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[Bugfix] guard missing attn_metadata in KV scales path #24290

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[Bugfix] guard missing attn_metadata in KV scales path #24290

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3 changes: 2 additions & 1 deletion vllm/attention/layer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -241,7 +241,8 @@ def forward(
"""
if self.calculate_kv_scales:
attn_metadata = get_forward_context().attn_metadata
if attn_metadata.enable_kv_scales_calculation:
if (attn_metadata is not None and getattr(
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@ProExpertProg ProExpertProg Sep 8, 2025

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@zpqiu one question: if enable_kv_scales_calculation=True but not set during compilation, wouldn't attention metadata possibly be None during the profile_run (which also triggers compilation) and then the graph is compiled without this, meaning it never runs even if later calculation is enabled?

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@zou3519 zou3519 Sep 8, 2025

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for piecewise cudagraphs, the dummy run runs without attention metadata https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu_model_runner.py#L2535

So the initial compile run has no attention metadata. This means that yes the graph will get compiled without this and it will be wrong later on

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@zou3519 zou3519 Sep 8, 2025 •
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My understanding is that the kv scales get computed on the first real input only and are then used in subsequent inputs.

To actually fix this, I think what we need is that the first real input should run without torch.compile and CUDAGraphs. All subsequent inputs should run with torch.compile and CUDAGraphs.

Then we need to actually make sure the torch.compile'd graph includes the kv scales.

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@zpqiu zpqiu Sep 9, 2025

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for piecewise cudagraphs, the dummy run runs without attention metadata https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu_model_runner.py#L2535

So the initial compile run has no attention metadata. This means that yes the graph will get compiled without this and it will be wrong later on

Thanks for pointing this out—you’re right. I printed the QKV scale values in vllm/v1/attention/backends/flash_attn.py forward() function, and they’re all the default 1.0, which suggests the dynamic scale computation didn’t take effect.

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@zpqiu zpqiu Sep 9, 2025

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My understanding is that the kv scales get computed on the first real input only and are then used in subsequent inputs.

To actually fix this, I think what we need is that the first real input should run without torch.compile and CUDAGraphs. All subsequent inputs should run with torch.compile and CUDAGraphs.

Then we need to actually make sure the torch.compile'd graph includes the kv scales.

Got it—I’ll try that approach. I’ll first sort out the profiling run logic.

attn_metadata, "enable_kv_scales_calculation", False)):
self.calc_kv_scales(query, key, value)
if self.use_output:
output_shape = (output_shape
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