0b884ebc6c
Signed-off-by: David Chen <530634352@qq.com> Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn> Signed-off-by: WeiQing Chen <40507679+david6666666@users.noreply.github.com> Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn> Co-authored-by: Isotr0py <2037008807@qq.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Base class for diffusion model quantization configurations."""
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from abc import ABC
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from typing import TYPE_CHECKING, ClassVar
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import torch
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if TYPE_CHECKING:
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig,
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)
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class DiffusionQuantizationConfig(ABC):
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"""Base class for diffusion model quantization configurations.
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This provides a thin wrapper over vLLM's quantization configs,
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allowing diffusion-model-specific defaults and future extensibility.
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Subclasses should:
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- Set quant_config_cls to the vLLM QuantizationConfig class
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- Call super().__init__() after creating self._vllm_config
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- Optionally override get_name() and get_min_capability() if needed
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"""
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# Subclasses should set this to the vLLM QuantizationConfig class
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quant_config_cls: ClassVar[type["QuantizationConfig"] | None] = None
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# The underlying vLLM config instance
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_vllm_config: "QuantizationConfig | None" = None
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def get_name(self) -> str:
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"""Return the quantization method name (e.g., 'fp8', 'int8').
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By default, delegates to the underlying vLLM config instance.
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"""
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if self._vllm_config is not None:
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return self._vllm_config.get_name()
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raise NotImplementedError("Subclass must initialize _vllm_config or override get_name().")
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def get_vllm_quant_config(self) -> "QuantizationConfig | None":
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"""Return the underlying vLLM QuantizationConfig for linear layers."""
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return self._vllm_config
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@classmethod
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def get_supported_act_dtypes(cls) -> list[torch.dtype]:
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"""Return supported activation dtypes."""
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return [torch.bfloat16, torch.float16]
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@classmethod
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def get_min_capability(cls) -> int:
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"""Minimum GPU compute capability required.
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By default, delegates to the underlying vLLM config class.
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"""
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if cls.quant_config_cls is not None:
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return cls.quant_config_cls.get_min_capability()
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return 80 # Ampere default
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