python.data-structure ========================= dictionary ^^^^^^^^^^ .. note:: Tags: :doc:`python.data-structure ` Support Level: SUPPORTED Original source code: .. code-block:: python import torch class Dictionary(torch.nn.Module): """ Dictionary structures are inlined and flattened along tracing. """ def __init__(self): super().__init__() def forward(self, x, y): elements = {} elements["x2"] = x * x y = y * elements["x2"] return {"y": y} Result: .. code-block:: 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(input_specs=[InputSpec(kind=, arg=TensorArgument(name='arg0_1'), target=None, persistent=None), InputSpec(kind=, arg=TensorArgument(name='arg1_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=, arg=TensorArgument(name='mul_1'), target=None)]) Range constraints: {} fn_with_kwargs ^^^^^^^^^^^^^^ .. note:: Tags: :doc:`python.data-structure ` Support Level: SUPPORTED Original source code: .. code-block:: python import torch ), tags={"python.data-structure"}, support_level=SupportLevel.SUPPORTED, ) class FnWithKwargs(torch.nn.Module): """ Keyword arguments are not supported at the moment. """ def __init__(self): super().__init__() def forward(self, pos0, tuple0, *myargs, mykw0, **mykwargs): 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: .. code-block:: 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(input_specs=[InputSpec(kind=, arg=TensorArgument(name='arg0_1'), target=None, persistent=None), InputSpec(kind=, arg=TensorArgument(name='arg1_1'), target=None, persistent=None), InputSpec(kind=, arg=TensorArgument(name='arg2_1'), target=None, persistent=None), InputSpec(kind=, arg=TensorArgument(name='arg3_1'), target=None, persistent=None), InputSpec(kind=, arg=TensorArgument(name='arg4_1'), target=None, persistent=None), InputSpec(kind=, arg=TensorArgument(name='arg5_1'), target=None, persistent=None), InputSpec(kind=, arg=TensorArgument(name='arg6_1'), target=None, persistent=None), InputSpec(kind=, arg=TensorArgument(name='arg7_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=, arg=TensorArgument(name='mul_6'), target=None)]) Range constraints: {} list_contains ^^^^^^^^^^^^^ .. note:: Tags: :doc:`torch.dynamic-shape `, :doc:`python.assert `, :doc:`python.data-structure ` Support Level: SUPPORTED Original source code: .. code-block:: python import torch class ListContains(torch.nn.Module): """ List containment relation can be checked on a dynamic shape or constants. """ def __init__(self): super().__init__() def forward(self, x): 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: .. code-block:: 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(input_specs=[InputSpec(kind=, arg=TensorArgument(name='arg0_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=, arg=TensorArgument(name='add'), target=None)]) Range constraints: {} list_unpack ^^^^^^^^^^^ .. note:: Tags: :doc:`python.data-structure `, :doc:`python.control-flow ` Support Level: SUPPORTED Original source code: .. code-block:: python from typing import List import torch class ListUnpack(torch.nn.Module): """ Lists are treated as static construct, therefore unpacking should be erased after tracing. """ def __init__(self): super().__init__() def forward(self, 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: .. code-block:: 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(input_specs=[InputSpec(kind=, arg=TensorArgument(name='arg0_1'), target=None, persistent=None), InputSpec(kind=, arg=TensorArgument(name='arg1_1'), target=None, persistent=None), InputSpec(kind=, arg=TensorArgument(name='arg2_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=, arg=TensorArgument(name='add'), target=None)]) Range constraints: {}