[Quantization] Expose input quantization scales through linear kernel contract

[jjjxia]

Purpose

Fused activation-quantization producers currently need consumer-owned quantization scales, but access them through quantization-specific Linear attributes. For example, static FP8 reads linear.input_scale, while NVFP4 previously read the downstream layer's global scale directly.

This PR extends the existing QuantizedActivation consumer contract so linear kernels expose the input quantization scales required by upstream fused producers.

The model-facing API remains unchanged:

maybe_fused_act_quant(act_fn, x, self.down_proj)

The main changes are:

• add InputQuantScales for consumer-owned input quantization scales;
• expose a scale accessor alongside input_quant_key;
• move FP8 and NVFP4 parameter lookup into their linear-kernel implementations;
• resolve scales at use time instead of caching tensors when the contract is installed, since weight post-processing may later create or replace those parameters;
• remove direct accesses to quantization-specific scale attributes from fused_act_quant.py.

This also corrects the NVFP4 manual-fusion path to use input_global_scale_inv, matching the regular NVFP4 input-quantization path. The previous test used a global scale of 1.0, which made the scale and its reciprocal indistinguishable; the updated test covers non-unit values.

I searched open PRs for input_quant_scales and input quantization scales and found no PR implementing the same consumer-scale contract.

Test Plan

Run the affected contract and fusion tests:

.venv/bin/python -m pytest \
  tests/fusion/test_quant_activation_contract.py \
  tests/kernels/test_fused_quant_activation.py \
  tests/compile/passes/test_silu_mul_quant_manual_fusion.py \
  -v

The contract tests cover eager and torch.compile(fullgraph=True) execution and verify that scale lookup observes parameters replaced after the contract is installed.

The NVFP4 fusion test uses non-unit global-scale values so a scale/inverse-scale mix-up is observable.

Test Result

140 passed, 4 skipped

The four skipped cases require SM100+ hardware for NVFP4 execution; the available GPU does not support those kernels.

The FP8 fusion path passes with the real kernel scale accessor. The contract tests also pass with parameter replacement after expose_input_quant_key() and under torch.compile(fullgraph=True).

During test validation, replacing the late-bound accessor with a cached scale causes the parameter-replacement tests to fail, and using input_global_scale instead of input_global_scale_inv causes the non-unit NVFP4 cases to fail.

No user-facing documentation update is required because this changes an internal quantization contract.

AI Assistance

OpenAI Codex assisted with implementation, testing, source inspection, and review. I reviewed the changed code and am responsible for understanding and defending the change end-to-end.

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• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

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Configuration used: Repository UI

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Run ID: e83c5540-2031-4017-9e72-6b041a1a4b85

📥 Commits

Reviewing files that changed from the base of the PR and between f4eccda and 7e1697ce404d69e18ce0390fce5200fe3de7de38.

📒 Files selected for processing (8)

• tests/compile/passes/test_silu_mul_quant_manual_fusion.py
• tests/fusion/test_quant_activation_contract.py
• tests/kernels/test_fused_quant_activation.py
• vllm/model_executor/kernels/linear/nvfp4/base.py
• vllm/model_executor/kernels/linear/nvfp4/flashinfer.py
• vllm/model_executor/kernels/linear/scaled_mm/ScaledMMLinearKernel.py
• vllm/model_executor/layers/fusion/fused_act_quant.py
• vllm/model_executor/layers/fusion/quant_activation.py

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📝 Summary

Summary by CodeRabbit

• New Features

  • Added a unified interface for retrieving activation quantization scales across supported kernels.
  • Added support for static and dynamic quantization scales in fused activation-quantization workflows.
  • Improved compatibility with parameter replacement and compiled execution.

• Bug Fixes

  • Fused quantization now uses current layer scale values, improving correctness after parameter loading or replacement.
  • NVFP4 and FP8 fusion paths now consistently resolve their required input scales.

Walkthrough

The change adds InputQuantScales and runtime scale accessors to the quantization contract. FP8 and NVFP4 kernels implement the accessors. Fused paths use them, and tests cover parameter replacement, compilation, and non-unit global scales.

Changes

Input quantization scale contract

Layer / File(s)	Summary
Scale contract vllm/model_executor/layers/fusion/quant_activation.py, vllm/model_executor/kernels/linear/nvfp4/base.py, vllm/model_executor/kernels/linear/scaled_mm/ScaledMMLinearKernel.py	Defines InputQuantScales and stores a layer accessor for resolving current scale parameters. Kernel documentation requires scale accessors when kernels expose quantization keys.
Kernel scale accessors vllm/model_executor/kernels/linear/nvfp4/flashinfer.py, vllm/model_executor/kernels/linear/scaled_mm/ScaledMMLinearKernel.py	NVFP4 kernels return global_scale_inv. FP8 kernels return the configured static input scale.
Fused quantization integration vllm/model_executor/layers/fusion/fused_act_quant.py	FP8 and NVFP4 fused paths obtain required scales through get_input_quant_scales.
Contract and fusion tests tests/fusion/test_quant_activation_contract.py, tests/kernels/test_fused_quant_activation.py, tests/compile/passes/test_silu_mul_quant_manual_fusion.py	Tests cover parameter replacement, torch.compile, mock accessors, and NVFP4 dynamic fusion with multiple global scale values.

Estimated code review effort: 3 (Moderate) | ~20 minutes

Merge Risk: ⚪ Minimal · up to 7e169

This change exposes input quantization scales through the linear-kernel contract and updates fused FP8/NVFP4 activation quantization to use it. The covered replacement, compilation, and non-unit scale paths indicate no remaining merge-blocking risk.

Sequence Diagram(s)

sequenceDiagram
  participant FusedActivationQuant
  participant LinearLayer
  participant Kernel
  FusedActivationQuant->>LinearLayer: get_input_quant_scales(linear)
  LinearLayer->>Kernel: resolve current scale parameters
  Kernel-->>LinearLayer: InputQuantScales
  LinearLayer-->>FusedActivationQuant: static_scale or global_scale_inv
  FusedActivationQuant->>FusedActivationQuant: quantize fused activation

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🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 52.63% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 19 functions across 8 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and concisely describes the primary change: exposing input quantization scales through the linear kernel contract.
Description check	✅ Passed	The description directly explains the contract changes, scale lookup behavior, NVFP4 correction, testing, and validation results.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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🛠️ Fix failing CI checks 💡

• Create stacked PR
• Commit on current branch

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @jjjxia.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @jjjxia.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork