File size: 75,113 Bytes
8898da0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
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
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
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
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
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
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
394
395
396
397
398
399
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
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
========================
START TIME: Thu Jul  4 02:27:55 UTC 2024
python3 version = Python 3.10.14
========================
The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.
Token is valid (permission: write).
Your token has been saved to /admin/home/ferdinand_mom/.cache/huggingface/token
Login successful
Already on 'bench_cluster'
M	examples/config_tiny_llama.py
M	examples/config_tiny_llama.yaml
M	examples/train_tiny_llama.sh
M	src/nanotron/models/llama.py
M	src/nanotron/trainer.py
Your branch is up to date with 'origin/bench_cluster'.
Job status: RUNNING
W0704 02:27:58.305000 140315521349440 torch/distributed/run.py:757] 
W0704 02:27:58.305000 140315521349440 torch/distributed/run.py:757] *****************************************
W0704 02:27:58.305000 140315521349440 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0704 02:27:58.305000 140315521349440 torch/distributed/run.py:757] *****************************************
[default0]:07/04/2024 02:28:14 [WARNING|DP=0|PP=0|TP=0|ip-26-0-171-88]: [Vocab Size Padding] Padded vocab (size: 50257) with 7 dummy tokens (new size: 50264)
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Config:
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                            run='%date_%jobid',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                            seed=42,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                            step=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                            consumed_train_samples=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                            benchmark_csv_path=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                            ignore_sanity_checks=True),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:        parallelism=ParallelismArgs(dp=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                    pp=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                    tp=8,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f04e6fd4700>,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                    tp_linear_async_communication=False,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                    expert_parallel_size=1),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:        model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 eos_token_id=2,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 hidden_act='silu',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 hidden_size=2048,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 initializer_range=0.02,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 intermediate_size=4096,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 is_llama_config=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 max_position_embeddings=4096,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 num_attention_heads=32,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 num_hidden_layers=24,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 num_key_value_heads=32,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 pad_token_id=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 pretraining_tp=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 rms_norm_eps=1e-05,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 rope_scaling=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 rope_theta=10000.0,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 tie_word_embeddings=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 use_cache=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                 vocab_size=50264),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                        init_method=RandomInit(std=0.025),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                        dtype=torch.bfloat16,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                        make_vocab_size_divisible_by=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                        ddp_bucket_cap_mb=25),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:        tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                tokenizer_revision=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                tokenizer_max_length=None),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:        checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                    checkpoint_interval=100000,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                    save_initial_state=False,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                    resume_checkpoint_path=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:        logging=LoggingArgs(log_level='info',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                            log_level_replica='info',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                            iteration_step_info_interval=1),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                          train_steps=20,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                          micro_batch_size=256,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                          batch_accumulation_per_replica=4,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                          val_check_interval=-1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                          limit_val_batches=0,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                          limit_test_batches=0),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:        optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                     adam_beta1=0.9,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                     adam_beta2=0.95,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                     torch_adam_is_fused=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                     name='adamW'),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                zero_stage=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                weight_decay=0.01,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                clip_grad=1.0,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                accumulate_grad_in_fp32=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                        lr_warmup_steps=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                        lr_warmup_style='linear',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                        lr_decay_style='linear',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                        lr_decay_steps=19,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                        lr_decay_starting_step=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                        min_decay_lr=1e-05)),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                      start_training_step=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                      data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                                 hf_dataset_splits='train',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                                 hf_dataset_config_name=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                                                 text_column_name='text'),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                    seed=42,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:                                                    num_loading_workers=0))],
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:        profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-1_tp-8_pp-1_mbz-256')),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:        lighteval=None)
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Model Config:
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: LlamaConfig(bos_token_id=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             eos_token_id=2,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             hidden_act='silu',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             hidden_size=2048,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             initializer_range=0.02,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             intermediate_size=4096,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             is_llama_config=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             max_position_embeddings=4096,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             num_attention_heads=32,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             num_hidden_layers=24,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             num_key_value_heads=32,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             pad_token_id=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             pretraining_tp=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             rms_norm_eps=1e-05,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             rope_scaling=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             rope_theta=10000.0,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             tie_word_embeddings=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             use_cache=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:             vocab_size=50264)
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Building model..
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Setting PP block ranks...
[default4]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=4|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default5]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=5|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default5]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=5|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default5]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=5|ip-26-0-171-88]: No checkpoint path provided.
[default4]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=4|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default4]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=4|ip-26-0-171-88]: No checkpoint path provided.
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Total number of parameters: 1.11G (2117.88MiB)
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Parametrizing model parameters using StandardParametrizator
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [ZeRO sharding] DP Rank 0 has 139M out of 139M (100.00%) params' optimizer states
[default2]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=2|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default2]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=2|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default2]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=2|ip-26-0-171-88]: No checkpoint path provided.
[default7]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=7|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default7]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=7|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default7]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=7|ip-26-0-171-88]: No checkpoint path provided.
[default6]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=6|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default6]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=6|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default3]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=3|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default3]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=3|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default6]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=6|ip-26-0-171-88]: No checkpoint path provided.
[default3]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=3|ip-26-0-171-88]: No checkpoint path provided.
[default1]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=1|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default1]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=1|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default1]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=1|ip-26-0-171-88]: No checkpoint path provided.
[default0]:07/04/2024 02:28:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/04/2024 02:28:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Using `datasets` library
[default0]:07/04/2024 02:28:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:07/04/2024 02:28:31 [WARNING|DP=0|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/04/2024 02:28:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [Training Plan] There are 1 training stages 
[default0]:07/04/2024 02:28:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [Stage Training Stage] start from step 1 
[default0]:07/04/2024 02:28:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: 
[default0]:07/04/2024 02:28:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [Start training] datetime: 2024-07-04 02:28:32.629932 | mbs: 256 | grad_accum: 4 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/04/2024 02:28:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/04/2024 02:28:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:  Memory usage: 1350.75MiB. Peak allocated 1350.76MiB. Peak reserved: 1384.00MiB
[default4]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=4|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=5|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=6|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=2|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=7|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=3|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:[rank2]: Traceback (most recent call last):
[default0]:[rank0]: Traceback (most recent call last):
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default2]:[rank2]:     trainer.train(dataloader)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default0]:[rank0]:     trainer.train(dataloader)
[default6]:[rank6]: Traceback (most recent call last):
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default2]:[rank2]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default6]:[rank6]:     trainer.train(dataloader)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default0]:[rank0]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default2]:[rank2]:     outputs = self.pipeline_engine.train_batch_iter(
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default0]:[rank0]:     outputs = self.pipeline_engine.train_batch_iter(
[default6]:[rank6]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default0]:[rank0]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default6]:[rank6]:     outputs = self.pipeline_engine.train_batch_iter(
[default2]:[rank2]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default6]:[rank6]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default0]:[rank0]:     output = model(**micro_batch)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default6]:[rank6]:     output = model(**micro_batch)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     output = model(**micro_batch)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:     sharded_logits = self.model(
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:     sharded_logits = self.model(
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:     sharded_logits = self.model(
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default0]:[rank0]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default2]:[rank2]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default0]:[rank0]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default6]:[rank6]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     output = self.pp_block(**new_kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default6]:[rank6]:     output = self.pp_block(**new_kwargs)
[default0]:[rank0]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     output = self.pp_block(**new_kwargs)
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default7]:[rank7]: Traceback (most recent call last):
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     merged_states = self.gate_up_proj(hidden_states)
[default7]:[rank7]:     trainer.train(dataloader)
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default7]:[rank7]:     outputs = self.pipeline_engine.train_batch_iter(
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default7]:[rank7]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default0]:[rank0]:     return column_linear(
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default6]:[rank6]:     merged_states = self.gate_up_proj(hidden_states)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:     merged_states = self.gate_up_proj(hidden_states)
[default0]:[rank0]:     return F.linear(input, weight, bias)
[default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU 
[default7]:[rank7]:     output = model(**micro_batch)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     sharded_logits = self.model(
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return column_linear(
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default2]:[rank2]:     return column_linear(
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default2]:[rank2]:     return F.linear(input, weight, bias)
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default6]:[rank6]:     return F.linear(input, weight, bias)
[default2]:[rank2]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU  has a total capacity of 79.33 GiB of which 1.71 GiB is free. Including non-PyTorch memory, this process has 77.61 GiB memory in use. Of the allocated memory 64.45 GiB is allocated by PyTorch, and 1.43 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default7]:[rank7]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default7]:[rank7]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU  has a total capacity of 79.33 GiB of which 1.71 GiB is free. Including non-PyTorch memory, this process has 77.61 GiB memory in use. Of the allocated memory 64.45 GiB is allocated by PyTorch, and 1.43 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default7]:[rank7]:     output = self.pp_block(**new_kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default7]:[rank7]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward
[default7]:[rank7]:     hidden_states = self.down_proj(self.split_silu_mul(merged_states))
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 128, in forward
[default7]:[rank7]:     return self.act(gate_states) * up_states
[default7]:[rank7]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 421.94 MiB is free. Including non-PyTorch memory, this process has 78.91 GiB memory in use. Of the allocated memory 67.45 GiB is allocated by PyTorch, and 436.23 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default1]:[rank1]: Traceback (most recent call last):
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default1]:[rank1]:     trainer.train(dataloader)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default1]:[rank1]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default1]:[rank1]:     outputs = self.pipeline_engine.train_batch_iter(
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default1]:[rank1]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default1]:[rank1]:     output = model(**micro_batch)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default1]:[rank1]:     sharded_logits = self.model(
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default1]:[rank1]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default1]:[rank1]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default1]:[rank1]:     output = self.pp_block(**new_kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]: Traceback (most recent call last):
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default3]:[rank3]:     trainer.train(dataloader)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default1]:[rank1]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:     outputs = self.pipeline_engine.train_batch_iter(
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default3]:[rank3]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default1]:[rank1]:     merged_states = self.gate_up_proj(hidden_states)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     output = model(**micro_batch)
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default1]:[rank1]:     return column_linear(
[default3]:[rank3]:     sharded_logits = self.model(
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:     return F.linear(input, weight, bias)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU  has a total capacity of 79.33 GiB of which 1.71 GiB is free. Including non-PyTorch memory, this process has 77.61 GiB memory in use. Of the allocated memory 64.45 GiB is allocated by PyTorch, and 1.43 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default3]:[rank3]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default3]:[rank3]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default3]:[rank3]:     output = self.pp_block(**new_kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default3]:[rank3]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default3]:[rank3]:     merged_states = self.gate_up_proj(hidden_states)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default3]:[rank3]:     return column_linear(
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default3]:[rank3]:     return F.linear(input, weight, bias)
[default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU  has a total capacity of 79.33 GiB of which 1.71 GiB is free. Including non-PyTorch memory, this process has 77.61 GiB memory in use. Of the allocated memory 64.45 GiB is allocated by PyTorch, and 1.43 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default5]:[rank5]: Traceback (most recent call last):
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default5]:[rank5]:     trainer.train(dataloader)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default5]:[rank5]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default5]:[rank5]:     outputs = self.pipeline_engine.train_batch_iter(
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default5]:[rank5]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default4]:[rank4]: Traceback (most recent call last):
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default5]:[rank5]:     output = model(**micro_batch)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     trainer.train(dataloader)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default4]:[rank4]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default5]:[rank5]:     sharded_logits = self.model(
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default4]:[rank4]:     outputs = self.pipeline_engine.train_batch_iter(
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default4]:[rank4]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default4]:[rank4]:     output = model(**micro_batch)
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default4]:[rank4]:     sharded_logits = self.model(
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default5]:[rank5]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default5]:[rank5]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default5]:[rank5]:     output = self.pp_block(**new_kwargs)
[default4]:[rank4]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default4]:[rank4]:     output = self.pp_block(**new_kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default4]:[rank4]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default4]:[rank4]:     merged_states = self.gate_up_proj(hidden_states)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default4]:[rank4]:     return column_linear(
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:     return F.linear(input, weight, bias)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default5]:[rank5]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU  has a total capacity of 79.33 GiB of which 1.71 GiB is free. Including non-PyTorch memory, this process has 77.61 GiB memory in use. Of the allocated memory 64.45 GiB is allocated by PyTorch, and 1.43 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default5]:[rank5]:     merged_states = self.gate_up_proj(hidden_states)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default5]:[rank5]:     return column_linear(
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default5]:[rank5]:     return F.linear(input, weight, bias)
[default5]:[rank5]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU  has a total capacity of 79.33 GiB of which 1.71 GiB is free. Including non-PyTorch memory, this process has 77.61 GiB memory in use. Of the allocated memory 64.45 GiB is allocated by PyTorch, and 1.43 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
E0704 02:28:53.525000 140315521349440 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 1144719) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10
Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 263, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2024-07-04_02:28:53
  host      : ip-26-0-171-88.ec2.internal
  rank      : 1 (local_rank: 1)
  exitcode  : 1 (pid: 1144720)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
  time      : 2024-07-04_02:28:53
  host      : ip-26-0-171-88.ec2.internal
  rank      : 2 (local_rank: 2)
  exitcode  : 1 (pid: 1144721)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[3]:
  time      : 2024-07-04_02:28:53
  host      : ip-26-0-171-88.ec2.internal
  rank      : 3 (local_rank: 3)
  exitcode  : 1 (pid: 1144722)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[4]:
  time      : 2024-07-04_02:28:53
  host      : ip-26-0-171-88.ec2.internal
  rank      : 4 (local_rank: 4)
  exitcode  : 1 (pid: 1144723)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[5]:
  time      : 2024-07-04_02:28:53
  host      : ip-26-0-171-88.ec2.internal
  rank      : 5 (local_rank: 5)
  exitcode  : 1 (pid: 1144724)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[6]:
  time      : 2024-07-04_02:28:53
  host      : ip-26-0-171-88.ec2.internal
  rank      : 6 (local_rank: 6)
  exitcode  : 1 (pid: 1144725)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[7]:
  time      : 2024-07-04_02:28:53
  host      : ip-26-0-171-88.ec2.internal
  rank      : 7 (local_rank: 7)
  exitcode  : 1 (pid: 1144726)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-07-04_02:28:53
  host      : ip-26-0-171-88.ec2.internal
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 1144719)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
srun: error: ip-26-0-171-88: task 0: Exited with exit code 1
Consider using `hf_transfer` for faster uploads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.