test_trainer.py

# coding=utf-8
# Copyright 2018 the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import dataclasses
import gc
import os
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, IntervalStrategy, PretrainedConfig, TrainingArguments, is_torch_available
from transformers.file_utils import WEIGHTS_NAME
from transformers.testing_utils import (
    TestCasePlus,
    get_tests_dir,
    require_datasets,
    require_optuna,
    require_ray,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.utils.hp_naming import TrialShortNamer


if is_torch_available():
    import torch
    from torch.utils.data import IterableDataset

    from transformers import (
        AutoModelForMaskedLM,
        AutoModelForSequenceClassification,
        DataCollatorForLanguageModeling,
        EarlyStoppingCallback,
        GlueDataset,
        GlueDataTrainingArguments,
        GPT2Config,
        GPT2LMHeadModel,
        LineByLineTextDataset,
        PreTrainedModel,
        TextDataset,
        Trainer,
        TrainerState,
    )
    from transformers.modeling_utils import unwrap_model


PATH_SAMPLE_TEXT = f"{get_tests_dir()}/fixtures/sample_text.txt"


class RegressionDataset:
    def __init__(self, a=2, b=3, length=64, seed=42, label_names=None):
        np.random.seed(seed)
        self.label_names = ["labels"] if label_names is None else label_names
        self.length = length
        self.x = np.random.normal(size=(length,)).astype(np.float32)
        self.ys = [a * self.x + b + np.random.normal(scale=0.1, size=(length,)) for _ in self.label_names]
        self.ys = [y.astype(np.float32) for y in self.ys]

    def __len__(self):
        return self.length

    def __getitem__(self, i):
        result = {name: y[i] for name, y in zip(self.label_names, self.ys)}
        result["input_x"] = self.x[i]
        return result


@dataclasses.dataclass
class RegressionTrainingArguments(TrainingArguments):
    a: float = 0.0
    b: float = 0.0


class RepeatDataset:
    def __init__(self, x, length=64):
        self.x = x
        self.length = length

    def __len__(self):
        return self.length

    def __getitem__(self, i):
        return {"input_ids": self.x, "labels": self.x}


class DynamicShapesDataset:
    def __init__(self, length=64, seed=42, batch_size=8):
        self.length = length
        np.random.seed(seed)
        sizes = np.random.randint(1, 20, (length // batch_size,))
        # For easy batching, we make every batch_size consecutive samples the same size.
        self.xs = [np.random.normal(size=(s,)) for s in sizes.repeat(batch_size)]
        self.ys = [np.random.normal(size=(s,)) for s in sizes.repeat(batch_size)]

    def __len__(self):
        return self.length

    def __getitem__(self, i):
        return {"input_x": self.xs[i], "labels": self.ys[i]}


class AlmostAccuracy:
    def __init__(self, thresh=0.25):
        self.thresh = thresh

    def __call__(self, eval_pred):
        predictions, labels = eval_pred
        true = np.abs(predictions - labels) <= self.thresh
        return {"accuracy": true.astype(np.float32).mean().item()}


class RegressionModelConfig(PretrainedConfig):
    def __init__(self, a=0, b=0, double_output=False, **kwargs):
        super().__init__(**kwargs)
        self.a = a
        self.b = b
        self.double_output = double_output


if is_torch_available():

    class SampleIterableDataset(IterableDataset):
        """
        Criteria is not whether it is IterableDataset or not, criteria is whether __len__ is implemented
        """

        def __init__(self, file_path, tokenizer):
            self.ds = TextDataset(file_path=file_path, tokenizer=tokenizer, block_size=64)

        def __iter__(self):
            for i in range(len(self.ds)):
                yield self.ds[i]

    class RegressionModel(torch.nn.Module):
        def __init__(self, a=0, b=0, double_output=False):
            super().__init__()
            self.a = torch.nn.Parameter(torch.tensor(a).float())
            self.b = torch.nn.Parameter(torch.tensor(b).float())
            self.double_output = double_output
            self.config = None

        def forward(self, input_x, labels=None, **kwargs):
            y = input_x * self.a + self.b
            if labels is None:
                return (y, y) if self.double_output else (y,)
            loss = torch.nn.functional.mse_loss(y, labels)
            return (loss, y, y) if self.double_output else (loss, y)

    class RegressionDictModel(torch.nn.Module):
        def __init__(self, a=0, b=0):
            super().__init__()
            self.a = torch.nn.Parameter(torch.tensor(a).float())
            self.b = torch.nn.Parameter(torch.tensor(b).float())
            self.config = None

        def forward(self, input_x, labels=None, **kwargs):
            y = input_x * self.a + self.b
            result = {"output": y}
            if labels is not None:
                result["loss"] = torch.nn.functional.mse_loss(y, labels)
            return result

    class RegressionPreTrainedModel(PreTrainedModel):
        config_class = RegressionModelConfig
        base_model_prefix = "regression"

        def __init__(self, config):
            super().__init__(config)
            self.a = torch.nn.Parameter(torch.tensor(config.a).float())
            self.b = torch.nn.Parameter(torch.tensor(config.b).float())
            self.double_output = config.double_output

        def forward(self, input_x, labels=None, **kwargs):
            y = input_x * self.a + self.b
            if labels is None:
                return (y, y) if self.double_output else (y,)
            loss = torch.nn.functional.mse_loss(y, labels)
            return (loss, y, y) if self.double_output else (loss, y)

    class TstLayer(torch.nn.Module):
        def __init__(self, hidden_size):
            super().__init__()
            self.linear1 = torch.nn.Linear(hidden_size, hidden_size)
            self.ln1 = torch.nn.LayerNorm(hidden_size)
            self.linear2 = torch.nn.Linear(hidden_size, hidden_size)
            self.ln2 = torch.nn.LayerNorm(hidden_size)
            self.bias = torch.nn.Parameter(torch.zeros(hidden_size))

        def forward(self, x):
            h = self.ln1(torch.nn.functional.relu(self.linear1(x)))
            h = torch.nn.functional.relu(self.linear2(x))
            return self.ln2(x + h + self.bias)

    def get_regression_trainer(a=0, b=0, double_output=False, train_len=64, eval_len=64, pretrained=True, **kwargs):
        label_names = kwargs.get("label_names", None)
        train_dataset = RegressionDataset(length=train_len, label_names=label_names)
        eval_dataset = RegressionDataset(length=eval_len, label_names=label_names)
        if pretrained:
            config = RegressionModelConfig(a=a, b=b, double_output=double_output)
            model = RegressionPreTrainedModel(config)
        else:
            model = RegressionModel(a=a, b=b, double_output=double_output)
        compute_metrics = kwargs.pop("compute_metrics", None)
        data_collator = kwargs.pop("data_collator", None)
        optimizers = kwargs.pop("optimizers", (None, None))
        output_dir = kwargs.pop("output_dir", "./regression")
        model_init = kwargs.pop("model_init", None)

        args = RegressionTrainingArguments(output_dir, a=a, b=b, **kwargs)
        return Trainer(
            model,
            args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            compute_metrics=compute_metrics,
            optimizers=optimizers,
            model_init=model_init,
        )


class TrainerIntegrationCommon:
    def check_saved_checkpoints(self, output_dir, freq, total, is_pretrained=True):
        file_list = [WEIGHTS_NAME, "training_args.bin", "optimizer.pt", "scheduler.pt", "trainer_state.json"]
        if is_pretrained:
            file_list.append("config.json")
        for step in range(freq, total, freq):
            checkpoint = os.path.join(output_dir, f"checkpoint-{step}")
            self.assertTrue(os.path.isdir(checkpoint))
            for filename in file_list:
                self.assertTrue(os.path.isfile(os.path.join(checkpoint, filename)))

    def check_best_model_has_been_loaded(
        self, output_dir, freq, total, trainer, metric, greater_is_better=False, is_pretrained=True
    ):
        checkpoint = os.path.join(output_dir, f"checkpoint-{(total // freq) * freq}")
        log_history = TrainerState.load_from_json(os.path.join(checkpoint, "trainer_state.json")).log_history

        values = [d[metric] for d in log_history]
        best_value = max(values) if greater_is_better else min(values)
        best_checkpoint = (values.index(best_value) + 1) * freq
        checkpoint = os.path.join(output_dir, f"checkpoint-{best_checkpoint}")
        if is_pretrained:
            best_model = RegressionPreTrainedModel.from_pretrained(checkpoint)
            best_model.to(trainer.args.device)
        else:
            best_model = RegressionModel()
            state_dict = torch.load(os.path.join(checkpoint, WEIGHTS_NAME))
            best_model.load_state_dict(state_dict)
            best_model.to(trainer.args.device)
        self.assertTrue(torch.allclose(best_model.a, trainer.model.a))
        self.assertTrue(torch.allclose(best_model.b, trainer.model.b))

        metrics = trainer.evaluate()
        self.assertEqual(metrics[metric], best_value)

    def check_trainer_state_are_the_same(self, trainer_state, trainer_state1):
        # We'll pop things so operate on copies.
        state = trainer_state.copy()
        state1 = trainer_state1.copy()
        # Log history main contain different logs for the time metrics (after resuming a training).
        log_history = state.pop("log_history", None)
        log_history1 = state1.pop("log_history", None)
        self.assertEqual(state, state1)
        for log, log1 in zip(log_history, log_history1):
            _ = log.pop("train_runtime", None)
            _ = log1.pop("train_runtime", None)
            _ = log.pop("train_samples_per_second", None)
            _ = log1.pop("train_samples_per_second", None)
            self.assertEqual(log, log1)


@require_torch
@require_sentencepiece
@require_tokenizers
class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
    def setUp(self):
        super().setUp()
        args = TrainingArguments(".")
        self.n_epochs = args.num_train_epochs
        self.batch_size = args.train_batch_size
        trainer = get_regression_trainer(learning_rate=0.1)
        trainer.train()
        self.default_trained_model = (trainer.model.a, trainer.model.b)

        trainer = get_regression_trainer(learning_rate=0.1, seed=314)
        trainer.train()
        self.alternate_trained_model = (trainer.model.a, trainer.model.b)

    def check_trained_model(self, model, alternate_seed=False):
        # Checks a training seeded with learning_rate = 0.1
        (a, b) = self.alternate_trained_model if alternate_seed else self.default_trained_model
        self.assertTrue(torch.allclose(model.a, a))
        self.assertTrue(torch.allclose(model.b, b))

    def test_trainer_works_with_dict(self):
        # Edge case because Apex with mode O2 will change our models to return dicts. This test checks it doesn't break
        # anything.
        train_dataset = RegressionDataset()
        eval_dataset = RegressionDataset()
        model = RegressionDictModel()
        args = TrainingArguments("./regression")
        trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
        trainer.train()
        _ = trainer.evaluate()
        _ = trainer.predict(eval_dataset)

    def test_evaluation_with_keys_to_drop(self):
        config = GPT2Config(vocab_size=100, n_positions=128, n_ctx=128, n_embd=32, n_layer=3, n_head=4)
        tiny_gpt2 = GPT2LMHeadModel(config)
        x = torch.randint(0, 100, (128,))
        eval_dataset = RepeatDataset(x)
        args = TrainingArguments("./test")
        trainer = Trainer(tiny_gpt2, args, eval_dataset=eval_dataset)
        # By default the past_key_values are removed
        result = trainer.predict(eval_dataset)
        self.assertTrue(isinstance(result.predictions, np.ndarray))
        # We can still get them by setting ignore_keys to []
        result = trainer.predict(eval_dataset, ignore_keys=[])
        self.assertTrue(isinstance(result.predictions, tuple))
        self.assertEqual(len(result.predictions), 2)

    def test_training_arguments_are_left_untouched(self):
        trainer = get_regression_trainer()
        trainer.train()
        args = TrainingArguments("./regression")
        dict1, dict2 = args.to_dict(), trainer.args.to_dict()
        for key in dict1.keys():
            # Logging dir can be slightly different as they default to something with the time.
            if key != "logging_dir":
                self.assertEqual(dict1[key], dict2[key])

    def test_reproducible_training(self):
        # Checks that training worked, model trained and seed made a reproducible training.
        trainer = get_regression_trainer(learning_rate=0.1)
        trainer.train()
        self.check_trained_model(trainer.model)

        # Checks that a different seed gets different (reproducible) results.
        trainer = get_regression_trainer(learning_rate=0.1, seed=314)
        trainer.train()
        self.check_trained_model(trainer.model, alternate_seed=True)

    def test_number_of_steps_in_training(self):
        # Regular training has n_epochs * len(train_dl) steps
        trainer = get_regression_trainer(learning_rate=0.1)
        train_output = trainer.train()
        self.assertEqual(train_output.global_step, self.n_epochs * 64 / self.batch_size)

        # Check passing num_train_epochs works (and a float version too):
        trainer = get_regression_trainer(learning_rate=0.1, num_train_epochs=1.5)
        train_output = trainer.train()
        self.assertEqual(train_output.global_step, int(1.5 * 64 / self.batch_size))

        # If we pass a max_steps, num_train_epochs is ignored
        trainer = get_regression_trainer(learning_rate=0.1, max_steps=10)
        train_output = trainer.train()
        self.assertEqual(train_output.global_step, 10)

    def test_train_and_eval_dataloaders(self):
        n_gpu = max(1, torch.cuda.device_count())
        trainer = get_regression_trainer(learning_rate=0.1, per_device_train_batch_size=16)
        self.assertEqual(trainer.get_train_dataloader().batch_size, 16 * n_gpu)
        trainer = get_regression_trainer(learning_rate=0.1, per_device_eval_batch_size=16)
        self.assertEqual(trainer.get_eval_dataloader().batch_size, 16 * n_gpu)

        # Check drop_last works
        trainer = get_regression_trainer(
            train_len=66, eval_len=74, learning_rate=0.1, per_device_train_batch_size=16, per_device_eval_batch_size=32
        )
        self.assertEqual(len(trainer.get_train_dataloader()), 66 // (16 * n_gpu) + 1)
        self.assertEqual(len(trainer.get_eval_dataloader()), 74 // (32 * n_gpu) + 1)

        trainer = get_regression_trainer(
            train_len=66,
            eval_len=74,
            learning_rate=0.1,
            per_device_train_batch_size=16,
            per_device_eval_batch_size=32,
            dataloader_drop_last=True,
        )
        self.assertEqual(len(trainer.get_train_dataloader()), 66 // (16 * n_gpu))
        self.assertEqual(len(trainer.get_eval_dataloader()), 74 // (32 * n_gpu))

        # Check passing a new dataset for evaluation works
        new_eval_dataset = RegressionDataset(length=128)
        self.assertEqual(len(trainer.get_eval_dataloader(new_eval_dataset)), 128 // (32 * n_gpu))

    @require_torch_multi_gpu
    def test_data_is_not_parallelized_when_model_is_parallel(self):
        model = RegressionModel()
        # Make the Trainer believe it's a parallelized model
        model.is_parallelizable = True
        model.model_parallel = True
        args = TrainingArguments("./regression", per_device_train_batch_size=16, per_device_eval_batch_size=16)
        trainer = Trainer(model, args, train_dataset=RegressionDataset(), eval_dataset=RegressionDataset())
        # Check the Trainer was fooled
        self.assertTrue(trainer.is_model_parallel)
        self.assertEqual(trainer.args.n_gpu, 1)

        # The batch size of the training and evaluation dataloaders should be 16, not 16 * n_gpu
        self.assertEqual(trainer.get_train_dataloader().batch_size, 16)
        self.assertEqual(len(trainer.get_train_dataloader()), 64 // 16)
        self.assertEqual(trainer.get_eval_dataloader().batch_size, 16)
        self.assertEqual(len(trainer.get_eval_dataloader()), 64 // 16)

    def test_evaluate(self):
        trainer = get_regression_trainer(a=1.5, b=2.5, compute_metrics=AlmostAccuracy())
        results = trainer.evaluate()

        x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
        pred = 1.5 * x + 2.5
        expected_loss = ((pred - y) ** 2).mean()
        self.assertAlmostEqual(results["eval_loss"], expected_loss)
        expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
        self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

        # With a number of elements not a round multiple of the batch size
        trainer = get_regression_trainer(a=1.5, b=2.5, eval_len=66, compute_metrics=AlmostAccuracy())
        results = trainer.evaluate()

        x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
        pred = 1.5 * x + 2.5
        expected_loss = ((pred - y) ** 2).mean()
        self.assertAlmostEqual(results["eval_loss"], expected_loss)
        expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
        self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

    def test_predict(self):
        trainer = get_regression_trainer(a=1.5, b=2.5)
        preds = trainer.predict(trainer.eval_dataset).predictions
        x = trainer.eval_dataset.x
        self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))

        # With a number of elements not a round multiple of the batch size
        trainer = get_regression_trainer(a=1.5, b=2.5, eval_len=66)
        preds = trainer.predict(trainer.eval_dataset).predictions
        x = trainer.eval_dataset.x
        self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))

        # With more than one output of the model
        trainer = get_regression_trainer(a=1.5, b=2.5, double_output=True)
        preds = trainer.predict(trainer.eval_dataset).predictions
        x = trainer.eval_dataset.x
        self.assertTrue(len(preds), 2)
        self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
        self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))

        # With more than one output/label of the model
        trainer = get_regression_trainer(a=1.5, b=2.5, double_output=True, label_names=["labels", "labels_2"])
        outputs = trainer.predict(trainer.eval_dataset)
        preds = outputs.predictions
        labels = outputs.label_ids
        x = trainer.eval_dataset.x
        self.assertTrue(len(preds), 2)
        self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
        self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))
        self.assertTrue(np.array_equal(labels[0], trainer.eval_dataset.ys[0]))
        self.assertTrue(np.array_equal(labels[1], trainer.eval_dataset.ys[1]))

    def test_dynamic_shapes(self):
        eval_dataset = DynamicShapesDataset(batch_size=self.batch_size)
        model = RegressionModel(a=2, b=1)
        args = TrainingArguments("./regression")
        trainer = Trainer(model, args, eval_dataset=eval_dataset)

        # Check evaluation can run to completion
        _ = trainer.evaluate()

        # Check predictions
        preds = trainer.predict(eval_dataset)
        for expected, seen in zip(eval_dataset.ys, preds.label_ids):
            self.assertTrue(np.array_equal(expected, seen[: expected.shape[0]]))
            self.assertTrue(np.all(seen[expected.shape[0] :] == -100))

        for expected, seen in zip(eval_dataset.xs, preds.predictions):
            self.assertTrue(np.array_equal(2 * expected + 1, seen[: expected.shape[0]]))
            self.assertTrue(np.all(seen[expected.shape[0] :] == -100))

        # Same tests with eval accumulation
        args = TrainingArguments("./regression", eval_accumulation_steps=2)
        trainer = Trainer(model, args, eval_dataset=eval_dataset)

        # Check evaluation can run to completion
        _ = trainer.evaluate()

        # Check predictions
        preds = trainer.predict(eval_dataset)
        for expected, seen in zip(eval_dataset.ys, preds.label_ids):
            self.assertTrue(np.array_equal(expected, seen[: expected.shape[0]]))
            self.assertTrue(np.all(seen[expected.shape[0] :] == -100))

        for expected, seen in zip(eval_dataset.xs, preds.predictions):
            self.assertTrue(np.array_equal(2 * expected + 1, seen[: expected.shape[0]]))
            self.assertTrue(np.all(seen[expected.shape[0] :] == -100))

    @require_datasets
    def test_trainer_with_datasets(self):
        import datasets

        np.random.seed(42)
        x = np.random.normal(size=(64,)).astype(np.float32)
        y = 2.0 * x + 3.0 + np.random.normal(scale=0.1, size=(64,))
        train_dataset = datasets.Dataset.from_dict({"input_x": x, "label": y})

        # Base training. Should have the same results as test_reproducible_training
        model = RegressionModel()
        args = TrainingArguments("./regression", learning_rate=0.1)
        trainer = Trainer(model, args, train_dataset=train_dataset)
        trainer.train()
        self.check_trained_model(trainer.model)

        # Can return tensors.
        train_dataset.set_format(type="torch", dtype=torch.float32)
        model = RegressionModel()
        trainer = Trainer(model, args, train_dataset=train_dataset)
        trainer.train()
        self.check_trained_model(trainer.model)

        # Adding one column not used by the model should have no impact
        z = np.random.normal(size=(64,)).astype(np.float32)
        train_dataset = datasets.Dataset.from_dict({"input_x": x, "label": y, "extra": z})
        model = RegressionModel()
        trainer = Trainer(model, args, train_dataset=train_dataset)
        trainer.train()
        self.check_trained_model(trainer.model)

    def test_custom_optimizer(self):
        train_dataset = RegressionDataset()
        args = TrainingArguments("./regression")
        model = RegressionModel()
        optimizer = torch.optim.SGD(model.parameters(), lr=1.0)
        lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: 1.0)
        trainer = Trainer(model, args, train_dataset=train_dataset, optimizers=(optimizer, lr_scheduler))
        trainer.train()

        (a, b) = self.default_trained_model
        self.assertFalse(torch.allclose(trainer.model.a, a))
        self.assertFalse(torch.allclose(trainer.model.b, b))
        self.assertEqual(trainer.optimizer.state_dict()["param_groups"][0]["lr"], 1.0)

    def test_model_init(self):
        train_dataset = RegressionDataset()
        args = TrainingArguments("./regression", learning_rate=0.1)
        trainer = Trainer(args=args, train_dataset=train_dataset, model_init=lambda: RegressionModel())
        trainer.train()
        self.check_trained_model(trainer.model)

        # Re-training should restart from scratch, thus lead the same results.
        trainer.train()
        self.check_trained_model(trainer.model)

        # Re-training should restart from scratch, thus lead the same results and new seed should be used.
        trainer.args.seed = 314
        trainer.train()
        self.check_trained_model(trainer.model, alternate_seed=True)

    def test_save_checkpoints(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(output_dir=tmpdir, save_steps=5)
            trainer.train()
            self.check_saved_checkpoints(tmpdir, 5, int(self.n_epochs * 64 / self.batch_size))

        # With a regular model that is not a PreTrainedModel
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(output_dir=tmpdir, save_steps=5, pretrained=False)
            trainer.train()
            self.check_saved_checkpoints(tmpdir, 5, int(self.n_epochs * 64 / self.batch_size), False)

    def test_gradient_accumulation(self):
        # Training with half the batch size but accumulation steps as 2 should give the same results.
        trainer = get_regression_trainer(
            gradient_accumulation_steps=2, per_device_train_batch_size=4, learning_rate=0.1
        )
        trainer.train()
        self.check_trained_model(trainer.model)

    @require_torch_multi_gpu
    def test_run_seq2seq_double_train_wrap_once(self):
        # test that we don't wrap the model more than once
        # since wrapping primarily happens on multi-gpu setup we want multiple gpus to test for
        # example DataParallel(DataParallel(model))

        trainer = get_regression_trainer()
        trainer.train()
        model_wrapped_before = trainer.model_wrapped
        trainer.train()
        model_wrapped_after = trainer.model_wrapped
        self.assertIs(model_wrapped_before, model_wrapped_after, "should be not wrapped twice")

    def test_can_resume_training(self):
        if torch.cuda.device_count() > 2:
            # This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
            # save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
            # won't be the same since the training dataloader is shuffled).
            return

        with tempfile.TemporaryDirectory() as tmpdir:
            kwargs = dict(output_dir=tmpdir, train_len=128, save_steps=5, learning_rate=0.1)
            trainer = get_regression_trainer(**kwargs)
            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()
            state = dataclasses.asdict(trainer.state)

            checkpoint = os.path.join(tmpdir, "checkpoint-5")

            # Reinitialize trainer
            trainer = get_regression_trainer(**kwargs)

            trainer.train(resume_from_checkpoint=checkpoint)
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
            self.check_trainer_state_are_the_same(state, state1)

            # Now check with a later checkpoint that it also works when we span over one epoch
            checkpoint = os.path.join(tmpdir, "checkpoint-15")

            # Reinitialize trainer and load model
            trainer = get_regression_trainer(**kwargs)

            trainer.train(resume_from_checkpoint=checkpoint)
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
            self.check_trainer_state_are_the_same(state, state1)

        # With a regular model that is not a PreTrainedModel
        with tempfile.TemporaryDirectory() as tmpdir:
            kwargs = dict(output_dir=tmpdir, train_len=128, save_steps=5, learning_rate=0.1, pretrained=False)

            trainer = get_regression_trainer(**kwargs)
            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()
            state = dataclasses.asdict(trainer.state)

            checkpoint = os.path.join(tmpdir, "checkpoint-5")

            # Reinitialize trainer and load model
            trainer = get_regression_trainer(**kwargs)

            trainer.train(resume_from_checkpoint=checkpoint)
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
            self.check_trainer_state_are_the_same(state, state1)

            # Now check with a later checkpoint that it also works when we span over one epoch
            checkpoint = os.path.join(tmpdir, "checkpoint-15")

            # Reinitialize trainer and load model
            trainer = get_regression_trainer(**kwargs)

            trainer.train(resume_from_checkpoint=checkpoint)
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
            self.check_trainer_state_are_the_same(state, state1)

        # Now check failures

        # 1. fail to find a bogus checkpoint
        trainer = get_regression_trainer()
        with self.assertRaises(Exception) as context:
            trainer.train(resume_from_checkpoint=f"{checkpoint}-bogus")
        self.assertTrue("Can't find a valid checkpoint at" in str(context.exception))

        # 2. fail to find any checkpoint - due a fresh output_dir
        output_dir2 = self.get_auto_remove_tmp_dir()
        trainer = get_regression_trainer(output_dir=output_dir2)
        with self.assertRaises(Exception) as context:
            trainer.train(resume_from_checkpoint=True)
        self.assertTrue("No valid checkpoint found in output directory" in str(context.exception))

    def test_resume_training_with_gradient_accumulation(self):
        if torch.cuda.device_count() > 2:
            # This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
            # save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
            # won't be the same since the training dataloader is shuffled).
            return
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                train_len=128,
                gradient_accumulation_steps=2,
                per_device_train_batch_size=4,
                save_steps=5,
                learning_rate=0.1,
            )
            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()
            state = dataclasses.asdict(trainer.state)

            checkpoint = os.path.join(tmpdir, "checkpoint-5")

            # Reinitialize trainer
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                train_len=128,
                gradient_accumulation_steps=2,
                per_device_train_batch_size=4,
                save_steps=5,
                learning_rate=0.1,
            )

            trainer.train(resume_from_checkpoint=checkpoint)
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
            self.check_trainer_state_are_the_same(state, state1)

    def test_load_best_model_at_end(self):
        total = int(self.n_epochs * 64 / self.batch_size)
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                output_dir=tmpdir,
                learning_rate=0.1,
                eval_steps=5,
                evaluation_strategy="steps",
                load_best_model_at_end=True,
            )
            self.assertFalse(trainer.args.greater_is_better)
            trainer.train()
            self.check_saved_checkpoints(tmpdir, 5, total)
            self.check_best_model_has_been_loaded(tmpdir, 5, total, trainer, "eval_loss")

        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                output_dir=tmpdir,
                learning_rate=0.1,
                eval_steps=5,
                evaluation_strategy="steps",
                load_best_model_at_end=True,
                metric_for_best_model="accuracy",
                compute_metrics=AlmostAccuracy(),
            )
            self.assertTrue(trainer.args.greater_is_better)
            trainer.train()
            self.check_saved_checkpoints(tmpdir, 5, total)
            self.check_best_model_has_been_loaded(tmpdir, 5, total, trainer, "eval_accuracy", greater_is_better=True)

        # Save is done every eval regardless of the strategy
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                output_dir=tmpdir,
                learning_rate=0.1,
                evaluation_strategy="epoch",
                load_best_model_at_end=True,
                metric_for_best_model="accuracy",
                compute_metrics=AlmostAccuracy(),
            )
            self.assertTrue(trainer.args.greater_is_better)
            trainer.train()
            self.check_saved_checkpoints(tmpdir, 64 // self.batch_size, total)
            self.check_best_model_has_been_loaded(
                tmpdir, 64 // self.batch_size, total, trainer, "eval_accuracy", greater_is_better=True
            )

        # Test this works with a non PreTrainedModel
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                learning_rate=0.1,
                eval_steps=5,
                evaluation_strategy="steps",
                load_best_model_at_end=True,
                pretrained=False,
            )
            self.assertFalse(trainer.args.greater_is_better)
            trainer.train()
            self.check_saved_checkpoints(tmpdir, 5, total, is_pretrained=False)
            self.check_best_model_has_been_loaded(tmpdir, 5, total, trainer, "eval_loss", is_pretrained=False)

    @slow
    def test_trainer_eval_mrpc(self):
        MODEL_ID = "bert-base-cased-finetuned-mrpc"
        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
        model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
        data_args = GlueDataTrainingArguments(
            task_name="mrpc", data_dir=f"{get_tests_dir()}/fixtures/tests_samples/MRPC", overwrite_cache=True
        )
        eval_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev")

        training_args = TrainingArguments(output_dir="./examples", no_cuda=True)
        trainer = Trainer(model=model, args=training_args, eval_dataset=eval_dataset)
        result = trainer.evaluate()
        self.assertLess(result["eval_loss"], 0.2)

    @slow
    def test_trainer_eval_lm(self):
        MODEL_ID = "distilroberta-base"
        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
        dataset = LineByLineTextDataset(
            tokenizer=tokenizer,
            file_path=PATH_SAMPLE_TEXT,
            block_size=tokenizer.max_len_single_sentence,
        )
        self.assertEqual(len(dataset), 31)

    def test_trainer_iterable_dataset(self):
        # Simulate Language Modeling with an IterableDataset, with no __len__ method
        # Pick-up a tiny model, so it works on CPU
        # See Issue #5990: https://github.com/huggingface/transformers/issues/5990
        MODEL_ID = "sshleifer/tiny-distilbert-base-cased"
        model = AutoModelForMaskedLM.from_pretrained(MODEL_ID)
        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
        train_dataset = SampleIterableDataset(file_path=PATH_SAMPLE_TEXT, tokenizer=tokenizer)
        training_args = TrainingArguments(output_dir="./examples", no_cuda=True, max_steps=2)
        data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15)

        training_args = TrainingArguments(output_dir="./examples", no_cuda=True, max_steps=2)
        trainer = Trainer(model=model, args=training_args, train_dataset=train_dataset, data_collator=data_collator)
        trainer.train()

        loader = trainer.get_train_dataloader()
        self.assertIsInstance(loader, torch.utils.data.DataLoader)
        self.assertIsInstance(loader.sampler, torch.utils.data.dataloader._InfiniteConstantSampler)

        # Exception if giving iterable dataset and no max_steps
        with self.assertRaises(ValueError):
            training_args = TrainingArguments(output_dir="./examples", no_cuda=True)
            _ = Trainer(model=model, args=training_args, train_dataset=train_dataset, data_collator=data_collator)

        # Exception if eval_dataset is iterable in __init__
        with self.assertRaises(ValueError):
            training_args = TrainingArguments(output_dir="./examples", no_cuda=True, max_steps=2)
            _ = Trainer(
                model=model,
                args=training_args,
                train_dataset=train_dataset,
                eval_dataset=train_dataset,
                data_collator=data_collator,
            )

        # Exception if predicting with iterable dataset
        with self.assertRaises(ValueError):
            training_args = TrainingArguments(output_dir="./examples", no_cuda=True)
            trainer = Trainer(model=model, args=training_args, data_collator=data_collator)
            trainer.predict(train_dataset)

        # Exception if evaluating with iterable dataset
        with self.assertRaises(ValueError):
            training_args = TrainingArguments(output_dir="./examples", no_cuda=True)
            trainer = Trainer(model=model, args=training_args, data_collator=data_collator)
            trainer.evaluate(train_dataset)

    def test_num_train_epochs_in_training(self):
        # len(train_dl) < gradient_accumulation_steps shouldn't give ``ZeroDivisionError`` when ``max_steps`` is given.
        # It should give 1 update step for each epoch.
        trainer = get_regression_trainer(
            max_steps=3, train_len=64, per_device_train_batch_size=16, gradient_accumulation_steps=5
        )
        train_output = trainer.train()
        self.assertEqual(train_output.global_step, 3)

        # Even ``max_steps`` is not specified, we still expect 1 update step for each epoch if
        # len(train_dl) < gradient_accumulation_steps.
        trainer = get_regression_trainer(train_len=64, per_device_train_batch_size=16, gradient_accumulation_steps=5)
        train_output = trainer.train()
        self.assertEqual(train_output.global_step, int(self.n_epochs))

    def test_early_stopping_callback(self):
        # early stopping stops training before num_training_epochs
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                num_train_epochs=20,
                gradient_accumulation_steps=1,
                per_device_train_batch_size=16,
                load_best_model_at_end=True,
                evaluation_strategy=IntervalStrategy.EPOCH,
                compute_metrics=AlmostAccuracy(),
                metric_for_best_model="accuracy",
            )
            trainer.add_callback(EarlyStoppingCallback(1, 0.0001))
            train_output = trainer.train()
            self.assertLess(train_output.global_step, 20 * 64 / 16)

        # Invalid inputs to trainer with early stopping callback result in assertion error
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                num_train_epochs=20,
                gradient_accumulation_steps=1,
                per_device_train_batch_size=16,
                evaluation_strategy=IntervalStrategy.EPOCH,
                compute_metrics=AlmostAccuracy(),
                metric_for_best_model="accuracy",
            )
            trainer.add_callback(EarlyStoppingCallback(1))
            self.assertEqual(trainer.state.global_step, 0)
            try:
                trainer.train()
            except AssertionError:
                self.assertEqual(trainer.state.global_step, 0)

    def test_flos_extraction(self):
        trainer = get_regression_trainer(learning_rate=0.1)

        def assert_flos_extraction(trainer, wrapped_model_to_check):
            self.assertEqual(trainer.model, unwrap_model(wrapped_model_to_check))
            self.assertGreaterEqual(getattr(unwrap_model(wrapped_model_to_check).config, "total_flos", 0), 0)

        # with plain model
        assert_flos_extraction(trainer, trainer.model)

        # with enforced DataParallel
        assert_flos_extraction(trainer, torch.nn.DataParallel(trainer.model))

        trainer.train()
        self.assertTrue(isinstance(trainer.state.total_flos, float))

    def check_mem_metrics(self, trainer, check_func):
        metrics = trainer.train().metrics
        check_func("init_mem_cpu_alloc_delta", metrics)
        check_func("train_mem_cpu_alloc_delta", metrics)
        if torch.cuda.device_count() > 0:
            check_func("init_mem_gpu_alloc_delta", metrics)
            check_func("train_mem_gpu_alloc_delta", metrics)

        metrics = trainer.evaluate()
        check_func("eval_mem_cpu_alloc_delta", metrics)
        if torch.cuda.device_count() > 0:
            check_func("eval_mem_gpu_alloc_delta", metrics)

        metrics = trainer.predict(RegressionDataset()).metrics
        check_func("test_mem_cpu_alloc_delta", metrics)
        if torch.cuda.device_count() > 0:
            check_func("test_mem_gpu_alloc_delta", metrics)

    def test_mem_metrics(self):

        # with mem metrics enabled
        trainer = get_regression_trainer()
        self.check_mem_metrics(trainer, self.assertIn)

        # with mem metrics disabled
        trainer = get_regression_trainer(skip_memory_metrics=True)
        self.check_mem_metrics(trainer, self.assertNotIn)

    @require_torch_gpu
    def test_fp16_full_eval(self):

        # this is a sensitive test so let's keep debugging printouts in place for quick diagnosis.
        # it's using pretty large safety margins, but small enough to detect broken functionality.
        debug = 0

        bs = 8
        # make the params somewhat big so that there will be enough RAM consumed to be able to
        # measure things. We should get about 64KB for a+b in fp32
        a = torch.ones(1000, bs) + 0.001
        b = torch.ones(1000, bs) - 0.001

        # 1. with mem metrics enabled
        trainer = get_regression_trainer(a=a, b=b, eval_len=16)
        metrics = trainer.evaluate()
        del trainer
        gc.collect()

        fp32_init = metrics["init_mem_gpu_alloc_delta"]
        fp32_eval = metrics["eval_mem_gpu_alloc_delta"]

        if debug:
            print(f"fp32_init {fp32_init}")
            print(f"fp32_eval {fp32_eval}")

        # here we expect the model to be preloaded in trainer.__init__ and consume around 64K gpu ram.
        # perfect world: fp32_init == 64<<10
        self.assertGreater(fp32_init, 59_000)
        # after eval should be no extra memory allocated - with a small margin (other than the peak
        # memory consumption for the forward calculation that gets recovered)
        # perfect world: fp32_eval == close to zero
        self.assertLess(fp32_eval, 5_000)

        # 2. with mem metrics disabled
        trainer = get_regression_trainer(a=a, b=b, eval_len=16, fp16_full_eval=True)
        metrics = trainer.evaluate()
        fp16_init = metrics["init_mem_gpu_alloc_delta"]
        fp16_eval = metrics["eval_mem_gpu_alloc_delta"]

        if debug:
            print(f"fp16_init {fp16_init}")
            print(f"fp16_eval {fp16_eval}")

        # here we expect the model to not be preloaded in trainer.__init__, so with a small margin it should be close to 0
        # perfect world: fp16_init == close to zero
        self.assertLess(fp16_init, 5_000)
        # here we put the model on device in eval and only `half()` of it, i.e. about 32K,(again we ignore the peak margin which gets returned back)
        # perfect world: fp32_init == 32<<10
        self.assertGreater(fp16_eval, 27_000)

        # 3. relative comparison fp32 vs full fp16
        # should be about half of fp16_init
        # perfect world: fp32_init/2 == fp16_eval
        self.assertAlmostEqual(fp16_eval, fp32_init / 2, delta=5_000)

    def test_no_wd_param_group(self):
        model = torch.nn.Sequential(TstLayer(128), torch.nn.ModuleList([TstLayer(128), TstLayer(128)]))
        trainer = Trainer(model=model)
        trainer.create_optimizer_and_scheduler(10)
        # fmt: off
        wd_names = ['0.linear1.weight', '0.linear2.weight', '1.0.linear1.weight', '1.0.linear2.weight', '1.1.linear1.weight', '1.1.linear2.weight']
        # fmt: on
        wd_params = [p for n, p in model.named_parameters() if n in wd_names]
        no_wd_params = [p for n, p in model.named_parameters() if n not in wd_names]
        self.assertListEqual(trainer.optimizer.param_groups[0]["params"], wd_params)
        self.assertListEqual(trainer.optimizer.param_groups[1]["params"], no_wd_params)


@require_torch
@require_optuna
class TrainerHyperParameterOptunaIntegrationTest(unittest.TestCase):
    def setUp(self):
        args = TrainingArguments(".")
        self.n_epochs = args.num_train_epochs
        self.batch_size = args.train_batch_size

    def test_hyperparameter_search(self):
        class MyTrialShortNamer(TrialShortNamer):
            DEFAULTS = {"a": 0, "b": 0}

        def hp_space(trial):
            return {}

        def model_init(trial):
            if trial is not None:
                a = trial.suggest_int("a", -4, 4)
                b = trial.suggest_int("b", -4, 4)
            else:
                a = 0
                b = 0
            config = RegressionModelConfig(a=a, b=b, double_output=False)

            return RegressionPreTrainedModel(config)

        def hp_name(trial):
            return MyTrialShortNamer.shortname(trial.params)

        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                learning_rate=0.1,
                logging_steps=1,
                evaluation_strategy=IntervalStrategy.EPOCH,
                num_train_epochs=4,
                disable_tqdm=True,
                load_best_model_at_end=True,
                logging_dir="runs",
                run_name="test",
                model_init=model_init,
            )
            trainer.hyperparameter_search(direction="minimize", hp_space=hp_space, hp_name=hp_name, n_trials=4)


@require_torch
@require_ray
class TrainerHyperParameterRayIntegrationTest(unittest.TestCase):
    def setUp(self):
        args = TrainingArguments(".")
        self.n_epochs = args.num_train_epochs
        self.batch_size = args.train_batch_size

    def test_hyperparameter_search(self):
        class MyTrialShortNamer(TrialShortNamer):
            DEFAULTS = {"a": 0, "b": 0}

        def hp_space(trial):
            from ray import tune

            return {
                "a": tune.randint(-4, 4),
                "b": tune.randint(-4, 4),
            }

        def model_init(config):
            model_config = RegressionModelConfig(a=config["a"], b=config["b"], double_output=False)

            return RegressionPreTrainedModel(model_config)

        def hp_name(params):
            return MyTrialShortNamer.shortname(params)

        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                learning_rate=0.1,
                logging_steps=1,
                evaluation_strategy=IntervalStrategy.EPOCH,
                num_train_epochs=4,
                disable_tqdm=True,
                load_best_model_at_end=True,
                logging_dir="runs",
                run_name="test",
                model_init=model_init,
            )
            trainer.hyperparameter_search(
                direction="minimize", hp_space=hp_space, hp_name=hp_name, backend="ray", n_trials=4
            )