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Change output_declarations in function_wrapper.py to be a NamedTuple (#5312)
* Add python typing module as build dependency * Change output_declarations to be a NamedTuple * Add mypy configuration files mypy-files.txt includes a list of all files that should be typed checked with mypy. Run mypy with `mypy @mypyfiles.txt`. mypy.ini includes mypy options. Unfortunately this can't be merged with mypy-files.txt. Update .travis.yml so that one doesn't have to specify what files to type check inside it. * Add RuntimeError on missing `typing` module Alerts users to the new build dependency.
This commit is contained in:
Richard Zou
committed by
Soumith Chintala
Soumith Chintala
parent
2130070785
commit
dcbbf346c2
@ -16,8 +16,7 @@ matrix:
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python: "2.7"
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install: pip install flake8
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script: flake8
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# mypy will complain about various files. Limiting it to only typed files
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- env: MYPY_TYPE_CHECK
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python: "3.6"
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install: pip install mypy mypy-extensions
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script: mypy --py2 aten/src/ATen/function_wrapper.py
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script: mypy @mypy-files.txt
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@ -171,7 +171,7 @@ On Linux
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export CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" # [anaconda root directory]
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# Install basic dependencies
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conda install numpy pyyaml mkl setuptools cmake cffi
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conda install numpy pyyaml mkl setuptools cmake cffi typing
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# Add LAPACK support for the GPU
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conda install -c pytorch magma-cuda80 # or magma-cuda90 if CUDA 9
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@ -6,12 +6,22 @@ from collections import OrderedDict
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from code_template import CodeTemplate
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try:
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from typing import Any, Dict, List, Generic, Optional, Set, Tuple, \
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Union, TypeVar
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from mypy_extensions import TypedDict
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TYPE_HINTS = True
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import typing
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except ImportError:
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TYPE_HINTS = False
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raise RuntimeError(
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'Missing build dependency: Unable to import the `typing` module. '
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'Please install it via `conda install typing` or `pip install typing`')
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from typing import Any, Dict, List, Generic, Optional, Set, Tuple, \
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Union, TypeVar, NamedTuple
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try:
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from mypy_extensions import TypedDict
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except ImportError:
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# Avoid the dependency on the mypy_extensions package.
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# It is required, however, for type checking.
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def TypedDict(name, attrs, total=True): # type: ignore
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return Dict[Any, Any]
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import sys
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if sys.version_info[0] == 3:
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@ -282,152 +292,163 @@ class nested_dict(object):
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return r
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return self.parent[x]
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if TYPE_HINTS:
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Environment = TypedDict('Environment', {
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'ScalarName': str,
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'THTensor': str,
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'THType': str,
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'THTensor': str,
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'Backend': str,
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'AccScalarName': str,
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})
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Environment = TypedDict('Environment', {
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'ScalarName': str,
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'THTensor': str,
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'THType': str,
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'THTensor': str,
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'Backend': str,
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'AccScalarName': str,
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})
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TopEnvironment = TypedDict('TopEnvironment', {
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'type_registrations': List[str],
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'type_headers': List[str],
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'type_method_declarations': List[str],
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'type_method_definitions': List[str],
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'type_method_inline_definitions': List[str],
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'tensor_method_declarations': List[str],
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'tensor_method_definitions': List[str],
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'function_declarations': List[str],
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'function_definitions': List[str],
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'type_ids': List[str],
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'native_function_declarations': List[str],
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})
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TopEnvironment = TypedDict('TopEnvironment', {
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'type_registrations': List[str],
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'type_headers': List[str],
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'type_method_declarations': List[str],
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'type_method_definitions': List[str],
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'type_method_inline_definitions': List[str],
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'tensor_method_declarations': List[str],
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'tensor_method_definitions': List[str],
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'function_declarations': List[str],
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'function_definitions': List[str],
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'type_ids': List[str],
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'native_function_declarations': List[str],
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})
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# A Declarations.cwrap formal argument
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# type can contain THTensor* types
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THFormal = TypedDict('THFormal', {
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'name': str,
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'type': str,
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'dynamic_type': str,
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'kwarg_only': bool,
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'is_nullable': bool,
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'default': str,
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'default_init': str,
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'python_default_init': str,
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'output': bool,
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'size': int,
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'declared_type': str,
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'ignore_check': bool,
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'allocate': bool,
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'mask': bool,
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'if_true': bool,
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'if_false': bool,
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'wrap_dim': str,
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# Broadcast is originally a str but gets unwrapped to a List or Dict in-place
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'broadcast': Any,
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'resize': str,
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'cpu_zero': bool,
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'zero': bool,
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}, total=False)
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# A Declarations.cwrap formal argument
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# type can contain THTensor* types
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THFormal = TypedDict('THFormal', {
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'name': str,
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'type': str,
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'dynamic_type': str,
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'kwarg_only': bool,
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'is_nullable': bool,
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'default': str,
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'default_init': str,
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'python_default_init': str,
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'output': bool,
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'size': int,
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'declared_type': str,
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'ignore_check': bool,
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'allocate': bool,
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'mask': bool,
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'if_true': bool,
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'if_false': bool,
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'wrap_dim': str,
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# Broadcast is originally a str but gets unwrapped to a List or Dict in-place
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'broadcast': Any,
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'resize': str,
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'cpu_zero': bool,
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'zero': bool,
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}, total=False)
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# A native_functions.yaml formal argument
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# type can contain Tensor, BoolTensor, IndexTensor types
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NativeFormal = TypedDict('NativeFormal', {
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'name': str,
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'type': str,
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'dynamic_type': str,
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'kwarg_only': bool,
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'is_nullable': bool,
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'default': str,
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'default_init': str,
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'python_default_init': str,
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'output': bool,
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'size': int,
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}, total=False)
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# A native_functions.yaml formal argument
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# type can contain Tensor, BoolTensor, IndexTensor types
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NativeFormal = TypedDict('NativeFormal', {
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'name': str,
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'type': str,
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'dynamic_type': str,
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'kwarg_only': bool,
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'is_nullable': bool,
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'default': str,
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'default_init': str,
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'python_default_init': str,
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'output': bool,
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'size': int,
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}, total=False)
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# Generic ATen formal.
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# type can contain Tensor& reference types.
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AtFormal = TypedDict('AtFormal', {
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'name': str,
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'type': str,
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'dynamic_type': str,
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'kwarg_only': bool,
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'is_nullable': bool,
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'default': str,
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'default_init': str,
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'python_default_init': str,
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'output': bool,
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'size': int,
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}, total=False)
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# Generic ATen formal.
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# type can contain Tensor& reference types.
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AtFormal = TypedDict('AtFormal', {
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'name': str,
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'type': str,
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'dynamic_type': str,
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'kwarg_only': bool,
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'is_nullable': bool,
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'default': str,
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'default_init': str,
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'python_default_init': str,
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'output': bool,
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'size': int,
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}, total=False)
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ReturnType = TypedDict('ReturnType', {
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'name': str,
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'type': str,
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'dynamic_type': str,
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}, total=False)
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ReturnType = TypedDict('ReturnType', {
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'name': str,
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'type': str,
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'dynamic_type': str,
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}, total=False)
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ReturnDecl = TypedDict('ReturnDecl', {
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'kind': str,
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'type': str,
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'arguments': List[int],
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}, total=False)
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ReturnDecl = TypedDict('ReturnDecl', {
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'kind': str,
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'type': str,
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'arguments': List[int],
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}, total=False)
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# Represents a buffer in nn.yaml
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NNBuffer = TypedDict('NNBuffer', {
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'name': str,
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})
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# Represents a buffer in nn.yaml
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NNBuffer = TypedDict('NNBuffer', {
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'name': str,
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})
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FunctionOption = TypedDict('FunctionOption', {
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'arguments': List[THFormal],
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'mode': str,
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'name': str,
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'return': ReturnDecl,
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'variants': str,
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'type_method_definition_dispatch': str,
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'cname': str,
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'backends': List[str],
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'api_name': str,
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'backend_type_pairs': List[Tuple[str, str]],
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'inplace': bool,
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'aten_dense_sparse': bool,
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'sparse': bool,
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'scalar_check': str,
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'aten_custom_call': str,
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'type_definition_body': List[str],
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# cimpls is really a List[FunctionOption]
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'cimpls': List[Any],
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'when_spares_dispatch': str,
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'actuals': List[str],
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'buffers': List[NNBuffer],
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'zero_dim_dispatch_when_scalar': str,
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'zero_dim_tensor_only': bool,
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'when_sparse_dispatch': str,
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'formals_list': List[AtFormal],
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'condition': str,
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'auto_gpu': bool,
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'cpu_half': bool,
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# options should be List[FunctionOption]
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'options': Any,
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'formals': List[str],
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'formals_with_defaults': List[str],
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'returns': List[ReturnType],
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'return_type': str,
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'return_call': str,
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'method_formals': List[str],
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'method_formals_with_defaults': List[str],
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'method_actuals': List[str],
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'const_mark': str,
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'method_prefix_derived': str,
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'broadcast_actuals': List[str],
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'broadcast_returns': List[str],
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'inferred_type': str,
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'broadcast_function': str,
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'broadcast_modified_actuals': List[str],
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'native_type_method_dispatch': str,
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})
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FunctionOption = TypedDict('FunctionOption', {
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'arguments': List[THFormal],
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'mode': str,
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'name': str,
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'return': ReturnDecl,
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'variants': str,
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'type_method_definition_dispatch': str,
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'cname': str,
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'backends': List[str],
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'api_name': str,
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'backend_type_pairs': List[Tuple[str, str]],
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'inplace': bool,
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'aten_dense_sparse': bool,
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'sparse': bool,
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'scalar_check': str,
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'aten_custom_call': str,
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'type_definition_body': List[str],
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# cimpls is really a List[FunctionOption]
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'cimpls': List[Any],
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'when_spares_dispatch': str,
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'actuals': List[str],
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'buffers': List[NNBuffer],
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'zero_dim_dispatch_when_scalar': str,
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'zero_dim_tensor_only': bool,
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'when_sparse_dispatch': str,
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'formals_list': List[AtFormal],
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'condition': str,
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'auto_gpu': bool,
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'cpu_half': bool,
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# options should be List[FunctionOption]
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'options': Any,
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'formals': List[str],
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'formals_with_defaults': List[str],
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'returns': List[ReturnType],
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'return_type': str,
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'return_call': str,
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'method_formals': List[str],
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'method_formals_with_defaults': List[str],
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'method_actuals': List[str],
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'const_mark': str,
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'method_prefix_derived': str,
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'broadcast_actuals': List[str],
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'broadcast_returns': List[str],
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'inferred_type': str,
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'broadcast_function': str,
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'broadcast_modified_actuals': List[str],
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'native_type_method_dispatch': str,
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})
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OutputDeclaration = NamedTuple('OutputDeclaration', [
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('name', str),
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('method_prefix_derived', str),
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('arguments', List[AtFormal]),
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('method_of', List[str]),
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('mode', str),
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('buffers', Optional[List[str]]),
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('returns', List[ReturnType]),
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('inplace', bool),
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('abstract', bool),
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])
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def is_real_argument_to_wrapper(argument):
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@ -458,7 +479,7 @@ def to_return_type(arg, option):
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def create_generic(top_env, declarations):
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# type: (TopEnvironment, List[FunctionOption]) -> List[OrderedDict]
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# type: (TopEnvironment, List[FunctionOption]) -> List[OutputDeclaration]
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# translates defaults from cwrap types to C++ values
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def translate_default(argument, type_str, default):
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# type: (THFormal, str, Any) -> Any
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@ -656,7 +677,7 @@ def create_generic(top_env, declarations):
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return body
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def process_option(option, output_options):
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# type: (FunctionOption, List[OrderedDict]) -> None
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# type: (FunctionOption, List[OutputDeclaration]) -> None
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option['inplace'] = re.search(
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'(^__i|[^_]_$)', option['api_name']) is not None
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@ -748,18 +769,18 @@ def create_generic(top_env, declarations):
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buffer_names = [buffer['name'] for buffer in option.get('buffers', [])]
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output_options.append(OrderedDict([
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('name', option['api_name']),
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('method_prefix_derived', option['method_prefix_derived']),
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('arguments', formals),
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('method_of', method_of),
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('mode', mode),
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('buffers', buffer_names),
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('returns', option['returns']),
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('inplace', option['inplace']),
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output_options.append(OutputDeclaration(
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name=option['api_name'],
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method_prefix_derived=option['method_prefix_derived'],
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arguments=formals,
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method_of=method_of,
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mode=mode,
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buffers=buffer_names,
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returns=option['returns'],
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inplace=option['inplace'],
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# See Note [Abstract ATen methods]
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('abstract', abstract),
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]))
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abstract=abstract,
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))
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def native_get_formals(option, include_constants=False):
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# type: (FunctionOption, bool) -> List[AtFormal]
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@ -843,7 +864,7 @@ def create_generic(top_env, declarations):
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return return_types
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|
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def process_native(option, output_options):
|
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# type: (FunctionOption, List[OrderedDict]) -> None
|
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# type: (FunctionOption, List[OutputDeclaration]) -> None
|
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option['inplace'] = re.search(
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'(^__i|[^_]_$)', option['api_name']) is not None
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|
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@ -932,21 +953,22 @@ def create_generic(top_env, declarations):
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FUNCTION_DEFINITION.substitute(env))
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method_of.append('namespace')
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|
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output_options.append(OrderedDict([
|
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('name', option['api_name']),
|
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('method_prefix_derived', option['method_prefix_derived']),
|
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('arguments', formals),
|
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('method_of', method_of),
|
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('mode', option['mode']),
|
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('returns', option['returns']),
|
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('inplace', option['inplace']),
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output_options.append(OutputDeclaration(
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name=option['api_name'],
|
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method_prefix_derived=option['method_prefix_derived'],
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arguments=formals,
|
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method_of=method_of,
|
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mode=option['mode'],
|
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buffers=None,
|
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returns=option['returns'],
|
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inplace=option['inplace'],
|
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# See Note [Abstract ATen methods]
|
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('abstract', abstract),
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]))
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abstract=abstract,
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))
|
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output_declarations = [] # type: List[OrderedDict]
|
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output_declarations = [] # type: List[OutputDeclaration]
|
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for declaration in declarations:
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output_options = [] # type: List[OrderedDict]
|
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output_options = [] # type: List[OutputDeclaration]
|
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for option in declaration['options']:
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try:
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if option['mode'] != 'native':
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|
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@ -165,19 +165,25 @@ def postprocess_output_declarations(output_declarations):
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# ensure each return has a name associated with it
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for decl in output_declarations:
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has_named_ret = False
|
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for n, ret in enumerate(decl['returns']):
|
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for n, ret in enumerate(decl.returns):
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if 'name' not in ret:
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assert not has_named_ret
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if decl['inplace']:
|
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if decl.inplace:
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ret['name'] = 'self'
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elif len(decl['returns']) == 1:
|
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elif len(decl.returns) == 1:
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ret['name'] = 'result'
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else:
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ret['name'] = 'result' + str(n)
|
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else:
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has_named_ret = True
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|
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return output_declarations
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def remove_key_if_none(dictionary, key):
|
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if key in dictionary.keys() and dictionary[key] is None:
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del dictionary[key]
|
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return dictionary
|
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|
||||
return [remove_key_if_none(decl._asdict(), 'buffers')
|
||||
for decl in output_declarations]
|
||||
|
||||
|
||||
def format_yaml(data):
|
||||
|
||||
11
mypy-README.md
Normal file
11
mypy-README.md
Normal file
@ -0,0 +1,11 @@
|
||||
### Optional type checking with mypy
|
||||
|
||||
mypy is an optional static typechecker that works with Python 3.
|
||||
To use it, install the following dependencies:
|
||||
```bash
|
||||
# Install dependencies
|
||||
pip install mypy mypy-extensions
|
||||
|
||||
# Run type checker in the pytorch/ directory
|
||||
mypy @mypy-files.txt
|
||||
```
|
||||
1
mypy-files.txt
Normal file
1
mypy-files.txt
Normal file
@ -0,0 +1 @@
|
||||
aten/src/ATen/function_wrapper.py
|
||||
@ -1 +1,2 @@
|
||||
pyyaml
|
||||
typing
|
||||
|
||||
Reference in New Issue
Block a user