a73bd6caf4
<!-- Thanks for sending a pull request! BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing/overview.html --> ### What this PR does / why we need it? <!-- - Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. - Please clarify why the changes are needed. For instance, the use case and bug description. - Fixes # --> Set div_mode to False to use the ACLNN kernel, which is crucial when using ACL Graph. ### Does this PR introduce _any_ user-facing change? <!-- Note that it means *any* user-facing change including all aspects such as API, interface or other behavior changes. Documentation-only updates are not considered user-facing changes. --> ### How was this patch tested? <!-- CI passed with new added/existing test. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
115 lines
4.2 KiB
Python
115 lines
4.2 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, Dict, Optional
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import torch
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import torch_npu
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def quant_per_tensor(in_tensor: torch.Tensor, input_scale: torch.Tensor,
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input_offset: torch.Tensor):
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return torch_npu.npu_quantize(in_tensor, input_scale, input_offset,
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torch.qint8, -1, False)
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class AscendW8A8LinearMethod:
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"""Linear method for Ascend W8A8.
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Args:
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w_sym: whether the linear weight is symmetrically quantized.
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"""
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def __init__(self) -> None:
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# aclnn quant matmul requires to transpose matrix B, set to true by default.
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self.transpose_weight = True
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@staticmethod
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def get_weight(
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype = torch.bfloat16,
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) -> Dict[str, Any]:
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params_dict = {
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"weight": torch.empty(output_size, input_size, dtype=torch.int8)
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}
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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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params_dict = {}
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params_dict["input_scale"] = torch.empty(1, dtype=params_dtype)
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params_dict["input_offset"] = torch.empty(1, dtype=torch.int8)
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return params_dict
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@staticmethod
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def get_perchannel_param(
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output_size: int,
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params_dtype: torch.dtype,
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) -> Dict[str, Any]:
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params_dict = {}
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params_dict["quant_bias"] = torch.empty(output_size, dtype=torch.int32)
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if params_dtype == torch.bfloat16:
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params_dict["deq_scale"] = torch.empty(output_size,
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dtype=torch.float32)
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elif params_dtype == torch.float16:
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params_dict["deq_scale"] = torch.empty(output_size,
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dtype=torch.int64)
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params_dict["weight_scale"] = torch.empty(output_size,
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1,
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dtype=params_dtype)
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params_dict["weight_offset"] = torch.empty(output_size,
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1,
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dtype=params_dtype)
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return params_dict
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@staticmethod
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def apply(
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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] = 0,
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) -> torch.Tensor:
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original_dtype = x.dtype
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if original_dtype != torch.int8:
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x = quant_per_tensor(
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x,
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layer.aclnn_input_scale,
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layer.aclnn_input_offset,
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)
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quant_bias = layer.quant_bias if tp_rank == 0 else None
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return torch_npu.npu_quant_matmul(
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x,
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layer.weight,
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layer.deq_scale,
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bias=quant_bias,
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output_dtype=original_dtype,
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)
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def process_weights_after_loading(self, layer):
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expanding_factor = layer.weight.data.shape[1]
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layer.aclnn_input_scale = 1 / torch.nn.Parameter(
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layer.input_scale.data.repeat(expanding_factor),
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requires_grad=False)
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layer.aclnn_input_offset = torch.nn.Parameter(
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layer.input_offset.data.repeat(expanding_factor),
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requires_grad=False).to(layer.aclnn_input_scale.dtype)
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if self.transpose_weight:
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layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
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layer.weight_scale.data = torch.flatten(layer.weight_scale.data)
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layer.weight_offset.data = torch.flatten(layer.weight_offset.data)
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