wehub-resource-sync
56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
# Copyright (c) 2022 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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import copy
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from .ptq_quantizer import (
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SUPPORT_ACT_QUANTIZERS,
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SUPPORT_WT_QUANTIZERS,
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KLQuantizer,
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PerChannelAbsmaxQuantizer,
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)
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class PTQConfig:
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"""
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The PTQ config shows how to quantize the inputs and outputs.
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"""
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def __init__(self, activation_quantizer, weight_quantizer):
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"""
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Constructor.
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Args:
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activation_quantizer(BaseQuantizer): The activation quantizer.
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It should be the instance of BaseQuantizer.
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weight_quantizer(BaseQuantizer): The weight quantizer.
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It should be the instance of BaseQuantizer.
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"""
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super().__init__()
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assert isinstance(activation_quantizer, tuple(SUPPORT_ACT_QUANTIZERS))
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assert isinstance(weight_quantizer, tuple(SUPPORT_WT_QUANTIZERS))
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self.in_act_quantizer = copy.deepcopy(activation_quantizer)
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self.out_act_quantizer = copy.deepcopy(activation_quantizer)
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self.wt_quantizer = copy.deepcopy(weight_quantizer)
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self.quant_hook_handle = None
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# In order to wrap simulated layers, use in_act_quantizer
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# to calculate the input thresholds for conv2d, linear and etc.
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self.enable_in_act_quantizer = False
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default_ptq_config = PTQConfig(KLQuantizer(), PerChannelAbsmaxQuantizer())
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