5018f2d8fd
### What this PR does / why we need it?
related to https://github.com/vllm-project/vllm-ascend/issues/4267
### Does this PR introduce _any_ user-facing change?
support w8a16 quantization now
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
### Test
tested using [aisbench](https://gitee.com/aisbench/benchmark/) with tp2
#### Precision
| ceval | mmlu | gsm8k
-- | -- | -- | --
bf16 | 90.46 | 89.17 | 96.21
w8a16 | 89.51 | 89.29 | 95.98
#### Performance
| input_len | output_len | concurrency | TTFT (ms) | TPOT (ms) | TPS
(Total) (tokens/s)
-- | -- | -- | -- | -- | -- | --
bf16 | 2048 | 2048 | 10 | 1911.7136 | 77.988 | 253.9866
w8a16 | 2048 | 2048 | 10 | 2128.6334 | 67.1633 | 293.9117
bf16 | 3500 | 1024 | 10 | 3076.2509 | 84.3525 | 506.949
w8a16 | 3500 | 1024 | 10 | 2685.2031 | 73.015 | 585.4717
---------
Signed-off-by: yyt <yangyit139@gmail.com>
Signed-off-by: TmacAaron <yangyit139@gmail.com>
Co-authored-by: realliujiaxu <realliujiaxu@163.com>
90 lines
2.9 KiB
Python
90 lines
2.9 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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from vllm_ascend.utils import maybe_trans_nz
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class AscendW8A16LinearMethod:
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"""Linear method for Ascend W8A16.
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"""
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def __init__(self) -> None:
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pass
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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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return {}
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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["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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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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@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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output = torch_npu.npu_weight_quant_batchmatmul(
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x=x,
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weight=layer.weight,
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antiquant_scale=layer.weight_scale,
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antiquant_offset=layer.weight_offset,
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bias=bias)
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return output
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def process_weights_after_loading(self, layer):
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layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
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layer.weight.data = maybe_trans_nz(layer.weight.data)
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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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