# Copyright (c) 2023 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. import sys sys.path.append("../../") import os import unittest import paddle import tempfile import numpy as np from paddle.vision.models import resnet18 from paddle.quantization import QuantConfig from paddle.quantization import PTQ from paddleslim.quant.observers import HistObserver, KLObserver, EMDObserver, MSEObserver, AVGObserver from paddleslim.quant.observers.hist import PercentHistObserverLayer from paddleslim.quant.observers.kl import KLObserverLayer from paddleslim.quant.observers.mse import MSEObserverLayer from paddleslim.quant.observers.avg import AVGObserverLayer from paddleslim.quant.observers.emd import EMDObserverLayer from paddleslim.quant.observers.kl import KLObserverLayer from paddleslim.quant.observers.mse_weight import MSEChannelWiseWeightObserver from paddleslim.quant.observers.abs_max_weight import AbsMaxChannelWiseWeightObserver from paddle.nn.quant.format import LinearDequanter, LinearQuanter import logging from paddleslim.common import get_logger _logger = get_logger(__name__, level=logging.INFO) class ImperativeLenet(paddle.nn.Layer): def __init__(self, num_classes=10, classifier_activation='softmax'): super(ImperativeLenet, self).__init__() self.features = paddle.nn.Sequential( paddle.nn.Conv2D( in_channels=1, out_channels=6, kernel_size=3, stride=1, padding=1), paddle.nn.AvgPool2D(kernel_size=2, stride=2), paddle.nn.Conv2D( in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=0), paddle.nn.AvgPool2D(kernel_size=2, stride=2)) self.fc = paddle.nn.Sequential( paddle.nn.Linear(in_features=400, out_features=120), paddle.nn.Linear(in_features=120, out_features=84), paddle.nn.Linear(in_features=84, out_features=num_classes), ) def forward(self, inputs): x = self.features(inputs) x = paddle.flatten(x, 1) x = self.fc(x) return x class TestPTQObserverAcc(unittest.TestCase): def __init__(self, activation_observer, weight_observer=None, *args, **kvargs): super(TestPTQObserverAcc, self).__init__(*args, **kvargs) self.act_observer = activation_observer self.weight_observer = weight_observer def setUp(self): paddle.set_device("cpu") self.init_case() self.dummy_input = paddle.rand([1, 3, 224, 224]) self.temp_dir = tempfile.TemporaryDirectory(dir="./") self.path = os.path.join(self.temp_dir.name, 'qat') if not os.path.exists('ILSVRC2012_data_demo'): os.system( 'wget -q https://sys-p0.bj.bcebos.com/slim_ci/ILSVRC2012_data_demo.tar.gz' ) os.system('tar -xf ILSVRC2012_data_demo.tar.gz') seed = 1 np.random.seed(seed) paddle.static.default_main_program().random_seed = seed paddle.static.default_startup_program().random_seed = seed def tearDown(self): self.temp_dir.cleanup() def runTest(self): self.test_convergence() def init_case(self): self.q_config = QuantConfig(activation=None, weight=None) self.q_config.add_type_config( paddle.nn.Conv2D, activation=self.act_observer, weight=self.weight_observer) def _count_layers(self, model, layer_type): count = 0 for _layer in model.sublayers(True): if isinstance(_layer, layer_type): count += 1 return count def test_convergence(self): model = ImperativeLenet() place = paddle.CUDAPlace(0) \ if paddle.is_compiled_with_cuda() else paddle.CPUPlace() transform = paddle.vision.transforms.Compose([ paddle.vision.transforms.Transpose(), paddle.vision.transforms.Normalize([127.5], [127.5]) ]) train_dataset = paddle.vision.datasets.MNIST( mode='train', backend='cv2', transform=transform) val_dataset = paddle.vision.datasets.MNIST( mode='test', backend='cv2', transform=transform) train_reader = paddle.io.DataLoader( train_dataset, drop_last=True, places=place, batch_size=64, return_list=True) test_reader = paddle.io.DataLoader( val_dataset, places=place, batch_size=64, return_list=True) def train(model): adam = paddle.optimizer.Adam( learning_rate=0.0001, parameters=model.parameters()) epoch_num = 1 for epoch in range(epoch_num): model.train() for batch_id, data in enumerate(train_reader): img = paddle.to_tensor(data[0]) label = paddle.to_tensor(data[1]) img = paddle.reshape(img, [-1, 1, 28, 28]) label = paddle.reshape(label, [-1, 1]) out = model(img) acc = paddle.metric.accuracy(out, label) loss = paddle.nn.functional.loss.cross_entropy(out, label) avg_loss = paddle.mean(loss) avg_loss.backward() adam.minimize(avg_loss) model.clear_gradients() if batch_id % 100 == 0: _logger.info( "Train | At epoch {} step {}: loss = {:}, acc= {:}". format(epoch, batch_id, avg_loss.numpy(), acc.numpy())) def test(model): model.eval() avg_acc = [[], []] for batch_id, data in enumerate(test_reader): img = paddle.to_tensor(data[0]) img = paddle.reshape(img, [-1, 1, 28, 28]) label = paddle.to_tensor(data[1]) label = paddle.reshape(label, [-1, 1]) out = model(img) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) avg_acc[0].append(acc_top1.numpy()) avg_acc[1].append(acc_top5.numpy()) if batch_id % 100 == 0: _logger.info( "Test | step {}: acc1 = {:}, acc5 = {:}".format( batch_id, acc_top1.numpy(), acc_top5.numpy())) _logger.info("Test | Average: acc_top1 {}, acc_top5 {}".format( np.mean(avg_acc[0]), np.mean(avg_acc[1]))) return np.mean(avg_acc[0]), np.mean(avg_acc[1]) def ptq_sample(model): model.eval() avg_acc = [[], []] for batch_id, data in enumerate(test_reader): img = paddle.to_tensor(data[0]) img = paddle.reshape(img, [-1, 1, 28, 28]) label = paddle.to_tensor(data[1]) label = paddle.reshape(label, [-1, 1]) out = model(img) if batch_id % 100 == 0: _logger.info("PTQ sampling | step {}".format(batch_id)) train(model) top1_1, top5_1 = test(model) ptq = PTQ(self.q_config) model.eval() quant_model = ptq.quantize(model, inplace=False) ptq_sample(quant_model) converted_model = ptq.convert(quant_model, inplace=True) top1_2, top5_2 = test(converted_model) _logger.info( "Before quantization: top1: {}, top5: {}".format(top1_1, top5_1)) _logger.info( "After quantization: top1: {}, top5: {}".format(top1_2, top5_2)) _logger.info("\n") diff = 0.01 self.assertTrue( top1_1 - top1_2 < diff, msg="The acc of quant model is too lower than fp32 model") _logger.info('done') return observer_suite = unittest.TestSuite() for _observer in [ AVGObserver(), EMDObserver(), MSEObserver(), KLObserver(bins_count=256), HistObserver(sign=True, symmetric=True), ]: observer_suite.addTest( TestPTQObserverAcc( activation_observer=_observer, weight_observer=_observer)) for _weight_observer in [ MSEChannelWiseWeightObserver(), AbsMaxChannelWiseWeightObserver(), ]: observer_suite.addTest( TestPTQObserverAcc( activation_observer=MSEObserver(), weight_observer=_weight_observer)) if __name__ == '__main__': runner = unittest.TextTestRunner(verbosity=2) runner.run(observer_suite) os.system('rm -rf ILSVRC2012_data_demo.tar.gz') os.system('rm -rf ILSVRC2012_data_demo')