SUMIN/HuggingFace_transformer
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Reformat source code with black.

Browse Source 
This is the result of:

    $ black --line-length 119 examples templates transformers utils hubconf.py setup.py

There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.

This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.
This commit is contained in:
Aymeric Augustin
2019-12-21 15:46:46 +01:00
parent 63e3827c6b
commit fa84ae26d6
200 changed files with 17452 additions and 12594 deletions
Download Patch File Download Diff File Expand all files Collapse all files

169
examples/benchmarks.py
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@@ -247,16 +247,18 @@ the wall, slowly on into the Social Predestination Room.
as they entered."""
def create_setup_and_compute(model_names: List[str],
gpu: bool = True,
tensorflow: bool = False,
average_over: int = 3,
torchscript: bool = False,
xla: bool = False,
amp: bool = False,
fp16: bool = False,
save_to_csv: bool = False,
csv_filename: str = f"results_{round(time())}.csv"):
def create_setup_and_compute(
model_names: List[str],
gpu: bool = True,
tensorflow: bool = False,
average_over: int = 3,
torchscript: bool = False,
xla: bool = False,
amp: bool = False,
fp16: bool = False,
save_to_csv: bool = False,
csv_filename: str = f"results_{round(time())}.csv",
):
if xla:
tf.config.optimizer.set_jit(True)
if amp:
@@ -266,7 +268,7 @@ def create_setup_and_compute(model_names: List[str],
dictionary = {model_name: {} for model_name in model_names}
results = _compute_tensorflow(model_names, dictionary, average_over, amp)
else:
device = 'cuda' if (gpu and torch.cuda.is_available()) else 'cpu'
device = "cuda" if (gpu and torch.cuda.is_available()) else "cpu"
dictionary = {model_name: {} for model_name in model_names}
results = _compute_pytorch(model_names, dictionary, average_over, device, torchscript, fp16)
@@ -276,34 +278,52 @@ def create_setup_and_compute(model_names: List[str],
for batch_size in results[model_name]["bs"]:
print("\t\t" + f"===== BATCH SIZE: {batch_size} =====")
for slice_size in results[model_name]["ss"]:
result = results[model_name]['results'][batch_size][slice_size]
result = results[model_name]["results"][batch_size][slice_size]
if isinstance(result, str):
print(f"\t\t{model_name}/{batch_size}/{slice_size}: "
f"{result}")
print(f"\t\t{model_name}/{batch_size}/{slice_size}: " f"{result}")
else:
print(f"\t\t{model_name}/{batch_size}/{slice_size}: "
f"{(round(1000 * result) / 1000)}"
f"s")
print(f"\t\t{model_name}/{batch_size}/{slice_size}: " f"{(round(1000 * result) / 1000)}" f"s")
if save_to_csv:
with open(csv_filename, mode='w') as csv_file:
fieldnames = ['model',
'1x8', '1x64', '1x128', '1x256', '1x512', '1x1024',
'2x8', '2x64', '2x128', '2x256', '2x512', '2x1024',
'4x8', '4x64', '4x128', '4x256', '4x512', '4x1024',
'8x8', '8x64', '8x128', '8x256', '8x512', '8x1024',
]
with open(csv_filename, mode="w") as csv_file:
fieldnames = [
"model",
"1x8",
"1x64",
"1x128",
"1x256",
"1x512",
"1x1024",
"2x8",
"2x64",
"2x128",
"2x256",
"2x512",
"2x1024",
"4x8",
"4x64",
"4x128",
"4x256",
"4x512",
"4x1024",
"8x8",
"8x64",
"8x128",
"8x256",
"8x512",
"8x1024",
]
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
writer.writeheader()
for model_name in model_names:
model_results = {
f'{bs}x{ss}': results[model_name]['results'][bs][ss]
f"{bs}x{ss}": results[model_name]["results"][bs][ss]
for bs in results[model_name]["results"]
for ss in results[model_name]['results'][bs]
for ss in results[model_name]["results"][bs]
}
writer.writerow({'model': model_name, **model_results})
writer.writerow({"model": model_name, **model_results})
def _compute_pytorch(model_names, dictionary, average_over, device, torchscript, fp16):
@@ -343,7 +363,7 @@ def _compute_pytorch(model_names, dictionary, average_over, device, torchscript,
print("Going through model with sequence of shape", sequence.shape)
runtimes = timeit.repeat(lambda: inference(sequence), repeat=average_over, number=3)
average_time = sum(runtimes)/float(len(runtimes)) / 3.0
average_time = sum(runtimes) / float(len(runtimes)) / 3.0
dictionary[model_name]["results"][batch_size][slice_size] = average_time
except RuntimeError as e:
print("Doesn't fit on GPU.", e)
@@ -379,7 +399,9 @@ def _compute_tensorflow(model_names, dictionary, average_over, amp):
if max_input_size is not None and slice_size > max_input_size:
dictionary[model_name]["results"][batch_size][slice_size] = "N/A"
else:
sequence = tf.stack([tf.squeeze(tf.constant(tokenized_sequence[:slice_size])[None, :])] * batch_size)
sequence = tf.stack(
[tf.squeeze(tf.constant(tokenized_sequence[:slice_size])[None, :])] * batch_size
)
try:
print("Going through model with sequence of shape", sequence.shape)
@@ -387,7 +409,7 @@ def _compute_tensorflow(model_names, dictionary, average_over, amp):
inference(sequence)
runtimes = timeit.repeat(lambda: inference(sequence), repeat=average_over, number=3)
average_time = sum(runtimes)/float(len(runtimes)) / 3.0
average_time = sum(runtimes) / float(len(runtimes)) / 3.0
dictionary[model_name]["results"][batch_size][slice_size] = average_time
except tf.errors.ResourceExhaustedError as e:
print("Doesn't fit on GPU.", e)
@@ -399,33 +421,64 @@ def _compute_tensorflow(model_names, dictionary, average_over, amp):
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--models", required=False, type=str, default='all', help="Model checkpoints to be provided "
"to the AutoModel classes. Leave "
"blank to benchmark the base version "
"of all available model "
"architectures.")
parser.add_argument("--torch", required=False, action="store_true", help="Benchmark the Pytorch version of the "
"models")
parser.add_argument("--torch_cuda", required=False, action="store_true", help="Pytorch only: run on available "
"cuda devices")
parser.add_argument("--torchscript", required=False, action="store_true", help="Pytorch only: trace the models "
"using torchscript")
parser.add_argument("--tensorflow", required=False, action="store_true", help="Benchmark the TensorFlow version "
"of the models. Will run on GPU if "
"the correct dependencies are "
"installed")
parser.add_argument(
"--models",
required=False,
type=str,
default="all",
help="Model checkpoints to be provided "
"to the AutoModel classes. Leave "
"blank to benchmark the base version "
"of all available model "
"architectures.",
)
parser.add_argument(
"--torch", required=False, action="store_true", help="Benchmark the Pytorch version of the " "models"
)
parser.add_argument(
"--torch_cuda", required=False, action="store_true", help="Pytorch only: run on available " "cuda devices"
)
parser.add_argument(
"--torchscript",
required=False,
action="store_true",
help="Pytorch only: trace the models " "using torchscript",
)
parser.add_argument(
"--tensorflow",
required=False,
action="store_true",
help="Benchmark the TensorFlow version "
"of the models. Will run on GPU if "
"the correct dependencies are "
"installed",
)
parser.add_argument("--xla", required=False, action="store_true", help="TensorFlow only: use XLA acceleration.")
parser.add_argument("--amp", required=False, action="store_true", help="TensorFlow only: use automatic mixed precision acceleration.")
parser.add_argument("--fp16", required=False, action="store_true", help="PyTorch only: use FP16 to accelerate inference.")
parser.add_argument("--keras_predict", required=False, action="store_true", help="Whether to use model.predict "
"instead of model() to do a "
"forward pass.")
parser.add_argument(
"--amp",
required=False,
action="store_true",
help="TensorFlow only: use automatic mixed precision acceleration.",
)
parser.add_argument(
"--fp16", required=False, action="store_true", help="PyTorch only: use FP16 to accelerate inference."
)
parser.add_argument(
"--keras_predict",
required=False,
action="store_true",
help="Whether to use model.predict " "instead of model() to do a " "forward pass.",
)
parser.add_argument("--save_to_csv", required=False, action="store_true", help="Save to a CSV file.")
parser.add_argument("--csv_filename", required=False, default=None, help="CSV filename used if saving results to csv.")
parser.add_argument("--average_over", required=False, default=30, type=int, help="Times an experiment will be run.")
parser.add_argument(
"--csv_filename", required=False, default=None, help="CSV filename used if saving results to csv."
)
parser.add_argument(
"--average_over", required=False, default=30, type=int, help="Times an experiment will be run."
)
args = parser.parse_args()
if args.models == 'all':
if args.models == "all":
args.models = [
"gpt2",
"bert-base-cased",
@@ -436,7 +489,7 @@ def main():
"distilbert-base-uncased",
"distilgpt2",
"roberta-base",
"ctrl"
"ctrl",
]
else:
args.models = args.models.split()
@@ -453,7 +506,7 @@ def main():
fp16=args.fp16,
save_to_csv=args.save_to_csv,
csv_filename=args.csv_filename,
average_over=args.average_over
average_over=args.average_over,
)
else:
raise ImportError("Trying to run a PyTorch benchmark but PyTorch was not found in the environment.")
@@ -467,11 +520,11 @@ def main():
amp=args.amp,
save_to_csv=args.save_to_csv,
csv_filename=args.csv_filename,
average_over=args.average_over
average_over=args.average_over,
)
else:
raise ImportError("Trying to run a TensorFlow benchmark but TensorFlow was not found in the environment.")
if __name__ == '__main__':
main()
if __name__ == "__main__":
main()