[Minor] Zero-initialize attn output buffer

[WoosukKwon]

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Purpose

Currently, we use torch.empty for initializing the attention output buffer. This could cause a numerical issue in the initial memory profiling run, because all the subsequent operators get uninitialized inputs that could contain NaNs. This PR fixes this by using torch.zeros instead.

Test Plan

Test Result

(Optional) Documentation Update

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[gemini-code-assist]

Summary of Changes

Hello @WoosukKwon, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a potential numerical instability issue within the attention mechanism by ensuring that the output buffer is properly zero-initialized. This change aims to prevent the propagation of uninitialized values, which could lead to NaNs, particularly during memory profiling runs.

Highlights

• Numerical Stability: Changed the attention output buffer initialization in vllm/attention/layer.py from torch.empty to torch.zeros to prevent potential numerical issues (e.g., NaNs) from uninitialized memory.

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[gemini-code-assist]

Code Review

This PR replaces torch.empty with torch.zeros for initializing the attention output buffer in vllm/attention/layer.py. This change mitigates potential numerical issues by ensuring the buffer starts with a clean state, which is especially important during initial memory profiling runs where uninitialized values could lead to incorrect profiling results or NaNs.

[robertgshaw2-redhat]

was this causing a problem? just curious

[WoosukKwon]

@robertgshaw2-redhat Potentially. If any kernel assumes that the value is non-NaN, this could be problematic.

[houseroad]

Looks good.

[BoyuanFeng]

@WoosukKwon do you have a repro for issues with torch.empty?

w/ torch.empty, we allocate but do not initialize. CUDAGraph would remove this allocation time.
w/ torch.zero, we allocate AND initialize, which requires an extra cuda/triton kernel. CUDAGraph cannot remove this kernel. This kernel adds ~1 us latency, which is on-par with a layer norm or rope kernel (~1.6 us).