4fcca137a7
### What this PR does / why we need it?
Adding `W4A8_DYNAMIC` quantization support for linear.
Dense models like Qwen3 can infer with `W4A8_DYNAMIC` quantization.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
Adding ut case in `tests/ut/quantization/test_w4a8_dynamic.py`
Adding e2e case in
`tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC`
to test qwen3 w4a8_dynamic quantized model
Note the w4a8_dynamic quantized model is quantized by `msit/msmodelslim`
of commit `d0abb0a47e1f1a473b866ad41b737fbc28fb1409`
1. Generate `W4A8_DYNAMIC` quantization weights using `msmodelslim`
```shell
git clone https://gitee.com/ascend/msit.git
cd msit/msmodelslim
git checkout d0abb0a47e1f1a473b866ad41b737fbc28fb1409
bash install.sh
```
2. Serve model using `vllm`
```shell
VLLM_USE_V1=1 python -m vllm.entrypoints.openai.api_server \
--model vllm-ascend/Qwen3-8B-W4A8 \
--port 8000 \
--quantization ascend \
--tensor_parallel_size 2 \
--enforce-eager
```
- vLLM version: v0.10.0
- vLLM main:
4cd7fe6cea
---------
Signed-off-by: ZhouXiang <zhouxiang100@huawei.com>
114 lines
4.6 KiB
Python
114 lines
4.6 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.config import get_current_vllm_config
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class AscendW4A8DynamicLinearMethod:
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"""Linear method for Ascend W4A8_DYNAMIC
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"""
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def __init__(self):
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self.transpose_weight = True
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try:
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self.group_size = get_current_vllm_config(
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).quant_config.quant_description.get("group_size", 256)
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except AttributeError:
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self.group_size = 256
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@staticmethod
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def get_weight(input_size: int, output_size: int,
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params_dtype: torch.dtype) -> 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(output_size: int,
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params_dtype: torch.dtype) -> Dict[str, Any]:
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return {}
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def get_pergroup_param(self, input_size: int, output_size: int,
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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=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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params_dict["weight_scale_second"] = torch.empty(output_size,
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input_size //
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self.group_size,
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dtype=params_dtype)
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params_dict["weight_offset_second"] = torch.empty(output_size,
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input_size //
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self.group_size,
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dtype=params_dtype)
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return params_dict
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@staticmethod
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def process_scale_second(weight: torch.Tensor, scale: torch.Tensor,
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per_group_scale: torch.Tensor):
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k, n = weight.shape
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group_num, n = per_group_scale.shape
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weight_high = weight.to(torch.float32).reshape(
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group_num, -1, n) * per_group_scale.reshape(group_num, 1, n)
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weight_high = weight_high.reshape(k, n)
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bias = 8 * (weight_high.to(torch.float32) * scale).sum(dim=0)
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antiquant_scale = (scale * per_group_scale).reshape(group_num, n)
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return antiquant_scale.npu(), bias
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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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return torch_npu.npu_weight_quant_batchmatmul(
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x,
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layer.weight,
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antiquant_scale=layer.weight_scale_second.to(x.dtype),
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antiquant_group_size=self.group_size,
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)
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def process_weights_after_loading(self, layer: torch.nn.Module):
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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 = layer.weight_scale.data.flatten().to(
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torch.float32)
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layer.weight_offset.data = layer.weight_offset.data.flatten()
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layer.weight_scale_second.data, scale_bias = self.process_scale_second(
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layer.weight.data,
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layer.weight_scale.data,
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layer.weight_scale_second.data.transpose(0, 1).contiguous(),
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)
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param = torch.nn.Parameter(scale_bias, requires_grad=False)
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layer.register_parameter("weight_scale_bias", param)
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layer.weight.data = torch_npu.npu_convert_weight_to_int4pack(
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layer.weight.data.to(torch.int32))
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