python.closure¶
cond_closed_over_variable¶
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]"):
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: {}
nested_function¶
Original source code:
# 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 __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, 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: {}