[https://nvbugs/5522851][fix] Correct the logic to update kv_lens_cuda #7790
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📝 WalkthroughWalkthroughAdds conditional KV-cache lens updates for chunked-context requests in both preprocess and postprocess paths, applied only when attention metadata is TrtllmAttentionMetadata. Introduces num_chunked_ctx_requests and updates kv_lens_cuda slices accordingly to maintain consistency for CUDA graph capture and normal runs. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant C as Caller
participant ME as ModelEngine
participant Pre as _preprocess_inputs
participant Meta as TrtllmAttentionMetadata
participant GPU as kv_lens_cuda
C->>ME: run(...)
ME->>Pre: preprocess(inputs, attn_metadata)
Pre->>Meta: inspect num_ctx_requests, num_seqs,<br/>num_gen_requests, num_chunked_ctx_requests
alt attn_metadata is TrtllmAttentionMetadata
alt num_chunked_ctx_requests > 0
Pre->>GPU: update kv_lens_cuda[num_ctx - num_chunked : num_ctx]<br/>= prev_offsets[:num_chunked]
else No chunked context
Pre->>GPU: update kv_lens_cuda[num_ctx : num_seqs]<br/>= prev_offsets[:num_gen]
end
else Other metadata types
Note over Pre,Meta: No kv_lens_cuda updates
end
ME-->>C: execute model step(s)
rect rgba(220,240,255,0.5)
note over ME: Postprocess path mirrors updates
ME->>ME: _postprocess_inputs(...)
ME->>Meta: re-evaluate counts
alt TrtllmAttentionMetadata
alt num_chunked_ctx_requests > 0
ME->>GPU: update same chunked slice
else No chunked context
ME->>GPU: update generation slice
end
else Other metadata
Note over ME,Meta: No kv_lens_cuda updates
end
end
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Actionable comments posted: 1
🧹 Nitpick comments (1)
tensorrt_llm/_torch/pyexecutor/model_engine.py (1)
1234-1243: Add sanity checks to prevent slice length mismatchesAdd cheap debug assertions before updating kv_lens_cuda to catch off‑by‑one/shape drift. Apply the diff below at this site and add the same check at the other similar site in tensorrt_llm/_torch/pyexecutor/model_engine.py (around lines 1185–1189 and 1226–1230). Note: attn_metadata.num_chunked_ctx_requests is set at ~lines 1698 and 1702.
# Only TrtllmAttentionMetadata has kv_lens_cuda. if isinstance(inputs['attn_metadata'], TrtllmAttentionMetadata): + # Debug sanity checks; safe to remove once proven stable. + if num_chunked_ctx_requests > 0: + assert 0 <= num_chunked_ctx_requests <= num_ctx_requests + else: + assert num_gen_requests == (num_seqs - num_ctx_requests) if num_chunked_ctx_requests > 0:
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tensorrt_llm/_torch/pyexecutor/model_engine.py(1 hunks)
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🧠 Learnings (3)
📓 Common learnings
Learnt from: thorjohnsen
PR: NVIDIA/TensorRT-LLM#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.
Learnt from: eopXD
PR: NVIDIA/TensorRT-LLM#6768
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:2010-2045
Timestamp: 2025-08-21T09:41:49.347Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, updateSequenceCacheBlockOffsets is specifically for updating bookkeeping when blocks are added during the context phase, not for refreshing offsets after detach operations. During detach operations, GenerationRequest::removeFrontBlock handles the necessary cache block bookkeeping internally.
📚 Learning: 2025-08-14T21:04:50.248Z
Learnt from: thorjohnsen
PR: NVIDIA/TensorRT-LLM#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:
tensorrt_llm/_torch/pyexecutor/model_engine.py
📚 Learning: 2025-08-21T09:41:49.347Z
Learnt from: eopXD
PR: NVIDIA/TensorRT-LLM#6768
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:2010-2045
Timestamp: 2025-08-21T09:41:49.347Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, updateSequenceCacheBlockOffsets is specifically for updating bookkeeping when blocks are added during the context phase, not for refreshing offsets after detach operations. During detach operations, GenerationRequest::removeFrontBlock handles the necessary cache block bookkeeping internally.
Applied to files:
tensorrt_llm/_torch/pyexecutor/model_engine.py
🧬 Code graph analysis (1)
tensorrt_llm/_torch/pyexecutor/model_engine.py (2)
tensorrt_llm/_torch/attention_backend/interface.py (2)
num_ctx_tokens(263-264)num_seqs(245-249)tensorrt_llm/_torch/attention_backend/trtllm.py (1)
TrtllmAttentionMetadata(532-1107)
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NVIDIA#7790) Signed-off-by: ziyixiong-nv <219238287+ziyixiong-nv@users.noreply.github.com>
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