def _create_iobinding(io_binding, inputs, model, device):
'''Creates IO binding for a `model` inputs and output'''
for idx, value_info in enumerate(model.graph.input):
io_binding.bind_ortvalue_input(value_info.name, OrtValue(_ortvalue_from_torch_tensor(inputs[idx])))
for value_info in model.graph.output:
io_binding.bind_output(value_info.name, device.type, device_id=get_device_index(device))
def run_forward(self, iobinding, run_options):
"""
Compute the forward subgraph until it hits the Yield Op.
:param iobinding: the iobinding object that has graph inputs/outputs bind.
:param run_options: See :class:`onnxruntime.RunOptions`.
"""
ortvalues, run_id = self._training_agent.run_forward(
iobinding._iobinding, run_options)
return [OrtValue(ortvalue) for ortvalue in ortvalues], run_id
def _ortvalue_from_torch_tensor(torch_tensor):
return OrtValue(
C.OrtValue.from_dlpack(to_dlpack(torch_tensor),
torch_tensor.dtype == torch.bool))
