modelscope/tests/trainers/hooks/test_optimizer_hook.py

# Copyright (c) Alibaba, Inc. and its affiliates.
import os
import shutil
import tempfile
import unittest

import json
import numpy as np
import torch
from torch import nn
from torch.optim import SGD
from torch.optim.lr_scheduler import MultiStepLR

from modelscope.trainers import build_trainer
from modelscope.utils.constant import ModelFile, TrainerStages
from modelscope.utils.test_utils import create_dummy_test_dataset

dummy_dataset = create_dummy_test_dataset(
    np.random.random(size=(2, 2)), np.random.randint(0, 2, (1, )), 10)


class DummyModel(nn.Module):

    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(2, 2)
        self.bn = nn.BatchNorm1d(2)

    def forward(self, feat, labels):
        x = self.linear(feat)
        x = self.bn(x)
        loss = torch.sum(x)
        return dict(logits=x, loss=loss)


class OptimizerHookTest(unittest.TestCase):

    def setUp(self):
        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
        self.tmp_dir = tempfile.TemporaryDirectory().name
        if not os.path.exists(self.tmp_dir):
            os.makedirs(self.tmp_dir)

    def tearDown(self):
        super().tearDown()
        shutil.rmtree(self.tmp_dir)

    def test_optimizer_hook(self):
        json_cfg = {
            'task': 'image_classification',
            'train': {
                'work_dir': self.tmp_dir,
                'dataloader': {
                    'batch_size_per_gpu': 2,
                    'workers_per_gpu': 1
                }
            }
        }

        config_path = os.path.join(self.tmp_dir, ModelFile.CONFIGURATION)
        with open(config_path, 'w') as f:
            json.dump(json_cfg, f)

        model = DummyModel()
        optimizer = SGD(model.parameters(), lr=0.01)
        lr_scheduler = MultiStepLR(optimizer, milestones=[1, 2])
        trainer_name = 'EpochBasedTrainer'
        kwargs = dict(
            cfg_file=config_path,
            model=model,
            train_dataset=dummy_dataset,
            optimizers=(optimizer, lr_scheduler),
            max_epochs=2)

        trainer = build_trainer(trainer_name, kwargs)
        train_dataloader = trainer._build_dataloader_with_dataset(
            trainer.train_dataset, **trainer.cfg.train.get('dataloader', {}))
        trainer.register_optimizers_hook()

        trainer.invoke_hook(TrainerStages.before_run)

        for _ in range(trainer._epoch, trainer._max_epochs):
            trainer.invoke_hook(TrainerStages.before_train_epoch)
            for _, data_batch in enumerate(train_dataloader):
                trainer.invoke_hook(TrainerStages.before_train_iter)
                trainer.train_step(trainer.model, data_batch)
                trainer.invoke_hook(TrainerStages.after_train_iter)

                self.assertEqual(
                    len(trainer.optimizer.param_groups[0]['params']), 4)
                for i in range(4):
                    self.assertTrue(trainer.optimizer.param_groups[0]['params']
                                    [i].requires_grad)

            trainer.invoke_hook(TrainerStages.after_train_epoch)
            trainer._epoch += 1
        trainer.invoke_hook(TrainerStages.after_run)


class TorchAMPOptimizerHookTest(unittest.TestCase):

    def setUp(self):
        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
        self.tmp_dir = tempfile.TemporaryDirectory().name
        if not os.path.exists(self.tmp_dir):
            os.makedirs(self.tmp_dir)

    def tearDown(self):
        super().tearDown()
        shutil.rmtree(self.tmp_dir)

    @unittest.skipIf(not torch.cuda.is_available(),
                     'skip this test when cuda is not available')
    def test_amp_optimizer_hook(self):
        json_cfg = {
            'task': 'image_classification',
            'train': {
                'work_dir': self.tmp_dir,
                'dataloader': {
                    'batch_size_per_gpu': 2,
                    'workers_per_gpu': 1
                }
            }
        }

        config_path = os.path.join(self.tmp_dir, ModelFile.CONFIGURATION)
        with open(config_path, 'w') as f:
            json.dump(json_cfg, f)

        model = DummyModel().cuda()
        optimizer = SGD(model.parameters(), lr=0.01)
        lr_scheduler = MultiStepLR(optimizer, milestones=[1, 2])
        trainer_name = 'EpochBasedTrainer'
        kwargs = dict(
            cfg_file=config_path,
            model=model,
            train_dataset=dummy_dataset,
            optimizers=(optimizer, lr_scheduler),
            max_epochs=2,
            use_fp16=True)

        trainer = build_trainer(trainer_name, kwargs)
        train_dataloader = trainer._build_dataloader_with_dataset(
            trainer.train_dataset, **trainer.cfg.train.get('dataloader', {}))
        trainer.register_optimizers_hook()

        trainer.invoke_hook(TrainerStages.before_run)

        for _ in range(trainer._epoch, trainer._max_epochs):
            trainer.invoke_hook(TrainerStages.before_train_epoch)
            for _, data_batch in enumerate(train_dataloader):
                for k, v in data_batch.items():
                    data_batch[k] = v.cuda()
                trainer.invoke_hook(TrainerStages.before_train_iter)
                trainer.train_step(trainer.model, data_batch)
                trainer.invoke_hook(TrainerStages.after_train_iter)

                self.assertEqual(trainer.train_outputs['logits'].dtype,
                                 torch.float16)

                # test if `after_train_iter`, whether the model is reset to fp32
                trainer.train_step(trainer.model, data_batch)
                self.assertEqual(trainer.train_outputs['logits'].dtype,
                                 torch.float32)

                self.assertEqual(
                    len(trainer.optimizer.param_groups[0]['params']), 4)
                for i in range(4):
                    self.assertTrue(trainer.optimizer.param_groups[0]['params']
                                    [i].requires_grad)

            trainer.invoke_hook(TrainerStages.after_train_epoch)
            trainer._epoch += 1
        trainer.invoke_hook(TrainerStages.after_run)


if __name__ == '__main__':
    unittest.main()