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