b6bc3d2f9d
NZ Format Support for Linear Layers: Implemented support for the NZ
(N-dimensional Z-order) format for linear layer weights on Ascend 310P,
enhancing performance for both quantized and unquantized layers.
Unquantized Linear Method for Ascend 310P: Introduced
AscendUnquantizedLinearMethod310 to specifically handle and apply NZ
format casting to unquantized linear layer weights during the loading
process.
MRotaryEmbedding Integration: Extended Rotary Embedding support by
adding AscendMRotaryEmbedding310 to provide an Ascend-specific
implementation for MRotaryEmbedding.
Quantization Method Updates: Updated the w8a8_static quantization method
to directly transpose weights and apply NZ format casting, ensuring
consistency with the new format.
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
66 lines
2.2 KiB
Python
66 lines
2.2 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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from __future__ import annotations
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import torch
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import torch.nn as nn
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import torch_npu
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from vllm.model_executor.layers.linear import (
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LinearBase,
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QuantizeMethodBase,
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UnquantizedLinearMethod,
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)
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from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
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from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ
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class AscendUnquantizedLinearMethod310(UnquantizedLinearMethod):
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def process_weights_after_loading(self, layer: nn.Module) -> None:
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super().process_weights_after_loading(layer)
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if "conv1d" not in getattr(layer, "prefix", ""):
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layer.weight.data = torch_npu.npu_format_cast(layer.weight.data, ACL_FORMAT_FRACTAL_NZ)
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class AscendLinearBase310(LinearBase):
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def __init__(
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self,
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input_size: int,
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output_size: int,
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skip_bias_add: bool = False,
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params_dtype: object | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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*,
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return_bias: bool = True,
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disable_tp: bool = False,
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):
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nn.Module.__init__(self)
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self.input_size = int(input_size)
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self.output_size = int(output_size)
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self.skip_bias_add = skip_bias_add
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self.params_dtype = torch.float16
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self.quant_config = quant_config
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self.prefix = prefix
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self.return_bias = return_bias
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self.disable_tp = disable_tp
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if quant_config is None:
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self.quant_method: QuantizeMethodBase | None = AscendUnquantizedLinearMethod310()
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else:
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self.quant_method = quant_config.get_quant_method(self, prefix=prefix)
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