File size: 3,140 Bytes
afcd0ff | 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 | #!/usr/bin/env python
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import subprocess
from typing import List
from transformers import TrainerCallback
from transformers.trainer_callback import TrainerControl, TrainerState
from transformers.training_args import TrainingArguments
from .evaluation import run_benchmark_jobs
from .hub import push_to_hub_revision
def is_slurm_available() -> bool:
# returns true if a slurm queueing system is available
try:
subprocess.run(["sinfo"], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
return True
except FileNotFoundError:
return False
class DummyConfig:
def __init__(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
class PushToHubRevisionCallback(TrainerCallback):
def __init__(self, model_config) -> None:
self.model_config = model_config
def on_save(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
if state.is_world_process_zero:
global_step = state.global_step
# WARNING: if you use dataclasses.replace(args, ...) the accelerator dist state will be broken, so I do this workaround
# Also if you instantiate a new SFTConfig, the accelerator dist state will be broken
dummy_config = DummyConfig(
hub_model_id=args.hub_model_id,
hub_model_revision=f"{args.hub_model_revision}-step-{global_step:09d}",
output_dir=f"{args.output_dir}/checkpoint-{global_step}",
system_prompt=args.system_prompt,
)
future = push_to_hub_revision(
dummy_config, extra_ignore_patterns=["*.pt"]
) # don't push the optimizer states
if is_slurm_available():
dummy_config.benchmarks = args.benchmarks
def run_benchmark_callback(_):
print(f"Checkpoint {global_step} pushed to hub.")
run_benchmark_jobs(dummy_config, self.model_config)
future.add_done_callback(run_benchmark_callback)
CALLBACKS = {
"push_to_hub_revision": PushToHubRevisionCallback,
}
def get_callbacks(train_config, model_config) -> List[TrainerCallback]:
callbacks = []
for callback_name in train_config.callbacks:
if callback_name not in CALLBACKS:
raise ValueError(f"Callback {callback_name} not found in CALLBACKS.")
callbacks.append(CALLBACKS[callback_name](model_config))
return callbacks
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