FEAT / Trainer: LOMO optimizer support (#30178)

* add V1 - adalomo not working yet

* add todo docs + refactor from comments

* adjust LR

* add docs

* add more elaborated test

* Apply suggestions from code review

Co-authored-by: Zach Mueller <muellerzr@gmail.com>

* fix

* push

* add accelerate check

* fix DDP case

* Apply suggestions from code review

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* fix

* init kwargs

* safely add attribute

* revert to enum logic

* Update src/transformers/trainer.py

---------

Co-authored-by: Zach Mueller <muellerzr@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

docs/source/en/trainer.md

@@ -382,6 +382,56 @@ trainer.train()
 
 Note layerwise optimization is a bit experimental and does not support DDP (Distributed Data Parallel), thus you can run the training script only on a single GPU. Please see [this appropriate section](https://github.com/jiaweizzhao/GaLore?tab=readme-ov-file#train-7b-model-with-a-single-gpu-with-24gb-memory) for more details. Other features such as gradient clipping, DeepSpeed, etc might not be supported out of the box. Please [raise an issue on GitHub](https://github.com/huggingface/transformers/issues) if you encounter such issue.
 
+## LOMO optimizer
+
+The LOMO optimizers have been introduced in [Full Parameter Fine-Tuning for Large Language Models with Limited Resources](https://hf.co/papers/2306.09782) and [AdaLomo: Low-memory Optimization with Adaptive Learning Rate](https://hf.co/papers/2310.10195). 
+They both consist of an efficient full-parameter fine-tuning method. These optimizers fuse the gradient computation and the parameter update in one step to reduce memory usage. Supported optimizers for LOMO are `"lomo"` and `"adalomo"`. First either install LOMO from pypi `pip install lomo-optim` or install it from source with `pip install git+https://github.com/OpenLMLab/LOMO.git`. 
+
+<Tip>
+
+According to the authors, it is recommended to use `AdaLomo` without `grad_norm` to get better performance and higher throughput.
+
+</Tip>
+
+Below is a simple script to demonstrate how to fine-tune [google/gemma-2b](https://huggingface.co/google/gemma-2b) on IMDB dataset in full precision:
+
+```python
+import torch
+import datasets
+from transformers import TrainingArguments, AutoTokenizer, AutoModelForCausalLM
+import trl
+
+train_dataset = datasets.load_dataset('imdb', split='train')
+
+args = TrainingArguments(
+    output_dir="./test-lomo",
+    max_steps=1000,
+    per_device_train_batch_size=4,
+    optim="adalomo",
+    gradient_checkpointing=True,
+    logging_strategy="steps",
+    logging_steps=1,
+    learning_rate=2e-6,
+    save_strategy="no",
+    run_name="lomo-imdb",
+)
+
+model_id = "google/gemma-2b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(0)
+
+trainer = trl.SFTTrainer(
+    model=model, 
+    args=args,
+    train_dataset=train_dataset,
+    dataset_text_field='text',
+    max_seq_length=1024,
+)
+
+trainer.train()
+```
+
 ## Accelerate and Trainer
 
 The [`Trainer`] class is powered by [Accelerate](https://hf.co/docs/accelerate), a library for easily training PyTorch models in distributed environments with support for integrations such as [FullyShardedDataParallel (FSDP)](https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/) and [DeepSpeed](https://www.deepspeed.ai/).