mirror of
https://github.com/modelscope/modelscope.git
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4dca4773db
1. Support csanmt exporting to savedmodel format 2. Create a new base class for text-ranking preprocessors, and move some parameters of mgeo_ranking_preprocessor to init method 3. Avoid Model & Preprocessor classes coupled with pytorch 4. Regression test supports comparing only model output 5. Support zero-shot exporting to onnx and torchscript Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11522461
332 lines
11 KiB
Python
332 lines
11 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import glob
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import os
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import shutil
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import tempfile
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import unittest
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import json
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import numpy as np
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import torch
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from torch import nn
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from torch.optim import SGD
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from torch.optim.lr_scheduler import StepLR
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from torch.utils.data import IterableDataset
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from modelscope.metainfo import Metrics, Trainers
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from modelscope.metrics.builder import MetricKeys
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from modelscope.models.base import Model, TorchModel
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from modelscope.trainers import EpochBasedTrainer, build_trainer
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from modelscope.utils.constant import LogKeys, ModeKeys, ModelFile, Tasks
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from modelscope.utils.test_utils import (DistributedTestCase,
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create_dummy_test_dataset, test_level)
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class DummyIterableDataset(IterableDataset):
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def __iter__(self):
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feat = np.random.random(size=(5, )).astype(np.float32)
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labels = np.random.randint(0, 4, (1, ))
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iterations = [{'feat': feat, 'labels': labels}] * 500
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return iter(iterations)
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dummy_dataset_small = create_dummy_test_dataset(
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np.random.random(size=(5, )), np.random.randint(0, 4, (1, )), 20)
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dummy_dataset_big = create_dummy_test_dataset(
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np.random.random(size=(5, )), np.random.randint(0, 4, (1, )), 40)
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class DummyModel(TorchModel):
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def __init__(self):
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super().__init__()
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self.linear = nn.Linear(5, 4)
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self.bn = nn.BatchNorm1d(4)
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def forward(self, feat, labels):
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x = self.linear(feat)
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x = self.bn(x)
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loss = torch.sum(x)
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return dict(logits=x, loss=loss)
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class DummyModelForwardInputs(DummyModel):
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def forward(self, inputs):
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feat, labels = inputs['feat'], inputs['labels']
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return super().forward(feat, labels)
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def train_func(work_dir,
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dist=False,
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iterable_dataset=False,
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forward_inputs=False,
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**kwargs):
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json_cfg = {
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'task': Tasks.image_classification,
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'model': {},
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'train': {
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'work_dir': work_dir,
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'dataloader': {
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'batch_size_per_gpu': 2,
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'workers_per_gpu': 1
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},
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'hooks': [{
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'type': 'EvaluationHook',
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'interval': 1
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}]
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},
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'evaluation': {
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'dataloader': {
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'batch_size_per_gpu': 1,
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'workers_per_gpu': 1,
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'shuffle': False
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},
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'metrics': [Metrics.seq_cls_metric]
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}
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}
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config_path = os.path.join(work_dir, ModelFile.CONFIGURATION)
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with open(config_path, 'w') as f:
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json.dump(json_cfg, f)
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if forward_inputs:
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model = DummyModelForwardInputs()
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else:
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model = DummyModel()
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optimmizer = SGD(model.parameters(), lr=0.01)
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lr_scheduler = StepLR(optimmizer, 2)
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trainer_name = Trainers.default
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if iterable_dataset:
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train_dataset = DummyIterableDataset()
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eval_dataset = DummyIterableDataset()
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else:
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train_dataset = dummy_dataset_big
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eval_dataset = dummy_dataset_small
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_kwargs = dict(
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cfg_file=config_path,
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model=model,
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data_collator=None,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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optimizers=(optimmizer, lr_scheduler),
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max_epochs=3,
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device='gpu',
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launcher='pytorch' if dist else None,
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**kwargs)
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trainer = build_trainer(trainer_name, _kwargs)
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trainer.train()
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@unittest.skipIf(not torch.cuda.is_available(), 'cuda unittest')
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class TrainerTestSingleGpu(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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self.tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(self.tmp_dir):
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os.makedirs(self.tmp_dir)
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def tearDown(self):
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super().tearDown()
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shutil.rmtree(self.tmp_dir)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_single_gpu(self):
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train_func(self.tmp_dir)
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results_files = os.listdir(self.tmp_dir)
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json_files = glob.glob(os.path.join(self.tmp_dir, '*.log.json'))
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self.assertEqual(len(json_files), 1)
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with open(json_files[0], 'r', encoding='utf-8') as f:
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lines = [i.strip() for i in f.readlines()]
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 1,
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LogKeys.ITER: 10,
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LogKeys.LR: 0.01
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}, json.loads(lines[0]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 1,
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LogKeys.ITER: 20,
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LogKeys.LR: 0.01
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}, json.loads(lines[1]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.EVAL,
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LogKeys.EPOCH: 1,
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LogKeys.ITER: 20
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}, json.loads(lines[2]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 2,
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LogKeys.ITER: 10,
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LogKeys.LR: 0.01
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}, json.loads(lines[3]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 2,
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LogKeys.ITER: 20,
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LogKeys.LR: 0.01
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}, json.loads(lines[4]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.EVAL,
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LogKeys.EPOCH: 2,
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LogKeys.ITER: 20
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}, json.loads(lines[5]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 3,
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LogKeys.ITER: 10,
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LogKeys.LR: 0.001
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}, json.loads(lines[6]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 3,
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LogKeys.ITER: 20,
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LogKeys.LR: 0.001
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}, json.loads(lines[7]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.EVAL,
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LogKeys.EPOCH: 3,
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LogKeys.ITER: 20
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}, json.loads(lines[8]))
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self.assertIn(f'{LogKeys.EPOCH}_1.pth', results_files)
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self.assertIn(f'{LogKeys.EPOCH}_2.pth', results_files)
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self.assertIn(f'{LogKeys.EPOCH}_3.pth', results_files)
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for i in [0, 1, 3, 4, 6, 7]:
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self.assertIn(LogKeys.DATA_LOAD_TIME, lines[i])
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self.assertIn(LogKeys.ITER_TIME, lines[i])
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for i in [2, 5, 8]:
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self.assertIn(MetricKeys.ACCURACY, lines[i])
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@unittest.skipIf(not torch.cuda.is_available()
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or torch.cuda.device_count() <= 1, 'distributed unittest')
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class TrainerTestMultiGpus(DistributedTestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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self.tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(self.tmp_dir):
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os.makedirs(self.tmp_dir)
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def tearDown(self):
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super().tearDown()
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shutil.rmtree(self.tmp_dir)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_multi_gpus(self):
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self.start(train_func, num_gpus=2, work_dir=self.tmp_dir, dist=True)
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results_files = os.listdir(self.tmp_dir)
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json_files = glob.glob(os.path.join(self.tmp_dir, '*.log.json'))
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self.assertEqual(len(json_files), 1)
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with open(json_files[0], 'r', encoding='utf-8') as f:
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lines = [i.strip() for i in f.readlines()]
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 1,
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LogKeys.ITER: 10,
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LogKeys.LR: 0.01
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}, json.loads(lines[0]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.EVAL,
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LogKeys.EPOCH: 1,
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LogKeys.ITER: 10
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}, json.loads(lines[1]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 2,
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LogKeys.ITER: 10,
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LogKeys.LR: 0.01
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}, json.loads(lines[2]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.EVAL,
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LogKeys.EPOCH: 2,
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LogKeys.ITER: 10
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}, json.loads(lines[3]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.TRAIN,
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LogKeys.EPOCH: 3,
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LogKeys.ITER: 10,
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LogKeys.LR: 0.001
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}, json.loads(lines[4]))
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self.assertDictContainsSubset(
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{
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LogKeys.MODE: ModeKeys.EVAL,
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LogKeys.EPOCH: 3,
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LogKeys.ITER: 10
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}, json.loads(lines[5]))
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self.assertIn(f'{LogKeys.EPOCH}_1.pth', results_files)
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self.assertIn(f'{LogKeys.EPOCH}_2.pth', results_files)
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self.assertIn(f'{LogKeys.EPOCH}_3.pth', results_files)
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for i in [0, 2, 4]:
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self.assertIn(LogKeys.DATA_LOAD_TIME, lines[i])
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self.assertIn(LogKeys.ITER_TIME, lines[i])
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for i in [1, 3, 5]:
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self.assertIn(MetricKeys.ACCURACY, lines[i])
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_multi_gpus_forward_inputs(self):
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self.start(
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train_func,
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num_gpus=2,
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work_dir=self.tmp_dir,
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dist=True,
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forward_inputs=True)
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results_files = os.listdir(self.tmp_dir)
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json_files = glob.glob(os.path.join(self.tmp_dir, '*.log.json'))
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self.assertEqual(len(json_files), 1)
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self.assertIn(f'{LogKeys.EPOCH}_1.pth', results_files)
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self.assertIn(f'{LogKeys.EPOCH}_2.pth', results_files)
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self.assertIn(f'{LogKeys.EPOCH}_3.pth', results_files)
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# TODO: support iters_per_epoch for dist mode
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@unittest.skipIf(True, 'need to adapt to DistributedSampler')
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def test_multi_gpus_with_iters_per_epoch(self):
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self.start(
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train_func,
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num_gpus=2,
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work_dir=self.tmp_dir,
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dist=True,
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iterable_dataset=True,
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train_iters_per_epoch=20,
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val_iters_per_epoch=10,
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)
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results_files = os.listdir(self.tmp_dir)
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json_files = glob.glob(os.path.join(self.tmp_dir, '*.log.json'))
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self.assertEqual(len(json_files), 1)
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with open(json_files[0], 'r', encoding='utf-8') as f:
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lines = [i.strip() for i in f.readlines()]
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print(results_files, lines)
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if __name__ == '__main__':
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unittest.main()
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