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, arg0_1: f32[3, 2], arg1_1: i64[]):
#
mul: f32[3, 2] = torch.ops.aten.mul.Tensor(arg0_1, arg0_1); arg0_1 = None
mul_1: f32[3, 2] = torch.ops.aten.mul.Tensor(arg1_1, mul); arg1_1 = mul = None
return (mul_1,)
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1', 'arg1_1'], user_outputs=['mul_1'], inputs_to_parameters={}, inputs_to_buffers={}, buffers_to_mutate={}, backward_signature=None, assertion_dep_token=None)
Symbol to range: {}
fn_with_kwargs¶
Original source code:
import torch
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, arg0_1: f32[4], arg1_1: f32[4], arg2_1: f32[4], arg3_1: f32[4], arg4_1: f32[4], arg5_1: f32[4], arg6_1: f32[4], arg7_1: f32[4]):
#
mul: f32[4] = torch.ops.aten.mul.Tensor(arg0_1, arg1_1); arg0_1 = arg1_1 = None
mul_1: f32[4] = torch.ops.aten.mul.Tensor(mul, arg2_1); mul = arg2_1 = None
mul_2: f32[4] = torch.ops.aten.mul.Tensor(mul_1, arg3_1); mul_1 = arg3_1 = None
mul_3: f32[4] = torch.ops.aten.mul.Tensor(mul_2, arg4_1); mul_2 = arg4_1 = None
mul_4: f32[4] = torch.ops.aten.mul.Tensor(mul_3, arg5_1); mul_3 = arg5_1 = None
mul_5: f32[4] = torch.ops.aten.mul.Tensor(mul_4, arg6_1); mul_4 = arg6_1 = None
mul_6: f32[4] = torch.ops.aten.mul.Tensor(mul_5, arg7_1); mul_5 = arg7_1 = None
return (mul_6,)
Graph Signature: ExportGraphSignature(parameters=[], buffers=[], user_inputs=['arg0_1', 'arg1_1', 'arg2_1', 'arg3_1', 'arg4_1', 'arg5_1', 'arg6_1', 'arg7_1'], user_outputs=['mul_6'], 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: {}
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: {}