wehub-resource-sync
131 lines
4.8 KiB
Python
131 lines
4.8 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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from __future__ import annotations
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import copy
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from typing import TYPE_CHECKING
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from paddle.distributed import fleet
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from .quantize import Quantization
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if TYPE_CHECKING:
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from paddle.nn import Layer
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from .config import QuantConfig
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class PTQ(Quantization):
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"""
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Applying post training quantization to the model.
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"""
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def __init__(self, config: QuantConfig) -> None:
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super().__init__(config)
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def _is_parallel_training(self):
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try:
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if fleet.worker_num() > 2:
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return True
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else:
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return False
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except Exception: # fleet is not initialized
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return False
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def quantize(self, model: Layer, inplace: bool = False) -> Layer:
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r"""
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Create a model for post-training quantization.
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The quantization configuration will be propagated in the model.
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And it will insert observers into the model to collect and compute
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quantization parameters.
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Args:
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model(Layer): The model to be quantized.
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inplace(bool): Whether to modify the model in-place.
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Return: The prepared model for post-training quantization.
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Examples:
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.. code-block:: pycon
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>>> from paddle.quantization import PTQ, QuantConfig
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>>> from paddle.quantization.observers import AbsmaxObserver
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>>> from paddle.vision.models import LeNet
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>>> observer = AbsmaxObserver()
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>>> q_config = QuantConfig(activation=observer, weight=observer)
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>>> ptq = PTQ(q_config)
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>>> model = LeNet()
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>>> model.eval()
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>>> quant_model = ptq.quantize(model)
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>>> print(quant_model)
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LeNet(
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(features): Sequential(
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(0): QuantedConv2D(
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(weight_quanter): AbsmaxObserverLayer()
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(activation_quanter): AbsmaxObserverLayer()
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)
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(1): ObserveWrapper(
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(_observer): AbsmaxObserverLayer()
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(_observed): ReLU()
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)
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(2): ObserveWrapper(
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(_observer): AbsmaxObserverLayer()
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(_observed): MaxPool2D(kernel_size=2, stride=2, padding=0, dilation=1)
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)
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(3): QuantedConv2D(
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(weight_quanter): AbsmaxObserverLayer()
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(activation_quanter): AbsmaxObserverLayer()
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)
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(4): ObserveWrapper(
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(_observer): AbsmaxObserverLayer()
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(_observed): ReLU()
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)
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(5): ObserveWrapper(
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(_observer): AbsmaxObserverLayer()
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(_observed): MaxPool2D(kernel_size=2, stride=2, padding=0, dilation=1)
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)
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)
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(fc): Sequential(
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(0): QuantedLinear(
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(weight_quanter): AbsmaxObserverLayer()
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(activation_quanter): AbsmaxObserverLayer()
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)
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(1): QuantedLinear(
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(weight_quanter): AbsmaxObserverLayer()
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(activation_quanter): AbsmaxObserverLayer()
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)
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(2): QuantedLinear(
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(weight_quanter): AbsmaxObserverLayer()
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(activation_quanter): AbsmaxObserverLayer()
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)
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)
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)
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"""
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_model = model
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if not inplace:
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assert not self._is_parallel_training(), (
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"'inplace' is not compatible with parallel training."
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)
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_model = copy.deepcopy(model)
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_model.eval()
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assert not model.training, (
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"Post-Training Quantization should not work on training models. Please set evaluation mode by model.eval()."
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)
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self._config._specify(_model)
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self._convert_to_quant_layers(_model, self._config)
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self._insert_activation_observers(_model, self._config)
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return _model
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