python.closure

cond_closed_over_variable

Note

Tags: python.closure, torch.cond

Support Level: SUPPORTED

Original source code:

import torch

from functorch.experimental.control_flow import cond


class CondClosedOverVariable(torch.nn.Module):
    """
    torch.cond() supports branches closed over arbitrary variables.
    """

    def forward(self, pred, x):
        def true_fn(val):
            return x * 2

        def false_fn(val):
            return x - 2

        return cond(pred, true_fn, false_fn, [x + 1])

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: "b8[]", arg1_1: "f32[3, 2]"):
                true_graph_0 = self.true_graph_0
            false_graph_0 = self.false_graph_0
            conditional = torch.ops.higher_order.cond(arg0_1, true_graph_0, false_graph_0, [arg1_1]);  arg0_1 = true_graph_0 = false_graph_0 = arg1_1 = None
            getitem: "f32[3, 2]" = conditional[0];  conditional = None
            return (getitem,)

        class <lambda>(torch.nn.Module):
            def forward(self, arg0_1: "f32[3, 2]"):
                        mul: "f32[3, 2]" = torch.ops.aten.mul.Tensor(arg0_1, 2);  arg0_1 = None
                return (mul,)

        class <lambda>(torch.nn.Module):
            def forward(self, arg0_1: "f32[3, 2]"):
                        sub: "f32[3, 2]" = torch.ops.aten.sub.Tensor(arg0_1, 2);  arg0_1 = None
                return (sub,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg1_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {}

nested_function

Note

Tags: python.closure

Support Level: SUPPORTED

Original source code:

import torch



class NestedFunction(torch.nn.Module):
    """
    Nested functions are traced through. Side effects on global captures
    are not supported though.
    """
    def __init__(self):
        super().__init__()

    def forward(self, a, b):
        x = a + b
        z = a - b

        def closure(y):
            nonlocal x
            x += 1
            return x * y + z

        return closure(x)

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: "f32[3, 2]", arg1_1: "f32[2]"):
                add: "f32[3, 2]" = torch.ops.aten.add.Tensor(arg0_1, arg1_1)

                sub: "f32[3, 2]" = torch.ops.aten.sub.Tensor(arg0_1, arg1_1);  arg0_1 = arg1_1 = None

                add_1: "f32[3, 2]" = torch.ops.aten.add.Tensor(add, 1);  add = None

                mul: "f32[3, 2]" = torch.ops.aten.mul.Tensor(add_1, add_1);  add_1 = None
            add_2: "f32[3, 2]" = torch.ops.aten.add.Tensor(mul, sub);  mul = sub = None
            return (add_2,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg1_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_2'), target=None)])
Range constraints: {}