Files
2025-11-27 15:44:17 +08:00

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Python

"""
Local users
------------
- 1 GPU (LoRA, useful for testing):
accelerate launch \
--config_file scripts/accelerate_configs/ddp.yaml --num_processes 1 \
examples/editflow/llada/pt.py \
--lora True
- 8 GPUs (DeepSpeed FSDP):
accelerate launch \
--config_file scripts/accelerate_configs/fsdp.yaml \
examples/editflow/llada/pt.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:1 scripts/train.slurm.sh \
--accelerate_config "fsdp" \
--script_path "examples/editflow/llada/pt.py"
- 24 Nodes, 192 GPUs (FSDP):
sbatch --nodes=24 --gres=gpu:8 scripts/train.slurm.sh \
--accelerate_config "fsdp" \
--script_path "examples/editflow/llada/pt.py"
"""
from dataclasses import dataclass
import transformers
import dllm
from examples.editflow import pt as editflow_pt
@dataclass
class ModelArguments(editflow_pt.ModelArguments):
model_name_or_path: str = "GSAI-ML/LLaDA-8B-Base"
lm_head_key: str = "model.transformer.ff_out"
@dataclass
class DataArguments(editflow_pt.DataArguments):
dataset_args: str = "mlfoundations/dclm-baseline-1.0[train:10_000_000,test:10_000]"
@dataclass
class TrainingArguments(editflow_pt.TrainingArguments):
output_dir: str = (
"models/EditFlow-LLaDA-8B-Base/dclm-baseline-1.0[train:10_000_000,test:10_000]"
)
if __name__ == "__main__":
# ----- Argument parsing -------------------------------------------------------
parser = transformers.HfArgumentParser(
(ModelArguments, DataArguments, TrainingArguments)
)
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
editflow_pt.train(
model_args=model_args,
data_args=data_args,
training_args=training_args,
ef_config_cls=dllm.pipelines.editflow.EditFlowLLaDAConfig,
)