wehub-resource-sync
108 lines
4.2 KiB
Python
108 lines
4.2 KiB
Python
# Copyright (c) 2023 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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"""Define stub used in quantization."""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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from ..layer.layers import Layer
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if TYPE_CHECKING:
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from paddle import Tensor
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from paddle.quantization import QuantConfig
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from paddle.quantization.factory import QuanterFactory
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class Stub(Layer):
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r"""
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The stub is used as placeholders that will be replaced by observers before PTQ or QAT.
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It is hard to assign a quantization configuration to a functional API called in
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the forward of a layer. Instead, we can create a stub and add it to the sublayers of the layer.
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And call the stub before the functional API in the forward. The observer held by the
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stub will observe or quantize the inputs of the functional API.
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Args:
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observer(QuanterFactory): The configured information of the observer to be inserted.
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It will use a global configuration to create the observers if the 'observer' 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.quant import Stub
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>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
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>>> from paddle.nn import Conv2D
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>>> from paddle.quantization import QAT, QuantConfig
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>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
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>>> class Model(paddle.nn.Layer):
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... def __init__(self, num_classes=10):
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... super().__init__()
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... self.conv = Conv2D(3, 6, 3, stride=1, padding=1)
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... self.quant = Stub(quanter)
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...
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... def forward(self, inputs):
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... out = self.conv(inputs)
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... out = self.quant(out)
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... return paddle.nn.functional.relu(out)
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>>> model = Model()
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>>> q_config = QuantConfig(activation=quanter, weight=quanter)
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>>> qat = QAT(q_config)
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>>> quant_model = qat.quantize(model)
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>>> print(quant_model)
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Model(
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(conv): QuantedConv2D(
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(weight_quanter): FakeQuanterWithAbsMaxObserverLayer()
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(activation_quanter): FakeQuanterWithAbsMaxObserverLayer()
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)
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(quant): QuanterStub(
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(_observer): FakeQuanterWithAbsMaxObserverLayer()
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)
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)
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"""
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def __init__(self, observer: QuanterFactory | None = None) -> None:
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super().__init__()
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self._observer = observer
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def forward(self, input: Tensor) -> Tensor:
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return input
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class QuanterStub(Layer):
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r"""
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It is an identity layer with an observer observing the input.
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Before QAT or PTQ, the stub in the model will be replaced with an instance of QuanterStub.
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The user should not use this class directly.
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Args:
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layer(paddle.nn.Layer): The stub layer with an observer configure factory. If the observer
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of the stub layer is none, it will use 'q_config' to create an observer instance.
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q_config(QuantConfig): The quantization configuration for the current stub layer.
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"""
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def __init__(self, layer: Stub, q_config: QuantConfig) -> None:
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super().__init__()
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self._observer = None
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if layer._observer is not None:
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self._observer = layer._observer._instance(layer)
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elif q_config.activation is not None:
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self._observer = q_config.activation._instance(layer)
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def forward(self, input: Tensor) -> Tensor:
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return self._observer(input) if self._observer is not None else input
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