File size: 12,687 Bytes
dfefe0b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 | diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py
index e0a49ee5795e..216d5cd42960 100755
--- a/src/transformers/trainer.py
+++ b/src/transformers/trainer.py
@@ -1237,6 +1237,10 @@ def get_optimizer_cls_and_kwargs(
OptimizerNames.ADAMW_8BIT,
OptimizerNames.PAGED_ADAMW,
OptimizerNames.PAGED_ADAMW_8BIT,
+ OptimizerNames.ADEMAMIX,
+ OptimizerNames.ADEMAMIX_8BIT,
+ OptimizerNames.PAGED_ADEMAMIX,
+ OptimizerNames.PAGED_ADEMAMIX_8BIT,
OptimizerNames.LION,
OptimizerNames.LION_8BIT,
OptimizerNames.PAGED_LION,
@@ -1266,6 +1270,33 @@ def get_optimizer_cls_and_kwargs(
# 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
+ elif "ademamix" in args.optim:
+ if is_bitsandbytes_available() and version.parse(
+ importlib.metadata.version("bitsandbytes")
+ ) < version.parse("0.44.0"):
+ raise ValueError(
+ "The AdEMAMix optimizer is not supported by your current version of `bitsandbytes`. "
+ "Please install `bitsandbytes` >= 0.44.0."
+ )
+
+ from bitsandbytes.optim import AdEMAMix
+
+ optimizer_cls = AdEMAMix
+ additional_optim_kwargs = {
+ "betas": (
+ float(optim_args.get("beta1", args.adam_beta1)),
+ float(optim_args.get("beta2", args.adam_beta2)),
+ float(optim_args.get("beta3", 0.9999)),
+ ),
+ "alpha": float(optim_args.get("alpha", 5.0)),
+ "eps": float(optim_args.get("eps", args.adam_epsilon)),
+ }
+
+ if "t_alpha" in optim_args:
+ additional_optim_kwargs["t_alpha"] = int(optim_args["t_alpha"])
+
+ if "t_beta3" in optim_args:
+ additional_optim_kwargs["t_beta3"] = int(optim_args["t_beta3"])
bnb_kwargs = {"optim_bits": optim_bits}
if "rmsprop" not in args.optim:
diff --git a/src/transformers/training_args.py b/src/transformers/training_args.py
index 02413c285832..596917928350 100644
--- a/src/transformers/training_args.py
+++ b/src/transformers/training_args.py
@@ -155,14 +155,18 @@ class OptimizerNames(ExplicitEnum):
ADAFACTOR = "adafactor"
ADAMW_ANYPRECISION = "adamw_anyprecision"
ADAMW_TORCH_4BIT = "adamw_torch_4bit"
+ ADEMAMIX = "ademamix"
SGD = "sgd"
ADAGRAD = "adagrad"
ADAMW_BNB = "adamw_bnb_8bit"
ADAMW_8BIT = "adamw_8bit" # just an alias for adamw_bnb_8bit
+ ADEMAMIX_8BIT = "ademamix_8bit"
LION_8BIT = "lion_8bit"
LION = "lion_32bit"
PAGED_ADAMW = "paged_adamw_32bit"
PAGED_ADAMW_8BIT = "paged_adamw_8bit"
+ PAGED_ADEMAMIX = "paged_ademamix_32bit"
+ PAGED_ADEMAMIX_8BIT = "paged_ademamix_8bit"
PAGED_LION = "paged_lion_32bit"
PAGED_LION_8BIT = "paged_lion_8bit"
RMSPROP = "rmsprop"
@@ -618,7 +622,7 @@ class TrainingArguments:
"adafactor". See `OptimizerNames` in [training_args.py](https://github.com/huggingface/transformers/blob/main/src/transformers/training_args.py)
for a full list of optimizers.
optim_args (`str`, *optional*):
- Optional arguments that are supplied to AnyPrecisionAdamW.
+ Optional arguments that are supplied to optimizers such as AnyPrecisionAdamW, AdEMAMix, and GaLore.
group_by_length (`bool`, *optional*, defaults to `False`):
Whether or not to group together samples of roughly the same length in the training dataset (to minimize
padding applied and be more efficient). Only useful if applying dynamic padding.
diff --git a/tests/trainer/test_trainer.py b/tests/trainer/test_trainer.py
index 14014e4a0947..0035ff7de8ba 100644
--- a/tests/trainer/test_trainer.py
+++ b/tests/trainer/test_trainer.py
@@ -15,6 +15,7 @@
import dataclasses
import gc
+import importlib
import json
import math
import os
@@ -32,6 +33,7 @@
import numpy as np
from huggingface_hub import HfFolder, ModelCard, create_branch, delete_repo, list_repo_commits, list_repo_files
+from packaging import version
from parameterized import parameterized
from requests.exceptions import HTTPError
@@ -1091,6 +1093,40 @@ def test_rmsprop_bnb(self):
# Check that it trains without errors
trainer.train()
+ @require_bitsandbytes
+ def test_ademamix_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="ademamix"
+ )
+ trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
+
+ # Check that it trains without errors
+ trainer.train()
+
+ @require_bitsandbytes
+ def test_ademamix_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="ademamix_8bit"
+ )
+ 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)
@@ -4187,6 +4223,13 @@ def hp_name(trial):
"lr": TrainingArguments.learning_rate,
}
+ default_ademamix_kwargs = {
+ "betas": (TrainingArguments.adam_beta1, TrainingArguments.adam_beta2, 0.9999),
+ "alpha": 5.0,
+ "eps": TrainingArguments.adam_epsilon,
+ "lr": TrainingArguments.learning_rate,
+ }
+
default_anyprecision_kwargs = {
"use_kahan_summation": False,
"momentum_dtype": torch.float32,
@@ -4291,6 +4334,36 @@ def hp_name(trial):
)
)
+ if version.parse(importlib.metadata.version("bitsandbytes")) >= version.parse("0.44.0"):
+ optim_test_params.append(
+ (
+ TrainingArguments(optim=OptimizerNames.ADEMAMIX, output_dir="None"),
+ bnb.optim.AdEMAMix,
+ default_ademamix_kwargs,
+ )
+ )
+ optim_test_params.append(
+ (
+ TrainingArguments(optim=OptimizerNames.ADEMAMIX_8BIT, output_dir="None"),
+ bnb.optim.AdEMAMix,
+ default_ademamix_kwargs,
+ )
+ )
+ optim_test_params.append(
+ (
+ TrainingArguments(optim=OptimizerNames.PAGED_ADEMAMIX_8BIT, output_dir="None"),
+ bnb.optim.AdEMAMix,
+ default_ademamix_kwargs,
+ )
+ )
+ optim_test_params.append(
+ (
+ TrainingArguments(optim=OptimizerNames.PAGED_ADEMAMIX, output_dir="None"),
+ bnb.optim.AdEMAMix,
+ default_ademamix_kwargs,
+ )
+ )
+
if is_torchdistx_available():
import torchdistx
@@ -4420,6 +4493,62 @@ def test_bnb_paged_adam8bit(self):
default_adam_kwargs,
)
+ def test_bnb_ademamix(self):
+ mock = Mock()
+ modules = {
+ "bitsandbytes": mock,
+ "bitsandbytes.optim": mock.optim,
+ "bitsandbytes.optim.AdEMAMix": mock.optim.AdEMAMix,
+ }
+ with patch.dict("sys.modules", modules):
+ self.check_optim_and_kwargs(
+ TrainingArguments(optim=OptimizerNames.ADEMAMIX, output_dir="None"),
+ mock.optim.AdEMAMix,
+ default_ademamix_kwargs,
+ )
+
+ def test_bnb_ademamix8bit(self):
+ mock = Mock()
+ modules = {
+ "bitsandbytes": mock,
+ "bitsandbytes.optim": mock.optim,
+ "bitsandbytes.optim.AdEMAMix": mock.optim.AdEMAMix,
+ }
+ with patch.dict("sys.modules", modules):
+ self.check_optim_and_kwargs(
+ TrainingArguments(optim=OptimizerNames.ADEMAMIX_8BIT, output_dir="None"),
+ mock.optim.AdEMAMix,
+ default_ademamix_kwargs,
+ )
+
+ def test_bnb_paged_ademamix(self):
+ mock = Mock()
+ modules = {
+ "bitsandbytes": mock,
+ "bitsandbytes.optim": mock.optim,
+ "bitsandbytes.optim.AdEMAMix": mock.optim.AdEMAMix,
+ }
+ with patch.dict("sys.modules", modules):
+ self.check_optim_and_kwargs(
+ TrainingArguments(optim=OptimizerNames.PAGED_ADEMAMIX, output_dir="None"),
+ mock.optim.AdEMAMix,
+ default_ademamix_kwargs,
+ )
+
+ def test_bnb_paged_ademamix8bit(self):
+ mock = Mock()
+ modules = {
+ "bitsandbytes": mock,
+ "bitsandbytes.optim": mock.optim,
+ "bitsandbytes.optim.AdEMAMix": mock.optim.AdEMAMix,
+ }
+ with patch.dict("sys.modules", modules):
+ self.check_optim_and_kwargs(
+ TrainingArguments(optim=OptimizerNames.PAGED_ADEMAMIX_8BIT, output_dir="None"),
+ mock.optim.AdEMAMix,
+ default_ademamix_kwargs,
+ )
+
def test_bnb_lion(self):
mock = Mock()
modules = {
@@ -4503,6 +4632,42 @@ def test_bnb_paged_adam8bit_no_bnb(self):
with self.assertRaises(ValueError):
Trainer.get_optimizer_cls_and_kwargs(args)
+ def test_bnb_ademamix_no_bnb(self):
+ args = TrainingArguments(optim=OptimizerNames.ADEMAMIX, output_dir="None")
+
+ # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
+ # bnb will fail even if `bitsandbytes` is installed.
+ with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
+ with self.assertRaises(ValueError):
+ Trainer.get_optimizer_cls_and_kwargs(args)
+
+ def test_bnb_ademamix8bit_no_bnb(self):
+ args = TrainingArguments(optim=OptimizerNames.ADEMAMIX_8BIT, output_dir="None")
+
+ # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
+ # bnb will fail even if `bitsandbytes` is installed.
+ with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
+ with self.assertRaises(ValueError):
+ Trainer.get_optimizer_cls_and_kwargs(args)
+
+ def test_bnb_paged_ademamix_no_bnb(self):
+ args = TrainingArguments(optim=OptimizerNames.PAGED_ADEMAMIX, output_dir="None")
+
+ # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
+ # bnb will fail even if `bitsandbytes` is installed.
+ with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
+ with self.assertRaises(ValueError):
+ Trainer.get_optimizer_cls_and_kwargs(args)
+
+ def test_bnb_paged_ademamix8bit_no_bnb(self):
+ args = TrainingArguments(optim=OptimizerNames.PAGED_ADEMAMIX_8BIT, output_dir="None")
+
+ # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
+ # bnb will fail even if `bitsandbytes` is installed.
+ with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
+ with self.assertRaises(ValueError):
+ Trainer.get_optimizer_cls_and_kwargs(args)
+
def test_bnb_paged_lion_no_bnb(self):
args = TrainingArguments(optim=OptimizerNames.PAGED_LION, output_dir="None")
|