xc-llm-ascend/vllm_ascend/quantization/methods/w8a8_mxfp8.py

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

from typing import Any, Dict, Optional

import torch
import torch_npu
from vllm.config import get_current_vllm_config

from .base import AscendLinearScheme
from .registry import register_scheme


@register_scheme("W8A8_MXFP8", "linear")
class AscendW8A8MXFP8DynamicLinearMethod(AscendLinearScheme):
    """Linear method for Ascend W8A8_MXFP8 (Microscaling FP8) quantization.
    
    This scheme uses microscaling FP8 quantization with per-group scales.
    The activation is dynamically quantized to FP8 (E4M3FN format) with
    microscaling, and weights are stored in FP8 format with per-group scales.
    """
    model_dtype = None

    def __init__(self):
        vllm_config = get_current_vllm_config()
        self.group_size = vllm_config.quant_config.quant_description.get(
            "group_size", 32)

    def get_weight(self, input_size: int, output_size: int,
                   params_dtype: torch.dtype) -> Dict[str, Any]:
        params_dict = {
            "weight":
            torch.empty(output_size, input_size, dtype=torch.float8_e4m3fn)
        }
        return params_dict

    def get_pergroup_param(self,
                           input_size: int,
                           output_size: int,
                           params_dtype: torch.dtype,
                           layer_type: Optional[str] = None) -> Dict[str, Any]:
        params_dict = {}
        params_dict["weight_scale"] = torch.empty(output_size,
                                                  input_size //
                                                  self.group_size,
                                                  dtype=torch.uint8)
        return params_dict

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
        tp_rank: Optional[int] = 0,
    ) -> torch.Tensor:

        quantized_x, dynamic_scale = torch_npu.npu_dynamic_mx_quant(
            x, dst_type=torch.float8_e4m3fn)
        pertoken_scale = dynamic_scale
        output_dtype = x.dtype

        output = torch_npu.npu_quant_matmul(
            quantized_x,
            layer.weight,
            layer.weight_scale,
            scale_dtype=torch_npu.float8_e8m0fnu,
            pertoken_scale=pertoken_scale,
            pertoken_scale_dtype=torch_npu.float8_e8m0fnu,
            bias=bias,
            output_dtype=output_dtype,
            group_sizes=[1, 1, self.group_size])

        return output

    def process_weights_after_loading(self, layer):
        n_dim, k_dim = layer.weight_scale.data.shape
        layer.weight_scale.data = layer.weight_scale.data.reshape(
            n_dim, k_dim // 2, 2)
        layer.weight.data = layer.weight.data.transpose(0, 1)
        layer.weight_scale.data = layer.weight_scale.data.transpose(0, 1)