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Added mixed precision support to benchmarks.py

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Timothy Liu 2019-10-30 00:44:23 +00:00 committed by Lysandre Debut
parent 2a5663c280
commit be36cf92fb
1 changed files with 14 additions and 4 deletions
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18
examples/benchmarks.py
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@ -253,18 +253,22 @@ def create_setup_and_compute(model_names: List[str],
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:
tf.config.optimizer.set_experimental_options({"auto_mixed_precision": True})
if tensorflow:
dictionary = {model_name: {} for model_name in model_names}
results = _compute_tensorflow(model_names, dictionary, average_over)
results = _compute_tensorflow(model_names, dictionary, average_over, amp)
else:
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)
results = _compute_pytorch(model_names, dictionary, average_over, device, torchscript, fp16)
print("=========== RESULTS ===========")
for model_name in model_names:
@ -302,7 +306,7 @@ def create_setup_and_compute(model_names: List[str],
writer.writerow({'model': model_name, **model_results})
def _compute_pytorch(model_names, dictionary, average_over, device, torchscript):
def _compute_pytorch(model_names, dictionary, average_over, device, torchscript, fp16):
for c, model_name in enumerate(model_names):
print(f"{c + 1} / {len(model_names)}")
config = AutoConfig.from_pretrained(model_name, torchscript=torchscript)
@ -319,6 +323,8 @@ def _compute_pytorch(model_names, dictionary, average_over, device, torchscript)
dictionary[model_name]["results"] = {i: {} for i in batch_sizes}
for batch_size in batch_sizes:
if fp16:
model.half()
model.to(device)
model.eval()
for slice_size in slice_sizes:
@ -346,7 +352,7 @@ def _compute_pytorch(model_names, dictionary, average_over, device, torchscript)
return dictionary
def _compute_tensorflow(model_names, dictionary, average_over):
def _compute_tensorflow(model_names, dictionary, average_over, amp):
for c, model_name in enumerate(model_names):
print(f"{c + 1} / {len(model_names)}")
config = AutoConfig.from_pretrained(model_name)
@ -409,6 +415,8 @@ def main():
"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.")
@ -442,6 +450,7 @@ def main():
tensorflow=False,
gpu=args.torch_cuda,
torchscript=args.torchscript,
fp16=args.fp16,
save_to_csv=args.save_to_csv,
csv_filename=args.csv_filename,
average_over=args.average_over
@ -455,6 +464,7 @@ def main():
model_names=args.models,
tensorflow=True,
xla=args.xla,
amp=args.amp,
save_to_csv=args.save_to_csv,
csv_filename=args.csv_filename,
average_over=args.average_over