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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 torch
from datasets import load_dataset
from peft import LoraConfig
from tqdm import tqdm
from transformers import AutoTokenizer, BitsAndBytesConfig, HfArgumentParser
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, is_xpu_available
from trl.core import LengthSampler
input_min_text_length = 6
input_max_text_length = 12
@dataclass
class ScriptArguments:
"""
The name of the Casual LM model we wish to fine with PPO
"""
model_name: Optional[str] = field(default="huggyllama/llama-7b", metadata={"help": "the model name"})
dataset_name: Optional[str] = field(default="Anthropic/hh-rlhf", metadata={"help": "the dataset name"})
rm_adapter: Optional[str] = field(
default="trl-lib/llama-7b-hh-rm-adapter", metadata={"help": "the rm adapter name"}
)
log_with: Optional[str] = field(default=None, metadata={"help": "use 'wandb' to log with wandb"})
use_safetensors: Optional[bool] = field(default=False, metadata={"help": "Use safetensors"})
seed: Optional[int] = field(default=0, metadata={"help": "the random seed"})
use_score_scaling: Optional[bool] = field(default=False, metadata={"help": "Use score scaling"})
use_score_norm: Optional[bool] = field(
default=False, metadata={"help": "Use score normalization. Only applicable if use_score_scaling is True"}
)
score_clip: Optional[float] = field(default=None, metadata={"help": "Score clipping"})
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
def create_and_prepare_dataset(tokenizer):
dataset = load_dataset(script_args.dataset_name, split="train[:1%]")
input_size = LengthSampler(input_min_text_length, input_max_text_length)
def tokenize(example):
text_size = input_size()
example["input_ids"] = tokenizer.encode(example["chosen"])[:text_size]
example["query"] = tokenizer.decode(example["input_ids"])
return example
dataset = dataset.map(tokenize, batched=False)
dataset.set_format("torch")
return dataset
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
nf4_config = BitsAndBytesConfig(
load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLMWithValueHead.from_pretrained(
script_args.model_name,
device_map={"": "xpu:0"} if is_xpu_available() else {"": 0},
peft_config=lora_config,
quantization_config=nf4_config,
reward_adapter=script_args.rm_adapter,
use_safetensors=script_args.use_safetensors,
)
tokenizer = AutoTokenizer.from_pretrained(script_args.model_name)
tokenizer.pad_token = tokenizer.eos_token
dataset = create_and_prepare_dataset(tokenizer)
def collator(data):
return dict((key, [d[key] for d in data]) for key in data[0])
config = PPOConfig(
model_name=script_args.model_name,
log_with=script_args.log_with,
learning_rate=1e-5,
batch_size=8,
mini_batch_size=2,
gradient_accumulation_steps=2,
optimize_cuda_cache=True,
seed=script_args.seed,
use_score_scaling=script_args.use_score_scaling,
use_score_norm=script_args.use_score_norm,
score_clip=script_args.score_clip,
)
ppo_trainer = PPOTrainer(
config,
model,
ref_model=None,
tokenizer=tokenizer,
dataset=dataset,
data_collator=collator,
)
generation_kwargs = {
"top_k": 0.0,
"top_p": 0.9,
"do_sample": True,
"pad_token_id": tokenizer.pad_token_id,
"max_new_tokens": 32,
}
for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
question_tensors = batch["input_ids"]
response_tensors = ppo_trainer.generate(
question_tensors,
return_prompt=False,
**generation_kwargs,
)
batch["response"] = tokenizer.batch_decode(response_tensors, skip_special_tokens=True)
# Compute reward score
texts = [q + r for q, r in zip(batch["query"], batch["response"])]
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(ppo_trainer.accelerator.device)
raw_rewards = ppo_trainer.accelerator.unwrap_model(ppo_trainer.model).compute_reward_score(**inputs)
rewards = [raw_rewards[i, -1, 1] for i in range(len(raw_rewards))] # take last token
# Run PPO step
stats = ppo_trainer.step(question_tensors, response_tensors, rewards)
ppo_trainer.log_stats(stats, batch, rewards)
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