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[https://nvbugs/6084445][fix] use DEEPGEMM for DeepSeek-V3-Lite fp8 chunked prefill on SM100/SM103#13257

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kaiyux merged 1 commit intoNVIDIA:mainNVIDIA/TensorRT-LLM:mainfrom
jmydurant:fix/nvbug_6084445jmydurant/TensorRT-LLM:fix/nvbug_6084445Copy head branch name to clipboard
Apr 24, 2026
Merged

[https://nvbugs/6084445][fix] use DEEPGEMM for DeepSeek-V3-Lite fp8 chunked prefill on SM100/SM103#13257
kaiyux merged 1 commit intoNVIDIA:mainNVIDIA/TensorRT-LLM:mainfrom
jmydurant:fix/nvbug_6084445jmydurant/TensorRT-LLM:fix/nvbug_6084445Copy head branch name to clipboard

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@jmydurant jmydurant commented Apr 21, 2026

Summary by CodeRabbit

  • Tests
    • Added integration test to validate model accuracy with optimized quantization and memory management parameters.
    • Updated test configuration to support advanced model optimization features in targeted scenarios.
    • Removed test waiver, marking the optimization scenario as standard test coverage.

Description

This change fixes the DeepSeek-V3-Lite fp8 chunked prefill path on Blackwell SM100/SM103 GPUs.

For TestDeepSeekV3Lite::test_chunked_prefill, the fp8 checkpoint was previously using the default MoE backend selection path. On B200/B300, that could fall back to a CUTLASS fp8 block-
scale MoE path that eventually hits a Hopper-only deep_gemm kernel path and fails at runtime.

This PR explicitly selects DEEPGEMM for the fp8 chunked prefill case on SM100/SM103, which matches the expected backend support for DeepSeek-V3-Lite FP8 block-scale MoE on Blackwell.

In addition, this PR adds a B300 single-GPU coverage entry for:
accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_chunked_prefill[quant_dtype=fp8-kv_cache_reuse=True-fp8kv=True-overlap_scheduler=True]

Since the issue is now fixed, the corresponding waiver is also removed.

Test Coverage

Verified on B300 single-GPU:

  • accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_chunked_prefill[quant_dtype=fp8-kv_cache_reuse=True-fp8kv=True-overlap_scheduler=True]

PR Checklist

Please review the following before submitting your PR:

  • PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.

  • PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.

  • Test cases are provided for new code paths (see test instructions)

  • Any new dependencies have been scanned for license and vulnerabilities

  • CODEOWNERS updated if ownership changes

  • Documentation updated as needed

  • Update tava architecture diagram if there is a significant design change in PR.

  • The reviewers assigned automatically/manually are appropriate for the PR.

  • Please check this after reviewing the above items as appropriate for this PR.

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@jmydurant jmydurant requested a review from a team as a code owner April 21, 2026 04:11
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coderabbitai Bot commented Apr 21, 2026

📝 Walkthrough

Walkthrough

The changes update PyTorch test configurations to conditionally enable MoE backend settings for fp8 quantization on SM100f GPUs, register the updated test in the test database, and remove a corresponding test skip waiver to enable execution.

Changes

Cohort / File(s) Summary
Test Implementation & Configuration
tests/integration/defs/accuracy/test_llm_api_pytorch.py, tests/integration/test_lists/test-db/l0_b300.yml
Added conditional MoE backend configuration (DEEPGEMM) when quant_dtype is fp8 in test_chunked_prefill, and registered the test with fp8 quantization parameters in the test database.
Test Waiver Management
tests/integration/test_lists/waives.txt
Removed skip waiver for the chunked_prefill test with fp8 quantization and kv_cache reuse, enabling test execution.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name Status Explanation Resolution
Docstring Coverage ⚠️ Warning Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Write docstrings for the functions missing them to satisfy the coverage threshold.
✅ Passed checks (4 passed)
Check name Status Explanation
Title check ✅ Passed The title clearly and specifically describes the main change: fixing DeepSeek-V3-Lite fp8 chunked prefill by using DEEPGEMM backend on SM100/SM103 GPUs, with proper NVBugs ticket reference.
Linked Issues check ✅ Passed Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check ✅ Passed Check skipped because no linked issues were found for this pull request.
Description check ✅ Passed The PR description is comprehensive and well-structured, covering the issue being fixed, the solution implemented, test coverage, and a completed PR checklist.

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/bot run --disable-fail-fast

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PR_Github #44615 [ run ] triggered by Bot. Commit: ab5dc34 Link to invocation

@xinhe-nv xinhe-nv force-pushed the fix/nvbug_6084445 branch from ab5dc34 to 56bbff2 Compare April 21, 2026 08:54
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PR_Github #44615 [ run ] completed with state SUCCESS. Commit: ab5dc34
/LLM/main/L0_MergeRequest_PR pipeline #34996 completed with status: 'FAILURE'

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/bot run --disable-fail-fast

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PR_Github #44741 [ run ] triggered by Bot. Commit: 56bbff2 Link to invocation

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PR_Github #44741 [ run ] completed with state SUCCESS. Commit: 56bbff2
/LLM/main/L0_MergeRequest_PR pipeline #35102 completed with status: 'FAILURE'

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Signed-off-by: Mingyang Jiang <13463932+jmydurant@users.noreply.github.com>
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/bot run --disable-fail-fast

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PR_Github #44888 [ run ] triggered by Bot. Commit: e9dead4 Link to invocation

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PR_Github #44888 [ run ] completed with state SUCCESS. Commit: e9dead4
/LLM/main/L0_MergeRequest_PR pipeline #35222 completed with status: 'FAILURE'

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/bot run --disable-fail-fast

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PR_Github #45097 [ run ] triggered by Bot. Commit: e9dead4 Link to invocation

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PR_Github #45097 [ run ] completed with state SUCCESS. Commit: e9dead4
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@kaiyux kaiyux merged commit dbf5f49 into NVIDIA:main Apr 24, 2026
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