dc6172efd3
### What this PR does / why we need it? Update attention nz and mla nz modules to improve TPOP 6ms performance Convert W_UV and W_UK_T to NPU format in mla_v1.py Convert layer.weight to NPU format in w8a8.py Signed-off-by: ttanzhiqiang <389825161@qq.com>
116 lines
4.3 KiB
Python
116 lines
4.3 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.data = torch_npu.npu_format_cast(layer.weight.data, 29)
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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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