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29b0094 | 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 | # 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.
"""
# Full training
python examples/scripts/dpo.py \
--dataset_name trl-lib/ultrafeedback_binarized \
--model_name_or_path Qwen/Qwen2-0.5B-Instruct \
--learning_rate 5.0e-7 \
--num_train_epochs 1 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 8 \
--gradient_checkpointing \
--logging_steps 25 \
--eval_strategy steps \
--eval_steps 50 \
--output_dir Qwen2-0.5B-DPO \
--no_remove_unused_columns
# LoRA:
python examples/scripts/dpo.py \
--dataset_name trl-lib/ultrafeedback_binarized \
--model_name_or_path Qwen/Qwen2-0.5B-Instruct \
--learning_rate 5.0e-6 \
--num_train_epochs 1 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 8 \
--gradient_checkpointing \
--logging_steps 25 \
--eval_strategy steps \
--eval_steps 50 \
--output_dir Qwen2-0.5B-DPO \
--no_remove_unused_columns \
--use_peft \
--lora_r 32 \
--lora_alpha 16
"""
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import (
DPOConfig,
DPOTrainer,
ModelConfig,
ScriptArguments,
TrlParser,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
from trl.trainer.utils import SIMPLE_CHAT_TEMPLATE
if __name__ == "__main__":
parser = TrlParser((ScriptArguments, DPOConfig, ModelConfig))
script_args, training_args, model_config = parser.parse_args_and_config()
################
# Model & Tokenizer
###################
torch_dtype = (
model_config.torch_dtype
if model_config.torch_dtype in ["auto", None]
else getattr(torch, model_config.torch_dtype)
)
quantization_config = get_quantization_config(model_config)
model_kwargs = dict(
revision=model_config.model_revision,
attn_implementation=model_config.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_config.model_name_or_path, trust_remote_code=model_config.trust_remote_code, **model_kwargs
)
peft_config = get_peft_config(model_config)
if peft_config is None:
ref_model = AutoModelForCausalLM.from_pretrained(
model_config.model_name_or_path, trust_remote_code=model_config.trust_remote_code, **model_kwargs
)
else:
ref_model = None
tokenizer = AutoTokenizer.from_pretrained(
model_config.model_name_or_path, trust_remote_code=model_config.trust_remote_code
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
if tokenizer.chat_template is None:
tokenizer.chat_template = SIMPLE_CHAT_TEMPLATE
if script_args.ignore_bias_buffers:
# torch distributed hack
model._ddp_params_and_buffers_to_ignore = [
name for name, buffer in model.named_buffers() if buffer.dtype == torch.bool
]
################
# Dataset
################
dataset = load_dataset(script_args.dataset_name)
##########
# Training
################
trainer = DPOTrainer(
model,
ref_model,
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,
peft_config=peft_config,
)
trainer.train()
if training_args.eval_strategy != "no":
metrics = trainer.evaluate()
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# 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)
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