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* refactor rl get_name_mappings_to_training * fix tp>1 * change variable name(ffn1->up_gate_proj/ffn2->down_proj) * change variable name(linear_weight->weight/linear_bias->bias) * add rl names mapping for vl * fix ernie 0.3B error * fix develop code * fix
47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. 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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"""
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import paddle
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from paddle.nn.quant import weight_dequantize
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from fastdeploy.model_executor.layers.quantization.weight_only import (
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GPUWeightOnlyLinearMethod, WeightOnlyConfig)
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class DCUWeightOnlyLinearMethod(GPUWeightOnlyLinearMethod):
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"""
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Weight only quantization method for linear layer on GPU
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The weights are loaded in the BF16 numerical format. After loading, the quantization coefficients will be computed,
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and the weights will be quantized to int8 or int4.
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"""
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def __init__(
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self,
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quant_config: WeightOnlyConfig,
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) -> None:
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super().__init__(quant_config)
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def apply(self, layer, x):
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dequant_out = weight_dequantize(
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x=layer.weight,
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scale=layer.weight_scale,
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algo=self.quant_config.algo,
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out_dtype=paddle.get_default_dtype()
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)
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linear_out = paddle.matmul(x, dequant_out)
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if layer.bias is not None:
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linear_out = paddle.add(linear_out, layer.bias)
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return linear_out
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