torch.cond#
cond_branch_class_method#
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) -> 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)
Result:
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:
# inputs
x: USER_INPUT
# outputs
sin: USER_OUTPUT
Range constraints: {}
cond_branch_nested_function#
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 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)
Result:
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:
# inputs
x: USER_INPUT
# outputs
add: USER_OUTPUT
Range constraints: {}
cond_branch_nonlocal_variables#
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 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)
Result:
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:
# inputs
c_lifted_tensor_0: CONSTANT_TENSOR target='lifted_tensor_0'
x: USER_INPUT
# outputs
add_2: USER_OUTPUT
Range constraints: {}
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])
example_args = (torch.tensor(True), torch.randn(3, 2))
tags = {"torch.cond", "python.closure"}
model = CondClosedOverVariable()
torch.export.export(model, example_args)
Result:
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:
# inputs
pred: USER_INPUT
x: USER_INPUT
# outputs
getitem: USER_OUTPUT
Range constraints: {}
cond_operands#
Original source code:
# 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)
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, x: "f32[s6, 2]", y: "f32[2]"):
# No stacktrace found for following nodes
sym_size_int_1: "Sym(s6)" = torch.ops.aten.sym_size.int(x, 0)
gt: "Sym(s6 > 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[s6, 2]" = cond[0]; cond = None
return (getitem,)
class true_graph_0(torch.nn.Module):
def forward(self, x: "f32[s6, 2]", y: "f32[2]"):
add: "f32[s6, 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[s6, 2]", y: "f32[2]"):
sub: "f32[s6, 2]" = torch.ops.aten.sub.Tensor(x, y); x = y = None
return (sub,)
Graph signature:
# inputs
x: USER_INPUT
y: USER_INPUT
# outputs
getitem: USER_OUTPUT
Range constraints: {s6: VR[0, int_oo]}
cond_predicate#
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 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)
Result:
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:
# inputs
x: USER_INPUT
# outputs
sin: USER_OUTPUT
Range constraints: {}
