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import os
import shutil
from dataclasses import dataclass, field
from typing import Optional
import torch
from accelerate import Accelerator
from datasets import load_dataset
from peft import AutoPeftModelForSequenceClassification, PeftConfig
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
DataCollatorWithPadding,
HfArgumentParser,
Trainer,
TrainingArguments,
)
shutil.disk_usage = lambda x: shutil._ntuple_diskusage(1, 1, 1)
@dataclass
class ScriptArguments:
output_dir: Optional[str] = field(
default="/home/toolkit/huggingface/openai_summarize_tldr_reward",
metadata={"help": "output folder"},
)
model_name: Optional[str] = field(
default="mnoukhov/pythia410m-tldr-sft-rm-adapter", metadata={"help": "the model name"}
)
new_column_name: Optional[str] = field(default="reward_baseline")
dataset_name: Optional[str] = field(
default="mnoukhov/openai_summarize_comparisons_tldrprompt", metadata={"help": "the dataset name"}
)
max_length: Optional[int] = field(default=560, metadata={"help": "maximum length for generation"})
train_split: Optional[str] = field(default="train[:20]", metadata={"help": "the dataset name"})
eval_split: Optional[str] = field(default=None, metadata={"help": "the dataset name"})
load_in_8bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 8 bits precision"})
load_in_4bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 4 bits precision"})
batch_size: Optional[int] = field(default=4)
bf16: Optional[bool] = field(default=False)
fp16: Optional[bool] = field(default=False)
fp16_model: Optional[bool] = field(default=False)
def create_and_prepare_model(args):
if args.load_in_8bit and args.load_in_4bit:
raise ValueError("You can't load the model in 8 bits and 4 bits at the same time")
elif args.load_in_8bit or args.load_in_4bit:
quantization_config = BitsAndBytesConfig(load_in_8bit=args.load_in_8bit, load_in_4bit=args.load_in_4bit)
device_map = {"": Accelerator().local_process_index}
else:
device_map = None
quantization_config = None
if args.bf16:
torch_dtype = torch.bfloat16
elif args.fp16_model:
torch_dtype = torch.float16
else:
torch_dtype = None
if "adapter" in args.model_name:
model_cls = AutoPeftModelForSequenceClassification
config = PeftConfig.from_pretrained(args.model_name)
tokenizer_name = config.base_model_name_or_path
else:
model_cls = AutoModelForSequenceClassification
tokenizer_name = args.model_name
model = model_cls.from_pretrained(
args.model_name,
quantization_config=quantization_config,
device_map=device_map,
num_labels=1,
torch_dtype=torch_dtype,
)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
if getattr(tokenizer, "pad_token", None) is None:
tokenizer.pad_token = tokenizer.eos_token
if getattr(model.config, "pad_token_id", None) is None:
model.config.pad_token_id = model.config.eos_token_id
return model, tokenizer
def create_and_prepare_dataset(args, tokenizer, split, num_proc=2):
dataset = load_dataset(args.dataset_name, split=split)
def combine_and_tokenize(examples):
if isinstance(examples["label"], str):
texts = examples["prompt"] + examples["label"]
else:
texts = [prompt + label for prompt, label in zip(examples["prompt"], examples["label"])]
return tokenizer(texts, truncation=True, padding=False, max_length=args.max_length)
original_columns = dataset["train"].column_names
dataset = dataset.map(
combine_and_tokenize,
batched=True,
num_proc=num_proc,
remove_columns=original_columns,
)
dataset.set_format("torch")
return dataset
def strip_prompt(examples):
examples["prompt"] = [prompt.strip() for prompt in examples["prompt"]]
return examples
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
model, tokenizer = create_and_prepare_model(script_args)
training_args = TrainingArguments(
output_dir=script_args.output_dir,
per_device_eval_batch_size=script_args.batch_size,
bf16=script_args.bf16,
fp16=script_args.fp16,
)
if script_args.fp16:
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
else:
data_collator = None
trainer = Trainer(
model=model,
args=training_args,
tokenizer=tokenizer,
data_collator=data_collator,
)
data_splits = {
"train": script_args.train_split,
"valid": script_args.eval_split,
}
original_datasets = create_and_prepare_dataset(script_args, tokenizer, split=data_splits)
augmented_dataset = load_dataset(script_args.dataset_name, split=data_splits)
augmented_dataset = augmented_dataset.map(strip_prompt, batched=True)
for key, dataset in original_datasets.items():
preds = trainer.predict(dataset)
reward_preds = preds[0].flatten()
if trainer.accelerator.is_local_main_process:
augmented_dataset[key] = augmented_dataset[key].add_column(script_args.new_column_name, reward_preds)
trainer.accelerator.wait_for_everyone()
if trainer.accelerator.is_main_process:
# augmented_dataset.save_to_disk(script_args.output_dir)
augmented_dataset.push_to_hub(os.path.basename(script_args.output_dir))
# trainer.accelerator.free_memro()
# if trainer.accelerator.is_local_main_process:
# trainer.model = gold_model
# trainer = Trainer(
# model=gold_model,
# args=training_args,
# tokenizer=tokenizer,
# data_collator=data_collator,
# )
# original_datasets = create_and_prepare_dataset(script_args, tokenizer, split=data_splits)
# if trainer.accelerator.is_local_main_process:
# import pdb
#
# pdb.set_trace()
# for key, dataset in original_datasets.items():
# preds = trainer.predict(dataset)
# gold_reward_preds = preds[0].flatten()
#
# if trainer.accelerator.is_local_main_process:
# augmented_dataset[key] = augmented_dataset[key].add_column("gold_reward_baseline", gold_reward_preds)
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