def test_pytorch_model_0_gpu_onnxruntime(self):
if 'CUDAExecutionProvider' not in onnxruntime.get_available_providers(
):
print(
"skip test_pytorch_model_0_gpu_onnxruntime since no gpu found")
return
input = _get_test_model_path('bert_pytorch_0')
output = 'temp.onnx'
optimize_by_onnxruntime(input,
use_gpu=True,
optimized_model_path=output)
model = ModelProto()
with open(output, "rb") as f:
model.ParseFromString(f.read())
os.remove(output)
bert_model = OnnxModel(model)
expected_node_count = {
'EmbedLayerNormalization': 1,
'Attention': 12,
'SkipLayerNormalization': 24,
'Gelu': 0,
'FastGelu': 12,
'BiasGelu': 0
}
self.verify_node_count(bert_model, expected_node_count,
'test_pytorch_model_0_gpu_onnxruntime')
def test_pytorch_model_0_cpu_onnxruntime(self):
input = BERT_TEST_MODELS['bert_pytorch_0']
output = 'temp.onnx'
optimize_by_onnxruntime(input, use_gpu=False, optimized_model_path=output)
model = ModelProto()
with open(output, "rb") as f:
model.ParseFromString(f.read())
os.remove(output)
bert_model = OnnxModel(model)
expected_node_count = {
'EmbedLayerNormalization': 1,
'Attention': 12,
'SkipLayerNormalization': 24,
'Gelu': 0,
'FastGelu': 0,
'BiasGelu': 12
}
self.verify_node_count(bert_model, expected_node_count)
def optimize_onnx_model_by_ort(onnx_model_path, ort_model_path, use_gpu, overwrite, model_fusion_statistics):
if overwrite or not os.path.exists(ort_model_path):
from optimizer import optimize_by_onnxruntime, get_fusion_statistics
# Use onnxruntime to optimize model, which will be saved to *_ort.onnx
opt_model = optimize_by_onnxruntime(onnx_model_path,
use_gpu=use_gpu,
optimized_model_path=ort_model_path,
opt_level=99)
model_fusion_statistics[ort_model_path] = get_fusion_statistics(ort_model_path)
else:
logger.info(f"Skip optimization since model existed: {ort_model_path}")
