generated from fastai/nbdev_template
-
Notifications
You must be signed in to change notification settings - Fork 1.4k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Add score scaling/normalization/clipping (#560)
* Add reward/score scaling/normalization/clipping * Run pre-commit to fix styles and remove some dupe code * Make sure score module and pretrained_model have the same dtype * Add multi_adapter_rl_v2.py * Add log_with * Add more verbose help message for use_score_norm * Fix score clipping for float16 * Minor fix
- Loading branch information
Showing
8 changed files
with
275 additions
and
16 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,150 @@ | ||
# 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 BitsAndBytesConfig, HfArgumentParser, LlamaTokenizer | ||
|
||
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer | ||
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={"": 0}, | ||
peft_config=lora_config, | ||
quantization_config=nf4_config, | ||
reward_adapter=script_args.rm_adapter, | ||
use_safetensors=script_args.use_safetensors, | ||
) | ||
tokenizer = LlamaTokenizer.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, | ||
} | ||
|
||
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.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) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.