2064afe380
### What this PR does / why we need it?
- This PR fixes an issue with weight format conversion for unquantized
models running on Ascend 310P devices.
- The changes refactor the logic for converting weights to the
FRACTAL_NZ format. Previously, this was handled in a 310P-specific
linear layer implementation (`AscendUnquantizedLinearMethod310`). This
implementation has been removed, and the logic is now centralized in the
`maybe_trans_nz` utility function. This function now checks if the
device is a 310P and applies the NZ format cast accordingly for
`float16`/`bfloat16` weights.
- This refactoring simplifies the code by removing platform-specific
duplication and ensures correct weight handling for unquantized models
on 310P.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
ut and local test
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
88 lines
3.0 KiB
Python
88 lines
3.0 KiB
Python
#
|
|
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
#
|
|
# 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.
|
|
# This file is a part of the vllm-ascend project.
|
|
#
|
|
|
|
from typing import Any
|
|
|
|
import torch
|
|
import torch_npu
|
|
|
|
from vllm_ascend.quantization.methods.base import AscendLinearScheme
|
|
from vllm_ascend.utils import maybe_trans_nz
|
|
|
|
from .registry import register_scheme
|
|
|
|
|
|
@register_scheme("W8A8S", "linear")
|
|
class AscendW8A8SLinearMethod310(AscendLinearScheme):
|
|
"""310P-only W8A8S Sparse linear scheme.
|
|
|
|
Notes:
|
|
- This scheme is discovered via 310P local registry.
|
|
"""
|
|
|
|
def get_weight(
|
|
self,
|
|
input_size: int,
|
|
output_size: int,
|
|
params_dtype: torch.dtype = torch.float16,
|
|
) -> dict[str, Any]:
|
|
return {"weight": torch.empty(output_size, input_size, dtype=torch.int8)}
|
|
|
|
def get_pertensor_param(self, params_dtype: torch.dtype) -> dict[str, Any]:
|
|
return {
|
|
"input_scale": torch.empty(1, dtype=params_dtype),
|
|
"input_offset": torch.empty(1, dtype=torch.int8),
|
|
}
|
|
|
|
def get_perchannel_param(self, output_size: int, params_dtype: torch.dtype) -> dict[str, Any]:
|
|
return {
|
|
"quant_bias": torch.empty(output_size, dtype=torch.int32),
|
|
"deq_scale": torch.empty(output_size, dtype=torch.int64),
|
|
}
|
|
|
|
def apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: torch.Tensor | None = None,
|
|
tp_rank: int | None = 0,
|
|
) -> torch.Tensor:
|
|
if x.dtype != torch.int8:
|
|
x = torch.ops.vllm.quantize(
|
|
x,
|
|
layer.aclnn_input_scale,
|
|
layer.aclnn_input_scale_reciprocal,
|
|
layer.aclnn_input_offset,
|
|
)
|
|
|
|
quant_bias = layer.quant_bias if tp_rank == 0 else None
|
|
|
|
return torch_npu.npu_quant_matmul(
|
|
x,
|
|
layer.weight.data.transpose(0, 1),
|
|
layer.deq_scale,
|
|
bias=quant_bias,
|
|
output_dtype=layer.params_dtype,
|
|
)
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
expanding_factor = layer.weight.data.shape[1]
|
|
layer.aclnn_input_scale = layer.input_scale.data.repeat(expanding_factor)
|
|
layer.aclnn_input_scale_reciprocal = 1.0 / layer.aclnn_input_scale.data
|
|
layer.aclnn_input_offset = layer.input_offset.data.repeat(expanding_factor).to(layer.aclnn_input_scale.dtype)
|
|
layer.weight.data = maybe_trans_nz(layer.weight.data)
|