com.microsoft - PythonOp#
PythonOp - 1 (com.microsoft)#
Version
name: PythonOp (GitHub)
domain: com.microsoft
since_version: 1
function:
support_level:
shape inference:
This version of the operator has been available since version 1 of domain com.microsoft.
Summary
Wrapper of Pytorch’s autograd.Function implementation.
Attributes
inplace: Indicate if the output should reuse input memory. Default value is
?
.input_convention (required): input_convention[i]==c means a non-tensor argument. input_convention[i]==d means a tensor. Default value is
?
.input_float_scalar_positions:
Default value is
?
.
input_float_scalars: Python float arguments. Default value is
?
.input_float_tuple_begins:
Default value is
?
.
input_float_tuple_positions:
Default value is
?
.
input_float_tuples:
Default value is
?
.
input_int_scalar_positions:
Default value is
?
.
input_int_scalars: Python int arguments. Default value is
?
.input_int_tuple_begins:
Default value is
?
.
input_int_tuple_positions:
Default value is
?
.
input_int_tuples: Python int-tuple arguments. Default value is
?
.input_pointer_scalar_positions:
Default value is
?
.
input_pointer_scalars:
Default value is
?
.
input_requires_grads (required): Flags to indicate whether the torch.autograd.apply’s inputs require gradients (including flags for both tensor and non-tensor inputs) Default value is
?
.input_tensor_ranks (required): Input tensors’ ranks of autograd.Function.apply. Default value is
?
.input_tensor_types (required): Input types of autograd.Function.apply. Default value is
?
.name (required): Name of custom class. Default value is
?
.output_tensor_ranks (required): Output tensors’ ranks of autograd.Function.apply. Default value is
?
.output_tensor_requires_grads (required): Flags to indicate which output has gradient Default value is
?
.output_tensor_types (required): Output types of autograd.Function.apply. Default value is
?
.training_mode: Indicate if the model is exported in training_mode, by default, False. Default value is
?
.
Inputs
Between 1 and 2147483647 inputs.
inputs (variadic) - T: Module outputs to be returned to pytorch.
Outputs
Between 2 and 2147483647 outputs.
context (heterogeneous) - TInt64: Address of context created in this operator. It can be used in backward.
outputs (variadic) - T: Outputs returned from pytorch.
Examples