riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-07-31 02:02:21 +06:00
Code Issues Actions 23 Packages Projects Releases Wiki Activity

FEAT [Trainer / bnb]: Add RMSProp from bitsandbytes to HF Trainer (#29082)

Browse Source 
* add RMSProp to Trainer

* revert some change

* Update src/transformers/trainer.py

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

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Younes Belkada 2024-02-20 02:43:02 +01:00 committed by GitHub
parent a7ff2f23a0
commit f7ef7cec6c
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 67 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
src/transformers/trainer.py
View File

@ -1084,9 +1084,12 @@ class Trainer:
OptimizerNames.LION_8BIT,
OptimizerNames.PAGED_LION,
OptimizerNames.PAGED_LION_8BIT,
OptimizerNames.RMSPROP_BNB,
OptimizerNames.RMSPROP_8BIT,
OptimizerNames.RMSPROP_32BIT,
]:
try:
from bitsandbytes.optim import AdamW, Lion
from bitsandbytes.optim import AdamW, Lion, RMSprop
is_paged = False
optim_bits = 32
@ -1101,8 +1104,16 @@ class Trainer:
elif "lion" in args.optim:
optimizer_cls = Lion
additional_optim_kwargs = {"betas": (args.adam_beta1, args.adam_beta2)}
elif "rmsprop" in args.optim:
optimizer_cls = RMSprop
# Above we pass all `adam_kwargs` to the optimizer, here
# we only pass `optim_args` which can be passed by the user.
additional_optim_kwargs = optim_args
bnb_kwargs = {"optim_bits": optim_bits}
if "rmsprop" not in args.optim:
bnb_kwargs["is_paged"] = is_paged
bnb_kwargs = {"is_paged": is_paged, "optim_bits": optim_bits}
optimizer_kwargs.update(additional_optim_kwargs)
optimizer_kwargs.update(bnb_kwargs)
except ImportError:

3
src/transformers/training_args.py
View File

@ -157,6 +157,9 @@ class OptimizerNames(ExplicitEnum):
PAGED_LION = "paged_lion_32bit"
PAGED_LION_8BIT = "paged_lion_8bit"
RMSPROP = "rmsprop"
RMSPROP_BNB = "rmsprop_bnb"
RMSPROP_8BIT = "rmsprop_bnb_8bit"
RMSPROP_32BIT = "rmsprop_bnb_32bit"
# TODO: `TrainingArguments` users rely on it being fully mutable. In the future see if we can narrow this to a few keys: https://github.com/huggingface/transformers/pull/25903

51
tests/trainer/test_trainer.py
View File

@ -58,6 +58,7 @@ from transformers.testing_utils import (
get_tests_dir,
is_staging_test,
require_accelerate,
require_bitsandbytes,
require_deepspeed,
require_intel_extension_for_pytorch,
require_optuna,
@ -872,6 +873,56 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
train_output = trainer.train()
self.assertEqual(train_output.global_step, 10)
@require_bitsandbytes
def test_rmsprop_bnb(self):
config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
tiny_gpt2 = GPT2LMHeadModel(config)
x = torch.randint(0, 100, (128,))
train_dataset = RepeatDataset(x)
with tempfile.TemporaryDirectory() as tmpdir:
# Trainer without inf/nan filter
args = TrainingArguments(
tmpdir, learning_rate=1e-9, logging_steps=5, logging_nan_inf_filter=False, optim="rmsprop_bnb"
)
trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
# Check that it trains without errors
trainer.train()
@require_bitsandbytes
def test_rmsprop_bnb_8bit(self):
config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
tiny_gpt2 = GPT2LMHeadModel(config)
x = torch.randint(0, 100, (128,))
train_dataset = RepeatDataset(x)
with tempfile.TemporaryDirectory() as tmpdir:
# Trainer without inf/nan filter
args = TrainingArguments(
tmpdir, learning_rate=1e-9, logging_steps=5, logging_nan_inf_filter=False, optim="rmsprop_bnb_8bit"
)
trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
# Check that it trains without errors
trainer.train()
@require_bitsandbytes
def test_rmsprop_bnb_32bit(self):
config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
tiny_gpt2 = GPT2LMHeadModel(config)
x = torch.randint(0, 100, (128,))
train_dataset = RepeatDataset(x)
with tempfile.TemporaryDirectory() as tmpdir:
# Trainer without inf/nan filter
args = TrainingArguments(
tmpdir, learning_rate=1e-9, logging_steps=5, logging_nan_inf_filter=False, optim="rmsprop_bnb_32bit"
)
trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
# Check that it trains without errors
trainer.train()
def test_neftune(self):
config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
tiny_gpt2 = GPT2LMHeadModel(config)