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[https://nvbugs/5670108][fix] Fix overlap scheduler race condition in…#10610

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SimengLiu-nv merged 2 commits intoNVIDIA:mainfrom
SimengLiu-nv:fix-kvcache-rewind
Jan 20, 2026
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[https://nvbugs/5670108][fix] Fix overlap scheduler race condition in…#10610
SimengLiu-nv merged 2 commits intoNVIDIA:mainfrom
SimengLiu-nv:fix-kvcache-rewind

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@SimengLiu-nv SimengLiu-nv commented Jan 12, 2026

… KV cache rewind.

Skip CONTEXT_INIT requests when rewinding KV cache for rejected draft tokens. With overlap scheduler, when the scheduler pauses a request and frees its KV cache at iteration N, the previous batch (N-1) may still try to rewind KV cache for a request whose state was reset to CONTEXT_INIT, causing "unordered_map::at" IndexError.

Summary by CodeRabbit

  • Refactor
    • Optimized internal resource management logic for improved efficiency in token handling operations.

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@SimengLiu-nv SimengLiu-nv requested a review from a team as a code owner January 12, 2026 21:10
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📝 Walkthrough

Walkthrough

Modified KV cache rewind logic in resource_manager.py to skip rewinding for requests in GENERATION_COMPLETE or CONTEXT_INIT states, and rewind only when py_rewind_len exceeds zero for other states. Added explanatory comments detailing the logic and its relation to overlap scheduling.

Changes

Cohort / File(s) Summary
KV Cache Rewind Logic
tensorrt_llm/_torch/pyexecutor/resource_manager.py
Simplified conditional logic in update_resources method for draft token rewinding: now explicitly skips GENERATION_COMPLETE and CONTEXT_INIT states before checking py_rewind_len > 0. Added clarifying comments explaining skipped states and overlap scheduling rationale.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~12 minutes

🚥 Pre-merge checks | ❌ 3
❌ Failed checks (2 warnings, 1 inconclusive)
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.
Description check ⚠️ Warning PR description is incomplete. It contains the fix explanation but lacks required sections: proper PR title format, detailed Description section, and Test Coverage details. Add a properly formatted PR title following [type] pattern, expand Description section with issue/solution details, and document relevant test coverage.
Title check ❓ Inconclusive The title is truncated and incomplete (ends with 'in…'), making it impossible to fully assess. However, the visible portion clearly relates to the bug fix described in the PR: fixing an overlap scheduler race condition in KV cache rewind. Complete the pull request title by removing the truncation marker and providing the full title text.

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Actionable comments posted: 1

🤖 Fix all issues with AI agents
In @tensorrt_llm/_torch/pyexecutor/resource_manager.py:
- Around line 607-619: Add the required NVIDIA copyright header (with the year
of latest meaningful modification) to the top of
tensorrt_llm/_torch/pyexecutor/resource_manager.py; ensure the header follows
the project's standard header format and licensing text used across TensorRT-LLM
source files, and keep the rest of the file (including functions/methods
referencing LlmRequestState, scheduled_batch.generation_requests, and
rewind_kv_cache / request.py_rewind_len) unchanged.
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Reviewing files that changed from the base of the PR and between 18a3376 and 1392fd3.

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  • tensorrt_llm/_torch/pyexecutor/resource_manager.py
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🧠 Learnings (7)
📓 Common learnings
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.
Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 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: 2025-08-21T09:41:49.347Z
Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 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/resource_manager.py
📚 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:

  • tensorrt_llm/_torch/pyexecutor/resource_manager.py
📚 Learning: 2025-08-20T06:48:45.368Z
Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6768
File: cpp/include/tensorrt_llm/batch_manager/kvCacheManager.h:0-0
Timestamp: 2025-08-20T06:48:45.368Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, updateSequenceCacheBlockOffsets is only called when adding a sequence, not during detach operations. During detach, the cache block bookkeeping is handled by GenerationRequest::removeFrontBlock.

Applied to files:

  • tensorrt_llm/_torch/pyexecutor/resource_manager.py
📚 Learning: 2025-08-15T06:46:54.897Z
Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6767
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-15T06:46:54.897Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp addToken function, newly allocated blocks are unshared by design. The beam search path in addToken (when sequence.getNumTokens() > windowSize) is currently broken/non-functional with SWA, so the block allocation doesn't follow a shared-then-unshared pattern.

Applied to files:

  • tensorrt_llm/_torch/pyexecutor/resource_manager.py
📚 Learning: 2025-08-15T06:46:53.813Z
Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6767
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-15T06:46:53.813Z
Learning: In the TensorRT-LLM KV cache manager, SWA (Sliding Window Attention) combined with beam search is currently in a broken/non-functional state and is planned for future rework. During preparatory refactoring phases, code related to SWA+beam search may intentionally remain in a non-working state until the broader rework is completed.

Applied to files:

  • tensorrt_llm/_torch/pyexecutor/resource_manager.py
📚 Learning: 2025-12-12T03:27:08.565Z
Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 9655
File: tensorrt_llm/_torch/pyexecutor/sampler.py:3031-3031
Timestamp: 2025-12-12T03:27:08.565Z
Learning: In files under tensorrt_llm/_torch/pyexecutor, avoid accessing torch.Tensor objects inside for-loops when iterating over requests. Convert batched tensors to Python lists beforehand using tensor.tolist(), and then iterate over those lists. This improves performance by reducing tensor-bound operations inside hot loops. Apply this pattern to similar code paths that process batches to access simple Python data structures (lists) inside loops.

Applied to files:

  • tensorrt_llm/_torch/pyexecutor/resource_manager.py
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tensorrt_llm/_torch/pyexecutor/resource_manager.py (1)
cpp/include/tensorrt_llm/batch_manager/llmRequest.h (1)
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⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
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/bot run --add-multi-gpu-test --disable-fail-fast

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LGTM

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/bot run --add-multi-gpu-test --disable-fail-fast

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PR_Github #31624 [ run ] triggered by Bot. Commit: 1392fd3

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PR_Github #31624 [ run ] completed with state SUCCESS. Commit: 1392fd3
/LLM/main/L0_MergeRequest_PR pipeline #24457 completed with status: 'FAILURE'

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/bot run --add-multi-gpu-test --disable-fail-fast

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PR_Github #32017 [ run ] triggered by Bot. Commit: 3ba1685

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PR_Github #32017 [ run ] completed with state SUCCESS. Commit: 3ba1685
/LLM/main/L0_MergeRequest_PR pipeline #24808 completed with status: 'FAILURE'

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/bot run --add-multi-gpu-test

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PR_Github #32066 [ run ] triggered by Bot. Commit: 3b6b174

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PR_Github #32066 [ run ] completed with state SUCCESS. Commit: 3b6b174
/LLM/main/L0_MergeRequest_PR pipeline #24852 completed with status: 'FAILURE'

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… KV cache rewind.

Skip CONTEXT_INIT requests when rewinding KV cache for rejected draft
tokens. With overlap scheduler, when the scheduler pauses a request and
frees its KV cache at iteration N, the previous batch (N-1) may still
try to rewind KV cache for a request whose state was reset to
CONTEXT_INIT, causing "unordered_map::at" IndexError.

Signed-off-by: SimengLiu-nv <simengl@nvidia.com>
…und 30 minutes.

Signed-off-by: SimengLiu-nv <simengl@nvidia.com>
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/bot run --add-multi-gpu-test

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PR_Github #32169 [ run ] triggered by Bot. Commit: e3dd67d

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PR_Github #32169 [ run ] completed with state SUCCESS. Commit: e3dd67d
/LLM/main/L0_MergeRequest_PR pipeline #24942 completed with status: 'FAILURE'

⚠️ Action Required:

  • Please check the failed tests and fix your PR
  • If you cannot view the failures, ask the CI triggerer to share details
  • Once fixed, request an NVIDIA team member to trigger CI again

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/bot run --add-multi-gpu-test

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PR_Github #32326 [ run ] triggered by Bot. Commit: e3dd67d

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PR_Github #32326 [ run ] completed with state SUCCESS. Commit: e3dd67d
/LLM/main/L0_MergeRequest_PR pipeline #25052 completed with status: 'SUCCESS'

@SimengLiu-nv SimengLiu-nv merged commit 3c8ed19 into NVIDIA:main Jan 20, 2026
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