[TRTLLM-8348][feat] Speed up concat k and copy k_nope in context phase using torch.compile #8044
Conversation
Signed-off-by: Tailing Yuan <yuantailing@gmail.com>
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/bot run |
📝 WalkthroughWalkthroughAdds two torch.compile-wrapped helper functions for copy and concatenation, and updates attention forward paths to use these helpers instead of direct in-place copy_ and torch.cat calls. Changes
Sequence Diagram(s)sequenceDiagram
participant Attn as Attention
participant CC as compiled_copy_
participant CAT as compiled_cat
rect rgb(245,248,255)
note over Attn: forward_context_default
Attn->>CC: copy_(k_nope → k)
CC-->>Attn: k updated
end
rect rgb(245,255,245)
note over Attn: forward_context_with_cached_kv
Attn->>CAT: cat([k_nope, k_pe.expand(...)], dim)
CAT-->>Attn: concatenated k
end
rect rgb(255,248,245)
note over Attn: forward_context_with_chunked_prefill
Attn->>CAT: cat([full_k_nope, full_k_pe.expand(...)], dim)
CAT-->>Attn: full_k concatenated
Attn->>CAT: cat(chunked_k parts, dim)
CAT-->>Attn: chunked_k concatenated
end
Estimated code review effort🎯 2 (Simple) | ⏱️ ~10 minutes Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
✅ Passed checks (1 passed)
✨ Finishing touches
🧪 Generate unit tests
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PR_Github #20169 [ run ] triggered by Bot |
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PR_Github #20169 [ run ] completed with state |
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@yuantailing I am curious about in which case compiled cat/copy can improve perf? strided layout? |
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Yes, compiled cat/copy improves perf when layout is strided. For For |
4 participants
Benchmark for DeepSeek-R1 with 8K-1K random dataset on context servers:
direct_copy_kernel_cudatriton_poi_fused_copy_0Please be aware that I have checked that although the time for Copy is reduced for 199us, the time for
MLA.forward_implas well as the time for each layer are not reduced for so much.Summary by CodeRabbit
Description
Test Coverage
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