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Reformat source code with black.
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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.
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@aaugustin
aaugustin committed Dec 21, 2019
1 parent 63e3827 commit fa84ae2
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Showing 200 changed files with 17,612 additions and 12,754 deletions.
169 changes: 111 additions & 58 deletions examples/benchmarks.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -247,16 +247,18 @@
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:
Expand All @@ -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)

Expand All @@ -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):
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -379,15 +399,17 @@ def inference(inputs):
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)
# To make sure that the model is traced + that the tensors are on the appropriate device
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)
Expand All @@ -399,33 +421,64 @@ def inference(inputs):
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",
Expand All @@ -436,7 +489,7 @@ def main():
"distilbert-base-uncased",
"distilgpt2",
"roberta-base",
"ctrl"
"ctrl",
]
else:
args.models = args.models.split()
Expand All @@ -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.")
Expand All @@ -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()
37 changes: 18 additions & 19 deletions examples/contrib/run_camembert.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -10,38 +10,37 @@

def fill_mask(masked_input, model, tokenizer, topk=5):
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>') == 1
assert masked_input.count("<mask>") == 1
input_ids = torch.tensor(tokenizer.encode(masked_input, add_special_tokens=True)).unsqueeze(0) # Batch size 1
logits = model(input_ids)[0] # The last hidden-state is the first element of the output tuple
masked_index = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
logits = logits[0, masked_index, :]
prob = logits.softmax(dim=0)
values, indices = prob.topk(k=topk, dim=0)
topk_predicted_token_bpe = ' '.join([tokenizer.convert_ids_to_tokens(indices[i].item())
for i in range(len(indices))])
topk_predicted_token_bpe = " ".join(
[tokenizer.convert_ids_to_tokens(indices[i].item()) for i in range(len(indices))]
)
masked_token = tokenizer.mask_token
topk_filled_outputs = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ')):
predicted_token = predicted_token_bpe.replace('\u2581', ' ')
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" ")):
predicted_token = predicted_token_bpe.replace("\u2581", " ")
if " {0}".format(masked_token) in masked_input:
topk_filled_outputs.append((
masked_input.replace(
' {0}'.format(masked_token), predicted_token
),
values[index].item(),
predicted_token,
))
topk_filled_outputs.append(
(
masked_input.replace(" {0}".format(masked_token), predicted_token),
values[index].item(),
predicted_token,
)
)
else:
topk_filled_outputs.append((
masked_input.replace(masked_token, predicted_token),
values[index].item(),
predicted_token,
))
topk_filled_outputs.append(
(masked_input.replace(masked_token, predicted_token), values[index].item(), predicted_token,)
)
return topk_filled_outputs


tokenizer = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertForMaskedLM.from_pretrained('camembert-base')
tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
model = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

masked_input = "Le camembert est <mask> :)"
Expand Down
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