python.data-structure¶
dictionary¶
Original source code:
import torch
def dictionary(x, y):
"""
Dictionary structures are inlined and flattened along tracing.
"""
elements = {}
elements["x2"] = x * x
y = y * elements["x2"]
return {"y": y}
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, l_x_: "f32[3, 2]", l_y_: "i64[]"):
mul: "f32[3, 2]" = torch.ops.aten.mul.Tensor(l_x_, l_x_); l_x_ = None
mul_1: "f32[3, 2]" = torch.ops.aten.mul.Tensor(l_y_, mul); l_y_ = mul = None
return (mul_1,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_y_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='mul_1'), target=None)])
Range constraints: {}
Equality constraints: []
fn_with_kwargs¶
Original source code:
import torch
),
tags={"python.data-structure"},
support_level=SupportLevel.SUPPORTED,
)
def fn_with_kwargs(pos0, tuple0, *myargs, mykw0, **mykwargs):
"""
Keyword arguments are not supported at the moment.
"""
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
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, out: "f32[4]", arg: "f32[4]", arg_1: "f32[4]", arg_2: "f32[4]", arg_3: "f32[4]", l_mykw0_: "f32[4]", l_mykwargs_input0_: "f32[4]", l_mykwargs_input1_: "f32[4]"):
mul: "f32[4]" = torch.ops.aten.mul.Tensor(out, arg); out = arg = None
mul_1: "f32[4]" = torch.ops.aten.mul.Tensor(mul, arg_1); mul = arg_1 = None
mul_2: "f32[4]" = torch.ops.aten.mul.Tensor(mul_1, arg_2); mul_1 = arg_2 = None
mul_3: "f32[4]" = torch.ops.aten.mul.Tensor(mul_2, arg_3); mul_2 = arg_3 = None
mul_4: "f32[4]" = torch.ops.aten.mul.Tensor(mul_3, l_mykw0_); mul_3 = l_mykw0_ = None
mul_5: "f32[4]" = torch.ops.aten.mul.Tensor(mul_4, l_mykwargs_input0_); mul_4 = l_mykwargs_input0_ = None
mul_6: "f32[4]" = torch.ops.aten.mul.Tensor(mul_5, l_mykwargs_input1_); mul_5 = l_mykwargs_input1_ = None
return (mul_6,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='out'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg_1'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg_2'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg_3'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_mykw0_'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_mykwargs_input0_'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_mykwargs_input1_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='mul_6'), target=None)])
Range constraints: {}
Equality constraints: []
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, l_x_: "f32[3, 2]"):
add: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, l_x_); l_x_ = None
return (add,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
Range constraints: {}
Equality constraints: []
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, x: "f32[3, 2]", l_args_1_: "i64[]", arg2: "i64[]"):
add: "f32[3, 2]" = torch.ops.aten.add.Tensor(x, l_args_1_); x = l_args_1_ = None
return (add,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_args_1_'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg2'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
Range constraints: {}
Equality constraints: []
