torch.dynamic-shape¶
cond_branch_class_method¶
Original source code:
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, arg0_1: f32[3]):
#
submodule_0 = self.submodule_0
submodule_1 = self.submodule_1
cond: f32[3] = torch.ops.higher_order.cond(False, submodule_0, submodule_1, [arg0_1]); submodule_0 = submodule_1 = arg0_1 = None
return (cond,)
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[3]):
cos: f32[3] = torch.ops.aten.cos.default(arg0_1); arg0_1 = None
return cos
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[3]):
sin: f32[3] = torch.ops.aten.sin.default(arg0_1); arg0_1 = None
return sin
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1'], user_outputs=['cond'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
cond_branch_nested_function¶
Original source code:
import torch
from functorch.experimental.control_flow import cond
def cond_branch_nested_function(x):
"""
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 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, arg0_1: f32[3]):
#
submodule_0 = self.submodule_0
submodule_1 = self.submodule_1
cond: f32[3] = torch.ops.higher_order.cond(True, submodule_0, submodule_1, [arg0_1]); submodule_0 = submodule_1 = arg0_1 = None
return (cond,)
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[3]):
add: f32[3] = torch.ops.aten.add.Tensor(arg0_1, arg0_1); arg0_1 = None
return add
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[3]):
sub: f32[3] = torch.ops.aten.sub.Tensor(arg0_1, arg0_1); arg0_1 = None
return sub
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1'], user_outputs=['cond'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
cond_branch_nonlocal_variables¶
Original source code:
import torch
from functorch.experimental.control_flow import cond
def cond_branch_nonlocal_variables(x):
"""
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.
"""
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, arg0_1: f32[6]):
#
add: f32[6] = torch.ops.aten.add.Tensor(arg0_1, 100)
_tensor_constant0: f32[] = self._tensor_constant0
lift_fresh_copy: f32[] = torch.ops.aten.lift_fresh_copy.default(_tensor_constant0); _tensor_constant0 = None
submodule_0 = self.submodule_0
submodule_1 = self.submodule_1
cond: f32[6] = torch.ops.higher_order.cond(True, submodule_0, submodule_1, [arg0_1, add, lift_fresh_copy]); submodule_0 = submodule_1 = arg0_1 = add = lift_fresh_copy = None
return (cond,)
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[6], arg1_1: f32[6], arg2_1: f32[]):
add: f32[6] = torch.ops.aten.add.Tensor(arg0_1, arg1_1); arg0_1 = arg1_1 = None
add_1: f32[6] = torch.ops.aten.add.Tensor(add, arg2_1); add = arg2_1 = None
return add_1
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[6], arg1_1: f32[6], arg2_1: f32[]):
sub: f32[6] = torch.ops.aten.sub.Tensor(arg0_1, arg1_1); arg0_1 = arg1_1 = None
sub_1: f32[6] = torch.ops.aten.sub.Tensor(sub, arg2_1); sub = arg2_1 = None
return sub_1
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1'], user_outputs=['cond'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
cond_operands¶
Original source code:
import torch
from torch._export import dynamic_dim
from functorch.experimental.control_flow import cond
x = torch.randn(3, 2)
y = torch.ones(2)
dynamic_constraint = dynamic_dim(x, 0)
def cond_operands(x, y):
"""
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 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, arg0_1: f32[s0, 2], arg1_1: f32[2]):
#
sym_size: Sym(s0) = torch.ops.aten.sym_size.int(arg0_1, 0)
gt: Sym(s0 > 2) = sym_size > 2; sym_size = None
submodule_0 = self.submodule_0
submodule_1 = self.submodule_1
cond: f32[s0, 2] = torch.ops.higher_order.cond(gt, submodule_0, submodule_1, [arg0_1, arg1_1]); gt = submodule_0 = submodule_1 = arg0_1 = arg1_1 = None
return (cond,)
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[s0, 2], arg1_1: f32[2]):
add: f32[s0, 2] = torch.ops.aten.add.Tensor(arg0_1, arg1_1); arg0_1 = arg1_1 = None
return add
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[s0, 2], arg1_1: f32[2]):
sub: f32[s0, 2] = torch.ops.aten.sub.Tensor(arg0_1, arg1_1); arg0_1 = arg1_1 = None
return sub
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1', 'arg1_1'], user_outputs=['cond'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {s0: RangeConstraint(min_val=2, max_val=9223372036854775806)}
cond_predicate¶
Original source code:
import torch
from functorch.experimental.control_flow import cond
def cond_predicate(x):
"""
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.
"""
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, arg0_1: f32[6, 4, 3]):
#
submodule_0 = self.submodule_0
submodule_1 = self.submodule_1
cond: f32[6, 4, 3] = torch.ops.higher_order.cond(False, submodule_0, submodule_1, [arg0_1]); submodule_0 = submodule_1 = arg0_1 = None
return (cond,)
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[6, 4, 3]):
cos: f32[6, 4, 3] = torch.ops.aten.cos.default(arg0_1); arg0_1 = None
return cos
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[6, 4, 3]):
sin: f32[6, 4, 3] = torch.ops.aten.sin.default(arg0_1); arg0_1 = None
return sin
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1'], user_outputs=['cond'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
dynamic_shape_constructor¶
Original source code:
import torch
def dynamic_shape_constructor(x):
"""
Tensor constructors should be captured with dynamic shape inputs rather
than being baked in with static shape.
"""
return torch.ones(x.shape[0] * 2)
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[3, 2]):
#
full: f32[6] = torch.ops.aten.full.default([6], 1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
return (full,)
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1'], user_outputs=['full'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
dynamic_shape_if_guard¶
Original source code:
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()
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[3, 2, 2]):
#
cos: f32[3, 2, 2] = torch.ops.aten.cos.default(arg0_1); arg0_1 = None
return (cos,)
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1'], user_outputs=['cos'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
dynamic_shape_map¶
Original source code:
import torch
from functorch.experimental.control_flow import map
def dynamic_shape_map(xs, y):
"""
functorch map() maps a function over the first tensor dimension.
"""
def body(x, y):
return x + y
return map(body, xs, y)
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[3, 2], arg1_1: f32[2]):
#
submodule_0 = self.submodule_0
map_impl = torch.ops.map_impl(submodule_0, 1, arg0_1, arg1_1); submodule_0 = arg0_1 = arg1_1 = None
getitem: f32[3, 2] = map_impl[0]; map_impl = None
return (getitem,)
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[2], arg1_1: f32[2]):
add: f32[2] = torch.ops.aten.add.Tensor(arg0_1, arg1_1); arg0_1 = arg1_1 = None
return [add]
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1', 'arg1_1'], user_outputs=['getitem'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
dynamic_shape_round¶
Original source code:
import torch
from torch._export import dynamic_dim
x = torch.ones(3, 2)
dynamic_constraint = dynamic_dim(x, 0)
def dynamic_shape_round(x):
"""
Calling round on dynamic shapes is not supported.
"""
return x[: round(x.shape[0] / 2)]
Result:
Unsupported: Calling round() on symbolic value is not supported. You can use floor() to implement this functionality
dynamic_shape_slicing¶
Original source code:
import torch
def dynamic_shape_slicing(x):
"""
Slices with dynamic shape arguments should be captured into the graph
rather than being baked in.
"""
return x[: x.shape[0] - 2, x.shape[1] - 1 :: 2]
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[3, 2]):
#
slice_1: f32[1, 2] = torch.ops.aten.slice.Tensor(arg0_1, 0, 0, 1); arg0_1 = 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(parameters=[], buffers=[], user_inputs=['arg0_1'], user_outputs=['slice_2'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
dynamic_shape_view¶
Original source code:
import torch
def dynamic_shape_view(x):
"""
Dynamic shapes should be propagated to view arguments instead of being
baked into the exported graph.
"""
new_x_shape = x.size()[:-1] + (2, 5)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1)
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[10, 10]):
#
view: f32[10, 2, 5] = torch.ops.aten.view.default(arg0_1, [10, 2, 5]); arg0_1 = None
permute: f32[10, 5, 2] = torch.ops.aten.permute.default(view, [0, 2, 1]); view = None
return (permute,)
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1'], user_outputs=['permute'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
list_contains¶
Original source code:
import torch
def list_contains(x):
"""
List containment relation can be checked on a dynamic shape or constants.
"""
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
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[3, 2]):
#
add: f32[3, 2] = torch.ops.aten.add.Tensor(arg0_1, arg0_1); arg0_1 = None
return (add,)
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1'], user_outputs=['add'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
scalar_output¶
Original source code:
import torch
from torch._export import dynamic_dim
x = torch.ones(3, 2)
dynamic_constraint = dynamic_dim(x, 1)
def scalar_output(x):
"""
Returning scalar values from the graph is supported, in addition to Tensor
outputs. Symbolic shapes are captured and rank is specialized.
"""
return x.shape[1] + 1
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[3, s0]):
#
sym_size: Sym(s0) = torch.ops.aten.sym_size.int(arg0_1, 1); arg0_1 = None
add: Sym(s0 + 1) = sym_size + 1; sym_size = None
return (add,)
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1'], user_outputs=['add'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {s0: RangeConstraint(min_val=2, max_val=9223372036854775806)}
