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https://github.com/PaddlePaddle/FastDeploy.git
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93fcf7e4ec
* w4afp8 支持per group * code style * 精度完成 * revert append attn utils * ffn1 动态量化 * ffn2 支持动态量化 * code style * code style * 修改单测 * 修改单测 * fix bug * Implement conditional parameter creation for layers Add parameter creation for up_gate_proj_in_scale when ep_size > 1. * code style * fix conflict * code style * code style * 修复w4aint8 精度 * fix ci --------- Co-authored-by: yuanxiaolan <yuanxiaolan01@baidu.com>
122 lines
4.4 KiB
Python
122 lines
4.4 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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from typing import Optional
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from fastdeploy.model_executor.layers.attention.attention import Attention
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from fastdeploy.model_executor.layers.moe.moe import FusedMoE
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from . import get_quantization_config
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from .quant_base import QuantConfigBase, QuantMethodBase
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class MixQuantConfig(QuantConfigBase):
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"""
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Quantization config for layers that has different quantization methods.
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"""
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def __init__(
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self,
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dense_quant_type: str,
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moe_quant_type: str,
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kv_cache_quant_type: str = None,
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image_moe_quant_type: str = None,
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is_channel_wise: bool = False,
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has_zero_point: bool = False,
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is_permuted: bool = True,
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is_quantized: bool = False,
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hadamard_block_size: int = 128,
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moe_dynamic_quant: bool = False,
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) -> None:
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super().__init__()
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self.dense_quant_type = dense_quant_type
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self.moe_quant_type = moe_quant_type
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self.kv_cache_quant_type = kv_cache_quant_type
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if image_moe_quant_type is None:
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self.image_moe_quant_type = moe_quant_type
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else:
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self.image_moe_quant_type = image_moe_quant_type
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self.is_channel_wise = is_channel_wise
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self.has_zero_point = has_zero_point
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self.quant_max_bound = 0
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self.quant_min_bound = 0
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self.quant_round_type = 0
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self.is_permuted = is_permuted
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self.is_checkpoint_bf16 = not is_quantized
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self.is_quantized = is_quantized
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self.hadamard_block_size = hadamard_block_size
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self.moe_dynamic_quant = moe_dynamic_quant
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def name(self) -> str:
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return "mix_quant"
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@classmethod
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def from_config(cls, config: dict) -> "MixQuantConfig":
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return cls(
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config["dense_quant_type"],
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config["moe_quant_type"],
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config.get("kv_cache_quant_type", None),
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config.get("image_moe_quant_type", None),
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config.get("is_channel_wise", False),
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config.get("has_zero_point", False),
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config.get("is_permuted", True),
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config.get("is_quantized", False),
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config.get("hadamard_block_size", 128),
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config.get("moe_dynamic_quant", False),
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)
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def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
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if isinstance(layer, FusedMoE):
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if layer.moe_tag == "Image":
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return (
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get_quantization_config(self.image_moe_quant_type)
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.from_config(
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{
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"is_permuted": self.is_permuted,
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"is_quantized": self.is_quantized,
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"hadamard_block_size": self.hadamard_block_size,
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}
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)
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.get_quant_method(layer)
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)
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else:
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return (
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get_quantization_config(self.moe_quant_type)
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.from_config(
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{
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"is_permuted": self.is_permuted,
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"is_quantized": self.is_quantized,
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"hadamard_block_size": self.hadamard_block_size,
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}
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)
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.get_quant_method(layer)
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)
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elif isinstance(layer, Attention):
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if self.kv_cache_quant_type is not None:
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return (
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get_quantization_config("kvcache")
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.from_config(self.kv_cache_quant_type, self.is_channel_wise, self.has_zero_point)
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.get_quant_method(layer)
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)
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else:
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return None
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else:
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return (
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get_quantization_config(self.dense_quant_type)
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.from_config({"is_quantized": self.is_quantized})
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.get_quant_method(layer)
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)
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