23524f2ca4
### What this PR does / why we need it?
This pull request focuses on a significant refactoring effort within the
vllm-ascend project, specifically targeting operations optimized for the
Ascend 310P hardware. The changes aim to streamline the implementation
of core components like quantization and multi-head attention, making
the codebase more maintainable and robust. Concurrently, new unit tests
have been introduced to ensure the correctness and reliability of these
refactored modules.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
E2E test with qwen3-32b w8a8
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
108 lines
3.7 KiB
Python
108 lines
3.7 KiB
Python
#
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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# This file is a part of the vllm-ascend project.
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#
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from typing import Any
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import torch
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import torch_npu
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from vllm_ascend.quantization.methods.base import AscendLinearScheme
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from .registry import register_scheme
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@register_scheme("W8A8", "linear")
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class AscendW8A8LinearMethod310(AscendLinearScheme):
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"""310P-only W8A8 static linear scheme.
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Notes:
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- This scheme is discovered via 310P local registry.
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"""
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def get_weight(
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self,
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype = torch.float16,
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) -> dict[str, Any]:
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return {"weight": torch.empty(output_size, input_size, dtype=torch.int8)}
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def get_pertensor_param(self, params_dtype: torch.dtype) -> dict[str, Any]:
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return {
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"input_scale": torch.empty(1, dtype=params_dtype),
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"input_offset": torch.empty(1, dtype=torch.int8),
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}
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def get_perchannel_param(self, output_size: int, params_dtype: torch.dtype) -> dict[str, Any]:
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params: dict[str, Any] = {}
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params["quant_bias"] = torch.empty(output_size, dtype=torch.int32)
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# NOTE: keep identical to your current working behavior.
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if params_dtype == torch.bfloat16:
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params["deq_scale"] = torch.empty(output_size, dtype=torch.float32)
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else:
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params["deq_scale"] = torch.empty(output_size, dtype=torch.int64)
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params["weight_scale"] = torch.empty(output_size, 1, dtype=params_dtype)
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params["weight_offset"] = torch.empty(output_size, 1, dtype=params_dtype)
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return params
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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: torch.Tensor | None = None,
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tp_rank: int | None = 0,
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) -> torch.Tensor:
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if x.dtype != torch.int8:
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x = torch.ops.vllm.quantize(
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x,
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layer.aclnn_input_scale,
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layer.aclnn_input_scale_reciprocal,
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layer.aclnn_input_offset,
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)
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quant_bias = layer.quant_bias if tp_rank == 0 else None
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return torch_npu.npu_quant_matmul(
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x,
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layer.weight,
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layer.deq_scale,
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bias=quant_bias,
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output_dtype=layer.params_dtype,
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)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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expanding_factor = layer.weight.data.shape[1]
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layer.aclnn_input_scale = torch.nn.Parameter(
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layer.input_scale.data.repeat(expanding_factor),
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requires_grad=False,
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)
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layer.aclnn_input_scale_reciprocal = torch.nn.Parameter(
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1.0 / layer.aclnn_input_scale.data,
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requires_grad=False,
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)
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layer.aclnn_input_offset = torch.nn.Parameter(
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layer.input_offset.data.repeat(expanding_factor),
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requires_grad=False,
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).to(layer.aclnn_input_scale.dtype)
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layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
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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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