torch.cond

cond_closed_over_variable

注意

标签：python 闭包, torch.cond

支持级别：支持

原始源代码：

# mypy: allow-untyped-defs
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])

example_args = (torch.tensor(True), torch.randn(3, 2))
tags = {"torch.cond", "python.closure"}
model = CondClosedOverVariable()


torch.export.export(model, example_args)

结果：

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, pred: "b8[]", x: "f32[3, 2]"):
                 add: "f32[3, 2]" = torch.ops.aten.add.Tensor(x, 1);  add = None

                 true_graph_0 = self.true_graph_0
            false_graph_0 = self.false_graph_0
            cond = torch.ops.higher_order.cond(pred, true_graph_0, false_graph_0, [x]);  pred = true_graph_0 = false_graph_0 = x = None
            getitem: "f32[3, 2]" = cond[0];  cond = None
            return (getitem,)

        class true_graph_0(torch.nn.Module):
            def forward(self, x: "f32[3, 2]"):
                         mul: "f32[3, 2]" = torch.ops.aten.mul.Tensor(x, 2);  x = None
                return (mul,)

        class false_graph_0(torch.nn.Module):
            def forward(self, x: "f32[3, 2]"):
                         sub: "f32[3, 2]" = torch.ops.aten.sub.Tensor(x, 2);  x = None
                return (sub,)

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

条件操作数

注意

标签：torch.dynamic-shape, torch.cond

支持级别：支持

原始源代码：

# mypy: allow-untyped-defs
import torch

from torch.export import Dim

x = torch.randn(3, 2)
y = torch.randn(2)
dim0_x = Dim("dim0_x")

class CondOperands(torch.nn.Module):
    """
    The operands passed to cond() must be:
    - a list of tensors
    - match arguments of `true_fn` and `false_fn`

    NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.
    """

    def forward(self, x, y):
        def true_fn(x, y):
            return x + y

        def false_fn(x, y):
            return x - y

        return torch.cond(x.shape[0] > 2, true_fn, false_fn, [x, y])

example_args = (x, y)
tags = {
    "torch.cond",
    "torch.dynamic-shape",
}
extra_inputs = (torch.randn(2, 2), torch.randn(2))
dynamic_shapes = {"x": {0: dim0_x}, "y": None}
model = CondOperands()


torch.export.export(model, example_args, dynamic_shapes=dynamic_shapes)

结果：

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[s0, 2]", y: "f32[2]"):
             #
            sym_size_int_1: "Sym(s0)" = torch.ops.aten.sym_size.int(x, 0)

                 gt: "Sym(s0 > 2)" = sym_size_int_1 > 2;  sym_size_int_1 = None

                 true_graph_0 = self.true_graph_0
            false_graph_0 = self.false_graph_0
            cond = torch.ops.higher_order.cond(gt, true_graph_0, false_graph_0, [x, y]);  gt = true_graph_0 = false_graph_0 = x = y = None
            getitem: "f32[s0, 2]" = cond[0];  cond = None
            return (getitem,)

        class true_graph_0(torch.nn.Module):
            def forward(self, x: "f32[s0, 2]", y: "f32[2]"):
                         add: "f32[s0, 2]" = torch.ops.aten.add.Tensor(x, y);  x = y = None
                return (add,)

        class false_graph_0(torch.nn.Module):
            def forward(self, x: "f32[s0, 2]", y: "f32[2]"):
                         sub: "f32[s0, 2]" = torch.ops.aten.sub.Tensor(x, y);  x = y = None
                return (sub,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='y'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {s0: VR[0, int_oo]}

条件分支类方法

注意

标签：torch.dynamic-shape, torch.cond

支持级别：支持

原始源代码：

# mypy: allow-untyped-defs
import torch

from functorch.experimental.control_flow import cond

class MySubModule(torch.nn.Module):
    def foo(self, x):
        return x.cos()

    def forward(self, x):
        return self.foo(x)

class CondBranchClassMethod(torch.nn.Module):
    """
    The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules:
      - both branches must take the same args, which must also match the branch args passed to cond.
      - both branches must return a single tensor
      - returned tensor must have the same tensor metadata, e.g. shape and dtype
      - branch function can be free function, nested function, lambda, class methods
      - branch function can not have closure variables
      - no inplace mutations on inputs or global variables


    This example demonstrates using class method in cond().

    NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.
    """

    def __init__(self) -> None:
        super().__init__()
        self.subm = MySubModule()

    def bar(self, x):
        return x.sin()

    def forward(self, x):
        return cond(x.shape[0] <= 2, self.subm.forward, self.bar, [x])

example_args = (torch.randn(3),)
tags = {
    "torch.cond",
    "torch.dynamic-shape",
}
model = CondBranchClassMethod()


torch.export.export(model, example_args)

结果：

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

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

条件分支嵌套函数 ¶

注意

标签：torch.dynamic-shape, torch.cond

支持级别：支持

原始源代码：

# mypy: allow-untyped-defs
import torch

from functorch.experimental.control_flow import cond

class CondBranchNestedFunction(torch.nn.Module):
    """
    The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules:
      - both branches must take the same args, which must also match the branch args passed to cond.
      - both branches must return a single tensor
      - returned tensor must have the same tensor metadata, e.g. shape and dtype
      - branch function can be free function, nested function, lambda, class methods
      - branch function can not have closure variables
      - no inplace mutations on inputs or global variables

    This example demonstrates using nested function in cond().

    NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.
    """

    def forward(self, x):
        def true_fn(x):
            def inner_true_fn(y):
                return x + y

            return inner_true_fn(x)

        def false_fn(x):
            def inner_false_fn(y):
                return x - y

            return inner_false_fn(x)

        return cond(x.shape[0] < 10, true_fn, false_fn, [x])

example_args = (torch.randn(3),)
tags = {
    "torch.cond",
    "torch.dynamic-shape",
}
model = CondBranchNestedFunction()


torch.export.export(model, example_args)

结果：

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

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

条件分支非局部变量 ¶

注意

标签：torch.dynamic-shape, torch.cond

支持级别：支持

原始源代码：

# mypy: allow-untyped-defs
import torch

from functorch.experimental.control_flow import cond

class CondBranchNonlocalVariables(torch.nn.Module):
    """
    The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules:
    - both branches must take the same args, which must also match the branch args passed to cond.
    - both branches must return a single tensor
    - returned tensor must have the same tensor metadata, e.g. shape and dtype
    - branch function can be free function, nested function, lambda, class methods
    - branch function can not have closure variables
    - no inplace mutations on inputs or global variables

    This example demonstrates how to rewrite code to avoid capturing closure variables in branch functions.

    The code below will not work because capturing closure variables is not supported.
    ```
    my_tensor_var = x + 100
    my_primitive_var = 3.14

    def true_fn(y):
        nonlocal my_tensor_var, my_primitive_var
        return y + my_tensor_var + my_primitive_var

    def false_fn(y):
        nonlocal my_tensor_var, my_primitive_var
        return y - my_tensor_var - my_primitive_var

    return cond(x.shape[0] > 5, true_fn, false_fn, [x])
    ```

    NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.
    """

    def forward(self, x):
        my_tensor_var = x + 100
        my_primitive_var = 3.14

        def true_fn(x, y, z):
            return x + y + z

        def false_fn(x, y, z):
            return x - y - z

        return cond(
            x.shape[0] > 5,
            true_fn,
            false_fn,
            [x, my_tensor_var, torch.tensor(my_primitive_var)],
        )

example_args = (torch.randn(6),)
tags = {
    "torch.cond",
    "torch.dynamic-shape",
}
model = CondBranchNonlocalVariables()


torch.export.export(model, example_args)

结果：

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, c_lifted_tensor_0: "f32[]", x: "f32[6]"):
                 add: "f32[6]" = torch.ops.aten.add.Tensor(x, 100)

                 lift_fresh_copy: "f32[]" = torch.ops.aten.lift_fresh_copy.default(c_lifted_tensor_0);  c_lifted_tensor_0 = None
            detach_: "f32[]" = torch.ops.aten.detach_.default(lift_fresh_copy);  lift_fresh_copy = None

                 add_1: "f32[6]" = torch.ops.aten.add.Tensor(x, add);  x = add = None
            add_2: "f32[6]" = torch.ops.aten.add.Tensor(add_1, detach_);  add_1 = detach_ = None
            return (add_2,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.CONSTANT_TENSOR: 4>, arg=TensorArgument(name='c_lifted_tensor_0'), target='lifted_tensor_0', persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_2'), target=None)])
Range constraints: {}

条件谓词 ¶

注意

标签：torch.dynamic-shape, torch.cond

支持级别：支持

原始源代码：

# mypy: allow-untyped-defs
import torch

from functorch.experimental.control_flow import cond

class CondPredicate(torch.nn.Module):
    """
    The conditional statement (aka predicate) passed to cond() must be one of the following:
      - torch.Tensor with a single element
      - boolean expression

    NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.
    """

    def forward(self, x):
        pred = x.dim() > 2 and x.shape[2] > 10

        return cond(pred, lambda x: x.cos(), lambda y: y.sin(), [x])

example_args = (torch.randn(6, 4, 3),)
tags = {
    "torch.cond",
    "torch.dynamic-shape",
}
model = CondPredicate()


torch.export.export(model, example_args)

结果：

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[6, 4, 3]"):
                 sin: "f32[6, 4, 3]" = torch.ops.aten.sin.default(x);  x = None
            return (sin,)

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