FEAT / Optim: Add GaLore optimizer #29588

younesbelkada · 2024-03-11T12:35:48Z

What does this PR do?

As per title, adds the GaLore optimizer from https://github.com/jiaweizzhao/GaLore

This is how I am currently testing the API:

import torch
import datasets
from transformers import TrainingArguments, AutoConfig, AutoTokenizer, AutoModelForCausalLM
import trl

train_dataset = datasets.load_dataset('imdb', split='train')

args = TrainingArguments(
    output_dir="./test-galore",
    max_steps=100,
    per_device_train_batch_size=2,
    optim="galore_adamw",
    optim_target_modules=["attn", "mlp"]
)

model_id = "mistralai/Mistral-7B-v0.1"

config = AutoConfig.from_pretrained(model_id)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_config(config).to(0)

trainer = trl.SFTTrainer(
    model=model, 
    args=args,
    train_dataset=train_dataset,
    dataset_text_field='text',
    max_seq_length=512,
)

trainer.train()

cc @pacman100 @muellerzr

HuggingFaceDocBuilderDev · 2024-03-11T12:58:39Z

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

younesbelkada · 2024-03-11T13:18:26Z

see OpenAccess-AI-Collective/axolotl#1370 (comment) for intermediate results

muellerzr · 2024-03-11T13:22:14Z

src/transformers/trainer.py

+            OptimizerNames.GALORE_ADAMW_8BIT,
+            OptimizerNames.GALORE_ADAFACTOR,
+        ]:
+            from galore_torch import GaLoreAdafactor, GaLoreAdamW, GaLoreAdamW8bit


We need an import check here, no? 😄

yeah indeed, many things can be optimized, for now it's a really rough draft, will focus on polishing everything next!

Great, ping me when you're ready for a full review :)

muellerzr

Thanks! Overall this looks good to me, nice! Excited to see this in transformers :)

Left some suggestions.

src/transformers/trainer.py

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

BenjaminBossan

Pretty cool work, thanks for adding this Younes. After this, we should also add this to PEFT ;)

BenjaminBossan · 2024-03-11T16:30:41Z

src/transformers/training_args.py

@@ -696,6 +699,11 @@ class TrainingArguments:
            for instruction fine-tuning. Check out the [original paper](https://arxiv.org/abs/2310.05914) and the
            [original code](https://github.com/neelsjain/NEFTune). Support transformers `PreTrainedModel` and also
            `PeftModel` from peft.
+        galore_target_modules (`List[str]`, *optional*):


It would be cool if we could use the same mechanism as for target_modules in PEFT:

allow str for regex match

if list of str, not only match exact names, but also if the module name ends with the target module name

passing all-linear to match all linear layers

But I understand that we don't want to copy the whole mechanism to transformers. Maybe we can think of a way to factor this code out in the future, so that we can re-use it in multiple places.

This should be now supported !

src/transformers/trainer.py

src/transformers/training_args.py

docs/source/en/trainer.md

BenjaminBossan · 2024-03-11T16:43:55Z

src/transformers/trainer.py

+
+            galore_params = []
+            for module_name, module in model.named_modules():
+                if not isinstance(module, nn.Linear):


I wonder if we should raise an error if the target module name matches but the layer type is not Linear. Let's assume that a user matches N linear layers and accidentally 1 other type like Embedding, currently the embedding layer would be ignored but the user doesn't get any error message or warning.

This sounds like a good idea!

Yes! I think we should pass a warning to be able to pass simple regex such as .*.attn.*. - lmk wdyt !

tests/trainer/test_trainer.py

amyeroberts

Thanks for adding!

Just a comment on the training args

docs/source/en/trainer.md

PenutChen · 2024-03-12T02:49:36Z

Is it possible to integrate per-layer weight updates as described in the galore paper?

It seems to reduce memory usage significantly:

But the original implementation is somewhat complex.

pacman100

Thank you @younesbelkada for adding GaLore 🔥! Others have added most of the comments that I agree with.

docs/source/en/trainer.md

tests/trainer/test_trainer.py

hiyouga · 2024-03-13T07:34:40Z

Is it possible to integrate per-layer weight updates as described in the galore paper?

It seems to reduce memory usage significantly:

But the original implementation is somewhat complex.

Sure, per-layer weight update is crucial to the current implementation of GaLore (may not be in the future). Without that, it would require an additional 14GB GRAM for the gradients for a 7B model, making full-parameter fine-tuning infeasible with a 24GB GPU.

You may refer to our discussion jiaweizzhao/GaLore#6 for more empirical results.

hiyouga · 2024-03-17T14:21:21Z

docs/source/en/trainer.md

+First make sure to install GaLore official repository:
+
+```bash
+pip install git+https://github.com/jiaweizzhao/GaLore


GaLore has released an official package: pip install galore-torch

https://github.com/jiaweizzhao/GaLore?tab=readme-ov-file#install-galore-optimizer

docs/source/en/trainer.md

src/transformers/trainer_utils.py

docs/source/en/trainer.md

Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com>

amyeroberts

Thanks for all the work adding this!

Just some small nits. It would be great if you could address the warning for the attention layers before merge

amyeroberts · 2024-03-18T14:47:24Z

docs/source/en/trainer.md

+pip install galore-torch
+```
+
+Then simply add one of `["galore_adamw", "galore_adafactor", "galore_adamw_8bit"]` in `optim` together with `optim_target_modules`, which can be a list of strings, regew or full path corresponding to the target module names you want to adapt. Below is an end-to-end example script (make sure to `pip install trl datasets`):


Suggested change

Then simply add one of `["galore_adamw", "galore_adafactor", "galore_adamw_8bit"]` in `optim` together with `optim_target_modules`, which can be a list of strings, regew or full path corresponding to the target module names you want to adapt. Below is an end-to-end example script (make sure to `pip install trl datasets`):

Then simply add one of `["galore_adamw", "galore_adafactor", "galore_adamw_8bit"]` in `optim` together with `optim_target_modules`, which can be a list of strings, regex or full path corresponding to the target module names you want to adapt. Below is an end-to-end example script (make sure to `pip install trl datasets`):

Arghf, sadly it has been taken care by 898a3c5 already

amyeroberts · 2024-03-18T14:49:15Z

docs/source/en/trainer.md

+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.


nice note :)

amyeroberts · 2024-03-18T14:52:18Z

src/transformers/training_args.py

@@ -1354,6 +1366,13 @@ class TrainingArguments:
        },
    )

+    optim_target_modules: Optional[List[str]] = field(


nit

Suggested change

optim_target_modules: Optional[List[str]] = field(

optim_target_modules: Optional[Union[str, List[str]]] = field(

FYI that suggestion broke the CI for some reason; 73dcabb fixed it I think Optional and Union are not compatible somehow

src/transformers/trainer.py

winglian · 2024-03-18T18:15:17Z

Nice catch @winglian ! 6ff3762 should fix it, can you try now?

It progresses to start training now, but I've been getting an odd error and I'm not sure if it's a transformers, axolotl, or galore_torch issue.

  File "/workspace/transformers/src/transformers/trainer.py", line 1774, in train
    return inner_training_loop(
  File "/workspace/transformers/src/transformers/trainer.py", line 2170, in _inner_training_loop
    self.optimizer.step()
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/optimizer.py", line 145, in step
    self.optimizer.step(closure)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 68, in wrapper
    return wrapped(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/optim/optimizer.py", line 373, in wrapper
    out = func(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/galore_torch/adamw.py", line 96, in step
    grad = state["projector"].project(grad, state["step"])
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/galore_torch/galore_projector.py", line 17, in project
    self.ortho_matrix = self.get_orthogonal_matrix(full_rank_grad, self.rank, type='right')
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/galore_torch/galore_projector.py", line 87, in get_orthogonal_matrix
    A = U[:, :rank] @ torch.diag(s[:rank])
TypeError: slice indices must be integers or None or have an __index__ method

winglian · 2024-03-18T18:58:42Z

So setting optim_args doesn't work and results in that error. Am I setting it incorrectly?

args = TrainingArguments(
    output_dir="./test-galore",
    max_steps=100,
    per_device_train_batch_size=1,
    optim="galore_adamw",
    optim_args="rank=128",
    optim_target_modules=["attn", "mlp"]
)

I believe this is the correct way according to

transformers/src/transformers/trainer.py

Lines 1047 to 1051 in 838b87a

    
           optim_args = {} 
        
           if args.optim_args: 
        
               for mapping in args.optim_args.replace(" ", "").split(","): 
        
                   key, value = mapping.split("=") 
        
                   optim_args[key] = value

winglian · 2024-03-18T19:05:02Z

@younesbelkada the bug is in the optim_args string splitting. it casts the value to a string, and galore expects an int. I expect it's going to have to need some fancy checks to determine if it's an int type or float type, since rank expects an int (doesn't work with float), but scale is a float. An easier solution might be to have TrainingArguments.optim_args be a Union[str, Dict[str, Any]] instead.

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

younesbelkada · 2024-03-19T08:29:57Z

Nice catch @winglian , in 57e7096 i should properly take care of everything and I can confirm it works fine. The correct way of passing galore args is the following:

            args = TrainingArguments(
                tmpdir,
                learning_rate=1e-9,
                logging_steps=5,
                optim="galore_adamw",
                optim_args="rank=64, update_proj_gap=100, scale=0.10",
                optim_target_modules=[r".*attn.*", r".*mlp.*"],
            )

fakerybakery · 2024-03-19T19:04:14Z

Hi,
According to the GaLore README:

We are working on the offical release of GaLore. In the meanwhile, please feel free to try the pre-release version and provide feedback to us. Currently, the pre-release version (e.g., GaLore optimizers) should provide a decent memory reduction and accurate simulation of GaLore algorithm.

When the official version of GaLore is released, will it be integrated into Transformers?

NicolasMejiaPetit · 2024-03-19T22:52:29Z

Is there anyway to get this optimizer fully offloaded to the cpu? Similar to how deepspeed does it with a large 32bit adam optimizer. That way the entire system can be working together with little interference through the pcei bus (compared to going into shared memory). Optimizing gpu memory to the max, allowing the gpu to handle the highest batch sizes possible, while the cpu deals with the optimizer.

kiddyboots216 · 2024-03-19T22:56:54Z

Is there anyway to get this optimizer fully offloaded to the cpu? Similar to how deepspeed does it with a large 32bit adam optimizer. That way the entire system can be working together with little interference through the pcei bus (compared to going into shared memory). Optimizing gpu memory to the max, allowing the gpu to handle the highest batch sizes possible, while the cpu deals with the optimizer.

If you use ZeRO stage 2/3 (offload all optimizer state to CPU) as done in deepspeed/FSDP then you will not have any VRAM usage from optimizer state. There's not much point in using GaLoRE if you're already offloading the optimizer state to the cpu, because GaLoRE only reduces the optimizer state. The layerwise backprop isn't part of GaLoRE, it can be done by any optimizer.

NicolasMejiaPetit · 2024-03-19T23:30:44Z

Is there anyway to get this optimizer fully offloaded to the cpu? Similar to how deepspeed does it with a large 32bit adam optimizer. That way the entire system can be working together with little interference through the pcei bus (compared to going into shared memory). Optimizing gpu memory to the max, allowing the gpu to handle the highest batch sizes possible, while the cpu deals with the optimizer.

If you use ZeRO stage 2/3 (offload all optimizer state to CPU) as done in deepspeed/FSDP then you will not have any VRAM usage from optimizer state. There's not much point in using GaLoRE if you're already offloading the optimizer state to the cpu, because GaLoRE only reduces the optimizer state. The layerwise backprop isn't part of GaLoRE, it can be done by any optimizer.

Wait you can do this without having to use adam 32bit cpu? So I could do like adam 8bit fully offloaded to cpu? Last I tried this it didn’t work but I must’ve not done it the way you are saying. cause I’m trying to get the cpu to do the work, not just hold the memory in system ram, (the way paged adam works).

Apologies for this being off topic. To make up for it, I started testing GaLoRE with Unsloth/hf trainer (on windows), I can confirm it works, and i’m getting great results. The loss is what I would normally get, with the added bonus that Its letting me do a batch size of 8 where previously with paged adam 8bit I was at batch size of 4 training a 7b model.

kiddyboots216 · 2024-03-19T23:47:08Z

Is there anyway to get this optimizer fully offloaded to the cpu? Similar to how deepspeed does it with a large 32bit adam optimizer. That way the entire system can be working together with little interference through the pcei bus (compared to going into shared memory). Optimizing gpu memory to the max, allowing the gpu to handle the highest batch sizes possible, while the cpu deals with the optimizer.

If you use ZeRO stage 2/3 (offload all optimizer state to CPU) as done in deepspeed/FSDP then you will not have any VRAM usage from optimizer state. There's not much point in using GaLoRE if you're already offloading the optimizer state to the cpu, because GaLoRE only reduces the optimizer state. The layerwise backprop isn't part of GaLoRE, it can be done by any optimizer.

Wait you can do this without having to use adam 32bit cpu? So I could do like adam 8bit fully offloaded to cpu? Last I tried this it didn’t work but I must’ve not done it the way you are saying. cause I’m trying to get the cpu to do the work, not just hold the memory in system ram, (the way paged adam works).

Could you try doing this the way it's referenced here TimDettmers/bitsandbytes#89 (comment) I'm not sure why it wouldn't work

(sorry to go off-topic on a PR with many people and commits on it)

* add galore v1 * add import * add tests and doc * fix doctest * forward contrib credits from discussions * forward contrib credits from discussions * Apply suggestions from code review Co-authored-by: Zach Mueller <muellerzr@gmail.com> * fix failing tests' * switch to `optim_target_modules` and clarify docs * more clarification * enhance lookup logic * update a test to add peak memory * add regex, all-linear and single string support * add layer-wise optimization through DummyOptimizers and LRSchedulers * forward contrib credits from discussions and original idea * add a section about DDP not supported in layerwise * Update src/transformers/trainer.py Co-authored-by: Zach Mueller <muellerzr@gmail.com> * fix self * check only if layer_wise * Update src/transformers/training_args.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * oops * make use of intervals * clarify comment * add matching tests * GaLoRe -> GaLore * move to `get_scheduler` * add note on docs * add a warning * adapt a bit the docs * update docstring * support original API * Update docs/source/en/trainer.md * slightly refactor * Update docs/source/en/trainer.md Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com> * Update src/transformers/training_args.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * fix args parsing and add tests * remove warning for regex * fix type hint * add note about extra args * make `is_regex` return optional --------- Co-authored-by: Maxime <maximegmd @users.noreply.github.com> Co-authored-by: Wing Lian <winglian @users.noreply.github.com> Co-authored-by: Zach Mueller <muellerzr@gmail.com> Co-authored-by: hiyouga <hiyouga@users.noreply.github.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com>

add galore v1

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WIP: galore optimizer OpenAccess-AI-Collective/axolotl#1370

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feat: GaLore support #29512

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[Feature Request] add layer-wise optimizers #29732

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Import error of the newest version of transformers #29847

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Transformers 4.39.0 has breaking change on torch compatibility. #29895

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FEAT / Optim: Add GaLore optimizer #29588

FEAT / Optim: Add GaLore optimizer #29588

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	Then simply add one of `["galore_adamw", "galore_adafactor", "galore_adamw_8bit"]` in `optim` together with `optim_target_modules`, which can be a list of strings, regew or full path corresponding to the target module names you want to adapt. Below is an end-to-end example script (make sure to `pip install trl datasets`):
	Then simply add one of `["galore_adamw", "galore_adafactor", "galore_adamw_8bit"]` in `optim` together with `optim_target_modules`, which can be a list of strings, regex or full path corresponding to the target module names you want to adapt. Below is an end-to-end example script (make sure to `pip install trl datasets`):

	optim_target_modules: Optional[List[str]] = field(
	optim_target_modules: Optional[Union[str, List[str]]] = field(

FEAT / Optim: Add GaLore optimizer #29588

FEAT / Optim: Add GaLore optimizer #29588

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younesbelkada commented Mar 11, 2024 • edited

What does this PR do?

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