[TRTLLM-8777][feat] Update DeepGEMM to the latest commit to include optimizations for DeepSeek-v3.2 #9380
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📝 WalkthroughWalkthroughThe pull request updates the deepgemm dependency hash in the build configuration to reference the swapab_sm100 branch and increases test dimensionality in DSA indexer tests while adjusting the ke offset calculation. Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~8 minutes
Pre-merge checks and finishing touches❌ Failed checks (1 inconclusive)
✅ Passed checks (2 passed)
✨ Finishing touches
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📒 Files selected for processing (2)
3rdparty/CMakeLists.txt(1 hunks)tests/unittest/_torch/attention/sparse/test_dsa_indexer.py(2 hunks)
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🧠 Learnings (2)
📚 Learning: 2025-08-14T21:04:50.248Z
Learnt from: thorjohnsen
Repo: NVIDIA/TensorRT-LLM PR: 6910
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-14T21:04:50.248Z
Learning: In KV cache onboarding logic during prefill in cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, when calculating which blocks fall within the attention window, use getTokensPerBlock() to advance token indices rather than block->getUniqueTokens().size(), because the calculation needs to consider the post-prefill state where blocks will be filled to capacity, not their current token count.
Applied to files:
tests/unittest/_torch/attention/sparse/test_dsa_indexer.py
📚 Learning: 2025-09-29T15:14:28.503Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation with asserts for total size and TP divisibility.
Applied to files:
tests/unittest/_torch/attention/sparse/test_dsa_indexer.py
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🔇 Additional comments (2)
3rdparty/CMakeLists.txt (1)
42-42: LGTM! DeepGEMM dependency update looks good.The commit hash update to include DeepSeek-v3.2 optimizations is appropriately documented with the branch reference.
tests/unittest/_torch/attention/sparse/test_dsa_indexer.py (1)
311-313: LGTM! Test dimension increases align with DeepGEMM optimizations.The larger test dimensions (num_heads: 32→64, seq_len: 512→2048, seq_len_kv: 1024→4096) appropriately validate the new larger MMA tile size optimizations mentioned in the PR objectives.
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Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
Signed-off-by: Fanrong Li <23290157+lfr-0531@users.noreply.github.com>
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Summary by CodeRabbit
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Description
Optimizations:
Fix the failed test in #9349.
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PR Checklist
Please review the following before submitting your PR:
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PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
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