ExportDB

# mypy: allow-untyped-defs
import torch
import torch._dynamo as torchdynamo


class AssumeConstantResult(torch.nn.Module):
"""
    Applying `assume_constant_result` decorator to burn make non-tracable code as constant.
    """

    @torchdynamo.assume_constant_result
    def get_item(self, y):
        return y.int().item()

    def forward(self, x, y):
        return x[: self.get_item(y)]

example_args = (torch.randn(3, 2), torch.tensor(4))
tags = {"torch.escape-hatch"}
model = AssumeConstantResult()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3, 2]", y: "i64[]"):
                 slice_1: "f32[3, 2]" = torch.ops.aten.slice.Tensor(x, 0, 0, 4);  x = None
            return (slice_1,)

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='slice_1'), target=None)])
Range constraints: {}

# mypy: allow-untyped-defs
import torch

class MyAutogradFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x):
        return x.clone()

    @staticmethod
    def backward(ctx, grad_output):
        return grad_output + 1

class AutogradFunction(torch.nn.Module):
"""
    TorchDynamo does not keep track of backward() on autograd functions. We recommend to
    use `allow_in_graph` to mitigate this problem.
    """

    def forward(self, x):
        return MyAutogradFunction.apply(x)

example_args = (torch.randn(3, 2),)
model = AutogradFunction()


torch.export.export(model, example_args)

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

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

# mypy: allow-untyped-defs
import torch

class ClassMethod(torch.nn.Module):
"""
    Class methods are inlined during tracing.
    """

    @classmethod
    def method(cls, x):
        return x + 1

    def __init__(self) -> None:
        super().__init__()
        self.linear = torch.nn.Linear(4, 2)

    def forward(self, x):
        x = self.linear(x)
        return self.method(x) * self.__class__.method(x) * type(self).method(x)

example_args = (torch.randn(3, 4),)
model = ClassMethod()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, p_linear_weight: "f32[2, 4]", p_linear_bias: "f32[2]", x: "f32[3, 4]"):
                 linear: "f32[3, 2]" = torch.ops.aten.linear.default(x, p_linear_weight, p_linear_bias);  x = p_linear_weight = p_linear_bias = None

                 add: "f32[3, 2]" = torch.ops.aten.add.Tensor(linear, 1)
            add_1: "f32[3, 2]" = torch.ops.aten.add.Tensor(linear, 1)

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

                 add_2: "f32[3, 2]" = torch.ops.aten.add.Tensor(linear, 1);  linear = None

                 mul_1: "f32[3, 2]" = torch.ops.aten.mul.Tensor(mul, add_2);  mul = add_2 = None
            return (mul_1,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_linear_weight'), target='linear.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='p_linear_bias'), target='linear.bias', 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='mul_1'), target=None)])
Range constraints: {}

# 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)

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: {}

# 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)

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: {}

# 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)

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: {}

# 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)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, pred: "b8[]", x: "f32[3, 2]"):
                 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: {}

# 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 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])

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)

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_3: "f32[s0, 2]" = torch.ops.aten.add.Tensor(x, y);  x = y = None
                return (add_3,)

        class false_graph_0(torch.nn.Module):
            def forward(self, x: "f32[s0, 2]", y: "f32[2]"):
                         sub_1: "f32[s0, 2]" = torch.ops.aten.sub.Tensor(x, y);  x = y = None
                return (sub_1,)

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]}

# 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)

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: {}

# mypy: allow-untyped-defs
import torch


class ConstrainAsSizeExample(torch.nn.Module):
"""
    If the value is not known at tracing time, you can provide hint so that we
    can trace further. Please look at torch._check and torch._check_is_size APIs.
    torch._check_is_size is used for values that NEED to be used for constructing
    tensor.
    """

    def forward(self, x):
        a = x.item()
        torch._check_is_size(a)
        torch._check(a <= 5)
        return torch.zeros((a, 5))


example_args = (torch.tensor(4),)
tags = {
    "torch.dynamic-value",
    "torch.escape-hatch",
}
model = ConstrainAsSizeExample()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "i64[]"):
                 item: "Sym(u0)" = torch.ops.aten.item.default(x);  x = None

             #
            sym_constrain_range_for_size_default = torch.ops.aten.sym_constrain_range_for_size.default(item);  sym_constrain_range_for_size_default = None

                 ge_3: "Sym(u0 >= 0)" = item >= 0
            _assert_scalar_default = torch.ops.aten._assert_scalar.default(ge_3, "Runtime assertion failed for expression u0 >= 0 on node 'ge_3'");  ge_3 = _assert_scalar_default = None
            le_1: "Sym(u0 <= 5)" = item <= 5
            _assert_scalar_default_1 = torch.ops.aten._assert_scalar.default(le_1, "Runtime assertion failed for expression u0 <= 5 on node 'le_1'");  le_1 = _assert_scalar_default_1 = None

                 zeros: "f32[u0, 5]" = torch.ops.aten.zeros.default([item, 5], device = device(type='cpu'), pin_memory = False);  item = None
            return (zeros,)

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='zeros'), target=None)])
Range constraints: {u0: VR[0, 5], u1: VR[0, 5], u2: VR[0, 5]}

# mypy: allow-untyped-defs
import torch


class ConstrainAsValueExample(torch.nn.Module):
"""
    If the value is not known at tracing time, you can provide hint so that we
    can trace further. Please look at torch._check and torch._check_is_size APIs.
    torch._check is used for values that don't need to be used for constructing
    tensor.
    """

    def forward(self, x, y):
        a = x.item()
        torch._check(a >= 0)
        torch._check(a <= 5)

        if a < 6:
            return y.sin()
        return y.cos()


example_args = (torch.tensor(4), torch.randn(5, 5))
tags = {
    "torch.dynamic-value",
    "torch.escape-hatch",
}
model = ConstrainAsValueExample()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "i64[]", y: "f32[5, 5]"):
                 item: "Sym(u0)" = torch.ops.aten.item.default(x);  x = None
            ge_1: "Sym(u0 >= 0)" = item >= 0
            _assert_scalar_default = torch.ops.aten._assert_scalar.default(ge_1, "Runtime assertion failed for expression u0 >= 0 on node 'ge_1'");  ge_1 = _assert_scalar_default = None
            le_1: "Sym(u0 <= 5)" = item <= 5;  item = None
            _assert_scalar_default_1 = torch.ops.aten._assert_scalar.default(le_1, "Runtime assertion failed for expression u0 <= 5 on node 'le_1'");  le_1 = _assert_scalar_default_1 = None

                 sin: "f32[5, 5]" = torch.ops.aten.sin.default(y);  y = None
            return (sin,)

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='sin'), target=None)])
Range constraints: {u0: VR[0, 5], u1: VR[0, 5], u2: VR[0, 5]}

# mypy: allow-untyped-defs
import functools

import torch

def test_decorator(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        return func(*args, **kwargs) + 1

    return wrapper

class Decorator(torch.nn.Module):
"""
    Decorators calls are inlined into the exported function during tracing.
    """

    @test_decorator
    def forward(self, x, y):
        return x + y

example_args = (torch.randn(3, 2), torch.randn(3, 2))
model = Decorator()


torch.export.export(model, example_args)

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

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

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='add_1'), target=None)])
Range constraints: {}

# mypy: allow-untyped-defs
import torch

class Dictionary(torch.nn.Module):
"""
    Dictionary structures are inlined and flattened along tracing.
    """

    def forward(self, x, y):
        elements = {}
        elements["x2"] = x * x
        y = y * elements["x2"]
        return {"y": y}

example_args = (torch.randn(3, 2), torch.tensor(4))
tags = {"python.data-structure"}
model = Dictionary()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3, 2]", y: "i64[]"):
                 mul: "f32[3, 2]" = torch.ops.aten.mul.Tensor(x, x);  x = None

                 mul_1: "f32[3, 2]" = torch.ops.aten.mul.Tensor(y, mul);  y = mul = None
            return (mul_1,)

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='mul_1'), target=None)])
Range constraints: {}

# mypy: allow-untyped-defs
import torch

class DynamicShapeAssert(torch.nn.Module):
"""
    A basic usage of python assertion.
    """

    def forward(self, x):
        # assertion with error message
        assert x.shape[0] > 2, f"{x.shape[0]} is greater than 2"
        # assertion without error message
        assert x.shape[0] > 1
        return x

example_args = (torch.randn(3, 2),)
tags = {"python.assert"}
model = DynamicShapeAssert()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3, 2]"):
            return (x,)

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

# mypy: allow-untyped-defs
import torch

class DynamicShapeConstructor(torch.nn.Module):
"""
    Tensor constructors should be captured with dynamic shape inputs rather
    than being baked in with static shape.
    """

    def forward(self, x):
        return torch.zeros(x.shape[0] * 2)

example_args = (torch.randn(3, 2),)
tags = {"torch.dynamic-shape"}
model = DynamicShapeConstructor()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3, 2]"):
                 zeros: "f32[6]" = torch.ops.aten.zeros.default([6], device = device(type='cpu'), pin_memory = False)
            return (zeros,)

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

# mypy: allow-untyped-defs
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()

example_args = (torch.randn(3, 2, 2),)
tags = {"torch.dynamic-shape", "python.control-flow"}
model = DynamicShapeIfGuard()


torch.export.export(model, example_args)

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

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

# mypy: allow-untyped-defs
import torch

from functorch.experimental.control_flow import map

class DynamicShapeMap(torch.nn.Module):
"""
    functorch map() maps a function over the first tensor dimension.
    """

    def forward(self, xs, y):
        def body(x, y):
            return x + y

        return map(body, xs, y)

example_args = (torch.randn(3, 2), torch.randn(2))
tags = {"torch.dynamic-shape", "torch.map"}
model = DynamicShapeMap()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, xs: "f32[3, 2]", y: "f32[2]"):
                 body_graph_0 = self.body_graph_0
            map_impl = torch.ops.higher_order.map_impl(body_graph_0, [xs], [y]);  body_graph_0 = xs = y = None
            getitem: "f32[3, 2]" = map_impl[0];  map_impl = None
            return (getitem,)

        class body_graph_0(torch.nn.Module):
            def forward(self, xs: "f32[2]", y: "f32[2]"):
                         add: "f32[2]" = torch.ops.aten.add.Tensor(xs, y);  xs = y = None
                return (add,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='xs'), 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: {}

# mypy: allow-untyped-defs
import torch

class DynamicShapeSlicing(torch.nn.Module):
"""
    Slices with dynamic shape arguments should be captured into the graph
    rather than being baked in.
    """

    def forward(self, x):
        return x[: x.shape[0] - 2, x.shape[1] - 1 :: 2]

example_args = (torch.randn(3, 2),)
tags = {"torch.dynamic-shape"}
model = DynamicShapeSlicing()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3, 2]"):
                 slice_1: "f32[1, 2]" = torch.ops.aten.slice.Tensor(x, 0, 0, 1);  x = None
            slice_2: "f32[1, 1]" = torch.ops.aten.slice.Tensor(slice_1, 1, 1, 9223372036854775807, 2);  slice_1 = None
            return (slice_2,)

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='slice_2'), target=None)])
Range constraints: {}

# mypy: allow-untyped-defs
import torch

class DynamicShapeView(torch.nn.Module):
"""
    Dynamic shapes should be propagated to view arguments instead of being
    baked into the exported graph.
    """

    def forward(self, x):
        new_x_shape = x.size()[:-1] + (2, 5)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1)

example_args = (torch.randn(10, 10),)
tags = {"torch.dynamic-shape"}
model = DynamicShapeView()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[10, 10]"):
                 view: "f32[10, 2, 5]" = torch.ops.aten.view.default(x, [10, 2, 5]);  x = None

                 permute: "f32[10, 5, 2]" = torch.ops.aten.permute.default(view, [0, 2, 1]);  view = None
            return (permute,)

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

# mypy: allow-untyped-defs
import torch

class FnWithKwargs(torch.nn.Module):
"""
    Keyword arguments are not supported at the moment.
    """

    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

example_args = (
    torch.randn(4),
    (torch.randn(4), torch.randn(4)),
    *[torch.randn(4), torch.randn(4)]
)
example_kwargs = {
    "mykw0": torch.randn(4),
    "input0": torch.randn(4),
    "input1": torch.randn(4),
}
tags = {"python.data-structure"}
model = FnWithKwargs()


torch.export.export(model, example_args, example_kwargs)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, pos0: "f32[4]", tuple0_0: "f32[4]", tuple0_1: "f32[4]", myargs_0: "f32[4]", myargs_1: "f32[4]", mykw0: "f32[4]", input0: "f32[4]", input1: "f32[4]"):
                 mul: "f32[4]" = torch.ops.aten.mul.Tensor(pos0, tuple0_0);  pos0 = tuple0_0 = None
            mul_1: "f32[4]" = torch.ops.aten.mul.Tensor(mul, tuple0_1);  mul = tuple0_1 = None

                 mul_2: "f32[4]" = torch.ops.aten.mul.Tensor(mul_1, myargs_0);  mul_1 = myargs_0 = None
            mul_3: "f32[4]" = torch.ops.aten.mul.Tensor(mul_2, myargs_1);  mul_2 = myargs_1 = None

                 mul_4: "f32[4]" = torch.ops.aten.mul.Tensor(mul_3, mykw0);  mul_3 = mykw0 = None

                 mul_5: "f32[4]" = torch.ops.aten.mul.Tensor(mul_4, input0);  mul_4 = input0 = None
            mul_6: "f32[4]" = torch.ops.aten.mul.Tensor(mul_5, input1);  mul_5 = input1 = None
            return (mul_6,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='pos0'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='tuple0_0'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='tuple0_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='myargs_0'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='myargs_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='mykw0'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='input0'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='input1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='mul_6'), target=None)])
Range constraints: {}

# mypy: allow-untyped-defs
import torch

class ListContains(torch.nn.Module):
"""
    List containment relation can be checked on a dynamic shape or constants.
    """

    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

example_args = (torch.randn(3, 2),)
tags = {"torch.dynamic-shape", "python.data-structure", "python.assert"}
model = ListContains()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3, 2]"):
                 add: "f32[3, 2]" = 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: {}

# mypy: allow-untyped-defs
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 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]

example_args = ([torch.randn(3, 2), torch.tensor(4), torch.tensor(5)],)
tags = {"python.control-flow", "python.data-structure"}
model = ListUnpack()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, args_0: "f32[3, 2]", args_1: "i64[]", args_2: "i64[]"):
                 add: "f32[3, 2]" = torch.ops.aten.add.Tensor(args_0, args_1);  args_0 = args_1 = None
            return (add,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='args_0'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='args_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='args_2'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
Range constraints: {}

# mypy: allow-untyped-defs
import torch

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

    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)

example_args = (torch.randn(3, 2), torch.randn(2))
tags = {"python.closure"}
model = NestedFunction()


torch.export.export(model, example_args)

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

                 sub: "f32[3, 2]" = torch.ops.aten.sub.Tensor(a, b);  a = b = 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='a'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='b'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_2'), target=None)])
Range constraints: {}

# mypy: allow-untyped-defs
import contextlib

import torch

class NullContextManager(torch.nn.Module):
"""
    Null context manager in Python will be traced out.
    """

    def forward(self, x):
"""
        Null context manager in Python will be traced out.
        """
        ctx = contextlib.nullcontext()
        with ctx:
            return x.sin() + x.cos()

example_args = (torch.randn(3, 2),)
tags = {"python.context-manager"}
model = NullContextManager()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3, 2]"):
                 sin: "f32[3, 2]" = torch.ops.aten.sin.default(x)
            cos: "f32[3, 2]" = torch.ops.aten.cos.default(x);  x = None
            add: "f32[3, 2]" = torch.ops.aten.add.Tensor(sin, cos);  sin = cos = 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: {}

# mypy: allow-untyped-defs
import torch

from torch.utils import _pytree as pytree

class PytreeFlatten(torch.nn.Module):
"""
    Pytree from PyTorch can be captured by TorchDynamo.
    """

    def forward(self, x):
        y, spec = pytree.tree_flatten(x)
        return y[0] + 1

example_args = ({1: torch.randn(3, 2), 2: torch.randn(3, 2)},),
model = PytreeFlatten()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x_0_1: "f32[3, 2]", x_0_2: "f32[3, 2]"):
                 add: "f32[3, 2]" = torch.ops.aten.add.Tensor(x_0_1, 1);  x_0_1 = None
            return (add,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x_0_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x_0_2'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
Range constraints: {}

# mypy: allow-untyped-defs
import torch

from torch.export import Dim

x = torch.randn(3, 2)
dim1_x = Dim("dim1_x")

class ScalarOutput(torch.nn.Module):
"""
    Returning scalar values from the graph is supported, in addition to Tensor
    outputs. Symbolic shapes are captured and rank is specialized.
    """
    def __init__(self) -> None:
        super().__init__()

    def forward(self, x):
        return x.shape[1] + 1

example_args = (x,)
tags = {"torch.dynamic-shape"}
dynamic_shapes = {"x": {1: dim1_x}}
model = ScalarOutput()


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

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3, s0]"):
             #
            sym_size_int_1: "Sym(s0)" = torch.ops.aten.sym_size.int(x, 1);  x = None

                 add: "Sym(s0 + 1)" = sym_size_int_1 + 1;  sym_size_int_1 = 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=SymIntArgument(name='add'), target=None)])
Range constraints: {s0: VR[0, int_oo]}

# mypy: allow-untyped-defs
from enum import Enum

import torch

class Animal(Enum):
    COW = "moo"

class SpecializedAttribute(torch.nn.Module):
"""
    Model attributes are specialized.
    """

    def __init__(self) -> None:
        super().__init__()
        self.a = "moo"
        self.b = 4

    def forward(self, x):
        if self.a == Animal.COW.value:
            return x * x + self.b
        else:
            raise ValueError("bad")

example_args = (torch.randn(3, 2),)
model = SpecializedAttribute()


torch.export.export(model, example_args)

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, x: "f32[3, 2]"):
                 mul: "f32[3, 2]" = torch.ops.aten.mul.Tensor(x, x);  x = None
            add: "f32[3, 2]" = torch.ops.aten.add.Tensor(mul, 4);  mul = 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: {}