test_trainer.py 114 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
# 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.

16
import dataclasses
17
import gc
18
import json
19
import math
20
import os
21
import random
Sylvain Gugger's avatar
Sylvain Gugger committed
22
import re
23
import subprocess
24
import sys
25
import tempfile
26
import time
Julien Chaumond's avatar
Julien Chaumond committed
27
import unittest
28
from itertools import product
29
from pathlib import Path
30
from unittest.mock import Mock, patch
Julien Chaumond's avatar
Julien Chaumond committed
31

Sylvain Gugger's avatar
Sylvain Gugger committed
32
import numpy as np
33
from huggingface_hub import HfFolder, Repository, delete_repo
34
from parameterized import parameterized
Sylvain Gugger's avatar
Sylvain Gugger committed
35
from requests.exceptions import HTTPError
36

37
38
39
40
41
42
43
44
from transformers import (
    AutoTokenizer,
    IntervalStrategy,
    PretrainedConfig,
    TrainingArguments,
    is_torch_available,
    logging,
)
45
from transformers.testing_utils import (
Sylvain Gugger's avatar
Sylvain Gugger committed
46
    ENDPOINT_STAGING,
47
    TOKEN,
Sylvain Gugger's avatar
Sylvain Gugger committed
48
    USER,
49
    CaptureLogger,
50
    TestCasePlus,
51
    get_gpu_count,
52
    get_tests_dir,
Sylvain Gugger's avatar
Sylvain Gugger committed
53
    is_staging_test,
Yih-Dar's avatar
Yih-Dar committed
54
    require_accelerate,
55
    require_intel_extension_for_pytorch,
56
    require_optuna,
57
    require_ray,
58
    require_safetensors,
59
    require_sentencepiece,
60
    require_sigopt,
61
62
    require_tokenizers,
    require_torch,
63
64
    require_torch_bf16_cpu,
    require_torch_bf16_gpu,
65
    require_torch_gpu,
66
    require_torch_multi_gpu,
67
    require_torch_non_multi_gpu,
68
    require_torch_tensorrt_fx,
69
    require_torch_tf32,
70
    require_torch_up_to_2_gpus,
71
    require_torchdynamo,
72
    require_wandb,
73
74
    slow,
)
75
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
76
from transformers.training_args import OptimizerNames
77
from transformers.utils import (
78
79
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
80
81
82
83
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    is_apex_available,
    is_bitsandbytes_available,
84
    is_safetensors_available,
85
86
    is_torchdistx_available,
)
87
from transformers.utils.hp_naming import TrialShortNamer
Julien Chaumond's avatar
Julien Chaumond committed
88
89
90
91


if is_torch_available():
    import torch
92
    from torch import nn
93
94
    from torch.utils.data import IterableDataset

95
    import transformers.optimization
Julien Chaumond's avatar
Julien Chaumond committed
96
97
    from transformers import (
        AutoModelForSequenceClassification,
98
        EarlyStoppingCallback,
Julien Chaumond's avatar
Julien Chaumond committed
99
100
        GlueDataset,
        GlueDataTrainingArguments,
101
102
        GPT2Config,
        GPT2LMHeadModel,
103
        LineByLineTextDataset,
104
        PreTrainedModel,
105
        Trainer,
106
        TrainerState,
Julien Chaumond's avatar
Julien Chaumond committed
107
    )
108
    from transformers.modeling_utils import unwrap_model
Julien Chaumond's avatar
Julien Chaumond committed
109

110
111
112
    if is_safetensors_available():
        import safetensors.torch

Julien Chaumond's avatar
Julien Chaumond committed
113

114
PATH_SAMPLE_TEXT = f"{get_tests_dir()}/fixtures/sample_text.txt"
Julien Chaumond's avatar
Julien Chaumond committed
115
116


Sylvain Gugger's avatar
Sylvain Gugger committed
117
class RegressionDataset:
Sylvain Gugger's avatar
Sylvain Gugger committed
118
    def __init__(self, a=2, b=3, length=64, seed=42, label_names=None):
Sylvain Gugger's avatar
Sylvain Gugger committed
119
        np.random.seed(seed)
Sylvain Gugger's avatar
Sylvain Gugger committed
120
        self.label_names = ["labels"] if label_names is None else label_names
Sylvain Gugger's avatar
Sylvain Gugger committed
121
122
        self.length = length
        self.x = np.random.normal(size=(length,)).astype(np.float32)
Sylvain Gugger's avatar
Sylvain Gugger committed
123
124
        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]
Julien Chaumond's avatar
Julien Chaumond committed
125

Sylvain Gugger's avatar
Sylvain Gugger committed
126
127
128
129
    def __len__(self):
        return self.length

    def __getitem__(self, i):
Sylvain Gugger's avatar
Sylvain Gugger committed
130
131
132
        result = {name: y[i] for name, y in zip(self.label_names, self.ys)}
        result["input_x"] = self.x[i]
        return result
Sylvain Gugger's avatar
Sylvain Gugger committed
133
134


135
136
137
138
139
@dataclasses.dataclass
class RegressionTrainingArguments(TrainingArguments):
    a: float = 0.0
    b: float = 0.0

140
141
142
143
144
    def __post_init__(self):
        super().__post_init__()
        # save resources not dealing with reporting (also avoids the warning when it's not set)
        self.report_to = []

145

146
147
148
149
150
151
152
153
154
155
156
157
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}


158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
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]}


Sylvain Gugger's avatar
Sylvain Gugger committed
174
175
176
177
178
179
180
181
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()}
182

Julien Chaumond's avatar
Julien Chaumond committed
183

184
class RegressionModelConfig(PretrainedConfig):
185
    def __init__(self, a=0, b=0, double_output=False, random_torch=True, **kwargs):
186
187
188
189
        super().__init__(**kwargs)
        self.a = a
        self.b = b
        self.double_output = double_output
190
        self.random_torch = random_torch
191
        self.hidden_size = 1
192
193


194
195
196
if is_torch_available():

    class SampleIterableDataset(IterableDataset):
197
198
        def __init__(self, a=2, b=3, length=64, seed=42, label_names=None):
            self.dataset = RegressionDataset(a=a, b=b, length=length, seed=seed, label_names=label_names)
199
200

        def __iter__(self):
201
202
            for i in range(len(self.dataset)):
                yield self.dataset[i]
203

204
205
206
207
208
209
210
211
212
213
    class FiniteIterableDataset(SampleIterableDataset):
        def __init__(self, a=2, b=3, length=64, seed=42, label_names=None):
            super().__init__(a, b, length, seed, label_names)
            self.current_sample = 0

        def __iter__(self):
            while self.current_sample < len(self.dataset):
                yield self.dataset[self.current_sample]
                self.current_sample += 1

214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
    class MultiLoader:
        def __init__(self, loaders):
            self.loaders = loaders

        def __len__(self):
            return sum(len(loader) for loader in self.loaders)

        def __iter__(self):
            for loader in self.loaders:
                yield from loader

    class CustomDataloaderTrainer(Trainer):
        def get_train_dataloader(self):
            dataloaders = [super().get_train_dataloader(), super().get_train_dataloader()]
            return MultiLoader(dataloaders)

        def get_eval_dataloader(self, eval_dataset):
            dataloaders = [super().get_eval_dataloader(eval_dataset), super().get_eval_dataloader(eval_dataset)]
            return MultiLoader(dataloaders)

234
    class RegressionModel(nn.Module):
235
        def __init__(self, a=0, b=0, double_output=False):
Sylvain Gugger's avatar
Sylvain Gugger committed
236
            super().__init__()
237
238
            self.a = nn.Parameter(torch.tensor(a).float())
            self.b = nn.Parameter(torch.tensor(b).float())
239
240
            self.double_output = double_output
            self.config = None
Sylvain Gugger's avatar
Sylvain Gugger committed
241

Stas Bekman's avatar
Stas Bekman committed
242
        def forward(self, input_x, labels=None, **kwargs):
Sylvain Gugger's avatar
Sylvain Gugger committed
243
244
            y = input_x * self.a + self.b
            if labels is None:
245
                return (y, y) if self.double_output else (y,)
246
            loss = nn.functional.mse_loss(y, labels)
247
            return (loss, y, y) if self.double_output else (loss, y)
Sylvain Gugger's avatar
Sylvain Gugger committed
248

249
    class RegressionDictModel(nn.Module):
250
251
        def __init__(self, a=0, b=0):
            super().__init__()
252
253
            self.a = nn.Parameter(torch.tensor(a).float())
            self.b = nn.Parameter(torch.tensor(b).float())
254
255
            self.config = None

Stas Bekman's avatar
Stas Bekman committed
256
        def forward(self, input_x, labels=None, **kwargs):
257
258
259
            y = input_x * self.a + self.b
            result = {"output": y}
            if labels is not None:
260
                result["loss"] = nn.functional.mse_loss(y, labels)
261
262
            return result

263
264
265
266
267
268
    class RegressionPreTrainedModel(PreTrainedModel):
        config_class = RegressionModelConfig
        base_model_prefix = "regression"

        def __init__(self, config):
            super().__init__(config)
269
270
            self.a = nn.Parameter(torch.tensor(config.a).float())
            self.b = nn.Parameter(torch.tensor(config.b).float())
271
272
            self.double_output = config.double_output

Stas Bekman's avatar
Stas Bekman committed
273
        def forward(self, input_x, labels=None, **kwargs):
274
275
276
            y = input_x * self.a + self.b
            if labels is None:
                return (y, y) if self.double_output else (y,)
277
            loss = nn.functional.mse_loss(y, labels)
278
279
            return (loss, y, y) if self.double_output else (loss, y)

280
281
282
283
284
285
    class RegressionRandomPreTrainedModel(PreTrainedModel):
        config_class = RegressionModelConfig
        base_model_prefix = "regression"

        def __init__(self, config):
            super().__init__(config)
286
287
            self.a = nn.Parameter(torch.tensor(config.a).float())
            self.b = nn.Parameter(torch.tensor(config.b).float())
288
            self.random_torch = config.random_torch
289
290
291

        def forward(self, input_x, labels=None, **kwargs):
            y = input_x * self.a + self.b
292
293
            if self.random_torch:
                torch_rand = torch.randn(1).squeeze()
294
295
296
            np_rand = np.random.rand()
            rand_rand = random.random()

297
298
299
            if self.random_torch:
                y += 0.05 * torch_rand
            y += 0.05 * torch.tensor(np_rand + rand_rand)
300
301
302

            if labels is None:
                return (y,)
303
            loss = nn.functional.mse_loss(y, labels)
304
305
            return (loss, y)

306
    class TstLayer(nn.Module):
307
308
        def __init__(self, hidden_size):
            super().__init__()
309
310
311
312
313
            self.linear1 = nn.Linear(hidden_size, hidden_size)
            self.ln1 = nn.LayerNorm(hidden_size)
            self.linear2 = nn.Linear(hidden_size, hidden_size)
            self.ln2 = nn.LayerNorm(hidden_size)
            self.bias = nn.Parameter(torch.zeros(hidden_size))
314
315

        def forward(self, x):
316
317
            h = self.ln1(nn.functional.relu(self.linear1(x)))
            h = nn.functional.relu(self.linear2(x))
318
319
            return self.ln2(x + h + self.bias)

320
    def get_regression_trainer(a=0, b=0, double_output=False, train_len=64, eval_len=64, pretrained=True, **kwargs):
Sylvain Gugger's avatar
Sylvain Gugger committed
321
322
323
        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)
324
325
326
327

        model_init = kwargs.pop("model_init", None)
        if model_init is not None:
            model = None
328
        else:
329
330
331
332
333
334
            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)

Sylvain Gugger's avatar
Sylvain Gugger committed
335
336
337
        compute_metrics = kwargs.pop("compute_metrics", None)
        data_collator = kwargs.pop("data_collator", None)
        optimizers = kwargs.pop("optimizers", (None, None))
338
        output_dir = kwargs.pop("output_dir", "./regression")
339
        preprocess_logits_for_metrics = kwargs.pop("preprocess_logits_for_metrics", None)
340
341

        args = RegressionTrainingArguments(output_dir, a=a, b=b, **kwargs)
Sylvain Gugger's avatar
Sylvain Gugger committed
342
343
344
345
346
347
348
349
        return Trainer(
            model,
            args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            compute_metrics=compute_metrics,
            optimizers=optimizers,
350
            model_init=model_init,
351
            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
Sylvain Gugger's avatar
Sylvain Gugger committed
352
353
        )

354

355
class TrainerIntegrationCommon:
356
357
358
    def check_saved_checkpoints(self, output_dir, freq, total, is_pretrained=True, safe_weights=False):
        weights_file = WEIGHTS_NAME if not safe_weights else SAFE_WEIGHTS_NAME
        file_list = [weights_file, "training_args.bin", "optimizer.pt", "scheduler.pt", "trainer_state.json"]
359
360
361
362
363
364
365
366
367
        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(
368
        self, output_dir, freq, total, trainer, metric, greater_is_better=False, is_pretrained=True, safe_weights=False
369
370
    ):
        checkpoint = os.path.join(output_dir, f"checkpoint-{(total // freq) * freq}")
371
        log_history = TrainerState.load_from_json(os.path.join(checkpoint, "trainer_state.json")).log_history
372
373
374
375
376
377
378
379
380
381

        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()
382
383
384
385
            if not safe_weights:
                state_dict = torch.load(os.path.join(checkpoint, WEIGHTS_NAME))
            else:
                state_dict = safetensors.torch.load_file(os.path.join(checkpoint, SAFE_WEIGHTS_NAME))
386
            best_model.load_state_dict(state_dict)
387
            best_model.to(trainer.args.device)
388
389
390
391
392
393
        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)

394
395
396
397
398
399
400
401
    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)
402
        skip_log_keys = ["train_runtime", "train_samples_per_second", "train_steps_per_second", "train_loss"]
403
        for log, log1 in zip(log_history, log_history1):
404
405
406
            for key in skip_log_keys:
                _ = log.pop(key, None)
                _ = log1.pop(key, None)
407
408
            self.assertEqual(log, log1)

409
    def convert_to_sharded_checkpoint(self, folder, save_safe=False, load_safe=False):
410
        # Converts a checkpoint of a regression model to a sharded checkpoint.
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
        if load_safe:
            loader = safetensors.torch.load_file
            weights_file = os.path.join(folder, SAFE_WEIGHTS_NAME)
        else:
            loader = torch.load
            weights_file = os.path.join(folder, WEIGHTS_NAME)

        if save_safe:
            extension = "safetensors"
            saver = safetensors.torch.save_file
            index_file = os.path.join(folder, SAFE_WEIGHTS_INDEX_NAME)
            shard_name = SAFE_WEIGHTS_NAME
        else:
            extension = "bin"
            saver = torch.save
            index_file = os.path.join(folder, WEIGHTS_INDEX_NAME)
            shard_name = WEIGHTS_NAME

        state_dict = loader(weights_file)

        os.remove(weights_file)
432
433
434
        keys = list(state_dict.keys())

        shard_files = [
435
436
            shard_name.replace(f".{extension}", f"-{idx+1:05d}-of-{len(keys):05d}.{extension}")
            for idx in range(len(keys))
437
438
439
        ]
        index = {"metadata": {}, "weight_map": {key: shard_files[i] for i, key in enumerate(keys)}}

440
        with open(index_file, "w", encoding="utf-8") as f:
441
442
443
444
            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
            f.write(content)

        for param_name, shard_file in zip(keys, shard_files):
445
            saver({param_name: state_dict[param_name]}, os.path.join(folder, shard_file))
446

447
448
449
450

@require_torch
@require_sentencepiece
@require_tokenizers
451
452
453
454
455
456
457
458
class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
    """
    Only tests that want to tap into the auto-pre-run 2 trainings:
    - self.default_trained_model
    - self.alternate_trained_model
    directly, or via check_trained_model
    """

459
460
    def setUp(self):
        super().setUp()
461
        args = TrainingArguments("..")
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
        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))

478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
    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_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_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_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)

543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
    def test_training_loss(self):
        n_gpus = max(1, get_gpu_count())

        # With even logs
        trainer = get_regression_trainer(logging_steps=64 / (8 * n_gpus))
        trainer.train()
        log_history = trainer.state.log_history

        losses = [log["loss"] for log in log_history if "loss" in log]
        train_loss = log_history[-1]["train_loss"]
        self.assertAlmostEqual(sum(losses) / len(losses), train_loss, places=4)

        # With uneven logs
        trainer = get_regression_trainer(logging_steps=5)
        trainer.train()
        log_history = trainer.state.log_history

        # Training loss should be the same as before
        new_train_loss = log_history[-1]["train_loss"]
        self.assertAlmostEqual(train_loss, new_train_loss, places=4)

564
565
566
567
568
569
570
571
572
573
574
575
576
577
    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)

578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
    def test_reduce_lr_on_plateau_args(self):
        # test passed arguments for a custom ReduceLROnPlateau scheduler
        train_dataset = RegressionDataset(length=64)
        eval_dataset = RegressionDataset(length=64)
        args = TrainingArguments(
            "./regression",
            evaluation_strategy="epoch",
            metric_for_best_model="eval_loss",
        )
        model = RegressionModel()
        optimizer = torch.optim.SGD(model.parameters(), lr=1.0)
        lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.2, patience=5, cooldown=2)
        trainer = Trainer(
            model, args, train_dataset=train_dataset, eval_dataset=eval_dataset, optimizers=(optimizer, lr_scheduler)
        )
        trainer.train()

        self.assertIsInstance(trainer.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau)
        self.assertEqual(trainer.lr_scheduler.factor, 0.2)
        self.assertEqual(trainer.lr_scheduler.patience, 5)
        self.assertEqual(trainer.lr_scheduler.cooldown, 2)

    def test_reduce_lr_on_plateau(self):
        # test the ReduceLROnPlateau scheduler

        class TrainerWithLRLogs(Trainer):
            def log(self, logs):
                # the LR is computed after metrics and does not exist for the first epoch
                if hasattr(self.lr_scheduler, "_last_lr"):
                    logs["learning_rate"] = self.lr_scheduler._last_lr
                super().log(logs)

        train_dataset = RegressionDataset(length=64)
        eval_dataset = RegressionDataset(length=64)

        args = TrainingArguments(
            "./regression",
            lr_scheduler_type="reduce_lr_on_plateau",
            evaluation_strategy="epoch",
            metric_for_best_model="eval_loss",
            num_train_epochs=10,
            learning_rate=0.2,
        )
        model = RegressionModel()
        trainer = TrainerWithLRLogs(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
        trainer.train()

        self.assertIsInstance(trainer.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau)
        patience = trainer.lr_scheduler.patience

        logs = trainer.state.log_history[1:]
        best_loss = logs[0]["eval_loss"]
        bad_epochs = 0
        for i, log in enumerate(logs[:-1]):  # Compare learning rate to next epoch's
            loss = log["eval_loss"]
            just_decreased = False
            if loss > best_loss:
                bad_epochs += 1
                if bad_epochs > patience:
                    self.assertLess(logs[i + 1]["learning_rate"][0], log["learning_rate"][0])
                    just_decreased = True
                    bad_epochs = 0
            else:
                best_loss = loss
                bad_epochs = 0
            if not just_decreased:
                self.assertEqual(logs[i + 1]["learning_rate"][0], log["learning_rate"][0])

646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
    def test_adafactor_lr_none(self):
        # test the special case where lr=None, since Trainer can't not have lr_scheduler

        from transformers.optimization import Adafactor, AdafactorSchedule

        train_dataset = RegressionDataset()
        args = TrainingArguments("./regression")
        model = RegressionModel()
        optimizer = Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None)
        lr_scheduler = AdafactorSchedule(optimizer)
        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.assertGreater(trainer.optimizer.state_dict()["param_groups"][0]["lr"], 0)

664
    @require_torch_gpu
665
    @require_torch_bf16_gpu
666
667
668
669
670
671
672
673
674
675
676
677
    def test_mixed_bf16(self):
        # very basic test
        trainer = get_regression_trainer(learning_rate=0.1, bf16=True)
        trainer.train()
        self.check_trained_model(trainer.model)

        # --bf16 --half_precision_backend apex can't be used together
        with self.assertRaises(ValueError):
            trainer = get_regression_trainer(learning_rate=0.1, bf16=True, half_precision_backend="apex")

        # will add more specific tests once there are some bugs to fix

678
679
680
681
682
683
684
685
    @require_torch_gpu
    @require_torch_tf32
    def test_tf32(self):
        # very basic test
        trainer = get_regression_trainer(learning_rate=0.1, tf32=True)
        trainer.train()
        self.check_trained_model(trainer.model)

686
687
688
689
690
691
692

@require_torch
@require_sentencepiece
@require_tokenizers
class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
    def setUp(self):
        super().setUp()
693
        args = TrainingArguments("..")
694
695
696
        self.n_epochs = args.num_train_epochs
        self.batch_size = args.train_batch_size

697
698
699
700
701
702
703
704
705
706
707
708
709
    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):
710
        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
711
712
713
714
715
716
717
718
719
720
721
722
723
        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)

724
725
726
    def test_training_arguments_are_left_untouched(self):
        trainer = get_regression_trainer()
        trainer.train()
727
        args = TrainingArguments("./regression", report_to=[])
728
729
        dict1, dict2 = args.to_dict(), trainer.args.to_dict()
        for key in dict1.keys():
730
            # Logging dir can be slightly different as they default to something with the time.
Sylvain Gugger's avatar
Sylvain Gugger committed
731
            if key != "logging_dir":
732
                self.assertEqual(dict1[key], dict2[key])
733

Sylvain Gugger's avatar
Sylvain Gugger committed
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
    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)

750
    @require_torch_bf16_cpu
751
752
753
754
755
756
    @require_intel_extension_for_pytorch
    def test_number_of_steps_in_training_with_ipex(self):
        for mix_bf16 in [True, False]:
            # Regular training has n_epochs * len(train_dl) steps
            trainer = get_regression_trainer(learning_rate=0.1, use_ipex=True, bf16=mix_bf16, no_cuda=True)
            train_output = trainer.train()
757
            self.assertEqual(train_output.global_step, self.n_epochs * 64 / trainer.args.train_batch_size)
758
759
760
761
762
763

            # Check passing num_train_epochs works (and a float version too):
            trainer = get_regression_trainer(
                learning_rate=0.1, num_train_epochs=1.5, use_ipex=True, bf16=mix_bf16, no_cuda=True
            )
            train_output = trainer.train()
764
            self.assertEqual(train_output.global_step, int(1.5 * 64 / trainer.args.train_batch_size))
765
766
767
768
769
770
771
772

            # If we pass a max_steps, num_train_epochs is ignored
            trainer = get_regression_trainer(
                learning_rate=0.1, max_steps=10, use_ipex=True, bf16=mix_bf16, no_cuda=True
            )
            train_output = trainer.train()
            self.assertEqual(train_output.global_step, 10)

773
    def test_logging_inf_nan_filter(self):
774
        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
        tiny_gpt2 = GPT2LMHeadModel(config)
        x = torch.randint(0, 100, (128,))
        train_dataset = RepeatDataset(x)

        # Trainer without inf/nan filter
        args = TrainingArguments("./test", learning_rate=1e9, logging_steps=5, logging_nan_inf_filter=False)
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
        trainer.train()
        log_history_no_filter = trainer.state.log_history

        # Trainer with inf/nan filter
        args = TrainingArguments("./test", learning_rate=1e9, logging_steps=5, logging_nan_inf_filter=True)
        trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
        trainer.train()
        log_history_filter = trainer.state.log_history

        def is_any_loss_nan_or_inf(log_history):
            losses = [l["loss"] for l in log_history[:-1]]
            return any(math.isnan(x) for x in losses) or any(math.isinf(x) for x in losses)

        self.assertTrue(is_any_loss_nan_or_inf(log_history_no_filter))
        self.assertFalse(is_any_loss_nan_or_inf(log_history_filter))

Sylvain Gugger's avatar
Sylvain Gugger committed
798
    def test_train_and_eval_dataloaders(self):
799
        n_gpu = max(1, torch.cuda.device_count())
Sylvain Gugger's avatar
Sylvain Gugger committed
800
        trainer = get_regression_trainer(learning_rate=0.1, per_device_train_batch_size=16)
801
        self.assertEqual(trainer.get_train_dataloader().total_batch_size, 16 * n_gpu)
Sylvain Gugger's avatar
Sylvain Gugger committed
802
        trainer = get_regression_trainer(learning_rate=0.1, per_device_eval_batch_size=16)
803
        self.assertEqual(trainer.get_eval_dataloader().total_batch_size, 16 * n_gpu)
Sylvain Gugger's avatar
Sylvain Gugger committed
804
805
806
807
808

        # 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
        )
809
810
        self.assertEqual(len(trainer.get_train_dataloader()), 66 // (16 * n_gpu) + 1)
        self.assertEqual(len(trainer.get_eval_dataloader()), 74 // (32 * n_gpu) + 1)
Sylvain Gugger's avatar
Sylvain Gugger committed
811
812
813
814
815
816
817
818
819

        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,
        )
820
821
        self.assertEqual(len(trainer.get_train_dataloader()), 66 // (16 * n_gpu))
        self.assertEqual(len(trainer.get_eval_dataloader()), 74 // (32 * n_gpu))
Sylvain Gugger's avatar
Sylvain Gugger committed
822

823
        # Check passing a new dataset for evaluation works
Sylvain Gugger's avatar
Sylvain Gugger committed
824
        new_eval_dataset = RegressionDataset(length=128)
825
        self.assertEqual(len(trainer.get_eval_dataloader(new_eval_dataset)), 128 // (32 * n_gpu))
Sylvain Gugger's avatar
Sylvain Gugger committed
826

827
828
829
830
831
832
833
834
835
    # tests that we do not require dataloader to have a .dataset attribute
    def test_dataloader_without_dataset(self):
        train_dataset = RegressionDataset(length=128)
        trainer = CustomDataloaderTrainer(
            model=RegressionModel(), train_dataset=train_dataset, eval_dataset=train_dataset
        )
        trainer.train()
        trainer.evaluate()

836
837
838
839
840
841
    @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
842
843
        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())
844
845
        # Check the Trainer was fooled
        self.assertTrue(trainer.is_model_parallel)
846
        self.assertEqual(trainer.args.n_gpu, 1)
847
848

        # The batch size of the training and evaluation dataloaders should be 16, not 16 * n_gpu
849
        self.assertEqual(trainer.get_train_dataloader().total_batch_size, 16)
850
        self.assertEqual(len(trainer.get_train_dataloader()), 64 // 16)
851
        self.assertEqual(trainer.get_eval_dataloader().total_batch_size, 16)
852
853
        self.assertEqual(len(trainer.get_eval_dataloader()), 64 // 16)

Sylvain Gugger's avatar
Sylvain Gugger committed
854
855
856
857
    def test_evaluate(self):
        trainer = get_regression_trainer(a=1.5, b=2.5, compute_metrics=AlmostAccuracy())
        results = trainer.evaluate()

Sylvain Gugger's avatar
Sylvain Gugger committed
858
        x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
Sylvain Gugger's avatar
Sylvain Gugger committed
859
860
861
862
863
864
865
866
867
868
        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()

Sylvain Gugger's avatar
Sylvain Gugger committed
869
        x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
Sylvain Gugger's avatar
Sylvain Gugger committed
870
871
872
873
874
875
        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)

876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
        # With logits preprocess
        trainer = get_regression_trainer(
            a=1.5,
            b=2.5,
            compute_metrics=AlmostAccuracy(),
            preprocess_logits_for_metrics=lambda logits, labels: logits + 1,
        )
        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 + 1, y))["accuracy"]
        self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
    def test_evaluate_with_jit(self):
        trainer = get_regression_trainer(a=1.5, b=2.5, compute_metrics=AlmostAccuracy(), jit_mode_eval=True)
        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(), jit_mode_eval=True
        )
        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 logits preprocess
        trainer = get_regression_trainer(
            a=1.5,
            b=2.5,
            compute_metrics=AlmostAccuracy(),
            preprocess_logits_for_metrics=lambda logits, labels: logits + 1,
            jit_mode_eval=True,
        )
        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 + 1, y))["accuracy"]
        self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

933
    @require_torch_bf16_cpu
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
    @require_intel_extension_for_pytorch
    def test_evaluate_with_ipex(self):
        for mix_bf16 in [True, False]:
            trainer = get_regression_trainer(
                a=1.5, b=2.5, use_ipex=True, compute_metrics=AlmostAccuracy(), bf16=mix_bf16, no_cuda=True
            )
            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,
                use_ipex=True,
                eval_len=66,
                compute_metrics=AlmostAccuracy(),
                bf16=mix_bf16,
                no_cuda=True,
            )
            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 logits preprocess
            trainer = get_regression_trainer(
                a=1.5,
                b=2.5,
                use_ipex=True,
                compute_metrics=AlmostAccuracy(),
                preprocess_logits_for_metrics=lambda logits, labels: logits + 1,
                bf16=mix_bf16,
                no_cuda=True,
            )
            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 + 1, y))["accuracy"]
            self.assertAlmostEqual(results["eval_accuracy"], expected_acc)

Sylvain Gugger's avatar
Sylvain Gugger committed
987
988
989
990
991
992
993
994
995
996
997
998
    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))

999
1000
1001
1002
        # 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
1003
        self.assertEqual(len(preds), 2)
1004
1005
1006
        self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
        self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))

Sylvain Gugger's avatar
Sylvain Gugger committed
1007
1008
1009
1010
1011
1012
        # 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
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
        self.assertEqual(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_predict_with_jit(self):
        trainer = get_regression_trainer(a=1.5, b=2.5, jit_mode_eval=True)
        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, jit_mode_eval=True)
        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, jit_mode_eval=True)
        preds = trainer.predict(trainer.eval_dataset).predictions
        x = trainer.eval_dataset.x
        self.assertEqual(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"], jit_mode_eval=True
        )
        outputs = trainer.predict(trainer.eval_dataset)
        preds = outputs.predictions
        labels = outputs.label_ids
        x = trainer.eval_dataset.x
1047
        self.assertEqual(len(preds), 2)
Sylvain Gugger's avatar
Sylvain Gugger committed
1048
1049
1050
1051
1052
        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]))

1053
    @require_torch_bf16_cpu
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
    @require_intel_extension_for_pytorch
    def test_predict_with_ipex(self):
        for mix_bf16 in [True, False]:
            trainer = get_regression_trainer(a=1.5, b=2.5, use_ipex=True, bf16=mix_bf16, no_cuda=True)
            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, use_ipex=True, bf16=mix_bf16, no_cuda=True)
            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, use_ipex=True, bf16=mix_bf16, no_cuda=True
            )
            preds = trainer.predict(trainer.eval_dataset).predictions
            x = trainer.eval_dataset.x
            self.assertEqual(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"],
                use_ipex=True,
                bf16=mix_bf16,
                no_cuda=True,
            )
            outputs = trainer.predict(trainer.eval_dataset)
            preds = outputs.predictions
            labels = outputs.label_ids
            x = trainer.eval_dataset.x
            self.assertEqual(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]))

1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
    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))

1134
    def test_log_level(self):
1135
        # testing only --log_level (--log_level_replica requires multiple gpus and DDP and is tested elsewhere)
1136
1137
1138
        logger = logging.get_logger()
        log_info_string = "Running training"

1139
1140
        # test with the default log_level - should be the same as before and thus we test depending on is_info
        is_info = logging.get_verbosity() <= 20
1141
1142
1143
        with CaptureLogger(logger) as cl:
            trainer = get_regression_trainer()
            trainer.train()
1144
1145
1146
1147
        if is_info:
            self.assertIn(log_info_string, cl.out)
        else:
            self.assertNotIn(log_info_string, cl.out)
1148

1149
        # test with low log_level - lower than info
1150
1151
1152
1153
1154
        with CaptureLogger(logger) as cl:
            trainer = get_regression_trainer(log_level="debug")
            trainer.train()
        self.assertIn(log_info_string, cl.out)

1155
        # test with high log_level - should be quiet
1156
1157
1158
1159
1160
        with CaptureLogger(logger) as cl:
            trainer = get_regression_trainer(log_level="error")
            trainer.train()
        self.assertNotIn(log_info_string, cl.out)

1161
1162
1163
1164
1165
1166
1167
1168
    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:
1169
            trainer = get_regression_trainer(output_dir=tmpdir, save_steps=5, pretrained=False)
1170
1171
1172
            trainer.train()
            self.check_saved_checkpoints(tmpdir, 5, int(self.n_epochs * 64 / self.batch_size), False)

1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
    @require_safetensors
    def test_safe_checkpoints(self):
        for save_safetensors in [True, False]:
            with tempfile.TemporaryDirectory() as tmpdir:
                trainer = get_regression_trainer(output_dir=tmpdir, save_steps=5, save_safetensors=save_safetensors)
                trainer.train()
                self.check_saved_checkpoints(
                    tmpdir, 5, int(self.n_epochs * 64 / self.batch_size), safe_weights=save_safetensors
                )

            # 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, save_safetensors=save_safetensors
                )
                trainer.train()
                self.check_saved_checkpoints(
                    tmpdir, 5, int(self.n_epochs * 64 / self.batch_size), False, safe_weights=save_safetensors
                )

1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
    @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")

1206
    @require_torch_up_to_2_gpus
1207
    def test_can_resume_training(self):
1208
1209
1210
        # 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).
1211

1212
        with tempfile.TemporaryDirectory() as tmpdir:
1213
1214
1215
1216
1217
1218
1219
            kwargs = {
                "output_dir": tmpdir,
                "train_len": 128,
                "save_steps": 5,
                "learning_rate": 0.1,
                "logging_steps": 5,
            }
1220
            trainer = get_regression_trainer(**kwargs)
1221
1222
1223
1224
1225
1226
            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()
            state = dataclasses.asdict(trainer.state)

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

1227
            # Reinitialize trainer
1228
            trainer = get_regression_trainer(**kwargs)
1229

1230
            trainer.train(resume_from_checkpoint=checkpoint)
1231
1232
1233
1234
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
1235
            self.check_trainer_state_are_the_same(state, state1)
1236

1237
1238
1239
1240
            # 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
1241
            trainer = get_regression_trainer(**kwargs)
1242

1243
            trainer.train(resume_from_checkpoint=checkpoint)
1244
1245
1246
1247
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
1248
            self.check_trainer_state_are_the_same(state, state1)
1249

1250
1251
        # With a regular model that is not a PreTrainedModel
        with tempfile.TemporaryDirectory() as tmpdir:
1252
1253
1254
1255
1256
1257
1258
            kwargs = {
                "output_dir": tmpdir,
                "train_len": 128,
                "save_steps": 5,
                "learning_rate": 0.1,
                "pretrained": False,
            }
1259
1260

            trainer = get_regression_trainer(**kwargs)
1261
1262
1263
1264
1265
1266
1267
            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
1268
            trainer = get_regression_trainer(**kwargs)
1269

1270
            trainer.train(resume_from_checkpoint=checkpoint)
1271
1272
1273
1274
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
1275
            self.check_trainer_state_are_the_same(state, state1)
1276

1277
1278
1279
1280
            # 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
1281
            trainer = get_regression_trainer(**kwargs)
1282

1283
            trainer.train(resume_from_checkpoint=checkpoint)
1284
1285
1286
1287
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
1288
            self.check_trainer_state_are_the_same(state, state1)
1289

1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
        # 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))

1305
    def test_resume_training_with_randomness(self):
1306
1307
1308
1309
        # For more than 1 GPUs, since the randomness is introduced in the model and with DataParallel (which is used
        # in this test for more than 2 GPUs), the calls to the torch RNG will happen in a random order (sometimes
        # GPU 0 will call first and sometimes GPU 1).
        random_torch = not torch.cuda.is_available() or torch.cuda.device_count() <= 1
1310
1311
1312
1313
1314
1315

        if torch.cuda.is_available():
            torch.backends.cudnn.deterministic = True
        train_dataset = RegressionDataset(length=128)
        eval_dataset = RegressionDataset()

1316
1317
1318
        with self.subTest("Test every step"):
            config = RegressionModelConfig(a=0, b=2, random_torch=random_torch)
            model = RegressionRandomPreTrainedModel(config)
1319

1320
1321
1322
            tmp_dir = self.get_auto_remove_tmp_dir()
            args = RegressionTrainingArguments(tmp_dir, save_steps=5, learning_rate=0.1)
            trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
1323

1324
1325
            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()
1326

1327
1328
1329
1330
1331
            model = RegressionRandomPreTrainedModel(config)
            trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
            trainer.train(resume_from_checkpoint=os.path.join(tmp_dir, "checkpoint-15"))
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()

1332
1333
            self.assertAlmostEqual(a, a1, delta=1e-5)
            self.assertAlmostEqual(b, b1, delta=1e-5)
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355

        with self.subTest("Test every epoch"):
            config = RegressionModelConfig(a=0, b=2, random_torch=random_torch)
            model = RegressionRandomPreTrainedModel(config)

            tmp_dir = self.get_auto_remove_tmp_dir()
            args = RegressionTrainingArguments(tmp_dir, save_strategy="epoch", learning_rate=0.1)
            trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)

            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()

            model = RegressionRandomPreTrainedModel(config)
            trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)

            checkpoints = [d for d in os.listdir(tmp_dir) if d.startswith("checkpoint-")]
            # There should be one checkpoint per epoch.
            self.assertEqual(len(checkpoints), 3)
            checkpoint_dir = sorted(checkpoints, key=lambda x: int(x.replace("checkpoint-", "")))[0]

            trainer.train(resume_from_checkpoint=os.path.join(tmp_dir, checkpoint_dir))
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
1356

1357
1358
            self.assertAlmostEqual(a, a1, delta=1e-5)
            self.assertAlmostEqual(b, b1, delta=1e-5)
1359

1360
    @slow
Yih-Dar's avatar
Yih-Dar committed
1361
    @require_accelerate
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
    @require_torch_non_multi_gpu
    def test_auto_batch_size_finder(self):
        if torch.cuda.is_available():
            torch.backends.cudnn.deterministic = True

        SRC_DIR = os.path.abspath(
            os.path.join(os.path.dirname(__file__), "..", "..", "examples", "pytorch", "text-classification")
        )
        sys.path.append(SRC_DIR)
        import run_glue

        with tempfile.TemporaryDirectory() as tmpdir:
            testargs = f"""
                run_glue.py
                --model_name_or_path distilbert-base-uncased
                --task_name mrpc
                --do_train
                --do_eval
                --max_seq_len 128
                --per_device_train_batch_size 4096
                --learning_rate 2e-5
                --num_train_epochs 1
                --output_dir {tmpdir}
                --auto_find_batch_size 0
                """.split()
            with self.assertRaises(RuntimeError):
                with patch.object(sys, "argv", testargs):
                    run_glue.main()

        testargs[-1] = "1"
        with patch.object(sys, "argv", testargs):
            run_glue.main()

1395
    # regression for this issue: https://github.com/huggingface/transformers/issues/12970
1396
    def test_training_with_resume_from_checkpoint_false(self):
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
        train_dataset = RegressionDataset(length=128)
        eval_dataset = RegressionDataset()

        config = RegressionModelConfig(a=0, b=2)
        model = RegressionRandomPreTrainedModel(config)

        tmp_dir = self.get_auto_remove_tmp_dir()
        args = RegressionTrainingArguments(tmp_dir, save_steps=5, learning_rate=0.1)
        trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)

        trainer.train(resume_from_checkpoint=False)

1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
    @require_torch_up_to_2_gpus
    def test_resume_training_with_shard_checkpoint(self):
        # 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).

        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(output_dir=tmpdir, train_len=128, 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")
            self.convert_to_sharded_checkpoint(checkpoint)

            # Reinitialize trainer
            trainer = get_regression_trainer(output_dir=tmpdir, train_len=128, 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)

1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
    @require_safetensors
    @require_torch_up_to_2_gpus
    def test_resume_training_with_safe_checkpoint(self):
        # 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).

        for initial_safe in [False, True]:
            for loaded_safe in [False, True]:
                with tempfile.TemporaryDirectory() as tmpdir:
                    trainer = get_regression_trainer(
                        output_dir=tmpdir,
                        train_len=128,
                        save_steps=5,
                        learning_rate=0.1,
                        save_safetensors=initial_safe,
                    )
                    trainer.train()
                    (a, b) = trainer.model.a.item(), trainer.model.b.item()
                    state = dataclasses.asdict(trainer.state)

                    checkpoint = os.path.join(tmpdir, "checkpoint-5")
                    self.convert_to_sharded_checkpoint(checkpoint, load_safe=initial_safe, save_safe=loaded_safe)

                    # Reinitialize trainer
                    trainer = get_regression_trainer(
                        output_dir=tmpdir, train_len=128, save_steps=5, learning_rate=0.1, save_safetensors=loaded_safe
                    )

                    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)

1470
    @require_torch_up_to_2_gpus
1471
    def test_resume_training_with_gradient_accumulation(self):
1472
1473
1474
1475
        # 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).

1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
        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")

1491
1492
1493
1494
1495
1496
1497
1498
1499
            # 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,
            )
1500

1501
            trainer.train(resume_from_checkpoint=checkpoint)
1502
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
1503
1504
1505
1506
1507
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
            self.check_trainer_state_are_the_same(state, state1)

1508
    @require_torch_up_to_2_gpus
1509
    def test_resume_training_with_frozen_params(self):
1510
1511
1512
1513
        # 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).

1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
        with tempfile.TemporaryDirectory() as tmpdir:
            trainer = get_regression_trainer(
                output_dir=tmpdir,
                train_len=128,
                per_device_train_batch_size=4,
                save_steps=5,
                learning_rate=0.1,
            )
            trainer.model.a.requires_grad_(False)
            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,
                per_device_train_batch_size=4,
                save_steps=5,
                learning_rate=0.1,
            )
            trainer.model.a.requires_grad_(False)

            trainer.train(resume_from_checkpoint=checkpoint)

            self.assertFalse(trainer.model.a.requires_grad)
            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
1543
1544
1545
            state1 = dataclasses.asdict(trainer.state)
            self.assertEqual(a, a1)
            self.assertEqual(b, b1)
1546
            self.check_trainer_state_are_the_same(state, state1)
1547

1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
    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",
1558
                save_steps=5,
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
                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",
1574
                save_steps=5,
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
                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)

        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",
1591
                save_strategy="epoch",
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
                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",
1610
                save_steps=5,
1611
                load_best_model_at_end=True,
1612
                pretrained=False,
1613
1614
1615
1616
1617
1618
            )
            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)

1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
    @require_safetensors
    def test_load_best_model_from_safetensors(self):
        total = int(self.n_epochs * 64 / self.batch_size)
        for save_safetensors, pretrained in product([False, True], [False, True]):
            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",
                    save_steps=5,
                    load_best_model_at_end=True,
                    save_safetensors=save_safetensors,
                    pretrained=pretrained,
                )
                self.assertFalse(trainer.args.greater_is_better)
                trainer.train()
                self.check_saved_checkpoints(tmpdir, 5, total, is_pretrained=pretrained, safe_weights=save_safetensors)
                self.check_best_model_has_been_loaded(
                    tmpdir, 5, total, trainer, "eval_loss", is_pretrained=pretrained, safe_weights=save_safetensors
                )

1643
    @slow
Julien Chaumond's avatar
Julien Chaumond committed
1644
1645
1646
1647
1648
    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(
1649
            task_name="mrpc", data_dir=f"{get_tests_dir()}/fixtures/tests_samples/MRPC", overwrite_cache=True
Julien Chaumond's avatar
Julien Chaumond committed
1650
        )
1651
        eval_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev")
Julien Chaumond's avatar
Julien Chaumond committed
1652
1653
1654
1655

        training_args = TrainingArguments(output_dir="./examples", no_cuda=True)
        trainer = Trainer(model=model, args=training_args, eval_dataset=eval_dataset)
        result = trainer.evaluate()
1656
        self.assertLess(result["eval_loss"], 0.2)
Julien Chaumond's avatar
Julien Chaumond committed
1657

1658
    @slow
Julien Chaumond's avatar
Julien Chaumond committed
1659
1660
1661
1662
    def test_trainer_eval_lm(self):
        MODEL_ID = "distilroberta-base"
        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
        dataset = LineByLineTextDataset(
Lysandre's avatar
Lysandre committed
1663
1664
1665
            tokenizer=tokenizer,
            file_path=PATH_SAMPLE_TEXT,
            block_size=tokenizer.max_len_single_sentence,
Julien Chaumond's avatar
Julien Chaumond committed
1666
1667
        )
        self.assertEqual(len(dataset), 31)
1668

1669
    def test_training_iterable_dataset(self):
1670
1671
        config = RegressionModelConfig()
        model = RegressionPreTrainedModel(config)
1672
1673
        # Adding one column not used by the model should have no impact
        train_dataset = SampleIterableDataset(label_names=["labels", "extra"])
1674

1675
        args = RegressionTrainingArguments(output_dir="./examples", max_steps=4)
1676
        trainer = Trainer(model=model, args=args, train_dataset=train_dataset)
1677
        trainer.train()
1678
        self.assertEqual(trainer.state.global_step, 4)
1679

1680
1681
        loader = trainer.get_train_dataloader()
        self.assertIsInstance(loader, torch.utils.data.DataLoader)
1682
1683
        self.assertIsInstance(loader.sampler, torch.utils.data.dataloader._InfiniteConstantSampler)

1684
1685
1686
    def test_evaluation_iterable_dataset(self):
        config = RegressionModelConfig(a=1.5, b=2.5)
        model = RegressionPreTrainedModel(config)
1687
1688
        # Adding one column not used by the model should have no impact
        eval_dataset = SampleIterableDataset(label_names=["labels", "extra"])
1689
1690
1691
1692

        args = RegressionTrainingArguments(output_dir="./examples")
        trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset, compute_metrics=AlmostAccuracy())
        results = trainer.evaluate()
1693

1694
1695
1696
1697
1698
1699
        x, y = trainer.eval_dataset.dataset.x, trainer.eval_dataset.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)
1700

1701
1702
1703
        # With a number of elements not a round multiple of the batch size
        eval_dataset = SampleIterableDataset(length=66)
        results = trainer.evaluate(eval_dataset)
1704

1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
        x, y = eval_dataset.dataset.x, eval_dataset.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_iterable_dataset(self):
        config = RegressionModelConfig(a=1.5, b=2.5)
        model = RegressionPreTrainedModel(config)
        eval_dataset = SampleIterableDataset()

        args = RegressionTrainingArguments(output_dir="./examples")
        trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset, compute_metrics=AlmostAccuracy())

        preds = trainer.predict(trainer.eval_dataset).predictions
        x = eval_dataset.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
1725
1726
        # Adding one column not used by the model should have no impact
        test_dataset = SampleIterableDataset(length=66, label_names=["labels", "extra"])
1727
1728
1729
        preds = trainer.predict(test_dataset).predictions
        x = test_dataset.dataset.x
        self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744

    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))
Marcin Zab艂ocki's avatar
Marcin Zab艂ocki committed
1745

1746
1747
    def test_early_stopping_callback(self):
        # early stopping stops training before num_training_epochs
1748
1749
1750
1751
1752
1753
1754
        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,
1755
                evaluation_strategy=IntervalStrategy.EPOCH,
1756
                save_strategy=IntervalStrategy.EPOCH,
1757
1758
1759
1760
1761
1762
                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)
1763
1764

        # Invalid inputs to trainer with early stopping callback result in assertion error
1765
1766
1767
1768
1769
1770
        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,
1771
                evaluation_strategy=IntervalStrategy.EPOCH,
1772
1773
1774
1775
                compute_metrics=AlmostAccuracy(),
                metric_for_best_model="accuracy",
            )
            trainer.add_callback(EarlyStoppingCallback(1))
1776
            self.assertEqual(trainer.state.global_step, 0)
1777
1778
1779
1780
            try:
                trainer.train()
            except AssertionError:
                self.assertEqual(trainer.state.global_step, 0)
1781

Marcin Zab艂ocki's avatar
Marcin Zab艂ocki committed
1782
1783
1784
1785
    def test_flos_extraction(self):
        trainer = get_regression_trainer(learning_rate=0.1)

        def assert_flos_extraction(trainer, wrapped_model_to_check):
1786
1787
            self.assertEqual(trainer.model, unwrap_model(wrapped_model_to_check))
            self.assertGreaterEqual(getattr(unwrap_model(wrapped_model_to_check).config, "total_flos", 0), 0)
Marcin Zab艂ocki's avatar
Marcin Zab艂ocki committed
1788
1789
1790
1791
1792

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

        # with enforced DataParallel
1793
        assert_flos_extraction(trainer, nn.DataParallel(trainer.model))
1794

1795
1796
1797
        trainer.train()
        self.assertTrue(isinstance(trainer.state.total_flos, float))

1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
    def check_checkpoint_deletion(self, trainer, output_dir, expected):
        # Make fake checkpoints
        for n in [5, 10, 15, 20, 25]:
            os.makedirs(os.path.join(output_dir, f"{PREFIX_CHECKPOINT_DIR}-{n}"), exist_ok=True)
        trainer._rotate_checkpoints(output_dir=output_dir)
        glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{PREFIX_CHECKPOINT_DIR}-*")]
        values = [int(re.match(f".*{PREFIX_CHECKPOINT_DIR}-([0-9]+)", d).groups()[0]) for d in glob_checkpoints]
        self.assertSetEqual(set(values), set(expected))

    def test_checkpoint_rotation(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            # Without best model at end
            trainer = get_regression_trainer(output_dir=tmp_dir, save_total_limit=2)
            self.check_checkpoint_deletion(trainer, tmp_dir, [20, 25])

            # With best model at end
1814
1815
1816
            trainer = get_regression_trainer(
                output_dir=tmp_dir, evaluation_strategy="steps", load_best_model_at_end=True, save_total_limit=2
            )
1817
1818
1819
1820
1821
            trainer.state.best_model_checkpoint = os.path.join(tmp_dir, "checkpoint-5")
            self.check_checkpoint_deletion(trainer, tmp_dir, [5, 25])

            # Edge case: we don't always honor save_total_limit=1 if load_best_model_at_end=True to be able to resume
            # from checkpoint
1822
1823
1824
            trainer = get_regression_trainer(
                output_dir=tmp_dir, evaluation_strategy="steps", load_best_model_at_end=True, save_total_limit=1
            )
1825
1826
1827
1828
1829
1830
            trainer.state.best_model_checkpoint = os.path.join(tmp_dir, "checkpoint-25")
            self.check_checkpoint_deletion(trainer, tmp_dir, [25])

            trainer.state.best_model_checkpoint = os.path.join(tmp_dir, "checkpoint-5")
            self.check_checkpoint_deletion(trainer, tmp_dir, [5, 25])

1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
    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
1851
        trainer = get_regression_trainer(skip_memory_metrics=False)
1852
1853
1854
1855
1856
1857
        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)

1858
1859
1860
1861
1862
    @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
1863
        n_gpus = get_gpu_count()
1864
1865

        bs = 8
1866
        eval_len = 16 * n_gpus
1867
1868
1869
1870
1871
        # 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

1872
        # 1. with fp16_full_eval disabled
1873
        trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, skip_memory_metrics=False)
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
        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)

1893
        # 2. with fp16_full_eval enabled
1894
        trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, fp16_full_eval=True, skip_memory_metrics=False)
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
        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)

1915
1916
    @require_torch_non_multi_gpu
    @require_torchdynamo
1917
    @require_torch_tensorrt_fx
1918
    def test_torchdynamo_full_eval(self):
Yih-Dar's avatar
Yih-Dar committed
1919
1920
        import torchdynamo

1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
        # torchdynamo at the moment doesn't support DP/DDP, therefore require a single gpu
        n_gpus = get_gpu_count()

        bs = 8
        eval_len = 16 * n_gpus
        # make the params are 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. Default - without TorchDynamo
        trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len)
        metrics = trainer.evaluate()
        original_eval_loss = metrics["eval_loss"]
        del trainer

        # 2. TorchDynamo eager
        trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, torchdynamo="eager")
        metrics = trainer.evaluate()
        self.assertAlmostEqual(metrics["eval_loss"], original_eval_loss)
        del trainer
Yih-Dar's avatar
Yih-Dar committed
1942
        torchdynamo.reset()
1943
1944
1945
1946
1947

        # 3. TorchDynamo nvfuser
        trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, torchdynamo="nvfuser")
        metrics = trainer.evaluate()
        self.assertAlmostEqual(metrics["eval_loss"], original_eval_loss)
Yih-Dar's avatar
Yih-Dar committed
1948
        torchdynamo.reset()
1949

1950
1951
1952
1953
        # 4. TorchDynamo fx2trt
        trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, torchdynamo="fx2trt")
        metrics = trainer.evaluate()
        self.assertAlmostEqual(metrics["eval_loss"], original_eval_loss)
Yih-Dar's avatar
Yih-Dar committed
1954
        torchdynamo.reset()
1955

1956
    @unittest.skip("torch 2.0.0 gives `ModuleNotFoundError: No module named 'torchdynamo'`.")
1957
1958
1959
1960
    @require_torch_non_multi_gpu
    @require_torchdynamo
    def test_torchdynamo_memory(self):
        # torchdynamo at the moment doesn't support DP/DDP, therefore require a single gpu
Yih-Dar's avatar
Yih-Dar committed
1961
1962
        import torchdynamo

1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
        class CustomTrainer(Trainer):
            def compute_loss(self, model, inputs, return_outputs=False):
                x = inputs["x"]
                output = model(x)
                if self.args.n_gpu == 1:
                    return output.mean()
                return output

        class MyModule(torch.nn.Module):
            """Simple module that does aggressive fusion"""

            def __init__(self):
                super().__init__()

            def forward(self, x):
                for _ in range(20):
Yih-Dar's avatar
Yih-Dar committed
1979
                    x = torch.cos(x)
1980
1981
1982
1983
                return x

        mod = MyModule()

1984
        # 1. without TorchDynamo (eager baseline)
1985
1986
1987
1988
1989
1990
1991
        a = torch.ones(1024, 1024, device="cuda", requires_grad=True)
        a.grad = None
        trainer = CustomTrainer(model=mod)
        # warmup
        for _ in range(10):
            orig_loss = trainer.training_step(mod, {"x": a})

1992
1993
1994
        # resets
        gc.collect()
        torch.cuda.empty_cache()
1995
        torch.cuda.reset_peak_memory_stats()
1996

1997
1998
        orig_loss = trainer.training_step(mod, {"x": a})
        orig_peak_mem = torch.cuda.max_memory_allocated()
Yih-Dar's avatar
Yih-Dar committed
1999
        torchdynamo.reset()
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
        del trainer

        # 2. TorchDynamo nvfuser
        a = torch.ones(1024, 1024, device="cuda", requires_grad=True)
        a.grad = None
        args = TrainingArguments(output_dir="None", torchdynamo="nvfuser")
        trainer = CustomTrainer(model=mod, args=args)
        # warmup
        for _ in range(10):
            loss = trainer.training_step(mod, {"x": a})

2011
2012
2013
        # resets
        gc.collect()
        torch.cuda.empty_cache()
2014
        torch.cuda.reset_peak_memory_stats()
2015

2016
2017
        loss = trainer.training_step(mod, {"x": a})
        peak_mem = torch.cuda.max_memory_allocated()
Yih-Dar's avatar
Yih-Dar committed
2018
        torchdynamo.reset()
2019
2020
2021
2022
2023
2024
2025
2026
2027
        del trainer

        # Functional check
        self.assertAlmostEqual(loss, orig_loss)

        # AOT Autograd recomputaion and nvfuser recomputation optimization
        # aggressively fuses the operations and reduce the memory footprint.
        self.assertGreater(orig_peak_mem, peak_mem * 2)

2028
    @require_torch_gpu
2029
    @require_torch_bf16_gpu
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
    def test_bf16_full_eval(self):
        # note: most of the logic is the same as test_fp16_full_eval

        # 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
        n_gpus = get_gpu_count()

        bs = 8
        eval_len = 16 * n_gpus
        # 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

2045
        # 1. with bf16_full_eval disabled
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
        trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, skip_memory_metrics=False)
        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)

2066
        # 2. with bf16_full_eval enabled
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
        trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, bf16_full_eval=True, skip_memory_metrics=False)
        metrics = trainer.evaluate()
        bf16_init = metrics["init_mem_gpu_alloc_delta"]
        bf16_eval = metrics["eval_mem_gpu_alloc_delta"]

        if debug:
            print(f"bf16_init {bf16_init}")
            print(f"bf16_eval {bf16_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: bf16_init == close to zero
        self.assertLess(bf16_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(bf16_eval, 27_000)

        # 3. relative comparison fp32 vs full bf16
        # should be about half of bf16_init
        # perfect world: fp32_init/2 == bf16_eval
        self.assertAlmostEqual(bf16_eval, fp32_init / 2, delta=5_000)

2088
    def test_no_wd_param_group(self):
2089
        model = nn.Sequential(TstLayer(128), nn.ModuleList([TstLayer(128), TstLayer(128)]))
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
        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)

2100

Sylvain Gugger's avatar
Sylvain Gugger committed
2101
2102
2103
2104
2105
@require_torch
@is_staging_test
class TrainerIntegrationWithHubTester(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
2106
2107
        cls._token = TOKEN
        HfFolder.save_token(TOKEN)
Sylvain Gugger's avatar
Sylvain Gugger committed
2108
2109
2110

    @classmethod
    def tearDownClass(cls):
2111
2112
        for model in ["test-trainer", "test-trainer-epoch", "test-trainer-step"]:
            try:
2113
                delete_repo(token=cls._token, repo_id=model)
2114
2115
            except HTTPError:
                pass
Sylvain Gugger's avatar
Sylvain Gugger committed
2116
2117

        try:
2118
            delete_repo(token=cls._token, repo_id="valid_org/test-trainer-org")
Sylvain Gugger's avatar
Sylvain Gugger committed
2119
2120
2121
2122
2123
        except HTTPError:
            pass

    def test_push_to_hub(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
2124
2125
2126
            trainer = get_regression_trainer(
                output_dir=os.path.join(tmp_dir, "test-trainer"),
                push_to_hub=True,
2127
                hub_token=self._token,
2128
2129
            )
            url = trainer.push_to_hub()
Sylvain Gugger's avatar
Sylvain Gugger committed
2130
2131
2132
2133
2134
2135

            # Extract repo_name from the url
            re_search = re.search(ENDPOINT_STAGING + r"/([^/]+/[^/]+)/", url)
            self.assertTrue(re_search is not None)
            repo_name = re_search.groups()[0]

2136
            self.assertEqual(repo_name, f"{USER}/test-trainer")
Sylvain Gugger's avatar
Sylvain Gugger committed
2137
2138
2139
2140
2141
2142
2143
2144
2145

            model = RegressionPreTrainedModel.from_pretrained(repo_name)
            self.assertEqual(model.a.item(), trainer.model.a.item())
            self.assertEqual(model.b.item(), trainer.model.b.item())

    def test_push_to_hub_in_organization(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(output_dir=tmp_dir)
            trainer.save_model()
2146
2147
2148
            trainer = get_regression_trainer(
                output_dir=os.path.join(tmp_dir, "test-trainer-org"),
                push_to_hub=True,
2149
2150
                hub_model_id="valid_org/test-trainer-org",
                hub_token=self._token,
2151
            )
2152
            url = trainer.push_to_hub()
Sylvain Gugger's avatar
Sylvain Gugger committed
2153
2154
2155
2156
2157

            # Extract repo_name from the url
            re_search = re.search(ENDPOINT_STAGING + r"/([^/]+/[^/]+)/", url)
            self.assertTrue(re_search is not None)
            repo_name = re_search.groups()[0]
2158
            self.assertEqual(repo_name, "valid_org/test-trainer-org")
Sylvain Gugger's avatar
Sylvain Gugger committed
2159

2160
            model = RegressionPreTrainedModel.from_pretrained("valid_org/test-trainer-org")
Sylvain Gugger's avatar
Sylvain Gugger committed
2161
2162
2163
            self.assertEqual(model.a.item(), trainer.model.a.item())
            self.assertEqual(model.b.item(), trainer.model.b.item())

2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
    def get_commit_history(self, repo):
        commit_logs = subprocess.run(
            "git log".split(),
            stderr=subprocess.PIPE,
            stdout=subprocess.PIPE,
            check=True,
            encoding="utf-8",
            cwd=repo,
        ).stdout
        commits = commit_logs.split("\n\n")[1::2]
        return [commit.strip() for commit in commits]

    def test_push_to_hub_with_saves_each_epoch(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=os.path.join(tmp_dir, "test-trainer-epoch"),
                push_to_hub=True,
                hub_token=self._token,
                save_strategy="epoch",
            )
            trainer.train()

2186
2187
2188
2189
            # Wait for the async pushes to be finished
            while trainer.push_in_progress is not None and not trainer.push_in_progress.is_done:
                time.sleep(0.5)

2190
        with tempfile.TemporaryDirectory() as tmp_dir:
2191
            _ = Repository(tmp_dir, clone_from=f"{USER}/test-trainer-epoch", token=self._token)
2192
            commits = self.get_commit_history(tmp_dir)
2193
2194
2195
2196
            self.assertIn("initial commit", commits)
            # We can't test that epoch 2 and 3 are in the commits without being flaky as those might be skipped if
            # the push for epoch 1 wasn't finished at the time.
            self.assertIn("Training in progress, epoch 1", commits)
2197
2198

    def test_push_to_hub_with_saves_each_n_steps(self):
2199
2200
2201
2202
        num_gpus = max(1, get_gpu_count())
        if num_gpus > 2:
            return

2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=os.path.join(tmp_dir, "test-trainer-step"),
                push_to_hub=True,
                hub_token=self._token,
                save_strategy="steps",
                save_steps=5,
            )
            trainer.train()

2213
2214
2215
2216
            # Wait for the async pushes to be finished
            while trainer.push_in_progress is not None and not trainer.push_in_progress.is_done:
                time.sleep(0.5)

2217
        with tempfile.TemporaryDirectory() as tmp_dir:
2218
            _ = Repository(tmp_dir, clone_from=f"{USER}/test-trainer-step", token=self._token)
2219
            commits = self.get_commit_history(tmp_dir)
2220
2221
2222
2223
            self.assertIn("initial commit", commits)
            # We can't test that epoch 2 and 3 are in the commits without being flaky as those might be skipped if
            # the push for epoch 1 wasn't finished at the time.
            self.assertIn("Training in progress, step 5", commits)
2224

Sylvain Gugger's avatar
Sylvain Gugger committed
2225

2226
2227
@require_torch
@require_optuna
2228
class TrainerHyperParameterOptunaIntegrationTest(unittest.TestCase):
2229
    def setUp(self):
2230
        args = TrainingArguments("..")
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
        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)

2255
2256
2257
2258
2259
        with tempfile.TemporaryDirectory() as tmp_dir:
            trainer = get_regression_trainer(
                output_dir=tmp_dir,
                learning_rate=0.1,
                logging_steps=1,
2260
                evaluation_strategy=IntervalStrategy.EPOCH,
2261
                save_strategy=IntervalStrategy.EPOCH,
2262
2263
2264
2265
2266
2267
2268
2269
                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)
2270
2271
2272
2273
2274
2275


@require_torch
@require_ray
class TrainerHyperParameterRayIntegrationTest(unittest.TestCase):
    def setUp(self):
2276
        args = TrainingArguments("..")
2277
2278
2279
        self.n_epochs = args.num_train_epochs
        self.batch_size = args.train_batch_size

2280
    def ray_hyperparameter_search(self):
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
        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):
2293
2294
2295
2296
2297
2298
2299
            if config is None:
                a = 0
                b = 0
            else:
                a = config["a"]
                b = config["b"]
            model_config = RegressionModelConfig(a=a, b=b, double_output=False)
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310

            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,
2311
                evaluation_strategy=IntervalStrategy.EPOCH,
2312
                save_strategy=IntervalStrategy.EPOCH,
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
                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
            )
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333

    def test_hyperparameter_search(self):
        self.ray_hyperparameter_search()

    def test_hyperparameter_search_ray_client(self):
        import ray
        from ray.util.client.ray_client_helpers import ray_start_client_server

        with ray_start_client_server():
            assert ray.util.client.ray.is_connected()
            self.ray_hyperparameter_search()
2334
2335


2336
@slow
2337
2338
2339
2340
@require_torch
@require_sigopt
class TrainerHyperParameterSigOptIntegrationTest(unittest.TestCase):
    def setUp(self):
2341
        args = TrainingArguments("..")
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
        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 [
                {"bounds": {"min": -4, "max": 4}, "name": "a", "type": "int"},
                {"bounds": {"min": -4, "max": 4}, "name": "b", "type": "int"},
            ]

        def model_init(trial):
            if trial is not None:
                a = trial.assignments["a"]
                b = trial.assignments["b"]
            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.assignments)

        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,
                save_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="sigopt", n_trials=4
            )
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395


optim_test_params = []
if is_torch_available():
    default_adam_kwargs = {
        "betas": (TrainingArguments.adam_beta1, TrainingArguments.adam_beta2),
        "eps": TrainingArguments.adam_epsilon,
        "lr": TrainingArguments.learning_rate,
    }

2396
2397
2398
2399
2400
    default_lion_kwargs = {
        "betas": (TrainingArguments.adam_beta1, TrainingArguments.adam_beta2),
        "lr": TrainingArguments.learning_rate,
    }

2401
2402
2403
2404
2405
2406
2407
    default_anyprecision_kwargs = {
        "use_kahan_summation": False,
        "momentum_dtype": torch.float32,
        "variance_dtype": torch.float32,
        "compensation_buffer_dtype": torch.bfloat16,
    }

2408
2409
    optim_test_params = [
        (
2410
            TrainingArguments(optim=OptimizerNames.ADAMW_HF, output_dir="None"),
2411
2412
2413
2414
            transformers.optimization.AdamW,
            default_adam_kwargs,
        ),
        (
2415
            TrainingArguments(optim=OptimizerNames.ADAMW_HF.value, output_dir="None"),
2416
2417
2418
2419
            transformers.optimization.AdamW,
            default_adam_kwargs,
        ),
        (
2420
            TrainingArguments(optim=OptimizerNames.ADAMW_TORCH, output_dir="None"),
2421
2422
2423
2424
            torch.optim.AdamW,
            default_adam_kwargs,
        ),
        (
2425
            TrainingArguments(optim=OptimizerNames.ADAFACTOR, output_dir="None"),
2426
2427
2428
2429
2430
2431
2432
2433
            transformers.optimization.Adafactor,
            {
                "scale_parameter": False,
                "relative_step": False,
                "lr": TrainingArguments.learning_rate,
            },
        ),
    ]
2434

2435
2436
2437
2438
2439
    if is_apex_available():
        import apex

        optim_test_params.append(
            (
2440
                TrainingArguments(optim=OptimizerNames.ADAMW_APEX_FUSED, output_dir="None"),
2441
2442
2443
2444
2445
                apex.optimizers.FusedAdam,
                default_adam_kwargs,
            )
        )

2446
2447
2448
2449
2450
    if is_bitsandbytes_available():
        import bitsandbytes as bnb

        optim_test_params.append(
            (
2451
                TrainingArguments(optim=OptimizerNames.ADAMW_BNB, output_dir="None"),
2452
                bnb.optim.AdamW,
2453
2454
2455
2456
                default_adam_kwargs,
            )
        )

2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
        optim_test_params.append(
            (
                TrainingArguments(optim=OptimizerNames.ADAMW_8BIT, output_dir="None"),
                bnb.optim.AdamW,
                default_adam_kwargs,
            )
        )

        optim_test_params.append(
            (
                TrainingArguments(optim=OptimizerNames.PAGED_ADAMW, output_dir="None"),
                bnb.optim.AdamW,
                default_adam_kwargs,
            )
        )

        optim_test_params.append(
            (
                TrainingArguments(optim=OptimizerNames.PAGED_ADAMW_8BIT, output_dir="None"),
                bnb.optim.AdamW,
                default_adam_kwargs,
            )
        )

        optim_test_params.append(
            (
                TrainingArguments(optim=OptimizerNames.LION, output_dir="None"),
                bnb.optim.Lion,
                default_lion_kwargs,
            )
        )

        optim_test_params.append(
            (
                TrainingArguments(optim=OptimizerNames.LION_8BIT, output_dir="None"),
                bnb.optim.Lion,
                default_lion_kwargs,
            )
        )

        optim_test_params.append(
            (
                TrainingArguments(optim=OptimizerNames.PAGED_LION_8BIT, output_dir="None"),
                bnb.optim.Lion,
                default_lion_kwargs,
            )
        )

2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
    if is_torchdistx_available():
        import torchdistx

        optim_test_params.append(
            (
                TrainingArguments(optim=OptimizerNames.ADAMW_ANYPRECISION, output_dir="None"),
                torchdistx.optimizers.AnyPrecisionAdamW,
                dict(default_adam_kwargs, **default_anyprecision_kwargs),
            )
        )

2516
2517
2518

@require_torch
class TrainerOptimizerChoiceTest(unittest.TestCase):
2519
2520
    def check_optim_and_kwargs(self, training_args: TrainingArguments, expected_cls, expected_kwargs):
        actual_cls, optim_kwargs = Trainer.get_optimizer_cls_and_kwargs(training_args)
2521
2522
2523
        self.assertEqual(expected_cls, actual_cls)
        self.assertIsNotNone(optim_kwargs)

2524
        for p, v in expected_kwargs.items():
2525
2526
2527
2528
2529
            self.assertTrue(p in optim_kwargs)
            actual_v = optim_kwargs[p]
            self.assertTrue(actual_v == v, f"Failed check for {p}. Expected {v}, but got {actual_v}.")

    @parameterized.expand(optim_test_params, skip_on_empty=True)
2530
    def test_optim_supported(self, training_args: TrainingArguments, expected_cls, expected_kwargs):
2531
        # exercises all the valid --optim options
2532
        self.check_optim_and_kwargs(training_args, expected_cls, expected_kwargs)
2533

2534
        trainer = get_regression_trainer(**training_args.to_dict())
2535
2536
2537
2538
        trainer.train()

    def test_fused_adam(self):
        # Pretend that apex is installed and mock apex.optimizers.FusedAdam exists.
2539
2540
        # Trainer.get_optimizer_cls_and_kwargs does not use FusedAdam. It only has to return the
        # class given, so mocking apex.optimizers.FusedAdam should be fine for testing and allow
2541
2542
2543
2544
2545
2546
2547
2548
2549
        # the test to run without requiring an apex installation.
        mock = Mock()
        modules = {
            "apex": mock,
            "apex.optimizers": mock.optimizers,
            "apex.optimizers.FusedAdam": mock.optimizers.FusedAdam,
        }
        with patch.dict("sys.modules", modules):
            self.check_optim_and_kwargs(
2550
                TrainingArguments(optim=OptimizerNames.ADAMW_APEX_FUSED, output_dir="None"),
2551
                mock.optimizers.FusedAdam,
2552
                default_adam_kwargs,
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
            )

    def test_fused_adam_no_apex(self):
        args = TrainingArguments(optim=OptimizerNames.ADAMW_APEX_FUSED, output_dir="None")

        # Pretend that apex does not exist, even if installed. By setting apex to None, importing
        # apex will fail even if apex is installed.
        with patch.dict("sys.modules", {"apex.optimizers": None}):
            with self.assertRaises(ValueError):
                Trainer.get_optimizer_cls_and_kwargs(args)
2563

2564
2565
2566
2567
2568
2569
2570
2571
2572
    def test_bnb_adam8bit(self):
        # Pretend that Bits and Bytes is installed and mock bnb.optim.Adam8bit exists.
        # Trainer.get_optimizer_cls_and_kwargs does not use Adam8bit. It only has to return the
        # class given, so mocking bnb.optim.Adam8bit should be fine for testing and allow
        # the test to run without requiring a bnb installation.
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
2573
            "bitsandbytes.optim.AdamW": mock.optim.AdamW,
2574
2575
2576
        }
        with patch.dict("sys.modules", modules):
            self.check_optim_and_kwargs(
2577
                TrainingArguments(optim=OptimizerNames.ADAMW_BNB, output_dir="None"),
2578
                mock.optim.AdamW,
2579
                default_adam_kwargs,
2580
2581
            )

2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
    def test_bnb_paged_adam8bit_alias(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.AdamW": mock.optim.AdamW,
        }
        with patch.dict("sys.modules", modules):
            self.check_optim_and_kwargs(
                TrainingArguments(optim=OptimizerNames.ADAMW_8BIT, output_dir="None"),
                mock.optim.AdamW,
                default_adam_kwargs,
            )

    def test_bnb_paged_adam(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.AdamW": mock.optim.AdamW,
        }
        with patch.dict("sys.modules", modules):
            self.check_optim_and_kwargs(
                TrainingArguments(optim=OptimizerNames.PAGED_ADAMW, output_dir="None"),
                mock.optim.AdamW,
                default_adam_kwargs,
            )

    def test_bnb_paged_adam8bit(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.AdamW": mock.optim.AdamW,
        }
        with patch.dict("sys.modules", modules):
            self.check_optim_and_kwargs(
                TrainingArguments(optim=OptimizerNames.PAGED_ADAMW_8BIT, output_dir="None"),
                mock.optim.AdamW,
                default_adam_kwargs,
            )

    def test_bnb_lion(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.Lion": mock.optim.Lion,
        }
        with patch.dict("sys.modules", modules):
            self.check_optim_and_kwargs(
                TrainingArguments(optim=OptimizerNames.LION, output_dir="None"),
                mock.optim.Lion,
                default_lion_kwargs,
            )

    def test_bnb_lion8bit(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.Lion": mock.optim.Lion,
        }
        with patch.dict("sys.modules", modules):
            self.check_optim_and_kwargs(
                TrainingArguments(optim=OptimizerNames.LION_8BIT, output_dir="None"),
                mock.optim.Lion,
                default_lion_kwargs,
            )

    def test_bnb_paged_lion8bit(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.Lion": mock.optim.Lion,
        }
        with patch.dict("sys.modules", modules):
            self.check_optim_and_kwargs(
                TrainingArguments(optim=OptimizerNames.PAGED_LION_8BIT, output_dir="None"),
                mock.optim.Lion,
                default_lion_kwargs,
            )

    def test_bnb_paged_lion(self):
        mock = Mock()
        modules = {
            "bitsandbytes": mock,
            "bitsandbytes.optim": mock.optim,
            "bitsandbytes.optim.Lion": mock.optim.Lion,
        }
        with patch.dict("sys.modules", modules):
            self.check_optim_and_kwargs(
                TrainingArguments(optim=OptimizerNames.PAGED_LION, output_dir="None"),
                mock.optim.Lion,
                default_lion_kwargs,
            )

2680
2681
2682
2683
2684
    def test_bnb_adam8bit_no_bnb(self):
        args = TrainingArguments(optim=OptimizerNames.ADAMW_BNB, output_dir="None")

        # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
        # bnb will fail even if bnb is installed.
Younes Belkada's avatar
Younes Belkada committed
2685
        with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
2686
2687
2688
            with self.assertRaises(ValueError):
                Trainer.get_optimizer_cls_and_kwargs(args)

2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
    def test_bnb_paged_adam_no_bnb(self):
        args = TrainingArguments(optim=OptimizerNames.PAGED_ADAMW, output_dir="None")

        # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
        # bnb will fail even if bnb is installed.
        with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
            with self.assertRaises(ValueError):
                Trainer.get_optimizer_cls_and_kwargs(args)

    def test_bnb_paged_adam8bit_no_bnb(self):
        args = TrainingArguments(optim=OptimizerNames.PAGED_ADAMW_8BIT, output_dir="None")

        # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
        # bnb will fail even if bnb is installed.
        with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
            with self.assertRaises(ValueError):
                Trainer.get_optimizer_cls_and_kwargs(args)

    def test_bnb_paged_lion_no_bnb(self):
        args = TrainingArguments(optim=OptimizerNames.PAGED_LION, output_dir="None")

        # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
        # bnb will fail even if bnb is installed.
        with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
            with self.assertRaises(ValueError):
                Trainer.get_optimizer_cls_and_kwargs(args)

    def test_bnb_paged_lion8bit_no_bnb(self):
        args = TrainingArguments(optim=OptimizerNames.PAGED_LION_8BIT, output_dir="None")

        # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
        # bnb will fail even if bnb is installed.
        with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
            with self.assertRaises(ValueError):
                Trainer.get_optimizer_cls_and_kwargs(args)

2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
    def test_anyprecision_adamw(self):
        # Pretend that torchdistx is installed and mock torchdistx.optimizers.AnyPrecisionAdamW exists.
        # Trainer.get_optimizer_cls_and_kwargs does not use AnyPrecisioinAdamW. It only has to return the
        # class given, so mocking torchdistx.optimizers.AnyPrecisionAdamW should be fine for testing and allow
        # the test to run without requiring a bnb installation.
        mock = Mock()
        modules = {
            "torchdistx": mock,
            "torchdistx.optimizers": mock.optimizers,
            "torchdistx.optimizers.AnyPrecisionAdamW.": mock.optimizers.AnyPrecisionAdamW,
        }
        with patch.dict("sys.modules", modules):
            self.check_optim_and_kwargs(
                TrainingArguments(optim=OptimizerNames.ADAMW_ANYPRECISION, output_dir="None"),
                mock.optimizers.AnyPrecisionAdamW,
                dict(default_adam_kwargs, **default_anyprecision_kwargs),
            )

    def test_no_torchdistx_anyprecision_adamw(self):
        args = TrainingArguments(optim=OptimizerNames.ADAMW_ANYPRECISION, output_dir="None")

        # Pretend that torchdistx does not exist, even if installed. By setting torchdistx to None, importing
        # torchdistx.optimizers will fail even if torchdistx is installed.
        with patch.dict("sys.modules", {"torchdistx.optimizers": None}):
            with self.assertRaises(ValueError):
                Trainer.get_optimizer_cls_and_kwargs(args)

2752
2753
2754
2755
2756

@require_torch
@require_wandb
class TrainerHyperParameterWandbIntegrationTest(unittest.TestCase):
    def setUp(self):
2757
        args = TrainingArguments("..")
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
        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 {
                "method": "random",
                "metric": {},
                "parameters": {
                    "a": {"distribution": "uniform", "min": 1e-6, "max": 1e-4},
                    "b": {"distribution": "int_uniform", "min": 1, "max": 6},
                },
            }

        def model_init(config):
            if config is None:
                a = 0
                b = 0
            else:
                a = config["a"]
                b = config["b"]
            model_config = RegressionModelConfig(a=a, b=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,
                save_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="wandb", n_trials=4, anonymous="must"
            )