1127 update to latest

dllm/examples/editflow/llada/adapt.py

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+"""
+Local users
+------------
+- 1 GPU (LoRA, useful for testing):
+    accelerate launch \
+        --config_file scripts/accelerate_configs/ddp.yaml --num_processes 1 \
+        examples/editflow/llada/adapt.py \
+        --lora True
+    
+- 8 GPUs (FSDP):
+    accelerate launch \
+        --config_file scripts/accelerate_configs/fsdp.yaml \
+        examples/editflow/llada/adapt.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/editflow/llada/adapt.py"
+
+- 2 Nodes, 16 GPUs (FSDP):
+    sbatch --nodes=2 --gres=gpu:8 scripts/train.slurm.sh \
+        --accelerate_config "fsdp" \
+        --script_path "examples/editflow/llada/adapt.py"
+"""
+
+from dataclasses import dataclass
+
+import torch
+import transformers
+
+import dllm
+from examples.editflow import sft as editflow_sft
+
+
+@dataclass
+class ModelArguments(editflow_sft.ModelArguments):
+    model_name_or_path: str = "GSAI-ML/LLaDA-8B-Instruct"
+    lm_head_key: str = "model.transformer.ff_out"
+    init_editflow_from_src: bool = True
+
+
+@dataclass
+class DataArguments(editflow_sft.DataArguments):
+    dataset_args: str = "allenai/tulu-3-sft-mixture[train:10000,test:1000]"
+
+
+@dataclass
+class TrainingArguments(editflow_sft.TrainingArguments):
+    output_dir: str = (
+        "models/EditFlow-LLaDA-8B-Instruct-Adapt/tulu-3-sft-mixture[train:10000,test:1000]"
+    )
+
+
+if __name__ == "__main__":
+    # ----- Argument parsing -------------------------------------------------------
+    parser = transformers.HfArgumentParser(
+        (ModelArguments, DataArguments, TrainingArguments)
+    )
+    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    dllm.utils.initial_training_setup(model_args, data_args, training_args)
+    # Create EditFlow model (bf16 init on CUDA)
+    ef_cfg = dllm.pipelines.editflow.EditFlowLLaDAConfig.from_pretrained(
+        model_args.model_name_or_path
+    )
+    with dllm.utils.init_device_context_manager():
+        model = transformers.AutoModel.from_config(ef_cfg, dtype=torch.bfloat16)
+        # Initialize EditFlow model from the src model: copies backbone & clones lm_head
+        if model_args.init_editflow_from_src:
+            src_model = transformers.AutoModelForMaskedLM.from_pretrained(
+                model_args.model_name_or_path, dtype=torch.bfloat16
+            )
+            dllm.pipelines.editflow.utils.init_editflow_from_src(
+                model, src_model, lm_head_key=model_args.lm_head_key
+            )
+            del src_model
+    model = dllm.utils.load_peft(model, model_args)
+
+    editflow_sft.train(
+        model_args=model_args,
+        data_args=data_args,
+        training_args=training_args,
+        model=model,
+    )