[https://nvbugs/5467548][fix] DeepSeek illegal memory access.

[bobboli]

Summary by CodeRabbit

• Bug Fixes
  • Fixed rare MLA/Flash MLA attention issues caused by stale cached memory by ensuring sequence metadata is cleared when reused. This improves correctness for incremental decoding and long-context generation, yielding more stable, accurate outputs and fewer edge-case failures when caches are reused.

Description

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[coderabbitai]

📝 Walkthrough

Walkthrough

Zero-initializes two MLA per-sequence buffers (block_ids_per_seq and kv_block_ids_per_seq) in TrtllmAttentionMetadata._post_init_with_buffers to ensure reused cached buffers from kv_cache_manager do not contain stale data. No signatures or control flow changed.

Changes

Cohort / File(s)	Summary
Attention metadata buffer init tensorrt_llm/_torch/attention_backend/trtllm.py	After preparing MLA/flash-MLA buffers, call fill_(0) on kv_block_ids_per_seq and block_ids_per_seq to zero preallocated cached buffers. No API or control-flow changes.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Possibly related PRs

• fix: fix illeagel memory access #6437 — Also fixes uninitialized MLA per-sequence buffer (block_ids_per_seq / related buffers) to prevent stale data when reusing cached buffers.

Suggested reviewers

• litaotju
• byshiue
• Naveassaf
• stnie

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• tensorrt_llm/_torch/attention_backend/trtllm.py (1 hunks)

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[bobboli]

/bot run --post-merge

[coderabbitai]

Actionable comments posted: 0

🧹 Nitpick comments (2)

tensorrt_llm/_torch/attention_backend/trtllm.py (2)

702-711: Zero-initializing block_ids_per_seq is the right fix to prevent stale indices.

This avoids reusing garbage when the buffer comes from the cache and num_blocks shrinks, which can manifest as illegal memory access in flash-MLA paths. Consider a tiny nit: use zero_() and leave a brief comment to document the rationale.

-                self.block_ids_per_seq.fill_(0)
+                # Reset cached view to avoid stale block IDs when reusing buffers.
+                self.block_ids_per_seq.zero_()

---

711-719: Likewise, zeroing kv_block_ids_per_seq is correct and consistent.

Same rationale as above. Prefer zero_() and add a short comment for future maintainers.

-                self.kv_block_ids_per_seq.fill_(0)
+                # Reset cached view to avoid stale KV block IDs when reusing buffers.
+                self.kv_block_ids_per_seq.zero_()

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• tensorrt_llm/_torch/attention_backend/trtllm.py (2 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#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.

📚 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/attention_backend/trtllm.py

📚 Learning: 2025-08-14T15:43:23.107Z

Learnt from: MatthiasKohl
PR: NVIDIA/TensorRT-LLM#6904
File: tensorrt_llm/_torch/attention_backend/trtllm.py:259-262
Timestamp: 2025-08-14T15:43:23.107Z
Learning: In TensorRT-LLM's attention backend, tensor parameters in the plan() method are assigned directly without validation (dtype, device, contiguity checks). This maintains consistency across all tensor inputs and follows the pattern of trusting callers to provide correctly formatted tensors.

Applied to files:

• tensorrt_llm/_torch/attention_backend/trtllm.py

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[tensorrt-cicd]

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[tensorrt-cicd]

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[bobboli]

/bot run --post-merge

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[bobboli]

/bot run --post-merge

[tensorrt-cicd]

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