53b494b1e4
## Summary
- Remove unused `set_rotation_config` and `apply_rotation` methods from
`AscendW4A4LaosDynamicLinearMethod`
- Remove unused `rotation_type` field and associated conditional
quantization parameters (`heads_rotation`, `kronecker_rotation_n`,
`kronecker_rotation_m`)
These rotation-related functions and parameters are never called in the
current W4A4 LAOS dynamic quantization workflow.
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
77 lines
2.8 KiB
Python
77 lines
2.8 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
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import torch
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import torch_npu
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from .base import AscendLinearScheme
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from .registry import register_scheme
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@register_scheme("W4A4_DYNAMIC", "linear")
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class AscendW4A4LaosDynamicLinearMethod(AscendLinearScheme):
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"""Linear method for Ascend W4A4_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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def get_weight(self, 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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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, 1, dtype=torch.float32)
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params_dict["weight_offset"] = torch.empty(output_size, 1, dtype=torch.float32)
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return params_dict
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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: torch.Tensor | None = None,
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tp_rank: int | None = 0,
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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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output = torch_npu.npu_quant_matmul(
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x,
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layer.weight.data,
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scale=layer.weight_scale.data.view(-1),
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pertoken_scale=pertoken_scale,
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bias=None,
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output_dtype=dtype,
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)
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if bias is not None:
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output = output + bias.to(dtype)
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return output
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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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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