def test_optimize_concurrency(concurrency_num):
result_path = "concurrency_opt_{}".format(concurrency_num)
opt_config = OptimizationConfig(model_path=ONNX_MODEL_PATH,
concurrency_num=concurrency_num,
result_path=result_path)
optimize(opt_config)
shutil.rmtree(result_path)
def test_optimize_openmp(openmp_enabled):
result_path = "openmp_opt"
opt_config = OptimizationConfig(model_path=ONNX_MODEL_PATH,
openmp_enabled=openmp_enabled,
result_path=result_path)
optimize(opt_config)
shutil.rmtree(result_path)
def test_optimize_transformer(transformer_enabled):
model_path = os.path.join(os.path.dirname(__file__), "other_models",
"TFBertForQuestionAnswering.onnx")
result_path = "transformer_opt_{}".format(transformer_enabled)
inputs_spec = {
"attention_mask": [1, 7],
"input_ids": [1, 7],
"token_type_ids": [1, 7]
}
if transformer_enabled:
transformer_args = "--model_type bert --num_heads 12"
opt_config = OptimizationConfig(
model_path=model_path,
inputs_spec=inputs_spec,
result_path=result_path,
transformer_enabled=transformer_enabled,
transformer_args=transformer_args)
optimize(opt_config)
else:
opt_config = OptimizationConfig(model_path=model_path,
inputs_spec=inputs_spec,
result_path=result_path)
optimize(opt_config)
assert os.path.exists(result_path)
shutil.rmtree(result_path)
def test_optimize_sample_data(sample_input_data_path):
result_path = "sample_data_opt"
opt_config = OptimizationConfig(
model_path=ONNX_MODEL_PATH,
sample_input_data_path=sample_input_data_path,
result_path=result_path)
optimize(opt_config)
shutil.rmtree(result_path)
def test_optimize_providers(providers_list):
result_path = "ep_opt_{}".format(providers_list)
opt_config = OptimizationConfig(model_path=ONNX_MODEL_PATH,
providers_list=providers_list,
result_path=result_path)
optimize(opt_config)
assert os.path.exists(result_path)
shutil.rmtree(result_path)
def test_optimize_quantization(quantization_enabled):
model_path = os.path.join(os.path.dirname(__file__), "other_models",
"TFBertForQuestionAnswering.onnx")
result_path = "quantization_opt_{}".format(quantization_enabled)
inputs_spec = {
"attention_mask": [1, 7],
"input_ids": [1, 7],
"token_type_ids": [1, 7]
}
opt_config = OptimizationConfig(model_path=model_path,
inputs_spec=inputs_spec,
quantization_enabled=quantization_enabled,
result_path=result_path)
optimize(opt_config)
assert os.path.exists(result_path)
shutil.rmtree(result_path)
def test_throughput_tuning(dynamic_batching_size):
result_path = "throughput_tuning_res"
model_path = os.path.join(os.path.dirname(__file__), "other_models",
"TFBertForQuestionAnswering.onnx")
opt_config = OptimizationConfig(
model_path=model_path,
inputs_spec={
"attention_mask": [-1, 7],
"input_ids": [-1, 7],
"token_type_ids": [-1, 7]
},
throughput_tuning_enabled=True,
max_latency_percentile=0.95,
max_latency_ms=100,
threads_num=1,
dynamic_batching_size=dynamic_batching_size,
result_path=result_path,
min_duration_sec=1)
optimize(opt_config)
shutil.rmtree(result_path)