File size: 87,892 Bytes
b7b1765 |
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 |
========================
START TIME: Wed Jul 3 23:26:01 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
W0703 23:26:10.004000 139973789022016 torch/distributed/run.py:757]
W0703 23:26:10.004000 139973789022016 torch/distributed/run.py:757] *****************************************
W0703 23:26:10.004000 139973789022016 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.
W0703 23:26:10.004000 139973789022016 torch/distributed/run.py:757] *****************************************
[default0]:07/03/2024 23:26:32 [WARNING|DP=0|PP=0|TP=0|ip-26-0-169-86]: [Vocab Size Padding] Padded vocab (size: 50257) with 3 dummy tokens (new size: 50260)
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Config:
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: run='%date_%jobid',
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: seed=42,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: step=None,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: consumed_train_samples=None,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: benchmark_csv_path=None,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: ignore_sanity_checks=True),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: parallelism=ParallelismArgs(dp=1,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: pp=2,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: tp=4,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f94edff88e0>,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: tp_linear_async_communication=False,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: expert_parallel_size=1),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: eos_token_id=2,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: hidden_act='silu',
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: hidden_size=2048,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: initializer_range=0.02,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: intermediate_size=4096,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: is_llama_config=True,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: max_position_embeddings=4096,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: num_attention_heads=32,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: num_hidden_layers=24,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: num_key_value_heads=32,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: pad_token_id=None,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: pretraining_tp=1,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: rms_norm_eps=1e-05,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: rope_scaling=None,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: rope_theta=10000.0,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: tie_word_embeddings=True,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: use_cache=True,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: vocab_size=50260),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: init_method=RandomInit(std=0.025),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: dtype=torch.bfloat16,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: make_vocab_size_divisible_by=1,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: ddp_bucket_cap_mb=25),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: tokenizer_revision=None,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: tokenizer_max_length=None),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: checkpoint_interval=100000,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: save_initial_state=False,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: resume_checkpoint_path=None,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: checkpoints_path_is_shared_file_system=False),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: logging=LoggingArgs(log_level='info',
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: log_level_replica='info',
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: iteration_step_info_interval=1),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: tokens=TokensArgs(sequence_length=4096,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: train_steps=20,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: micro_batch_size=4,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: batch_accumulation_per_replica=256,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: val_check_interval=-1,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: limit_val_batches=0,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: limit_test_batches=0),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: adam_beta1=0.9,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: adam_beta2=0.95,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: torch_adam_is_fused=True,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: name='adamW'),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: zero_stage=1,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: weight_decay=0.01,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: clip_grad=1.0,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: accumulate_grad_in_fp32=True,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: lr_warmup_steps=1,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: lr_warmup_style='linear',
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: lr_decay_style='linear',
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: lr_decay_steps=19,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: lr_decay_starting_step=None,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: min_decay_lr=1e-05)),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: start_training_step=1,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: hf_dataset_splits='train',
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: hf_dataset_config_name=None,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: dataset_processing_num_proc_per_process=64,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: dataset_overwrite_cache=False,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: text_column_name='text'),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: seed=42,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: num_loading_workers=0))],
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-1_tp-4_pp-2_mbz-4')),
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: lighteval=None)
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Model Config:
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: LlamaConfig(bos_token_id=1,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: eos_token_id=2,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: hidden_act='silu',
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: hidden_size=2048,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: initializer_range=0.02,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: intermediate_size=4096,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: is_llama_config=True,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: max_position_embeddings=4096,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: num_attention_heads=32,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: num_hidden_layers=24,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: num_key_value_heads=32,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: pad_token_id=None,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: pretraining_tp=1,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: rms_norm_eps=1e-05,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: rope_scaling=None,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: rope_theta=10000.0,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: tie_word_embeddings=True,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: use_cache=True,
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: vocab_size=50260)
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Building model..
[default0]:07/03/2024 23:26:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Setting PP block ranks...
[default5]:07/03/2024 23:26:47 [INFO|DP=0|PP=1|TP=1|ip-26-0-169-86]: Local number of parameters: 131M (249.16MiB)
[default5]:07/03/2024 23:26:47 [INFO|DP=0|PP=1|TP=1|ip-26-0-169-86]: [After model building] Memory usage: 260.10MiB. Peak allocated: 262.13MiB Peak reserved: 264.00MiB
[default5]:07/03/2024 23:26:47 [INFO|DP=0|PP=1|TP=1|ip-26-0-169-86]: No checkpoint path provided.
[default1]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=1|ip-26-0-169-86]: Local number of parameters: 173M (329.19MiB)
[default1]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=1|ip-26-0-169-86]: [After model building] Memory usage: 344.13MiB. Peak allocated: 346.16MiB Peak reserved: 348.00MiB
[default1]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=1|ip-26-0-169-86]: No checkpoint path provided.
[default0]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Total number of parameters: 1.21G (2313.42MiB)
[default0]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Local number of parameters: 173M (329.19MiB)
[default0]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: [After model building] Memory usage: 344.13MiB. Peak allocated: 346.16MiB Peak reserved: 348.00MiB
[default0]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: No checkpoint path provided.
[default0]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Parametrizing model parameters using StandardParametrizator
[default7]:07/03/2024 23:26:47 [INFO|DP=0|PP=1|TP=3|ip-26-0-169-86]: Local number of parameters: 131M (249.16MiB)
[default7]:07/03/2024 23:26:47 [INFO|DP=0|PP=1|TP=3|ip-26-0-169-86]: [After model building] Memory usage: 260.10MiB. Peak allocated: 262.13MiB Peak reserved: 264.00MiB
[default7]:07/03/2024 23:26:47 [INFO|DP=0|PP=1|TP=3|ip-26-0-169-86]: No checkpoint path provided.
[default2]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=2|ip-26-0-169-86]: Local number of parameters: 173M (329.19MiB)
[default2]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=2|ip-26-0-169-86]: [After model building] Memory usage: 344.13MiB. Peak allocated: 346.16MiB Peak reserved: 348.00MiB
[default2]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=2|ip-26-0-169-86]: No checkpoint path provided.
[default4]:07/03/2024 23:26:47 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-86]: Local number of parameters: 131M (249.16MiB)
[default4]:07/03/2024 23:26:47 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-86]: [After model building] Memory usage: 260.10MiB. Peak allocated: 262.13MiB Peak reserved: 264.00MiB
[default4]:07/03/2024 23:26:47 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-86]: No checkpoint path provided.
[default6]:07/03/2024 23:26:47 [INFO|DP=0|PP=1|TP=2|ip-26-0-169-86]: Local number of parameters: 131M (249.16MiB)
[default6]:07/03/2024 23:26:47 [INFO|DP=0|PP=1|TP=2|ip-26-0-169-86]: [After model building] Memory usage: 260.10MiB. Peak allocated: 262.13MiB Peak reserved: 264.00MiB
[default6]:07/03/2024 23:26:47 [INFO|DP=0|PP=1|TP=2|ip-26-0-169-86]: No checkpoint path provided.
[default3]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=3|ip-26-0-169-86]: Local number of parameters: 173M (329.19MiB)
[default3]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=3|ip-26-0-169-86]: [After model building] Memory usage: 344.13MiB. Peak allocated: 346.16MiB Peak reserved: 348.00MiB
[default3]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=3|ip-26-0-169-86]: No checkpoint path provided.
[default0]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/03/2024 23:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: [ZeRO sharding] DP Rank 0 has 173M out of 173M (100.00%) params' optimizer states
[default0]:07/03/2024 23:26:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/03/2024 23:26:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Using `datasets` library
[default0]:07/03/2024 23:26:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:07/03/2024 23:26:49 [WARNING|DP=0|PP=0|TP=0|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 23:26:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: [Training Plan] There are 1 training stages
[default0]:07/03/2024 23:26:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: [Stage Training Stage] start from step 1
[default0]:07/03/2024 23:26:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]:
[default0]:07/03/2024 23:26:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: [Start training] datetime: 2024-07-03 23:26:52.108515 | mbs: 4 | grad_accum: 256 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/03/2024 23:26:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/03/2024 23:26:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Memory usage: 1660.89MiB. Peak allocated 1660.89MiB. Peak reserved: 1668.00MiB
[default1]:07/03/2024 23:26:52 [WARNING|DP=0|PP=0|TP=1|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 23:26:52 [WARNING|DP=0|PP=1|TP=3|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 23:26:52 [WARNING|DP=0|PP=0|TP=2|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 23:26:52 [WARNING|DP=0|PP=1|TP=2|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 23:26:52 [WARNING|DP=0|PP=1|TP=0|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 23:26:52 [WARNING|DP=0|PP=0|TP=3|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 23:26:57 [WARNING|DP=0|PP=1|TP=1|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]: warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]: warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]: warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]: warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]: warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]: warnings.warn(
[default0]:07/03/2024 23:27:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Memory usage: 1732.03MiB. Peak allocated 13063.24MiB. Peak reserved: 13348.00MiB
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]: warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]: warnings.warn(
[default0]:07/03/2024 23:27:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Memory usage: 3048.84MiB. Peak allocated 3048.84MiB. Peak reserved: 13348.00MiB
[default4]:07/03/2024 23:27:44 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-86]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 45.1K | tokens_per_sec: 92.9K | tokens_per_sec_per_gpu: 11.6K | global_batch_size: 1.02K | lm_loss: 11.1 | lr: 0.0001 | model_tflops_per_gpu: 105 | hardware_tflops_per_gpu: 105 | grad_norm: 15 | cuda_memory_allocated: 2.44G | cuda_max_memory_reserved: 8.57G | hd_total_memory_tb: 312G | hd_used_memory_tb: 68.8G | hd_free_memory_tb: 243G
[default0]:07/03/2024 23:28:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Memory usage: 3048.84MiB. Peak allocated 14380.05MiB. Peak reserved: 14534.00MiB
[default0]:07/03/2024 23:28:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Memory usage: 3048.84MiB. Peak allocated 3048.88MiB. Peak reserved: 14534.00MiB
[default4]:07/03/2024 23:28:07 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-86]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 23.1K | tokens_per_sec: 181K | tokens_per_sec_per_gpu: 22.7K | global_batch_size: 1.02K | lm_loss: 11.1 | lr: 9.53e-05 | model_tflops_per_gpu: 206 | hardware_tflops_per_gpu: 206 | grad_norm: 15.1 | cuda_memory_allocated: 2.44G | cuda_max_memory_reserved: 8.57G | hd_total_memory_tb: 312G | hd_used_memory_tb: 68.8G | hd_free_memory_tb: 243G
[default4]:07/03/2024 23:28:31 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-86]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 24.8K | tokens_per_sec: 169K | tokens_per_sec_per_gpu: 21.1K | global_batch_size: 1.02K | lm_loss: 11.4 | lr: 9.05e-05 | model_tflops_per_gpu: 192 | hardware_tflops_per_gpu: 192 | grad_norm: 106 | cuda_memory_allocated: 2.44G | cuda_max_memory_reserved: 8.57G | hd_total_memory_tb: 312G | hd_used_memory_tb: 68.8G | hd_free_memory_tb: 243G
[default0]:07/03/2024 23:28:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Memory usage: 3048.84MiB. Peak allocated 14380.05MiB. Peak reserved: 14646.00MiB
[default0]:07/03/2024 23:28:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Memory usage: 3048.84MiB. Peak allocated 3048.88MiB. Peak reserved: 14646.00MiB
[default0]:STAGE:2024-07-03 23:28:31 2274198:2274198 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[default4]:07/03/2024 23:29:04 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-86]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 33K | tokens_per_sec: 127K | tokens_per_sec_per_gpu: 15.9K | global_batch_size: 1.02K | lm_loss: 11.7 | lr: 8.58e-05 | model_tflops_per_gpu: 144 | hardware_tflops_per_gpu: 144 | grad_norm: 24.5 | cuda_memory_allocated: 2.44G | cuda_max_memory_reserved: 8.57G | hd_total_memory_tb: 312G | hd_used_memory_tb: 68.8G | hd_free_memory_tb: 243G
[default0]:07/03/2024 23:29:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Memory usage: 3048.84MiB. Peak allocated 14380.05MiB. Peak reserved: 14646.00MiB
[default0]:07/03/2024 23:29:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Memory usage: 3048.84MiB. Peak allocated 3048.88MiB. Peak reserved: 14646.00MiB
[default4]:07/03/2024 23:29:38 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-86]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 33.3K | tokens_per_sec: 126K | tokens_per_sec_per_gpu: 15.7K | global_batch_size: 1.02K | lm_loss: 10 | lr: 8.11e-05 | model_tflops_per_gpu: 143 | hardware_tflops_per_gpu: 143 | grad_norm: 11
[default0]:07/03/2024 23:29:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-86]: Memory usage: 3048.84MiB. Peak allocated 14380.05MiB. Peak reserved: 14646.00MiB
[default4]:07/03/2024 23:30:11 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-86]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 33.1K | tokens_per_sec: 127K | tokens_per_sec_per_gpu: 15.9K | global_batch_size: 1.02K | lm_loss: 9.46 | lr: 7.63e-05 | model_tflops_per_gpu: 144 | hardware_tflops_per_gpu: 144 | grad_norm: 7.2
[default0]:STAGE:2024-07-03 23:31:36 2274198:2274198 ActivityProfilerController.cpp:320] Completed Stage: Collection
[default0]:STAGE:2024-07-03 23:31:45 2274198:2274198 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[default1]:[rank1]:[E ProcessGroupNCCL.cpp:563] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=175141, OpType=_REDUCE_SCATTER_BASE, NumelIn=33554432, NumelOut=8388608, Timeout(ms)=600000) ran for 600016 milliseconds before timing out.
[default6]:[rank6]:[E ProcessGroupNCCL.cpp:563] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=13834, OpType=RECV, NumelIn=7, NumelOut=7, Timeout(ms)=600000) ran for 600052 milliseconds before timing out.
[default2]:[rank2]:[E ProcessGroupNCCL.cpp:563] [Rank 2] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=175141, OpType=_REDUCE_SCATTER_BASE, NumelIn=33554432, NumelOut=8388608, Timeout(ms)=600000) ran for 600054 milliseconds before timing out.
[default7]:[rank7]:[E ProcessGroupNCCL.cpp:563] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=13834, OpType=RECV, NumelIn=7, NumelOut=7, Timeout(ms)=600000) ran for 600052 milliseconds before timing out.
[default3]:[rank3]:[E ProcessGroupNCCL.cpp:563] [Rank 3] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=175141, OpType=_REDUCE_SCATTER_BASE, NumelIn=33554432, NumelOut=8388608, Timeout(ms)=600000) ran for 600092 milliseconds before timing out.
[default5]:[rank5]:[E ProcessGroupNCCL.cpp:563] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=13834, OpType=RECV, NumelIn=7, NumelOut=7, Timeout(ms)=600000) ran for 600055 milliseconds before timing out.
[default4]:[rank4]:[E ProcessGroupNCCL.cpp:563] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=13834, OpType=RECV, NumelIn=7, NumelOut=7, Timeout(ms)=600000) ran for 600065 milliseconds before timing out.
[default6]:[rank6]: Traceback (most recent call last):
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default6]:[rank6]: trainer.train(dataloader)
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default6]:[rank6]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default6]:[rank6]: outputs = self.pipeline_engine.train_batch_iter(
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default6]:[rank6]: 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 44, in forward
[default6]:[rank6]: 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 1532, in _wrapped_call_impl
[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
[default6]:[rank6]: return forward_call(*args, **kwargs)
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[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
[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
[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
[default6]:[rank6]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default6]:[rank6]: hidden_encoder_states = encoder_block(**hidden_encoder_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)
[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)
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 126, in forward
[default6]:[rank6]: new_kwargs[name] = recv_from_pipeline_state_buffer(
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/functional.py", line 117, in recv_from_pipeline_state_buffer
[default6]:[rank6]: pipeline_state.run_communication()
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 150, in run_communication
[default6]:[rank6]: recv_activation_tensor = recv_activation()
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 31, in __call__
[default6]:[rank6]: return self.p2p.recv_tensors(num_tensors=1, from_rank=self.from_rank)[0]
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 353, in recv_tensors
[default6]:[rank6]: buffers, futures = self.irecv_tensors(num_tensors=num_tensors, from_rank=from_rank, tag=tag)
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 326, in irecv_tensors
[default6]:[rank6]: meta = self._recv_meta(from_rank=from_rank, tag=tag)
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 267, in _recv_meta
[default6]:[rank6]: self.second_metadata = torch.empty(second_metadata_num_elements, dtype=torch.long, device=self.device)
[default6]:[rank6]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate more than 1EB memory.
[default7]:[rank7]: Traceback (most recent call last):
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default7]:[rank7]: trainer.train(dataloader)
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default7]:[rank7]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[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(
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[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
[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
[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 891, in forward
[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
[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 764, in forward
[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
[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 126, in forward
[default7]:[rank7]: new_kwargs[name] = recv_from_pipeline_state_buffer(
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/functional.py", line 117, in recv_from_pipeline_state_buffer
[default7]:[rank7]: pipeline_state.run_communication()
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 150, in run_communication
[default7]:[rank7]: recv_activation_tensor = recv_activation()
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 31, in __call__
[default7]:[rank7]: return self.p2p.recv_tensors(num_tensors=1, from_rank=self.from_rank)[0]
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 353, in recv_tensors
[default7]:[rank7]: buffers, futures = self.irecv_tensors(num_tensors=num_tensors, from_rank=from_rank, tag=tag)
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 326, in irecv_tensors
[default7]:[rank7]: meta = self._recv_meta(from_rank=from_rank, tag=tag)
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 267, in _recv_meta
[default7]:[rank7]: self.second_metadata = torch.empty(second_metadata_num_elements, dtype=torch.long, device=self.device)
[default7]:[rank7]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate more than 1EB memory.
[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)
[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
[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)
[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 764, in forward
[default5]:[rank5]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[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)
[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 126, in forward
[default5]:[rank5]: new_kwargs[name] = recv_from_pipeline_state_buffer(
[default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/functional.py", line 117, in recv_from_pipeline_state_buffer
[default5]:[rank5]: pipeline_state.run_communication()
[default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 150, in run_communication
[default5]:[rank5]: recv_activation_tensor = recv_activation()
[default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 31, in __call__
[default5]:[rank5]: return self.p2p.recv_tensors(num_tensors=1, from_rank=self.from_rank)[0]
[default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 353, in recv_tensors
[default5]:[rank5]: buffers, futures = self.irecv_tensors(num_tensors=num_tensors, from_rank=from_rank, tag=tag)
[default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 326, in irecv_tensors
[default5]:[rank5]: meta = self._recv_meta(from_rank=from_rank, tag=tag)
[default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 267, in _recv_meta
[default5]:[rank5]: self.second_metadata = torch.empty(second_metadata_num_elements, dtype=torch.long, device=self.device)
[default5]:[rank5]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate more than 1EB memory.
[default4]:[rank4]: Traceback (most recent call last):
[default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[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)
[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)
[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)
[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)
[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
[default4]:[rank4]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default4]:[rank4]: hidden_encoder_states = encoder_block(**hidden_encoder_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/parallel/pipeline_parallel/block.py", line 126, in forward
[default4]:[rank4]: new_kwargs[name] = recv_from_pipeline_state_buffer(
[default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/functional.py", line 117, in recv_from_pipeline_state_buffer
[default4]:[rank4]: pipeline_state.run_communication()
[default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 150, in run_communication
[default4]:[rank4]: recv_activation_tensor = recv_activation()
[default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 31, in __call__
[default4]:[rank4]: return self.p2p.recv_tensors(num_tensors=1, from_rank=self.from_rank)[0]
[default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 353, in recv_tensors
[default4]:[rank4]: buffers, futures = self.irecv_tensors(num_tensors=num_tensors, from_rank=from_rank, tag=tag)
[default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 326, in irecv_tensors
[default4]:[rank4]: meta = self._recv_meta(from_rank=from_rank, tag=tag)
[default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 267, in _recv_meta
[default4]:[rank4]: self.second_metadata = torch.empty(second_metadata_num_elements, dtype=torch.long, device=self.device)
[default4]:[rank4]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate more than 1EB memory.
[default6]:[rank6]:[E ProcessGroupNCCL.cpp:1537] [PG 4 Rank 1] Timeout at NCCL work: 13834, last enqueued NCCL work: 13834, last completed NCCL work: 13833.
[default6]:[rank6]:[E ProcessGroupNCCL.cpp:577] [Rank 1] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[default6]:[rank6]:[E ProcessGroupNCCL.cpp:583] [Rank 1] To avoid data inconsistency, we are taking the entire process down.
[default6]:[rank6]:[E ProcessGroupNCCL.cpp:1414] [PG 4 Rank 1] Process group watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=13834, OpType=RECV, NumelIn=7, NumelOut=7, Timeout(ms)=600000) ran for 600052 milliseconds before timing out.
[default6]:Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:565 (most recent call first):
[default6]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7ffa8926d897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default6]:frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x1d2 (0x7ffa8a546c62 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default6]:frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1a0 (0x7ffa8a54ba80 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default6]:frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7ffa8a54cdcc in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default6]:frame #4: <unknown function> + 0xd3e95 (0x7ffad5fe5e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default6]:frame #5: <unknown function> + 0x8609 (0x7ffadb02c609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default6]:frame #6: clone + 0x43 (0x7ffadadf7353 in /lib/x86_64-linux-gnu/libc.so.6)
[default6]:
[default6]:terminate called after throwing an instance of 'c10::DistBackendError'
[default6]: what(): [PG 4 Rank 1] Process group watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=13834, OpType=RECV, NumelIn=7, NumelOut=7, Timeout(ms)=600000) ran for 600052 milliseconds before timing out.
[default6]:Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:565 (most recent call first):
[default6]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7ffa8926d897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default6]:frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x1d2 (0x7ffa8a546c62 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default6]:frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1a0 (0x7ffa8a54ba80 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default6]:frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7ffa8a54cdcc in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default6]:frame #4: <unknown function> + 0xd3e95 (0x7ffad5fe5e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default6]:frame #5: <unknown function> + 0x8609 (0x7ffadb02c609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default6]:frame #6: clone + 0x43 (0x7ffadadf7353 in /lib/x86_64-linux-gnu/libc.so.6)
[default6]:
[default6]:Exception raised from ncclCommWatchdog at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1418 (most recent call first):
[default6]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7ffa8926d897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default6]:frame #1: <unknown function> + 0xe32119 (0x7ffa8a1d0119 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default6]:frame #2: <unknown function> + 0xd3e95 (0x7ffad5fe5e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default6]:frame #3: <unknown function> + 0x8609 (0x7ffadb02c609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default6]:frame #4: clone + 0x43 (0x7ffadadf7353 in /lib/x86_64-linux-gnu/libc.so.6)
[default6]:
[default7]:[rank7]:[E ProcessGroupNCCL.cpp:1537] [PG 4 Rank 1] Timeout at NCCL work: 13834, last enqueued NCCL work: 13834, last completed NCCL work: 13833.
[default7]:[rank7]:[E ProcessGroupNCCL.cpp:577] [Rank 1] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[default7]:[rank7]:[E ProcessGroupNCCL.cpp:583] [Rank 1] To avoid data inconsistency, we are taking the entire process down.
[default7]:[rank7]:[E ProcessGroupNCCL.cpp:1414] [PG 4 Rank 1] Process group watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=13834, OpType=RECV, NumelIn=7, NumelOut=7, Timeout(ms)=600000) ran for 600052 milliseconds before timing out.
[default7]:Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:565 (most recent call first):
[default7]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f08104fd897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default7]:frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x1d2 (0x7f08117d6c62 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default7]:frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1a0 (0x7f08117dba80 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default7]:frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7f08117dcdcc in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default7]:frame #4: <unknown function> + 0xd3e95 (0x7f085d275e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default7]:frame #5: <unknown function> + 0x8609 (0x7f08622bc609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default7]:frame #6: clone + 0x43 (0x7f0862087353 in /lib/x86_64-linux-gnu/libc.so.6)
[default7]:
[default7]:terminate called after throwing an instance of 'c10::DistBackendError'
[default7]: what(): [PG 4 Rank 1] Process group watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=13834, OpType=RECV, NumelIn=7, NumelOut=7, Timeout(ms)=600000) ran for 600052 milliseconds before timing out.
[default7]:Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:565 (most recent call first):
[default7]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f08104fd897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default7]:frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x1d2 (0x7f08117d6c62 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default7]:frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1a0 (0x7f08117dba80 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default7]:frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7f08117dcdcc in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default7]:frame #4: <unknown function> + 0xd3e95 (0x7f085d275e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default7]:frame #5: <unknown function> + 0x8609 (0x7f08622bc609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default7]:frame #6: clone + 0x43 (0x7f0862087353 in /lib/x86_64-linux-gnu/libc.so.6)
[default7]:
[default7]:Exception raised from ncclCommWatchdog at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1418 (most recent call first):
[default7]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f08104fd897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default7]:frame #1: <unknown function> + 0xe32119 (0x7f0811460119 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default7]:frame #2: <unknown function> + 0xd3e95 (0x7f085d275e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default7]:frame #3: <unknown function> + 0x8609 (0x7f08622bc609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default7]:frame #4: clone + 0x43 (0x7f0862087353 in /lib/x86_64-linux-gnu/libc.so.6)
[default7]:
[default5]:[rank5]:[E ProcessGroupNCCL.cpp:1537] [PG 4 Rank 1] Timeout at NCCL work: 13834, last enqueued NCCL work: 13834, last completed NCCL work: 13833.
[default5]:[rank5]:[E ProcessGroupNCCL.cpp:577] [Rank 1] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[default5]:[rank5]:[E ProcessGroupNCCL.cpp:583] [Rank 1] To avoid data inconsistency, we are taking the entire process down.
[default5]:[rank5]:[E ProcessGroupNCCL.cpp:1414] [PG 4 Rank 1] Process group watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=13834, OpType=RECV, NumelIn=7, NumelOut=7, Timeout(ms)=600000) ran for 600055 milliseconds before timing out.
[default5]:Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:565 (most recent call first):
[default5]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f80e58fb897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default5]:frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x1d2 (0x7f80e6bd4c62 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default5]:frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1a0 (0x7f80e6bd9a80 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default5]:frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7f80e6bdadcc in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default5]:frame #4: <unknown function> + 0xd3e95 (0x7f8132673e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default5]:frame #5: <unknown function> + 0x8609 (0x7f81376ba609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default5]:frame #6: clone + 0x43 (0x7f8137485353 in /lib/x86_64-linux-gnu/libc.so.6)
[default5]:
[default5]:terminate called after throwing an instance of 'c10::DistBackendError'
[default5]: what(): [PG 4 Rank 1] Process group watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=13834, OpType=RECV, NumelIn=7, NumelOut=7, Timeout(ms)=600000) ran for 600055 milliseconds before timing out.
[default5]:Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:565 (most recent call first):
[default5]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f80e58fb897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default5]:frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x1d2 (0x7f80e6bd4c62 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default5]:frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1a0 (0x7f80e6bd9a80 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default5]:frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7f80e6bdadcc in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default5]:frame #4: <unknown function> + 0xd3e95 (0x7f8132673e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default5]:frame #5: <unknown function> + 0x8609 (0x7f81376ba609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default5]:frame #6: clone + 0x43 (0x7f8137485353 in /lib/x86_64-linux-gnu/libc.so.6)
[default5]:
[default5]:Exception raised from ncclCommWatchdog at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1418 (most recent call first):
[default5]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f80e58fb897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default5]:frame #1: <unknown function> + 0xe32119 (0x7f80e685e119 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default5]:frame #2: <unknown function> + 0xd3e95 (0x7f8132673e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default5]:frame #3: <unknown function> + 0x8609 (0x7f81376ba609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default5]:frame #4: clone + 0x43 (0x7f8137485353 in /lib/x86_64-linux-gnu/libc.so.6)
[default5]:
[default4]:[rank4]:[E ProcessGroupNCCL.cpp:1537] [PG 4 Rank 1] Timeout at NCCL work: 13834, last enqueued NCCL work: 13834, last completed NCCL work: 13833.
[default4]:[rank4]:[E ProcessGroupNCCL.cpp:577] [Rank 1] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[default4]:[rank4]:[E ProcessGroupNCCL.cpp:583] [Rank 1] To avoid data inconsistency, we are taking the entire process down.
[default4]:[rank4]:[E ProcessGroupNCCL.cpp:1414] [PG 4 Rank 1] Process group watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=13834, OpType=RECV, NumelIn=7, NumelOut=7, Timeout(ms)=600000) ran for 600065 milliseconds before timing out.
[default4]:Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:565 (most recent call first):
[default4]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f4c6224c897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default4]:frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x1d2 (0x7f4c63525c62 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default4]:frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1a0 (0x7f4c6352aa80 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default4]:frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7f4c6352bdcc in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default4]:frame #4: <unknown function> + 0xd3e95 (0x7f4caefc4e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default4]:frame #5: <unknown function> + 0x8609 (0x7f4cb400b609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default4]:frame #6: clone + 0x43 (0x7f4cb3dd6353 in /lib/x86_64-linux-gnu/libc.so.6)
[default4]:
[default4]:terminate called after throwing an instance of 'c10::DistBackendError'
[default4]: what(): [PG 4 Rank 1] Process group watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=13834, OpType=RECV, NumelIn=7, NumelOut=7, Timeout(ms)=600000) ran for 600065 milliseconds before timing out.
[default4]:Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:565 (most recent call first):
[default4]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f4c6224c897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default4]:frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x1d2 (0x7f4c63525c62 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default4]:frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1a0 (0x7f4c6352aa80 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default4]:frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7f4c6352bdcc in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default4]:frame #4: <unknown function> + 0xd3e95 (0x7f4caefc4e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default4]:frame #5: <unknown function> + 0x8609 (0x7f4cb400b609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default4]:frame #6: clone + 0x43 (0x7f4cb3dd6353 in /lib/x86_64-linux-gnu/libc.so.6)
[default4]:
[default4]:Exception raised from ncclCommWatchdog at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1418 (most recent call first):
[default4]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f4c6224c897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default4]:frame #1: <unknown function> + 0xe32119 (0x7f4c631af119 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default4]:frame #2: <unknown function> + 0xd3e95 (0x7f4caefc4e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default4]:frame #3: <unknown function> + 0x8609 (0x7f4cb400b609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default4]:frame #4: clone + 0x43 (0x7f4cb3dd6353 in /lib/x86_64-linux-gnu/libc.so.6)
[default4]:
W0703 23:40:16.055000 139973789022016 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2274198 closing signal SIGTERM
W0703 23:40:16.055000 139973789022016 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2274199 closing signal SIGTERM
W0703 23:40:16.055000 139973789022016 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2274200 closing signal SIGTERM
W0703 23:40:16.055000 139973789022016 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2274201 closing signal SIGTERM
E0703 23:40:20.862000 139973789022016 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: -6) local_rank: 4 (pid: 2274202) 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 : 2024-07-03_23:40:16
host : ip-26-0-169-86.ec2.internal
rank : 5 (local_rank: 5)
exitcode : -6 (pid: 2274203)
error_file: <N/A>
traceback : Signal 6 (SIGABRT) received by PID 2274203
[2]:
time : 2024-07-03_23:40:16
host : ip-26-0-169-86.ec2.internal
rank : 6 (local_rank: 6)
exitcode : -6 (pid: 2274204)
error_file: <N/A>
traceback : Signal 6 (SIGABRT) received by PID 2274204
[3]:
time : 2024-07-03_23:40:16
host : ip-26-0-169-86.ec2.internal
rank : 7 (local_rank: 7)
exitcode : -6 (pid: 2274205)
error_file: <N/A>
traceback : Signal 6 (SIGABRT) received by PID 2274205
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2024-07-03_23:40:16
host : ip-26-0-169-86.ec2.internal
rank : 4 (local_rank: 4)
exitcode : -6 (pid: 2274202)
error_file: <N/A>
traceback : Signal 6 (SIGABRT) received by PID 2274202
============================================================
srun: error: ip-26-0-169-86: 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.
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 0%| | 0.00/848M [00:00<?, ?B/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 2%|▏ | 16.0M/848M [00:00<00:17, 47.0MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 4%|▍ | 32.0M/848M [00:00<00:15, 53.5MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 6%|▌ | 48.0M/848M [00:00<00:13, 60.5MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 8%|▊ | 64.0M/848M [00:01<00:15, 50.5MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 9%|▉ | 80.0M/848M [00:01<00:14, 53.4MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 11%|█▏ | 96.0M/848M [00:01<00:14, 53.6MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 13%|█▎ | 112M/848M [00:02<00:13, 52.9MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 15%|█▌ | 128M/848M [00:02<00:12, 58.2MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 17%|█▋ | 144M/848M [00:02<00:12, 55.9MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 19%|█▉ | 160M/848M [00:02<00:12, 55.7MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 21%|██ | 176M/848M [00:03<00:11, 56.4MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 23%|██▎ | 192M/848M [00:03<00:11, 58.3MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 25%|██▍ | 208M/848M [00:03<00:10, 62.5MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 26%|██▋ | 224M/848M [00:03<00:10, 59.5MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 28%|██▊ | 240M/848M [00:04<00:09, 61.0MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 30%|███ | 256M/848M [00:04<00:09, 61.7MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 32%|███▏ | 272M/848M [00:04<00:08, 66.4MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 34%|███▍ | 288M/848M [00:04<00:08, 66.2MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 36%|███▌ | 304M/848M [00:05<00:08, 66.5MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 38%|███▊ | 320M/848M [00:05<00:08, 64.2MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 40%|███▉ | 336M/848M [00:05<00:08, 63.8MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 41%|████▏ | 352M/848M [00:06<00:08, 57.5MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 43%|████▎ | 368M/848M [00:06<00:09, 48.8MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 45%|████▌ | 384M/848M [00:06<00:09, 49.2MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 47%|████▋ | 400M/848M [00:07<00:08, 53.8MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 49%|████▉ | 416M/848M [00:07<00:07, 58.9MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 51%|█████ | 432M/848M [00:07<00:06, 63.6MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 53%|█████▎ | 448M/848M [00:07<00:07, 56.8MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 55%|█████▍ | 464M/848M [00:08<00:06, 59.5MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 57%|█████▋ | 480M/848M [00:08<00:05, 62.2MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 58%|█████▊ | 496M/848M [00:08<00:05, 60.5MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 60%|██████ | 512M/848M [00:08<00:05, 59.1MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 62%|██████▏ | 528M/848M [00:09<00:05, 57.9MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 64%|██████▍ | 544M/848M [00:09<00:04, 61.4MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 66%|██████▌ | 560M/848M [00:09<00:05, 55.7MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 68%|██████▊ | 576M/848M [00:09<00:04, 60.3MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 70%|██████▉ | 592M/848M [00:10<00:04, 54.9MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 72%|███████▏ | 608M/848M [00:10<00:03, 61.6MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 74%|███████▎ | 624M/848M [00:10<00:03, 61.2MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 75%|███████▌ | 640M/848M [00:10<00:03, 60.7MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 77%|███████▋ | 656M/848M [00:11<00:02, 64.7MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 79%|███████▉ | 672M/848M [00:11<00:02, 59.0MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 81%|████████ | 688M/848M [00:11<00:02, 62.4MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 83%|████████▎ | 704M/848M [00:11<00:02, 68.7MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 85%|████████▍ | 720M/848M [00:12<00:01, 71.8MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 87%|████████▋ | 736M/848M [00:12<00:01, 62.4MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 89%|████████▊ | 752M/848M [00:13<00:02, 41.2MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 91%|█████████ | 768M/848M [00:13<00:01, 45.6MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 92%|█████████▏| 784M/848M [00:13<00:01, 49.3MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 94%|█████████▍| 800M/848M [00:14<00:01, 39.3MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 96%|█████████▌| 816M/848M [00:14<00:00, 39.6MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 98%|█████████▊| 832M/848M [00:14<00:00, 42.4MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 100%|█████████▉| 848M/848M [00:15<00:00, 46.9MB/s]
ip-26-0-169-86_2274198.1720050009949646955.pt.trace.json.tmp: 100%|██████████| 848M/848M [00:15<00:00, 55.4MB/s]
|