ggml-org
diff --git a/‎ggml-cuda/fattn.cu
Copy file name to clipboardExpand all lines: ggml-cuda/fattn.cu
+9-6Lines changed: 9 additions & 6 deletions b/‎ggml-cuda/fattn.cu
Copy file name to clipboardExpand all lines: ggml-cuda/fattn.cu
+9-6Lines changed: 9 additions & 6 deletions
diff --git a/‎llama.cpp
Copy file name to clipboardExpand all lines: llama.cpp
+2-2Lines changed: 2 additions & 2 deletions b/‎llama.cpp
Copy file name to clipboardExpand all lines: llama.cpp
+2-2Lines changed: 2 additions & 2 deletions
@@ -4,6 +4,7 @@
 #include <mma.h>
 
 #define FATTN_KQ_STRIDE 256
+#define HALF_MAX_HALF   __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction.
 
 template<int D, int parallel_blocks> // D == head size
 __launch_bounds__(((D + WARP_SIZE - 1) / WARP_SIZE)*WARP_SIZE, 1)
@@ -59,13 +60,13 @@ static __global__ void flash_attn_vec_ext_f16(
     KQ[tid] = -INFINITY;
     half2 * KQ2 = (half2 *) KQ;
 
-    half kqmax = -INFINITY;
+    half kqmax = -HALF_MAX_HALF;
     half kqsum = 0.0f;
 
     __shared__ half kqmax_shared[WARP_SIZE];
     __shared__ half kqsum_shared[WARP_SIZE];
     if (threadIdx.y == 0) {
-        kqmax_shared[threadIdx.x] = -INFINITY;
+        kqmax_shared[threadIdx.x] = -HALF_MAX_HALF;
         kqsum_shared[threadIdx.x] = 0.0f;
     }
     __syncthreads();
@@ -139,7 +140,7 @@ static __global__ void flash_attn_vec_ext_f16(
         if (tid < D) {
 #pragma unroll
             for (int k0 = 0; k0 < D; k0 += 2) {
-                if (256 % D != 0 && k_VKQ_0 + k0 >= ne11) {
+                if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
                     break;
                 }
 
@@ -253,9 +254,9 @@ static __global__ void flash_attn_ext_f16(
     __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
     half2 * KQ2 = (half2 *) KQ;
 
-    half2    KQ_rowsum[ncols/nwarps] = {{0.0f,      0.0f}};
-    half2       KQ_max[ncols/nwarps] = {{-INFINITY, -INFINITY}};
-    half2 KQ_max_scale[ncols/nwarps] = {{0.0f,      0.0f}};
+    half2    KQ_rowsum[ncols/nwarps] = {{ 0.0f,           0.0f}};
+    half2       KQ_max[ncols/nwarps] = {{-HALF_MAX_HALF, -HALF_MAX_HALF}};
+    half2 KQ_max_scale[ncols/nwarps] = {{ 0.0f,           0.0f}};
 
     __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
     half2 * VKQ2 = (half2 *) VKQ;
@@ -578,6 +579,8 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) &&
                                 "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big");
 
+    GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");
+
     ggml_cuda_set_device(ctx.device);
 
     const cudaStream_t main_stream = ctx.stream();
 
@@ -9973,7 +9973,7 @@ static int llama_decode_internal(
                 // a heuristic, to avoid attending the full cache if it is not yet utilized
                 // after enough generations, the benefit from this heuristic disappears
                 // if we start defragmenting the cache, the benefit from this will be more important
-                kv_self.n = std::min(kv_self.size, std::max(128u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 128)));
+                kv_self.n = std::min(kv_self.size, std::max(256u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 256)));
                 //kv_self.n = llama_kv_cache_cell_max(kv_self);
             }
         }
@@ -13909,7 +13909,7 @@ struct llama_context * llama_new_context_with_model(
     cparams.rope_freq_scale  = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;
 
     // this is necessary due to kv_self.n being padded later during inference
-    cparams.n_ctx = GGML_PAD(cparams.n_ctx, 32);
+    cparams.n_ctx = GGML_PAD(cparams.n_ctx, 256);
 
     // with causal attention, the batch size is limited by the context size
     cparams.n_batch          = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;