wehub-resource-sync
123 lines
4.9 KiB
Python
123 lines
4.9 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
from __future__ import annotations
|
|
|
|
import copy
|
|
from typing import TYPE_CHECKING
|
|
|
|
from .quantize import Quantization
|
|
|
|
if TYPE_CHECKING:
|
|
from paddle.nn import Layer
|
|
|
|
from .config import QuantConfig
|
|
|
|
|
|
class QAT(Quantization):
|
|
r"""
|
|
Tools used to prepare model for quantization-aware training.
|
|
Args:
|
|
config(QuantConfig): Quantization configuration
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> from paddle.quantization import QAT, QuantConfig
|
|
>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
|
|
>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
|
|
>>> q_config = QuantConfig(activation=quanter, weight=quanter)
|
|
>>> qat = QAT(q_config)
|
|
"""
|
|
|
|
def __init__(self, config: QuantConfig) -> None:
|
|
super().__init__(config)
|
|
|
|
def quantize(self, model: Layer, inplace: bool = False) -> Layer:
|
|
r"""
|
|
Create a model for quantization-aware training.
|
|
|
|
The quantization configuration will be propagated in the model.
|
|
And it will insert fake quanters into the model to simulate the quantization.
|
|
|
|
Args:
|
|
model(Layer): The model to be quantized.
|
|
inplace(bool): Whether to modify the model in-place.
|
|
|
|
Return: The prepared model for quantization-aware training.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> from paddle.quantization import QAT, QuantConfig
|
|
>>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
|
|
>>> from paddle.vision.models import LeNet
|
|
|
|
>>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
|
|
>>> q_config = QuantConfig(activation=quanter, weight=quanter)
|
|
>>> qat = QAT(q_config)
|
|
>>> model = LeNet()
|
|
>>> quant_model = qat.quantize(model)
|
|
>>> print(quant_model)
|
|
LeNet(
|
|
(features): Sequential(
|
|
(0): QuantedConv2D(
|
|
(weight_quanter): FakeQuanterWithAbsMaxObserverLayer()
|
|
(activation_quanter): FakeQuanterWithAbsMaxObserverLayer()
|
|
)
|
|
(1): ObserveWrapper(
|
|
(_observer): FakeQuanterWithAbsMaxObserverLayer()
|
|
(_observed): ReLU()
|
|
)
|
|
(2): ObserveWrapper(
|
|
(_observer): FakeQuanterWithAbsMaxObserverLayer()
|
|
(_observed): MaxPool2D(kernel_size=2, stride=2, padding=0, dilation=1)
|
|
)
|
|
(3): QuantedConv2D(
|
|
(weight_quanter): FakeQuanterWithAbsMaxObserverLayer()
|
|
(activation_quanter): FakeQuanterWithAbsMaxObserverLayer()
|
|
)
|
|
(4): ObserveWrapper(
|
|
(_observer): FakeQuanterWithAbsMaxObserverLayer()
|
|
(_observed): ReLU()
|
|
)
|
|
(5): ObserveWrapper(
|
|
(_observer): FakeQuanterWithAbsMaxObserverLayer()
|
|
(_observed): MaxPool2D(kernel_size=2, stride=2, padding=0, dilation=1)
|
|
)
|
|
)
|
|
(fc): Sequential(
|
|
(0): QuantedLinear(
|
|
(weight_quanter): FakeQuanterWithAbsMaxObserverLayer()
|
|
(activation_quanter): FakeQuanterWithAbsMaxObserverLayer()
|
|
)
|
|
(1): QuantedLinear(
|
|
(weight_quanter): FakeQuanterWithAbsMaxObserverLayer()
|
|
(activation_quanter): FakeQuanterWithAbsMaxObserverLayer()
|
|
)
|
|
(2): QuantedLinear(
|
|
(weight_quanter): FakeQuanterWithAbsMaxObserverLayer()
|
|
(activation_quanter): FakeQuanterWithAbsMaxObserverLayer()
|
|
)
|
|
)
|
|
)
|
|
"""
|
|
assert model.training, (
|
|
"Quantization-Aware Training should work on training models. Please set training mode by model.train()."
|
|
)
|
|
_model = model if inplace else copy.deepcopy(model)
|
|
self._config._specify(_model)
|
|
self._convert_to_quant_layers(_model, self._config)
|
|
self._insert_activation_observers(_model, self._config)
|
|
return _model
|