wehub-resource-sync
144 lines
4.6 KiB
Python
144 lines
4.6 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 paddle
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class LayerInfo:
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"""
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Store the arg names of the inputs and outputs.
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"""
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def __init__(self, layer, input_names, weight_names, output_names):
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super().__init__()
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self.layer = layer
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self.input_names = input_names
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self.weight_names = weight_names
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self.output_names = output_names
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PTQ_LAYERS_INFO = [
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LayerInfo(paddle.nn.Conv2D, ['Input'], ['Filter'], ['Output']),
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LayerInfo(paddle.nn.Linear, ['X'], ['Y'], ['Out']),
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LayerInfo(paddle.nn.BatchNorm2D, ['X'], [], ['Y']),
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LayerInfo(paddle.nn.AdaptiveMaxPool2D, ['X'], [], ['Out']),
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LayerInfo(paddle.nn.AdaptiveAvgPool2D, ['X'], [], ['Out']),
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LayerInfo(paddle.nn.AvgPool2D, ['X'], [], ['Out']),
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LayerInfo(paddle.nn.MaxPool2D, ['X'], [], ['Out']),
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LayerInfo(paddle.nn.ReLU, ['X'], [], ['Out']),
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LayerInfo(paddle.nn.ReLU6, ['X'], [], ['Out']),
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LayerInfo(paddle.nn.Hardswish, ['X'], [], ['Out']),
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LayerInfo(paddle.nn.Swish, ['X'], [], ['Out']),
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LayerInfo(paddle.nn.Sigmoid, ['X'], [], ['Out']),
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LayerInfo(paddle.nn.Softmax, ['X'], [], ['Out']),
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LayerInfo(paddle.nn.Tanh, ['X'], [], ['Out']),
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LayerInfo(paddle.nn.quant.add, ['X', 'Y'], [], ['Out']),
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]
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QUANT_LAYERS_INFO = [
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LayerInfo(
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paddle.nn.quant.quant_layers.QuantizedConv2D,
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['Input'],
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['Filter'],
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['Output'],
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),
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LayerInfo(
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paddle.nn.quant.quant_layers.QuantizedLinear, ['X'], ['Y'], ['Out']
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),
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]
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SIMULATED_LAYERS = [paddle.nn.Conv2D, paddle.nn.Linear]
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class PTQRegistry:
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"""
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Register the supported layers for PTQ and provide layers info.
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"""
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supported_layers_map = {}
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registered_layers_map = {}
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is_inited = False
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def __init__(self):
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super().__init__()
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@classmethod
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def _init(cls):
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if not cls.is_inited:
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for layer_info in PTQ_LAYERS_INFO:
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cls.supported_layers_map[layer_info.layer] = layer_info
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all_layers_info = PTQ_LAYERS_INFO + QUANT_LAYERS_INFO
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for layer_info in all_layers_info:
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cls.registered_layers_map[layer_info.layer] = layer_info
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cls.is_inited = True
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@classmethod
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def is_supported_layer(cls, layer):
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"""
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Analyze whether the layer supports quantization.
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Args:
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layer(Layer): The input layer can be a python class or an instance.
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Returns:
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flag(bool): Whether the layer is supported.
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"""
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cls._init()
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return layer in cls.supported_layers_map or isinstance(
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layer, tuple(cls.supported_layers_map.keys())
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)
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@classmethod
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def is_registered_layer(cls, layer):
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"""
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Analyze whether the layer is register layer_info.
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Args:
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layer(Layer): The input layer can be a python class or an instance.
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Returns:
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flag(bool): Whether the layer is register layer_info.
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"""
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cls._init()
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return layer in cls.registered_layers_map or isinstance(
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layer, tuple(cls.registered_layers_map.keys())
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)
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@classmethod
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def is_simulated_quant_layer(cls, layer):
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"""
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Analyze whether the layer is simulated quant layer.
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Args:
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layer(Layer): The input layer can be a python class or an instance.
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Returns:
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flag(bool): Whether the layer is supported.
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"""
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return layer in SIMULATED_LAYERS or isinstance(
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layer, tuple(SIMULATED_LAYERS)
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)
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@classmethod
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def layer_info(cls, layer):
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"""
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Get the information for the layer.
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Args:
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layer(Layer): The input layer can be a python class or an instance.
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Returns:
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layer_info(LayerInfo): The layer info of the input layer.
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"""
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assert cls.is_registered_layer(layer), (
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"The input layer is not register."
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)
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for layer_key, layer_info in cls.registered_layers_map.items():
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if layer == layer_key or isinstance(layer, layer_key):
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return layer_info
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