tensor_build

torch_to_nnef.op.aten.tensor_build

arange

arange(g, node, name_to_tensor, inference_target, **kwargs)

This operator can not be exactly exported to NNEF.

In general NNEF spec is against dynamism it could provide so

we implement it as a simple constant variable.

copy

copy(g, node, name_to_tensor, inference_target, torch_graph, null_ref, **kwargs)

Map PyTorch: 'aten:copy', 'aten:clone' to NNEF.

empty_like

empty_like(**kwargs)

Operator can not be exactly exported to NNEF if dynamic.

With tract we use use expansion

fill

fill(g, node, name_to_tensor, torch_graph, inference_target, op_helper, **kwargs)

Map PyTorch: 'aten:fill', 'aten:fill_' to NNEF.

full

full(g, node, name_to_tensor, torch_graph, inference_target, **kwargs)

Map PyTorch: 'aten:full' to NNEF.

full_like

full_like(**kwargs)

Operator can not be exactly exported to NNEF if dynamic.

With tract we use use expansion

new_zeros

new_zeros(g, node, name_to_tensor, torch_graph, inference_target, **kwargs)

Map PyTorch: 'aten:new_zeros' to NNEF.

ones

ones(g, node, name_to_tensor, torch_graph, inference_target, **kwargs)

This operator can not be exactly exported to NNEF.

In general NNEF spec is against dynamism it could provide so

we implement it as a simple constant variable.

ones_like

ones_like(**kwargs)

Operator can not be exactly exported to NNEF if dynamic.

With tract we use use expansion

tril

tril(g, node, name_to_tensor, inference_target, **kwargs)

Map PyTorch: 'aten:tril' to NNEF.

triu

triu(g, node, name_to_tensor, inference_target, **kwargs)

Map PyTorch: 'aten:triu' to NNEF.

zeros

zeros(g, node, name_to_tensor, torch_graph, inference_target, **kwargs)

Map PyTorch: 'aten:zeros' to NNEF.

zeros_like

zeros_like(**kwargs)

Operator can not be exactly exported to NNEF if dynamic.

With tract we use use exapnsion