Training customization
TRL is designed with modularity in mind so that users to be able to efficiently customize the training loop for their needs. Below are some examples on how you can apply and test different techniques. Note: Although these examples use the DPOTrainer, the customization applies to most (if not all) trainers.
Train on multiple GPUs / nodes
The trainers in TRL use 🤗 Accelerate to enable distributed training across multiple GPUs or nodes. To do so, first create an 🤗 Accelerate config file by running
accelerate config
and answering the questions according to your multi-gpu / multi-node setup. You can then launch distributed training by running:
accelerate launch your_script.py
We also provide config files in the examples folder that can be used as templates. To use these templates, simply pass the path to the config file when launching a job, e.g.:
accelerate launch --config_file=examples/accelerate_configs/multi_gpu.yaml --num_processes {NUM_GPUS} path_to_script.py --all_arguments_of_the_scriptRefer to the examples page for more details.
Distributed training with DeepSpeed
All of the trainers in TRL can be run on multiple GPUs together with DeepSpeed ZeRO-{1,2,3} for efficient sharding of the optimizer states, gradients, and model weights. To do so, run:
accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero{1,2,3}.yaml --num_processes {NUM_GPUS} path_to_your_script.py --all_arguments_of_the_scriptNote that for ZeRO-3, a small tweak is needed to initialize your reward model on the correct device via the zero3_init_context_manager() context manager. In particular, this is needed to avoid DeepSpeed hanging after a fixed number of training steps. Here is a snippet of what is involved from the sentiment_tuning example:
ds_plugin = ppo_trainer.accelerator.state.deepspeed_plugin
if ds_plugin is not None and ds_plugin.is_zero3_init_enabled():
with ds_plugin.zero3_init_context_manager(enable=False):
sentiment_pipe = pipeline("sentiment-analysis", model="lvwerra/distilbert-imdb", device=device)
else:
sentiment_pipe = pipeline("sentiment-analysis", model="lvwerra/distilbert-imdb", device=device)Consult the 🤗 Accelerate documentation for more information about the DeepSpeed plugin.
Use different optimizers and schedulers
By default, the DPOTrainer creates a torch.optim.AdamW optimizer. You can create and define a different optimizer and pass it to DPOTrainer as follows:
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from torch import optim
from trl import DPOConfig, DPOTrainer
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")
optimizer = optim.SGD(model.parameters(), lr=training_args.learning_rate)
trainer = DPOTrainer(
model=model,
args=training_args,
train_dataset=dataset,
tokenizer=tokenizer,
optimizers=(optimizer, None),
)
trainer.train()Add a learning rate scheduler
You can also play with your training by adding learning rate schedulers.
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from torch import optim
from trl import DPOConfig, DPOTrainer
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")
optimizer = optim.AdamW(model.parameters(), lr=training_args.learning_rate)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
trainer = DPOTrainer(
model=model,
args=training_args,
train_dataset=dataset,
tokenizer=tokenizer,
optimizers=(optimizer, lr_scheduler),
)
trainer.train()Memory efficient fine-tuning by sharing layers
Another tool you can use for more memory efficient fine-tuning is to share layers between the reference model and the model you want to train.
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import create_reference_model, DPOConfig, DPOTrainer
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
ref_model = create_reference_model(model, num_shared_layers=6)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train[:1%]")
training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")
trainer = DPOTrainer(
model=model,
ref_model=ref_model,
args=training_args,
train_dataset=dataset,
tokenizer=tokenizer,
)
trainer.train()Pass 8-bit reference models
Since trl supports all keyword arguments when loading a model from transformers using from_pretrained, you can also leverage load_in_8bit from transformers for more memory efficient fine-tuning.
Read more about 8-bit model loading in transformers here.
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import DPOConfig, DPOTrainer
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
ref_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", quantization_config= quantization_config)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")
trainer = DPOTrainer(
model=model,
ref_model=ref_model,
args=training_args,
train_dataset=dataset,
tokenizer=tokenizer,
)
trainer.train()Use the CUDA cache optimizer
When training large models, you should better handle the CUDA cache by iteratively clearing it. To do so, simply pass optimize_cuda_cache=True to DPOConfig:
training_args = DPOConfig(..., optimize_cuda_cache=True)