mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-03 12:50:06 +06:00
replace directly-specified-test-dirs with tmp_dir
This commit is contained in:
ryohei-a-shimizu
parent
d911458b59
commit
310a6d962e
@ -552,10 +552,10 @@ if is_torch_available():
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compute_metrics = kwargs.pop("compute_metrics", None)
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data_collator = kwargs.pop("data_collator", None)
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optimizers = kwargs.pop("optimizers", (None, None))
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output_dir = kwargs.pop("output_dir", "./regression")
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preprocess_logits_for_metrics = kwargs.pop("preprocess_logits_for_metrics", None)
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args = RegressionTrainingArguments(output_dir, a=a, b=b, keep_report_to=keep_report_to, **kwargs)
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kwargs.pop("output_dir") # remove output_dir from kwargs
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = RegressionTrainingArguments(tmp_dir, a=a, b=b, keep_report_to=keep_report_to, **kwargs)
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return Trainer(
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model,
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args,
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@ -713,7 +713,8 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
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# Base training. Should have the same results as test_reproducible_training
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model = RegressionModel()
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args = TrainingArguments("./regression", learning_rate=0.1, report_to="none")
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(tmp_dir, learning_rate=0.1, report_to="none")
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trainer = Trainer(model, args, train_dataset=train_dataset)
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trainer.train()
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self.check_trained_model(trainer.model)
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@ -735,7 +736,8 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
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def test_model_init(self):
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train_dataset = RegressionDataset()
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args = TrainingArguments("./regression", learning_rate=0.1, report_to="none")
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(tmp_dir, learning_rate=0.1, report_to="none")
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trainer = Trainer(args=args, train_dataset=train_dataset, model_init=lambda: RegressionModel())
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trainer.train()
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self.check_trained_model(trainer.model)
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@ -782,10 +784,11 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
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"disable_tqdm": True,
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}
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args = TrainingArguments(
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"./generation",
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**args_kwargs,
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir,
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**args_kwargs,
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)
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trainer = Trainer(
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model,
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args,
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@ -797,12 +800,13 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
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trainer.train()
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grad_accum_loss_callback = StoreLossCallback()
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args = TrainingArguments(
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"./generation",
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**args_kwargs,
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gradient_accumulation_steps=2,
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per_device_train_batch_size=4,
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir,
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**args_kwargs,
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gradient_accumulation_steps=2,
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per_device_train_batch_size=4,
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)
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set_seed(42)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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trainer = Trainer(
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@ -879,10 +883,11 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
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"disable_tqdm": True,
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}
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args = TrainingArguments(
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"./generation",
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**args_kwargs,
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir,
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**args_kwargs,
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)
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trainer = Trainer(
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model,
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args,
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@ -894,12 +899,13 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
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trainer.train()
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grad_accum_loss_callback = StoreLossCallback()
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args = TrainingArguments(
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"./generation",
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**args_kwargs,
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gradient_accumulation_steps=2,
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per_device_train_batch_size=4,
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir,
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**args_kwargs,
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gradient_accumulation_steps=2,
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per_device_train_batch_size=4,
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)
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set_seed(42)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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trainer = Trainer(
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@ -987,7 +993,8 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
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def test_custom_optimizer(self):
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train_dataset = RegressionDataset()
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args = TrainingArguments("./regression", report_to="none")
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(tmp_dir, report_to="none")
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model = RegressionModel()
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optimizer = torch.optim.SGD(model.parameters(), lr=1.0)
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lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: 1.0)
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@ -1005,14 +1012,15 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
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model = RegressionModel()
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num_steps, num_warmup_steps = 10, 2
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extra_kwargs = {"power": 5.0, "lr_end": 1e-5} # Non-default arguments
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args = TrainingArguments(
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"./regression",
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lr_scheduler_type="polynomial",
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lr_scheduler_kwargs=extra_kwargs,
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learning_rate=0.2,
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warmup_steps=num_warmup_steps,
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report_to="none",
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir,
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lr_scheduler_type="polynomial",
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lr_scheduler_kwargs=extra_kwargs,
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learning_rate=0.2,
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warmup_steps=num_warmup_steps,
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report_to="none",
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)
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trainer = Trainer(model, args, train_dataset=train_dataset)
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trainer.create_optimizer_and_scheduler(num_training_steps=num_steps)
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@ -1032,14 +1040,15 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
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model = RegressionModel()
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num_steps, num_warmup_steps = 10, 2
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extra_kwargs = {"min_lr": 1e-5} # Non-default arguments
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args = TrainingArguments(
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"./regression",
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lr_scheduler_type="cosine_with_min_lr",
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lr_scheduler_kwargs=extra_kwargs,
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learning_rate=0.2,
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warmup_steps=num_warmup_steps,
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report_to="none",
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir,
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lr_scheduler_type="cosine_with_min_lr",
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lr_scheduler_kwargs=extra_kwargs,
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learning_rate=0.2,
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warmup_steps=num_warmup_steps,
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report_to="none",
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)
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trainer = Trainer(model, args, train_dataset=train_dataset)
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trainer.create_optimizer_and_scheduler(num_training_steps=num_steps)
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@ -1055,12 +1064,13 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
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# test passed arguments for a custom ReduceLROnPlateau scheduler
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train_dataset = RegressionDataset(length=64)
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eval_dataset = RegressionDataset(length=64)
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args = TrainingArguments(
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"./regression",
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eval_strategy="epoch",
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metric_for_best_model="eval_loss",
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report_to="none",
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir,
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eval_strategy="epoch",
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metric_for_best_model="eval_loss",
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report_to="none",
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)
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model = RegressionModel()
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optimizer = torch.optim.SGD(model.parameters(), lr=1.0)
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lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.2, patience=5, cooldown=2)
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@ -1087,15 +1097,16 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
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train_dataset = RegressionDataset(length=64)
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eval_dataset = RegressionDataset(length=64)
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args = TrainingArguments(
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"./regression",
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lr_scheduler_type="reduce_lr_on_plateau",
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eval_strategy="epoch",
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metric_for_best_model="eval_loss",
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num_train_epochs=10,
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learning_rate=0.2,
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report_to="none",
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir,
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lr_scheduler_type="reduce_lr_on_plateau",
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eval_strategy="epoch",
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metric_for_best_model="eval_loss",
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num_train_epochs=10,
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learning_rate=0.2,
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report_to="none",
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)
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model = RegressionModel()
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trainer = TrainerWithLRLogs(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
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trainer.train()
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@ -1127,7 +1138,8 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
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from transformers.optimization import Adafactor, AdafactorSchedule
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train_dataset = RegressionDataset()
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args = TrainingArguments("./regression", report_to="none")
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(tmp_dir, report_to="none")
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model = RegressionModel()
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optimizer = Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None)
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lr_scheduler = AdafactorSchedule(optimizer)
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@ -1179,7 +1191,8 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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train_dataset = RegressionDataset()
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eval_dataset = RegressionDataset()
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model = RegressionDictModel()
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args = TrainingArguments("./regression", report_to="none")
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(tmp_dir, report_to="none")
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trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
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trainer.train()
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_ = trainer.evaluate()
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@ -1190,7 +1203,8 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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tiny_gpt2 = GPT2LMHeadModel(config)
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x = torch.randint(0, 100, (128,))
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eval_dataset = RepeatDataset(x)
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args = TrainingArguments("./test", report_to="none")
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(tmp_dir, report_to="none")
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trainer = Trainer(tiny_gpt2, args, eval_dataset=eval_dataset)
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# By default the past_key_values are removed
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result = trainer.predict(eval_dataset)
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@ -1203,7 +1217,8 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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def test_training_arguments_are_left_untouched(self):
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trainer = get_regression_trainer()
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trainer.train()
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args = TrainingArguments("./regression", report_to=[])
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(tmp_dir, report_to=[])
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dict1, dict2 = args.to_dict(), trainer.args.to_dict()
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for key in dict1.keys():
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# Logging dir can be slightly different as they default to something with the time.
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@ -1450,14 +1465,15 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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train_dataset = RepeatDataset(x)
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# Trainer without inf/nan filter
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args = TrainingArguments(
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"./test",
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learning_rate=1e-9,
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logging_steps=5,
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logging_nan_inf_filter=False,
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neftune_noise_alpha=0.4,
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report_to="none",
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir,
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learning_rate=1e-9,
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logging_steps=5,
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logging_nan_inf_filter=False,
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neftune_noise_alpha=0.4,
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report_to="none",
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)
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trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
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trainer.model = trainer._activate_neftune(trainer.model)
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@ -1472,14 +1488,15 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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# redefine the model
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tiny_gpt2 = GPT2LMHeadModel(config)
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# Trainer without inf/nan filter
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args = TrainingArguments(
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"./test",
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learning_rate=1e-9,
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logging_steps=5,
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logging_nan_inf_filter=False,
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neftune_noise_alpha=0.4,
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report_to="none",
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir,
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learning_rate=1e-9,
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logging_steps=5,
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logging_nan_inf_filter=False,
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neftune_noise_alpha=0.4,
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report_to="none",
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)
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trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
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# Check that it trains without errors
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@ -1504,17 +1521,19 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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train_dataset = RepeatDataset(x)
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# Trainer without inf/nan filter
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args = TrainingArguments(
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"./test", learning_rate=1e9, logging_steps=5, logging_nan_inf_filter=False, report_to="none"
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir, learning_rate=1e9, logging_steps=5, logging_nan_inf_filter=False, report_to="none"
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)
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trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
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trainer.train()
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log_history_no_filter = trainer.state.log_history
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# Trainer with inf/nan filter
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args = TrainingArguments(
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"./test", learning_rate=1e9, logging_steps=5, logging_nan_inf_filter=True, report_to="none"
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir, learning_rate=1e9, logging_steps=5, logging_nan_inf_filter=True, report_to="none"
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)
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trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
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trainer.train()
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log_history_filter = trainer.state.log_history
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@ -1576,7 +1595,8 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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train_dataset = RegressionDataset()
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config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
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tiny_gpt2 = GPT2LMHeadModel(config)
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args = TrainingArguments("./test", report_to="none", dataloader_persistent_workers=False)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(tmp_dir, report_to="none", dataloader_persistent_workers=False)
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# Single evaluation dataset
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eval_dataset = RegressionDataset()
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@ -1619,12 +1639,13 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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train_dataset = RegressionDataset()
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config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
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tiny_gpt2 = GPT2LMHeadModel(config)
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args = TrainingArguments(
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"./test",
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report_to="none",
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dataloader_persistent_workers=True,
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dataloader_num_workers=2,
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir,
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report_to="none",
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dataloader_persistent_workers=True,
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dataloader_num_workers=2,
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)
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# Single evaluation dataset
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eval_dataset = RegressionDataset()
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@ -1678,10 +1699,11 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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self.assertNotEqual(modeling_llama.apply_rotary_pos_emb, liger_rotary_pos_emb)
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self.assertFalse(isinstance(tiny_llama.model.norm, LigerRMSNorm))
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args = TrainingArguments(
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"./test",
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use_liger_kernel=True,
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir,
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use_liger_kernel=True,
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)
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Trainer(tiny_llama, args)
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# Spot check that modeling code and model instance variables are patched
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@ -2162,9 +2184,10 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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# Make the Trainer believe it's a parallelized model
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model.is_parallelizable = True
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model.model_parallel = True
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args = TrainingArguments(
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"./regression", per_device_train_batch_size=16, per_device_eval_batch_size=16, report_to="none"
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)
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(
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tmp_dir, per_device_train_batch_size=16, per_device_eval_batch_size=16, report_to="none"
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)
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trainer = Trainer(model, args, train_dataset=RegressionDataset(), eval_dataset=RegressionDataset())
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# Check the Trainer was fooled
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self.assertTrue(trainer.is_model_parallel)
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@ -2518,7 +2541,8 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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def test_dynamic_shapes(self):
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eval_dataset = DynamicShapesDataset(batch_size=self.batch_size)
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model = RegressionModel(a=2, b=1)
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args = TrainingArguments("./regression", report_to="none")
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(tmp_dir, report_to="none")
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trainer = Trainer(model, args, eval_dataset=eval_dataset)
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# Check evaluation can run to completion
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@ -2535,7 +2559,8 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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self.assertTrue(np.all(seen[expected.shape[0] :] == -100))
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# Same tests with eval accumulation
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args = TrainingArguments("./regression", eval_accumulation_steps=2, report_to="none")
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with tempfile.TemporaryDirectory() as tmp_dir:
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args = TrainingArguments(tmp_dir, eval_accumulation_steps=2, report_to="none")
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trainer = Trainer(model, args, eval_dataset=eval_dataset)
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# Check evaluation can run to completion
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@ -3185,7 +3210,8 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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)
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eval_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev")
|
||||
|
||||
training_args = TrainingArguments(output_dir="./examples", use_cpu=True, report_to="none")
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
training_args = TrainingArguments(output_dir=tmp_dir, use_cpu=True, report_to="none")
|
||||
trainer = Trainer(model=model, args=training_args, eval_dataset=eval_dataset)
|
||||
result = trainer.evaluate()
|
||||
self.assertLess(result["eval_loss"], 0.2)
|
||||
@ -3202,12 +3228,13 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
|
||||
)
|
||||
for example in dataset.examples:
|
||||
example["labels"] = example["input_ids"]
|
||||
training_args = TrainingArguments(
|
||||
output_dir="./examples",
|
||||
use_cpu=True,
|
||||
per_device_eval_batch_size=1,
|
||||
report_to="none",
|
||||
)
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
training_args = TrainingArguments(
|
||||
output_dir=tmp_dir,
|
||||
use_cpu=True,
|
||||
per_device_eval_batch_size=1,
|
||||
report_to="none",
|
||||
)
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
@ -3237,7 +3264,8 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
|
||||
# Adding one column not used by the model should have no impact
|
||||
train_dataset = SampleIterableDataset(label_names=["labels", "extra"])
|
||||
|
||||
args = RegressionTrainingArguments(output_dir="./examples", max_steps=4)
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
args = RegressionTrainingArguments(output_dir=tmp_dir, max_steps=4)
|
||||
trainer = Trainer(model=model, args=args, train_dataset=train_dataset)
|
||||
trainer.train()
|
||||
self.assertEqual(trainer.state.global_step, 4)
|
||||
@ -3252,7 +3280,8 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
|
||||
# Adding one column not used by the model should have no impact
|
||||
eval_dataset = SampleIterableDataset(label_names=["labels", "extra"])
|
||||
|
||||
args = RegressionTrainingArguments(output_dir="./examples")
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
args = RegressionTrainingArguments(output_dir=tmp_dir)
|
||||
trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset, compute_metrics=AlmostAccuracy())
|
||||
results = trainer.evaluate()
|
||||
|
||||
@ -3279,7 +3308,8 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
|
||||
model = RegressionPreTrainedModel(config)
|
||||
eval_dataset = SampleIterableDataset()
|
||||
|
||||
args = RegressionTrainingArguments(output_dir="./examples")
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
args = RegressionTrainingArguments(output_dir=tmp_dir)
|
||||
trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset, compute_metrics=AlmostAccuracy())
|
||||
|
||||
preds = trainer.predict(trainer.eval_dataset).predictions
|
||||
@ -4052,7 +4082,8 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
|
||||
train_dataset = RegressionDataset()
|
||||
eval_dataset = RegressionDataset()
|
||||
model = RegressionDictModel()
|
||||
args = TrainingArguments("./regression", report_to="none", eval_use_gather_object=True)
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
args = TrainingArguments(tmp_dir, report_to="none", eval_use_gather_object=True)
|
||||
trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
|
||||
trainer.train()
|
||||
_ = trainer.evaluate()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user