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# python.data-structure

## dictionary

Note

Tags: python.data-structure

Support Level: SUPPORTED

Original source code:

    import torch

    def dictionary(x, y):
        """
        Dictionary structures are inlined and flattened along tracing.
        """
        elements = {}
        elements["x2"] = x * x
        y = y * elements["x2"]
        return {"y": y}

Result:

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, arg0_1: f32[3, 2], arg1_1: i64[]):
                #
                mul: f32[3, 2] = torch.ops.aten.mul.Tensor(arg0_1, arg0_1);  arg0_1 = None
                mul_1: f32[3, 2] = torch.ops.aten.mul.Tensor(arg1_1, mul);  arg1_1 = mul = None
                return (mul_1,)

    Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1', 'arg1_1'], user_outputs=['mul_1'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
    Symbol to range: {}

## fn_with_kwargs

Note

Tags: python.data-structure

Support Level: SUPPORTED

Original source code:

    import torch

    def fn_with_kwargs(pos0, tuple0, *myargs, mykw0, **mykwargs):
        """
        Keyword arguments are not supported at the moment.
        """
        out = pos0
        for arg in tuple0:
            out = out * arg
        for arg in myargs:
            out = out * arg
        out = out * mykw0
        out = out * mykwargs["input0"] * mykwargs["input1"]
        return out

Result:

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, arg0_1: f32[4], arg1_1: f32[4], arg2_1: f32[4], arg3_1: f32[4], arg4_1: f32[4], arg5_1: f32[4], arg6_1: f32[4], arg7_1: f32[4]):
                #
                mul: f32[4] = torch.ops.aten.mul.Tensor(arg0_1, arg1_1);  arg0_1 = arg1_1 = None
                mul_1: f32[4] = torch.ops.aten.mul.Tensor(mul, arg2_1);  mul = arg2_1 = None
                mul_2: f32[4] = torch.ops.aten.mul.Tensor(mul_1, arg3_1);  mul_1 = arg3_1 = None
                mul_3: f32[4] = torch.ops.aten.mul.Tensor(mul_2, arg4_1);  mul_2 = arg4_1 = None
                mul_4: f32[4] = torch.ops.aten.mul.Tensor(mul_3, arg5_1);  mul_3 = arg5_1 = None
                mul_5: f32[4] = torch.ops.aten.mul.Tensor(mul_4, arg6_1);  mul_4 = arg6_1 = None
                mul_6: f32[4] = torch.ops.aten.mul.Tensor(mul_5, arg7_1);  mul_5 = arg7_1 = None
                return (mul_6,)

    Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1', 'arg1_1', 'arg2_1', 'arg3_1', 'arg4_1', 'arg5_1', 'arg6_1', 'arg7_1'], user_outputs=['mul_6'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
    Symbol to range: {}

## list_contains

Note

Tags: python.data-structure, python.assert, torch.dynamic-shape

Support Level: SUPPORTED

Original source code:

    import torch

    def list_contains(x):
        """
        List containment relation can be checked on a dynamic shape or constants.
        """
        assert x.size(-1) in [6, 2]
        assert x.size(0) not in [4, 5, 6]
        assert "monkey" not in ["cow", "pig"]
        return x + x

Result:

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, arg0_1: f32[3, 2]):
                #
                add: f32[3, 2] = torch.ops.aten.add.Tensor(arg0_1, arg0_1);  arg0_1 = None
                return (add,)

    Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1'], user_outputs=['add'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
    Symbol to range: {}

## list_unpack

Note

Tags: python.data-structure, python.control-flow

Support Level: SUPPORTED

Original source code:

    from typing import List

    import torch

    def list_unpack(args: List[torch.Tensor]):
        """
        Lists are treated as static construct, therefore unpacking should be
        erased after tracing.
        """
        x, *y = args
        return x + y[0]

Result:

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, arg0_1: f32[3, 2], arg1_1: i64[], arg2_1: i64[]):
                #
                add: f32[3, 2] = torch.ops.aten.add.Tensor(arg0_1, arg1_1);  arg0_1 = arg1_1 = None
                return (add,)

    Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1', 'arg1_1', 'arg2_1'], user_outputs=['add'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
    Symbol to range: {}

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