test_deepspeed.py 43.6 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.

15
import dataclasses
16
import io
17
import itertools
18
import json
19
20
import os
import unittest
21
from copy import deepcopy
22

23
from parameterized import parameterized
Stas Bekman's avatar
Stas Bekman committed
24
from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
25
from transformers import AutoModel, TrainingArguments, is_torch_available, logging
26
from transformers.deepspeed import HfDeepSpeedConfig, is_deepspeed_available
27
from transformers.testing_utils import (
28
    CaptureLogger,
29
    CaptureStd,
30
    CaptureStderr,
31
    LoggingLevel,
32
33
34
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
35
    mockenv_context,
36
    require_deepspeed,
37
    require_optuna,
38
39
40
41
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
42
from transformers.trainer_utils import get_last_checkpoint, set_seed
43
from transformers.utils import WEIGHTS_NAME, is_torch_bf16_available
44

45

46
if is_torch_available():
Stas Bekman's avatar
Stas Bekman committed
47
48
49
50
51
    from tests.trainer.test_trainer import (  # noqa
        RegressionModelConfig,
        RegressionPreTrainedModel,
        get_regression_trainer,
    )
52
53


54
set_seed(42)
55

56
57
58
# default torch.distributed port
DEFAULT_MASTER_PORT = "10999"

59
T5_SMALL = "t5-small"
60
T5_TINY = "patrickvonplaten/t5-tiny-random"
61
GPT2_TINY = "sshleifer/tiny-gpt2"
62
63


64
65
66
67
68
def load_json(path):
    with open(path) as f:
        return json.load(f)


Stas Bekman's avatar
Stas Bekman committed
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
def get_master_port(real_launcher=False):
    """
    When using a single gpu launcher emulation (i.e. not deepspeed or python -m torch.distributed)
    the issue is that once the port is tied it can't be used anywhere else outside of this process,
    since torch.dist doesn't free the port until the process exits. Therefore for the sake of being
    able to run both emulated launcher and normal launcher tests we need 2 distinct ports.

    This function will give the right port in the right context. For real launcher it'll give the
    base port, for emulated launcher it'll give the base port + 1. In both cases a string is
    returned.

    Args:
        `real_launcher`: whether a real launcher is going to be used, or the emulated one

    """

    master_port_base = os.environ.get("DS_TEST_PORT", DEFAULT_MASTER_PORT)
    if not real_launcher:
        master_port_base = str(int(master_port_base) + 1)
    return master_port_base


91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
def require_deepspeed_aio(test_case):
    """
    Decorator marking a test that requires deepspeed aio (nvme)
    """
    if not is_deepspeed_available():
        return unittest.skip("test requires deepspeed")(test_case)

    import deepspeed
    from deepspeed.ops.aio import AsyncIOBuilder

    if not deepspeed.ops.__compatible_ops__[AsyncIOBuilder.NAME]:
        return unittest.skip("test requires deepspeed async-io")(test_case)
    else:
        return test_case


107
108
if is_deepspeed_available():
    from deepspeed.utils import logger as deepspeed_logger  # noqa
109
    from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
110
    from transformers.deepspeed import deepspeed_config, is_deepspeed_zero3_enabled  # noqa
111

112
113
114
115
116
117
118

def get_launcher(distributed=False):
    # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
    # - it won't be able to handle that
    # 2. for now testing with just 2 gpus max (since some quality tests may give different
    # results with mode gpus because we use very little data)
    num_gpus = min(2, get_gpu_count()) if distributed else 1
Stas Bekman's avatar
Stas Bekman committed
119
    master_port = get_master_port(real_launcher=True)
120
    return f"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()
121
122


123
124
ZERO2 = "zero2"
ZERO3 = "zero3"
125
126
127
128

FP16 = "fp16"
BF16 = "bf16"

129
stages = [ZERO2, ZERO3]
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
if is_torch_bf16_available():
    dtypes = [FP16, BF16]
else:
    dtypes = [FP16]


def parameterized_custom_name_func(func, param_num, param):
    # customize the test name generator function as we want both params to appear in the sub-test
    # name, as by default it shows only the first param
    param_based_name = parameterized.to_safe_name("_".join(str(x) for x in param.args))
    return f"{func.__name__}_{param_based_name}"


# Cartesian-product of zero stages with models to test
params = list(itertools.product(stages, dtypes))
145
146


147
148
149
150
151
152
153
154
155
156
@require_deepspeed
@require_torch_gpu
class CoreIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
    """
    Testing non-Trainer DeepSpeed integration
    """

    def setUp(self):
        super().setUp()

Stas Bekman's avatar
Stas Bekman committed
157
        master_port = get_master_port(real_launcher=False)
158
        self.dist_env_1_gpu = dict(
159
            MASTER_ADDR="localhost", MASTER_PORT=master_port, RANK="0", LOCAL_RANK="0", WORLD_SIZE="1"
160
161
        )

162
163
    def test_init_zero3_fp16(self):
        # test that zero.Init() works correctly under zero3/fp16
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
        ds_config = {
            "train_batch_size": 1,
            "zero_optimization": {
                "stage": 3,
            },
        }

        dschf = HfDeepSpeedConfig(ds_config)

        self.assertTrue(dschf.is_zero3())
        self.assertTrue(is_deepspeed_zero3_enabled())

        with LoggingLevel(logging.INFO):
            with mockenv_context(**self.dist_env_1_gpu):
                logger = logging.get_logger("transformers.modeling_utils")
                with CaptureLogger(logger) as cl:
                    AutoModel.from_pretrained(T5_TINY)
        self.assertIn("Detected DeepSpeed ZeRO-3", cl.out)

        # now remove zero optimization
        del ds_config["zero_optimization"]
        dschf = HfDeepSpeedConfig(ds_config)

        self.assertFalse(dschf.is_zero3())
        self.assertFalse(is_deepspeed_zero3_enabled())

        with LoggingLevel(logging.INFO):
            with mockenv_context(**self.dist_env_1_gpu):
                logger = logging.get_logger("transformers.modeling_utils")
                with CaptureLogger(logger) as cl:
                    AutoModel.from_pretrained(T5_TINY)
        self.assertNotIn("Detected DeepSpeed ZeRO-3", cl.out)


198
@require_deepspeed
199
@require_torch_gpu
200
201
202
203
204
class TrainerIntegrationDeepSpeed(TestCasePlus, TrainerIntegrationCommon):
    """

    This class is for testing directly via get_regression_trainer

205
206
207
208
209
210
211
212
213
214
215
216
217
    It mixes in `TrainerIntegrationCommon` which already has a lot of helper validation methods
    which we can re-use here.

    Important: this class' setup can only work with a single gpu because it runs within the current
    pytest worker. For multi-gpu tests use TestDeepSpeedWithLauncher.

    Note: if any of the tests of this class get run there will be at least one gpu occupied by them
    until this pytest worker exits. This is because the gpu memory allocated by the cuda-kernels
    won't be released until this pytest worker exits.

    This may appear as some run-away tests if you watch `nvidia-smi` while other tests that fork new
    processes are run. So there will be one or two "stale" processes reported in `nvidia-smi`. This
    is not a bug.
218
    """
219
220
221

    def setUp(self):
        super().setUp()
222
223
224
225
226

        args = TrainingArguments(".")
        self.n_epochs = args.num_train_epochs
        self.batch_size = args.train_batch_size

Stas Bekman's avatar
Stas Bekman committed
227
        master_port = get_master_port(real_launcher=False)
228
        self.dist_env_1_gpu = dict(
229
            MASTER_ADDR="localhost", MASTER_PORT=master_port, RANK="0", LOCAL_RANK="0", WORLD_SIZE="1"
230
        )
231

232
233
234
235
        self.ds_config_file = dict(
            zero2=f"{self.test_file_dir_str}/ds_config_zero2.json",
            zero3=f"{self.test_file_dir_str}/ds_config_zero3.json",
        )
236
237
238

        # use self.get_config_dict(stage) to use these to ensure the original is not modified
        with io.open(self.ds_config_file[ZERO2], "r", encoding="utf-8") as f:
239
            config_zero2 = json.load(f)
240
        with io.open(self.ds_config_file[ZERO3], "r", encoding="utf-8") as f:
241
            config_zero3 = json.load(f)
242
            # The following setting slows things down, so don't enable it by default unless needed by a test.
243
            # It's in the file as a demo for users since we want everything to work out of the box even if slower.
244
245
            config_zero3["zero_optimization"]["stage3_gather_16bit_weights_on_model_save"] = False

246
247
248
249
250
251
252
253
        self.ds_config_dict = dict(
            zero2=config_zero2,
            zero3=config_zero3,
        )

    def get_config_dict(self, stage):
        # As some tests modify the dict, always make a copy
        return deepcopy(self.ds_config_dict[stage])
254
255

    # --- These tests are enough to run on one of zero stages --- #
256

257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
    def test_hf_ds_config_mismatch(self):

        ds_config = self.get_config_dict(ZERO2)

        # Purposefully configure these values to mismatch TrainingArguments values.
        # This currently doesn't cover all keys (but it could)
        per_device_train_batch_size = 2
        ds_config["train_micro_batch_size_per_gpu"] = per_device_train_batch_size + 2

        ds_config["train_batch_size"] = 1000

        gradient_accumulation_steps = 2
        ds_config["gradient_accumulation_steps"] = gradient_accumulation_steps + 2

        max_grad_norm = 1.0
        ds_config["gradient_clipping"] = max_grad_norm + 0.1

        adam_beta1, adam_beta2 = 0.9, 0.99
        ds_config["optimizer"]["params"]["betas"] = [adam_beta1 - 0.1, adam_beta2 - 0.1]

        fp16 = True
        ds_config["fp16"]["enabled"] = not fp16

        keys = [
            "per_device_train_batch_size",
            "train_batch_size",
            "gradient_accumulation_steps",
            "max_grad_norm",
            "betas",
            "fp16",
        ]

        with mockenv_context(**self.dist_env_1_gpu):
            trainer = get_regression_trainer(
                local_rank=0,
                fp16=fp16,
                deepspeed=ds_config,
                per_device_train_batch_size=per_device_train_batch_size,
                gradient_accumulation_steps=gradient_accumulation_steps,
                max_grad_norm=max_grad_norm,
                adam_beta1=adam_beta1,
                adam_beta2=adam_beta2,
            )
            with self.assertRaises(Exception) as context:
                trainer.train()

        for key in keys:
            self.assertTrue(
                key in str(context.exception),
                f"{key} is not in the exception message:\n{context.exception}",
            )

309
310
311
312
313
314
315
316
317
    # Test various combos
    # 1. DS scheduler + DS optimizer: this is already tested by most other tests
    # 2. HF scheduler + HF optimizer:
    # 3. DS scheduler + HF optimizer:
    # 4. HF scheduler + DS optimizer:

    def test_hf_scheduler_hf_optimizer(self):
        a = 0
        with mockenv_context(**self.dist_env_1_gpu):
318
319
320
            ds_config_zero2_dict = self.get_config_dict(ZERO2)
            del ds_config_zero2_dict["optimizer"]  # force default HF Trainer optimizer
            del ds_config_zero2_dict["scheduler"]  # force default HF Trainer scheduler
321
            ds_config_zero2_dict["zero_optimization"]["offload_optimizer"]["device"] = "none"
322
            ds_config_zero2_dict["fp16"]["initial_scale_power"] = 1  # force optimizer on the first step
323
            trainer = get_regression_trainer(a=a, local_rank=0, fp16=True, deepspeed=ds_config_zero2_dict)
324
325
326
327
328
329
330
            trainer.train()
        new_a = trainer.model.a.item()
        self.assertNotEqual(new_a, a)

    def test_ds_scheduler_hf_optimizer(self):
        a = 0
        with mockenv_context(**self.dist_env_1_gpu):
331
332
            ds_config_zero2_dict = self.get_config_dict(ZERO2)
            del ds_config_zero2_dict["optimizer"]  # force default HF Trainer optimizer
333
            ds_config_zero2_dict["zero_optimization"]["offload_optimizer"]["device"] = "none"
334
            ds_config_zero2_dict["fp16"]["initial_scale_power"] = 1  # force optimizer on the first step
335
            trainer = get_regression_trainer(a=a, local_rank=0, fp16=True, deepspeed=ds_config_zero2_dict)
336
337
338
339
340
            trainer.train()
        new_a = trainer.model.a.item()
        self.assertNotEqual(new_a, a)

    def test_hf_scheduler_ds_optimizer(self):
341
        a = 0
342
        with mockenv_context(**self.dist_env_1_gpu):
343
344
            ds_config_zero2_dict = self.get_config_dict(ZERO2)
            del ds_config_zero2_dict["scheduler"]  # force default HF Trainer scheduler
345
            ds_config_zero2_dict["zero_optimization"]["offload_optimizer"]["device"] = "none"
346
            ds_config_zero2_dict["fp16"]["initial_scale_power"] = 1  # force optimizer on the first step
347
            trainer = get_regression_trainer(local_rank=0, fp16=True, deepspeed=ds_config_zero2_dict)
348
349
350
            trainer.train()
        new_a = trainer.model.a.item()
        self.assertNotEqual(new_a, a)
351

352
    @require_deepspeed_aio
353
354
355
356
357
358
359
360
361
    def test_stage3_nvme_offload(self):
        with mockenv_context(**self.dist_env_1_gpu):
            # this actually doesn't have to be on NVMe, any storage will do since this test only
            # runs a simple check that we can use some directory as if it were NVMe
            nvme_path = self.get_auto_remove_tmp_dir()
            nvme_config = dict(device="nvme", nvme_path=nvme_path)
            ds_config_zero3_dict = self.get_config_dict(ZERO3)
            ds_config_zero3_dict["zero_optimization"]["offload_optimizer"] = nvme_config
            ds_config_zero3_dict["zero_optimization"]["offload_param"] = nvme_config
362
            trainer = get_regression_trainer(local_rank=0, fp16=True, deepspeed=ds_config_zero3_dict)
363
            with CaptureLogger(deepspeed_logger) as cl:
364
                trainer.train()
365
            self.assertIn("DeepSpeed info", cl.out, "expected DeepSpeed logger output but got none")
366

367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
    @require_optuna
    def test_hyperparameter_search(self):
        with mockenv_context(**self.dist_env_1_gpu):

            ds_config_zero3_dict = self.get_config_dict(ZERO3)

            # hyperparameter_search requires model_init() to recreate the model for each trial
            def model_init():
                config = RegressionModelConfig(a=0, b=0, double_output=False)
                model = RegressionPreTrainedModel(config)
                return model

            trainer = get_regression_trainer(
                local_rank=0,
                fp16=True,
                model_init=model_init,
                deepspeed=ds_config_zero3_dict,
            )

            n_trials = 3
            with CaptureLogger(deepspeed_logger) as cl:
                with CaptureStd() as cs:
                    trainer.hyperparameter_search(direction="maximize", n_trials=n_trials)
            self.assertIn("DeepSpeed info", cl.out, "expected DeepSpeed logger output but got none")
            self.assertIn(f"Trial {n_trials-1} finished with value", cs.err, "expected hyperparameter_search output")
            self.assertIn("Best is trial", cs.err, "expected hyperparameter_search output")

394
395
    # --- These tests need to run on both zero stages --- #

396
397
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_hf_optimizer_with_offload(self, stage, dtype):
398
        # non-DS optimizers can be used with ZERO-offload (as long as they have both CPU and GPU implementation (except LAMB))
399
400
401
        ds_config_dict = self.get_config_dict(stage)
        del ds_config_dict["optimizer"]  # force default HF Trainer optimizer
        # force cpu offload
402
        ds_config_dict["zero_optimization"]["offload_optimizer"]["device"] = "cpu"
403
        with mockenv_context(**self.dist_env_1_gpu):
404
405
406
            kwargs = dict(local_rank=0, deepspeed=ds_config_dict)
            kwargs[dtype] = True
            trainer = get_regression_trainer(**kwargs)
407
            with CaptureLogger(deepspeed_logger) as cl:
408
                trainer.train()
409
            self.assertIn("DeepSpeed info", cl.out, "expected DeepSpeed logger output but got none")
410

411
412
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_fake_notebook_no_launcher(self, stage, dtype):
413
414
415
416
417
        # this setup emulates a notebook where a launcher needs to be emulated by hand

        # note that unittest resets sys.stdout each test, so `CaptureStd` will work here to capture
        # DeepSpeed log if this test happens to run first in this pytest worker. But it will fail if
        # it's run not as a first test as `sys.stdout` will no longer be the same. So we either have
418
419
        # to reset `deepspeed_logger.handlers[0].setStream(sys.stdout)` or directly capture from the deepspeed_logger.
        with mockenv_context(**self.dist_env_1_gpu):
420
421
422
423
            kwargs = dict(local_rank=0, deepspeed=self.get_config_dict(stage))
            kwargs[dtype] = True
            trainer = get_regression_trainer(**kwargs)

424
            with CaptureLogger(deepspeed_logger) as cl:
425
                trainer.train()
426
            self.assertIn("DeepSpeed info", cl.out, "expected DeepSpeed logger output but got none")
427

428
429
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_early_get_last_lr(self, stage, dtype):
430
431
432
433
434
435
436
437
        # with deepspeed's fp16 and dynamic loss scale enabled the optimizer/scheduler steps may
        # not run for the first few dozen steps while loss scale is too large, and thus during
        # that time `get_last_lr` will fail if called during that warm up stage,
        #
        # setting `logging_steps=1` forces an early `trainer._maybe_log_save_evaluate()` which calls
        # `self.lr_scheduler.get_last_lr()` and originally it'd fail on the very first step.
        with mockenv_context(**self.dist_env_1_gpu):
            a = b = 0.0
438
            kwargs = dict(
439
440
441
442
                a=a,
                b=b,
                local_rank=0,
                train_len=8,
443
                deepspeed=self.get_config_dict(stage),
444
445
446
                per_device_train_batch_size=8,
                logging_steps=1,
            )
447
448
449
            kwargs[dtype] = True
            trainer = get_regression_trainer(**kwargs)

450
            trainer.train()
451
452
            post_train_a = trainer.model.a.item()

453
454
            # XXX: for some reason the following check fails with zero3/fp16 and any/bf16 - not a
            # broken but a different qualitative outcome - as if optimizer did run
455
456
457
458
            # oddly getting 1.0 for both a and b from 0.0 - there is a bug somewhere
            # print(trainer.model.a.item())
            # print(trainer.model.b.item())
            # need to investigate at some point
459
            if (stage == ZERO3 and dtype == FP16) or (dtype == BF16):
460
                return
461
462
463

            # it's enough that train didn't fail for this test, but we must check that
            # optimizer/scheduler didn't run (since if it did this test isn't testing the right thing)
464
            self.assertEqual(post_train_a, a)
465

466
467
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_gradient_accumulation(self, stage, dtype):
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
        # this test measures that we get identical weights and similar loss with:
        # 1. per_device_train_batch_size=8, gradient_accumulation_steps=1
        # 2. per_device_train_batch_size=4, gradient_accumulation_steps=2
        # since the 2nd should produce the effective batch of 1st, with the same results
        #
        # I can get an identical loss for a small train_len=32, plus the power of the initial
        # dynamic loss scale value set to:
        #   "fp16.initial_scale_power": 1
        # plus having the same WarmupLR's warmup_min_lr == warmup_max_lr in the config file
        # but for some reason going to train_len=64 the weights, weights start to mismatch with this setup.
        # the culprit seems to be `initial_scale_power` - putting it back to its default 32 keeps the weights identical

        train_len = 64
        a = b = 0.0

483
484
485
486
487
488
489
        kwargs = dict(
            a=a,
            b=b,
            local_rank=0,
            train_len=train_len,
            deepspeed=self.get_config_dict(stage),
        )
490
        kwargs[dtype] = True
491

492
493
        with mockenv_context(**self.dist_env_1_gpu):
            no_grad_accum_trainer = get_regression_trainer(
494
495
                **kwargs,
                per_device_train_batch_size=16,
496
497
498
499
500
501
502
503
504
505
506
                gradient_accumulation_steps=1,
            )
            no_grad_accum_result = no_grad_accum_trainer.train()
            no_grad_accum_loss = no_grad_accum_result.training_loss
            no_grad_accum_a = no_grad_accum_trainer.model.a.item()
            no_grad_accum_b = no_grad_accum_trainer.model.b.item()
            # make sure the optimizer kicked in - if it hasn't changed from the original value of a then make train_len bigger
            self.assertNotEqual(no_grad_accum_a, a)

        with mockenv_context(**self.dist_env_1_gpu):
            yes_grad_accum_trainer = get_regression_trainer(
507
                **kwargs,
508
                per_device_train_batch_size=4,
509
                gradient_accumulation_steps=4,
510
511
512
513
514
515
516
            )
            yes_grad_accum_result = yes_grad_accum_trainer.train()
            yes_grad_accum_loss = yes_grad_accum_result.training_loss
            yes_grad_accum_a = yes_grad_accum_trainer.model.a.item()
            yes_grad_accum_b = yes_grad_accum_trainer.model.b.item()
            self.assertNotEqual(yes_grad_accum_a, a)

517
518
519
520
        # training with half the batch size but accumulation steps as 2 should give the same
        # weights, but sometimes get a slight difference still of 1e-6
        self.assertAlmostEqual(no_grad_accum_a, yes_grad_accum_a, places=5)
        self.assertAlmostEqual(no_grad_accum_b, yes_grad_accum_b, places=5)
521
522

        # see the note above how to get identical loss on a small bs
523
        self.assertAlmostEqual(no_grad_accum_loss, yes_grad_accum_loss, places=2)
524

525
    def check_saved_checkpoints_deepspeed(self, output_dir, freq, total, stage, dtype):
526
527
528
        # adapted from TrainerIntegrationCommon.check_saved_checkpoints

        file_list = [WEIGHTS_NAME, "training_args.bin", "trainer_state.json", "config.json"]
529
530
531
532
533
534
535
536

        if stage == ZERO2:
            ds_file_list = ["mp_rank_00_model_states.pt"]
        elif stage == ZERO3:
            ds_file_list = ["zero_pp_rank_0_mp_rank_00_model_states.pt"]
        else:
            raise ValueError(f"unknown stage {stage}")

537
538
        if dtype == "bf16":
            ds_file_list.append("bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt")
539
540
541

        for step in range(freq, total, freq):
            checkpoint = os.path.join(output_dir, f"checkpoint-{step}")
542
            self.assertTrue(os.path.isdir(checkpoint), f"[{stage}] {checkpoint} dir is not found")
543
544
545

            # common files
            for filename in file_list:
546
547
                path = os.path.join(checkpoint, filename)
                self.assertTrue(os.path.isfile(path), f"[{stage}] {path} is not found")
548
549
550
551
552
553

            # ds files
            ds_path = os.path.join(checkpoint, f"global_step{step}")
            for filename in ds_file_list:
                # filename = os.path.join(path, filename)
                # print(filename)
554
555
                path = os.path.join(ds_path, filename)
                self.assertTrue(os.path.isfile(path), f"[{stage}] {path} is not found")
556

557
558
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_save_checkpoints(self, stage, dtype):
559
560
        # adapted from  TrainerIntegrationTest.test_save_checkpoints

561
        freq = 5
562
        output_dir = self.get_auto_remove_tmp_dir()
563
        ds_config_dict = self.get_config_dict(stage)
564
565
566
        if dtype == FP16:
            ds_config_dict["fp16"]["initial_scale_power"] = 1  # force optimizer on the first step
        # XXX:
567
        if stage == ZERO3:
568
            ds_config_dict["zero_optimization"]["stage3_gather_16bit_weights_on_model_save"] = True
569
570
571

        # save checkpoints
        with mockenv_context(**self.dist_env_1_gpu):
572
            kwargs = dict(
573
574
575
576
                output_dir=output_dir,
                save_steps=freq,
                deepspeed=ds_config_dict,
            )
577
578
            kwargs[dtype] = True
            trainer = get_regression_trainer(**kwargs)
579
580
581
            trainer.train()

        total = int(self.n_epochs * 64 / self.batch_size)
582
        self.check_saved_checkpoints_deepspeed(output_dir, freq, total, stage, dtype)
583

584
585
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_can_resume_training_errors(self, stage, dtype):
586
587
588
589

        with mockenv_context(**self.dist_env_1_gpu):
            ds_config_dict = self.get_config_dict(stage)
            output_dir = self.get_auto_remove_tmp_dir()
590
591
592
            kwargs = dict(output_dir=output_dir, deepspeed=ds_config_dict)
            kwargs[dtype] = True
            trainer = get_regression_trainer(**kwargs)
593
594
595
596
597
598
599
600

            # 1. fail to find any checkpoint - due a fresh output_dir
            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),
                f"got exception: {context.exception}",
            )
601

602
603
604
605
606
607
608
609
            # 2. fail to find a bogus checkpoint
            with self.assertRaises(Exception) as context:
                checkpoint = os.path.join(output_dir, "checkpoint-5")
                trainer.train(resume_from_checkpoint=f"{checkpoint}-bogus")
            self.assertTrue(
                "Can't find a valid checkpoint at" in str(context.exception), f"got exception: {context.exception}"
            )

610
611
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_can_resume_training_normal(self, stage, dtype):
612
613
        # adapted from TrainerIntegrationTest.test_can_resume_training
        # test normal resume for each stage separately, error-handling is tested in a different test
614
        output_dir = self.get_auto_remove_tmp_dir("./xxx", after=False)
615
        ds_config_dict = self.get_config_dict(stage)
616
617
618
        if dtype == FP16:
            ds_config_dict["fp16"]["initial_scale_power"] = 1  # force optimizer on the first step
        # XXX:
619
        if stage == ZERO3:
620
            ds_config_dict["zero_optimization"]["stage3_gather_16bit_weights_on_model_save"] = True
621

622
623
        kwargs = dict(output_dir=output_dir, train_len=128, save_steps=5, learning_rate=0.1, deepspeed=ds_config_dict)
        kwargs[dtype] = True
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655

        with mockenv_context(**self.dist_env_1_gpu):
            trainer = get_regression_trainer(**kwargs)
            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()
            state = dataclasses.asdict(trainer.state)

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

            # Reinitialize trainer
            trainer = get_regression_trainer(**kwargs)

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

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

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

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

656
657
658
659
660
            # Finally, should be able to resume with the same trainer/same deepspeed engine instance
            # XXX: but currently this not possible due DS bug: https://github.com/microsoft/DeepSpeed/issues/1612
            # trainer.train(resume_from_checkpoint=checkpoint)
            # a workaround needs to be used that re-creates the deepspeed engine

661
662
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_load_state_dict_from_zero_checkpoint(self, stage, dtype):
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
        # test that we can load fp32 weights directly from the zero checkpoint into the current model

        output_dir = self.get_auto_remove_tmp_dir()  # "./xxx", after=False, before=False)

        ds_config_dict = self.get_config_dict(stage)

        kwargs = dict(
            output_dir=output_dir,
            train_len=4,
            per_device_train_batch_size=4,
            num_train_epochs=1,
            save_strategy="steps",
            save_steps=1,
            learning_rate=0.1,
            deepspeed=ds_config_dict,
        )
679
        kwargs[dtype] = True
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695

        with mockenv_context(**self.dist_env_1_gpu):
            trainer = get_regression_trainer(**kwargs)
            trainer.train()
            (a, b) = trainer.model.a.item(), trainer.model.b.item()
            state = dataclasses.asdict(trainer.state)

            checkpoint_dir = get_last_checkpoint(output_dir)
            model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)

            (a1, b1) = model.a.item(), 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)

696
697
698
699
    def test_config_object(self):
        # test that we can switch from zero2 to zero3 in the same process for example
        # test is_zero, etc.
        output_dir = self.get_auto_remove_tmp_dir()
700
        kwargs = dict(output_dir=output_dir, train_len=8, fp16=True)
701

702
703
        ds_config_zero3_dict = self.get_config_dict(ZERO3)
        ds_config_zero2_dict = self.get_config_dict(ZERO2)
704

705
        with mockenv_context(**self.dist_env_1_gpu):
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
            trainer = get_regression_trainer(deepspeed=ds_config_zero3_dict, **kwargs)
            self.assertTrue(is_deepspeed_zero3_enabled())

            # test we can repeat that and with train this time
            trainer = get_regression_trainer(deepspeed=ds_config_zero3_dict, **kwargs)
            trainer.train()
            self.assertTrue(is_deepspeed_zero3_enabled())

            # test zero3 is disabled
            trainer = get_regression_trainer(deepspeed=ds_config_zero2_dict, **kwargs)
            self.assertFalse(is_deepspeed_zero3_enabled())

            # check config obj
            config = deepspeed_config()
            self.assertTrue(bool(config), "Deepspeed config should be accessible")

            del trainer
            # now weakref should gc the global and we shouldn't get anything here
            config = deepspeed_config()
            self.assertFalse(is_deepspeed_zero3_enabled())
            self.assertFalse(bool(config), "Deepspeed config should not be accessible")

728
729
730
731

@slow
@require_deepspeed
@require_torch_gpu
732
class TestDeepSpeedWithLauncher(TestCasePlus):
Patrick von Platen's avatar
Patrick von Platen committed
733
    """This class is for testing via an external script - can do multiple gpus"""
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749

    # Tests to devise #
    #
    # 1. predict_with_generate on multigpu - need to figure out how to give input sequences so that
    # the 2 gpus will generate prediction sequences that aren't of the same length - this is because
    # we had to code a special feature to sync the gpus when the predicted sequences aren't of the
    # same length. In general this will tested as a side-effect through a variety of other tests -
    # it'll simply hang trying to synchronize with other gpus if this problem is encountered. So as
    # long as we have a few full tests running on zero3 + predict_with_generate this should be
    # mostly covered.
    #
    # but there are 5 variations on beam search in `generate`- with identical code branched with `if
    # synced_gpus`
    #
    # 2. most tests should probably be run on both: zero2 and zero3 configs
    #
750

751
    @require_torch_multi_gpu
752
753
754
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_basic_distributed(self, stage, dtype):
        self.run_and_check(stage=stage, dtype=dtype, distributed=True)
755

756
757
    def test_do_eval_no_train(self):
        # testing only zero3 since zero2 makes no sense with inference
758
        self.run_and_check(
759
            stage=ZERO3,
760
            dtype=FP16,
761
762
            eval_steps=1,
            distributed=False,
763
764
            do_train=False,
            do_eval=True,
765
        )
766

767
768
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_fp32_non_distributed(self, stage, dtype):
769
770
771
772
        # real model needs too much GPU memory under stage2+fp32, so using tiny random model here -
        # therefore no quality checks, just basic completion checks are done
        self.run_and_check(
            stage=stage,
773
            dtype=dtype,
774
775
776
777
778
            model_name=T5_TINY,
            distributed=False,
            do_train=True,
            do_eval=True,
            quality_checks=False,
779
            fp32=True,
780
781
782
        )

    @require_torch_multi_gpu
783
784
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_fp32_distributed(self, stage, dtype):
785
786
787
788
        # real model needs too much GPU memory under stage2+fp32, so using tiny random model here -
        # therefore no quality checks, just basic completion checks are done
        self.run_and_check(
            stage=stage,
789
            dtype=dtype,
790
791
792
793
794
            model_name=T5_TINY,
            distributed=True,
            do_train=True,
            do_eval=True,
            quality_checks=False,
795
            fp32=True,
796
797
        )

798
799
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_resume_train_not_from_ds_checkpoint(self, stage, dtype):
800
801
802
803
804
        # do normal training and then resume not from the deepspeed checkpoint but explicitly from
        # the saved model dir

        do_train = True
        do_eval = False
805
        kwargs = dict(stage=stage, dtype=dtype, eval_steps=1, distributed=True, do_train=do_train, do_eval=do_eval)
806
807
808
809
810
811
812
813
814
815

        # 1. normal training
        output_dir = self.run_and_check(**kwargs)

        # 2. now resume explicitly from the saved weights, by passing --model_name_or_path output_dir
        # - i.e. the same path the model was saved to in step 1
        output_dir = self.run_trainer(**kwargs, model_name=output_dir)

        self.do_checks(output_dir, do_train=do_train, do_eval=do_eval)

816
    @require_torch_multi_gpu
817
    @parameterized.expand(["bf16", "fp16", "fp32"])
818
    def test_inference(self, dtype):
819
820
821
        if dtype == "bf16" and not is_torch_bf16_available():
            self.skipTest("test requires bfloat16 hardware support")

822
823
        # this is just inference, so no optimizer should be loaded
        # it only works for z3 (makes no sense with z1-z2)
824
        fp32 = True if dtype == "fp32" else False
825
826
        self.run_and_check(
            stage=ZERO3,
827
            dtype=FP16,
828
829
830
831
832
            model_name=T5_TINY,
            distributed=True,
            do_train=False,
            do_eval=True,
            quality_checks=False,
833
            fp32=fp32,
834
835
        )

836
    def do_checks(self, output_dir, do_train=True, do_eval=True, quality_checks=True):
837
838
839
840

        if do_train:
            train_metrics = load_json(os.path.join(output_dir, "train_results.json"))
            self.assertIn("train_samples_per_second", train_metrics)
841
842
            if quality_checks:
                self.assertGreater(train_metrics["train_samples_per_second"], 0.5)
843
844
845
846

        if do_eval:
            eval_metrics = load_json(os.path.join(output_dir, "eval_results.json"))
            self.assertIn("eval_bleu", eval_metrics)
847
848
            if quality_checks:
                self.assertGreater(eval_metrics["eval_bleu"], 1)
849
850

    # XXX: need to do better validation beyond just that the run was successful
851
852
853
    def run_and_check(
        self,
        stage,
854
        dtype,
855
856
857
858
859
860
        model_name: str = T5_SMALL,
        eval_steps: int = 10,
        distributed: bool = True,
        do_train: bool = True,
        do_eval: bool = True,
        quality_checks: bool = True,
861
        fp32: bool = False,
862
863
        extra_args_str: str = None,
        remove_args_str: str = None,
864
865
866
    ):

        # we are doing quality testing so using a small real model
867
        output_dir = self.run_trainer(
868
            stage=stage,
869
            dtype=dtype,
870
            model_name=model_name,
871
            eval_steps=eval_steps,
872
            num_train_epochs=1,
873
874
            do_train=do_train,
            do_eval=do_eval,
875
            distributed=distributed,
876
            fp32=fp32,
877
878
879
            extra_args_str=extra_args_str,
            remove_args_str=remove_args_str,
        )
880

881
        self.do_checks(output_dir, do_train=do_train, do_eval=do_eval, quality_checks=quality_checks)
882
883

        return output_dir
884
885
886

    def run_trainer(
        self,
887
        stage: str,
888
        dtype: str,
889
        model_name: str,
890
891
892
893
        eval_steps: int = 10,
        num_train_epochs: int = 1,
        do_train: bool = False,
        do_eval: bool = True,
894
        distributed: bool = True,
895
        fp32: bool = False,
896
897
898
        extra_args_str: str = None,
        remove_args_str: str = None,
    ):
899
        max_len = 32
Sylvain Gugger's avatar
Sylvain Gugger committed
900
        data_dir = self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
901
902
903
        output_dir = self.get_auto_remove_tmp_dir()
        args = f"""
            --model_name_or_path {model_name}
904
905
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
906
907
908
909
910
911
912
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_source_length {max_len}
            --max_target_length {max_len}
            --val_max_target_length {max_len}
            --warmup_steps 8
            --predict_with_generate
913
914
            --save_steps 0
            --eval_steps {eval_steps}
915
916
            --group_by_length
            --label_smoothing_factor 0.1
917
918
            --source_lang en
            --target_lang ro
919
            --report_to none
920
        """.split()
921
922
        args.extend(["--source_prefix", '"translate English to Romanian: "'])

923
924
        if not fp32:
            args.extend([f"--{dtype}"])
925

926
927
928
929
930
931
932
        actions = 0
        if do_train:
            actions += 1
            args.extend(
                f"""
            --do_train
            --num_train_epochs {str(num_train_epochs)}
933
            --max_train_samples 16
934
935
936
937
938
939
940
941
942
943
            --per_device_train_batch_size 2
            --learning_rate 3e-3
            """.split()
            )

        if do_eval:
            actions += 1
            args.extend(
                """
            --do_eval
944
            --max_eval_samples 16
945
946
947
948
949
            --per_device_eval_batch_size 2
            """.split()
            )

        assert actions > 0, "need at least do_train or do_eval for the test to run"
950
951
952
953

        if extra_args_str is not None:
            args.extend(extra_args_str.split())

954
        # currently only works for bool args
955
956
957
958
        if remove_args_str is not None:
            remove_args = remove_args_str.split()
            args = [x for x in args if x not in remove_args]

959
        ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()
Sylvain Gugger's avatar
Sylvain Gugger committed
960
        script = [f"{self.examples_dir_str}/pytorch/translation/run_translation.py"]
961
        launcher = get_launcher(distributed)
962
963

        cmd = launcher + script + args + ds_args
964
        # keep for quick debug
965
966
967
968
969
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        execute_subprocess_async(cmd, env=self.get_env())

        return output_dir

970
971
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_clm(self, stage, dtype):
972
973
974
975
976
977
        # this test exercises model.resize_token_embeddings() which requires param gathering outside
        # of forward - it's not used by `run_translation.py`, but it is in `run_clm.py`

        data_dir = self.tests_dir / "fixtures"
        output_dir = self.get_auto_remove_tmp_dir()
        args = f"""
978
            --model_name_or_path {GPT2_TINY}
979
980
981
982
983
984
            --train_file {data_dir}/sample_text.txt
            --validation_file {data_dir}/sample_text.txt
            --output_dir {output_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
985
986
987
988
            --max_train_samples 16
            --max_eval_samples 16
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
989
990
            --num_train_epochs 1
            --warmup_steps 8
991
            --block_size 64
992
            --report_to none
993
994
            """.split()

995
996
        args.extend([f"--{dtype}"])

997
        ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()
Sylvain Gugger's avatar
Sylvain Gugger committed
998
        script = [f"{self.examples_dir_str}/pytorch/language-modeling/run_clm.py"]
999
        launcher = get_launcher(distributed=True)
1000
1001
1002
1003

        cmd = launcher + script + args + ds_args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
1004
1005
        execute_subprocess_async(cmd, env=self.get_env())

1006
    def test_clm_from_config_zero3_fp16(self):
1007
1008
1009
1010
1011
1012
        # this test exercises AutoModel.from_config(config) - to ensure zero.Init is called

        data_dir = self.tests_dir / "fixtures"
        output_dir = self.get_auto_remove_tmp_dir()
        args = f"""
            --model_type gpt2
1013
            --tokenizer_name {GPT2_TINY}
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
            --train_file {data_dir}/sample_text.txt
            --validation_file {data_dir}/sample_text.txt
            --output_dir {output_dir}
            --overwrite_output_dir
            --do_train
            --max_train_samples 4
            --per_device_train_batch_size 2
            --num_train_epochs 1
            --warmup_steps 8
            --block_size 8
            --fp16
            --report_to none
            """.split()

        ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config_zero3.json".split()
        script = [f"{self.examples_dir_str}/pytorch/language-modeling/run_clm.py"]
1030
        launcher = get_launcher(distributed=True)
1031
1032
1033
1034
1035
1036

        cmd = launcher + script + args + ds_args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        with CaptureStderr() as cs:
            execute_subprocess_async(cmd, env=self.get_env())
1037
        self.assertIn("Detected DeepSpeed ZeRO-3", cs.err)
1038

1039
1040
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_load_best_model(self, stage, dtype):
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
        # this test exercises --load_best_model_at_end - the key is being able to resume after some training

        data_dir = self.tests_dir / "fixtures/tests_samples/wmt_en_ro"
        output_dir = self.get_auto_remove_tmp_dir()
        args = f"""
            --model_name_or_path {T5_TINY}
            --tokenizer_name {T5_TINY}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --source_lang en
            --target_lang ro
            --do_train
            --max_train_samples 3
            --do_eval
            --max_eval_samples 1
            --logging_strategy steps
            --logging_steps 1
            --evaluation_strategy steps
            --eval_steps 1
            --save_strategy steps
            --save_steps 1
            --load_best_model_at_end
            --per_device_train_batch_size 1
            --per_device_eval_batch_size 1
            --num_train_epochs 1
            --report_to none
            """.split()
        args.extend(["--source_prefix", "translate English to Romanian: "])

1072
1073
        args.extend([f"--{dtype}"])

1074
        ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()
1075
1076
1077
1078
1079
1080
        script = [f"{self.examples_dir_str}/pytorch/translation/run_translation.py"]
        launcher = get_launcher(distributed=False)

        cmd = launcher + script + args + ds_args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
1081
        with CaptureStd() as cs:
1082
1083
            execute_subprocess_async(cmd, env=self.get_env())
        # enough to test it didn't fail
1084
        self.assertIn("DeepSpeed info", cs.out)