python.control-flow

dynamic_shape_if_guard

Note

Tags: torch.dynamic-shape, python.control-flow

Support Level: SUPPORTED

Original source code:

import torch



class DynamicShapeIfGuard(torch.nn.Module):
    """
    `if` statement with backed dynamic shape predicate will be specialized into
    one particular branch and generate a guard. However, export will fail if the
    the dimension is marked as dynamic shape from higher level API.
    """

    def forward(self, x):
        if x.shape[0] == 3:
            return x.cos()

        return x.sin()

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, l_x_: "f32[3, 2, 2]"):
                cos: "f32[3, 2, 2]" = torch.ops.aten.cos.default(l_x_);  l_x_ = None
            return (cos,)

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

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, x: "f32[3, 2]", l_args_1_: "i64[]", arg2: "i64[]"):
                add: "f32[3, 2]" = torch.ops.aten.add.Tensor(x, l_args_1_);  x = l_args_1_ = None
            return (add,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_args_1_'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg2'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
Range constraints: {}
Equality constraints: []

static_for_loop

Note

Tags: python.control-flow

Support Level: SUPPORTED

Original source code:

import torch



class StaticForLoop(torch.nn.Module):
    """
    A for loop with constant number of iterations should be unrolled in the exported graph.
    """

    def __init__(self):
        super().__init__()

    def forward(self, x):
        ret = []
        for i in range(10):  # constant
            ret.append(i + x)
        return ret

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, l_x_: "f32[3, 2]"):
                add: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, 0)
            add_1: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, 1)
            add_2: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, 2)
            add_3: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, 3)
            add_4: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, 4)
            add_5: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, 5)
            add_6: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, 6)
            add_7: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, 7)
            add_8: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, 8)
            add_9: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, 9);  l_x_ = None
            return (add, add_1, add_2, add_3, add_4, add_5, add_6, add_7, add_8, add_9)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_1'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_2'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_3'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_4'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_5'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_6'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_7'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_8'), target=None), OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_9'), target=None)])
Range constraints: {}
Equality constraints: []

static_if

Note

Tags: python.control-flow

Support Level: SUPPORTED

Original source code:

import torch



class StaticIf(torch.nn.Module):
    """
    `if` statement with static predicate value should be traced through with the
    taken branch.
    """

    def __init__(self):
        super().__init__()

    def forward(self, x):
        if len(x.shape) == 3:
            return x + torch.ones(1, 1, 1)

        return x

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, l_x_: "f32[3, 2, 2]"):
                ones: "f32[1, 1, 1]" = torch.ops.aten.ones.default([1, 1, 1], device = device(type='cpu'), pin_memory = False)
            add: "f32[3, 2, 2]" = torch.ops.aten.add.Tensor(l_x_, ones);  l_x_ = ones = None
            return (add,)

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