[sequence_id] + increments: List[int] = [] + while node is not self._root: + increments.append(node.position_increment) + parent = node.parent + assert parent is not None + node = parent + increments.reverse() + assert keep_len <= len(increments) + return sum(increments[:keep_len]) + + def copy( + self, + source_sequence_id: int, + dest_sequence_id: int, + source_length: int, + keep_len: int, + ) -> None: + assert source_sequence_id >= 0 + assert dest_sequence_id >= 0 + assert source_sequence_id in self._tails + assert dest_sequence_id not in self._tails + assert 0 <= keep_len <= source_length + node = self._tails[source_sequence_id] + path: List[SequenceHistory.Node] = [] + for _ in range(source_length - keep_len): + parent = node.parent + assert parent is not None + node = parent + while node is not self._root: + path.append(node) + parent = node.parent + assert parent is not None + node = parent + parent = self._root + position_length = 0 + for child in reversed(path): + parent.children[dest_sequence_id] = child + child.sequences.add(dest_sequence_id) + position_length += child.position_increment + parent = child + if keep_len == 0: + self._tails.pop(dest_sequence_id, None) + self._position_lengths.pop(dest_sequence_id, None) + else: + self._tails[dest_sequence_id] = path[0] + self._position_lengths[dest_sequence_id] = position_length + + def truncate( + self, + sequence_id: int, + current_length: int, + keep_len: int, + ) -> None: + assert sequence_id >= 0 + assert sequence_id in self._tails + assert 0 <= keep_len <= current_length + node = self._tails[sequence_id] + drop = current_length - keep_len + position_length = self._position_lengths.get(sequence_id, 0) + while node is not self._root and drop > 0: + node.sequences.remove(sequence_id) + parent = node.parent + assert parent is not None + child = parent.children.get(sequence_id) + if child is node: + del parent.children[sequence_id] + if not node.sequences: + self.size -= 1 + position_length -= node.position_increment + node = parent + drop -= 1 + if node is self._root: + self._tails.pop(sequence_id, None) + self._position_lengths.pop(sequence_id, None) + else: + self._tails[sequence_id] = node + self._position_lengths[sequence_id] = max(0, position_length) + + +class DraftProvider(abc.ABC): + @abc.abstractmethod + def draft( + self, + input_ids: np.ndarray, + /, + *, + seq_id: int, + max_tokens: Optional[int], + ) -> np.ndarray: + raise NotImplementedError() + + def can_draft(self, input_length: int, /, *, seq_id: int) -> bool: + return True + + def process(self, batch: Any, /) -> None: + pass + + def accept(self, seq_id: int, accepted_draft_tokens: int) -> None: + pass + + def truncate(self, seq_id: int, keep_len: int) -> None: + pass + + def copy_sequence( + self, + source_seq_id: int, + dest_seq_id: int, + p0: int, + p1: int, + ) -> None: + pass + + def set_target_processing_enabled(self, enabled: bool) -> None: + pass + + def close(self) -> None: + pass + + +class PromptLookupDecoding(DraftProvider): + def __init__(self, max_ngram_size: int = 2, num_pred_tokens: int = 10) -> None: + self._max_ngram_size = max_ngram_size + self._num_pred_tokens = num_pred_tokens + + def draft( + self, + input_ids: np.ndarray, + /, + *, + seq_id: int, + max_tokens: Optional[int], + ) -> np.ndarray: + input_length = input_ids.shape[0] + if input_length < 2: + return np.array([], dtype=np.intc) + num_pred_tokens = self._num_pred_tokens + if max_tokens is not None: + num_pred_tokens = min(num_pred_tokens, max_tokens) + if num_pred_tokens <= 0: + return np.array([], dtype=np.intc) + max_ngram_size = min(self._max_ngram_size, input_length - 1) + for ngram_size in range(max_ngram_size, 0, -1): + windows = np.lib.stride_tricks.sliding_window_view(input_ids, (ngram_size,)) + ngram = input_ids[-ngram_size:] + matches = np.all(windows == ngram, axis=1) + match_indices = np.nonzero(matches)[0] + for index in match_indices: + start = index + ngram_size + if start >= input_length: + continue + end = min(start + num_pred_tokens, input_length) + if start < end: + return input_ids[start:end].astype(np.intc, copy=False) + return np.array([], dtype=np.intc) + + +class MTPDraftProvider(DraftProvider): + batched_draft = True + sampled_batch_draft = True + + @dataclass + class DraftManyState: + result_index: int + seq_id: int + first_pos: int + keep_len: int + n_predict: int + token: int + drafted: List[int] + embedding: np.ndarray + cache_key: Tuple[Tuple[int, ...], int] + + def __init__( + self, + *, + model: "Model", + context_params: Any, + num_pred_tokens: int, + top_k: int, + p_min: float, + ) -> None: + self.target_ctx = model.ctx + self.model = model.llama_model + self.n_seq_max = model.n_seq_max + self.n_vocab = model.n_vocab + self.n_embd = int(llama_cpp.llama_model_n_embd(self.model)) + self.num_pred_tokens = max(0, int(num_pred_tokens)) + self.top_k = max(1, int(top_k)) + self.p_min = max(0.0, min(1.0, float(p_min))) + ( + self.target_hidden_norm_weight, + self.draft_hidden_norm_weight, + self.hidden_norm_epsilon, + ) = self._load_hidden_norm_weights(model.model_path) + self.ctx = llama_cpp.llama_init_from_model(self.model, context_params) + if self.ctx is None: + raise RuntimeError("failed to create MTP draft context") + self.n_batch = int(llama_cpp.llama_n_batch(self.ctx)) + mem = llama_cpp.llama_get_memory(self.ctx) + if mem is None: + llama_cpp.llama_free(self.ctx) + raise RuntimeError("failed to access MTP draft memory") + self.mem = mem + self.batch = llama_cpp.llama_batch_init(self.n_batch, self.n_embd, 1) + self._batch_tokens = (llama_cpp.llama_token * self.n_batch)() + self.batch.token = self._batch_tokens + self.batch_embeddings = np.ctypeslib.as_array( + self.batch.embd, + shape=(self.n_batch * self.n_embd,), + ) + self._samplers: List[Any] = [] + self.pending_h = np.zeros((self.n_seq_max, self.n_embd), dtype=np.float32) + self.verify_h: List[np.ndarray] = [ + np.empty((0, self.n_embd), dtype=np.float32) + for _ in range(self.n_seq_max) + ] + self.verify_h_pos: List[List[int]] = [[] for _ in range(self.n_seq_max)] + self.verify_h_rows = [0] * self.n_seq_max + self.ready = [False] * self.n_seq_max + self.ready_pos = [0] * self.n_seq_max + self.context_pos = [0] * self.n_seq_max + self.decode_seconds_total = 0.0 + self.decode_calls_total = 0 + self.decode_tokens_total = 0 + self.decode_failures_total = 0 + self.target_processing_enabled = False + self.set_target_processing_enabled(True) + llama_cpp_ext.llama_set_embeddings_pre_norm( + self.ctx, + True, + True, + ) + self._init_samplers() + + @staticmethod + def _load_gguf_reader() -> Any: + try: + from gguf import GGUFReader # type: ignore[import-not-found] + return GGUFReader + except ImportError: + pass + + gguf_init = ( + Path(__file__).resolve().parents[2] + / "vendor/llama.cpp/gguf-py/gguf/__init__.py" + ) + spec = importlib.util.spec_from_file_location( + "gguf", + gguf_init, + submodule_search_locations=[str(gguf_init.parent)], + ) + if spec is None or spec.loader is None: + raise RuntimeError("MTP requires the gguf Python reader from llama.cpp") + module = importlib.util.module_from_spec(spec) + previous = sys.modules.get("gguf") + # Package-relative imports in gguf-py expect the package to be registered. + sys.modules["gguf"] = module + try: + spec.loader.exec_module(module) + except Exception: + if previous is None: + sys.modules.pop("gguf", None) + else: + sys.modules["gguf"] = previous + raise + return module.GGUFReader + + @staticmethod + def _gguf_field_contents(reader: Any, name: str) -> Any: + field = reader.fields.get(name) + if field is None: + return None + return field.contents() + + def _load_hidden_norm_weights( + self, + model_path: str, + ) -> Tuple[np.ndarray, np.ndarray, float]: + GGUFReader = self._load_gguf_reader() + + target_weight: Optional[np.ndarray] = None + draft_weights: List[np.ndarray] = [] + reader = GGUFReader(model_path) + arch = self._gguf_field_contents(reader, "general.architecture") + if arch not in {"qwen35", "qwen35moe"}: + raise RuntimeError( + "draft-mtp currently supports qwen35/qwen35moe GGUF models, " + f"got {arch!r}" + ) + nextn_layers = self._gguf_field_contents( + reader, + f"{arch}.nextn_predict_layers", + ) + # The current MTP path follows llama.cpp's Qwen3.5 one-nextn-layer graph. + if int(nextn_layers or 0) != 1: + raise RuntimeError( + "draft-mtp currently supports exactly one Qwen3.5 nextn prediction layer" + ) + epsilon = self._gguf_field_contents( + reader, + f"{arch}.attention.layer_norm_rms_epsilon", + ) + if epsilon is None: + raise RuntimeError( + f"MTP requires {arch}.attention.layer_norm_rms_epsilon" + ) + for tensor in reader.tensors: + if tensor.name == "output_norm.weight": + target_weight = np.asarray(tensor.data, dtype=np.float32).copy() + elif tensor.name.endswith(".nextn.shared_head_norm.weight"): + draft_weights.append(np.asarray(tensor.data, dtype=np.float32).copy()) + if target_weight is None: + raise RuntimeError("MTP requires output_norm.weight in GGUF model") + if len(draft_weights) > 1: + raise RuntimeError( + "MTP requires at most one blk.*.nextn.shared_head_norm.weight in GGUF model" + ) + draft_weight = draft_weights[0] if draft_weights else target_weight + if target_weight.shape != (self.n_embd,): + raise RuntimeError( + "MTP target norm weight shape does not match model embedding size " + f"({target_weight.shape} != ({self.n_embd},))" + ) + if draft_weight.shape != (self.n_embd,): + raise RuntimeError( + "MTP draft norm weight shape does not match model embedding size " + f"({draft_weight.shape} != ({self.n_embd},))" + ) + return target_weight, draft_weight, float(epsilon) + + def _normalize_hidden_rows(self, rows: np.ndarray, weight: np.ndarray) -> np.ndarray: + rows = np.asarray(rows, dtype=np.float32) + scale = np.reciprocal( + np.sqrt( + np.mean(np.square(rows), axis=-1, keepdims=True) + + self.hidden_norm_epsilon + ) + ) + return rows * scale * weight.reshape(1, -1) + + def _normalize_target_hidden_rows(self, rows: np.ndarray) -> np.ndarray: + return self._normalize_hidden_rows(rows, self.target_hidden_norm_weight) + + def _normalize_draft_hidden_row( + self, + row: Union[np.ndarray, ctypes.POINTER(ctypes.c_float)], + ) -> np.ndarray: + if isinstance(row, np.ndarray): + row_array = row.reshape(1, -1) + else: + row_array = np.ctypeslib.as_array(row, shape=(self.n_embd,)).reshape(1, -1) + return self._normalize_hidden_rows( + row_array, + self.draft_hidden_norm_weight, + )[0] + + def _init_samplers(self) -> None: + for seq_id in range(self.n_seq_max): + params = llama_cpp.llama_sampler_chain_default_params() + params.no_perf = True + sampler = llama_cpp.llama_sampler_chain_init(params) + if self.top_k > 1: + llama_cpp.llama_sampler_chain_add( + sampler, + llama_cpp.llama_sampler_init_top_k(self.top_k), + ) + llama_cpp.llama_sampler_chain_add( + sampler, + llama_cpp.llama_sampler_init_greedy(), + ) + self._samplers.append(sampler) + + def _passes_p_min(self, output_index: int) -> bool: + if self.p_min <= 0.0: + return True + logits_ptr = llama_cpp.llama_get_logits_ith(self.ctx, output_index) + if not logits_ptr: + return False + logits = np.ctypeslib.as_array(logits_ptr, shape=(self.n_vocab,)) + n_values = min(self.top_k, self.n_vocab) + if n_values <= 0: + return False + if n_values == self.n_vocab: + values = logits + else: + top_indices = np.argpartition(logits, -n_values)[-n_values:] + values = logits[top_indices] + max_logit = float(np.max(values)) + weights = np.exp(values.astype(np.float64, copy=False) - max_logit) + total = float(np.sum(weights)) + if total <= 0.0 or not math.isfinite(total): + return False + return 1.0 / total >= self.p_min + + def _sample_token(self, output_index: int = 0, *, seq_id: int = 0) -> Optional[int]: + if seq_id < 0 or seq_id >= len(self._samplers): + return None + if not self._passes_p_min(output_index): + return None + sampler = self._samplers[seq_id] + token = int(llama_cpp.llama_sampler_sample(sampler, self.ctx, output_index)) + if token == llama_cpp.LLAMA_TOKEN_NULL: + return None + return token + + def _reset_sampler(self, seq_id: int) -> None: + if 0 <= seq_id < len(self._samplers): + llama_cpp.llama_sampler_reset(self._samplers[seq_id]) + + def close(self) -> None: + self.set_target_processing_enabled(False) + self.batch.token = ctypes.POINTER(llama_cpp.llama_token)() + llama_cpp.llama_batch_free(self.batch) + llama_cpp.llama_free(self.ctx) + for sampler in self._samplers: + llama_cpp.llama_sampler_free(sampler) + self._samplers.clear() + + def set_target_processing_enabled(self, enabled: bool) -> None: + if self.target_processing_enabled == enabled: + return + llama_cpp_ext.llama_set_embeddings_pre_norm( + self.target_ctx, + enabled, + False, + ) + self.target_processing_enabled = enabled + + def _clear_batch(self) -> None: + self.batch.n_tokens = 0 + + def _batch_embeddings(self) -> np.ndarray: + return self.batch_embeddings + + def _set_batch_embedding_row( + self, + row: int, + embedding: Union[np.ndarray, ctypes.POINTER(ctypes.c_float)], + ) -> None: + row_start = row * self.n_embd + row_end = row_start + self.n_embd + if isinstance(embedding, np.ndarray): + self._batch_embeddings()[row_start:row_end] = embedding + return + self._batch_embeddings()[row_start:row_end] = np.ctypeslib.as_array( + embedding, + shape=(self.n_embd,), + ) + + def _add_batch_token( + self, + *, + token: int, + pos: int, + seq_id: int, + logits: bool, + ) -> None: + slot = int(self.batch.n_tokens) + if slot >= self.n_batch: + raise RuntimeError("MTP draft batch capacity exceeded") + self.batch.token[slot] = int(token) + self.batch.pos[slot] = int(pos) + self.batch.seq_id[slot][0] = int(seq_id) + self.batch.n_seq_id[slot] = 1 + self.batch.logits[slot] = int(logits) + self.batch.n_tokens += 1 + + def _try_decode_batch(self) -> bool: + n_tokens = int(self.batch.n_tokens) + if n_tokens <= 0: + return True + started_at = time.perf_counter() + result = int(llama_cpp.llama_decode(self.ctx, self.batch)) + self.decode_seconds_total += time.perf_counter() - started_at + self.decode_calls_total += 1 + self.decode_tokens_total += n_tokens + if result != 0: + self.decode_failures_total += 1 + return False + return True + + def _decode_batch(self) -> None: + n_tokens = int(self.batch.n_tokens) + if n_tokens <= 0: + return + started_at = time.perf_counter() + result = int(llama_cpp.llama_decode(self.ctx, self.batch)) + self.decode_seconds_total += time.perf_counter() - started_at + self.decode_calls_total += 1 + self.decode_tokens_total += n_tokens + if result != 0: + self.decode_failures_total += 1 + raise RuntimeError(f"MTP draft decode failed with code {result}") + + def metric_definitions( + self, + ) -> List[Tuple[str, str, str, Union[int, float]]]: + decode_tokens_seconds = ( + self.decode_tokens_total / self.decode_seconds_total + if self.decode_seconds_total > 0.0 + else 0.0 + ) + return [ + ( + "counter", + "batch_server:mtp_decode_seconds_total", + "Time spent inside llama_decode() for the MTP draft context.", + self.decode_seconds_total, + ), + ( + "counter", + "batch_server:mtp_decode_calls_total", + "Number of llama_decode() calls made by the MTP draft context.", + self.decode_calls_total, + ), + ( + "counter", + "batch_server:mtp_decode_tokens_total", + "Number of batch rows decoded by the MTP draft context.", + self.decode_tokens_total, + ), + ( + "counter", + "batch_server:mtp_decode_failures_total", + "Number of failed MTP draft context decode calls.", + self.decode_failures_total, + ), + ( + "gauge", + "batch_server:mtp_decode_tokens_seconds", + "Average MTP draft context decode throughput in batch rows/s.", + decode_tokens_seconds, + ), + ] + + def can_draft(self, input_length: int, /, *, seq_id: int) -> bool: + if ( + input_length <= 0 + or seq_id < 0 + or seq_id >= self.n_seq_max + or not self.ready[seq_id] + ): + return False + return self.ready_pos[seq_id] == input_length - 1 + + def process(self, batch: Any, /) -> None: + n_tokens = int(batch.n_tokens) + if ( + n_tokens <= 0 + or not self.target_processing_enabled + or not bool(batch.token) + or bool(batch.embd) + ): + return + + h_tgt = llama_cpp_ext.llama_get_embeddings_pre_norm(self.target_ctx) + if not h_tgt: + raise RuntimeError("missing target pre-norm embeddings for MTP") + h_tgt_rows = np.ctypeslib.as_array( + h_tgt, + shape=(n_tokens, self.n_embd), + ) + h_tgt_rows = self._normalize_target_hidden_rows(h_tgt_rows) + + previous_row_by_seq: Dict[int, int] = {} + first_pos_by_seq: Dict[int, int] = {} + target_rows_by_seq: Dict[int, List[int]] = {} + aligned_by_seq: Dict[int, bool] = {} + + for start in range(0, n_tokens, self.n_batch): + self._process_rows( + batch, + h_tgt_rows, + start, + min(start + self.n_batch, n_tokens), + previous_row_by_seq, + first_pos_by_seq, + target_rows_by_seq, + aligned_by_seq, + ) + + for seq_id, rows in target_rows_by_seq.items(): + if not aligned_by_seq.get(seq_id, False): + self.ready[seq_id] = False + continue + if rows[-1] - rows[0] + 1 == len(rows): + target_rows = h_tgt_rows[rows[0] : rows[-1] + 1] + else: + target_rows = h_tgt_rows[rows].copy() + target_positions = [int(batch.pos[source_index]) for source_index in rows] + self.verify_h[seq_id] = target_rows + self.verify_h_pos[seq_id] = target_positions + self.verify_h_rows[seq_id] = len(rows) + self.pending_h[seq_id] = target_rows[-1] + self.ready[seq_id] = True + self.ready_pos[seq_id] = target_positions[-1] + 1 + + def _process_rows( + self, + batch: Any, + h_tgt_rows: np.ndarray, + start: int, + end: int, + previous_row_by_seq: Dict[int, int], + first_pos_by_seq: Dict[int, int], + target_rows_by_seq: Dict[int, List[int]], + aligned_by_seq: Dict[int, bool], + ) -> None: + added_pos_by_seq: Dict[int, int] = {} + self._clear_batch() + for index in range(start, end): + if int(batch.n_seq_id[index]) != 1: + raise RuntimeError("MTP requires one sequence id per batch token") + seq_id = int(batch.seq_id[index][0]) + if seq_id < 0 or seq_id >= self.n_seq_max: + raise RuntimeError(f"MTP sequence id out of range: {seq_id}") + pos = int(batch.pos[index]) + first_pos = first_pos_by_seq.setdefault(seq_id, pos) + aligned = first_pos <= 0 or ( + self.ready[seq_id] and self.ready_pos[seq_id] == first_pos + ) + aligned_by_seq.setdefault(seq_id, aligned) + mtp_pos = ( + pos - 1 + if previous_row_by_seq.get(seq_id) is None + else int(batch.pos[previous_row_by_seq[seq_id]]) + ) + if aligned and mtp_pos >= 0 and mtp_pos >= self.context_pos[seq_id]: + previous_row = previous_row_by_seq.get(seq_id) + slot = int(self.batch.n_tokens) + self._add_batch_token( + token=int(batch.token[index]), + pos=mtp_pos, + seq_id=seq_id, + logits=False, + ) + if previous_row is None: + self._set_batch_embedding_row(slot, self.pending_h[seq_id]) + else: + self._set_batch_embedding_row(slot, h_tgt_rows[previous_row]) + added_pos_by_seq[seq_id] = mtp_pos + previous_row_by_seq[seq_id] = index + target_rows_by_seq.setdefault(seq_id, []).append(index) + + if int(self.batch.n_tokens) > 0: + self._decode_batch() + for seq_id, pos in added_pos_by_seq.items(): + self.context_pos[seq_id] = max(self.context_pos[seq_id], pos + 1) + + def draft( + self, + input_ids: np.ndarray, + /, + *, + seq_id: int, + max_tokens: Optional[int], + ) -> np.ndarray: + if ( + self.num_pred_tokens <= 0 + or input_ids.size == 0 + or seq_id < 0 + or seq_id >= self.n_seq_max + or not self.ready[seq_id] + ): + return np.array([], dtype=np.intc) + n_predict = self.num_pred_tokens + if max_tokens is not None: + n_predict = min(n_predict, max_tokens) + if n_predict <= 0: + return np.array([], dtype=np.intc) + + n_past = int(input_ids.size) - 1 + if self.ready_pos[seq_id] != n_past: + return np.array([], dtype=np.intc) + first_pos = n_past - 1 + if first_pos < 0: + return np.array([], dtype=np.intc) + + token = int(input_ids[-1]) + drafted: List[int] = [] + if self.context_pos[seq_id] > first_pos: + self.truncate(seq_id, first_pos) + if self.context_pos[seq_id] < first_pos: + self.ready[seq_id] = False + return np.array([], dtype=np.intc) + + self._reset_sampler(seq_id) + self._clear_batch() + self._add_batch_token( + token=token, + pos=first_pos, + seq_id=seq_id, + logits=True, + ) + self._set_batch_embedding_row(0, self.pending_h[seq_id]) + if not self._try_decode_batch(): + self.truncate(seq_id, first_pos) + return np.array([], dtype=np.intc) + self.context_pos[seq_id] = n_past + + while len(drafted) < n_predict: + sampled_token = self._sample_token(seq_id=seq_id) + if sampled_token is None: + break + token = sampled_token + drafted.append(token) + if len(drafted) >= n_predict: + break + h_row = llama_cpp_ext.llama_get_embeddings_pre_norm_ith(self.ctx, 0) + if not h_row: + break + h_row = self._normalize_draft_hidden_row(h_row) + self._clear_batch() + self._add_batch_token( + token=token, + pos=first_pos + len(drafted), + seq_id=seq_id, + logits=True, + ) + self._set_batch_embedding_row(0, h_row) + if not self._try_decode_batch(): + break + self.context_pos[seq_id] = first_pos + len(drafted) + 1 + + if not drafted: + self.truncate(seq_id, n_past) + return np.array([], dtype=np.intc) + self.truncate(seq_id, n_past) + return np.asarray(drafted, dtype=np.intc) + + def draft_many( + self, + requests: Sequence[Tuple[np.ndarray, int, Optional[int]]], + /, + ) -> List[np.ndarray]: + results = [np.array([], dtype=np.intc) for _ in requests] + active: List["MTPDraftProvider.DraftManyState"] = [] + for result_index, (input_ids, seq_id, max_tokens) in enumerate(requests): + if ( + self.num_pred_tokens <= 0 + or input_ids.size == 0 + or seq_id < 0 + or seq_id >= self.n_seq_max + or not self.ready[seq_id] + ): + continue + n_predict = self.num_pred_tokens + if max_tokens is not None: + n_predict = min(n_predict, max_tokens) + if n_predict <= 0: + continue + if len(active) >= self.n_batch: + break + + n_past = int(input_ids.size) - 1 + if self.ready_pos[seq_id] != n_past: + continue + first_pos = n_past - 1 + if first_pos < 0: + continue + if self.context_pos[seq_id] > first_pos: + self.truncate(seq_id, first_pos) + if self.context_pos[seq_id] < first_pos: + self.ready[seq_id] = False + continue + self._reset_sampler(seq_id) + active.append( + self.DraftManyState( + result_index=result_index, + seq_id=seq_id, + first_pos=first_pos, + keep_len=n_past, + n_predict=n_predict, + token=int(input_ids[-1]), + drafted=[], + embedding=self.pending_h[seq_id], + cache_key=( + tuple(int(token) for token in input_ids.tolist()), + n_predict, + ), + ) + ) + + if not active: + return results + + touched = list(active) + try: + if all(state.n_predict == 1 for state in active): + grouped: Dict[ + Tuple[Tuple[int, ...], int], + List["MTPDraftProvider.DraftManyState"], + ] = {} + for state in active: + grouped.setdefault(state.cache_key, []).append(state) + + representatives = [states[0] for states in grouped.values()] + self._clear_batch() + for row, state in enumerate(representatives): + self._add_batch_token( + token=state.token, + pos=state.first_pos, + seq_id=state.seq_id, + logits=True, + ) + self._set_batch_embedding_row(row, state.embedding) + + if self._try_decode_batch(): + sampled_tokens = [ + self._sample_token(row, seq_id=state.seq_id) + for row, state in enumerate(representatives) + ] + for representative, sampled_token in zip( + representatives, + sampled_tokens, + ): + if sampled_token is None: + continue + self.context_pos[representative.seq_id] = max( + self.context_pos[representative.seq_id], + representative.keep_len, + ) + for state in grouped[representative.cache_key]: + state.drafted.append(sampled_token) + active = [] + + while active: + self._clear_batch() + for row, state in enumerate(active): + self._add_batch_token( + token=state.token, + pos=state.first_pos + len(state.drafted), + seq_id=state.seq_id, + logits=True, + ) + self._set_batch_embedding_row(row, state.embedding) + + if not self._try_decode_batch(): + break + + next_active: List["MTPDraftProvider.DraftManyState"] = [] + sampled_tokens = [ + self._sample_token(row, seq_id=state.seq_id) + for row, state in enumerate(active) + ] + for row, (state, sampled_token) in enumerate(zip(active, sampled_tokens)): + decoded_pos = state.first_pos + len(state.drafted) + self.context_pos[state.seq_id] = max( + self.context_pos[state.seq_id], + decoded_pos + 1, + ) + if sampled_token is None: + continue + h_row_ptr = llama_cpp_ext.llama_get_embeddings_pre_norm_ith( + self.ctx, row + ) + state.drafted.append(sampled_token) + if len(state.drafted) >= state.n_predict: + continue + if not h_row_ptr: + continue + h_row = self._normalize_draft_hidden_row(h_row_ptr) + state.token = sampled_token + state.embedding = h_row + next_active.append(state) + active = next_active + finally: + for state in touched: + self.truncate(state.seq_id, state.keep_len) + + for state in touched: + if state.drafted: + results[state.result_index] = np.asarray( + state.drafted, + dtype=np.intc, + ) + return results + + @dataclass(frozen=True) + class SampledContextRow: + seq_id: int + draft_pos: int + token: int + source_row: Optional[int] + + @dataclass(frozen=True) + class SampledPendingRow: + update_index: int + seq_id: int + draft_pos: int + token: int + source_row: int + + @dataclass(frozen=True) + class SampledOutput: + update_index: int + seq_id: int + output_index: int + keep_len: int + ready_pos: int + + @dataclass(frozen=True) + class SampledBatchUpdate: + seq_id: int + start_pos: int + tokens: List[int] + row_indices: List[int] + target_count: int + sample_index: int + pending_token: Optional[int] + max_tokens: Optional[int] + + @dataclass + class SampledBatchPlan: + context_rows: List["MTPDraftProvider.SampledContextRow"] + pending_rows: List["MTPDraftProvider.SampledPendingRow"] + sample_pending_index_by_update: Dict[int, int] + + @dataclass + class SampledDraftState: + update_index: int + seq_id: int + keep_len: int + pos: int + token: int + drafted: List[int] + n_predict: int + embedding: np.ndarray + + def _build_sampled_batch_plan( + self, + updates: Sequence["MTPDraftProvider.SampledBatchUpdate"], + /, + ) -> "MTPDraftProvider.SampledBatchPlan": + context_rows: List["MTPDraftProvider.SampledContextRow"] = [] + pending_rows: List["MTPDraftProvider.SampledPendingRow"] = [] + sample_pending_index_by_update: Dict[int, int] = {} + + for update_index, update in enumerate(updates): + seq_id = update.seq_id + if seq_id < 0 or seq_id >= self.n_seq_max: + continue + start_pos = update.start_pos + tokens = update.tokens + row_indices = update.row_indices + target_count = update.target_count + sample_index = update.sample_index + pending_token = update.pending_token + if ( + pending_token is None + or start_pos <= 0 + or target_count <= 0 + or target_count > len(tokens) + or target_count > len(row_indices) + or sample_index < 0 + or sample_index >= target_count + ): + continue + + for target_index in range(sample_index + 1): + mtp_pos = start_pos + target_index - 1 + if mtp_pos < 0: + continue + source_row = ( + None + if target_index == 0 + else row_indices[target_index - 1] + ) + context_rows.append( + self.SampledContextRow( + seq_id=seq_id, + draft_pos=mtp_pos, + token=tokens[target_index], + source_row=source_row, + ) + ) + + actual_pos = start_pos + sample_index + pending_rows.append( + self.SampledPendingRow( + update_index=update_index, + seq_id=seq_id, + draft_pos=actual_pos, + token=pending_token, + source_row=row_indices[sample_index], + ) + ) + sample_pending_index_by_update[update_index] = len(pending_rows) - 1 + + return self.SampledBatchPlan( + context_rows=context_rows, + pending_rows=pending_rows, + sample_pending_index_by_update=sample_pending_index_by_update, + ) + + def _decode_sampled_context_rows( + self, + context_rows: Sequence["MTPDraftProvider.SampledContextRow"], + h_tgt_rows: np.ndarray, + /, + ) -> None: + self._clear_batch() + decoded_context_rows: List[Tuple[int, int]] = [] + for row in context_rows: + if row.draft_pos < self.context_pos[row.seq_id]: + continue + if row.source_row is None: + embedding = self.pending_h[row.seq_id] + else: + embedding = h_tgt_rows[row.source_row] + self._add_batch_token( + token=row.token, + pos=row.draft_pos, + seq_id=row.seq_id, + logits=False, + ) + self._set_batch_embedding_row(int(self.batch.n_tokens) - 1, embedding) + decoded_context_rows.append((row.seq_id, row.draft_pos)) + + if int(self.batch.n_tokens) > 0: + self._decode_batch() + for seq_id, draft_pos in decoded_context_rows: + self.context_pos[seq_id] = max( + self.context_pos[seq_id], + draft_pos + 1, + ) + + def _decode_sampled_pending_rows( + self, + plan: "MTPDraftProvider.SampledBatchPlan", + h_tgt_rows: np.ndarray, + /, + ) -> List["MTPDraftProvider.SampledOutput"]: + sampled_outputs: List["MTPDraftProvider.SampledOutput"] = [] + pending_rows = plan.pending_rows + + self._clear_batch() + for pending_index, row in enumerate(pending_rows): + if row.draft_pos < self.context_pos[row.seq_id]: + continue + is_sample_pending = ( + pending_index + == plan.sample_pending_index_by_update.get(row.update_index) + ) + slot = int(self.batch.n_tokens) + self._add_batch_token( + token=row.token, + pos=row.draft_pos, + seq_id=row.seq_id, + logits=is_sample_pending, + ) + self._set_batch_embedding_row(slot, h_tgt_rows[row.source_row]) + if is_sample_pending: + sampled_outputs.append( + self.SampledOutput( + update_index=row.update_index, + seq_id=row.seq_id, + output_index=slot, + keep_len=row.draft_pos + 1, + ready_pos=row.draft_pos + 1, + ) + ) + + self._decode_batch() + for row in pending_rows: + self.context_pos[row.seq_id] = max( + self.context_pos[row.seq_id], + row.draft_pos + 1, + ) + + return sampled_outputs + + def _start_sampled_draft_states( + self, + updates: Sequence["MTPDraftProvider.SampledBatchUpdate"], + sampled_outputs: Sequence["MTPDraftProvider.SampledOutput"], + results: List[np.ndarray], + /, + ) -> List["MTPDraftProvider.SampledDraftState"]: + active: List["MTPDraftProvider.SampledDraftState"] = [] + for output in sampled_outputs: + self._reset_sampler(output.seq_id) + sampled_token = self._sample_token( + output.output_index, + seq_id=output.seq_id, + ) + if sampled_token is None: + continue + update = updates[output.update_index] + seq_id = update.seq_id + self.ready[seq_id] = True + n_predict = self.num_pred_tokens + max_tokens = update.max_tokens + if max_tokens is not None: + n_predict = min(n_predict, max_tokens) + if n_predict <= 0: + continue + if n_predict > 1: + h_row = llama_cpp_ext.llama_get_embeddings_pre_norm_ith( + self.ctx, output.output_index + ) + if h_row: + active.append( + self.SampledDraftState( + update_index=output.update_index, + seq_id=seq_id, + keep_len=output.keep_len, + pos=output.keep_len, + token=sampled_token, + drafted=[sampled_token], + n_predict=n_predict, + embedding=self._normalize_draft_hidden_row(h_row), + ) + ) + results[output.update_index] = np.asarray([sampled_token], dtype=np.intc) + + return active + + def _extend_sampled_draft_states( + self, + active: List["MTPDraftProvider.SampledDraftState"], + results: List[np.ndarray], + cleanup_keep_len_by_seq: Dict[int, int], + /, + ) -> None: + touched = list(active) + try: + while active: + self._clear_batch() + for batch_row, state in enumerate(active): + self._add_batch_token( + token=state.token, + pos=state.pos, + seq_id=state.seq_id, + logits=True, + ) + self._set_batch_embedding_row(batch_row, state.embedding) + + if not self._try_decode_batch(): + break + + next_active: List["MTPDraftProvider.SampledDraftState"] = [] + sampled_tokens = [ + self._sample_token(batch_row, seq_id=state.seq_id) + for batch_row, state in enumerate(active) + ] + for batch_row, (state, sampled_token) in enumerate( + zip(active, sampled_tokens) + ): + self.context_pos[state.seq_id] = max( + self.context_pos[state.seq_id], + state.pos + 1, + ) + if sampled_token is None: + continue + state.drafted.append(sampled_token) + if len(state.drafted) >= state.n_predict: + continue + h_row = llama_cpp_ext.llama_get_embeddings_pre_norm_ith( + self.ctx, batch_row + ) + if not h_row: + continue + h_row = self._normalize_draft_hidden_row(h_row) + state.token = sampled_token + state.embedding = h_row + state.pos += 1 + next_active.append(state) + active = next_active + finally: + for state in touched: + cleanup_keep_len_by_seq[state.seq_id] = state.keep_len + for seq_id, keep_len in cleanup_keep_len_by_seq.items(): + self._truncate_memory(seq_id, keep_len) + + for state in touched: + if state.drafted: + results[state.update_index] = np.asarray( + state.drafted, + dtype=np.intc, + ) + + def process_sampled_batch( + self, + updates: Sequence["MTPDraftProvider.SampledBatchUpdate"], + /, + ) -> List[np.ndarray]: + results = [np.array([], dtype=np.intc) for _ in updates] + if self.num_pred_tokens <= 0 or not updates: + return results + h_tgt = llama_cpp_ext.llama_get_embeddings_pre_norm(self.target_ctx) + if not h_tgt: + raise RuntimeError("missing target pre-norm embeddings for MTP") + n_target_rows = max( + ( + max(update.row_indices) + 1 + for update in updates + if update.row_indices + ), + default=0, + ) + if n_target_rows <= 0: + return results + h_tgt_rows = np.ctypeslib.as_array(h_tgt, shape=(n_target_rows, self.n_embd)) + h_tgt_rows = self._normalize_target_hidden_rows(h_tgt_rows) + + plan = self._build_sampled_batch_plan(updates) + if not plan.context_rows and not plan.pending_rows: + return results + if ( + len(plan.context_rows) > self.n_batch + or len(plan.pending_rows) > self.n_batch + ): + raise RuntimeError("MTP draft batch capacity exceeded") + + self._decode_sampled_context_rows(plan.context_rows, h_tgt_rows) + sampled_outputs = self._decode_sampled_pending_rows(plan, h_tgt_rows) + cleanup_keep_len_by_seq: Dict[int, int] = {} + for output in sampled_outputs: + update = updates[output.update_index] + sample_index = update.sample_index + sample_source_row = update.row_indices[sample_index] + self.pending_h[output.seq_id] = h_tgt_rows[sample_source_row] + self.ready[output.seq_id] = True + self.ready_pos[output.seq_id] = output.ready_pos + cleanup_keep_len_by_seq[output.seq_id] = output.keep_len + + for seq_id, keep_len in cleanup_keep_len_by_seq.items(): + self._truncate_memory(seq_id, keep_len) + + if sampled_outputs: + active = self._start_sampled_draft_states( + updates, + sampled_outputs, + results, + ) + self._extend_sampled_draft_states( + active, + results, + cleanup_keep_len_by_seq, + ) + + return results + + def accept(self, seq_id: int, accepted_draft_tokens: int) -> None: + if seq_id < 0 or seq_id >= self.n_seq_max: + return + n_rows = self.verify_h_rows[seq_id] + if n_rows <= 0: + return + row = min(max(accepted_draft_tokens, 0), n_rows - 1) + self.pending_h[seq_id] = self.verify_h[seq_id][row] + self.ready[seq_id] = True + self.ready_pos[seq_id] = self.verify_h_pos[seq_id][row] + 1 + + def _truncate_memory(self, seq_id: int, keep_len: int) -> None: + if seq_id < 0 or seq_id >= self.n_seq_max: + return + if not llama_cpp.llama_memory_seq_rm( + self.mem, + seq_id, + keep_len, + -1, + ): + raise RuntimeError( + f"failed to truncate MTP draft sequence {seq_id} at position {keep_len}" + ) + self.context_pos[seq_id] = min(self.context_pos[seq_id], keep_len) + + def truncate(self, seq_id: int, keep_len: int) -> None: + if seq_id < 0 or seq_id >= self.n_seq_max: + return + self._truncate_memory(seq_id, keep_len) + if keep_len <= 0: + self.pending_h[seq_id].fill(0.0) + self.verify_h[seq_id] = np.empty((0, self.n_embd), dtype=np.float32) + self.verify_h_pos[seq_id] = [] + self.verify_h_rows[seq_id] = 0 + self.ready[seq_id] = False + self.ready_pos[seq_id] = 0 + self.context_pos[seq_id] = 0 + return + + if self.ready_pos[seq_id] != keep_len: + self.ready[seq_id] = False + + def copy_sequence( + self, + source_seq_id: int, + dest_seq_id: int, + p0: int, + p1: int, + ) -> None: + if ( + source_seq_id < 0 + or source_seq_id >= self.n_seq_max + or dest_seq_id < 0 + or dest_seq_id >= self.n_seq_max + ): + return + llama_cpp.llama_memory_seq_cp( + self.mem, + source_seq_id, + dest_seq_id, + p0, + p1, + ) + source_ready_pos = self.ready_pos[source_seq_id] + copied_full_ready_state = p1 < 0 or p1 == source_ready_pos + if self.ready[source_seq_id] and copied_full_ready_state: + self.pending_h[dest_seq_id] = self.pending_h[source_seq_id] + self.verify_h[dest_seq_id] = self.verify_h[source_seq_id].copy() + self.verify_h_pos[dest_seq_id] = list(self.verify_h_pos[source_seq_id]) + self.verify_h_rows[dest_seq_id] = self.verify_h_rows[source_seq_id] + self.ready[dest_seq_id] = True + self.ready_pos[dest_seq_id] = source_ready_pos + self.context_pos[dest_seq_id] = min( + self.context_pos[source_seq_id], + source_ready_pos, + ) + return + + self.pending_h[dest_seq_id].fill(0.0) + self.verify_h[dest_seq_id] = np.empty((0, self.n_embd), dtype=np.float32) + self.verify_h_pos[dest_seq_id] = [] + self.verify_h_rows[dest_seq_id] = 0 + self.ready[dest_seq_id] = False + self.ready_pos[dest_seq_id] = 0 + self.context_pos[dest_seq_id] = 0 + + +class CompletionRequestCancelledError(RuntimeError): + pass + + +class CompletionRequestValidationError(ValueError): + pass + + +class CompletionResponseParsingError(RuntimeError): + pass + + +def omit_additional_properties(schema: Dict[str, Any]) -> None: + schema.pop("additionalProperties", None) + + +class CompletionChunkLogprobs(TypedDict): + tokens: List[str] + text_offset: List[int] + token_logprobs: List[Optional[float]] + top_logprobs: List[Optional[Dict[str, float]]] + + +class CompletionChunkChoice(TypedDict): + text: str + index: int + logprobs: Optional[CompletionChunkLogprobs] + finish_reason: Optional[str] + + +class CompletionChunk(TypedDict): + id: str + object: Literal["text_completion"] + created: int + model: str + choices: List[CompletionChunkChoice] + + +CompletionStream = Generator[CompletionChunk, None, OpenAICompletion] +CompletionPrompt = Union[str, List[int], List[str], List[List[int]]] + + +class CreateCompletionRequest(BaseModel): + model_config = ConfigDict(extra="ignore") + + prompt: CompletionPrompt = "" + suffix: Optional[str] = None + max_tokens: Optional[int] = Field(default=16, ge=0) + temperature: float = 0.8 + top_p: float = Field(default=0.95, ge=0.0, le=1.0) + echo: bool = False + stop: Optional[Union[str, List[str]]] = None + stream: bool = False + logprobs: Optional[int] = Field(default=None, ge=0) + presence_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0) + frequency_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0) + logit_bias: Optional[Dict[str, float]] = None + seed: Optional[int] = None + model: Optional[str] = None + n: int = Field(default=1, ge=1) + best_of: Optional[int] = Field(default=None, ge=1) + user: Optional[str] = None + + @field_validator("logit_bias") + @classmethod + def validate_logit_bias(cls, value: Optional[Dict[str, float]]) -> Optional[Dict[str, float]]: + if value is None: + return None + result: Dict[str, float] = {} + for key, bias in value.items(): + int(key) + result[key] = float(bias) + return result + + @model_validator(mode="after") + def validate_after(self) -> "CreateCompletionRequest": + if self.best_of is None: + self.best_of = self.n + if self.best_of < self.n: + raise ValueError("best_of must be greater than or equal to n") + if self.stream and self.best_of > 1: + raise ValueError("best_of is not supported for streaming completions") + if len(self.normalized_prompt()) > 1 and self.stream: + raise ValueError("streaming does not support multiple prompts") + return self + + def normalized_prompt(self) -> List[Union[str, List[int]]]: + if isinstance(self.prompt, str): + return [self.prompt] + if all(isinstance(token, int) for token in self.prompt): + return [cast(List[int], self.prompt)] + if all(isinstance(prompt, str) for prompt in self.prompt): + return cast(List[Union[str, List[int]]], list(cast(List[str], self.prompt))) + if all( + isinstance(prompt, list) + and all(isinstance(token, int) for token in prompt) + for prompt in self.prompt + ): + return cast( + List[Union[str, List[int]]], + list(cast(List[List[int]], self.prompt)), + ) + raise ValueError("prompt must be a string, token ids, list of strings, or list of token-id lists") + + +class ChatCompletionFunctionCall(BaseModel): + model_config = ConfigDict( + extra="allow", + json_schema_extra=omit_additional_properties, + ) + + name: str + arguments: Optional[str] = None + + +class ChatCompletionToolCall(BaseModel): + model_config = ConfigDict(extra="ignore") + + id: Optional[str] = None + type: Literal["function"] = "function" + function: ChatCompletionFunctionCall + + +class ChatCompletionRequestMessage(BaseModel): + model_config = ConfigDict( + extra="allow", + json_schema_extra=omit_additional_properties, + ) + + role: Literal["system", "developer", "user", "assistant", "tool", "function"] = Field( + default="user" + ) + content: Optional[Union[str, List[Dict[str, Any]]]] = Field(default="") + name: Optional[str] = Field(default=None) + tool_call_id: Optional[str] = Field(default=None) + function_call: Optional[ChatCompletionFunctionCall] = Field(default=None) + tool_calls: Optional[List[ChatCompletionToolCall]] = Field(default=None) + + +class ChatTemplateFunctionDefinition(TypedDict, total=False): + name: str + description: Optional[str] + parameters: Optional[Dict[str, Any]] + strict: Optional[bool] + content_type: Optional[str] + + +class ChatTemplateTool(TypedDict, total=False): + type: Literal["function"] + original_type: str + function: ChatTemplateFunctionDefinition + + +class ChatTemplateFunctionCall(TypedDict, total=False): + name: str + + +class ChatTemplateToolChoice(TypedDict, total=False): + type: Literal["function"] + function: ChatTemplateFunctionCall + + +class ChatTemplateResponseFormatJsonSchema(TypedDict, total=False): + name: Optional[str] + description: Optional[str] + schema: Dict[str, Any] + strict: Optional[bool] + + +class ChatTemplateResponseFormat(TypedDict, total=False): + type: Literal["text", "json_object", "json_schema"] + schema: Dict[str, Any] + json_schema: ChatTemplateResponseFormatJsonSchema + + +class ChatCompletionFunctionDefinition(BaseModel): + model_config = ConfigDict(extra="ignore") + + name: str + description: Optional[str] = None + parameters: Optional[Dict[str, Any]] = None + + def to_template_function(self) -> ChatTemplateFunctionDefinition: + function: ChatTemplateFunctionDefinition = {"name": self.name} + if self.description is not None: + function["description"] = self.description + if self.parameters is not None: + function["parameters"] = self.parameters + return function + + +class ChatCompletionToolFunction(BaseModel): + model_config = ConfigDict(extra="ignore") + + name: str + description: Optional[str] = None + parameters: Optional[Dict[str, Any]] = None + strict: Optional[bool] = None + + def to_template_function(self) -> ChatTemplateFunctionDefinition: + function: ChatTemplateFunctionDefinition = {"name": self.name} + if self.description is not None: + function["description"] = self.description + if self.parameters is not None: + function["parameters"] = self.parameters + if self.strict is not None: + function["strict"] = self.strict + return function + + +class ChatCompletionTool(BaseModel): + model_config = ConfigDict(extra="ignore") + + type: Literal["function"] + function: ChatCompletionToolFunction + + def to_template_tool(self) -> ChatTemplateTool: + return { + "type": self.type, + "function": self.function.to_template_function(), + } + + +class ChatCompletionFunctionCallOption(BaseModel): + model_config = ConfigDict(extra="ignore") + + name: str + + def to_template_function_call(self) -> ChatTemplateFunctionCall: + return {"name": self.name} + + +class ChatCompletionToolChoiceObject(BaseModel): + model_config = ConfigDict(extra="ignore") + + type: Literal["function"] + function: ChatCompletionFunctionCallOption + + def to_template_tool_choice(self) -> ChatTemplateToolChoice: + return { + "type": self.type, + "function": self.function.to_template_function_call(), + } + + +class ChatCompletionResponseFormatJsonSchema(BaseModel): + model_config = ConfigDict(extra="ignore", populate_by_name=True) + + name: Optional[str] = None + description: Optional[str] = None + schema_: Optional[Dict[str, Any]] = Field(default=None, alias="schema") + strict: Optional[bool] = None + + def to_template_json_schema(self) -> ChatTemplateResponseFormatJsonSchema: + json_schema: ChatTemplateResponseFormatJsonSchema = {} + if self.name is not None: + json_schema["name"] = self.name + if self.description is not None: + json_schema["description"] = self.description + if self.schema_ is not None: + json_schema["schema"] = self.schema_ + if self.strict is not None: + json_schema["strict"] = self.strict + return json_schema + + +class ChatCompletionResponseFormat(BaseModel): + model_config = ConfigDict(extra="ignore", populate_by_name=True) + + type: Literal["text", "json_object", "json_schema"] + schema_: Optional[Dict[str, Any]] = Field(default=None, alias="schema") + json_schema: Optional[ChatCompletionResponseFormatJsonSchema] = None + + def to_template_response_format(self) -> ChatTemplateResponseFormat: + response_format: ChatTemplateResponseFormat = {"type": self.type} + if self.schema_ is not None: + response_format["schema"] = self.schema_ + if self.json_schema is not None: + response_format["json_schema"] = self.json_schema.to_template_json_schema() + return response_format + + +class CreateChatCompletionRequest(BaseModel): + model_config = ConfigDict(extra="ignore") + + messages: List[ChatCompletionRequestMessage] = Field(default_factory=list) + max_tokens: Optional[int] = Field(default=None, ge=0) + temperature: float = 0.8 + top_p: float = Field(default=0.95, ge=0.0, le=1.0) + stop: Optional[Union[str, List[str]]] = None + stream: bool = False + logprobs: Optional[bool] = Field(default=False) + top_logprobs: Optional[int] = Field(default=None, ge=0) + presence_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0) + frequency_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0) + logit_bias: Optional[Dict[str, float]] = None + seed: Optional[int] = None + model: Optional[str] = None + n: int = Field(default=1, ge=1) + user: Optional[str] = None + functions: Optional[List[ChatCompletionFunctionDefinition]] = None + function_call: Optional[ + Union[Literal["none", "auto"], ChatCompletionFunctionCallOption] + ] = None + tools: Optional[List[ChatCompletionTool]] = None + tool_choice: Optional[ + Union[Literal["none", "auto", "required"], ChatCompletionToolChoiceObject] + ] = None + response_format: Optional[ChatCompletionResponseFormat] = None + reasoning_effort: Optional[ + Literal["low", "medium", "high", "minimal", "none"] + ] = None + + @field_validator("logit_bias") + @classmethod + def validate_logit_bias(cls, value: Optional[Dict[str, float]]) -> Optional[Dict[str, float]]: + if value is None: + return None + result: Dict[str, float] = {} + for key, bias in value.items(): + int(key) + result[key] = float(bias) + return result + + @model_validator(mode="after") + def validate_after(self) -> "CreateChatCompletionRequest": + if self.top_logprobs is not None and not self.logprobs: + raise ValueError("top_logprobs requires logprobs=true") + return self + + +class ResponsesFunctionTool(BaseModel): + model_config = ConfigDict(extra="ignore") + + type: Literal["function"] + name: str + description: Optional[str] = None + parameters: Optional[Dict[str, Any]] = None + strict: Optional[bool] = None + + def to_chat_template_tool(self) -> ChatTemplateTool: + return { + "type": "function", + "function": { + "name": self.name, + "description": self.description, + "parameters": self.parameters, + "strict": self.strict, + }, + } + + +class ResponsesCustomToolFormat(BaseModel): + model_config = ConfigDict(extra="ignore") + + syntax: Optional[str] = None + definition: Optional[str] = None + + +class ResponsesCustomTool(BaseModel): + model_config = ConfigDict(extra="ignore") + + type: Literal["custom"] + name: str + description: Optional[str] = None + format: Optional[ResponsesCustomToolFormat] = None + strict: Optional[bool] = None + + def to_chat_template_tool(self) -> ChatTemplateTool: + tool_format = self.format + syntax = tool_format.syntax if tool_format is not None else None + definition = tool_format.definition if tool_format is not None else None + description = self.description or "" + text_tool_guidance = ( + "This is a text tool. When calling it, the " + "`function.arguments` field itself must be the raw input string. " + "Do not wrap the input in JSON and do not use an object such as " + "'{\"input\": \"...\"}' or '{\"patch\": \"...\"}'." + ) + if isinstance(syntax, str) and isinstance(definition, str): + if description: + description = ( + f"{description}\n\n{text_tool_guidance}\n\n" + f"{syntax}:\n{definition}" + ) + else: + description = f"{text_tool_guidance}\n\n{syntax}:\n{definition}" + elif description: + description = f"{description}\n\n{text_tool_guidance}" + else: + description = text_tool_guidance + return { + "type": "function", + "original_type": "custom", + "function": { + "name": self.name, + "description": description or None, + "parameters": { + "type": "object", + "properties": { + "input": { + "type": "string", + "description": ( + "Raw input text for this tool. " + "For apply_patch, put the full patch here." + ), + }, + }, + "required": ["input"], + "additionalProperties": False, + }, + "strict": self.strict, + "content_type": "text", + }, + } + + +class ResponsesWebSearchTool(BaseModel): + model_config = ConfigDict(extra="ignore") + + type: Literal["web_search"] + + +ResponsesToolDefinition = Union[ + ResponsesFunctionTool, + ResponsesCustomTool, + ResponsesWebSearchTool, +] + + +class ResponsesToolChoiceObject(BaseModel): + model_config = ConfigDict(extra="ignore") + + type: Literal["function", "custom"] + name: str + + def to_chat_template_tool_choice(self) -> ChatTemplateToolChoice: + return { + "type": "function", + "function": { + "name": self.name, + }, + } + + +ResponsesToolChoice = Union[ + Literal["auto", "none", "required"], + ResponsesToolChoiceObject, +] + + +class ResponsesTextFormatJsonSchema(BaseModel): + model_config = ConfigDict(extra="ignore", populate_by_name=True) + + schema_: Dict[str, Any] = Field(alias="schema") + + def to_template_json_schema(self) -> ChatTemplateResponseFormatJsonSchema: + return {"schema": self.schema_} + + +class ResponsesTextFormat(BaseModel): + model_config = ConfigDict(extra="ignore", populate_by_name=True) + + type: Literal["text", "json_object", "json_schema"] + schema_: Optional[Dict[str, Any]] = Field(default=None, alias="schema") + json_schema: Optional[ResponsesTextFormatJsonSchema] = None + + def to_chat_response_format(self) -> ChatTemplateResponseFormat: + response_format: ChatTemplateResponseFormat = {"type": self.type} + if self.schema_ is not None: + response_format["schema"] = self.schema_ + if self.json_schema is not None: + response_format["json_schema"] = self.json_schema.to_template_json_schema() + return response_format + + +class ResponsesTextOptions(BaseModel): + model_config = ConfigDict(extra="ignore") + + format: Optional[ResponsesTextFormat] = None + + +class ResponsesReasoningOptions(BaseModel): + model_config = ConfigDict(extra="ignore") + + effort: Optional[Literal["low", "medium", "high"]] = None + + +class CreateResponseRequest(BaseModel): + model_config = ConfigDict(extra="ignore") + + input: Any = "" + instructions: Optional[str] = None + max_output_tokens: Optional[int] = Field(default=None, ge=0) + temperature: Optional[float] = Field(default=0.8, ge=0.0, le=2.0) + top_p: Optional[float] = Field(default=0.95, ge=0.0, le=1.0) + stream: bool = False + top_logprobs: Optional[int] = Field(default=None, ge=0) + model: Optional[str] = None + tools: Optional[List["ResponsesToolDefinition"]] = None + tool_choice: Optional["ResponsesToolChoice"] = None + parallel_tool_calls: bool = True + text: Optional["ResponsesTextOptions"] = None + reasoning: Optional["ResponsesReasoningOptions"] = None + metadata: Optional[Dict[str, str]] = None + user: Optional[str] = None + previous_response_id: Optional[str] = None + conversation: Optional[str] = None + store: Optional[bool] = None + truncation: Optional[Literal["auto", "disabled"]] = None + + +class ResponseCreateWebSocketRequest(BaseModel): + model_config = ConfigDict(extra="ignore") + + type: Literal["response.create"] + model: Optional[str] = None + input: Any = "" + instructions: Optional[str] = None + max_output_tokens: Optional[int] = Field(default=None, ge=0) + temperature: Optional[float] = Field(default=0.8, ge=0.0, le=2.0) + top_p: Optional[float] = Field(default=0.95, ge=0.0, le=1.0) + stream: bool = True + top_logprobs: Optional[int] = Field(default=None, ge=0) + tools: Optional[List["ResponsesToolDefinition"]] = None + tool_choice: Optional["ResponsesToolChoice"] = None + parallel_tool_calls: bool = True + text: Optional["ResponsesTextOptions"] = None + reasoning: Optional["ResponsesReasoningOptions"] = None + metadata: Optional[Dict[str, str]] = None + user: Optional[str] = None + previous_response_id: Optional[str] = None + conversation: Optional[str] = None + store: Optional[bool] = None + truncation: Optional[Literal["auto", "disabled"]] = None + generate: Optional[bool] = None + + def to_create_response_request(self) -> CreateResponseRequest: + return CreateResponseRequest.model_validate( + self.model_dump(mode="python", exclude={"type"}) + ) + + +class ModelCardResponse(BaseModel): + id: str + object: Literal["model"] = "model" + created: int + owned_by: str + + +class ModelListResponse(BaseModel): + object: Literal["list"] = "list" + data: List[ModelCardResponse] + + +class HealthzResponse(BaseModel): + status: Literal["ok"] = "ok" + + +@dataclass +class ResponsesWebSocketState: + input_items: List[Any] + output_items: List[Dict[str, Any]] + + +class ConfigFile(BaseModel): + class ServerOptions(BaseModel): + host: str = "127.0.0.1" + port: int = 8000 + + class DiskCacheOptions(BaseModel): + path: str + max_bytes: int = Field(ge=0) + min_tokens: int = Field(default=128, ge=1) + + class FromPretrainedOptions(BaseModel): + repo_id: str + filename: str + additional_files: Optional[List[str]] = None + local_dir: Optional[str] = None + local_dir_use_symlinks: Union[bool, Literal["auto"]] = "auto" + cache_dir: Optional[str] = None + + @staticmethod + def _pattern_has_glob(pattern: str) -> bool: + return any(char in pattern for char in "*?[]") + + @staticmethod + def _subfolder_for_repo_file(repo_file: str) -> Optional[str]: + subfolder = str(Path(repo_file).parent) + return None if subfolder == "." else subfolder + + def _cached_repo_file_list(self) -> List[str]: + from huggingface_hub import scan_cache_dir + + try: + cache_info = scan_cache_dir(self.cache_dir) + except Exception: + return [] + + cached_files: set[str] = set() + for repo in cache_info.repos: + if repo.repo_type != "model" or repo.repo_id != self.repo_id: + continue + for revision in repo.revisions: + for cached_file in revision.files: + cached_files.add(cached_file.file_name) + return sorted(cached_files) + + def _download_repo_file(self, repo_file: str) -> str: + from huggingface_hub import hf_hub_download + + filename = Path(repo_file).name + subfolder = self._subfolder_for_repo_file(repo_file) + + download_kwargs: Dict[str, Any] = dict( + repo_id=self.repo_id, + filename=filename, + subfolder=subfolder, + local_dir=self.local_dir, + cache_dir=self.cache_dir, + ) + hf_hub_download_signature = inspect.signature(hf_hub_download) + supports_local_dir_use_symlinks = ( + "local_dir_use_symlinks" in hf_hub_download_signature.parameters + or any( + parameter.kind is inspect.Parameter.VAR_KEYWORD + for parameter in hf_hub_download_signature.parameters.values() + ) + ) + if supports_local_dir_use_symlinks: + download_kwargs["local_dir_use_symlinks"] = self.local_dir_use_symlinks + + try: + resolved_path = hf_hub_download(**download_kwargs) + except Exception as exc: + try: + cached_path = cast( + str, + hf_hub_download( + repo_id=self.repo_id, + filename=filename, + subfolder=subfolder, + cache_dir=self.cache_dir, + local_files_only=True, + ), + ) + except Exception: + raise exc + + if self.local_dir is None: + return cached_path + + resolved_path = os.path.join(self.local_dir, repo_file) + resolved_parent = os.path.dirname(resolved_path) + if resolved_parent: + os.makedirs(resolved_parent, exist_ok=True) + shutil.copy2(cached_path, resolved_path) + + return cast(str, resolved_path) + + @staticmethod + def _match_single_file(pattern: str, file_list: Sequence[str]) -> str: + matching_files = [file for file in file_list if fnmatch.fnmatch(file, pattern)] + if len(matching_files) == 0: + raise ValueError( + f"No file found matching {pattern}\n\n" + f"Available Files:\n{json.dumps(list(file_list))}" + ) + if len(matching_files) > 1: + raise ValueError( + f"Multiple files found matching {pattern}\n\n" + f"Available Files:\n{json.dumps(list(file_list))}" + ) + return matching_files[0] + + def resolve_model_path(self) -> str: + try: + from huggingface_hub import HfFileSystem + from huggingface_hub.utils import validate_repo_id + except ImportError as exc: + try: + from huggingface_hub import HfFileSystem + from huggingface_hub.utils._validators import validate_repo_id + except ImportError: + raise ImportError( + "model.from_pretrained requires the huggingface-hub package. " + "You can install it with `pip install huggingface-hub`." + ) from exc + + validate_repo_id(self.repo_id) + requested_patterns = [self.filename, *(self.additional_files or [])] + if not any(self._pattern_has_glob(pattern) for pattern in requested_patterns): + model_path = self._download_repo_file(self.filename) + if self.additional_files: + for additional_file_name in self.additional_files: + self._download_repo_file(additional_file_name) + return model_path + + try: + hffs = HfFileSystem() + files = [ + file["name"] if isinstance(file, dict) else file + for file in hffs.ls(self.repo_id, recursive=True) + ] + file_list = [str(Path(file).relative_to(self.repo_id)) for file in files] + except Exception as exc: + file_list = self._cached_repo_file_list() + if not file_list: + raise exc + + matching_file = self._match_single_file(self.filename, file_list) + model_path = self._download_repo_file(matching_file) + + if self.additional_files: + for additional_file_name in self.additional_files: + matching_additional_file = self._match_single_file( + additional_file_name, file_list + ) + self._download_repo_file(matching_additional_file) + + return model_path + + class LoraOptions(BaseModel): + path: Optional[str] = None + from_pretrained: Optional["ConfigFile.FromPretrainedOptions"] = None + scale: float = 1.0 + + @model_validator(mode="after") + def validate_source(self) -> "ConfigFile.LoraOptions": + if (self.path is None) == (self.from_pretrained is None): + raise ValueError("exactly one of lora.path or lora.from_pretrained is required") + return self + + def resolve_path(self) -> str: + if self.from_pretrained is not None: + return self.from_pretrained.resolve_model_path() + assert self.path is not None + return self.path + + class MTMDEmbeddingCacheOptions(BaseModel): + path: str + max_bytes: int = Field(ge=0) + + class MTMDOptions(BaseModel): + mmproj_path: Optional[str] = None + mmproj_from_pretrained: Optional["ConfigFile.FromPretrainedOptions"] = None + embedding_cache: Optional["ConfigFile.MTMDEmbeddingCacheOptions"] = None + allowed_media_domains: Optional[List[str]] = None + allowed_local_media_path: Optional[str] = None + image_max_bytes: int = Field(default=20 * 1024 * 1024, ge=1) + audio_max_bytes: int = Field(default=100 * 1024 * 1024, ge=1) + image_timeout_seconds: float = Field(default=10.0, gt=0.0) + + @model_validator(mode="after") + def validate_source(self) -> "ConfigFile.MTMDOptions": + if (self.mmproj_path is None) == (self.mmproj_from_pretrained is None): + raise ValueError( + "exactly one of model.mtmd.mmproj_path or " + "model.mtmd.mmproj_from_pretrained is required" + ) + return self + + def resolve_mmproj_path(self) -> str: + if self.mmproj_from_pretrained is not None: + return self.mmproj_from_pretrained.resolve_model_path() + assert self.mmproj_path is not None + return self.mmproj_path + + class ModelOptions(BaseModel): + path: Optional[str] = None + alias: Optional[str] = None + chat_template: Optional[str] = None + from_pretrained: Optional["ConfigFile.FromPretrainedOptions"] = None + loras: List["ConfigFile.LoraOptions"] = Field(default_factory=list) + mtmd: Optional["ConfigFile.MTMDOptions"] = None + n_gpu_layers: Optional[int] = None + split_mode: Optional[int] = None + main_gpu: Optional[int] = None + tensor_split: Optional[List[float]] = None + vocab_only: Optional[bool] = None + use_mmap: Optional[bool] = None + use_mlock: Optional[bool] = None + kv_overrides: Optional[Dict[str, Union[bool, int, float, str]]] = None + n_ctx: Optional[int] = None + n_batch: Optional[int] = None + n_ubatch: Optional[int] = None + n_seq_max: Optional[int] = None + threads: Optional[int] = Field( + default_factory=lambda: max(multiprocessing.cpu_count(), 1) + ) + threads_batch: Optional[int] = Field( + default_factory=lambda: max(multiprocessing.cpu_count(), 1) + ) + rope_scaling_type: Optional[int] = None + pooling_type: Optional[int] = None + attention_type: Optional[int] = None + rope_freq_base: Optional[float] = None + rope_freq_scale: Optional[float] = None + yarn_ext_factor: Optional[float] = None + yarn_attn_factor: Optional[float] = None + yarn_beta_fast: Optional[float] = None + yarn_beta_slow: Optional[float] = None + yarn_orig_ctx: Optional[int] = None + offload_kqv: Optional[bool] = None + flash_attn: Optional[bool] = None + op_offload: Optional[bool] = None + swa_full: Optional[bool] = None + no_perf: Optional[bool] = None + type_k: Optional[int] = None + type_v: Optional[int] = None + max_seq_len: Optional[int] = None + max_output_tokens: Optional[int] = Field(default=None, ge=0) + kv_unified: bool = True + draft_model: Optional[Literal["prompt-lookup-decoding", "draft-mtp"]] = None + draft_model_num_pred_tokens: int = 16 + draft_model_max_ngram_size: int = 2 + draft_model_top_k: int = Field(default=1, ge=1) + draft_model_p_min: float = Field(default=0.0, ge=0.0, le=1.0) + draft_model_max_batch_size: Optional[int] = Field(default=None, ge=1) + draft_model_threads: Optional[int] = Field(default=None, gt=0) + draft_model_threads_batch: Optional[int] = Field(default=None, gt=0) + response_schema: Optional[Dict[str, Any]] = None + store_logits: bool = True + + @model_validator(mode="after") + def validate_source(self) -> "ConfigFile.ModelOptions": + if (self.path is None) == (self.from_pretrained is None): + raise ValueError("exactly one of model.path or model.from_pretrained is required") + return self + + @field_validator("chat_template", mode="before") + @classmethod + def normalize_chat_template(cls, value: Any) -> Any: + if isinstance(value, list): + if not all(isinstance(item, str) for item in value): + raise TypeError("model.chat_template list entries must be strings") + return "".join(value) + return value + + def resolve_model_path(self) -> str: + if self.from_pretrained is not None: + return self.from_pretrained.resolve_model_path() + assert self.path is not None + return self.path + + server: "ConfigFile.ServerOptions" = Field(default_factory=lambda: ConfigFile.ServerOptions()) + model: "ConfigFile.ModelOptions" + disk_cache: Optional["ConfigFile.DiskCacheOptions"] = None + + @classmethod + def load(cls, path: str) -> "ConfigFile": + return cls.model_validate_json(Path(path).read_text()) + + +ConfigFile.model_rebuild() + + +@dataclass(frozen=True) +class MediaInput: + kind: Literal["image", "audio"] + url: Optional[str] = None + data: Optional[str] = None + format: Optional[str] = None + + +class Jinja2ChatFormatter: + def __init__(self, template: str, *, bos_token: str, eos_token: str) -> None: + self._eos_token = eos_token + self._bos_token = bos_token + self._template_text = template + environment = ImmutableSandboxedEnvironment( + loader=jinja2.BaseLoader(), + trim_blocks=True, + lstrip_blocks=True, + ) + environment.filters["from_json"] = self._from_json + self._template = environment.from_string(template) + + @staticmethod + def _from_json(value: Any) -> Any: + if isinstance(value, str): + return json.loads(value) + return value + + @staticmethod + def media_inputs_from_messages( + messages: Sequence[ChatCompletionRequestMessage], + ) -> List[MediaInput]: + media_inputs: List[MediaInput] = [] + for message in messages: + content = message.content + if not isinstance(content, list): + continue + for part in content: + if not isinstance(part, dict): + continue + part_type = part.get("type") + if part_type in {"image_url", "input_image", "image"}: + image_url = part.get("image_url") or part.get("url") + if isinstance(image_url, str): + media_inputs.append(MediaInput(kind="image", url=image_url)) + elif isinstance(image_url, dict) and isinstance(image_url.get("url"), str): + media_inputs.append(MediaInput(kind="image", url=cast(str, image_url["url"]))) + else: + raise ValueError("image_url content part requires a URL string") + continue + if part_type == "audio_url": + audio_url = part.get("audio_url") + if isinstance(audio_url, str): + media_inputs.append(MediaInput(kind="audio", url=audio_url)) + elif isinstance(audio_url, dict) and isinstance(audio_url.get("url"), str): + media_inputs.append(MediaInput(kind="audio", url=cast(str, audio_url["url"]))) + else: + raise ValueError("audio_url content part requires a URL string") + continue + if part_type == "input_audio": + input_audio = part.get("input_audio") + if isinstance(input_audio, dict): + data = input_audio.get("data") + audio_format = input_audio.get("format") + else: + data = part.get("data") + audio_format = part.get("format") + if not isinstance(data, str): + raise ValueError("input_audio content part requires base64 data") + if audio_format is not None and not isinstance(audio_format, str): + raise ValueError("input_audio format must be a string") + media_inputs.append( + MediaInput( + kind="audio", + data=data, + format=cast(Optional[str], audio_format), + ) + ) + continue + return media_inputs + + @staticmethod + def _literal_content_for_template( + text: str, + media_marker: Optional[str], + ) -> str: + if media_marker is not None and media_marker in text: + raise ValueError("message content contains reserved multimodal marker text") + return text + + @staticmethod + def _content_part_for_template( + part: Any, + media_marker: Optional[str], + ) -> str: + if isinstance(part, str): + return Jinja2ChatFormatter._literal_content_for_template(part, media_marker) + if not isinstance(part, dict): + raise ValueError("content parts must be strings or objects") + part_type = part.get("type") + if part_type in {"image_url", "input_image", "image", "audio_url", "input_audio"}: + if media_marker is None: + raise ValueError("multimodal content requires model.mtmd") + return media_marker + if part_type in {"text", "input_text"}: + text = part.get("text") + if not isinstance(text, str): + raise ValueError(f"content part {part_type!r} requires string text") + return Jinja2ChatFormatter._literal_content_for_template(text, media_marker) + raise ValueError(f"unsupported content part type: {part_type!r}") + + @staticmethod + def _messages_for_template( + messages: Sequence[ChatCompletionRequestMessage], + media_marker: Optional[str], + ) -> List[Dict[str, Any]]: + converted: List[Dict[str, Any]] = [] + for message in messages: + data = message.model_dump(exclude_none=True) + content = data.get("content") + if isinstance(content, list): + data["content"] = "".join( + Jinja2ChatFormatter._content_part_for_template( + part, + media_marker, + ) + for part in content + ) + elif isinstance(content, str): + data["content"] = Jinja2ChatFormatter._literal_content_for_template( + content, + media_marker, + ) + converted.append(data) + return converted + + def _render( + self, + *, + messages: List[ChatCompletionRequestMessage], + media_marker: Optional[str] = None, + functions: Optional[List[ChatTemplateFunctionDefinition]] = None, + function_call: Optional[Union[str, ChatTemplateFunctionCall]] = None, + tools: Optional[List[ChatTemplateTool]] = None, + tool_choice: Optional[Union[str, ChatTemplateToolChoice]] = None, + reasoning_effort: Optional[str] = None, + add_generation_prompt: bool, + strftime_now: Callable[[str], str], + ) -> str: + def raise_exception(message: str) -> None: + raise ValueError(message) + + return cast( + str, + self._template.render( + messages=self._messages_for_template(messages, media_marker), + eos_token=self._eos_token, + bos_token=self._bos_token, + raise_exception=raise_exception, + add_generation_prompt=add_generation_prompt, + functions=functions, + function_call=function_call, + tools=tools, + tool_choice=tool_choice, + reasoning_effort=reasoning_effort, + strftime_now=strftime_now, + ), + ) + + def format( + self, + *, + messages: List[ChatCompletionRequestMessage], + media_marker: Optional[str] = None, + functions: Optional[List[ChatTemplateFunctionDefinition]] = None, + function_call: Optional[Union[str, ChatTemplateFunctionCall]] = None, + tools: Optional[List[ChatTemplateTool]] = None, + tool_choice: Optional[Union[str, ChatTemplateToolChoice]] = None, + reasoning_effort: Optional[str] = None, + ) -> Tuple[str, str, List[str]]: + render_time = datetime.now() + + def strftime_now(format_string: str) -> str: + return render_time.strftime(format_string) + + chat_template = self._render( + messages=messages, + media_marker=media_marker, + functions=functions, + function_call=function_call, + tools=tools, + tool_choice=tool_choice, + reasoning_effort=reasoning_effort, + add_generation_prompt=False, + strftime_now=strftime_now, + ) + prompt = self._render( + messages=messages, + media_marker=media_marker, + functions=functions, + function_call=function_call, + tools=tools, + tool_choice=tool_choice, + reasoning_effort=reasoning_effort, + add_generation_prompt=True, + strftime_now=strftime_now, + ) + generation_prompt = prompt[len(chat_template) :] if prompt.startswith(chat_template) else "" + stop = [self._eos_token] if self._eos_token else [] + return prompt, generation_prompt, stop + + +@dataclass +class Token: + token: int + text_bytes: bytes + token_logprob: Optional[float] + top_logprobs: Optional[Dict[str, float]] + + @classmethod + def from_token( + cls, + *, + model: Model, + prev_tokens: Sequence[int], + prev_text_bytes: Optional[Union[bytes, bytearray]] = None, + token: int, + ) -> "Token": + return cls( + token=token, + text_bytes=( + model.token_bytes_with_prev_bytes(prev_tokens, prev_text_bytes, token) + if prev_text_bytes is not None + else model.token_bytes_with_prev(prev_tokens, token) + ), + token_logprob=None, + top_logprobs=None, + ) + + @classmethod + def from_logits( + cls, + *, + model: Model, + formatter: OpenAIFormatter, + prev_tokens: Sequence[int], + prev_text_bytes: Optional[Union[bytes, bytearray]] = None, + token: int, + logits: np.ndarray, + logprobs_count: Optional[int], + need_token_logprob: bool = False, + ) -> "Token": + text_bytes = ( + model.token_bytes_with_prev_bytes(prev_tokens, prev_text_bytes, token) + if prev_text_bytes is not None + else model.token_bytes_with_prev(prev_tokens, token) + ) + if not model.store_logits: + return cls( + token=token, + text_bytes=text_bytes, + token_logprob=None, + top_logprobs=None, + ) + if logprobs_count is None and not need_token_logprob: + return cls( + token=token, + text_bytes=text_bytes, + token_logprob=None, + top_logprobs=None, + ) + logprobs = CompletionScheduler.logits_to_logprobs(logits) + token_logprob = float(logprobs[token]) + top_logprobs: Optional[Dict[str, float]] = None + if logprobs_count is not None: + top_count = min(max(logprobs_count, 0), model.n_vocab) + if top_count > 0: + top_indices = np.argpartition(logprobs, -top_count)[-top_count:] + top_indices = top_indices[np.argsort(logprobs[top_indices])[::-1]] + top_logprobs = { + formatter.decode_text(model.token_bytes(int(index))): float( + logprobs[int(index)] + ) + for index in top_indices + } + else: + top_logprobs = {} + top_logprobs[formatter.decode_text(text_bytes)] = token_logprob + return cls( + token=token, + text_bytes=text_bytes, + token_logprob=token_logprob, + top_logprobs=top_logprobs, + ) + + +@dataclass +class Completion: + request_id: str + index: int + seq_id: int + sampler: "Sampler" + prompt_tokens: List[int] + prompt_length: int + prompt_text: str + multimodal_prompt: bool + max_total_tokens: int + stop_sequences: List[bytes] + logprobs: Optional[int] + completion_tokens: List[int] = field(default_factory=list) + token_records: List[Token] = field(default_factory=list) + rendered_bytes: bytearray = field(default_factory=bytearray) + detokenized_prefix_bytes: bytearray = field(default_factory=bytearray) + pending_input_tokens: List[int] = field(default_factory=list) + draft_tokens: List[int] = field(default_factory=list) + pending_finish_reason: Optional[str] = None + returned_token_count: int = 0 + finished: bool = False + finish_reason: Optional[str] = None + score_sum: float = 0.0 + rank_by_score: bool = False + + @property + def total_tokens(self) -> int: + return self.prompt_length + len(self.completion_tokens) + + @property + def completion_token_count(self) -> int: + return len(self.completion_tokens) + + @property + def needs_token_logprob(self) -> bool: + return self.logprobs is not None or self.rank_by_score + + @property + def max_stop_sequence_length(self) -> int: + return max((len(stop) for stop in self.stop_sequences), default=0) + + +@dataclass +class PromptSegment: + @dataclass + class Media: + embeddings: np.ndarray + positions: np.ndarray + non_causal: bool = False + + kind: Literal["text", "image", "audio"] + start_pos: int + n_pos: int + identity_tokens: List[int] + decode_start_pos: int + decode_n_pos: int + text_tokens: List[int] = field(default_factory=list) + media: Optional["PromptSegment.Media"] = None + + @property + def end_pos(self) -> int: + return self.start_pos + self.n_pos + + @property + def batch_rows(self) -> int: + if self.kind != "text": + if self.media is None: + return 0 + return int(self.media.embeddings.shape[0]) + return len(self.text_tokens) + + @property + def decoder_position_increments(self) -> List[int]: + if not self.identity_tokens: + return [] + if self.kind == "text": + return [1] * len(self.identity_tokens) + return [*([0] * (len(self.identity_tokens) - 1)), self.decode_n_pos] + + def rows_for_capacity(self, offset: int, capacity: int) -> int: + if capacity <= 0: + return 0 + return min(capacity, self.end_pos - self.start_pos - offset) + + def media_slice( + self, + row_offset: int, + row_count: int, + ) -> Tuple[np.ndarray, np.ndarray, List[int]]: + if self.media is None: + raise RuntimeError("media segment is missing embeddings or positions") + row_start = row_offset + row_end = row_offset + row_count + embeddings = self.media.embeddings[row_start:row_end] + if len(self.media.positions) == self.batch_rows: + positions = self.media.positions[row_start:row_end] + else: + positions = ( + self.media.positions.reshape(4, self.batch_rows)[:, row_start:row_end] + .reshape(-1) + ) + return ( + embeddings, + positions, + self.decoder_position_increments[row_start:row_end], + ) + + +@dataclass +class PromptPlan: + text: str + generation_prompt: str + text_tokens: List[int] + identity_tokens: List[int] + segments: List[PromptSegment] + text_token_index_by_pos: Dict[int, int] = field(default_factory=dict) + + @classmethod + def from_tokens( + cls, + text: str, + tokens: List[int], + *, + generation_prompt: str = "", + ) -> "PromptPlan": + segment = PromptSegment( + kind="text", + start_pos=0, + n_pos=len(tokens), + identity_tokens=list(tokens), + decode_start_pos=0, + decode_n_pos=len(tokens), + text_tokens=list(tokens), + ) + return cls( + text=text, + generation_prompt=generation_prompt, + text_tokens=list(tokens), + identity_tokens=list(tokens), + segments=[segment] if tokens else [], + text_token_index_by_pos={pos: pos for pos in range(len(tokens))}, + ) + + @property + def length(self) -> int: + return len(self.identity_tokens) + + @property + def eval_token_count(self) -> int: + return self.length + + def position_increments_up_to(self, pos: int) -> List[int]: + increments: List[int] = [] + for segment in self.segments: + if pos <= segment.start_pos: + break + take = min(pos, segment.end_pos) - segment.start_pos + if take <= 0: + continue + increments.extend(segment.decoder_position_increments[:take]) + if pos <= segment.end_pos: + break + return increments + + def is_boundary(self, pos: int) -> bool: + if pos <= 0 or pos >= self.length: + return True + return any(segment.start_pos == pos or segment.end_pos == pos for segment in self.segments) + + def is_reusable_boundary(self, pos: int) -> bool: + if pos <= 0 or pos >= self.length: + return True + if self.is_boundary(pos): + return True + return self.segment_at(pos).kind == "text" + + def clamp_to_reusable_boundary(self, pos: int) -> int: + if pos <= 0: + return 0 + if pos >= self.length: + return self.length + if self.is_reusable_boundary(pos): + return pos + segment = self.segment_at(pos) + return segment.start_pos + + def decoder_pos_up_to(self, pos: int) -> int: + if not self.is_reusable_boundary(pos): + raise RuntimeError("decoder position requested inside a non-reusable media segment") + if pos <= 0: + return 0 + if pos >= self.length: + if not self.segments: + return 0 + segment = self.segments[-1] + return segment.decode_start_pos + segment.decode_n_pos + segment = self.segment_at(pos) + if segment.kind == "text": + return segment.decode_start_pos + (pos - segment.start_pos) + if pos == segment.start_pos: + return segment.decode_start_pos + if pos == segment.end_pos: + return segment.decode_start_pos + segment.decode_n_pos + raise RuntimeError("decoder position requested inside a media segment") + + def segment_at(self, pos: int) -> PromptSegment: + for segment in self.segments: + if segment.start_pos <= pos < segment.end_pos: + return segment + raise RuntimeError(f"missing prompt segment at position {pos}") + + def has_text_token_at(self, pos: int) -> bool: + return pos in self.text_token_index_by_pos + + def text_token_at(self, pos: int) -> int: + return self.text_tokens[self.text_token_index_by_pos[pos]] + + def prev_text_tokens_at(self, pos: int) -> List[int]: + return self.text_tokens[: self.text_token_index_by_pos[pos]] + + +@dataclass(frozen=True) +class PreparedCompletionParts: + payload: CreateCompletionRequest + prompt_text: str + generation_prompt: str + prompt_plan: PromptPlan + grammar_text: Optional[str] + tool_name: Optional[str] + + +@dataclass(frozen=True) +class ResponsesChatRequestParts: + messages: List[ChatCompletionRequestMessage] + max_tokens: Optional[int] + temperature: float + top_p: float + stream: bool + logprobs: bool + top_logprobs: Optional[int] + model: Optional[str] + user: Optional[str] + tools: Optional[List[ChatTemplateTool]] + tool_choice: Optional[Union[Literal["auto", "none", "required"], ChatTemplateToolChoice]] + response_format: Optional[ChatTemplateResponseFormat] + reasoning_effort: Optional[str] + + +@dataclass +class SchedulerMetrics: + started_at: float = field(default_factory=time.time) + requests_submitted_total: int = 0 + requests_admitted_total: int = 0 + requests_completed_total: int = 0 + requests_cancelled_total: int = 0 + requests_failed_total: int = 0 + prompt_tokens_total: int = 0 + prompt_seconds_total: float = 0.0 + tokens_predicted_total: int = 0 + tokens_predicted_seconds_total: float = 0.0 + scheduler_step_seconds_total: float = 0.0 + process_batch_seconds_total: float = 0.0 + sample_seconds_total: float = 0.0 + draft_seconds_total: float = 0.0 + draft_process_seconds_total: float = 0.0 + draft_generate_seconds_total: float = 0.0 + draft_sampled_batch_seconds_total: float = 0.0 + draft_process_calls_total: int = 0 + draft_generate_calls_total: int = 0 + draft_sampled_batch_calls_total: int = 0 + draft_batches_verified_total: int = 0 + draft_target_tokens_verified_total: int = 0 + draft_target_tokens_wasted_total: int = 0 + draft_tokens_reused_as_pending_total: int = 0 + n_decode_total: int = 0 + scheduler_step_calls_total: int = 0 + process_batch_calls_total: int = 0 + sample_calls_total: int = 0 + n_tokens_max: int = 0 + n_busy_slots_total: int = 0 + checkpoint_hits_total: int = 0 + checkpoint_saves_total: int = 0 + checkpoint_evictions_total: int = 0 + sequence_cache_hits_total: int = 0 + sequence_cache_save_requests_total: int = 0 + sequence_cache_lookup_failures_total: int = 0 + sequence_cache_load_failures_total: int = 0 + sequence_cache_save_failures_total: int = 0 + sequence_cache_tokens_loaded_total: int = 0 + + def observe_decode( + self, + items: Sequence[Any], + elapsed_seconds: float, + ) -> None: + if not items: + return + total_tokens = sum( + item.batch_token_count + for item in items + ) + if total_tokens <= 0: + return + prompt_tokens = sum( + item.batch_token_count + for item in items + if getattr(item, "kind", None) == "prefill" + ) + generation_tokens = total_tokens - prompt_tokens + self.n_decode_total += 1 + self.n_busy_slots_total += len(items) + self.n_tokens_max = max(self.n_tokens_max, total_tokens) + self.prompt_tokens_total += prompt_tokens + if prompt_tokens > 0: + self.prompt_seconds_total += elapsed_seconds * (prompt_tokens / total_tokens) + if generation_tokens > 0: + self.tokens_predicted_seconds_total += elapsed_seconds * ( + generation_tokens / total_tokens + ) + + def observe_predicted_token(self) -> None: + self.tokens_predicted_total += 1 + + +class SequenceCache(Protocol): + """Optional external sequence-state cache; storage and eviction stay outside the scheduler.""" + + @dataclass(frozen=True) + class Match: + tokens: Tuple[int, ...] + has_prompt_logits: bool = False + + @property + def length(self) -> int: + return len(self.tokens) + + @dataclass + class Load: + tokens: List[int] + state_bytes: np.ndarray + prompt_logits: Optional[np.ndarray] = None + + def lookup(self, tokens: Sequence[int]) -> Optional["SequenceCache.Match"]: + ... + + def load( + self, + match: "SequenceCache.Match", + ) -> Optional["SequenceCache.Load"]: + ... + + def save( + self, + tokens: Sequence[int], + state_bytes: np.ndarray, + prompt_logits: Optional[np.ndarray], + ) -> None: + ... + + +@dataclass +class CompletionRequest: + payload: CreateCompletionRequest + prompt_text: str + prompt_tokens: List[int] + prompt_plan: PromptPlan + effective_max_len: int + internal_completion_count: int + prompt_visible_start: int + id: str = field(default_factory=lambda: f"cmpl-{uuid.uuid4().hex}") + created: int = field(default_factory=lambda: int(time.time())) + prompt_cursor: int = 0 + match_sequence_id: int = -1 + match_length: int = 0 + sequence_cache_match: Optional[SequenceCache.Match] = None + sequence_cache_match_length: int = 0 + prompt_logits: Optional[np.ndarray] = None + base_seq_id: Optional[int] = None + sibling_seq_ids: List[int] = field(default_factory=list) + completion_seq_ids: List[int] = field(default_factory=list) + completions: List[Completion] = field(default_factory=list) + admitted: bool = False + prompt_done: bool = False + prompt_checkpoint_saved: bool = False + cancelled: bool = False + prompt_records: List[Token] = field(default_factory=list) + grammar_text: Optional[str] = None + grammar_root: str = "root" + chat_tool_name: Optional[str] = None + on_stream_chunk: Optional[Callable[[CompletionChunk], None]] = None + on_done: Optional[Callable[[OpenAICompletion], None]] = None + on_error: Optional[Callable[[BaseException], None]] = None + + @classmethod + def from_prepared( + cls, + *, + payload: CreateCompletionRequest, + prompt_text: str, + prompt_plan: PromptPlan, + max_seq_len: int, + max_output_tokens: Optional[int], + prompt_visible_start: int, + prompt_records: Optional[List[Token]] = None, + grammar_text: Optional[str] = None, + chat_tool_name: Optional[str] = None, + on_stream_chunk: Optional[Callable[[CompletionChunk], None]] = None, + on_done: Optional[Callable[[OpenAICompletion], None]] = None, + on_error: Optional[Callable[[BaseException], None]] = None, + ) -> "CompletionRequest": + prompt_tokens = list(prompt_plan.identity_tokens) + prompt_eval_tokens = prompt_plan.eval_token_count + if prompt_eval_tokens > max_seq_len: + raise CompletionRequestValidationError("prompt exceeds context window") + ctx_limit = prompt_plan.length + (max_seq_len - prompt_eval_tokens) + if max_output_tokens is not None: + ctx_limit = min(ctx_limit, prompt_plan.length + max_output_tokens) + if payload.max_tokens is None: + effective_max_len = ctx_limit + else: + effective_max_len = min(ctx_limit, prompt_plan.length + payload.max_tokens) + if effective_max_len < prompt_plan.length: + raise CompletionRequestValidationError("prompt exceeds context window") + internal_completion_count = payload.best_of if payload.best_of is not None else payload.n + request = cls( + payload=payload, + prompt_text=prompt_text, + prompt_tokens=prompt_tokens, + prompt_plan=prompt_plan, + effective_max_len=effective_max_len, + internal_completion_count=internal_completion_count, + prompt_visible_start=prompt_visible_start, + prompt_records=list(prompt_records or []), + grammar_text=grammar_text, + chat_tool_name=chat_tool_name, + on_stream_chunk=on_stream_chunk, + on_done=on_done, + on_error=on_error, + ) + return request + + def selected_completions(self) -> List[Completion]: + completions = list(self.completions) + best_of = self.payload.best_of if self.payload.best_of is not None else self.payload.n + if best_of > self.payload.n: + completions.sort( + key=lambda completion: ( + completion.score_sum / max(1, completion.completion_token_count), + completion.score_sum, + ), + reverse=True, + ) + return sorted(completions[: self.payload.n], key=lambda completion: completion.index) + + def capture_prompt_logprobs( + self, + *, + model: Model, + formatter: OpenAIFormatter, + output_indices: Sequence[Optional[int]], + output_positions: Sequence[int], + output_count: int, + output_index_to_logits_index: Callable[[Optional[int], int], Optional[int]], + ) -> None: + if self.payload.logprobs is None or not self.payload.echo: + return + for token_offset, output_index in enumerate(output_indices): + if output_index is None: + continue + next_pos = output_positions[token_offset] + 1 + if not self.prompt_plan.has_text_token_at(next_pos): + continue + text_token_index = self.prompt_plan.text_token_index_by_pos[next_pos] + if text_token_index < self.prompt_visible_start: + continue + logits_index = output_index_to_logits_index(output_index, output_count) + assert logits_index is not None + next_token = self.prompt_plan.text_token_at(next_pos) + record = Token.from_logits( + model=model, + formatter=formatter, + prev_tokens=self.prompt_plan.prev_text_tokens_at(next_pos), + token=next_token, + logits=model.logits(logits_index), + logprobs_count=self.payload.logprobs, + ) + expected_index = text_token_index - self.prompt_visible_start + if expected_index == len(self.prompt_records): + self.prompt_records.append(record) + elif expected_index < len(self.prompt_records): + self.prompt_records[expected_index] = record + else: + raise RuntimeError("prompt logprob order mismatch") + + +class ResponseParser: + @dataclass + class PartialJsonValue: + text: str + schema_type: Optional[str] = None + complete: bool = False + + @dataclass + class PartialJsonObject: + value: "OrderedDict[str, Any]" + complete: bool = False + + @dataclass + class DirectState: + deltas: bool + pending: str + mode: int + done: bool + saw_tool_calls: bool + tool_call_count: int + assistant_prefix: str + leading_capture_field: Optional[str] + leading_capture_start: str + leading_capture_end: str + leading_capture_strip_after: bool + leading_capture_implicit: bool + content_end_markers: Tuple[str, ...] + trim_before_iterator: bool + iterator_start: str + iterator_end: str + stop_markers: Tuple[str, ...] + function_start: str + function_name_end: str + function_end: str + parameter_start: str + parameter_name_end: str + parameter_end: str + + @dataclass + class ItemState: + pending: str + mode: int + tool_call_index: int + tool_name: str + parameter_count: int + visible_tool_call: Optional[Dict[str, Any]] + visible_function: Optional[Dict[str, Any]] + current_parameter: Optional[str] + current_parameter_value: str + current_schema_type: Optional[str] + current_parameter_schema: Dict[str, Any] + + @dataclass + class StreamState: + kind: str + pending: str + mode: str + current_item: Optional[Dict[str, Any]] + current_segment: Optional[Dict[str, Any]] + done: bool + saw_tool_calls: bool + parsed: Optional[Dict[str, Any]] = None + buffer: str = "" + tool_call_count: int = 0 + + _STREAM_PLAN_CACHE: Dict[int, Tuple[Any, Optional[Dict[str, Any]]]] = {} + _TOOL_SCHEMA_CACHE: Dict[int, Tuple[Any, Dict[str, Dict[str, Any]]]] = {} + __slots__ = ( + "_schema", + "_tools", + "_completion_id", + "_choice_index", + "_generation_prompt", + "_tool_schemas", + "_started", + "_text_parts", + "_message", + "_stream_plan", + "_direct", + "_item", + "_stream_state", + "_stream_failed", + ) + DIRECT_MODE_ASSISTANT_PREFIX = 0 + DIRECT_MODE_PRELUDE = 1 + DIRECT_MODE_LEADING_CAPTURE = 2 + DIRECT_MODE_CONTENT = 3 + DIRECT_MODE_TOOL_ITEM = 4 + DIRECT_MODE_AFTER_TOOL_ITEM = 5 + ITEM_MODE_FUNCTION_HEADER = 0 + ITEM_MODE_AFTER_FUNCTION_HEADER = 1 + ITEM_MODE_PARAMETER_NAME = 2 + ITEM_MODE_PARAMETER_VALUE = 3 + ITEM_MODE_AFTER_PARAMETER = 4 + ITEM_MODE_DONE = 5 + + def __init__( + self, + schema: Dict[str, Any], + *, + tools: Optional[List[ChatTemplateTool]] = None, + completion_id: str = "", + choice_index: int = 0, + generation_prompt: str = "", + ) -> None: + self._schema = schema + self._tools = tools + self._completion_id = completion_id + self._choice_index = choice_index + self._generation_prompt = generation_prompt + self._tool_schemas = self._cached_tool_schema_map(tools) + self._started = False + self._text_parts: List[str] = [] + self._message: Dict[str, Any] = {} + self._stream_plan = self._cached_stream_plan(schema) + self._direct = ResponseParser.DirectState( + deltas=bool(self._stream_plan is not None and self._stream_plan.get("direct_deltas")), + pending="", + mode=self.DIRECT_MODE_PRELUDE, + done=False, + saw_tool_calls=False, + tool_call_count=0, + assistant_prefix="", + leading_capture_field=None, + leading_capture_start="", + leading_capture_end="", + leading_capture_strip_after=False, + leading_capture_implicit=False, + content_end_markers=(), + trim_before_iterator=False, + iterator_start="", + iterator_end="", + stop_markers=(), + function_start="", + function_name_end="", + function_end="", + parameter_start="", + parameter_name_end="", + parameter_end="", + ) + self._item = ResponseParser.ItemState( + pending="", + mode=self.ITEM_MODE_FUNCTION_HEADER, + tool_call_index=0, + tool_name="", + parameter_count=0, + visible_tool_call=None, + visible_function=None, + current_parameter=None, + current_parameter_value="", + current_schema_type=None, + current_parameter_schema={}, + ) + if self._direct.deltas and self._stream_plan is not None: + direct_init = self._stream_plan["direct_init"] + ( + self._direct.assistant_prefix, + self._direct.leading_capture_field, + self._direct.leading_capture_start, + self._direct.leading_capture_end, + self._direct.leading_capture_strip_after, + self._direct.leading_capture_implicit, + self._direct.trim_before_iterator, + self._direct.content_end_markers, + self._direct.stop_markers, + self._direct.iterator_start, + self._direct.iterator_end, + self._direct.function_start, + self._direct.function_name_end, + self._direct.function_end, + self._direct.parameter_start, + self._direct.parameter_name_end, + self._direct.parameter_end, + ) = direct_init + self._direct.mode = ( + self.DIRECT_MODE_ASSISTANT_PREFIX + if self._direct.assistant_prefix + else self.DIRECT_MODE_PRELUDE + ) + self._stream_state = None + else: + self._stream_state = ( + self._new_stream_state(self._stream_plan) if self._stream_plan is not None else None + ) + self._stream_failed = False + if self._generation_prompt and self._stream_plan is not None: + success, _ = self._advance_stream_state(self._generation_prompt) + if not success: + self._stream_failed = True + + @property + def started(self) -> bool: + return self._started + + @staticmethod + def _regex_capture(text: str, pattern: str) -> Optional[Any]: + match = re.search(pattern, text, re.S) + if match is None: + return None + group_dict = match.groupdict() + if group_dict: + return {key: value for key, value in group_dict.items() if value is not None} + return match.group(1) if match.lastindex else match.group(0) + + @staticmethod + def _gemma4_tool_call_to_json(text: str) -> str: + strings: List[str] = [] + + def capture(match: re.Match[str]) -> str: + strings.append(match.group(1)) + return f"\x00{len(strings) - 1}\x00" + + text = re.sub(r'<\|"\|>(.*?)<\|"\|>', capture, text, flags=re.S) + text = re.sub(r"(?<=[{,])(\w+):", r'"\1":', text) + for index, value in enumerate(strings): + text = text.replace(f"\x00{index}\x00", json.dumps(value)) + return text + + @staticmethod + def _regex_literal_prefix(pattern: str) -> str: + literal: List[str] = [] + index = 0 + while index < len(pattern): + char = pattern[index] + if char == "\\": + index += 1 + if index >= len(pattern): + break + escaped = pattern[index] + if escaped == "n": + literal.append("\n") + elif escaped == "t": + literal.append("\t") + else: + literal.append(escaped) + index += 1 + continue + if char in "[](){}.*+?|^$": + break + literal.append(char) + index += 1 + return "".join(literal) + + @staticmethod + def _literal_suffix_prefix_length(text: str, literal: str) -> int: + max_length = min(len(text), len(literal) - 1) + if max_length <= 0: + return 0 + tail = text[-max_length:] + if literal[0] not in tail: + return 0 + for prefix_length in range(max_length, 0, -1): + if text.endswith(literal[:prefix_length]): + return prefix_length + return 0 + + @classmethod + def _consume_until_literal( + cls, + text: str, + literal: str, + ) -> Tuple[str, bool, str, str]: + if not literal: + return text, True, "", "" + literal_length = len(literal) + search_from = 0 + first_char = literal[0] + while True: + marker_index = text.find(first_char, search_from) + if marker_index < 0: + return text, False, "", "" + if text.startswith(literal, marker_index): + return text[:marker_index], True, text[marker_index + literal_length :], "" + suffix = text[marker_index:] + if literal.startswith(suffix): + return text[:marker_index], False, "", suffix + search_from = marker_index + 1 + + @staticmethod + def _compile_iterator_pattern(pattern: str) -> Optional[Tuple[str, str]]: + if "(.*?)" not in pattern: + return None + prefix_pattern, suffix_pattern = pattern.split("(.*?)", 1) + prefix = ResponseParser._regex_literal_prefix(prefix_pattern.lstrip("^")) + suffix = ResponseParser._regex_literal_prefix(suffix_pattern) + if not prefix: + return None + return prefix, suffix + + @classmethod + def _compile_iterator_block_pattern( + cls, + pattern: str, + ) -> Optional[Dict[str, Any]]: + first_group = pattern.find("(") + if first_group < 0: + return None + depth = 0 + last_group = -1 + escaped = False + for index in range(first_group, len(pattern)): + char = pattern[index] + if escaped: + escaped = False + continue + if char == "\\": + escaped = True + continue + if char == "(": + depth += 1 + continue + if char == ")": + depth -= 1 + if depth == 0: + last_group = index + break + if last_group < first_group: + return None + start = cls._regex_literal_prefix(pattern[:first_group].lstrip("^")) + if not start: + return None + suffix_pattern = pattern[last_group + 1 :] + if suffix_pattern == "": + return {"start": start, "end": "", "allow_eof": True} + eof_variant = suffix_pattern.endswith("|$)") and suffix_pattern.startswith("(?:") + if eof_variant: + suffix_pattern = suffix_pattern[3:-3] + end = cls._regex_literal_prefix(suffix_pattern) + if not end: + return None + return {"start": start, "end": end, "allow_eof": eof_variant} + + @classmethod + def _compile_capture_pattern( + cls, + pattern: str, + ) -> Optional[Dict[str, Any]]: + normalized = pattern.lstrip("^") + if "(.*?)" in normalized: + prefix_pattern, suffix_pattern = normalized.split("(.*?)", 1) + start = cls._regex_literal_prefix(prefix_pattern) + if not start: + return None + if suffix_pattern == "": + return {"start": start, "end": "", "allow_eof": True} + eof_variant = suffix_pattern.endswith("|$)") and suffix_pattern.startswith("(?:") + if eof_variant: + suffix_pattern = suffix_pattern[3:-3] + end = cls._regex_literal_prefix(suffix_pattern) + if not end: + return None + return {"start": start, "end": end, "allow_eof": eof_variant} + if "(.*)" in normalized: + prefix_pattern, suffix_pattern = normalized.split("(.*)", 1) + start = cls._regex_literal_prefix(prefix_pattern) + if not start: + return None + if suffix_pattern == "": + return {"start": start, "end": "", "allow_eof": True} + eof_variant = suffix_pattern.endswith("|$)") and suffix_pattern.startswith("(?:") + if eof_variant: + suffix_pattern = suffix_pattern[3:-3] + end = cls._regex_literal_prefix(suffix_pattern) + if not end: + return None + return {"start": start, "end": end, "allow_eof": eof_variant} + return None + + @classmethod + def _compile_word_capture_pattern( + cls, + pattern: str, + ) -> Optional[Dict[str, Any]]: + normalized = pattern.lstrip("^") + if r"(\w+)" not in normalized: + return None + prefix_pattern, suffix_pattern = normalized.split(r"(\w+)", 1) + start = cls._regex_literal_prefix(prefix_pattern) + if not start: + return None + end = cls._regex_literal_prefix(suffix_pattern) if suffix_pattern else "" + return { + "kind": "word", + "start": start, + "end": end, + } + + @classmethod + def _consume_until_any_literal( + cls, + text: str, + literals: Sequence[str], + ) -> Tuple[str, Optional[str], str, str]: + for literal in literals: + if literal and len(text) < len(literal) and literal.startswith(text): + return "", None, "", text + literal_first_chars = {literal[0] for literal in literals if literal} + if literal_first_chars and not any(char in text for char in literal_first_chars): + return text, None, "", "" + earliest_index: Optional[int] = None + earliest_literal: Optional[str] = None + for literal in literals: + marker_index = text.find(literal) + if marker_index < 0: + continue + if earliest_index is None or marker_index < earliest_index: + earliest_index = marker_index + earliest_literal = literal + if earliest_index is not None and earliest_literal is not None: + return ( + text[:earliest_index], + earliest_literal, + text[earliest_index + len(earliest_literal) :], + "", + ) + overlap = 0 + for literal in literals: + overlap = max(overlap, cls._literal_suffix_prefix_length(text, literal)) + if overlap: + return text[:-overlap], None, "", text[-overlap:] + return text, None, "", "" + + @classmethod + def _compile_tool_call_item_plan( + cls, + item_schema: Dict[str, Any], + ) -> Optional[Dict[str, Any]]: + item_properties = item_schema.get("properties") + if not isinstance(item_properties, dict): + return None + function_schema = item_properties.get("function") + if not isinstance(function_schema, dict): + return None + function_properties = function_schema.get("properties") + if not isinstance(function_properties, dict): + return None + name_schema = function_properties.get("name") + arguments_schema = function_properties.get("arguments") + if not isinstance(name_schema, dict) or not isinstance(arguments_schema, dict): + return None + name_regex = name_schema.get("x-regex") + argument_regex = arguments_schema.get("x-regex") + argument_key_value = arguments_schema.get("x-regex-key-value") + if ( + name_regex == r"^<function=([^>\n]+)>\n" + and argument_regex == r"^<function=[^>\n]+>\n(.*?)\n</function>$" + and argument_key_value + == r"<parameter=(?P<key>[^>\n]+)>\n(?P<value>.*?)\n</parameter>" + ): + return { + "kind": "tagged-parameters", + "schema": item_schema, + "function_start": "<function=", + "function_name_end": ">\n", + "function_end": "</function>", + "parameter_start": "<parameter=", + "parameter_name_end": ">\n", + "parameter_end": "\n</parameter>", + } + name_capture = ( + cls._compile_word_capture_pattern(name_regex) + if isinstance(name_regex, str) + else None + ) + arguments_capture = ( + cls._compile_capture_pattern(argument_regex) + if isinstance(argument_regex, str) + else None + ) + if ( + isinstance(name_capture, dict) + and isinstance(arguments_capture, dict) + and arguments_schema.get("x-parser") == "json" + ): + arguments_value_schema = { + key: value + for key, value in arguments_schema.items() + if key != "x-regex" + } + return { + "kind": "json-message", + "schema": item_schema, + "name_capture": name_capture, + "arguments_capture": arguments_capture, + "arguments_schema": arguments_value_schema, + } + return { + "kind": "buffered", + "schema": item_schema, + } + + @classmethod + def _compile_segment_message_plan( + cls, + schema: Dict[str, Any], + ) -> Optional[Dict[str, Any]]: + if schema.get("type") != "object": + return None + if isinstance(schema.get("x-regex"), str): + return None + properties = schema.get("properties") + if not isinstance(properties, dict): + return None + segments: List[Dict[str, Any]] = [] + field_segment_count = 0 + for field_name, value_schema in properties.items(): + if not isinstance(value_schema, dict): + continue + if field_name == "tool_calls": + iterator_pattern = value_schema.get("x-regex-iterator") + if not isinstance(iterator_pattern, str): + continue + iterator_capture = cls._compile_iterator_block_pattern(iterator_pattern) + if not isinstance(iterator_capture, dict) or not iterator_capture["start"]: + return None + items_schema = value_schema.get("items") + if not isinstance(items_schema, dict): + return None + item_plan = cls._compile_tool_call_item_plan(items_schema) + if item_plan is None: + return None + segments.append( + { + "kind": "iterator", + "field": field_name, + "start": iterator_capture["start"], + "end": iterator_capture["end"], + "allow_eof": iterator_capture["allow_eof"], + "item": item_plan, + } + ) + continue + field_regex = value_schema.get("x-regex") + if not isinstance(field_regex, str): + continue + capture = cls._compile_capture_pattern(field_regex) + if not isinstance(capture, dict) or not capture["start"]: + return None + segments.append( + { + "kind": "field", + "field": field_name, + "start": capture["start"], + "end": capture["end"], + "allow_eof": capture["allow_eof"], + } + ) + field_segment_count += 1 + if not segments or field_segment_count == 0: + return None + start_literals = tuple(segment["start"] for segment in segments) + if len(start_literals) != len(set(start_literals)): + return None + return { + "kind": "segment-message", + "segments": segments, + "segment_starts": start_literals, + "segments_by_start": {segment["start"]: segment for segment in segments}, + } + + @classmethod + def _compile_tagged_message_plan( + cls, + schema: Dict[str, Any], + ) -> Optional[Dict[str, Any]]: + if schema.get("type") != "object": + return None + properties = schema.get("properties") + if not isinstance(properties, dict): + return None + tool_calls_schema = properties.get("tool_calls") + if not isinstance(tool_calls_schema, dict): + return None + iterator_pattern = tool_calls_schema.get("x-regex-iterator") + if not isinstance(iterator_pattern, str): + return None + iterator = cls._compile_iterator_pattern(iterator_pattern) + if iterator is None: + return None + items_schema = tool_calls_schema.get("items") + if not isinstance(items_schema, dict): + return None + item_plan = cls._compile_tool_call_item_plan(items_schema) + if item_plan is None: + return None + content_schema = properties.get("content") + content_regex = ( + content_schema.get("x-regex") + if isinstance(content_schema, dict) + else None + ) + object_regex = schema.get("x-regex") if isinstance(schema.get("x-regex"), str) else None + assistant_prefix: Optional[str] = None + if isinstance(content_regex, str) and r"<\|im_start\|>assistant\n" in content_regex: + assistant_prefix = "<|im_start|>assistant\n" + leading_capture: Optional[Dict[str, Any]] = None + for field_name, value_schema in properties.items(): + if not isinstance(value_schema, dict): + continue + field_regex = value_schema.get("x-regex") + if not isinstance(field_regex, str): + continue + if "<think>\\n" in field_regex and "</think>" in field_regex: + leading_capture = { + "field": field_name, + "start": "<think>\n", + "end": "</think>", + "strip_after": True, + "implicit_at_start": "(?:<think>\\n)?" in field_regex, + } + break + if leading_capture is None and isinstance(object_regex, str): + if ( + "(?P<thinking>" in object_regex + and r"<\|channel\>thought\n" in object_regex + and r"\<channel\|\>" in object_regex + ): + leading_capture = { + "field": "thinking", + "start": "<|channel>thought\n", + "end": "<channel|>", + "strip_after": False, + "implicit_at_start": False, + } + end_markers: List[str] = [] + iterator_start, iterator_end = iterator + if "content" in properties: + end_markers.append(iterator_start) + if isinstance(content_regex, str) and r"<\|im_end\|>" in content_regex: + end_markers.append("<|im_end|>") + if isinstance(object_regex, str) and r"<turn\|>" in object_regex: + end_markers.append("<turn|>") + if not end_markers and iterator_start: + end_markers.append(iterator_start) + trim_before_iterator = ( + isinstance(content_regex, str) + and r"\s*<tool_call>\n" in content_regex + ) + end_marker_tuple = tuple(end_markers) + direct_deltas = item_plan["kind"] == "tagged-parameters" + direct_init = ( + ( + assistant_prefix or "", + leading_capture["field"] if leading_capture is not None else None, + leading_capture["start"] if leading_capture is not None else "", + leading_capture["end"] if leading_capture is not None else "", + bool(leading_capture.get("strip_after")) if leading_capture is not None else False, + bool(leading_capture.get("implicit_at_start")) if leading_capture is not None else False, + trim_before_iterator, + end_marker_tuple, + tuple(marker for marker in end_marker_tuple if marker != iterator_start), + iterator_start, + iterator_end, + item_plan["function_start"], + item_plan["function_name_end"], + item_plan["function_end"], + item_plan["parameter_start"], + item_plan["parameter_name_end"], + item_plan["parameter_end"], + ) + if direct_deltas + else None + ) + return { + "kind": "tagged-message", + "direct_deltas": direct_deltas, + "assistant_prefix": assistant_prefix, + "leading_capture": leading_capture, + "content_field": "content" if "content" in properties else None, + "content_end_markers": end_marker_tuple, + "trim_before_iterator": trim_before_iterator, + "stop_markers": tuple(marker for marker in end_marker_tuple if marker != iterator_start), + "direct_init": direct_init, + "iterator": { + "start": iterator_start, + "end": iterator_end, + "item": item_plan, + }, + } + + @classmethod + def _compile_stream_plan( + cls, + response_schema: Optional[Dict[str, Any]], + ) -> Optional[Dict[str, Any]]: + if not isinstance(response_schema, dict): + return None + if response_schema.get("x-parser") == "json": + return {"kind": "json-root"} + segment_plan = cls._compile_segment_message_plan(response_schema) + if segment_plan is not None: + return segment_plan + return cls._compile_tagged_message_plan(response_schema) + + @classmethod + def _cached_stream_plan( + cls, + response_schema: Optional[Dict[str, Any]], + ) -> Optional[Dict[str, Any]]: + if not isinstance(response_schema, dict): + return None + cache_key = id(response_schema) + cached = cls._STREAM_PLAN_CACHE.get(cache_key) + if cached is not None and cached[0] is response_schema: + return cached[1] + plan = cls._compile_stream_plan(response_schema) + cls._STREAM_PLAN_CACHE[cache_key] = (response_schema, plan) + return plan + + @staticmethod + def _tool_schema_map( + tools: Optional[List[ChatTemplateTool]], + ) -> Dict[str, Dict[str, Any]]: + if tools is None: + return {} + mapping: Dict[str, Dict[str, Any]] = {} + for tool in tools: + if tool.get("type") != "function": + continue + function = tool.get("function", {}) + name = function.get("name") + parameters = function.get("parameters") + if isinstance(name, str) and isinstance(parameters, dict): + mapping[name] = { + "parameters": parameters, + "content_type": function.get("content_type"), + } + return mapping + + @classmethod + def _cached_tool_schema_map( + cls, + tools: Optional[List[ChatTemplateTool]], + ) -> Dict[str, Dict[str, Any]]: + if tools is None: + return {} + cache_key = id(tools) + cached = cls._TOOL_SCHEMA_CACHE.get(cache_key) + if cached is not None and cached[0] is tools: + return cached[1] + mapping = cls._tool_schema_map(tools) + cls._TOOL_SCHEMA_CACHE[cache_key] = (tools, mapping) + return mapping + + def _parameter_schema_for_tool( + self, tool_name: str, parameter_name: str + ) -> Dict[str, Any]: + tool_schema = self._tool_schemas.get(tool_name) + if not isinstance(tool_schema, dict): + return {} + parameters = tool_schema.get("parameters") + if not isinstance(parameters, dict): + return {} + properties = parameters.get("properties") + if not isinstance(properties, dict): + return {} + parameter_schema = properties.get(parameter_name) + if not isinstance(parameter_schema, dict): + return {} + return parameter_schema + + def _tool_content_type(self, tool_name: str) -> Optional[str]: + tool_schema = self._tool_schemas.get(tool_name) + if not isinstance(tool_schema, dict): + return None + content_type = tool_schema.get("content_type") + if isinstance(content_type, str): + return content_type + return None + + def _has_text_tools(self) -> bool: + return any( + isinstance(tool_schema, dict) and tool_schema.get("content_type") == "text" + for tool_schema in self._tool_schemas.values() + ) + + @staticmethod + def _append_parsed_text(parsed: Dict[str, Any], key: str, text: str) -> None: + if not text: + return + existing = parsed.get(key) + if isinstance(existing, str): + parsed[key] = existing + text + else: + parsed[key] = text + + def _append_visible_text(self, key: str, text: str) -> None: + if not text: + return + existing = self._message.get(key) + if isinstance(existing, str): + self._message[key] = existing + text + else: + self._message[key] = text + + @staticmethod + def _advance_json_scanner( + item_state: Dict[str, Any], + text: str, + *, + schema_type: Optional[str], + ) -> bool: + started = cast(bool, item_state["json_started"]) + complete = cast(bool, item_state["json_complete"]) + depth = cast(int, item_state["json_depth"]) + in_string = cast(bool, item_state["json_in_string"]) + escaped = cast(bool, item_state["json_escaped"]) + for char in text: + if complete: + if not char.isspace(): + return False + continue + if not started: + if char.isspace(): + continue + if schema_type == "object" and char != "{": + return False + if schema_type == "array" and char != "[": + return False + if char == "{": + started = True + depth = 1 + continue + if char == "[": + started = True + depth = 1 + continue + return False + if in_string: + if escaped: + escaped = False + elif char == "\\": + escaped = True + elif char == '"': + in_string = False + continue + if char == '"': + in_string = True + continue + if char in "{[": + depth += 1 + continue + if char in "}]": + depth -= 1 + if depth < 0: + return False + if depth == 0: + complete = True + continue + item_state["json_started"] = started + item_state["json_complete"] = complete + item_state["json_depth"] = depth + item_state["json_in_string"] = in_string + item_state["json_escaped"] = escaped + return True + + def _new_tool_call_state(self, item_plan: Dict[str, Any]) -> Dict[str, Any]: + if item_plan["kind"] == "buffered": + return {"kind": "buffered", "buffer": ""} + if item_plan["kind"] == "json-message": + return { + "kind": "json-message", + "pending": "", + "mode": "function-name", + "tool_call": { + "type": "function", + "function": { + "name": "", + "arguments": "", + }, + }, + "arguments_text": "", + "json_started": False, + "json_complete": False, + "json_depth": 0, + "json_in_string": False, + "json_escaped": False, + } + return { + "kind": "tagged-parameters", + "pending": "", + "mode": "function-header", + "tool_call": { + "type": "function", + "function": { + "name": "", + "arguments": ResponseParser.PartialJsonObject(OrderedDict(), complete=False), + }, + }, + "current_parameter": None, + "current_schema_type": None, + } + + def _new_stream_state(self, plan: Dict[str, Any]) -> ResponseParser.StreamState: + if plan["kind"] == "json-root": + return ResponseParser.StreamState( + kind="json-root", + pending="", + mode="prelude", + current_item=None, + current_segment=None, + done=False, + saw_tool_calls=False, + parsed={"role": "assistant"}, + buffer="", + ) + if plan.get("direct_deltas"): + return ResponseParser.StreamState( + kind="tagged-message", + pending="", + mode="assistant-prefix" if plan.get("assistant_prefix") else "prelude", + current_item=None, + current_segment=None, + done=False, + saw_tool_calls=False, + tool_call_count=0, + ) + if plan["kind"] == "segment-message": + return ResponseParser.StreamState( + kind="segment-message", + pending="", + mode="segment-start", + current_item=None, + current_segment=None, + done=False, + saw_tool_calls=False, + parsed={"role": "assistant"}, + ) + return ResponseParser.StreamState( + kind="tagged-message", + pending="", + mode="assistant-prefix" if plan.get("assistant_prefix") else "prelude", + current_item=None, + current_segment=None, + done=False, + saw_tool_calls=False, + parsed={"role": "assistant"}, + ) + + def _start_direct_tool_call(self, tool_call_index: int) -> None: + self._item.pending = "" + self._item.mode = self.ITEM_MODE_FUNCTION_HEADER + self._item.tool_call_index = tool_call_index + self._item.tool_name = "" + self._item.parameter_count = 0 + tool_calls = self._message.setdefault("tool_calls", []) + assert isinstance(tool_calls, list) + while len(tool_calls) <= tool_call_index: + tool_calls.append({"function": {"name": "", "arguments": ""}}) + visible_tool_call = tool_calls[tool_call_index] + assert isinstance(visible_tool_call, dict) + self._item.visible_tool_call = visible_tool_call + function = visible_tool_call.setdefault("function", {}) + assert isinstance(function, dict) + self._item.visible_function = function + if tool_call_index == 0: + self._message["function_call"] = self._item.visible_function + self._item.current_parameter = None + self._item.current_parameter_value = "" + self._item.current_schema_type = None + self._item.current_parameter_schema = {} + + def _advance_direct_tool_call_state(self, text: str) -> Tuple[bool, List[Dict[str, Any]]]: + deltas: List[Dict[str, Any]] = [] + mode = self._item.mode + tool_call_index = self._item.tool_call_index + tool_name = self._item.tool_name + parameter_count = self._item.parameter_count + visible_tool_call = self._item.visible_tool_call + visible_function = self._item.visible_function + current_parameter = self._item.current_parameter + current_parameter_value = self._item.current_parameter_value + current_schema_type = self._item.current_schema_type + current_parameter_schema = self._item.current_parameter_schema + buffer = self._item.pending + text + function_start = self._direct.function_start + function_name_end = self._direct.function_name_end + function_end = self._direct.function_end + parameter_start = self._direct.parameter_start + parameter_name_end = self._direct.parameter_name_end + parameter_end = self._direct.parameter_end + + while True: + if mode == self.ITEM_MODE_FUNCTION_HEADER: + if buffer.startswith(function_start): + buffer = buffer[len(function_start) :] + elif function_start.startswith(buffer): + self._item.pending = buffer + self._item.mode = mode + return True, deltas + else: + self._item.pending = "" + return False, deltas + delimiter_index = buffer.find(function_name_end) + if delimiter_index < 0: + self._item.pending = function_start + buffer + self._item.mode = mode + return True, deltas + function_name = buffer[:delimiter_index] + if not function_name: + self._item.pending = "" + return False, deltas + tool_name = function_name + assert visible_tool_call is not None + assert visible_function is not None + visible_function["name"] = function_name + visible_function["arguments"] = "{" + visible_tool_call["id"] = ( + f"call_{self._choice_index}_{function_name}_{self._completion_id}_{tool_call_index}" + ) + visible_tool_call["type"] = "function" + deltas.append( + { + "tool_calls": [ + { + "index": tool_call_index, + "id": ( + f"call_{self._choice_index}_{function_name}_" + f"{self._completion_id}_{tool_call_index}" + ), + "type": "function", + "function": {"name": function_name, "arguments": "{"}, + } + ] + } + ) + buffer = buffer[delimiter_index + len(function_name_end) :] + mode = self.ITEM_MODE_AFTER_FUNCTION_HEADER + continue + if mode == self.ITEM_MODE_AFTER_FUNCTION_HEADER: + if buffer.startswith("\n"): + buffer = buffer[1:] + continue + if buffer.startswith(parameter_start): + mode = self.ITEM_MODE_PARAMETER_NAME + continue + if buffer.startswith(function_end): + buffer = buffer[len(function_end) :] + assert visible_function is not None + visible_function["arguments"] += "}" + deltas.append( + { + "tool_calls": [ + { + "index": tool_call_index, + "function": {"arguments": "}"}, + } + ] + } + ) + mode = self.ITEM_MODE_DONE + continue + if parameter_start.startswith(buffer) or function_end.startswith(buffer): + self._item.pending = buffer + self._item.mode = mode + self._item.tool_name = tool_name + return True, deltas + if not buffer: + self._item.pending = "" + self._item.mode = mode + self._item.tool_name = tool_name + return True, deltas + self._item.pending = "" + return False, deltas + if mode == self.ITEM_MODE_PARAMETER_NAME: + if buffer.startswith(parameter_start): + buffer = buffer[len(parameter_start) :] + elif parameter_start.startswith(buffer): + self._item.pending = buffer + self._item.mode = mode + self._item.tool_name = tool_name + self._item.parameter_count = parameter_count + return True, deltas + else: + self._item.pending = "" + return False, deltas + delimiter_index = buffer.find(parameter_name_end) + if delimiter_index < 0: + self._item.pending = parameter_start + buffer + self._item.mode = mode + self._item.tool_name = tool_name + self._item.parameter_count = parameter_count + return True, deltas + parameter_name = buffer[:delimiter_index] + if not parameter_name: + self._item.pending = "" + return False, deltas + parameter_schema = self._parameter_schema_for_tool(tool_name, parameter_name) + schema_type = parameter_schema.get("type") if isinstance(parameter_schema, dict) else None + current_parameter = parameter_name + current_parameter_value = "" + current_schema_type = schema_type if isinstance(schema_type, str) else None + current_parameter_schema = parameter_schema + key_prefix = json.dumps(parameter_name, ensure_ascii=False, separators=(",", ":")) + ":" + if parameter_count > 0: + key_prefix = "," + key_prefix + if schema_type in {None, "string"}: + key_prefix += '"' + parameter_count += 1 + assert visible_function is not None + visible_function["arguments"] += key_prefix + deltas.append( + { + "tool_calls": [ + { + "index": tool_call_index, + "function": {"arguments": key_prefix}, + } + ] + } + ) + buffer = buffer[delimiter_index + len(parameter_name_end) :] + mode = self.ITEM_MODE_PARAMETER_VALUE + continue + if mode == self.ITEM_MODE_PARAMETER_VALUE: + value_delta, matched, remainder, pending = self._consume_until_literal(buffer, parameter_end) + current_parameter_value += value_delta + if value_delta: + assert visible_function is not None + visible_function["arguments"] += value_delta + deltas.append( + { + "tool_calls": [ + { + "index": tool_call_index, + "function": {"arguments": value_delta}, + } + ] + } + ) + if not matched: + self._item.pending = pending + self._item.mode = mode + self._item.tool_name = tool_name + self._item.parameter_count = parameter_count + self._item.current_parameter = current_parameter + self._item.current_parameter_value = current_parameter_value + self._item.current_schema_type = current_schema_type + self._item.current_parameter_schema = current_parameter_schema + return True, deltas + self._coerce_tool_argument( + cast(str, current_parameter_value), + cast(Dict[str, Any], current_parameter_schema), + tool_name=tool_name, + argument_name=cast(str, current_parameter), + ) + if current_schema_type in {None, "string"}: + assert visible_function is not None + visible_function["arguments"] += '"' + deltas.append( + { + "tool_calls": [ + { + "index": tool_call_index, + "function": {"arguments": '"'}, + } + ] + } + ) + current_parameter = None + current_parameter_value = "" + current_schema_type = None + current_parameter_schema = {} + buffer = remainder + mode = self.ITEM_MODE_AFTER_PARAMETER + continue + if mode == self.ITEM_MODE_AFTER_PARAMETER: + if buffer.startswith("\n"): + buffer = buffer[1:] + continue + if buffer.startswith(parameter_start): + mode = self.ITEM_MODE_PARAMETER_NAME + continue + if buffer.startswith(function_end): + buffer = buffer[len(function_end) :] + assert visible_function is not None + visible_function["arguments"] += "}" + deltas.append( + { + "tool_calls": [ + { + "index": tool_call_index, + "function": {"arguments": "}"}, + } + ] + } + ) + mode = self.ITEM_MODE_DONE + continue + if parameter_start.startswith(buffer) or function_end.startswith(buffer): + self._item.pending = buffer + self._item.mode = mode + self._item.tool_name = tool_name + self._item.parameter_count = parameter_count + return True, deltas + if not buffer: + self._item.pending = "" + self._item.mode = mode + self._item.tool_name = tool_name + self._item.parameter_count = parameter_count + return True, deltas + self._item.pending = "" + return False, deltas + if mode == self.ITEM_MODE_DONE: + if buffer.strip(): + self._item.pending = "" + return False, deltas + self._item.pending = buffer + self._item.mode = mode + self._item.tool_name = tool_name + self._item.parameter_count = parameter_count + return True, deltas + + def _advance_direct_stream_state(self, text: str) -> Tuple[bool, List[Dict[str, Any]]]: + deltas: List[Dict[str, Any]] = [] + mode = self._direct.mode + tool_call_count = self._direct.tool_call_count + saw_tool_calls = self._direct.saw_tool_calls + done = self._direct.done + buffer = self._direct.pending + text + assistant_prefix = self._direct.assistant_prefix + leading_capture_field = self._direct.leading_capture_field + leading_capture_start = self._direct.leading_capture_start + leading_capture_end = self._direct.leading_capture_end + leading_capture_strip_after = self._direct.leading_capture_strip_after + leading_capture_implicit = self._direct.leading_capture_implicit + iterator_start = self._direct.iterator_start + iterator_end = self._direct.iterator_end + content_end_markers = self._direct.content_end_markers + stop_markers = self._direct.stop_markers + + while True: + if mode == self.DIRECT_MODE_ASSISTANT_PREFIX: + if buffer.startswith(assistant_prefix): + buffer = buffer[len(assistant_prefix) :] + mode = self.DIRECT_MODE_PRELUDE + continue + if assistant_prefix.startswith(buffer): + self._direct.pending = buffer + self._direct.mode = mode + return True, deltas + mode = self.DIRECT_MODE_PRELUDE + continue + if mode == self.DIRECT_MODE_PRELUDE: + if leading_capture_field is not None: + if buffer.startswith(leading_capture_start): + buffer = buffer[len(leading_capture_start) :] + mode = self.DIRECT_MODE_LEADING_CAPTURE + continue + if leading_capture_start.startswith(buffer): + self._direct.pending = buffer + self._direct.mode = mode + return True, deltas + if leading_capture_implicit: + tool_index = buffer.find(iterator_start) + end_index = buffer.find(leading_capture_end) + overlap = self._literal_suffix_prefix_length(buffer, leading_capture_end) + if ((end_index >= 0 and (tool_index < 0 or end_index < tool_index)) or overlap): + mode = self.DIRECT_MODE_LEADING_CAPTURE + continue + mode = self.DIRECT_MODE_CONTENT + continue + if mode == self.DIRECT_MODE_LEADING_CAPTURE: + segment, matched, remainder, pending = self._consume_until_literal(buffer, leading_capture_end) + if segment: + assert leading_capture_field is not None + self._append_visible_text(leading_capture_field, segment) + deltas.append({leading_capture_field: segment}) + if not matched: + self._direct.pending = pending + self._direct.mode = mode + self._direct.tool_call_count = tool_call_count + self._direct.saw_tool_calls = saw_tool_calls + self._direct.done = done + return True, deltas + buffer = remainder.lstrip() if leading_capture_strip_after else remainder + mode = self.DIRECT_MODE_CONTENT + continue + if mode == self.DIRECT_MODE_CONTENT: + segment, matched_marker, remainder, pending = self._consume_until_any_literal( + buffer, + content_end_markers, + ) + content_segment = segment.rstrip() if matched_marker == iterator_start and self._direct.trim_before_iterator else segment + if content_segment: + self._append_visible_text("content", content_segment) + deltas.append({"content": content_segment}) + if matched_marker is None: + self._direct.pending = pending + self._direct.mode = mode + self._direct.tool_call_count = tool_call_count + self._direct.saw_tool_calls = saw_tool_calls + self._direct.done = done + return True, deltas + if matched_marker == iterator_start: + saw_tool_calls = True + self._start_direct_tool_call(tool_call_count) + tool_call_count += 1 + mode = self.DIRECT_MODE_TOOL_ITEM + buffer = remainder + continue + done = True + self._direct.pending = "" + self._direct.mode = mode + self._direct.tool_call_count = tool_call_count + self._direct.saw_tool_calls = saw_tool_calls + self._direct.done = done + return True, deltas + if mode == self.DIRECT_MODE_TOOL_ITEM: + item_text, matched, remainder, pending = self._consume_until_literal(buffer, iterator_end) + if item_text: + success, item_deltas = self._advance_direct_tool_call_state(item_text) + if not success: + self._direct.pending = "" + self._direct.mode = mode + self._direct.tool_call_count = tool_call_count + self._direct.saw_tool_calls = saw_tool_calls + self._direct.done = done + return False, deltas + deltas.extend(item_deltas) + if not matched: + self._direct.pending = pending + self._direct.mode = mode + self._direct.tool_call_count = tool_call_count + self._direct.saw_tool_calls = saw_tool_calls + self._direct.done = done + return True, deltas + buffer = remainder.lstrip() + mode = self.DIRECT_MODE_AFTER_TOOL_ITEM + continue + if mode == self.DIRECT_MODE_AFTER_TOOL_ITEM: + if not buffer: + self._direct.pending = "" + self._direct.mode = mode + self._direct.tool_call_count = tool_call_count + self._direct.saw_tool_calls = saw_tool_calls + self._direct.done = done + return True, deltas + if buffer.startswith(iterator_start): + saw_tool_calls = True + self._start_direct_tool_call(tool_call_count) + tool_call_count += 1 + mode = self.DIRECT_MODE_TOOL_ITEM + buffer = buffer[len(iterator_start) :] + continue + for marker in stop_markers: + if buffer.startswith(marker): + done = True + self._direct.pending = "" + self._direct.mode = mode + self._direct.tool_call_count = tool_call_count + self._direct.saw_tool_calls = saw_tool_calls + self._direct.done = done + return True, deltas + if any(marker.startswith(buffer) for marker in content_end_markers) or buffer.isspace(): + self._direct.pending = buffer + self._direct.mode = mode + self._direct.tool_call_count = tool_call_count + self._direct.saw_tool_calls = saw_tool_calls + self._direct.done = done + return True, deltas + self._direct.pending = "" + self._direct.mode = mode + self._direct.tool_call_count = tool_call_count + self._direct.saw_tool_calls = saw_tool_calls + self._direct.done = done + return False, deltas + + def _advance_tool_call_state( + self, + item_state: Dict[str, Any], + text: str, + item_plan: Dict[str, Any], + ) -> Tuple[bool, List[Dict[str, Any]]]: + deltas: List[Dict[str, Any]] = [] + if item_plan["kind"] == "buffered": + item_state["buffer"] = item_state["buffer"] + text + return True, deltas + if item_plan["kind"] == "json-message": + buffer = item_state["pending"] + text + item_state["pending"] = "" + while True: + mode = item_state["mode"] + if mode == "function-name": + name_capture = item_plan["name_capture"] + name_prefix = name_capture["start"] + if buffer.startswith(name_prefix): + remainder = buffer[len(name_prefix) :] + elif name_prefix.startswith(buffer): + item_state["pending"] = buffer + return True, deltas + else: + return False, deltas + name_end = 0 + while name_end < len(remainder) and ( + remainder[name_end].isalnum() or remainder[name_end] == "_" + ): + name_end += 1 + if name_end == 0: + if not remainder: + item_state["pending"] = buffer + return True, deltas + return False, deltas + if name_end == len(remainder): + item_state["pending"] = buffer + return True, deltas + function_name = remainder[:name_end] + item_state["tool_call"]["function"]["name"] = function_name + tool_call_index = cast(int, item_state["tool_call_index"]) + deltas.append( + { + "tool_calls": [ + { + "index": tool_call_index, + "id": ( + f"call_{self._choice_index}_{function_name}_" + f"{self._completion_id}_{tool_call_index}" + ), + "type": "function", + "function": {"name": function_name}, + } + ] + } + ) + buffer = remainder[name_end:] + item_state["mode"] = "seek-arguments" + continue + if mode == "seek-arguments": + arguments_capture = item_plan["arguments_capture"] + _, matched, remainder, pending = self._consume_until_literal( + buffer, + arguments_capture["start"], + ) + if not matched: + item_state["pending"] = pending + return True, deltas + buffer = remainder + item_state["mode"] = "arguments" + continue + if mode == "arguments": + if buffer: + item_state["arguments_text"] = item_state["arguments_text"] + buffer + deltas.append( + { + "tool_calls": [ + { + "index": cast(int, item_state["tool_call_index"]), + "function": {"arguments": buffer}, + } + ] + } + ) + item_state["pending"] = "" + arguments_schema = cast(Dict[str, Any], item_plan["arguments_schema"]) + schema_type = arguments_schema.get("type") + if not self._advance_json_scanner( + item_state, + buffer, + schema_type=schema_type if isinstance(schema_type, str) else None, + ): + return False, deltas + return True, deltas + + buffer = item_state["pending"] + text + item_state["pending"] = "" + while True: + mode = item_state["mode"] + if mode == "function-header": + function_start = item_plan["function_start"] + if buffer.startswith(function_start): + buffer = buffer[len(function_start) :] + elif function_start.startswith(buffer): + item_state["pending"] = buffer + return True, deltas + else: + return False, deltas + function_name_end = item_plan["function_name_end"] + delimiter_index = buffer.find(function_name_end) + if delimiter_index < 0: + item_state["pending"] = function_start + buffer + return True, deltas + function_name = buffer[:delimiter_index] + if not function_name: + return False, deltas + item_state["tool_call"]["function"]["name"] = function_name + deltas.append( + { + "tool_calls": [ + { + "index": item_state["tool_call_index"], + "id": ( + f"call_{self._choice_index}_{function_name}_" + f"{self._completion_id}_{item_state['tool_call_index']}" + ), + "type": "function", + "function": { + "name": function_name, + "arguments": "{", + }, + } + ] + } + ) + buffer = buffer[delimiter_index + len(function_name_end) :] + item_state["mode"] = "after-function-header" + continue + if mode == "after-function-header": + if buffer.startswith("\n"): + buffer = buffer[1:] + continue + function_end = item_plan["function_end"] + parameter_start = item_plan["parameter_start"] + if buffer.startswith(parameter_start): + item_state["mode"] = "parameter-name" + continue + if buffer.startswith(function_end): + buffer = buffer[len(function_end) :] + item_state["mode"] = "done" + deltas.append( + { + "tool_calls": [ + { + "index": item_state["tool_call_index"], + "function": {"arguments": "}"}, + } + ] + } + ) + continue + if parameter_start.startswith(buffer) or function_end.startswith(buffer): + item_state["pending"] = buffer + return True, deltas + if not buffer: + return True, deltas + return False, deltas + if mode == "parameter-name": + parameter_start = item_plan["parameter_start"] + if buffer.startswith(parameter_start): + buffer = buffer[len(parameter_start) :] + elif parameter_start.startswith(buffer): + item_state["pending"] = buffer + return True, deltas + else: + return False, deltas + parameter_name_end = item_plan["parameter_name_end"] + delimiter_index = buffer.find(parameter_name_end) + if delimiter_index < 0: + item_state["pending"] = parameter_start + buffer + return True, deltas + parameter_name = buffer[:delimiter_index] + if not parameter_name: + return False, deltas + function = item_state["tool_call"]["function"] + arguments = cast(ResponseParser.PartialJsonObject, function["arguments"]) + tool_name = function["name"] + parameter_schema = self._parameter_schema_for_tool(tool_name, parameter_name) + schema_type = ( + parameter_schema.get("type") + if isinstance(parameter_schema, dict) + else None + ) + arguments.value[parameter_name] = ResponseParser.PartialJsonValue( + text="", + schema_type=schema_type if isinstance(schema_type, str) else None, + complete=False, + ) + key_prefix = json.dumps( + parameter_name, + ensure_ascii=False, + separators=(",", ":"), + ) + ":" + if len(arguments.value) > 1: + key_prefix = "," + key_prefix + if schema_type in {None, "string"}: + key_prefix += '"' + deltas.append( + { + "tool_calls": [ + { + "index": item_state["tool_call_index"], + "function": {"arguments": key_prefix}, + } + ] + } + ) + item_state["current_parameter"] = parameter_name + item_state["current_schema_type"] = ( + schema_type if isinstance(schema_type, str) else None + ) + buffer = buffer[delimiter_index + len(parameter_name_end) :] + item_state["mode"] = "parameter-value" + continue + if mode == "parameter-value": + parameter_end = item_plan["parameter_end"] + value_delta, matched, remainder, pending = self._consume_until_literal( + buffer, + parameter_end, + ) + function = item_state["tool_call"]["function"] + arguments = cast(ResponseParser.PartialJsonObject, function["arguments"]) + parameter_name = cast(str, item_state["current_parameter"]) + current_value = arguments.value[parameter_name] + assert isinstance(current_value, ResponseParser.PartialJsonValue) + current_value.text = current_value.text + value_delta + if value_delta: + deltas.append( + { + "tool_calls": [ + { + "index": item_state["tool_call_index"], + "function": {"arguments": value_delta}, + } + ] + } + ) + if not matched: + item_state["pending"] = pending + return True, deltas + tool_name = function["name"] + parameter_schema = self._parameter_schema_for_tool(tool_name, parameter_name) + self._coerce_tool_argument( + current_value.text, + parameter_schema, + tool_name=tool_name, + argument_name=parameter_name, + ) + current_value.complete = True + if current_value.schema_type in {None, "string"}: + deltas.append( + { + "tool_calls": [ + { + "index": item_state["tool_call_index"], + "function": {"arguments": '"'}, + } + ] + } + ) + item_state["current_parameter"] = None + item_state["current_schema_type"] = None + buffer = remainder + item_state["mode"] = "after-parameter" + continue + if mode == "after-parameter": + if buffer.startswith("\n"): + buffer = buffer[1:] + continue + function_end = item_plan["function_end"] + parameter_start = item_plan["parameter_start"] + if buffer.startswith(parameter_start): + item_state["mode"] = "parameter-name" + continue + if buffer.startswith(function_end): + buffer = buffer[len(function_end) :] + cast( + ResponseParser.PartialJsonObject, + item_state["tool_call"]["function"]["arguments"], + ).complete = True + item_state["mode"] = "done" + deltas.append( + { + "tool_calls": [ + { + "index": item_state["tool_call_index"], + "function": {"arguments": "}"}, + } + ] + } + ) + continue + if parameter_start.startswith(buffer) or function_end.startswith(buffer): + item_state["pending"] = buffer + return True, deltas + if not buffer: + return True, deltas + return False, deltas + if mode == "done": + if buffer.strip(): + return False, deltas + item_state["pending"] = buffer + return True, deltas + + def _finish_tool_call_state( + self, + item_state: Dict[str, Any], + item_plan: Dict[str, Any], + ) -> Optional[Dict[str, Any]]: + if item_plan["kind"] == "buffered": + parsed_item = self._parse_response_value( + item_state["buffer"], + item_plan["schema"], + partial=False, + ) + return self._normalize_tool_call_item(parsed_item, partial=False) + if item_plan["kind"] == "json-message": + if item_state["mode"] != "arguments": + return None + if item_state["pending"] or not item_state["json_started"] or not item_state["json_complete"]: + return None + try: + arguments = self._parse_response_value( + item_state["arguments_text"], + item_plan["arguments_schema"], + partial=False, + ) + except CompletionResponseParsingError: + return None + if arguments is None: + return None + item_state["tool_call"]["function"]["arguments"] = arguments + return cast(Dict[str, Any], item_state["tool_call"]) + if item_state["pending"] or item_state["current_parameter"] is not None: + return None + if item_state["mode"] not in {"done", "after-function-header"}: + return None + return cast(Dict[str, Any], item_state["tool_call"]) + + def _tool_call_delta( + self, + *, + tool_call: Dict[str, Any], + tool_call_index: int, + partial: bool, + ) -> Dict[str, Any]: + function = cast(Dict[str, Any], tool_call["function"]) + return { + "tool_calls": [ + { + "index": tool_call_index, + "id": ( + f"call_{self._choice_index}_{function['name']}_" + f"{self._completion_id}_{tool_call_index}" + ), + "type": tool_call.get("type", "function"), + "function": { + "name": function["name"], + "arguments": self._serialize_tool_arguments( + function["arguments"], + partial=partial, + ), + }, + } + ] + } + + def _advance_stream_state(self, text: str) -> Tuple[bool, List[Dict[str, Any]]]: + deltas: List[Dict[str, Any]] = [] + if self._stream_plan is None: + return False, deltas + if self._direct.deltas: + return self._advance_direct_stream_state(text) + if self._stream_state is None: + return False, deltas + if self._stream_plan["kind"] == "segment-message": + state = self._stream_state + parsed = cast(Dict[str, Any], state.parsed) + buffer = state.pending + text + state.pending = "" + while True: + if state.mode == "segment-start": + _, matched_start, remainder, pending = self._consume_until_any_literal( + buffer, + cast(Tuple[str, ...], self._stream_plan["segment_starts"]), + ) + if matched_start is None: + state.pending = buffer if not pending else pending + return True, deltas + state.current_segment = cast( + Dict[str, Any], + self._stream_plan["segments_by_start"][matched_start], + ) + buffer = remainder + state.mode = ( + "segment-tool-item" + if state.current_segment["kind"] == "iterator" + else "segment-field" + ) + if state.current_segment["kind"] == "iterator": + item_state = self._new_tool_call_state(state.current_segment["item"]) + if item_state["kind"] in {"tagged-parameters", "json-message"}: + tool_calls = cast( + List[Dict[str, Any]], + parsed.setdefault("tool_calls", []), + ) + tool_calls.append(item_state["tool_call"]) + item_state["tool_call_index"] = len(tool_calls) - 1 + state.current_item = item_state + state.saw_tool_calls = True + continue + if state.mode == "segment-field": + current_segment = state.current_segment + if not isinstance(current_segment, dict): + return False, deltas + segment_text, matched, remainder, pending = self._consume_until_literal( + buffer, + cast(str, current_segment["end"]), + ) + field_name = cast(str, current_segment["field"]) + self._append_parsed_text(parsed, field_name, segment_text) + if segment_text: + deltas.append({field_name: segment_text}) + if not matched: + state.pending = pending + return True, deltas + buffer = remainder + state.current_segment = None + state.mode = "segment-start" + continue + if state.mode == "segment-tool-item": + current_segment = state.current_segment + if not isinstance(current_segment, dict): + return False, deltas + active_item_state = state.current_item + if not isinstance(active_item_state, dict): + return False, deltas + item_text, matched, remainder, pending = self._consume_until_literal( + buffer, + cast(str, current_segment["end"]), + ) + if item_text: + success, item_deltas = self._advance_tool_call_state( + active_item_state, + item_text, + cast(Dict[str, Any], current_segment["item"]), + ) + if not success: + return False, deltas + deltas.extend(item_deltas) + if not matched: + state.pending = pending + return True, deltas + tool_call = self._finish_tool_call_state( + active_item_state, + cast(Dict[str, Any], current_segment["item"]), + ) + if tool_call is None: + return False, deltas + if active_item_state["kind"] == "buffered": + tool_calls = cast(List[Dict[str, Any]], parsed.setdefault("tool_calls", [])) + tool_call_index = len(tool_calls) + tool_calls.append(tool_call) + deltas.append( + self._tool_call_delta( + tool_call=tool_call, + tool_call_index=tool_call_index, + partial=False, + ) + ) + state.current_item = None + state.current_segment = None + state.mode = "segment-start" + buffer = remainder + continue + return False, deltas + if self._stream_plan["kind"] == "json-root": + buffer = self._stream_state.buffer + text + try: + parsed = self.parse_chat_response(buffer, partial=True) + except CompletionResponseParsingError: + return False, deltas + self._stream_state.buffer = buffer + self._stream_state.parsed = parsed + return True, deltas + + state = self._stream_state + plan = self._stream_plan + buffer = state.pending + text + state.pending = "" + parsed = cast(Dict[str, Any], state.parsed) + iterator_start = plan["iterator"]["start"] + iterator_end = plan["iterator"]["end"] + while True: + mode = state.mode + if mode == "assistant-prefix": + assistant_prefix = plan.get("assistant_prefix") + if not assistant_prefix: + state.mode = "prelude" + continue + if buffer.startswith(assistant_prefix): + buffer = buffer[len(assistant_prefix) :] + state.mode = "prelude" + continue + if assistant_prefix.startswith(buffer): + state.pending = buffer + return True, deltas + state.mode = "prelude" + continue + if mode == "prelude": + leading_capture = plan.get("leading_capture") + if leading_capture is not None: + capture_start = leading_capture["start"] + if buffer.startswith(capture_start): + buffer = buffer[len(capture_start) :] + state.mode = "leading-capture" + continue + if capture_start.startswith(buffer): + state.pending = buffer + return True, deltas + if leading_capture.get("implicit_at_start"): + capture_end = leading_capture["end"] + tool_index = buffer.find(iterator_start) + end_index = buffer.find(capture_end) + overlap = self._literal_suffix_prefix_length(buffer, capture_end) + if ( + end_index >= 0 + and (tool_index < 0 or end_index < tool_index) + ) or overlap: + state.mode = "leading-capture" + continue + state.mode = "content" + continue + if mode == "leading-capture": + leading_capture = cast(Dict[str, Any], plan["leading_capture"]) + segment, matched, remainder, pending = self._consume_until_literal( + buffer, + leading_capture["end"], + ) + self._append_parsed_text(parsed, leading_capture["field"], segment) + if segment: + deltas.append({leading_capture["field"]: segment}) + if not matched: + state.pending = pending + return True, deltas + buffer = remainder.lstrip() if leading_capture.get("strip_after") else remainder + state.mode = "content" + continue + if mode == "content": + content_field = plan.get("content_field") + end_markers = cast(List[str], plan["content_end_markers"]) + segment, matched_marker, remainder, pending = self._consume_until_any_literal( + buffer, + end_markers, + ) + if content_field is not None: + content_segment = ( + segment.rstrip() + if matched_marker == iterator_start and plan.get("trim_before_iterator") + else segment + ) + self._append_parsed_text(parsed, content_field, content_segment) + if content_segment: + deltas.append({content_field: content_segment}) + if matched_marker is None: + state.pending = pending + return True, deltas + if matched_marker == iterator_start: + item_state = self._new_tool_call_state(plan["iterator"]["item"]) + state.saw_tool_calls = True + if item_state["kind"] == "tagged-parameters": + tool_calls = cast(List[Dict[str, Any]], parsed.setdefault("tool_calls", [])) + tool_calls.append(item_state["tool_call"]) + item_state["tool_call_index"] = len(tool_calls) - 1 + state.current_item = item_state + state.mode = "tool-item" + buffer = remainder + continue + state.done = True + state.pending = "" + return True, deltas + if mode == "tool-item": + current_item_state: Optional[Dict[str, Any]] = state.current_item + if not isinstance(current_item_state, dict): + return False, deltas + item_text, matched, remainder, pending = self._consume_until_literal( + buffer, + iterator_end, + ) + if item_text: + success, item_deltas = self._advance_tool_call_state( + current_item_state, + item_text, + plan["iterator"]["item"], + ) + if not success: + return False, deltas + deltas.extend(item_deltas) + if not matched: + state.pending = pending + return True, deltas + tool_call = self._finish_tool_call_state( + current_item_state, + plan["iterator"]["item"], + ) + if tool_call is None: + return False, deltas + if current_item_state["kind"] == "buffered": + parsed.setdefault("tool_calls", []).append(tool_call) + state.current_item = None + buffer = remainder.lstrip() + state.mode = "after-tool-item" + continue + if mode == "after-tool-item": + end_markers = [ + iterator_start, + *[ + marker + for marker in cast(List[str], plan["content_end_markers"]) + if marker != iterator_start + ], + ] + if not buffer: + state.pending = "" + return True, deltas + if buffer.startswith(iterator_start): + item_state = self._new_tool_call_state(plan["iterator"]["item"]) + state.saw_tool_calls = True + if item_state["kind"] == "tagged-parameters": + tool_calls = cast(List[Dict[str, Any]], parsed.setdefault("tool_calls", [])) + tool_calls.append(item_state["tool_call"]) + item_state["tool_call_index"] = len(tool_calls) - 1 + state.current_item = item_state + state.mode = "tool-item" + buffer = buffer[len(iterator_start) :] + continue + for marker in end_markers[1:]: + if buffer.startswith(marker): + state.done = True + state.pending = "" + return True, deltas + if any(marker.startswith(buffer) for marker in end_markers): + state.pending = buffer + return True, deltas + if buffer.isspace(): + state.pending = buffer + return True, deltas + return False, deltas + + @staticmethod + def _partial_regex_key_value_item( + pattern: str, + text: str, + *, + min_start: int, + ) -> Optional[Tuple[str, str]]: + value_group = "(?P<value>" + value_group_start = pattern.find(value_group) + if value_group_start < 0: + return None + partial_pattern = pattern[:value_group_start] + r"(?P<value>.*)\Z" + partial_match: Optional[re.Match[str]] = None + for match in re.finditer(partial_pattern, text, re.S): + if match.start() < min_start: + continue + partial_match = match + if partial_match is None: + return None + group_dict = partial_match.groupdict() + key = group_dict.get("key") + value = group_dict.get("value") + if key is None or value is None: + return None + value_pattern = "(?P<value>.*?)" + if value_pattern in pattern: + suffix_literal = ResponseParser._regex_literal_prefix( + pattern.split(value_pattern, 1)[1] + ) + for suffix_length in range(len(suffix_literal), 0, -1): + suffix_prefix = suffix_literal[:suffix_length] + if value.endswith(suffix_prefix): + value = value[:-suffix_length] + break + return key, value + + def _trim_partial_tool_call_prefix( + self, + *, + response_text: str, + parsed: Dict[str, Any], + ) -> None: + if not isinstance(parsed.get("content"), str): + return + tool_calls_schema = self._schema.get("properties", {}).get("tool_calls") + if not isinstance(tool_calls_schema, dict): + return + iterator_pattern = tool_calls_schema.get("x-regex-iterator") + if not isinstance(iterator_pattern, str) or "(.*?)" not in iterator_pattern: + return + prefix_pattern = iterator_pattern.split("(.*?)", 1)[0] + literal_prefix = self._regex_literal_prefix(prefix_pattern) + if not literal_prefix: + return + content = cast(str, parsed["content"]) + for prefix_length in range(len(literal_prefix) - 1, 0, -1): + prefix = literal_prefix[:prefix_length] + if content.endswith(prefix) and response_text.endswith(prefix): + trimmed = content[:-prefix_length] + parsed["content"] = trimmed if trimmed else None + break + + def _parse_response_value( + self, + text: Any, + schema: Dict[str, Any], + *, + partial: bool, + ) -> Any: + if "const" in schema: + return schema["const"] + if text is None: + return None + node_content: Any = text + node_regex = schema.get("x-regex") + if node_regex is not None: + if not isinstance(node_content, str): + raise CompletionResponseParsingError( + "response_schema x-regex requires string input" + ) + captured_content = self._regex_capture(node_content, node_regex) + if captured_content is None: + if ( + partial + and schema.get("type") == "object" + and "x-regex-key-value" in schema + ): + captured_content = node_content + else: + return None + node_content = captured_content + node_regex_iterator = schema.get("x-regex-iterator") + if node_regex_iterator is not None: + if schema.get("type") != "array": + raise CompletionResponseParsingError( + "response_schema x-regex-iterator requires array type" + ) + if not isinstance(node_content, str): + raise CompletionResponseParsingError( + "response_schema x-regex-iterator requires string input" + ) + array_values = [] + matches = list(re.finditer(node_regex_iterator, node_content, re.S)) + for match in matches: + item_text = self._regex_capture(match.group(0), node_regex_iterator) + if item_text is not None: + array_values.append(item_text) + if partial and "(.*?)" in node_regex_iterator: + prefix_pattern, suffix_pattern = node_regex_iterator.split("(.*?)", 1) + prefix_matches = list(re.finditer(prefix_pattern, node_content, re.S)) + if prefix_matches: + last_prefix_match = prefix_matches[-1] + if not matches or matches[-1].start() != last_prefix_match.start(): + tail = node_content[last_prefix_match.end() :] + if re.search(suffix_pattern, tail, re.S) is None: + array_values.append(tail) + if not array_values: + return None + node_content = array_values + node_regex_key_value = schema.get("x-regex-key-value") + if node_regex_key_value is not None: + if schema.get("type") != "object": + raise CompletionResponseParsingError( + "response_schema x-regex-key-value requires object type" + ) + if not isinstance(node_content, str): + raise CompletionResponseParsingError( + "response_schema x-regex-key-value requires string input" + ) + key_values: Dict[str, str] = {} + matches = list(re.finditer(node_regex_key_value, node_content, re.S)) + for match in matches: + group_dict = match.groupdict() + if "key" not in group_dict or "value" not in group_dict: + raise CompletionResponseParsingError( + "response_schema x-regex-key-value must define key and value groups" + ) + key = group_dict["key"] + value = group_dict["value"] + if key is None or value is None: + raise CompletionResponseParsingError( + "response_schema x-regex-key-value matched null key or value" + ) + key_values[key] = value + if partial: + min_start = matches[-1].end() if matches else 0 + partial_item = self._partial_regex_key_value_item( + node_regex_key_value, + node_content, + min_start=min_start, + ) + if partial_item is not None: + key_values[partial_item[0]] = partial_item[1] + if not key_values: + return None + node_content = key_values + parser_name = schema.get("x-parser") + if parser_name is not None: + if parser_name != "json": + if parser_name != "gemma4-tool-call": + raise CompletionResponseParsingError( + f"unsupported response_schema x-parser: {parser_name}" + ) + if not isinstance(node_content, str): + raise CompletionResponseParsingError( + "response_schema x-parser='gemma4-tool-call' requires string input" + ) + node_content = self._gemma4_tool_call_to_json(node_content) + parser_name = "json" + if parser_name == "json": + if not isinstance(node_content, str): + raise CompletionResponseParsingError( + "response_schema x-parser='json' requires string input" + ) + try: + parsed = from_json(node_content, allow_partial=partial) + except ValueError as exc: + if ( + self._has_text_tools() + and schema.get("type") == "object" + and schema.get("additionalProperties") is True + and not schema.get("properties") + ): + return node_content + if partial: + return None + raise CompletionResponseParsingError( + "response did not match response_schema JSON parser" + ) from exc + stripped_schema = { + key: value + for key, value in schema.items() + if key + not in { + "x-parser", + "x-parser-args", + "x-regex", + "x-regex-iterator", + "x-regex-key-value", + } + } + return self._parse_response_value( + parsed, stripped_schema, partial=partial + ) + schema_type = schema.get("type") + if schema_type == "string": + return node_content + if schema_type == "array": + if isinstance(node_content, list): + array_values = [] + item_schema = schema.get("items", {}) + for item in node_content: + parsed_item = self._parse_response_value( + item, + item_schema, + partial=partial, + ) + if parsed_item is not None: + array_values.append(parsed_item) + return array_values + return [] + if schema_type == "object": + properties = schema.get("properties", {}) + if isinstance(node_content, dict): + parsed_object: Dict[str, Any] = {} + for key, value_schema in properties.items(): + value = self._parse_response_value( + node_content.get(key), + value_schema, + partial=partial, + ) + if value is None: + continue + if isinstance(value, list) and not value: + continue + parsed_object[key] = value + additional_properties = schema.get("additionalProperties") + if additional_properties is True: + for key, value in node_content.items(): + if key not in parsed_object and key not in properties: + parsed_object[key] = value + elif isinstance(additional_properties, dict): + for key, value in node_content.items(): + if key in parsed_object or key in properties: + continue + parsed_value = self._parse_response_value( + value, + additional_properties, + partial=partial, + ) + if parsed_value is not None: + parsed_object[key] = parsed_value + if not partial: + missing = [ + key + for key in schema.get("required", []) + if key not in parsed_object + ] + if missing: + raise CompletionResponseParsingError( + f"response did not match response_schema: missing {', '.join(missing)}" + ) + return parsed_object + parsed_object_from_text: Dict[str, Any] = {} + for key, value_schema in properties.items(): + value = self._parse_response_value( + node_content, value_schema, partial=partial + ) + if value is None: + continue + if isinstance(value, list) and not value: + continue + parsed_object_from_text[key] = value + if not partial: + missing = [ + key + for key in schema.get("required", []) + if key not in parsed_object_from_text + ] + if missing: + raise CompletionResponseParsingError( + f"response did not match response_schema: missing {', '.join(missing)}" + ) + return parsed_object_from_text + if schema_type == "integer": + if isinstance(node_content, int) and not isinstance(node_content, bool): + return node_content + if partial and isinstance(node_content, str) and not node_content: + return None + try: + return int(node_content) + except (TypeError, ValueError): + return None + if schema_type == "number": + if isinstance(node_content, (int, float)) and not isinstance( + node_content, bool + ): + return node_content + if partial and isinstance(node_content, str) and not node_content: + return None + try: + return float(node_content) + except (TypeError, ValueError): + return None + if schema_type == "boolean": + if isinstance(node_content, bool): + return node_content + if node_content in {"true", "True", 1, "1"}: + return True + if node_content in {"false", "False", 0, "0"}: + return False + return None + one_of = schema.get("oneOf") + if isinstance(one_of, list): + for option in one_of: + value = self._parse_response_value( + node_content, option, partial=partial + ) + if value is not None: + return value + return None + if schema_type is None or schema_type == "any": + return node_content + return None + + def _coerce_tool_argument( + self, + raw_value: str, + schema: Dict[str, Any], + *, + tool_name: str, + argument_name: str, + ) -> Any: + if "oneOf" in schema: + last_error: Optional[BaseException] = None + for variant in schema["oneOf"]: + try: + return self._coerce_tool_argument( + raw_value, + variant, + tool_name=tool_name, + argument_name=argument_name, + ) + except BaseException as exc: + last_error = exc + raise CompletionResponseParsingError( + f"tool '{tool_name}' argument '{argument_name}' did not match any allowed schema" + ) from last_error + schema_type = schema.get("type") + if isinstance(schema_type, list): + last_type_error: Optional[BaseException] = None + for variant_type in schema_type: + try: + return self._coerce_tool_argument( + raw_value, + {**schema, "type": variant_type}, + tool_name=tool_name, + argument_name=argument_name, + ) + except BaseException as exc: + last_type_error = exc + raise CompletionResponseParsingError( + f"tool '{tool_name}' argument '{argument_name}' did not match any allowed type" + ) from last_type_error + if schema_type in {None, "string"}: + return raw_value + try: + decoded = json.loads(raw_value) + except json.JSONDecodeError as exc: + raise CompletionResponseParsingError( + f"tool '{tool_name}' argument '{argument_name}' is not valid JSON for type '{schema_type}'" + ) from exc + if schema_type == "integer": + if isinstance(decoded, bool) or not isinstance(decoded, int): + raise CompletionResponseParsingError( + f"tool '{tool_name}' argument '{argument_name}' must be an integer" + ) + return decoded + if schema_type == "number": + if isinstance(decoded, bool) or not isinstance(decoded, (int, float)): + raise CompletionResponseParsingError( + f"tool '{tool_name}' argument '{argument_name}' must be a number" + ) + return decoded + if schema_type == "boolean": + if not isinstance(decoded, bool): + raise CompletionResponseParsingError( + f"tool '{tool_name}' argument '{argument_name}' must be a boolean" + ) + return decoded + if schema_type == "object": + if not isinstance(decoded, dict): + raise CompletionResponseParsingError( + f"tool '{tool_name}' argument '{argument_name}' must be an object" + ) + return decoded + if schema_type == "array": + if not isinstance(decoded, list): + raise CompletionResponseParsingError( + f"tool '{tool_name}' argument '{argument_name}' must be an array" + ) + return decoded + return decoded + + def _coerce_tool_arguments( + self, + tool_name: str, + arguments: Dict[str, str], + *, + partial: bool, + ) -> Dict[str, Any]: + if self._tool_content_type(tool_name) == "text": + raw_input = arguments.get("input", "") + if not isinstance(raw_input, str): + raw_input = str(raw_input) + return {"input": raw_input} + if self._tools is None: + raise CompletionResponseParsingError( + f"response included tool call '{tool_name}' but the request declared no tools" + ) + tool = next( + ( + candidate + for candidate in self._tools + if candidate.get("type") == "function" + and candidate.get("function", {}).get("name") == tool_name + ), + None, + ) + if tool is None: + raise CompletionResponseParsingError( + f"response included undeclared tool call '{tool_name}'" + ) + parameters = tool.get("function", {}).get("parameters") or { + "type": "object", + "properties": {}, + "required": [], + } + properties = parameters.get("properties", {}) + coerced: Dict[str, Any] = {} + for argument_name, raw_value in arguments.items(): + if argument_name not in properties: + raise CompletionResponseParsingError( + f"tool '{tool_name}' returned unexpected argument '{argument_name}'" + ) + coerced[argument_name] = self._coerce_tool_argument( + raw_value, + properties[argument_name], + tool_name=tool_name, + argument_name=argument_name, + ) + if not partial: + missing = [ + argument_name + for argument_name in parameters.get("required", []) + if argument_name not in coerced + ] + if missing: + raise CompletionResponseParsingError( + f"tool '{tool_name}' response is missing required arguments: {', '.join(missing)}" + ) + return coerced + + def parse_chat_response( + self, + response_text: str, + *, + partial: bool, + ) -> Dict[str, Any]: + full_response_text = self._generation_prompt + response_text + parsed = self._parse_response_value( + full_response_text, + self._schema, + partial=partial, + ) + if not isinstance(parsed, dict): + raise CompletionResponseParsingError("response_schema must produce an object") + if partial: + self._trim_partial_tool_call_prefix( + response_text=full_response_text, + parsed=parsed, + ) + tool_calls = parsed.get("tool_calls") + if isinstance(tool_calls, list): + normalized_tool_calls: List[Dict[str, Any]] = [] + for tool_call in tool_calls: + normalized = self._normalize_tool_call_item(tool_call, partial=partial) + if normalized is None: + continue + normalized_tool_calls.append(normalized) + if normalized_tool_calls: + parsed["tool_calls"] = normalized_tool_calls + else: + parsed.pop("tool_calls", None) + for field in ("reasoning_content", "thinking"): + value = parsed.get(field) + if isinstance(value, str) and not value.strip(): + parsed.pop(field, None) + return parsed + + def _normalize_tool_call_item( + self, + tool_call: Any, + *, + partial: bool, + ) -> Optional[Dict[str, Any]]: + if not isinstance(tool_call, dict): + if partial: + return None + raise CompletionResponseParsingError("tool_calls items must be objects") + function = tool_call.get("function") + if not isinstance(function, dict): + if partial: + return None + raise CompletionResponseParsingError( + "tool_calls items must define a function object" + ) + tool_name = function.get("name") + if not isinstance(tool_name, str) or not tool_name: + if partial: + return None + raise CompletionResponseParsingError( + "tool_calls function name must be a non-empty string" + ) + if self._tool_content_type(tool_name) == "text": + arguments = function.get("arguments", "") + if not isinstance(arguments, str): + if partial: + return None + raise CompletionResponseParsingError( + "tool_calls function arguments must be a string for text tools" + ) + return { + "type": tool_call.get("type", "function"), + "function": { + "name": tool_name, + "arguments": arguments, + }, + } + arguments = function.get("arguments", {}) + if not isinstance(arguments, (dict, ResponseParser.PartialJsonObject)): + if partial: + return None + raise CompletionResponseParsingError( + "tool_calls function arguments must parse to an object" + ) + argument_values = ( + arguments.value + if isinstance(arguments, ResponseParser.PartialJsonObject) + else arguments + ) + raw_arguments: Dict[str, str] = {} + for argument_name, argument_value in argument_values.items(): + if isinstance(argument_value, ResponseParser.PartialJsonValue): + raw_arguments[argument_name] = argument_value.text + else: + raw_arguments[argument_name] = str(argument_value) + normalized_arguments = ( + arguments + if isinstance(arguments, ResponseParser.PartialJsonObject) + or any( + isinstance(value, ResponseParser.PartialJsonValue) + for value in argument_values.values() + ) + else self._coerce_tool_arguments( + tool_name, + raw_arguments, + partial=partial, + ) + ) + return { + "type": tool_call.get("type", "function"), + "function": { + "name": tool_name, + "arguments": normalized_arguments, + }, + } + + @classmethod + def _serialize_partial_json_prefix(cls, value: Any) -> str: + if isinstance(value, ResponseParser.PartialJsonValue): + if value.complete and value.schema_type in {None, "string"}: + return json.dumps(value.text, ensure_ascii=False, separators=(",", ":")) + if value.schema_type in {None, "string"}: + return json.dumps(value.text, ensure_ascii=False, separators=(",", ":"))[:-1] + return value.text + if isinstance(value, ResponseParser.PartialJsonObject): + return cls._serialize_partial_json_prefix(value.value) + if isinstance(value, dict): + items = list(value.items()) + if not items: + return "{" + rendered = ["{"] + last_index = len(items) - 1 + for index, (key, item_value) in enumerate(items): + if index > 0: + rendered.append(",") + rendered.append(json.dumps(key, ensure_ascii=False, separators=(",", ":"))) + rendered.append(":") + if index == last_index: + rendered.append(cls._serialize_partial_json_prefix(item_value)) + else: + rendered.append( + json.dumps(item_value, ensure_ascii=False, separators=(",", ":")) + ) + return "".join(rendered) + if isinstance(value, list): + if not value: + return "[" + rendered = ["["] + last_index = len(value) - 1 + for index, item_value in enumerate(value): + if index > 0: + rendered.append(",") + if index == last_index: + rendered.append(cls._serialize_partial_json_prefix(item_value)) + else: + rendered.append( + json.dumps(item_value, ensure_ascii=False, separators=(",", ":")) + ) + return "".join(rendered) + if isinstance(value, str): + return json.dumps(value, ensure_ascii=False, separators=(",", ":"))[:-1] + return json.dumps(value, ensure_ascii=False, separators=(",", ":")) + + @classmethod + def _contains_partial_json_value(cls, value: Any) -> bool: + if isinstance(value, ResponseParser.PartialJsonValue): + return True + if isinstance(value, ResponseParser.PartialJsonObject): + return True + if isinstance(value, dict): + return any(cls._contains_partial_json_value(item) for item in value.values()) + if isinstance(value, list): + return any(cls._contains_partial_json_value(item) for item in value) + return False + + @classmethod + def _serialize_partial_json_state(cls, value: Any) -> Tuple[str, bool]: + if isinstance(value, ResponseParser.PartialJsonValue): + if value.schema_type in {None, "string"}: + rendered = json.dumps(value.text, ensure_ascii=False, separators=(",", ":")) + if value.complete: + return rendered, True + return rendered[:-1], False + return value.text, value.complete + if isinstance(value, ResponseParser.PartialJsonObject): + rendered, children_complete = cls._serialize_partial_json_state(value.value) + if value.complete and children_complete: + return rendered, True + if rendered.endswith("}"): + return rendered[:-1], False + return rendered, False + if isinstance(value, dict): + parts = ["{"] + items = list(value.items()) + for index, (key, item_value) in enumerate(items): + if index > 0: + parts.append(",") + parts.append(json.dumps(key, ensure_ascii=False, separators=(",", ":"))) + parts.append(":") + rendered_item, item_complete = cls._serialize_partial_json_state(item_value) + parts.append(rendered_item) + if index == len(items) - 1 and not item_complete: + return "".join(parts), False + parts.append("}") + return "".join(parts), True + if isinstance(value, list): + parts = ["["] + for index, item_value in enumerate(value): + if index > 0: + parts.append(",") + rendered_item, item_complete = cls._serialize_partial_json_state(item_value) + parts.append(rendered_item) + if index == len(value) - 1 and not item_complete: + return "".join(parts), False + parts.append("]") + return "".join(parts), True + return json.dumps(value, ensure_ascii=False, separators=(",", ":")), True + + @classmethod + def _serialize_tool_arguments(cls, arguments: Any, *, partial: bool = False) -> str: + if partial: + if cls._contains_partial_json_value(arguments): + return cls._serialize_partial_json_state(arguments)[0] + return cls._serialize_partial_json_prefix(arguments) + if isinstance(arguments, ResponseParser.PartialJsonObject): + arguments = arguments.value + return json.dumps(arguments, ensure_ascii=False, separators=(",", ":")) + + def _parsed_chat_message( + self, + *, + parsed: Dict[str, Any], + partial: bool = False, + ) -> Dict[str, Any]: + message: Dict[str, Any] = { + "role": parsed.get("role", "assistant"), + } + for key, value in parsed.items(): + if key in {"role", "content", "tool_calls"}: + continue + if value is None or value == "": + continue + message[key] = value + content = parsed.get("content") + tool_calls = parsed.get("tool_calls") + if isinstance(tool_calls, list) and tool_calls: + normalized_tool_calls = [] + for tool_call_index, tool_call in enumerate(tool_calls): + function = tool_call["function"] + if self._tool_content_type(function["name"]) == "text": + raw_arguments = function["arguments"] + arguments = ( + raw_arguments + if isinstance(raw_arguments, str) + else str(raw_arguments) + ) + else: + arguments = self._serialize_tool_arguments( + function["arguments"], + partial=partial, + ) + normalized_tool_calls.append( + { + "id": f"call_{self._choice_index}_{function['name']}_{self._completion_id}_{tool_call_index}", + "type": tool_call.get("type", "function"), + "function": { + "name": function["name"], + "arguments": arguments, + }, + } + ) + message["content"] = content if content not in {None, ""} else None + message["tool_calls"] = normalized_tool_calls + if len(normalized_tool_calls) == 1: + message["function_call"] = dict(normalized_tool_calls[0]["function"]) + return message + message["content"] = content if content is not None else "" + return message + + def _message_deltas( + self, + previous_message: Dict[str, Any], + message: Dict[str, Any], + ) -> List[Dict[str, Any]]: + deltas: List[Dict[str, Any]] = [] + for key, value in message.items(): + if key in {"role", "content", "tool_calls", "function_call"}: + continue + old_value = previous_message.get(key, "") + if not isinstance(value, str): + if key not in previous_message and value is not None: + deltas.append({key: value}) + continue + if not value: + continue + if isinstance(old_value, str) and value.startswith(old_value): + delta_value = value[len(old_value) :] + else: + delta_value = value + if delta_value: + deltas.append({key: delta_value}) + + new_content = message.get("content") + old_content = previous_message.get("content", "") + if isinstance(new_content, str) and new_content: + if isinstance(old_content, str) and new_content.startswith(old_content): + content_delta = new_content[len(old_content) :] + else: + content_delta = new_content + if content_delta: + deltas.append({"content": content_delta}) + + new_tool_calls = cast(List[Dict[str, Any]], message.get("tool_calls", [])) + old_tool_calls = cast(List[Dict[str, Any]], previous_message.get("tool_calls", [])) + for tool_call_index, tool_call in enumerate(new_tool_calls): + old_tool_call = old_tool_calls[tool_call_index] if tool_call_index < len(old_tool_calls) else None + delta_tool_call: Dict[str, Any] = {"index": tool_call_index} + if old_tool_call is None: + delta_tool_call["id"] = tool_call["id"] + delta_tool_call["type"] = tool_call["type"] + function = cast(Dict[str, Any], tool_call["function"]) + old_function = ( + cast(Dict[str, Any], old_tool_call["function"]) + if old_tool_call is not None + else {} + ) + function_delta: Dict[str, Any] = {} + if function.get("name") and function.get("name") != old_function.get("name"): + function_delta["name"] = function["name"] + arguments = cast(str, function.get("arguments", "")) + old_arguments = cast(str, old_function.get("arguments", "")) + if old_tool_call is None and arguments == "{}": + argument_delta = "" + elif arguments.startswith(old_arguments): + argument_delta = arguments[len(old_arguments) :] + else: + argument_delta = arguments + if argument_delta: + function_delta["arguments"] = argument_delta + if function_delta: + delta_tool_call["function"] = function_delta + if len(delta_tool_call) > 1: + deltas.append({"tool_calls": [delta_tool_call]}) + return deltas + + @staticmethod + def _apply_message_delta(message: Dict[str, Any], delta: Dict[str, Any]) -> None: + if "role" in delta: + message["role"] = delta["role"] + for key, value in delta.items(): + if key in {"role", "tool_calls", "function_call"}: + continue + if isinstance(value, str): + existing = message.get(key) + if isinstance(existing, str): + message[key] = existing + value + else: + message[key] = value + else: + message[key] = value + tool_call_deltas = delta.get("tool_calls") + if not isinstance(tool_call_deltas, list): + return + tool_calls = cast(List[Dict[str, Any]], message.setdefault("tool_calls", [])) + for tool_delta in tool_call_deltas: + if not isinstance(tool_delta, dict): + continue + index = tool_delta.get("index") + if not isinstance(index, int): + continue + while len(tool_calls) <= index: + tool_calls.append({"function": {"name": "", "arguments": ""}}) + tool_call = tool_calls[index] + if "id" in tool_delta: + tool_call["id"] = tool_delta["id"] + if "type" in tool_delta: + tool_call["type"] = tool_delta["type"] + function_delta = tool_delta.get("function") + if not isinstance(function_delta, dict): + continue + function = cast(Dict[str, Any], tool_call.setdefault("function", {})) + name_delta = function_delta.get("name") + if isinstance(name_delta, str): + function["name"] = cast(str, function.get("name", "")) + name_delta + arguments_delta = function_delta.get("arguments") + if isinstance(arguments_delta, str): + function["arguments"] = cast(str, function.get("arguments", "")) + arguments_delta + if tool_calls: + message["function_call"] = dict(cast(Dict[str, Any], tool_calls[0]["function"])) + + def parse_completion_message(self, response_text: str) -> Dict[str, Any]: + parsed = self.parse_chat_response(response_text, partial=False) + return self._parsed_chat_message(parsed=parsed) + + def _stream_state_message(self, *, partial: bool) -> Dict[str, Any]: + assert self._stream_state is not None + if self._stream_plan is not None and self._stream_plan.get("direct_deltas"): + copied = { + key: ( + [ + { + child_key: ( + dict(child_value) + if isinstance(child_value, dict) + else child_value + ) + for child_key, child_value in tool_call.items() + } + for tool_call in cast(List[Dict[str, Any]], value) + ] + if key == "tool_calls" and isinstance(value, list) + else value + ) + for key, value in self._message.items() + } + if copied.get("tool_calls"): + copied["function_call"] = dict( + cast(List[Dict[str, Any]], copied["tool_calls"])[0]["function"] + ) + return copied + parsed = cast(Dict[str, Any], self._stream_state.parsed) + return self._parsed_chat_message(parsed=parsed, partial=partial) + + def _stream_state_complete(self) -> bool: + if self._stream_plan is None: + return False + if self._direct.deltas: + return ( + not self._direct.pending + and self._direct.mode in {self.DIRECT_MODE_CONTENT, self.DIRECT_MODE_AFTER_TOOL_ITEM} + ) + if self._stream_state is None: + return False + if self._stream_plan["kind"] == "json-root": + return True + if self._stream_plan["kind"] == "segment-message": + return ( + not self._stream_state.pending + and self._stream_state.current_item is None + and self._stream_state.current_segment is None + and self._stream_state.mode == "segment-start" + ) + return ( + not self._stream_state.pending + and self._stream_state.current_item is None + and self._stream_state.mode in {"content", "after-tool-item"} + ) + + @staticmethod + def _chat_chunk_payload( + *, + chunk_id: str, + created: int, + model: str, + index: int, + delta: Dict[str, Any], + finish_reason: Optional[str], + logprobs: Optional[Dict[str, Any]] = None, + ) -> Dict[str, Any]: + choice: Dict[str, Any] = { + "index": index, + "delta": delta, + "finish_reason": finish_reason, + } + if logprobs is not None: + choice["logprobs"] = logprobs + return { + "id": "chat" + chunk_id, + "object": "chat.completion.chunk", + "created": created, + "model": model, + "choices": [choice], + } + + def _chunk_payloads( + self, + *, + chunk_id: str, + created: int, + model: str, + deltas: List[Dict[str, Any]], + finish_reason: Optional[str], + logprobs: Optional[Dict[str, Any]], + leading_delta: Optional[Dict[str, Any]] = None, + ) -> List[Dict[str, Any]]: + payloads: List[Dict[str, Any]] = [] + logprobs_sent = False + chat_chunk_id = "chat" + chunk_id + index = self._choice_index + if leading_delta is not None: + payloads.append( + { + "id": chat_chunk_id, + "object": "chat.completion.chunk", + "created": created, + "model": model, + "choices": [ + { + "index": index, + "delta": leading_delta, + "finish_reason": None, + } + ], + } + ) + for delta in deltas: + payload_logprobs = None + if ( + logprobs is not None + and delta.get("role") != "assistant" + and not logprobs_sent + ): + payload_logprobs = logprobs + logprobs_sent = True + choice: Dict[str, Any] = { + "index": index, + "delta": delta, + "finish_reason": None, + } + if payload_logprobs is not None: + choice["logprobs"] = payload_logprobs + payloads.append( + { + "id": chat_chunk_id, + "object": "chat.completion.chunk", + "created": created, + "model": model, + "choices": [choice], + } + ) + if finish_reason is not None: + payloads.append( + { + "id": chat_chunk_id, + "object": "chat.completion.chunk", + "created": created, + "model": model, + "choices": [ + { + "index": index, + "delta": {}, + "finish_reason": finish_reason, + } + ], + } + ) + return payloads + + def consume_completion_chunk( + self, + text: str, + *, + chunk_id: str, + created: int, + model: str, + finish_reason: Optional[str], + logprobs: Optional[Dict[str, Any]] = None, + ) -> List[Dict[str, Any]]: + role_delta: Optional[Dict[str, Any]] = None + if not self._started: + self._started = True + self._message["role"] = "assistant" + role_delta = {"role": "assistant"} + self._text_parts.append(text) + + if self._stream_plan is not None and not self._stream_failed: + success, stream_deltas = self._advance_stream_state(text) + if success: + if self._direct.deltas: + if finish_reason is None: + return self._chunk_payloads( + chunk_id=chunk_id, + created=created, + model=model, + deltas=stream_deltas, + finish_reason=None, + logprobs=logprobs, + leading_delta=role_delta, + ) + if self._stream_state_complete(): + return self._chunk_payloads( + chunk_id=chunk_id, + created=created, + model=model, + deltas=stream_deltas, + finish_reason=( + "tool_calls" if self._direct.saw_tool_calls else finish_reason + ), + logprobs=logprobs, + leading_delta=role_delta, + ) + self._stream_failed = True + elif self._stream_plan["kind"] == "segment-message": + if role_delta is not None: + stream_deltas = [role_delta, *stream_deltas] + for delta in stream_deltas: + self._apply_message_delta(self._message, delta) + if finish_reason is None: + return self._chunk_payloads( + chunk_id=chunk_id, + created=created, + model=model, + deltas=stream_deltas, + finish_reason=None, + logprobs=logprobs, + ) + if self._stream_state_complete(): + return self._chunk_payloads( + chunk_id=chunk_id, + created=created, + model=model, + deltas=stream_deltas, + finish_reason=( + "tool_calls" if self._message.get("tool_calls") else finish_reason + ), + logprobs=logprobs, + ) + self._stream_failed = True + else: + previous_message = self._message + partial_deltas: List[Dict[str, Any]] = [] + assert self._stream_state is not None + parsed = cast(Dict[str, Any], self._stream_state.parsed) + message = self._parsed_chat_message( + parsed=parsed, + partial=finish_reason is None, + ) + if finish_reason is None: + if role_delta is not None: + partial_deltas.append(role_delta) + partial_deltas.extend(self._message_deltas(previous_message, message)) + self._message = message + return self._chunk_payloads( + chunk_id=chunk_id, + created=created, + model=model, + deltas=partial_deltas, + finish_reason=None, + logprobs=logprobs, + ) + if self._stream_state_complete(): + if role_delta is not None: + partial_deltas.append(role_delta) + partial_deltas.extend(self._message_deltas(previous_message, message)) + self._message = message + return self._chunk_payloads( + chunk_id=chunk_id, + created=created, + model=model, + deltas=partial_deltas, + finish_reason=( + "tool_calls" if message.get("tool_calls") else finish_reason + ), + logprobs=logprobs, + ) + self._stream_failed = True + else: + self._stream_failed = True + + response_text = "".join(self._text_parts) + parsed = self.parse_chat_response(response_text, partial=finish_reason is None) + message = self._parsed_chat_message(parsed=parsed, partial=finish_reason is None) + previous_message = self._message + deltas: List[Dict[str, Any]] = [] + if role_delta is not None: + deltas.append(role_delta) + deltas.extend(self._message_deltas(previous_message, message)) + self._message = message + return self._chunk_payloads( + chunk_id=chunk_id, + created=created, + model=model, + deltas=deltas, + finish_reason=( + "tool_calls" + if finish_reason is not None and message.get("tool_calls") + else finish_reason + ), + logprobs=logprobs, + ) + + +class OpenAIFormatter: + @dataclass + class ReturnedToken: + index: int + text_bytes: bytes + token: Token + text_offset: int + + @dataclass + class ResponsesOutputItem: + output_index: int + item: Dict[str, Any] + content_index: Optional[int] = None + + @dataclass + class ResponsesStream: + body: CreateResponseRequest + response_id: str + created_at: float + model: str + output: List[Dict[str, Any]] = field(default_factory=list) + sequence_number: int = 0 + started: bool = False + assistant_meta: Dict[str, Any] = field(default_factory=dict) + reasoning_item: Optional["OpenAIFormatter.ResponsesOutputItem"] = None + message_item: Optional["OpenAIFormatter.ResponsesOutputItem"] = None + tool_items: Dict[int, "OpenAIFormatter.ResponsesOutputItem"] = field( + default_factory=dict + ) + final_status: Optional[str] = None + incomplete_details: Optional[Dict[str, Any]] = None + + def __init__(self, model: Model) -> None: + self.model = model + + @staticmethod + def decode_text(data: bytes) -> str: + return data.decode("utf-8", errors="ignore") + + @staticmethod + def encode_sse_payload(payload: BaseModel | Dict[str, Any]) -> bytes: + data = ( + payload.model_dump(mode="json", exclude_none=True) + if isinstance(payload, BaseModel) + else payload + ) + return ( + "data: " + f"{json.dumps(data, ensure_ascii=False, separators=(',', ':'))}\n\n" + ).encode("utf-8") + + @staticmethod + def next_stream_chunk( + stream: Iterator[CompletionChunk], + ) -> Tuple[bool, Optional[CompletionChunk]]: + try: + return False, next(stream) + except StopIteration: + return True, None + + @staticmethod + def next_stream_output( + stream: Iterator[CompletionChunk], + ) -> Tuple[bool, Optional[CompletionChunk], Optional[OpenAICompletion]]: + try: + return False, next(stream), None + except StopIteration as stop: + return True, None, cast(Optional[OpenAICompletion], stop.value) + + @staticmethod + def collect_completion(stream: Iterator[Any]) -> OpenAICompletion: + iterator = iter(stream) + while True: + try: + next(iterator) + except StopIteration as stop: + result = stop.value + if result is None: + raise RuntimeError("missing completion result") + return cast(OpenAICompletion, result) + + @staticmethod + def _tools_for_response_parser( + *, + functions: Optional[List[ChatTemplateFunctionDefinition]] = None, + tools: Optional[List[ChatTemplateTool]] = None, + ) -> Optional[List[ChatTemplateTool]]: + if functions is not None: + return [ + { + "type": "function", + "function": function, + } + for function in functions + ] + return tools + + def _chat_template_text(self) -> str: + if self.model.chat_formatter is None: + return "" + return cast(str, self.model.chat_formatter._template_text) + + def _uses_harmony_channels(self) -> bool: + template = self._chat_template_text() + return "<|channel|>" in template or "<|recipient|>" in template + + def _uses_reasoning_content(self) -> bool: + template = self._chat_template_text() + return "reasoning_content" in template or "<think>" in template + + @staticmethod + def _chat_message(data: Dict[str, Any]) -> ChatCompletionRequestMessage: + return ChatCompletionRequestMessage.model_validate(data) + + def _instructions_role(self) -> Literal["developer", "system"]: + return "developer" if "developer" in self._chat_template_text() else "system" + + @staticmethod + def _response_reasoning_effort( + body: CreateResponseRequest, + ) -> Optional[str]: + reasoning = body.reasoning + if reasoning is None: + return None + effort = reasoning.effort + if effort in {"low", "medium", "high"}: + return cast(str, effort) + return None + + @staticmethod + def _response_text_from_content(content: Any) -> str: + if isinstance(content, str): + return content + if not isinstance(content, list): + raise CompletionRequestValidationError("responses input content must be a string or list") + parts: List[str] = [] + for part in content: + if not isinstance(part, dict): + raise CompletionRequestValidationError("responses content parts must be objects") + part_type = part.get("type") + if part_type in {"input_text", "output_text", "reasoning_text", "summary_text", "text"}: + text = part.get("text") + if not isinstance(text, str): + raise CompletionRequestValidationError( + f"responses content part {part_type!r} requires string text" + ) + parts.append(text) + continue + raise CompletionRequestValidationError( + f"unsupported responses content part type: {part_type!r}" + ) + return "".join(parts) + + @staticmethod + def _response_chat_content_from_content( + content: Any, + ) -> Union[str, List[Dict[str, Any]]]: + if isinstance(content, str): + return content + if not isinstance(content, list): + raise CompletionRequestValidationError("responses input content must be a string or list") + parts: List[Dict[str, Any]] = [] + for part in content: + if not isinstance(part, dict): + raise CompletionRequestValidationError("responses content parts must be objects") + part_type = part.get("type") + if part_type in {"input_text", "output_text", "reasoning_text", "summary_text", "text"}: + text = part.get("text") + if not isinstance(text, str): + raise CompletionRequestValidationError( + f"responses content part {part_type!r} requires string text" + ) + parts.append({"type": "text", "text": text}) + continue + if part_type == "input_image": + image_url = part.get("image_url") + if not isinstance(image_url, str): + raise CompletionRequestValidationError( + "responses input_image content part requires image_url" + ) + parts.append({"type": "image_url", "image_url": {"url": image_url}}) + continue + if part_type == "input_audio": + input_audio = part.get("input_audio") + if isinstance(input_audio, dict): + data = input_audio.get("data") + audio_format = input_audio.get("format") + else: + data = part.get("data") + audio_format = part.get("format") + if not isinstance(data, str): + raise CompletionRequestValidationError( + "responses input_audio content part requires base64 data" + ) + if audio_format is not None and not isinstance(audio_format, str): + raise CompletionRequestValidationError( + "responses input_audio format must be a string" + ) + audio_part: Dict[str, Any] = {"data": data} + if audio_format is not None: + audio_part["format"] = audio_format + parts.append({"type": "input_audio", "input_audio": audio_part}) + continue + if part_type == "audio_url": + audio_url = part.get("audio_url") + if not isinstance(audio_url, str): + raise CompletionRequestValidationError( + "responses audio_url content part requires audio_url" + ) + parts.append({"type": "audio_url", "audio_url": {"url": audio_url}}) + continue + raise CompletionRequestValidationError( + f"unsupported responses content part type: {part_type!r}" + ) + if all(part.get("type") == "text" for part in parts): + return "".join(cast(str, part.get("text", "")) for part in parts) + return parts + + @staticmethod + def _response_reasoning_text(item: Dict[str, Any]) -> str: + content = item.get("content") + if isinstance(content, list) and content: + return OpenAIFormatter._response_text_from_content(content) + summary = item.get("summary") + if isinstance(summary, list) and summary: + return OpenAIFormatter._response_text_from_content(summary) + return "" + + def _response_reasoning_input_message( + self, + *, + text: str, + ) -> ChatCompletionRequestMessage: + if self._uses_harmony_channels(): + return self._chat_message( + { + "role": "assistant", + "content": text, + "channel": "analysis", + } + ) + if self._uses_reasoning_content(): + return self._chat_message( + { + "role": "assistant", + "content": "", + "reasoning_content": text, + } + ) + return self._chat_message( + { + "role": "assistant", + "content": text, + } + ) + + @staticmethod + def _response_tool_call_input_message( + *, + name: str, + arguments: str, + call_id: str, + content_type: Optional[str] = None, + ) -> ChatCompletionRequestMessage: + function: Dict[str, Any] = { + "name": name, + "arguments": arguments, + } + if isinstance(content_type, str) and content_type: + function["content_type"] = content_type + tool_call = { + "id": call_id, + "type": "function", + "function": function, + } + message: Dict[str, Any] = { + "role": "assistant", + "content": None, + "tool_calls": [tool_call], + "function_call": dict(function), + } + return OpenAIFormatter._chat_message(message) + + def _response_function_call_input_message( + self, + item: Dict[str, Any], + ) -> ChatCompletionRequestMessage: + name = item.get("name") + if not isinstance(name, str) or not name: + raise CompletionRequestValidationError("function_call input requires name") + arguments = item.get("arguments", "") + if not isinstance(arguments, str): + raise CompletionRequestValidationError("function_call input requires string arguments") + call_id = item.get("call_id") or item.get("id") or f"call_{uuid.uuid4().hex}" + return self._response_tool_call_input_message( + name=name, + arguments=arguments, + call_id=call_id, + content_type="json", + ) + + def _response_custom_tool_call_input_message( + self, + item: Dict[str, Any], + ) -> ChatCompletionRequestMessage: + name = item.get("name") + if not isinstance(name, str) or not name: + raise CompletionRequestValidationError("custom_tool_call input requires name") + input_text = item.get("input", "") + if not isinstance(input_text, str): + raise CompletionRequestValidationError("custom_tool_call input requires string input") + call_id = item.get("call_id") or item.get("id") or f"call_{uuid.uuid4().hex}" + content_type = item.get("content_type") + if not isinstance(content_type, str) or not content_type: + content_type = "text" + return self._response_tool_call_input_message( + name=name, + arguments=input_text, + call_id=call_id, + content_type=content_type, + ) + + def responses_input_to_chat_messages( + self, + body: CreateResponseRequest, + ) -> List[ChatCompletionRequestMessage]: + if body.conversation is not None: + raise CompletionRequestValidationError( + "conversation is not supported in stateless /v1/responses" + ) + if body.store: + raise CompletionRequestValidationError( + "store=true is not supported in stateless /v1/responses" + ) + if body.truncation not in {None, "disabled"}: + raise CompletionRequestValidationError( + "only truncation='disabled' is supported" + ) + + messages: List[ChatCompletionRequestMessage] = [] + if body.instructions is not None: + messages.append( + self._chat_message( + { + "role": self._instructions_role(), + "content": body.instructions, + } + ) + ) + + if isinstance(body.input, str): + messages.append( + self._chat_message( + { + "role": "user", + "content": body.input, + } + ) + ) + return messages + + items = body.input + if isinstance(items, dict): + items = [items] + if not isinstance(items, list): + raise CompletionRequestValidationError( + "responses input must be a string, object, or list" + ) + + function_names_by_call_id: Dict[str, str] = {} + for item in items: + if not isinstance(item, dict): + raise CompletionRequestValidationError( + "responses input items must be objects" + ) + item_type = item.get("type") + if item_type is None and "role" in item: + item_type = "message" + if item_type == "message": + role = item.get("role", "user") + if role not in { + "user", + "assistant", + "system", + "developer", + "tool", + "function", + }: + raise CompletionRequestValidationError( + f"unsupported responses message role: {role!r}" + ) + if role == "function": + role = "tool" + data: Dict[str, Any] = { + "role": role, + "content": self._response_chat_content_from_content( + item.get("content", "") + ), + } + phase = item.get("phase") + if isinstance(phase, str): + data["phase"] = phase + if role == "assistant" and self._uses_harmony_channels(): + if phase == "commentary": + data["channel"] = "commentary" + elif phase == "final_answer": + data["channel"] = "final" + messages.append(self._chat_message(data)) + continue + if item_type == "reasoning": + text = self._response_reasoning_text(item) + if text: + messages.append(self._response_reasoning_input_message(text=text)) + continue + if item_type == "function_call": + message = self._response_function_call_input_message(item) + call_id = item.get("call_id") or item.get("id") + name = item.get("name") + if isinstance(call_id, str) and isinstance(name, str): + function_names_by_call_id[call_id] = name + messages.append(message) + continue + if item_type == "custom_tool_call": + message = self._response_custom_tool_call_input_message(item) + call_id = item.get("call_id") or item.get("id") + name = item.get("name") + if isinstance(call_id, str) and isinstance(name, str): + function_names_by_call_id[call_id] = name + messages.append(message) + continue + if item_type == "function_call_output": + call_id = item.get("call_id") + if not isinstance(call_id, str) or not call_id: + raise CompletionRequestValidationError( + "function_call_output input requires call_id" + ) + tool_output_data: Dict[str, Any] = { + "role": "tool", + "tool_call_id": call_id, + "content": self._response_text_from_content(item.get("output", "")), + } + name = function_names_by_call_id.get(call_id) + if name is not None: + tool_output_data["name"] = name + messages.append(self._chat_message(tool_output_data)) + continue + if item_type == "custom_tool_call_output": + call_id = item.get("call_id") + if not isinstance(call_id, str) or not call_id: + raise CompletionRequestValidationError( + "custom_tool_call_output input requires call_id" + ) + tool_output_data = { + "role": "tool", + "tool_call_id": call_id, + "content": self._response_text_from_content(item.get("output", "")), + } + name = function_names_by_call_id.get(call_id) + if name is not None: + tool_output_data["name"] = name + messages.append(self._chat_message(tool_output_data)) + continue + raise CompletionRequestValidationError( + f"unsupported responses input item type: {item_type!r}" + ) + return messages + + @staticmethod + def _clone_response_input_items(input_items: Any) -> List[Any]: + if isinstance(input_items, list): + return copy.deepcopy(input_items) + if isinstance(input_items, dict): + return [copy.deepcopy(input_items)] + return [copy.deepcopy(input_items)] + + @staticmethod + def _normalize_response_output_item_for_input( + item: Dict[str, Any], + ) -> Optional[Dict[str, Any]]: + item_type = item.get("type") + if item_type == "message": + role = item.get("role") + if not isinstance(role, str): + return None + normalized: Dict[str, Any] = { + "type": "message", + "role": role, + "content": copy.deepcopy(item.get("content", [])), + } + for key in ("id", "phase", "status"): + value = item.get(key) + if value is not None: + normalized[key] = copy.deepcopy(value) + return normalized + if item_type == "reasoning": + normalized = { + "type": "reasoning", + "content": copy.deepcopy(item.get("content", [])), + } + for key in ("id", "summary", "status"): + value = item.get(key) + if value is not None: + normalized[key] = copy.deepcopy(value) + return normalized + if item_type == "function_call": + normalized = { + "type": "function_call", + "call_id": copy.deepcopy(item.get("call_id")), + "name": copy.deepcopy(item.get("name")), + "arguments": copy.deepcopy(item.get("arguments", "")), + } + namespace = item.get("namespace") + item_id = item.get("id") + if item_id is not None: + normalized["id"] = copy.deepcopy(item_id) + if namespace is not None: + normalized["namespace"] = copy.deepcopy(namespace) + return normalized + if item_type == "custom_tool_call": + normalized = { + "type": "custom_tool_call", + "call_id": copy.deepcopy(item.get("call_id")), + "name": copy.deepcopy(item.get("name")), + "input": copy.deepcopy(item.get("input", "")), + } + namespace = item.get("namespace") + content_type = item.get("content_type") + item_id = item.get("id") + if item_id is not None: + normalized["id"] = copy.deepcopy(item_id) + if namespace is not None: + normalized["namespace"] = copy.deepcopy(namespace) + if content_type is not None: + normalized["content_type"] = copy.deepcopy(content_type) + return normalized + return None + + def _responses_tools_to_chat_tools( + self, + tools: Optional[List[ResponsesToolDefinition]], + ) -> Optional[List[ChatTemplateTool]]: + if tools is None: + return None + chat_tools: List[ChatTemplateTool] = [] + for tool in tools: + if isinstance(tool, ResponsesWebSearchTool): + continue + if isinstance(tool, ResponsesFunctionTool): + chat_tools.append(tool.to_chat_template_tool()) + continue + if isinstance(tool, ResponsesCustomTool): + chat_tools.append(tool.to_chat_template_tool()) + continue + raise CompletionRequestValidationError( + f"unsupported responses tool type: {tool.type!r}" + ) + return chat_tools + + @staticmethod + def _responses_tool_choice_to_chat_tool_choice( + tool_choice: Optional[ResponsesToolChoice], + ) -> Optional[Union[Literal["auto", "none", "required"], ChatTemplateToolChoice]]: + if tool_choice is None or isinstance(tool_choice, str): + return tool_choice + if tool_choice.type in {"function", "custom"}: + return tool_choice.to_chat_template_tool_choice() + raise CompletionRequestValidationError( + f"unsupported responses tool_choice type: {tool_choice.type!r}" + ) + + @staticmethod + def _response_format_type(response_format: Optional[ChatTemplateResponseFormat]) -> Optional[str]: + if response_format is None: + return None + format_type = response_format.get("type") + if isinstance(format_type, str): + return format_type + return None + + @staticmethod + def _grammar_for_response_format( + response_format: Optional[ChatTemplateResponseFormat], + ) -> Optional[str]: + format_type = OpenAIFormatter._response_format_type(response_format) + if format_type is None or format_type == "text": + return None + if format_type == "json_object": + return JSON_GBNF + if format_type == "json_schema": + assert response_format is not None + schema = response_format.get("schema") + if schema is None and isinstance(response_format.get("json_schema"), dict): + schema = cast(Dict[str, Any], response_format["json_schema"]).get("schema") + if not isinstance(schema, dict): + raise CompletionRequestValidationError( + "json_schema response format requires a schema object" + ) + return JsonSchemaConverter.to_gbnf( + json.dumps(schema, ensure_ascii=False, separators=(",", ":")) + ) + raise CompletionRequestValidationError( + f"unsupported response format type: {format_type!r}" + ) + + def response_request_to_chat_parts( + self, + body: CreateResponseRequest, + ) -> ResponsesChatRequestParts: + chat_tools = self._responses_tools_to_chat_tools(body.tools) + response_format = ( + None + if body.text is None or body.text.format is None + else body.text.format.to_chat_response_format() + ) + return ResponsesChatRequestParts( + messages=self.responses_input_to_chat_messages(body), + max_tokens=body.max_output_tokens, + temperature=0.8 if body.temperature is None else body.temperature, + top_p=0.95 if body.top_p is None else body.top_p, + stream=body.stream, + logprobs=body.top_logprobs is not None, + top_logprobs=body.top_logprobs, + model=body.model, + user=body.user, + tools=chat_tools, + tool_choice=self._responses_tool_choice_to_chat_tool_choice(body.tool_choice), + response_format=response_format, + reasoning_effort=self._response_reasoning_effort(body), + ) + + def _response_parser( + self, + *, + tools: Optional[List[ChatTemplateTool]] = None, + completion_id: str = "", + choice_index: int = 0, + generation_prompt: str = "", + ) -> ResponseParser: + if self.model.response_schema is None: + raise CompletionResponseParsingError("model does not define response_schema") + return ResponseParser( + self.model.response_schema, + tools=tools, + completion_id=completion_id, + choice_index=choice_index, + generation_prompt=generation_prompt, + ) + + def parse_chat_response( + self, + response_text: str, + *, + tools: Optional[List[ChatTemplateTool]] = None, + partial: bool, + generation_prompt: str = "", + ) -> Dict[str, Any]: + return self._response_parser( + tools=tools, + generation_prompt=generation_prompt, + ).parse_chat_response( + response_text, + partial=partial, + ) + + def _chat_tool_name_and_grammar( + self, + *, + tools: Optional[List[ChatTemplateTool]], + function_call: Optional[Union[Literal["none", "auto"], ChatTemplateFunctionCall]], + tool_choice: Optional[Union[Literal["none", "auto", "required"], ChatTemplateToolChoice]], + response_format: Optional[ChatTemplateResponseFormat], + ) -> Tuple[Optional[str], Optional[str]]: + selected_tool_choice: Optional[Union[str, ChatTemplateToolChoice]] = tool_choice + if function_call is not None: + if isinstance(function_call, str): + selected_tool_choice = function_call + else: + selected_tool_choice = { + "type": "function", + "function": { + "name": function_call["name"], + }, + } + grammar_text = self._grammar_for_response_format(response_format) + if not isinstance(selected_tool_choice, dict): + return None, grammar_text + if tools is None: + raise CompletionRequestValidationError("tool choice requires tools") + tool_name = selected_tool_choice["function"]["name"] + tool = next((tool for tool in tools if tool["function"]["name"] == tool_name), None) + if tool is None: + raise CompletionRequestValidationError( + f"Tool choice '{tool_name}' not found in tools." + ) + if self.model.response_schema is None: + return tool_name, JSON_GBNF + return tool_name, grammar_text + + def completion_request_from_chat_request( + self, + body: CreateChatCompletionRequest, + ) -> PreparedCompletionParts: + functions = ( + [function.to_template_function() for function in body.functions] + if body.functions is not None + else None + ) + tools = ( + [tool.to_template_tool() for tool in body.tools] + if body.tools is not None + else None + ) + function_call = ( + body.function_call + if body.function_call is None or isinstance(body.function_call, str) + else body.function_call.to_template_function_call() + ) + tool_choice = ( + body.tool_choice + if body.tool_choice is None or isinstance(body.tool_choice, str) + else body.tool_choice.to_template_tool_choice() + ) + response_format = ( + body.response_format.to_template_response_format() + if body.response_format is not None + else None + ) + parser_tools = self._tools_for_response_parser( + functions=functions, + tools=tools, + ) + try: + prompt_text, generation_prompt, prompt_plan, formatter_stop = self.model.build_chat_prompt( + body.messages, + functions=functions, + function_call=function_call, + tools=tools, + tool_choice=tool_choice, + reasoning_effort=body.reasoning_effort, + ) + tool_name, grammar_text = self._chat_tool_name_and_grammar( + tools=parser_tools, + function_call=function_call, + tool_choice=tool_choice, + response_format=response_format, + ) + except ValueError as exc: + raise CompletionRequestValidationError(str(exc)) from exc + request_stop: List[str] = [] + if body.stop is None: + request_stop = [] + elif isinstance(body.stop, str): + request_stop = [body.stop] + else: + request_stop = list(body.stop) + stop_sequences = [stop for stop in [*request_stop, *formatter_stop] if stop] + deduped_stop: List[str] = [] + seen_stop: set[str] = set() + for stop in stop_sequences: + if stop not in seen_stop: + deduped_stop.append(stop) + seen_stop.add(stop) + payload = CreateCompletionRequest( + prompt=prompt_text, + max_tokens=body.max_tokens, + temperature=body.temperature, + top_p=body.top_p, + echo=False, + stop=deduped_stop or None, + stream=body.stream, + logprobs=( + 0 if body.logprobs and body.top_logprobs is None else body.top_logprobs + ), + presence_penalty=body.presence_penalty, + frequency_penalty=body.frequency_penalty, + logit_bias=body.logit_bias, + seed=body.seed, + model=body.model, + n=body.n, + user=body.user, + ) + return PreparedCompletionParts( + payload=payload, + prompt_text=prompt_text, + generation_prompt=generation_prompt, + prompt_plan=prompt_plan, + grammar_text=grammar_text, + tool_name=tool_name, + ) + + def completion_request_from_response_chat_parts( + self, + body: ResponsesChatRequestParts, + ) -> PreparedCompletionParts: + try: + prompt_text, generation_prompt, prompt_plan, formatter_stop = self.model.build_chat_prompt( + body.messages, + tools=body.tools, + tool_choice=body.tool_choice, + reasoning_effort=body.reasoning_effort, + ) + tool_name, grammar_text = self._chat_tool_name_and_grammar( + tools=body.tools, + function_call=None, + tool_choice=body.tool_choice, + response_format=body.response_format, + ) + except ValueError as exc: + raise CompletionRequestValidationError(str(exc)) from exc + deduped_stop: List[str] = [] + seen_stop: set[str] = set() + for stop in formatter_stop: + if stop and stop not in seen_stop: + deduped_stop.append(stop) + seen_stop.add(stop) + payload = CreateCompletionRequest( + prompt=prompt_text, + max_tokens=body.max_tokens, + temperature=body.temperature, + top_p=body.top_p, + echo=False, + stop=deduped_stop or None, + stream=body.stream, + logprobs=( + 0 if body.logprobs and body.top_logprobs is None else body.top_logprobs + ), + seed=None, + model=body.model, + n=1, + user=body.user, + ) + return PreparedCompletionParts( + payload=payload, + prompt_text=prompt_text, + generation_prompt=generation_prompt, + prompt_plan=prompt_plan, + grammar_text=grammar_text, + tool_name=tool_name, + ) + + @staticmethod + def _response_phase_from_message(message: Dict[str, Any]) -> Optional[str]: + phase = message.get("phase") + if phase in {"commentary", "final_answer"}: + return cast(str, phase) + channel = message.get("channel") + if channel == "commentary": + return "commentary" + if channel == "final": + return "final_answer" + return None + + @staticmethod + def _response_reasoning_text_from_message(message: Dict[str, Any]) -> str: + thinking = message.get("thinking") + if isinstance(thinking, str) and thinking: + return thinking + reasoning_content = message.get("reasoning_content") + if isinstance(reasoning_content, str) and reasoning_content: + return reasoning_content + if message.get("channel") == "analysis": + content = message.get("content") + if isinstance(content, str): + return content + return "" + + @staticmethod + def _response_output_text_from_message(message: Dict[str, Any]) -> str: + if message.get("channel") == "analysis": + return "" + content = message.get("content") + if isinstance(content, str): + return content + return "" + + @staticmethod + def _response_logprobs_from_chat_logprobs( + logprobs: Optional[Dict[str, Any]], + ) -> List[Dict[str, Any]]: + if logprobs is None: + return [] + content = logprobs.get("content") + if not isinstance(content, list): + return [] + response_logprobs: List[Dict[str, Any]] = [] + for entry in content: + if not isinstance(entry, dict): + continue + token = entry.get("token") + logprob = entry.get("logprob") + if not isinstance(token, str) or not isinstance(logprob, (int, float)): + continue + top_logprobs = entry.get("top_logprobs") + converted_top: List[Dict[str, Any]] = [] + if isinstance(top_logprobs, list): + for top in top_logprobs: + if not isinstance(top, dict): + continue + converted_top.append( + { + "token": top.get("token"), + "logprob": top.get("logprob"), + } + ) + response_logprobs.append( + { + "token": token, + "logprob": float(logprob), + "top_logprobs": converted_top or None, + } + ) + return response_logprobs + + def _response_logprobs_from_completion( + self, + logprobs: Optional[CompletionLogprobs], + ) -> List[Dict[str, Any]]: + if logprobs is None: + return [] + response_logprobs: List[Dict[str, Any]] = [] + tokens = logprobs.tokens or [] + token_logprobs_list = logprobs.token_logprobs or [] + top_logprobs_list = logprobs.top_logprobs or [] + for token, token_logprob, top_logprobs in zip( + tokens, + token_logprobs_list, + top_logprobs_list, + ): + if token_logprob is None: + continue + converted_top: List[Dict[str, Any]] = [] + if top_logprobs is not None: + for top_token, top_logprob in top_logprobs.items(): + converted_top.append( + { + "token": top_token, + "logprob": float(top_logprob), + } + ) + response_logprobs.append( + { + "token": token, + "logprob": float(token_logprob), + "top_logprobs": converted_top or None, + } + ) + return response_logprobs + + @staticmethod + def _response_reasoning_item( + *, + item_id: str, + text: str, + status: str, + ) -> Dict[str, Any]: + return { + "id": item_id, + "type": "reasoning", + "summary": [], + "content": [ + { + "type": "reasoning_text", + "text": text, + } + ], + "status": status, + } + + @staticmethod + def _response_message_item( + *, + item_id: str, + text: str, + status: str, + phase: Optional[str], + logprobs: Optional[List[Dict[str, Any]]] = None, + ) -> Dict[str, Any]: + content_item: Dict[str, Any] = { + "type": "output_text", + "text": text, + "annotations": [], + } + if logprobs: + content_item["logprobs"] = logprobs + item: Dict[str, Any] = { + "id": item_id, + "type": "message", + "role": "assistant", + "content": [content_item], + "status": status, + } + if phase is not None: + item["phase"] = phase + return item + + @staticmethod + def _response_function_call_item( + *, + item_id: str, + call_id: str, + name: str, + arguments: str, + status: str, + ) -> Dict[str, Any]: + item: Dict[str, Any] = { + "id": item_id, + "type": "function_call", + "call_id": call_id, + "name": name, + "arguments": arguments, + "status": status, + } + if "." in name: + namespace, bare_name = name.split(".", 1) + item["namespace"] = namespace + item["name"] = bare_name + return item + + @staticmethod + def _response_custom_tool_call_item( + *, + item_id: str, + call_id: str, + name: str, + input_text: str, + ) -> Dict[str, Any]: + item: Dict[str, Any] = { + "id": item_id, + "type": "custom_tool_call", + "call_id": call_id, + "name": name, + "input": input_text, + } + if "." in name: + namespace, bare_name = name.split(".", 1) + item["namespace"] = namespace + item["name"] = bare_name + return item + + @staticmethod + def _responses_tool_type_by_name( + tools: Optional[List[Any]], + ) -> Dict[str, str]: + if tools is None: + return {} + tool_types: Dict[str, str] = {} + for tool in tools: + if isinstance(tool, BaseModel): + tool = tool.model_dump(mode="python", exclude_none=True) + if not isinstance(tool, dict): + continue + tool_type = tool.get("original_type") or tool.get("type") + if not isinstance(tool_type, str): + continue + function = tool.get("function") + if isinstance(function, dict): + name = function.get("name") + else: + name = tool.get("name") + if isinstance(name, str) and name: + tool_types[name] = tool_type + return tool_types + + def _response_output_items_from_message( + self, + *, + response_id: str, + message: Dict[str, Any], + logprobs: Optional[List[Dict[str, Any]]] = None, + tools: Optional[List[ChatTemplateTool]] = None, + ) -> List[Dict[str, Any]]: + items: List[Dict[str, Any]] = [] + tool_types_by_name = self._responses_tool_type_by_name(tools) + reasoning_text = self._response_reasoning_text_from_message(message) + if reasoning_text: + items.append( + self._response_reasoning_item( + item_id=f"rs_{response_id}_0", + text=reasoning_text, + status="completed", + ) + ) + tool_calls = message.get("tool_calls") + if isinstance(tool_calls, list): + for tool_call_index, tool_call in enumerate(tool_calls): + if not isinstance(tool_call, dict): + continue + function = tool_call.get("function") + if not isinstance(function, dict): + continue + name = function.get("name") + arguments = function.get("arguments") + if not isinstance(name, str) or not isinstance(arguments, str): + continue + call_id = tool_call.get("id") + if not isinstance(call_id, str) or not call_id: + call_id = f"call_{response_id}_{tool_call_index}" + tool_type = tool_types_by_name.get(name, "function") + if tool_type == "custom": + items.append( + self._response_custom_tool_call_item( + item_id=f"fc_{response_id}_{tool_call_index}", + call_id=call_id, + name=name, + input_text=arguments, + ) + ) + continue + items.append( + self._response_function_call_item( + item_id=f"fc_{response_id}_{tool_call_index}", + call_id=call_id, + name=name, + arguments=arguments, + status="completed", + ) + ) + output_text = self._response_output_text_from_message(message) + if output_text: + items.append( + self._response_message_item( + item_id=f"msg_{response_id}_0", + text=output_text, + status="completed", + phase=self._response_phase_from_message(message), + logprobs=logprobs, + ) + ) + return items + + def _responses_usage_from_completion( + self, + *, + usage: Optional[CompletionUsage], + output_items: Sequence[Dict[str, Any]], + ) -> Optional[Dict[str, Any]]: + if usage is None: + return None + reasoning_tokens = 0 + for item in output_items: + if item.get("type") != "reasoning": + continue + content = item.get("content") + if not isinstance(content, list): + continue + for part in content: + if isinstance(part, dict) and part.get("type") == "reasoning_text": + text = part.get("text") + if isinstance(text, str) and text: + reasoning_tokens += len( + self.model.tokenize(text, add_bos=False, special=True) + ) + return { + "input_tokens": usage.prompt_tokens, + "input_tokens_details": { + "cached_tokens": 0, + }, + "output_tokens": usage.completion_tokens, + "output_tokens_details": { + "reasoning_tokens": min(reasoning_tokens, usage.completion_tokens), + }, + "total_tokens": usage.total_tokens, + } + + def _response_status_and_incomplete_details( + self, + *, + finish_reason: Optional[str], + ) -> Tuple[str, Optional[Dict[str, str]]]: + if finish_reason == "length": + return "incomplete", {"reason": "max_output_tokens"} + return "completed", None + + def _response_object( + self, + *, + body: CreateResponseRequest, + response_id: str, + created_at: float, + model: str, + output_items: Sequence[Dict[str, Any]], + usage: Optional[Dict[str, Any]], + status: str, + incomplete_details: Optional[Dict[str, str]], + completed_at: Optional[float], + ) -> Dict[str, Any]: + return { + "id": response_id, + "object": "response", + "created_at": created_at, + "completed_at": completed_at, + "error": None, + "incomplete_details": incomplete_details, + "instructions": body.instructions, + "metadata": body.metadata, + "model": model, + "output": list(output_items), + "parallel_tool_calls": body.parallel_tool_calls, + "reasoning": { + "effort": self._response_reasoning_effort(body), + "summary": None, + }, + "store": False, + "temperature": body.temperature, + "tool_choice": ( + body.tool_choice.model_dump(mode="python", exclude_none=True) + if isinstance(body.tool_choice, BaseModel) + else body.tool_choice or "auto" + ), + "tools": ( + [ + tool.model_dump(mode="python", exclude_none=True) + if isinstance(tool, BaseModel) + else tool + for tool in body.tools + ] + if body.tools + else [] + ), + "top_p": body.top_p, + "max_output_tokens": body.max_output_tokens, + "previous_response_id": None, + "status": status, + "text": ( + body.text.model_dump(mode="python", exclude_none=True, by_alias=True) + if body.text is not None + else {"format": {"type": "text"}} + ), + "top_logprobs": body.top_logprobs, + "truncation": body.truncation, + "usage": usage, + "user": body.user, + } + + def _response_from_chat_message( + self, + *, + body: CreateResponseRequest, + response_id: str, + created_at: float, + model: str, + message: Dict[str, Any], + finish_reason: Optional[str], + usage: Optional[CompletionUsage], + logprobs: Optional[List[Dict[str, Any]]] = None, + ) -> Dict[str, Any]: + status, incomplete_details = self._response_status_and_incomplete_details( + finish_reason=finish_reason, + ) + output_items = self._response_output_items_from_message( + response_id=response_id, + message=message, + logprobs=logprobs, + tools=cast(Optional[List[Any]], body.tools), + ) + return self._response_object( + body=body, + response_id=response_id, + created_at=created_at, + model=model, + output_items=output_items, + usage=self._responses_usage_from_completion( + usage=usage, + output_items=output_items, + ), + status=status, + incomplete_details=incomplete_details, + completed_at=time.time() if status in {"completed", "incomplete"} else None, + ) + + def convert_completion_response_to_response( + self, + completion: OpenAICompletion, + body: CreateResponseRequest, + tool_name: Optional[str] = None, + *, + tools: Optional[List[ChatTemplateTool]] = None, + generation_prompt: str = "", + ) -> Dict[str, Any]: + chat_response = self.convert_completion_response_to_chat( + completion, + tool_name, + tools=tools, + generation_prompt=generation_prompt, + ) + if isinstance(chat_response, BaseModel): + chat_payload = chat_response.model_dump(mode="json", exclude_none=True) + else: + chat_payload = chat_response + choice = cast(Dict[str, Any], chat_payload["choices"][0]) + message = cast(Dict[str, Any], choice["message"]) + response_id = "resp_" + completion.id + logprobs = self._response_logprobs_from_completion(completion.choices[0].logprobs) + return self._response_from_chat_message( + body=body, + response_id=response_id, + created_at=float(completion.created), + model=completion.model, + message=message, + finish_reason=cast(Optional[str], choice.get("finish_reason")), + usage=completion.usage, + logprobs=logprobs or None, + ) + + @staticmethod + def _response_event( + state: "OpenAIFormatter.ResponsesStream", + event_type: str, + **payload: Any, + ) -> Dict[str, Any]: + state.sequence_number += 1 + event = { + "type": event_type, + "sequence_number": state.sequence_number, + } + event.update(payload) + return event + + def _response_stream_response( + self, + state: "OpenAIFormatter.ResponsesStream", + *, + status: str, + usage: Optional[Dict[str, Any]], + incomplete_details: Optional[Dict[str, str]], + completed_at: Optional[float], + ) -> Dict[str, Any]: + return self._response_object( + body=state.body, + response_id=state.response_id, + created_at=state.created_at, + model=state.model, + output_items=state.output, + usage=usage, + status=status, + incomplete_details=incomplete_details, + completed_at=completed_at, + ) + + def start_response_stream( + self, + state: "OpenAIFormatter.ResponsesStream", + ) -> List[Dict[str, Any]]: + if state.started: + return [] + state.started = True + response = self._response_stream_response( + state, + status="in_progress", + usage=None, + incomplete_details=None, + completed_at=None, + ) + return [ + self._response_event(state, "response.created", response=response), + self._response_event(state, "response.in_progress", response=response), + ] + + @staticmethod + def _response_item_status( + finish_reason: Optional[str], + ) -> str: + if finish_reason == "length": + return "incomplete" + return "completed" + + def _add_response_stream_item( + self, + state: "OpenAIFormatter.ResponsesStream", + item: Dict[str, Any], + *, + content_index: Optional[int] = None, + ) -> "OpenAIFormatter.ResponsesOutputItem": + item_state = OpenAIFormatter.ResponsesOutputItem( + output_index=len(state.output), + item=item, + content_index=content_index, + ) + state.output.append(item) + return item_state + + def _ensure_reasoning_stream_item( + self, + state: "OpenAIFormatter.ResponsesStream", + ) -> Tuple[List[Dict[str, Any]], "OpenAIFormatter.ResponsesOutputItem"]: + if state.reasoning_item is not None: + return [], state.reasoning_item + item = self._response_reasoning_item( + item_id=f"rs_{state.response_id}_{len(state.output)}", + text="", + status="in_progress", + ) + item_state = self._add_response_stream_item(state, item, content_index=0) + state.reasoning_item = item_state + part = cast(List[Dict[str, Any]], item["content"])[0] + return [ + self._response_event( + state, + "response.output_item.added", + output_index=item_state.output_index, + item=item, + ), + self._response_event( + state, + "response.content_part.added", + item_id=cast(str, item["id"]), + output_index=item_state.output_index, + content_index=0, + part=part, + ), + ], item_state + + def _ensure_message_stream_item( + self, + state: "OpenAIFormatter.ResponsesStream", + ) -> Tuple[List[Dict[str, Any]], "OpenAIFormatter.ResponsesOutputItem"]: + if state.message_item is not None: + return [], state.message_item + item = self._response_message_item( + item_id=f"msg_{state.response_id}_{len(state.output)}", + text="", + status="in_progress", + phase=cast(Optional[str], state.assistant_meta.get("phase")), + ) + item_state = self._add_response_stream_item(state, item, content_index=0) + state.message_item = item_state + part = cast(List[Dict[str, Any]], item["content"])[0] + return [ + self._response_event( + state, + "response.output_item.added", + output_index=item_state.output_index, + item=item, + ), + self._response_event( + state, + "response.content_part.added", + item_id=cast(str, item["id"]), + output_index=item_state.output_index, + content_index=0, + part=part, + ), + ], item_state + + def _ensure_tool_stream_item( + self, + state: "OpenAIFormatter.ResponsesStream", + *, + tool_call_index: int, + call_id: Optional[str], + name: Optional[str], + ) -> Tuple[List[Dict[str, Any]], "OpenAIFormatter.ResponsesOutputItem"]: + existing = state.tool_items.get(tool_call_index) + if existing is not None: + return [], existing + tool_types_by_name = self._responses_tool_type_by_name(state.body.tools) + tool_type = tool_types_by_name.get(name or "", "function") + item_id = f"fc_{state.response_id}_{tool_call_index}" + resolved_call_id = call_id or f"call_{state.response_id}_{tool_call_index}" + resolved_name = name or "" + if tool_type == "custom": + item = self._response_custom_tool_call_item( + item_id=item_id, + call_id=resolved_call_id, + name=resolved_name, + input_text="", + ) + else: + item = self._response_function_call_item( + item_id=item_id, + call_id=resolved_call_id, + name=resolved_name, + arguments="", + status="in_progress", + ) + item_state = self._add_response_stream_item(state, item) + state.tool_items[tool_call_index] = item_state + return [ + self._response_event( + state, + "response.output_item.added", + output_index=item_state.output_index, + item=item, + ) + ], item_state + + def _update_tool_stream_item( + self, + item: Dict[str, Any], + *, + call_id: Optional[str], + name_delta: Optional[str], + arguments_delta: Optional[str], + ) -> None: + if isinstance(call_id, str) and call_id: + item["call_id"] = call_id + if isinstance(name_delta, str) and name_delta: + current_name = cast(str, item.get("name", "")) + if not current_name: + item["name"] = name_delta + elif name_delta == current_name or name_delta.startswith(current_name): + item["name"] = name_delta + elif not current_name.endswith(name_delta): + item["name"] = current_name + name_delta + if isinstance(arguments_delta, str) and arguments_delta: + if item.get("type") == "custom_tool_call": + raw_arguments = cast(str, item.get("_raw_arguments", "")) + arguments_delta + item["_raw_arguments"] = raw_arguments + normalized_input = self._normalize_text_tool_payload(raw_arguments) + if normalized_input is not None: + item["input"] = normalized_input + else: + item["arguments"] = ( + cast(str, item.get("arguments", "")) + arguments_delta + ) + + @staticmethod + def _normalize_text_tool_payload(payload: str) -> Optional[str]: + if payload == "": + return "" + stripped = payload.lstrip() + if not stripped: + return "" + if stripped[0] not in '{["': + return payload + try: + decoded = json.loads(payload) + except Exception: + return None + if isinstance(decoded, str): + return decoded + if isinstance(decoded, dict): + input_value = decoded.get("input") + if isinstance(input_value, str): + return input_value + if len(decoded) == 1: + sole_value = next(iter(decoded.values())) + if isinstance(sole_value, str): + return sole_value + return payload + + def _finalize_response_stream_items( + self, + state: "OpenAIFormatter.ResponsesStream", + *, + finish_reason: Optional[str], + ) -> List[Dict[str, Any]]: + events: List[Dict[str, Any]] = [] + item_status = self._response_item_status(finish_reason) + + if ( + state.reasoning_item is not None + and state.reasoning_item.item["status"] == "in_progress" + ): + item = state.reasoning_item.item + item["status"] = item_status + part = cast(List[Dict[str, Any]], item["content"])[0] + events.append( + self._response_event( + state, + "response.reasoning_text.done", + item_id=cast(str, item["id"]), + output_index=state.reasoning_item.output_index, + content_index=cast(int, state.reasoning_item.content_index), + text=part["text"], + ) + ) + events.append( + self._response_event( + state, + "response.content_part.done", + item_id=cast(str, item["id"]), + output_index=state.reasoning_item.output_index, + content_index=cast(int, state.reasoning_item.content_index), + part=part, + ) + ) + events.append( + self._response_event( + state, + "response.output_item.done", + output_index=state.reasoning_item.output_index, + item=item, + ) + ) + + if ( + state.message_item is not None + and state.message_item.item["status"] == "in_progress" + ): + item = state.message_item.item + item["status"] = item_status + part = cast(List[Dict[str, Any]], item["content"])[0] + events.append( + self._response_event( + state, + "response.output_text.done", + item_id=cast(str, item["id"]), + output_index=state.message_item.output_index, + content_index=cast(int, state.message_item.content_index), + text=part["text"], + logprobs=part.get("logprobs", []), + ) + ) + events.append( + self._response_event( + state, + "response.content_part.done", + item_id=cast(str, item["id"]), + output_index=state.message_item.output_index, + content_index=cast(int, state.message_item.content_index), + part=part, + ) + ) + events.append( + self._response_event( + state, + "response.output_item.done", + output_index=state.message_item.output_index, + item=item, + ) + ) + + for tool_call_index in sorted(state.tool_items): + item_state = state.tool_items[tool_call_index] + item = item_state.item + item.pop("_raw_arguments", None) + if ( + item.get("status") != "in_progress" + and item.get("type") != "custom_tool_call" + ): + continue + if item.get("type") == "custom_tool_call": + events.append( + self._response_event( + state, + "response.custom_tool_call_input.done", + item_id=cast(str, item["id"]), + output_index=item_state.output_index, + input=cast(str, item.get("input", "")), + ) + ) + else: + item["status"] = item_status + events.append( + self._response_event( + state, + "response.function_call_arguments.done", + item_id=cast(str, item["id"]), + output_index=item_state.output_index, + name=cast(str, item["name"]), + arguments=cast(str, item["arguments"]), + ) + ) + events.append( + self._response_event( + state, + "response.output_item.done", + output_index=item_state.output_index, + item=item, + ) + ) + + state.final_status, state.incomplete_details = ( + self._response_status_and_incomplete_details( + finish_reason=finish_reason, + ) + ) + return events + + def convert_chat_chunk_to_response_events( + self, + chunk: ChatCompletionChunk | Dict[str, Any], + state: "OpenAIFormatter.ResponsesStream", + ) -> List[Dict[str, Any]]: + payload = ( + chunk.model_dump(mode="json", exclude_none=True) + if isinstance(chunk, BaseModel) + else chunk + ) + events = self.start_response_stream(state) + choice = cast(Dict[str, Any], payload["choices"][0]) + delta = cast(Dict[str, Any], choice.get("delta", {})) + finish_reason = cast(Optional[str], choice.get("finish_reason")) + logprobs = self._response_logprobs_from_chat_logprobs( + cast(Optional[Dict[str, Any]], choice.get("logprobs")) + ) + + phase = delta.get("phase") + if isinstance(phase, str): + state.assistant_meta["phase"] = phase + if state.message_item is not None: + state.message_item.item["phase"] = phase + + reasoning_delta = delta.get("reasoning_content") + if not isinstance(reasoning_delta, str) or not reasoning_delta: + candidate = delta.get("thinking") + reasoning_delta = candidate if isinstance(candidate, str) else None + if isinstance(reasoning_delta, str) and reasoning_delta: + added, item_state = self._ensure_reasoning_stream_item(state) + events.extend(added) + part = cast(List[Dict[str, Any]], item_state.item["content"])[0] + part["text"] += reasoning_delta + events.append( + self._response_event( + state, + "response.reasoning_text.delta", + item_id=cast(str, item_state.item["id"]), + output_index=item_state.output_index, + content_index=cast(int, item_state.content_index), + delta=reasoning_delta, + ) + ) + + content_delta = delta.get("content") + if isinstance(content_delta, str) and content_delta: + added, item_state = self._ensure_message_stream_item(state) + events.extend(added) + part = cast(List[Dict[str, Any]], item_state.item["content"])[0] + part["text"] += content_delta + if logprobs: + cast(List[Dict[str, Any]], part.setdefault("logprobs", [])).extend( + logprobs + ) + events.append( + self._response_event( + state, + "response.output_text.delta", + item_id=cast(str, item_state.item["id"]), + output_index=item_state.output_index, + content_index=cast(int, item_state.content_index), + delta=content_delta, + logprobs=logprobs, + ) + ) + + tool_calls = delta.get("tool_calls") + if isinstance(tool_calls, list): + for tool_call in tool_calls: + if not isinstance(tool_call, dict): + continue + tool_call_index = tool_call.get("index", 0) + if not isinstance(tool_call_index, int): + continue + function = tool_call.get("function") + if not isinstance(function, dict): + continue + added, item_state = self._ensure_tool_stream_item( + state, + tool_call_index=tool_call_index, + call_id=cast(Optional[str], tool_call.get("id")), + name=cast(Optional[str], function.get("name")), + ) + events.extend(added) + previous_input = cast(str, item_state.item.get("input", "")) + self._update_tool_stream_item( + item_state.item, + call_id=cast(Optional[str], tool_call.get("id")), + name_delta=cast(Optional[str], function.get("name")), + arguments_delta=cast(Optional[str], function.get("arguments")), + ) + arguments_delta = function.get("arguments") + if isinstance(arguments_delta, str) and arguments_delta: + if item_state.item.get("type") == "custom_tool_call": + current_input = cast(str, item_state.item.get("input", "")) + if not current_input or current_input == previous_input: + continue + delta_text = current_input + if current_input.startswith(previous_input): + delta_text = current_input[len(previous_input) :] + events.append( + self._response_event( + state, + "response.custom_tool_call_input.delta", + item_id=cast(str, item_state.item["id"]), + output_index=item_state.output_index, + delta=delta_text, + ) + ) + continue + events.append( + self._response_event( + state, + "response.function_call_arguments.delta", + item_id=cast(str, item_state.item["id"]), + output_index=item_state.output_index, + delta=arguments_delta, + ) + ) + + if finish_reason is not None: + events.extend( + self._finalize_response_stream_items( + state, + finish_reason=finish_reason, + ) + ) + return events + + def response_stream_terminal_events( + self, + state: "OpenAIFormatter.ResponsesStream", + completion: Optional[OpenAICompletion], + ) -> List[Dict[str, Any]]: + if not state.started: + state.started = True + if completion is not None and state.final_status is None: + finish_reason = None + if completion.choices: + finish_reason = completion.choices[0].finish_reason + self._finalize_response_stream_items(state, finish_reason=finish_reason) + status = state.final_status or "completed" + response = self._response_stream_response( + state, + status=status, + usage=( + self._responses_usage_from_completion( + usage=completion.usage if completion is not None else None, + output_items=state.output, + ) + ), + incomplete_details=state.incomplete_details, + completed_at=time.time() if status in {"completed", "incomplete"} else None, + ) + event_type = "response.incomplete" if status == "incomplete" else "response.completed" + return [self._response_event(state, event_type, response=response)] + + def aggregate_completion_results( + self, + results: Sequence[OpenAICompletion], + ) -> OpenAICompletion: + choices: List[CompletionChoice] = [] + prompt_tokens = 0 + completion_tokens = 0 + total_tokens = 0 + for result in results: + for choice in result.choices: + choices.append(choice.model_copy(update={"index": len(choices)})) + assert result.usage is not None + prompt_tokens += result.usage.prompt_tokens + completion_tokens += result.usage.completion_tokens + total_tokens += result.usage.total_tokens + return OpenAICompletion( + id=f"cmpl-{uuid.uuid4().hex}", + object="text_completion", + created=int(time.time()), + model=self.model.model_path, + choices=choices, + usage=CompletionUsage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + ), + ) + + @staticmethod + def _convert_completion_logprobs_to_chat_choice( + logprobs: Optional[CompletionLogprobs], + ) -> Optional[ChatCompletionChoiceLogprobs]: + if logprobs is None: + return None + tokens = logprobs.tokens or [] + token_logprobs_list = logprobs.token_logprobs or [] + top_logprobs_list = logprobs.top_logprobs or [] + return ChatCompletionChoiceLogprobs( + content=[ + ChatCompletionTokenLogprob( + token=token, + bytes=None, + logprob=token_logprob if token_logprob is not None else 0.0, + top_logprobs=( + [ + TopLogprob( + token=top_token, + logprob=top_logprob, + bytes=None, + ) + for top_token, top_logprob in top_logprobs.items() + ] + if top_logprobs is not None + else [] + ), + ) + for token, token_logprob, top_logprobs in zip( + tokens, + token_logprobs_list, + top_logprobs_list, + ) + ], + refusal=None, + ) + + @staticmethod + def _convert_completion_logprobs_to_chat_chunk( + logprobs: Optional[CompletionLogprobs], + ) -> Optional[ChatCompletionChunkChoiceLogprobs]: + choice_logprobs = OpenAIFormatter._convert_completion_logprobs_to_chat_choice(logprobs) + if choice_logprobs is None: + return None + return ChatCompletionChunkChoiceLogprobs.model_validate( + choice_logprobs.model_dump(mode="python", exclude_none=True) + ) + + @staticmethod + def _chat_message_from_completion_choice( + completion_id: str, + choice: CompletionChoice, + tool_name: Optional[str], + ) -> ChatCompletionMessage: + if tool_name is None: + return ChatCompletionMessage( + role="assistant", + content=choice.text, + ) + tool_id = f"call_{choice.index}_{tool_name}_{completion_id}" + arguments = choice.text + return ChatCompletionMessage( + role="assistant", + content=None, + function_call=ChatCompletionMessageFunctionCall( + name=tool_name, + arguments=arguments, + ), + tool_calls=[ + ChatCompletionMessageToolCall( + id=tool_id, + type="function", + function=ChatCompletionMessageToolCallFunction( + name=tool_name, + arguments=arguments, + ), + ) + ], + ) + + @staticmethod + def _chat_chunk_payload( + *, + chunk_id: str, + created: int, + model: str, + index: int, + delta: Dict[str, Any], + finish_reason: Optional[str], + logprobs: Optional[Dict[str, Any]] = None, + ) -> Dict[str, Any]: + choice: Dict[str, Any] = { + "index": index, + "delta": delta, + "finish_reason": finish_reason, + } + if logprobs is not None: + choice["logprobs"] = logprobs + return { + "id": "chat" + chunk_id, + "object": "chat.completion.chunk", + "created": created, + "model": model, + "choices": [choice], + } + + def convert_completion_response_to_chat( + self, + completion: OpenAICompletion, + tool_name: Optional[str] = None, + *, + functions: Optional[List[ChatTemplateFunctionDefinition]] = None, + tools: Optional[List[ChatTemplateTool]] = None, + generation_prompt: str = "", + ) -> ChatCompletion | Dict[str, Any]: + parser_tools = self._tools_for_response_parser( + functions=functions, + tools=tools, + ) + if self.model.response_schema is not None: + choices: List[Dict[str, Any]] = [] + for choice in completion.choices: + parser = self._response_parser( + tools=parser_tools, + completion_id=completion.id, + choice_index=choice.index, + generation_prompt=generation_prompt, + ) + message = parser.parse_completion_message(choice.text) + logprobs = self._convert_completion_logprobs_to_chat_choice(choice.logprobs) + choices.append( + { + "index": choice.index, + "message": message, + "logprobs": ( + logprobs.model_dump(mode="json", exclude_none=True) + if logprobs is not None + else None + ), + "finish_reason": ( + "tool_calls" + if message.get("tool_calls") + else choice.finish_reason + ), + } + ) + return { + "id": "chat" + completion.id, + "object": "chat.completion", + "created": completion.created, + "model": completion.model, + "choices": choices, + "usage": ( + completion.usage.model_dump(mode="json", exclude_none=True) + if completion.usage is not None + else None + ), + } + return ChatCompletion( + id="chat" + completion.id, + object="chat.completion", + created=completion.created, + model=completion.model, + choices=[ + ChatCompletionChoice( + index=choice.index, + message=self._chat_message_from_completion_choice( + completion.id, + choice, + tool_name, + ), + logprobs=self._convert_completion_logprobs_to_chat_choice(choice.logprobs), + finish_reason=cast(Any, ( + "tool_calls" if tool_name is not None else choice.finish_reason + )), + ) + for choice in completion.choices + ], + usage=completion.usage, + ) + + def convert_completion_chunk_to_chat_chunks( + self, + chunk: CompletionChunk, + started_indices: set[int], + tool_name: Optional[str] = None, + *, + functions: Optional[List[ChatTemplateFunctionDefinition]] = None, + tools: Optional[List[ChatTemplateTool]] = None, + parsed_states: Optional[Dict[int, Any]] = None, + generation_prompt: str = "", + ) -> List[ChatCompletionChunk | Dict[str, Any]]: + parser_tools = self._tools_for_response_parser( + functions=functions, + tools=tools, + ) + if self.model.response_schema is not None: + parsed_chunks: List[Dict[str, Any]] = [] + if parsed_states is None: + parsed_states = {} + for choice in chunk["choices"]: + index = choice["index"] + parser = parsed_states.get(index) + if not isinstance(parser, ResponseParser): + parser = self._response_parser( + tools=parser_tools, + completion_id=chunk["id"], + choice_index=index, + generation_prompt=generation_prompt, + ) + parsed_states[index] = parser + logprobs = self._convert_completion_logprobs_to_chat_chunk( + CompletionLogprobs.model_validate(choice["logprobs"]) + if choice["logprobs"] is not None + else None + ) + parsed_chunks.extend( + parser.consume_completion_chunk( + choice["text"], + chunk_id=chunk["id"], + created=chunk["created"], + model=chunk["model"], + finish_reason=choice["finish_reason"], + logprobs=( + logprobs.model_dump(mode="json", exclude_none=True) + if logprobs is not None + else None + ), + ) + ) + if parser.started: + started_indices.add(index) + return cast(List[ChatCompletionChunk | Dict[str, Any]], parsed_chunks) + chat_chunks: List[ChatCompletionChunk] = [] + for choice in chunk["choices"]: + index = choice["index"] + if index not in started_indices: + started_indices.add(index) + chat_chunks.append( + ChatCompletionChunk( + id="chat" + chunk["id"], + object="chat.completion.chunk", + created=chunk["created"], + model=chunk["model"], + choices=[ + ChatCompletionChunkChoice( + index=index, + delta=( + ChoiceDelta( + role="assistant", + content=None, + function_call=None, + tool_calls=None, + ) + if tool_name is not None + else ChoiceDelta(role="assistant") + ), + logprobs=None, + finish_reason=None, + ) + ], + ) + ) + if tool_name is not None: + delta: ChoiceDelta + if choice["finish_reason"] is None: + tool_id = f"call_{index}_{tool_name}_{chunk['id']}" + delta = ChoiceDelta( + role=None, + content=None, + function_call=ChoiceDeltaFunctionCall( + name=tool_name, + arguments=choice["text"], + ), + tool_calls=[ + ChoiceDeltaToolCall( + index=0, + id=tool_id, + type="function", + function=ChoiceDeltaToolCallFunction( + name=tool_name, + arguments=choice["text"], + ), + ) + ], + ) + else: + delta = ChoiceDelta( + role=None, + content=None, + function_call=None, + tool_calls=None, + ) + chat_chunks.append( + ChatCompletionChunk( + id="chat" + chunk["id"], + object="chat.completion.chunk", + created=chunk["created"], + model=chunk["model"], + choices=[ + ChatCompletionChunkChoice( + index=index, + delta=delta, + logprobs=self._convert_completion_logprobs_to_chat_chunk( + CompletionLogprobs.model_validate(choice["logprobs"]) + if choice["logprobs"] is not None + else None + ), + finish_reason=( + "tool_calls" + if choice["finish_reason"] is not None + else None + ), + ) + ], + ) + ) + continue + chat_chunks.append( + ChatCompletionChunk( + id="chat" + chunk["id"], + object="chat.completion.chunk", + created=chunk["created"], + model=chunk["model"], + choices=[ + ChatCompletionChunkChoice( + index=index, + delta=( + ChoiceDelta(content=choice["text"]) + if choice["finish_reason"] is None + else ChoiceDelta() + ), + logprobs=self._convert_completion_logprobs_to_chat_chunk( + CompletionLogprobs.model_validate(choice["logprobs"]) + if choice["logprobs"] is not None + else None + ), + finish_reason=cast(Any, choice["finish_reason"]), + ) + ], + ) + ) + return cast(List[ChatCompletionChunk | Dict[str, Any]], chat_chunks) + + def returned_output_end( + self, + completion: Completion, + finish_reason: Optional[str], + ) -> int: + completion_bytes: bytes | bytearray + if completion.rendered_bytes: + completion_bytes = completion.rendered_bytes + else: + completion_bytes = b"".join(record.text_bytes for record in completion.token_records) + returned_end = len(completion_bytes) + if finish_reason == "stop": + stops = [stop for stop in completion.stop_sequences if stop in completion_bytes] + if stops: + returned_end = min(completion_bytes.index(stop) for stop in stops) + elif finish_reason is None: + holdback = 0 + for stop in completion.stop_sequences: + for size in range(min(len(stop), returned_end), 0, -1): + if completion_bytes.endswith(stop[:size]): + holdback = max(holdback, size) + break + returned_end -= holdback + return returned_end + + def returned_tokens( + self, + completion: Completion, + finish_reason: Optional[str], + *, + start_index: int = 0, + ) -> List["OpenAIFormatter.ReturnedToken"]: + returned_end = self.returned_output_end(completion, finish_reason) + returned_tokens: List[OpenAIFormatter.ReturnedToken] = [] + prefix_bytes = b"" + for index, record in enumerate(completion.token_records): + token_start = len(prefix_bytes) + if token_start >= returned_end: + break + token_end = token_start + len(record.text_bytes) + text_bytes = record.text_bytes + if token_end > returned_end: + if finish_reason is None: + break + text_bytes = text_bytes[: returned_end - token_start] + if index >= start_index: + returned_tokens.append( + OpenAIFormatter.ReturnedToken( + index=index, + text_bytes=text_bytes, + token=record, + text_offset=len(self.decode_text(prefix_bytes)), + ) + ) + prefix_bytes += record.text_bytes + return returned_tokens + + def stream_completion_chunks( + self, + request: CompletionRequest, + completion: Completion, + finish_reason: Optional[str], + ) -> List[CompletionChunk]: + returned_tokens = self.returned_tokens( + completion, + finish_reason, + start_index=completion.returned_token_count, + ) + chunks: List[CompletionChunk] = [] + if completion.logprobs is not None: + for returned_token in returned_tokens: + token = returned_token.token + chunks.append( + { + "id": request.id, + "object": "text_completion", + "created": request.created, + "model": self.model.model_path, + "choices": [ + { + "text": self.decode_text(returned_token.text_bytes), + "index": completion.index, + "logprobs": { + "tokens": [self.decode_text(token.text_bytes)], + "text_offset": [ + len(completion.prompt_text) + returned_token.text_offset + ], + "token_logprobs": [token.token_logprob], + "top_logprobs": [token.top_logprobs], + }, + "finish_reason": None, + } + ], + } + ) + completion.returned_token_count = returned_token.index + 1 + return chunks + + chunk_tokens: List[OpenAIFormatter.ReturnedToken] = [] + for returned_token in returned_tokens: + chunk_tokens.append(returned_token) + chunk_bytes = b"".join(token.text_bytes for token in chunk_tokens) + if returned_token.text_bytes != returned_token.token.text_bytes: + chunks.append( + { + "id": request.id, + "object": "text_completion", + "created": request.created, + "model": self.model.model_path, + "choices": [ + { + "text": self.decode_text(returned_token.text_bytes), + "index": completion.index, + "logprobs": None, + "finish_reason": None, + } + ], + } + ) + completion.returned_token_count = returned_token.index + 1 + chunk_tokens = [] + continue + try: + text = chunk_bytes.decode("utf-8") + except UnicodeError: + continue + chunks.append( + { + "id": request.id, + "object": "text_completion", + "created": request.created, + "model": self.model.model_path, + "choices": [ + { + "text": text, + "index": completion.index, + "logprobs": None, + "finish_reason": None, + } + ], + } + ) + completion.returned_token_count = returned_token.index + 1 + chunk_tokens = [] + return chunks + + def completion_finish_chunk( + self, + request: CompletionRequest, + completion: Completion, + finish_reason: str, + ) -> CompletionChunk: + return { + "id": request.id, + "object": "text_completion", + "created": request.created, + "model": self.model.model_path, + "choices": [ + { + "text": "", + "index": completion.index, + "logprobs": None, + "finish_reason": finish_reason, + } + ], + } + + def _build_completion_choice( + self, + request: CompletionRequest, + completion: Completion, + ) -> CompletionChoice: + returned_tokens = self.returned_tokens(completion, completion.finish_reason) + text_bytes = b"".join(returned_token.text_bytes for returned_token in returned_tokens) + text = self.decode_text(text_bytes) + if request.payload.echo: + text = completion.prompt_text + text + logprobs: Optional[CompletionLogprobs] = None + if completion.logprobs is not None: + offsets: List[int] = [] + token_texts: List[str] = [] + token_logprobs: List[Optional[float]] = [] + top_logprobs: List[Optional[Dict[str, float]]] = [] + text_cursor = request.prompt_text if not request.payload.echo else "" + if request.payload.echo: + for record in request.prompt_records: + offsets.append(len(text_cursor)) + token_texts.append(self.decode_text(record.text_bytes)) + token_logprobs.append(record.token_logprob) + top_logprobs.append(record.top_logprobs) + text_cursor += self.decode_text(record.text_bytes) + text_cursor = request.prompt_text + for returned_token in returned_tokens: + token = returned_token.token + offsets.append(len(completion.prompt_text) + returned_token.text_offset) + if request.payload.echo: + offsets[-1] = len(text_cursor) + token_texts.append(self.decode_text(token.text_bytes)) + token_logprobs.append(token.token_logprob) + top_logprobs.append(token.top_logprobs) + text_cursor += self.decode_text(token.text_bytes) + logprobs = CompletionLogprobs.model_construct( + text_offset=offsets, + token_logprobs=token_logprobs, + tokens=token_texts, + top_logprobs=top_logprobs, + ) + return CompletionChoice( + text=text, + index=completion.index, + logprobs=logprobs, + finish_reason=cast(Any, completion.finish_reason), + ) + + def build_completion_response( + self, + request: CompletionRequest, + completions: Sequence[Completion], + ) -> OpenAICompletion: + completion_tokens = sum(completion.completion_token_count for completion in completions) + prompt_tokens = request.prompt_plan.eval_token_count + return OpenAICompletion( + id=request.id, + object="text_completion", + created=request.created, + model=self.model.model_path, + choices=[ + self._build_completion_choice(request, completion) + for completion in completions + ], + usage=CompletionUsage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ), + ) + + +class Sampler: + TOKEN_DATA_DTYPE = np.dtype( + [("id", np.intc), ("logit", np.single), ("p", np.single)], + align=True, + ) + + def __init__( + self, + *, + seed: int, + vocab: llama_cpp.llama_vocab_p, + n_vocab: int, + top_p: float, + temperature: float, + frequency_penalty: float, + presence_penalty: float, + logit_bias: Optional[Dict[int, float]], + grammar_text: Optional[str] = None, + grammar_root: str = "root", + ) -> None: + params = llama_cpp.llama_sampler_chain_default_params() + self._sampler = llama_cpp.llama_sampler_chain_init(params) + self._closed = False + self._sample_logits_size = 0 + self._sample_logits_token_data: Optional[Any] = None + self._sample_logits_token_array: Optional[Any] = None + self._sample_logits_recarray: Optional[np.recarray] = None + if logit_bias: + bias_array = (llama_cpp.llama_logit_bias * len(logit_bias))() + for index, (token, bias) in enumerate(logit_bias.items()): + bias_array[index].token = ctypes.c_int32(token) + bias_array[index].bias = float(bias) + llama_cpp.llama_sampler_chain_add( + self._sampler, + llama_cpp.llama_sampler_init_logit_bias( + n_vocab, len(logit_bias), bias_array + ), + ) + self.bias_array = bias_array + if frequency_penalty != 0.0 or presence_penalty != 0.0: + llama_cpp.llama_sampler_chain_add( + self._sampler, + llama_cpp.llama_sampler_init_penalties( + 64, + 1.0, + frequency_penalty, + presence_penalty, + ), + ) + if grammar_text is not None: + grammar_sampler = llama_cpp.llama_sampler_init_grammar( + vocab, + grammar_text.encode("utf-8"), + grammar_root.encode("utf-8"), + ) + if grammar_sampler is None: + raise RuntimeError("failed to initialize grammar sampler") + llama_cpp.llama_sampler_chain_add(self._sampler, grammar_sampler) + if temperature < 0.0: + llama_cpp.llama_sampler_chain_add( + self._sampler, llama_cpp.llama_sampler_init_dist(seed) + ) + return + if temperature == 0.0: + llama_cpp.llama_sampler_chain_add( + self._sampler, llama_cpp.llama_sampler_init_greedy() + ) + return + min_keep = 1 + llama_cpp.llama_sampler_chain_add( + self._sampler, llama_cpp.llama_sampler_init_top_p(top_p, min_keep) + ) + llama_cpp.llama_sampler_chain_add( + self._sampler, llama_cpp.llama_sampler_init_temp(temperature) + ) + llama_cpp.llama_sampler_chain_add( + self._sampler, llama_cpp.llama_sampler_init_dist(seed) + ) + + def sample(self, ctx: llama_cpp.llama_context_p, output_index: int) -> int: + return int(llama_cpp.llama_sampler_sample(self._sampler, ctx, output_index)) + + def _ensure_sample_logits_buffer(self, size: int) -> None: + if size == self._sample_logits_size and self._sample_logits_recarray is not None: + return + token_data = (llama_cpp.llama_token_data * size)() + token_data_address = ctypes.addressof(token_data) + recarray = np.recarray( + shape=(size,), + dtype=self.TOKEN_DATA_DTYPE, + buf=cast( + Any, + (llama_cpp.llama_token_data * size).from_address(token_data_address), + ), + ) + recarray.id[:] = np.arange(size, dtype=np.intc) + token_array = llama_cpp.llama_token_data_array( + data=token_data, + size=size, + selected=-1, + sorted=False, + ) + self._sample_logits_size = size + self._sample_logits_token_data = token_data + self._sample_logits_token_array = token_array + self._sample_logits_recarray = recarray + + def sample_logits(self, logits: np.ndarray) -> int: + self._ensure_sample_logits_buffer(len(logits)) + assert self._sample_logits_recarray is not None + assert self._sample_logits_token_array is not None + self._sample_logits_recarray.logit[:] = logits + self._sample_logits_recarray.p.fill(0.0) + self._sample_logits_token_array.selected = -1 + self._sample_logits_token_array.sorted = False + llama_cpp.llama_sampler_apply( + self._sampler, + cast(Any, ctypes.byref(self._sample_logits_token_array)), + ) + token = int(self._sample_logits_recarray.id[self._sample_logits_token_array.selected]) + llama_cpp.llama_sampler_accept(self._sampler, token) + return token + + def close(self) -> None: + if not self._closed: + llama_cpp.llama_sampler_free(self._sampler) + self._closed = True + + +@dataclass +class MTMDEmbedding: + key: str + embeddings: np.ndarray + + +class MTMDEmbeddingCache: + _metadata_version = "1" + + def __init__( + self, + *, + path: str, + max_bytes: int, + model_fingerprint: str, + mmproj_fingerprint: str, + ) -> None: + self.path = Path(path) + self.max_bytes = max_bytes + self.model_fingerprint = model_fingerprint + self.mmproj_fingerprint = mmproj_fingerprint + self.path.mkdir(parents=True, exist_ok=True) + + @staticmethod + def _safe_open(path: Path) -> Any: + try: + from safetensors import safe_open + except ImportError as exc: + raise RuntimeError( + "model.mtmd.embedding_cache requires safetensors. " + "Install it with `pip install safetensors`." + ) from exc + return safe_open(str(path), framework="numpy") + + @staticmethod + def _save_file( + tensors: Dict[str, np.ndarray], + path: Path, + metadata: Dict[str, str], + ) -> None: + from safetensors.numpy import save_file + + save_file(tensors, str(path), metadata=metadata) + + @staticmethod + def fingerprint_file(path: str) -> str: + stat = os.stat(path) + payload = f"{Path(path).resolve()}:{stat.st_size}:{stat.st_mtime_ns}" + return hashlib.sha256(payload.encode("utf-8")).hexdigest() + + @staticmethod + def _build_key( + *, + model_fingerprint: str, + mmproj_fingerprint: str, + kind: Literal["image", "audio"], + media_bytes: bytes, + ) -> str: + digest = hashlib.sha256(f"{kind}:".encode("utf-8") + media_bytes).hexdigest() + payload = ":".join( + [ + MTMDEmbeddingCache._metadata_version, + model_fingerprint, + mmproj_fingerprint, + kind, + digest, + ] + ) + return hashlib.sha256(payload.encode("utf-8")).hexdigest() + + def key_for_media(self, kind: Literal["image", "audio"], media_bytes: bytes) -> str: + return self._build_key( + model_fingerprint=self.model_fingerprint, + mmproj_fingerprint=self.mmproj_fingerprint, + kind=kind, + media_bytes=media_bytes, + ) + + def _path_for_key(self, key: str) -> Path: + return self.path / f"{key}.safetensors" + + def load(self, key: str) -> Optional[MTMDEmbedding]: + entry_path = self._path_for_key(key) + if not entry_path.exists(): + return None + with self._safe_open(entry_path) as tensors: + metadata = tensors.metadata() + if metadata.get("version") != self._metadata_version: + return None + if metadata.get("model") != self.model_fingerprint: + return None + if metadata.get("mmproj") != self.mmproj_fingerprint: + return None + embeddings = np.array(tensors.get_tensor("embeddings"), copy=True) + return MTMDEmbedding(key=key, embeddings=embeddings.astype(np.float32, copy=False)) + + def save(self, key: str, embeddings: np.ndarray) -> None: + if self.max_bytes == 0: + return + tmp_path = self.path / f".{key}.{uuid.uuid4().hex}.tmp" + final_path = self._path_for_key(key) + metadata = { + "version": self._metadata_version, + "model": self.model_fingerprint, + "mmproj": self.mmproj_fingerprint, + "key": key, + } + self._save_file( + {"embeddings": np.ascontiguousarray(embeddings, dtype=np.float32)}, + tmp_path, + metadata, + ) + os.replace(tmp_path, final_path) + self.evict_if_needed() + + def evict_if_needed(self) -> None: + if self.max_bytes <= 0: + return + entries = [path for path in self.path.glob("*.safetensors") if path.is_file()] + total = sum(path.stat().st_size for path in entries) + if total <= self.max_bytes: + return + for path in sorted(entries, key=lambda item: item.stat().st_mtime_ns): + if total <= self.max_bytes: + break + try: + size = path.stat().st_size + path.unlink() + total -= size + except OSError: + continue + + +class MTMDProcessor: + def __init__( + self, + *, + model_path: str, + llama_model: Any, + chat_formatter: Jinja2ChatFormatter, + tokenize: Callable[..., List[int]], + n_embd_inp: int, + n_batch: int, + n_ubatch: int, + n_threads_batch: int, + mmproj_path: str, + embedding_cache: Optional[MTMDEmbeddingCache], + allowed_media_domains: Optional[List[str]], + allowed_local_media_path: Optional[str], + image_max_bytes: int, + audio_max_bytes: int, + image_timeout_seconds: float, + ) -> None: + self.chat_formatter = chat_formatter + self.tokenize = tokenize + self.n_embd_inp = n_embd_inp + self.n_batch = n_batch + self.n_ubatch = n_ubatch + self.mmproj_path = mmproj_path + self.embedding_cache = embedding_cache + self.model_fingerprint = MTMDEmbeddingCache.fingerprint_file(model_path) + self.mmproj_fingerprint = MTMDEmbeddingCache.fingerprint_file(mmproj_path) + self.allowed_media_domains = ( + {domain.lower() for domain in allowed_media_domains} + if allowed_media_domains is not None + else set() + ) + self.allowed_local_media_path = ( + Path(allowed_local_media_path).expanduser().resolve() + if allowed_local_media_path is not None + else None + ) + self.image_max_bytes = image_max_bytes + self.audio_max_bytes = audio_max_bytes + self.image_timeout_seconds = image_timeout_seconds + self.lock = threading.Lock() + params = mtmd_cpp.mtmd_context_params_default() + params.n_threads = max(1, n_threads_batch) + self.ctx = mtmd_cpp.mtmd_init_from_file( + mmproj_path.encode("utf-8"), + llama_model, + params, + ) + if self.ctx is None: + raise RuntimeError(f"failed to load MTMD context: {mmproj_path}") + self.supports_vision = bool(mtmd_cpp.mtmd_support_vision(self.ctx)) + self.supports_audio = bool(mtmd_cpp.mtmd_support_audio(self.ctx)) + if not self.supports_vision and not self.supports_audio: + mtmd_cpp.mtmd_free(self.ctx) + self.ctx = None + raise RuntimeError(f"MTMD projector does not support image or audio input: {mmproj_path}") + self.media_marker = mtmd_cpp.mtmd_default_marker().decode("utf-8") + + def close(self) -> None: + if self.ctx is not None: + mtmd_cpp.mtmd_free(self.ctx) + self.ctx = None + + def _max_bytes_for_media(self, kind: Literal["image", "audio"]) -> int: + if kind == "image": + return self.image_max_bytes + return self.audio_max_bytes + + def _load_media_file(self, kind: Literal["image", "audio"], media_url: str) -> bytes: + if self.allowed_local_media_path is None: + raise CompletionRequestValidationError("local media path is not allowed") + parsed = urllib.parse.urlsplit(media_url) + if parsed.netloc not in {"", "localhost"}: + raise CompletionRequestValidationError("local media path is not allowed") + path = Path(urllib.parse.unquote(parsed.path)).expanduser().resolve() + try: + path.relative_to(self.allowed_local_media_path) + except ValueError as exc: + raise CompletionRequestValidationError("local media path is not allowed") from exc + max_bytes = self._max_bytes_for_media(kind) + try: + if path.stat().st_size > max_bytes: + raise CompletionRequestValidationError(f"{kind} exceeds model.mtmd.{kind}_max_bytes") + data = path.read_bytes() + except OSError as exc: + raise CompletionRequestValidationError(f"failed to read local {kind}: {exc}") from exc + if len(data) > max_bytes: + raise CompletionRequestValidationError(f"{kind} exceeds model.mtmd.{kind}_max_bytes") + return data + + def _validate_remote_media_url(self, media_url: str) -> str: + parsed = urllib.parse.urlsplit(media_url) + if parsed.scheme not in {"http", "https"} or not parsed.hostname: + raise CompletionRequestValidationError( + "only data:, file:, http:, and https: media URLs are supported" + ) + if parsed.username is not None or parsed.password is not None: + raise CompletionRequestValidationError("remote media domain is not allowed") + hostname = parsed.hostname.lower() + if self.allowed_media_domains and hostname not in self.allowed_media_domains: + raise CompletionRequestValidationError("remote media domain is not allowed") + return urllib.parse.urlunsplit(parsed) + + @staticmethod + def _urlopen_without_redirects( + request: urllib.request.Request, + *, + timeout: float, + ) -> Any: + class NoRedirectHandler(urllib.request.HTTPRedirectHandler): + def redirect_request(self, req, fp, code, msg, headers, newurl): # type: ignore[no-untyped-def] + return None + + opener = urllib.request.build_opener(NoRedirectHandler) + return opener.open(request, timeout=timeout) + + def _load_media_url(self, kind: Literal["image", "audio"], media_url: str) -> bytes: + max_bytes = self._max_bytes_for_media(kind) + if media_url.startswith("data:"): + try: + _, encoded = media_url.split(",", 1) + except ValueError as exc: + raise CompletionRequestValidationError(f"invalid data {kind} URL") from exc + data = base64.b64decode(encoded, validate=False) + if len(data) > max_bytes: + raise CompletionRequestValidationError(f"{kind} exceeds model.mtmd.{kind}_max_bytes") + return data + if media_url.startswith("file:"): + return self._load_media_file(kind, media_url) + media_url = self._validate_remote_media_url(media_url) + request = urllib.request.Request( + media_url, + headers={"User-Agent": "llama-cpp-python-batch-mtmd/0"}, + ) + try: + with self._urlopen_without_redirects(request, timeout=self.image_timeout_seconds) as response: + data = response.read(max_bytes + 1) + except (OSError, TimeoutError, urllib.error.URLError) as exc: + raise CompletionRequestValidationError( + f"failed to fetch {kind} URL: {exc}" + ) from exc + if len(data) > max_bytes: + raise CompletionRequestValidationError(f"{kind} exceeds model.mtmd.{kind}_max_bytes") + return data + + def load_media(self, media: MediaInput) -> bytes: + if media.url is not None: + return self._load_media_url(media.kind, media.url) + if media.kind != "audio" or media.data is None: + raise CompletionRequestValidationError(f"{media.kind} input requires a URL") + try: + data = base64.b64decode(media.data, validate=False) + except (ValueError, binascii.Error) as exc: + raise CompletionRequestValidationError("input_audio data must be valid base64") from exc + if len(data) > self.audio_max_bytes: + raise CompletionRequestValidationError("audio exceeds model.mtmd.audio_max_bytes") + return data + + def _create_bitmap(self, media_bytes: bytes, kind: Literal["image", "audio"]) -> Any: + buffer = (ctypes.c_uint8 * len(media_bytes)).from_buffer_copy(media_bytes) + bitmap = mtmd_cpp.mtmd_helper_bitmap_init_from_buf( + self.ctx, + buffer, + len(media_bytes), + ) + if bitmap is None: + raise CompletionRequestValidationError(f"failed to create MTMD {kind} bitmap") + return bitmap + + def _media_identity_tokens(self, kind: Literal["image", "audio"], key: str, n_pos: int) -> List[int]: + tokens: List[int] = [] + for index in range(n_pos): + digest = hashlib.sha256(f"{kind}:{key}:{index}".encode("utf-8")).digest() + tokens.append(-1 - (int.from_bytes(digest[:4], "little") & 0x3FFFFFFF)) + return tokens + + def _encode_media_chunk( + self, + *, + kind: Literal["image", "audio"], + key: str, + chunk: Any, + ) -> np.ndarray: + n_tokens = int(mtmd_cpp.mtmd_input_chunk_get_n_tokens(chunk)) + if self.embedding_cache is not None: + cached = self.embedding_cache.load(key) + if ( + cached is not None + and cached.embeddings.shape == (n_tokens, self.n_embd_inp) + ): + return cached.embeddings + result = int(mtmd_cpp.mtmd_encode_chunk(self.ctx, chunk)) + if result != 0: + raise CompletionRequestValidationError( + f"failed to encode {kind} chunk: error code {result}" + ) + output = mtmd_cpp.mtmd_get_output_embd(self.ctx) + if output is None: + raise CompletionRequestValidationError(f"MTMD {kind} encoder returned no embeddings") + flat = np.ctypeslib.as_array(output, shape=(n_tokens * self.n_embd_inp,)) + embeddings = np.array(flat, dtype=np.float32, copy=True).reshape( + n_tokens, + self.n_embd_inp, + ) + if self.embedding_cache is not None: + self.embedding_cache.save(key, embeddings) + return embeddings + + def _positions_for_chunk(self, chunk: Any, start_pos: int) -> np.ndarray: + n_tokens = int(mtmd_cpp.mtmd_input_chunk_get_n_tokens(chunk)) + if not mtmd_cpp.mtmd_decode_use_mrope(self.ctx): + return np.arange(start_pos, start_pos + n_tokens, dtype=np.int32) + chunk_type = int(mtmd_cpp.mtmd_input_chunk_get_type(chunk)) + if chunk_type == mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_AUDIO: + positions = np.empty((4, n_tokens), dtype=np.int32) + positions[:] = np.arange(start_pos, start_pos + n_tokens, dtype=np.int32) + return positions.reshape(-1) + image_tokens = mtmd_cpp.mtmd_input_chunk_get_tokens_image(chunk) + if image_tokens is None: + raise CompletionRequestValidationError("MTMD image chunk has no image tokens") + positions = np.empty((4, n_tokens), dtype=np.int32) + for index in range(n_tokens): + pos = mtmd_cpp.mtmd_image_tokens_get_decoder_pos( + image_tokens, + llama_cpp.llama_pos(start_pos), + index, + ) + positions[0, index] = int(pos.t) + positions[1, index] = int(pos.y) + positions[2, index] = int(pos.x) + positions[3, index] = int(pos.z) + return positions.reshape(-1) + + def build_prompt_plan( + self, + *, + messages: List[ChatCompletionRequestMessage], + functions: Optional[List[ChatTemplateFunctionDefinition]] = None, + function_call: Optional[Union[str, ChatTemplateFunctionCall]] = None, + tools: Optional[List[ChatTemplateTool]] = None, + tool_choice: Optional[Union[str, ChatTemplateToolChoice]] = None, + reasoning_effort: Optional[str] = None, + ) -> PromptPlan: + media_inputs = Jinja2ChatFormatter.media_inputs_from_messages(messages) + if not media_inputs: + prompt, generation_prompt, _ = self.chat_formatter.format( + messages=messages, + functions=functions, + function_call=function_call, + tools=tools, + tool_choice=tool_choice, + reasoning_effort=reasoning_effort, + ) + tokens = self.tokenize(prompt, add_bos=False, special=True) + return PromptPlan.from_tokens(prompt, tokens, generation_prompt=generation_prompt) + if any(media.kind == "image" for media in media_inputs) and not self.supports_vision: + raise CompletionRequestValidationError("MTMD projector does not support images") + if any(media.kind == "audio" for media in media_inputs) and not self.supports_audio: + raise CompletionRequestValidationError("MTMD projector does not support audio") + with self.lock: + return self._build_prompt_plan_locked( + messages=messages, + media_inputs=media_inputs, + functions=functions, + function_call=function_call, + tools=tools, + tool_choice=tool_choice, + reasoning_effort=reasoning_effort, + ) + + def _build_prompt_plan_locked( + self, + *, + messages: List[ChatCompletionRequestMessage], + media_inputs: List[MediaInput], + functions: Optional[List[ChatTemplateFunctionDefinition]], + function_call: Optional[Union[str, ChatTemplateFunctionCall]], + tools: Optional[List[ChatTemplateTool]], + tool_choice: Optional[Union[str, ChatTemplateToolChoice]], + reasoning_effort: Optional[str], + ) -> PromptPlan: + prompt, generation_prompt, _ = self.chat_formatter.format( + messages=messages, + media_marker=self.media_marker, + functions=functions, + function_call=function_call, + tools=tools, + tool_choice=tool_choice, + reasoning_effort=reasoning_effort, + ) + media_bytes_by_index = [self.load_media(media) for media in media_inputs] + bitmaps: List[Any] = [] + chunks: Optional[Any] = None + try: + bitmaps = [ + self._create_bitmap(media_bytes, media.kind) + for media, media_bytes in zip(media_inputs, media_bytes_by_index) + ] + input_text = mtmd_cpp.mtmd_input_text() + input_text.text = prompt.encode("utf-8") + input_text.add_special = False + input_text.parse_special = True + chunks = mtmd_cpp.mtmd_input_chunks_init() + if chunks is None: + raise CompletionRequestValidationError("failed to create MTMD input chunks") + bitmap_array = (mtmd_cpp.mtmd_bitmap_p_ctypes * len(bitmaps))(*bitmaps) + result = int( + mtmd_cpp.mtmd_tokenize( + self.ctx, + chunks, + ctypes.byref(input_text), + bitmap_array, + len(bitmaps), + ) + ) + if result != 0: + raise CompletionRequestValidationError( + f"failed to tokenize MTMD prompt: error code {result}" + ) + segments: List[PromptSegment] = [] + identity_tokens: List[int] = [] + text_tokens: List[int] = [] + text_token_index_by_pos: Dict[int, int] = {} + identity_pos = 0 + decode_pos = 0 + media_index = 0 + n_chunks = int(mtmd_cpp.mtmd_input_chunks_size(chunks)) + for chunk_index in range(n_chunks): + chunk = mtmd_cpp.mtmd_input_chunks_get(chunks, chunk_index) + if chunk is None: + continue + chunk_type = int(mtmd_cpp.mtmd_input_chunk_get_type(chunk)) + if chunk_type == mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_TEXT: + n_tokens_out = ctypes.c_size_t() + tokens_ptr = mtmd_cpp.mtmd_input_chunk_get_tokens_text( + chunk, + ctypes.byref(n_tokens_out), + ) + tokens = ( + [int(tokens_ptr[index]) for index in range(int(n_tokens_out.value))] + if tokens_ptr + else [] + ) + if tokens: + start_pos = identity_pos + segments.append( + PromptSegment( + kind="text", + start_pos=start_pos, + n_pos=len(tokens), + identity_tokens=list(tokens), + decode_start_pos=decode_pos, + decode_n_pos=len(tokens), + text_tokens=list(tokens), + ) + ) + for offset, token in enumerate(tokens): + text_token_index_by_pos[start_pos + offset] = len(text_tokens) + text_tokens.append(token) + identity_tokens.extend(tokens) + identity_pos += len(tokens) + decode_pos += len(tokens) + continue + if chunk_type == mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_IMAGE: + kind: Literal["image", "audio"] = "image" + if not self.supports_vision: + raise CompletionRequestValidationError("MTMD projector does not support images") + elif chunk_type == mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_AUDIO: + kind = "audio" + if not self.supports_audio: + raise CompletionRequestValidationError("MTMD projector does not support audio") + else: + raise CompletionRequestValidationError("unsupported MTMD input chunk type") + if media_index >= len(media_bytes_by_index): + raise CompletionRequestValidationError("MTMD media chunk count mismatch") + media = media_inputs[media_index] + media_bytes = media_bytes_by_index[media_index] + media_index += 1 + if media.kind != kind: + raise CompletionRequestValidationError("MTMD media chunk modality mismatch") + key = ( + self.embedding_cache.key_for_media(kind, media_bytes) + if self.embedding_cache is not None + else MTMDEmbeddingCache._build_key( + model_fingerprint=self.model_fingerprint, + mmproj_fingerprint=self.mmproj_fingerprint, + kind=kind, + media_bytes=media_bytes, + ) + ) + decode_n_pos = int(mtmd_cpp.mtmd_input_chunk_get_n_pos(chunk)) + if decode_n_pos <= 0: + raise CompletionRequestValidationError("MTMD media chunk has no decoder positions") + embeddings = self._encode_media_chunk(kind=kind, key=key, chunk=chunk) + n_tokens = int(embeddings.shape[0]) + if n_tokens <= 0: + raise CompletionRequestValidationError("MTMD media chunk has no embeddings") + non_causal = bool(mtmd_cpp.mtmd_decode_use_non_causal(self.ctx, chunk)) + segment_identity = self._media_identity_tokens(kind, key, n_tokens) + positions = self._positions_for_chunk(chunk, decode_pos) + segment = PromptSegment( + kind=kind, + start_pos=identity_pos, + n_pos=n_tokens, + identity_tokens=segment_identity, + decode_start_pos=decode_pos, + decode_n_pos=decode_n_pos, + media=PromptSegment.Media( + embeddings=embeddings, + positions=positions, + non_causal=non_causal, + ), + ) + if non_causal and embeddings.shape[0] > min(self.n_batch, self.n_ubatch): + raise CompletionRequestValidationError( + f"non-causal {kind} embedding chunk exceeds model batch limits; " + "increase n_batch and n_ubatch" + ) + segments.append(segment) + identity_tokens.extend(segment_identity) + identity_pos += n_tokens + decode_pos += decode_n_pos + if media_index != len(media_bytes_by_index): + raise CompletionRequestValidationError("not all media inputs were consumed by MTMD") + return PromptPlan( + text=prompt, + generation_prompt=generation_prompt, + text_tokens=text_tokens, + identity_tokens=identity_tokens, + segments=segments, + text_token_index_by_pos=text_token_index_by_pos, + ) + finally: + if chunks is not None: + mtmd_cpp.mtmd_input_chunks_free(chunks) + for bitmap in bitmaps: + mtmd_cpp.mtmd_bitmap_free(bitmap) + + +class Model: + @dataclass(frozen=True) + class LoraAdapter: + path: str + scale: float = 1.0 + + def __init__( + self, + *, + model_path: str, + model_alias: Optional[str] = None, + chat_template: Optional[str] = None, + loras: Optional[List["Model.LoraAdapter"]] = None, + n_gpu_layers: Optional[int] = None, + split_mode: Optional[int] = None, + main_gpu: Optional[int] = None, + tensor_split: Optional[List[float]] = None, + vocab_only: Optional[bool] = None, + use_mmap: Optional[bool] = None, + use_mlock: Optional[bool] = None, + kv_overrides: Optional[Dict[str, Union[bool, int, float, str]]] = None, + n_ctx: Optional[int], + n_batch: Optional[int], + n_ubatch: Optional[int] = None, + n_seq_max: Optional[int], + n_threads: Optional[int], + n_threads_batch: Optional[int], + rope_scaling_type: Optional[int] = None, + pooling_type: Optional[int] = None, + attention_type: Optional[int] = None, + rope_freq_base: Optional[float] = None, + rope_freq_scale: Optional[float] = None, + yarn_ext_factor: Optional[float] = None, + yarn_attn_factor: Optional[float] = None, + yarn_beta_fast: Optional[float] = None, + yarn_beta_slow: Optional[float] = None, + yarn_orig_ctx: Optional[int] = None, + offload_kqv: Optional[bool] = None, + flash_attn: Optional[bool] = None, + op_offload: Optional[bool] = None, + swa_full: Optional[bool] = None, + no_perf: Optional[bool] = None, + type_k: Optional[int] = None, + type_v: Optional[int] = None, + kv_unified: bool = True, + max_seq_len: Optional[int] = None, + max_output_tokens: Optional[int] = None, + draft_model: Optional[str] = None, + draft_model_num_pred_tokens: int = 16, + draft_model_max_ngram_size: int = 2, + draft_model_top_k: int = 1, + draft_model_p_min: float = 0.0, + draft_model_max_batch_size: Optional[int] = None, + draft_model_threads: Optional[int] = None, + draft_model_threads_batch: Optional[int] = None, + response_schema: Optional[Dict[str, Any]] = None, + store_logits: bool = True, + ) -> None: + llama_cpp.llama_backend_init() + self.backend_initialized = True + self.model_path = model_path + self.model_alias = model_alias + self.chat_template_override = chat_template + self.response_schema = response_schema + self.store_logits = store_logits + self.max_output_tokens = max_output_tokens + self.draft_model_max_batch_size = draft_model_max_batch_size + self.draft_provider: Optional[DraftProvider] = None + self.loras = list(loras or []) + self._lora_adapters: List[Any] = [] + self._lora_adapter_array: Optional[Any] = None + self._lora_scales_array: Optional[Any] = None + model_params, self._c_tensor_split, self._kv_overrides_array = ( + self.build_model_params( + n_gpu_layers=n_gpu_layers, + split_mode=split_mode, + main_gpu=main_gpu, + tensor_split=tensor_split, + vocab_only=vocab_only, + use_mmap=use_mmap, + use_mlock=use_mlock, + kv_overrides=kv_overrides, + ) + ) + llama_model = llama_cpp.llama_model_load_from_file( + model_path.encode("utf-8"), + model_params, + ) + if llama_model is None: + raise RuntimeError(f"failed to load model: {model_path}") + self.llama_model = llama_model + vocab = llama_cpp.llama_model_get_vocab(llama_model) + if vocab is None: + raise RuntimeError("failed to access model vocabulary") + self.vocab = vocab + if llama_cpp.llama_model_has_encoder(llama_model): + raise RuntimeError("encoder models are not supported") + if not llama_cpp.llama_model_has_decoder(llama_model): + raise RuntimeError("decoder is required") + if llama_cpp.llama_model_is_recurrent(llama_model): + self.memory_model = "recurrent" + elif llama_cpp.llama_model_is_hybrid(llama_model): + self.memory_model = "hybrid" + else: + self.memory_model = ( + "attention-unified" if kv_unified else "attention-partitioned" + ) + normalized_draft_model = draft_model + required_mtp_batch = max(1, draft_model_num_pred_tokens + 1) + if normalized_draft_model == "draft-mtp": + if n_batch is not None and n_batch < required_mtp_batch: + raise RuntimeError( + "MTP requires n_batch to fit the pending token plus draft tokens " + f"(required {required_mtp_batch}, got {n_batch})" + ) + self.draft_target_batching = normalized_draft_model is not None + if ( + normalized_draft_model is not None + and normalized_draft_model != "draft-mtp" + and not self.memory_model.startswith("attention") + ): + raise RuntimeError( + "speculative decoding is only supported for attention models" + ) + n_ctx_train = int(llama_cpp.llama_model_n_ctx_train(llama_model)) + target_n_rs_seq = ( + max(1, draft_model_num_pred_tokens) + if normalized_draft_model == "draft-mtp" + else None + ) + + context_params = self.build_context_params( + n_ctx=n_ctx if n_ctx is not None else n_ctx_train, + n_batch=n_batch, + n_ubatch=n_ubatch, + n_seq_max=n_seq_max, + n_threads=n_threads, + n_threads_batch=n_threads_batch, + rope_scaling_type=rope_scaling_type, + pooling_type=pooling_type, + attention_type=attention_type, + rope_freq_base=rope_freq_base, + rope_freq_scale=rope_freq_scale, + yarn_ext_factor=yarn_ext_factor, + yarn_attn_factor=yarn_attn_factor, + yarn_beta_fast=yarn_beta_fast, + yarn_beta_slow=yarn_beta_slow, + yarn_orig_ctx=yarn_orig_ctx, + offload_kqv=offload_kqv, + flash_attn=flash_attn, + op_offload=op_offload, + swa_full=swa_full, + no_perf=no_perf, + type_k=type_k, + type_v=type_v, + kv_unified=kv_unified, + n_rs_seq=target_n_rs_seq, + ctx_type=None, + ) + ctx = llama_cpp.llama_init_from_model(llama_model, context_params) + if ctx is None: + raise RuntimeError("failed to create context") + self.ctx = ctx + mem = llama_cpp.llama_get_memory(ctx) + if mem is None: + raise RuntimeError("failed to access model memory") + self.mem = mem + self.n_ctx = int(llama_cpp.llama_n_ctx(ctx)) + self.n_ctx_seq = int(llama_cpp.llama_n_ctx_seq(ctx)) + self.n_seq_max = int(llama_cpp.llama_n_seq_max(ctx)) + self.n_rs_seq = int(llama_cpp.llama_n_rs_seq(ctx)) + self.n_batch = int(llama_cpp.llama_n_batch(ctx)) + self.n_ubatch = int(llama_cpp.llama_n_ubatch(ctx)) + self.n_threads_batch = ( + n_threads_batch + if n_threads_batch is not None + else max(multiprocessing.cpu_count(), 1) + ) + self.mtmd_processor: Optional[MTMDProcessor] = None + self._embedding_batch: Optional[llama_cpp.llama_batch] = None + self._embedding_batch_refs: List[Any] = [] + if normalized_draft_model == "draft-mtp" and self.n_batch < required_mtp_batch: + raise RuntimeError( + "MTP requires runtime n_batch to fit the pending token plus draft tokens " + f"(required {required_mtp_batch}, got {self.n_batch})" + ) + if target_n_rs_seq is not None and self.n_rs_seq < target_n_rs_seq: + raise RuntimeError( + "MTP requires retained recurrent-state slots for rollback " + f"(required {target_n_rs_seq}, got {self.n_rs_seq})" + ) + self.n_ctx_train = n_ctx_train + self.n_vocab = int(llama_cpp.llama_vocab_n_tokens(self.vocab)) + self.n_embd_inp = int(llama_cpp.llama_model_n_embd_inp(self.llama_model)) + self.kv_unified = kv_unified + self.max_seq_len_limit = min(self.request_context_limit, self.n_ctx_train) + if max_seq_len is None: + self.max_seq_len = self.max_seq_len_limit + else: + if max_seq_len <= 0: + llama_cpp.llama_free(self.ctx) + llama_cpp.llama_model_free(self.llama_model) + if self.backend_initialized: + llama_cpp.llama_backend_free() + self.backend_initialized = False + raise RuntimeError("max_seq_len must be greater than 0") + if max_seq_len > self.max_seq_len_limit: + llama_cpp.llama_free(self.ctx) + llama_cpp.llama_model_free(self.llama_model) + if self.backend_initialized: + llama_cpp.llama_backend_free() + self.backend_initialized = False + raise RuntimeError( + f"max_seq_len ({max_seq_len}) exceeds runtime limit ({self.max_seq_len_limit})" + ) + self.max_seq_len = max_seq_len + self.batch = llama_cpp.llama_batch_init(self.n_batch, 0, self.n_seq_max) + self.bos_token = int(llama_cpp.llama_vocab_bos(self.vocab)) + self.eos_token = int(llama_cpp.llama_vocab_eos(self.vocab)) + self.cls_token = self.bos_token + self.sep_token = int(llama_cpp.llama_vocab_sep(self.vocab)) + self.fim_pre_token = int(llama_cpp.llama_vocab_fim_pre(self.vocab)) + self.fim_mid_token = int(llama_cpp.llama_vocab_fim_mid(self.vocab)) + self.fim_suf_token = int(llama_cpp.llama_vocab_fim_suf(self.vocab)) + self.add_bos_token = bool(llama_cpp.llama_vocab_get_add_bos(self.vocab)) + self.add_eos_token = bool(llama_cpp.llama_vocab_get_add_eos(self.vocab)) + self.add_space_prefix = ( + self._meta_value("tokenizer.ggml.add_space_prefix") != "false" + ) + if normalized_draft_model is None: + self.draft_provider = None + elif normalized_draft_model == "prompt-lookup-decoding": + self.draft_provider = PromptLookupDecoding( + max_ngram_size=draft_model_max_ngram_size, + num_pred_tokens=draft_model_num_pred_tokens, + ) + elif normalized_draft_model == "draft-mtp": + if self.n_ubatch < self.n_seq_max: + mtp_n_batch = self.n_batch + else: + mtp_n_batch = min( + self.n_batch, + max(self.n_ubatch, self.n_seq_max, required_mtp_batch), + ) + mtp_context_params = self.build_context_params( + n_ctx=self.n_ctx, + n_batch=mtp_n_batch, + n_ubatch=min(self.n_ubatch, mtp_n_batch), + n_seq_max=self.n_seq_max, + n_threads=( + draft_model_threads + if draft_model_threads is not None + else n_threads + ), + n_threads_batch=( + draft_model_threads_batch + if draft_model_threads_batch is not None + else ( + draft_model_threads + if draft_model_threads is not None + else n_threads_batch + ) + ), + rope_scaling_type=rope_scaling_type, + pooling_type=pooling_type, + attention_type=attention_type, + rope_freq_base=rope_freq_base, + rope_freq_scale=rope_freq_scale, + yarn_ext_factor=yarn_ext_factor, + yarn_attn_factor=yarn_attn_factor, + yarn_beta_fast=yarn_beta_fast, + yarn_beta_slow=yarn_beta_slow, + yarn_orig_ctx=yarn_orig_ctx, + offload_kqv=offload_kqv, + flash_attn=flash_attn, + op_offload=op_offload, + swa_full=swa_full, + no_perf=no_perf, + type_k=type_k, + type_v=type_v, + kv_unified=kv_unified, + n_rs_seq=target_n_rs_seq, + ctx_type=llama_cpp.LLAMA_CONTEXT_TYPE_MTP, + n_outputs_max=min(mtp_n_batch, self.n_seq_max), + ) + try: + self.draft_provider = MTPDraftProvider( + model=self, + context_params=mtp_context_params, + num_pred_tokens=draft_model_num_pred_tokens, + top_k=draft_model_top_k, + p_min=draft_model_p_min, + ) + except BaseException: + llama_cpp.llama_batch_free(self.batch) + llama_cpp.llama_free(self.ctx) + self._free_lora_adapters() + llama_cpp.llama_model_free(self.llama_model) + if self.backend_initialized: + llama_cpp.llama_backend_free() + self.backend_initialized = False + raise + else: + raise RuntimeError(f"unsupported draft model: {draft_model}") + try: + self._load_lora_adapters(self.loras) + self._apply_lora_adapters(self.ctx, "target") + if isinstance(self.draft_provider, MTPDraftProvider): + self._apply_lora_adapters(self.draft_provider.ctx, "MTP draft") + except BaseException: + if self.draft_provider is not None: + self.draft_provider.close() + llama_cpp.llama_batch_free(self.batch) + llama_cpp.llama_free(self.ctx) + self._free_lora_adapters() + llama_cpp.llama_model_free(self.llama_model) + if self.backend_initialized: + llama_cpp.llama_backend_free() + self.backend_initialized = False + raise + self.chat_formatter = self._build_chat_formatter() + + @staticmethod + def build_model_params( + *, + n_gpu_layers: Optional[int], + split_mode: Optional[int], + main_gpu: Optional[int], + tensor_split: Optional[List[float]], + vocab_only: Optional[bool], + use_mmap: Optional[bool], + use_mlock: Optional[bool], + kv_overrides: Optional[Dict[str, Union[bool, int, float, str]]], + ) -> Tuple[Any, Optional[Any], Optional[Any]]: + model_params = llama_cpp.llama_model_default_params() + if n_gpu_layers is not None: + model_params.n_gpu_layers = 0x7FFFFFFF if n_gpu_layers == -1 else n_gpu_layers + if split_mode is not None: + model_params.split_mode = split_mode + if main_gpu is not None: + model_params.main_gpu = main_gpu + tensor_split_ref = None + if tensor_split is not None: + if len(tensor_split) > llama_cpp.LLAMA_MAX_DEVICES: + raise ValueError( + "tensor_split exceeds maximum supported devices " + f"({llama_cpp.LLAMA_MAX_DEVICES})" + ) + float_array = ctypes.c_float * llama_cpp.LLAMA_MAX_DEVICES + tensor_split_ref = float_array(*tensor_split) + model_params.tensor_split = tensor_split_ref + if vocab_only is not None: + model_params.vocab_only = vocab_only + if use_mmap is not None: + model_params.use_mmap = use_mmap + if use_mlock is not None: + model_params.use_mlock = use_mlock + + kv_overrides_ref = None + if kv_overrides is not None: + kv_overrides_ref = ( + llama_cpp.llama_model_kv_override * (len(kv_overrides) + 1) + )() + for index, (key, value) in enumerate(kv_overrides.items()): + kv_overrides_ref[index].key = key.encode("utf-8") + if isinstance(value, bool): + kv_overrides_ref[index].tag = llama_cpp.LLAMA_KV_OVERRIDE_TYPE_BOOL + kv_overrides_ref[index].value.val_bool = value + elif isinstance(value, int): + kv_overrides_ref[index].tag = llama_cpp.LLAMA_KV_OVERRIDE_TYPE_INT + kv_overrides_ref[index].value.val_i64 = value + elif isinstance(value, float): + kv_overrides_ref[index].tag = llama_cpp.LLAMA_KV_OVERRIDE_TYPE_FLOAT + kv_overrides_ref[index].value.val_f64 = value + elif isinstance(value, str): + encoded = value.encode("utf-8") + if len(encoded) > 128: + raise ValueError(f"kv_overrides value for {key} is too long") + encoded = encoded.ljust(128, b"\0") + kv_overrides_ref[index].tag = llama_cpp.LLAMA_KV_OVERRIDE_TYPE_STR + address = cast( + int, + ctypes.addressof(kv_overrides_ref[index].value) + + cast( + Any, + llama_cpp.llama_model_kv_override_value.val_str, + ).offset, + ) + buffer_start = ctypes.cast(address, ctypes.POINTER(ctypes.c_char)) + ctypes.memmove(buffer_start, encoded, 128) + else: + raise ValueError(f"unsupported kv_override value for {key}: {value!r}") + kv_overrides_ref[-1].key = b"\0" + model_params.kv_overrides = kv_overrides_ref + return model_params, tensor_split_ref, kv_overrides_ref + + @staticmethod + def build_context_params( + *, + n_ctx: Optional[int], + n_batch: Optional[int], + n_ubatch: Optional[int], + n_seq_max: Optional[int], + n_threads: Optional[int], + n_threads_batch: Optional[int], + rope_scaling_type: Optional[int], + pooling_type: Optional[int], + attention_type: Optional[int], + rope_freq_base: Optional[float], + rope_freq_scale: Optional[float], + yarn_ext_factor: Optional[float], + yarn_attn_factor: Optional[float], + yarn_beta_fast: Optional[float], + yarn_beta_slow: Optional[float], + yarn_orig_ctx: Optional[int], + offload_kqv: Optional[bool], + flash_attn: Optional[bool], + op_offload: Optional[bool], + swa_full: Optional[bool], + no_perf: Optional[bool], + type_k: Optional[int], + type_v: Optional[int], + kv_unified: bool, + n_rs_seq: Optional[int] = None, + ctx_type: Optional[int] = None, + n_outputs_max: Optional[int] = None, + ) -> Any: + context_params = llama_cpp.llama_context_default_params() + if n_ctx is not None: + context_params.n_ctx = n_ctx + if n_batch is not None: + context_params.n_batch = min(int(context_params.n_ctx), n_batch) + if n_ubatch is not None: + context_params.n_ubatch = min(int(context_params.n_batch), n_ubatch) + if n_seq_max is not None: + context_params.n_seq_max = n_seq_max + if n_rs_seq is not None: + context_params.n_rs_seq = n_rs_seq + if n_threads is not None: + context_params.n_threads = n_threads + if n_threads_batch is not None: + context_params.n_threads_batch = n_threads_batch + if ctx_type is not None: + context_params.ctx_type = ctx_type + if n_outputs_max is not None: + context_params.n_outputs_max = n_outputs_max + if rope_scaling_type is not None: + context_params.rope_scaling_type = rope_scaling_type + if pooling_type is not None: + context_params.pooling_type = pooling_type + if attention_type is not None: + context_params.attention_type = attention_type + if rope_freq_base is not None: + context_params.rope_freq_base = rope_freq_base + if rope_freq_scale is not None: + context_params.rope_freq_scale = rope_freq_scale + if yarn_ext_factor is not None: + context_params.yarn_ext_factor = yarn_ext_factor + if yarn_attn_factor is not None: + context_params.yarn_attn_factor = yarn_attn_factor + if yarn_beta_fast is not None: + context_params.yarn_beta_fast = yarn_beta_fast + if yarn_beta_slow is not None: + context_params.yarn_beta_slow = yarn_beta_slow + if yarn_orig_ctx is not None: + context_params.yarn_orig_ctx = yarn_orig_ctx + if offload_kqv is not None: + context_params.offload_kqv = offload_kqv + if flash_attn is not None: + context_params.flash_attn_type = ( + llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED + if flash_attn + else llama_cpp.LLAMA_FLASH_ATTN_TYPE_DISABLED + ) + if op_offload is not None: + context_params.op_offload = op_offload + if swa_full is not None: + context_params.swa_full = swa_full + if no_perf is not None: + context_params.no_perf = no_perf + if type_k is not None: + context_params.type_k = type_k + if type_v is not None: + context_params.type_v = type_v + context_params.kv_unified = kv_unified + return context_params + + @property + def exact_checkpoints_only(self) -> bool: + return self.memory_model in {"recurrent", "hybrid"} + + @property + def has_attention_budget(self) -> bool: + return self.memory_model != "recurrent" + + @property + def attention_partitioned(self) -> bool: + return self.memory_model == "attention-partitioned" + + @property + def request_context_limit(self) -> int: + if self.attention_partitioned: + return self.n_ctx_seq + return self.n_ctx + + def _load_lora_adapters(self, loras: List["Model.LoraAdapter"]) -> None: + for lora in loras: + adapter = llama_cpp.llama_adapter_lora_init( + self.llama_model, + lora.path.encode("utf-8"), + ) + if adapter is None: + raise RuntimeError(f"failed to load LoRA adapter: {lora.path}") + self._lora_adapters.append(adapter) + + if not self._lora_adapters: + return + + adapter_array_type = llama_cpp.llama_adapter_lora_p_ctypes * len( + self._lora_adapters + ) + scale_array_type = ctypes.c_float * len(self._lora_adapters) + self._lora_adapter_array = adapter_array_type(*self._lora_adapters) + self._lora_scales_array = scale_array_type( + *(float(lora.scale) for lora in loras) + ) + + def _apply_lora_adapters(self, ctx: Any, context_name: str) -> None: + if not self._lora_adapters: + return + if self._lora_adapter_array is None or self._lora_scales_array is None: + raise RuntimeError("LoRA adapter arrays are not initialized") + result = llama_cpp.llama_set_adapters_lora( + ctx, + self._lora_adapter_array, + len(self._lora_adapters), + self._lora_scales_array, + ) + if result: + raise RuntimeError(f"failed to apply LoRA adapters to {context_name} context") + + def _free_lora_adapters(self) -> None: + while self._lora_adapters: + adapter = self._lora_adapters.pop() + llama_cpp.llama_adapter_lora_free(adapter) + self._lora_adapter_array = None + self._lora_scales_array = None + + def close(self) -> None: + if self.mtmd_processor is not None: + self.mtmd_processor.close() + self.mtmd_processor = None + if self.draft_provider is not None: + self.draft_provider.close() + llama_cpp.llama_batch_free(self.batch) + llama_cpp.llama_free(self.ctx) + self._free_lora_adapters() + llama_cpp.llama_model_free(self.llama_model) + if self.backend_initialized: + llama_cpp.llama_backend_free() + self.backend_initialized = False + + def _meta_value(self, key: str) -> Optional[str]: + encoded = key.encode("utf-8") + capacity = 256 + while True: + buffer = ctypes.create_string_buffer(capacity) + count = int( + llama_cpp.llama_model_meta_val_str( + self.llama_model, + encoded, + cast(Any, buffer), + capacity, + ) + ) + if count < 0: + return None + if count < capacity: + return buffer.value.decode("utf-8", errors="ignore") + capacity = count + 1 + + def _build_chat_formatter(self) -> Optional[Jinja2ChatFormatter]: + template_text = self.chat_template_override + if template_text is None: + template = llama_cpp.llama_model_chat_template(self.llama_model, None) + if template: + template_text = template.decode("utf-8", errors="ignore") + if not template_text: + return None + bos_token = "" + eos_token = "" + if self.bos_token != -1: + bos_text = llama_cpp.llama_vocab_get_text(self.vocab, self.bos_token) + bos_token = bos_text.decode("utf-8", errors="ignore") if bos_text else "" + if self.eos_token != -1: + eos_text = llama_cpp.llama_vocab_get_text(self.vocab, self.eos_token) + eos_token = eos_text.decode("utf-8", errors="ignore") if eos_text else "" + return Jinja2ChatFormatter( + template=template_text, + bos_token=bos_token, + eos_token=eos_token, + ) + + def tokenize( + self, + text: str, + *, + add_bos: bool = True, + special: bool = True, + ) -> List[int]: + encoded = text.encode("utf-8") + capacity = max(32, len(encoded) + 32) + while True: + tokens = (llama_cpp.llama_token * capacity)() + count = int( + llama_cpp.llama_tokenize( + self.vocab, + encoded, + len(encoded), + tokens, + capacity, + add_bos, + special, + ) + ) + if count >= 0: + return [int(tokens[index]) for index in range(count)] + capacity = max(capacity * 2, -count) + + def build_prompt_tokens(self, prompt: str, suffix: Optional[str]) -> List[int]: + if suffix is None: + return self.tokenize(prompt) + if min(self.fim_pre_token, self.fim_mid_token, self.fim_suf_token) < 0: + raise ValueError("suffix is not supported by this model") + bos_tokens = [self.cls_token if self.cls_token != -1 else self.bos_token] + eos_tokens = [self.sep_token if self.sep_token != -1 else self.eos_token] + if not self.add_bos_token or bos_tokens[:1] == [-1]: + bos_tokens = [] + if not self.add_eos_token and self.sep_token == -1: + eos_tokens = [] + suffix_text = suffix + suffix_space_prefix = 0 + if self.add_space_prefix and suffix_text: + suffix_text = "☺" + suffix_text + suffix_space_prefix = 2 + prefix_tokens = [self.fim_pre_token] + self.tokenize( + prompt, + add_bos=False, + special=False, + ) + suffix_tokens = [self.fim_suf_token] + if suffix_text: + suffix_tokens.extend( + self.tokenize( + suffix_text, + add_bos=False, + special=False, + )[suffix_space_prefix:] + ) + return bos_tokens + prefix_tokens + suffix_tokens + [self.fim_mid_token] + eos_tokens + + def build_chat_prompt( + self, + messages: List[ChatCompletionRequestMessage], + *, + functions: Optional[List[ChatTemplateFunctionDefinition]] = None, + function_call: Optional[Union[str, ChatTemplateFunctionCall]] = None, + tools: Optional[List[ChatTemplateTool]] = None, + tool_choice: Optional[Union[str, ChatTemplateToolChoice]] = None, + reasoning_effort: Optional[str] = None, + ) -> Tuple[str, str, PromptPlan, List[str]]: + if self.chat_formatter is None: + raise ValueError("model does not provide a GGUF chat template") + if self.mtmd_processor is not None: + prompt_plan = self.mtmd_processor.build_prompt_plan( + messages=messages, + functions=functions, + function_call=function_call, + tools=tools, + tool_choice=tool_choice, + reasoning_effort=reasoning_effort, + ) + formatter_stop = [self.chat_formatter._eos_token] if self.chat_formatter._eos_token else [] + return ( + prompt_plan.text, + prompt_plan.generation_prompt, + prompt_plan, + formatter_stop, + ) + prompt, generation_prompt, formatter_stop = self.chat_formatter.format( + messages=messages, + functions=functions, + function_call=function_call, + tools=tools, + tool_choice=tool_choice, + reasoning_effort=reasoning_effort, + ) + prompt_tokens = self.tokenize(prompt, add_bos=False, special=True) + return ( + prompt, + generation_prompt, + PromptPlan.from_tokens( + prompt, + prompt_tokens, + generation_prompt=generation_prompt, + ), + formatter_stop, + ) + + def detokenize(self, tokens: Sequence[int]) -> bytes: + if not tokens: + return b"" + array = (llama_cpp.llama_token * len(tokens))(*tokens) + capacity = max(64, len(tokens) * 16) + while True: + buffer = ctypes.create_string_buffer(capacity) + count = int( + llama_cpp.llama_detokenize( + self.vocab, + array, + len(tokens), + cast(Any, buffer), + capacity, + True, + True, + ) + ) + if count >= 0: + return bytes(buffer.raw[:count]) + capacity = max(capacity * 2, -count) + + def token_bytes(self, token: int) -> bytes: + return self.detokenize([token]) + + def token_bytes_with_prev(self, prev_tokens: Sequence[int], token: int) -> bytes: + current = self.detokenize([*prev_tokens, token]) + previous = self.detokenize(prev_tokens) + return current[len(previous) :] + + def token_bytes_with_prev_bytes( + self, + prev_tokens: Sequence[int], + prev_text_bytes: Union[bytes, bytearray], + token: int, + ) -> bytes: + current = self.detokenize([*prev_tokens, token]) + return current[len(prev_text_bytes) :] + + def clear_batch(self) -> None: + self.batch.n_tokens = 0 + self._embedding_batch = None + self._embedding_batch_refs = [] + + def add_batch_tokens( + self, + *, + seq_id: int, + start_pos: int, + tokens: Sequence[int], + output_indices: Sequence[Optional[int]], + ) -> None: + if not tokens: + return + for index, token in enumerate(tokens): + slot = self.batch.n_tokens + self.batch.token[slot] = token + self.batch.pos[slot] = start_pos + index + self.batch.seq_id[slot][0] = seq_id + self.batch.n_seq_id[slot] = 1 + self.batch.logits[slot] = int(output_indices[index] is not None) + self.batch.n_tokens += 1 + + def add_batch_embeddings( + self, + *, + seq_id: int, + embeddings: np.ndarray, + positions: np.ndarray, + output_indices: Sequence[Optional[int]], + ) -> None: + if self.batch.n_tokens: + raise RuntimeError("cannot mix token and embedding batches") + if self._embedding_batch is not None: + raise RuntimeError("only one embedding batch is supported per scheduler step") + embeddings = np.ascontiguousarray(embeddings, dtype=np.float32) + positions = np.ascontiguousarray(positions, dtype=np.int32).reshape(-1) + n_tokens = int(embeddings.shape[0]) + if n_tokens == 0: + return + if embeddings.ndim != 2 or embeddings.shape[1] != self.n_embd_inp: + raise RuntimeError("embedding batch shape does not match model input embedding size") + if len(positions) not in {n_tokens, n_tokens * 4}: + raise RuntimeError("embedding position length mismatch") + if len(output_indices) != n_tokens: + raise RuntimeError("embedding output index length mismatch") + pos_array = (llama_cpp.llama_pos * len(positions))( + *[int(pos) for pos in positions] + ) + n_seq_id_array = (ctypes.c_int32 * n_tokens)(*[1] * n_tokens) + seq_id_array = (llama_cpp.llama_seq_id * 1)(llama_cpp.llama_seq_id(seq_id)) + seq_ids_array = (ctypes.POINTER(llama_cpp.llama_seq_id) * (n_tokens + 1))() + for index in range(n_tokens): + seq_ids_array[index] = seq_id_array + logits_array = (ctypes.c_int8 * n_tokens)( + *[int(output_index is not None) for output_index in output_indices] + ) + batch = llama_cpp.llama_batch( + n_tokens=n_tokens, + token=None, + embd=embeddings.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), + pos=pos_array, + n_seq_id=n_seq_id_array, + seq_id=seq_ids_array, + logits=logits_array, + ) + self._embedding_batch = batch + self._embedding_batch_refs = [ + embeddings, + positions, + pos_array, + n_seq_id_array, + seq_id_array, + seq_ids_array, + logits_array, + ] + + def decode(self) -> None: + batch = self._embedding_batch if self._embedding_batch is not None else self.batch + result = int(llama_cpp.llama_decode(self.ctx, batch)) + if result != 0: + raise RuntimeError(f"llama_decode failed with code {result}") + + def process_draft_batch(self) -> None: + if self.draft_provider is not None: + self.draft_provider.process(self.batch) + + def set_draft_processing_enabled(self, enabled: bool) -> None: + if self.draft_provider is not None: + self.draft_provider.set_target_processing_enabled(enabled) + + def accept_draft_tokens(self, seq_id: int, accepted_draft_tokens: int) -> None: + if self.draft_provider is not None: + self.draft_provider.accept(seq_id, accepted_draft_tokens) + + def truncate_draft_sequence(self, seq_id: int, keep_len: int) -> None: + if self.draft_provider is not None: + self.draft_provider.truncate(seq_id, keep_len) + + def copy_draft_sequence( + self, + source_seq_id: int, + dest_seq_id: int, + p0: int, + p1: int, + ) -> None: + if self.draft_provider is not None: + self.draft_provider.copy_sequence(source_seq_id, dest_seq_id, p0, p1) + + def logits(self, output_index: int) -> np.ndarray: + ptr = llama_cpp.llama_get_logits_ith(self.ctx, output_index) + if not ptr: + raise RuntimeError(f"missing logits output {output_index}") + return np.ctypeslib.as_array(ptr, shape=(self.n_vocab,)).copy() + + +class SequenceDiskCache(SequenceCache): + """Directory-backed cache for serialized llama.cpp sequence state.""" + + @dataclass + class Entry: + entry_id: int + path: Path + tokens: Tuple[int, ...] + size_bytes: int + has_prompt_logits: bool + last_accessed: float + + @dataclass(frozen=True) + class Header: + tokens: Tuple[int, ...] + has_prompt_logits: bool + + @dataclass + class Payload: + tokens: List[int] + state_bytes: np.ndarray + prompt_logits: Optional[np.ndarray] + + FORMAT_VERSION = "1" + + TENSOR_TOKENS = "tokens" + TENSOR_STATE = "state" + TENSOR_PROMPT_LOGITS = "prompt_logits" + + METADATA_FORMAT = "sequence_cache_format" + METADATA_COMPATIBILITY_KEY = "compatibility_key" + + def __init__( + self, + *, + path: Union[str, Path], + max_bytes: int, + compatibility_key: str, + min_tokens: int = 128, + ) -> None: + self.path = Path(path) + self.path.mkdir(parents=True, exist_ok=True) + self.max_bytes = max(0, int(max_bytes)) + self.min_tokens = max(1, int(min_tokens)) + self.compatibility_key = compatibility_key + self._metadata = { + self.METADATA_FORMAT: self.FORMAT_VERSION, + self.METADATA_COMPATIBILITY_KEY: compatibility_key, + } + self._trie = RadixTrie() + self._entries_by_id: Dict[int, SequenceDiskCache.Entry] = {} + self._entries_by_tokens: Dict[Tuple[int, ...], SequenceDiskCache.Entry] = {} + self._next_entry_id = 0 + self._size_bytes = 0 + self._load_entries() + self._evict_if_needed() + + @staticmethod + def fingerprint_file(path: str) -> str: + stat = os.stat(path) + payload = f"{Path(path).resolve()}:{stat.st_size}:{stat.st_mtime_ns}" + return hashlib.sha256(payload.encode("utf-8")).hexdigest() + + @classmethod + def compatibility_key_for_model(cls, model: "Model") -> str: + payload: Dict[str, Any] = { + "model": cls.fingerprint_file(model.model_path), + "memory_model": model.memory_model, + "loras": [ + { + "adapter": cls.fingerprint_file(lora.path), + "scale": float(lora.scale), + } + for lora in model.loras + ], + } + if model.memory_model == "attention-partitioned": + payload["attention_streams"] = model.n_seq_max + encoded = json.dumps( + payload, + sort_keys=True, + separators=(",", ":"), + ).encode("utf-8") + return hashlib.sha256(encoded).hexdigest() + + @staticmethod + def _safe_open(path: Path) -> Any: + try: + from safetensors import safe_open + except ImportError as exc: + raise ImportError( + "disk_cache requires safetensors. Install it with `pip install safetensors`." + ) from exc + + return safe_open(str(path), framework="numpy") + + @staticmethod + def _save_file( + tensors: Dict[str, np.ndarray], + path: Path, + metadata: Dict[str, str], + ) -> None: + from safetensors.numpy import save_file + + save_file(tensors, str(path), metadata=metadata) + + def _metadata_compatible(self, metadata: Optional[Dict[str, str]]) -> bool: + if metadata is None: + return False + return all(metadata.get(key) == value for key, value in self._metadata.items()) + + def _read_entry_header(self, path: Path) -> Optional["SequenceDiskCache.Header"]: + with self._safe_open(path) as tensors: + if not self._metadata_compatible(tensors.metadata()): + return None + tokens_array = tensors.get_tensor(self.TENSOR_TOKENS) + has_prompt_logits = self.TENSOR_PROMPT_LOGITS in tensors.keys() + tokens = tuple(int(token) for token in tokens_array.tolist()) + if len(tokens) < self.min_tokens: + return None + return SequenceDiskCache.Header( + tokens=tokens, + has_prompt_logits=has_prompt_logits, + ) + + def _read_entry_payload( + self, + entry: "SequenceDiskCache.Entry", + ) -> "SequenceDiskCache.Payload": + with self._safe_open(entry.path) as tensors: + tokens = [ + int(token) + for token in tensors.get_tensor(self.TENSOR_TOKENS).tolist() + ] + state_bytes = np.ascontiguousarray( + tensors.get_tensor(self.TENSOR_STATE), + dtype=np.uint8, + ).copy() + prompt_logits = ( + tensors.get_tensor(self.TENSOR_PROMPT_LOGITS) + if entry.has_prompt_logits + and self.TENSOR_PROMPT_LOGITS in tensors.keys() + else None + ) + return SequenceDiskCache.Payload( + tokens=tokens, + state_bytes=state_bytes, + prompt_logits=prompt_logits, + ) + + def _write_entry( + self, + path: Path, + tensors: Dict[str, np.ndarray], + ) -> None: + tmp_path = path.with_name(f"{path.name}.tmp") + try: + self._save_file(tensors, tmp_path, self._metadata) + os.replace(tmp_path, path) + except Exception: + try: + tmp_path.unlink() + except FileNotFoundError: + pass + raise + + def _load_entries(self) -> None: + for path in sorted(self.path.glob("*.safetensors")): + try: + header = self._read_entry_header(path) + if header is None: + continue + self._add_entry( + path=path, + tokens=header.tokens, + has_prompt_logits=header.has_prompt_logits, + last_accessed=path.stat().st_mtime, + ) + except Exception: # noqa: BLE001 + continue + + def _add_entry( + self, + *, + path: Path, + tokens: Tuple[int, ...], + has_prompt_logits: bool, + last_accessed: Optional[float] = None, + ) -> None: + existing = self._entries_by_tokens.get(tokens) + if existing is not None: + self._remove_entry(existing) + entry_id = self._next_entry_id + self._next_entry_id += 1 + size_bytes = path.stat().st_size + entry = SequenceDiskCache.Entry( + entry_id=entry_id, + path=path, + tokens=tokens, + size_bytes=size_bytes, + has_prompt_logits=has_prompt_logits, + last_accessed=last_accessed if last_accessed is not None else time.time(), + ) + self._entries_by_id[entry_id] = entry + self._entries_by_tokens[tokens] = entry + self._trie.extend(entry_id, tokens) + self._size_bytes += size_bytes + + def _remove_entry(self, entry: "SequenceDiskCache.Entry") -> None: + if entry.entry_id in self._trie.sequence_lengths: + self._trie.truncate(entry.entry_id, 0) + self._entries_by_id.pop(entry.entry_id, None) + if self._entries_by_tokens.get(entry.tokens) is entry: + self._entries_by_tokens.pop(entry.tokens, None) + self._size_bytes = max(0, self._size_bytes - entry.size_bytes) + try: + entry.path.unlink() + except FileNotFoundError: + pass + + @staticmethod + def _touch(entry: "SequenceDiskCache.Entry") -> None: + entry.last_accessed = time.time() + try: + os.utime(entry.path, None) + except OSError: + pass + + def _oldest_entry(self) -> Optional["SequenceDiskCache.Entry"]: + if not self._entries_by_id: + return None + return min( + self._entries_by_id.values(), + key=lambda item: (item.last_accessed, item.size_bytes), + ) + + def _evict_until(self, target_bytes: int) -> None: + while self._size_bytes > target_bytes: + entry = self._oldest_entry() + if entry is None: + return + self._remove_entry(entry) + + def _evict_if_needed(self) -> None: + if self.max_bytes <= 0: + for entry in list(self._entries_by_id.values()): + self._remove_entry(entry) + return + if self._size_bytes > self.max_bytes: + self._evict_until(self.max_bytes) + self._evict_until(int(self.max_bytes * 0.9)) + + def lookup(self, tokens: Sequence[int]) -> Optional[SequenceCache.Match]: + if not tokens: + return None + entry_id, _ = self._trie.longest_prefix(tokens) + entry = self._entries_by_id.get(entry_id) + if entry is None: + return None + self._touch(entry) + return SequenceCache.Match( + tokens=entry.tokens, + has_prompt_logits=entry.has_prompt_logits, + ) + + def load( + self, + match: SequenceCache.Match, + ) -> Optional[SequenceCache.Load]: + entry = self._entries_by_tokens.get(match.tokens) + if entry is None: + return None + + payload = self._read_entry_payload(entry) + self._touch(entry) + return SequenceCache.Load( + tokens=payload.tokens, + state_bytes=payload.state_bytes, + prompt_logits=( + np.asarray(payload.prompt_logits, dtype=np.float32) + if payload.prompt_logits is not None + else None + ), + ) + + def save( + self, + tokens: Sequence[int], + state_bytes: np.ndarray, + prompt_logits: Optional[np.ndarray], + ) -> None: + if len(tokens) < self.min_tokens or self.max_bytes <= 0: + return + state = np.asarray(state_bytes, dtype=np.uint8) + if state.size <= 0: + return + state = np.ascontiguousarray(state).copy() + entry_tokens = tuple(int(token) for token in tokens) + tensors: Dict[str, np.ndarray] = { + self.TENSOR_TOKENS: np.asarray(entry_tokens, dtype=np.int32), + self.TENSOR_STATE: state, + } + if prompt_logits is not None: + tensors[self.TENSOR_PROMPT_LOGITS] = np.asarray(prompt_logits, dtype=np.float32) + path = self.path / f"{uuid.uuid4().hex}.safetensors" + self._write_entry(path, tensors) + self._add_entry( + path=path, + tokens=entry_tokens, + has_prompt_logits=prompt_logits is not None, + ) + self._evict_if_needed() + + +class MemoryPolicy(abc.ABC): + def __init__(self, scheduler: CompletionScheduler) -> None: + self.scheduler = scheduler + + def reclaim_order(self, best_free: Optional[int]) -> List[int]: + reclaim_order = [seq_id for seq_id in self.scheduler.free_sequences if seq_id != best_free] + if best_free is not None: + reclaim_order.append(best_free) + return reclaim_order + + @staticmethod + def generation_kv_for_request( + request: CompletionRequest, + prompt_length: int, + ) -> int: + return request.internal_completion_count * ( + request.effective_max_len - prompt_length + ) + + @staticmethod + def attention_kv_required( + prompt_kv: int, + reused_kv: int, + generation_kv: int, + ) -> int: + return prompt_kv - reused_kv + generation_kv + + def try_set_sequence_cache_match( + self, + request: CompletionRequest, + resident_reuse_len: int, + required_sequence_ids: int, + required_attn_kv: Optional[int] = None, + skip_attention_budget_when_unbounded: bool = False, + ) -> bool: + cache_match_length = self.scheduler.find_sequence_cache_match( + request, + resident_reuse_len, + ) + if cache_match_length <= resident_reuse_len: + return False + has_sequence_budget = len(self.scheduler.unused_sequences) >= required_sequence_ids + has_attention_budget = required_attn_kv is None or ( + skip_attention_budget_when_unbounded + and not self.scheduler.model.has_attention_budget + ) or self.scheduler.sequence_history.size + required_attn_kv <= self.scheduler.model.n_ctx + if has_sequence_budget and has_attention_budget: + return True + self.scheduler.clear_sequence_cache_match(request) + return False + + @abc.abstractmethod + def can_admit(self, request: CompletionRequest) -> bool: + raise NotImplementedError + + @abc.abstractmethod + def admit_request(self, request: CompletionRequest) -> None: + raise NotImplementedError + + @abc.abstractmethod + def copy_prompt_state( + self, + source_sequence_id: int, + dest_sequence_id: int, + keep_len: int, + ) -> None: + raise NotImplementedError + + +class AttentionMemoryPolicy(MemoryPolicy): + def match_prefix(self, tokens: Sequence[int]) -> Tuple[int, int]: + return self.scheduler.radix_trie.longest_prefix( + tokens, + preferred_sequences=self.scheduler.free_sequences, + ) + + @staticmethod + def reuse_len_for_request(request: CompletionRequest, match_length: int) -> int: + needs_generation = ( + request.payload.max_tokens != 0 + and request.effective_max_len > len(request.prompt_tokens) + ) + reuse_len = match_length + if needs_generation and request.prompt_tokens: + reuse_len = min(reuse_len, len(request.prompt_tokens) - 1) + return request.prompt_plan.clamp_to_reusable_boundary(reuse_len) + + def admit_request(self, request: CompletionRequest) -> None: + match_seq_id = request.match_sequence_id + match_length = request.match_length + reuse_len = self.reuse_len_for_request(request, match_length) + claimable = match_seq_id in self.scheduler.free_sequences + if request.sequence_cache_match is not None: + base_seq_id = self.scheduler.claim_unused_sequence() + reuse_len, request.prompt_logits = self.scheduler.hydrate_sequence_cache_match( + request, + base_seq_id, + ) + elif claimable: + base_seq_id = self.scheduler.claim_free_sequence(match_seq_id) + if self.scheduler.radix_trie.length(base_seq_id) > reuse_len: + self.scheduler.truncate_sequence(base_seq_id, reuse_len) + else: + base_seq_id = self.scheduler.claim_unused_sequence() + if reuse_len > 0 and match_seq_id >= 0: + self.copy_prompt_state(match_seq_id, base_seq_id, reuse_len) + sibling_count = request.internal_completion_count - 1 + sibling_seq_ids: List[int] = [] + for _ in range(sibling_count): + seq_id = self.scheduler.claim_unused_sequence() + sibling_seq_ids.append(seq_id) + self.scheduler.activate_request( + request, + base_seq_id=base_seq_id, + sibling_seq_ids=sibling_seq_ids, + ) + request.prompt_cursor = reuse_len + if request.prompt_cursor == len(request.prompt_tokens): + request.prompt_done = True + self.scheduler.maybe_save_sequence_cache(request) + self.scheduler.start_completions( + request, + prompt_output_index=None, + prompt_logits=request.prompt_logits, + ) + + +class UnifiedAttentionMemoryPolicy(AttentionMemoryPolicy): + def can_admit(self, request: CompletionRequest) -> bool: + match_seq_id, match_length = self.match_prefix(request.prompt_tokens) + match_length = request.prompt_plan.clamp_to_reusable_boundary(match_length) + request.match_sequence_id = match_seq_id + request.match_length = match_length + claimable = match_seq_id in self.scheduler.free_sequences + required_sequence_ids = request.internal_completion_count - int(claimable) + prompt_length = len(request.prompt_tokens) + prompt_kv = request.prompt_plan.eval_token_count + reuse_len = self.reuse_len_for_request(request, match_length) + prefix_credit = match_length if claimable else reuse_len + prefix_credit_kv = max(0, min(prefix_credit, request.prompt_plan.length)) + generation_kv = self.generation_kv_for_request(request, prompt_length) + if self.try_set_sequence_cache_match( + request, + resident_reuse_len=reuse_len, + required_sequence_ids=request.internal_completion_count, + required_attn_kv=self.attention_kv_required( + prompt_kv, + reused_kv=0, + generation_kv=generation_kv, + ), + ): + return True + required_kv = self.attention_kv_required( + prompt_kv, + reused_kv=prefix_credit_kv, + generation_kv=generation_kv, + ) + if ( + len(self.scheduler.unused_sequences) >= required_sequence_ids + and self.scheduler.sequence_history.size + required_kv <= self.scheduler.model.n_ctx + ): + return True + best_free = match_seq_id if claimable else None + for seq_id in self.reclaim_order(best_free): + if len(self.scheduler.unused_sequences) < required_sequence_ids: + self.scheduler.delete_free_sequence(seq_id) + elif self.scheduler.sequence_history.size + required_kv > self.scheduler.model.n_ctx: + if seq_id == best_free and request.match_length > 0: + self.scheduler.truncate_free_sequence(seq_id, request.match_length) + else: + self.scheduler.delete_free_sequence(seq_id) + if ( + len(self.scheduler.unused_sequences) >= required_sequence_ids + and self.scheduler.sequence_history.size + required_kv <= self.scheduler.model.n_ctx + ): + request.match_sequence_id, request.match_length = self.match_prefix( + request.prompt_tokens, + ) + request.match_length = request.prompt_plan.clamp_to_reusable_boundary( + request.match_length + ) + return True + return False + + def copy_prompt_state( + self, + source_sequence_id: int, + dest_sequence_id: int, + keep_len: int, + ) -> None: + self.scheduler.copy_sequence_state( + source_sequence_id, + dest_sequence_id, + keep_len, + ) + + +class PartitionedAttentionMemoryPolicy(AttentionMemoryPolicy): + def can_admit(self, request: CompletionRequest) -> bool: + match_seq_id, match_length = self.match_prefix(request.prompt_tokens) + match_length = request.prompt_plan.clamp_to_reusable_boundary(match_length) + request.match_sequence_id = match_seq_id + request.match_length = match_length + claimable = match_seq_id in self.scheduler.free_sequences + required_sequence_ids = request.internal_completion_count - int(claimable) + reuse_len = self.reuse_len_for_request(request, match_length) + if self.try_set_sequence_cache_match( + request, + resident_reuse_len=reuse_len, + required_sequence_ids=request.internal_completion_count, + ): + return True + if len(self.scheduler.unused_sequences) >= required_sequence_ids: + return True + best_free = match_seq_id if claimable else None + for seq_id in self.reclaim_order(best_free): + self.scheduler.delete_free_sequence(seq_id) + if len(self.scheduler.unused_sequences) >= required_sequence_ids: + request.match_sequence_id, request.match_length = self.match_prefix( + request.prompt_tokens, + ) + request.match_length = request.prompt_plan.clamp_to_reusable_boundary( + request.match_length + ) + return True + return False + + def copy_prompt_state( + self, + source_sequence_id: int, + dest_sequence_id: int, + keep_len: int, + ) -> None: + self.scheduler.copy_sequence_state( + source_sequence_id, + dest_sequence_id, + keep_len, + copy_all_state=True, + ) + + +class CheckpointMemoryPolicy(MemoryPolicy): + def exact_checkpoint_match(self, tokens: Sequence[int]) -> Tuple[int, int]: + match_seq_id, match_length = self.scheduler.radix_trie.longest_prefix( + tokens, + preferred_sequences=self.scheduler.free_sequences, + exact_only=True, + ) + if match_seq_id not in self.scheduler.free_sequences: + return -1, 0 + return match_seq_id, match_length + + def can_admit(self, request: CompletionRequest) -> bool: + match_seq_id, match_length = self.exact_checkpoint_match(request.prompt_tokens) + request.match_sequence_id = match_seq_id + request.match_length = match_length + claimable = match_seq_id in self.scheduler.free_sequences + required_sequence_ids = request.internal_completion_count - int(claimable) + prompt_length = len(request.prompt_tokens) + prompt_kv = request.prompt_plan.eval_token_count + generation_kv = self.generation_kv_for_request(request, prompt_length) + if self.try_set_sequence_cache_match( + request, + resident_reuse_len=match_length, + required_sequence_ids=request.internal_completion_count, + required_attn_kv=self.attention_kv_required( + prompt_kv, + reused_kv=0, + generation_kv=generation_kv, + ), + skip_attention_budget_when_unbounded=True, + ): + return True + required_attn_kv = self.attention_kv_required( + prompt_kv, + reused_kv=max(0, min(match_length, request.prompt_plan.length)), + generation_kv=generation_kv, + ) + if len(self.scheduler.unused_sequences) >= required_sequence_ids and ( + not self.scheduler.model.has_attention_budget + or self.scheduler.sequence_history.size + required_attn_kv <= self.scheduler.model.n_ctx + ): + return True + best_free = match_seq_id if claimable else None + for seq_id in self.reclaim_order(best_free): + self.scheduler.delete_free_sequence(seq_id) + if len(self.scheduler.unused_sequences) >= required_sequence_ids and ( + not self.scheduler.model.has_attention_budget + or self.scheduler.sequence_history.size + required_attn_kv <= self.scheduler.model.n_ctx + ): + request.match_sequence_id, request.match_length = self.exact_checkpoint_match( + request.prompt_tokens, + ) + return True + return False + + def admit_request(self, request: CompletionRequest) -> None: + match_seq_id = request.match_sequence_id + match_length = request.match_length + claimable = match_seq_id in self.scheduler.free_sequences + if request.sequence_cache_match is not None: + base_seq_id = self.scheduler.claim_unused_sequence() + match_length, request.prompt_logits = self.scheduler.hydrate_sequence_cache_match( + request, + base_seq_id, + ) + elif claimable: + base_seq_id = self.scheduler.claim_free_sequence(match_seq_id) + request.prompt_logits = self.scheduler.checkpoint_logits.get(base_seq_id) + self.scheduler.metrics.checkpoint_hits_total += 1 + else: + base_seq_id = self.scheduler.claim_unused_sequence() + request.prompt_logits = None + sibling_count = request.internal_completion_count - 1 + sibling_seq_ids: List[int] = [] + for _ in range(sibling_count): + seq_id = self.scheduler.claim_unused_sequence() + sibling_seq_ids.append(seq_id) + self.scheduler.activate_request( + request, + base_seq_id=base_seq_id, + sibling_seq_ids=sibling_seq_ids, + ) + request.prompt_cursor = match_length + if request.prompt_cursor == len(request.prompt_tokens): + request.prompt_done = True + self.scheduler.maybe_save_prompt_checkpoint(request) + self.scheduler.maybe_save_sequence_cache(request) + self.scheduler.start_completions( + request, + prompt_output_index=None, + prompt_logits=request.prompt_logits, + ) + + def copy_prompt_state( + self, + source_sequence_id: int, + dest_sequence_id: int, + keep_len: int, + ) -> None: + self.scheduler.copy_sequence_state( + source_sequence_id, + dest_sequence_id, + keep_len, + ) + + +class CompletionScheduler: + @dataclass + class BatchItem: + @dataclass + class Prefill: + embeddings: Optional[np.ndarray] = None + positions: Optional[np.ndarray] = None + non_causal: bool = False + prompt_advance_to: Optional[int] = None + + @dataclass + class Decode: + completion_index: int + pending_count: int + accepted_draft_count: int = 0 + sampled_pending_token: Optional[int] = None + rollback_keep_len: Optional[int] = None + rollback_accepted_draft_count: int = 0 + rollback_draft_processed: bool = False + deferred_accept_draft_count: Optional[int] = None + deferred_truncate_draft_len: Optional[int] = None + + kind: Literal["prefill", "decode"] + request_id: str + seq_id: int + start_pos: int + llama_start_pos: int + tokens: List[int] + identity_tokens: List[int] + output_indices: List[Optional[int]] + output_positions: List[int] + position_increments: List[int] + prefill_state: Optional["CompletionScheduler.BatchItem.Prefill"] = None + decode_state: Optional["CompletionScheduler.BatchItem.Decode"] = None + + def require_prefill(self) -> "CompletionScheduler.BatchItem.Prefill": + if self.prefill_state is None: + raise RuntimeError("batch item is not a prefill item") + return self.prefill_state + + def require_decode(self) -> "CompletionScheduler.BatchItem.Decode": + if self.decode_state is None: + raise RuntimeError("batch item is not a decode item") + return self.decode_state + + @property + def batch_token_count(self) -> int: + if ( + self.prefill_state is not None + and self.prefill_state.embeddings is not None + ): + return int(self.prefill_state.embeddings.shape[0]) + return len(self.tokens) + + @classmethod + def prefill( + cls, + *, + request_id: str, + seq_id: int, + start_pos: int, + llama_start_pos: int, + tokens: List[int], + identity_tokens: List[int], + output_indices: List[Optional[int]], + output_positions: List[int], + position_increments: List[int], + embeddings: Optional[np.ndarray] = None, + positions: Optional[np.ndarray] = None, + non_causal: bool = False, + prompt_advance_to: Optional[int] = None, + ) -> "CompletionScheduler.BatchItem": + return cls( + kind="prefill", + request_id=request_id, + seq_id=seq_id, + start_pos=start_pos, + llama_start_pos=llama_start_pos, + tokens=tokens, + identity_tokens=identity_tokens, + output_indices=output_indices, + output_positions=output_positions, + position_increments=position_increments, + prefill_state=cls.Prefill( + embeddings=embeddings, + positions=positions, + non_causal=non_causal, + prompt_advance_to=prompt_advance_to, + ), + ) + + @classmethod + def decode( + cls, + *, + request_id: str, + seq_id: int, + start_pos: int, + llama_start_pos: int, + tokens: List[int], + identity_tokens: List[int], + output_indices: List[Optional[int]], + output_positions: List[int], + position_increments: List[int], + completion_index: int, + pending_count: int, + ) -> "CompletionScheduler.BatchItem": + return cls( + kind="decode", + request_id=request_id, + seq_id=seq_id, + start_pos=start_pos, + llama_start_pos=llama_start_pos, + tokens=tokens, + identity_tokens=identity_tokens, + output_indices=output_indices, + output_positions=output_positions, + position_increments=position_increments, + decode_state=cls.Decode( + completion_index=completion_index, + pending_count=pending_count, + ), + ) + + def __init__( + self, + model: Model, + sequence_cache: Optional[SequenceCache] = None, + ) -> None: + self.model = model + self.sequence_cache = sequence_cache + self.formatter = OpenAIFormatter(model) + self.radix_trie = RadixTrie() + self.sequence_history = SequenceHistory() + self.metrics = SchedulerMetrics() + self.checkpoint_logits: Dict[int, np.ndarray] = {} + self.claimed_sequences: set[int] = set() + self.free_sequences: "OrderedDict[int, None]" = OrderedDict() + self.unused_sequences: List[int] = list(range(self.model.n_seq_max - 1, -1, -1)) + self.requests: Dict[str, CompletionRequest] = {} + self.pending_requests: Deque[CompletionRequest] = deque() + self.active_request_ids: set[str] = set() + self.closed = False + self.sequence_round_robin = 0 + self.speculative_stats: Dict[str, int] = { + "draft_proposals": 0, + "draft_tokens_proposed": 0, + "draft_tokens_accepted": 0, + "draft_tokens_rejected": 0, + } + self.draft_acceptance_length_counts: Dict[int, int] = {} + self.defer_sampled_draft_processing = False + self.memory_policy = self.build_memory_policy() + + def build_memory_policy(self) -> MemoryPolicy: + if self.model.exact_checkpoints_only: + return CheckpointMemoryPolicy(self) + if self.model.attention_partitioned: + return PartitionedAttentionMemoryPolicy(self) + return UnifiedAttentionMemoryPolicy(self) + + @staticmethod + def request_needs_prompt_logits(request: CompletionRequest) -> bool: + return request.payload.max_tokens != 0 and request.effective_max_len > len( + request.prompt_tokens + ) + + @staticmethod + def request_needs_uncached_prompt_logprobs(request: CompletionRequest) -> bool: + return request.payload.echo and request.payload.logprobs is not None + + @staticmethod + def clear_sequence_cache_match(request: CompletionRequest) -> None: + request.sequence_cache_match = None + request.sequence_cache_match_length = 0 + + def can_lookup_sequence_cache(self, request: CompletionRequest) -> bool: + return ( + self.sequence_cache is not None + and bool(request.prompt_tokens) + and not self.request_needs_uncached_prompt_logprobs(request) + ) + + def is_sequence_cache_match_usable( + self, + request: CompletionRequest, + match: SequenceCache.Match, + resident_reuse_len: int, + ) -> bool: + match_length = match.length + if ( + match_length <= resident_reuse_len + or match_length > len(request.prompt_tokens) + or tuple(request.prompt_tokens[:match_length]) != match.tokens + or not request.prompt_plan.is_reusable_boundary(match_length) + ): + return False + return not ( + self.request_needs_prompt_logits(request) + and match_length == len(request.prompt_tokens) + and not match.has_prompt_logits + ) + + def find_sequence_cache_match( + self, + request: CompletionRequest, + resident_reuse_len: int, + ) -> int: + self.clear_sequence_cache_match(request) + if not self.can_lookup_sequence_cache(request): + return 0 + assert self.sequence_cache is not None + try: + match = self.sequence_cache.lookup(request.prompt_tokens) + except Exception: # noqa: BLE001 + self.metrics.sequence_cache_lookup_failures_total += 1 + return 0 + if match is None: + return 0 + if not self.is_sequence_cache_match_usable( + request, + match, + resident_reuse_len, + ): + return 0 + request.sequence_cache_match = match + request.sequence_cache_match_length = match.length + return match.length + + def fail_sequence_cache_load( + self, + request: CompletionRequest, + seq_id: int, + ) -> Tuple[int, Optional[np.ndarray]]: + llama_cpp.llama_memory_seq_rm(self.model.mem, seq_id, 0, -1) + self.model.truncate_draft_sequence(seq_id, 0) + self.clear_sequence_cache_match(request) + self.metrics.sequence_cache_load_failures_total += 1 + return 0, None + + def load_sequence_state_bytes( + self, + seq_id: int, + state_bytes: np.ndarray, + ) -> bool: + state = np.ascontiguousarray(state_bytes, dtype=np.uint8) + state_size = int(state.size) + if state_size <= 0: + return False + state_buffer = (ctypes.c_uint8 * state_size).from_buffer(cast(Any, state)) + loaded_bytes = int( + llama_cpp.llama_state_seq_set_data( + self.model.ctx, + state_buffer, + state_size, + cast(llama_cpp.llama_seq_id, seq_id), + ) + ) + return loaded_bytes == state_size + + def save_sequence_state_bytes(self, seq_id: int) -> Optional[np.ndarray]: + c_seq_id = cast(llama_cpp.llama_seq_id, seq_id) + state_size = int(llama_cpp.llama_state_seq_get_size(self.model.ctx, c_seq_id)) + if state_size <= 0: + return None + state_buffer = (ctypes.c_uint8 * state_size)() + state_bytes = int( + llama_cpp.llama_state_seq_get_data( + self.model.ctx, + state_buffer, + state_size, + c_seq_id, + ) + ) + if state_bytes <= 0: + return None + return np.ctypeslib.as_array(state_buffer, shape=(state_bytes,)).copy() + + def hydrate_sequence_cache_match( + self, + request: CompletionRequest, + seq_id: int, + ) -> Tuple[int, Optional[np.ndarray]]: + match = request.sequence_cache_match + if self.sequence_cache is None or match is None: + return 0, None + try: + loaded = self.sequence_cache.load(match) + except Exception: # noqa: BLE001 + return self.fail_sequence_cache_load(request, seq_id) + if loaded is None: + return self.fail_sequence_cache_load(request, seq_id) + tokens = list(loaded.tokens) + expected_length = request.sequence_cache_match_length + if ( + len(tokens) != expected_length + or tokens != request.prompt_tokens[:expected_length] + ): + return self.fail_sequence_cache_load(request, seq_id) + if ( + len(tokens) == len(request.prompt_tokens) + and self.request_needs_prompt_logits(request) + and loaded.prompt_logits is None + ): + return self.fail_sequence_cache_load(request, seq_id) + if not self.load_sequence_state_bytes(seq_id, loaded.state_bytes): + return self.fail_sequence_cache_load(request, seq_id) + self.radix_trie.extend(seq_id, tokens) + self.sequence_history.extend( + seq_id, + tokens, + request.prompt_plan.position_increments_up_to(len(tokens)), + ) + self.model.truncate_draft_sequence(seq_id, 0) + self.metrics.sequence_cache_hits_total += 1 + self.metrics.sequence_cache_tokens_loaded_total += len(tokens) + if len(tokens) == len(request.prompt_tokens): + return len(tokens), loaded.prompt_logits + return len(tokens), None + + def maybe_save_sequence_cache(self, request: CompletionRequest) -> None: + if ( + self.sequence_cache is None + or request.base_seq_id is None + or not request.prompt_tokens + or request.sequence_cache_match_length == len(request.prompt_tokens) + ): + return + state_bytes = self.save_sequence_state_bytes(request.base_seq_id) + if state_bytes is None: + return + try: + self.sequence_cache.save( + request.prompt_tokens, + state_bytes, + request.prompt_logits, + ) + except Exception: # noqa: BLE001 + self.metrics.sequence_cache_save_failures_total += 1 + return + self.metrics.sequence_cache_save_requests_total += 1 + + def close(self) -> None: + self.closed = True + self.model.close() + + def submit_request(self, request: CompletionRequest) -> str: + if self.closed: + raise RuntimeError("scheduler closed") + self.requests[request.id] = request + self.pending_requests.append(request) + self.metrics.requests_submitted_total += 1 + return request.id + + def cancel(self, request_id: str) -> None: + request = self.requests.get(request_id) + if request is not None: + request.cancelled = True + + def is_idle(self) -> bool: + return not self.pending_requests and not self.active_request_ids + + @staticmethod + def logits_to_logprobs(logits: np.ndarray) -> np.ndarray: + logits = logits.astype(np.float32, copy=False) + max_logit = float(np.max(logits)) + shifted = logits - max_logit + return shifted - math.log(float(np.sum(np.exp(shifted, dtype=np.float64)))) + + def step(self) -> bool: + step_started_at = time.perf_counter() + if self.closed: + return False + try: + self.admit_waiting() + batch_items = self.build_batch() + if not batch_items: + if self.maybe_fill_batched_draft_tokens(): + self.finalize_cancelled() + return True + return self.finalize_cancelled() + draft_processing_enabled = self.batch_needs_draft_processing(batch_items) + self.defer_sampled_draft_processing = ( + draft_processing_enabled + and self.supports_sampled_draft_processing + and all(item.kind == "decode" for item in batch_items) + ) + self.model.set_draft_processing_enabled(draft_processing_enabled) + self.model.clear_batch() + for item in batch_items: + prefill = item.prefill_state + if prefill is not None and prefill.embeddings is not None: + if prefill.positions is None: + raise RuntimeError("embedding batch item is missing positions") + self.model.add_batch_embeddings( + seq_id=item.seq_id, + embeddings=prefill.embeddings, + positions=prefill.positions, + output_indices=item.output_indices, + ) + else: + self.model.add_batch_tokens( + seq_id=item.seq_id, + start_pos=item.llama_start_pos, + tokens=item.tokens, + output_indices=item.output_indices, + ) + decode_started_at = time.perf_counter() + try: + non_causal_decode = any( + item.prefill_state is not None and item.prefill_state.non_causal + for item in batch_items + ) + try: + if non_causal_decode: + llama_cpp.llama_set_causal_attn(self.model.ctx, False) + self.model.decode() + finally: + if non_causal_decode: + llama_cpp.llama_set_causal_attn(self.model.ctx, True) + decode_elapsed = time.perf_counter() - decode_started_at + if draft_processing_enabled and not self.defer_sampled_draft_processing: + draft_process_started_at = time.perf_counter() + self.model.process_draft_batch() + draft_process_elapsed = time.perf_counter() - draft_process_started_at + self.metrics.draft_process_seconds_total += draft_process_elapsed + self.metrics.draft_process_calls_total += 1 + self.observe_draft_process(batch_items, draft_process_elapsed) + except BaseException as exc: # noqa: BLE001 + for request_id in list(self.active_request_ids): + self.fail_request(self.requests[request_id], exc) + for request in list(self.pending_requests): + self.fail_request(request, exc) + return True + self.metrics.observe_decode( + batch_items, + decode_elapsed, + ) + process_batch_started_at = time.perf_counter() + self.process_batch(batch_items) + self.metrics.process_batch_seconds_total += ( + time.perf_counter() - process_batch_started_at + ) + self.metrics.process_batch_calls_total += 1 + if self.defer_sampled_draft_processing: + try: + self.process_sampled_draft_batch(batch_items) + finally: + self.apply_deferred_draft_rollbacks(batch_items) + self.defer_sampled_draft_processing = False + self.finalize_cancelled() + return True + finally: + self.metrics.scheduler_step_seconds_total += ( + time.perf_counter() - step_started_at + ) + self.metrics.scheduler_step_calls_total += 1 + + def observe_draft_process( + self, + items: Sequence["CompletionScheduler.BatchItem"], + elapsed_seconds: float, + ) -> None: + if self.model.draft_provider is None or elapsed_seconds <= 0.0: + return + self.metrics.draft_seconds_total += elapsed_seconds + + def item_allows_mtp_processing(self, item: "CompletionScheduler.BatchItem") -> bool: + request = self.requests.get(item.request_id) + if request is None: + return False + if item.kind == "prefill": + return not any(segment.kind != "text" for segment in request.prompt_plan.segments) + decode = item.decode_state + if decode is None: + return False + return not request.completions[decode.completion_index].multimodal_prompt + + def batch_needs_draft_processing( + self, + batch_items: Sequence["CompletionScheduler.BatchItem"], + ) -> bool: + if self.model.draft_provider is None: + return False + if not self.draft_batch_size_allowed(batch_items): + return False + if isinstance(self.model.draft_provider, MTPDraftProvider) and not all( + self.item_allows_mtp_processing(item) for item in batch_items + ): + return False + for item in batch_items: + request = self.requests.get(item.request_id) + if request is None: + continue + if item.kind == "prefill": + if self.request_needs_prompt_logits(request): + return True + continue + decode = item.decode_state + if decode is None: + continue + completion = request.completions[decode.completion_index] + if not completion.finished: + return True + return False + + def draft_batch_size_allowed( + self, + batch_items: Sequence["CompletionScheduler.BatchItem"], + ) -> bool: + max_batch_size = getattr(self.model, "draft_model_max_batch_size", None) + if max_batch_size is None: + return True + draft_batch_size = sum( + 1 + for item in batch_items + if item.kind == "decode" and item.decode_state is not None + ) + return draft_batch_size <= max_batch_size + + def active_draft_batch_size_allowed(self, draft_batch_size: int) -> bool: + max_batch_size = getattr(self.model, "draft_model_max_batch_size", None) + return max_batch_size is None or draft_batch_size <= max_batch_size + + def admit_waiting(self) -> None: + while self.pending_requests: + request = self.pending_requests[0] + if request.cancelled: + self.fail_request(request, CompletionRequestCancelledError("request cancelled")) + continue + if not self.can_admit(request): + break + self.pending_requests.popleft() + self.admit_request(request) + + def can_admit(self, request: CompletionRequest) -> bool: + return self.memory_policy.can_admit(request) + + def admit_request(self, request: CompletionRequest) -> None: + self.memory_policy.admit_request(request) + if request.admitted: + self.metrics.requests_admitted_total += 1 + + def build_batch(self) -> List[CompletionScheduler.BatchItem]: + prompt_requests = [ + self.requests[request_id] + for request_id in self.active_request_ids + if self.requests[request_id].admitted + and not self.requests[request_id].prompt_done + and not self.requests[request_id].cancelled + ] + completions = [ + completion + for request_id in self.active_request_ids + for completion in self.requests[request_id].completions + if (completion.pending_input_tokens or completion.draft_tokens) + and not completion.finished + ] + if not prompt_requests and not completions: + return [] + + for request in prompt_requests: + if request.prompt_cursor < len(request.prompt_tokens): + segment = self._current_prompt_segment(request) + if segment.kind != "text": + token_count = min( + segment.batch_rows + if segment.media is not None and segment.media.non_causal + else self._pending_tokens_length(request), + self.model.n_batch, + ) + if segment.batch_rows > self.model.n_batch: + if segment.media is not None and segment.media.non_causal: + raise RuntimeError( + "non-causal media prompt segment exceeds model.n_batch; " + "increase n_batch" + ) + item, _ = self._build_pending_batch_item( + request, + token_count, + 0, + ) + return [item] + + ordered_sequences = self._ordered_pending_sequences(prompt_requests, completions) + allocations = self._allocate_pending_tokens_for_model(ordered_sequences) + if ordered_sequences: + self.sequence_round_robin += 1 + + items: List[CompletionScheduler.BatchItem] = [] + output_index = 0 + for source in ordered_sequences: + token_count = allocations.get(self._pending_sequence_id(source), 0) + if token_count <= 0: + continue + item, output_index = self._build_pending_batch_item( + source, + token_count, + output_index, + ) + items.append(item) + return self._split_mixed_mtp_batch(items) + + def _split_mixed_mtp_batch( + self, + items: List["CompletionScheduler.BatchItem"], + ) -> List["CompletionScheduler.BatchItem"]: + if not isinstance(self.model.draft_provider, MTPDraftProvider) or len(items) <= 1: + return items + eligibility = [self.item_allows_mtp_processing(item) for item in items] + if all(eligibility) or not any(eligibility): + return items + keep_eligible = eligibility[0] + split_items = [ + item + for item, item_eligible in zip(items, eligibility) + if item_eligible == keep_eligible + ] + self._renumber_output_indices(split_items) + return split_items + + @staticmethod + def _renumber_output_indices( + items: Sequence["CompletionScheduler.BatchItem"], + ) -> None: + next_output_index = 0 + for item in items: + for index, output_index in enumerate(item.output_indices): + if output_index is None: + continue + item.output_indices[index] = next_output_index + next_output_index += 1 + + def _allocate_pending_tokens_for_model( + self, + ordered_sequences: Sequence[Union[CompletionRequest, Completion]], + ) -> Dict[int, int]: + if not self._needs_homogeneous_recurrent_draft_batching(): + return self._allocate_pending_tokens(ordered_sequences, self.model.n_batch) + + active = [ + source + for source in ordered_sequences + if self._pending_tokens_length(source) > 0 + ] + if not active: + return {} + + first_draft_count = self._recurrent_draft_batch_token_count(active[0]) + if first_draft_count > 0: + return self._allocate_homogeneous_recurrent_draft_tokens( + active, + first_draft_count, + ) + + non_draft_sources = [ + source + for source in active + if self._recurrent_draft_batch_token_count(source) == 0 + ] + return self._allocate_pending_tokens(non_draft_sources, self.model.n_batch) + + def _needs_homogeneous_recurrent_draft_batching(self) -> bool: + return bool( + self.model.draft_provider is not None + and getattr(self.model, "draft_target_batching", False) + and self.model.exact_checkpoints_only + and self.model.n_rs_seq > 0 + ) + + def _recurrent_draft_batch_token_count( + self, + source: Union[CompletionRequest, Completion], + ) -> int: + if isinstance(source, CompletionRequest): + return 0 + if ( + source.finished + or source.pending_finish_reason is not None + or not source.pending_input_tokens + or not source.draft_tokens + ): + return 0 + token_count = min( + len(source.pending_input_tokens) + len(source.draft_tokens), + len(source.pending_input_tokens) + self.model.n_rs_seq, + ) + return token_count if token_count > len(source.pending_input_tokens) else 0 + + def _allocate_homogeneous_recurrent_draft_tokens( + self, + sources: Sequence[Union[CompletionRequest, Completion]], + token_count: int, + ) -> Dict[int, int]: + # Recurrent rollback snapshots are only valid if the whole per-sequence + # pending+draft group fits in a single llama.cpp ubatch. + safe_capacity = min(self.model.n_batch, self.model.n_ubatch) + if token_count > safe_capacity: + return self._allocate_recurrent_pending_only_tokens(sources, safe_capacity) + + allocations: Dict[int, int] = {} + remaining_capacity = safe_capacity + for source in sources: + if remaining_capacity < token_count: + break + if self._recurrent_draft_batch_token_count(source) != token_count: + continue + seq_id = self._pending_sequence_id(source) + allocations[seq_id] = token_count + remaining_capacity -= token_count + return allocations + + def _allocate_recurrent_pending_only_tokens( + self, + sources: Sequence[Union[CompletionRequest, Completion]], + capacity: int, + ) -> Dict[int, int]: + allocations: Dict[int, int] = {} + remaining_capacity = capacity + for source in sources: + if remaining_capacity <= 0: + break + if self._recurrent_draft_batch_token_count(source) == 0: + continue + if isinstance(source, CompletionRequest): + continue + pending_count = min(len(source.pending_input_tokens), remaining_capacity) + if pending_count <= 0: + continue + allocations[source.seq_id] = pending_count + remaining_capacity -= pending_count + return allocations + + def _ordered_pending_sequences( + self, + prompt_requests: Sequence[CompletionRequest], + completions: Sequence[Completion], + ) -> List[Union[CompletionRequest, Completion]]: + if not prompt_requests and not completions: + return [] + prompt_by_request_id = {request.id: request for request in prompt_requests} + completions_by_request_id: Dict[str, List[Completion]] = {} + for completion in completions: + completions_by_request_id.setdefault(completion.request_id, []).append(completion) + sequence_list: List[Union[CompletionRequest, Completion]] = [] + for request_id, request in self.requests.items(): + if request_id not in self.active_request_ids or request.cancelled: + continue + prompt_request = prompt_by_request_id.get(request_id) + if prompt_request is not None: + sequence_list.append(prompt_request) + sequence_list.extend(completions_by_request_id.get(request_id, [])) + if not sequence_list: + return [] + start = self.sequence_round_robin % len(sequence_list) + return sequence_list[start:] + sequence_list[:start] + + def _pending_sequence_id( + self, + source: Union[CompletionRequest, Completion], + ) -> int: + if isinstance(source, CompletionRequest): + if source.base_seq_id is None: + raise RuntimeError("prompt sequence is missing base seq id") + return source.base_seq_id + return source.seq_id + + def _current_prompt_segment(self, request: CompletionRequest) -> PromptSegment: + return request.prompt_plan.segment_at(request.prompt_cursor) + + def _pending_tokens_length( + self, + source: Union[CompletionRequest, Completion], + ) -> int: + if isinstance(source, CompletionRequest): + if source.prompt_cursor >= len(source.prompt_tokens): + return 0 + segment = self._current_prompt_segment(source) + if segment.kind != "text": + segment_offset = source.prompt_cursor - segment.start_pos + if segment.media is not None and segment.media.non_causal: + if segment_offset != 0: + raise RuntimeError("non-causal media segment was partially scheduled") + return segment.batch_rows + return segment.end_pos - source.prompt_cursor + return segment.end_pos - source.prompt_cursor + draft_count = ( + len(source.draft_tokens) + if source.pending_finish_reason is None + and getattr(self.model, "draft_target_batching", True) + else 0 + ) + return len(source.pending_input_tokens) + draft_count + + def _pending_allocation_for_capacity( + self, + source: Union[CompletionRequest, Completion], + already_allocated: int, + capacity: int, + ) -> int: + if capacity <= 0: + return 0 + if not isinstance(source, CompletionRequest): + remaining = self._pending_tokens_length(source) - already_allocated + return min(remaining, capacity) + segment = self._current_prompt_segment(source) + if segment.kind == "text": + remaining = self._pending_tokens_length(source) - already_allocated + return min(remaining, capacity) + if segment.media is not None and segment.media.non_causal: + if already_allocated == 0 and segment.batch_rows <= capacity: + return segment.batch_rows + return 0 + segment_offset = source.prompt_cursor - segment.start_pos + already_allocated + return segment.rows_for_capacity(segment_offset, capacity) + + def _allocate_pending_tokens( + self, + sources: Sequence[Union[CompletionRequest, Completion]], + capacity: int, + ) -> Dict[int, int]: + allocations: Dict[int, int] = {} + active = [source for source in sources if self._pending_tokens_length(source) > 0] + remaining_capacity = capacity + + for source in active: + if remaining_capacity <= 0: + break + seq_id = self._pending_sequence_id(source) + allocation = self._pending_allocation_for_capacity( + source, + allocations.get(seq_id, 0), + remaining_capacity, + ) + if allocation <= 0: + continue + allocations[seq_id] = allocations.get(seq_id, 0) + allocation + remaining_capacity -= allocation + + while remaining_capacity > 0 and active: + share = max(1, remaining_capacity // len(active)) + progress = False + next_active: List[Union[CompletionRequest, Completion]] = [] + for source in active: + seq_id = self._pending_sequence_id(source) + remaining_tokens = self._pending_tokens_length(source) - allocations.get(seq_id, 0) + if remaining_tokens <= 0: + continue + allocation = self._pending_allocation_for_capacity( + source, + allocations.get(seq_id, 0), + min(share, remaining_capacity), + ) + if allocation <= 0: + next_active.append(source) + continue + allocations[seq_id] = allocations.get(seq_id, 0) + allocation + remaining_capacity -= allocation + progress = True + if remaining_tokens > allocation and remaining_capacity > 0: + next_active.append(source) + if not progress: + break + active = next_active + return allocations + + def _build_pending_batch_item( + self, + source: Union[CompletionRequest, Completion], + token_count: int, + output_index: int, + ) -> Tuple["CompletionScheduler.BatchItem", int]: + if isinstance(source, CompletionRequest): + if source.base_seq_id is None: + raise RuntimeError("prompt sequence is missing base seq id") + segment = self._current_prompt_segment(source) + if segment.kind != "text": + segment_offset = source.prompt_cursor - segment.start_pos + non_causal = segment.media is not None and segment.media.non_causal + if non_causal: + if segment_offset != 0 or token_count < segment.batch_rows: + raise RuntimeError("non-causal media segment must be scheduled atomically") + row_count = segment.batch_rows + embeddings, positions, position_increments = segment.media_slice( + 0, + row_count, + ) + else: + row_count = segment.rows_for_capacity(segment_offset, token_count) + if row_count <= 0: + raise RuntimeError("media prompt allocation is too small") + embeddings, positions, position_increments = segment.media_slice( + segment_offset, + row_count, + ) + logical_start = source.prompt_cursor + logical_end = logical_start + row_count + output_indices = [None] * row_count + output_positions = [logical_start] * row_count + if output_positions: + output_positions[-1] = logical_end - 1 + needs_last_output = ( + (source.payload.echo and source.payload.logprobs is not None + and source.prompt_plan.has_text_token_at(logical_end)) + or self.model.exact_checkpoints_only + or logical_end == len(source.prompt_tokens) + ) + if needs_last_output and output_indices: + output_indices[-1] = output_index + output_index += 1 + return ( + CompletionScheduler.BatchItem.prefill( + request_id=source.id, + seq_id=source.base_seq_id, + start_pos=logical_start, + llama_start_pos=segment.decode_start_pos + sum( + segment.decoder_position_increments[:segment_offset] + ), + tokens=[], + identity_tokens=list( + segment.identity_tokens[ + segment_offset : segment_offset + row_count + ] + ), + output_indices=output_indices, + output_positions=output_positions, + position_increments=position_increments, + embeddings=embeddings, + positions=positions, + non_causal=non_causal, + prompt_advance_to=logical_end, + ), + output_index, + ) + segment_offset = source.prompt_cursor - segment.start_pos + max_count = min(token_count, segment.end_pos - source.prompt_cursor) + chunk = list(segment.text_tokens[segment_offset : segment_offset + max_count]) + identity_chunk = list( + segment.identity_tokens[segment_offset : segment_offset + max_count] + ) + ends_prompt = source.prompt_cursor + len(identity_chunk) == len(source.prompt_tokens) + output_indices: List[Optional[int]] = [None] * len(chunk) + output_positions = [source.prompt_cursor + index for index in range(len(chunk))] + for index, output_position in enumerate(output_positions): + if ( + source.payload.echo + and source.payload.logprobs is not None + and source.prompt_plan.has_text_token_at(output_position + 1) + ): + output_indices[index] = output_index + output_index += 1 + if self.model.exact_checkpoints_only and chunk and output_indices[-1] is None: + output_indices[-1] = output_index + output_index += 1 + elif ends_prompt and chunk and output_indices[-1] is None: + output_indices[-1] = output_index + output_index += 1 + return ( + CompletionScheduler.BatchItem.prefill( + request_id=source.id, + seq_id=source.base_seq_id, + start_pos=source.prompt_cursor, + llama_start_pos=segment.decode_start_pos + segment_offset, + tokens=chunk, + identity_tokens=identity_chunk, + output_indices=output_indices, + output_positions=output_positions, + position_increments=segment.decoder_position_increments[ + segment_offset : segment_offset + len(identity_chunk) + ], + ), + output_index, + ) + + request = self.requests[source.request_id] + pending_count = min(token_count, len(source.pending_input_tokens)) + draft_count = ( + max(0, token_count - pending_count) + if getattr(self.model, "draft_target_batching", True) + else 0 + ) + scheduled_tokens = [ + *source.pending_input_tokens[:pending_count], + *( + source.draft_tokens[:draft_count] + if source.pending_finish_reason is None + else [] + ), + ] + output_indices = list(range(output_index, output_index + len(scheduled_tokens))) + start_pos = self.radix_trie.length(source.seq_id) + return ( + CompletionScheduler.BatchItem.decode( + request_id=request.id, + seq_id=source.seq_id, + start_pos=start_pos, + llama_start_pos=self.sequence_history.position_length(source.seq_id), + tokens=list(scheduled_tokens), + identity_tokens=list(scheduled_tokens), + output_indices=output_indices, + output_positions=[ + start_pos + index + for index in range(len(scheduled_tokens)) + ], + position_increments=[1] * len(scheduled_tokens), + completion_index=source.index, + pending_count=pending_count, + ), + output_index + len(scheduled_tokens), + ) + + def process_batch(self, items: List[CompletionScheduler.BatchItem]) -> None: + output_count = sum( + output_index is not None + for item in items + for output_index in item.output_indices + ) + for item in items: + request = self.requests[item.request_id] + if request.cancelled: + continue + self.radix_trie.extend(item.seq_id, item.identity_tokens) + self.sequence_history.extend( + item.seq_id, + item.identity_tokens, + item.position_increments, + ) + if item.kind == "prefill": + request.capture_prompt_logprobs( + model=self.model, + formatter=self.formatter, + output_indices=item.output_indices, + output_positions=item.output_positions, + output_count=output_count, + output_index_to_logits_index=self.output_index_to_logits_index, + ) + prompt_output_index = self.output_index_to_logits_index( + self.last_output_index(item.output_indices), + output_count, + ) + prompt_logits = ( + self.model.logits(prompt_output_index) + if prompt_output_index is not None + else None + ) + prefill = item.require_prefill() + if prefill.prompt_advance_to is not None: + request.prompt_cursor = prefill.prompt_advance_to + else: + request.prompt_cursor += len(item.identity_tokens) + if request.prompt_cursor == len(request.prompt_tokens): + request.prompt_done = True + request.prompt_logits = prompt_logits + self.maybe_save_prompt_checkpoint(request) + self.maybe_save_sequence_cache(request) + self.start_completions( + request, + prompt_output_index=prompt_output_index, + prompt_logits=request.prompt_logits, + ) + else: + decode = item.require_decode() + completion = request.completions[decode.completion_index] + self.process_generation_item( + completion, + item, + output_count, + ) + self.finalize_request_if_ready(request) + if not self.defer_sampled_draft_processing: + self.maybe_fill_batched_draft_tokens() + + @property + def should_defer_draft_fill(self) -> bool: + return bool( + self.model.draft_provider is not None + and getattr(self.model.draft_provider, "batched_draft", False) + ) + + @property + def supports_sampled_draft_processing(self) -> bool: + return bool( + self.model.draft_provider is not None + and getattr(self.model.draft_provider, "sampled_batch_draft", False) + ) + + def maybe_fill_draft_tokens(self, completion: Completion) -> None: + if ( + self.model.draft_provider is None + or completion.finished + or completion.pending_finish_reason is not None + or completion.draft_tokens + ): + return + remaining_tokens = completion.max_total_tokens - completion.total_tokens + if not getattr(self.model, "draft_target_batching", True): + remaining_tokens = min(remaining_tokens, 1) + if remaining_tokens <= 0: + return + input_ids = self.draft_input_ids(completion) + if isinstance(self.model.draft_provider, MTPDraftProvider) and completion.multimodal_prompt: + return + can_draft = getattr(self.model.draft_provider, "can_draft", None) + if can_draft is not None and not can_draft( + int(input_ids.shape[0]), + seq_id=completion.seq_id, + ): + return + draft_started_at = time.perf_counter() + try: + proposed = self.model.draft_provider.draft( + input_ids, + seq_id=completion.seq_id, + max_tokens=remaining_tokens, + ) + finally: + draft_elapsed = time.perf_counter() - draft_started_at + self.metrics.draft_seconds_total += draft_elapsed + self.metrics.draft_generate_seconds_total += draft_elapsed + self.metrics.draft_generate_calls_total += 1 + if proposed.size == 0: + return + limited = [int(token) for token in proposed[:remaining_tokens]] + if not limited: + return + completion.draft_tokens = limited + self.speculative_stats["draft_proposals"] += 1 + self.speculative_stats["draft_tokens_proposed"] += len(limited) + + def maybe_fill_batched_draft_tokens(self) -> bool: + if not self.should_defer_draft_fill or self.model.draft_provider is None: + return False + draft_many = getattr(self.model.draft_provider, "draft_many", None) + if draft_many is None: + return False + + completions: List[Completion] = [] + draft_requests: List[Tuple[np.ndarray, int, Optional[int]]] = [] + remaining_by_completion: List[int] = [] + for request_id in self.active_request_ids: + request = self.requests.get(request_id) + if request is None or not request.admitted: + continue + for completion in request.completions: + if ( + completion.finished + or completion.pending_finish_reason is not None + or completion.draft_tokens + ): + continue + remaining_tokens = completion.max_total_tokens - completion.total_tokens + if not getattr(self.model, "draft_target_batching", True): + remaining_tokens = min(remaining_tokens, 1) + if remaining_tokens <= 0: + continue + input_ids = self.draft_input_ids(completion) + if ( + isinstance(self.model.draft_provider, MTPDraftProvider) + and completion.multimodal_prompt + ): + continue + can_draft = getattr(self.model.draft_provider, "can_draft", None) + if can_draft is not None and not can_draft( + int(input_ids.shape[0]), + seq_id=completion.seq_id, + ): + continue + completions.append(completion) + draft_requests.append((input_ids, completion.seq_id, remaining_tokens)) + remaining_by_completion.append(remaining_tokens) + + if not draft_requests: + return False + if not self.active_draft_batch_size_allowed(len(draft_requests)): + return False + + draft_started_at = time.perf_counter() + proposed_many = draft_many(draft_requests) + draft_elapsed = time.perf_counter() - draft_started_at + self.metrics.draft_seconds_total += draft_elapsed + self.metrics.draft_generate_seconds_total += draft_elapsed + self.metrics.draft_generate_calls_total += 1 + made_progress = False + + for completion, proposed, remaining_tokens in zip( + completions, + proposed_many, + remaining_by_completion, + ): + if proposed.size == 0: + continue + limited = [int(token) for token in proposed[:remaining_tokens]] + if not limited: + continue + self.speculative_stats["draft_proposals"] += 1 + self.speculative_stats["draft_tokens_proposed"] += len(limited) + completion.draft_tokens = limited + made_progress = True + return made_progress + + def draft_input_ids(self, completion: Completion) -> np.ndarray: + return np.array( + [*completion.prompt_tokens, *completion.completion_tokens], + dtype=np.intc, + ) + + def process_sampled_draft_batch( + self, + items: Sequence["CompletionScheduler.BatchItem"], + ) -> None: + if self.model.draft_provider is None: + return + process_sampled_batch = getattr( + self.model.draft_provider, + "process_sampled_batch", + None, + ) + if process_sampled_batch is None: + return + if not self.draft_batch_size_allowed(items): + return + + output_count = sum( + output_index is not None + for item in items + for output_index in item.output_indices + ) + completions: List[Completion] = [] + update_items: List["CompletionScheduler.BatchItem"] = [] + updates: List["MTPDraftProvider.SampledBatchUpdate"] = [] + remaining_by_completion: List[int] = [] + batch_row_offset = 0 + for item in items: + item_batch_rows = list( + range(batch_row_offset, batch_row_offset + len(item.tokens)) + ) + batch_row_offset += len(item.tokens) + decode = item.decode_state + if item.kind != "decode" or decode is None: + continue + request = self.requests.get(item.request_id) + if request is None: + continue + completion = request.completions[decode.completion_index] + if completion.finished or completion.pending_finish_reason is not None: + continue + remaining_tokens = completion.max_total_tokens - completion.total_tokens + if remaining_tokens <= 0: + continue + resolved_output_indices = [ + self.output_index_to_logits_index(output_index, output_count) + for output_index in item.output_indices + ] + if any(output_index is None for output_index in resolved_output_indices): + continue + sample_index = decode.pending_count + decode.accepted_draft_count - 1 + target_count = len(item.tokens) + if sample_index < 0 or sample_index >= target_count: + continue + if decode.sampled_pending_token is None: + continue + completions.append(completion) + update_items.append(item) + updates.append( + MTPDraftProvider.SampledBatchUpdate( + seq_id=item.seq_id, + start_pos=item.llama_start_pos, + tokens=item.tokens, + row_indices=item_batch_rows, + target_count=target_count, + sample_index=sample_index, + pending_token=decode.sampled_pending_token, + max_tokens=remaining_tokens, + ) + ) + remaining_by_completion.append(remaining_tokens) + + if not updates: + return + + draft_started_at = time.perf_counter() + proposed_many = process_sampled_batch(updates) + for item in update_items: + item.require_decode().rollback_draft_processed = True + draft_elapsed = time.perf_counter() - draft_started_at + self.metrics.draft_seconds_total += draft_elapsed + self.metrics.draft_sampled_batch_seconds_total += draft_elapsed + self.metrics.draft_sampled_batch_calls_total += 1 + for completion, proposed, remaining_tokens in zip( + completions, + proposed_many, + remaining_by_completion, + ): + if proposed.size == 0: + continue + limited = [int(token) for token in proposed[:remaining_tokens]] + if not limited: + continue + completion.draft_tokens = limited + self.speculative_stats["draft_proposals"] += 1 + self.speculative_stats["draft_tokens_proposed"] += len(limited) + + def rollback_draft_verification( + self, + item: "CompletionScheduler.BatchItem", + keep_len: int, + accepted_draft_count: int, + *, + defer_draft_state: bool, + ) -> None: + decode = item.require_decode() + if defer_draft_state: + decode.rollback_keep_len = keep_len + decode.rollback_accepted_draft_count = accepted_draft_count + decode.rollback_draft_processed = False + return + self.model.accept_draft_tokens(item.seq_id, accepted_draft_count) + self.truncate_sequence(item.seq_id, keep_len) + + def apply_deferred_draft_rollbacks( + self, + items: Sequence["CompletionScheduler.BatchItem"], + ) -> None: + for item in items: + decode = item.require_decode() + rollback_keep_len = decode.rollback_keep_len + if rollback_keep_len is None: + deferred_accept_draft_count = decode.deferred_accept_draft_count + if ( + not decode.rollback_draft_processed + and deferred_accept_draft_count is not None + ): + self.model.accept_draft_tokens( + item.seq_id, + deferred_accept_draft_count, + ) + deferred_truncate_draft_len = decode.deferred_truncate_draft_len + if deferred_truncate_draft_len is not None: + self.model.truncate_draft_sequence( + item.seq_id, + deferred_truncate_draft_len, + ) + decode.deferred_accept_draft_count = None + decode.deferred_truncate_draft_len = None + decode.rollback_draft_processed = False + continue + + draft_state_needs_fallback = not decode.rollback_draft_processed + if draft_state_needs_fallback: + self.model.accept_draft_tokens( + item.seq_id, + decode.rollback_accepted_draft_count, + ) + self.truncate_sequence( + item.seq_id, + rollback_keep_len, + truncate_draft=draft_state_needs_fallback, + ) + decode.rollback_keep_len = None + decode.rollback_accepted_draft_count = 0 + decode.rollback_draft_processed = False + decode.deferred_accept_draft_count = None + decode.deferred_truncate_draft_len = None + + def sample_completion_token(self, completion: Completion, output_index: int) -> int: + sample_started_at = time.perf_counter() + try: + return completion.sampler.sample(self.model.ctx, output_index) + finally: + self.metrics.sample_seconds_total += time.perf_counter() - sample_started_at + self.metrics.sample_calls_total += 1 + + def process_generation_item( + self, + completion: Completion, + item: CompletionScheduler.BatchItem, + output_count: int, + ) -> None: + if completion.finished: + return + decode = item.require_decode() + pending_count = decode.pending_count + resolved_output_indices = [ + self.output_index_to_logits_index(output_index, output_count) + for output_index in item.output_indices + ] + if any(output_index is None for output_index in resolved_output_indices): + raise RuntimeError("generation outputs are required") + logits_indices: List[int] = [] + for output_index in resolved_output_indices: + assert output_index is not None + logits_indices.append(int(output_index)) + if completion.pending_finish_reason is not None: + if self.model.store_logits: + self.checkpoint_logits[completion.seq_id] = self.model.logits( + logits_indices[-1] + ) + completion.pending_input_tokens = completion.pending_input_tokens[pending_count:] + finish_reason: str = completion.pending_finish_reason + completion.pending_finish_reason = None + self.finish_completion(completion, finish_reason) + return + + if pending_count: + completion.pending_input_tokens = completion.pending_input_tokens[pending_count:] + + decoded_draft_tokens = item.tokens[pending_count:] + accepted_draft_count = 0 + defer_draft_state = self.defer_sampled_draft_processing + if decoded_draft_tokens and pending_count <= 0: + raise RuntimeError("draft verification requires at least one pending token") + if decoded_draft_tokens: + self.metrics.draft_batches_verified_total += 1 + self.metrics.draft_target_tokens_verified_total += len(decoded_draft_tokens) + + for draft_index, draft_token in enumerate(decoded_draft_tokens): + verify_output_index = pending_count + draft_index - 1 + if verify_output_index >= len(logits_indices): + raise RuntimeError("missing target output for draft verification") + logits_index = logits_indices[verify_output_index] + sampled_token = self.sample_completion_token(completion, logits_index) + need_logits = self.model.store_logits or completion.needs_token_logprob + logits = self.model.logits(logits_index) if need_logits else None + if self.model.store_logits and logits is not None: + self.checkpoint_logits[completion.seq_id] = logits + if sampled_token != draft_token: + self.record_draft_acceptance_length(accepted_draft_count) + rejected = max(0, len(completion.draft_tokens) - accepted_draft_count) + if rejected > 0: + self.speculative_stats["draft_tokens_rejected"] += rejected + self.metrics.draft_target_tokens_wasted_total += ( + max(0, len(decoded_draft_tokens) - accepted_draft_count - 1) + ) + keep_len = item.start_pos + pending_count + accepted_draft_count + self.rollback_draft_verification( + item, + keep_len, + accepted_draft_count, + defer_draft_state=defer_draft_state, + ) + completion.draft_tokens.clear() + prev_tokens = [*completion.prompt_tokens, *completion.completion_tokens] + if logits is not None: + record = Token.from_logits( + model=self.model, + formatter=self.formatter, + prev_tokens=prev_tokens, + prev_text_bytes=completion.detokenized_prefix_bytes, + token=sampled_token, + logits=logits, + logprobs_count=completion.logprobs, + need_token_logprob=completion.needs_token_logprob, + ) + else: + record = Token.from_token( + model=self.model, + prev_tokens=prev_tokens, + prev_text_bytes=completion.detokenized_prefix_bytes, + token=sampled_token, + ) + mismatch_finish_reason: Optional[str] = self.handle_completion_token( + completion, + sampled_token, + record, + decoded=False, + ) + if mismatch_finish_reason is not None: + completion.pending_finish_reason = mismatch_finish_reason + else: + decode.sampled_pending_token = sampled_token + decode.accepted_draft_count = accepted_draft_count + return + prev_tokens = [*completion.prompt_tokens, *completion.completion_tokens] + if logits is not None: + record = Token.from_logits( + model=self.model, + formatter=self.formatter, + prev_tokens=prev_tokens, + prev_text_bytes=completion.detokenized_prefix_bytes, + token=draft_token, + logits=logits, + logprobs_count=completion.logprobs, + need_token_logprob=completion.needs_token_logprob, + ) + else: + record = Token.from_token( + model=self.model, + prev_tokens=prev_tokens, + prev_text_bytes=completion.detokenized_prefix_bytes, + token=draft_token, + ) + accepted_finish_reason: Optional[str] = self.handle_completion_token( + completion, + draft_token, + record, + decoded=True, + ) + accepted_draft_count += 1 + self.speculative_stats["draft_tokens_accepted"] += 1 + if accepted_finish_reason is not None: + self.record_draft_acceptance_length(accepted_draft_count) + rejected = max(0, len(completion.draft_tokens) - accepted_draft_count) + if rejected > 0: + self.speculative_stats["draft_tokens_rejected"] += rejected + self.metrics.draft_target_tokens_wasted_total += ( + len(decoded_draft_tokens) - accepted_draft_count + ) + keep_len = item.start_pos + pending_count + accepted_draft_count + self.rollback_draft_verification( + item, + keep_len, + accepted_draft_count, + defer_draft_state=defer_draft_state, + ) + completion.draft_tokens.clear() + decode.accepted_draft_count = accepted_draft_count + self.finish_completion(completion, accepted_finish_reason) + return + + decode.accepted_draft_count = accepted_draft_count + if decoded_draft_tokens: + self.record_draft_acceptance_length(accepted_draft_count) + if not defer_draft_state: + self.model.accept_draft_tokens(completion.seq_id, accepted_draft_count) + elif decoded_draft_tokens: + decode.deferred_accept_draft_count = accepted_draft_count + if accepted_draft_count: + completion.draft_tokens = completion.draft_tokens[accepted_draft_count:] + + final_logits_index = logits_indices[-1] + next_token = self.sample_completion_token(completion, final_logits_index) + need_final_logits = self.model.store_logits or completion.needs_token_logprob + final_logits = ( + self.model.logits(final_logits_index) if need_final_logits else None + ) + if self.model.store_logits and final_logits is not None: + self.checkpoint_logits[completion.seq_id] = final_logits + if completion.draft_tokens and next_token != completion.draft_tokens[0]: + self.speculative_stats["draft_tokens_rejected"] += len(completion.draft_tokens) + if not defer_draft_state: + self.model.truncate_draft_sequence( + completion.seq_id, + item.start_pos + len(item.tokens), + ) + else: + decode.deferred_truncate_draft_len = item.start_pos + len(item.tokens) + completion.draft_tokens.clear() + elif completion.draft_tokens and next_token == completion.draft_tokens[0]: + if getattr(self.model, "draft_target_batching", True): + completion.draft_tokens = completion.draft_tokens[1:] + else: + completion.draft_tokens.clear() + self.speculative_stats["draft_tokens_accepted"] += 1 + self.metrics.draft_tokens_reused_as_pending_total += 1 + + prev_tokens = [*completion.prompt_tokens, *completion.completion_tokens] + if final_logits is not None: + record = Token.from_logits( + model=self.model, + formatter=self.formatter, + prev_tokens=prev_tokens, + prev_text_bytes=completion.detokenized_prefix_bytes, + token=next_token, + logits=final_logits, + logprobs_count=completion.logprobs, + need_token_logprob=completion.needs_token_logprob, + ) + else: + record = Token.from_token( + model=self.model, + prev_tokens=prev_tokens, + prev_text_bytes=completion.detokenized_prefix_bytes, + token=next_token, + ) + final_finish_reason: Optional[str] = self.handle_completion_token( + completion, + next_token, + record, + decoded=False, + ) + if final_finish_reason is not None: + completion.pending_finish_reason = final_finish_reason + else: + decode.sampled_pending_token = next_token + + def maybe_save_prompt_checkpoint(self, request: CompletionRequest) -> None: + if ( + not self.model.exact_checkpoints_only + or request.prompt_checkpoint_saved + or request.base_seq_id is None + or not request.prompt_tokens + or request.prompt_logits is None + or not self.unused_sequences + ): + return + checkpoint_seq_id = self.unused_sequences.pop() + self.copy_sequence_state( + request.base_seq_id, + checkpoint_seq_id, + len(request.prompt_tokens), + ) + self.checkpoint_logits[checkpoint_seq_id] = request.prompt_logits + self.free_sequences[checkpoint_seq_id] = None + self.free_sequences.move_to_end(checkpoint_seq_id) + request.prompt_checkpoint_saved = True + self.metrics.checkpoint_saves_total += 1 + + @staticmethod + def output_index_to_logits_index( + output_index: Optional[int], + output_count: int, + ) -> Optional[int]: + if output_index is None: + return None + return output_index - output_count + + @staticmethod + def last_output_index(output_indices: Sequence[Optional[int]]) -> Optional[int]: + for output_index in reversed(output_indices): + if output_index is not None: + return output_index + return None + + def start_completions( + self, + request: CompletionRequest, + prompt_output_index: Optional[int], + prompt_logits: Optional[np.ndarray] = None, + ) -> None: + if request.completions: + return + assert request.base_seq_id is not None + prompt_tokens = list(request.prompt_plan.text_tokens) + prompt_length = len(request.prompt_tokens) + multimodal_prompt = any( + segment.kind != "text" for segment in request.prompt_plan.segments + ) + prompt_text = request.prompt_text + if request.payload.stop is None: + stop_sequences: List[bytes] = [] + elif isinstance(request.payload.stop, str): + stop_sequences = [request.payload.stop.encode("utf-8")] + else: + stop_sequences = [item.encode("utf-8") for item in request.payload.stop] + logit_bias = ( + {int(token): float(bias) for token, bias in request.payload.logit_bias.items()} + if request.payload.logit_bias + else None + ) + prompt_text_bytes = self.model.detokenize(prompt_tokens) if prompt_tokens else b"" + for offset, seq_id in enumerate(request.completion_seq_ids): + if offset > 0: + if prompt_length: + self.memory_policy.copy_prompt_state( + request.base_seq_id, + seq_id, + prompt_length, + ) + sampler = Sampler( + seed=(request.payload.seed or llama_cpp.LLAMA_DEFAULT_SEED) + offset, + vocab=self.model.vocab, + n_vocab=self.model.n_vocab, + top_p=request.payload.top_p, + temperature=request.payload.temperature, + frequency_penalty=request.payload.frequency_penalty or 0.0, + presence_penalty=request.payload.presence_penalty or 0.0, + logit_bias=logit_bias, + grammar_text=request.grammar_text, + grammar_root=request.grammar_root, + ) + request.completions.append( + Completion( + request_id=request.id, + index=offset, + seq_id=seq_id, + sampler=sampler, + prompt_tokens=prompt_tokens, + prompt_length=prompt_length, + prompt_text=prompt_text, + multimodal_prompt=multimodal_prompt, + max_total_tokens=request.effective_max_len, + stop_sequences=stop_sequences, + logprobs=request.payload.logprobs, + detokenized_prefix_bytes=bytearray(prompt_text_bytes), + rank_by_score=( + request.payload.best_of is not None + and request.payload.best_of > request.payload.n + ), + ) + ) + if request.payload.max_tokens == 0 or request.effective_max_len == prompt_length: + for completion in request.completions: + self.finish_completion(completion, "length") + self.finalize_request_if_ready(request) + return + if prompt_output_index is None: + if prompt_logits is not None: + for completion in request.completions: + self.sample_completion_from_logits(completion, prompt_logits) + return + raise RuntimeError("prompt output is required to start generation") + for completion in request.completions: + self.sample_completion(completion, prompt_output_index) + + def sample_completion( + self, + completion: Completion, + output_index: Optional[int], + ) -> None: + if completion.finished: + return + if output_index is None: + raise RuntimeError("missing logits output") + token = self.sample_completion_token(completion, output_index) + prev_tokens = [*completion.prompt_tokens, *completion.completion_tokens] + if self.model.store_logits or completion.needs_token_logprob: + logits = self.model.logits(output_index) + if self.model.store_logits: + self.checkpoint_logits[completion.seq_id] = logits + record = Token.from_logits( + model=self.model, + formatter=self.formatter, + prev_tokens=prev_tokens, + prev_text_bytes=completion.detokenized_prefix_bytes, + token=token, + logits=logits, + logprobs_count=completion.logprobs, + need_token_logprob=completion.needs_token_logprob, + ) + else: + record = Token.from_token( + model=self.model, + prev_tokens=prev_tokens, + prev_text_bytes=completion.detokenized_prefix_bytes, + token=token, + ) + finish_reason = self.handle_completion_token( + completion, + token, + record, + decoded=False, + ) + if finish_reason is not None: + completion.pending_finish_reason = finish_reason + + def sample_completion_from_logits( + self, + completion: Completion, + logits: np.ndarray, + ) -> None: + if completion.finished: + return + token = completion.sampler.sample_logits(logits) + prev_tokens = [*completion.prompt_tokens, *completion.completion_tokens] + if self.model.store_logits or completion.needs_token_logprob: + if self.model.store_logits: + self.checkpoint_logits[completion.seq_id] = logits.copy() + record = Token.from_logits( + model=self.model, + formatter=self.formatter, + prev_tokens=prev_tokens, + prev_text_bytes=completion.detokenized_prefix_bytes, + token=token, + logits=logits, + logprobs_count=completion.logprobs, + need_token_logprob=completion.needs_token_logprob, + ) + else: + record = Token.from_token( + model=self.model, + prev_tokens=prev_tokens, + prev_text_bytes=completion.detokenized_prefix_bytes, + token=token, + ) + finish_reason = self.handle_completion_token( + completion, + token, + record, + decoded=False, + ) + if finish_reason is not None: + completion.pending_finish_reason = finish_reason + + def handle_completion_token( + self, + completion: Completion, + token: int, + record: Token, + *, + decoded: bool, + ) -> Optional[str]: + if record.token_logprob is not None: + completion.score_sum += record.token_logprob + rendered_start = len(completion.rendered_bytes) + completion.completion_tokens.append(token) + self.metrics.observe_predicted_token() + completion.token_records.append(record) + completion.rendered_bytes.extend(record.text_bytes) + completion.detokenized_prefix_bytes.extend(record.text_bytes) + finish_reason: Optional[str] = None + if llama_cpp.llama_vocab_is_eog(self.model.vocab, token): + finish_reason = "stop" + elif completion.total_tokens >= completion.max_total_tokens: + finish_reason = "length" + else: + max_stop_length = completion.max_stop_sequence_length + search_start = max(0, rendered_start - max_stop_length + 1) + if any( + completion.rendered_bytes.find(stop, search_start) != -1 + for stop in completion.stop_sequences + ): + finish_reason = "stop" + if not decoded: + completion.pending_input_tokens.append(token) + if ( + completion.request_id in self.requests + and self.requests[completion.request_id].payload.stream + and finish_reason is None + ): + self.flush_stream_updates(completion, finish_reason=None) + if not decoded and finish_reason is None and not self.should_defer_draft_fill: + self.maybe_fill_draft_tokens(completion) + return finish_reason + + def flush_stream_updates( + self, + completion: Completion, + finish_reason: Optional[str], + ) -> None: + request = self.requests[completion.request_id] + for payload in self.formatter.stream_completion_chunks( + request, + completion, + finish_reason, + ): + if request.on_stream_chunk is not None: + request.on_stream_chunk(payload) + + def finish_completion(self, completion: Completion, finish_reason: str) -> None: + if completion.finished: + return + completion.finished = True + completion.finish_reason = finish_reason + completion.pending_input_tokens.clear() + completion.draft_tokens.clear() + request = self.requests[completion.request_id] + if request.payload.stream: + self.flush_stream_updates(completion, finish_reason=finish_reason) + if request.on_stream_chunk is not None: + request.on_stream_chunk( + self.formatter.completion_finish_chunk( + request, + completion, + finish_reason, + ) + ) + + def finalize_request_if_ready(self, request: CompletionRequest) -> None: + if not request.completions or not all(completion.finished for completion in request.completions): + return + selected = request.selected_completions() + result = self.formatter.build_completion_response(request, selected) + self.metrics.requests_completed_total += 1 + self.release_request(request) + if request.on_done is not None: + request.on_done(result) + + def truncate_sequence( + self, + seq_id: int, + keep_len: int, + *, + truncate_draft: bool = True, + ) -> None: + current_len = self.radix_trie.length(seq_id) + if current_len <= keep_len: + return + keep_pos = self.sequence_history.position_length_for_prefix(seq_id, keep_len) + if not llama_cpp.llama_memory_seq_rm(self.model.mem, seq_id, keep_pos, -1): + raise RuntimeError( + f"failed to truncate model sequence {seq_id} at position {keep_pos}" + ) + if truncate_draft: + self.model.truncate_draft_sequence(seq_id, keep_pos) + self.truncate_sequence_metadata(seq_id, current_len, keep_len) + + def copy_sequence_state( + self, + source_sequence_id: int, + dest_sequence_id: int, + keep_len: int, + *, + copy_all_state: bool = False, + ) -> None: + if keep_len <= 0: + return + source_length = self.radix_trie.length(source_sequence_id) + keep_pos = self.sequence_history.position_length_for_prefix( + source_sequence_id, + keep_len, + ) + copy_p0 = 0 + copy_p1 = keep_pos + if copy_all_state or not self.model.kv_unified: + copy_p0 = -1 + copy_p1 = -1 + llama_cpp.llama_memory_seq_cp( + self.model.mem, + source_sequence_id, + dest_sequence_id, + copy_p0, + copy_p1, + ) + self.model.copy_draft_sequence( + source_sequence_id, + dest_sequence_id, + copy_p0, + copy_p1, + ) + if copy_all_state and source_length > keep_len: + if not llama_cpp.llama_memory_seq_rm( + self.model.mem, + dest_sequence_id, + keep_pos, + -1, + ): + raise RuntimeError( + f"failed to truncate copied model sequence {dest_sequence_id} " + f"at position {keep_pos}" + ) + self.model.truncate_draft_sequence(dest_sequence_id, keep_pos) + self.radix_trie.copy(source_sequence_id, dest_sequence_id, keep_len) + self.sequence_history.copy( + source_sequence_id, + dest_sequence_id, + source_length, + keep_len, + ) + + def truncate_sequence_metadata( + self, + seq_id: int, + current_len: int, + keep_len: int, + ) -> None: + self.radix_trie.truncate(seq_id, keep_len) + self.sequence_history.truncate(seq_id, current_len, keep_len) + self.checkpoint_logits.pop(seq_id, None) + + def truncate_free_sequence(self, seq_id: int, keep_len: int) -> None: + if seq_id not in self.free_sequences: + return + self.truncate_sequence(seq_id, keep_len) + self.free_sequences.move_to_end(seq_id) + + def delete_free_sequence(self, seq_id: int) -> None: + if seq_id not in self.free_sequences: + return + self.truncate_sequence(seq_id, 0) + del self.free_sequences[seq_id] + self.checkpoint_logits.pop(seq_id, None) + self.unused_sequences.append(seq_id) + self.metrics.checkpoint_evictions_total += 1 + + def claim_unused_sequence(self) -> int: + seq_id = self.unused_sequences.pop() + self.claimed_sequences.add(seq_id) + return seq_id + + def claim_free_sequence(self, seq_id: int) -> int: + del self.free_sequences[seq_id] + self.claimed_sequences.add(seq_id) + return seq_id + + def activate_request( + self, + request: CompletionRequest, + *, + base_seq_id: int, + sibling_seq_ids: List[int], + ) -> None: + request.base_seq_id = base_seq_id + request.sibling_seq_ids = sibling_seq_ids + request.completion_seq_ids = [base_seq_id, *sibling_seq_ids] + request.admitted = True + self.active_request_ids.add(request.id) + + def release_request(self, request: CompletionRequest) -> None: + for completion in request.completions: + completion.sampler.close() + for seq_id in request.completion_seq_ids: + if seq_id in self.claimed_sequences: + self.claimed_sequences.remove(seq_id) + self.free_sequences[seq_id] = None + self.free_sequences.move_to_end(seq_id) + self.active_request_ids.discard(request.id) + self.requests.pop(request.id, None) + + def remove_pending_request(self, request: CompletionRequest) -> None: + try: + self.pending_requests.remove(request) + except ValueError: + pass + + def fail_request(self, request: CompletionRequest, exc: BaseException) -> None: + self.remove_pending_request(request) + if request.id in self.active_request_ids or request.admitted: + self.release_request(request) + else: + self.requests.pop(request.id, None) + if isinstance(exc, CompletionRequestCancelledError): + self.metrics.requests_cancelled_total += 1 + else: + self.metrics.requests_failed_total += 1 + if request.on_error is not None: + request.on_error(exc) + + @staticmethod + def _format_prometheus_value(value: Union[int, float]) -> str: + if isinstance(value, int): + return str(value) + if not math.isfinite(value): + raise ValueError(f"invalid Prometheus metric value: {value}") + return format(value, ".16g") + + def sequence_cache_metric_definitions( + self, + ) -> List[Tuple[str, str, str, Union[int, float]]]: + return [ + ( + "counter", + "batch_server:sequence_cache_hits_total", + "Number of external sequence cache entries hydrated.", + self.metrics.sequence_cache_hits_total, + ), + ( + "counter", + "batch_server:sequence_cache_save_requests_total", + "Number of external sequence cache save requests.", + self.metrics.sequence_cache_save_requests_total, + ), + ( + "counter", + "batch_server:sequence_cache_load_failures_total", + "Number of external sequence cache load failures.", + self.metrics.sequence_cache_load_failures_total, + ), + ( + "counter", + "batch_server:sequence_cache_lookup_failures_total", + "Number of external sequence cache lookup failures.", + self.metrics.sequence_cache_lookup_failures_total, + ), + ( + "counter", + "batch_server:sequence_cache_save_failures_total", + "Number of external sequence cache save failures.", + self.metrics.sequence_cache_save_failures_total, + ), + ( + "counter", + "batch_server:sequence_cache_tokens_loaded_total", + "Number of prompt tokens hydrated from external sequence cache.", + self.metrics.sequence_cache_tokens_loaded_total, + ), + ] + + def speculative_metric_definitions( + self, + ) -> List[Tuple[str, str, str, Union[int, float]]]: + proposed = self.speculative_stats["draft_tokens_proposed"] + accepted = self.speculative_stats["draft_tokens_accepted"] + proposals = self.speculative_stats["draft_proposals"] + verified_proposals = sum(self.draft_acceptance_length_counts.values()) + verified_accepted = sum( + accepted_tokens * count + for accepted_tokens, count in self.draft_acceptance_length_counts.items() + ) + acceptance_rate = accepted / proposed if proposed > 0 else 0.0 + average_proposed_tokens = proposed / proposals if proposals > 0 else 0.0 + average_verified_acceptance_length = ( + verified_accepted / verified_proposals if verified_proposals > 0 else 0.0 + ) + return [ + ( + "counter", + "batch_server:draft_proposals_total", + "Number of speculative draft proposal batches generated.", + proposals, + ), + ( + "counter", + "batch_server:draft_tokens_proposed_total", + "Number of speculative draft tokens proposed.", + proposed, + ), + ( + "counter", + "batch_server:draft_tokens_accepted_total", + "Number of speculative draft tokens accepted.", + accepted, + ), + ( + "counter", + "batch_server:draft_tokens_rejected_total", + "Number of speculative draft tokens rejected.", + self.speculative_stats["draft_tokens_rejected"], + ), + ( + "counter", + "batch_server:draft_batches_verified_total", + "Number of completion items that target-verified draft tokens.", + self.metrics.draft_batches_verified_total, + ), + ( + "counter", + "batch_server:draft_target_tokens_verified_total", + "Number of draft tokens decoded by the target for verification.", + self.metrics.draft_target_tokens_verified_total, + ), + ( + "counter", + "batch_server:draft_target_tokens_wasted_total", + "Number of target-verified draft tokens rejected before emission.", + self.metrics.draft_target_tokens_wasted_total, + ), + ( + "counter", + "batch_server:draft_tokens_reused_as_pending_total", + "Number of draft tokens accepted by the next pending-token sample.", + self.metrics.draft_tokens_reused_as_pending_total, + ), + ( + "gauge", + "batch_server:draft_acceptance_rate", + "Fraction of proposed draft tokens accepted, including reused pending matches.", + acceptance_rate, + ), + ( + "gauge", + "batch_server:draft_average_proposed_tokens", + "Average number of tokens in generated draft proposals.", + average_proposed_tokens, + ), + ( + "gauge", + "batch_server:draft_average_verified_acceptance_length", + "Average accepted draft-token prefix length for target-verified proposals only.", + average_verified_acceptance_length, + ), + ] + + def record_draft_acceptance_length(self, accepted_tokens: int) -> None: + accepted_tokens = max(0, int(accepted_tokens)) + self.draft_acceptance_length_counts[accepted_tokens] = ( + self.draft_acceptance_length_counts.get(accepted_tokens, 0) + 1 + ) + + def draft_acceptance_length_metric_lines(self) -> List[str]: + lines = [ + "# HELP batch_server:draft_acceptance_length_total Number of verified draft proposals by accepted-token prefix length.", + "# TYPE batch_server:draft_acceptance_length_total counter", + ] + for accepted_tokens, count in sorted(self.draft_acceptance_length_counts.items()): + lines.append( + f'batch_server:draft_acceptance_length_total{{accepted_tokens="{accepted_tokens}"}} {count}' + ) + return lines + + def render_prometheus_metrics(self) -> str: + active_completions = sum( + 1 + for request_id in self.active_request_ids + for completion in self.requests[request_id].completions + if not completion.finished + ) + checkpoint_entries = sum( + 1 for seq_id in self.free_sequences if seq_id in self.checkpoint_logits + ) + prompt_tokens_seconds = ( + self.metrics.prompt_tokens_total / self.metrics.prompt_seconds_total + if self.metrics.prompt_seconds_total > 0 + else 0.0 + ) + predicted_tokens_seconds = ( + self.metrics.tokens_predicted_total + / self.metrics.tokens_predicted_seconds_total + if self.metrics.tokens_predicted_seconds_total > 0 + else 0.0 + ) + n_busy_slots_per_decode = ( + self.metrics.n_busy_slots_total / self.metrics.n_decode_total + if self.metrics.n_decode_total > 0 + else 0.0 + ) + draft_provider_metrics: List[Tuple[str, str, str, Union[int, float]]] = [] + if self.model.draft_provider is not None: + draft_metric_definitions = getattr( + self.model.draft_provider, + "metric_definitions", + None, + ) + if draft_metric_definitions is not None: + draft_provider_metrics = cast( + List[Tuple[str, str, str, Union[int, float]]], + draft_metric_definitions(), + ) + metrics_def: List[Tuple[str, str, str, Union[int, float]]] = [ + ( + "counter", + "llamacpp:prompt_tokens_total", + "Number of prompt tokens processed.", + self.metrics.prompt_tokens_total, + ), + ( + "counter", + "llamacpp:prompt_seconds_total", + "Estimated prompt processing time in seconds.", + self.metrics.prompt_seconds_total, + ), + ( + "counter", + "llamacpp:tokens_predicted_total", + "Number of generated tokens processed.", + self.metrics.tokens_predicted_total, + ), + ( + "counter", + "llamacpp:tokens_predicted_seconds_total", + "Estimated generation processing time in seconds.", + self.metrics.tokens_predicted_seconds_total, + ), + ( + "counter", + "batch_server:scheduler_step_seconds_total", + "Total scheduler step wall time.", + self.metrics.scheduler_step_seconds_total, + ), + ( + "counter", + "batch_server:process_batch_seconds_total", + "Time spent processing decoded batches after llama_decode().", + self.metrics.process_batch_seconds_total, + ), + ( + "counter", + "batch_server:sample_seconds_total", + "Time spent sampling target logits.", + self.metrics.sample_seconds_total, + ), + ( + "counter", + "batch_server:draft_seconds_total", + "Speculative draft processing time in seconds.", + self.metrics.draft_seconds_total, + ), + ( + "counter", + "batch_server:draft_process_seconds_total", + "Time spent feeding target batches into the draft context.", + self.metrics.draft_process_seconds_total, + ), + ( + "counter", + "batch_server:draft_generate_seconds_total", + "Time spent generating speculative draft tokens.", + self.metrics.draft_generate_seconds_total, + ), + ( + "counter", + "batch_server:draft_sampled_batch_seconds_total", + "Time spent generating speculative tokens from sampled target batches.", + self.metrics.draft_sampled_batch_seconds_total, + ), + ( + "counter", + "batch_server:draft_process_calls_total", + "Number of target-to-draft context processing phases.", + self.metrics.draft_process_calls_total, + ), + ( + "counter", + "batch_server:draft_generate_calls_total", + "Number of speculative draft generation phases.", + self.metrics.draft_generate_calls_total, + ), + ( + "counter", + "batch_server:draft_sampled_batch_calls_total", + "Number of sampled target batch draft phases.", + self.metrics.draft_sampled_batch_calls_total, + ), + ( + "counter", + "llamacpp:n_decode_total", + "Total number of llama_decode() calls.", + self.metrics.n_decode_total, + ), + ( + "counter", + "batch_server:scheduler_step_calls_total", + "Number of scheduler steps.", + self.metrics.scheduler_step_calls_total, + ), + ( + "counter", + "batch_server:process_batch_calls_total", + "Number of decoded batch processing phases.", + self.metrics.process_batch_calls_total, + ), + ( + "counter", + "batch_server:sample_calls_total", + "Number of target logit sampling calls.", + self.metrics.sample_calls_total, + ), + ( + "counter", + "llamacpp:n_tokens_max", + "Largest observed n_tokens.", + self.metrics.n_tokens_max, + ), + ( + "gauge", + "llamacpp:n_busy_slots_per_decode", + "Average number of busy sequences per llama_decode() call.", + n_busy_slots_per_decode, + ), + ( + "gauge", + "llamacpp:prompt_tokens_seconds", + "Average prompt throughput in tokens/s.", + prompt_tokens_seconds, + ), + ( + "gauge", + "llamacpp:predicted_tokens_seconds", + "Average generation throughput in tokens/s.", + predicted_tokens_seconds, + ), + ( + "gauge", + "llamacpp:requests_processing", + "Number of requests processing.", + len(self.active_request_ids), + ), + ( + "gauge", + "llamacpp:requests_deferred", + "Number of requests deferred.", + len(self.pending_requests), + ), + ( + "counter", + "batch_server:requests_submitted_total", + "Number of requests submitted.", + self.metrics.requests_submitted_total, + ), + ( + "counter", + "batch_server:requests_admitted_total", + "Number of requests admitted.", + self.metrics.requests_admitted_total, + ), + ( + "counter", + "batch_server:requests_completed_total", + "Number of requests completed successfully.", + self.metrics.requests_completed_total, + ), + ( + "counter", + "batch_server:requests_cancelled_total", + "Number of requests cancelled.", + self.metrics.requests_cancelled_total, + ), + ( + "counter", + "batch_server:requests_failed_total", + "Number of requests failed.", + self.metrics.requests_failed_total, + ), + ( + "gauge", + "batch_server:active_completions", + "Number of active unfinished completions.", + active_completions, + ), + ( + "gauge", + "batch_server:claimed_sequences", + "Number of claimed sequence ids.", + len(self.claimed_sequences), + ), + ( + "gauge", + "batch_server:free_sequences", + "Number of reusable free sequence ids.", + len(self.free_sequences), + ), + ( + "gauge", + "batch_server:unused_sequences", + "Number of unused sequence ids.", + len(self.unused_sequences), + ), + ( + "gauge", + "batch_server:checkpoint_entries", + "Number of free sequence checkpoints with stored logits.", + checkpoint_entries, + ), + ( + "counter", + "batch_server:checkpoint_hits_total", + "Number of exact checkpoint hits.", + self.metrics.checkpoint_hits_total, + ), + ( + "counter", + "batch_server:checkpoint_saves_total", + "Number of prompt checkpoints saved.", + self.metrics.checkpoint_saves_total, + ), + ( + "counter", + "batch_server:checkpoint_evictions_total", + "Number of free checkpoints evicted.", + self.metrics.checkpoint_evictions_total, + ), + *self.sequence_cache_metric_definitions(), + *self.speculative_metric_definitions(), + *draft_provider_metrics, + ( + "gauge", + "batch_server:radix_trie_sequences", + "Number of sequences tracked in the radix trie.", + len(self.radix_trie.sequence_lengths), + ), + ( + "gauge", + "batch_server:radix_trie_tokens", + "Total tokens tracked in the radix trie.", + self.sequence_history.size, + ), + ] + lines: List[str] = [] + for metric_type, name, help_text, value in metrics_def: + lines.append(f"# HELP {name} {help_text}") + lines.append(f"# TYPE {name} {metric_type}") + lines.append(f"{name} {self._format_prometheus_value(value)}") + lines.extend(self.draft_acceptance_length_metric_lines()) + lines.append("") + return "\n".join(lines) + + def finalize_cancelled(self) -> bool: + finalized = False + for request in list(self.pending_requests): + if request.cancelled: + self.fail_request(request, CompletionRequestCancelledError("request cancelled")) + finalized = True + for request_id in list(self.active_request_ids): + request = self.requests[request_id] + if request.cancelled: + self.fail_request(request, CompletionRequestCancelledError("request cancelled")) + finalized = True + return finalized + + +class CompletionService: + def __init__(self, scheduler: CompletionScheduler) -> None: + self.scheduler = scheduler + self.formatter = scheduler.formatter + self.condition = threading.Condition() + self.commands: Deque[Callable[[], None]] = deque() + self.closed = False + self.worker = threading.Thread( + target=self.run_loop, + name="completion-service", + daemon=True, + ) + self.worker.start() + + def close(self) -> None: + with self.condition: + self.closed = True + self.condition.notify_all() + self.worker.join() + self.scheduler.close() + + def enqueue(self, command: Callable[[], None]) -> None: + with self.condition: + if self.closed: + raise RuntimeError("completion service closed") + self.commands.append(command) + self.condition.notify_all() + + def call_on_scheduler(self, callback: Callable[[], Any]) -> Any: + result_box: Dict[str, Any] = {} + error_box: Dict[str, BaseException] = {} + done = threading.Event() + + def run_callback() -> None: + try: + result_box["result"] = callback() + except BaseException as exc: # noqa: BLE001 + error_box["error"] = exc + finally: + done.set() + + self.enqueue(run_callback) + done.wait() + if "error" in error_box: + raise error_box["error"] + return result_box.get("result") + + def render_prometheus_metrics(self) -> str: + metrics = self.call_on_scheduler(self.scheduler.render_prometheus_metrics) + return cast(str, metrics) + + def run_loop(self) -> None: + while True: + with self.condition: + while not self.closed and not self.commands and self.scheduler.is_idle(): + self.condition.wait() + if self.closed and not self.commands and self.scheduler.is_idle(): + return + commands = list(self.commands) + self.commands.clear() + for command in commands: + command() + progressed = self.scheduler.step() + if progressed: + continue + with self.condition: + if self.closed and not self.commands and self.scheduler.is_idle(): + return + if not self.commands: + self.condition.wait(timeout=0.01) + + def submit_request( + self, + request: CompletionRequest, + ) -> Tuple[CompletionStream, Callable[[], None]]: + mailbox: "queue.Queue[object]" = queue.Queue() + done = threading.Event() + cancelled = threading.Event() + sentinel = object() + result_box: Dict[str, OpenAICompletion] = {} + error_box: Dict[str, BaseException] = {} + + def on_stream_chunk(chunk: CompletionChunk) -> None: + mailbox.put(chunk) + + def on_done(result: OpenAICompletion) -> None: + result_box["result"] = result + done.set() + mailbox.put(sentinel) + + def on_error(exc: BaseException) -> None: + error_box["error"] = exc + done.set() + mailbox.put(sentinel) + + request.on_stream_chunk = on_stream_chunk + request.on_done = on_done + request.on_error = on_error + + def cancel() -> None: + if cancelled.is_set(): + return + cancelled.set() + try: + def cancel_request() -> None: + self.scheduler.cancel(request.id) + + self.enqueue(cancel_request) + except RuntimeError: + pass + + def stream() -> CompletionStream: + try: + while True: + item = mailbox.get() + if item is sentinel: + break + yield cast(CompletionChunk, item) + finally: + if not done.is_set(): + cancel() + if "error" in error_box: + raise error_box["error"] + result = result_box.get("result") + if result is None: + raise RuntimeError("missing completion result") + return result + + def submit_request() -> None: + self.scheduler.submit_request(request) + + self.enqueue(submit_request) + return stream(), cancel + + def submit( + self, + payload: CreateCompletionRequest, + ) -> Tuple[CompletionStream, Callable[[], None]]: + request = self.request_from_payload(payload) + return self.submit_request(request) + + def request_from_payload( + self, + payload: CreateCompletionRequest, + ) -> CompletionRequest: + prompt_text, prompt_plan = self.prepare_completion_prompt(payload) + return self.request_from_prepared( + payload=payload, + prompt_text=prompt_text, + prompt_plan=prompt_plan, + ) + + def request_from_prepared( + self, + *, + payload: CreateCompletionRequest, + prompt_text: str, + prompt_plan: PromptPlan, + grammar_text: Optional[str] = None, + chat_tool_name: Optional[str] = None, + on_stream_chunk: Optional[Callable[[CompletionChunk], None]] = None, + on_done: Optional[Callable[[OpenAICompletion], None]] = None, + on_error: Optional[Callable[[BaseException], None]] = None, + ) -> CompletionRequest: + model = self.scheduler.model + prompt_visible_start = self.prompt_visible_start(prompt_plan) + return CompletionRequest.from_prepared( + payload=payload, + prompt_text=prompt_text, + prompt_plan=prompt_plan, + max_seq_len=model.max_seq_len, + max_output_tokens=model.max_output_tokens, + prompt_visible_start=prompt_visible_start, + prompt_records=self.initial_prompt_records( + payload, + prompt_plan, + prompt_visible_start, + ), + grammar_text=grammar_text, + chat_tool_name=chat_tool_name, + on_stream_chunk=on_stream_chunk, + on_done=on_done, + on_error=on_error, + ) + + def prepare_completion_prompt( + self, + payload: CreateCompletionRequest, + ) -> Tuple[str, PromptPlan]: + model = self.scheduler.model + prompts = payload.normalized_prompt() + if len(prompts) != 1: + raise CompletionRequestValidationError("multiple prompts are not supported") + prompt_item = prompts[0] + if isinstance(prompt_item, str): + prompt_text = prompt_item + try: + prompt_tokens = model.build_prompt_tokens(prompt_text, payload.suffix) + except ValueError as exc: + raise CompletionRequestValidationError(str(exc)) from exc + else: + if payload.suffix is not None: + raise CompletionRequestValidationError( + "suffix is not supported with token id prompts" + ) + prompt_tokens = list(prompt_item) + prompt_text = model.detokenize(prompt_tokens).decode("utf-8", errors="ignore") + return prompt_text, PromptPlan.from_tokens(prompt_text, prompt_tokens) + + def prompt_visible_start(self, prompt_plan: PromptPlan) -> int: + model = self.scheduler.model + if prompt_plan.text_tokens and prompt_plan.text_tokens[0] == model.bos_token: + return 1 + return 0 + + def initial_prompt_records( + self, + payload: CreateCompletionRequest, + prompt_plan: PromptPlan, + prompt_visible_start: int, + ) -> List[Token]: + if not (payload.echo and payload.logprobs is not None): + return [] + if prompt_visible_start >= len(prompt_plan.text_tokens): + return [] + model = self.scheduler.model + first_pos = prompt_visible_start + first_token = prompt_plan.text_tokens[first_pos] + return [ + Token( + token=first_token, + text_bytes=model.token_bytes_with_prev( + prompt_plan.text_tokens[:first_pos], + first_token, + ), + token_logprob=None, + top_logprobs=None, + ) + ] + + +def create_app() -> FastAPI: + app = FastAPI() + app.state.service = None + app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + async def watch_http_disconnect( + http_request: Request, + cancel: Callable[[], None], + ) -> None: + while True: + message = await http_request.receive() + if message["type"] == "http.disconnect": + cancel() + return + + async def disconnected_cancelled_response_or_raise( + http_request: Request, + exc: BaseException, + ) -> Response: + if isinstance(exc, CompletionRequestCancelledError): + if await http_request.is_disconnected(): + return Response(status_code=204) + raise exc + + def bad_request(exc: CompletionRequestValidationError) -> HTTPException: + return HTTPException(status_code=400, detail=str(exc)) + + async def collect_completion_result( + formatter: OpenAIFormatter, + http_request: Request, + stream: CompletionStream, + cancel: Callable[[], None], + ) -> OpenAICompletion | Response: + disconnect_task = asyncio.create_task( + watch_http_disconnect(http_request, cancel) + ) + try: + return await asyncio.to_thread(formatter.collect_completion, stream) + except asyncio.CancelledError: + cancel() + raise + except BaseException as exc: + return await disconnected_cancelled_response_or_raise(http_request, exc) + finally: + disconnect_task.cancel() + stream.close() + + async def collect_completion_results( + formatter: OpenAIFormatter, + http_request: Request, + submissions: Sequence[ + Tuple[ + CompletionStream, + Callable[[], None], + ] + ], + ) -> List[OpenAICompletion] | Response: + streams = [stream for stream, _ in submissions] + cancel_all = [cancel for _, cancel in submissions] + + def cancel_all_requests() -> None: + for cancel in cancel_all: + cancel() + + disconnect_task = asyncio.create_task( + watch_http_disconnect( + http_request, + cancel_all_requests, + ) + ) + try: + return await asyncio.gather( + *( + asyncio.to_thread(formatter.collect_completion, stream) + for stream in streams + ) + ) + except asyncio.CancelledError: + cancel_all_requests() + raise + except BaseException as exc: + return await disconnected_cancelled_response_or_raise(http_request, exc) + finally: + disconnect_task.cancel() + for stream in streams: + stream.close() + + async def stream_sse_chunks( + formatter: OpenAIFormatter, + http_request: Request, + stream: CompletionStream, + cancel: Callable[[], None], + chunk_payloads: Callable[[CompletionChunk], Iterable[Any]], + ) -> AsyncIterator[bytes]: + disconnect_task = asyncio.create_task( + watch_http_disconnect(http_request, cancel) + ) + try: + while True: + done, chunk = await asyncio.to_thread( + formatter.next_stream_chunk, + stream, + ) + if done: + break + assert chunk is not None + for payload in chunk_payloads(chunk): + yield formatter.encode_sse_payload(payload) + yield b"data: [DONE]\n\n" + except asyncio.CancelledError: + cancel() + raise + except BaseException as exc: + cancel() + if ( + isinstance(exc, CompletionRequestCancelledError) + and await http_request.is_disconnected() + ): + return + raise + finally: + disconnect_task.cancel() + + async def stream_sse_outputs( + formatter: OpenAIFormatter, + http_request: Request, + stream: CompletionStream, + cancel: Callable[[], None], + *, + initial_payloads: Iterable[BaseModel | Dict[str, Any]], + chunk_payloads: Callable[[CompletionChunk], Iterable[Any]], + done_payloads: Callable[ + [Optional[OpenAICompletion]], Iterable[BaseModel | Dict[str, Any]] + ], + ) -> AsyncIterator[bytes]: + disconnect_task = asyncio.create_task( + watch_http_disconnect(http_request, cancel) + ) + try: + for payload in initial_payloads: + yield formatter.encode_sse_payload(payload) + while True: + done, chunk, result = await asyncio.to_thread( + formatter.next_stream_output, + stream, + ) + if done: + for payload in done_payloads(result): + yield formatter.encode_sse_payload(payload) + break + assert chunk is not None + for payload in chunk_payloads(chunk): + yield formatter.encode_sse_payload(payload) + yield b"data: [DONE]\n\n" + except asyncio.CancelledError: + cancel() + raise + except BaseException as exc: + cancel() + if ( + isinstance(exc, CompletionRequestCancelledError) + and await http_request.is_disconnected() + ): + return + raise + finally: + disconnect_task.cancel() + + async def stream_websocket_responses( + websocket: WebSocket, + formatter: OpenAIFormatter, + stream: CompletionStream, + _: Callable[[], None], + *, + initial_payloads: Iterable[BaseModel | Dict[str, Any]], + chunk_payloads: Callable[[CompletionChunk], Iterable[Any]], + done_payloads: Callable[ + [Optional[OpenAICompletion]], Iterable[BaseModel | Dict[str, Any]] + ], + ) -> None: + try: + for payload in initial_payloads: + await websocket.send_json(payload) + while True: + done, chunk, result = await asyncio.to_thread( + formatter.next_stream_output, + stream, + ) + if done: + for payload in done_payloads(result): + await websocket.send_json(payload) + break + assert chunk is not None + for payload in chunk_payloads(chunk): + await websocket.send_json(payload) + finally: + stream.close() + + @app.post("/v1/completions") + async def create_completion( # pyright: ignore[reportUnusedFunction] + http_request: Request, + body: CreateCompletionRequest, + ): + service: CompletionService = app.state.service + formatter = service.formatter + prompts = body.normalized_prompt() + if len(prompts) > 1: + if body.stream: + raise HTTPException( + status_code=400, + detail="streaming does not support multiple prompts", + ) + try: + submissions = [ + service.submit(body.model_copy(update={"prompt": prompt})) + for prompt in prompts + ] + except CompletionRequestValidationError as exc: + raise bad_request(exc) from exc + results = await collect_completion_results( + formatter, http_request, submissions + ) + if isinstance(results, Response): + return results + return JSONResponse( + formatter.aggregate_completion_results(results).model_dump( + mode="json", + exclude_none=True, + ) + ) + try: + stream, cancel = service.submit( + body.model_copy(update={"prompt": prompts[0]}) + ) + except CompletionRequestValidationError as exc: + raise bad_request(exc) from exc + if body.stream: + return StreamingResponse( + stream_sse_chunks( + formatter, + http_request, + stream, + cancel, + lambda chunk: [chunk], + ), + media_type="text/event-stream", + ) + result = await collect_completion_result( + formatter, http_request, stream, cancel + ) + if isinstance(result, Response): + return result + return JSONResponse(result.model_dump(mode="json", exclude_none=True)) + + @app.post("/v1/chat/completions") + async def create_chat_completion( # pyright: ignore[reportUnusedFunction] + http_request: Request, body: CreateChatCompletionRequest + ): + service: CompletionService = app.state.service + formatter = service.formatter + try: + parts = formatter.completion_request_from_chat_request( + body, + ) + except CompletionRequestValidationError as exc: + raise bad_request(exc) from exc + template_functions = ( + [function.to_template_function() for function in body.functions] + if body.functions is not None + else None + ) + template_tools = ( + [tool.to_template_tool() for tool in body.tools] + if body.tools is not None + else None + ) + try: + request = service.request_from_prepared( + payload=parts.payload, + prompt_text=parts.prompt_text, + prompt_plan=parts.prompt_plan, + grammar_text=parts.grammar_text, + chat_tool_name=parts.tool_name, + ) + stream, cancel = service.submit_request(request) + except CompletionRequestValidationError as exc: + raise bad_request(exc) from exc + if body.stream: + started_indices: set[int] = set() + + def chat_chunk_payloads( + completion_chunk: CompletionChunk, + ) -> Iterable[BaseModel | Dict[str, Any]]: + return formatter.convert_completion_chunk_to_chat_chunks( + completion_chunk, + started_indices, + parts.tool_name, + functions=template_functions, + tools=template_tools, + parsed_states=parsed_states, + generation_prompt=parts.generation_prompt, + ) + + parsed_states: Dict[int, Dict[str, Any]] = {} + return StreamingResponse( + stream_sse_chunks( + formatter, + http_request, + stream, + cancel, + chat_chunk_payloads, + ), + media_type="text/event-stream", + ) + completion = await collect_completion_result( + formatter, http_request, stream, cancel + ) + if isinstance(completion, Response): + return completion + chat_response = formatter.convert_completion_response_to_chat( + completion, + parts.tool_name, + functions=template_functions, + tools=template_tools, + generation_prompt=parts.generation_prompt, + ) + if isinstance(chat_response, BaseModel): + return JSONResponse( + chat_response.model_dump(mode="json", exclude_none=True) + ) + return JSONResponse(chat_response) + + @app.post("/v1/responses") + async def create_response( # pyright: ignore[reportUnusedFunction] + http_request: Request, + body: CreateResponseRequest, + ): + service: CompletionService = app.state.service + formatter = service.formatter + try: + chat_parts = formatter.response_request_to_chat_parts(body) + parts = formatter.completion_request_from_response_chat_parts(chat_parts) + except CompletionRequestValidationError as exc: + raise bad_request(exc) from exc + response_tools = chat_parts.tools + try: + request = service.request_from_prepared( + payload=parts.payload, + prompt_text=parts.prompt_text, + prompt_plan=parts.prompt_plan, + grammar_text=parts.grammar_text, + chat_tool_name=parts.tool_name, + ) + stream, cancel = service.submit_request(request) + except CompletionRequestValidationError as exc: + raise bad_request(exc) from exc + if body.stream: + started_indices: set[int] = set() + parsed_states: Dict[int, ResponseParser] = {} + stream_state = OpenAIFormatter.ResponsesStream( + body=body, + response_id="resp_" + request.id, + created_at=float(request.created), + model=service.scheduler.model.model_path, + ) + + def response_chunk_payloads( + completion_chunk: CompletionChunk, + ) -> Iterable[BaseModel | Dict[str, Any]]: + chat_chunks = formatter.convert_completion_chunk_to_chat_chunks( + completion_chunk, + started_indices, + parts.tool_name, + tools=response_tools, + parsed_states=parsed_states, + generation_prompt=parts.generation_prompt, + ) + payloads: List[BaseModel | Dict[str, Any]] = [] + for chat_chunk in chat_chunks: + payloads.extend( + formatter.convert_chat_chunk_to_response_events( + chat_chunk, + stream_state, + ) + ) + return payloads + + return StreamingResponse( + stream_sse_outputs( + formatter, + http_request, + stream, + cancel, + initial_payloads=formatter.start_response_stream(stream_state), + chunk_payloads=response_chunk_payloads, + done_payloads=lambda completion: ( + formatter.response_stream_terminal_events( + stream_state, + completion, + ) + ), + ), + media_type="text/event-stream", + ) + completion = await collect_completion_result( + formatter, http_request, stream, cancel + ) + if isinstance(completion, Response): + return completion + return JSONResponse( + formatter.convert_completion_response_to_response( + completion, + body, + parts.tool_name, + tools=response_tools, + generation_prompt=parts.generation_prompt, + ) + ) + + @app.websocket("/v1/responses") + async def responses_websocket( # pyright: ignore[reportUnusedFunction] + websocket: WebSocket, + ): + await websocket.accept() + service: CompletionService = app.state.service + formatter = service.formatter + # HTTP /v1/responses remains stateless. The websocket transport keeps + # per-connection response history so Codex can use previous_response_id + # within a single live session. + websocket_response_history: Dict[str, ResponsesWebSocketState] = {} + + def websocket_request_with_ephemeral_history( + ws_body: ResponseCreateWebSocketRequest, + ) -> CreateResponseRequest: + body = ws_body.to_create_response_request() + previous_response_id = body.previous_response_id + if previous_response_id is None: + return body + prior_state = websocket_response_history.get(previous_response_id) + if prior_state is None: + raise CompletionRequestValidationError( + f"unknown previous_response_id: {previous_response_id}" + ) + current_items = formatter._clone_response_input_items(body.input) + replay_items = formatter._clone_response_input_items( + prior_state.input_items + ) + for item in prior_state.output_items: + normalized = formatter._normalize_response_output_item_for_input(item) + if normalized is not None: + replay_items.append(normalized) + replay_items.extend(current_items) + return body.model_copy( + update={ + "input": replay_items, + "previous_response_id": None, + } + ) + + async def send_error(message: str) -> None: + await websocket.send_json( + { + "type": "error", + "error": { + "message": message, + }, + } + ) + + try: + while True: + try: + payload = await websocket.receive_json() + except WebSocketDisconnect: + break + except Exception: + await send_error("invalid websocket request payload") + continue + + try: + ws_body = ResponseCreateWebSocketRequest.model_validate(payload) + body = websocket_request_with_ephemeral_history(ws_body) + if ws_body.generate is False: + body = body.model_copy(update={"max_output_tokens": 0}) + chat_parts = formatter.response_request_to_chat_parts(body) + parts = formatter.completion_request_from_response_chat_parts(chat_parts) + response_tools = chat_parts.tools + request = service.request_from_prepared( + payload=parts.payload, + prompt_text=parts.prompt_text, + prompt_plan=parts.prompt_plan, + grammar_text=parts.grammar_text, + chat_tool_name=parts.tool_name, + ) + stream, cancel = service.submit_request(request) + except CompletionRequestValidationError as exc: + await send_error(str(exc)) + continue + + started_indices: set[int] = set() + parsed_states: Dict[int, ResponseParser] = {} + stream_state = OpenAIFormatter.ResponsesStream( + body=body, + response_id="resp_" + request.id, + created_at=float(request.created), + model=service.scheduler.model.model_path, + ) + + def response_chunk_payloads( + completion_chunk: CompletionChunk, + ) -> Iterable[BaseModel | Dict[str, Any]]: + chat_chunks = formatter.convert_completion_chunk_to_chat_chunks( + completion_chunk, + started_indices, + parts.tool_name, + tools=response_tools, + parsed_states=parsed_states, + generation_prompt=parts.generation_prompt, + ) + payloads: List[BaseModel | Dict[str, Any]] = [] + for chat_chunk in chat_chunks: + payloads.extend( + formatter.convert_chat_chunk_to_response_events( + chat_chunk, + stream_state, + ) + ) + return payloads + + try: + await stream_websocket_responses( + websocket, + formatter, + stream, + cancel, + initial_payloads=formatter.start_response_stream(stream_state), + chunk_payloads=response_chunk_payloads, + done_payloads=lambda completion: ( + formatter.response_stream_terminal_events( + stream_state, + completion, + ) + ), + ) + websocket_response_history[stream_state.response_id] = ( + ResponsesWebSocketState( + input_items=formatter._clone_response_input_items( + body.input + ), + output_items=copy.deepcopy(stream_state.output), + ) + ) + except WebSocketDisconnect: + cancel() + break + except BaseException as exc: + cancel() + await send_error(str(exc)) + except WebSocketDisconnect: + pass + + @app.get("/v1/models", response_model=ModelListResponse) + async def list_models() -> ModelListResponse: # pyright: ignore[reportUnusedFunction] + service = app.state.service + model = service.scheduler.model + model_id = getattr(model, "model_alias", None) or model.model_path + created = int(time.time()) + return ModelListResponse( + data=[ + ModelCardResponse( + id=model_id, + created=created, + owned_by="llama-cpp-python", + ) + ] + ) + + @app.get("/healthz", response_model=HealthzResponse) + async def healthz() -> HealthzResponse: # pyright: ignore[reportUnusedFunction] + return HealthzResponse() + + @app.get("/metrics") + async def metrics() -> Response: # pyright: ignore[reportUnusedFunction] + service = cast(Optional[CompletionService], getattr(app.state, "service", None)) + if service is None: + raise HTTPException(status_code=503, detail="completion service unavailable") + payload = await asyncio.to_thread(service.render_prometheus_metrics) + return Response( + payload, + media_type="text/plain; version=0.0.4; charset=utf-8", + ) + + return app + + +APP = create_app() + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("-C", "--config-file", required=True) + args = parser.parse_args() + config = ConfigFile.load(args.config_file) + model_path = config.model.resolve_model_path() + loras = [ + Model.LoraAdapter(path=lora.resolve_path(), scale=lora.scale) + for lora in config.model.loras + ] + model = Model( + model_path=model_path, + model_alias=config.model.alias, + chat_template=config.model.chat_template, + loras=loras, + n_gpu_layers=config.model.n_gpu_layers, + split_mode=config.model.split_mode, + main_gpu=config.model.main_gpu, + tensor_split=config.model.tensor_split, + vocab_only=config.model.vocab_only, + use_mmap=config.model.use_mmap, + use_mlock=config.model.use_mlock, + kv_overrides=config.model.kv_overrides, + n_ctx=config.model.n_ctx, + n_batch=config.model.n_batch, + n_ubatch=config.model.n_ubatch, + n_seq_max=config.model.n_seq_max, + n_threads=config.model.threads, + n_threads_batch=config.model.threads_batch, + rope_scaling_type=config.model.rope_scaling_type, + pooling_type=config.model.pooling_type, + attention_type=config.model.attention_type, + rope_freq_base=config.model.rope_freq_base, + rope_freq_scale=config.model.rope_freq_scale, + yarn_ext_factor=config.model.yarn_ext_factor, + yarn_attn_factor=config.model.yarn_attn_factor, + yarn_beta_fast=config.model.yarn_beta_fast, + yarn_beta_slow=config.model.yarn_beta_slow, + yarn_orig_ctx=config.model.yarn_orig_ctx, + offload_kqv=config.model.offload_kqv, + flash_attn=config.model.flash_attn, + op_offload=config.model.op_offload, + swa_full=config.model.swa_full, + no_perf=config.model.no_perf, + type_k=config.model.type_k, + type_v=config.model.type_v, + kv_unified=config.model.kv_unified, + max_seq_len=config.model.max_seq_len, + max_output_tokens=config.model.max_output_tokens, + draft_model=config.model.draft_model, + draft_model_num_pred_tokens=config.model.draft_model_num_pred_tokens, + draft_model_max_ngram_size=config.model.draft_model_max_ngram_size, + draft_model_top_k=config.model.draft_model_top_k, + draft_model_p_min=config.model.draft_model_p_min, + draft_model_max_batch_size=config.model.draft_model_max_batch_size, + draft_model_threads=config.model.draft_model_threads, + draft_model_threads_batch=config.model.draft_model_threads_batch, + response_schema=config.model.response_schema, + store_logits=config.model.store_logits, + ) + if config.model.mtmd is not None: + if model.chat_formatter is None: + raise RuntimeError("MTMD requires a GGUF chat template") + mmproj_path = config.model.mtmd.resolve_mmproj_path() + embedding_cache: Optional[MTMDEmbeddingCache] = None + if config.model.mtmd.embedding_cache is not None: + embedding_cache = MTMDEmbeddingCache( + path=config.model.mtmd.embedding_cache.path, + max_bytes=config.model.mtmd.embedding_cache.max_bytes, + model_fingerprint=MTMDEmbeddingCache.fingerprint_file(model_path), + mmproj_fingerprint=MTMDEmbeddingCache.fingerprint_file(mmproj_path), + ) + model.mtmd_processor = MTMDProcessor( + model_path=model.model_path, + llama_model=model.llama_model, + chat_formatter=model.chat_formatter, + tokenize=model.tokenize, + n_embd_inp=model.n_embd_inp, + n_batch=model.n_batch, + n_ubatch=model.n_ubatch, + n_threads_batch=model.n_threads_batch, + mmproj_path=mmproj_path, + embedding_cache=embedding_cache, + allowed_media_domains=config.model.mtmd.allowed_media_domains, + allowed_local_media_path=config.model.mtmd.allowed_local_media_path, + image_max_bytes=config.model.mtmd.image_max_bytes, + audio_max_bytes=config.model.mtmd.audio_max_bytes, + image_timeout_seconds=config.model.mtmd.image_timeout_seconds, + ) + sequence_cache: Optional[SequenceCache] = None + if config.disk_cache is not None: + sequence_cache = SequenceDiskCache( + path=config.disk_cache.path, + max_bytes=config.disk_cache.max_bytes, + min_tokens=config.disk_cache.min_tokens, + compatibility_key=SequenceDiskCache.compatibility_key_for_model(model), + ) + scheduler = CompletionScheduler(model, sequence_cache=sequence_cache) + APP.state.service = CompletionService(scheduler) + try: + uvicorn.run( + APP, host=config.server.host, port=config.server.port, log_level="info" + ) + finally: + APP.state.service.close() + + +if __name__ == "__main__": + main() diff --git a/llama_cpp/llama_cpp_ext.py b/llama_cpp/llama_cpp_ext.py new file mode 100644 index 0000000000..f6ab9197b8 --- /dev/null +++ b/llama_cpp/llama_cpp_ext.py @@ -0,0 +1,99 @@ +"""Experimental bindings for non-public llama.cpp APIs from `llama-ext.h`. + +This module is not part of the stable llama-cpp-python public API. +Downstream code should not import or depend on it directly. +""" + +from __future__ import annotations + +import ctypes +import functools + +from typing import Any, Iterable, Union + +from . import llama_cpp + +_lib = llama_cpp._lib + + +def _ctypes_function_from_names( + names: Iterable[str], + argtypes: list[Any], + restype: Any, +): + """Decorator for extension functions whose exported symbol name can vary by ABI.""" + + def decorator(f): + missing: list[str] = [] + for name in names: + try: + func = getattr(_lib, name) + except AttributeError: + missing.append(name) + continue + func.argtypes = argtypes + func.restype = restype + functools.wraps(f)(func) + return func + raise AttributeError( + f"None of the shared library symbols were found: {', '.join(missing)}" + ) + + return decorator + + +# LLAMA_API void llama_set_embeddings_pre_norm(struct llama_context * ctx, bool value, bool masked); +@_ctypes_function_from_names( + ( + "llama_set_embeddings_pre_norm", + "_Z29llama_set_embeddings_pre_normP13llama_contextbb", + "?llama_set_embeddings_pre_norm@@YAXPEAUllama_context@@_N1@Z", + ), + [llama_cpp.llama_context_p_ctypes, ctypes.c_bool, ctypes.c_bool], + None, +) +def llama_set_embeddings_pre_norm( + ctx: llama_cpp.llama_context_p, + value: bool, + masked: bool, + /, +): + """Set whether the context outputs pre-norm embeddings or not.""" + ... + + +# LLAMA_API float * llama_get_embeddings_pre_norm(struct llama_context * ctx); +@_ctypes_function_from_names( + ( + "llama_get_embeddings_pre_norm", + "_Z29llama_get_embeddings_pre_normP13llama_context", + "?llama_get_embeddings_pre_norm@@YAPEAMPEAUllama_context@@@Z", + ), + [llama_cpp.llama_context_p_ctypes], + ctypes.POINTER(ctypes.c_float), +) +def llama_get_embeddings_pre_norm( + ctx: llama_cpp.llama_context_p, + /, +): + """Get the pre-norm embeddings from the last evaluation.""" + ... + + +# LLAMA_API float * llama_get_embeddings_pre_norm_ith(struct llama_context * ctx, int32_t i); +@_ctypes_function_from_names( + ( + "llama_get_embeddings_pre_norm_ith", + "_Z33llama_get_embeddings_pre_norm_ithP13llama_contexti", + "?llama_get_embeddings_pre_norm_ith@@YAPEAMPEAUllama_context@@H@Z", + ), + [llama_cpp.llama_context_p_ctypes, ctypes.c_int32], + ctypes.POINTER(ctypes.c_float), +) +def llama_get_embeddings_pre_norm_ith( + ctx: llama_cpp.llama_context_p, + i: Union[ctypes.c_int32, int], + /, +): + """Get the pre-norm embeddings for the ith output row from the last evaluation.""" + ... From cf1883063bfa8b80cdd95a5e65fb923e97381105 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 7 Jun 2026 03:22:56 -0700 Subject: [PATCH 123/138] feat: update llama.cpp to ggml-org/llama.cpp@465b1f0e7 (#2278) --- CHANGELOG.md | 2 +- examples/server/server.py | 1 + llama_cpp/llama_chat_format.py | 2 ++ llama_cpp/mtmd_cpp.py | 11 ++++++----- vendor/llama.cpp | 2 +- 5 files changed, 11 insertions(+), 7 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index bbe7b13bdc..7c6a7d969b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,7 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] -- feat: update llama.cpp to ggml-org/llama.cpp@5a69c9743 +- feat: update llama.cpp to ggml-org/llama.cpp@465b1f0e7 - feat(example): Updated server example (batch processing, multi-token prediction, `/v1/responses` api, response parsing) by @abetlen in #2174 ## [0.3.26] diff --git a/examples/server/server.py b/examples/server/server.py index 6a500ceec9..590a214208 100644 --- a/examples/server/server.py +++ b/examples/server/server.py @@ -10102,6 +10102,7 @@ def _create_bitmap(self, media_bytes: bytes, kind: Literal["image", "audio"]) -> self.ctx, buffer, len(media_bytes), + False, ) if bitmap is None: raise CompletionRequestValidationError(f"failed to create MTMD {kind} bitmap") diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index 4298642f67..0034bdae98 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -2841,6 +2841,7 @@ def _create_bitmap_from_bytes(self, image_bytes: bytes): self.mtmd_ctx, (ctypes.c_uint8 * len(image_bytes)).from_buffer(bytearray(image_bytes)), len(image_bytes), + False, ) if bitmap is None: @@ -3332,6 +3333,7 @@ def _create_bitmap_from_bytes(self, image_bytes: bytes): self.mtmd_ctx, (ctypes.c_uint8 * len(image_bytes)).from_buffer(bytearray(image_bytes)), len(image_bytes), + False, ) if bitmap is None: diff --git a/llama_cpp/mtmd_cpp.py b/llama_cpp/mtmd_cpp.py index 8c2ebbfef4..abe4c7a679 100644 --- a/llama_cpp/mtmd_cpp.py +++ b/llama_cpp/mtmd_cpp.py @@ -551,29 +551,30 @@ def mtmd_test_create_input_chunks() -> Optional[mtmd_input_chunks_p]: ################################################ -# MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname); +# MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname, bool placeholder); @ctypes_function( "mtmd_helper_bitmap_init_from_file", - [mtmd_context_p_ctypes, c_char_p], + [mtmd_context_p_ctypes, c_char_p, c_bool], mtmd_bitmap_p_ctypes, ) def mtmd_helper_bitmap_init_from_file( - ctx: mtmd_context_p, fname: bytes, / + ctx: mtmd_context_p, fname: bytes, placeholder: Union[c_bool, bool], / ) -> Optional[mtmd_bitmap_p]: """Initialize an MTMD bitmap from a file.""" ... -# MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len); +# MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len, bool placeholder); @ctypes_function( "mtmd_helper_bitmap_init_from_buf", - [mtmd_context_p_ctypes, POINTER(c_uint8), c_size_t], + [mtmd_context_p_ctypes, POINTER(c_uint8), c_size_t, c_bool], mtmd_bitmap_p_ctypes, ) def mtmd_helper_bitmap_init_from_buf( ctx: mtmd_context_p, buf: CtypesArray[c_uint8], length: Union[c_size_t, int], + placeholder: Union[c_bool, bool], /, ) -> Optional[mtmd_bitmap_p]: ... diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 5a69c97439..465b1f0e75 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 5a69c974392020e514c3b2b2910bb92f847cb4c9 +Subproject commit 465b1f0e75c590426cff3ca998bcd25297071a5b From 380177b7b9c09f018d51de7d6410ab8adf5c9754 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 7 Jun 2026 03:51:23 -0700 Subject: [PATCH 124/138] chore: bump version to 0.3.27 (#2279) --- CHANGELOG.md | 2 ++ llama_cpp/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7c6a7d969b..229921847f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.27] + - feat: update llama.cpp to ggml-org/llama.cpp@465b1f0e7 - feat(example): Updated server example (batch processing, multi-token prediction, `/v1/responses` api, response parsing) by @abetlen in #2174 diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index bbfb73de3f..369f24ca88 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.26" +__version__ = "0.3.27" From fe927bd000f045ee1b488fbe326421033b8b2716 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 7 Jun 2026 15:09:29 -0700 Subject: [PATCH 125/138] feat(example): add OpenAI-compatible embeddings endpoint (#2281) * feat(example): add embeddings endpoint * docs: update changelog for embeddings endpoint * test: remove server embeddings example test * feat(example): auto-detect embedding model mode --- CHANGELOG.md | 2 + examples/server/README.md | 27 +- .../server/configs/bge-small-en-v1.5.json | 22 + examples/server/server.py | 395 +++++++++++++++++- 4 files changed, 438 insertions(+), 8 deletions(-) create mode 100644 examples/server/configs/bge-small-en-v1.5.json diff --git a/CHANGELOG.md b/CHANGELOG.md index 229921847f..4912e00e45 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat(example): add OpenAI-compatible embeddings endpoint by @abetlen in #2281 + ## [0.3.27] - feat: update llama.cpp to ggml-org/llama.cpp@465b1f0e7 diff --git a/examples/server/README.md b/examples/server/README.md index 1f6e0d3dbe..6a1cc44480 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -1,7 +1,7 @@ # Server Example This example is an updated OpenAI-compatible web server that depends only on the low-level C bindings. -It supports batched inference, prompt caching, response parsing, `/v1/responses`, disk sequence caching, MTP, LoRA, and multimodal image/audio inputs. +It supports batched inference, prompt caching, response parsing, `/v1/responses`, `/v1/embeddings`, disk sequence caching, MTP, LoRA, and multimodal image/audio inputs. ## Setup @@ -46,6 +46,7 @@ The smallest checked-in example uses Qwen3.5 0.8B so the server can be started o | Config | Model | Notes | | --- | --- | --- | +| [`configs/bge-small-en-v1.5.json`](configs/bge-small-en-v1.5.json) | [`CompendiumLabs/bge-small-en-v1.5-gguf`](https://huggingface.co/CompendiumLabs/bge-small-en-v1.5-gguf) | Small embedding model config for `/v1/embeddings`. | | [`configs/qwen3.5-0.8b.json`](configs/qwen3.5-0.8b.json) | [`lmstudio-community/Qwen3.5-0.8B-GGUF`](https://huggingface.co/lmstudio-community/Qwen3.5-0.8B-GGUF) | Default small multimodal example. | | [`configs/gemma-4-12b-it-qat.json`](configs/gemma-4-12b-it-qat.json) | [`unsloth/gemma-4-12B-it-qat-GGUF`](https://huggingface.co/unsloth/gemma-4-12B-it-qat-GGUF) | Larger Gemma 4 QAT multimodal config with projector. | | [`configs/qwen3.6-27b.json`](configs/qwen3.6-27b.json) | [`unsloth/Qwen3.6-27B-GGUF`](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF) | Larger Qwen3.6 multimodal config. | @@ -86,11 +87,33 @@ response = client.responses.create( print(response.output_text) ``` +### Embeddings + +Start the server with an embedding config before calling `/v1/embeddings`. + +```bash +cd examples/server +uv run --script server.py -C configs/bge-small-en-v1.5.json +``` + +```python +from openai import OpenAI + +client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="not-used") + +response = client.embeddings.create( + model="bge-small-en-v1.5", + input=["The food was delicious.", "The meal was excellent."], +) +print(len(response.data[0].embedding)) +``` + ## API Surface | Endpoint | Purpose | Reference | | --- | --- | --- | | `POST /v1/completions` | Legacy text completions with streaming, stop sequences, logprobs, penalties, seeds, and grammar-backed JSON output. | [OpenAI Completions API](https://platform.openai.com/docs/api-reference/completions) | +| `POST /v1/embeddings` | OpenAI-compatible embeddings for embedding-mode GGUF models, including string inputs, token inputs, base64 output, and dimensions truncation. | [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings) | | `POST /v1/chat/completions` | Chat completions with streaming, tools, forced tool choice, reasoning parsing, multimodal content parts, and structured response parsing. | [OpenAI Chat API](https://platform.openai.com/docs/api-reference/chat) | | `POST /v1/responses` | Stateless Responses API compatibility for clients that use response items and response events. | [OpenAI Responses API](https://platform.openai.com/docs/api-reference/responses) | | `WS /v1/responses` | Stateful websocket Responses transport with per-connection `previous_response_id` replay. | [OpenAI Responses API](https://platform.openai.com/docs/api-reference/responses) | @@ -190,6 +213,8 @@ Most model runtime fields map to `llama_model_params` or `llama_context_params` | `threads` | Decode thread count. | | `threads_batch` | Prefill and batch thread count. | | `kv_unified` | Selects unified or per-sequence memory layout. | +| `embedding` | Overrides embedding mode; omit to auto-detect pooled embedding GGUFs from model metadata. | +| `pooling_type` | Overrides pooled embedding behavior for embedding models, such as `1` for mean pooling. | | `store_logits` | Keeps logits after decode when needed by sampling or diagnostics. | | `use_mmap` | Memory maps model weights. | | `use_mlock` | Attempts to lock model pages into RAM. | diff --git a/examples/server/configs/bge-small-en-v1.5.json b/examples/server/configs/bge-small-en-v1.5.json new file mode 100644 index 0000000000..3fc8016df8 --- /dev/null +++ b/examples/server/configs/bge-small-en-v1.5.json @@ -0,0 +1,22 @@ +{ + "server": { + "host": "0.0.0.0", + "port": 8000 + }, + "model": { + "alias": "bge-small-en-v1.5", + "from_pretrained": { + "repo_id": "CompendiumLabs/bge-small-en-v1.5-gguf", + "filename": "bge-small-en-v1.5-q4_k_m.gguf" + }, + "n_ctx": 512, + "n_seq_max": 16, + "n_batch": 512, + "n_ubatch": 512, + "threads": 4, + "threads_batch": 8, + "kv_unified": true, + "store_logits": false, + "use_mmap": true + } +} diff --git a/examples/server/server.py b/examples/server/server.py index 590a214208..2530cf4bec 100644 --- a/examples/server/server.py +++ b/examples/server/server.py @@ -2457,6 +2457,7 @@ class CompletionChunk(TypedDict): CompletionStream = Generator[CompletionChunk, None, OpenAICompletion] CompletionPrompt = Union[str, List[int], List[str], List[List[int]]] +EmbeddingInput = Union[str, List[str], List[int], List[List[int]]] class CreateCompletionRequest(BaseModel): @@ -2522,6 +2523,126 @@ def normalized_prompt(self) -> List[Union[str, List[int]]]: raise ValueError("prompt must be a string, token ids, list of strings, or list of token-id lists") +class CreateEmbeddingRequest(BaseModel): + model_config = ConfigDict(extra="ignore") + + input: EmbeddingInput + model: str + encoding_format: Literal["float", "base64"] = "float" + dimensions: Optional[int] = Field(default=None, ge=1) + user: Optional[str] = None + + @staticmethod + def _validate_text_input(text: str) -> str: + if text == "": + raise ValueError("embedding input must not contain empty strings") + return text + + @staticmethod + def _validate_token_input(tokens: List[int]) -> List[int]: + if not tokens: + raise ValueError("embedding token input must not be empty") + if len(tokens) > 2048: + raise ValueError("embedding token input must not exceed 2048 tokens") + return tokens + + @model_validator(mode="after") + def validate_after(self) -> "CreateEmbeddingRequest": + self.normalized_input() + return self + + def normalized_input(self) -> List[Union[str, List[int]]]: + if isinstance(self.input, str): + return [self._validate_text_input(self.input)] + if all(isinstance(token, int) for token in self.input): + return [self._validate_token_input(cast(List[int], self.input))] + if all(isinstance(item, str) for item in self.input): + if len(self.input) > 2048: + raise ValueError("embedding input array must not exceed 2048 items") + return [ + self._validate_text_input(item) + for item in cast(List[str], self.input) + ] + if all( + isinstance(item, list) + and all(isinstance(token, int) for token in item) + for item in self.input + ): + if len(self.input) > 2048: + raise ValueError("embedding input array must not exceed 2048 items") + return [ + self._validate_token_input(item) + for item in cast(List[List[int]], self.input) + ] + raise ValueError( + "embedding input must be a string, list of strings, token ids, or list of token-id lists" + ) + + +class EmbeddingDataResponse(BaseModel): + object: Literal["embedding"] = "embedding" + embedding: Union[List[float], str] + index: int + + +class EmbeddingUsageResponse(BaseModel): + prompt_tokens: int + total_tokens: int + + +class CreateEmbeddingResponse(BaseModel): + object: Literal["list"] = "list" + data: List[EmbeddingDataResponse] + model: str + usage: EmbeddingUsageResponse + + @staticmethod + def encode_embedding( + embedding: Sequence[float], + encoding_format: Literal["float", "base64"], + dimensions: Optional[int], + ) -> Union[List[float], str]: + if dimensions is not None: + if dimensions > len(embedding): + raise CompletionRequestValidationError( + f"dimensions ({dimensions}) exceeds embedding size ({len(embedding)})" + ) + embedding = embedding[:dimensions] + if encoding_format == "float": + return [float(value) for value in embedding] + array = np.asarray(embedding, dtype=np.float32) + return base64.b64encode(array.tobytes()).decode("ascii") + + @classmethod + def from_embeddings( + cls, + *, + model: str, + embeddings: Sequence[Sequence[float]], + total_tokens: int, + encoding_format: Literal["float", "base64"], + dimensions: Optional[int], + ) -> "CreateEmbeddingResponse": + return cls( + data=[ + EmbeddingDataResponse( + embedding=cls.encode_embedding( + embedding, + encoding_format, + dimensions, + ), + index=index, + ) + for index, embedding in enumerate(embeddings) + ], + model=model, + usage=EmbeddingUsageResponse( + prompt_tokens=total_tokens, + total_tokens=total_tokens, + ), + ) + + class ChatCompletionFunctionCall(BaseModel): model_config = ConfigDict( extra="allow", @@ -3214,6 +3335,7 @@ class ModelOptions(BaseModel): rope_scaling_type: Optional[int] = None pooling_type: Optional[int] = None attention_type: Optional[int] = None + embedding: Optional[bool] = None rope_freq_base: Optional[float] = None rope_freq_scale: Optional[float] = None yarn_ext_factor: Optional[float] = None @@ -10408,6 +10530,7 @@ def __init__( rope_scaling_type: Optional[int] = None, pooling_type: Optional[int] = None, attention_type: Optional[int] = None, + embedding: Optional[bool] = None, rope_freq_base: Optional[float] = None, rope_freq_scale: Optional[float] = None, yarn_ext_factor: Optional[float] = None, @@ -10473,9 +10596,13 @@ def __init__( if vocab is None: raise RuntimeError("failed to access model vocabulary") self.vocab = vocab - if llama_cpp.llama_model_has_encoder(llama_model): + embedding = self.resolve_embedding_mode(llama_model, embedding) + self.embedding = embedding + self.has_encoder = bool(llama_cpp.llama_model_has_encoder(llama_model)) + self.has_decoder = bool(llama_cpp.llama_model_has_decoder(llama_model)) + if self.has_encoder and not embedding: raise RuntimeError("encoder models are not supported") - if not llama_cpp.llama_model_has_decoder(llama_model): + if not self.has_decoder and not (embedding and self.has_encoder): raise RuntimeError("decoder is required") if llama_cpp.llama_model_is_recurrent(llama_model): self.memory_model = "recurrent" @@ -10519,6 +10646,7 @@ def __init__( rope_scaling_type=rope_scaling_type, pooling_type=pooling_type, attention_type=attention_type, + embedding=embedding, rope_freq_base=rope_freq_base, rope_freq_scale=rope_freq_scale, yarn_ext_factor=yarn_ext_factor, @@ -10542,7 +10670,7 @@ def __init__( raise RuntimeError("failed to create context") self.ctx = ctx mem = llama_cpp.llama_get_memory(ctx) - if mem is None: + if mem is None and not embedding: raise RuntimeError("failed to access model memory") self.mem = mem self.n_ctx = int(llama_cpp.llama_n_ctx(ctx)) @@ -10571,7 +10699,11 @@ def __init__( ) self.n_ctx_train = n_ctx_train self.n_vocab = int(llama_cpp.llama_vocab_n_tokens(self.vocab)) + self.n_embd = int(llama_cpp.llama_model_n_embd(self.llama_model)) self.n_embd_inp = int(llama_cpp.llama_model_n_embd_inp(self.llama_model)) + self.n_embd_out = int(llama_cpp.llama_model_n_embd_out(self.llama_model)) + if self.n_embd_out <= 0: + self.n_embd_out = self.n_embd self.kv_unified = kv_unified self.max_seq_len_limit = min(self.request_context_limit, self.n_ctx_train) if max_seq_len is None: @@ -10644,6 +10776,7 @@ def __init__( rope_scaling_type=rope_scaling_type, pooling_type=pooling_type, attention_type=attention_type, + embedding=embedding, rope_freq_base=rope_freq_base, rope_freq_scale=rope_freq_scale, yarn_ext_factor=yarn_ext_factor, @@ -10786,6 +10919,7 @@ def build_context_params( rope_scaling_type: Optional[int], pooling_type: Optional[int], attention_type: Optional[int], + embedding: bool, rope_freq_base: Optional[float], rope_freq_scale: Optional[float], yarn_ext_factor: Optional[float], @@ -10830,6 +10964,7 @@ def build_context_params( context_params.pooling_type = pooling_type if attention_type is not None: context_params.attention_type = attention_type + context_params.embeddings = embedding if rope_freq_base is not None: context_params.rope_freq_base = rope_freq_base if rope_freq_scale is not None: @@ -10940,14 +11075,34 @@ def close(self) -> None: llama_cpp.llama_backend_free() self.backend_initialized = False - def _meta_value(self, key: str) -> Optional[str]: + @staticmethod + def _model_meta_key_by_index(llama_model: Any, index: int) -> Optional[str]: + capacity = 256 + while True: + buffer = ctypes.create_string_buffer(capacity) + count = int( + llama_cpp.llama_model_meta_key_by_index( + llama_model, + index, + cast(Any, buffer), + capacity, + ) + ) + if count < 0: + return None + if count < capacity: + return buffer.value.decode("utf-8", errors="ignore") + capacity = count + 1 + + @staticmethod + def _model_meta_value(llama_model: Any, key: str) -> Optional[str]: encoded = key.encode("utf-8") capacity = 256 while True: buffer = ctypes.create_string_buffer(capacity) count = int( llama_cpp.llama_model_meta_val_str( - self.llama_model, + llama_model, encoded, cast(Any, buffer), capacity, @@ -10959,6 +11114,50 @@ def _meta_value(self, key: str) -> Optional[str]: return buffer.value.decode("utf-8", errors="ignore") capacity = count + 1 + @staticmethod + def _parse_pooling_type(value: str) -> Optional[int]: + normalized = value.strip().lower() + try: + return int(normalized) + except ValueError: + return { + "none": llama_cpp.LLAMA_POOLING_TYPE_NONE, + "mean": llama_cpp.LLAMA_POOLING_TYPE_MEAN, + "cls": llama_cpp.LLAMA_POOLING_TYPE_CLS, + "last": llama_cpp.LLAMA_POOLING_TYPE_LAST, + "rank": llama_cpp.LLAMA_POOLING_TYPE_RANK, + }.get(normalized) + + @classmethod + def detect_embedding_model(cls, llama_model: Any) -> bool: + for index in range(int(llama_cpp.llama_model_meta_count(llama_model))): + key = cls._model_meta_key_by_index(llama_model, index) + if key is None or not key.endswith(".pooling_type"): + continue + value = cls._model_meta_value(llama_model, key) + if value is None: + continue + pooling_type = cls._parse_pooling_type(value) + return pooling_type in { + llama_cpp.LLAMA_POOLING_TYPE_MEAN, + llama_cpp.LLAMA_POOLING_TYPE_CLS, + llama_cpp.LLAMA_POOLING_TYPE_LAST, + } + return False + + @classmethod + def resolve_embedding_mode( + cls, + llama_model: Any, + embedding: Optional[bool], + ) -> bool: + if embedding is not None: + return embedding + return cls.detect_embedding_model(llama_model) + + def _meta_value(self, key: str) -> Optional[str]: + return self._model_meta_value(self.llama_model, key) + def _build_chat_formatter(self) -> Optional[Jinja2ChatFormatter]: template_text = self.chat_template_override if template_text is None: @@ -11131,6 +11330,10 @@ def clear_batch(self) -> None: self._embedding_batch = None self._embedding_batch_refs = [] + def clear_memory(self) -> None: + if self.mem is not None: + llama_cpp.llama_memory_clear(self.mem, True) + def add_batch_tokens( self, *, @@ -11206,9 +11409,14 @@ def add_batch_embeddings( def decode(self) -> None: batch = self._embedding_batch if self._embedding_batch is not None else self.batch - result = int(llama_cpp.llama_decode(self.ctx, batch)) + if self.embedding and self.has_encoder: + operation = "llama_encode" + result = int(llama_cpp.llama_encode(self.ctx, batch)) + else: + operation = "llama_decode" + result = int(llama_cpp.llama_decode(self.ctx, batch)) if result != 0: - raise RuntimeError(f"llama_decode failed with code {result}") + raise RuntimeError(f"{operation} failed with code {result}") def process_draft_batch(self) -> None: if self.draft_provider is not None: @@ -11242,6 +11450,103 @@ def logits(self, output_index: int) -> np.ndarray: raise RuntimeError(f"missing logits output {output_index}") return np.ctypeslib.as_array(ptr, shape=(self.n_vocab,)).copy() + def embed( + self, + inputs: Sequence[Union[str, List[int]]], + ) -> Tuple[List[List[float]], int]: + if not self.embedding: + raise CompletionRequestValidationError( + "model.embedding must be true to use /v1/embeddings" + ) + pooling_type = int(llama_cpp.llama_pooling_type(self.ctx)) + if pooling_type == llama_cpp.LLAMA_POOLING_TYPE_NONE: + raise CompletionRequestValidationError( + "/v1/embeddings requires a pooled embedding model; " + "set model.pooling_type to MEAN, CLS, or LAST" + ) + if pooling_type == llama_cpp.LLAMA_POOLING_TYPE_RANK: + raise CompletionRequestValidationError( + "/v1/embeddings does not support reranking pooling" + ) + if len(inputs) > 2048: + raise CompletionRequestValidationError( + "embedding input batch size exceeds 2048" + ) + + embeddings: List[List[float]] = [] + total_tokens = 0 + batch_sizes: List[int] = [] + batch_token_count = 0 + + def decode_embedding_batch() -> None: + nonlocal batch_token_count + if not batch_sizes: + return + self.clear_memory() + self.decode() + self.clear_batch() + for seq_id in range(len(batch_sizes)): + ptr = llama_cpp.llama_get_embeddings_seq( + self.ctx, + llama_cpp.llama_seq_id(seq_id), + ) + if not ptr: + raise RuntimeError(f"missing embedding output for input {seq_id}") + embeddings.append( + np.ctypeslib.as_array(ptr, shape=(self.n_embd_out,)).astype( + float + ).tolist() + ) + batch_sizes.clear() + batch_token_count = 0 + + try: + self.clear_batch() + self.clear_memory() + for input_item in inputs: + tokens = ( + self.tokenize(input_item) + if isinstance(input_item, str) + else list(input_item) + ) + n_tokens = len(tokens) + if n_tokens == 0: + raise CompletionRequestValidationError( + "embedding input must not be empty" + ) + if n_tokens > self.n_ctx_seq: + raise CompletionRequestValidationError( + f"embedding input has {n_tokens} tokens, exceeding n_ctx_seq ({self.n_ctx_seq})" + ) + if n_tokens > self.n_batch: + raise CompletionRequestValidationError( + f"embedding input has {n_tokens} tokens, exceeding n_batch ({self.n_batch})" + ) + if total_tokens + n_tokens > 300_000: + raise CompletionRequestValidationError( + "embedding request exceeds 300000 total tokens" + ) + if ( + batch_token_count + n_tokens > self.n_batch + or len(batch_sizes) >= self.n_seq_max + ): + decode_embedding_batch() + seq_id = len(batch_sizes) + self.add_batch_tokens( + seq_id=seq_id, + start_pos=0, + tokens=tokens, + output_indices=[0] * n_tokens, + ) + batch_sizes.append(n_tokens) + batch_token_count += n_tokens + total_tokens += n_tokens + decode_embedding_batch() + finally: + self.clear_batch() + self.clear_memory() + return embeddings, total_tokens + class SequenceDiskCache(SequenceCache): """Directory-backed cache for serialized llama.cpp sequence state.""" @@ -12065,6 +12370,37 @@ def build_memory_policy(self) -> MemoryPolicy: return PartitionedAttentionMemoryPolicy(self) return UnifiedAttentionMemoryPolicy(self) + def clear_resident_state(self) -> None: + self.model.clear_memory() + self.model.clear_batch() + self.radix_trie = RadixTrie() + self.sequence_history = SequenceHistory() + self.checkpoint_logits.clear() + self.claimed_sequences.clear() + self.free_sequences.clear() + self.unused_sequences = list(range(self.model.n_seq_max - 1, -1, -1)) + for seq_id in range(self.model.n_seq_max): + self.model.truncate_draft_sequence(seq_id, 0) + + def create_embedding( + self, + payload: CreateEmbeddingRequest, + ) -> CreateEmbeddingResponse: + if not self.is_idle(): + raise RuntimeError("embedding requests require an idle scheduler") + self.clear_resident_state() + try: + embeddings, total_tokens = self.model.embed(payload.normalized_input()) + return CreateEmbeddingResponse.from_embeddings( + model=payload.model, + embeddings=embeddings, + total_tokens=total_tokens, + encoding_format=payload.encoding_format, + dimensions=payload.dimensions, + ) + finally: + self.clear_resident_state() + @staticmethod def request_needs_prompt_logits(request: CompletionRequest) -> bool: return request.payload.max_tokens != 0 and request.effective_max_len > len( @@ -14420,6 +14756,39 @@ def run_callback() -> None: raise error_box["error"] return result_box.get("result") + def call_on_idle_scheduler(self, callback: Callable[[], Any]) -> Any: + result_box: Dict[str, Any] = {} + error_box: Dict[str, BaseException] = {} + done = threading.Event() + + def run_callback() -> None: + if not self.scheduler.is_idle(): + with self.condition: + self.commands.appendleft(run_callback) + self.condition.notify_all() + return + try: + result_box["result"] = callback() + except BaseException as exc: # noqa: BLE001 + error_box["error"] = exc + finally: + done.set() + + self.enqueue(run_callback) + done.wait() + if "error" in error_box: + raise error_box["error"] + return result_box.get("result") + + def create_embedding( + self, + payload: CreateEmbeddingRequest, + ) -> CreateEmbeddingResponse: + embedding = self.call_on_idle_scheduler( + lambda: self.scheduler.create_embedding(payload) + ) + return cast(CreateEmbeddingResponse, embedding) + def render_prometheus_metrics(self) -> str: metrics = self.call_on_scheduler(self.scheduler.render_prometheus_metrics) return cast(str, metrics) @@ -14874,6 +15243,17 @@ async def create_completion( # pyright: ignore[reportUnusedFunction] return result return JSONResponse(result.model_dump(mode="json", exclude_none=True)) + @app.post("/v1/embeddings", response_model=CreateEmbeddingResponse) + async def create_embedding( # pyright: ignore[reportUnusedFunction] + body: CreateEmbeddingRequest, + ) -> JSONResponse: + service: CompletionService = app.state.service + try: + embedding = await asyncio.to_thread(service.create_embedding, body) + except CompletionRequestValidationError as exc: + raise bad_request(exc) from exc + return JSONResponse(embedding.model_dump(mode="json", exclude_none=True)) + @app.post("/v1/chat/completions") async def create_chat_completion( # pyright: ignore[reportUnusedFunction] http_request: Request, body: CreateChatCompletionRequest @@ -15250,6 +15630,7 @@ def main() -> None: rope_scaling_type=config.model.rope_scaling_type, pooling_type=config.model.pooling_type, attention_type=config.model.attention_type, + embedding=config.model.embedding, rope_freq_base=config.model.rope_freq_base, rope_freq_scale=config.model.rope_freq_scale, yarn_ext_factor=config.model.yarn_ext_factor, From db66da32d12408cc5bc62c27d09e09ba570dc131 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 7 Jun 2026 15:20:36 -0700 Subject: [PATCH 126/138] feat: update llama.cpp to ggml-org/llama.cpp@9e3b928fd (#2282) --- CHANGELOG.md | 1 + llama_cpp/llama_cpp.py | 7 +++++++ vendor/llama.cpp | 2 +- 3 files changed, 9 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4912e00e45..e20ed73c29 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: update llama.cpp to ggml-org/llama.cpp@9e3b928fd - feat(example): add OpenAI-compatible embeddings endpoint by @abetlen in #2281 ## [0.3.27] diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index 44c25a519f..21f85c81c3 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -949,6 +949,10 @@ class llama_sampler_seq_config(ctypes.Structure): # // ref: https://github.com/ggml-org/llama.cpp/pull/14363 # struct llama_sampler_seq_config * samplers; # size_t n_samplers; +# +# // a source/target/parent context +# // can be utilized in various ways, for example by sharing results or llama_memory between 2 contexts +# struct llama_context * ctx_other; # }; class llama_context_params(ctypes.Structure): """Parameters for llama_context @@ -989,6 +993,7 @@ class llama_context_params(ctypes.Structure): kv_unified (bool): use a unified buffer across the input sequences when computing the attention samplers (ctypes.POINTER(llama_sampler_seq_config)): backend sampler chain configuration n_samplers (int): number of backend sampler chain configurations + ctx_other (llama_context_p): source, target, or parent context """ if TYPE_CHECKING: @@ -1027,6 +1032,7 @@ class llama_context_params(ctypes.Structure): kv_unified: bool samplers: ctypes.POINTER(llama_sampler_seq_config) n_samplers: int + ctx_other: llama_context_p _fields_ = [ ("n_ctx", ctypes.c_uint32), @@ -1064,6 +1070,7 @@ class llama_context_params(ctypes.Structure): ("kv_unified", ctypes.c_bool), ("samplers", ctypes.POINTER(llama_sampler_seq_config)), ("n_samplers", ctypes.c_size_t), + ("ctx_other", llama_context_p_ctypes), ] diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 465b1f0e75..9e3b928fd8 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 465b1f0e75c590426cff3ca998bcd25297071a5b +Subproject commit 9e3b928fd8c9d14dbf15a8768b9fdd7e5c721d66 From fddee272fc78e0e6b74cad8da10eb19347cad909 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 7 Jun 2026 16:19:29 -0700 Subject: [PATCH 127/138] feat(example): align server MTP support with llama.cpp (#2283) * feat(example): align server MTP support with llama.cpp * docs: update changelog for server MTP alignment * fix: remove stale pre-norm extension bindings * fix(example): disable sampled MTP for shared memory contexts --- CHANGELOG.md | 1 + examples/server/README.md | 16 ++ examples/server/server.py | 408 +++++++++++++++++-------------------- llama_cpp/llama_cpp_ext.py | 54 +++-- 4 files changed, 238 insertions(+), 241 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index e20ed73c29..b07244ed7b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat(example): align server MTP support with llama.cpp by @abetlen in #2283 - feat: update llama.cpp to ggml-org/llama.cpp@9e3b928fd - feat(example): add OpenAI-compatible embeddings endpoint by @abetlen in #2281 diff --git a/examples/server/README.md b/examples/server/README.md index 6a1cc44480..ff04374fc5 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -434,6 +434,22 @@ Use MTP when the loaded model and llama.cpp build expose the required draft stat } ``` +By default `draft-mtp` creates the MTP context from the target model. +Set `draft_model_path` or `draft_model_from_pretrained` when the model uses a separate assistant GGUF. + +```json +{ + "model": { + "draft_model": "draft-mtp", + "draft_model_num_pred_tokens": 2, + "draft_model_from_pretrained": { + "repo_id": "example/gemma-assistant-GGUF", + "filename": "assistant.gguf" + } + } +} +``` + MTP currently applies to text-only requests. ## Disk Sequence Cache diff --git a/examples/server/server.py b/examples/server/server.py index 2530cf4bec..64a16f0bd1 100644 --- a/examples/server/server.py +++ b/examples/server/server.py @@ -36,7 +36,6 @@ import copy import shutil import inspect -import importlib.util import sys import urllib.error import urllib.parse @@ -1262,27 +1261,33 @@ def __init__( self, *, model: "Model", + draft_model: Any, context_params: Any, num_pred_tokens: int, top_k: int, p_min: float, ) -> None: self.target_ctx = model.ctx - self.model = model.llama_model + self.model = draft_model self.n_seq_max = model.n_seq_max self.n_vocab = model.n_vocab - self.n_embd = int(llama_cpp.llama_model_n_embd(self.model)) + self.n_embd = int(llama_cpp.llama_model_n_embd_out(self.model)) + if self.n_embd <= 0: + self.n_embd = int(llama_cpp.llama_model_n_embd(self.model)) + if self.n_embd != model.n_embd: + raise RuntimeError( + "MTP draft model output embedding size must match target model " + f"embedding size ({self.n_embd} != {model.n_embd})" + ) self.num_pred_tokens = max(0, int(num_pred_tokens)) self.top_k = max(1, int(top_k)) self.p_min = max(0.0, min(1.0, float(p_min))) - ( - self.target_hidden_norm_weight, - self.draft_hidden_norm_weight, - self.hidden_norm_epsilon, - ) = self._load_hidden_norm_weights(model.model_path) self.ctx = llama_cpp.llama_init_from_model(self.model, context_params) if self.ctx is None: raise RuntimeError("failed to create MTP draft context") + ctx_other = llama_cpp_ext.llama_get_ctx_other(self.ctx) + self.is_mem_shared = bool(ctx_other and ctx_other == self.target_ctx) + self.sampled_batch_draft = not self.is_mem_shared self.n_batch = int(llama_cpp.llama_n_batch(self.ctx)) mem = llama_cpp.llama_get_memory(self.ctx) if mem is None: @@ -1313,135 +1318,13 @@ def __init__( self.decode_failures_total = 0 self.target_processing_enabled = False self.set_target_processing_enabled(True) - llama_cpp_ext.llama_set_embeddings_pre_norm( + llama_cpp_ext.llama_set_embeddings_nextn( self.ctx, True, True, ) self._init_samplers() - @staticmethod - def _load_gguf_reader() -> Any: - try: - from gguf import GGUFReader # type: ignore[import-not-found] - return GGUFReader - except ImportError: - pass - - gguf_init = ( - Path(__file__).resolve().parents[2] - / "vendor/llama.cpp/gguf-py/gguf/__init__.py" - ) - spec = importlib.util.spec_from_file_location( - "gguf", - gguf_init, - submodule_search_locations=[str(gguf_init.parent)], - ) - if spec is None or spec.loader is None: - raise RuntimeError("MTP requires the gguf Python reader from llama.cpp") - module = importlib.util.module_from_spec(spec) - previous = sys.modules.get("gguf") - # Package-relative imports in gguf-py expect the package to be registered. - sys.modules["gguf"] = module - try: - spec.loader.exec_module(module) - except Exception: - if previous is None: - sys.modules.pop("gguf", None) - else: - sys.modules["gguf"] = previous - raise - return module.GGUFReader - - @staticmethod - def _gguf_field_contents(reader: Any, name: str) -> Any: - field = reader.fields.get(name) - if field is None: - return None - return field.contents() - - def _load_hidden_norm_weights( - self, - model_path: str, - ) -> Tuple[np.ndarray, np.ndarray, float]: - GGUFReader = self._load_gguf_reader() - - target_weight: Optional[np.ndarray] = None - draft_weights: List[np.ndarray] = [] - reader = GGUFReader(model_path) - arch = self._gguf_field_contents(reader, "general.architecture") - if arch not in {"qwen35", "qwen35moe"}: - raise RuntimeError( - "draft-mtp currently supports qwen35/qwen35moe GGUF models, " - f"got {arch!r}" - ) - nextn_layers = self._gguf_field_contents( - reader, - f"{arch}.nextn_predict_layers", - ) - # The current MTP path follows llama.cpp's Qwen3.5 one-nextn-layer graph. - if int(nextn_layers or 0) != 1: - raise RuntimeError( - "draft-mtp currently supports exactly one Qwen3.5 nextn prediction layer" - ) - epsilon = self._gguf_field_contents( - reader, - f"{arch}.attention.layer_norm_rms_epsilon", - ) - if epsilon is None: - raise RuntimeError( - f"MTP requires {arch}.attention.layer_norm_rms_epsilon" - ) - for tensor in reader.tensors: - if tensor.name == "output_norm.weight": - target_weight = np.asarray(tensor.data, dtype=np.float32).copy() - elif tensor.name.endswith(".nextn.shared_head_norm.weight"): - draft_weights.append(np.asarray(tensor.data, dtype=np.float32).copy()) - if target_weight is None: - raise RuntimeError("MTP requires output_norm.weight in GGUF model") - if len(draft_weights) > 1: - raise RuntimeError( - "MTP requires at most one blk.*.nextn.shared_head_norm.weight in GGUF model" - ) - draft_weight = draft_weights[0] if draft_weights else target_weight - if target_weight.shape != (self.n_embd,): - raise RuntimeError( - "MTP target norm weight shape does not match model embedding size " - f"({target_weight.shape} != ({self.n_embd},))" - ) - if draft_weight.shape != (self.n_embd,): - raise RuntimeError( - "MTP draft norm weight shape does not match model embedding size " - f"({draft_weight.shape} != ({self.n_embd},))" - ) - return target_weight, draft_weight, float(epsilon) - - def _normalize_hidden_rows(self, rows: np.ndarray, weight: np.ndarray) -> np.ndarray: - rows = np.asarray(rows, dtype=np.float32) - scale = np.reciprocal( - np.sqrt( - np.mean(np.square(rows), axis=-1, keepdims=True) - + self.hidden_norm_epsilon - ) - ) - return rows * scale * weight.reshape(1, -1) - - def _normalize_target_hidden_rows(self, rows: np.ndarray) -> np.ndarray: - return self._normalize_hidden_rows(rows, self.target_hidden_norm_weight) - - def _normalize_draft_hidden_row( - self, - row: Union[np.ndarray, ctypes.POINTER(ctypes.c_float)], - ) -> np.ndarray: - if isinstance(row, np.ndarray): - row_array = row.reshape(1, -1) - else: - row_array = np.ctypeslib.as_array(row, shape=(self.n_embd,)).reshape(1, -1) - return self._normalize_hidden_rows( - row_array, - self.draft_hidden_norm_weight, - )[0] - def _init_samplers(self) -> None: for seq_id in range(self.n_seq_max): params = llama_cpp.llama_sampler_chain_default_params() @@ -1507,7 +1390,7 @@ def close(self) -> None: def set_target_processing_enabled(self, enabled: bool) -> None: if self.target_processing_enabled == enabled: return - llama_cpp_ext.llama_set_embeddings_pre_norm( + llama_cpp_ext.llama_set_embeddings_nextn( self.target_ctx, enabled, False, @@ -1641,14 +1524,13 @@ def process(self, batch: Any, /) -> None: ): return - h_tgt = llama_cpp_ext.llama_get_embeddings_pre_norm(self.target_ctx) + h_tgt = llama_cpp_ext.llama_get_embeddings_nextn(self.target_ctx) if not h_tgt: - raise RuntimeError("missing target pre-norm embeddings for MTP") + raise RuntimeError("missing target nextn embeddings for MTP") h_tgt_rows = np.ctypeslib.as_array( h_tgt, shape=(n_tokens, self.n_embd), ) - h_tgt_rows = self._normalize_target_hidden_rows(h_tgt_rows) previous_row_by_seq: Dict[int, int] = {} first_pos_by_seq: Dict[int, int] = {} @@ -1708,17 +1590,17 @@ def _process_rows( self.ready[seq_id] and self.ready_pos[seq_id] == first_pos ) aligned_by_seq.setdefault(seq_id, aligned) - mtp_pos = ( - pos - 1 - if previous_row_by_seq.get(seq_id) is None - else int(batch.pos[previous_row_by_seq[seq_id]]) - ) - if aligned and mtp_pos >= 0 and mtp_pos >= self.context_pos[seq_id]: - previous_row = previous_row_by_seq.get(seq_id) + previous_row = previous_row_by_seq.get(seq_id) + if ( + aligned + and not self.is_mem_shared + and pos >= 0 + and pos >= self.context_pos[seq_id] + ): slot = int(self.batch.n_tokens) self._add_batch_token( token=int(batch.token[index]), - pos=mtp_pos, + pos=pos, seq_id=seq_id, logits=False, ) @@ -1726,7 +1608,7 @@ def _process_rows( self._set_batch_embedding_row(slot, self.pending_h[seq_id]) else: self._set_batch_embedding_row(slot, h_tgt_rows[previous_row]) - added_pos_by_seq[seq_id] = mtp_pos + added_pos_by_seq[seq_id] = pos previous_row_by_seq[seq_id] = index target_rows_by_seq.setdefault(seq_id, []).append(index) @@ -1760,15 +1642,15 @@ def draft( n_past = int(input_ids.size) - 1 if self.ready_pos[seq_id] != n_past: return np.array([], dtype=np.intc) - first_pos = n_past - 1 + first_pos = n_past if first_pos < 0: return np.array([], dtype=np.intc) token = int(input_ids[-1]) drafted: List[int] = [] - if self.context_pos[seq_id] > first_pos: + if not self.is_mem_shared and self.context_pos[seq_id] > first_pos: self.truncate(seq_id, first_pos) - if self.context_pos[seq_id] < first_pos: + if not self.is_mem_shared and self.context_pos[seq_id] < first_pos: self.ready[seq_id] = False return np.array([], dtype=np.intc) @@ -1782,9 +1664,11 @@ def draft( ) self._set_batch_embedding_row(0, self.pending_h[seq_id]) if not self._try_decode_batch(): - self.truncate(seq_id, first_pos) + if not self.is_mem_shared: + self.truncate(seq_id, first_pos) return np.array([], dtype=np.intc) - self.context_pos[seq_id] = n_past + if not self.is_mem_shared: + self.context_pos[seq_id] = first_pos + 1 while len(drafted) < n_predict: sampled_token = self._sample_token(seq_id=seq_id) @@ -1794,26 +1678,28 @@ def draft( drafted.append(token) if len(drafted) >= n_predict: break - h_row = llama_cpp_ext.llama_get_embeddings_pre_norm_ith(self.ctx, 0) + h_row = llama_cpp_ext.llama_get_embeddings_nextn_ith(self.ctx, 0) if not h_row: break - h_row = self._normalize_draft_hidden_row(h_row) self._clear_batch() self._add_batch_token( token=token, - pos=first_pos + len(drafted), + pos=first_pos if self.is_mem_shared else first_pos + len(drafted), seq_id=seq_id, logits=True, ) self._set_batch_embedding_row(0, h_row) if not self._try_decode_batch(): break - self.context_pos[seq_id] = first_pos + len(drafted) + 1 + if not self.is_mem_shared: + self.context_pos[seq_id] = first_pos + len(drafted) + 1 if not drafted: - self.truncate(seq_id, n_past) + if not self.is_mem_shared: + self.truncate(seq_id, n_past) return np.array([], dtype=np.intc) - self.truncate(seq_id, n_past) + if not self.is_mem_shared: + self.truncate(seq_id, n_past) return np.asarray(drafted, dtype=np.intc) def draft_many( @@ -1843,12 +1729,12 @@ def draft_many( n_past = int(input_ids.size) - 1 if self.ready_pos[seq_id] != n_past: continue - first_pos = n_past - 1 + first_pos = n_past if first_pos < 0: continue - if self.context_pos[seq_id] > first_pos: + if not self.is_mem_shared and self.context_pos[seq_id] > first_pos: self.truncate(seq_id, first_pos) - if self.context_pos[seq_id] < first_pos: + if not self.is_mem_shared and self.context_pos[seq_id] < first_pos: self.ready[seq_id] = False continue self._reset_sampler(seq_id) @@ -1904,10 +1790,11 @@ def draft_many( ): if sampled_token is None: continue - self.context_pos[representative.seq_id] = max( - self.context_pos[representative.seq_id], - representative.keep_len, - ) + if not self.is_mem_shared: + self.context_pos[representative.seq_id] = max( + self.context_pos[representative.seq_id], + representative.first_pos + 1, + ) for state in grouped[representative.cache_key]: state.drafted.append(sampled_token) active = [] @@ -1917,7 +1804,11 @@ def draft_many( for row, state in enumerate(active): self._add_batch_token( token=state.token, - pos=state.first_pos + len(state.drafted), + pos=( + state.first_pos + if self.is_mem_shared + else state.first_pos + len(state.drafted) + ), seq_id=state.seq_id, logits=True, ) @@ -1932,14 +1823,19 @@ def draft_many( for row, state in enumerate(active) ] for row, (state, sampled_token) in enumerate(zip(active, sampled_tokens)): - decoded_pos = state.first_pos + len(state.drafted) - self.context_pos[state.seq_id] = max( - self.context_pos[state.seq_id], - decoded_pos + 1, + decoded_pos = ( + state.first_pos + if self.is_mem_shared + else state.first_pos + len(state.drafted) ) + if not self.is_mem_shared: + self.context_pos[state.seq_id] = max( + self.context_pos[state.seq_id], + decoded_pos + 1, + ) if sampled_token is None: continue - h_row_ptr = llama_cpp_ext.llama_get_embeddings_pre_norm_ith( + h_row_ptr = llama_cpp_ext.llama_get_embeddings_nextn_ith( self.ctx, row ) state.drafted.append(sampled_token) @@ -1947,14 +1843,17 @@ def draft_many( continue if not h_row_ptr: continue - h_row = self._normalize_draft_hidden_row(h_row_ptr) state.token = sampled_token - state.embedding = h_row + state.embedding = np.ctypeslib.as_array( + h_row_ptr, + shape=(self.n_embd,), + ).copy() next_active.append(state) active = next_active finally: - for state in touched: - self.truncate(state.seq_id, state.keep_len) + if not self.is_mem_shared: + for state in touched: + self.truncate(state.seq_id, state.keep_len) for state in touched: if state.drafted: @@ -2036,7 +1935,7 @@ def _build_sampled_batch_plan( pending_token = update.pending_token if ( pending_token is None - or start_pos <= 0 + or start_pos < 0 or target_count <= 0 or target_count > len(tokens) or target_count > len(row_indices) @@ -2046,9 +1945,7 @@ def _build_sampled_batch_plan( continue for target_index in range(sample_index + 1): - mtp_pos = start_pos + target_index - 1 - if mtp_pos < 0: - continue + mtp_pos = start_pos + target_index source_row = ( None if target_index == 0 @@ -2063,7 +1960,7 @@ def _build_sampled_batch_plan( ) ) - actual_pos = start_pos + sample_index + actual_pos = start_pos + sample_index + 1 pending_rows.append( self.SampledPendingRow( update_index=update_index, @@ -2090,6 +1987,8 @@ def _decode_sampled_context_rows( self._clear_batch() decoded_context_rows: List[Tuple[int, int]] = [] for row in context_rows: + if self.is_mem_shared: + continue if row.draft_pos < self.context_pos[row.seq_id]: continue if row.source_row is None: @@ -2124,7 +2023,10 @@ def _decode_sampled_pending_rows( self._clear_batch() for pending_index, row in enumerate(pending_rows): - if row.draft_pos < self.context_pos[row.seq_id]: + if ( + not self.is_mem_shared + and row.draft_pos < self.context_pos[row.seq_id] + ): continue is_sample_pending = ( pending_index @@ -2144,17 +2046,18 @@ def _decode_sampled_pending_rows( update_index=row.update_index, seq_id=row.seq_id, output_index=slot, - keep_len=row.draft_pos + 1, - ready_pos=row.draft_pos + 1, + keep_len=row.draft_pos, + ready_pos=row.draft_pos, ) ) self._decode_batch() - for row in pending_rows: - self.context_pos[row.seq_id] = max( - self.context_pos[row.seq_id], - row.draft_pos + 1, - ) + if not self.is_mem_shared: + for row in pending_rows: + self.context_pos[row.seq_id] = max( + self.context_pos[row.seq_id], + row.draft_pos + 1, + ) return sampled_outputs @@ -2184,7 +2087,7 @@ def _start_sampled_draft_states( if n_predict <= 0: continue if n_predict > 1: - h_row = llama_cpp_ext.llama_get_embeddings_pre_norm_ith( + h_row = llama_cpp_ext.llama_get_embeddings_nextn_ith( self.ctx, output.output_index ) if h_row: @@ -2193,11 +2096,18 @@ def _start_sampled_draft_states( update_index=output.update_index, seq_id=seq_id, keep_len=output.keep_len, - pos=output.keep_len, + pos=( + output.keep_len + if self.is_mem_shared + else output.keep_len + 1 + ), token=sampled_token, drafted=[sampled_token], n_predict=n_predict, - embedding=self._normalize_draft_hidden_row(h_row), + embedding=np.ctypeslib.as_array( + h_row, + shape=(self.n_embd,), + ).copy(), ) ) results[output.update_index] = np.asarray([sampled_token], dtype=np.intc) @@ -2235,31 +2145,36 @@ def _extend_sampled_draft_states( for batch_row, (state, sampled_token) in enumerate( zip(active, sampled_tokens) ): - self.context_pos[state.seq_id] = max( - self.context_pos[state.seq_id], - state.pos + 1, - ) + if not self.is_mem_shared: + self.context_pos[state.seq_id] = max( + self.context_pos[state.seq_id], + state.pos + 1, + ) if sampled_token is None: continue state.drafted.append(sampled_token) if len(state.drafted) >= state.n_predict: continue - h_row = llama_cpp_ext.llama_get_embeddings_pre_norm_ith( + h_row = llama_cpp_ext.llama_get_embeddings_nextn_ith( self.ctx, batch_row ) if not h_row: continue - h_row = self._normalize_draft_hidden_row(h_row) state.token = sampled_token - state.embedding = h_row - state.pos += 1 + state.embedding = np.ctypeslib.as_array( + h_row, + shape=(self.n_embd,), + ).copy() + if not self.is_mem_shared: + state.pos += 1 next_active.append(state) active = next_active finally: for state in touched: cleanup_keep_len_by_seq[state.seq_id] = state.keep_len - for seq_id, keep_len in cleanup_keep_len_by_seq.items(): - self._truncate_memory(seq_id, keep_len) + if not self.is_mem_shared: + for seq_id, keep_len in cleanup_keep_len_by_seq.items(): + self._truncate_memory(seq_id, keep_len) for state in touched: if state.drafted: @@ -2276,9 +2191,9 @@ def process_sampled_batch( results = [np.array([], dtype=np.intc) for _ in updates] if self.num_pred_tokens <= 0 or not updates: return results - h_tgt = llama_cpp_ext.llama_get_embeddings_pre_norm(self.target_ctx) + h_tgt = llama_cpp_ext.llama_get_embeddings_nextn(self.target_ctx) if not h_tgt: - raise RuntimeError("missing target pre-norm embeddings for MTP") + raise RuntimeError("missing target nextn embeddings for MTP") n_target_rows = max( ( max(update.row_indices) + 1 @@ -2290,7 +2205,6 @@ def process_sampled_batch( if n_target_rows <= 0: return results h_tgt_rows = np.ctypeslib.as_array(h_tgt, shape=(n_target_rows, self.n_embd)) - h_tgt_rows = self._normalize_target_hidden_rows(h_tgt_rows) plan = self._build_sampled_batch_plan(updates) if not plan.context_rows and not plan.pending_rows: @@ -2313,8 +2227,9 @@ def process_sampled_batch( self.ready_pos[output.seq_id] = output.ready_pos cleanup_keep_len_by_seq[output.seq_id] = output.keep_len - for seq_id, keep_len in cleanup_keep_len_by_seq.items(): - self._truncate_memory(seq_id, keep_len) + if not self.is_mem_shared: + for seq_id, keep_len in cleanup_keep_len_by_seq.items(): + self._truncate_memory(seq_id, keep_len) if sampled_outputs: active = self._start_sampled_draft_states( @@ -2344,6 +2259,9 @@ def accept(self, seq_id: int, accepted_draft_tokens: int) -> None: def _truncate_memory(self, seq_id: int, keep_len: int) -> None: if seq_id < 0 or seq_id >= self.n_seq_max: return + if self.is_mem_shared: + self.context_pos[seq_id] = min(self.context_pos[seq_id], keep_len) + return if not llama_cpp.llama_memory_seq_rm( self.mem, seq_id, @@ -2386,13 +2304,14 @@ def copy_sequence( or dest_seq_id >= self.n_seq_max ): return - llama_cpp.llama_memory_seq_cp( - self.mem, - source_seq_id, - dest_seq_id, - p0, - p1, - ) + if not self.is_mem_shared: + llama_cpp.llama_memory_seq_cp( + self.mem, + source_seq_id, + dest_seq_id, + p0, + p1, + ) source_ready_pos = self.ready_pos[source_seq_id] copied_full_ready_state = p1 < 0 or p1 == source_ready_pos if self.ready[source_seq_id] and copied_full_ready_state: @@ -3354,6 +3273,10 @@ class ModelOptions(BaseModel): max_output_tokens: Optional[int] = Field(default=None, ge=0) kv_unified: bool = True draft_model: Optional[Literal["prompt-lookup-decoding", "draft-mtp"]] = None + draft_model_path: Optional[str] = None + draft_model_from_pretrained: Optional[ + "ConfigFile.FromPretrainedOptions" + ] = None draft_model_num_pred_tokens: int = 16 draft_model_max_ngram_size: int = 2 draft_model_top_k: int = Field(default=1, ge=1) @@ -3368,8 +3291,21 @@ class ModelOptions(BaseModel): def validate_source(self) -> "ConfigFile.ModelOptions": if (self.path is None) == (self.from_pretrained is None): raise ValueError("exactly one of model.path or model.from_pretrained is required") + if ( + self.draft_model_path is not None + and self.draft_model_from_pretrained is not None + ): + raise ValueError( + "model.draft_model_path and model.draft_model_from_pretrained " + "are mutually exclusive" + ) return self + def resolve_draft_model_path(self) -> Optional[str]: + if self.draft_model_from_pretrained is not None: + return self.draft_model_from_pretrained.resolve_model_path() + return self.draft_model_path + @field_validator("chat_template", mode="before") @classmethod def normalize_chat_template(cls, value: Any) -> Any: @@ -10549,6 +10485,7 @@ def __init__( max_seq_len: Optional[int] = None, max_output_tokens: Optional[int] = None, draft_model: Optional[str] = None, + draft_model_path: Optional[str] = None, draft_model_num_pred_tokens: int = 16, draft_model_max_ngram_size: int = 2, draft_model_top_k: int = 1, @@ -10573,6 +10510,7 @@ def __init__( self._lora_adapters: List[Any] = [] self._lora_adapter_array: Optional[Any] = None self._lora_scales_array: Optional[Any] = None + self.draft_llama_model: Optional[Any] = None model_params, self._c_tensor_split, self._kv_overrides_array = ( self.build_model_params( n_gpu_layers=n_gpu_layers, @@ -10630,11 +10568,6 @@ def __init__( "speculative decoding is only supported for attention models" ) n_ctx_train = int(llama_cpp.llama_model_n_ctx_train(llama_model)) - target_n_rs_seq = ( - max(1, draft_model_num_pred_tokens) - if normalized_draft_model == "draft-mtp" - else None - ) context_params = self.build_context_params( n_ctx=n_ctx if n_ctx is not None else n_ctx_train, @@ -10662,7 +10595,7 @@ def __init__( type_k=type_k, type_v=type_v, kv_unified=kv_unified, - n_rs_seq=target_n_rs_seq, + n_rs_seq=None, ctx_type=None, ) ctx = llama_cpp.llama_init_from_model(llama_model, context_params) @@ -10692,11 +10625,6 @@ def __init__( "MTP requires runtime n_batch to fit the pending token plus draft tokens " f"(required {required_mtp_batch}, got {self.n_batch})" ) - if target_n_rs_seq is not None and self.n_rs_seq < target_n_rs_seq: - raise RuntimeError( - "MTP requires retained recurrent-state slots for rollback " - f"(required {target_n_rs_seq}, got {self.n_rs_seq})" - ) self.n_ctx_train = n_ctx_train self.n_vocab = int(llama_cpp.llama_vocab_n_tokens(self.vocab)) self.n_embd = int(llama_cpp.llama_model_n_embd(self.llama_model)) @@ -10747,6 +10675,22 @@ def __init__( num_pred_tokens=draft_model_num_pred_tokens, ) elif normalized_draft_model == "draft-mtp": + draft_llama_model = self.llama_model + if draft_model_path is not None: + draft_llama_model = llama_cpp.llama_model_load_from_file( + draft_model_path.encode("utf-8"), + model_params, + ) + if draft_llama_model is None: + llama_cpp.llama_batch_free(self.batch) + llama_cpp.llama_free(self.ctx) + self._free_lora_adapters() + llama_cpp.llama_model_free(self.llama_model) + if self.backend_initialized: + llama_cpp.llama_backend_free() + self.backend_initialized = False + raise RuntimeError(f"failed to load MTP draft model: {draft_model_path}") + self.draft_llama_model = draft_llama_model if self.n_ubatch < self.n_seq_max: mtp_n_batch = self.n_batch else: @@ -10792,13 +10736,15 @@ def __init__( type_k=type_k, type_v=type_v, kv_unified=kv_unified, - n_rs_seq=target_n_rs_seq, + n_rs_seq=0, ctx_type=llama_cpp.LLAMA_CONTEXT_TYPE_MTP, n_outputs_max=min(mtp_n_batch, self.n_seq_max), + ctx_other=self.ctx, ) try: self.draft_provider = MTPDraftProvider( model=self, + draft_model=draft_llama_model, context_params=mtp_context_params, num_pred_tokens=draft_model_num_pred_tokens, top_k=draft_model_top_k, @@ -10808,6 +10754,9 @@ def __init__( llama_cpp.llama_batch_free(self.batch) llama_cpp.llama_free(self.ctx) self._free_lora_adapters() + if self.draft_llama_model is not None: + llama_cpp.llama_model_free(self.draft_llama_model) + self.draft_llama_model = None llama_cpp.llama_model_free(self.llama_model) if self.backend_initialized: llama_cpp.llama_backend_free() @@ -10818,7 +10767,10 @@ def __init__( try: self._load_lora_adapters(self.loras) self._apply_lora_adapters(self.ctx, "target") - if isinstance(self.draft_provider, MTPDraftProvider): + if ( + isinstance(self.draft_provider, MTPDraftProvider) + and self.draft_llama_model is None + ): self._apply_lora_adapters(self.draft_provider.ctx, "MTP draft") except BaseException: if self.draft_provider is not None: @@ -10826,6 +10778,9 @@ def __init__( llama_cpp.llama_batch_free(self.batch) llama_cpp.llama_free(self.ctx) self._free_lora_adapters() + if self.draft_llama_model is not None: + llama_cpp.llama_model_free(self.draft_llama_model) + self.draft_llama_model = None llama_cpp.llama_model_free(self.llama_model) if self.backend_initialized: llama_cpp.llama_backend_free() @@ -10938,6 +10893,7 @@ def build_context_params( n_rs_seq: Optional[int] = None, ctx_type: Optional[int] = None, n_outputs_max: Optional[int] = None, + ctx_other: Optional[Any] = None, ) -> Any: context_params = llama_cpp.llama_context_default_params() if n_ctx is not None: @@ -10958,6 +10914,8 @@ def build_context_params( context_params.ctx_type = ctx_type if n_outputs_max is not None: context_params.n_outputs_max = n_outputs_max + if ctx_other is not None: + context_params.ctx_other = ctx_other if rope_scaling_type is not None: context_params.rope_scaling_type = rope_scaling_type if pooling_type is not None: @@ -11070,6 +11028,9 @@ def close(self) -> None: llama_cpp.llama_batch_free(self.batch) llama_cpp.llama_free(self.ctx) self._free_lora_adapters() + if self.draft_llama_model is not None: + llama_cpp.llama_model_free(self.draft_llama_model) + self.draft_llama_model = None llama_cpp.llama_model_free(self.llama_model) if self.backend_initialized: llama_cpp.llama_backend_free() @@ -15649,6 +15610,7 @@ def main() -> None: max_seq_len=config.model.max_seq_len, max_output_tokens=config.model.max_output_tokens, draft_model=config.model.draft_model, + draft_model_path=config.model.resolve_draft_model_path(), draft_model_num_pred_tokens=config.model.draft_model_num_pred_tokens, draft_model_max_ngram_size=config.model.draft_model_max_ngram_size, draft_model_top_k=config.model.draft_model_top_k, diff --git a/llama_cpp/llama_cpp_ext.py b/llama_cpp/llama_cpp_ext.py index f6ab9197b8..284811086a 100644 --- a/llama_cpp/llama_cpp_ext.py +++ b/llama_cpp/llama_cpp_ext.py @@ -42,58 +42,76 @@ def decorator(f): return decorator -# LLAMA_API void llama_set_embeddings_pre_norm(struct llama_context * ctx, bool value, bool masked); +# LLAMA_API void llama_set_embeddings_nextn(struct llama_context * ctx, bool value, bool masked); @_ctypes_function_from_names( ( - "llama_set_embeddings_pre_norm", - "_Z29llama_set_embeddings_pre_normP13llama_contextbb", - "?llama_set_embeddings_pre_norm@@YAXPEAUllama_context@@_N1@Z", + "llama_set_embeddings_nextn", + "_Z26llama_set_embeddings_nextnP13llama_contextbb", + "?llama_set_embeddings_nextn@@YAXPEAUllama_context@@_N1@Z", ), [llama_cpp.llama_context_p_ctypes, ctypes.c_bool, ctypes.c_bool], None, ) -def llama_set_embeddings_pre_norm( +def llama_set_embeddings_nextn( ctx: llama_cpp.llama_context_p, value: bool, masked: bool, /, ): - """Set whether the context outputs pre-norm embeddings or not.""" + """Set whether the context outputs nextn embeddings or not.""" ... -# LLAMA_API float * llama_get_embeddings_pre_norm(struct llama_context * ctx); +# LLAMA_API float * llama_get_embeddings_nextn(struct llama_context * ctx); @_ctypes_function_from_names( ( - "llama_get_embeddings_pre_norm", - "_Z29llama_get_embeddings_pre_normP13llama_context", - "?llama_get_embeddings_pre_norm@@YAPEAMPEAUllama_context@@@Z", + "llama_get_embeddings_nextn", + "_Z26llama_get_embeddings_nextnP13llama_context", + "?llama_get_embeddings_nextn@@YAPEAMPEAUllama_context@@@Z", ), [llama_cpp.llama_context_p_ctypes], ctypes.POINTER(ctypes.c_float), ) -def llama_get_embeddings_pre_norm( +def llama_get_embeddings_nextn( ctx: llama_cpp.llama_context_p, /, ): - """Get the pre-norm embeddings from the last evaluation.""" + """Get the nextn embeddings from the last evaluation.""" ... -# LLAMA_API float * llama_get_embeddings_pre_norm_ith(struct llama_context * ctx, int32_t i); +# LLAMA_API float * llama_get_embeddings_nextn_ith(struct llama_context * ctx, int32_t i); @_ctypes_function_from_names( ( - "llama_get_embeddings_pre_norm_ith", - "_Z33llama_get_embeddings_pre_norm_ithP13llama_contexti", - "?llama_get_embeddings_pre_norm_ith@@YAPEAMPEAUllama_context@@H@Z", + "llama_get_embeddings_nextn_ith", + "_Z30llama_get_embeddings_nextn_ithP13llama_contexti", + "?llama_get_embeddings_nextn_ith@@YAPEAMPEAUllama_context@@H@Z", ), [llama_cpp.llama_context_p_ctypes, ctypes.c_int32], ctypes.POINTER(ctypes.c_float), ) -def llama_get_embeddings_pre_norm_ith( +def llama_get_embeddings_nextn_ith( ctx: llama_cpp.llama_context_p, i: Union[ctypes.c_int32, int], /, ): - """Get the pre-norm embeddings for the ith output row from the last evaluation.""" + """Get the nextn embeddings for the ith output row from the last evaluation.""" + ... + + +# LLAMA_API llama_context * llama_get_ctx_other(struct llama_context * ctx); +@_ctypes_function_from_names( + ( + "llama_get_ctx_other", + "_Z19llama_get_ctx_otherP13llama_context", + "?llama_get_ctx_other@@YAPEAUllama_context@@PEAU1@@Z", + ), + [llama_cpp.llama_context_p_ctypes], + llama_cpp.llama_context_p_ctypes, +) +def llama_get_ctx_other( + ctx: llama_cpp.llama_context_p, + /, +): + """Get the context linked through llama_context_params.ctx_other.""" ... From 8e470ac585a2e4363e0c089c479015ed8470ecb1 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 7 Jun 2026 16:49:11 -0700 Subject: [PATCH 128/138] chore: bump version to 0.3.28 (#2284) --- CHANGELOG.md | 2 ++ llama_cpp/__init__.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index b07244ed7b..b83ccc83bf 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.28] + - feat(example): align server MTP support with llama.cpp by @abetlen in #2283 - feat: update llama.cpp to ggml-org/llama.cpp@9e3b928fd - feat(example): add OpenAI-compatible embeddings endpoint by @abetlen in #2281 diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index 369f24ca88..13668893fe 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.27" +__version__ = "0.3.28" From a72325b0d5be7592afe87f01571e913e04a8e394 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 7 Jun 2026 18:48:40 -0700 Subject: [PATCH 129/138] fix(example): avoid duplicate streamed response deltas (#2285) --- examples/server/server.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/server/server.py b/examples/server/server.py index 64a16f0bd1..ec83fe6e3c 100644 --- a/examples/server/server.py +++ b/examples/server/server.py @@ -8695,7 +8695,7 @@ def _ensure_reasoning_stream_item( state, "response.output_item.added", output_index=item_state.output_index, - item=item, + item=copy.deepcopy(item), ), self._response_event( state, @@ -8703,7 +8703,7 @@ def _ensure_reasoning_stream_item( item_id=cast(str, item["id"]), output_index=item_state.output_index, content_index=0, - part=part, + part=copy.deepcopy(part), ), ], item_state @@ -8727,7 +8727,7 @@ def _ensure_message_stream_item( state, "response.output_item.added", output_index=item_state.output_index, - item=item, + item=copy.deepcopy(item), ), self._response_event( state, @@ -8735,7 +8735,7 @@ def _ensure_message_stream_item( item_id=cast(str, item["id"]), output_index=item_state.output_index, content_index=0, - part=part, + part=copy.deepcopy(part), ), ], item_state @@ -8777,7 +8777,7 @@ def _ensure_tool_stream_item( state, "response.output_item.added", output_index=item_state.output_index, - item=item, + item=copy.deepcopy(item), ) ], item_state From 411e0f40e767b2ac3b1155be73916ea94c2ddfc6 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 7 Jun 2026 20:29:46 -0700 Subject: [PATCH 130/138] fix(example): derive streaming response parser boundaries from schema (#2287) --- examples/server/server.py | 329 +++++++++++++++++++++++++++++--------- 1 file changed, 249 insertions(+), 80 deletions(-) diff --git a/examples/server/server.py b/examples/server/server.py index ec83fe6e3c..28fc8f4eb3 100644 --- a/examples/server/server.py +++ b/examples/server/server.py @@ -4344,6 +4344,11 @@ def capture(match: re.Match[str]) -> str: @staticmethod def _regex_literal_prefix(pattern: str) -> str: + literal, _ = ResponseParser._regex_literal_prefix_and_remainder(pattern) + return literal + + @staticmethod + def _regex_literal_prefix_and_remainder(pattern: str) -> Tuple[str, str]: literal: List[str] = [] index = 0 while index < len(pattern): @@ -4365,7 +4370,181 @@ def _regex_literal_prefix(pattern: str) -> str: break literal.append(char) index += 1 - return "".join(literal) + return "".join(literal), pattern[index:] + + @staticmethod + def _find_regex_group_end(pattern: str, start: int) -> int: + depth = 0 + escaped = False + in_character_class = False + for index in range(start, len(pattern)): + char = pattern[index] + if escaped: + escaped = False + continue + if char == "\\": + escaped = True + continue + if char == "[": + in_character_class = True + continue + if char == "]" and in_character_class: + in_character_class = False + continue + if in_character_class: + continue + if char == "(": + depth += 1 + continue + if char == ")": + depth -= 1 + if depth == 0: + return index + return -1 + + @classmethod + def _consume_optional_literal_prefix( + cls, + pattern: str, + ) -> Optional[Tuple[str, str]]: + if not pattern.startswith("(?:"): + return None + group_end = cls._find_regex_group_end(pattern, 0) + if group_end < 0 or group_end + 1 >= len(pattern) or pattern[group_end + 1] != "?": + return None + literal, remainder = cls._regex_literal_prefix_and_remainder(pattern[3:group_end]) + if not literal or remainder: + return None + return literal, pattern[group_end + 2 :] + + @staticmethod + def _split_regex_alternatives(pattern: str) -> List[str]: + alternatives: List[str] = [] + start = 0 + depth = 0 + escaped = False + in_character_class = False + for index, char in enumerate(pattern): + if escaped: + escaped = False + continue + if char == "\\": + escaped = True + continue + if char == "[": + in_character_class = True + continue + if char == "]" and in_character_class: + in_character_class = False + continue + if in_character_class: + continue + if char == "(": + depth += 1 + continue + if char == ")": + depth -= 1 + continue + if char == "|" and depth == 0: + alternatives.append(pattern[start:index]) + start = index + 1 + alternatives.append(pattern[start:]) + return alternatives + + @classmethod + def _regex_lookahead_literal_specs(cls, pattern: str) -> List[Tuple[str, bool]]: + if not pattern.startswith("(?="): + return [] + group_end = cls._find_regex_group_end(pattern, 0) + if group_end < 0: + return [] + literals: List[Tuple[str, bool]] = [] + for alternative in cls._split_regex_alternatives(pattern[3:group_end]): + strip_leading_whitespace = False + while alternative.startswith(r"\s*"): + strip_leading_whitespace = True + alternative = alternative[3:] + if alternative == "$": + continue + if alternative.endswith("$"): + alternative = alternative[:-1] + literal, _ = cls._regex_literal_prefix_and_remainder(alternative) + if literal: + literals.append((literal, strip_leading_whitespace)) + return literals + + @classmethod + def _regex_capture_parts( + cls, + pattern: str, + ) -> Optional[Tuple[str, str]]: + normalized = pattern.lstrip("^") + captures = [ + (index, token) + for token in ("(.*?)", "(.*)") + if (index := normalized.find(token)) >= 0 + ] + if not captures: + return None + capture_index, capture_token = min(captures, key=lambda item: item[0]) + return normalized[:capture_index], normalized[capture_index + len(capture_token) :] + + @classmethod + def _regex_capture_end_literal_specs(cls, pattern: str) -> List[Tuple[str, bool]]: + capture_parts = cls._regex_capture_parts(pattern) + if capture_parts is None: + return [] + _, suffix_pattern = capture_parts + literal_specs = cls._regex_lookahead_literal_specs(suffix_pattern) + if literal_specs: + return literal_specs + literal, _ = cls._regex_literal_prefix_and_remainder(suffix_pattern) + return [(literal, False)] if literal else [] + + @classmethod + def _regex_capture_end_literals(cls, pattern: str) -> List[str]: + return [literal for literal, _ in cls._regex_capture_end_literal_specs(pattern)] + + @classmethod + def _regex_leading_capture( + cls, + *, + field_name: str, + field_regex: str, + content_regex: Optional[str], + ) -> Optional[Dict[str, Any]]: + capture_parts = cls._regex_capture_parts(field_regex) + if capture_parts is None: + return None + prefix_pattern, _ = capture_parts + prefix_pattern = prefix_pattern.lstrip("^") + optional_prefix = cls._consume_optional_literal_prefix(prefix_pattern) + if optional_prefix is not None: + prefix_pattern = optional_prefix[1] + implicit_at_start = False + optional_capture_start = cls._consume_optional_literal_prefix(prefix_pattern) + if optional_capture_start is not None: + capture_start, prefix_pattern = optional_capture_start + implicit_at_start = True + else: + capture_start, prefix_pattern = cls._regex_literal_prefix_and_remainder(prefix_pattern) + if not capture_start or prefix_pattern: + return None + end_literals = cls._regex_capture_end_literals(field_regex) + if not end_literals: + return None + capture_end = end_literals[0] + strip_after = False + if isinstance(content_regex, str): + escaped_end = re.escape(capture_end) + strip_after = bool(re.search(escaped_end + r"\\s\*", content_regex)) + return { + "field": field_name, + "start": capture_start, + "end": capture_end, + "strip_after": strip_after, + "implicit_at_start": implicit_at_start, + } @staticmethod def _literal_suffix_prefix_length(text: str, literal: str) -> int: @@ -4702,7 +4881,14 @@ def _compile_tagged_message_plan( return None iterator = cls._compile_iterator_pattern(iterator_pattern) if iterator is None: - return None + iterator_capture = cls._compile_iterator_block_pattern(iterator_pattern) + if ( + not isinstance(iterator_capture, dict) + or not iterator_capture["start"] + or iterator_capture["allow_eof"] + ): + return None + iterator = (iterator_capture["start"], iterator_capture["end"]) items_schema = tool_calls_schema.get("items") if not isinstance(items_schema, dict): return None @@ -4715,10 +4901,11 @@ def _compile_tagged_message_plan( if isinstance(content_schema, dict) else None ) - object_regex = schema.get("x-regex") if isinstance(schema.get("x-regex"), str) else None assistant_prefix: Optional[str] = None - if isinstance(content_regex, str) and r"<\|im_start\|>assistant\n" in content_regex: - assistant_prefix = "<|im_start|>assistant\n" + if isinstance(content_regex, str): + optional_prefix = cls._consume_optional_literal_prefix(content_regex.lstrip("^")) + if optional_prefix is not None: + assistant_prefix = optional_prefix[0] leading_capture: Optional[Dict[str, Any]] = None for field_name, value_schema in properties.items(): if not isinstance(value_schema, dict): @@ -4726,43 +4913,31 @@ def _compile_tagged_message_plan( field_regex = value_schema.get("x-regex") if not isinstance(field_regex, str): continue - if "<think>\\n" in field_regex and "</think>" in field_regex: - leading_capture = { - "field": field_name, - "start": "<think>\n", - "end": "</think>", - "strip_after": True, - "implicit_at_start": "(?:<think>\\n)?" in field_regex, - } + if field_name == "content": + continue + capture = cls._regex_leading_capture( + field_name=field_name, + field_regex=field_regex, + content_regex=content_regex, + ) + if capture is not None: + leading_capture = capture break - if leading_capture is None and isinstance(object_regex, str): - if ( - "(?P<thinking>" in object_regex - and r"<\|channel\>thought\n" in object_regex - and r"\<channel\|\>" in object_regex - ): - leading_capture = { - "field": "thinking", - "start": "<|channel>thought\n", - "end": "<channel|>", - "strip_after": False, - "implicit_at_start": False, - } end_markers: List[str] = [] + content_end_marker_specs: List[Tuple[str, bool]] = [] iterator_start, iterator_end = iterator if "content" in properties: end_markers.append(iterator_start) - if isinstance(content_regex, str) and r"<\|im_end\|>" in content_regex: - end_markers.append("<|im_end|>") - if isinstance(object_regex, str) and r"<turn\|>" in object_regex: - end_markers.append("<turn|>") + if isinstance(content_regex, str): + content_end_marker_specs = cls._regex_capture_end_literal_specs(content_regex) + end_markers.extend(literal for literal, _ in content_end_marker_specs) if not end_markers and iterator_start: end_markers.append(iterator_start) - trim_before_iterator = ( - isinstance(content_regex, str) - and r"\s*<tool_call>\n" in content_regex + deduped_end_markers = tuple(dict.fromkeys(end_markers)) + trim_before_iterator = any( + literal == iterator_start and strip_leading_whitespace + for literal, strip_leading_whitespace in content_end_marker_specs ) - end_marker_tuple = tuple(end_markers) direct_deltas = item_plan["kind"] == "tagged-parameters" direct_init = ( ( @@ -4773,8 +4948,8 @@ def _compile_tagged_message_plan( bool(leading_capture.get("strip_after")) if leading_capture is not None else False, bool(leading_capture.get("implicit_at_start")) if leading_capture is not None else False, trim_before_iterator, - end_marker_tuple, - tuple(marker for marker in end_marker_tuple if marker != iterator_start), + deduped_end_markers, + tuple(marker for marker in deduped_end_markers if marker != iterator_start), iterator_start, iterator_end, item_plan["function_start"], @@ -4793,9 +4968,9 @@ def _compile_tagged_message_plan( "assistant_prefix": assistant_prefix, "leading_capture": leading_capture, "content_field": "content" if "content" in properties else None, - "content_end_markers": end_marker_tuple, + "content_end_markers": deduped_end_markers, "trim_before_iterator": trim_before_iterator, - "stop_markers": tuple(marker for marker in end_marker_tuple if marker != iterator_start), + "stop_markers": tuple(marker for marker in deduped_end_markers if marker != iterator_start), "direct_init": direct_init, "iterator": { "start": iterator_start, @@ -7192,19 +7367,17 @@ def consume_completion_chunk( logprobs=logprobs, leading_delta=role_delta, ) - if self._stream_state_complete(): - return self._chunk_payloads( - chunk_id=chunk_id, - created=created, - model=model, - deltas=stream_deltas, - finish_reason=( - "tool_calls" if self._direct.saw_tool_calls else finish_reason - ), - logprobs=logprobs, - leading_delta=role_delta, - ) - self._stream_failed = True + return self._chunk_payloads( + chunk_id=chunk_id, + created=created, + model=model, + deltas=stream_deltas, + finish_reason=( + "tool_calls" if self._direct.saw_tool_calls else finish_reason + ), + logprobs=logprobs, + leading_delta=role_delta, + ) elif self._stream_plan["kind"] == "segment-message": if role_delta is not None: stream_deltas = [role_delta, *stream_deltas] @@ -7219,18 +7392,16 @@ def consume_completion_chunk( finish_reason=None, logprobs=logprobs, ) - if self._stream_state_complete(): - return self._chunk_payloads( - chunk_id=chunk_id, - created=created, - model=model, - deltas=stream_deltas, - finish_reason=( - "tool_calls" if self._message.get("tool_calls") else finish_reason - ), - logprobs=logprobs, - ) - self._stream_failed = True + return self._chunk_payloads( + chunk_id=chunk_id, + created=created, + model=model, + deltas=stream_deltas, + finish_reason=( + "tool_calls" if self._message.get("tool_calls") else finish_reason + ), + logprobs=logprobs, + ) else: previous_message = self._message partial_deltas: List[Dict[str, Any]] = [] @@ -7238,7 +7409,7 @@ def consume_completion_chunk( parsed = cast(Dict[str, Any], self._stream_state.parsed) message = self._parsed_chat_message( parsed=parsed, - partial=finish_reason is None, + partial=finish_reason is None or not self._stream_state_complete(), ) if finish_reason is None: if role_delta is not None: @@ -7253,22 +7424,20 @@ def consume_completion_chunk( finish_reason=None, logprobs=logprobs, ) - if self._stream_state_complete(): - if role_delta is not None: - partial_deltas.append(role_delta) - partial_deltas.extend(self._message_deltas(previous_message, message)) - self._message = message - return self._chunk_payloads( - chunk_id=chunk_id, - created=created, - model=model, - deltas=partial_deltas, - finish_reason=( - "tool_calls" if message.get("tool_calls") else finish_reason - ), - logprobs=logprobs, - ) - self._stream_failed = True + if role_delta is not None: + partial_deltas.append(role_delta) + partial_deltas.extend(self._message_deltas(previous_message, message)) + self._message = message + return self._chunk_payloads( + chunk_id=chunk_id, + created=created, + model=model, + deltas=partial_deltas, + finish_reason=( + "tool_calls" if message.get("tool_calls") else finish_reason + ), + logprobs=logprobs, + ) else: self._stream_failed = True From 7eb494d83ef5fe67f348a0b1a14627fa8da50e36 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 7 Jun 2026 22:35:22 -0700 Subject: [PATCH 131/138] fix(ci): repair Linux accelerator wheels (#2286) --- .github/workflows/build-wheels-cuda.yaml | 53 +++++++++++++++-- .github/workflows/build-wheels-rocm.yaml | 69 ++++++++++++++++++---- .github/workflows/build-wheels-vulkan.yaml | 57 ++++++++++-------- CHANGELOG.md | 2 + 4 files changed, 140 insertions(+), 41 deletions(-) diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index 905b9bee9b..59dc3558bf 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -1,6 +1,12 @@ name: Build Wheels (CUDA) -on: workflow_dispatch +on: + workflow_dispatch: + inputs: + release_tag: + description: Release tag to upload wheel assets to + required: false + type: string permissions: contents: write @@ -131,7 +137,9 @@ jobs: if ($IsWindows) { python -m pip install build wheel ninja } else { - python -m pip install build wheel + sudo apt-get update + sudo apt-get install -y patchelf + python -m pip install auditwheel build wheel } - name: Build Wheel @@ -169,6 +177,12 @@ jobs: $env:CPLUS_INCLUDE_PATH = "$cudaRoot/include$pathSeparator$env:CPLUS_INCLUDE_PATH" $env:LIBRARY_PATH = "$cudaRoot/lib$pathSeparator$env:CONDA_PREFIX/lib$pathSeparator$env:LIBRARY_PATH" $env:LD_LIBRARY_PATH = "$cudaRoot/lib$pathSeparator$env:CONDA_PREFIX/lib$pathSeparator$env:LD_LIBRARY_PATH" + $cudaLibraryPaths = @( + (Join-Path $cudaRoot 'lib'), + (Join-Path $cudaRoot 'lib64'), + (Join-Path $env:CONDA_PREFIX 'lib') + ) | Where-Object { Test-Path $_ } + Write-Output "CUDA_LIBRARY_PATHS=$($cudaLibraryPaths -join ':')" >> $env:GITHUB_ENV } elseif ($IsWindows) { $ninjaPath = ((Get-Command ninja -ErrorAction Stop).Source).Replace('\', '/') $env:CMAKE_GENERATOR = 'Ninja' @@ -218,15 +232,44 @@ jobs: # SM. This keeps the wheel under GitHub's 2 GiB release-asset limit. $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=$cudaArchs -DCMAKE_CUDA_FLAGS=-allow-unsupported-compiler -DCMAKE_CUDA_FLAGS_INIT=-allow-unsupported-compiler $env:CMAKE_ARGS" $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off' + if ($IsLinux) { + $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_OPENMP=OFF' + } python -m build --wheel # Publish tags that reflect the actual installed toolkit version. Write-Output "CUDA_VERSION=$cudaTagVersion" >> $env:GITHUB_ENV + - name: Repair Linux wheel + if: runner.os == 'Linux' + shell: bash + run: | + set -euxo pipefail + mkdir -p wheelhouse + export LD_LIBRARY_PATH="$PWD/llama_cpp/lib:${CUDA_LIBRARY_PATHS}:${LD_LIBRARY_PATH:-}" + auditwheel_bin="${CONDA}/envs/llamacpp/bin/auditwheel" + "${auditwheel_bin}" repair \ + --exclude libcuda.so \ + --exclude libcuda.so.1 \ + --exclude libcudart.so.11.0 \ + --exclude libcudart.so.12 \ + --exclude libcudart.so.13 \ + --exclude libcublas.so.11 \ + --exclude libcublas.so.12 \ + --exclude libcublas.so.13 \ + --exclude libcublasLt.so.11 \ + --exclude libcublasLt.so.12 \ + --exclude libcublasLt.so.13 \ + -w wheelhouse \ + dist/*.whl + rm dist/*.whl + cp wheelhouse/*.whl dist/ + "${auditwheel_bin}" show dist/*.whl + - uses: softprops/action-gh-release@v3 - if: startsWith(github.ref, 'refs/tags/') + if: startsWith(github.ref, 'refs/tags/') || (github.event_name == 'workflow_dispatch' && inputs.release_tag != '') with: files: dist/* - # Set tag_name to <tag>-cu<cuda_version> - tag_name: ${{ github.ref_name }}-cu${{ env.CUDA_VERSION }} + # Set tag_name to <tag>-cu<cuda_version>. + tag_name: ${{ github.event_name == 'workflow_dispatch' && inputs.release_tag || github.ref_name }}-cu${{ env.CUDA_VERSION }} env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/build-wheels-rocm.yaml b/.github/workflows/build-wheels-rocm.yaml index 6ad0b49541..70fca5edb7 100644 --- a/.github/workflows/build-wheels-rocm.yaml +++ b/.github/workflows/build-wheels-rocm.yaml @@ -1,6 +1,20 @@ name: Build Wheels (ROCm) -on: workflow_dispatch +on: + workflow_dispatch: + inputs: + release_tag: + description: Release tag to upload wheel assets to + required: false + type: string + amdgpu_targets: + description: AMDGPU targets to compile into the Linux ROCm wheel + required: false + default: gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1200;gfx1201 + windows_amdgpu_targets: + description: AMDGPU targets to compile into the Windows HIP Radeon wheel + required: false + default: gfx1150;gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032 permissions: contents: write @@ -24,7 +38,7 @@ jobs: - name: Install system dependencies run: | apt-get update - apt-get install -y --no-install-recommends git cmake lsb-release ninja-build + apt-get install -y --no-install-recommends git cmake lsb-release ninja-build patchelf - uses: actions/checkout@v6 with: @@ -37,9 +51,12 @@ jobs: - name: Install build dependencies run: | python -m pip install --upgrade pip - python -m pip install build wheel + python -m pip install auditwheel build wheel - name: Build ROCm wheel + env: + MATRIX_AMDGPU_TARGETS: ${{ matrix.amdgpu_targets }} + INPUT_AMDGPU_TARGETS: ${{ inputs.amdgpu_targets }} run: | export ROCM_PATH="${ROCM_PATH:-/opt/rocm}" export HIP_PATH="${HIP_PATH:-$ROCM_PATH}" @@ -56,11 +73,38 @@ jobs: rocm_tag="$(hipconfig --version | sed -E 's/^([0-9]+)\.([0-9]+).*/\1\2/')" echo "ROCM_VERSION=$rocm_tag" >> "$GITHUB_ENV" - amdgpu_targets="${{ matrix.amdgpu_targets }}" - export CMAKE_ARGS="-DGGML_HIP=on -DGGML_NATIVE=off -DGGML_AVX=off -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off -DAMDGPU_TARGETS=$amdgpu_targets -DCMAKE_HIP_ARCHITECTURES=$amdgpu_targets" + amdgpu_targets="${INPUT_AMDGPU_TARGETS:-$MATRIX_AMDGPU_TARGETS}" + export CMAKE_ARGS="-DGGML_HIP=on -DGGML_NATIVE=off -DGGML_OPENMP=OFF -DGGML_AVX=off -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off -DAMDGPU_TARGETS=$amdgpu_targets -DCMAKE_HIP_ARCHITECTURES=$amdgpu_targets" python -m build --wheel + + - name: Repair Linux wheel + run: | + export ROCM_PATH="${ROCM_PATH:-/opt/rocm}" + export LD_LIBRARY_PATH="$PWD/llama_cpp/lib:$ROCM_PATH/lib:$ROCM_PATH/lib64:${LD_LIBRARY_PATH:-}" mkdir -p wheelhouse - cp dist/*.whl wheelhouse/ + python -m auditwheel repair \ + --exclude libamdhip64.so \ + --exclude libamdhip64.so.6 \ + --exclude libamdhip64.so.7 \ + --exclude libhiprtc.so \ + --exclude libhiprtc.so.6 \ + --exclude libhiprtc.so.7 \ + --exclude libhipblas.so \ + --exclude libhipblas.so.2 \ + --exclude libhipblas.so.3 \ + --exclude libhipblaslt.so \ + --exclude libhipblaslt.so.0 \ + --exclude libhipblaslt.so.1 \ + --exclude librocblas.so \ + --exclude librocblas.so.4 \ + --exclude librocblas.so.5 \ + --exclude libhsa-runtime64.so.1 \ + --exclude libhsakmt.so.1 \ + -w wheelhouse \ + dist/*.whl + rm dist/*.whl + cp wheelhouse/*.whl dist/ + python -m auditwheel show dist/*.whl - uses: actions/upload-artifact@v7 with: @@ -139,11 +183,14 @@ jobs: & $clangPath.FullName --version - name: Build HIP wheel + env: + MATRIX_AMDGPU_TARGETS: ${{ matrix.amdgpu_targets }} + INPUT_AMDGPU_TARGETS: ${{ inputs.windows_amdgpu_targets }} run: | $ErrorActionPreference = "Stop" $hipPath = Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Split-Path | Split-Path $rocwmmaInclude = (Join-Path $PWD 'opt\rocm-7.2.1\include').Replace('\', '/') - $amdgpuTargets = "${{ matrix.amdgpu_targets }}" + $amdgpuTargets = if ($env:INPUT_AMDGPU_TARGETS) { $env:INPUT_AMDGPU_TARGETS } else { $env:MATRIX_AMDGPU_TARGETS } $env:HIP_PATH = $hipPath $env:ROCM_PATH = $hipPath @@ -198,7 +245,7 @@ jobs: release_rocm: name: Release ROCm needs: [build_wheels] - if: startsWith(github.ref, 'refs/tags/') + if: startsWith(github.ref, 'refs/tags/') || (github.event_name == 'workflow_dispatch' && inputs.release_tag != '') runs-on: ubuntu-latest steps: @@ -211,14 +258,14 @@ jobs: with: files: dist/* # Set release name to <tag>-rocm<rocm_version>. - tag_name: ${{ github.ref_name }}-rocm72 + tag_name: ${{ github.event_name == 'workflow_dispatch' && inputs.release_tag || github.ref_name }}-rocm72 env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} release_hip: name: Release HIP needs: [build_wheels_windows_hip] - if: startsWith(github.ref, 'refs/tags/') + if: startsWith(github.ref, 'refs/tags/') || (github.event_name == 'workflow_dispatch' && inputs.release_tag != '') runs-on: ubuntu-latest steps: @@ -231,6 +278,6 @@ jobs: with: files: dist/* # Set release name to <tag>-hip-radeon. - tag_name: ${{ github.ref_name }}-hip-radeon + tag_name: ${{ github.event_name == 'workflow_dispatch' && inputs.release_tag || github.ref_name }}-hip-radeon env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/build-wheels-vulkan.yaml b/.github/workflows/build-wheels-vulkan.yaml index 8790cc20e9..822662c3fb 100644 --- a/.github/workflows/build-wheels-vulkan.yaml +++ b/.github/workflows/build-wheels-vulkan.yaml @@ -1,6 +1,12 @@ name: Build Wheels (Vulkan) -on: workflow_dispatch +on: + workflow_dispatch: + inputs: + release_tag: + description: Release tag to upload wheel assets to + required: false + type: string permissions: contents: write @@ -40,23 +46,6 @@ jobs: python-version: ${{ matrix.pyver }} cache: "pip" - - name: Install Vulkan SDK - if: runner.os == 'Linux' - run: | - curl -fL \ - "https://sdk.lunarg.com/sdk/download/${VULKAN_SDK_VERSION}/linux/vulkansdk-linux-x86_64-${VULKAN_SDK_VERSION}.tar.xz" \ - -o vulkan-sdk.tar.xz - echo "${VULKAN_SDK_LINUX_SHA256} vulkan-sdk.tar.xz" | sha256sum -c - - mkdir -p "$RUNNER_TEMP/vulkan-sdk" - tar -xf vulkan-sdk.tar.xz -C "$RUNNER_TEMP/vulkan-sdk" - source "$RUNNER_TEMP/vulkan-sdk/${VULKAN_SDK_VERSION}/setup-env.sh" - { - echo "VULKAN_SDK=$VULKAN_SDK" - echo "LD_LIBRARY_PATH=$VULKAN_SDK/lib:${LD_LIBRARY_PATH:-}" - } >> "$GITHUB_ENV" - echo "$VULKAN_SDK/bin" >> "$GITHUB_PATH" - "$VULKAN_SDK/bin/glslc" --version - - name: Install Vulkan SDK if: runner.os == 'Windows' shell: pwsh @@ -71,6 +60,7 @@ jobs: & "$vulkanSdk\Bin\glslc.exe" --version - name: Install build dependencies + if: runner.os == 'Windows' run: | python -m pip install --upgrade pip python -m pip install build wheel @@ -81,11 +71,28 @@ jobs: - name: Build Vulkan wheel if: runner.os == 'Linux' - run: | - export CMAKE_ARGS="-DGGML_NATIVE=off -DGGML_METAL=OFF -DGGML_VULKAN=on" - python -m build --wheel - mkdir -p wheelhouse - cp dist/*.whl wheelhouse/ + uses: pypa/cibuildwheel@v3.4.1 + env: + CIBW_BUILD: "cp38-manylinux_*" + CIBW_ARCHS: "auto64" + CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014" + CIBW_BEFORE_ALL_LINUX: > + yum install -y xz && + curl -L https://micro.mamba.pm/api/micromamba/linux-64/latest -o /tmp/micromamba.tar.bz2 && + mkdir -p /tmp/micromamba && + tar -xjf /tmp/micromamba.tar.bz2 -C /tmp/micromamba bin/micromamba && + /tmp/micromamba/bin/micromamba create -y -p /opt/vulkan -c conda-forge shaderc libvulkan-loader spirv-headers && + /opt/vulkan/bin/glslc --version && + curl -fL "https://sdk.lunarg.com/sdk/download/${{ env.VULKAN_SDK_VERSION }}/linux/vulkansdk-linux-x86_64-${{ env.VULKAN_SDK_VERSION }}.tar.xz" -o /tmp/vulkan-sdk.tar.xz && + echo "${{ env.VULKAN_SDK_LINUX_SHA256 }} /tmp/vulkan-sdk.tar.xz" | sha256sum -c - && + mkdir -p /opt/vulkan-sdk && + tar -xf /tmp/vulkan-sdk.tar.xz -C /opt/vulkan-sdk + CIBW_ENVIRONMENT_LINUX: > + CMAKE_ARGS="-DGGML_NATIVE=off -DGGML_METAL=OFF -DGGML_OPENMP=OFF -DGGML_VULKAN=on -DCMAKE_PREFIX_PATH=/opt/vulkan -DVulkan_INCLUDE_DIR=/opt/vulkan-sdk/${{ env.VULKAN_SDK_VERSION }}/x86_64/include -DVulkan_LIBRARY=/opt/vulkan/lib/libvulkan.so -DVulkan_GLSLC_EXECUTABLE=/opt/vulkan/bin/glslc" + CIBW_REPAIR_WHEEL_COMMAND_LINUX: "LD_LIBRARY_PATH=/project/llama_cpp/lib:/opt/vulkan/lib auditwheel repair --exclude libvulkan.so.1 -w {dest_dir} {wheel}" + with: + package-dir: . + output-dir: wheelhouse - name: Build Vulkan wheel if: runner.os == 'Windows' @@ -105,7 +112,7 @@ jobs: release: name: Release needs: [build_wheels] - if: startsWith(github.ref, 'refs/tags/') + if: startsWith(github.ref, 'refs/tags/') || (github.event_name == 'workflow_dispatch' && inputs.release_tag != '') runs-on: ubuntu-latest steps: @@ -118,6 +125,6 @@ jobs: with: files: dist/* # Set release name to <tag>-vulkan. - tag_name: ${{ github.ref_name }}-vulkan + tag_name: ${{ github.event_name == 'workflow_dispatch' && inputs.release_tag || github.ref_name }}-vulkan env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/CHANGELOG.md b/CHANGELOG.md index b83ccc83bf..e358b871fe 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix(ci): Repair Linux accelerator wheels for manylinux publishing + ## [0.3.28] - feat(example): align server MTP support with llama.cpp by @abetlen in #2283 From d4ac2c2cc0dfab3e078c72101961b93a1261d791 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Sun, 7 Jun 2026 22:56:19 -0700 Subject: [PATCH 132/138] fix(example): support multi-step Responses tool streaming (#2288) * fix(example): support multi-step Responses tool streaming * docs: add Responses tool streaming changelog --- CHANGELOG.md | 1 + examples/server/server.py | 112 +++++++++++++++++++++++++++++++++++++- 2 files changed, 112 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index e358b871fe..ac792bc2a9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- fix(example): support multi-step Responses tool streaming by @abetlen in #2288 - fix(ci): Repair Linux accelerator wheels for manylinux publishing ## [0.3.28] diff --git a/examples/server/server.py b/examples/server/server.py index 28fc8f4eb3..fb00501cfe 100644 --- a/examples/server/server.py +++ b/examples/server/server.py @@ -2812,6 +2812,7 @@ def to_chat_template_tool(self) -> ChatTemplateTool: class ResponsesCustomToolFormat(BaseModel): model_config = ConfigDict(extra="ignore") + type: Optional[str] = None syntax: Optional[str] = None definition: Optional[str] = None @@ -2880,10 +2881,24 @@ class ResponsesWebSearchTool(BaseModel): type: Literal["web_search"] +class ResponsesNamespaceTool(BaseModel): + model_config = ConfigDict(extra="ignore") + + type: Literal["namespace"] + + +class ResponsesImageGenerationTool(BaseModel): + model_config = ConfigDict(extra="ignore") + + type: Literal["image_generation"] + + ResponsesToolDefinition = Union[ ResponsesFunctionTool, ResponsesCustomTool, ResponsesWebSearchTool, + ResponsesNamespaceTool, + ResponsesImageGenerationTool, ] @@ -5069,6 +5084,68 @@ def _tool_content_type(self, tool_name: str) -> Optional[str]: return content_type return None + def _raw_string_tool_arguments(self, tool_name: str, value: str) -> Optional[Dict[str, str]]: + if self._tools is None: + return None + for tool in self._tools: + if tool.get("type") != "function": + continue + function = tool.get("function", {}) + if function.get("name") != tool_name: + continue + parameters = function.get("parameters") + if not isinstance(parameters, dict): + return None + required = parameters.get("required") + if not isinstance(required, list) or len(required) != 1: + return None + argument_name = required[0] + if not isinstance(argument_name, str): + return None + properties = parameters.get("properties") + if not isinstance(properties, dict): + return None + argument_schema = properties.get(argument_name) + if not isinstance(argument_schema, dict): + return None + argument_type = argument_schema.get("type") + if argument_type == "string" or ( + isinstance(argument_type, list) and "string" in argument_type + ): + return {argument_name: value} + return None + return None + + @classmethod + def _raw_object_tool_arguments(cls, value: str) -> Optional[Dict[str, Any]]: + candidates = [value] + stripped = value.strip() + if stripped.startswith("{{") and stripped.endswith("}}"): + candidates.append(stripped[1:-1]) + for candidate in candidates: + normalized = cls._gemma4_tool_call_to_json(candidate) + for allow_partial in (False, True): + try: + parsed = from_json(normalized, allow_partial=allow_partial) + except ValueError: + continue + if isinstance(parsed, dict): + return { + key: cls._trim_partial_gemma_quote_marker(value) + if isinstance(value, str) + else value + for key, value in parsed.items() + } + return None + + @staticmethod + def _trim_partial_gemma_quote_marker(value: str) -> str: + quote_marker = '<|"|>' + for prefix_length in range(len(quote_marker) - 1, 0, -1): + if value.endswith(quote_marker[:prefix_length]): + return value[:-prefix_length] + return value + def _has_text_tools(self) -> bool: return any( isinstance(tool_schema, dict) and tool_schema.get("content_type") == "text" @@ -5637,6 +5714,18 @@ def _advance_direct_stream_state(self, text: str) -> Tuple[bool, List[Dict[str, self._direct.saw_tool_calls = saw_tool_calls self._direct.done = done return True, deltas + if leading_capture_field is not None: + if buffer.startswith(leading_capture_start): + buffer = buffer[len(leading_capture_start) :] + mode = self.DIRECT_MODE_LEADING_CAPTURE + continue + if leading_capture_start.startswith(buffer): + self._direct.pending = buffer + self._direct.mode = mode + self._direct.tool_call_count = tool_call_count + self._direct.saw_tool_calls = saw_tool_calls + self._direct.done = done + return True, deltas if buffer.startswith(iterator_start): saw_tool_calls = True self._start_direct_tool_call(tool_call_count) @@ -6302,6 +6391,16 @@ def _advance_stream_state(self, text: str) -> Tuple[bool, List[Dict[str, Any]]]: if not buffer: state.pending = "" return True, deltas + leading_capture = plan.get("leading_capture") + if leading_capture is not None: + capture_start = leading_capture["start"] + if buffer.startswith(capture_start): + buffer = buffer[len(capture_start) :] + state.mode = "leading-capture" + continue + if capture_start.startswith(buffer): + state.pending = buffer + return True, deltas if buffer.startswith(iterator_start): item_state = self._new_tool_call_state(plan["iterator"]["item"]) state.saw_tool_calls = True @@ -6866,6 +6965,10 @@ def _normalize_tool_call_item( }, } arguments = function.get("arguments", {}) + if isinstance(arguments, str): + arguments = self._raw_object_tool_arguments(arguments) or self._raw_string_tool_arguments( + tool_name, arguments + ) if not isinstance(arguments, (dict, ResponseParser.PartialJsonObject)): if partial: return None @@ -8009,7 +8112,14 @@ def _responses_tools_to_chat_tools( return None chat_tools: List[ChatTemplateTool] = [] for tool in tools: - if isinstance(tool, ResponsesWebSearchTool): + if isinstance( + tool, + ( + ResponsesWebSearchTool, + ResponsesNamespaceTool, + ResponsesImageGenerationTool, + ), + ): continue if isinstance(tool, ResponsesFunctionTool): chat_tools.append(tool.to_chat_template_tool()) From e8191f002770aa4631d960dae165dcb84df39531 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Mon, 8 Jun 2026 02:25:24 -0700 Subject: [PATCH 133/138] fix(example): correct GPT-OSS tool calling config for server example (#2289) --- examples/server/configs/gpt-oss-120b.json | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/examples/server/configs/gpt-oss-120b.json b/examples/server/configs/gpt-oss-120b.json index 2fa7a76f1e..64aaf040d7 100644 --- a/examples/server/configs/gpt-oss-120b.json +++ b/examples/server/configs/gpt-oss-120b.json @@ -42,7 +42,7 @@ }, "tool_calls": { "type": "array", - "x-regex-iterator": "(<\\|start\\|>assistant to=functions\\.\\w+<\\|channel\\|>commentary json<\\|message\\|>.*?<\\|call\\|>)", + "x-regex-iterator": "(<\\|start\\|>assistant<\\|channel\\|>commentary to=functions\\.\\w+ <\\|constrain\\|>json<\\|message\\|>.*?<\\|call\\|>)", "items": { "type": "object", "properties": { @@ -54,11 +54,11 @@ "properties": { "name": { "type": "string", - "x-regex": "^<\\|start\\|>assistant to=functions\\.(\\w+)<\\|channel\\|>commentary json<\\|message\\|>" + "x-regex": "^<\\|start\\|>assistant<\\|channel\\|>commentary to=functions\\.(\\w+) <\\|constrain\\|>json<\\|message\\|>" }, "arguments": { "type": "object", - "x-regex": "^<\\|start\\|>assistant to=functions\\.\\w+<\\|channel\\|>commentary json<\\|message\\|>(.*?)<\\|call\\|>$", + "x-regex": "^<\\|start\\|>assistant<\\|channel\\|>commentary to=functions\\.\\w+ <\\|constrain\\|>json<\\|message\\|>(.*?)<\\|call\\|>$", "x-parser": "json", "additionalProperties": true } @@ -410,7 +410,21 @@ "\n", "{#- Generation prompt #}\n", "{%- if add_generation_prompt -%}\n", + "{%- set forced_tool_name = none %}\n", + "{%- if tool_choice is mapping %}\n", + " {%- if tool_choice.function is defined and tool_choice.function.name is defined %}\n", + " {%- set forced_tool_name = tool_choice.function.name %}\n", + " {%- elif tool_choice.name is defined %}\n", + " {%- set forced_tool_name = tool_choice.name %}\n", + " {%- endif %}\n", + "{%- elif function_call is mapping and function_call.name is defined %}\n", + " {%- set forced_tool_name = function_call.name %}\n", + "{%- endif %}\n", + "{%- if forced_tool_name %}\n", + "{{- \"<|start|>assistant<|channel|>commentary to=functions.\" + forced_tool_name + \" <|constrain|>json<|message|>\" }}\n", + "{%- else %}\n", "<|start|>assistant\n", + "{%- endif %}\n", "{%- endif -%}" ] } From e1079999f8a339d39c60dee4d751a3c311b2d72d Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Mon, 8 Jun 2026 08:14:59 -0700 Subject: [PATCH 134/138] feat: update llama.cpp to 8f83d6c27 (#2290) --- CHANGELOG.md | 1 + llama_cpp/mtmd_cpp.py | 217 ++++++++++++++++++++++++++++++++++++++++-- vendor/llama.cpp | 2 +- 3 files changed, 213 insertions(+), 7 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ac792bc2a9..10209aacfb 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat: update llama.cpp to ggml-org/llama.cpp@8f83d6c27 - fix(example): support multi-step Responses tool streaming by @abetlen in #2288 - fix(ci): Repair Linux accelerator wheels for manylinux publishing diff --git a/llama_cpp/mtmd_cpp.py b/llama_cpp/mtmd_cpp.py index abe4c7a679..919cefb357 100644 --- a/llama_cpp/mtmd_cpp.py +++ b/llama_cpp/mtmd_cpp.py @@ -3,9 +3,11 @@ import os import warnings from ctypes import ( + CFUNCTYPE, c_bool, c_char_p, c_int, + c_int64, c_uint8, c_uint32, c_size_t, @@ -62,6 +64,9 @@ mtmd_bitmap_p = NewType("mtmd_bitmap_p", int) mtmd_bitmap_p_ctypes = c_void_p +mtmd_helper_video_p = NewType("mtmd_helper_video_p", int) +mtmd_helper_video_p_ctypes = c_void_p + mtmd_image_tokens_p = NewType("mtmd_image_tokens_p", int) mtmd_image_tokens_p_ctypes = c_void_p @@ -151,6 +156,60 @@ class mtmd_caps(Structure): ] +mtmd_bitmap_lazy_callback = CFUNCTYPE( + c_int, + c_size_t, + c_void_p, + POINTER(mtmd_bitmap_p_ctypes), + POINTER(c_char_p), +) + + +class mtmd_helper_bitmap_wrapper(Structure): + """Bitmap wrapper returned by MTMD helper media loaders.""" + + if TYPE_CHECKING: + bitmap: Optional[mtmd_bitmap_p] + video_ctx: Optional[mtmd_helper_video_p] + + _fields_ = [ + ("bitmap", mtmd_bitmap_p_ctypes), + ("video_ctx", mtmd_helper_video_p_ctypes), + ] + + +class mtmd_helper_video_info(Structure): + """Metadata for a decoded video stream.""" + + if TYPE_CHECKING: + width: int + height: int + fps: float + n_frames: int + + _fields_ = [ + ("width", c_uint32), + ("height", c_uint32), + ("fps", c_float), + ("n_frames", c_int), + ] + + +class mtmd_helper_video_init_params(Structure): + """Parameters for initializing an MTMD helper video stream.""" + + if TYPE_CHECKING: + fps_target: float + ffmpeg_bin_dir: Optional[bytes] + timestamp_interval_ms: int + + _fields_ = [ + ("fps_target", c_float), + ("ffmpeg_bin_dir", c_char_p), + ("timestamp_interval_ms", c_int64), + ] + + ################################################ # mtmd.h functions ################################################ @@ -234,6 +293,13 @@ def mtmd_get_audio_sample_rate(ctx: mtmd_context_p, /) -> int: ... +# MTMD_API const char * mtmd_get_marker(const mtmd_context * ctx); +@ctypes_function("mtmd_get_marker", [mtmd_context_p_ctypes], c_char_p) +def mtmd_get_marker(ctx: mtmd_context_p, /) -> Optional[bytes]: + """Get the current media marker string.""" + ... + + # Deprecated compatibility wrapper for the renamed mtmd_get_audio_sample_rate(). def mtmd_get_audio_bitrate(ctx: mtmd_context_p, /) -> int: warnings.warn( @@ -325,6 +391,26 @@ def mtmd_bitmap_set_id(bitmap: mtmd_bitmap_p, id: Optional[bytes], /): ... +# MTMD_API mtmd_bitmap * mtmd_bitmap_init_lazy(mtmd_context * ctx, +# const char * id, +# void * user_data, +# mtmd_bitmap_lazy_callback callback); +@ctypes_function( + "mtmd_bitmap_init_lazy", + [mtmd_context_p_ctypes, c_char_p, c_void_p, mtmd_bitmap_lazy_callback], + mtmd_bitmap_p_ctypes, +) +def mtmd_bitmap_init_lazy( + ctx: mtmd_context_p, + id: Optional[bytes], + user_data: c_void_p, + callback: mtmd_bitmap_lazy_callback, + /, +) -> Optional[mtmd_bitmap_p]: + """Initialize a lazy MTMD bitmap.""" + ... + + # MTMD_API mtmd_input_chunks * mtmd_input_chunks_init(void); @ctypes_function("mtmd_input_chunks_init", [], mtmd_input_chunks_p_ctypes) def mtmd_input_chunks_init() -> Optional[mtmd_input_chunks_p]: ... @@ -551,32 +637,63 @@ def mtmd_test_create_input_chunks() -> Optional[mtmd_input_chunks_p]: ################################################ -# MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname, bool placeholder); +# MTMD_API bool mtmd_helper_support_video(mtmd_context * ctx); +@ctypes_function( + "mtmd_helper_support_video", + [mtmd_context_p_ctypes], + c_bool, +) +def mtmd_helper_support_video(ctx: mtmd_context_p, /) -> bool: + """Check whether MTMD helper video support is available.""" + ... + + +# MTMD_API struct mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname, bool placeholder); @ctypes_function( "mtmd_helper_bitmap_init_from_file", [mtmd_context_p_ctypes, c_char_p, c_bool], - mtmd_bitmap_p_ctypes, + mtmd_helper_bitmap_wrapper, ) +def mtmd_helper_bitmap_init_from_file_wrapper( + ctx: mtmd_context_p, fname: bytes, placeholder: Union[c_bool, bool], / +) -> mtmd_helper_bitmap_wrapper: + """Initialize an MTMD bitmap wrapper from a file.""" + ... + + def mtmd_helper_bitmap_init_from_file( ctx: mtmd_context_p, fname: bytes, placeholder: Union[c_bool, bool], / ) -> Optional[mtmd_bitmap_p]: """Initialize an MTMD bitmap from a file.""" - ... + return mtmd_helper_bitmap_init_from_file_wrapper(ctx, fname, placeholder).bitmap -# MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len, bool placeholder); +# MTMD_API struct mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len, bool placeholder); @ctypes_function( "mtmd_helper_bitmap_init_from_buf", [mtmd_context_p_ctypes, POINTER(c_uint8), c_size_t, c_bool], - mtmd_bitmap_p_ctypes, + mtmd_helper_bitmap_wrapper, ) +def mtmd_helper_bitmap_init_from_buf_wrapper( + ctx: mtmd_context_p, + buf: CtypesArray[c_uint8], + length: Union[c_size_t, int], + placeholder: Union[c_bool, bool], + /, +) -> mtmd_helper_bitmap_wrapper: ... + + def mtmd_helper_bitmap_init_from_buf( ctx: mtmd_context_p, buf: CtypesArray[c_uint8], length: Union[c_size_t, int], placeholder: Union[c_bool, bool], /, -) -> Optional[mtmd_bitmap_p]: ... +) -> Optional[mtmd_bitmap_p]: + """Initialize an MTMD bitmap from a buffer.""" + return mtmd_helper_bitmap_init_from_buf_wrapper( + ctx, buf, length, placeholder + ).bitmap # MTMD_API size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks); @@ -719,6 +836,94 @@ def mtmd_helper_decode_image_chunk( ... +# MTMD_API struct mtmd_helper_video_init_params mtmd_helper_video_init_params_default(void); +@ctypes_function( + "mtmd_helper_video_init_params_default", [], mtmd_helper_video_init_params +) +def mtmd_helper_video_init_params_default() -> mtmd_helper_video_init_params: + """Return the default MTMD helper video initialization parameters.""" + ... + + +# MTMD_API mtmd_helper_video * mtmd_helper_video_init( +# struct mtmd_context * mctx, +# const char * path, +# struct mtmd_helper_video_init_params params); +@ctypes_function( + "mtmd_helper_video_init", + [mtmd_context_p_ctypes, c_char_p, mtmd_helper_video_init_params], + mtmd_helper_video_p_ctypes, +) +def mtmd_helper_video_init( + ctx: mtmd_context_p, + path: bytes, + params: mtmd_helper_video_init_params, + /, +) -> Optional[mtmd_helper_video_p]: + """Initialize an MTMD helper video stream from a file path.""" + ... + + +# MTMD_API mtmd_helper_video * mtmd_helper_video_init_from_buf( +# struct mtmd_context * mctx, +# const unsigned char * buf, size_t len, +# struct mtmd_helper_video_init_params params); +@ctypes_function( + "mtmd_helper_video_init_from_buf", + [mtmd_context_p_ctypes, POINTER(c_uint8), c_size_t, mtmd_helper_video_init_params], + mtmd_helper_video_p_ctypes, +) +def mtmd_helper_video_init_from_buf( + ctx: mtmd_context_p, + buf: CtypesArray[c_uint8], + length: Union[c_size_t, int], + params: mtmd_helper_video_init_params, + /, +) -> Optional[mtmd_helper_video_p]: + """Initialize an MTMD helper video stream from a buffer.""" + ... + + +# MTMD_API void mtmd_helper_video_free(mtmd_helper_video * ctx); +@ctypes_function("mtmd_helper_video_free", [mtmd_helper_video_p_ctypes], None) +def mtmd_helper_video_free(ctx: mtmd_helper_video_p, /): + """Free an MTMD helper video stream.""" + ... + + +# MTMD_API struct mtmd_helper_video_info mtmd_helper_video_get_info(const mtmd_helper_video * ctx); +@ctypes_function( + "mtmd_helper_video_get_info", + [mtmd_helper_video_p_ctypes], + mtmd_helper_video_info, +) +def mtmd_helper_video_get_info(ctx: mtmd_helper_video_p, /) -> mtmd_helper_video_info: + """Get metadata for an MTMD helper video stream.""" + ... + + +# MTMD_API int32_t mtmd_helper_video_read_next(mtmd_helper_video * ctx, +# mtmd_bitmap ** out_bitmap, +# char ** out_text); +@ctypes_function( + "mtmd_helper_video_read_next", + [ + mtmd_helper_video_p_ctypes, + POINTER(mtmd_bitmap_p_ctypes), + POINTER(c_char_p), + ], + c_int, +) +def mtmd_helper_video_read_next( + ctx: mtmd_helper_video_p, + out_bitmap: "_Pointer[mtmd_bitmap_p_ctypes]", + out_text: "_Pointer[c_char_p]", + /, +) -> int: + """Read the next bitmap or text chunk from an MTMD helper video stream.""" + ... + + # MTMD_API void mtmd_log_set(ggml_log_callback log_callback, void * user_data); @ctypes_function( "mtmd_log_set", diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 9e3b928fd8..8f83d6c271 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 9e3b928fd8c9d14dbf15a8768b9fdd7e5c721d66 +Subproject commit 8f83d6c271d194bde2d410145a0ce73bc42e85cd From 051dda2515f240fe5e93ddbf6124f9c9dead3f73 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Mon, 8 Jun 2026 21:49:57 -0700 Subject: [PATCH 135/138] feat(example): support server video inputs and Gemma text tool calls (#2291) * feat(example): support server video inputs and Gemma text tool calls * fix(example): pass media count to MTMD tokenizer * fix(example): avoid MTMD video stdin double close * fix(example): parse Gemma text tool bracket calls --- CHANGELOG.md | 1 + .../server/configs/gemma-4-12b-it-qat.json | 6 +- examples/server/server.py | 410 +++++++++++++++--- 3 files changed, 354 insertions(+), 63 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 10209aacfb..e733f37e6e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +- feat(example): support server video inputs and Gemma text tool calls by @abetlen in #2291 - feat: update llama.cpp to ggml-org/llama.cpp@8f83d6c27 - fix(example): support multi-step Responses tool streaming by @abetlen in #2288 - fix(ci): Repair Linux accelerator wheels for manylinux publishing diff --git a/examples/server/configs/gemma-4-12b-it-qat.json b/examples/server/configs/gemma-4-12b-it-qat.json index fefeda8da0..60ffd2a987 100644 --- a/examples/server/configs/gemma-4-12b-it-qat.json +++ b/examples/server/configs/gemma-4-12b-it-qat.json @@ -44,7 +44,7 @@ }, "tool_calls": { "type": "array", - "x-regex-iterator": "<\\|tool_call>(call:[^\\{]+\\{.*?\\})<tool_call\\|>", + "x-regex-iterator": "<\\|tool_call>(call:[^\\{\\[]+[\\{\\[].*?[\\}\\]])<tool_call\\|>", "items": { "type": "object", "properties": { @@ -56,11 +56,11 @@ "properties": { "name": { "type": "string", - "x-regex": "^call:(\\w+)\\{" + "x-regex": "^call:(\\w+)[\\{\\[]" }, "arguments": { "type": "object", - "x-regex": "^call:\\w+(\\{.*\\})$", + "x-regex": "^call:\\w+(\\{.*\\}|\\[.*\\])$", "x-parser": "gemma4-tool-call", "additionalProperties": true } diff --git a/examples/server/server.py b/examples/server/server.py index fb00501cfe..e8034a2146 100644 --- a/examples/server/server.py +++ b/examples/server/server.py @@ -37,6 +37,7 @@ import shutil import inspect import sys +import tempfile import urllib.error import urllib.parse import urllib.request @@ -3224,6 +3225,7 @@ class MTMDOptions(BaseModel): allowed_local_media_path: Optional[str] = None image_max_bytes: int = Field(default=20 * 1024 * 1024, ge=1) audio_max_bytes: int = Field(default=100 * 1024 * 1024, ge=1) + video_max_bytes: int = Field(default=512 * 1024 * 1024, ge=1) image_timeout_seconds: float = Field(default=10.0, gt=0.0) @model_validator(mode="after") @@ -3350,7 +3352,7 @@ def load(cls, path: str) -> "ConfigFile": @dataclass(frozen=True) class MediaInput: - kind: Literal["image", "audio"] + kind: Literal["image", "audio", "video"] url: Optional[str] = None data: Optional[str] = None format: Optional[str] = None @@ -3426,6 +3428,33 @@ def media_inputs_from_messages( ) ) continue + if part_type in {"video_url", "input_video", "video"}: + input_video = part.get("input_video") + if isinstance(input_video, dict): + data = input_video.get("data") + video_url = input_video.get("video_url") or input_video.get("url") + video_format = input_video.get("format") + else: + data = part.get("data") + video_url = part.get("video_url") or part.get("url") + video_format = part.get("format") + if isinstance(video_url, dict): + video_url = video_url.get("url") + if isinstance(data, str): + if video_format is not None and not isinstance(video_format, str): + raise ValueError("input_video format must be a string") + media_inputs.append( + MediaInput( + kind="video", + data=data, + format=cast(Optional[str], video_format), + ) + ) + elif isinstance(video_url, str): + media_inputs.append(MediaInput(kind="video", url=video_url)) + else: + raise ValueError("video content part requires base64 data or a URL string") + continue return media_inputs @staticmethod @@ -3447,7 +3476,16 @@ def _content_part_for_template( if not isinstance(part, dict): raise ValueError("content parts must be strings or objects") part_type = part.get("type") - if part_type in {"image_url", "input_image", "image", "audio_url", "input_audio"}: + if part_type in { + "image_url", + "input_image", + "image", + "audio_url", + "input_audio", + "video_url", + "input_video", + "video", + }: if media_marker is None: raise ValueError("multimodal content requires model.mtmd") return media_marker @@ -3694,7 +3732,7 @@ class Media: positions: np.ndarray non_causal: bool = False - kind: Literal["text", "image", "audio"] + kind: Literal["text", "image", "audio", "video"] start_pos: int n_pos: int identity_tokens: List[int] @@ -4351,6 +4389,9 @@ def capture(match: re.Match[str]) -> str: strings.append(match.group(1)) return f"\x00{len(strings) - 1}\x00" + stripped = text.strip() + if stripped.startswith("[") and stripped.endswith("]"): + text = "{" + stripped[1:-1] + "}" text = re.sub(r'<\|"\|>(.*?)<\|"\|>', capture, text, flags=re.S) text = re.sub(r"(?<=[{,])(\w+):", r'"\1":', text) for index, value in enumerate(strings): @@ -5116,6 +5157,87 @@ def _raw_string_tool_arguments(self, tool_name: str, value: str) -> Optional[Dic return None return None + def _single_string_tool_argument_name(self, tool_name: str) -> Optional[str]: + if self._tools is None: + return None + for tool in self._tools: + if tool.get("type") != "function": + continue + function = tool.get("function", {}) + if function.get("name") != tool_name: + continue + parameters = function.get("parameters") + if not isinstance(parameters, dict): + return None + required = parameters.get("required") + if not isinstance(required, list) or len(required) != 1: + return None + argument_name = required[0] + if not isinstance(argument_name, str): + return None + properties = parameters.get("properties") + if not isinstance(properties, dict): + return None + argument_schema = properties.get(argument_name) + if not isinstance(argument_schema, dict): + return None + argument_type = argument_schema.get("type") + if argument_type == "string" or ( + isinstance(argument_type, list) and "string" in argument_type + ): + return argument_name + return None + return None + + def _text_tool_argument_from_object( + self, + tool_name: str, + arguments: Dict[str, Any], + ) -> Optional[str]: + input_value = arguments.get("input") + if isinstance(input_value, str): + return input_value + argument_name = self._single_string_tool_argument_name(tool_name) + if argument_name is not None: + argument_value = arguments.get(argument_name) + if isinstance(argument_value, str): + return argument_value + if len(arguments) == 1: + argument_value = next(iter(arguments.values())) + if isinstance(argument_value, str): + return argument_value + return None + + def _text_tool_arguments( + self, + tool_name: str, + arguments: Any, + *, + partial: bool, + ) -> Optional[str]: + if isinstance(arguments, str): + parsed_arguments = self._raw_object_tool_arguments(arguments) + if parsed_arguments is not None: + text = self._text_tool_argument_from_object(tool_name, parsed_arguments) + if text is not None: + return text + if partial: + return None + return json.dumps(parsed_arguments, ensure_ascii=False, separators=(",", ":")) + return arguments + if isinstance(arguments, ResponseParser.PartialJsonObject): + arguments = arguments.value + if isinstance(arguments, dict): + text = self._text_tool_argument_from_object(tool_name, arguments) + if text is not None: + return text + if partial: + return None + return json.dumps(arguments, ensure_ascii=False, separators=(",", ":")) + if partial: + return None + return str(arguments) + @classmethod def _raw_object_tool_arguments(cls, value: str) -> Optional[Dict[str, Any]]: candidates = [value] @@ -6950,13 +7072,13 @@ def _normalize_tool_call_item( "tool_calls function name must be a non-empty string" ) if self._tool_content_type(tool_name) == "text": - arguments = function.get("arguments", "") - if not isinstance(arguments, str): - if partial: - return None - raise CompletionResponseParsingError( - "tool_calls function arguments must be a string for text tools" - ) + arguments = self._text_tool_arguments( + tool_name, + function.get("arguments", ""), + partial=partial, + ) + if arguments is None: + return None return { "type": tool_call.get("type", "function"), "function": { @@ -7141,12 +7263,13 @@ def _parsed_chat_message( for tool_call_index, tool_call in enumerate(tool_calls): function = tool_call["function"] if self._tool_content_type(function["name"]) == "text": - raw_arguments = function["arguments"] - arguments = ( - raw_arguments - if isinstance(raw_arguments, str) - else str(raw_arguments) + arguments = self._text_tool_arguments( + function["name"], + function["arguments"], + partial=partial, ) + if arguments is None: + continue else: arguments = self._serialize_tool_arguments( function["arguments"], @@ -10195,7 +10318,7 @@ def _build_key( *, model_fingerprint: str, mmproj_fingerprint: str, - kind: Literal["image", "audio"], + kind: Literal["image", "audio", "video"], media_bytes: bytes, ) -> str: digest = hashlib.sha256(f"{kind}:".encode("utf-8") + media_bytes).hexdigest() @@ -10210,7 +10333,7 @@ def _build_key( ) return hashlib.sha256(payload.encode("utf-8")).hexdigest() - def key_for_media(self, kind: Literal["image", "audio"], media_bytes: bytes) -> str: + def key_for_media(self, kind: Literal["image", "audio", "video"], media_bytes: bytes) -> str: return self._build_key( model_fingerprint=self.model_fingerprint, mmproj_fingerprint=self.mmproj_fingerprint, @@ -10273,6 +10396,19 @@ def evict_if_needed(self) -> None: continue +@dataclass +class MTMDLoadedMedia: + media: MediaInput + media_bytes: bytes + key: str + bitmap: Any + video_ctx: Optional[Any] = None + video_temp_path: Optional[Path] = None + video_callback: Optional[Any] = None + video_frame_count: int = 0 + video_frames_used: int = 0 + + class MTMDProcessor: def __init__( self, @@ -10291,6 +10427,7 @@ def __init__( allowed_local_media_path: Optional[str], image_max_bytes: int, audio_max_bytes: int, + video_max_bytes: int, image_timeout_seconds: float, ) -> None: self.chat_formatter = chat_formatter @@ -10314,6 +10451,7 @@ def __init__( ) self.image_max_bytes = image_max_bytes self.audio_max_bytes = audio_max_bytes + self.video_max_bytes = video_max_bytes self.image_timeout_seconds = image_timeout_seconds self.lock = threading.Lock() params = mtmd_cpp.mtmd_context_params_default() @@ -10327,23 +10465,33 @@ def __init__( raise RuntimeError(f"failed to load MTMD context: {mmproj_path}") self.supports_vision = bool(mtmd_cpp.mtmd_support_vision(self.ctx)) self.supports_audio = bool(mtmd_cpp.mtmd_support_audio(self.ctx)) + self.supports_video = self.supports_vision and bool( + mtmd_cpp.mtmd_helper_support_video(self.ctx) + ) if not self.supports_vision and not self.supports_audio: mtmd_cpp.mtmd_free(self.ctx) self.ctx = None raise RuntimeError(f"MTMD projector does not support image or audio input: {mmproj_path}") - self.media_marker = mtmd_cpp.mtmd_default_marker().decode("utf-8") + media_marker = mtmd_cpp.mtmd_get_marker(self.ctx) + if media_marker is None: + mtmd_cpp.mtmd_free(self.ctx) + self.ctx = None + raise RuntimeError(f"MTMD projector does not expose a media marker: {mmproj_path}") + self.media_marker = media_marker.decode("utf-8") def close(self) -> None: if self.ctx is not None: mtmd_cpp.mtmd_free(self.ctx) self.ctx = None - def _max_bytes_for_media(self, kind: Literal["image", "audio"]) -> int: + def _max_bytes_for_media(self, kind: Literal["image", "audio", "video"]) -> int: if kind == "image": return self.image_max_bytes - return self.audio_max_bytes + if kind == "audio": + return self.audio_max_bytes + return self.video_max_bytes - def _load_media_file(self, kind: Literal["image", "audio"], media_url: str) -> bytes: + def _load_media_file(self, kind: Literal["image", "audio", "video"], media_url: str) -> bytes: if self.allowed_local_media_path is None: raise CompletionRequestValidationError("local media path is not allowed") parsed = urllib.parse.urlsplit(media_url) @@ -10391,7 +10539,7 @@ def redirect_request(self, req, fp, code, msg, headers, newurl): # type: ignore opener = urllib.request.build_opener(NoRedirectHandler) return opener.open(request, timeout=timeout) - def _load_media_url(self, kind: Literal["image", "audio"], media_url: str) -> bytes: + def _load_media_url(self, kind: Literal["image", "audio", "video"], media_url: str) -> bytes: max_bytes = self._max_bytes_for_media(kind) if media_url.startswith("data:"): try: @@ -10423,29 +10571,134 @@ def _load_media_url(self, kind: Literal["image", "audio"], media_url: str) -> by def load_media(self, media: MediaInput) -> bytes: if media.url is not None: return self._load_media_url(media.kind, media.url) - if media.kind != "audio" or media.data is None: + if media.kind not in {"audio", "video"} or media.data is None: raise CompletionRequestValidationError(f"{media.kind} input requires a URL") try: data = base64.b64decode(media.data, validate=False) except (ValueError, binascii.Error) as exc: - raise CompletionRequestValidationError("input_audio data must be valid base64") from exc - if len(data) > self.audio_max_bytes: - raise CompletionRequestValidationError("audio exceeds model.mtmd.audio_max_bytes") + raise CompletionRequestValidationError(f"input_{media.kind} data must be valid base64") from exc + max_bytes = self._max_bytes_for_media(media.kind) + if len(data) > max_bytes: + raise CompletionRequestValidationError( + f"{media.kind} exceeds model.mtmd.{media.kind}_max_bytes" + ) return data - def _create_bitmap(self, media_bytes: bytes, kind: Literal["image", "audio"]) -> Any: + def _create_loaded_media( + self, + media: MediaInput, + media_bytes: bytes, + ) -> MTMDLoadedMedia: + key = ( + self.embedding_cache.key_for_media(media.kind, media_bytes) + if self.embedding_cache is not None + else MTMDEmbeddingCache._build_key( + model_fingerprint=self.model_fingerprint, + mmproj_fingerprint=self.mmproj_fingerprint, + kind=media.kind, + media_bytes=media_bytes, + ) + ) + if media.kind == "video": + return self._create_loaded_video_media(media, media_bytes, key) buffer = (ctypes.c_uint8 * len(media_bytes)).from_buffer_copy(media_bytes) - bitmap = mtmd_cpp.mtmd_helper_bitmap_init_from_buf( + wrapper = mtmd_cpp.mtmd_helper_bitmap_init_from_buf_wrapper( self.ctx, buffer, len(media_bytes), False, ) + bitmap = wrapper.bitmap if bitmap is None: - raise CompletionRequestValidationError(f"failed to create MTMD {kind} bitmap") - return bitmap + raise CompletionRequestValidationError(f"failed to create MTMD {media.kind} bitmap") + mtmd_cpp.mtmd_bitmap_set_id(bitmap, key.encode("utf-8")) + video_frame_count = 0 + video_ctx = wrapper.video_ctx + if video_ctx: + video_info = mtmd_cpp.mtmd_helper_video_get_info(video_ctx) + video_frame_count = max(0, int(video_info.n_frames)) + return MTMDLoadedMedia( + media=media, + media_bytes=media_bytes, + key=key, + bitmap=bitmap, + video_ctx=video_ctx, + video_frame_count=video_frame_count, + ) + + @staticmethod + def _video_temp_suffix(media: MediaInput) -> str: + extension = (media.format or "mp4").lstrip(".").lower() + if not extension or any(not char.isalnum() for char in extension): + extension = "video" + return f".{extension}" + + def _create_loaded_video_media( + self, + media: MediaInput, + media_bytes: bytes, + key: str, + ) -> MTMDLoadedMedia: + temp_file = tempfile.NamedTemporaryFile( + prefix="llama-cpp-python-mtmd-", + suffix=self._video_temp_suffix(media), + delete=False, + ) + temp_path = Path(temp_file.name) + try: + with temp_file: + temp_file.write(media_bytes) + params = mtmd_cpp.mtmd_helper_video_init_params_default() + video_ctx = mtmd_cpp.mtmd_helper_video_init( + self.ctx, + str(temp_path).encode("utf-8"), + params, + ) + if video_ctx is None: + raise CompletionRequestValidationError("failed to create MTMD video context") + + def read_next( + _chunk_index: int, + _user_data: Any, + out_bitmap: Any, + out_text: Any, + ) -> int: + return int(mtmd_cpp.mtmd_helper_video_read_next(video_ctx, out_bitmap, out_text)) + + callback = mtmd_cpp.mtmd_bitmap_lazy_callback(read_next) + bitmap = mtmd_cpp.mtmd_bitmap_init_lazy( + self.ctx, + key.encode("utf-8"), + ctypes.c_void_p(), + callback, + ) + if bitmap is None: + mtmd_cpp.mtmd_helper_video_free(video_ctx) + raise CompletionRequestValidationError("failed to create MTMD video bitmap") + video_info = mtmd_cpp.mtmd_helper_video_get_info(video_ctx) + return MTMDLoadedMedia( + media=media, + media_bytes=media_bytes, + key=key, + bitmap=bitmap, + video_ctx=video_ctx, + video_temp_path=temp_path, + video_callback=callback, + video_frame_count=max(0, int(video_info.n_frames)), + ) + except Exception: + try: + temp_path.unlink() + except OSError: + pass + raise - def _media_identity_tokens(self, kind: Literal["image", "audio"], key: str, n_pos: int) -> List[int]: + def _media_identity_tokens( + self, + kind: Literal["image", "audio", "video"], + key: str, + n_pos: int, + ) -> List[int]: tokens: List[int] = [] for index in range(n_pos): digest = hashlib.sha256(f"{kind}:{key}:{index}".encode("utf-8")).digest() @@ -10455,7 +10708,7 @@ def _media_identity_tokens(self, kind: Literal["image", "audio"], key: str, n_po def _encode_media_chunk( self, *, - kind: Literal["image", "audio"], + kind: Literal["image", "audio", "video"], key: str, chunk: Any, ) -> np.ndarray: @@ -10535,6 +10788,8 @@ def build_prompt_plan( raise CompletionRequestValidationError("MTMD projector does not support images") if any(media.kind == "audio" for media in media_inputs) and not self.supports_audio: raise CompletionRequestValidationError("MTMD projector does not support audio") + if any(media.kind == "video" for media in media_inputs) and not self.supports_video: + raise CompletionRequestValidationError("MTMD projector does not support video") with self.lock: return self._build_prompt_plan_locked( messages=messages, @@ -10567,13 +10822,19 @@ def _build_prompt_plan_locked( reasoning_effort=reasoning_effort, ) media_bytes_by_index = [self.load_media(media) for media in media_inputs] - bitmaps: List[Any] = [] + loaded_media: List[MTMDLoadedMedia] = [] chunks: Optional[Any] = None try: - bitmaps = [ - self._create_bitmap(media_bytes, media.kind) + loaded_media = [ + self._create_loaded_media(media, media_bytes) for media, media_bytes in zip(media_inputs, media_bytes_by_index) ] + loaded_media_by_key = {media.key: media for media in loaded_media} + video_media = [media for media in loaded_media if media.media.kind == "video"] + if len(video_media) > 1 and any(media.video_frame_count <= 0 for media in video_media): + raise CompletionRequestValidationError( + "multiple videos require MTMD to report frame counts" + ) input_text = mtmd_cpp.mtmd_input_text() input_text.text = prompt.encode("utf-8") input_text.add_special = False @@ -10581,14 +10842,16 @@ def _build_prompt_plan_locked( chunks = mtmd_cpp.mtmd_input_chunks_init() if chunks is None: raise CompletionRequestValidationError("failed to create MTMD input chunks") - bitmap_array = (mtmd_cpp.mtmd_bitmap_p_ctypes * len(bitmaps))(*bitmaps) + bitmap_array = (mtmd_cpp.mtmd_bitmap_p_ctypes * len(loaded_media))( + *(media.bitmap for media in loaded_media) + ) result = int( mtmd_cpp.mtmd_tokenize( self.ctx, chunks, ctypes.byref(input_text), bitmap_array, - len(bitmaps), + len(loaded_media), ) ) if result != 0: @@ -10601,7 +10864,8 @@ def _build_prompt_plan_locked( text_token_index_by_pos: Dict[int, int] = {} identity_pos = 0 decode_pos = 0 - media_index = 0 + video_index = 0 + used_media_keys = set() n_chunks = int(mtmd_cpp.mtmd_input_chunks_size(chunks)) for chunk_index in range(n_chunks): chunk = mtmd_cpp.mtmd_input_chunks_get(chunks, chunk_index) @@ -10640,32 +10904,50 @@ def _build_prompt_plan_locked( decode_pos += len(tokens) continue if chunk_type == mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_IMAGE: - kind: Literal["image", "audio"] = "image" + chunk_kind: Literal["image", "audio"] = "image" if not self.supports_vision: raise CompletionRequestValidationError("MTMD projector does not support images") elif chunk_type == mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_AUDIO: - kind = "audio" + chunk_kind = "audio" if not self.supports_audio: raise CompletionRequestValidationError("MTMD projector does not support audio") else: raise CompletionRequestValidationError("unsupported MTMD input chunk type") - if media_index >= len(media_bytes_by_index): - raise CompletionRequestValidationError("MTMD media chunk count mismatch") - media = media_inputs[media_index] - media_bytes = media_bytes_by_index[media_index] - media_index += 1 - if media.kind != kind: - raise CompletionRequestValidationError("MTMD media chunk modality mismatch") - key = ( - self.embedding_cache.key_for_media(kind, media_bytes) - if self.embedding_cache is not None - else MTMDEmbeddingCache._build_key( - model_fingerprint=self.model_fingerprint, - mmproj_fingerprint=self.mmproj_fingerprint, - kind=kind, - media_bytes=media_bytes, - ) - ) + chunk_id_bytes = mtmd_cpp.mtmd_input_chunk_get_id(chunk) + chunk_id = chunk_id_bytes.decode("utf-8") if chunk_id_bytes else "" + media = loaded_media_by_key.get(chunk_id) + video_frame_index: Optional[int] = None + if media is None and chunk_kind == "image" and video_media: + while ( + video_index < len(video_media) + and video_media[video_index].video_frame_count > 0 + and video_media[video_index].video_frames_used + >= video_media[video_index].video_frame_count + ): + video_index += 1 + if video_index >= len(video_media): + raise CompletionRequestValidationError("MTMD video frame count mismatch") + media = video_media[video_index] + video_frame_index = media.video_frames_used + media.video_frames_used += 1 + if media is None: + raise CompletionRequestValidationError("MTMD media chunk identity mismatch") + if media.media.kind == "video": + if chunk_kind != "image": + raise CompletionRequestValidationError("MTMD video chunk modality mismatch") + kind: Literal["image", "audio", "video"] = "video" + if video_frame_index is None: + video_frame_index = media.video_frames_used + media.video_frames_used += 1 + key = hashlib.sha256( + f"{media.key}:frame:{video_frame_index}".encode("utf-8") + ).hexdigest() + else: + if media.media.kind != chunk_kind: + raise CompletionRequestValidationError("MTMD media chunk modality mismatch") + kind = media.media.kind + key = media.key + used_media_keys.add(media.key) decode_n_pos = int(mtmd_cpp.mtmd_input_chunk_get_n_pos(chunk)) if decode_n_pos <= 0: raise CompletionRequestValidationError("MTMD media chunk has no decoder positions") @@ -10698,7 +10980,7 @@ def _build_prompt_plan_locked( identity_tokens.extend(segment_identity) identity_pos += n_tokens decode_pos += decode_n_pos - if media_index != len(media_bytes_by_index): + if used_media_keys != {media.key for media in loaded_media}: raise CompletionRequestValidationError("not all media inputs were consumed by MTMD") return PromptPlan( text=prompt, @@ -10711,8 +10993,15 @@ def _build_prompt_plan_locked( finally: if chunks is not None: mtmd_cpp.mtmd_input_chunks_free(chunks) - for bitmap in bitmaps: - mtmd_cpp.mtmd_bitmap_free(bitmap) + for media in loaded_media: + mtmd_cpp.mtmd_bitmap_free(media.bitmap) + if media.video_ctx: + mtmd_cpp.mtmd_helper_video_free(media.video_ctx) + if media.video_temp_path is not None: + try: + media.video_temp_path.unlink() + except OSError: + pass class Model: @@ -15927,6 +16216,7 @@ def main() -> None: allowed_local_media_path=config.model.mtmd.allowed_local_media_path, image_max_bytes=config.model.mtmd.image_max_bytes, audio_max_bytes=config.model.mtmd.audio_max_bytes, + video_max_bytes=config.model.mtmd.video_max_bytes, image_timeout_seconds=config.model.mtmd.image_timeout_seconds, ) sequence_cache: Optional[SequenceCache] = None From 0edb5d8d2d68461e706f4b2ecfb69060e4e99486 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Tue, 9 Jun 2026 00:19:48 -0700 Subject: [PATCH 136/138] feat: update llama.cpp to ggml-org/llama.cpp@e3471b3e7 (#2294) --- CHANGELOG.md | 2 +- vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index e733f37e6e..7cc732b935 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,7 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] - feat(example): support server video inputs and Gemma text tool calls by @abetlen in #2291 -- feat: update llama.cpp to ggml-org/llama.cpp@8f83d6c27 +- feat: update llama.cpp to ggml-org/llama.cpp@e3471b3e7 - fix(example): support multi-step Responses tool streaming by @abetlen in #2288 - fix(ci): Repair Linux accelerator wheels for manylinux publishing diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 8f83d6c271..e3471b3e73 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 8f83d6c271d194bde2d410145a0ce73bc42e85cd +Subproject commit e3471b3e7306fe120dc8f38a2263c1293fc2add7 From b5eefc82e0fd415d5547c81367c29b159c0268d3 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Wed, 10 Jun 2026 00:44:25 -0700 Subject: [PATCH 137/138] feat: update llama.cpp to ggml-org/llama.cpp@76da2450a (#2295) --- CHANGELOG.md | 2 +- vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7cc732b935..1fc8750c87 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,7 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] - feat(example): support server video inputs and Gemma text tool calls by @abetlen in #2291 -- feat: update llama.cpp to ggml-org/llama.cpp@e3471b3e7 +- feat: update llama.cpp to ggml-org/llama.cpp@76da2450a - fix(example): support multi-step Responses tool streaming by @abetlen in #2288 - fix(ci): Repair Linux accelerator wheels for manylinux publishing diff --git a/vendor/llama.cpp b/vendor/llama.cpp index e3471b3e73..76da2450a4 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit e3471b3e7306fe120dc8f38a2263c1293fc2add7 +Subproject commit 76da2450a4f2cc9ce6c7fc8229e25dc0a4b41e5d From 19ea70cff57e2f60ee333fbe515640e53c83f994 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Wed, 10 Jun 2026 21:49:38 -0700 Subject: [PATCH 138/138] feat: update llama.cpp to ggml-org/llama.cpp@ac4cddeb0 (#2297) --- CHANGELOG.md | 2 +- vendor/llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 1fc8750c87..5c4c744d9e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,7 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] - feat(example): support server video inputs and Gemma text tool calls by @abetlen in #2291 -- feat: update llama.cpp to ggml-org/llama.cpp@76da2450a +- feat: update llama.cpp to ggml-org/llama.cpp@ac4cddeb0 - fix(example): support multi-step Responses tool streaming by @abetlen in #2288 - fix(ci): Repair Linux accelerator wheels for manylinux publishing diff --git a/vendor/llama.cpp b/vendor/llama.cpp index 76da2450a4..ac4cddeb0d 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit 76da2450a4f2cc9ce6c7fc8229e25dc0a4b41e5d +Subproject commit ac4cddeb0dbd778f650bf568f6f08344a06abe3a