dllm/examples/llada/sft.py at main · dte/dllm · GitHub

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"""
Local users
------------
- 1 GPU (4bit quant & LoRA, useful for testing):
    accelerate launch \
        --config_file scripts/accelerate_configs/ddp.yaml --num_processes 1 \
        examples/llada/sft.py \
        --load_in_4bit True --lora True

- 8 GPUs (FSDP):
    accelerate launch \
        --config_file scripts/accelerate_configs/fsdp.yaml \
        examples/llada/sft.py

Slurm users
# Note: run `mkdir logs` before running sbatch; and adjust
#       `partition` and `quotatype` in `scripts/train.slurm.sh` for your cluster.
------------
- 1 Node, 8 GPUs (FSDP):
    sbatch --gres=gpu:8 scripts/train.slurm.sh \
        --accelerate_config "fsdp" \
        --script_path "examples/llada/sft.py"

- 2 Nodes, 16 GPUs (FSDP):
    sbatch --nodes=2 --gres=gpu:8 scripts/train.slurm.sh \
        --accelerate_config "fsdp" \
        --script_path "examples/llada/sft.py"
"""

import os
from dataclasses import dataclass, field
from functools import partial

import transformers
import accelerate

import dllm


@dataclass
class ModelArguments(dllm.utils.ModelArguments):
    model_name_or_path: str = "GSAI-ML/LLaDA-8B-Base"


@dataclass
class DataArguments(dllm.utils.DataArguments):
    dataset_args: str = "allenai/tulu-3-sft-mixture[train:10000,test:1000]"
    load_preprocessed_data: bool = False
    mask_prompt_loss: bool = field(
        default=True,
        metadata={"help": "Whether to mask the loss on the prompt tokens"},
    )


@dataclass
class TrainingArguments(dllm.utils.TrainingArguments):
    output_dir: str = "models/LLaDA-8B-SFT/tulu-3-sft-mixture[train:10000,test:1000]"
    group_by_length: bool = True


def train():
    # ----- Argument parsing -------------------------------------------------------
    parser = transformers.HfArgumentParser(
        (ModelArguments, DataArguments, TrainingArguments)
    )
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    dllm.utils.print_args_main(model_args, data_args, training_args)
    dllm.utils.initial_training_setup(model_args, data_args, training_args)

    # ----- Model ------------------------------------------------------------------
    model = dllm.utils.get_model(model_args=model_args)
    # ----- Tokenizer --------------------------------------------------------------
    tokenizer = dllm.utils.get_tokenizer(model_args=model_args)

    # ----- Dataset ----------------------------------------------------------------
    with accelerate.PartialState().local_main_process_first():
        dataset = dllm.data.load_sft_dataset(
            data_args.dataset_args,
            load_preprocessed_data=data_args.load_preprocessed_data,
        )
        if not data_args.load_preprocessed_data:
            map_fn = partial(
                dllm.utils.default_sft_map_fn,
                tokenizer=tokenizer,
                mask_prompt_loss=data_args.mask_prompt_loss,
            )
            dataset = dataset.map(map_fn, num_proc=data_args.num_proc)
        # truncate / filter long sequences if needed
        dataset = dllm.utils.post_process_dataset(dataset, data_args)

    # ----- Training --------------------------------------------------------------
    accelerate.PartialState().wait_for_everyone()
    dllm.utils.print_main("start training...")
    trainer = dllm.core.trainers.MDLMTrainer(
        model=model,
        tokenizer=tokenizer,
        train_dataset=dataset["train"],
        eval_dataset=dataset.get("test", None),
        args=training_args,
        data_collator=dllm.utils.NoAttentionMaskCollator(
            tokenizer,
            return_tensors="pt",
            padding=True,
            label_pad_token_id=tokenizer.pad_token_id,  # finetune on padding <eos_token>
        ),
    )
    trainer.train()
    trainer.save_model(os.path.join(training_args.output_dir, "checkpoint-final"))
    trainer.processing_class.save_pretrained(
        os.path.join(training_args.output_dir, "checkpoint-final")
    )


if __name__ == "__main__":
    train()