Spaces:
Paused
Paused
File size: 5,355 Bytes
2f5127c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
# Copyright 2020-2025 The HuggingFace 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.
"""
Usage:
python examples/scripts/dpo_online.py \
--model_name_or_path trl-lib/pythia-1b-deduped-tldr-sft \
--reward_model_path trl-lib/pythia-1b-deduped-tldr-rm \
--dataset_name trl-lib/tldr \
--learning_rate 5.0e-7 \
--output_dir pythia-1b-tldr-online-dpo \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 16 \
--warmup_ratio 0.1 \
--missing_eos_penalty 1.0
With LoRA:
python examples/scripts/dpo_online.py \
--model_name_or_path trl-lib/pythia-1b-deduped-tldr-sft \
--reward_model_path trl-lib/pythia-1b-deduped-tldr-rm \
--dataset_name trl-lib/tldr \
--learning_rate 5.0e-6 \
--output_dir pythia-1b-tldr-online-dpo \
--per_device_train_batch_size 16 \
--gradient_accumulation_steps 8 \
--warmup_ratio 0.1 \
--missing_eos_penalty 1.0 \
--use_peft
"""
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, GenerationConfig
from trl import (
HfPairwiseJudge,
LogCompletionsCallback,
ModelConfig,
OnlineDPOConfig,
OnlineDPOTrainer,
OpenAIPairwiseJudge,
PairRMJudge,
ScriptArguments,
TrlParser,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
from trl.trainer.utils import SIMPLE_CHAT_TEMPLATE
JUDGES = {"pair_rm": PairRMJudge, "openai": OpenAIPairwiseJudge, "hf": HfPairwiseJudge}
if __name__ == "__main__":
parser = TrlParser((ScriptArguments, OnlineDPOConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_and_config()
training_args.gradient_checkpointing_kwargs = {"use_reentrant": True}
torch_dtype = (
model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
)
quantization_config = get_quantization_config(model_args)
model_kwargs = dict(
revision=model_args.model_revision,
attn_implementation=model_args.attn_implementation,
torch_dtype=torch_dtype,
use_cache=False if training_args.gradient_checkpointing else True,
device_map=get_kbit_device_map() if quantization_config is not None else None,
quantization_config=quantization_config,
)
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, **model_kwargs
)
if training_args.reward_model_path is not None:
reward_model = AutoModelForSequenceClassification.from_pretrained(
training_args.reward_model_path,
num_labels=1,
trust_remote_code=model_args.trust_remote_code,
**model_kwargs,
)
reward_tokenizer = AutoTokenizer.from_pretrained(
training_args.reward_model_path,
trust_remote_code=model_args.trust_remote_code,
truncation=True,
truncation_side="left", # since we judge the completion, truncating left is more appropriate
)
else:
reward_model = None
reward_tokenizer = None
if training_args.judge is not None:
judge_cls = JUDGES[training_args.judge]
judge = judge_cls()
else:
judge = None
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
padding_side="left",
trust_remote_code=model_args.trust_remote_code,
**model_kwargs,
)
if tokenizer.chat_template is None:
tokenizer.chat_template = SIMPLE_CHAT_TEMPLATE
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
trainer = OnlineDPOTrainer(
model=model,
reward_model=reward_model,
judge=judge,
args=training_args,
train_dataset=dataset[script_args.dataset_train_split],
eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
processing_class=tokenizer,
reward_processing_class=reward_tokenizer,
peft_config=get_peft_config(model_args),
)
if training_args.eval_strategy != "no":
generation_config = GenerationConfig(
max_new_tokens=training_args.max_new_tokens, do_sample=True, temperature=training_args.temperature
)
completions_callback = LogCompletionsCallback(trainer, generation_config, num_prompts=8)
trainer.add_callback(completions_callback)
trainer.train()
# Save and push to hub
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
|