当前位置：首页 > news >正文

PyTorch中的pyi檔案生成機制

news 2025/9/2 19:07:58

PyTorch中的pyi檔案生成機制

前言
pyi檔
由py生成pyi.in
由pyi.in生成pyi
- torch/CMakeLists.txt
- tools/pyi/gen_pyi.py
- gen_pyi
- - native_functions
  - - rand.names ＆ rand.names_out
    - rand.generator_with_names & rand.generator_with_names_out
    - rand
    - rand.generator
    - rand.out
    - rand.generator_out
    - add.Tensor && add.out
    - add_.Tensor && add.out
    - add.out
  - function_signatures
  - - rand.names & rand.names_out
    - rand.generator_with_names & rand.generator_with_names_out
    - rand
    - rand.generator
    - rand.out
    - rand.generator_out
    - add.Tensor && add.out
    - add_.Tensor && add.out
    - add.out
  - sig_groups
  - - rand.generator_with_names & rand.generator_with_names_out
    - rand.generator & rand.generator_out
    - rand.names & rand.names_out
    - rand & rand.out
    - add.Tensor & add.out
    - add & add.Tensor & add.out
  - unsorted_function_hints
  - - rand
    - add
  - function_hints
  - - rand
    - add
  - hinted_function_names
  - all_symbols
  - all_directive
  - env
- gen_nn_functional
- datapipe.pyi
- 生成結果
使用pyi做類型檢查

前言

在PyTorch中如果查找python函數的定義，十有八九會跳轉到torch/_C/_VariableFunctions.pyi這個檔案。但是如果去PyTorch的github repo上尋找這個檔案，只能找到一個跟它名字類似的torch/_C/_VariableFunctions.pyi.in，卻找不到torch/_C/_VariableFunctions.pyi這個檔案本身。

如果打開torch/_C/_VariableFunctions.pyi去看：

# @generated from torch/_C/_VariableFunctions.pyi.in

才發現原來首行就說了：它是在編譯時才由torch/_C/_VariableFunctions.pyi.in動態生成的。

本篇就是要探討PyTorch中pyi檔案的生成機制。pyi檔案的生成過程大致可分為以下兩步：

由py生成pyi.in
由pyi.in生成pyi

但在這之前，先來看一下pyi檔在Python中的作用為何。

pyi檔

首先來看一下pyi這個檔案類型的名稱由來，根據What does “i” represent in Python .pyi extension?：

The i in .pyi stands for ‘interface’.The .pyi extension was first mentioned in this GitHub issue thread where JukkaL says:I'd probably prefer an extension with just a single dot. It also needs to be something that is not in use (it should not be used by cython, etc.). .pys seems to be used in Windows (or was). Maybe .pyi, where i stands for an interface definition?

可以知道，pyi中的i代表的是interface。

pyi implements "stub" file (definition from Martin Fowler)Stubs: provide canned answers to calls made during the test, usually not responding at all to anything outside what's programmed in for the test.

而它代表的涵意則是stub(樁/存根)，詳見樁 (計算機)：

樁[1]（Stub / Method Stub ）是指用來替換一部分功能的程序段。樁程序可以用來模擬已有程序的行為（比如一個遠端機器的過程）或是對將要開發的代碼的一種臨時替代。因此，打樁技術在程序移植、分布式計算、通用軟體開發和測試中用處很大。

正如pyi文件是干嘛的？（一文读懂Python的存根文件和类型检查）中所說，而pyi檔的作用只是在IDE中給type hint的，並不是必須的。

在PyTorch中也是一樣，torch/_C/_VariableFunctions.pyi僅用於類型提示。Python函數與C++函數的關聯實際上是由torch/csrc/autograd/generated/python_torch_functions_i.cpp所指定，而該檔案也是在編譯時自動生成的，詳見PyTorch中的python_torch_functions_i.cpp檔案生成機制。

由py生成pyi.in

PyTorch源碼中有以下.pyi.in檔：

torch/_C/__init__.pyi.in
torch/_C/_nn.pyi.in
torch/_C/return_types.pyi.in
torch/_C/_VariableFunctions.pyi.in
torch/nn/functional.pyi.in
torch/utils/data/datapipes/datapipe.pyi.in

根據torch/nn/functional.pyi.in中的注釋：

# These stubs were generated by running stubgen (`stubgen --parse-only functional.py`), followed by manual cleaning.

functional.pyi.in是用mypy的stubgen工具由functional.py生成後手動編輯而成的。

試著自己對torch/nn/functional.py跑跑看stubgen，首先把functional.py這個檔案複製到一個合適的地方，然後下：

stubgen functional.py

如果出現以下跟import相關的錯誤，先手動把對應的行數注釋掉就好：

Critical error during semantic analysis: functional.py:23: error: No parent module -- cannot perform relative import
functional.py:24: error: No parent module -- cannot perform relative import

先只關注以下這一段：

def fractional_max_pool2d_with_indices(input: Tensor, kernel_size: BroadcastingList2[int],output_size: Optional[BroadcastingList2[int]] = None,output_ratio: Optional[BroadcastingList2[float]] = None,return_indices: bool = False,_random_samples: Optional[Tensor] = None
) -> Tuple[Tensor, Tensor]:# ...fractional_max_pool2d = boolean_dispatch(arg_name="return_indices",arg_index=4,default=False,if_true=fractional_max_pool2d_with_indices,if_false=_fractional_max_pool2d,module_name=__name__,func_name="fractional_max_pool2d",
)

生成的functional.pyi裡對應的內容：

# ...
def fractional_max_pool2d_with_indices(input: Tensor, kernel_size: BroadcastingList2[int], output_size: Optional[BroadcastingList2[int]] = ..., output_ratio: Optional[BroadcastingList2[float]] = ..., return_indices: bool = ..., _random_samples: Optional[Tensor] = ...) -> Tuple[Tensor, Tensor]: ...fractional_max_pool2d: Incomplete
# ...

fractional_max_pool2d_with_indices這個函數的簽名與原本的幾乎一致，而fractional_max_pool2d則因為無法推斷被標注為Incomplete。

照理說.pyi.in檔是由.py檔生成的，但是torch/_C目錄下的.pyi.in檔都沒有對應的.py檔，推測是由多個.py檔合併到同一個.pyi.in檔而來的。

由pyi.in生成pyi

一般來說.pyi檔是由stubgen生成的，但在PyTorch中則是先用stubgen生成並手動編輯後得到pyi.in檔，然後再利用Python腳本由.pyi.in檔生成的。

torch/CMakeLists.txt

torch/CMakeLists.txt

新增一個名為torch_python_stubs的custom target，依賴於如下的pyi檔。(關於add_custom_target和接下來會看到的add_custom_command詳見cmake的add_custom_command及add_custom_target。)

add_custom_target(torch_python_stubs DEPENDS"${TORCH_SRC_DIR}/_C/__init__.pyi""${TORCH_SRC_DIR}/_C/_VariableFunctions.pyi""${TORCH_SRC_DIR}/nn/functional.pyi""${TORCH_SRC_DIR}/utils/data/datapipes/datapipe.pyi"
)

查看如下add_custom_command的OUTPUT參數，可以知道這個custom command正是用於生成torch_python_stubs所依賴的前三個pyi檔。至於剩下的datapipe.pyi是如何生成的，詳見datapipe.pyi章節。

file(GLOB_RECURSE torchgen_python "${PROJECT_SOURCE_DIR}/torchgen/*.py")
file(GLOB_RECURSE autograd_python "${TOOLS_PATH}/autograd/*.py")
file(GLOB_RECURSE pyi_python "${TOOLS_PATH}/pyi/*.py")
add_custom_command(OUTPUT"${TORCH_SRC_DIR}/_C/__init__.pyi""${TORCH_SRC_DIR}/_C/_VariableFunctions.pyi""${TORCH_SRC_DIR}/nn/functional.pyi"COMMAND"${PYTHON_EXECUTABLE}" -mtools.pyi.gen_pyi--native-functions-path "aten/src/ATen/native/native_functions.yaml"--tags-path "aten/src/ATen/native/tags.yaml"--deprecated-functions-path "tools/autograd/deprecated.yaml"DEPENDS"${TORCH_SRC_DIR}/_C/__init__.pyi.in""${TORCH_SRC_DIR}/_C/_VariableFunctions.pyi.in""${TORCH_SRC_DIR}/nn/functional.pyi.in""${TORCH_ROOT}/aten/src/ATen/native/native_functions.yaml""${TORCH_ROOT}/aten/src/ATen/native/tags.yaml""${TORCH_ROOT}/tools/autograd/deprecated.yaml"${pyi_python}${autograd_python}${torchgen_python}WORKING_DIRECTORY"${TORCH_ROOT}"
)

這一段的入口是add_custom_command中的COMMAND，它透過"${PYTHON_EXECUTABLE}" -mtools.pyi.gen_pyi調用tools/pyi/gen_pyi.py，輸入則是DEPENDS區塊中寫的_C/__init__.pyi.in, _C/_VariableFunctions.pyi.in 及nn/functional.pyi.in，程序執行完後會生成OUTPUT區塊中寫的三個pyi檔。

torch/_C/_nn.pyi和torch/_C/return_types.pyi也是由tools/pyi/gen_pyi.py生成的，為什麼沒寫在add_custom_target和add_custom_command的DEPENDS和OUTPUT裡?

新增一個名為torch_python的shared library，運行過後會生成build/lib/libtorch_python.so。

add_library(torch_python SHARED ${TORCH_PYTHON_SRCS})

接著宣告torch_python依賴於torch_python_stubs這個custom target。

add_dependencies(torch_python torch_python_stubs)

在非MacOS的系統上都會建構一個名為nnapi_backend的library，它的依賴中就有torch_python。

# Skip building this library under MacOS, since it is currently failing to build on Mac
# Github issue #61930
if(NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")# Add Android Nnapi delegate libraryadd_library(nnapi_backend SHARED${TORCH_SRC_DIR}/csrc/jit/backends/nnapi/nnapi_backend_lib.cpp${TORCH_SRC_DIR}/csrc/jit/backends/nnapi/nnapi_backend_preprocess.cpp)# Pybind11 requires explicit linking of the torch_python librarytarget_link_libraries(nnapi_backend PRIVATE torch torch_python pybind::pybind11)
endif()

總結一下，就是有nnapi_backend -> torch_python -> torch_python_stubs -> torch/_C/__init__.pyi, torch/_C/_VariableFunctions.pyi, torch/nn/functional.pyi間的層層依賴，所以要建構nnapi_backend這個library時才會調用tools/pyi/gen_pyi.py去生成.pyi檔。

tools/pyi/gen_pyi.py

CMakeLists.txt中透過"${PYTHON_EXECUTABLE}" -mtools.pyi.gen_pyi調用了tools/pyi/gen_pyi.py，它的功用是由.pyi.in檔生成.pyi檔。

def main() -> None:parser = argparse.ArgumentParser(description="Generate type stubs for PyTorch")parser.add_argument("--native-functions-path",metavar="NATIVE",default="aten/src/ATen/native/native_functions.yaml",help="path to native_functions.yaml",)parser.add_argument("--tags-path",metavar="TAGS",default="aten/src/ATen/native/tags.yaml",help="path to tags.yaml",)parser.add_argument("--deprecated-functions-path",metavar="DEPRECATED",default="tools/autograd/deprecated.yaml",help="path to deprecated.yaml",)parser.add_argument("--out", metavar="OUT", default=".", help="path to output directory")args = parser.parse_args()fm = FileManager(install_dir=args.out, template_dir=".", dry_run=False)gen_pyi(args.native_functions_path, args.tags_path, args.deprecated_functions_path, fm)if __name__ == "__main__":main()

gen_pyi.py中的注釋：

- We start off with a hand-written __init__.pyi.in file.  Thisfile contains type definitions for everything we cannot automaticallygenerate, including pure Python definitions directly in __init__.py(the latter case should be pretty rare).- We go through automatically bound functions based on thetype information recorded in native_functions.yaml andgenerate type hints for them (generate_type_hints)

native_functions.yaml中記錄了自動綁定函數（automatically bound functions，猜測是Python與C++函數的綁定）的類型資訊，gen_pyi.py會依據這些類型資訊用generate_type_hints函數（待會會在unsorted_function_hints一節出現）生成類型提示。

gen_pyi

tools/pyi/gen_pyi.py

這個函數的功用是由_C/__init__.pyi.in, _C/_VariableFunctions.pyi.in及torch/_C/return_types.pyi.in生成_C/__init__.pyi, _C/_VariableFunctions.pyi, torch/_VF.pyi和torch/return_types.pyi。

def gen_pyi(native_yaml_path: str,tags_yaml_path: str,deprecated_yaml_path: str,fm: FileManager,
) -> None:"""gen_pyi()This function generates a pyi file for torch."""# ...

前三個參數預設為：

native_yaml_path：aten/src/ATen/native/native_functions.yaml
tags_yaml_path：aten/src/ATen/native/tags.yaml
deprecated_yaml_path：tools/autograd/deprecated.yaml

fm建構子中的兩個參數如下：

install_dir：args.out，也就是’.’
template_dir：‘.’

native_functions

解析native_functions.yaml與tags.yaml，得到native_functions變數：

    native_functions = parse_native_yaml(native_yaml_path, tags_yaml_path).native_functionsnative_functions = list(filter(should_generate_py_binding, native_functions))

native_functions是一個NativeFunction的列表，表示aten命名空間裡的函數，其第零個元素如下：

NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='_cast_Byte', inplace=False, dunder_method=False, functional_overload=False), overload_name=''), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), post_self_positional=(Argument(name='non_blocking', type=BaseType(name=<BaseTy.bool: 9>), default='False', annotation=None),), pre_tensor_options_kwarg_only=(), tensor_options=None, post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=9), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=False, has_composite_implicit_autograd_kernel=True, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags=set())

代表rand函數的元素如下，aten::rand函數有六種overload name，分別為names, generator_with_names, 空字串, generator, out, generator_out。可與native_functions.yaml交互參看：

rand.names ＆ rand.names_out

- func: rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensordevice_check: NoCheckdevice_guard: Falsedispatch:CompositeExplicitAutograd: randautogen: rand.names_outtags: nondeterministic_seeded

yaml檔中的autogen欄位中有rand.names_out，對照native_functions中的元素，可以發現NativeFunction的autogen成員也有一個overload_name為names_out的OperatorName。

NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), overload_name='names'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='names', type=OptionalType(elem=ListType(elem=BaseType(name=<BaseTy.Dimname: 5>), size=None)), default=None, annotation=None),), tensor_options=TensorOptionsArguments(dtype=Argument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', annotation=None), layout=Argument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', annotation=None), device=Argument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', annotation=None), pin_memory=Argument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='None', annotation=None)), post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=False, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4254), autogen=[OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), overload_name='names_out')], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False,has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'})

rand.generator_with_names & rand.generator_with_names_out

- func: rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensordevice_check: NoCheckdevice_guard: Falsetags: nondeterministic_seededdispatch:CompositeExplicitAutograd: randautogen: rand.generator_with_names_out

yaml檔中的autogen欄位中有rand.generator_with_names_out，對照下面，可以發現NativeFunction的autogen成員也有一個overload_name為generator_with_names_out的OperatorName。

NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), overload_name='generator_with_names'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, annotation=None), Argument(name='names', type=OptionalType(elem=ListType(elem=BaseType(name=<BaseTy.Dimname: 5>), size=None)), default=None, annotation=None)), tensor_options=TensorOptionsArguments(dtype=Argument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', annotation=None), layout=Argument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', annotation=None), device=Argument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', annotation=None), pin_memory=Argument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='None', annotation=None)), post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=False, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4262), autogen=[OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), overload_name='generator_with_names_out')], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'})

rand

- func: rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensortags: nondeterministic_seededdispatch:CompositeExplicitAutograd: rand

NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), overload_name=''), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(), tensor_options=TensorOptionsArguments(dtype=Argument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', annotation=None), layout=Argument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', annotation=None), device=Argument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', annotation=None), pin_memory=Argument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='None', annotation=None)), post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4270), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'})

rand.generator

- func: rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensortags: nondeterministic_seededdispatch:CompositeExplicitAutograd: rand

NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), overload_name='generator'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, annotation=None),), tensor_options=TensorOptionsArguments(dtype=Argument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', annotation=None), layout=Argument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', annotation=None), device=Argument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', annotation=None), pin_memory=Argument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='None', annotation=None)), post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4275), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'})

rand.out

- func: rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)tags: nondeterministic_seededdispatch:CompositeExplicitAutograd: rand_out

NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), overload_name='out'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(), tensor_options=None, post_tensor_options_kwarg_only=(), out=(Argument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4280), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'})

rand.generator_out

- func: rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)tags: nondeterministic_seeded

NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), overload_name='generator_out'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=(Argument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4285), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=False, has_composite_implicit_autograd_kernel=True, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'})

因為rand.names和rand.generator_with_names會生成對應的out版本的函數，所以由native_functions.yaml裡六個rand相關函數最後可以生成C++ aten命名空間裡的八個函數。

add.Tensor && add.out

傳入self及other，回傳結果的add函數。

- func: add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensordevice_check: NoCheck   # TensorIteratorstructured_delegate: add.outvariants: function, methoddispatch:SparseCPU, SparseCUDA: add_sparseSparseCsrCPU, SparseCsrCUDA: add_sparse_csrMkldnnCPU: mkldnn_addZeroTensor: add_zerotensorNestedTensorCPU, NestedTensorCUDA: NestedTensor_add_Tensortags: [canonical, pointwise]

NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='Tensor'), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), post_self_positional=(Argument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>, <Variant.method: 2>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=497), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='out'), structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'pointwise', 'canonical'})

add_.Tensor && add.out

直接修改self參數的inplace版本。

- func: add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)device_check: NoCheck   # TensorIteratorvariants: methodstructured_delegate: add.outdispatch:SparseCPU, SparseCUDA: add_sparse_SparseCsrCPU, SparseCsrCUDA: add_sparse_csr_MkldnnCPU: mkldnn_add_NestedTensorCPU, NestedTensorCUDA: NestedTensor_add__Tensortags: pointwise

根據pytorch native README.md：

Tensor(a!) - members of a may be written to thus mutating the underlying data.

Tensor(a!) self這個寫法表示self參數同時是入參也是出參。

NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='add', inplace=True, dunder_method=False, functional_overload=False), overload_name='Tensor'), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=()))), post_self_positional=(Argument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.method: 2>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=509), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='out'), structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'pointwise'})

add.out

有出參out版本的add函數。

- func: add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)device_check: NoCheck   # TensorIteratorstructured: Truestructured_inherits: TensorIteratorBaseufunc_inner_loop:Generic: add (AllAndComplex, BFloat16, Half, ComplexHalf)ScalarOnly: add (Bool)dispatch:SparseCPU: add_out_sparse_cpuSparseCUDA: add_out_sparse_cudaSparseCsrCPU: add_out_sparse_csr_cpuSparseCsrCUDA: add_out_sparse_csr_cudaMkldnnCPU: mkldnn_add_outMPS: add_out_mpstags: pointwise

NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='out'), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), post_self_positional=(Argument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=(Argument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=520), autogen=[], ufunc_inner_loop={<UfuncKey.Generic: 7>: UfuncInnerLoop(name='add', supported_dtypes=<torchgen.utils.OrderedSet object at 0x7f600cff7910>, ufunc_key=<UfuncKey.Generic: 7>), <UfuncKey.ScalarOnly: 6>: UfuncInnerLoop(name='add', supported_dtypes=<torchgen.utils.OrderedSet object at 0x7f600cff7b80>, ufunc_key=<UfuncKey.ScalarOnly: 6>)}, structured=True, structured_delegate=None, structured_inherits='TensorIteratorBase', precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'pointwise'})

function_signatures

    function_signatures = load_signatures(native_functions, deprecated_yaml_path, method=False, pyi=True)

function_signatures是一個PythonSignatureNativeFunctionPair的列表，其第零個元素如下：

PythonSignatureNativeFunctionPair(signature=PythonSignature(name='_cast_Byte', input_args=(PythonArgument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None), PythonArgument(name='non_blocking', type=BaseType(name=<BaseTy.bool: 9>), default='False', default_init=None)), input_kwargs=(), output_args=None, returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), tensor_options_args=(), method=False), function=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='_cast_Byte', inplace=False, dunder_method=False, functional_overload=False), overload_name=''), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), post_self_positional=(Argument(name='non_blocking', type=BaseType(name=<BaseTy.bool: 9>), default='False', annotation=None),), pre_tensor_options_kwarg_only=(), tensor_options=None, post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=9), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=False, has_composite_implicit_autograd_kernel=True, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags=set()))

代表rand函數的元素如下，共六個，可與剛才的native_functions一一對應：

rand.names & rand.names_out

PythonSignatureNativeFunctionPair(signature=PythonSignature(name='rand', input_args=(PythonArgument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, default_init=None),), input_kwargs=(PythonArgument(name='names', type=OptionalType(elem=ListType(elem=BaseType(name=<BaseTy.Dimname: 5>), size=None)), default=None, default_init=None),), output_args=None, returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), tensor_options_args=(PythonArgument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', default_init=None), PythonArgument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', default_init=None), PythonArgument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', default_init='torch::tensors::get_default_device()'), PythonArgument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None), PythonArgument(name='requires_grad', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None)), method=False), function=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), 
overload_name='names'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='names', type=OptionalType(elem=ListType(elem=BaseType(name=<BaseTy.Dimname: 5>), size=None)), default=None, annotation=None),), tensor_options=TensorOptionsArguments(dtype=Argument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', annotation=None), layout=Argument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', annotation=None), device=Argument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', annotation=None), pin_memory=Argument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='None', annotation=None)), post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=False, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4254), 
autogen=[OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), overload_name='names_out')], 
ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'}))

注意names會生成names_out函數。

rand.generator_with_names & rand.generator_with_names_out

PythonSignatureNativeFunctionPair(signature=PythonSignature(name='rand', input_args=(PythonArgument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, default_init=None),), input_kwargs=(PythonArgument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, default_init=None), PythonArgument(name='names', type=OptionalType(elem=ListType(elem=BaseType(name=<BaseTy.Dimname: 5>), size=None)), default=None, default_init=None)), output_args=None, returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), tensor_options_args=(PythonArgument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', default_init=None), PythonArgument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', default_init=None), PythonArgument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', default_init='torch::tensors::get_default_device()'), PythonArgument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None), PythonArgument(name='requires_grad', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None)), method=False), function=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), 
overload_name='generator_with_names'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, annotation=None), Argument(name='names', type=OptionalType(elem=ListType(elem=BaseType(name=<BaseTy.Dimname: 5>), size=None)), default=None, annotation=None)), tensor_options=TensorOptionsArguments(dtype=Argument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', annotation=None), layout=Argument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', annotation=None), device=Argument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', annotation=None), pin_memory=Argument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='None', annotation=None)), post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=False, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4262), 
autogen=[OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), overload_name='generator_with_names_out')], 
ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'}))

注意generator_with_names會生成generator_with_names_out函數。

rand

PythonSignatureNativeFunctionPair(signature=PythonSignature(name='rand', input_args=(PythonArgument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, default_init=None),), input_kwargs=(), output_args=None, returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), tensor_options_args=(PythonArgument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', default_init=None), PythonArgument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', default_init=None), PythonArgument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', default_init='torch::tensors::get_default_device()'), PythonArgument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None), PythonArgument(name='requires_grad', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None)), method=False), function=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), 
overload_name=''), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(), tensor_options=TensorOptionsArguments(dtype=Argument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', annotation=None), layout=Argument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', annotation=None), device=Argument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', annotation=None), pin_memory=Argument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='None', annotation=None)), post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4270), 
autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'}))

rand.generator

PythonSignatureNativeFunctionPair(signature=PythonSignature(name='rand', input_args=(PythonArgument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, default_init=None),), input_kwargs=(PythonArgument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, default_init=None),), output_args=None, returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), tensor_options_args=(PythonArgument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', default_init=None), PythonArgument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', default_init=None), PythonArgument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', default_init='torch::tensors::get_default_device()'), PythonArgument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None), PythonArgument(name='requires_grad', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None)), method=False), function=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), 
overload_name='generator'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, annotation=None),), tensor_options=TensorOptionsArguments(dtype=Argument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', annotation=None), layout=Argument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', annotation=None), device=Argument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', annotation=None), pin_memory=Argument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='None', annotation=None)), post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4275), 
autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'}))

rand.out

PythonSignatureNativeFunctionPair(signature=PythonSignature(name='rand', input_args=(PythonArgument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, default_init=None),), input_kwargs=(), output_args=PythonOutArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default='None', default_init=None, outputs=(PythonArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None),)), returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), tensor_options_args=(PythonArgument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', default_init=None), PythonArgument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', default_init=None), PythonArgument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', default_init='torch::tensors::get_default_device()'), PythonArgument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None), PythonArgument(name='requires_grad', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None)), method=False), function=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), 
overload_name='out'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(), tensor_options=None, post_tensor_options_kwarg_only=(), out=(Argument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4280), 
autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'}))

rand.generator_out

PythonSignatureNativeFunctionPair(signature=PythonSignature(name='rand', input_args=(PythonArgument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, default_init=None),), input_kwargs=(PythonArgument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, default_init=None),), output_args=PythonOutArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default='None', default_init=None, outputs=(PythonArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None),)), returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), tensor_options_args=(PythonArgument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', default_init=None), PythonArgument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', default_init=None), PythonArgument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', default_init='torch::tensors::get_default_device()'), PythonArgument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None), PythonArgument(name='requires_grad', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None)), method=False), function=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), 
overload_name='generator_out'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=(Argument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4285), 
autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=False, has_composite_implicit_autograd_kernel=True, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'}))

代表add函數的元素如下，共三個，也可與剛才的native_functions一一對應。

add.Tensor && add.out

PythonSignatureNativeFunctionPair(signature=PythonSignature(name='add', input_args=(PythonArgument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None), PythonArgument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None)), input_kwargs=(PythonArgument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', default_init=None),), output_args=None, returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), tensor_options_args=(), method=False), function=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='Tensor'), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), post_self_positional=(Argument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>, <Variant.method: 2>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=497), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='out'), structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'pointwise', 'canonical'}))

add_.Tensor && add.out

PythonSignatureNativeFunctionPair(signature=PythonSignature(name='add_', input_args=(PythonArgument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None), PythonArgument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None)), input_kwargs=(PythonArgument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', default_init=None),), output_args=None, returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), tensor_options_args=(), method=False), function=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='add', inplace=True, dunder_method=False, functional_overload=False), overload_name='Tensor'), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=()))), post_self_positional=(Argument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.method: 2>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=509), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='out'), structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'pointwise'}))

add.out

PythonSignatureNativeFunctionPair(signature=PythonSignature(name='add', input_args=(PythonArgument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None), PythonArgument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None)), input_kwargs=(PythonArgument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', default_init=None),), output_args=PythonOutArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default='None', default_init=None, outputs=(PythonArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None),)), returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), tensor_options_args=(), method=False), function=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='out'), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), post_self_positional=(Argument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=(Argument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=520), autogen=[], ufunc_inner_loop={<UfuncKey.Generic: 7>: UfuncInnerLoop(name='add', supported_dtypes=<torchgen.utils.OrderedSet object at 0x7f600cff7910>, ufunc_key=<UfuncKey.Generic: 7>), <UfuncKey.ScalarOnly: 6>: UfuncInnerLoop(name='add', supported_dtypes=<torchgen.utils.OrderedSet object at 0x7f600cff7b80>, ufunc_key=<UfuncKey.ScalarOnly: 6>)}, structured=True, structured_delegate=None, structured_inherits='TensorIteratorBase', precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'pointwise'}))

sig_groups

sig_groups是一個PythonSignatureGroup的列表，PythonSignatureGroup則是由PythonSignature和NativeFunction組成的pair。

PythonSignatureGroup跟PythonSignatureNativeFunctionPair比起來多了一個outplace。

    sig_groups = get_py_torch_functions(function_signatures)

sig_groups的第零個元素如下：

PythonSignatureGroup(signature=PythonSignature(name='__and__', input_args=(PythonArgument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None), PythonArgument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None)), input_kwargs=(), output_args=None, returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), tensor_options_args=(), method=False), base=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='and', inplace=False, dunder_method=True, functional_overload=False), overload_name='Tensor'), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), post_self_positional=(Argument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None),), pre_tensor_options_kwarg_only=(), tensor_options=None, post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.method: 2>, <Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=7635), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=False, has_composite_implicit_autograd_kernel=True, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags=set()), outplace=None)

代表rand函數的四個元素如下。原先的八個函數依據有沒有out被整理成兩兩一對，共四對。

rand.generator_with_names & rand.generator_with_names_out

PythonSignatureGroup(signature=PythonSignature(name='rand', input_args=(PythonArgument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, default_init=None),), input_kwargs=(PythonArgument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, default_init=None), PythonArgument(name='names', type=OptionalType(elem=ListType(elem=BaseType(name=<BaseTy.Dimname: 5>), size=None)), default=None, default_init=None)), output_args=None, returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), tensor_options_args=(PythonArgument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', default_init=None), PythonArgument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', default_init=None), PythonArgument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', default_init='torch::tensors::get_default_device()'), PythonArgument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None), PythonArgument(name='requires_grad', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None)), method=False), base=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), overload_name='generator_with_names'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, annotation=None), Argument(name='names', type=OptionalType(elem=ListType(elem=BaseType(name=<BaseTy.Dimname: 5>), size=None)), default=None, annotation=None)), tensor_options=TensorOptionsArguments(dtype=Argument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', annotation=None), layout=Argument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', annotation=None), device=Argument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', annotation=None), pin_memory=Argument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='None', annotation=None)), post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=False, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4262), autogen=[OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), overload_name='generator_with_names_out')], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'}), outplace=None)

rand.generator & rand.generator_out

PythonSignatureGroup(signature=PythonSignature(name='rand', input_args=(PythonArgument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, default_init=None),), input_kwargs=(PythonArgument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, default_init=None),), output_args=PythonOutArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default='None', default_init=None, outputs=(PythonArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None),)), returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), tensor_options_args=(PythonArgument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', default_init=None), PythonArgument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', default_init=None), PythonArgument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', default_init='torch::tensors::get_default_device()'), PythonArgument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None), PythonArgument(name='requires_grad', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None)), method=False), base=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), 
overload_name='generator'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, annotation=None),), tensor_options=TensorOptionsArguments(dtype=Argument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', annotation=None), layout=Argument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', annotation=None), device=Argument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', annotation=None), pin_memory=Argument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='None', annotation=None)), post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4275), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'}), outplace=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), 
overload_name='generator_out'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='generator', type=OptionalType(elem=BaseType(name=<BaseTy.Generator: 1>)), default=None, annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=(Argument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4285), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=False, has_composite_implicit_autograd_kernel=True, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'}))

rand.names & rand.names_out

PythonSignatureGroup(signature=PythonSignature(name='rand', input_args=(PythonArgument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, default_init=None),), input_kwargs=(PythonArgument(name='names', type=OptionalType(elem=ListType(elem=BaseType(name=<BaseTy.Dimname: 5>), size=None)), default=None, default_init=None),), output_args=None, returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), tensor_options_args=(PythonArgument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', default_init=None), PythonArgument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', default_init=None), PythonArgument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', default_init='torch::tensors::get_default_device()'), PythonArgument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None), PythonArgument(name='requires_grad', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None)), method=False), base=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), 
overload_name='names'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='names', type=OptionalType(elem=ListType(elem=BaseType(name=<BaseTy.Dimname: 5>), size=None)), default=None, annotation=None),), tensor_options=TensorOptionsArguments(dtype=Argument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', annotation=None), layout=Argument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', annotation=None), device=Argument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', annotation=None), pin_memory=Argument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='None', annotation=None)), post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=False, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4254), autogen=[OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), 
overload_name='names_out')], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'}), outplace=None)

rand & rand.out

PythonSignatureGroup(signature=PythonSignature(name='rand', input_args=(PythonArgument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, default_init=None),), input_kwargs=(), output_args=PythonOutArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default='None', default_init=None, outputs=(PythonArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None),)), returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), tensor_options_args=(PythonArgument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', default_init=None), PythonArgument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', default_init=None), PythonArgument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', default_init='torch::tensors::get_default_device()'), PythonArgument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None), PythonArgument(name='requires_grad', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='False', default_init=None)), method=False), base=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), 
overload_name=''), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(), tensor_options=TensorOptionsArguments(dtype=Argument(name='dtype', type=OptionalType(elem=BaseType(name=<BaseTy.ScalarType: 2>)), default='None', annotation=None), layout=Argument(name='layout', type=OptionalType(elem=BaseType(name=<BaseTy.Layout: 10>)), default='None', annotation=None), device=Argument(name='device', type=OptionalType(elem=BaseType(name=<BaseTy.Device: 11>)), default='None', annotation=None), pin_memory=Argument(name='pin_memory', type=OptionalType(elem=BaseType(name=<BaseTy.bool: 9>)), default='None', annotation=None)), post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4270), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'}), outplace=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='rand', inplace=False, dunder_method=False, functional_overload=False), 
overload_name='out'), arguments=Arguments(pre_self_positional=(), self_arg=None, post_self_positional=(Argument(name='size', type=ListType(elem=BaseType(name=<BaseTy.SymInt: 17>), size=None), default=None, annotation=None),), pre_tensor_options_kwarg_only=(), tensor_options=None, post_tensor_options_kwarg_only=(), out=(Argument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.ExactSame: 1>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=4280), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=None, structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=True, has_composite_explicit_autograd_non_functional_kernel=False, tags={'nondeterministic_seeded'}))

上面共有四個PythonSignatureGroup元素，先來看第一個元素，其base成員的func的overload_name為generator_with_names，autogen的overload_name則為generator_with_names_out。第二個的則分別為generator和generator_out。第三個的分別為names和names_out。第四個的分別為空字串和out。

到這裡得到了八個rand相關函數。

add.Tensor & add.out

PythonSignatureGroup(signature=PythonSignature(name='add', input_args=(PythonArgument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None), PythonArgument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None)), input_kwargs=(PythonArgument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', default_init=None),), output_args=PythonOutArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default='None', default_init=None, outputs=(PythonArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None),)), returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), tensor_options_args=(), method=False), base=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='Tensor'), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), post_self_positional=(Argument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>, <Variant.method: 2>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=497), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='out'), structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'pointwise', 'canonical'}), outplace=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='out'), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), post_self_positional=(Argument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=(Argument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=520), autogen=[], ufunc_inner_loop={<UfuncKey.Generic: 7>: UfuncInnerLoop(name='add', supported_dtypes=<torchgen.utils.OrderedSet object at 0x7f600cff7910>, ufunc_key=<UfuncKey.Generic: 7>), <UfuncKey.ScalarOnly: 6>: UfuncInnerLoop(name='add', supported_dtypes=<torchgen.utils.OrderedSet object at 0x7f600cff7b80>, ufunc_key=<UfuncKey.ScalarOnly: 6>)}, structured=True, structured_delegate=None, structured_inherits='TensorIteratorBase', precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'pointwise'}))

add & add.Tensor & add.out

PythonSignatureGroup(signature=PythonSignatureDeprecated(name='add', input_args=(PythonArgument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None), PythonArgument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default=None, default_init=None), PythonArgument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None)), input_kwargs=(), output_args=PythonOutArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default='None', default_init=None, outputs=(PythonArgument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, default_init=None),)), returns=PythonReturns(returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), tensor_options_args=(), method=False, deprecated_schema=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name=''), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), post_self_positional=(Argument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default=None, annotation=None), Argument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), pre_tensor_options_kwarg_only=(), tensor_options=None, post_tensor_options_kwarg_only=(), out=(Argument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), deprecated_args_exprs=('out', 'self', 'other', 'alpha')), base=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='Tensor'), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), post_self_positional=(Argument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=()), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=None),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>, <Variant.method: 2>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=497), autogen=[], ufunc_inner_loop={}, structured=False, structured_delegate=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='out'), structured_inherits=None, precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'pointwise', 'canonical'}), outplace=NativeFunction(namespace='aten', func=FunctionSchema(name=OperatorName(name=BaseOperatorName(base='add', inplace=False, dunder_method=False, functional_overload=False), overload_name='out'), arguments=Arguments(pre_self_positional=(), self_arg=SelfArgument(argument=Argument(name='self', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None)), post_self_positional=(Argument(name='other', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=None),), pre_tensor_options_kwarg_only=(Argument(name='alpha', type=BaseType(name=<BaseTy.Scalar: 12>), default='1', annotation=None),), tensor_options=None, post_tensor_options_kwarg_only=(), out=(Argument(name='out', type=BaseType(name=<BaseTy.Tensor: 3>), default=None, annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), returns=(Return(name=None, type=BaseType(name=<BaseTy.Tensor: 3>), annotation=Annotation(alias_set=('a',), is_write=True, alias_set_after=())),)), use_const_ref_for_mutable_tensors=False, device_guard=True, device_check=<DeviceCheckType.NoCheck: 0>, python_module=None, category_override=None, variants={<Variant.function: 1>}, manual_kernel_registration=False, manual_cpp_binding=False, loc=Location(file='aten/src/ATen/native/native_functions.yaml', line=520), autogen=[], ufunc_inner_loop={<UfuncKey.Generic: 7>: UfuncInnerLoop(name='add', supported_dtypes=<torchgen.utils.OrderedSet object at 0x7f600cff7910>, ufunc_key=<UfuncKey.Generic: 7>), <UfuncKey.ScalarOnly: 6>: UfuncInnerLoop(name='add', supported_dtypes=<torchgen.utils.OrderedSet object at 0x7f600cff7b80>, ufunc_key=<UfuncKey.ScalarOnly: 6>)}, structured=True, structured_delegate=None, structured_inherits='TensorIteratorBase', precomputed=None, cpp_no_default_args=set(), is_abstract=True, has_composite_implicit_autograd_kernel=False, has_composite_implicit_autograd_nested_tensor_kernel=False, has_composite_explicit_autograd_kernel=False, has_composite_explicit_autograd_non_functional_kernel=False, tags={'pointwise'}))

unsorted_function_hints

     for group in sorted(sig_groups, key=lambda g: g.signature.name):name = group.signature.nameunsorted_function_hints[name] += generate_type_hints(group)named_tuple = returns_named_tuple_pyi(group.signature)if named_tuple is not None and not group.signature.deprecated:# deprecated namedtuples are currently not included for torch functionstuple_name, tuple_def = named_tupleif tuple_name in namedtuples:assert namedtuples[tuple_name] == tuple_defelse:namedtuples[tuple_name] = tuple_def

unsorted_function_hints是一個defaultdict，key為函數名稱，value則為list of string。

rand

代表rand函數的元素如下：

'rand': ['def rand(size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...', 'def rand(*size: _int, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...', 'def rand(size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...', 'def rand(*size: _int, generator: Optional[Generator], out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...', 'def rand(size: Sequence[Union[_int, SymInt]], *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...', 'def rand(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...', 'def rand(size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...', 'def rand(*size: _int, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...']

上面是八個overload的rand函數，可以將它們分為四組：有generator及names參數，只有generator參數，只有name參數，沒有generator和names參數。每組又可分為size參數是Sequence的及是int的。到這裡已經可以與torch/_C/_VariableFunctions.pyi一一對應了。

add

找到名為add的key，其value list共有三個元素，分別對應add.Tensor，add_.Tensor，add.out。

'def add(input: Union[Tensor, Number], other: Union[Tensor, Number], *, alpha: Optional[Number]=1, out: Optional[Tensor]=None) -> Tensor: ...'

'def add(self: Tensor, alpha: Number, other: Tensor) -> Tensor: ...'

'def add(self: Tensor, alpha: Number, other: Tensor, *, out: Tensor) -> Tensor: ...'

function_hints

    function_hints = []for name, hints in sorted(unsorted_function_hints.items()):if len(hints) > 1:hints = ["@overload\n" + h for h in hints]function_hints += hints

function_hints是一個list of string：

['@overload\ndef __and_...ensor: ...', '@overload\ndef __and_...ensor: ...', '@overload\ndef __lshi...ensor: ...', '@overload\ndef __lshi...ensor: ...', '@overload\ndef __or__...ensor: ...', '@overload\ndef __or__...ensor: ...', '@overload\ndef __rshi...ensor: ...', '@overload\ndef __rshi...ensor: ...', '@overload\ndef __xor_...ensor: ...', '@overload\ndef __xor_...ensor: ...', 'def _adaptive_avg_po...ensor: ...', 'def _adaptive_avg_po...ensor: ...', 'def _add_batch_dim(i...ensor: ...', '@overload\ndef _add_r...ensor: ...', ...]

其第零個元素如下：

'@overload\ndef __and__(input: Tensor, other: Tensor) -> Tensor: ...'

rand

代表rand函數的八個元素如下。其實跟unsorted_function_hints裡的大同小異，差別只在於前面多加了’@overload\n’。

'@overload\ndef rand(size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...'

'@overload\ndef rand(*size: _int, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...'

'@overload\ndef rand(size: Sequence[Union[_int, SymInt]], *, generator: Optional[Generator], out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...'

'@overload\ndef rand(*size: _int, generator: Optional[Generator], out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...'

'@overload\ndef rand(size: Sequence[Union[_int, SymInt]], *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...'

'@overload\ndef rand(*size: _int, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...'

'@overload\ndef rand(size: Sequence[Union[_int, SymInt]], *, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...'

'@overload\ndef rand(*size: _int, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...'

add

代表add函數的三個元素如下：

'@overload\ndef add(input: Union[Tensor, Number], other: Union[Tensor, Number], *, alpha: Optional[Number]=1, out: Optional[Tensor]=None) -> Tensor: ...'

'@overload\ndef add(self: Tensor, alpha: Number, other: Tensor) -> Tensor: ...'

'@overload\ndef add(self: Tensor, alpha: Number, other: Tensor, *, out: Tensor) -> Tensor: ...'

hinted_function_names

    # Generate __all__ directive# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# Include only the functions that contain hints, to prevent undefined# symbols to be included in the `__all__` directive.hinted_function_names = [name for name, hint in unsorted_function_hints.items() if hint]

hinted_function_names是一個list of string，看起來就只是一個有hint的函數名稱的列表：

['sparse_csr_tensor', '_sparse_csr_tensor_unsafe', 'sparse_csc_tensor', '_sparse_csc_tensor_unsafe', 'sparse_bsr_tensor', '_sparse_bsr_tensor_unsafe', 'sparse_bsc_tensor', '_sparse_bsc_tensor_unsafe', 'set_flush_denormal', 'get_default_dtype', 'asarray', 'from_numpy', 'frombuffer', 'numel', ...]

其中也包含了：

'rand', 'rand_like', 'randint_like', 'randn', 'randn_like', 'randperm'

及'add'。

all_symbols

    all_symbols = sorted(list(namedtuples.keys()) + hinted_function_names)

all_symbols

['__and__', '__lshift__', '__or__', '__rshift__', '__xor__', '_adaptive_avg_pool2d', '_adaptive_avg_pool3d', '_add_batch_dim', '_add_relu', '_add_relu_', '_addmm_activation', '_aminmax', '_amp_foreach_non_fin...d_unscale_', '_amp_update_scale_', ...]

其中也包含了：

'rand', 'rand_like', 'randint_like', 'randn', 'randn_like', 'randperm'

及'add'。

all_directive

接下來將all_symbols轉為string，用\n切割成多段個組成一個字串的列表，即為all_directive：

    all_directive = pformat(all_symbols, width=100, compact=True).split("\n")all_directive[0] = "__all__ = {}".format(all_directive[0])

第零個元素如下：

"__all__ = ['__and__', '__lshift__', '__or__', '__rshift__', '__xor__', '_adaptive_avg_pool2d',"

其中包含add的元素如下：

" 'adaptive_max_pool1d', 'add', 'addbmm', 'addcdiv', 'addcmul', 'addmm', 'addmv', 'addmv_', 'addr',"

包含rand的元素如下：

" 'rad2deg_', 'rand', 'rand_like', 'randint', 'randint_like', 'randn', 'randn_like', 'randperm',"

最後一個元素如下：

" 'vsplit', 'vstack', 'where', 'xlogy', 'xlogy_', 'zero_', 'zeros', 'zeros_like']"

env

到這裡為止得到了function_hints和all_directive，這兩個變數與其它變數共同組成了env：

    env = {"namedtuple_defs": namedtuple_defs,"function_hints": function_hints,"tensor_method_hints": tensor_method_hints,"legacy_class_hints": legacy_class_hints,"legacy_storage_base_hints": legacy_storage_base_hints,"dtype_class_hints": dtype_class_hints,"dispatch_key_hints": dispatch_key_hints,"all_directive": all_directive,}

運算後的env如下：

{"namedtuple_defs":["_fake_quantize_per_t... Tensor)])","_fused_moving_avg_ob... Tensor)])","_linalg_det = NamedT... Tensor)])","_linalg_eigh = Named... Tensor)])","_linalg_slogdet = Na... Tensor)])","_linalg_solve_ex = N... Tensor)])","_linalg_svd = NamedT... Tensor)])","_lu_with_info = Name... Tensor)])","_unpack_dual = Named... Tensor)])","..."],"function_hints":["@overload\ndef __and_...ensor: ...","@overload\ndef __and_...ensor: ...","@overload\ndef __lshi...ensor: ...","@overload\ndef __lshi...ensor: ...","@overload\ndef __or__...ensor: ...","@overload\ndef __or__...ensor: ...","@overload\ndef __rshi...ensor: ...","@overload\ndef __rshi...ensor: ...","@overload\ndef __xor_...ensor: ...","..."],"tensor_method_hints":["def __abs__(self) ->...ensor: ...","def __add__(self, ot...ensor: ...","@overload\ndef __and_...ensor: ...","@overload\ndef __and_...ensor: ...","@overload\ndef __and_...ensor: ...","def __bool__(self) -....bool: ...","def __complex__(self...mplex: ...","def __div__(self, ot...ensor: ...","def __eq__(self, oth...[override]","..."],"legacy_class_hints":["class DoubleTensor(T...nsor): ...","class FloatTensor(Tensor): ...","class LongTensor(Tensor): ...","class IntTensor(Tensor): ...","class ShortTensor(Tensor): ...","class HalfTensor(Tensor): ...","class CharTensor(Tensor): ...","class ByteTensor(Tensor): ...","class BoolTensor(Tensor): ..."],"legacy_storage_base_hints":["class StorageBase(object): ..."],"dtype_class_hints":["float32: dtype = ...","float: dtype = ...","float64: dtype = ...","double: dtype = ...","float16: dtype = ...","bfloat16: dtype = ...","half: dtype = ...","uint8: dtype = ...","int8: dtype = ...","..."],"dispatch_key_hints":["Undefined: DispatchKey = ...","FPGA: DispatchKey = ...","ORT: DispatchKey = ...","Vulkan: DispatchKey = ...","Metal: DispatchKey = ...","MKLDNN: DispatchKey = ...","OpenGL: DispatchKey = ...","OpenCL: DispatchKey = ...","IDEEP: DispatchKey = ...","..."],"all_directive":["__all__ = ['__and__...,"," ...,"," '_aminmax', ...,"," ...,"," '_cast_Float', ...,"," ...,"," ...,"," ...,"," '_convolution_mode...,","..."]
}

接著把env傳入FileManager的成員函數write_with_template：

    # ...fm.write_with_template("torch/_C/__init__.pyi","torch/_C/__init__.pyi.in",lambda: {"generated_comment": "@" + "generated from torch/_C/__init__.pyi.in",**env,},)fm.write_with_template("torch/_C/_VariableFunctions.pyi","torch/_C/_VariableFunctions.pyi.in",lambda: {"generated_comment": "@"+ "generated from torch/_C/_VariableFunctions.pyi.in",**env,},)fm.write_with_template("torch/_VF.pyi","torch/_C/_VariableFunctions.pyi.in",lambda: {"generated_comment": "@"+ "generated from torch/_C/_VariableFunctions.pyi.in",**env,},)fm.write_with_template("torch/return_types.pyi","torch/_C/return_types.pyi.in",lambda: {"generated_comment": "@" + "generated from torch/_C/return_types.pyi",**env,},)gen_nn_functional(fm)

可以看到這段代碼裡調用了FileManager的write_with_template及gen_nn_functional，以下先看gen_nn_functional。

參考Unpacking Operators in Python的Merging Dictionaries章節，{"a": 1, **my_dict}這種寫法是先把my_dict拆開(unpacking)，然後再與"a": 1共同構成一個新的字典。

lambda: {}這種寫法則表示一個無需參數並回傳一個字典的lambda函數。

注意到在呼叫write_with_template時，最後一個參數後面多了一個,。根據Should I add a trailing comma after the last argument in a function call? [closed]，在呼叫函數時，如果參數是分多行寫的，比較建議的寫法是在最後加上一個,。

回想一開始看到的，共會由六個pyi.in檔生成六個對應的pyi檔，此處只生成了四個pyi檔，剩下兩個（functional.pyi，_nn.pyi）則是在gen_nn_functional中調用FileManager.write_with_template生成。

FileManager.write_with_template函數會以模板為基礎，按照替換函數所指定的方式，生成pyi檔，本函數已獨立成篇，詳見PyTorch檔案生成機制中的FileManager.write_with_template。

gen_nn_functional

gen_nn_functional函數同樣位於tools/pyi/gen_pyi.py，它的作用是由torch/nn/functional.pyi.in及torch/_C/_nn.pyi.in生成torch/nn/functional.pyi和torch/_C/_nn.pyi.in。

def gen_nn_functional(fm: FileManager) -> None:# Functions imported into `torch.nn.functional` from `torch`, perhaps being filtered# through an `_add_docstr` callimports = ["conv1d","conv2d","conv3d","conv_transpose1d","conv_transpose2d","conv_transpose3d","conv_tbc","avg_pool1d","relu_","selu_","celu_","rrelu_","pixel_shuffle","pixel_unshuffle","channel_shuffle","native_channel_shuffle","pdist","cosine_similarity",]# Functions generated by `torch._jit_internal.boolean_dispatch`dispatches = ["fractional_max_pool2d","fractional_max_pool3d","max_pool1d","max_pool2d","max_pool3d","adaptive_max_pool1d","adaptive_max_pool2d","adaptive_max_pool3d",]# Functions directly imported from `torch._C`from_c = ["avg_pool2d","avg_pool3d","hardtanh_","elu_","leaky_relu_","logsigmoid","softplus","softshrink","one_hot",]import_code = ["from .. import {0} as {0}".format(_) for _ in imports]# TODO make these types more precisedispatch_code = ["{}: Callable".format(_) for _ in (dispatches + from_c)]fm.write_with_template("torch/nn/functional.pyi","torch/nn/functional.pyi.in",lambda: {"imported_hints": import_code,"dispatched_hints": dispatch_code,},)# functional.pyi already contains the definitions for those functions# so, we don't export then to itfrom_c.extend(["hardtanh", "leaky_relu", "hardsigmoid"])dispatch_code = ["{}: Callable".format(_) for _ in (dispatches + from_c)]fm.write_with_template("torch/_C/_nn.pyi","torch/_C/_nn.pyi.in",lambda: {"imported_hints": import_code,"dispatched_hints": dispatch_code,},)

可以看到這個函數最後也是調用FileManager的write_with_template生成.pyi檔。

write_with_template已獨立成篇，詳見PyTorch檔案生成機制中的FileManager.write_with_template。

datapipe.pyi

回頭看CMakeLists.txt：

file(GLOB_RECURSE datapipe_files "${TORCH_SRC_DIR}/utils/data/datapipes/*.py")
add_custom_command(OUTPUT"${TORCH_SRC_DIR}/utils/data/datapipes/datapipe.pyi"COMMAND"${PYTHON_EXECUTABLE}" ${TORCH_SRC_DIR}/utils/data/datapipes/gen_pyi.pyDEPENDS"${TORCH_SRC_DIR}/utils/data/datapipes/datapipe.pyi.in"${datapipe_files}WORKING_DIRECTORY"${TORCH_ROOT}"
)

datapipe.pyi也是由類似的方式透過utils/data/datapipes/gen_pyi.py由datapipe.pyi.in生成的。

torch/utils/data/datapipes/datapipe.pyi.in中的注釋：

# This base template ("datapipe.pyi.in") is generated from mypy stubgen with minimal editing for code injection
# The output file will be "datapipe.pyi". This is executed as part of torch/CMakeLists.txt
# Note that, for mypy, .pyi file takes precedent over .py file, such that we must define the interface for other
# classes/objects here, even though we are not injecting extra code into them at the moment.

生成結果

以torch/_C/_VariableFunctions.pyi.in為例：

generated_comment

# ${generated_comment}

被替換成：

# @generated from torch/_C/_VariableFunctions.pyi.in

function_hints

${function_hints}

被替換成：

@overload
def __and__(input: Tensor, other: Tensor) -> Tensor: ...
# ...
def zeros_like(input: Tensor, *, memory_format: Optional[memory_format] = None, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Union[_device, str, None]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ...

all_directive

${all_directive}

被替換成：

__all__ = ['__and__', '__lshift__', '__or__', '__rshift__', '__xor__', '_adaptive_avg_pool2d',
# ...'view_copy', 'vsplit', 'vstack', 'where', 'xlogy', 'xlogy_', 'zero_', 'zeros', 'zeros_like']

其餘部份皆與torch/_C/_VariableFunctions.pyi.in相同。

使用pyi做類型檢查

在torch/__init__.py中有以下這麼一段：

# Appease the type checker: it can't deal with direct setting of globals().
# Note that we will see "too many" functions when reexporting this way; there
# is not a good way to fix this problem.  Perhaps, try to redesign VariableFunctions
# so that this import is good enough
if TYPE_CHECKING:# Some type signatures pulled in from _VariableFunctions here clash with# signatures already imported. For now these clashes are ignored; see# PR #43339 for details.from torch._C._VariableFunctions import *  # type: ignore[misc] # noqa: F403