python.数据结构¶

字典¶

注意

支持级别：支持

原始源代码：

import torch

结果:

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(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg1_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='mul_1'), target=None)])
Range constraints: {}

带关键字参数的函数¶

注意

标签: python.数据结构

支持级别：支持

原始源代码：

import torch

结果:

```html
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(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg1_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg2_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg3_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg4_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg5_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg6_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg7_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind<span class

list_contains¶

注意

标签: torch.dynamic-shape, python.data-structure, python.assert

支持级别：支持

原始源代码：

import torch

结果:

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(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
Range constraints: {}

列表解包¶

注意

标签: python.数据结构, python.控制流程

支持级别：支持

原始源代码：

from typing import List

import torch

结果:

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(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg1_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg2_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
Range constraints: {}