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import json
import datasets
from tests.trainer.test_trainer import StoreLossCallback
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.testing_utils import (
TestCasePlus,
backend_device_count,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_accelerator,
torch_device,
)
class TestTrainerDistributedLoss(TestCasePlus):
@require_torch_multi_accelerator
def test_trainer(self):
device_count = backend_device_count(torch_device)
min_bs = 2
output_dir = self.get_auto_remove_tmp_dir()
for gpu_num, enable, bs, name in (
(1, True, min_bs * device_count, "base"),
(device_count, False, min_bs, "broken"),
(device_count, True, min_bs, "fixed"),
):
distributed_args = f"""--nproc_per_node={gpu_num}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed_loss.py
""".split()
args = f"--output_dir {output_dir}/{name} --per_device_train_batch_size {bs} --average_tokens_across_devices {enable}".split()
cmd = ["torchrun"] + distributed_args + args
execute_subprocess_async(cmd, env=self.get_env())
with open(f"{output_dir}/base_losses.json") as f:
base_loss = json.load(f)
with open(f"{output_dir}/broken_losses.json") as f:
broken_loss = json.load(f)
with open(f"{output_dir}/fixed_losses.json") as f:
fixed_loss = json.load(f)
broken_diff = [abs(base_loss[i] - broken_loss[i]) for i in range(len(base_loss))]
fixed_diff = [abs(base_loss[i] - fixed_loss[i]) for i in range(len(base_loss))]
sum_base = sum(base_loss)
sum_broken = sum(broken_loss)
relative_broken = abs(sum_base - sum_broken) / max(sum_base, sum_broken)
# the gap may be smaller for other models, but it still ok.
self.assertGreater(max(broken_diff), 0.5)
self.assertLess(max(fixed_diff), 0.005)
self.assertLess(relative_broken, 0.1)
def run_distributed_training(training_args):
set_seed(42)
model_name = "nickypro/tinyllama-15M"
dataset_name = "wikitext"
dataset_config = "wikitext-2-raw-v1"
dataset = datasets.load_dataset(dataset_name, dataset_config, split="train[:100]")
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
def tokenize_function(examples):
return tokenizer(examples["text"], max_length=16, padding="max_length", truncation=True)
tokenized_dataset = dataset.map(tokenize_function, batched=True)
tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
model = AutoModelForCausalLM.from_pretrained(model_name)
loss_callback = StoreLossCallback()
training_args.logging_steps = 1
training_args.max_steps = 10
training_args.learning_rate = 3e-4
training_args.disable_tqdm = True
training_args.dataloader_drop_last = True
training_args.report_to = []
trainer = Trainer(
model,
training_args,
train_dataset=tokenized_dataset,
callbacks=[loss_callback],
data_collator=data_collator,
)
trainer.train()
with open(training_args.output_dir + "_losses.json", "w") as f:
json.dump(loss_callback.losses, f)
if __name__ == "__main__":
parser = HfArgumentParser((TrainingArguments,))
training_args = parser.parse_args_into_dataclasses()[0]
run_distributed_training(training_args)
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