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49 lines
No EOL
1.8 KiB
Python
49 lines
No EOL
1.8 KiB
Python
import transformers
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from peft import (
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LoraConfig,
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)
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from datasets import load_dataset
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from modeling_icae_multi_span import ICAE, ModelArguments, DataArguments, TrainingArguments
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from training_utils import instruct_ft_tokenize_function, DataCollatorForDynamicPadding, train_model
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def main():
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parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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print(model_args)
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print(data_args)
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training_args.gradient_checkpointing_kwargs = {"use_reentrant": False} # manually add this argument in the code
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lora_config = LoraConfig(
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r=model_args.lora_r,
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lora_alpha=32,
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM"
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)
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# check model_args.mem_size and min_tokens_for_lm
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assert (training_args.fixed_mem_size & (training_args.fixed_mem_size - 1)) == 0, "training_args.fixed_mem_size must be a power of 2"
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memory_size = training_args.fixed_mem_size
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train_file = "/path/to/train/file"
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eval_file = "/path/to/dev/file"
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print("Loading dataset...")
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dataset = load_dataset("json", data_files={"train": train_file, "eval": eval_file})
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train_dataset = dataset["train"]
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eval_dataset = dataset["eval"]
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model = ICAE(model_args, training_args, lora_config)
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MEM_TOKENS = list(range(model.vocab_size, model.vocab_size + memory_size))
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train_dataset = train_dataset.map(instruct_ft_tokenize_function, batched=True, fn_kwargs={"model": model, "mem": MEM_TOKENS})
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eval_dataset = eval_dataset.map(instruct_ft_tokenize_function, batched=True, fn_kwargs={"model": model, "mem": MEM_TOKENS})
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train_model(model, train_dataset, eval_dataset, training_args)
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main() |