test_deepspeed.py 47.4 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
from functools import partial
23

24
import datasets
25
from parameterized import parameterized
26

27
import tests.trainer.test_trainer
Stas Bekman's avatar
Stas Bekman committed
28
from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
29
from transformers import AutoModel, TrainingArguments, is_torch_available, logging
30
31
32
33
34
from transformers.integrations.deepspeed import (
    HfDeepSpeedConfig,
    is_deepspeed_available,
    unset_hf_deepspeed_config,
)
35
from transformers.testing_utils import (
36
    CaptureLogger,
37
    CaptureStd,
38
    CaptureStderr,
39
    LoggingLevel,
40
41
42
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
43
    mockenv_context,
44
    require_deepspeed,
45
    require_optuna,
46
47
48
49
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
50
from transformers.trainer_utils import get_last_checkpoint, set_seed
51
from transformers.utils import WEIGHTS_NAME, is_torch_bf16_gpu_available
52

53

54
if is_torch_available():
Stas Bekman's avatar
Stas Bekman committed
55
56
57
58
    from tests.trainer.test_trainer import (  # noqa
        RegressionModelConfig,
        RegressionPreTrainedModel,
    )
59

60
61
62
    # hack to restore original logging level pre #21700
    get_regression_trainer = partial(tests.trainer.test_trainer.get_regression_trainer, log_level="info")

63

64
set_seed(42)
65

66
67
68
# default torch.distributed port
DEFAULT_MASTER_PORT = "10999"

69
T5_SMALL = "t5-small"
70
T5_TINY = "patrickvonplaten/t5-tiny-random"
71
GPT2_TINY = "sshleifer/tiny-gpt2"
72
73


74
75
76
77
78
def load_json(path):
    with open(path) as f:
        return json.load(f)


Stas Bekman's avatar
Stas Bekman committed
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
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


101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
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


117
118
if is_deepspeed_available():
    from deepspeed.utils import logger as deepspeed_logger  # noqa
119
    from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
120
    from transformers.integrations.deepspeed import deepspeed_config, is_deepspeed_zero3_enabled  # noqa
121

122
123
124
125
126
127
128

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
129
    master_port = get_master_port(real_launcher=True)
130
    return f"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()
131
132


133
134
ZERO2 = "zero2"
ZERO3 = "zero3"
135
136
137
138

FP16 = "fp16"
BF16 = "bf16"

139
stages = [ZERO2, ZERO3]
140
if is_torch_bf16_gpu_available():
141
142
143
144
145
146
147
148
149
150
151
152
153
154
    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))
155
156


157
158
159
160
161
162
163
164
165
166
@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
167
        master_port = get_master_port(real_launcher=False)
168
169
170
171
172
173
174
        self.dist_env_1_gpu = {
            "MASTER_ADDR": "localhost",
            "MASTER_PORT": master_port,
            "RANK": "0",
            "LOCAL_RANK": "0",
            "WORLD_SIZE": "1",
        }
175

176
177
178
179
180
181
    def tearDown(self):
        super().tearDown()

        # reset the ds config global so that tests state doesn't leak
        unset_hf_deepspeed_config()

182
183
    def test_init_zero3_fp16(self):
        # test that zero.Init() works correctly under zero3/fp16
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
        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)


218
class TrainerIntegrationDeepSpeedWithCustomConfig(TestCasePlus):
219
220
    def setUp(self):
        super().setUp()
221
222
223
224
225

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

Stas Bekman's avatar
Stas Bekman committed
226
        master_port = get_master_port(real_launcher=False)
227
228
229
230
231
232
233
        self.dist_env_1_gpu = {
            "MASTER_ADDR": "localhost",
            "MASTER_PORT": master_port,
            "RANK": "0",
            "LOCAL_RANK": "0",
            "WORLD_SIZE": "1",
        }
234

235
236
237
238
        self.ds_config_file = {
            "zero2": f"{self.test_file_dir_str}/ds_config_zero2.json",
            "zero3": f"{self.test_file_dir_str}/ds_config_zero3.json",
        }
239
240
241

        # 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:
242
            config_zero2 = json.load(f)
243
        with io.open(self.ds_config_file[ZERO3], "r", encoding="utf-8") as f:
244
            config_zero3 = json.load(f)
245
            # The following setting slows things down, so don't enable it by default unless needed by a test.
246
            # It's in the file as a demo for users since we want everything to work out of the box even if slower.
247
248
            config_zero3["zero_optimization"]["stage3_gather_16bit_weights_on_model_save"] = False

249
250
251
252
        self.ds_config_dict = {
            "zero2": config_zero2,
            "zero3": config_zero3,
        }
253

254
255
256
257
258
259
    def tearDown(self):
        super().tearDown()

        # reset the ds config global so that tests state doesn't leak
        unset_hf_deepspeed_config()

260
261
262
    def get_config_dict(self, stage):
        # As some tests modify the dict, always make a copy
        return deepcopy(self.ds_config_dict[stage])
263

264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286

@require_deepspeed
@require_torch_gpu
class TrainerIntegrationDeepSpeed(TrainerIntegrationDeepSpeedWithCustomConfig, TrainerIntegrationCommon):
    """

    This class is for testing directly via get_regression_trainer

    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.
    """

287
    # --- These tests are enough to run on one of zero stages --- #
288

289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
    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}",
            )

340
341
342
343
344
345
346
347
348
    # 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):
349
350
351
            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
352
            ds_config_zero2_dict["zero_optimization"]["offload_optimizer"]["device"] = "none"
353
            ds_config_zero2_dict["fp16"]["initial_scale_power"] = 1  # force optimizer on the first step
354
            trainer = get_regression_trainer(a=a, local_rank=0, fp16=True, deepspeed=ds_config_zero2_dict)
355
356
357
358
359
360
361
            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):
362
363
            ds_config_zero2_dict = self.get_config_dict(ZERO2)
            del ds_config_zero2_dict["optimizer"]  # force default HF Trainer optimizer
364
            ds_config_zero2_dict["zero_optimization"]["offload_optimizer"]["device"] = "none"
365
            ds_config_zero2_dict["fp16"]["initial_scale_power"] = 1  # force optimizer on the first step
366
            trainer = get_regression_trainer(a=a, local_rank=0, fp16=True, deepspeed=ds_config_zero2_dict)
367
368
369
370
371
372
            trainer.train()
        new_a = trainer.model.a.item()
        self.assertNotEqual(new_a, a)

    def test_hf_scheduler_ds_optimizer(self):
        with mockenv_context(**self.dist_env_1_gpu):
373
374
            ds_config_zero2_dict = self.get_config_dict(ZERO2)
            del ds_config_zero2_dict["scheduler"]  # force default HF Trainer scheduler
375
            ds_config_zero2_dict["zero_optimization"]["offload_optimizer"]["device"] = "none"
376
            ds_config_zero2_dict["fp16"]["initial_scale_power"] = 1  # force optimizer on the first step
377
            trainer = get_regression_trainer(local_rank=0, fp16=True, deepspeed=ds_config_zero2_dict)
378
379
380
381
382
383
384
            with self.assertRaises(Exception) as context:
                trainer.train()
        self.assertIn(
            "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
            "Please configure a scheduler in the DeepSpeed config.",
            str(context.exception),
        )
385

386
    @require_deepspeed_aio
387
388
389
390
391
    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()
392
            nvme_config = {"device": "nvme", "nvme_path": nvme_path}
393
394
395
            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
396
            trainer = get_regression_trainer(local_rank=0, fp16=True, deepspeed=ds_config_zero3_dict)
397
            with CaptureLogger(deepspeed_logger) as cl:
398
                trainer.train()
399
            self.assertIn("DeepSpeed info", cl.out, "expected DeepSpeed logger output but got none")
400

401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
    @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")

427
428
    # --- These tests need to run on both zero stages --- #

429
430
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_hf_optimizer_with_offload(self, stage, dtype):
431
        # non-DS optimizers can be used with ZERO-offload (as long as they have both CPU and GPU implementation (except LAMB))
432
433
434
        ds_config_dict = self.get_config_dict(stage)
        del ds_config_dict["optimizer"]  # force default HF Trainer optimizer
        # force cpu offload
435
        ds_config_dict["zero_optimization"]["offload_optimizer"]["device"] = "cpu"
436
        ds_config_dict["zero_force_ds_cpu_optimizer"] = False  # offload is not efficient w/o CPUAdam
437
        with mockenv_context(**self.dist_env_1_gpu):
438
            kwargs = {"local_rank": 0, "deepspeed": ds_config_dict}
439
440
            kwargs[dtype] = True
            trainer = get_regression_trainer(**kwargs)
441
            with CaptureLogger(deepspeed_logger) as cl:
442
                trainer.train()
443
            self.assertIn("DeepSpeed info", cl.out, "expected DeepSpeed logger output but got none")
444

445
446
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_fake_notebook_no_launcher(self, stage, dtype):
447
448
449
450
451
        # 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
452
453
        # to reset `deepspeed_logger.handlers[0].setStream(sys.stdout)` or directly capture from the deepspeed_logger.
        with mockenv_context(**self.dist_env_1_gpu):
454
            kwargs = {"local_rank": 0, "deepspeed": self.get_config_dict(stage)}
455
456
457
            kwargs[dtype] = True
            trainer = get_regression_trainer(**kwargs)

458
            with CaptureLogger(deepspeed_logger) as cl:
459
                trainer.train()
460
            self.assertIn("DeepSpeed info", cl.out, "expected DeepSpeed logger output but got none")
461

462
463
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_early_get_last_lr(self, stage, dtype):
464
465
466
467
468
469
470
471
        # 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
472
473
474
475
476
477
478
479
480
            kwargs = {
                "a": a,
                "b": b,
                "local_rank": 0,
                "train_len": 8,
                "deepspeed": self.get_config_dict(stage),
                "per_device_train_batch_size": 8,
                "logging_steps": 1,
            }
481
482
483
            kwargs[dtype] = True
            trainer = get_regression_trainer(**kwargs)

484
            trainer.train()
485
486
            post_train_a = trainer.model.a.item()

487
488
            # 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
489
490
491
492
            # 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
493
            if (stage == ZERO3 and dtype == FP16) or (dtype == BF16):
494
                return
495
496
497

            # 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)
498
            self.assertEqual(post_train_a, a)
499

500
501
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_gradient_accumulation(self, stage, dtype):
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
        # 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

517
518
519
520
521
522
523
        kwargs = {
            "a": a,
            "b": b,
            "local_rank": 0,
            "train_len": train_len,
            "deepspeed": self.get_config_dict(stage),
        }
524
        kwargs[dtype] = True
525

526
527
        with mockenv_context(**self.dist_env_1_gpu):
            no_grad_accum_trainer = get_regression_trainer(
528
529
                **kwargs,
                per_device_train_batch_size=16,
530
531
532
533
534
535
536
537
538
539
540
                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(
541
                **kwargs,
542
                per_device_train_batch_size=4,
543
                gradient_accumulation_steps=4,
544
545
546
547
548
549
550
            )
            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)

551
552
553
554
        # 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)
555
556

        # see the note above how to get identical loss on a small bs
557
        self.assertAlmostEqual(no_grad_accum_loss, yes_grad_accum_loss, places=2)
558

559
    def check_saved_checkpoints_deepspeed(self, output_dir, freq, total, stage, dtype):
560
561
562
        # adapted from TrainerIntegrationCommon.check_saved_checkpoints

        file_list = [WEIGHTS_NAME, "training_args.bin", "trainer_state.json", "config.json"]
563
564
565
566
567
568
569
570

        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}")

571
572
        if dtype == "bf16":
            ds_file_list.append("bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt")
573
574
575

        for step in range(freq, total, freq):
            checkpoint = os.path.join(output_dir, f"checkpoint-{step}")
576
            self.assertTrue(os.path.isdir(checkpoint), f"[{stage}] {checkpoint} dir is not found")
577
578
579

            # common files
            for filename in file_list:
580
581
                path = os.path.join(checkpoint, filename)
                self.assertTrue(os.path.isfile(path), f"[{stage}] {path} is not found")
582
583
584
585
586
587

            # 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)
588
589
                path = os.path.join(ds_path, filename)
                self.assertTrue(os.path.isfile(path), f"[{stage}] {path} is not found")
590

591
592
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_save_checkpoints(self, stage, dtype):
593
594
        # adapted from  TrainerIntegrationTest.test_save_checkpoints

595
        freq = 5
596
        output_dir = self.get_auto_remove_tmp_dir()
597
        ds_config_dict = self.get_config_dict(stage)
598
599
600
        if dtype == FP16:
            ds_config_dict["fp16"]["initial_scale_power"] = 1  # force optimizer on the first step
        # XXX:
601
        if stage == ZERO3:
602
            ds_config_dict["zero_optimization"]["stage3_gather_16bit_weights_on_model_save"] = True
603
604
605

        # save checkpoints
        with mockenv_context(**self.dist_env_1_gpu):
606
607
608
609
610
            kwargs = {
                "output_dir": output_dir,
                "save_steps": freq,
                "deepspeed": ds_config_dict,
            }
611
612
            kwargs[dtype] = True
            trainer = get_regression_trainer(**kwargs)
613
614
615
            trainer.train()

        total = int(self.n_epochs * 64 / self.batch_size)
616
        self.check_saved_checkpoints_deepspeed(output_dir, freq, total, stage, dtype)
617

618
619
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_can_resume_training_errors(self, stage, dtype):
620
621
622
        with mockenv_context(**self.dist_env_1_gpu):
            ds_config_dict = self.get_config_dict(stage)
            output_dir = self.get_auto_remove_tmp_dir()
623
            kwargs = {"output_dir": output_dir, "deepspeed": ds_config_dict}
624
625
            kwargs[dtype] = True
            trainer = get_regression_trainer(**kwargs)
626
627
628
629
630
631
632
633

            # 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}",
            )
634

635
636
637
638
639
640
641
642
            # 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}"
            )

643
644
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_can_resume_training_normal(self, stage, dtype):
645
646
        # adapted from TrainerIntegrationTest.test_can_resume_training
        # test normal resume for each stage separately, error-handling is tested in a different test
647
        output_dir = self.get_auto_remove_tmp_dir("./xxx", after=False)
648
        ds_config_dict = self.get_config_dict(stage)
649
650
651
        if dtype == FP16:
            ds_config_dict["fp16"]["initial_scale_power"] = 1  # force optimizer on the first step
        # XXX:
652
        if stage == ZERO3:
653
            ds_config_dict["zero_optimization"]["stage3_gather_16bit_weights_on_model_save"] = True
654

655
656
657
658
659
660
661
        kwargs = {
            "output_dir": output_dir,
            "train_len": 128,
            "save_steps": 5,
            "learning_rate": 0.1,
            "deepspeed": ds_config_dict,
        }
662
        kwargs[dtype] = True
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694

        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)

695
696
697
698
699
            # 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

700
701
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_load_state_dict_from_zero_checkpoint(self, stage, dtype):
702
703
704
705
706
707
        # 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)

708
709
710
711
712
713
714
715
716
717
        kwargs = {
            "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,
        }
718
        kwargs[dtype] = True
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734

        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)

735
736
737
738
    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()
739
        kwargs = {"output_dir": output_dir, "train_len": 8, "fp16": True}
740

741
742
        ds_config_zero3_dict = self.get_config_dict(ZERO3)
        ds_config_zero2_dict = self.get_config_dict(ZERO2)
743

744
        with mockenv_context(**self.dist_env_1_gpu):
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
            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")

761
762
            # with accelerate integration below line is additionally required for this test to pass
            trainer.accelerator.state._reset_state()
763
764
765
766
767
768
            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")

769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_load_best_model(self, stage, dtype):
        # Test that forced deepspeed reinit doesn't break the model. the forced re-init after
        # loading the best model in Trainer is there to workaround this bug in Deepspeed
        # https://github.com/microsoft/DeepSpeed/issues/1612
        #
        # The test is derived from a repro script submitted in this Issue:
        # https://github.com/huggingface/transformers/issues/17114
        #
        # One additional feature of this test is that we use a non-AdamW optimizer to test that
        # deepspeed doesn't fallback to AdamW, which would prevent the optimizer states from loading
        # correctly

        from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer  # noqa

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

        ds_config_dict = self.get_config_dict(stage)
        del ds_config_dict["optimizer"]  # will use HF Trainer optimizer
        del ds_config_dict["scheduler"]  # will use HF Trainer scheduler
789
        ds_config_dict["zero_force_ds_cpu_optimizer"] = False  # offload is not efficient w/o CPUAdam
790
791
792
        # must use this setting to get the reload path exercised
        ds_config_dict["zero_optimization"]["stage3_gather_16bit_weights_on_model_save"] = True

793
        with mockenv_context(**self.dist_env_1_gpu):
794
            args_dict = {
795
796
                "per_device_train_batch_size": 1,
                "per_device_eval_batch_size": 1,
797
798
799
800
801
802
803
804
805
806
807
808
809
                "gradient_accumulation_steps": 1,
                "learning_rate": 1e-4,
                "num_train_epochs": 1,
                "do_train": True,
                "do_eval": True,
                "optim": "adafactor",
                "evaluation_strategy": "steps",
                "eval_steps": 1,
                "save_strategy": "steps",
                "save_steps": 1,
                "load_best_model_at_end": True,
                "max_steps": 1,
                "deepspeed": ds_config_dict,
810
                "report_to": "none",
811
812
813
            }

            training_args = TrainingArguments(output_dir, **args_dict)
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
            tokenizer = T5Tokenizer.from_pretrained(T5_TINY)
            model = T5ForConditionalGeneration.from_pretrained(T5_TINY)

            def _add_eos_to_examples(example):
                example["input_text"] = f"question: {example['question']}  context: {example['context']}"
                example["target_text"] = example["answers"]["text"][0] if len(example["answers"]["text"]) > 0 else ""
                return example

            def _convert_to_features(example_batch):
                input_encodings = tokenizer.batch_encode_plus(
                    example_batch["input_text"], pad_to_max_length=True, max_length=512, truncation=True
                )
                target_encodings = tokenizer.batch_encode_plus(
                    example_batch["target_text"], pad_to_max_length=True, max_length=16, truncation=True
                )

                encodings = {
                    "input_ids": input_encodings["input_ids"],
                    "attention_mask": input_encodings["attention_mask"],
                    "labels": target_encodings["input_ids"],
                }

                return encodings

            def get_dataset():
                data_file = str(self.tests_dir / "fixtures/tests_samples/SQUAD/sample.json")
840
                data_files = {"train": data_file, "validation": data_file}
841
842
843
844
845
846
847
                raw_datasets = datasets.load_dataset("json", data_files=data_files, field="data")
                train_dataset = raw_datasets["train"].map(_add_eos_to_examples).map(_convert_to_features, batched=True)
                valid_dataset = deepcopy(train_dataset)
                return train_dataset, valid_dataset

            train_dataset, eval_dataset = get_dataset()

848
849
850
851
852
853
854
855
856
857
            trainer = Trainer(
                model=model,
                tokenizer=tokenizer,
                args=training_args,
                train_dataset=train_dataset,
                eval_dataset=eval_dataset,
            )
            trainer.train()  # crash 1 was here
            trainer.evaluate()  # crash 2 was here

858
859
860
861

@slow
@require_deepspeed
@require_torch_gpu
862
class TestDeepSpeedWithLauncher(TestCasePlus):
Patrick von Platen's avatar
Patrick von Platen committed
863
    """This class is for testing via an external script - can do multiple gpus"""
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879

    # 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
    #
880

881
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
Yih-Dar's avatar
Yih-Dar committed
882
    @require_torch_multi_gpu
883
884
    def test_basic_distributed(self, stage, dtype):
        self.run_and_check(stage=stage, dtype=dtype, distributed=True)
885

886
887
    def test_do_eval_no_train(self):
        # testing only zero3 since zero2 makes no sense with inference
888
        self.run_and_check(
889
            stage=ZERO3,
890
            dtype=FP16,
891
892
            eval_steps=1,
            distributed=False,
893
894
            do_train=False,
            do_eval=True,
895
        )
896

897
898
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_fp32_non_distributed(self, stage, dtype):
899
900
901
902
        # 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,
903
            dtype=dtype,
904
905
906
907
908
            model_name=T5_TINY,
            distributed=False,
            do_train=True,
            do_eval=True,
            quality_checks=False,
909
            fp32=True,
910
911
        )

912
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
Yih-Dar's avatar
Yih-Dar committed
913
    @require_torch_multi_gpu
914
    def test_fp32_distributed(self, stage, dtype):
915
916
917
918
        # 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,
919
            dtype=dtype,
920
921
922
923
924
            model_name=T5_TINY,
            distributed=True,
            do_train=True,
            do_eval=True,
            quality_checks=False,
925
            fp32=True,
926
927
        )

928
929
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_resume_train_not_from_ds_checkpoint(self, stage, dtype):
930
931
932
933
934
        # do normal training and then resume not from the deepspeed checkpoint but explicitly from
        # the saved model dir

        do_train = True
        do_eval = False
935
936
937
938
939
940
941
942
        kwargs = {
            "stage": stage,
            "dtype": dtype,
            "eval_steps": 1,
            "distributed": True,
            "do_train": do_train,
            "do_eval": do_eval,
        }
943
944
945
946
947
948
949
950
951
952

        # 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)

953
    @parameterized.expand(["bf16", "fp16", "fp32"])
Yih-Dar's avatar
Yih-Dar committed
954
    @require_torch_multi_gpu
955
    def test_inference(self, dtype):
956
        if dtype == "bf16" and not is_torch_bf16_gpu_available():
957
958
            self.skipTest("test requires bfloat16 hardware support")

959
960
        # this is just inference, so no optimizer should be loaded
        # it only works for z3 (makes no sense with z1-z2)
961
        fp32 = True if dtype == "fp32" else False
962
963
        self.run_and_check(
            stage=ZERO3,
964
            dtype=FP16,
965
966
967
968
969
            model_name=T5_TINY,
            distributed=True,
            do_train=False,
            do_eval=True,
            quality_checks=False,
970
            fp32=fp32,
971
972
        )

973
    def do_checks(self, output_dir, do_train=True, do_eval=True, quality_checks=True):
974
975
976
        if do_train:
            train_metrics = load_json(os.path.join(output_dir, "train_results.json"))
            self.assertIn("train_samples_per_second", train_metrics)
977
978
            if quality_checks:
                self.assertGreater(train_metrics["train_samples_per_second"], 0.5)
979
980
981
982

        if do_eval:
            eval_metrics = load_json(os.path.join(output_dir, "eval_results.json"))
            self.assertIn("eval_bleu", eval_metrics)
983
984
            if quality_checks:
                self.assertGreater(eval_metrics["eval_bleu"], 1)
985
986

    # XXX: need to do better validation beyond just that the run was successful
987
988
989
    def run_and_check(
        self,
        stage,
990
        dtype,
991
992
993
994
995
996
        model_name: str = T5_SMALL,
        eval_steps: int = 10,
        distributed: bool = True,
        do_train: bool = True,
        do_eval: bool = True,
        quality_checks: bool = True,
997
        fp32: bool = False,
998
999
        extra_args_str: str = None,
        remove_args_str: str = None,
1000
1001
    ):
        # we are doing quality testing so using a small real model
1002
        output_dir = self.run_trainer(
1003
            stage=stage,
1004
            dtype=dtype,
1005
            model_name=model_name,
1006
            eval_steps=eval_steps,
1007
            num_train_epochs=1,
1008
1009
            do_train=do_train,
            do_eval=do_eval,
1010
            distributed=distributed,
1011
            fp32=fp32,
1012
1013
1014
            extra_args_str=extra_args_str,
            remove_args_str=remove_args_str,
        )
1015

1016
        self.do_checks(output_dir, do_train=do_train, do_eval=do_eval, quality_checks=quality_checks)
1017
1018

        return output_dir
1019
1020
1021

    def run_trainer(
        self,
1022
        stage: str,
1023
        dtype: str,
1024
        model_name: str,
1025
1026
1027
1028
        eval_steps: int = 10,
        num_train_epochs: int = 1,
        do_train: bool = False,
        do_eval: bool = True,
1029
        distributed: bool = True,
1030
        fp32: bool = False,
1031
1032
1033
        extra_args_str: str = None,
        remove_args_str: str = None,
    ):
1034
        max_len = 32
Sylvain Gugger's avatar
Sylvain Gugger committed
1035
        data_dir = self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
1036
1037
1038
        output_dir = self.get_auto_remove_tmp_dir()
        args = f"""
            --model_name_or_path {model_name}
1039
1040
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
1041
1042
1043
1044
1045
1046
1047
            --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
1048
1049
            --save_steps 0
            --eval_steps {eval_steps}
1050
1051
            --group_by_length
            --label_smoothing_factor 0.1
1052
1053
            --source_lang en
            --target_lang ro
1054
            --report_to none
1055
        """.split()
1056
1057
        args.extend(["--source_prefix", '"translate English to Romanian: "'])

1058
1059
        if not fp32:
            args.extend([f"--{dtype}"])
1060

1061
1062
1063
1064
1065
1066
1067
        actions = 0
        if do_train:
            actions += 1
            args.extend(
                f"""
            --do_train
            --num_train_epochs {str(num_train_epochs)}
1068
            --max_train_samples 16
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
            --per_device_train_batch_size 2
            --learning_rate 3e-3
            """.split()
            )

        if do_eval:
            actions += 1
            args.extend(
                """
            --do_eval
1079
            --max_eval_samples 16
1080
1081
1082
1083
1084
            --per_device_eval_batch_size 2
            """.split()
            )

        assert actions > 0, "need at least do_train or do_eval for the test to run"
1085
1086
1087
1088

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

1089
        # currently only works for bool args
1090
1091
1092
1093
        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]

1094
        ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()
Sylvain Gugger's avatar
Sylvain Gugger committed
1095
        script = [f"{self.examples_dir_str}/pytorch/translation/run_translation.py"]
1096
        launcher = get_launcher(distributed)
1097
1098

        cmd = launcher + script + args + ds_args
1099
        # keep for quick debug
1100
1101
1102
1103
1104
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        execute_subprocess_async(cmd, env=self.get_env())

        return output_dir

1105
1106
    @parameterized.expand(params, name_func=parameterized_custom_name_func)
    def test_clm(self, stage, dtype):
1107
1108
1109
1110
1111
1112
        # 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"""
1113
            --model_name_or_path {GPT2_TINY}
1114
1115
1116
1117
1118
1119
            --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
1120
1121
1122
1123
            --max_train_samples 16
            --max_eval_samples 16
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
1124
1125
            --num_train_epochs 1
            --warmup_steps 8
1126
            --block_size 64
1127
            --report_to none
1128
1129
            """.split()

1130
1131
        args.extend([f"--{dtype}"])

1132
        ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()
Sylvain Gugger's avatar
Sylvain Gugger committed
1133
        script = [f"{self.examples_dir_str}/pytorch/language-modeling/run_clm.py"]
1134
        launcher = get_launcher(distributed=True)
1135
1136
1137
1138

        cmd = launcher + script + args + ds_args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
1139
1140
        execute_subprocess_async(cmd, env=self.get_env())

1141
    def test_clm_from_config_zero3_fp16(self):
1142
1143
1144
1145
1146
1147
        # 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
1148
            --tokenizer_name {GPT2_TINY}
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
            --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"]
1165
        launcher = get_launcher(distributed=True)
1166
1167
1168
1169
1170
1171

        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())
1172
        self.assertIn("Detected DeepSpeed ZeRO-3", cs.err)