fix(quantization): Fix AWQ dequantize on Intel XPU and refactor AutoAWQ config

[Alex-ai-future]

Purpose

This PR fixes the AWQ dequantize issue on Intel XPU (#41469) and refactors the AutoAWQ config for better XPU performance support. The refactoring approach is inspired by #38288 (consolidate GPTQ).

Key changes:

1. Fix AWQ dequantize on XPU: Added AutoAWQXPULinearMethod using Intel's oneDNN int4 GEMM kernel for proper XPU support
2. Unified AutoAWQConfig: Merged awq_marlin.py and awq.py into a single config supporting multiple backends (Triton, Marlin, XPU)
3. Fixed override_quantization_method: Added explicit quant_method == "awq" check to prevent incorrect override of non-AWQ models
4. Platform-aware backend selection: get_quant_method now automatically selects the optimal kernel based on platform

AWQ Routing Logic

Config Mapping

"awq" | "awq_marlin" | "auto_awq" → AutoAWQConfig
"cpu_awq" → CPUAWQConfig

LinearMethod Selection

┌─ is_xpu()? ────── Yes → AutoAWQXPULinearMethod  (oneDNN int4 GEMM)
│
├─ is_cuda()? ───── Yes ──┐
│                         │
│                    use_marlin? ─── Yes ──┬─ layer compatible? → AutoAWQMarlinLinearMethod
│                    (no batch invariant,  │                       (Marlin kernel)
│                     SM ≥ 75, valid       │
│                     group_size)          └─ layer not compatible → AutoAWQLinearMethod
│                                                                     (Triton/C++ fallback)
│
├─ AMD ROCm ────── VLLM_USE_TRITON_AWQ=1 (auto-set)
│                  → AutoAWQLinearMethod → Triton kernel
│
└─ Other/CPU ──────→ AutoAWQLinearMethod or CPUAWQLinearMethod

MoE Layer Routing

isinstance(layer, RoutedExperts)?
├─ Yes ──┬─ layer skipped? → UnquantizedFusedMoEMethod
│        │
│        ├─ check_moe_marlin_supports_layer()?
│        │   ├─ Yes → AutoAWQMoEMethod (Marlin-based WNA16)
│        │   └─ No  → MoeWNA16Config.get_quant_method()
│        │              → select_wna16_moe_backend() → Triton/Oracle backend
│        │
│        └─ AutoAWQMoEMethod internally uses:
│             select_wna16_moe_backend() based on platform/weight bits
└─ No → continue to Linear layer routing

Platform Summary

Platform	LinearMethod	Kernel	Notes
NVIDIA CUDA (Marlin supported)	AutoAWQMarlinLinearMethod	Marlin	Fastest, Tensor Core optimized
NVIDIA CUDA (Marlin unsupported)	AutoAWQLinearMethod	C++ AWQ or Triton	Falls back if layer/group_size incompatible
AMD ROCm	AutoAWQLinearMethod	Triton	VLLM_USE_TRITON_AWQ auto-set to 1
Intel XPU	AutoAWQXPULinearMethod	oneDNN int4 GEMM	Platform-specific kernel
CPU	CPUAWQLinearMethod	CPU kernel	Separate config (cpu_awq)

MoE Summary

Condition	Method	Kernel
Marlin compatible	AutoAWQMoEMethod	Marlin-based WNA16
Not compatible	MoeWNA16Config	Triton/Oracle WNA16
Layer skipped	UnquantizedFusedMoEMethod	No quantization

Fallback Priority

Fastest ───────────────────────────────────→ Slowest
Marlin → oneDNN (XPU) → C++ AWQ → Triton
(CUDA)   (Intel)        (CUDA)    (Cross-platform)

Test Plan

.venv/bin/python -m pytest tests/quantization/test_auto_awq.py -v

Test Result

All 7 tests pass (7/7).

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[gemini-code-assist]

Code Review

This pull request adds a verify_quantization method to the XPUPlatform class to automatically enable Triton AWQ for AWQ quantization. Feedback identifies that the XPUPlatform class lacks the supported_quantization attribute, which prevents proper validation by the base class. Additionally, the reviewer pointed out that modifying os.environ directly may fail to update cached environment variables in the driver process, potentially causing initialization or profiling issues.

[gemini-code-assist]

The XPUPlatform class is missing the supported_quantization attribute definition. Currently, it inherits an empty list from the base Platform class, which causes super().verify_quantization(quant) (on line 383) to skip all validation. To properly enforce quantization support on XPU and utilize the base class's verification logic, you should define supported_quantization in this class (e.g., including "awq" and "fp8").

[gemini-code-assist]

Modifying os.environ["VLLM_USE_TRITON_AWQ"] here is likely to be ignored by the vllm.envs module in the current process due to environment variable caching.

In vLLM, environment variables are cached early during startup (via enable_envs_cache() in entrypoints). When envs.VLLM_USE_TRITON_AWQ is accessed on line 384, it populates the cache if it hasn't been already. Setting os.environ on line 389 will not update this cached value. Consequently, any subsequent code in the driver process (including single-process or TP=1 execution) that checks envs.VLLM_USE_TRITON_AWQ will still see the old value (likely False). While spawned worker processes will see the updated environment variable because XPU uses spawn, the inconsistency in the driver process can lead to incorrect kernel selection or failures during initialization and profiling.

[Alex-ai-future]

This is a very safe fix. I hope you can check. CC @jikunshang @xuechendi

[Alex-ai-future]

Try to handle it with torch.ops._xpu_C.int4_gemm_w4a16

[mergify]

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

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, @Alex-ai-future.

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, @Alex-ai-future.

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

[mayuyuace]

@jikunshang
I have tested Qwen/Qwen2.5-Omni-7B-AWQ.
Result is meaningful:
tp1:

tp2:

@Alex-ai-future
Can you resolve the code conflicts?

[jikunshang]

please fix this error... thanks
https://buildkite.com/vllm/intel-ci/builds/4927#019ed87d-1732-4272-9292-073297e5bf1e

[Alex-ai-future]

@jikunshang CI update:

• Quantization: Fixed (test_auto_round.py import path updated)
• Intel CI: Infra issue — pipeline-gen venv pip upgrade failed (not code-related)
• AMD CI: exit 139, but PR [Kernel] Add PDL support for DeepGEMM kernel #46006 also has AMD CI failures in the same build — likely project-level AMD CI issue

Could you re-trigger or help investigate the Intel/AMD CI infra? Thanks!