[AutoRound] Support WAN2.2 W4A16 quantization model

.buildkite/test-nightly.yml

@@ -959,7 +959,7 @@ steps:
       - label: ":full_moon: Diffusion X2V · Function Test"
         timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_hunyuan_video_15_expansion.py  -m "full_model and cuda" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_hunyuan_video_15_expansion.py tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and cuda" --run-level "full_model"
         agents:
           queue: "mithril-h100-pool"
         plugins:

docs/user_guide/quantization/autoround.md

@@ -32,7 +32,9 @@ guide. AutoRound is Intel-supported.
 |-------|------------|-------|--------|---------|
 | FLUX.1-dev | `vllm-project-org/FLUX.1-dev-AutoRound-w4a16` | Diffusion transformer | W4A16 | GPTQ-Marlin or Intel-supported AutoRound backend |
 | Qwen-Image | Not listed | Diffusion transformer | W4A16 | Not validated |
-| Wan2.2 | Not listed | Diffusion transformer | W4A16 | Not validated |
+| Wan2.2-I2V | `Intel/Wan2.2-I2V-A14B-Diffusers-int4-AutoRound` | Diffusion transformer | W4A16 | GPTQ-Marlin or Intel-supported AutoRound backend |
+| Wan2.2-T2V | `Intel/Wan2.2-T2V-A14B-Diffusers-int4-AutoRound` | Diffusion transformer | W4A16 | GPTQ-Marlin or Intel-supported AutoRound backend |
+| Wan2.2-TI2V | `Intel/Wan2.2-TI2V-5B-Diffusers-int4-AutoRound` | Diffusion transformer | W4A16 | GPTQ-Marlin or Intel-supported AutoRound backend |
 
 ### Multi-Stage Omni/TTS Model (Qwen3-Omni, Qwen3-TTS)
 

docs/user_guide/quantization/mxfp4.md

@@ -397,7 +397,7 @@ names** discovered in Step 1. No code changes to the model are required.
 ```python
 omni = Omni(
     model="/path/to/your-model",
-    quantization_config={
+    quantization={
         "method": "mxfp4_dualscale",
         "ignored_layers": [
             "blocks.0.attn1.to_qkv",   # runtime name, not diffusers name

tests/diffusion/models/wan2_2/test_wan22_quant_config_propagation.py

@@ -0,0 +1,301 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for Wan2.2 quant_config propagation through transformer creation.
+
+Tests cover:
+- create_transformer_from_config passes quant_config and prefix
+- create_vace_transformer_from_config passes quant_config and prefix
+- set_tf_model_config propagates quant_config to OmniDiffusionConfig
+- patch_wan_rms_norm safely iterates sys.modules with concurrent modifications
+- I2V transformer_2 quant_config is built from config dict
+"""
+
+import sys
+from types import SimpleNamespace
+
+import pytest
+from pytest_mock import MockerFixture
+
+import vllm_omni.diffusion.models.wan2_2.pipeline_wan2_2 as wan22_module
+import vllm_omni.diffusion.models.wan2_2.pipeline_wan2_2_vace as wan22_vace_module
+from vllm_omni.diffusion.models.wan2_2.pipeline_wan2_2 import (
+    create_transformer_from_config,
+)
+from vllm_omni.diffusion.models.wan2_2.pipeline_wan2_2_vace import (
+    create_vace_transformer_from_config,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
+
+
+# ---------------------------------------------------------------------------
+# create_transformer_from_config: quant_config / prefix forwarding
+# ---------------------------------------------------------------------------
+
+
+class TestCreateTransformerQuant:
+    """Verify quant_config and prefix are forwarded to WanTransformer3DModel."""
+
+    def test_quant_config_passed_through(self, mocker: MockerFixture):
+        captured = {}
+
+        class FakeTransformer:
+            def __init__(self, **kwargs):
+                captured.update(kwargs)
+
+        mocker.patch.object(wan22_module, "WanTransformer3DModel", FakeTransformer)
+
+        fake_qc = mocker.MagicMock()
+        create_transformer_from_config(
+            {"patch_size": [1, 2, 2], "num_layers": 2},
+            quant_config=fake_qc,
+        )
+        assert captured.get("quant_config") is fake_qc
+
+    def test_prefix_passed_through(self, mocker: MockerFixture):
+        captured = {}
+
+        class FakeTransformer:
+            def __init__(self, **kwargs):
+                captured.update(kwargs)
+
+        mocker.patch.object(wan22_module, "WanTransformer3DModel", FakeTransformer)
+
+        create_transformer_from_config(
+            {"patch_size": [1, 2, 2]},
+            prefix="model.transformer.",
+        )
+        assert captured.get("prefix") == "model.transformer."
+
+    def test_quant_config_none_by_default(self, mocker: MockerFixture):
+        captured = {}
+
+        class FakeTransformer:
+            def __init__(self, **kwargs):
+                captured.update(kwargs)
+
+        mocker.patch.object(wan22_module, "WanTransformer3DModel", FakeTransformer)
+
+        create_transformer_from_config({"patch_size": [1, 2, 2]})
+        # When quant_config is None and prefix is "", they are not added
+        assert "quant_config" not in captured or captured["quant_config"] is None
+
+    def test_quant_config_and_prefix_together(self, mocker: MockerFixture):
+        captured = {}
+
+        class FakeTransformer:
+            def __init__(self, **kwargs):
+                captured.update(kwargs)
+
+        mocker.patch.object(wan22_module, "WanTransformer3DModel", FakeTransformer)
+
+        fake_qc = mocker.MagicMock()
+        create_transformer_from_config(
+            {"patch_size": [1, 2, 2], "num_attention_heads": 4},
+            quant_config=fake_qc,
+            prefix="blocks.",
+        )
+        assert captured["quant_config"] is fake_qc
+        assert captured["prefix"] == "blocks."
+
+
+# ---------------------------------------------------------------------------
+# create_vace_transformer_from_config: quant_config / prefix forwarding
+# ---------------------------------------------------------------------------
+
+
+class TestCreateVaceTransformerQuant:
+    """Verify quant_config and prefix are forwarded to WanVACETransformer3DModel."""
+
+    def test_quant_config_passed_through(self, mocker: MockerFixture):
+        captured = {}
+
+        class FakeVACETransformer:
+            def __init__(self, **kwargs):
+                captured.update(kwargs)
+
+        mocker.patch.object(wan22_vace_module, "WanVACETransformer3DModel", FakeVACETransformer)
+
+        fake_qc = mocker.MagicMock()
+        create_vace_transformer_from_config(
+            {"patch_size": [1, 2, 2], "num_layers": 2},
+            quant_config=fake_qc,
+        )
+        assert captured.get("quant_config") is fake_qc
+
+    def test_prefix_passed_through(self, mocker: MockerFixture):
+        captured = {}
+
+        class FakeVACETransformer:
+            def __init__(self, **kwargs):
+                captured.update(kwargs)
+
+        mocker.patch.object(wan22_vace_module, "WanVACETransformer3DModel", FakeVACETransformer)
+
+        create_vace_transformer_from_config(
+            {"patch_size": [1, 2, 2]},
+            prefix="vace.",
+        )
+        assert captured.get("prefix") == "vace."
+
+
+# ---------------------------------------------------------------------------
+# set_tf_model_config: propagation of quant_config
+# ---------------------------------------------------------------------------
+
+
+class TestSetTfModelConfig:
+    """Test that set_tf_model_config propagates quant_config correctly."""
+
+    def _make_od_config(self):
+        """Create a minimal OmniDiffusionConfig-like object for testing."""
+        from vllm_omni.diffusion.data import OmniDiffusionConfig
+
+        cfg = object.__new__(OmniDiffusionConfig)
+        cfg.quantization_config = None
+        cfg.tf_model_config = None
+        return cfg
+
+    def test_propagates_quant_config_when_none(self, mocker: MockerFixture):
+        cfg = self._make_od_config()
+        fake_qc = mocker.MagicMock()
+        tf_config = SimpleNamespace(quant_config=fake_qc, quant_method="auto-round")
+
+        cfg.set_tf_model_config(tf_config)
+
+        assert cfg.tf_model_config is tf_config
+        assert cfg.quantization_config is fake_qc
+
+    def test_does_not_overwrite_existing_quantization_config(self, mocker: MockerFixture):
+        cfg = self._make_od_config()
+        existing_qc = mocker.MagicMock()
+        cfg.quantization_config = existing_qc
+        tf_config = SimpleNamespace(quant_config=mocker.MagicMock())
+
+        cfg.set_tf_model_config(tf_config)
+
+        assert cfg.tf_model_config is tf_config
+        assert cfg.quantization_config is existing_qc  # not overwritten
+
+    def test_no_propagation_when_tf_quant_config_is_none(self, mocker: MockerFixture):
+        cfg = self._make_od_config()
+        tf_config = SimpleNamespace(quant_config=None)
+
+        cfg.set_tf_model_config(tf_config)
+
+        assert cfg.tf_model_config is tf_config
+        assert cfg.quantization_config is None
+
+
+# ---------------------------------------------------------------------------
+# patch_wan_rms_norm: sys.modules snapshot safety
+# ---------------------------------------------------------------------------
+
+
+class TestPatchWanRmsNorm:
+    """Test that patch_wan_rms_norm doesn't raise on concurrent module registration."""
+
+    def test_patches_modules_with_wan_rms_norm(self):
+        from vllm_omni.diffusion.layers.norm import RMSNormVAE
+        from vllm_omni.diffusion.models.wan2_2.patch_diffusers import patch_wan_rms_norm
+
+        # Create a fake module that has WanRMS_norm
+        fake_module = SimpleNamespace(WanRMS_norm=lambda x: x)
+        sys.modules["_test_fake_wan_module"] = fake_module
+
+        try:
+            patch_wan_rms_norm()
+            assert fake_module.WanRMS_norm is RMSNormVAE
+        finally:
+            del sys.modules["_test_fake_wan_module"]
+
+    def test_no_error_when_modules_change_during_iteration(self):
+        """Regression test: list() snapshot prevents RuntimeError."""
+        from vllm_omni.diffusion.models.wan2_2.patch_diffusers import patch_wan_rms_norm
+
+        # Simulate a module being added during iteration by a side effect
+        original_items = sys.modules.items
+
+        def items_with_side_effect():
+            # This would cause RuntimeError without list() snapshot
+            result = list(original_items())
+            # Add a new module to simulate concurrent modification
+            sys.modules["_test_dynamic_module"] = SimpleNamespace()
+            return result
+
+        try:
+            # The function uses list(sys.modules.items()) so it takes a snapshot
+            # Just verify it doesn't raise
+            patch_wan_rms_norm()
+        finally:
+            sys.modules.pop("_test_dynamic_module", None)
+
+
+# ---------------------------------------------------------------------------
+# I2V transformer_2 quant_config extraction
+# ---------------------------------------------------------------------------
+
+
+class TestI2VTransformer2QuantConfig:
+    """Test the transformer_2 quant_config build logic from pipeline_wan2_2_i2v."""
+
+    def test_transformer_2_quant_config_built_from_dict(self):
+        """When transformer_2 config has quantization_config dict, build_quant_config is called."""
+        from vllm_omni.quantization.factory import build_quant_config
+
+        t2_config = {
+            "patch_size": [1, 2, 2],
+            "num_layers": 2,
+            "quantization_config": {
+                "quant_method": "auto-round",
+                "bits": 4,
+                "group_size": 128,
+                "sym": True,
+                "packing_format": "auto_round:auto_gptq",
+            },
+        }
+
+        # Replicate the logic from pipeline_wan2_2_i2v.py
+        t2_quant = t2_config.get("quantization_config")
+        if isinstance(t2_quant, dict) and "quant_method" in t2_quant:
+            method = t2_quant["quant_method"]
+            kwargs = {k: v for k, v in t2_quant.items() if k != "quant_method"}
+            t2_quant = build_quant_config(method, **kwargs)
+        else:
+            t2_quant = None
+
+        from vllm.model_executor.layers.quantization.inc import INCConfig
+
+        assert isinstance(t2_quant, INCConfig)
+        assert t2_quant.weight_bits == 4
+        assert t2_quant.group_size == 128
+
+    def test_transformer_2_quant_config_none_when_missing(self):
+        """When transformer_2 config has no quantization_config, result is None."""
+        t2_config = {
+            "patch_size": [1, 2, 2],
+            "num_layers": 2,
+        }
+
+        t2_quant = t2_config.get("quantization_config")
+        if isinstance(t2_quant, dict) and "quant_method" in t2_quant:
+            pass  # won't enter
+        else:
+            t2_quant = None
+
+        assert t2_quant is None
+
+    def test_transformer_2_quant_config_none_when_dict_lacks_method(self):
+        """When quantization_config is a dict but missing quant_method, result is None."""
+        t2_config = {
+            "patch_size": [1, 2, 2],
+            "quantization_config": {"bits": 4},  # no quant_method key
+        }
+
+        t2_quant = t2_config.get("quantization_config")
+        if isinstance(t2_quant, dict) and "quant_method" in t2_quant:
+            pass
+        else:
+            t2_quant = None
+
+        assert t2_quant is None