Shortcuts

torch.cond

cond_branch_class_method

Note

Tags: torch.cond, torch.dynamic-shape

Support Level: SUPPORTED

Original source code:

# 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):
        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])

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3]"):
             # File: /opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_higher_order_ops/cond.py:112 in cond, code: return cond_op(pred, true_fn, false_fn, operands)
            true_graph_0 = self.true_graph_0
            false_graph_0 = self.false_graph_0
            conditional = torch.ops.higher_order.cond(False, true_graph_0, false_graph_0, [x]);  true_graph_0 = false_graph_0 = x = None
            getitem: "f32[3]" = conditional[0];  conditional = None
            return (getitem,)

        class <lambda>(torch.nn.Module):
            def forward(self, x: "f32[3]"):
                         cos: "f32[3]" = torch.ops.aten.cos.default(x);  x = None
                return (cos,)

        class <lambda>(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='getitem'), target=None)])
Range constraints: {}

cond_branch_nested_function

Note

Tags: torch.cond, torch.dynamic-shape

Support Level: SUPPORTED

Original source code:

# 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 __init__(self):
        super().__init__()

    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])

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3]"):
             # File: /opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_higher_order_ops/cond.py:112 in cond, code: return cond_op(pred, true_fn, false_fn, operands)
            true_graph_0 = self.true_graph_0
            false_graph_0 = self.false_graph_0
            conditional = torch.ops.higher_order.cond(True, true_graph_0, false_graph_0, [x]);  true_graph_0 = false_graph_0 = x = None
            getitem: "f32[3]" = conditional[0];  conditional = None
            return (getitem,)

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

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

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='getitem'), target=None)])
Range constraints: {}

cond_branch_nonlocal_variables

Note

Tags: torch.cond, torch.dynamic-shape

Support Level: SUPPORTED

Original source code:

# 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 __init__(self):
        super().__init__()

    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)],
        )

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, c_lifted_tensor_0: "f32[]", x: "f32[6]"):
             # File: /opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_export/db/examples/cond_branch_nonlocal_variables.py:50 in forward, code: my_tensor_var = x + 100
            add: "f32[6]" = torch.ops.aten.add.Tensor(x, 100)

             # File: /opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_export/db/examples/cond_branch_nonlocal_variables.py:63 in forward, code: [x, my_tensor_var, torch.tensor(my_primitive_var)],
            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

             # File: /opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_higher_order_ops/cond.py:112 in cond, code: return cond_op(pred, true_fn, false_fn, operands)
            true_graph_0 = self.true_graph_0
            false_graph_0 = self.false_graph_0
            conditional = torch.ops.higher_order.cond(True, true_graph_0, false_graph_0, [x, add, detach]);  true_graph_0 = false_graph_0 = x = add = detach = None
            getitem: "f32[6]" = conditional[0];  conditional = None
            return (getitem,)

        class <lambda>(torch.nn.Module):
            def forward(self, x: "f32[6]", add: "f32[6]", detach: "f32[]"):
                         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,)

        class <lambda>(torch.nn.Module):
            def forward(self, x: "f32[6]", add: "f32[6]", detach: "f32[]"):
                         sub: "f32[6]" = torch.ops.aten.sub.Tensor(x, add);  x = add = None
                sub_1: "f32[6]" = torch.ops.aten.sub.Tensor(sub, detach);  sub = detach = None
                return (sub_1,)

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='getitem'), target=None)])
Range constraints: {}

cond_closed_over_variable

Note

Tags: python.closure, torch.cond

Support Level: SUPPORTED

Original source code:

# 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])

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, pred: "b8[]", x: "f32[3, 2]"):
             # File: /opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_higher_order_ops/cond.py:112 in cond, code: return cond_op(pred, true_fn, false_fn, operands)
            true_graph_0 = self.true_graph_0
            false_graph_0 = self.false_graph_0
            conditional = 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]" = conditional[0];  conditional = None
            return (getitem,)

        class <lambda>(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 <lambda>(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: {}

cond_operands

Note

Tags: torch.cond, torch.dynamic-shape

Support Level: SUPPORTED

Original source code:

# mypy: allow-untyped-defs
import torch

from torch.export import Dim
from functorch.experimental.control_flow import cond

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

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

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

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

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[s0, 2]", y: "f32[2]"):
             # File: /opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_higher_order_ops/cond.py:112 in cond, code: return cond_op(pred, true_fn, false_fn, operands)
            sym_size_int: "Sym(s0)" = torch.ops.aten.sym_size.int(x, 0)
            gt: "Sym(s0 > 2)" = sym_size_int > 2;  sym_size_int = None
            true_graph_0 = self.true_graph_0
            false_graph_0 = self.false_graph_0
            conditional = 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]" = conditional[0];  conditional = None
            return (getitem,)

        class <lambda>(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 <lambda>(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]}

cond_predicate

Note

Tags: torch.cond, torch.dynamic-shape

Support Level: SUPPORTED

Original source code:

# 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 __init__(self):
        super().__init__()

    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])

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[6, 4, 3]"):
             # File: /opt/conda/envs/py_3.8/lib/python3.8/site-packages/torch/_higher_order_ops/cond.py:112 in cond, code: return cond_op(pred, true_fn, false_fn, operands)
            true_graph_0 = self.true_graph_0
            false_graph_0 = self.false_graph_0
            conditional = torch.ops.higher_order.cond(False, true_graph_0, false_graph_0, [x]);  true_graph_0 = false_graph_0 = x = None
            getitem: "f32[6, 4, 3]" = conditional[0];  conditional = None
            return (getitem,)

        class <lambda>(torch.nn.Module):
            def forward(self, x: "f32[6, 4, 3]"):
                         cos: "f32[6, 4, 3]" = torch.ops.aten.cos.default(x);  x = None
                return (cos,)

        class <lambda>(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='getitem'), target=None)])
Range constraints: {}

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources