automatic generation of type checks for overload#911
automatic generation of type checks for overload#911vlad-perevezentsev wants to merge 12 commits intoIntelPython:numba_typingfrom
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Hello @vlad-perevezentsev! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found: There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻 Comment last updated at 2020-09-29 14:36:35 UTC |
vlad-perevezentsev
requested review from
AlexanderKalistratov,
Hardcode84,
KsanaKozlova and
densmirn
numba_typing/overload_list.py
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| return self._types_dict[p_type](self, p_type, n_type) | ||
| return self._types_dict[p_type](n_type) | ||
| except KeyError: | ||
| print((f'A check for the {p_type} was not found. {n_type}')) |
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So we will (implicitly) return None from match in this case, is this intended behavior? Please add explicit return or throw
numba_typing/overload_list.py
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| def choose_func_by_sig(sig_list, values_dict, defaults_dict={}): | ||
| checker = TypeChecker() | ||
| for sig in sig_list: # sig = (Signature,func) |
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Instead of accessing sig[0] and sig[1] each time you can write for sig, func in sig_list:
numba_typing/overload_list.py
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| import numpy | ||
| import numba | ||
| from numba import types | ||
| from numba import typeof | ||
| from numba.extending import overload | ||
| from type_annotations import product_annotations, get_func_annotations | ||
| from numba import njit | ||
| import typing | ||
| from numba import NumbaDeprecationWarning, NumbaPendingDeprecationWarning | ||
| import warnings | ||
| from numba.typed import List, Dict | ||
| from inspect import getfullargspec |
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There are some unused imports here, like numpy, typeof and njit, please clean them up.
numba_typing/overload_list.py
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| warnings.simplefilter('ignore', category=NumbaDeprecationWarning) | ||
| warnings.simplefilter('ignore', category=NumbaPendingDeprecationWarning) |
numba_typing/overload_list.py
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| def check_tuple_type(self, p_type, n_type): | ||
| res = False | ||
| if isinstance(n_type, types.Tuple): |
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Instead of using n_type.key you could use n_type.types which is defined for both Tuple and UniTuple, so you can unite both branches:
Something like:
if not isinstance(n_type, types.Tuple, types.UniTuple):
return False
for p_val, n_val in zip(p_type.__args__, n_type.types):
if not self.match(p_val, n_val):
return False
return TrueAnd btw you need to check that size of p_type.__args__ and n_type.types are the same.
And I believe you don't have tests for the case when they are different.
numba_typing/overload_list.py
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| return self._types_dict[p_type](self, p_type, n_type) | ||
| return self._types_dict[p_type](n_type) | ||
| except KeyError: | ||
| print((f'A check for the {p_type} was not found.')) |
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We should rise an exception
numba_typing/overload_list.py
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| def match(self, p_type, n_type): | ||
| try: | ||
| if p_type == typing.Any: |
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Let's do it like this:
if p_type == typing.Any:
return True
if self._is_generic(p_type):
origin_type = self._get_origin(p_type)
if origin_type == typing.Generic:
return self.match_generic(p_type, n_type)
return self._types_dict[origin_type](self, p_type, n_type)
if isinstance(p_type, typing.TypeVar):
return self.match_typevar(p_type, n_type)
if p_type in (list, tuple):
return self._types_dict[p_type](self, p_type, n_type)
return self._types_dict[p_type](n_type)
numba_typing/overload_list.py
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| return None | ||
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| def match_typevar(self, p_type, n_type): | ||
| if not self._typevars_dict.get(p_type) and n_type not in self._typevars_dict.values(): |
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Why do you need condition n_type not in self._typevars_dict.values() ?
| if not self._typevars_dict.get(p_type) and n_type not in self._typevars_dict.values(): | ||
| self._typevars_dict[p_type] = n_type | ||
| return True | ||
| return self._typevars_dict.get(p_type) == n_type |
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I believe it should be self.match. E.g. list and 'types.List' are synonyms but will fail equality check ('list != types.List').
And I'm assuming you don't have such tests?
numba_typing/overload_list.py
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| def match_generic(self, p_type, n_type): | ||
| res = True | ||
| for arg in p_type.__args__: | ||
| res = res and self.match(arg, n_type) |
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What's doesn't feel right. Do we have any test for this case?
numba_typing/overload_list.py
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| if sig.defaults.get(name, False): | ||
| full_match = full_match and sig.defaults[name] == typ.literal_value | ||
| else: |
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Do we need this else?
numba_typing/overload_list.py
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| def check_list_type(self, p_type, n_type): | ||
| res = isinstance(n_type, types.List) or isinstance(n_type, types.ListType) |
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res = isinstance(n_type, (types.List, types.ListType))
numba_typing/overload_list.py
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| def check_list_type(self, p_type, n_type): | ||
| res = isinstance(n_type, types.List) or isinstance(n_type, types.ListType) | ||
| if isinstance(p_type, type): |
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isinstance(p_type, (list, typing.List))?
numba_typing/overload_list.py
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| elif isinstance(p_type, typing.TypeVar): | ||
| return self.match_typevar(p_type, n_type) | ||
| else: | ||
| if p_type in (list, tuple): |
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Shouldn't dict be here too?
numba_typing/overload_list.py
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| class TypeChecker: | ||
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| _types_dict = {int: check_int_type, float: check_float_type, bool: check_bool_type, |
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I'd prefer this checks to be added using add_type_check function
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This code actually modifies original function globals. This shouldn't happen, please fix. |
numba_typing/overload_list.py
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| TypeChecker.add_type_check(dict, check_dict_type) | ||
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| def choose_func_by_sig(sig_list, values_dict, defaults_dict): |
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Let's do it this way:
def choose_func_by_sig(sig_list, values_dict, defaults_dict):
def check_signature(sig_params, types_dict):
checker = TypeChecker()
for name, typ in types_dict.items(): # name,type = 'a',int64
if isinstance(typ, types.Literal):
typ = typ.literal_type
if not checker.match(sig_params[name], typ):
return False
return True
for sig, func in sig_list: # sig = (Signature,func)
for param in sig.parameters: # param = {'a':int,'b':int}
if check_signature(param, values_dict):
return func
return None
numba_typing/test_overload_list.py
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| return typ.__name__ | ||
| return typ | ||
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| value_keys = ", ".join("{}".format(key) for key in values_dict.keys()) |
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please use f'{key}' instead. Here and everywhere
numba_typing/test_overload_list.py
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| D_list['qwe'] = List([3, 4, 5]) | ||
| str_1 = 'qwe' | ||
| str_2 = 'qaz' | ||
| test_cases = [('int', [1, 2], {'a': int, 'b': int}), ('float', [1.0, 2.0], {'a': float, 'b': float}), |
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{'a': int, 'b': int} why do you need two parameters of the same type?
What are you actually testing?
numba_typing/test_overload_list.py
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| str_2 = 'qaz' | ||
| test_cases = [('int', [1, 2], {'a': int, 'b': int}), ('float', [1.0, 2.0], {'a': float, 'b': float}), | ||
| ('bool', [True, True], {'a': bool, 'b': bool}), ('str', ['str_1', 'str_2'], {'a': str, 'b': str}), | ||
| ('list', [[1, 2], [3, 4]], {'a': typing.List[int], 'b':list}), |
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Why do you need second list parameter?
numba_typing/test_overload_list.py
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| ('tuple', [(1, 2.0), ('3', False)], {'a': typing.Tuple[int, float], 'b':tuple}), | ||
| ('dict', ['D', 'D_1'], {'a': typing.Dict[str, int], 'b': typing.Dict[int, bool]}), | ||
| ('union_1', [1, False], {'a': typing.Union[int, str], 'b': typing.Union[float, bool]}), | ||
| ('union_2', ['str_1', False], {'a': typing.Union[int, str], 'b': typing.Union[float, bool]}), |
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And why you are not testing second Union parameter?
numba_typing/test_overload_list.py
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| ('TypeVar_ListT_DictK_ListT', ['L', 'D_list'], {'a': 'typing.List[T]', | ||
| 'b': 'typing.Dict[K, typing.List[T]]'})] | ||
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| test_cases_default = [('int_defaults', [1], {'a': int}, {'b': 0}), ('float_defaults', [1.0], {'a': float}, {'b': 0.0}), |
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And what about both - annotation and default value?
numba_typing/test_overload_list.py
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| ('TypeVar_ListT_T', ['L', 5], {'a': 'typing.List[T]', 'b': 'T'}), | ||
| ('TypeVar_ListT_DictKT', ['L', 'D'], {'a': 'typing.List[T]', 'b': 'typing.Dict[K, T]'}), | ||
| ('TypeVar_ListT_DictK_ListT', ['L', 'D_list'], {'a': 'typing.List[T]', | ||
| 'b': 'typing.Dict[K, typing.List[T]]'})] |
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Are where any tests for TypeVar with specified types restriction?
numba_typing/test_overload_list.py
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| ('union_2', ['str_1', False], {'a': typing.Union[int, str], 'b': typing.Union[float, bool]}), | ||
| ('nested_list', ['L_int', 'L_float'], {'a': typing.List[typing.List[int]], | ||
| 'b': typing.List[typing.List[typing.List[float]]]}), | ||
| ('TypeVar_TT', ['L_f', [3.0, 4.0]], {'a': 'T', 'b': 'T'}), |
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Any negative tests for such case?
numba_typing/test_overload_list.py
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| ('tuple_defaults', [(1, 2)], {'a': tuple}, {'b': (0, 0)})] | ||
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| for name, val, annotation in test_cases: |
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I'd prefer you to group all cases into 4-5 tests with subtests. Something like this:
def test_common_types():
test_cases = [({'a': 0}, {'a': int}),
({'a': 0.}, {'a': float}),
...]
for case in test_cases:
with self.Subtest(case=case):
run_test(case)
numba_typing/overload_list.py
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| result = choose_func_by_sig(sig_list, values_dict) | ||
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| if result is None: | ||
| raise TypeError(f'Unsupported types a={a}, b={b}') |
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Looks both a and b are undefined.
numba_typing/overload_list.py
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| for sig, _ in list_signature: | ||
| for param in sig.parameters: | ||
| if len(param) != len(values_dict.items()): |
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You don't need to call method items() here.
numba_typing/overload_list.py
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| for name, val in defaults_dict.items(): | ||
| if sig_def.get(name) is None: | ||
| raise AttributeError(f'{name} does not match the signature of the function passed to overload_list') | ||
| if not sig_def[name] == val: |
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This condition looks pretty strange. Maybe if sig_def[name] != val?
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