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69104c0f8a
1. 将 single_gpu_test 与 multi_gpu_test 中的 model.forward 部分分离为 EpochBasedTrainer 中的 evaluation_step，为部分 evaluation 阶段不调用 forward 的模型提供更好的灵活性
2. 重构代码将文本生成模型 Model 层的输入输出统一为 Tensor，Tensor 到 str 的 decode 过程移动到 pipeline 中完成
3. pipeline 后处理添加对中文和中文标点与英文混杂时空格的处理，使 decode 后中英文混杂输出正确
4. 添加 TextGenerationTrainer 修复了部分模型 evaluation 过程 forward 输出单个 token 计算 metrics 的问题
5. 修复了 rouge 无法接收空字符串的问题
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10473768
127 lines
3.5 KiB
Python
127 lines
3.5 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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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 torch
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from torch import nn
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from torch.utils.data import DataLoader
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from modelscope.metrics.builder import MetricKeys
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from modelscope.metrics.sequence_classification_metric import \
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SequenceClassificationMetric
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from modelscope.models.base import Model
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from modelscope.trainers import EpochBasedTrainer
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from modelscope.trainers.utils.inference import multi_gpu_test, single_gpu_test
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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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from modelscope.utils.torch_utils import get_dist_info, init_dist
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dummy_dataset = create_dummy_test_dataset(
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torch.rand((5, )), torch.randint(0, 4, (1, )), 20)
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class DummyModel(nn.Module, Model):
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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 DummyTrainer(EpochBasedTrainer):
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def __init__(self, model):
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self.model = model
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def test_func(dist=False):
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dummy_model = DummyModel()
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dataset = dummy_dataset.to_torch_dataset()
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dummy_loader = DataLoader(
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dataset,
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batch_size=2,
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)
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metric_class = SequenceClassificationMetric()
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if dist:
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init_dist(launcher='pytorch')
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rank, world_size = get_dist_info()
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device = torch.device(f'cuda:{rank}')
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dummy_model.cuda()
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if world_size > 1:
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from torch.nn.parallel.distributed import DistributedDataParallel
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dummy_model = DistributedDataParallel(
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dummy_model, device_ids=[torch.cuda.current_device()])
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test_func = multi_gpu_test
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else:
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test_func = single_gpu_test
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dummy_trainer = DummyTrainer(dummy_model)
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metric_results = test_func(
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dummy_trainer,
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dummy_loader,
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device=device,
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metric_classes=[metric_class])
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return metric_results
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@unittest.skipIf(not torch.cuda.is_available(), 'cuda unittest')
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class SingleGpuTestTest(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() >= 1, 'skip test in current test level')
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def test_single_gpu_test(self):
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metric_results = test_func()
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self.assertIn(MetricKeys.ACCURACY, metric_results)
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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 MultiGpuTestTest(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_gpu_test(self):
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self.start(
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test_func,
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num_gpus=2,
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assert_callback=lambda x: self.assertIn(MetricKeys.ACCURACY, x),
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dist=True)
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if __name__ == '__main__':
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unittest.main()
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