Customize the Training

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.

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

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accelerate config

and answering the questions according to your multi-gpu / multi-node setup. You can then launch distributed training by running:

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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.:

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accelerate launch --config_file=examples/accelerate_configs/multi_gpu.yaml --num_processes {NUM_GPUS} path_to_script.py --all_arguments_of_the_script

Refer 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:

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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_script

Note 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:

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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

By default, the PPOTrainer creates a torch.optim.Adam optimizer. You can create and define a different optimizer and pass it to PPOTrainer:

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import torch
from transformers import GPT2Tokenizer
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead

# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# 2. define config
ppo_config = {'batch_size': 1, 'learning_rate':1e-5}
config = PPOConfig(**ppo_config)


# 2. Create optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=config.learning_rate)


# 3. initialize trainer
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer, optimizer=optimizer)

For memory efficient fine-tuning, you can also pass Adam8bit optimizer from bitsandbytes:

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import torch
import bitsandbytes as bnb

from transformers import GPT2Tokenizer
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead

# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# 2. define config
ppo_config = {'batch_size': 1, 'learning_rate':1e-5}
config = PPOConfig(**ppo_config)


# 2. Create optimizer
optimizer = bnb.optim.Adam8bit(model.parameters(), lr=config.learning_rate)

# 3. initialize trainer
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer, optimizer=optimizer)

Use LION optimizer

You can use the new LION optimizer from Google as well, first take the source code of the optimizer definition here, and copy it so that you can import the optimizer. Make sure to initialize the optimizer by considering the trainable parameters only for a more memory efficient training:

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optimizer = Lion(filter(lambda p: p.requires_grad, self.model.parameters()), lr=self.config.learning_rate)

...
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer, optimizer=optimizer)

We advise you to use the learning rate that you would use for Adam divided by 3 as pointed out here. We observed an improvement when using this optimizer compared to classic Adam (check the full logs here):

Add a learning rate scheduler

You can also play with your training by adding learning rate schedulers!

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import torch
from transformers import GPT2Tokenizer
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead

# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# 2. define config
ppo_config = {'batch_size': 1, 'learning_rate':1e-5}
config = PPOConfig(**ppo_config)


# 2. Create optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=config.learning_rate)
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)

# 3. initialize trainer
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer, optimizer=optimizer, lr_scheduler=lr_scheduler)

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.

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import torch
from transformers import AutoTokenizer
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead, create_reference_model

# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained('bigscience/bloom-560m')
model_ref = create_reference_model(model, num_shared_layers=6)
tokenizer = AutoTokenizer.from_pretrained('bigscience/bloom-560m')

# 2. initialize trainer
ppo_config = {'batch_size': 1}
config = PPOConfig(**ppo_config)
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer)

Pass 8-bit reference models

Since trl supports all key word 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.

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# 0. imports
# pip install bitsandbytes
import torch
from transformers import AutoTokenizer
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead

# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained('bigscience/bloom-560m')
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained('bigscience/bloom-560m', device_map="auto", load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained('bigscience/bloom-560m')

# 2. initialize trainer
ppo_config = {'batch_size': 1}
config = PPOConfig(**ppo_config)
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer)

Use the CUDA cache optimizer

When training large models, you should better handle the CUDA cache by iteratively clearing it. Do do so, simply pass optimize_cuda_cache=True to PPOConfig:

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config = PPOConfig(..., optimize_cuda_cache=True)

Use score scaling/normalization/clipping

As suggested by Secrets of RLHF in Large Language Models Part I: PPO, we support score (aka reward) scaling/normalization/clipping to improve training stability via PPOConfig:

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from trl import PPOConfig

ppo_config = {
    use_score_scaling=True,
    use_score_norm=True,
    score_clip=0.5,
}
config = PPOConfig(**ppo_config)

To run ppo.py, you can use the following command:

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python examples/scripts/ppo.py --log_with wandb --use_score_scaling --use_score_norm --score_clip 0.5