3b997fdd32
### What this PR does / why we need it?
support mxfp8 quantization (qwen liner layer)
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: wangyao <iwangyao@outlook.com>
99 lines
3.3 KiB
Python
99 lines
3.3 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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from vllm.config import get_current_vllm_config
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class AscendW8A8MXFP8DynamicLinearMethod:
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"""Linear method for Ascend W8A8_DYNAMIC.
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"""
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model_dtype = None
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def __init__(self):
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vllm_config = get_current_vllm_config()
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self.group_size = vllm_config.quant_config.quant_description.get(
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"group_size", 32)
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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":
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torch.empty(output_size, input_size, dtype=torch.float8_e4m3fn)
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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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return {}
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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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params_dict = {}
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params_dict["weight_scale"] = torch.empty(output_size,
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input_size //
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self.group_size,
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dtype=torch.uint8)
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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: Optional[torch.Tensor] = None,
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tp_rank: Optional[int] = 0,
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) -> torch.Tensor:
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quantized_x, dynamic_scale = torch_npu.npu_dynamic_mx_quant(
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x, dst_type=torch.float8_e4m3fn)
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pertoken_scale = dynamic_scale
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output_dtype = x.dtype
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output = torch_npu.npu_quant_matmul(
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quantized_x,
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layer.weight,
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layer.weight_scale,
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scale_dtype=torch_npu.float8_e8m0fnu,
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pertoken_scale=pertoken_scale,
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pertoken_scale_dtype=torch_npu.float8_e8m0fnu,
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bias=bias,
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output_dtype=output_dtype,
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group_sizes=[1, 1, self.group_size])
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return output
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def process_weights_after_loading(self, layer):
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n_dim, k_dim = layer.weight_scale.data.shape
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layer.weight_scale.data = layer.weight_scale.data.reshape(
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n_dim, k_dim // 2, 2)
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layer.weight.data = layer.weight.data.transpose(0, 1)
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layer.weight_scale.data = layer.weight_scale.data.transpose(0, 1)
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