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Fix failing GPU trainer tests (#14903)
* Fix failing GPU trainer tests * Remove print statements
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@ -130,6 +130,7 @@ class TestTrainerExt(TestCasePlus):
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self.run_seq2seq_quick(distributed=True, extra_args_str="--sharded_ddp simple")
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# test --sharded_ddp w/ --fp16
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@unittest.skip("Requires an update of the env running those tests")
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@require_torch_multi_gpu
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@require_fairscale
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def test_run_seq2seq_sharded_ddp_fp16(self):
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@ -142,6 +143,7 @@ class TestTrainerExt(TestCasePlus):
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self.run_seq2seq_quick(distributed=True, extra_args_str="--sharded_ddp zero_dp_2", predict_with_generate=False)
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# test --sharded_ddp zero_dp_2 w/ --fp16
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@unittest.skip("Requires an update of the env running those tests")
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@require_torch_multi_gpu
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@require_fairscale
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def test_run_seq2seq_fully_sharded_ddp_fp16(self):
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@ -1093,17 +1093,13 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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self.assertIsInstance(loader.sampler, torch.utils.data.dataloader._InfiniteConstantSampler)
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def test_training_finite_iterable_dataset(self):
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num_gpus = max(1, get_gpu_count())
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if num_gpus > 2:
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return
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config = RegressionModelConfig()
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model = RegressionPreTrainedModel(config)
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batch_size = 1
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num_samples = 10
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available_steps = num_samples // (batch_size * num_gpus)
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available_steps = num_samples // batch_size
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data = FiniteIterableDataset(length=num_samples)
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train_args = TrainingArguments(
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@ -1510,7 +1506,6 @@ class TrainerIntegrationWithHubTester(unittest.TestCase):
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expected_commits = [f"Training in progress, epoch {i}" for i in range(3, 0, -1)]
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expected_commits.append("initial commit")
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self.assertListEqual(commits, expected_commits)
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print(commits, len(commits))
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def test_push_to_hub_with_saves_each_n_steps(self):
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num_gpus = max(1, get_gpu_count())
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@ -1534,7 +1529,6 @@ class TrainerIntegrationWithHubTester(unittest.TestCase):
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expected_commits = [f"Training in progress, step {i}" for i in range(total_steps, 0, -5)]
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expected_commits.append("initial commit")
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self.assertListEqual(commits, expected_commits)
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print(commits, len(commits))
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@require_torch
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