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reward_modeling.py
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reward_modeling.py
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# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from typing import Optional
import tyro
from accelerate import Accelerator
from datasets import load_dataset
from peft import LoraConfig
from tqdm import tqdm
from transformers import AutoModelForSequenceClassification, AutoTokenizer, BitsAndBytesConfig
from trl import RewardConfig, RewardTrainer, is_xpu_available
import torch
# torch.cuda.empty_cache()
tqdm.pandas()
@dataclass
class ScriptArguments:
model_name: str = "google/flan-t5-small"
"""the model name"""
dataset_name: str = "Anthropic/hh-rlhf"
"""the dataset name"""
dataset_text_field: str = "text"
"""the text field of the dataset"""
eval_split: str = "none"
"""the dataset split to evaluate on; default to 'none' (no evaluation)"""
load_in_8bit: bool = False
"""load the model in 8 bits precision"""
load_in_4bit: bool = False
"""load the model in 4 bits precision"""
trust_remote_code: bool = True
"""Enable `trust_remote_code`"""
reward_config: RewardConfig = field(
default_factory=lambda: RewardConfig(
output_dir="output",
per_device_train_batch_size=8,
num_train_epochs=20,
gradient_accumulation_steps=16,
gradient_checkpointing=True,
gradient_checkpointing_kwargs={"use_reentrant": False},
learning_rate=1.41e-5,
report_to="tensorboard",
remove_unused_columns=False,
optim="adamw_torch",
logging_steps=20,
evaluation_strategy="no",
max_length=512,
save_steps=5
)
)
use_peft: bool = True
"""whether to use peft"""
peft_config: Optional[LoraConfig] = field(
default_factory=lambda: LoraConfig(
r=4,
lora_alpha=16,
bias="none",
task_type="SEQ_CLS",
modules_to_save=["scores"],
),
)
args = tyro.cli(ScriptArguments)
args.reward_config.evaluation_strategy = "steps" if args.eval_split != "none" else "no"
# Step 1: Load the model
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)
# Copy the model to each device
device_map = (
{"": f"xpu:{Accelerator().local_process_index}"}
if is_xpu_available()
else {"": Accelerator().local_process_index}
)
else:
device_map = None
quantization_config = None
model = AutoModelForSequenceClassification.from_pretrained(
args.model_name,
quantization_config=quantization_config,
device_map=device_map,
trust_remote_code=args.trust_remote_code,
num_labels=1,
)
# Step 2: Load the dataset and pre-process it
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
# train_dataset = load_dataset(args.dataset_name, split="train")
train_dataset = load_dataset('csv', data_files='output/pref_data.csv', split='train')
# Tokenize chosen/rejected pairs of inputs
# Adapt this section to your needs for custom datasets
def preprocess_function(examples, truncate=512):
token_to_char_multiplier = 3
new_examples = {
"input_ids_chosen": [],
"attention_mask_chosen": [],
"input_ids_rejected": [],
"attention_mask_rejected": [],
}
for chosen, rejected in zip(examples["chosen"], examples["rejected"]):
# print('chosen ', chosen, ' rejected ', rejected)
tokenized_chosen = tokenizer(chosen[:truncate * token_to_char_multiplier])
tokenized_rejected = tokenizer(rejected[:truncate * token_to_char_multiplier])
new_examples["input_ids_chosen"].append(tokenized_chosen["input_ids"])
new_examples["attention_mask_chosen"].append(tokenized_chosen["attention_mask"])
new_examples["input_ids_rejected"].append(tokenized_rejected["input_ids"])
new_examples["attention_mask_rejected"].append(tokenized_rejected["attention_mask"])
return new_examples
# Preprocess the dataset and filter out examples that are longer than args.max_length
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
num_proc=4,
)
train_dataset = train_dataset.filter(
lambda x: len(x["input_ids_chosen"]) <= args.reward_config.max_length
and len(x["input_ids_rejected"]) <= args.reward_config.max_length
)
print("len train ", len(train_dataset))
if args.eval_split == "none":
eval_dataset = None
else:
eval_dataset = load_dataset(args.dataset_name, split=args.eval_split)
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
num_proc=4,
)
eval_dataset = eval_dataset.filter(
lambda x: len(x["input_ids_chosen"]) <= args.reward_config.max_length
and len(x["input_ids_rejected"]) <= args.reward_config.max_length
)
# Step 4: Define the LoraConfig
if args.use_peft:
peft_config = args.peft_config
else:
peft_config = None
# Step 5: Define the Trainer
trainer = RewardTrainer(
model=model,
tokenizer=tokenizer,
args=args.reward_config,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
peft_config=peft_config,
)
trainer.train()