chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,497 @@
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# 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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from __future__ import annotations
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import copy
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from typing import TYPE_CHECKING
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import paddle
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from paddle import nn
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from .wrapper import ObserveWrapper
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if TYPE_CHECKING:
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from paddle.nn import Layer
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from .factory import QuanterFactory
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# TODO: Implement quanted layer and fill the mapping dict
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DEFAULT_QAT_LAYER_MAPPINGS: dict[Layer, Layer] = {
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nn.quant.Stub: nn.quant.stub.QuanterStub,
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nn.Linear: nn.quant.qat.QuantedLinear,
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nn.Conv2D: nn.quant.qat.QuantedConv2D,
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}
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DEFAULT_LEAVES = [nn.ReLU, nn.AvgPool2D]
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class SingleLayerConfig:
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r"""
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Configure how to quantize the activations and weights of a single layer.
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Args:
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activation(QuanterFactory): The factory to create instance of quanter used to quantize activations.
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weight(QuanterFactory): The factory to create instance of quanter used to quantize weights.
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"""
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def __init__(
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self, activation: QuanterFactory, weight: QuanterFactory
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) -> None:
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self._activation = activation
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self._weight = weight
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@property
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def activation(self) -> QuanterFactory:
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return self._activation
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@property
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def weight(self) -> QuanterFactory:
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return self._weight
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def __str__(self):
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return f"activation: {self._activation}\nweight: {self._weight}"
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class QuantConfig:
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r"""
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Configure how to quantize a model or a part of the model. It will map each layer to
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an instance of SingleLayerConfig by the settings. It provides diverse methods to set
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the strategies of quantization.
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Args:
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activation(QuanterFactory | None): The global quantizer used to quantize the activations.
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weight(QuanterFactory | None): The global quantizer used to quantize the weights.
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Examples:
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.. code-block:: pycon
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>>> from paddle.quantization import QuantConfig
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>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
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>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
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>>> q_config = QuantConfig(activation=quanter, weight=quanter)
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>>> print(q_config)
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Global config:
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activation: FakeQuanterWithAbsMaxObserver(name=None,moving_rate=0.9,bit_length=8,dtype=float32)
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weight: FakeQuanterWithAbsMaxObserver(name=None,moving_rate=0.9,bit_length=8,dtype=float32)
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"""
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def __init__(
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self, activation: QuanterFactory | None, weight: QuanterFactory | None
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) -> None:
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if activation is None and weight is None:
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self._global_config = None
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else:
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self._global_config = SingleLayerConfig(activation, weight)
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self._layer2config = {}
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self._prefix2config = {}
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self._type2config = {}
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self._model = None
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self._qat_layer_mapping = copy.deepcopy(DEFAULT_QAT_LAYER_MAPPINGS)
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self._customized_qat_layer_mapping = {}
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self._customized_leaves = []
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def add_layer_config(
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self,
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layer: Layer | list[Layer],
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activation: QuanterFactory | None = None,
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weight: QuanterFactory | None = None,
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) -> None:
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r"""
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Set the quantization config by layer. It has the highest priority among
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all the setting methods.
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Args:
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layer(Layer|list[Layer]]): One or a list of layers.
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activation(QuanterFactory | None): Quanter used for activations. Default is None.
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weight(QuanterFactory | None): Quanter used for weights. Default is None.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.nn import Linear
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>>> from paddle.quantization import QuantConfig
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>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
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>>> class Model(paddle.nn.Layer):
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... def __init__(self):
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... super().__init__()
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... self.fc = Linear(576, 120)
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>>> model = Model()
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>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
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>>> q_config = QuantConfig(activation=None, weight=None)
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>>> q_config.add_layer_config([model.fc], activation=quanter, weight=quanter)
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>>> # doctest: +SKIP('random memory address')
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>>> print(q_config)
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Global config:
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None
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Layer prefix config:
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{'linear_0': <paddle.quantization.config.SingleLayerConfig object at 0x7fe41a680ee0>}
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"""
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if isinstance(layer, list):
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for _element in layer:
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self.add_layer_config(
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_element, activation=activation, weight=weight
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)
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else:
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self.add_name_config(
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layer.full_name(), activation=activation, weight=weight
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)
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def add_name_config(
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self,
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layer_name: str | list[str],
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activation: QuanterFactory | None = None,
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weight: QuanterFactory | None = None,
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) -> None:
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r"""
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Set the quantization config by full name of layer. Its priority is
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lower than `add_layer_config`.
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Args:
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layer_name(str|list[str]): One or a list of layers' full name.
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activation(QuanterFactory | None): Quanter used for activations. Default is None.
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weight(QuanterFactory | None): Quanter used for weights. Default is None.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.nn import Linear
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>>> from paddle.quantization import QuantConfig
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>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
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>>> class Model(paddle.nn.Layer):
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... def __init__(self):
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... super().__init__()
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... self.fc = Linear(576, 120)
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>>> model = Model()
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>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
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>>> q_config = QuantConfig(activation=None, weight=None)
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>>> q_config.add_name_config([model.fc.full_name()], activation=quanter, weight=quanter)
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>>> # doctest: +SKIP('random memory address')
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>>> print(q_config)
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Global config:
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None
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Layer prefix config:
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{'linear_0': <paddle.quantization.config.SingleLayerConfig object at 0x7fe41a680fd0>}
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"""
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if isinstance(layer_name, str):
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config = SingleLayerConfig(activation, weight)
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self._prefix2config[layer_name] = config
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if isinstance(layer_name, list):
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for _element in layer_name:
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self.add_name_config(
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_element, activation=activation, weight=weight
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)
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def add_type_config(
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self,
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layer_type: type[Layer] | list[type[Layer]],
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activation: QuanterFactory | None = None,
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weight: QuanterFactory | None = None,
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) -> None:
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r"""
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Set the quantization config by the type of layer. The `layer_type` should be
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subclass of `paddle.nn.Layer`. Its priority is lower than `add_layer_config`
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and `add_name_config`.
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Args:
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layer_type(type[Layer] | list[type[Layer]]): One or a list of layers' type. It should be subclass of
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`paddle.nn.Layer`. Python built-in function `type()` can be used to get the type of a layer.
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activation(QuanterFactory | None): Quanter used for activations. Default is None.
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weight(QuanterFactory | None): Quanter used for weights. Default is None.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.nn import Linear
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>>> from paddle.quantization import QuantConfig
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>>> from paddle.quantization.quanters import (
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... FakeQuanterWithAbsMaxObserver,
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... )
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>>> class Model(paddle.nn.Layer):
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... def __init__(self):
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... super().__init__()
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... self.fc = Linear(576, 120)
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>>> model = Model()
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>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
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>>> q_config = QuantConfig(activation=None, weight=None)
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>>> q_config.add_type_config([Linear], activation=quanter, weight=quanter)
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>>> # doctest: +SKIP('random memory address')
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>>> print(q_config)
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Global config:
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None
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Layer type config:
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{<class 'paddle.nn.layer.common.Linear'>: <paddle.quantization.config.SingleLayerConfig object at 0x7fe41a680a60>}
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"""
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if isinstance(layer_type, type) and issubclass(
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layer_type, paddle.nn.Layer
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):
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config = SingleLayerConfig(activation, weight)
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self._type2config[layer_type] = config
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if isinstance(layer_type, list):
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for _element in layer_type:
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self.add_type_config(
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_element, activation=activation, weight=weight
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)
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def add_qat_layer_mapping(
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self, source: type[Layer], target: type[Layer]
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) -> None:
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r"""
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Add rules converting layers to simulated quantization layers
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before quantization-aware training. It will convert layers
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with type `source` to layers with type `target`. `source` and
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`target` should be subclass of `paddle.nn.Layer`. And a default
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mapping is provided by property `default_qat_layer_mapping`.
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Args:
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source(type[Layer]): The type of layers that will be converted.
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target(type[Layer]): The type of layers that will be converted to.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.nn import Conv2D
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>>> from paddle.quantization import QuantConfig
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>>> from paddle.quantization.quanters import (
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... FakeQuanterWithAbsMaxObserver,
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... )
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>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
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>>> q_config = QuantConfig(activation=None, weight=None)
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>>> class CustomizedQuantedConv2D(paddle.nn.Layer):
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... def forward(self, x):
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... pass
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... # add some code for quantization simulation
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>>> q_config.add_qat_layer_mapping(Conv2D, CustomizedQuantedConv2D)
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"""
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assert isinstance(source, type) and issubclass(
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source, paddle.nn.Layer
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), (
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"The source layer to be placed should be a subclass of paddle.nn.Layer"
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)
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assert isinstance(target, type) and issubclass(
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target, paddle.nn.Layer
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), "The target layer should be a subclass of paddle.nn.qat.Layer"
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self._qat_layer_mapping[source] = target
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self._customized_qat_layer_mapping[source] = target
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def add_customized_leaf(self, layer_type: type[Layer]) -> None:
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r"""
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Declare the customized layer as leaf of model for quantization.
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The leaf layer is quantized as one layer. The sublayers of
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leaf layer will not be quantized.
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Args:
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layer_type(type[Layer]): The type of layer to be declared as leaf.
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Examples:
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.. code-block:: pycon
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>>> from paddle.nn import Sequential
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>>> from paddle.quantization import QuantConfig
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>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
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>>> q_config = QuantConfig(activation=None, weight=None)
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>>> q_config.add_customized_leaf(Sequential)
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"""
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self._customized_leaves.append(layer_type)
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@property
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def customized_leaves(self) -> list[type[Layer]]:
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r"""
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Get all the customized leaves.
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"""
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return self._customized_leaves
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def _need_observe(self, layer: Layer):
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r"""
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Whether the layer should be observed by observer.
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"""
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return self._is_leaf(layer) and self._has_observer_config(layer)
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def _get_qat_layer(self, layer: Layer):
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q_config = self._get_config_by_layer(layer)
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target_type = self._customized_qat_layer_mapping.get(
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type(layer), self.qat_layer_mappings.get(type(layer))
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)
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return target_type(layer, q_config)
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def _has_observer_config(self, layer: Layer):
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r"""
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Whether the layer has been configured for activation quantization.
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"""
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_config = self._get_config_by_layer(layer)
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return _config is not None and _config.activation is not None
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def _is_leaf(self, layer: Layer):
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return (
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self._is_default_leaf(layer)
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or self._is_real_leaf(layer)
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or self._is_customized_leaf(layer)
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)
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def _is_default_leaf(self, layer: Layer):
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return type(layer) in DEFAULT_LEAVES
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def _is_real_leaf(self, layer: Layer):
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r"""
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The leaf is real leaf when it has no sublayers.
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"""
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return layer._sub_layers is None or len(layer._sub_layers) == 0
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def _is_customized_leaf(self, layer: Layer):
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return type(layer) in self.customized_leaves
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def _get_observer(self, layer: Layer):
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r"""
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Create an instance of observer or quanter according to the
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given layer's quantization config.
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"""
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_config = self._get_config_by_layer(layer)
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_observer = None if _config is None else _config.activation
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return None if _observer is None else _observer._instance(layer)
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def _get_observe_wrapper(self, layer: Layer):
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_observer = self._get_observer(layer)
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return ObserveWrapper(_observer, layer)
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@property
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def qat_layer_mappings(self) -> dict[Layer, Layer]:
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return self._qat_layer_mapping
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@property
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def default_qat_layer_mapping(self) -> dict[Layer, Layer]:
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return DEFAULT_QAT_LAYER_MAPPINGS
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@property
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def global_config(self) -> SingleLayerConfig:
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return self._global_config
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def _get_config_by_layer(self, layer) -> SingleLayerConfig:
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return self._layer2config.get(layer, None)
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def _is_quantifiable(self, layer: Layer):
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r"""
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The layer is quantifiable when it configured by activation quanter/observer
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or weight quanter/observer.
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"""
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return layer in self._layer2config
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def _specify(self, model: Layer):
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r"""
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Specify the quantization config of each sublayer in model.
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For each layer in sublayers of mode,
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1. Set the config by global config
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2. Overwrite the config with parents' config
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3. Overwrite the config with config set by layer's type
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4. Overwrite the config with config set by layer's full name
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5. Overwrite the config with config set by layer
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Args:
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model(Layer): The model to be specified by the config.
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Examples:
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.. code-block:: pycon
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|
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>>> import paddle
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>>> from paddle.nn import Linear, Sequential
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>>> from paddle.quantization import QuantConfig
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>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
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>>> class Model(paddle.nn.Layer):
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... def __init__(self):
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... super().__init__()
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... self.fc = Sequential(Linear(576, 120), Linear(576, 120))
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>>> model = Model()
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>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
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>>> q_config = QuantConfig(activation=None, weight=None)
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>>> q_config.add_layer_config([model.fc], activation=quanter, weight=quanter)
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>>> q_config._specify(model)
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"""
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self._model = model
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self._specify_helper(self._model)
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def _specify_helper(self, model: Layer):
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for child in model.children():
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layer_prefix = child.full_name()
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config = self._layer2config.get(model, self.global_config)
|
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config = self._type2config.get(type(child), config)
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config = self._prefix2config.get(layer_prefix, config)
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if config is not None:
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self._layer2config[child] = config
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self._specify_helper(child)
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return self
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def details(self) -> str:
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r"""
|
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Get the formatted details of current config.
|
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"""
|
||||
if self._model is None:
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return self.__str__()
|
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return self._details_helper(self._model)
|
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|
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def _details_helper(self, layer: Layer):
|
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sublayer_lines = []
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for name, sublayer in layer.named_children():
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sublayer_str = self._details_helper(sublayer)
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sublayer_str = self._addindent(sublayer_str, 2)
|
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if sublayer in self._layer2config:
|
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sublayer_lines.append(
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'('
|
||||
+ name
|
||||
+ '): '
|
||||
+ sublayer_str
|
||||
+ ', '
|
||||
+ str(self._layer2config[sublayer])
|
||||
)
|
||||
|
||||
final_str = layer.__class__.__name__ + '('
|
||||
if sublayer_lines:
|
||||
final_str += '\n ' + '\n '.join(sublayer_lines) + '\n'
|
||||
|
||||
final_str += ')'
|
||||
return final_str
|
||||
|
||||
def _addindent(self, string, indent):
|
||||
s1 = string.split('\n')
|
||||
if len(s1) == 1:
|
||||
return string
|
||||
s2 = []
|
||||
for idx, line in enumerate(s1):
|
||||
if idx > 0:
|
||||
s2.append(str((indent * ' ') + line))
|
||||
return s1[0] + '\n' + '\n'.join(s2)
|
||||
|
||||
def __str__(self):
|
||||
result = ""
|
||||
result += f"Global config:\n{self._global_config}\n"
|
||||
if len(self._type2config) > 0:
|
||||
result += f"Layer type config:\n{self._type2config}\n"
|
||||
if len(self._prefix2config) > 0:
|
||||
result += f"Layer prefix config: \n{self._prefix2config}\n"
|
||||
return result
|
||||
Reference in New Issue
Block a user