def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument(
"-m",
"--models",
required=False,
nargs="+",
type=str,
default=["bert-base-cased", "roberta-base", "gpt2"],
choices=list(MODELS.keys()),
help="Pre-trained models in the list: " + ", ".join(MODELS.keys()),
)
parser.add_argument(
"--model_source",
required=False,
nargs=1,
type=str,
default="pt",
choices=["pt", "tf"],
help="Export onnx from pt or tf",
)
parser.add_argument(
"--model_class",
required=False,
type=str,
default=None,
choices=list(MODEL_CLASSES),
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES),
)
parser.add_argument(
"-e",
"--engines",
required=False,
nargs="+",
type=str,
default=["onnxruntime"],
choices=["onnxruntime", "torch", "torchscript", "tensorflow"],
help="Engines to benchmark",
)
parser.add_argument(
"-c",
"--cache_dir",
required=False,
type=str,
default=os.path.join(".", "cache_models"),
help="Directory to cache pre-trained models",
)
parser.add_argument(
"--onnx_dir",
required=False,
type=str,
default=os.path.join(".", "onnx_models"),
help="Directory to store onnx models",
)
parser.add_argument("-g",
"--use_gpu",
required=False,
action="store_true",
help="Run on gpu device")
parser.add_argument(
"--provider",
required=False,
type=str,
default=None,
help="Execution provider to use",
)
parser.add_argument(
"-p",
"--precision",
type=Precision,
default=Precision.FLOAT32,
choices=list(Precision),
help=
"Precision of model to run. fp32 for full precision, fp16 for half precision, and int8 for quantization",
)
parser.add_argument("--verbose",
required=False,
action="store_true",
help="Print more information")
parser.add_argument(
"--overwrite",
required=False,
action="store_true",
help="Overwrite existing models",
)
parser.add_argument(
"-o",
"--optimizer_info",
type=OptimizerInfo,
default=OptimizerInfo.BYSCRIPT,
choices=list(OptimizerInfo),
help=
"Optimizer info: Use optimizer.py to optimize onnx model as default. Can also choose from by_ort and no_opt",
)
parser.add_argument(
"-v",
"--validate_onnx",
required=False,
action="store_true",
help="Validate ONNX model",
)
parser.add_argument(
"-f",
"--fusion_csv",
required=False,
default=None,
help="CSV file for saving summary results of graph optimization.",
)
parser.add_argument(
"-d",
"--detail_csv",
required=False,
default=None,
help="CSV file for saving detail results.",
)
parser.add_argument(
"-r",
"--result_csv",
required=False,
default=None,
help="CSV file for saving summary results.",
)
parser.add_argument(
"-i",
"--input_counts",
required=False,
nargs="+",
default=[1],
type=int,
choices=[1, 2, 3],
help=
"Number of ONNX model inputs. Please use 1 for fair comparison with Torch or TorchScript.",
)
parser.add_argument(
"-t",
"--test_times",
required=False,
default=100,
type=int,
help="Number of repeat times to get average inference latency.",
)
parser.add_argument("-b",
"--batch_sizes",
nargs="+",
type=int,
default=[1])
parser.add_argument(
"-s",
"--sequence_lengths",
nargs="+",
type=int,
default=[4, 8, 16, 32, 64, 128, 256],
)
parser.add_argument(
"--disable_ort_io_binding",
required=False,
action="store_true",
help="Disable running ONNX Runtime with binded inputs and outputs. ",
)
parser.set_defaults(disable_ort_io_binding=False)
parser.add_argument(
"-n",
"--num_threads",
required=False,
nargs="+",
type=int,
default=[0],
help="Threads to use",
)
parser.add_argument(
"--force_num_layers",
required=False,
type=int,
default=None,
help="Manually set the model's layer number",
)
FusionOptions.add_arguments(parser)
args = parser.parse_args()
return args
def _parse_arguments():
parser = argparse.ArgumentParser(
description=
'Graph optimization tool for ONNX Runtime. It transforms ONNX graph to use optimized operators for Transformer models.'
)
parser.add_argument('--input',
required=True,
type=str,
help="input onnx model path")
parser.add_argument('--output',
required=True,
type=str,
help="optimized onnx model path")
parser.add_argument('--model_type',
required=False,
type=str.lower,
default="bert",
choices=list(MODEL_TYPES.keys()),
help="Model type selected in the list: " +
", ".join(MODEL_TYPES.keys()))
parser.add_argument(
'--num_heads',
required=False,
type=int,
default=0,
help=
"number of attention heads like 12 for bert-base and 16 for bert-large. Default is 0 to detect automatically for BERT. For other model type, this parameter need specify correctly."
)
parser.add_argument(
'--hidden_size',
required=False,
type=int,
default=0,
help=
"hidden size like 768 for bert-base and 1024 for bert-large. Default is 0 to detect automatically for BERT. For other model type, this parameter need specify correctly."
)
parser.add_argument(
'--input_int32',
required=False,
action='store_true',
help=
"Use int32 (instead of int64) inputs. It could avoid unnecessary data cast when EmbedLayerNormalization is fused for BERT."
)
parser.set_defaults(input_int32=False)
parser.add_argument(
'--float16',
required=False,
action='store_true',
help=
"Convert all weights and nodes in float32 to float16. It has potential loss in precision compared to mixed precision conversion (see convert_float_to_float16)."
)
parser.set_defaults(float16=False)
FusionOptions.add_arguments(parser)
parser.add_argument('--verbose',
required=False,
action='store_true',
help="show debug information.")
parser.set_defaults(verbose=False)
parser.add_argument(
'--use_gpu',
required=False,
action='store_true',
help=
"Use GPU for inference. Set this flag if your model is intended for GPU when opt_level > 1."
)
parser.set_defaults(use_gpu=False)
parser.add_argument(
'--only_onnxruntime',
required=False,
action='store_true',
help="optimized by onnxruntime only, and no graph fusion in Python")
parser.set_defaults(only_onnxruntime=False)
parser.add_argument(
'--opt_level',
required=False,
type=int,
choices=[0, 1, 2, 99],
default=None,
help=
"onnxruntime optimization level. 0 will disable onnxruntime graph optimization. The recommended value is 1. When opt_level > 1 is used, optimized model for GPU might not run in CPU. Level 2 and 99 are intended for --only_onnxruntime."
)
parser.add_argument(
'--use_external_data_format',
required=False,
action='store_true',
help="use external data format to store large model (>2GB)")
parser.set_defaults(use_external_data_format=False)
args = parser.parse_args()
return args
def _parse_arguments():
parser = argparse.ArgumentParser(
description=
'Graph optimization tool for ONNX Runtime. It transforms ONNX graph to use optimized operators for Transformer models.'
)
parser.add_argument('--input', required=True, type=str, help="input onnx model path")
parser.add_argument('--output', required=True, type=str, help="optimized onnx model path")
parser.add_argument('--model_type',
required=False,
type=str.lower,
default="bert",
choices=list(MODEL_TYPES.keys()),
help="Model type selected in the list: " + ", ".join(MODEL_TYPES.keys()))
parser.add_argument(
'--num_heads',
required=False,
type=int,
default=12,
help=
"number of attention heads. 12 for bert-base model and 16 for bert-large. For BERT, set it to 0 to detect automatically."
)
parser.add_argument(
'--hidden_size',
required=False,
type=int,
default=768,
help=
"bert model hidden size. 768 for bert-base model and 1024 for bert-large. For BERT, set it to 0 to detect automatically."
)
parser.add_argument('--input_int32',
required=False,
action='store_true',
help="Use int32 (instead of int64) tensor as input to avoid unnecessary data cast.")
parser.set_defaults(input_int32=False)
parser.add_argument(
'--float16',
required=False,
action='store_true',
help=
"If your target device is V100 or T4 GPU, try this to convert float32 to float16 for best performance (with potential loss in precision)."
)
parser.set_defaults(float16=False)
FusionOptions.add_arguments(parser)
parser.add_argument('--verbose', required=False, action='store_true', help="show debug information.")
parser.set_defaults(verbose=False)
parser.add_argument(
'--use_gpu',
required=False,
action='store_true',
help="use GPU for inference. Set this flag if your model is intended for GPU and opt_level > 1.")
parser.set_defaults(use_gpu=False)
parser.add_argument('--only_onnxruntime',
required=False,
action='store_true',
help="optimized by onnxruntime only, and no graph fusion in Python")
parser.set_defaults(only_onnxruntime=False)
parser.add_argument(
'--opt_level',
required=False,
type=int,
choices=[0, 1, 2, 99],
default=None,
help=
"onnxruntime optimization level. 0 will disable onnxruntime graph optimization. Graph fusion in Python is not impacted by setting."
)
parser.add_argument('--use_external_data_format',
required=False,
action='store_true',
help="use external data format")
parser.set_defaults(use_external_data_format=False)
args = parser.parse_args()
return args
