128 lines
4.1 KiB
Python
128 lines
4.1 KiB
Python
"""
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Local users
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------------
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- 1 GPU:
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accelerate launch \
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--config_file scripts/accelerate_configs/ddp.yaml --num_processes 1 \
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examples/bert/pt.py
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- 8 GPUs (DDP):
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accelerate launch \
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--config_file scripts/accelerate_configs/ddp.yaml \
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examples/bert/pt.py
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Slurm users
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# Note: run `mkdir logs` before running sbatch; and adjust
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# `partition` and `quotatype` in `scripts/train.slurm.sh` for your cluster.
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------------
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- 8 GPUs (DDP):
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sbatch --gres=gpu:8 scripts/train.slurm.sh \
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--accelerate_config "ddp" \
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--script_path "examples/bert/pt.py"
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"""
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import os
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import functools
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from dataclasses import dataclass, field
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import transformers
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import accelerate
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import dllm
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logger = dllm.utils.get_default_logger(__name__)
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@dataclass
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class ModelArguments(dllm.utils.ModelArguments):
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model_name_or_path: str = "answerdotai/ModernBERT-large"
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@dataclass
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class DataArguments(dllm.utils.DataArguments):
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dataset_args: str = "Trelis/tiny-shakespeare"
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text_field: str = "Text"
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max_length: int = 128
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streaming: bool = False
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drop_tail: bool = True
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insert_eos: bool = field(
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default=True,
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metadata={
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"help": "False when adjacent samples from the datasets are semantically coherent."
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},
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)
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@dataclass
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class TrainingArguments(dllm.utils.TrainingArguments):
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output_dir: str = "models/ModernBERT-base/tiny-shakespeare"
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num_train_epochs: int = 20
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learning_rate: float = 1e-4
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per_device_train_batch_size: int = 64
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per_device_eval_batch_size: int = 64
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eval_steps: float = 0.1
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save_steps: float = 0.1
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def train():
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# ----- Argument parsing -------------------------------------------------------
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parser = transformers.HfArgumentParser(
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(ModelArguments, DataArguments, TrainingArguments)
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)
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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dllm.utils.print_args_main(model_args, data_args, training_args)
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dllm.utils.initial_training_setup(model_args, data_args, training_args)
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# ----- Model ------------------------------------------------------------------
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model = dllm.utils.get_model(model_args=model_args)
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# ----- Tokenizer --------------------------------------------------------------
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tokenizer = dllm.utils.get_tokenizer(model_args=model_args)
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# ----- Dataset ----------------------------------------------------------------
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with accelerate.PartialState().local_main_process_first():
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dataset = dllm.data.load_pt_dataset(
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data_args.dataset_args,
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streaming=data_args.streaming,
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)
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dataset = dataset.map(
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functools.partial(
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dllm.utils.tokenize_and_group,
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tokenizer=tokenizer,
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text_field=data_args.text_field,
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seq_length=data_args.max_length,
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insert_eos=data_args.insert_eos,
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drop_tail=data_args.drop_tail,
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),
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batched=True,
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remove_columns=dataset["train"].column_names,
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**({} if data_args.streaming else {"num_proc": data_args.num_proc}),
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**({} if data_args.streaming else {"desc": "Mapping dataset to PT format"}),
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)
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if data_args.streaming:
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dataset = dataset.shuffle(seed=training_args.seed)
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# ----- Training --------------------------------------------------------------
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accelerate.PartialState().wait_for_everyone()
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logger.info("Start training...")
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trainer = dllm.core.trainers.MDLMTrainer(
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model=model,
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tokenizer=tokenizer,
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train_dataset=dataset["train"],
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eval_dataset=dataset.get("test", None),
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args=training_args,
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data_collator=transformers.DataCollatorForSeq2Seq(
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tokenizer,
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return_tensors="pt",
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padding=True,
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),
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)
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trainer.train()
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trainer.save_model(os.path.join(training_args.output_dir, "checkpoint-final"))
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trainer.processing_class.save_pretrained(
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os.path.join(training_args.output_dir, "checkpoint-final")
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)
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if __name__ == "__main__":
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train()
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