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[#6507][fix] Fix precision issue due to KV layout mismatch for split/concat kernels#6917

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yuxianq merged 3 commits intoNVIDIA:mainfromZhangGe6:fix_pd_diff_tp
Nov 13, 2025

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@ZhangGe6ZhangGe6 commentedAug 14, 2025•
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The split/concat kernels expect KV cache in[max_num_pages, 2, num_kv_heads, page_size, head_dim] layout. However, the actually used KV cache layout exposed byKVCacheManager.get_buffers() is[max_num_pages, 2, page_size, num_kv_heads, head_dim]. This layout mismatch causes wrong indexing for split/concat kernels, leading to incorrect transferred prefill KV cache. This patch is a quick fix for flashinfer attn_backend.

Summary by CodeRabbit

  • New Features

    • Option to select KV cache memory layout (NHD or HND) for attention caching.

  • Refactor

    • Default KV cache layout changed to HND.
    • Resource manager API updated to accept and validate layout choices.

  • Chores

    • KV buffer retrieval and reshaping adjusted to honor the chosen layout while preserving compatibility.

  • Tests

    • Unit tests tweaked to reflect KV cache construction specifics.

Description

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coderabbitaibot commentedAug 14, 2025•
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📝 Walkthrough

Walkthrough

Default KV layout for FlashInfer attention changed from "NHD" to "HND". KV cache retrieval now accepts and uses akv_layout parameter; callers (FlashInfer and Star attention) passmetadata.kv_layout when requesting buffers.KVCacheManager.get_buffers validateskv_layout and applies layout-dependent reshape logic for "NHD" and "HND".

Changes

Cohort / File(s)Summary
Attention backend (FlashInfer)
tensorrt_llm/_torch/attention_backend/flashinfer.py		FlashInferAttentionMetadata.kv_layout default switched to"HND";forward_impl now calls KV cache retrieval withkv_layout=metadata.kv_layout; added comments describing the intended HND buffer layout.
Attention backend (Star)
tensorrt_llm/_torch/attention_backend/star_flashinfer.py		StarAttention.forward updated to fetch KV buffers viametadata.kv_cache_manager.get_buffers(..., kv_layout=metadata.kv_layout) (passes layout through).
Resource manager (KV cache buffers)
tensorrt_llm/_torch/pyexecutor/resource_manager.py		KVCacheManager.get_buffers signature extended toget_buffers(self, layer_idx: int, kv_layout: str = "NHD") -> Optional[torch.Tensor]; asserts valid layout; implements two reshape pathways:"NHD" -> [..., kv_factor, tokens_per_block, num_kv_heads, head_dim] and"HND" -> [..., kv_factor, num_kv_heads, tokens_per_block, head_dim]; docs updated.
Tests
tests/unittest/_torch/test_attention.py		Only whitespace adjustments around thepage_size line in two tensor shape constructions; no behavioral or API changes.

Sequence Diagram(s)

sequenceDiagram  participant Fwd as FlashInferAttention.forward_impl  participant Star as StarAttention.forward  participant Meta as FlashInferAttentionMetadata  participant KV as KVCacheManager  Note over Fwd,Star: read metadata.kv_layout and request KV buffers with layout  Fwd->>Meta: read kv_layout  Fwd->>KV: get_buffers(layer_idx, kv_layout)  Star->>Meta: read kv_layout  Star->>KV: get_buffers(layer_idx, kv_layout)  alt kv_layout == "NHD"    KV-->>Fwd: buffers reshaped to [max_pages, kv_factor, tokens_per_block, num_kv_heads, head_dim]    KV-->>Star: buffers reshaped to [max_pages, kv_factor, tokens_per_block, num_kv_heads, head_dim]  else kv_layout == "HND"    KV-->>Fwd: buffers reshaped to [max_pages, kv_factor, num_kv_heads, tokens_per_block, head_dim]    KV-->>Star: buffers reshaped to [max_pages, kv_factor, num_kv_heads, tokens_per_block, head_dim]  end  Fwd->>Fwd: continue attention compute with returned buffers  Star->>Star: continue attention compute with returned buffers
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Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

  • Pay attention to:
    • Correctness of reshape dimension ordering and indexing inget_buffers.
    • All callsites ofget_buffers to ensurekv_layout is passed or default is acceptable.
    • Default change ofFlashInferAttentionMetadata.kv_layout to"HND" and any runtime assumptions about previous"NHD" ordering.

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Title check✅ PassedThe PR title clearly summarizes the main change: fixing a KV layout mismatch issue affecting split/concat kernels. It is specific, concise, and directly related to the primary objective.
Description check✅ PassedThe PR description explains the issue (KV layout mismatch between expected and actual formats), the impact (incorrect indexing and precision errors), and the solution. However, the description is minimal and the Test Coverage section is empty.
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Actionable comments posted: 1

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tensorrt_llm/_torch/attention_backend/flashinfer.py (1)

1-3:Add missing 2025 NVIDIA copyright header

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+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. import math import os import weakref
tensorrt_llm/_torch/pyexecutor/resource_manager.py (1)

1-4:Add missing 2025 NVIDIA copyright header

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+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. import copy import enum import math from abc import ABC, abstractmethod
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tensorrt_llm/_torch/attention_backend/flashinfer.py (1)

59-61:Default kv_layout to HND aligns with split/concat kernels; consider making layout a planning key

Setting kv_layout="HND" matches the kernel expectation [max_num_pages, 2, num_kv_heads, page_size, head_dim]. One caveat: kv_layout currently isn’t part of the planning key (PlanParams), but affects wrapper construction. If kv_layout changes over the lifetime of the metadata, cached wrappers could be reused with the wrong layout. Either ensure kv_layout is immutable per metadata instance or include it in the plan key.

If you opt to make the plan cache layout-aware, I can draft a small patch to add kv_layout to PlanParams and its equality key.

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Learnt from: thorjohnsenPR: NVIDIA/TensorRT-LLM#6910File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0Timestamp: 2025-08-14T21:04:50.208ZLearning: 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.
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tensorrt_llm/_torch/attention_backend/flashinfer.py (1)

498-500:KV buffers now requested with kv_layout — LGTM

Fetching KV cache with kv_layout=metadata.kv_layout is correct and resolves the previous layout mismatch against split/concat kernels.

Note: KVCacheManager.get_buffers is annotated to return Optional[torch.Tensor] but this site assumes a tensor (uses .dtype). Either:

  • tighten the return annotation to torch.Tensor (preferred; see suggested change in resource_manager.py), or
  • add a guard here:
 kv_cache = metadata.kv_cache_manager.get_buffers(     self.layer_idx, kv_layout=metadata.kv_layout)+assert kv_cache is not None, "KV buffers are not allocated"

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Requesting@chuangz0'z review as expert on split/concat kernels.

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@yuxianq to review

@ZhangGe6 Please use "git commit --amend -s" to sign off the commit. Thanks.

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@yuxianq I am working on polishing this PR and will update it tomorrow. Thanks for your suggestions!

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@liji-nv I used "git commit --amend -s" to sign off the commit. Thanks for reminding.

@yuxianq I updated the PR according to review suggestions, please take a review again, thanks.

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Learnt from: thorjohnsenRepo: NVIDIA/TensorRT-LLM PR: 6910File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0Timestamp: 2025-08-14T21:04:50.248ZLearning: 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.
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@yuxianqyuxianq changed the title[Fix][Disaggregated]: Fix precision issue due to KV layout mismatch for split/concat kernels[#6507][fix]: Fix precision issue due to KV layout mismatch for split/concat kernelsNov 7, 2025
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/bot run --disable-fail-fast

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@ZhangGe6 It seems that the last 3 commits have not been signed off, please sign off all commits to pass the DCO check, thanks~

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PR_Github #23783 [ run ] triggered by Bot. Commit:a69b2b5

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PR_Github #23783 [ run ] completed with stateSUCCESS. Commit:a69b2b5
/LLM/main/L0_MergeRequest_PR pipeline #17903 completed with status: 'FAILURE'

@ZhangGe6ZhangGe6 changed the title[#6507][fix]: Fix precision issue due to KV layout mismatch for split/concat kernels[#6507][fix] Fix precision issue due to KV layout mismatch for split/concat kernelsNov 8, 2025
…split/concat kernelsThe split/concat kernels expect KV cache in"[max_num_pages, 2, num_kv_heads, page_size, head_dim]" layout. However,the actually used KV cache layout exposed by"KVCacheManager.get_buffers()" is"[max_num_pages, 2, page_size, num_kv_heads, head_dim]".This layout mismatch causes wrong indexing for split/concat kernels,leading to incorrect transferred prefill KV cache. This patch is a quick fixfor flashinfer attn_backend.Signed-off-by: ZhangGe6 <sjtu.zg123@gmail.com>
@ZhangGe6ZhangGe6 reopened thisNov 8, 2025
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@yuxianq Hi, I trimmed my commits and signed off them, thanks for reminding. In addition, I modified theflashinfer_kv_cache layout intest_attention.py for correctness issue. Please take a review.

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/bot run --disable-fail-fast

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PR_Github #23938 [ run ] triggered by Bot. Commit:5cbcfcb

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PR_Github #23938 [ run ] completed with stateSUCCESS. Commit:5cbcfcb
/LLM/main/L0_MergeRequest_PR pipeline #18025 completed with status: 'FAILURE'

Signed-off-by: Yuxian Qiu <142763828+yuxianq@users.noreply.github.com>
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@ZhangGe6 I have fixed some CI errors for this PR, please allow me to push to your branch or cherry-pick bugfix commit fromhttps://github.com/yuxianq/TensorRT-LLM/commits/fix_pd_diff_tp by yourself, I will rerun the CI. Thanks~

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@ZhangGe6 I have fixed some CI errors for this PR, please allow me to push to your branch or cherry-pick bugfix commit fromhttps://github.com/yuxianq/TensorRT-LLM/commits/fix_pd_diff_tp by yourself, I will rerun the CI. Thanks~

@yuxianq OK, you can push to my branch directly. Feel free to remind me if there is something I can/should do.

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@ZhangGe6 I getAuthentication error: Authentication required: You must have push access to verify locks when I push to your branch, please give me push permission of your repo, thanks~

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@yuxianq Hi, I have sent you an invitation to collaborate on my forked TensorRT-LLM repo (via "Setting -> Access -> Collaborator -> Add people"). Please accept it and try again.

I'm not yet familiar with GitHub operations. Let me know if I missed anything, or I can cherry-pick your bugfix commit later (maybe tonight).

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/bot run --disable-fail-fast

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@ZhangGe6 It works. I have started to rerun CI. Thanks~

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PR_Github #24236 [ run ] triggered by Bot. Commit:be3350b

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PR_Github #24236 [ run ] completed with stateSUCCESS. Commit:be3350b
/LLM/main/L0_MergeRequest_PR pipeline #18282 completed with status: 'FAILURE'

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/bot run --disable-fail-fast

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PR_Github #24301 [ run ] triggered by Bot. Commit:3c949b5

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PR_Github #24301 [ run ] completed with stateSUCCESS. Commit:3c949b5
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/bot run --disable-fail-fast

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PR_Github #24354 [ run ] triggered by Bot. Commit:3c949b5

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PR_Github #24354 [ run ] completed with stateSUCCESS. Commit:3c949b5
/LLM/main/L0_MergeRequest_PR pipeline #18380 completed with status: 'SUCCESS'

@yuxianqyuxianq merged commit49df731 intoNVIDIA:mainNov 13, 2025
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zheyuf pushed a commit to zheyuf/TensorRT-LLM that referenced this pull requestNov 19, 2025
…split/concat kernels (NVIDIA#6917)Signed-off-by: ZhangGe6 <sjtu.zg123@gmail.com>Co-authored-by: Yuxian Qiu <142763828+yuxianq@users.noreply.github.com>
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