mirror of
https://github.com/modelscope/modelscope.git
synced 2026-02-25 04:30:48 +01:00
a0bc5549a1
Design doc: https://yuque.alibaba-inc.com/suluyan.sly/yh1rvu/yx0owblyebpa2b3l?singleDoc#flU3s 1. Add parallel_group field in trainer to support DP, TP, PP. 2. Move the construction of common hooks(except optimizer/lrscheduler hook) to trainer's init method to support after_init stage. after_init is to support DP, TP, PP's initializing https://aone.alibaba-inc.com/v2/workitem#viewIdentifier=1c46ee8637e0c978f115b6f7&openWorkitemIdentifier=48099986 3. Add before_eval/after_eval stage to support model wrapping. to solve the order problem of apex amp & ddp wrapping. https://aone.alibaba-inc.com/v2/workitem#viewIdentifier=1c46ee8637e0c978f115b6f7&openWorkitemIdentifier=48099986 4. Exporter supports lazy importing. https://aone.alibaba-inc.com/v2/workitem#viewIdentifier=1c46ee8637e0c978f115b6f7&openWorkitemIdentifier=48122780 5. Fold all megatron imports to megatron hook. https://aone.alibaba-inc.com/v2/workitem#viewIdentifier=1c46ee8637e0c978f115b6f7&openWorkitemIdentifier=48099986 6. Add compile method to TorchModel ,Pipeline,Trainer to support torch2.0 https://aone.alibaba-inc.com/v2/workitem#viewIdentifier=1c46ee8637e0c978f115b6f7&openWorkitemIdentifier=46869415 7. Fix bug: Lrscheduler builder does not support torch2.0 8. Add callbacks for trainer https://aone.alibaba-inc.com/v2/workitem#viewIdentifier=1c46ee8637e0c978f115b6f7&openWorkitemIdentifier=48210342 Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11849932
113 lines
3.6 KiB
Python
113 lines
3.6 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
|
|
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 TorchModel
|
|
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=(5, )), np.random.randint(0, 4, (1, )), 10)
|
|
|
|
|
|
class DummyModel(TorchModel):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.linear = nn.Linear(5, 10)
|
|
self.bn = nn.BatchNorm1d(10)
|
|
|
|
def forward(self, feat, labels):
|
|
x = self.linear(feat)
|
|
|
|
x = self.bn(x)
|
|
loss = torch.sum(x)
|
|
return dict(logits=x, loss=loss)
|
|
|
|
|
|
class SparsityHookTest(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_sparsity_hook(self):
|
|
json_cfg = {
|
|
'task': 'image_classification',
|
|
'train': {
|
|
'work_dir':
|
|
self.tmp_dir,
|
|
'dataloader': {
|
|
'batch_size_per_gpu': 2,
|
|
'workers_per_gpu': 1
|
|
},
|
|
'hooks': [{
|
|
'type': 'SparsityHook',
|
|
'pruning_method': 'pst',
|
|
'config': {
|
|
'weight_rank': 1,
|
|
'mask_rank': 1,
|
|
'final_sparsity': 0.9,
|
|
'frequency': 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=[2, 4])
|
|
trainer_name = Trainers.default
|
|
kwargs = dict(
|
|
cfg_file=config_path,
|
|
model=model,
|
|
train_dataset=dummy_dataset,
|
|
optimizers=(optimizer, lr_scheduler),
|
|
max_epochs=5,
|
|
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.train_dataloader = train_dataloader
|
|
trainer.data_loader = train_dataloader
|
|
trainer.invoke_hook(TrainerStages.before_run)
|
|
for i 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)
|
|
trainer.invoke_hook(TrainerStages.after_train_epoch)
|
|
trainer.invoke_hook(TrainerStages.after_run)
|
|
|
|
self.assertEqual(
|
|
torch.mean(1.0 * (trainer.model.linear.weight == 0)), 0.9)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|