mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-24 20:19:22 +01:00
267 lines
9.7 KiB
Python
267 lines
9.7 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
import os
|
|
import shutil
|
|
import tempfile
|
|
import unittest
|
|
|
|
import json
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from torch import nn
|
|
from torch.optim import SGD
|
|
from torch.optim.lr_scheduler import MultiStepLR
|
|
|
|
from modelscope.metainfo import Trainers
|
|
from modelscope.models.base import Model
|
|
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, )), np.random.randint(0, 2, (1, )), 10)
|
|
|
|
|
|
class DummyModel(nn.Module, Model):
|
|
|
|
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 = F.cross_entropy(x, labels.to(torch.long).squeeze())
|
|
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 = Trainers.default
|
|
kwargs = dict(
|
|
cfg_file=config_path,
|
|
model=model,
|
|
train_dataset=dummy_dataset,
|
|
optimizers=(optimizer, lr_scheduler),
|
|
max_epochs=2,
|
|
device='cpu')
|
|
|
|
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._hooks = [
|
|
hook for hook in trainer._hooks if hook.__class__.__name__ not in
|
|
['CheckpointHook', 'TextLoggerHook', 'IterTimerHook']
|
|
]
|
|
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 = Trainers.default
|
|
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.register_processors()
|
|
trainer._hooks = [
|
|
hook for hook in trainer._hooks if hook.__class__.__name__ not in
|
|
['CheckpointHook', 'TextLoggerHook', 'IterTimerHook']
|
|
]
|
|
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)
|
|
|
|
|
|
class TorchApexOptimizerHookTest(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.skip('Apex works abnormally with torch 1.13')
|
|
def test_apex_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 = Trainers.default
|
|
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.register_hook_from_cfg([{'type': 'ApexAMPOptimizerHook'}])
|
|
trainer._hooks = [
|
|
hook for hook in trainer._hooks if hook.__class__.__name__ not in
|
|
['CheckpointHook', 'TextLoggerHook', 'IterTimerHook']
|
|
]
|
|
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(
|
|
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()
|