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python/paddle/fluid/contrib/slim/tests/test_imperative_qat_amp.py
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| # copyright (c) 2018 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 print_function | ||
|
|
||
| import os | ||
| import numpy as np | ||
| import random | ||
| import shutil | ||
| import time | ||
| import unittest | ||
| import logging | ||
|
|
||
| import paddle | ||
| import paddle.fluid as fluid | ||
| from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware | ||
| from paddle.fluid.log_helper import get_logger | ||
| from paddle.dataset.common import download | ||
|
|
||
| from imperative_test_utils import fix_model_dict, ImperativeLenet | ||
|
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| os.environ["CPU_NUM"] = "1" | ||
| if paddle.is_compiled_with_cuda(): | ||
| fluid.set_flags({"FLAGS_cudnn_deterministic": True}) | ||
|
|
||
| _logger = get_logger( | ||
| __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') | ||
|
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|
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| class TestImperativeQatAmp(unittest.TestCase): | ||
| """ | ||
| Test the combination of qat and amp. | ||
| """ | ||
|
|
||
| @classmethod | ||
| def setUpClass(cls): | ||
| timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime()) | ||
| cls.root_path = os.path.join(os.getcwd(), | ||
| "imperative_qat_amp_" + timestamp) | ||
| cls.save_path = os.path.join(cls.root_path, "model") | ||
|
|
||
| cls.download_path = 'dygraph_int8/download' | ||
| cls.cache_folder = os.path.expanduser('~/.cache/paddle/dataset/' + | ||
| cls.download_path) | ||
|
|
||
| cls.lenet_url = "https://paddle-inference-dist.cdn.bcebos.com/int8/unittest_model_data/lenet_pretrained.tar.gz" | ||
| cls.lenet_md5 = "953b802fb73b52fae42896e3c24f0afb" | ||
|
|
||
| seed = 1 | ||
| np.random.seed(seed) | ||
| paddle.static.default_main_program().random_seed = seed | ||
| paddle.static.default_startup_program().random_seed = seed | ||
|
|
||
| @classmethod | ||
| def tearDownClass(cls): | ||
| try: | ||
| shutil.rmtree(cls.root_path) | ||
| except Exception as e: | ||
| print("Failed to delete {} due to {}".format(cls.root_path, str(e))) | ||
|
|
||
| def cache_unzipping(self, target_folder, zip_path): | ||
| if not os.path.exists(target_folder): | ||
| cmd = 'mkdir {0} && tar xf {1} -C {0}'.format(target_folder, | ||
| zip_path) | ||
| os.system(cmd) | ||
|
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||
| def download_model(self, data_url, data_md5, folder_name): | ||
| download(data_url, self.download_path, data_md5) | ||
| file_name = data_url.split('/')[-1] | ||
| zip_path = os.path.join(self.cache_folder, file_name) | ||
| print('Data is downloaded at {0}'.format(zip_path)) | ||
|
|
||
| data_cache_folder = os.path.join(self.cache_folder, folder_name) | ||
| self.cache_unzipping(data_cache_folder, zip_path) | ||
| return data_cache_folder | ||
|
|
||
| def set_vars(self): | ||
| self.qat = ImperativeQuantAware() | ||
|
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| self.train_batch_num = 30 | ||
| self.train_batch_size = 32 | ||
| self.test_batch_num = 100 | ||
| self.test_batch_size = 32 | ||
| self.eval_acc_top1 = 0.99 | ||
|
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| def model_train(self, model, batch_num=-1, batch_size=32, use_amp=False): | ||
| model.train() | ||
|
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||
| train_reader = paddle.batch( | ||
| paddle.dataset.mnist.train(), batch_size=batch_size) | ||
| adam = paddle.optimizer.Adam( | ||
| learning_rate=0.001, parameters=model.parameters()) | ||
| scaler = paddle.amp.GradScaler(init_loss_scaling=500) | ||
|
|
||
| for batch_id, data in enumerate(train_reader()): | ||
| x_data = np.array([x[0].reshape(1, 28, 28) | ||
| for x in data]).astype('float32') | ||
| y_data = np.array( | ||
| [x[1] for x in data]).astype('int64').reshape(-1, 1) | ||
|
|
||
| img = paddle.to_tensor(x_data) | ||
| label = paddle.to_tensor(y_data) | ||
|
|
||
| if use_amp: | ||
| with paddle.amp.auto_cast(): | ||
| out = model(img) | ||
| acc = fluid.layers.accuracy(out, label) | ||
| loss = fluid.layers.cross_entropy(out, label) | ||
| avg_loss = fluid.layers.mean(loss) | ||
| scaled_loss = scaler.scale(avg_loss) | ||
| scaled_loss.backward() | ||
|
|
||
| scaler.minimize(adam, scaled_loss) | ||
| adam.clear_gradients() | ||
| else: | ||
| out = model(img) | ||
| acc = fluid.layers.accuracy(out, label) | ||
| loss = fluid.layers.cross_entropy(out, label) | ||
| avg_loss = fluid.layers.mean(loss) | ||
| avg_loss.backward() | ||
|
|
||
| adam.minimize(avg_loss) | ||
| model.clear_gradients() | ||
|
|
||
| if batch_id % 100 == 0: | ||
| _logger.info("Train | step {}: loss = {:}, acc= {:}".format( | ||
| batch_id, avg_loss.numpy(), acc.numpy())) | ||
|
|
||
| if batch_num > 0 and batch_id + 1 >= batch_num: | ||
| break | ||
|
|
||
| def model_test(self, model, batch_num=-1, batch_size=32, use_amp=False): | ||
| model.eval() | ||
|
|
||
| test_reader = paddle.batch( | ||
| paddle.dataset.mnist.test(), batch_size=batch_size) | ||
|
|
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| acc_top1_list = [] | ||
| for batch_id, data in enumerate(test_reader()): | ||
| x_data = np.array([x[0].reshape(1, 28, 28) | ||
| for x in data]).astype('float32') | ||
| y_data = np.array( | ||
| [x[1] for x in data]).astype('int64').reshape(-1, 1) | ||
|
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| img = paddle.to_tensor(x_data) | ||
| label = paddle.to_tensor(y_data) | ||
|
|
||
| with paddle.amp.auto_cast(use_amp): | ||
| out = model(img) | ||
| acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) | ||
| acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) | ||
|
|
||
| acc_top1_list.append(float(acc_top1.numpy())) | ||
| if batch_id % 100 == 0: | ||
| _logger.info("Test | At step {}: acc1 = {:}, acc5 = {:}".format( | ||
| batch_id, acc_top1.numpy(), acc_top5.numpy())) | ||
|
|
||
| if batch_num > 0 and batch_id + 1 >= batch_num: | ||
| break | ||
|
|
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| acc_top1 = sum(acc_top1_list) / len(acc_top1_list) | ||
| return acc_top1 | ||
|
|
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| def test_ptq(self): | ||
| start_time = time.time() | ||
|
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| self.set_vars() | ||
|
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| params_path = self.download_model(self.lenet_url, self.lenet_md5, | ||
| "lenet") | ||
| params_path += "/lenet_pretrained/lenet.pdparams" | ||
|
|
||
| with fluid.dygraph.guard(): | ||
| model = ImperativeLenet() | ||
| model_state_dict = paddle.load(params_path) | ||
| model.set_state_dict(model_state_dict) | ||
|
|
||
| _logger.info("Test fp32 model") | ||
| fp32_acc_top1 = self.model_test(model, self.test_batch_num, | ||
| self.test_batch_size) | ||
|
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| self.qat.quantize(model) | ||
|
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| use_amp = True | ||
| self.model_train(model, self.train_batch_num, self.train_batch_size, | ||
| use_amp) | ||
|
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| _logger.info("Test int8 model") | ||
| int8_acc_top1 = self.model_test(model, self.test_batch_num, | ||
| self.test_batch_size, use_amp) | ||
|
|
||
| _logger.info('fp32_acc_top1: %f, int8_acc_top1: %f' % | ||
| (fp32_acc_top1, int8_acc_top1)) | ||
| self.assertTrue( | ||
| int8_acc_top1 > fp32_acc_top1 - 0.01, | ||
| msg='fp32_acc_top1: %f, int8_acc_top1: %f' % | ||
| (fp32_acc_top1, int8_acc_top1)) | ||
|
|
||
| input_spec = [ | ||
| paddle.static.InputSpec( | ||
| shape=[None, 1, 28, 28], dtype='float32') | ||
| ] | ||
| paddle.jit.save(layer=model, path=self.save_path, input_spec=input_spec) | ||
| print('Quantized model saved in {%s}' % self.save_path) | ||
|
|
||
| end_time = time.time() | ||
| print("total time: %ss" % (end_time - start_time)) | ||
|
|
||
|
|
||
| if __name__ == '__main__': | ||
| unittest.main() |
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