trained model using kto - sanity check

kto_pipeline.py

@@ -0,0 +1,122 @@
+# import os
+# os.environ['CUDA_VISIBLE_DEVICES'] = "3"
+
+import torch
+from dataclasses import dataclass
+
+from accelerate import PartialState
+from datasets import load_dataset, DatasetDict
+from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
+
+from trl import KTOConfig, KTOTrainer, ModelConfig, get_peft_config, maybe_unpair_preference_dataset, setup_chat_format
+
+
+
+print(f'GPU number: {torch.cuda.current_device()}')
+
+
+# Define and parse arguments.
+@dataclass
+class ScriptArguments:
+    """
+    The arguments for the KTO training script.
+    """
+
+    dataset_name: str = "trl-lib/kto-mix-14k"
+
+
+# Initialize the arguments directly
+script_args = ScriptArguments(
+    dataset_name="trl-lib/kto-mix-14k"
+)
+
+training_args = KTOConfig(
+    output_dir="/raid/lingo/jen_ben/HF-RLHF/kto_oct_26", # MODFIFY
+    num_train_epochs=100,
+    per_device_train_batch_size=32,
+    learning_rate=5e-7,
+    lr_scheduler_type="cosine",
+    gradient_accumulation_steps=1,
+    logging_steps=10,
+    eval_steps=500,
+    warmup_ratio=0.1,
+    bf16=True,
+    logging_first_step=True
+)
+
+model_args = ModelConfig(
+    model_name_or_path="trl-lib/qwen1.5-1.8b-sft",
+    # any additional model-specific arguments
+)
+
+
+# Load a pretrained model
+model = AutoModelForCausalLM.from_pretrained(
+    model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
+)
+ref_model = AutoModelForCausalLM.from_pretrained(
+    model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
+)
+print(f'loaded model')
+
+# load a tokenaizer
+tokenizer = AutoTokenizer.from_pretrained(
+    model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
+)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+# If we are aligning a base model, we use ChatML as the default template
+if tokenizer.chat_template is None:
+    model, tokenizer = setup_chat_format(model, tokenizer)
+print(f'loaded tokenizer')
+
+# Load the dataset
+dataset = load_dataset(script_args.dataset_name)
+
+# If needed, reformat a DPO-formatted dataset (prompt, chosen, rejected) to a KTO-format (prompt, completion, label)
+dataset = maybe_unpair_preference_dataset(dataset, num_proc=training_args.dataset_num_proc)
+print(f'loaded dataset')
+
+
+# Apply chat template
+def format_dataset(example):
+    example["prompt"] = tokenizer.apply_chat_template(example["prompt"], tokenize=False)
+    example["completion"] = tokenizer.apply_chat_template(example["completion"], tokenize=False)
+    return example
+
+
+# Compute that only on the main process for faster data processing.
+# see: https://github.com/huggingface/trl/pull/1255
+with PartialState().local_main_process_first():
+    dataset = dataset.map(format_dataset, num_proc=training_args.dataset_num_proc)
+
+
+
+# Initialize the KTO trainer
+trainer = KTOTrainer(
+    model,
+    ref_model,
+    args=training_args,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["test"],
+    tokenizer=tokenizer,
+    peft_config=get_peft_config(model_args),
+)
+
+print(f'start training')
+
+trainer.train()
+
+print(f'finished training')
+
+metrics = trainer.evaluate()
+print(f'metrics: \n {metrics}')
+trainer.log_metrics("eval", metrics)
+trainer.save_metrics("eval", metrics)
+
+
+# Save and push to hub
+trainer.save_model(training_args.output_dir)
+if training_args.push_to_hub:
+    trainer.push_to_hub(dataset_name=script_args.dataset_name)