Optimizers

Transformers offers two native optimizers, AdamW and AdaFactor. It also provides integrations for more specialized optimizers. Install the library that offers the optimizer and drop it in the optim parameter in TrainingArguments.

This guide will show you how to use these optimizers with Trainer using TrainingArguments shown below.

import torch
from transformers import TrainingArguments, AutoTokenizer, AutoModelForCausalLM, Trainer

args = TrainingArguments(
    output_dir="./test-optimizer",
    max_steps=1000,
    per_device_train_batch_size=4,
    logging_strategy="steps",
    logging_steps=1,
    learning_rate=2e-5,
    save_strategy="no",
    run_name="optimizer-name",
)

APOLLO

pip install apollo-torch

Approximated Gradient Scaling for Memory Efficient LLM Optimization (APOLLO) is a memory-efficient optimizer that allows full parameter learning for both pretraining and fine-tuning. It maintains AdamW-level performance with SGD-like memory efficiency. For extreme memory efficiency, you can use APOLLO-Mini, a rank 1 variant of APOLLO. APOLLO optimizers support:

• Ultra-low rank efficiency. You can use a much lower rank than GaLoRE, rank 1 is sufficient.
• Avoid expensive SVD computations. APOLLO leverages random projections to avoid training stalls.

Use the optim_target_modules parameter to specify which layers to train.

import torch
from transformers import TrainingArguments

args = TrainingArguments(
    output_dir="./test-apollo",
    max_steps=100,
    per_device_train_batch_size=2,
+   optim="apollo_adamw",
+   optim_target_modules=[r".*.attn.*", r".*.mlp.*"],
    logging_strategy="steps",
    logging_steps=1,
    learning_rate=2e-5,
    save_strategy="no",
    run_name="apollo_adamw",
)

For additional training options, use optim_args to define hyperparameters like rank, scale, and more. Refer to the table below for a complete list of available hyperparameters.

The example below enables the APOLLO-Mini optimizer.

from transformers import TrainingArguments

args = TrainingArguments(
    output_dir="./test-apollo_mini",
    max_steps=100,
    per_device_train_batch_size=2,
    optim="apollo_adamw",
    optim_target_modules=[r".*.attn.*", r".*.mlp.*"],
    optim_args="proj=random,rank=1,scale=128.0,scale_type=tensor,update_proj_gap=200",
)

GrokAdamW

pip install grokadamw

GrokAdamW is an optimizer designed to help models that benefit from grokking, a term used to describe delayed generalization because of slow-varying gradients. It is particularly useful for models requiring more advanced optimization techniques to achieve better performance and stability.

import torch
from transformers import TrainingArguments

args = TrainingArguments(
    output_dir="./test-grokadamw",
    max_steps=1000,
    per_device_train_batch_size=4,
+   optim="grokadamw",
    logging_strategy="steps",
    logging_steps=1,
    learning_rate=2e-5,
    save_strategy="no",
    run_name="grokadamw",
)

LOMO

pip install lomo-optim

Low-Memory Optimization (LOMO) is a family of optimizers, LOMO and AdaLomo, designed for low-memory full-parameter finetuning of LLMs. Both LOMO optimizers fuse the gradient computation and parameter update in one step to reduce memory usage. AdaLomo builds on top of LOMO by incorporating an adaptive learning rate for each parameter like the Adam optimizer.

args = TrainingArguments(
    output_dir="./test-lomo",
    max_steps=1000,
    per_device_train_batch_size=4,
+   optim="adalomo",
    gradient_checkpointing=True,
    gradient_checkpointing=True,
    logging_strategy="steps",
    logging_steps=1,
    learning_rate=2e-6,
    save_strategy="no",
    run_name="adalomo",
)

Schedule Free

pip install schedulefree

Schedule Free optimizer (SFO) replaces the base optimizers momentum with a combination of averaging and interpolation. Unlike a traditional scheduler, SFO completely removes the need to anneal the learning rate.

SFO supports the RAdam (schedule_free_radam), AdamW (schedule_free_adamw) and SGD (schedule_free_sgd) optimizers. The RAdam scheduler doesn’t require warmup_steps or warmup_ratio.

By default, it is recommended to set lr_scheduler_type="constant". Other lr_scheduler_type values may also work, but combining SFO optimizers with other learning rate schedules could affect SFOs intended behavior and performance.

args = TrainingArguments(
    output_dir="./test-schedulefree",
    max_steps=1000,
    per_device_train_batch_size=4,
+   optim="schedule_free_radamw,
+   lr_scheduler_type="constant",
    gradient_checkpointing=True,
    logging_strategy="steps",
    logging_steps=1,
    learning_rate=2e-6,
    save_strategy="no",
    run_name="sfo",
)