fix: Propagate Quantization Configuration to HunyuanVideo-1.5 I2V Transformer to Enable FP8 Layers (#4245)

Signed-off-by: weizhoublue <weizhou.lan@daocloud.io>

tests/diffusion/models/hunyuan_video/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

tests/diffusion/models/hunyuan_video/test_hunyuan_video_quant_config_propagation.py

@@ -0,0 +1,160 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for HunyuanVideo-1.5 quant_config propagation through transformer creation.
+
+Tests cover:
+- HunyuanVideo15Pipeline passes quant_config to HunyuanVideo15Transformer3DModel
+- HunyuanVideo15I2VPipeline passes quant_config to HunyuanVideo15Transformer3DModel
+"""
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock, patch
+
+import pytest
+import torch
+
+import vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5 as t2v_module
+import vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5_i2v as i2v_module
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
+
+
+class TestHunyuanVideoQuantConfigPropagation:
+    """Verify quant_config is propagated to the transformer model in HunyuanVideo-1.5 pipelines."""
+
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5.get_local_device",
+        return_value="cpu",
+    )
+    @patch("vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5.prefetch_subfolders")
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5.from_pretrained_with_prefetch",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5.Qwen2Tokenizer.from_pretrained",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5.ByT5Tokenizer.from_pretrained",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5.AutoConfig.from_pretrained",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5.T5EncoderModel",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5.FlowMatchEulerDiscreteScheduler.from_pretrained",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5.get_transformer_config_kwargs",
+        return_value={},
+    )
+    def test_t2v_quant_config_passed_through(
+        self,
+        mock_get_kwargs,
+        mock_scheduler,
+        mock_t5_encoder,
+        mock_auto_config,
+        mock_byt5_tok,
+        mock_qwen2_tok,
+        mock_from_pretrained,
+        mock_prefetch,
+        mock_get_device,
+    ):
+        captured_kwargs = {}
+
+        class FakeTransformer:
+            def __init__(self, **kwargs):
+                captured_kwargs.update(kwargs)
+
+        with patch.object(t2v_module, "HunyuanVideo15Transformer3DModel", FakeTransformer):
+            fake_qc = MagicMock()
+            od_config = SimpleNamespace(
+                model="fake-t2v-model",
+                dtype=torch.bfloat16,
+                flow_shift=None,
+                tf_model_config=SimpleNamespace(),
+                quantization_config=fake_qc,
+                enable_diffusion_pipeline_profiler=False,
+            )
+
+            t2v_module.HunyuanVideo15Pipeline(od_config=od_config)
+
+            assert captured_kwargs.get("quant_config") is fake_qc
+
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5_i2v.get_local_device",
+        return_value="cpu",
+    )
+    @patch("vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5_i2v.prefetch_subfolders")
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5_i2v.from_pretrained_with_prefetch",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5_i2v.Qwen2Tokenizer.from_pretrained",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5_i2v.ByT5Tokenizer.from_pretrained",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5_i2v.AutoConfig.from_pretrained",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5_i2v.T5EncoderModel",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5_i2v.SiglipImageProcessor.from_pretrained",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5_i2v.FlowMatchEulerDiscreteScheduler.from_pretrained",
+        return_value=MagicMock(),
+    )
+    @patch(
+        "vllm_omni.diffusion.models.hunyuan_video.pipeline_hunyuan_video_1_5_i2v.get_transformer_config_kwargs",
+        return_value={},
+    )
+    def test_i2v_quant_config_passed_through(
+        self,
+        mock_get_kwargs,
+        mock_scheduler,
+        mock_siglip_processor,
+        mock_t5_encoder,
+        mock_auto_config,
+        mock_byt5_tok,
+        mock_qwen2_tok,
+        mock_from_pretrained,
+        mock_prefetch,
+        mock_get_device,
+    ):
+        captured_kwargs = {}
+
+        class FakeTransformer:
+            def __init__(self, **kwargs):
+                captured_kwargs.update(kwargs)
+
+        with patch.object(i2v_module, "HunyuanVideo15Transformer3DModel", FakeTransformer):
+            fake_qc = MagicMock()
+            od_config = SimpleNamespace(
+                model="fake-i2v-model",
+                dtype=torch.bfloat16,
+                flow_shift=None,
+                tf_model_config=SimpleNamespace(),
+                quantization_config=fake_qc,
+                enable_diffusion_pipeline_profiler=False,
+            )
+
+            i2v_module.HunyuanVideo15I2VPipeline(od_config=od_config)
+
+            assert captured_kwargs.get("quant_config") is fake_qc