def prepare(cls, model, device='CPU', **kwargs):
super(DummyBackend, cls).prepare(model, device, **kwargs)
# Save model to disk as temp_model.onnx.
onnx.save(model, "temp_model.onnx")
# Call frontend to process temp_model.onnx, bit code will be generated.
execute_commands([ONNX_MLIR, "temp_model.onnx"])
return ExecutionSession("./temp_model.so",
"_dyn_entry_point_main_graph")
def prepare(cls, model, device='CPU', **kwargs):
super(DummyBackend, cls).prepare(model, device, **kwargs)
# Save model to disk as temp_model.onnx.
onnx.save(model, "temp_model.onnx")
# Call frontend to process temp_model.onnx, bit code will be generated.
execute_commands([ONNF, "temp_model.onnx"])
# Call llc to generate object file from bitcode.
execute_commands(
[LLC, "-filetype=obj", "-relocation-model=pic", "model.bc"])
# Generate shared library from object file, linking with c runtime.
execute_commands([
CXX, "-shared", "-fPIC", "model.o", "-o", "model.so",
"-L" + RUNTIME_DIR, "-lcruntime"
])
return ExecutionSession("./model.so", "_dyn_entry_point_main_graph")