python.control-flow¶
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: {}
list_unpack¶
Original source code:
from typing import List
import torch
def list_unpack(args: List[torch.Tensor]):
"""
Lists are treated as static construct, therefore unpacking should be
erased after tracing.
"""
x, *y = args
return x + y[0]
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[3, 2], arg1_1: i64[], arg2_1: i64[]):
#
add: f32[3, 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', 'arg2_1'], user_outputs=['add'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
static_for_loop¶
Original source code:
import torch
class StaticForLoop(torch.nn.Module):
"""
A for loop with constant number of iterations should be unrolled in the exported graph.
"""
def __init__(self):
super().__init__()
def forward(self, x):
ret = []
for i in range(10): # constant
ret.append(i + x)
return ret
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, 0)
add_1: f32[3, 2] = torch.ops.aten.add.Tensor(arg0_1, 1)
add_2: f32[3, 2] = torch.ops.aten.add.Tensor(arg0_1, 2)
add_3: f32[3, 2] = torch.ops.aten.add.Tensor(arg0_1, 3)
add_4: f32[3, 2] = torch.ops.aten.add.Tensor(arg0_1, 4)
add_5: f32[3, 2] = torch.ops.aten.add.Tensor(arg0_1, 5)
add_6: f32[3, 2] = torch.ops.aten.add.Tensor(arg0_1, 6)
add_7: f32[3, 2] = torch.ops.aten.add.Tensor(arg0_1, 7)
add_8: f32[3, 2] = torch.ops.aten.add.Tensor(arg0_1, 8)
add_9: f32[3, 2] = torch.ops.aten.add.Tensor(arg0_1, 9); arg0_1 = None
return (add, add_1, add_2, add_3, add_4, add_5, add_6, add_7, add_8, add_9)
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1'], user_outputs=['add', 'add_1', 'add_2', 'add_3', 'add_4', 'add_5', 'add_6', 'add_7', 'add_8', 'add_9'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
static_if¶
Original source code:
import torch
class StaticIf(torch.nn.Module):
"""
`if` statement with static predicate value should be traced through with the
taken branch.
"""
def __init__(self):
super().__init__()
def forward(self, x):
if len(x.shape) == 3:
return x + torch.ones(1, 1, 1)
return x
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[3, 2, 2]):
#
full: f32[1, 1, 1] = torch.ops.aten.full.default([1, 1, 1], 1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
add: f32[3, 2, 2] = torch.ops.aten.add.Tensor(arg0_1, full); arg0_1 = full = 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: {}
