ef9d8367f5
Introduce W4A4 LAOS Quantization for better model compression and
inference efficiency on Ascend devices.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
111 lines
4.8 KiB
Python
111 lines
4.8 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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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 Any, Callable, Dict, Optional
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import torch
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import torch_npu
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import torch.nn.functional as F
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class AscendW4A4LaosDynamicLinearMethod:
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"""Linear method for Ascend W4A4_LAOS_DYNAMIC.
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This class implements W4A4 quantization with LAOS approach and dynamic activation quantization.
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- Weight: 4-bit quantization (per-channel) with scale and offset, stored as int8.
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- Activation: 4-bit dynamic quantization.
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"""
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def __init__(self):
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self.transpose_weight = True
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self.rotation_type = None
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def set_rotation_config(self, prefix, metadata):
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layer_idx = prefix.split(".")[2]
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if prefix.endswith("o_proj"):
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layers = metadata["quarot"]["heads_rotation"]["layers"]
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if layer_idx in layers:
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return "heads_rotation"
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if prefix.endswith("down_proj"):
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layers = metadata["quarot"]["kronecker_rotation"]["layers"]
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if layer_idx in layers:
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return "kronecker_rotation"
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@staticmethod
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def get_weight(input_size: int, output_size: int, params_dtype: torch.dtype) -> Dict[str, Any]:
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params_dict = {"weight": torch.empty(output_size, input_size, dtype=torch.int8)}
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return params_dict
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@staticmethod
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def get_pertensor_param(params_dtype: torch.dtype) -> Dict[str, Any]:
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return {}
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def get_perchannel_param(self, output_size: int, params_dtype: torch.dtype) -> Dict[str, Any]:
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params_dict = {}
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params_dict["weight_scale"] = torch.empty(output_size,
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1,
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dtype=torch.float32)
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params_dict["weight_offset"] = torch.empty(output_size,
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1,
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dtype=torch.float32)
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if self.rotation_type == "heads_rotation":
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params_dict["heads_rotation"] = torch.zeros((64, 64), dtype=torch.float32)
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if self.rotation_type == "kronecker_rotation":
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params_dict["kronecker_rotation_n"] = torch.zeros((160, 160), dtype=torch.float32)
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params_dict["kronecker_rotation_m"] = torch.zeros((160, 160), dtype=torch.float32)
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return params_dict
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def get_pergroup_param(self,
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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layer_type: Optional[str] = None) -> Dict[str, Any]:
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return {}
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def apply_rotation(self, layer, x):
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init_shape = x.shape
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dtype = x.dtype
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if self.rotation_type == "heads_rotation":
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Q1 = layer.heads_rotation
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scaled_x = x.reshape(-1, Q1.shape[1], 128)
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scaled_x = torch.matmul(Q1.T, scaled_x).reshape(init_shape)
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return scaled_x.to(dtype)
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if self.rotation_type == "kronecker_rotation":
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Q1 = layer.kronecker_rotation_m
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Q2 = layer.kronecker_rotation_n
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scaled_x = x.reshape(-1, Q1.shape[0], Q2.shape[0])
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scaled_x = torch.matmul(scaled_x, Q2)
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scaled_x = torch.matmul(Q1.T, scaled_x)
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scaled_x = scaled_x.reshape(init_shape)
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return scaled_x.to(dtype)
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return x
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None,
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tp_rank: Optional[int] = None,
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) -> torch.Tensor:
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dtype = x.dtype
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x, pertoken_scale = torch_npu.npu_dynamic_quant(x, dst_type=torch.quint4x2)
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pertoken_scale = pertoken_scale.reshape(-1, 1)
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pertoken_scale = pertoken_scale.squeeze(-1)
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y2 = torch_npu.npu_quant_matmul(x, layer.weight.data, scale=layer.weight_scale.data.view(-1), pertoken_scale=pertoken_scale, bias=None, output_dtype=dtype)
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return y2
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def process_weights_after_loading(self, layer: torch.nn.Module):
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layer.weight_scale.data = layer.weight_scale.data.to(torch.float32)
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layer.weight.data = torch_npu.npu_convert_weight_to_int4pack(layer.weight.data.to(torch.int32))
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if self.transpose_weight:
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layer.weight.data = layer.weight.data.transpose(-1, -2)
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