def __call__(self, *inputs, **kwargs):
pipeline = Pipeline.current()
if pipeline is None:
Pipeline._raise_no_current_pipeline("TorchPythonFunction")
if self.stream is None:
self.stream = torch.cuda.Stream(device=pipeline.device_id)
return super(TorchPythonFunction, self).__call__(*inputs, **kwargs)
def __call__(self, *inputs, **kwargs):
pipeline = Pipeline.current()
if pipeline is None:
Pipeline._raise_no_current_pipeline("NumbaFunction")
inputs = ops._preprocess_inputs(inputs, self._impl_name, self._device,
None)
if pipeline is None:
Pipeline._raise_pipeline_required("NumbaFunction operator")
if (len(inputs) > self._schema.MaxNumInput()
or len(inputs) < self._schema.MinNumInput()):
raise ValueError(("Operator {} expects from {} to " +
"{} inputs, but received {}.").format(
type(self).__name__,
self._schema.MinNumInput(),
self._schema.MaxNumInput(), len(inputs)))
for inp in inputs:
if not isinstance(inp, _DataNode):
raise TypeError((
"Expected inputs of type `DataNode`. Received input of type '{}'. "
+ "Python Operators do not support Multiple Input Sets."
).format(type(inp).__name__))
op_instance = ops._OperatorInstance(inputs, self, **kwargs)
op_instance.spec.AddArg("run_fn", self.run_fn)
op_instance.spec.AddArg("setup_fn",
self.setup_fn) if self.setup_fn else None
op_instance.spec.AddArg("out_types", self.out_types)
op_instance.spec.AddArg("in_types", self.in_types)
op_instance.spec.AddArg("outs_ndim", self.outs_ndim)
op_instance.spec.AddArg("ins_ndim", self.ins_ndim)
op_instance.spec.AddArg("device", self.device)
op_instance.spec.AddArg("batch_processing", self.batch_processing)
if self.device == 'gpu':
op_instance.spec.AddArg("blocks", self.blocks)
op_instance.spec.AddArg("threads_per_block",
self.threads_per_block)
if self.num_outputs == 0:
t_name = self._impl_name + "_id_" + str(op_instance.id) + "_sink"
t = _DataNode(t_name, self._device, op_instance)
pipeline.add_sink(t)
return
outputs = []
for i in range(self.num_outputs):
t_name = op_instance._name
if self.num_outputs > 1:
t_name += "[{}]".format(i)
t = _DataNode(t_name, self._device, op_instance)
op_instance.spec.AddOutput(t.name, t.device)
op_instance.append_output(t)
pipeline.add_sink(t)
outputs.append(t)
return outputs[0] if len(outputs) == 1 else outputs
