upload
Browse files- config.json +1 -1
- latest +1 -1
- model.safetensors +1 -1
- trainer_state.json +2509 -605
- training_args.bin +2 -2
- zero_to_fp32.py +14 -84
config.json
CHANGED
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@@ -1,5 +1,5 @@
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{
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-
"_name_or_path": "/
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"architectures": [
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"Qwen2ForCausalLM"
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],
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{
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+
"_name_or_path": "moussaKam/fr-qwen-1.5B-base",
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"architectures": [
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"Qwen2ForCausalLM"
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],
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latest
CHANGED
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@@ -1 +1 @@
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-
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+
global_step3962
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model.safetensors
CHANGED
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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size 3554214752
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:4b7c002ba46b916c79fff8f94759cc0cf8fbec829a682cd017fb9e23609dfab5
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size 3554214752
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trainer_state.json
CHANGED
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@@ -1,887 +1,2791 @@
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{
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"best_metric": null,
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"best_model_checkpoint": null,
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-
"epoch":
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-
"eval_steps": 500
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-
"global_step":
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"log_history": [
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{
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-
"epoch": 0.
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-
"grad_norm":
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-
"learning_rate":
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-
"loss": 1.
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"step": 50
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},
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{
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"epoch": 0.
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"grad_norm": 0.
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"learning_rate":
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"loss":
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"step":
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|
|
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"total_flos": 2.0380844918675866e+18,
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| 2804 |
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| 2805 |
}
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0ba6c8e40dc2d34ebe219f16f05b9e60b23b1741dcfccb9613485fc1e913f881
|
| 3 |
+
size 7032
|
zero_to_fp32.py
CHANGED
|
@@ -10,10 +10,7 @@
|
|
| 10 |
# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
|
| 11 |
# application.
|
| 12 |
#
|
| 13 |
-
# example:
|
| 14 |
-
# python zero_to_fp32.py . output_dir/
|
| 15 |
-
# or
|
| 16 |
-
# python zero_to_fp32.py . output_dir/ --safe_serialization
|
| 17 |
|
| 18 |
import argparse
|
| 19 |
import torch
|
|
@@ -21,8 +18,6 @@ import glob
|
|
| 21 |
import math
|
| 22 |
import os
|
| 23 |
import re
|
| 24 |
-
import json
|
| 25 |
-
from tqdm import tqdm
|
| 26 |
from collections import OrderedDict
|
| 27 |
from dataclasses import dataclass
|
| 28 |
|
|
@@ -144,6 +139,7 @@ def parse_model_states(files):
|
|
| 144 |
|
| 145 |
|
| 146 |
def parse_optim_states(files, ds_checkpoint_dir):
|
|
|
|
| 147 |
total_files = len(files)
|
| 148 |
state_dicts = []
|
| 149 |
for f in files:
|
|
@@ -424,10 +420,12 @@ def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero
|
|
| 424 |
offset = 0
|
| 425 |
total_numel = 0
|
| 426 |
total_params = 0
|
| 427 |
-
for name, shape in
|
|
|
|
| 428 |
unpartitioned_numel = shape.numel()
|
| 429 |
total_numel += unpartitioned_numel
|
| 430 |
total_params += 1
|
|
|
|
| 431 |
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
| 432 |
|
| 433 |
if debug:
|
|
@@ -523,75 +521,21 @@ def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None, exclude_f
|
|
| 523 |
return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
|
| 524 |
|
| 525 |
|
| 526 |
-
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
|
| 527 |
-
output_dir,
|
| 528 |
-
max_shard_size="5GB",
|
| 529 |
-
safe_serialization=False,
|
| 530 |
-
tag=None,
|
| 531 |
-
exclude_frozen_parameters=False):
|
| 532 |
"""
|
| 533 |
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
|
| 534 |
loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
|
| 535 |
|
| 536 |
Args:
|
| 537 |
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
| 538 |
-
- ``
|
| 539 |
-
- ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
|
| 540 |
-
- ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
|
| 541 |
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
| 542 |
- ``exclude_frozen_parameters``: exclude frozen parameters
|
| 543 |
"""
|
| 544 |
-
# Dependency pre-check
|
| 545 |
-
if safe_serialization:
|
| 546 |
-
try:
|
| 547 |
-
from safetensors.torch import save_file
|
| 548 |
-
except ImportError:
|
| 549 |
-
print('If you want to use `safe_serialization`, please `pip install safetensors`')
|
| 550 |
-
raise
|
| 551 |
-
if max_shard_size is not None:
|
| 552 |
-
try:
|
| 553 |
-
from huggingface_hub import split_torch_state_dict_into_shards
|
| 554 |
-
except ImportError:
|
| 555 |
-
print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
|
| 556 |
-
raise
|
| 557 |
-
|
| 558 |
-
# Convert zero checkpoint to state_dict
|
| 559 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters)
|
| 560 |
|
| 561 |
-
|
| 562 |
-
|
| 563 |
-
|
| 564 |
-
filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
|
| 565 |
-
state_dict_split = split_torch_state_dict_into_shards(state_dict,
|
| 566 |
-
filename_pattern=filename_pattern,
|
| 567 |
-
max_shard_size=max_shard_size)
|
| 568 |
-
else:
|
| 569 |
-
from collections import namedtuple
|
| 570 |
-
StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
|
| 571 |
-
state_dict_split = StateDictSplit(is_sharded=False,
|
| 572 |
-
filename_to_tensors={weights_name: list(state_dict.keys())})
|
| 573 |
-
|
| 574 |
-
# Save the model
|
| 575 |
-
filename_to_tensors = state_dict_split.filename_to_tensors.items()
|
| 576 |
-
for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
|
| 577 |
-
shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors}
|
| 578 |
-
output_path = os.path.join(output_dir, shard_file)
|
| 579 |
-
if safe_serialization:
|
| 580 |
-
save_file(shard, output_path, metadata={"format": "pt"})
|
| 581 |
-
else:
|
| 582 |
-
torch.save(shard, output_path)
|
| 583 |
-
|
| 584 |
-
# Save index if sharded
|
| 585 |
-
if state_dict_split.is_sharded:
|
| 586 |
-
index = {
|
| 587 |
-
"metadata": state_dict_split.metadata,
|
| 588 |
-
"weight_map": state_dict_split.tensor_to_filename,
|
| 589 |
-
}
|
| 590 |
-
save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
|
| 591 |
-
save_index_file = os.path.join(output_dir, save_index_file)
|
| 592 |
-
with open(save_index_file, "w", encoding="utf-8") as f:
|
| 593 |
-
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
|
| 594 |
-
f.write(content)
|
| 595 |
|
| 596 |
|
| 597 |
def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
|
@@ -634,27 +578,15 @@ def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
|
| 634 |
|
| 635 |
|
| 636 |
if __name__ == "__main__":
|
|
|
|
| 637 |
parser = argparse.ArgumentParser()
|
| 638 |
parser.add_argument("checkpoint_dir",
|
| 639 |
type=str,
|
| 640 |
help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
|
| 641 |
-
parser.add_argument("output_dir",
|
| 642 |
-
type=str,
|
| 643 |
-
help="directory to the pytorch fp32 state_dict output files"
|
| 644 |
-
"(e.g. path/checkpoint-12-output/)")
|
| 645 |
parser.add_argument(
|
| 646 |
-
"
|
| 647 |
type=str,
|
| 648 |
-
|
| 649 |
-
help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
|
| 650 |
-
"lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
|
| 651 |
-
"We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
|
| 652 |
-
"without CPU OOM issues.")
|
| 653 |
-
parser.add_argument(
|
| 654 |
-
"--safe_serialization",
|
| 655 |
-
default=False,
|
| 656 |
-
action='store_true',
|
| 657 |
-
help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
|
| 658 |
parser.add_argument("-t",
|
| 659 |
"--tag",
|
| 660 |
type=str,
|
|
@@ -667,8 +599,6 @@ if __name__ == "__main__":
|
|
| 667 |
debug = args.debug
|
| 668 |
|
| 669 |
convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
|
| 670 |
-
args.
|
| 671 |
-
max_shard_size=args.max_shard_size,
|
| 672 |
-
safe_serialization=args.safe_serialization,
|
| 673 |
tag=args.tag,
|
| 674 |
exclude_frozen_parameters=args.exclude_frozen_parameters)
|
|
|
|
| 10 |
# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
|
| 11 |
# application.
|
| 12 |
#
|
| 13 |
+
# example: python zero_to_fp32.py . pytorch_model.bin
|
|
|
|
|
|
|
|
|
|
| 14 |
|
| 15 |
import argparse
|
| 16 |
import torch
|
|
|
|
| 18 |
import math
|
| 19 |
import os
|
| 20 |
import re
|
|
|
|
|
|
|
| 21 |
from collections import OrderedDict
|
| 22 |
from dataclasses import dataclass
|
| 23 |
|
|
|
|
| 139 |
|
| 140 |
|
| 141 |
def parse_optim_states(files, ds_checkpoint_dir):
|
| 142 |
+
|
| 143 |
total_files = len(files)
|
| 144 |
state_dicts = []
|
| 145 |
for f in files:
|
|
|
|
| 420 |
offset = 0
|
| 421 |
total_numel = 0
|
| 422 |
total_params = 0
|
| 423 |
+
for name, shape in param_shapes.items():
|
| 424 |
+
|
| 425 |
unpartitioned_numel = shape.numel()
|
| 426 |
total_numel += unpartitioned_numel
|
| 427 |
total_params += 1
|
| 428 |
+
|
| 429 |
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
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if debug:
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return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
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+
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None, exclude_frozen_parameters=False):
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"""
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Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
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loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
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Args:
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- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
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+
- ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
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- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
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- ``exclude_frozen_parameters``: exclude frozen parameters
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"""
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+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters)
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+
print(f"Saving fp32 state dict to {output_file}")
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+
torch.save(state_dict, output_file)
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def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
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if __name__ == "__main__":
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+
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parser = argparse.ArgumentParser()
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parser.add_argument("checkpoint_dir",
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type=str,
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help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
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| 586 |
parser.add_argument(
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+
"output_file",
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type=str,
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+
help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
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parser.add_argument("-t",
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"--tag",
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type=str,
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debug = args.debug
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| 601 |
convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
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+
args.output_file,
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| 603 |
tag=args.tag,
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exclude_frozen_parameters=args.exclude_frozen_parameters)
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