Files
FastDeploy/fastdeploy/model_executor/layers/quantization/w4a8.py
yinwei 20c7b741f4 [XPU] Support W4A8C8-TP4-300B Model (#4068)
* support w4a8

* delete ep block attn

* delete moe_topk_select

* update note

* update

* delte useless info

* update

* add some note

* fix some format

* update scale info

* add ans baseline

---------

Co-authored-by: YuBaoku <49938469+EmmonsCurse@users.noreply.github.com>
2025-10-10 15:41:32 +08:00

58 lines
1.9 KiB
Python

"""
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
from typing import Optional
from fastdeploy.platforms import current_platform
from .quant_base import QuantConfigBase, QuantMethodBase
class W4A8Config(QuantConfigBase):
"""
quantization config for weight 4bits and activation 8bits
"""
def __init__(self, is_permuted, hadamard_block_size) -> None:
super().__init__()
self.is_permuted = is_permuted
self.hadamard_block_size = hadamard_block_size
def name(self) -> str:
return "w4a8"
@classmethod
def from_config(cls, config: dict) -> "W4A8Config":
is_permuted = config.get("is_permuted", True)
hadamard_block_size = config.get("hadamard_block_size", 128)
return cls(is_permuted, hadamard_block_size)
def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
if current_platform.is_cuda():
from fastdeploy.model_executor.layers.moe.fused_moe_cutlass_backend import (
CutlassW4A8MoEMethod,
)
return CutlassW4A8MoEMethod(self)
elif current_platform.is_xpu():
from fastdeploy.model_executor.layers.backends.xpu.moe.fused_moe import (
XPUW4A8MoEMethod,
)
return XPUW4A8MoEMethod(self)
else:
raise ValueError(f"Unsupported layer type {type(layer)} for w4a8")