File size: 6,728 Bytes
e0be88b |
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 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from transformers import is_torch_available
from transformers.testing_utils import (
TestCasePlus,
backend_device_count,
execute_subprocess_async,
get_torch_dist_unique_port,
require_accelerate,
require_fp8,
require_torch_multi_accelerator,
run_first,
torch_device,
)
if is_torch_available():
import torch
import torch.distributed
import torch.utils.data
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForSeq2Seq,
EvalPrediction,
GenerationConfig,
HfArgumentParser,
PreTrainedTokenizerBase,
Seq2SeqTrainer,
Seq2SeqTrainingArguments,
)
class DummyTextDataset(torch.utils.data.Dataset[str]):
def __init__(self, tokenizer: PreTrainedTokenizerBase) -> None:
data = 4 * [
"Hello world!",
"The quick brown fox jumps over the lazy dog.",
]
self.data = [
{k: v.squeeze(0) for k, v in tokenizer(item, return_tensors="pt", return_attention_mask=True).items()}
for item in data
]
for item in self.data:
item["labels"] = item["input_ids"]
def __len__(self) -> int:
return len(self.data)
def __getitem__(self, i: int) -> str:
return self.data[i]
class TestFSDPTrainer(TestCasePlus):
@require_torch_multi_accelerator
@require_accelerate
@run_first
def test_trainer(self):
output_dir = self.get_auto_remove_tmp_dir()
cmd = [
"accelerate",
"launch",
"--use_fsdp",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"--num_processes",
f"{backend_device_count(torch_device)}",
"--fsdp_transformer_layer_cls_to_wrap",
"GPT2Block",
f"{self.test_file_dir}/test_trainer_fsdp.py",
"--output_dir",
f"{output_dir}",
"--report_to",
"none",
]
execute_subprocess_async(cmd, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
class TestFSDPTrainerFP8(TestCasePlus):
@require_torch_multi_accelerator
@require_accelerate
@require_fp8
@run_first
def test_trainer(self):
output_dir = self.get_auto_remove_tmp_dir()
cmd = [
"accelerate",
"launch",
"--use_fsdp",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"--num_processes",
f"{backend_device_count(torch_device)}",
"--mixed_precision",
"fp8",
"--fsdp_transformer_layer_cls_to_wrap",
"GPT2Block",
f"{self.test_file_dir}/test_trainer_fsdp.py",
"--output_dir",
f"{output_dir}",
"--report_to",
"none",
]
execute_subprocess_async(cmd, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
class TestFSDPTrainerWrap(TestCasePlus):
@require_torch_multi_accelerator
@require_accelerate
@run_first
def test_trainer(self):
output_dir = self.get_auto_remove_tmp_dir()
cmd = [
"accelerate",
"launch",
"--use_fsdp",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"--num_processes",
f"{backend_device_count(torch_device)}",
"--fsdp_transformer_layer_cls_to_wrap",
"GPT2Block",
f"{self.test_file_dir}/test_trainer_fsdp.py",
"--output_dir",
f"{output_dir}",
"--report_to",
"none",
"--auto_find_batch_size",
"True",
]
execute_subprocess_async(cmd, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
class TestFSDPTrainerTorchCompile(TestCasePlus):
@require_torch_multi_accelerator
@require_accelerate
@run_first
def test_trainer(self):
output_dir = self.get_auto_remove_tmp_dir()
cmd = [
"accelerate",
"launch",
"--use_fsdp",
"--main_process_port",
f"{get_torch_dist_unique_port()}",
"--num_processes",
f"{backend_device_count(torch_device)}",
"--fsdp_transformer_layer_cls_to_wrap",
"GPT2Block",
f"{self.test_file_dir}/test_trainer_fsdp.py",
"--torch_compile_mode",
"default",
"--output_dir",
f"{output_dir}",
"--report_to",
"none",
]
execute_subprocess_async(cmd, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
parser = HfArgumentParser((Seq2SeqTrainingArguments,))
training_args = parser.parse_args_into_dataclasses()[0]
training_args.per_device_eval_batch_size = 1
training_args.use_legacy_prediction_loop = False
training_args.predict_with_generate = True
training_args.generation_config = GenerationConfig(max_length=30)
pretrained_model_name = "hf-internal-testing/tiny-random-gpt2"
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name)
tokenizer.pad_token = tokenizer.eos_token
device = torch.device(torch.distributed.get_rank())
model = AutoModelForCausalLM.from_pretrained(pretrained_model_name).to(device)
def compute_metrics(p: EvalPrediction) -> dict[str, bool]:
return {"accuracy": (p.predictions == p.label_ids).mean()}
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
data_collator=DataCollatorForSeq2Seq(tokenizer, model),
eval_dataset=DummyTextDataset(tokenizer),
compute_metrics=compute_metrics,
)
metrics = trainer.evaluate()
|