[Feature]: FP8 Quantization Support for DiT (#1034)

Signed-off-by: lishunyang <lishunyang12@163.com>
Signed-off-by: SYLAR <125541396+lishunyang12@users.noreply.github.com>

vllm_omni/diffusion/quantization/base.py

@@ -0,0 +1,61 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Base class for diffusion model quantization configurations."""
+
+from abc import ABC
+from typing import TYPE_CHECKING, ClassVar
+
+import torch
+
+if TYPE_CHECKING:
+    from vllm.model_executor.layers.quantization.base_config import (
+        QuantizationConfig,
+    )
+
+
+class DiffusionQuantizationConfig(ABC):
+    """Base class for diffusion model quantization configurations.
+
+    This provides a thin wrapper over vLLM's quantization configs,
+    allowing diffusion-model-specific defaults and future extensibility.
+
+    Subclasses should:
+        - Set quant_config_cls to the vLLM QuantizationConfig class
+        - Call super().__init__() after creating self._vllm_config
+        - Optionally override get_name() and get_min_capability() if needed
+    """
+
+    # Subclasses should set this to the vLLM QuantizationConfig class
+    quant_config_cls: ClassVar[type["QuantizationConfig"] | None] = None
+
+    # The underlying vLLM config instance
+    _vllm_config: "QuantizationConfig | None" = None
+
+    @classmethod
+    def get_name(cls) -> str:
+        """Return the quantization method name (e.g., 'fp8', 'int8').
+
+        By default, delegates to the underlying vLLM config class.
+        """
+        if cls.quant_config_cls is not None:
+            return cls.quant_config_cls.get_name()
+        raise NotImplementedError("Subclass must set quant_config_cls or override get_name()")
+
+    def get_vllm_quant_config(self) -> "QuantizationConfig | None":
+        """Return the underlying vLLM QuantizationConfig for linear layers."""
+        return self._vllm_config
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        """Return supported activation dtypes."""
+        return [torch.bfloat16, torch.float16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        """Minimum GPU compute capability required.
+
+        By default, delegates to the underlying vLLM config class.
+        """
+        if cls.quant_config_cls is not None:
+            return cls.quant_config_cls.get_min_capability()
+        return 80  # Ampere default