File size: 27,722 Bytes
af6e330 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 |
import argparse
import ast
import copy
import json
import logging
import yaml
from open_lm.attention import ATTN_ACTIVATIONS, ATTN_SEQ_SCALARS
class ParseKwargs(argparse.Action):
def __call__(self, parser, namespace, values, option_string=None):
kw = {}
for value in values:
key, value = value.split("=")
try:
kw[key] = ast.literal_eval(value)
except ValueError:
kw[key] = str(value) # fallback to string (avoid need to escape on command line)
setattr(namespace, self.dest, kw)
def add_model_args(parser):
"""Add arguments that change the underlying architecture.
These arguments need to be added to the eval code. Ideally, these should be moved to our model configs when we make
a backward-incompatible release."""
parser.add_argument(
"--model-norm",
type=str,
default="default_layer_norm",
choices=[
"default_layer_norm",
"lp_layer_norm",
"gain_only_lp_layer_norm",
"gain_only_layer_norm",
"no_wb_layer_norm",
"rms_norm",
],
help="Type of normalization to employ in the model. This might be overridden by the model config.",
)
parser.add_argument(
"--ffn-type",
choices=["swiglu", "swiglu_torch", "gelu", "gemma_geglu"],
default="swiglu",
help="Type of feedforward layer to use. This might be overridden by the model config.",
)
parser.add_argument(
"--qk-norm",
action="store_true",
default=False,
help="apply --model-norm to qk as in: https://arxiv.org/abs/2302.05442. This might be overridden by the model config.",
)
parser.add_argument(
"--positional-embedding-type",
type=str,
choices=["rotary", "head_rotary", "llama_rotary", "none"],
default="rotary",
help="Type of positional embedding to use. This might be overridden by the model config.",
)
parser.add_argument(
"--moe-freq",
type=int,
default=0,
help="if set > 0, we will add MoE layer to every moe_freq layer.",
)
parser.add_argument(
"--moe-num-experts",
type=int,
default=None,
help="Number of experts for MoE",
)
parser.add_argument(
"--moe-weight-parallelism",
action="store_true",
help="Add weight parallelism to MoE",
)
parser.add_argument(
"--moe-expert-model-parallelism",
action="store_true",
help="Add expert model parallelism to MoE",
)
parser.add_argument(
"--moe-capacity-factor",
type=float,
default=1.25,
help="MoE capacity factor",
)
parser.add_argument(
"--moe-loss-weight",
type=float,
default=0.1,
help="MoE loss weight",
)
parser.add_argument(
"--moe-top-k",
type=int,
default=2,
help="MoE top k experts",
)
parser.add_argument(
"--attn-name",
type=str,
default="auto",
choices=["auto", "xformers_attn", "xformers_attn_variable_length", "torch_attn", "custom_attn"],
help="type of attention to use",
)
parser.add_argument(
"--attn-activation",
type=str,
default=None,
choices=list(ATTN_ACTIVATIONS.keys()),
help="activation to use with custom_attn",
)
parser.add_argument(
"--attn-seq-scalar",
type=str,
default=None,
choices=list(ATTN_SEQ_SCALARS.keys()),
help="different ways to set L, where L^alpha divides attention logits post activation",
)
parser.add_argument(
"--attn-seq-scalar-alpha",
type=float,
default=None,
help="power alpha to raise L to, where L^alpha divides attention logits post activation",
)
def check_replacement_type(replacement, original):
"""Checks that `replacement`, which is intended to replace `original` is of
the right type. The type is correct if it matches exactly or is one of a few
cases in which the type can be easily coerced.
Taken from YACS: https://github.com/rbgirshick/yacs/blob/32d5e4ac300eca6cd3b839097dde39c4017a1070/yacs/config.py#L494
"""
# The types must match (with some exceptions)
if type(original) == type(replacement):
return True
# If either of them is None, accept the type.
if replacement is None or original is None:
return True
return False
def maybe_load_config(parser, args):
config_parser = argparse.ArgumentParser()
config_parser.add_argument("--config", type=str)
args, unknown_args = config_parser.parse_known_args(args)
if not args.config:
return None
assert not unknown_args, "No arguments can be passed if --config is provided."
logging.info(f"Loading config from: {args.config}")
with open(args.config, "r") as f:
if args.config.endswith(".yaml") or args.config.endswith(".yml"):
config = yaml.safe_load(f)
elif args.config.endswith(".json"):
config = json.load(f)
else:
raise ValueError(f"Unknown config format: {args.config}")
default_args = vars(parser.parse_args([]))
default_arg_keys = default_args.keys()
updated_args = copy.deepcopy(default_args)
for config_key, config_value in config.items():
config_key = config_key.replace("-", "_")
if config_key not in default_arg_keys:
raise ValueError(f"Unknown config key: {config_key}")
default_value = default_args[config_key]
is_valid = check_replacement_type(replacement=config_value, original=default_value)
if not is_valid:
raise ValueError(
f"Type mismatch (config: {type(config_value)} vs. argparse: {type(default_value)}) with values "
f"(config: {config_value} vs. argparse: {default_value}) for config. key: {config_key}"
)
updated_args[config_key] = config_value
return updated_args
def check_args(args):
# data checks
if args.dataset_type == "synthetic":
assert args.train_data is None, "--train-data must not be specified if --dataset-type='synthetic'"
assert args.dataset_manifest is None, "--dataset-manifest must not be specified if --dataset-type='synthetic'"
if args.val_data is not None and args.global_val_batch_size is None:
# Make sure that val batch size is set to micro batch size
args.global_val_batch_size = args.global_batch_size // args.accum_freq
assert (
args.train_data is None or args.dataset_manifest is None
), "--dataset-manifest and --train-data cannot both be set"
# custom_attn checks
if args.attn_name == "custom_attn":
assert (
args.attn_activation is not None
and args.attn_seq_scalar is not None
and args.attn_seq_scalar_alpha is not None
), "must provide attn-activation, attn-seq-scalar, attn-seq-scalar-alpha to use non-linear-attn"
else:
assert (
args.attn_activation is None and args.attn_seq_scalar is None and args.attn_seq_scalar_alpha is None
), "attn-activation, attn-seq-scalar, attn-seq-scalar-alpha must be None unless using non-linear-attn"
# masking checks
if args.squash_mask_left:
assert (
args.target_mask_left is not None and args.target_mask_individual is not None
), "must pass target-mask-left and target-mask-individual to use squash-mask-left"
if args.target_mask_left is not None and args.target_mask_individual == args.target_mask_left:
raise ValueError(
f"--target-mask-left and --target-mask-individual set to same value of {args.target_mask_left}."
)
# hf checks
if args.hf_model is not None and args.hf_seq_len is None:
raise ValueError("If passing --hf-model, must also pass --hf-seq-len to be used for training/fine-tuning.")
if args.hf_model is not None and args.fsdp and args.hf_fsdp_block is None:
raise ValueError("If passing --hf-model and --fsdp, must also pass --hf-fsdp-block.")
resume_latest = args.resume == "latest"
# resuming checkpoing checks
if resume_latest:
# If using remote_sync, need to check the remote instead of the local checkpoints folder.
if args.remote_sync is not None:
if args.save_most_recent:
raise ValueError("Cannot use save-most-recent with remote_sync and resume latest.")
if args.remote_sync_protocol != "s3":
raise ValueError("Sync protocol not supported when using resume latest.")
if args.lr_scheduler not in {"cosine", "const", "const-cooldown"}:
raise ValueError(
f"Unknown scheduler, {args.lr_scheduler}. Available options are: cosine, const, const-cooldown."
)
if args.experimental_meta_device:
print("WARNING: Meta device initialization requested, but this is not currently fully tested.")
if args.moe_freq != 0 or args.moe_num_experts is not None:
assert (
args.moe_freq != 0 and args.moe_num_experts is not None
), "For MoE training, pass --moe-freq and --moe-num-experts"
try:
import megablocks
except ImportError:
raise ValueError("Megablocks not installed. To train MoE, install with pip install megablocks.")
def parse_args(args):
parser = argparse.ArgumentParser()
parser.add_argument(
"--train-data",
type=str,
nargs="+",
default=None,
help="Path to file(s) with training data. When using webdataset, multiple datasources can be combined using the `::` separator.",
)
parser.add_argument(
"--train-data-mix-weights",
type=float,
nargs="+",
default=None,
help=(
"When using multiple data sources with webdataset and sampling with replacement, this can be used to upsample specific data sources. "
"Similar to --train-data, this should be a string with as many numbers as there are data sources, separated by `::` (e.g. 1::2::0.5) "
"By default, datapoints are sampled uniformly regardless of the dataset sizes."
),
)
parser.add_argument(
"--train-data-upsampling-factors",
type=str,
default=None,
help=(
"When using multiple data sources with webdataset and sampling with replacement, this can be used to upsample specific data sources. "
"Similar to --train-data, this should be a string with as many numbers as there are data sources, separated by `::` (e.g. 1::2::0.5) "
"By default, datapoints are sampled uniformly regardless of the dataset sizes."
),
)
parser.add_argument(
"--val-data",
type=str,
nargs="+",
default=None,
help=(
"Path to file(s) with validation data. Note: each space seperated entry will be processed seperately and writen as seperate entries "
"in a results.jsonl file."
),
)
parser.add_argument(
"--data-key",
type=str,
default="txt",
help="what is the extension",
)
parser.add_argument(
"--train-num-samples",
type=int,
default=None,
help="Number of samples in dataset. Required for webdataset if not available in info file.",
)
parser.add_argument(
"--val-num-samples",
type=int,
default=None,
help="Number of samples in dataset. Useful for webdataset if not available in info file.",
)
parser.add_argument(
"--dataset-type",
choices=["webdataset", "auto", "synthetic","jsonl"],
default="auto",
help="Which type of dataset to process.",
)
parser.add_argument(
"--dataset-resampled",
default=False,
action="store_true",
help="Whether to use sampling with replacement for webdataset shard selection.",
)
parser.add_argument(
"--dataset-manifest",
type=str,
nargs="+",
default=None,
help="Uses manifest to construct a train set.",
)
parser.add_argument(
"--disable-buffer",
action="store_true",
default=False,
help="Turns off the shuffle buffer.",
)
parser.add_argument(
"--logs",
type=str,
default="./logs/",
help="Where to store tensorboard logs. Use None to avoid storing logs.",
)
parser.add_argument(
"--log-local",
action="store_true",
default=False,
help="log files on local master, otherwise global master only.",
)
parser.add_argument(
"--name",
type=str,
default=None,
help="Optional identifier for the experiment when storing logs. Otherwise use current time.",
)
parser.add_argument("--workers", type=int, default=1, help="Number of dataloader workers per GPU.")
parser.add_argument("--global-batch-size", type=int, default=64, help="Global batch size.")
parser.add_argument("--epochs", type=int, default=32, help="Number of epochs to train for.")
parser.add_argument(
"--epochs-cooldown",
type=int,
default=None,
help="When scheduler w/ cooldown used, perform cooldown from total_epochs - cooldown_epochs onwards.",
)
parser.add_argument("--optimizer", default="adamw", help="Optimizer.")
parser.add_argument("--lr", type=float, default=5.0e-4, help="Learning rate.")
parser.add_argument("--beta1", type=float, default=0.9, help="Adam beta 1.")
parser.add_argument("--beta2", type=float, default=0.95, help="Adam beta 2.")
parser.add_argument("--eps", type=float, default=1.0e-8, help="Adam epsilon.")
parser.add_argument("--wd", type=float, default=0.2, help="Weight decay.")
parser.add_argument("--warmup", type=int, default=10000, help="Number of steps to warmup for.")
parser.add_argument(
"--z-loss-coefficient",
type=float,
default=0.0,
help="regularization term to make sure logits not too big, based on: https://github.com/google-research/t5x/blob/main/t5x/losses.py#L33-L38",
)
parser.add_argument(
"--log-logit-mean",
default=False,
action="store_true",
help="Whether to log the logit mean to wandb etc.",
)
parser.add_argument(
"--use-bn-sync",
default=False,
action="store_true",
help="Whether to use batch norm sync.",
)
parser.add_argument(
"--skip-scheduler",
action="store_true",
default=False,
help="Use this flag to skip the learning rate decay.",
)
parser.add_argument(
"--lr-scheduler",
type=str,
default="cosine",
help="LR scheduler. One of: 'cosine', 'const' (constant), 'const-cooldown' (constant w/ cooldown). Default: cosine",
)
parser.add_argument(
"--lr-cooldown-end",
type=float,
default=0.0,
help="End learning rate for cooldown schedule. Default: 0",
)
parser.add_argument(
"--lr-cooldown-power",
type=float,
default=1.0,
help="Power for polynomial cooldown schedule. Default: 1.0 (linear decay)",
)
parser.add_argument(
"--force-min-lr",
type=float,
default=0.0,
help="Force the LR to stop decaying at this value.",
)
parser.add_argument("--save-frequency", type=int, default=1, help="How often to save checkpoints.")
parser.add_argument(
"--save-most-recent",
action="store_true",
default=False,
help="Always save the most recent model trained to epoch_latest.pt.",
)
parser.add_argument(
"--torchcompile",
action="store_true",
default=False,
help="Compile the model, requires torch >=2.0.",
)
parser.add_argument(
"--val-frequency",
type=int,
default=1,
help="How often to run evaluation with val-data (in epochs). Last epoch validated if val-data provided.",
)
parser.add_argument(
"--global-val-batch-size",
type=int,
default=None,
help="Batch size to be used with val-data.",
)
parser.add_argument(
"--val-data-key",
type=str,
nargs="+",
default=None,
help="what is the extension fore each val-data source.",
)
parser.add_argument(
"--val-seq-ci",
default=False,
action="store_true",
help="comput sequence loss 0.95 ci.",
)
parser.add_argument(
"--val-tok-ci",
default=False,
action="store_true",
help="compute token loss 0.95 ci.",
)
parser.add_argument(
"--val-max-pop-ci",
default=None,
action="store",
type=int,
help="when running CIs what is the maximum population size for the inner loop",
)
parser.add_argument(
"--val-iter-ci",
default=10_000,
action="store",
type=int,
help="how many times to sample to construct the CI for the outer loop",
)
parser.add_argument(
"--resume",
default=None,
type=str,
help="path to latest checkpoint (default: none)",
)
parser.add_argument(
"--precision",
choices=["amp", "amp_bf16", "amp_bfloat16", "bf16", "fp16", "fp32"],
default="amp",
help="Floating point precision.",
)
parser.add_argument(
"--model",
type=str,
default="open_lm_1b",
help="Name of the model_config to use. Can also pass a custom json config.",
)
parser.add_argument(
"--hf-model",
type=str,
default=None,
help="Huggingface model/tokenizer name for AutoModelForCausalLM.",
)
parser.add_argument(
"--hf-seq-len",
type=int,
default=None,
help="Sequence length for use with a --hf-model.",
)
parser.add_argument(
"--hf-fsdp-block",
type=str,
default=None,
help="transformer_layer_cls name in a --hf-model used for fsdp's transformer_auto_wrap_policy.",
)
parser.add_argument(
"--pretrained",
default=None,
type=str,
help="Use a pretrained CLIP model weights with the specified tag or file path.",
)
parser.add_argument(
"--load-pretrained-state",
default=False,
action="store_true",
help="Include the opt and schedule state when loading a pre-trained model.",
)
parser.add_argument(
"--grad-checkpointing",
default=False,
action="store_true",
help="Enable gradient checkpointing.",
)
parser.add_argument(
"--torchscript",
default=False,
action="store_true",
help="torch.jit.script the model",
)
parser.add_argument(
"--trace",
default=False,
action="store_true",
help="torch.jit.trace the model for inference / eval only",
)
parser.add_argument(
"--accum-freq",
type=int,
default=1,
help="Update the model every --accum-freq steps.",
)
# arguments for distributed training
parser.add_argument(
"--dist-url",
default="env://",
type=str,
help="url used to set up distributed training",
)
parser.add_argument("--dist-backend", default="nccl", type=str, help="distributed backend")
parser.add_argument(
"--fsdp",
default=False,
action="store_true",
help="Use FullyShardedDataParallel for distributed training.",
)
parser.add_argument(
"--fsdp-cpu-offload",
default=False,
action="store_true",
help="CPU offloading for FSDP and checkpoint saving. This does not work with gradient accumulation.",
)
parser.add_argument(
"--fsdp-use-orig-params",
default=False,
action="store_true",
help="Passed into the FSDP constructor. This does not work for OPT models. Enables param_groups for weight_decay.",
)
parser.add_argument(
"--fsdp-amp",
default=False,
action="store_true",
help="Use FullyShardedDataParallel for distributed training.",
)
parser.add_argument(
"--fsdp-pure-bf16",
default=False,
action="store_true",
help="Use pure bf16 FullyShardedDataParallel for distributed training.",
)
parser.add_argument(
"--fsdp-backward-prefetch",
default=False,
action="store_true",
)
parser.add_argument(
"--fsdp-hybrid",
default=False,
action="store_true",
)
parser.add_argument(
"--fsdp-hybrid-o2",
default=False,
action="store_true",
)
parser.add_argument(
"--fsdp-checkpoint",
default=False,
action="store_true",
)
parser.add_argument(
"--fsdp-limit-all-gathers",
default=False,
action="store_true",
)
parser.add_argument(
"--report-to",
default="",
type=str,
help="Options are ['wandb', 'tensorboard', 'wandb,tensorboard']",
)
parser.add_argument("--wandb-notes", default="", type=str, help="Notes if logging with wandb")
parser.add_argument(
"--wandb-project-name",
type=str,
default="open-lm",
help="Name of the project if logging with wandb.",
)
parser.add_argument(
"--debug",
default=False,
action="store_true",
help="If true, more information is logged.",
)
parser.add_argument(
"--average",
type=str,
nargs="+",
default=None,
help=("Apply model average on these checkpoints with the specified coefficients by --average-coefficients."),
)
parser.add_argument(
"--average-coefficients",
type=float,
nargs="+",
default=None,
help=("Average the model weights with the specified coefficients, model weights specified by --average."),
)
parser.add_argument(
"--copy-codebase",
default=False,
action="store_true",
help="If true, we copy the entire base on the log directory, and execute from there.",
)
parser.add_argument(
"--ddp-static-graph",
default=False,
action="store_true",
help="Enable static graph optimization for DDP in PyTorch >= 1.11.",
)
parser.add_argument(
"--no-set-device-rank",
default=False,
action="store_true",
help="Don't set device index from local rank (when CUDA_VISIBLE_DEVICES restricted to one per proc).",
)
parser.add_argument("--seed", type=int, default=0, help="Default random seed.")
parser.add_argument("--grad-clip-norm", type=float, default=None, help="Gradient clip.")
parser.add_argument(
"--log-every-n-steps",
type=int,
default=100,
help="Log every n steps to tensorboard/console/wandb.",
)
parser.add_argument(
"--remote-sync",
type=str,
default=None,
help="Optinoally sync with a remote path specified by this arg",
)
parser.add_argument(
"--remote-sync-frequency",
type=int,
default=300,
help="How frequently to sync to a remote directly if --remote-sync is not None.",
)
parser.add_argument(
"--remote-sync-protocol",
choices=["s3", "fsspec"],
default="s3",
help="How to do the remote sync backup if --remote-sync is not None.",
)
parser.add_argument(
"--delete-previous-checkpoint",
default=False,
action="store_true",
help="If true, delete previous checkpoint after storing a new one.",
)
parser.add_argument(
"--distill-model",
default=None,
help="Which model arch to distill from, if any.",
)
parser.add_argument(
"--distill-pretrained",
default=None,
help="Which pre-trained weights to distill from, if any.",
)
parser.add_argument(
"--use-bnb-linear",
default=None,
help="Replace the network linear layers from the bitsandbytes library. " "Allows int8 training/inference, etc.",
)
parser.add_argument(
"--target-mask-left",
type=int,
default=None,
help="Mask the loss to the left of a specified token (including the specified token).",
)
parser.add_argument(
"--squash-mask-left",
default=False,
action="store_true",
help="squash the target-mask-left tokens in the sequence and pad from right with target-mask-individual",
)
parser.add_argument(
"--target-mask-individual",
type=int,
default=None,
help="Mask the loss for a special pad token. Useful for sequences shorter than sequence lenght.",
)
parser.add_argument(
"--ignore-parse-errors",
action="store_true",
default=False,
help="If true, ignore parse errors in data loading. This should ideally be False, as errors in dataloading can point to bigger issues in your dataset. However, this can be useful when training on a large dataset which has a couple errors.",
)
parser.add_argument(
"--experimental-meta-device",
action="store_true",
default=False,
help="If True, initialize the model on meta device. This can be useful for loading large models, but is not currently fully tested.",
)
parser.add_argument(
"--force-distributed",
action="store_true",
help="Allow forcing distributed mode even when running on one gpu. Mostly useful for testing.",
)
parser.add_argument(
"--preset-world-size",
type=int,
default=None,
help="Explicitly set the world size. Useful in cases where a different number of gpus per node need to be used.",
)
parser.add_argument(
"--multiple-data-passes",
action="store_true",
help="If set, allow model to do multiple data passes over our dataset, in order to reach the desired number of tokens.",
)
parser.add_argument(
"--averagers",
type=str,
default=None,
help="Optinoally average checkpoints along the trajectory.",
)
parser.add_argument(
"--log-avg-model-training-loss",
type=int,
default=0,
help="Whether to log the average model training loss. if not 0, it will log the average loss over the specified number of steps.",
)
parser.add_argument(
"--data-tolerate-error-p",
type=float,
default=0.09, # Roughly the number required to not repeat more than 10% of data.
help="This is the percentage of expected tokens above which the checkpoint is considered failed because of not having seen enough data.",
)
parser.add_argument(
"--data-tolerate-num-ckpts",
type=int,
default=0,
help="This is the maximum number of failed checkpoints (due to not having seen enough tokens) that are allowed",
)
add_model_args(parser)
config = maybe_load_config(parser, args)
if config is not None:
args = argparse.Namespace(**config)
logging.info(f"Loaded config from file: {args=}")
else:
args = parser.parse_args(args)
check_args(args)
return args
|