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import gc
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import torch
from datasets import Dataset, DatasetInfo, builder, load_dataset
from peft import AutoPeftModelForCausalLM, PeftModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, TrainingArguments
from vllm import LLM, SamplingParams
from vllm.model_executor.parallel_utils.parallel_state import destroy_model_parallel
from trl import DPOTrainer
builder.has_sufficient_disk_space = lambda needed_bytes, directory=".": True
@dataclass
class ScriptArguments:
output_dir: Optional[str] = field(
default="compare_results",
metadata={"help": "output folder"},
)
num_gpus: Optional[int] = field(default=1)
model_name: Optional[str] = field(default="EleutherAI/pythia-410m", metadata={"help": "the model name"})
revision: Optional[str] = field(default="main", metadata={"help": "the model revision"})
tokenizer_name: Optional[str] = field(default=None, metadata={"help": "the tokenizer name"})
dataset_name: Optional[str] = field(
default="arianhosseini/openai_summarize_unlabelled", metadata={"help": "the dataset name"}
)
dataset_prompt_field: Optional[str] = field(default="query")
train_split: Optional[str] = field(default="train[:20]", metadata={"help": "the dataset name"})
batch_size: Optional[int] = field(default=4)
max_prompt_length: Optional[int] = field(default=512, metadata={"help": "Input sequence length"})
temperature: Optional[float] = field(default=0.7, metadata={"help": "Gen temperature"})
top_p: Optional[float] = field(default=1.0, metadata={"help": "Gen temperature"})
max_new_tokens: Optional[int] = field(default=48, metadata={"help": "max new tokens"})
dtype: Optional[str] = field(default="auto")
lora_model: Optional[bool] = field(default=False)
base_model_name: Optional[str] = field(default=None, metadata={"help": "the model name"})
base_model_revision: Optional[str] = field(default=None)
def prepare_vllm_model(script_args):
if script_args.tokenizer_name is not None:
tokenizer_name = script_args.tokenizer_name
else:
tokenizer_name = script_args.model_name
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
if tokenizer_name.startswith("EleutherAI"):
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
tokenizer.padding_side = "left"
if script_args.lora_model:
# peft model that needs to be merged
if script_args.base_model_name is not None:
base_model = AutoModelForCausalLM.from_pretrained(
script_args.base_model_name, revision=script_args.base_model_revision
)
# merge the model and save
model = PeftModelForCausalLM.from_pretrained(
base_model, script_args.model_name, revision=script_args.revision, device_map="cpu"
)
else:
model = AutoPeftModelForCausalLM.from_pretrained(
script_args.model_name, revision=script_args.revision, device_map="cpu"
)
merged = model.merge_and_unload()
model_save_path = f"/home/toolkit/trl_results/{script_args.model_name}_merged/{script_args.revision}"
merged.save_pretrained(model_save_path)
del model
del merged
model_name = model_save_path
revision = None
else:
model_name = script_args.model_name
revision = script_args.revision
llm = LLM(
model=model_name,
revision=revision,
dtype=script_args.dtype,
tokenizer=tokenizer_name,
tensor_parallel_size=script_args.num_gpus,
trust_remote_code=True,
)
llm.set_tokenizer(tokenizer)
return llm, tokenizer
def strip_prompt(examples):
examples["prompt"] = [prompt.strip() for prompt in examples["prompt"]]
return examples
def generate_vllm(script_args):
llm, _ = prepare_vllm_model(script_args)
dataset = load_dataset(script_args.dataset_name, split=script_args.train_split)
prompts = dataset[script_args.dataset_prompt_field]
sampling_params = SamplingParams(
temperature=script_args.temperature,
max_tokens=script_args.max_new_tokens,
top_p=script_args.top_p,
n=2,
)
generations = llm.generate(prompts, sampling_params)
print(f"generated {len(generations)} samples")
def dataset_generator():
for gen in generations:
if len(gen.outputs) == 2:
yield {
"prompt": gen.prompt,
"chosen": gen.outputs[0].text,
"rejected": gen.outputs[1].text,
}
else:
print("skipping gen, only 1 output")
ds_info = DatasetInfo(
f"{script_args.dataset_name} split {script_args.train_split} prompts used to generate with {script_args.model_name}"
f" temp {script_args.temperature} top_p {script_args.top_p} "
)
generated_dataset = Dataset.from_generator(dataset_generator, info=ds_info)
destroy_model_parallel()
del llm.llm_engine.driver_worker
del llm
gc.collect()
torch.cuda.empty_cache()
torch.distributed.destroy_process_group()
return generated_dataset
def relabel(script_args, dataset):
torch_dtype = script_args.dtype if script_args.dtype in ["auto", None] else getattr(torch, script_args.dtype)
if script_args.base_model_name is not None:
base_model = AutoModelForCausalLM.from_pretrained(
script_args.base_model_name,
revision=script_args.base_model_revision,
)
# merge the model and save
model = PeftModelForCausalLM.from_pretrained(
base_model,
script_args.model_name,
revision=script_args.revision,
torch_dtype=torch_dtype,
)
else:
model = AutoPeftModelForCausalLM.from_pretrained(
script_args.model_name,
revision=script_args.revision,
torch_dtype=torch_dtype,
)
if script_args.tokenizer_name is not None:
tokenizer_name = script_args.tokenizer_name
else:
tokenizer_name = script_args.model_name
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
if tokenizer_name.startswith("EleutherAI"):
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
training_args = TrainingArguments(per_device_eval_batch_size=int(script_args.batch_size), output_dir=".")
dpo_trainer = DPOTrainer(
model=model,
tokenizer=tokenizer,
args=training_args,
max_length=script_args.max_new_tokens + script_args.max_prompt_length,
max_target_length=script_args.max_new_tokens,
max_prompt_length=script_args.max_prompt_length,
)
def relabel_with_preds(batch: Dict[str, List]):
relabel_batch = {
"prompt": [],
"chosen": [],
"rejected": [],
"pred_chosen": [],
"pred_rejected": [],
}
for prompt, chosen, rejected, pred_chosen, pred_rejected in zip(
batch["prompt"],
batch["chosen"],
batch["rejected"],
batch["pred_chosen"],
batch["pred_rejected"],
):
relabel_batch["prompt"].append(prompt)
if pred_chosen >= pred_rejected:
relabel_batch["chosen"].append(chosen)
relabel_batch["rejected"].append(rejected)
relabel_batch["pred_chosen"].append(pred_chosen)
relabel_batch["pred_rejected"].append(pred_rejected)
else:
relabel_batch["chosen"].append(rejected)
relabel_batch["rejected"].append(chosen)
relabel_batch["pred_chosen"].append(pred_rejected)
relabel_batch["pred_rejected"].append(pred_chosen)
return relabel_batch
dpo_trainer.accelerator.print("Prediction")
preds, _, metrics = dpo_trainer.predict(dataset)
(
chosen_rewards,
rejected_rewards,
policy_chosen_logps,
policy_rejected_logps,
reference_chosen_logps,
reference_rejected_logps,
) = preds
dpo_trainer.accelerator.print(f"metrics {metrics}")
if dpo_trainer.accelerator.is_local_main_process:
print("Relabelling Dataset and Saving")
dataset = dataset.add_column("pred_chosen", chosen_rewards)
dataset = dataset.add_column("pred_rejected", rejected_rewards)
relabel_dataset = dataset.map(
relabel_with_preds,
batched=True,
)
description = f"{script_args.dataset_name} relabelled with {script_args.model_name}"
relabel_dataset._info.description = description
relabel_dataset.push_to_hub(os.path.basename(script_args.output_dir), split="train")
def generate_relabel_args_dict(args_dict):
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_dict(args_dict)[0]
dataset = generate_vllm(script_args)
relabel(script_args, dataset)
if __name__ == "__main__":
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
generate_vllm(script_args)
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