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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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16
examples/tutorial/new_api/glue_bert/data.py
View File

@@ -5,7 +5,6 @@ from colossalai.booster.plugin.dp_plugin_base import DPPluginBase
class GLUEDataBuilder:
task_text_field_map = {
"cola": ["sentence"],
"sst2": ["sentence"],
@@ -84,10 +83,9 @@ class GLUEDataBuilder:
AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True)
def train_dataloader(self):
return self.plugin.prepare_dataloader(self.dataset["train"],
batch_size=self.train_batch_size,
shuffle=True,
drop_last=True)
return self.plugin.prepare_dataloader(
self.dataset["train"], batch_size=self.train_batch_size, shuffle=True, drop_last=True
)
def val_dataloader(self):
if len(self.eval_splits) == 1:
@@ -108,7 +106,6 @@ class GLUEDataBuilder:
]
def convert_to_features(self, example_batch):
# Either encode single sentence or sentence pairs
if len(self.text_fields) > 1:
texts_or_text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]]))
@@ -116,10 +113,9 @@ class GLUEDataBuilder:
texts_or_text_pairs = example_batch[self.text_fields[0]]
# Tokenize the text/text pairs
features = self.tokenizer.batch_encode_plus(texts_or_text_pairs,
max_length=self.max_seq_length,
padding='max_length',
truncation=True)
features = self.tokenizer.batch_encode_plus(
texts_or_text_pairs, max_length=self.max_seq_length, padding="max_length", truncation=True
)
# Rename label to labels to make it easier to pass to model forward
features["labels"] = example_batch["label"]

80
examples/tutorial/new_api/glue_bert/finetune.py
View File

@@ -33,8 +33,14 @@ def move_to_cuda(batch):
@torch.no_grad()
def evaluate(model: nn.Module, test_dataloader: Union[DataLoader, List[DataLoader]], num_labels: int, task_name: str,
eval_splits: List[str], coordinator: DistCoordinator):
def evaluate(
model: nn.Module,
test_dataloader: Union[DataLoader, List[DataLoader]],
num_labels: int,
task_name: str,
eval_splits: List[str],
coordinator: DistCoordinator,
):
metric = datasets.load_metric("glue", task_name, process_id=coordinator.rank, num_process=coordinator.world_size)
model.eval()
@@ -58,7 +64,7 @@ def evaluate(model: nn.Module, test_dataloader: Union[DataLoader, List[DataLoade
results = metric.compute()
dist.all_reduce(accum_loss.div_(len(dataloader)))
if coordinator.is_master():
results['loss'] = accum_loss.item() / coordinator.world_size
results["loss"] = accum_loss.item() / coordinator.world_size
return results
if isinstance(test_dataloader, DataLoader):
@@ -68,14 +74,21 @@ def evaluate(model: nn.Module, test_dataloader: Union[DataLoader, List[DataLoade
final_results = {}
for split, sub_loader in zip(eval_splits, test_dataloader):
results = evaluate_subset(sub_loader)
final_results.update({f'{k}_{split}': v for k, v in results.items()})
final_results.update({f"{k}_{split}": v for k, v in results.items()})
return final_results
def train_epoch(epoch: int, model: nn.Module, optimizer: Optimizer, lr_scheduler, train_dataloader: DataLoader,
booster: Booster, coordinator: DistCoordinator):
def train_epoch(
epoch: int,
model: nn.Module,
optimizer: Optimizer,
lr_scheduler,
train_dataloader: DataLoader,
booster: Booster,
coordinator: DistCoordinator,
):
model.train()
with tqdm(train_dataloader, desc=f'Epoch [{epoch + 1}/{NUM_EPOCHS}]', disable=not coordinator.is_master()) as pbar:
with tqdm(train_dataloader, desc=f"Epoch [{epoch + 1}/{NUM_EPOCHS}]", disable=not coordinator.is_master()) as pbar:
for batch in pbar:
# Forward pass
batch = move_to_cuda(batch)
@@ -89,7 +102,7 @@ def train_epoch(epoch: int, model: nn.Module, optimizer: Optimizer, lr_scheduler
lr_scheduler.step()
# Print log info
pbar.set_postfix({'loss': loss.item()})
pbar.set_postfix({"loss": loss.item()})
def main():
@@ -97,14 +110,16 @@ def main():
# Parse Arguments
# ==============================
parser = argparse.ArgumentParser()
parser.add_argument('-t', '--task', default='mrpc', help="GLUE task to run")
parser.add_argument('-p',
'--plugin',
type=str,
default='torch_ddp',
choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'],
help="plugin to use")
parser.add_argument('--target_f1', type=float, default=None, help="target f1 score. Raise exception if not reached")
parser.add_argument("-t", "--task", default="mrpc", help="GLUE task to run")
parser.add_argument(
"-p",
"--plugin",
type=str,
default="torch_ddp",
choices=["torch_ddp", "torch_ddp_fp16", "gemini", "low_level_zero"],
help="plugin to use",
)
parser.add_argument("--target_f1", type=float, default=None, help="target f1 score. Raise exception if not reached")
args = parser.parse_args()
# ==============================
@@ -115,19 +130,19 @@ def main():
# local_batch_size = BATCH_SIZE // coordinator.world_size
lr = LEARNING_RATE * coordinator.world_size
model_name = 'bert-base-uncased'
model_name = "bert-base-uncased"
# ==============================
# Instantiate Plugin and Booster
# ==============================
booster_kwargs = {}
if args.plugin == 'torch_ddp_fp16':
booster_kwargs['mixed_precision'] = 'fp16'
if args.plugin.startswith('torch_ddp'):
if args.plugin == "torch_ddp_fp16":
booster_kwargs["mixed_precision"] = "fp16"
if args.plugin.startswith("torch_ddp"):
plugin = TorchDDPPlugin()
elif args.plugin == 'gemini':
plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2**5)
elif args.plugin == 'low_level_zero':
elif args.plugin == "gemini":
plugin = GeminiPlugin(placement_policy="cuda", strict_ddp_mode=True, initial_scale=2**5)
elif args.plugin == "low_level_zero":
plugin = LowLevelZeroPlugin(initial_scale=2**5)
booster = Booster(plugin=plugin, **booster_kwargs)
@@ -135,11 +150,9 @@ def main():
# ==============================
# Prepare Dataloader
# ==============================
data_builder = GLUEDataBuilder(model_name,
plugin,
args.task,
train_batch_size=BATCH_SIZE,
eval_batch_size=BATCH_SIZE)
data_builder = GLUEDataBuilder(
model_name, plugin, args.task, train_batch_size=BATCH_SIZE, eval_batch_size=BATCH_SIZE
)
train_dataloader = data_builder.train_dataloader()
test_dataloader = data_builder.test_dataloader()
@@ -185,14 +198,15 @@ def main():
for epoch in range(NUM_EPOCHS):
train_epoch(epoch, model, optimizer, lr_scheduler, train_dataloader, booster, coordinator)
results = evaluate(model, test_dataloader, data_builder.num_labels, args.task, data_builder.eval_splits,
coordinator)
results = evaluate(
model, test_dataloader, data_builder.num_labels, args.task, data_builder.eval_splits, coordinator
)
if coordinator.is_master():
print(results)
if args.target_f1 is not None and 'f1' in results:
assert results['f1'] >= args.target_f1, f'f1 score {results["f1"]} is lower than target {args.target_f1}'
if args.target_f1 is not None and "f1" in results:
assert results["f1"] >= args.target_f1, f'f1 score {results["f1"]} is lower than target {args.target_f1}'
if __name__ == '__main__':
if __name__ == "__main__":
main()