fix: fix illeagel memory access

[dongjiyingdjy]

Summary by CodeRabbit

• Bug Fixes
  • Improved reliability by initializing certain internal tensors with zeros instead of uninitialized values, enhancing stability during specific operations.

Description

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[coderabbitai]

Walkthrough

The update changes the initialization of the block_ids_per_seq tensor in the TrtllmAttentionMetadata class's __post_init__ method. Instead of using torch.empty, which creates an uninitialized tensor, the code now uses torch.zeros to initialize the tensor with zeros. No other logic or interfaces are modified.

Changes

Cohort / File(s)	Change Summary
Attention Tensor Initialization tensorrt_llm/_torch/attention_backend/trtllm.py	Changed tensor initialization from torch.empty to torch.zeros for block_ids_per_seq in TrtllmAttentionMetadata.__post_init__ when enable_flash_mla is true.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~2 minutes

Poem

A tensor once empty, now filled up with care,
Zeros in place, no randomness there.
In the land of attention, this change is quite neat—
A hop and a skip, initialization complete!
🐇✨

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📥 Commits

Reviewing files that changed from the base of the PR and between e11255e and fe4d01a.

📒 Files selected for processing (1)

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

🧰 Additional context used

📓 Path-based instructions (2)

**/*.py

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Files:

• tensorrt_llm/_torch/attention_backend/trtllm.py

**/*.{cpp,h,hpp,cc,cxx,cu,py}

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Files:

• tensorrt_llm/_torch/attention_backend/trtllm.py

🧠 Learnings (2)

📓 Common learnings

Learnt from: yechank-nvidia
PR: NVIDIA/TensorRT-LLM#6254
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:1201-1204
Timestamp: 2025-07-22T09:22:14.726Z
Learning: In TensorRT-LLM's multimodal processing pipeline, shared tensor recovery using `from_shared_tensor()` is only needed during the context phase. Generation requests reuse the already-recovered tensor data and only need to call `strip_for_generation()` to remove unnecessary multimodal data while preserving the recovered tensors. This avoids redundant tensor recovery operations during generation.

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

Learnt from: yechank-nvidia
PR: #6254
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:1201-1204
Timestamp: 2025-07-22T09:22:14.726Z
Learning: In TensorRT-LLM's multimodal processing pipeline, shared tensor recovery using from_shared_tensor() is only needed during the context phase. Generation requests reuse the already-recovered tensor data and only need to call strip_for_generation() to remove unnecessary multimodal data while preserving the recovered tensors. This avoids redundant tensor recovery operations during generation.

🔇 Additional comments (1)

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

581-581: LGTM! Critical fix for illegal memory access.

Changing from torch.empty to torch.zeros prevents illegal memory access by ensuring the block_ids_per_seq tensor is initialized with safe default values instead of potentially garbage memory content. This aligns with the initialization pattern used for kv_block_ids_per_seq below.

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