diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/__pycache__/visitor.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/__pycache__/visitor.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3d2fab38d68e49e825e755e53cd1d8913562e300 Binary files /dev/null and b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/__pycache__/visitor.cpython-311.pyc differ diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/_identifier.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/_identifier.py new file mode 100644 index 0000000000000000000000000000000000000000..928c1503c7d414a8a86bbf5a82c68d42cb089bd2 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/_identifier.py @@ -0,0 +1,6 @@ +import re + +# generated by scripts/generate_identifier_pattern.py +pattern = re.compile( + r"[\w·̀-ͯ·҃-֑҇-ׇֽֿׁׂׅׄؐ-ًؚ-ٰٟۖ-ۜ۟-۪ۤۧۨ-ܑۭܰ-݊ަ-ް߫-߽߳ࠖ-࠙ࠛ-ࠣࠥ-ࠧࠩ-࡙࠭-࡛࣓-ࣣ࣡-ःऺ-़ा-ॏ॑-ॗॢॣঁ-ঃ়া-ৄেৈো-্ৗৢৣ৾ਁ-ਃ਼ਾ-ੂੇੈੋ-੍ੑੰੱੵઁ-ઃ઼ા-ૅે-ૉો-્ૢૣૺ-૿ଁ-ଃ଼ା-ୄେୈୋ-୍ୖୗୢୣஂா-ூெ-ைொ-்ௗఀ-ఄా-ౄె-ైొ-్ౕౖౢౣಁ-ಃ಼ಾ-ೄೆ-ೈೊ-್ೕೖೢೣഀ-ഃ഻഼ാ-ൄെ-ൈൊ-്ൗൢൣංඃ්ා-ුූෘ-ෟෲෳัิ-ฺ็-๎ັິ-ູົຼ່-ໍ༹༘༙༵༷༾༿ཱ-྄྆྇ྍ-ྗྙ-ྼ࿆ါ-ှၖ-ၙၞ-ၠၢ-ၤၧ-ၭၱ-ၴႂ-ႍႏႚ-ႝ፝-፟ᜒ-᜔ᜲ-᜴ᝒᝓᝲᝳ឴-៓៝᠋-᠍ᢅᢆᢩᤠ-ᤫᤰ-᤻ᨗ-ᨛᩕ-ᩞ᩠-᩿᩼᪰-᪽ᬀ-ᬄ᬴-᭄᭫-᭳ᮀ-ᮂᮡ-ᮭ᯦-᯳ᰤ-᰷᳐-᳔᳒-᳨᳭ᳲ-᳴᳷-᳹᷀-᷹᷻-᷿‿⁀⁔⃐-⃥⃜⃡-⃰℘℮⳯-⵿⳱ⷠ-〪ⷿ-゙゚〯꙯ꙴ-꙽ꚞꚟ꛰꛱ꠂ꠆ꠋꠣ-ꠧꢀꢁꢴ-ꣅ꣠-꣱ꣿꤦ-꤭ꥇ-꥓ꦀ-ꦃ꦳-꧀ꧥꨩ-ꨶꩃꩌꩍꩻ-ꩽꪰꪲ-ꪴꪷꪸꪾ꪿꫁ꫫ-ꫯꫵ꫶ꯣ-ꯪ꯬꯭ﬞ︀-️︠-︯︳︴﹍-﹏＿𐇽𐋠𐍶-𐍺𐨁-𐨃𐨅𐨆𐨌-𐨏𐨸-𐨿𐨺𐫦𐫥𐴤-𐽆𐴧-𐽐𑀀-𑀂𑀸-𑁆𑁿-𑂂𑂰-𑂺𑄀-𑄂𑄧-𑄴𑅅𑅆𑅳𑆀-𑆂𑆳-𑇀𑇉-𑇌𑈬-𑈷𑈾𑋟-𑋪𑌀-𑌃𑌻𑌼𑌾-𑍄𑍇𑍈𑍋-𑍍𑍗𑍢𑍣𑍦-𑍬𑍰-𑍴𑐵-𑑆𑑞𑒰-𑓃𑖯-𑖵𑖸-𑗀𑗜𑗝𑘰-𑙀𑚫-𑚷𑜝-𑜫𑠬-𑠺𑨁-𑨊𑨳-𑨹𑨻-𑨾𑩇𑩑-𑩛𑪊-𑪙𑰯-𑰶𑰸-𑰿𑲒-𑲧𑲩-𑲶𑴱-𑴶𑴺𑴼𑴽𑴿-𑵅𑵇𑶊-𑶎𑶐𑶑𑶓-𑶗𑻳-𑻶𖫰-𖫴𖬰-𖬶𖽑-𖽾𖾏-𖾒𛲝𛲞𝅥-𝅩𝅭-𝅲𝅻-𝆂𝆅-𝆋𝆪-𝆭𝉂-𝉄𝨀-𝨶𝨻-𝩬𝩵𝪄𝪛-𝪟𝪡-𝪯𞀀-𞀆𞀈-𞀘𞀛-𞀡𞀣𞀤𞀦-𞣐𞀪-𞣖𞥄-𞥊󠄀-󠇯]+" # noqa: B950 +) diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/async_utils.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/async_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..715d70119bba3f02350827da184cedbd08f65185 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/async_utils.py @@ -0,0 +1,84 @@ +import inspect +import typing as t +from functools import WRAPPER_ASSIGNMENTS +from functools import wraps + +from .utils import _PassArg +from .utils import pass_eval_context + +V = t.TypeVar("V") + + +def async_variant(normal_func): # type: ignore + def decorator(async_func): # type: ignore + pass_arg = _PassArg.from_obj(normal_func) + need_eval_context = pass_arg is None + + if pass_arg is _PassArg.environment: + + def is_async(args: t.Any) -> bool: + return t.cast(bool, args[0].is_async) + + else: + + def is_async(args: t.Any) -> bool: + return t.cast(bool, args[0].environment.is_async) + + # Take the doc and annotations from the sync function, but the + # name from the async function. Pallets-Sphinx-Themes + # build_function_directive expects __wrapped__ to point to the + # sync function. + async_func_attrs = ("__module__", "__name__", "__qualname__") + normal_func_attrs = tuple(set(WRAPPER_ASSIGNMENTS).difference(async_func_attrs)) + + @wraps(normal_func, assigned=normal_func_attrs) + @wraps(async_func, assigned=async_func_attrs, updated=()) + def wrapper(*args, **kwargs): # type: ignore + b = is_async(args) + + if need_eval_context: + args = args[1:] + + if b: + return async_func(*args, **kwargs) + + return normal_func(*args, **kwargs) + + if need_eval_context: + wrapper = pass_eval_context(wrapper) + + wrapper.jinja_async_variant = True + return wrapper + + return decorator + + +_common_primitives = {int, float, bool, str, list, dict, tuple, type(None)} + + +async def auto_await(value: t.Union[t.Awaitable["V"], "V"]) -> "V": + # Avoid a costly call to isawaitable + if type(value) in _common_primitives: + return t.cast("V", value) + + if inspect.isawaitable(value): + return await t.cast("t.Awaitable[V]", value) + + return t.cast("V", value) + + +async def auto_aiter( + iterable: "t.Union[t.AsyncIterable[V], t.Iterable[V]]", +) -> "t.AsyncIterator[V]": + if hasattr(iterable, "__aiter__"): + async for item in t.cast("t.AsyncIterable[V]", iterable): + yield item + else: + for item in iterable: + yield item + + +async def auto_to_list( + value: "t.Union[t.AsyncIterable[V], t.Iterable[V]]", +) -> t.List["V"]: + return [x async for x in auto_aiter(value)] diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/constants.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..41a1c23b0a7fe134b1f662545876eb65b31b071e --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/constants.py @@ -0,0 +1,20 @@ +#: list of lorem ipsum words used by the lipsum() helper function +LOREM_IPSUM_WORDS = """\ +a ac accumsan ad adipiscing aenean aliquam aliquet amet ante aptent arcu at +auctor augue bibendum blandit class commodo condimentum congue consectetuer +consequat conubia convallis cras cubilia cum curabitur curae cursus dapibus +diam dictum dictumst dignissim dis dolor donec dui duis egestas eget eleifend +elementum elit enim erat eros est et etiam eu euismod facilisi facilisis fames +faucibus felis fermentum feugiat fringilla fusce gravida habitant habitasse hac +hendrerit hymenaeos iaculis id imperdiet in inceptos integer interdum ipsum +justo lacinia lacus laoreet lectus leo libero ligula litora lobortis lorem +luctus maecenas magna magnis malesuada massa mattis mauris metus mi molestie +mollis montes morbi mus nam nascetur natoque nec neque netus nibh nisi nisl non +nonummy nostra nulla nullam nunc odio orci ornare parturient pede pellentesque +penatibus per pharetra phasellus placerat platea porta porttitor posuere +potenti praesent pretium primis proin pulvinar purus quam quis quisque rhoncus +ridiculus risus rutrum sagittis sapien scelerisque sed sem semper senectus sit +sociis sociosqu sodales sollicitudin suscipit suspendisse taciti tellus tempor +tempus tincidunt torquent tortor tristique turpis ullamcorper ultrices +ultricies urna ut varius vehicula vel velit venenatis vestibulum vitae vivamus +viverra volutpat vulputate""" diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/ext.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/ext.py new file mode 100644 index 0000000000000000000000000000000000000000..fade1fa3bc89b0d85d6ae3915277b835c67d7030 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/jinja2/ext.py @@ -0,0 +1,869 @@ +"""Extension API for adding custom tags and behavior.""" +import pprint +import re +import typing as t + +from markupsafe import Markup + +from . import defaults +from . import nodes +from .environment import Environment +from .exceptions import TemplateAssertionError +from .exceptions import TemplateSyntaxError +from .runtime import concat # type: ignore +from .runtime import Context +from .runtime import Undefined +from .utils import import_string +from .utils import pass_context + +if t.TYPE_CHECKING: + import typing_extensions as te + from .lexer import Token + from .lexer import TokenStream + from .parser import Parser + + class _TranslationsBasic(te.Protocol): + def gettext(self, message: str) -> str: + ... + + def ngettext(self, singular: str, plural: str, n: int) -> str: + pass + + class _TranslationsContext(_TranslationsBasic): + def pgettext(self, context: str, message: str) -> str: + ... + + def npgettext(self, context: str, singular: str, plural: str, n: int) -> str: + ... + + _SupportedTranslations = t.Union[_TranslationsBasic, _TranslationsContext] + + +# I18N functions available in Jinja templates. If the I18N library +# provides ugettext, it will be assigned to gettext. +GETTEXT_FUNCTIONS: t.Tuple[str, ...] = ( + "_", + "gettext", + "ngettext", + "pgettext", + "npgettext", +) +_ws_re = re.compile(r"\s*\n\s*") + + +class Extension: + """Extensions can be used to add extra functionality to the Jinja template + system at the parser level. Custom extensions are bound to an environment + but may not store environment specific data on `self`. The reason for + this is that an extension can be bound to another environment (for + overlays) by creating a copy and reassigning the `environment` attribute. + + As extensions are created by the environment they cannot accept any + arguments for configuration. One may want to work around that by using + a factory function, but that is not possible as extensions are identified + by their import name. The correct way to configure the extension is + storing the configuration values on the environment. Because this way the + environment ends up acting as central configuration storage the + attributes may clash which is why extensions have to ensure that the names + they choose for configuration are not too generic. ``prefix`` for example + is a terrible name, ``fragment_cache_prefix`` on the other hand is a good + name as includes the name of the extension (fragment cache). + """ + + identifier: t.ClassVar[str] + + def __init_subclass__(cls) -> None: + cls.identifier = f"{cls.__module__}.{cls.__name__}" + + #: if this extension parses this is the list of tags it's listening to. + tags: t.Set[str] = set() + + #: the priority of that extension. This is especially useful for + #: extensions that preprocess values. A lower value means higher + #: priority. + #: + #: .. versionadded:: 2.4 + priority = 100 + + def __init__(self, environment: Environment) -> None: + self.environment = environment + + def bind(self, environment: Environment) -> "Extension": + """Create a copy of this extension bound to another environment.""" + rv = object.__new__(self.__class__) + rv.__dict__.update(self.__dict__) + rv.environment = environment + return rv + + def preprocess( + self, source: str, name: t.Optional[str], filename: t.Optional[str] = None + ) -> str: + """This method is called before the actual lexing and can be used to + preprocess the source. The `filename` is optional. The return value + must be the preprocessed source. + """ + return source + + def filter_stream( + self, stream: "TokenStream" + ) -> t.Union["TokenStream", t.Iterable["Token"]]: + """It's passed a :class:`~jinja2.lexer.TokenStream` that can be used + to filter tokens returned. This method has to return an iterable of + :class:`~jinja2.lexer.Token`\\s, but it doesn't have to return a + :class:`~jinja2.lexer.TokenStream`. + """ + return stream + + def parse(self, parser: "Parser") -> t.Union[nodes.Node, t.List[nodes.Node]]: + """If any of the :attr:`tags` matched this method is called with the + parser as first argument. The token the parser stream is pointing at + is the name token that matched. This method has to return one or a + list of multiple nodes. + """ + raise NotImplementedError() + + def attr( + self, name: str, lineno: t.Optional[int] = None + ) -> nodes.ExtensionAttribute: + """Return an attribute node for the current extension. This is useful + to pass constants on extensions to generated template code. + + :: + + self.attr('_my_attribute', lineno=lineno) + """ + return nodes.ExtensionAttribute(self.identifier, name, lineno=lineno) + + def call_method( + self, + name: str, + args: t.Optional[t.List[nodes.Expr]] = None, + kwargs: t.Optional[t.List[nodes.Keyword]] = None, + dyn_args: t.Optional[nodes.Expr] = None, + dyn_kwargs: t.Optional[nodes.Expr] = None, + lineno: t.Optional[int] = None, + ) -> nodes.Call: + """Call a method of the extension. This is a shortcut for + :meth:`attr` + :class:`jinja2.nodes.Call`. + """ + if args is None: + args = [] + if kwargs is None: + kwargs = [] + return nodes.Call( + self.attr(name, lineno=lineno), + args, + kwargs, + dyn_args, + dyn_kwargs, + lineno=lineno, + ) + + +@pass_context +def _gettext_alias( + __context: Context, *args: t.Any, **kwargs: t.Any +) -> t.Union[t.Any, Undefined]: + return __context.call(__context.resolve("gettext"), *args, **kwargs) + + +def _make_new_gettext(func: t.Callable[[str], str]) -> t.Callable[..., str]: + @pass_context + def gettext(__context: Context, __string: str, **variables: t.Any) -> str: + rv = __context.call(func, __string) + if __context.eval_ctx.autoescape: + rv = Markup(rv) + # Always treat as a format string, even if there are no + # variables. This makes translation strings more consistent + # and predictable. This requires escaping + return rv % variables # type: ignore + + return gettext + + +def _make_new_ngettext(func: t.Callable[[str, str, int], str]) -> t.Callable[..., str]: + @pass_context + def ngettext( + __context: Context, + __singular: str, + __plural: str, + __num: int, + **variables: t.Any, + ) -> str: + variables.setdefault("num", __num) + rv = __context.call(func, __singular, __plural, __num) + if __context.eval_ctx.autoescape: + rv = Markup(rv) + # Always treat as a format string, see gettext comment above. + return rv % variables # type: ignore + + return ngettext + + +def _make_new_pgettext(func: t.Callable[[str, str], str]) -> t.Callable[..., str]: + @pass_context + def pgettext( + __context: Context, __string_ctx: str, __string: str, **variables: t.Any + ) -> str: + variables.setdefault("context", __string_ctx) + rv = __context.call(func, __string_ctx, __string) + + if __context.eval_ctx.autoescape: + rv = Markup(rv) + + # Always treat as a format string, see gettext comment above. + return rv % variables # type: ignore + + return pgettext + + +def _make_new_npgettext( + func: t.Callable[[str, str, str, int], str] +) -> t.Callable[..., str]: + @pass_context + def npgettext( + __context: Context, + __string_ctx: str, + __singular: str, + __plural: str, + __num: int, + **variables: t.Any, + ) -> str: + variables.setdefault("context", __string_ctx) + variables.setdefault("num", __num) + rv = __context.call(func, __string_ctx, __singular, __plural, __num) + + if __context.eval_ctx.autoescape: + rv = Markup(rv) + + # Always treat as a format string, see gettext comment above. + return rv % variables # type: ignore + + return npgettext + + +class InternationalizationExtension(Extension): + """This extension adds gettext support to Jinja.""" + + tags = {"trans"} + + # TODO: the i18n extension is currently reevaluating values in a few + # situations. Take this example: + # {% trans count=something() %}{{ count }} foo{% pluralize + # %}{{ count }} fooss{% endtrans %} + # something is called twice here. One time for the gettext value and + # the other time for the n-parameter of the ngettext function. + + def __init__(self, environment: Environment) -> None: + super().__init__(environment) + environment.globals["_"] = _gettext_alias + environment.extend( + install_gettext_translations=self._install, + install_null_translations=self._install_null, + install_gettext_callables=self._install_callables, + uninstall_gettext_translations=self._uninstall, + extract_translations=self._extract, + newstyle_gettext=False, + ) + + def _install( + self, translations: "_SupportedTranslations", newstyle: t.Optional[bool] = None + ) -> None: + # ugettext and ungettext are preferred in case the I18N library + # is providing compatibility with older Python versions. + gettext = getattr(translations, "ugettext", None) + if gettext is None: + gettext = translations.gettext + ngettext = getattr(translations, "ungettext", None) + if ngettext is None: + ngettext = translations.ngettext + + pgettext = getattr(translations, "pgettext", None) + npgettext = getattr(translations, "npgettext", None) + self._install_callables( + gettext, ngettext, newstyle=newstyle, pgettext=pgettext, npgettext=npgettext + ) + + def _install_null(self, newstyle: t.Optional[bool] = None) -> None: + import gettext + + translations = gettext.NullTranslations() + + if hasattr(translations, "pgettext"): + # Python < 3.8 + pgettext = translations.pgettext + else: + + def pgettext(c: str, s: str) -> str: + return s + + if hasattr(translations, "npgettext"): + npgettext = translations.npgettext + else: + + def npgettext(c: str, s: str, p: str, n: int) -> str: + return s if n == 1 else p + + self._install_callables( + gettext=translations.gettext, + ngettext=translations.ngettext, + newstyle=newstyle, + pgettext=pgettext, + npgettext=npgettext, + ) + + def _install_callables( + self, + gettext: t.Callable[[str], str], + ngettext: t.Callable[[str, str, int], str], + newstyle: t.Optional[bool] = None, + pgettext: t.Optional[t.Callable[[str, str], str]] = None, + npgettext: t.Optional[t.Callable[[str, str, str, int], str]] = None, + ) -> None: + if newstyle is not None: + self.environment.newstyle_gettext = newstyle # type: ignore + if self.environment.newstyle_gettext: # type: ignore + gettext = _make_new_gettext(gettext) + ngettext = _make_new_ngettext(ngettext) + + if pgettext is not None: + pgettext = _make_new_pgettext(pgettext) + + if npgettext is not None: + npgettext = _make_new_npgettext(npgettext) + + self.environment.globals.update( + gettext=gettext, ngettext=ngettext, pgettext=pgettext, npgettext=npgettext + ) + + def _uninstall(self, translations: "_SupportedTranslations") -> None: + for key in ("gettext", "ngettext", "pgettext", "npgettext"): + self.environment.globals.pop(key, None) + + def _extract( + self, + source: t.Union[str, nodes.Template], + gettext_functions: t.Sequence[str] = GETTEXT_FUNCTIONS, + ) -> t.Iterator[ + t.Tuple[int, str, t.Union[t.Optional[str], t.Tuple[t.Optional[str], ...]]] + ]: + if isinstance(source, str): + source = self.environment.parse(source) + return extract_from_ast(source, gettext_functions) + + def parse(self, parser: "Parser") -> t.Union[nodes.Node, t.List[nodes.Node]]: + """Parse a translatable tag.""" + lineno = next(parser.stream).lineno + + context = None + context_token = parser.stream.next_if("string") + + if context_token is not None: + context = context_token.value + + # find all the variables referenced. Additionally a variable can be + # defined in the body of the trans block too, but this is checked at + # a later state. + plural_expr: t.Optional[nodes.Expr] = None + plural_expr_assignment: t.Optional[nodes.Assign] = None + num_called_num = False + variables: t.Dict[str, nodes.Expr] = {} + trimmed = None + while parser.stream.current.type != "block_end": + if variables: + parser.stream.expect("comma") + + # skip colon for python compatibility + if parser.stream.skip_if("colon"): + break + + token = parser.stream.expect("name") + if token.value in variables: + parser.fail( + f"translatable variable {token.value!r} defined twice.", + token.lineno, + exc=TemplateAssertionError, + ) + + # expressions + if parser.stream.current.type == "assign": + next(parser.stream) + variables[token.value] = var = parser.parse_expression() + elif trimmed is None and token.value in ("trimmed", "notrimmed"): + trimmed = token.value == "trimmed" + continue + else: + variables[token.value] = var = nodes.Name(token.value, "load") + + if plural_expr is None: + if isinstance(var, nodes.Call): + plural_expr = nodes.Name("_trans", "load") + variables[token.value] = plural_expr + plural_expr_assignment = nodes.Assign( + nodes.Name("_trans", "store"), var + ) + else: + plural_expr = var + num_called_num = token.value == "num" + + parser.stream.expect("block_end") + + plural = None + have_plural = False + referenced = set() + + # now parse until endtrans or pluralize + singular_names, singular = self._parse_block(parser, True) + if singular_names: + referenced.update(singular_names) + if plural_expr is None: + plural_expr = nodes.Name(singular_names[0], "load") + num_called_num = singular_names[0] == "num" + + # if we have a pluralize block, we parse that too + if parser.stream.current.test("name:pluralize"): + have_plural = True + next(parser.stream) + if parser.stream.current.type != "block_end": + token = parser.stream.expect("name") + if token.value not in variables: + parser.fail( + f"unknown variable {token.value!r} for pluralization", + token.lineno, + exc=TemplateAssertionError, + ) + plural_expr = variables[token.value] + num_called_num = token.value == "num" + parser.stream.expect("block_end") + plural_names, plural = self._parse_block(parser, False) + next(parser.stream) + referenced.update(plural_names) + else: + next(parser.stream) + + # register free names as simple name expressions + for name in referenced: + if name not in variables: + variables[name] = nodes.Name(name, "load") + + if not have_plural: + plural_expr = None + elif plural_expr is None: + parser.fail("pluralize without variables", lineno) + + if trimmed is None: + trimmed = self.environment.policies["ext.i18n.trimmed"] + if trimmed: + singular = self._trim_whitespace(singular) + if plural: + plural = self._trim_whitespace(plural) + + node = self._make_node( + singular, + plural, + context, + variables, + plural_expr, + bool(referenced), + num_called_num and have_plural, + ) + node.set_lineno(lineno) + if plural_expr_assignment is not None: + return [plural_expr_assignment, node] + else: + return node + + def _trim_whitespace(self, string: str, _ws_re: t.Pattern[str] = _ws_re) -> str: + return _ws_re.sub(" ", string.strip()) + + def _parse_block( + self, parser: "Parser", allow_pluralize: bool + ) -> t.Tuple[t.List[str], str]: + """Parse until the next block tag with a given name.""" + referenced = [] + buf = [] + + while True: + if parser.stream.current.type == "data": + buf.append(parser.stream.current.value.replace("%", "%%")) + next(parser.stream) + elif parser.stream.current.type == "variable_begin": + next(parser.stream) + name = parser.stream.expect("name").value + referenced.append(name) + buf.append(f"%({name})s") + parser.stream.expect("variable_end") + elif parser.stream.current.type == "block_begin": + next(parser.stream) + block_name = ( + parser.stream.current.value + if parser.stream.current.type == "name" + else None + ) + if block_name == "endtrans": + break + elif block_name == "pluralize": + if allow_pluralize: + break + parser.fail( + "a translatable section can have only one pluralize section" + ) + elif block_name == "trans": + parser.fail( + "trans blocks can't be nested; did you mean `endtrans`?" + ) + parser.fail( + f"control structures in translatable sections are not allowed; " + f"saw `{block_name}`" + ) + elif parser.stream.eos: + parser.fail("unclosed translation block") + else: + raise RuntimeError("internal parser error") + + return referenced, concat(buf) + + def _make_node( + self, + singular: str, + plural: t.Optional[str], + context: t.Optional[str], + variables: t.Dict[str, nodes.Expr], + plural_expr: t.Optional[nodes.Expr], + vars_referenced: bool, + num_called_num: bool, + ) -> nodes.Output: + """Generates a useful node from the data provided.""" + newstyle = self.environment.newstyle_gettext # type: ignore + node: nodes.Expr + + # no variables referenced? no need to escape for old style + # gettext invocations only if there are vars. + if not vars_referenced and not newstyle: + singular = singular.replace("%%", "%") + if plural: + plural = plural.replace("%%", "%") + + func_name = "gettext" + func_args: t.List[nodes.Expr] = [nodes.Const(singular)] + + if context is not None: + func_args.insert(0, nodes.Const(context)) + func_name = f"p{func_name}" + + if plural_expr is not None: + func_name = f"n{func_name}" + func_args.extend((nodes.Const(plural), plural_expr)) + + node = nodes.Call(nodes.Name(func_name, "load"), func_args, [], None, None) + + # in case newstyle gettext is used, the method is powerful + # enough to handle the variable expansion and autoescape + # handling itself + if newstyle: + for key, value in variables.items(): + # the function adds that later anyways in case num was + # called num, so just skip it. + if num_called_num and key == "num": + continue + node.kwargs.append(nodes.Keyword(key, value)) + + # otherwise do that here + else: + # mark the return value as safe if we are in an + # environment with autoescaping turned on + node = nodes.MarkSafeIfAutoescape(node) + if variables: + node = nodes.Mod( + node, + nodes.Dict( + [ + nodes.Pair(nodes.Const(key), value) + for key, value in variables.items() + ] + ), + ) + return nodes.Output([node]) + + +class ExprStmtExtension(Extension): + """Adds a `do` tag to Jinja that works like the print statement just + that it doesn't print the return value. + """ + + tags = {"do"} + + def parse(self, parser: "Parser") -> nodes.ExprStmt: + node = nodes.ExprStmt(lineno=next(parser.stream).lineno) + node.node = parser.parse_tuple() + return node + + +class LoopControlExtension(Extension): + """Adds break and continue to the template engine.""" + + tags = {"break", "continue"} + + def parse(self, parser: "Parser") -> t.Union[nodes.Break, nodes.Continue]: + token = next(parser.stream) + if token.value == "break": + return nodes.Break(lineno=token.lineno) + return nodes.Continue(lineno=token.lineno) + + +class DebugExtension(Extension): + """A ``{% debug %}`` tag that dumps the available variables, + filters, and tests. + + .. code-block:: html+jinja + + 
{% debug %}
+ + .. code-block:: text + + {'context': {'cycler': , + ..., + 'namespace': }, + 'filters': ['abs', 'attr', 'batch', 'capitalize', 'center', 'count', 'd', + ..., 'urlencode', 'urlize', 'wordcount', 'wordwrap', 'xmlattr'], + 'tests': ['!=', '<', '<=', '==', '>', '>=', 'callable', 'defined', + ..., 'odd', 'sameas', 'sequence', 'string', 'undefined', 'upper']} + + .. versionadded:: 2.11.0 + """ + + tags = {"debug"} + + def parse(self, parser: "Parser") -> nodes.Output: + lineno = parser.stream.expect("name:debug").lineno + context = nodes.ContextReference() + result = self.call_method("_render", [context], lineno=lineno) + return nodes.Output([result], lineno=lineno) + + def _render(self, context: Context) -> str: + result = { + "context": context.get_all(), + "filters": sorted(self.environment.filters.keys()), + "tests": sorted(self.environment.tests.keys()), + } + + # Set the depth since the intent is to show the top few names. + return pprint.pformat(result, depth=3, compact=True) + + +def extract_from_ast( + ast: nodes.Template, + gettext_functions: t.Sequence[str] = GETTEXT_FUNCTIONS, + babel_style: bool = True, +) -> t.Iterator[ + t.Tuple[int, str, t.Union[t.Optional[str], t.Tuple[t.Optional[str], ...]]] +]: + """Extract localizable strings from the given template node. Per + default this function returns matches in babel style that means non string + parameters as well as keyword arguments are returned as `None`. This + allows Babel to figure out what you really meant if you are using + gettext functions that allow keyword arguments for placeholder expansion. + If you don't want that behavior set the `babel_style` parameter to `False` + which causes only strings to be returned and parameters are always stored + in tuples. As a consequence invalid gettext calls (calls without a single + string parameter or string parameters after non-string parameters) are + skipped. + + This example explains the behavior: + + >>> from jinja2 import Environment + >>> env = Environment() + >>> node = env.parse('{{ (_("foo"), _(), ngettext("foo", "bar", 42)) }}') + >>> list(extract_from_ast(node)) + [(1, '_', 'foo'), (1, '_', ()), (1, 'ngettext', ('foo', 'bar', None))] + >>> list(extract_from_ast(node, babel_style=False)) + [(1, '_', ('foo',)), (1, 'ngettext', ('foo', 'bar'))] + + For every string found this function yields a ``(lineno, function, + message)`` tuple, where: + + * ``lineno`` is the number of the line on which the string was found, + * ``function`` is the name of the ``gettext`` function used (if the + string was extracted from embedded Python code), and + * ``message`` is the string, or a tuple of strings for functions + with multiple string arguments. + + This extraction function operates on the AST and is because of that unable + to extract any comments. For comment support you have to use the babel + extraction interface or extract comments yourself. + """ + out: t.Union[t.Optional[str], t.Tuple[t.Optional[str], ...]] + + for node in ast.find_all(nodes.Call): + if ( + not isinstance(node.node, nodes.Name) + or node.node.name not in gettext_functions + ): + continue + + strings: t.List[t.Optional[str]] = [] + + for arg in node.args: + if isinstance(arg, nodes.Const) and isinstance(arg.value, str): + strings.append(arg.value) + else: + strings.append(None) + + for _ in node.kwargs: + strings.append(None) + if node.dyn_args is not None: + strings.append(None) + if node.dyn_kwargs is not None: + strings.append(None) + + if not babel_style: + out = tuple(x for x in strings if x is not None) + + if not out: + continue + else: + if len(strings) == 1: + out = strings[0] + else: + out = tuple(strings) + + yield node.lineno, node.node.name, out + + +class _CommentFinder: + """Helper class to find comments in a token stream. Can only + find comments for gettext calls forwards. Once the comment + from line 4 is found, a comment for line 1 will not return a + usable value. + """ + + def __init__( + self, tokens: t.Sequence[t.Tuple[int, str, str]], comment_tags: t.Sequence[str] + ) -> None: + self.tokens = tokens + self.comment_tags = comment_tags + self.offset = 0 + self.last_lineno = 0 + + def find_backwards(self, offset: int) -> t.List[str]: + try: + for _, token_type, token_value in reversed( + self.tokens[self.offset : offset] + ): + if token_type in ("comment", "linecomment"): + try: + prefix, comment = token_value.split(None, 1) + except ValueError: + continue + if prefix in self.comment_tags: + return [comment.rstrip()] + return [] + finally: + self.offset = offset + + def find_comments(self, lineno: int) -> t.List[str]: + if not self.comment_tags or self.last_lineno > lineno: + return [] + for idx, (token_lineno, _, _) in enumerate(self.tokens[self.offset :]): + if token_lineno > lineno: + return self.find_backwards(self.offset + idx) + return self.find_backwards(len(self.tokens)) + + +def babel_extract( + fileobj: t.BinaryIO, + keywords: t.Sequence[str], + comment_tags: t.Sequence[str], + options: t.Dict[str, t.Any], +) -> t.Iterator[ + t.Tuple[ + int, str, t.Union[t.Optional[str], t.Tuple[t.Optional[str], ...]], t.List[str] + ] +]: + """Babel extraction method for Jinja templates. + + .. versionchanged:: 2.3 + Basic support for translation comments was added. If `comment_tags` + is now set to a list of keywords for extraction, the extractor will + try to find the best preceding comment that begins with one of the + keywords. For best results, make sure to not have more than one + gettext call in one line of code and the matching comment in the + same line or the line before. + + .. versionchanged:: 2.5.1 + The `newstyle_gettext` flag can be set to `True` to enable newstyle + gettext calls. + + .. versionchanged:: 2.7 + A `silent` option can now be provided. If set to `False` template + syntax errors are propagated instead of being ignored. + + :param fileobj: the file-like object the messages should be extracted from + :param keywords: a list of keywords (i.e. function names) that should be + recognized as translation functions + :param comment_tags: a list of translator tags to search for and include + in the results. + :param options: a dictionary of additional options (optional) + :return: an iterator over ``(lineno, funcname, message, comments)`` tuples. + (comments will be empty currently) + """ + extensions: t.Dict[t.Type[Extension], None] = {} + + for extension_name in options.get("extensions", "").split(","): + extension_name = extension_name.strip() + + if not extension_name: + continue + + extensions[import_string(extension_name)] = None + + if InternationalizationExtension not in extensions: + extensions[InternationalizationExtension] = None + + def getbool(options: t.Mapping[str, str], key: str, default: bool = False) -> bool: + return options.get(key, str(default)).lower() in {"1", "on", "yes", "true"} + + silent = getbool(options, "silent", True) + environment = Environment( + options.get("block_start_string", defaults.BLOCK_START_STRING), + options.get("block_end_string", defaults.BLOCK_END_STRING), + options.get("variable_start_string", defaults.VARIABLE_START_STRING), + options.get("variable_end_string", defaults.VARIABLE_END_STRING), + options.get("comment_start_string", defaults.COMMENT_START_STRING), + options.get("comment_end_string", defaults.COMMENT_END_STRING), + options.get("line_statement_prefix") or defaults.LINE_STATEMENT_PREFIX, + options.get("line_comment_prefix") or defaults.LINE_COMMENT_PREFIX, + getbool(options, "trim_blocks", defaults.TRIM_BLOCKS), + getbool(options, "lstrip_blocks", defaults.LSTRIP_BLOCKS), + defaults.NEWLINE_SEQUENCE, + getbool(options, "keep_trailing_newline", defaults.KEEP_TRAILING_NEWLINE), + tuple(extensions), + cache_size=0, + auto_reload=False, + ) + + if getbool(options, "trimmed"): + environment.policies["ext.i18n.trimmed"] = True + if getbool(options, "newstyle_gettext"): + environment.newstyle_gettext = True # type: ignore + + source = fileobj.read().decode(options.get("encoding", "utf-8")) + try: + node = environment.parse(source) + tokens = list(environment.lex(environment.preprocess(source))) + except TemplateSyntaxError: + if not silent: + raise + # skip templates with syntax errors + return + + finder = _CommentFinder(tokens, comment_tags) + for lineno, func, message in extract_from_ast(node, keywords): + yield lineno, func, message, finder.find_comments(lineno) + + +#: nicer import names +i18n = InternationalizationExtension +do = ExprStmtExtension +loopcontrols = LoopControlExtension +debug = DebugExtension diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/nvidia_cuda_nvrtc_cu11-11.8.89.dist-info/METADATA b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/nvidia_cuda_nvrtc_cu11-11.8.89.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..274c82b512f01f59e92def273aaa29d6a006d596 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/nvidia_cuda_nvrtc_cu11-11.8.89.dist-info/METADATA @@ -0,0 +1,35 @@ +Metadata-Version: 2.1 +Name: nvidia-cuda-nvrtc-cu11 +Version: 11.8.89 +Summary: NVRTC native runtime libraries +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: cuda_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: Other/Proprietary License +Classifier: Natural Language :: English +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Topic :: Software Development +Classifier: Topic :: Software Development :: Libraries +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Requires-Python: >=3 +License-File: License.txt + +NVRTC native runtime libraries diff --git 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b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/__init__.py @@ -0,0 +1,18 @@ +from typing import List, Optional + +from pip._internal.utils import _log + +# init_logging() must be called before any call to logging.getLogger() +# which happens at import of most modules. +_log.init_logging() + + +def main(args: Optional[List[str]] = None) -> int: + """This is preserved for old console scripts that may still be referencing + it. + + For additional details, see https://github.com/pypa/pip/issues/7498. + """ + from pip._internal.utils.entrypoints import _wrapper + + return _wrapper(args) diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/__pycache__/main.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/__pycache__/main.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8f6f2912bd2b2193d8dcb89642f99a9c1e24e616 Binary files /dev/null and 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b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/build_env.py @@ -0,0 +1,319 @@ +"""Build Environment used for isolation during sdist building +""" + +import logging +import os +import pathlib +import site +import sys +import textwrap +from collections import OrderedDict +from types import TracebackType +from typing import TYPE_CHECKING, Iterable, List, Optional, Set, Tuple, Type, Union + +from pip._vendor.certifi import where +from pip._vendor.packaging.version import Version + +from pip import __file__ as pip_location +from pip._internal.cli.spinners import open_spinner +from pip._internal.locations import get_platlib, get_purelib, get_scheme +from pip._internal.metadata import get_default_environment, get_environment +from pip._internal.utils.logging import VERBOSE +from pip._internal.utils.packaging import get_requirement +from pip._internal.utils.subprocess import call_subprocess +from pip._internal.utils.temp_dir import TempDirectory, tempdir_kinds + +if TYPE_CHECKING: + from pip._internal.index.package_finder import PackageFinder + +logger = logging.getLogger(__name__) + + +def _dedup(a: str, b: str) -> Union[Tuple[str], Tuple[str, str]]: + return (a, b) if a != b else (a,) + + +class _Prefix: + def __init__(self, path: str) -> None: + self.path = path + self.setup = False + scheme = get_scheme("", prefix=path) + self.bin_dir = scheme.scripts + self.lib_dirs = _dedup(scheme.purelib, scheme.platlib) + + +def get_runnable_pip() -> str: + """Get a file to pass to a Python executable, to run the currently-running pip. + + This is used to run a pip subprocess, for installing requirements into the build + environment. + """ + source = pathlib.Path(pip_location).resolve().parent + + if not source.is_dir(): + # This would happen if someone is using pip from inside a zip file. In that + # case, we can use that directly. + return str(source) + + return os.fsdecode(source / "__pip-runner__.py") + + +def _get_system_sitepackages() -> Set[str]: + """Get system site packages + + Usually from site.getsitepackages, + but fallback on `get_purelib()/get_platlib()` if unavailable + (e.g. in a virtualenv created by virtualenv<20) + + Returns normalized set of strings. + """ + if hasattr(site, "getsitepackages"): + system_sites = site.getsitepackages() + else: + # virtualenv < 20 overwrites site.py without getsitepackages + # fallback on get_purelib/get_platlib. + # this is known to miss things, but shouldn't in the cases + # where getsitepackages() has been removed (inside a virtualenv) + system_sites = [get_purelib(), get_platlib()] + return {os.path.normcase(path) for path in system_sites} + + +class BuildEnvironment: + """Creates and manages an isolated environment to install build deps""" + + def __init__(self) -> None: + temp_dir = TempDirectory(kind=tempdir_kinds.BUILD_ENV, globally_managed=True) + + self._prefixes = OrderedDict( + (name, _Prefix(os.path.join(temp_dir.path, name))) + for name in ("normal", "overlay") + ) + + self._bin_dirs: List[str] = [] + self._lib_dirs: List[str] = [] + for prefix in reversed(list(self._prefixes.values())): + self._bin_dirs.append(prefix.bin_dir) + self._lib_dirs.extend(prefix.lib_dirs) + + # Customize site to: + # - ensure .pth files are honored + # - prevent access to system site packages + system_sites = _get_system_sitepackages() + + self._site_dir = os.path.join(temp_dir.path, "site") + if not os.path.exists(self._site_dir): + os.mkdir(self._site_dir) + with open( + os.path.join(self._site_dir, "sitecustomize.py"), "w", encoding="utf-8" + ) as fp: + fp.write( + textwrap.dedent( + """ + import os, site, sys + + # First, drop system-sites related paths. + original_sys_path = sys.path[:] + known_paths = set() + for path in {system_sites!r}: + site.addsitedir(path, known_paths=known_paths) + system_paths = set( + os.path.normcase(path) + for path in sys.path[len(original_sys_path):] + ) + original_sys_path = [ + path for path in original_sys_path + if os.path.normcase(path) not in system_paths + ] + sys.path = original_sys_path + + # Second, add lib directories. + # ensuring .pth file are processed. + for path in {lib_dirs!r}: + assert not path in sys.path + site.addsitedir(path) + """ + ).format(system_sites=system_sites, lib_dirs=self._lib_dirs) + ) + + def __enter__(self) -> None: + self._save_env = { + name: os.environ.get(name, None) + for name in ("PATH", "PYTHONNOUSERSITE", "PYTHONPATH") + } + + path = self._bin_dirs[:] + old_path = self._save_env["PATH"] + if old_path: + path.extend(old_path.split(os.pathsep)) + + pythonpath = [self._site_dir] + + os.environ.update( + { + "PATH": os.pathsep.join(path), + "PYTHONNOUSERSITE": "1", + "PYTHONPATH": os.pathsep.join(pythonpath), + } + ) + + def __exit__( + self, + exc_type: Optional[Type[BaseException]], + exc_val: Optional[BaseException], + exc_tb: Optional[TracebackType], + ) -> None: + for varname, old_value in self._save_env.items(): + if old_value is None: + os.environ.pop(varname, None) + else: + os.environ[varname] = old_value + + def check_requirements( + self, reqs: Iterable[str] + ) -> Tuple[Set[Tuple[str, str]], Set[str]]: + """Return 2 sets: + - conflicting requirements: set of (installed, wanted) reqs tuples + - missing requirements: set of reqs + """ + missing = set() + conflicting = set() + if reqs: + env = ( + get_environment(self._lib_dirs) + if hasattr(self, "_lib_dirs") + else get_default_environment() + ) + for req_str in reqs: + req = get_requirement(req_str) + # We're explicitly evaluating with an empty extra value, since build + # environments are not provided any mechanism to select specific extras. + if req.marker is not None and not req.marker.evaluate({"extra": ""}): + continue + dist = env.get_distribution(req.name) + if not dist: + missing.add(req_str) + continue + if isinstance(dist.version, Version): + installed_req_str = f"{req.name}=={dist.version}" + else: + installed_req_str = f"{req.name}==={dist.version}" + if not req.specifier.contains(dist.version, prereleases=True): + conflicting.add((installed_req_str, req_str)) + # FIXME: Consider direct URL? + return conflicting, missing + + def install_requirements( + self, + finder: "PackageFinder", + requirements: Iterable[str], + prefix_as_string: str, + *, + kind: str, + ) -> None: + prefix = self._prefixes[prefix_as_string] + assert not prefix.setup + prefix.setup = True + if not requirements: + return + self._install_requirements( + get_runnable_pip(), + finder, + requirements, + prefix, + kind=kind, + ) + + @staticmethod + def _install_requirements( + pip_runnable: str, + finder: "PackageFinder", + requirements: Iterable[str], + prefix: _Prefix, + *, + kind: str, + ) -> None: + args: List[str] = [ + sys.executable, + pip_runnable, + "install", + "--ignore-installed", + "--no-user", + "--prefix", + prefix.path, + "--no-warn-script-location", + "--disable-pip-version-check", + # The prefix specified two lines above, thus + # target from config file or env var should be ignored + "--target", + "", + ] + if logger.getEffectiveLevel() <= logging.DEBUG: + args.append("-vv") + elif logger.getEffectiveLevel() <= VERBOSE: + args.append("-v") + for format_control in ("no_binary", "only_binary"): + formats = getattr(finder.format_control, format_control) + args.extend( + ( + "--" + format_control.replace("_", "-"), + ",".join(sorted(formats or {":none:"})), + ) + ) + + index_urls = finder.index_urls + if index_urls: + args.extend(["-i", index_urls[0]]) + for extra_index in index_urls[1:]: + args.extend(["--extra-index-url", extra_index]) + else: + args.append("--no-index") + for link in finder.find_links: + args.extend(["--find-links", link]) + + for host in finder.trusted_hosts: + args.extend(["--trusted-host", host]) + if finder.allow_all_prereleases: + args.append("--pre") + if finder.prefer_binary: + args.append("--prefer-binary") + args.append("--") + args.extend(requirements) + extra_environ = {"_PIP_STANDALONE_CERT": where()} + with open_spinner(f"Installing {kind}") as spinner: + call_subprocess( + args, + command_desc=f"pip subprocess to install {kind}", + spinner=spinner, + extra_environ=extra_environ, + ) + + +class NoOpBuildEnvironment(BuildEnvironment): + """A no-op drop-in replacement for BuildEnvironment""" + + def __init__(self) -> None: + pass + + def __enter__(self) -> None: + pass + + def __exit__( + self, + exc_type: Optional[Type[BaseException]], + exc_val: Optional[BaseException], + exc_tb: Optional[TracebackType], + ) -> None: + pass + + def cleanup(self) -> None: + pass + + def install_requirements( + self, + finder: "PackageFinder", + requirements: Iterable[str], + prefix_as_string: str, + *, + kind: str, + ) -> None: + raise NotImplementedError() diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/__init__.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9a89a838b9a5cb264e9ae9d269fbedca6e2d6333 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/__init__.py @@ -0,0 +1,21 @@ +from pip._internal.distributions.base import AbstractDistribution +from pip._internal.distributions.sdist import SourceDistribution +from pip._internal.distributions.wheel import WheelDistribution +from pip._internal.req.req_install import InstallRequirement + + +def make_distribution_for_install_requirement( + install_req: InstallRequirement, +) -> AbstractDistribution: + """Returns a Distribution for the given InstallRequirement""" + # Editable requirements will always be source distributions. They use the + # legacy logic until we create a modern standard for them. + if install_req.editable: + return SourceDistribution(install_req) + + # If it's a wheel, it's a WheelDistribution + if install_req.is_wheel: + return WheelDistribution(install_req) + + # Otherwise, a SourceDistribution + return SourceDistribution(install_req) diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/__pycache__/installed.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/__pycache__/installed.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8997fcb1767494722f5410f9f570705fb62eafb0 Binary files /dev/null and b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/__pycache__/installed.cpython-311.pyc differ diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/__pycache__/sdist.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/__pycache__/sdist.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..64eaddcc4f11da2053ffeb62d690a4ec0af359ba Binary files /dev/null and b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/__pycache__/sdist.cpython-311.pyc differ diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/__pycache__/wheel.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/__pycache__/wheel.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3b5b059d4664e0c3d7562add720e29d5054fb82c Binary files /dev/null and b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/__pycache__/wheel.cpython-311.pyc differ diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/base.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/base.py new file mode 100644 index 0000000000000000000000000000000000000000..6e4d0c91a901c46ab20be813af083cd19809318a --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/base.py @@ -0,0 +1,53 @@ +import abc +from typing import TYPE_CHECKING, Optional + +from pip._internal.metadata.base import BaseDistribution +from pip._internal.req import InstallRequirement + +if TYPE_CHECKING: + from pip._internal.index.package_finder import PackageFinder + + +class AbstractDistribution(metaclass=abc.ABCMeta): + """A base class for handling installable artifacts. + + The requirements for anything installable are as follows: + + - we must be able to determine the requirement name + (or we can't correctly handle the non-upgrade case). + + - for packages with setup requirements, we must also be able + to determine their requirements without installing additional + packages (for the same reason as run-time dependencies) + + - we must be able to create a Distribution object exposing the + above metadata. + + - if we need to do work in the build tracker, we must be able to generate a unique + string to identify the requirement in the build tracker. + """ + + def __init__(self, req: InstallRequirement) -> None: + super().__init__() + self.req = req + + @abc.abstractproperty + def build_tracker_id(self) -> Optional[str]: + """A string that uniquely identifies this requirement to the build tracker. + + If None, then this dist has no work to do in the build tracker, and + ``.prepare_distribution_metadata()`` will not be called.""" + raise NotImplementedError() + + @abc.abstractmethod + def get_metadata_distribution(self) -> BaseDistribution: + raise NotImplementedError() + + @abc.abstractmethod + def prepare_distribution_metadata( + self, + finder: "PackageFinder", + build_isolation: bool, + check_build_deps: bool, + ) -> None: + raise NotImplementedError() diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/installed.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/installed.py new file mode 100644 index 0000000000000000000000000000000000000000..ab8d53be7408626719c27aa29fdc2e143b7c380a --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/installed.py @@ -0,0 +1,29 @@ +from typing import Optional + +from pip._internal.distributions.base import AbstractDistribution +from pip._internal.index.package_finder import PackageFinder +from pip._internal.metadata import BaseDistribution + + +class InstalledDistribution(AbstractDistribution): + """Represents an installed package. + + This does not need any preparation as the required information has already + been computed. + """ + + @property + def build_tracker_id(self) -> Optional[str]: + return None + + def get_metadata_distribution(self) -> BaseDistribution: + assert self.req.satisfied_by is not None, "not actually installed" + return self.req.satisfied_by + + def prepare_distribution_metadata( + self, + finder: PackageFinder, + build_isolation: bool, + check_build_deps: bool, + ) -> None: + pass diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/sdist.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/sdist.py new file mode 100644 index 0000000000000000000000000000000000000000..28ea5cea16cdf9b740809553cbf2d3bf8d626e1e --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/sdist.py @@ -0,0 +1,158 @@ +import logging +from typing import TYPE_CHECKING, Iterable, Optional, Set, Tuple + +from pip._internal.build_env import BuildEnvironment +from pip._internal.distributions.base import AbstractDistribution +from pip._internal.exceptions import InstallationError +from pip._internal.metadata import BaseDistribution +from pip._internal.utils.subprocess import runner_with_spinner_message + +if TYPE_CHECKING: + from pip._internal.index.package_finder import PackageFinder + +logger = logging.getLogger(__name__) + + +class SourceDistribution(AbstractDistribution): + """Represents a source distribution. + + The preparation step for these needs metadata for the packages to be + generated, either using PEP 517 or using the legacy `setup.py egg_info`. + """ + + @property + def build_tracker_id(self) -> Optional[str]: + """Identify this requirement uniquely by its link.""" + assert self.req.link + return self.req.link.url_without_fragment + + def get_metadata_distribution(self) -> BaseDistribution: + return self.req.get_dist() + + def prepare_distribution_metadata( + self, + finder: "PackageFinder", + build_isolation: bool, + check_build_deps: bool, + ) -> None: + # Load pyproject.toml, to determine whether PEP 517 is to be used + self.req.load_pyproject_toml() + + # Set up the build isolation, if this requirement should be isolated + should_isolate = self.req.use_pep517 and build_isolation + if should_isolate: + # Setup an isolated environment and install the build backend static + # requirements in it. + self._prepare_build_backend(finder) + # Check that if the requirement is editable, it either supports PEP 660 or + # has a setup.py or a setup.cfg. This cannot be done earlier because we need + # to setup the build backend to verify it supports build_editable, nor can + # it be done later, because we want to avoid installing build requirements + # needlessly. Doing it here also works around setuptools generating + # UNKNOWN.egg-info when running get_requires_for_build_wheel on a directory + # without setup.py nor setup.cfg. + self.req.isolated_editable_sanity_check() + # Install the dynamic build requirements. + self._install_build_reqs(finder) + # Check if the current environment provides build dependencies + should_check_deps = self.req.use_pep517 and check_build_deps + if should_check_deps: + pyproject_requires = self.req.pyproject_requires + assert pyproject_requires is not None + conflicting, missing = self.req.build_env.check_requirements( + pyproject_requires + ) + if conflicting: + self._raise_conflicts("the backend dependencies", conflicting) + if missing: + self._raise_missing_reqs(missing) + self.req.prepare_metadata() + + def _prepare_build_backend(self, finder: "PackageFinder") -> None: + # Isolate in a BuildEnvironment and install the build-time + # requirements. + pyproject_requires = self.req.pyproject_requires + assert pyproject_requires is not None + + self.req.build_env = BuildEnvironment() + self.req.build_env.install_requirements( + finder, pyproject_requires, "overlay", kind="build dependencies" + ) + conflicting, missing = self.req.build_env.check_requirements( + self.req.requirements_to_check + ) + if conflicting: + self._raise_conflicts("PEP 517/518 supported requirements", conflicting) + if missing: + logger.warning( + "Missing build requirements in pyproject.toml for %s.", + self.req, + ) + logger.warning( + "The project does not specify a build backend, and " + "pip cannot fall back to setuptools without %s.", + " and ".join(map(repr, sorted(missing))), + ) + + def _get_build_requires_wheel(self) -> Iterable[str]: + with self.req.build_env: + runner = runner_with_spinner_message("Getting requirements to build wheel") + backend = self.req.pep517_backend + assert backend is not None + with backend.subprocess_runner(runner): + return backend.get_requires_for_build_wheel() + + def _get_build_requires_editable(self) -> Iterable[str]: + with self.req.build_env: + runner = runner_with_spinner_message( + "Getting requirements to build editable" + ) + backend = self.req.pep517_backend + assert backend is not None + with backend.subprocess_runner(runner): + return backend.get_requires_for_build_editable() + + def _install_build_reqs(self, finder: "PackageFinder") -> None: + # Install any extra build dependencies that the backend requests. + # This must be done in a second pass, as the pyproject.toml + # dependencies must be installed before we can call the backend. + if ( + self.req.editable + and self.req.permit_editable_wheels + and self.req.supports_pyproject_editable + ): + build_reqs = self._get_build_requires_editable() + else: + build_reqs = self._get_build_requires_wheel() + conflicting, missing = self.req.build_env.check_requirements(build_reqs) + if conflicting: + self._raise_conflicts("the backend dependencies", conflicting) + self.req.build_env.install_requirements( + finder, missing, "normal", kind="backend dependencies" + ) + + def _raise_conflicts( + self, conflicting_with: str, conflicting_reqs: Set[Tuple[str, str]] + ) -> None: + format_string = ( + "Some build dependencies for {requirement} " + "conflict with {conflicting_with}: {description}." + ) + error_message = format_string.format( + requirement=self.req, + conflicting_with=conflicting_with, + description=", ".join( + f"{installed} is incompatible with {wanted}" + for installed, wanted in sorted(conflicting_reqs) + ), + ) + raise InstallationError(error_message) + + def _raise_missing_reqs(self, missing: Set[str]) -> None: + format_string = ( + "Some build dependencies for {requirement} are missing: {missing}." + ) + error_message = format_string.format( + requirement=self.req, missing=", ".join(map(repr, sorted(missing))) + ) + raise InstallationError(error_message) diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/wheel.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/wheel.py new file mode 100644 index 0000000000000000000000000000000000000000..bfadd39dcb77dfdaa2cca24e8a6db7e5beac181e --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/distributions/wheel.py @@ -0,0 +1,42 @@ +from typing import TYPE_CHECKING, Optional + +from pip._vendor.packaging.utils import canonicalize_name + +from pip._internal.distributions.base import AbstractDistribution +from pip._internal.metadata import ( + BaseDistribution, + FilesystemWheel, + get_wheel_distribution, +) + +if TYPE_CHECKING: + from pip._internal.index.package_finder import PackageFinder + + +class WheelDistribution(AbstractDistribution): + """Represents a wheel distribution. + + This does not need any preparation as wheels can be directly unpacked. + """ + + @property + def build_tracker_id(self) -> Optional[str]: + return None + + def get_metadata_distribution(self) -> BaseDistribution: + """Loads the metadata from the wheel file into memory and returns a + Distribution that uses it, not relying on the wheel file or + requirement. + """ + assert self.req.local_file_path, "Set as part of preparation during download" + assert self.req.name, "Wheels are never unnamed" + wheel = FilesystemWheel(self.req.local_file_path) + return get_wheel_distribution(wheel, canonicalize_name(self.req.name)) + + def prepare_distribution_metadata( + self, + finder: "PackageFinder", + build_isolation: bool, + check_build_deps: bool, + ) -> None: + pass diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/index/collector.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/index/collector.py new file mode 100644 index 0000000000000000000000000000000000000000..5f8fdee3d46271652d498cbfc865a25c50f2cab0 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/index/collector.py @@ -0,0 +1,494 @@ +""" +The main purpose of this module is to expose LinkCollector.collect_sources(). +""" + +import collections +import email.message +import functools +import itertools +import json +import logging +import os +import urllib.parse +import urllib.request +from dataclasses import dataclass +from html.parser import HTMLParser +from optparse import Values +from typing import ( + Callable, + Dict, + Iterable, + List, + MutableMapping, + NamedTuple, + Optional, + Protocol, + Sequence, + Tuple, + Union, +) + +from pip._vendor import requests +from pip._vendor.requests import Response +from pip._vendor.requests.exceptions import RetryError, SSLError + +from pip._internal.exceptions import NetworkConnectionError +from pip._internal.models.link import Link +from pip._internal.models.search_scope import SearchScope +from pip._internal.network.session import PipSession +from pip._internal.network.utils import raise_for_status +from pip._internal.utils.filetypes import is_archive_file +from pip._internal.utils.misc import redact_auth_from_url +from pip._internal.vcs import vcs + +from .sources import CandidatesFromPage, LinkSource, build_source + +logger = logging.getLogger(__name__) + +ResponseHeaders = MutableMapping[str, str] + + +def _match_vcs_scheme(url: str) -> Optional[str]: + """Look for VCS schemes in the URL. + + Returns the matched VCS scheme, or None if there's no match. + """ + for scheme in vcs.schemes: + if url.lower().startswith(scheme) and url[len(scheme)] in "+:": + return scheme + return None + + +class _NotAPIContent(Exception): + def __init__(self, content_type: str, request_desc: str) -> None: + super().__init__(content_type, request_desc) + self.content_type = content_type + self.request_desc = request_desc + + +def _ensure_api_header(response: Response) -> None: + """ + Check the Content-Type header to ensure the response contains a Simple + API Response. + + Raises `_NotAPIContent` if the content type is not a valid content-type. + """ + content_type = response.headers.get("Content-Type", "Unknown") + + content_type_l = content_type.lower() + if content_type_l.startswith( + ( + "text/html", + "application/vnd.pypi.simple.v1+html", + "application/vnd.pypi.simple.v1+json", + ) + ): + return + + raise _NotAPIContent(content_type, response.request.method) + + +class _NotHTTP(Exception): + pass + + +def _ensure_api_response(url: str, session: PipSession) -> None: + """ + Send a HEAD request to the URL, and ensure the response contains a simple + API Response. + + Raises `_NotHTTP` if the URL is not available for a HEAD request, or + `_NotAPIContent` if the content type is not a valid content type. + """ + scheme, netloc, path, query, fragment = urllib.parse.urlsplit(url) + if scheme not in {"http", "https"}: + raise _NotHTTP() + + resp = session.head(url, allow_redirects=True) + raise_for_status(resp) + + _ensure_api_header(resp) + + +def _get_simple_response(url: str, session: PipSession) -> Response: + """Access an Simple API response with GET, and return the response. + + This consists of three parts: + + 1. If the URL looks suspiciously like an archive, send a HEAD first to + check the Content-Type is HTML or Simple API, to avoid downloading a + large file. Raise `_NotHTTP` if the content type cannot be determined, or + `_NotAPIContent` if it is not HTML or a Simple API. + 2. Actually perform the request. Raise HTTP exceptions on network failures. + 3. Check the Content-Type header to make sure we got a Simple API response, + and raise `_NotAPIContent` otherwise. + """ + if is_archive_file(Link(url).filename): + _ensure_api_response(url, session=session) + + logger.debug("Getting page %s", redact_auth_from_url(url)) + + resp = session.get( + url, + headers={ + "Accept": ", ".join( + [ + "application/vnd.pypi.simple.v1+json", + "application/vnd.pypi.simple.v1+html; q=0.1", + "text/html; q=0.01", + ] + ), + # We don't want to blindly returned cached data for + # /simple/, because authors generally expecting that + # twine upload && pip install will function, but if + # they've done a pip install in the last ~10 minutes + # it won't. Thus by setting this to zero we will not + # blindly use any cached data, however the benefit of + # using max-age=0 instead of no-cache, is that we will + # still support conditional requests, so we will still + # minimize traffic sent in cases where the page hasn't + # changed at all, we will just always incur the round + # trip for the conditional GET now instead of only + # once per 10 minutes. + # For more information, please see pypa/pip#5670. + "Cache-Control": "max-age=0", + }, + ) + raise_for_status(resp) + + # The check for archives above only works if the url ends with + # something that looks like an archive. However that is not a + # requirement of an url. Unless we issue a HEAD request on every + # url we cannot know ahead of time for sure if something is a + # Simple API response or not. However we can check after we've + # downloaded it. + _ensure_api_header(resp) + + logger.debug( + "Fetched page %s as %s", + redact_auth_from_url(url), + resp.headers.get("Content-Type", "Unknown"), + ) + + return resp + + +def _get_encoding_from_headers(headers: ResponseHeaders) -> Optional[str]: + """Determine if we have any encoding information in our headers.""" + if headers and "Content-Type" in headers: + m = email.message.Message() + m["content-type"] = headers["Content-Type"] + charset = m.get_param("charset") + if charset: + return str(charset) + return None + + +class CacheablePageContent: + def __init__(self, page: "IndexContent") -> None: + assert page.cache_link_parsing + self.page = page + + def __eq__(self, other: object) -> bool: + return isinstance(other, type(self)) and self.page.url == other.page.url + + def __hash__(self) -> int: + return hash(self.page.url) + + +class ParseLinks(Protocol): + def __call__(self, page: "IndexContent") -> Iterable[Link]: ... + + +def with_cached_index_content(fn: ParseLinks) -> ParseLinks: + """ + Given a function that parses an Iterable[Link] from an IndexContent, cache the + function's result (keyed by CacheablePageContent), unless the IndexContent + `page` has `page.cache_link_parsing == False`. + """ + + @functools.lru_cache(maxsize=None) + def wrapper(cacheable_page: CacheablePageContent) -> List[Link]: + return list(fn(cacheable_page.page)) + + @functools.wraps(fn) + def wrapper_wrapper(page: "IndexContent") -> List[Link]: + if page.cache_link_parsing: + return wrapper(CacheablePageContent(page)) + return list(fn(page)) + + return wrapper_wrapper + + +@with_cached_index_content +def parse_links(page: "IndexContent") -> Iterable[Link]: + """ + Parse a Simple API's Index Content, and yield its anchor elements as Link objects. + """ + + content_type_l = page.content_type.lower() + if content_type_l.startswith("application/vnd.pypi.simple.v1+json"): + data = json.loads(page.content) + for file in data.get("files", []): + link = Link.from_json(file, page.url) + if link is None: + continue + yield link + return + + parser = HTMLLinkParser(page.url) + encoding = page.encoding or "utf-8" + parser.feed(page.content.decode(encoding)) + + url = page.url + base_url = parser.base_url or url + for anchor in parser.anchors: + link = Link.from_element(anchor, page_url=url, base_url=base_url) + if link is None: + continue + yield link + + +@dataclass(frozen=True) +class IndexContent: + """Represents one response (or page), along with its URL. + + :param encoding: the encoding to decode the given content. + :param url: the URL from which the HTML was downloaded. + :param cache_link_parsing: whether links parsed from this page's url + should be cached. PyPI index urls should + have this set to False, for example. + """ + + content: bytes + content_type: str + encoding: Optional[str] + url: str + cache_link_parsing: bool = True + + def __str__(self) -> str: + return redact_auth_from_url(self.url) + + +class HTMLLinkParser(HTMLParser): + """ + HTMLParser that keeps the first base HREF and a list of all anchor + elements' attributes. + """ + + def __init__(self, url: str) -> None: + super().__init__(convert_charrefs=True) + + self.url: str = url + self.base_url: Optional[str] = None + self.anchors: List[Dict[str, Optional[str]]] = [] + + def handle_starttag(self, tag: str, attrs: List[Tuple[str, Optional[str]]]) -> None: + if tag == "base" and self.base_url is None: + href = self.get_href(attrs) + if href is not None: + self.base_url = href + elif tag == "a": + self.anchors.append(dict(attrs)) + + def get_href(self, attrs: List[Tuple[str, Optional[str]]]) -> Optional[str]: + for name, value in attrs: + if name == "href": + return value + return None + + +def _handle_get_simple_fail( + link: Link, + reason: Union[str, Exception], + meth: Optional[Callable[..., None]] = None, +) -> None: + if meth is None: + meth = logger.debug + meth("Could not fetch URL %s: %s - skipping", link, reason) + + +def _make_index_content( + response: Response, cache_link_parsing: bool = True +) -> IndexContent: + encoding = _get_encoding_from_headers(response.headers) + return IndexContent( + response.content, + response.headers["Content-Type"], + encoding=encoding, + url=response.url, + cache_link_parsing=cache_link_parsing, + ) + + +def _get_index_content(link: Link, *, session: PipSession) -> Optional["IndexContent"]: + url = link.url.split("#", 1)[0] + + # Check for VCS schemes that do not support lookup as web pages. + vcs_scheme = _match_vcs_scheme(url) + if vcs_scheme: + logger.warning( + "Cannot look at %s URL %s because it does not support lookup as web pages.", + vcs_scheme, + link, + ) + return None + + # Tack index.html onto file:// URLs that point to directories + scheme, _, path, _, _, _ = urllib.parse.urlparse(url) + if scheme == "file" and os.path.isdir(urllib.request.url2pathname(path)): + # add trailing slash if not present so urljoin doesn't trim + # final segment + if not url.endswith("/"): + url += "/" + # TODO: In the future, it would be nice if pip supported PEP 691 + # style responses in the file:// URLs, however there's no + # standard file extension for application/vnd.pypi.simple.v1+json + # so we'll need to come up with something on our own. + url = urllib.parse.urljoin(url, "index.html") + logger.debug(" file: URL is directory, getting %s", url) + + try: + resp = _get_simple_response(url, session=session) + except _NotHTTP: + logger.warning( + "Skipping page %s because it looks like an archive, and cannot " + "be checked by a HTTP HEAD request.", + link, + ) + except _NotAPIContent as exc: + logger.warning( + "Skipping page %s because the %s request got Content-Type: %s. " + "The only supported Content-Types are application/vnd.pypi.simple.v1+json, " + "application/vnd.pypi.simple.v1+html, and text/html", + link, + exc.request_desc, + exc.content_type, + ) + except NetworkConnectionError as exc: + _handle_get_simple_fail(link, exc) + except RetryError as exc: + _handle_get_simple_fail(link, exc) + except SSLError as exc: + reason = "There was a problem confirming the ssl certificate: " + reason += str(exc) + _handle_get_simple_fail(link, reason, meth=logger.info) + except requests.ConnectionError as exc: + _handle_get_simple_fail(link, f"connection error: {exc}") + except requests.Timeout: + _handle_get_simple_fail(link, "timed out") + else: + return _make_index_content(resp, cache_link_parsing=link.cache_link_parsing) + return None + + +class CollectedSources(NamedTuple): + find_links: Sequence[Optional[LinkSource]] + index_urls: Sequence[Optional[LinkSource]] + + +class LinkCollector: + """ + Responsible for collecting Link objects from all configured locations, + making network requests as needed. + + The class's main method is its collect_sources() method. + """ + + def __init__( + self, + session: PipSession, + search_scope: SearchScope, + ) -> None: + self.search_scope = search_scope + self.session = session + + @classmethod + def create( + cls, + session: PipSession, + options: Values, + suppress_no_index: bool = False, + ) -> "LinkCollector": + """ + :param session: The Session to use to make requests. + :param suppress_no_index: Whether to ignore the --no-index option + when constructing the SearchScope object. + """ + index_urls = [options.index_url] + options.extra_index_urls + if options.no_index and not suppress_no_index: + logger.debug( + "Ignoring indexes: %s", + ",".join(redact_auth_from_url(url) for url in index_urls), + ) + index_urls = [] + + # Make sure find_links is a list before passing to create(). + find_links = options.find_links or [] + + search_scope = SearchScope.create( + find_links=find_links, + index_urls=index_urls, + no_index=options.no_index, + ) + link_collector = LinkCollector( + session=session, + search_scope=search_scope, + ) + return link_collector + + @property + def find_links(self) -> List[str]: + return self.search_scope.find_links + + def fetch_response(self, location: Link) -> Optional[IndexContent]: + """ + Fetch an HTML page containing package links. + """ + return _get_index_content(location, session=self.session) + + def collect_sources( + self, + project_name: str, + candidates_from_page: CandidatesFromPage, + ) -> CollectedSources: + # The OrderedDict calls deduplicate sources by URL. + index_url_sources = collections.OrderedDict( + build_source( + loc, + candidates_from_page=candidates_from_page, + page_validator=self.session.is_secure_origin, + expand_dir=False, + cache_link_parsing=False, + project_name=project_name, + ) + for loc in self.search_scope.get_index_urls_locations(project_name) + ).values() + find_links_sources = collections.OrderedDict( + build_source( + loc, + candidates_from_page=candidates_from_page, + page_validator=self.session.is_secure_origin, + expand_dir=True, + cache_link_parsing=True, + project_name=project_name, + ) + for loc in self.find_links + ).values() + + if logger.isEnabledFor(logging.DEBUG): + lines = [ + f"* {s.link}" + for s in itertools.chain(find_links_sources, index_url_sources) + if s is not None and s.link is not None + ] + lines = [ + f"{len(lines)} location(s) to search " + f"for versions of {project_name}:" + ] + lines + logger.debug("\n".join(lines)) + + return CollectedSources( + find_links=list(find_links_sources), + index_urls=list(index_url_sources), + ) diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/operations/__pycache__/__init__.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/operations/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 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update_env_context_manager(**changes: str) -> Generator[None, None, None]: + target = os.environ + + # Save values from the target and change them. + non_existent_marker = object() + saved_values: Dict[str, Union[object, str]] = {} + for name, new_value in changes.items(): + try: + saved_values[name] = target[name] + except KeyError: + saved_values[name] = non_existent_marker + target[name] = new_value + + try: + yield + finally: + # Restore original values in the target. + for name, original_value in saved_values.items(): + if original_value is non_existent_marker: + del target[name] + else: + assert isinstance(original_value, str) # for mypy + target[name] = original_value + + +@contextlib.contextmanager +def get_build_tracker() -> Generator["BuildTracker", None, None]: + root = os.environ.get("PIP_BUILD_TRACKER") + with contextlib.ExitStack() as ctx: + if root is None: + root = ctx.enter_context(TempDirectory(kind="build-tracker")).path + ctx.enter_context(update_env_context_manager(PIP_BUILD_TRACKER=root)) + logger.debug("Initialized build tracking at %s", root) + + with BuildTracker(root) as tracker: + yield tracker + + +class TrackerId(str): + """Uniquely identifying string provided to the build tracker.""" + + +class BuildTracker: + """Ensure that an sdist cannot request itself as a setup requirement. + + When an sdist is prepared, it identifies its setup requirements in the + context of ``BuildTracker.track()``. If a requirement shows up recursively, this + raises an exception. + + This stops fork bombs embedded in malicious packages.""" + + def __init__(self, root: str) -> None: + self._root = root + self._entries: Dict[TrackerId, InstallRequirement] = {} + logger.debug("Created build tracker: %s", self._root) + + def __enter__(self) -> "BuildTracker": + logger.debug("Entered build tracker: %s", self._root) + return self + + def __exit__( + self, + exc_type: Optional[Type[BaseException]], + exc_val: Optional[BaseException], + exc_tb: Optional[TracebackType], + ) -> None: + self.cleanup() + + def _entry_path(self, key: TrackerId) -> str: + hashed = hashlib.sha224(key.encode()).hexdigest() + return os.path.join(self._root, hashed) + + def add(self, req: InstallRequirement, key: TrackerId) -> None: + """Add an InstallRequirement to build tracking.""" + + # Get the file to write information about this requirement. + entry_path = self._entry_path(key) + + # Try reading from the file. If it exists and can be read from, a build + # is already in progress, so a LookupError is raised. + try: + with open(entry_path) as fp: + contents = fp.read() + except FileNotFoundError: + pass + else: + message = f"{req.link} is already being built: {contents}" + raise LookupError(message) + + # If we're here, req should really not be building already. + assert key not in self._entries + + # Start tracking this requirement. + with open(entry_path, "w", encoding="utf-8") as fp: + fp.write(str(req)) + self._entries[key] = req + + logger.debug("Added %s to build tracker %r", req, self._root) + + def remove(self, req: InstallRequirement, key: TrackerId) -> None: + """Remove an InstallRequirement from build tracking.""" + + # Delete the created file and the corresponding entry. + os.unlink(self._entry_path(key)) + del self._entries[key] + + logger.debug("Removed %s from build tracker %r", req, self._root) + + def cleanup(self) -> None: + for key, req in list(self._entries.items()): + self.remove(req, key) + + logger.debug("Removed build tracker: %r", self._root) + + @contextlib.contextmanager + def track(self, req: InstallRequirement, key: str) -> Generator[None, None, None]: + """Ensure that `key` cannot install itself as a setup requirement. + + :raises LookupError: If `key` was already provided in a parent invocation of + the context introduced by this method.""" + tracker_id = TrackerId(key) + self.add(req, tracker_id) + yield + self.remove(req, tracker_id) diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/operations/build/wheel.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/operations/build/wheel.py new file mode 100644 index 0000000000000000000000000000000000000000..064811ad11bb07b2b7bc8e30ec6c03f21997d6b2 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/operations/build/wheel.py @@ -0,0 +1,37 @@ +import logging +import os +from typing import Optional + +from pip._vendor.pyproject_hooks import BuildBackendHookCaller + +from pip._internal.utils.subprocess import runner_with_spinner_message + +logger = logging.getLogger(__name__) + + +def build_wheel_pep517( + name: str, + backend: BuildBackendHookCaller, + metadata_directory: str, + tempd: str, +) -> Optional[str]: + """Build one InstallRequirement using the PEP 517 build process. + + Returns path to wheel if successfully built. Otherwise, returns None. + """ + assert metadata_directory is not None + try: + logger.debug("Destination directory: %s", tempd) + + runner = runner_with_spinner_message( + f"Building wheel for {name} (pyproject.toml)" + ) + with backend.subprocess_runner(runner): + wheel_name = backend.build_wheel( + tempd, + metadata_directory=metadata_directory, + ) + except Exception: + logger.error("Failed building wheel for %s", name) + return None + return os.path.join(tempd, wheel_name) diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/req/__pycache__/constructors.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/req/__pycache__/constructors.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d9d2c19dc5faba1fc318e541b41f907fb9472f97 Binary files /dev/null and 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size +so, moving them and support code dedicated to them outside of that class +helps creates for better understandability for the rest of the code. + +These are meant to be used elsewhere within pip to create instances of +InstallRequirement. +""" + +import copy +import logging +import os +import re +from dataclasses import dataclass +from typing import Collection, Dict, List, Optional, Set, Tuple, Union + +from pip._vendor.packaging.markers import Marker +from pip._vendor.packaging.requirements import InvalidRequirement, Requirement +from pip._vendor.packaging.specifiers import Specifier + +from pip._internal.exceptions import InstallationError +from pip._internal.models.index import PyPI, TestPyPI +from pip._internal.models.link import Link +from pip._internal.models.wheel import Wheel +from pip._internal.req.req_file import ParsedRequirement +from pip._internal.req.req_install import InstallRequirement +from pip._internal.utils.filetypes import is_archive_file +from pip._internal.utils.misc import is_installable_dir +from pip._internal.utils.packaging import get_requirement +from pip._internal.utils.urls import path_to_url +from pip._internal.vcs import is_url, vcs + +__all__ = [ + "install_req_from_editable", + "install_req_from_line", + "parse_editable", +] + +logger = logging.getLogger(__name__) +operators = Specifier._operators.keys() + + +def _strip_extras(path: str) -> Tuple[str, Optional[str]]: + m = re.match(r"^(.+)(\[[^\]]+\])$", path) + extras = None + if m: + path_no_extras = m.group(1) + extras = m.group(2) + else: + path_no_extras = path + + return path_no_extras, extras + + +def convert_extras(extras: Optional[str]) -> Set[str]: + if not extras: + return set() + return get_requirement("placeholder" + extras.lower()).extras + + +def _set_requirement_extras(req: Requirement, new_extras: Set[str]) -> Requirement: + """ + Returns a new requirement based on the given one, with the supplied extras. If the + given requirement already has extras those are replaced (or dropped if no new extras + are given). + """ + match: Optional[re.Match[str]] = re.fullmatch( + # see https://peps.python.org/pep-0508/#complete-grammar + r"([\w\t .-]+)(\[[^\]]*\])?(.*)", + str(req), + flags=re.ASCII, + ) + # ireq.req is a valid requirement so the regex should always match + assert ( + match is not None + ), f"regex match on requirement {req} failed, this should never happen" + pre: Optional[str] = match.group(1) + post: Optional[str] = match.group(3) + assert ( + pre is not None and post is not None + ), f"regex group selection for requirement {req} failed, this should never happen" + extras: str = "[{}]".format(",".join(sorted(new_extras)) if new_extras else "") + return get_requirement(f"{pre}{extras}{post}") + + +def parse_editable(editable_req: str) -> Tuple[Optional[str], str, Set[str]]: + """Parses an editable requirement into: + - a requirement name + - an URL + - extras + - editable options + Accepted requirements: + svn+http://blahblah@rev#egg=Foobar[baz]&subdirectory=version_subdir + .[some_extra] + """ + + url = editable_req + + # If a file path is specified with extras, strip off the extras. + url_no_extras, extras = _strip_extras(url) + + if os.path.isdir(url_no_extras): + # Treating it as code that has already been checked out + url_no_extras = path_to_url(url_no_extras) + + if url_no_extras.lower().startswith("file:"): + package_name = Link(url_no_extras).egg_fragment + if extras: + return ( + package_name, + url_no_extras, + get_requirement("placeholder" + extras.lower()).extras, + ) + else: + return package_name, url_no_extras, set() + + for version_control in vcs: + if url.lower().startswith(f"{version_control}:"): + url = f"{version_control}+{url}" + break + + link = Link(url) + + if not link.is_vcs: + backends = ", ".join(vcs.all_schemes) + raise InstallationError( + f"{editable_req} is not a valid editable requirement. " + f"It should either be a path to a local project or a VCS URL " + f"(beginning with {backends})." + ) + + package_name = link.egg_fragment + if not package_name: + raise InstallationError( + f"Could not detect requirement name for '{editable_req}', " + "please specify one with #egg=your_package_name" + ) + return package_name, url, set() + + +def check_first_requirement_in_file(filename: str) -> None: + """Check if file is parsable as a requirements file. + + This is heavily based on ``pkg_resources.parse_requirements``, but + simplified to just check the first meaningful line. + + :raises InvalidRequirement: If the first meaningful line cannot be parsed + as an requirement. + """ + with open(filename, encoding="utf-8", errors="ignore") as f: + # Create a steppable iterator, so we can handle \-continuations. + lines = ( + line + for line in (line.strip() for line in f) + if line and not line.startswith("#") # Skip blank lines/comments. + ) + + for line in lines: + # Drop comments -- a hash without a space may be in a URL. + if " #" in line: + line = line[: line.find(" #")] + # If there is a line continuation, drop it, and append the next line. + if line.endswith("\\"): + line = line[:-2].strip() + next(lines, "") + get_requirement(line) + return + + +def deduce_helpful_msg(req: str) -> str: + """Returns helpful msg in case requirements file does not exist, + or cannot be parsed. + + :params req: Requirements file path + """ + if not os.path.exists(req): + return f" File '{req}' does not exist." + msg = " The path does exist. " + # Try to parse and check if it is a requirements file. + try: + check_first_requirement_in_file(req) + except InvalidRequirement: + logger.debug("Cannot parse '%s' as requirements file", req) + else: + msg += ( + f"The argument you provided " + f"({req}) appears to be a" + f" requirements file. If that is the" + f" case, use the '-r' flag to install" + f" the packages specified within it." + ) + return msg + + +@dataclass(frozen=True) +class RequirementParts: + requirement: Optional[Requirement] + link: Optional[Link] + markers: Optional[Marker] + extras: Set[str] + + +def parse_req_from_editable(editable_req: str) -> RequirementParts: + name, url, extras_override = parse_editable(editable_req) + + if name is not None: + try: + req: Optional[Requirement] = get_requirement(name) + except InvalidRequirement as exc: + raise InstallationError(f"Invalid requirement: {name!r}: {exc}") + else: + req = None + + link = Link(url) + + return RequirementParts(req, link, None, extras_override) + + +# ---- The actual constructors follow ---- + + +def install_req_from_editable( + editable_req: str, + comes_from: Optional[Union[InstallRequirement, str]] = None, + *, + use_pep517: Optional[bool] = None, + isolated: bool = False, + global_options: Optional[List[str]] = None, + hash_options: Optional[Dict[str, List[str]]] = None, + constraint: bool = False, + user_supplied: bool = False, + permit_editable_wheels: bool = False, + config_settings: Optional[Dict[str, Union[str, List[str]]]] = None, +) -> InstallRequirement: + parts = parse_req_from_editable(editable_req) + + return InstallRequirement( + parts.requirement, + comes_from=comes_from, + user_supplied=user_supplied, + editable=True, + permit_editable_wheels=permit_editable_wheels, + link=parts.link, + constraint=constraint, + use_pep517=use_pep517, + isolated=isolated, + global_options=global_options, + hash_options=hash_options, + config_settings=config_settings, + extras=parts.extras, + ) + + +def _looks_like_path(name: str) -> bool: + """Checks whether the string "looks like" a path on the filesystem. + + This does not check whether the target actually exists, only judge from the + appearance. + + Returns true if any of the following conditions is true: + * a path separator is found (either os.path.sep or os.path.altsep); + * a dot is found (which represents the current directory). + """ + if os.path.sep in name: + return True + if os.path.altsep is not None and os.path.altsep in name: + return True + if name.startswith("."): + return True + return False + + +def _get_url_from_path(path: str, name: str) -> Optional[str]: + """ + First, it checks whether a provided path is an installable directory. If it + is, returns the path. + + If false, check if the path is an archive file (such as a .whl). + The function checks if the path is a file. If false, if the path has + an @, it will treat it as a PEP 440 URL requirement and return the path. + """ + if _looks_like_path(name) and os.path.isdir(path): + if is_installable_dir(path): + return path_to_url(path) + # TODO: The is_installable_dir test here might not be necessary + # now that it is done in load_pyproject_toml too. + raise InstallationError( + f"Directory {name!r} is not installable. Neither 'setup.py' " + "nor 'pyproject.toml' found." + ) + if not is_archive_file(path): + return None + if os.path.isfile(path): + return path_to_url(path) + urlreq_parts = name.split("@", 1) + if len(urlreq_parts) >= 2 and not _looks_like_path(urlreq_parts[0]): + # If the path contains '@' and the part before it does not look + # like a path, try to treat it as a PEP 440 URL req instead. + return None + logger.warning( + "Requirement %r looks like a filename, but the file does not exist", + name, + ) + return path_to_url(path) + + +def parse_req_from_line(name: str, line_source: Optional[str]) -> RequirementParts: + if is_url(name): + marker_sep = "; " + else: + marker_sep = ";" + if marker_sep in name: + name, markers_as_string = name.split(marker_sep, 1) + markers_as_string = markers_as_string.strip() + if not markers_as_string: + markers = None + else: + markers = Marker(markers_as_string) + else: + markers = None + name = name.strip() + req_as_string = None + path = os.path.normpath(os.path.abspath(name)) + link = None + extras_as_string = None + + if is_url(name): + link = Link(name) + else: + p, extras_as_string = _strip_extras(path) + url = _get_url_from_path(p, name) + if url is not None: + link = Link(url) + + # it's a local file, dir, or url + if link: + # Handle relative file URLs + if link.scheme == "file" and re.search(r"\.\./", link.url): + link = Link(path_to_url(os.path.normpath(os.path.abspath(link.path)))) + # wheel file + if link.is_wheel: + wheel = Wheel(link.filename) # can raise InvalidWheelFilename + req_as_string = f"{wheel.name}=={wheel.version}" + else: + # set the req to the egg fragment. when it's not there, this + # will become an 'unnamed' requirement + req_as_string = link.egg_fragment + + # a requirement specifier + else: + req_as_string = name + + extras = convert_extras(extras_as_string) + + def with_source(text: str) -> str: + if not line_source: + return text + return f"{text} (from {line_source})" + + def _parse_req_string(req_as_string: str) -> Requirement: + try: + return get_requirement(req_as_string) + except InvalidRequirement as exc: + if os.path.sep in req_as_string: + add_msg = "It looks like a path." + add_msg += deduce_helpful_msg(req_as_string) + elif "=" in req_as_string and not any( + op in req_as_string for op in operators + ): + add_msg = "= is not a valid operator. Did you mean == ?" + else: + add_msg = "" + msg = with_source(f"Invalid requirement: {req_as_string!r}: {exc}") + if add_msg: + msg += f"\nHint: {add_msg}" + raise InstallationError(msg) + + if req_as_string is not None: + req: Optional[Requirement] = _parse_req_string(req_as_string) + else: + req = None + + return RequirementParts(req, link, markers, extras) + + +def install_req_from_line( + name: str, + comes_from: Optional[Union[str, InstallRequirement]] = None, + *, + use_pep517: Optional[bool] = None, + isolated: bool = False, + global_options: Optional[List[str]] = None, + hash_options: Optional[Dict[str, List[str]]] = None, + constraint: bool = False, + line_source: Optional[str] = None, + user_supplied: bool = False, + config_settings: Optional[Dict[str, Union[str, List[str]]]] = None, +) -> InstallRequirement: + """Creates an InstallRequirement from a name, which might be a + requirement, directory containing 'setup.py', filename, or URL. + + :param line_source: An optional string describing where the line is from, + for logging purposes in case of an error. + """ + parts = parse_req_from_line(name, line_source) + + return InstallRequirement( + parts.requirement, + comes_from, + link=parts.link, + markers=parts.markers, + use_pep517=use_pep517, + isolated=isolated, + global_options=global_options, + hash_options=hash_options, + config_settings=config_settings, + constraint=constraint, + extras=parts.extras, + user_supplied=user_supplied, + ) + + +def install_req_from_req_string( + req_string: str, + comes_from: Optional[InstallRequirement] = None, + isolated: bool = False, + use_pep517: Optional[bool] = None, + user_supplied: bool = False, +) -> InstallRequirement: + try: + req = get_requirement(req_string) + except InvalidRequirement as exc: + raise InstallationError(f"Invalid requirement: {req_string!r}: {exc}") + + domains_not_allowed = [ + PyPI.file_storage_domain, + TestPyPI.file_storage_domain, + ] + if ( + req.url + and comes_from + and comes_from.link + and comes_from.link.netloc in domains_not_allowed + ): + # Explicitly disallow pypi packages that depend on external urls + raise InstallationError( + "Packages installed from PyPI cannot depend on packages " + "which are not also hosted on PyPI.\n" + f"{comes_from.name} depends on {req} " + ) + + return InstallRequirement( + req, + comes_from, + isolated=isolated, + use_pep517=use_pep517, + user_supplied=user_supplied, + ) + + +def install_req_from_parsed_requirement( + parsed_req: ParsedRequirement, + isolated: bool = False, + use_pep517: Optional[bool] = None, + user_supplied: bool = False, + config_settings: Optional[Dict[str, Union[str, List[str]]]] = None, +) -> InstallRequirement: + if parsed_req.is_editable: + req = install_req_from_editable( + parsed_req.requirement, + comes_from=parsed_req.comes_from, + use_pep517=use_pep517, + constraint=parsed_req.constraint, + isolated=isolated, + user_supplied=user_supplied, + config_settings=config_settings, + ) + + else: + req = install_req_from_line( + parsed_req.requirement, + comes_from=parsed_req.comes_from, + use_pep517=use_pep517, + isolated=isolated, + global_options=( + parsed_req.options.get("global_options", []) + if parsed_req.options + else [] + ), + hash_options=( + parsed_req.options.get("hashes", {}) if parsed_req.options else {} + ), + constraint=parsed_req.constraint, + line_source=parsed_req.line_source, + user_supplied=user_supplied, + config_settings=config_settings, + ) + return req + + +def install_req_from_link_and_ireq( + link: Link, ireq: InstallRequirement +) -> InstallRequirement: + return InstallRequirement( + req=ireq.req, + comes_from=ireq.comes_from, + editable=ireq.editable, + link=link, + markers=ireq.markers, + use_pep517=ireq.use_pep517, + isolated=ireq.isolated, + global_options=ireq.global_options, + hash_options=ireq.hash_options, + config_settings=ireq.config_settings, + user_supplied=ireq.user_supplied, + ) + + +def install_req_drop_extras(ireq: InstallRequirement) -> InstallRequirement: + """ + Creates a new InstallationRequirement using the given template but without + any extras. Sets the original requirement as the new one's parent + (comes_from). + """ + return InstallRequirement( + req=( + _set_requirement_extras(ireq.req, set()) if ireq.req is not None else None + ), + comes_from=ireq, + editable=ireq.editable, + link=ireq.link, + markers=ireq.markers, + use_pep517=ireq.use_pep517, + isolated=ireq.isolated, + global_options=ireq.global_options, + hash_options=ireq.hash_options, + constraint=ireq.constraint, + extras=[], + config_settings=ireq.config_settings, + user_supplied=ireq.user_supplied, + permit_editable_wheels=ireq.permit_editable_wheels, + ) + + +def install_req_extend_extras( + ireq: InstallRequirement, + extras: Collection[str], +) -> InstallRequirement: + """ + Returns a copy of an installation requirement with some additional extras. + Makes a shallow copy of the ireq object. + """ + result = copy.copy(ireq) + result.extras = {*ireq.extras, *extras} + result.req = ( + _set_requirement_extras(ireq.req, result.extras) + if ireq.req is not None + else None + ) + return result diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/req/req_set.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/req/req_set.py new file mode 100644 index 0000000000000000000000000000000000000000..ec7a6e07a25acfa978030c65ae7c1d8609163249 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/req/req_set.py @@ -0,0 +1,82 @@ +import logging +from collections import OrderedDict +from typing import Dict, List + +from pip._vendor.packaging.utils import canonicalize_name + +from pip._internal.req.req_install import InstallRequirement + +logger = logging.getLogger(__name__) + + +class RequirementSet: + def __init__(self, check_supported_wheels: bool = True) -> None: + """Create a RequirementSet.""" + + self.requirements: Dict[str, InstallRequirement] = OrderedDict() + self.check_supported_wheels = check_supported_wheels + + self.unnamed_requirements: List[InstallRequirement] = [] + + def __str__(self) -> str: + requirements = sorted( + (req for req in self.requirements.values() if not req.comes_from), + key=lambda req: canonicalize_name(req.name or ""), + ) + return " ".join(str(req.req) for req in requirements) + + def __repr__(self) -> str: + requirements = sorted( + self.requirements.values(), + key=lambda req: canonicalize_name(req.name or ""), + ) + + format_string = "<{classname} object; {count} requirement(s): {reqs}>" + return format_string.format( + classname=self.__class__.__name__, + count=len(requirements), + reqs=", ".join(str(req.req) for req in requirements), + ) + + def add_unnamed_requirement(self, install_req: InstallRequirement) -> None: + assert not install_req.name + self.unnamed_requirements.append(install_req) + + def add_named_requirement(self, install_req: InstallRequirement) -> None: + assert install_req.name + + project_name = canonicalize_name(install_req.name) + self.requirements[project_name] = install_req + + def has_requirement(self, name: str) -> bool: + project_name = canonicalize_name(name) + + return ( + project_name in self.requirements + and not self.requirements[project_name].constraint + ) + + def get_requirement(self, name: str) -> InstallRequirement: + project_name = canonicalize_name(name) + + if project_name in self.requirements: + return self.requirements[project_name] + + raise KeyError(f"No project with the name {name!r}") + + @property + def all_requirements(self) -> List[InstallRequirement]: + return self.unnamed_requirements + list(self.requirements.values()) + + @property + def requirements_to_install(self) -> List[InstallRequirement]: + """Return the list of requirements that need to be installed. + + TODO remove this property together with the legacy resolver, since the new + resolver only returns requirements that need to be installed. + """ + return [ + install_req + for install_req in self.all_requirements + if not install_req.constraint and not install_req.satisfied_by + ] diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/req/req_uninstall.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/req/req_uninstall.py new file mode 100644 index 0000000000000000000000000000000000000000..26df20844b379bd7c06f3db7d75cddad5755d22e --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/req/req_uninstall.py @@ -0,0 +1,633 @@ +import functools +import os +import sys +import sysconfig +from importlib.util import cache_from_source +from typing import Any, Callable, Dict, Generator, Iterable, List, Optional, Set, Tuple + +from pip._internal.exceptions import LegacyDistutilsInstall, UninstallMissingRecord +from pip._internal.locations import get_bin_prefix, get_bin_user +from pip._internal.metadata import BaseDistribution +from pip._internal.utils.compat import WINDOWS +from pip._internal.utils.egg_link import egg_link_path_from_location +from pip._internal.utils.logging import getLogger, indent_log +from pip._internal.utils.misc import ask, normalize_path, renames, rmtree +from pip._internal.utils.temp_dir import AdjacentTempDirectory, TempDirectory +from pip._internal.utils.virtualenv import running_under_virtualenv + +logger = getLogger(__name__) + + +def _script_names( + bin_dir: str, script_name: str, is_gui: bool +) -> Generator[str, None, None]: + """Create the fully qualified name of the files created by + {console,gui}_scripts for the given ``dist``. + Returns the list of file names + """ + exe_name = os.path.join(bin_dir, script_name) + yield exe_name + if not WINDOWS: + return + yield f"{exe_name}.exe" + yield f"{exe_name}.exe.manifest" + if is_gui: + yield f"{exe_name}-script.pyw" + else: + yield f"{exe_name}-script.py" + + +def _unique( + fn: Callable[..., Generator[Any, None, None]] +) -> Callable[..., Generator[Any, None, None]]: + @functools.wraps(fn) + def unique(*args: Any, **kw: Any) -> Generator[Any, None, None]: + seen: Set[Any] = set() + for item in fn(*args, **kw): + if item not in seen: + seen.add(item) + yield item + + return unique + + +@_unique +def uninstallation_paths(dist: BaseDistribution) -> Generator[str, None, None]: + """ + Yield all the uninstallation paths for dist based on RECORD-without-.py[co] + + Yield paths to all the files in RECORD. For each .py file in RECORD, add + the .pyc and .pyo in the same directory. + + UninstallPathSet.add() takes care of the __pycache__ .py[co]. + + If RECORD is not found, raises an error, + with possible information from the INSTALLER file. + + https://packaging.python.org/specifications/recording-installed-packages/ + """ + location = dist.location + assert location is not None, "not installed" + + entries = dist.iter_declared_entries() + if entries is None: + raise UninstallMissingRecord(distribution=dist) + + for entry in entries: + path = os.path.join(location, entry) + yield path + if path.endswith(".py"): + dn, fn = os.path.split(path) + base = fn[:-3] + path = os.path.join(dn, base + ".pyc") + yield path + path = os.path.join(dn, base + ".pyo") + yield path + + +def compact(paths: Iterable[str]) -> Set[str]: + """Compact a path set to contain the minimal number of paths + necessary to contain all paths in the set. If /a/path/ and + /a/path/to/a/file.txt are both in the set, leave only the + shorter path.""" + + sep = os.path.sep + short_paths: Set[str] = set() + for path in sorted(paths, key=len): + should_skip = any( + path.startswith(shortpath.rstrip("*")) + and path[len(shortpath.rstrip("*").rstrip(sep))] == sep + for shortpath in short_paths + ) + if not should_skip: + short_paths.add(path) + return short_paths + + +def compress_for_rename(paths: Iterable[str]) -> Set[str]: + """Returns a set containing the paths that need to be renamed. + + This set may include directories when the original sequence of paths + included every file on disk. + """ + case_map = {os.path.normcase(p): p for p in paths} + remaining = set(case_map) + unchecked = sorted({os.path.split(p)[0] for p in case_map.values()}, key=len) + wildcards: Set[str] = set() + + def norm_join(*a: str) -> str: + return os.path.normcase(os.path.join(*a)) + + for root in unchecked: + if any(os.path.normcase(root).startswith(w) for w in wildcards): + # This directory has already been handled. + continue + + all_files: Set[str] = set() + all_subdirs: Set[str] = set() + for dirname, subdirs, files in os.walk(root): + all_subdirs.update(norm_join(root, dirname, d) for d in subdirs) + all_files.update(norm_join(root, dirname, f) for f in files) + # If all the files we found are in our remaining set of files to + # remove, then remove them from the latter set and add a wildcard + # for the directory. + if not (all_files - remaining): + remaining.difference_update(all_files) + wildcards.add(root + os.sep) + + return set(map(case_map.__getitem__, remaining)) | wildcards + + +def compress_for_output_listing(paths: Iterable[str]) -> Tuple[Set[str], Set[str]]: + """Returns a tuple of 2 sets of which paths to display to user + + The first set contains paths that would be deleted. Files of a package + are not added and the top-level directory of the package has a '*' added + at the end - to signify that all it's contents are removed. + + The second set contains files that would have been skipped in the above + folders. + """ + + will_remove = set(paths) + will_skip = set() + + # Determine folders and files + folders = set() + files = set() + for path in will_remove: + if path.endswith(".pyc"): + continue + if path.endswith("__init__.py") or ".dist-info" in path: + folders.add(os.path.dirname(path)) + files.add(path) + + _normcased_files = set(map(os.path.normcase, files)) + + folders = compact(folders) + + # This walks the tree using os.walk to not miss extra folders + # that might get added. + for folder in folders: + for dirpath, _, dirfiles in os.walk(folder): + for fname in dirfiles: + if fname.endswith(".pyc"): + continue + + file_ = os.path.join(dirpath, fname) + if ( + os.path.isfile(file_) + and os.path.normcase(file_) not in _normcased_files + ): + # We are skipping this file. Add it to the set. + will_skip.add(file_) + + will_remove = files | {os.path.join(folder, "*") for folder in folders} + + return will_remove, will_skip + + +class StashedUninstallPathSet: + """A set of file rename operations to stash files while + tentatively uninstalling them.""" + + def __init__(self) -> None: + # Mapping from source file root to [Adjacent]TempDirectory + # for files under that directory. + self._save_dirs: Dict[str, TempDirectory] = {} + # (old path, new path) tuples for each move that may need + # to be undone. + self._moves: List[Tuple[str, str]] = [] + + def _get_directory_stash(self, path: str) -> str: + """Stashes a directory. + + Directories are stashed adjacent to their original location if + possible, or else moved/copied into the user's temp dir.""" + + try: + save_dir: TempDirectory = AdjacentTempDirectory(path) + except OSError: + save_dir = TempDirectory(kind="uninstall") + self._save_dirs[os.path.normcase(path)] = save_dir + + return save_dir.path + + def _get_file_stash(self, path: str) -> str: + """Stashes a file. + + If no root has been provided, one will be created for the directory + in the user's temp directory.""" + path = os.path.normcase(path) + head, old_head = os.path.dirname(path), None + save_dir = None + + while head != old_head: + try: + save_dir = self._save_dirs[head] + break + except KeyError: + pass + head, old_head = os.path.dirname(head), head + else: + # Did not find any suitable root + head = os.path.dirname(path) + save_dir = TempDirectory(kind="uninstall") + self._save_dirs[head] = save_dir + + relpath = os.path.relpath(path, head) + if relpath and relpath != os.path.curdir: + return os.path.join(save_dir.path, relpath) + return save_dir.path + + def stash(self, path: str) -> str: + """Stashes the directory or file and returns its new location. + Handle symlinks as files to avoid modifying the symlink targets. + """ + path_is_dir = os.path.isdir(path) and not os.path.islink(path) + if path_is_dir: + new_path = self._get_directory_stash(path) + else: + new_path = self._get_file_stash(path) + + self._moves.append((path, new_path)) + if path_is_dir and os.path.isdir(new_path): + # If we're moving a directory, we need to + # remove the destination first or else it will be + # moved to inside the existing directory. + # We just created new_path ourselves, so it will + # be removable. + os.rmdir(new_path) + renames(path, new_path) + return new_path + + def commit(self) -> None: + """Commits the uninstall by removing stashed files.""" + for save_dir in self._save_dirs.values(): + save_dir.cleanup() + self._moves = [] + self._save_dirs = {} + + def rollback(self) -> None: + """Undoes the uninstall by moving stashed files back.""" + for p in self._moves: + logger.info("Moving to %s\n from %s", *p) + + for new_path, path in self._moves: + try: + logger.debug("Replacing %s from %s", new_path, path) + if os.path.isfile(new_path) or os.path.islink(new_path): + os.unlink(new_path) + elif os.path.isdir(new_path): + rmtree(new_path) + renames(path, new_path) + except OSError as ex: + logger.error("Failed to restore %s", new_path) + logger.debug("Exception: %s", ex) + + self.commit() + + @property + def can_rollback(self) -> bool: + return bool(self._moves) + + +class UninstallPathSet: + """A set of file paths to be removed in the uninstallation of a + requirement.""" + + def __init__(self, dist: BaseDistribution) -> None: + self._paths: Set[str] = set() + self._refuse: Set[str] = set() + self._pth: Dict[str, UninstallPthEntries] = {} + self._dist = dist + self._moved_paths = StashedUninstallPathSet() + # Create local cache of normalize_path results. Creating an UninstallPathSet + # can result in hundreds/thousands of redundant calls to normalize_path with + # the same args, which hurts performance. + self._normalize_path_cached = functools.lru_cache(normalize_path) + + def _permitted(self, path: str) -> bool: + """ + Return True if the given path is one we are permitted to + remove/modify, False otherwise. + + """ + # aka is_local, but caching normalized sys.prefix + if not running_under_virtualenv(): + return True + return path.startswith(self._normalize_path_cached(sys.prefix)) + + def add(self, path: str) -> None: + head, tail = os.path.split(path) + + # we normalize the head to resolve parent directory symlinks, but not + # the tail, since we only want to uninstall symlinks, not their targets + path = os.path.join(self._normalize_path_cached(head), os.path.normcase(tail)) + + if not os.path.exists(path): + return + if self._permitted(path): + self._paths.add(path) + else: + self._refuse.add(path) + + # __pycache__ files can show up after 'installed-files.txt' is created, + # due to imports + if os.path.splitext(path)[1] == ".py": + self.add(cache_from_source(path)) + + def add_pth(self, pth_file: str, entry: str) -> None: + pth_file = self._normalize_path_cached(pth_file) + if self._permitted(pth_file): + if pth_file not in self._pth: + self._pth[pth_file] = UninstallPthEntries(pth_file) + self._pth[pth_file].add(entry) + else: + self._refuse.add(pth_file) + + def remove(self, auto_confirm: bool = False, verbose: bool = False) -> None: + """Remove paths in ``self._paths`` with confirmation (unless + ``auto_confirm`` is True).""" + + if not self._paths: + logger.info( + "Can't uninstall '%s'. No files were found to uninstall.", + self._dist.raw_name, + ) + return + + dist_name_version = f"{self._dist.raw_name}-{self._dist.raw_version}" + logger.info("Uninstalling %s:", dist_name_version) + + with indent_log(): + if auto_confirm or self._allowed_to_proceed(verbose): + moved = self._moved_paths + + for_rename = compress_for_rename(self._paths) + + for path in sorted(compact(for_rename)): + moved.stash(path) + logger.verbose("Removing file or directory %s", path) + + for pth in self._pth.values(): + pth.remove() + + logger.info("Successfully uninstalled %s", dist_name_version) + + def _allowed_to_proceed(self, verbose: bool) -> bool: + """Display which files would be deleted and prompt for confirmation""" + + def _display(msg: str, paths: Iterable[str]) -> None: + if not paths: + return + + logger.info(msg) + with indent_log(): + for path in sorted(compact(paths)): + logger.info(path) + + if not verbose: + will_remove, will_skip = compress_for_output_listing(self._paths) + else: + # In verbose mode, display all the files that are going to be + # deleted. + will_remove = set(self._paths) + will_skip = set() + + _display("Would remove:", will_remove) + _display("Would not remove (might be manually added):", will_skip) + _display("Would not remove (outside of prefix):", self._refuse) + if verbose: + _display("Will actually move:", compress_for_rename(self._paths)) + + return ask("Proceed (Y/n)? ", ("y", "n", "")) != "n" + + def rollback(self) -> None: + """Rollback the changes previously made by remove().""" + if not self._moved_paths.can_rollback: + logger.error( + "Can't roll back %s; was not uninstalled", + self._dist.raw_name, + ) + return + logger.info("Rolling back uninstall of %s", self._dist.raw_name) + self._moved_paths.rollback() + for pth in self._pth.values(): + pth.rollback() + + def commit(self) -> None: + """Remove temporary save dir: rollback will no longer be possible.""" + self._moved_paths.commit() + + @classmethod + def from_dist(cls, dist: BaseDistribution) -> "UninstallPathSet": + dist_location = dist.location + info_location = dist.info_location + if dist_location is None: + logger.info( + "Not uninstalling %s since it is not installed", + dist.canonical_name, + ) + return cls(dist) + + normalized_dist_location = normalize_path(dist_location) + if not dist.local: + logger.info( + "Not uninstalling %s at %s, outside environment %s", + dist.canonical_name, + normalized_dist_location, + sys.prefix, + ) + return cls(dist) + + if normalized_dist_location in { + p + for p in {sysconfig.get_path("stdlib"), sysconfig.get_path("platstdlib")} + if p + }: + logger.info( + "Not uninstalling %s at %s, as it is in the standard library.", + dist.canonical_name, + normalized_dist_location, + ) + return cls(dist) + + paths_to_remove = cls(dist) + develop_egg_link = egg_link_path_from_location(dist.raw_name) + + # Distribution is installed with metadata in a "flat" .egg-info + # directory. This means it is not a modern .dist-info installation, an + # egg, or legacy editable. + setuptools_flat_installation = ( + dist.installed_with_setuptools_egg_info + and info_location is not None + and os.path.exists(info_location) + # If dist is editable and the location points to a ``.egg-info``, + # we are in fact in the legacy editable case. + and not info_location.endswith(f"{dist.setuptools_filename}.egg-info") + ) + + # Uninstall cases order do matter as in the case of 2 installs of the + # same package, pip needs to uninstall the currently detected version + if setuptools_flat_installation: + if info_location is not None: + paths_to_remove.add(info_location) + installed_files = dist.iter_declared_entries() + if installed_files is not None: + for installed_file in installed_files: + paths_to_remove.add(os.path.join(dist_location, installed_file)) + # FIXME: need a test for this elif block + # occurs with --single-version-externally-managed/--record outside + # of pip + elif dist.is_file("top_level.txt"): + try: + namespace_packages = dist.read_text("namespace_packages.txt") + except FileNotFoundError: + namespaces = [] + else: + namespaces = namespace_packages.splitlines(keepends=False) + for top_level_pkg in [ + p + for p in dist.read_text("top_level.txt").splitlines() + if p and p not in namespaces + ]: + path = os.path.join(dist_location, top_level_pkg) + paths_to_remove.add(path) + paths_to_remove.add(f"{path}.py") + paths_to_remove.add(f"{path}.pyc") + paths_to_remove.add(f"{path}.pyo") + + elif dist.installed_by_distutils: + raise LegacyDistutilsInstall(distribution=dist) + + elif dist.installed_as_egg: + # package installed by easy_install + # We cannot match on dist.egg_name because it can slightly vary + # i.e. setuptools-0.6c11-py2.6.egg vs setuptools-0.6rc11-py2.6.egg + paths_to_remove.add(dist_location) + easy_install_egg = os.path.split(dist_location)[1] + easy_install_pth = os.path.join( + os.path.dirname(dist_location), + "easy-install.pth", + ) + paths_to_remove.add_pth(easy_install_pth, "./" + easy_install_egg) + + elif dist.installed_with_dist_info: + for path in uninstallation_paths(dist): + paths_to_remove.add(path) + + elif develop_egg_link: + # PEP 660 modern editable is handled in the ``.dist-info`` case + # above, so this only covers the setuptools-style editable. + with open(develop_egg_link) as fh: + link_pointer = os.path.normcase(fh.readline().strip()) + normalized_link_pointer = paths_to_remove._normalize_path_cached( + link_pointer + ) + assert os.path.samefile( + normalized_link_pointer, normalized_dist_location + ), ( + f"Egg-link {develop_egg_link} (to {link_pointer}) does not match " + f"installed location of {dist.raw_name} (at {dist_location})" + ) + paths_to_remove.add(develop_egg_link) + easy_install_pth = os.path.join( + os.path.dirname(develop_egg_link), "easy-install.pth" + ) + paths_to_remove.add_pth(easy_install_pth, dist_location) + + else: + logger.debug( + "Not sure how to uninstall: %s - Check: %s", + dist, + dist_location, + ) + + if dist.in_usersite: + bin_dir = get_bin_user() + else: + bin_dir = get_bin_prefix() + + # find distutils scripts= scripts + try: + for script in dist.iter_distutils_script_names(): + paths_to_remove.add(os.path.join(bin_dir, script)) + if WINDOWS: + paths_to_remove.add(os.path.join(bin_dir, f"{script}.bat")) + except (FileNotFoundError, NotADirectoryError): + pass + + # find console_scripts and gui_scripts + def iter_scripts_to_remove( + dist: BaseDistribution, + bin_dir: str, + ) -> Generator[str, None, None]: + for entry_point in dist.iter_entry_points(): + if entry_point.group == "console_scripts": + yield from _script_names(bin_dir, entry_point.name, False) + elif entry_point.group == "gui_scripts": + yield from _script_names(bin_dir, entry_point.name, True) + + for s in iter_scripts_to_remove(dist, bin_dir): + paths_to_remove.add(s) + + return paths_to_remove + + +class UninstallPthEntries: + def __init__(self, pth_file: str) -> None: + self.file = pth_file + self.entries: Set[str] = set() + self._saved_lines: Optional[List[bytes]] = None + + def add(self, entry: str) -> None: + entry = os.path.normcase(entry) + # On Windows, os.path.normcase converts the entry to use + # backslashes. This is correct for entries that describe absolute + # paths outside of site-packages, but all the others use forward + # slashes. + # os.path.splitdrive is used instead of os.path.isabs because isabs + # treats non-absolute paths with drive letter markings like c:foo\bar + # as absolute paths. It also does not recognize UNC paths if they don't + # have more than "\\sever\share". Valid examples: "\\server\share\" or + # "\\server\share\folder". + if WINDOWS and not os.path.splitdrive(entry)[0]: + entry = entry.replace("\\", "/") + self.entries.add(entry) + + def remove(self) -> None: + logger.verbose("Removing pth entries from %s:", self.file) + + # If the file doesn't exist, log a warning and return + if not os.path.isfile(self.file): + logger.warning("Cannot remove entries from nonexistent file %s", self.file) + return + with open(self.file, "rb") as fh: + # windows uses '\r\n' with py3k, but uses '\n' with py2.x + lines = fh.readlines() + self._saved_lines = lines + if any(b"\r\n" in line for line in lines): + endline = "\r\n" + else: + endline = "\n" + # handle missing trailing newline + if lines and not lines[-1].endswith(endline.encode("utf-8")): + lines[-1] = lines[-1] + endline.encode("utf-8") + for entry in self.entries: + try: + logger.verbose("Removing entry: %s", entry) + lines.remove((entry + endline).encode("utf-8")) + except ValueError: + pass + with open(self.file, "wb") as fh: + fh.writelines(lines) + + def rollback(self) -> bool: + if self._saved_lines is None: + logger.error("Cannot roll back changes to %s, none were made", self.file) + return False + logger.debug("Rolling %s back to previous state", self.file) + with open(self.file, "wb") as fh: + fh.writelines(self._saved_lines) + return True diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/resolution/__init__.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/resolution/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/resolution/legacy/__init__.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/pip/_internal/resolution/legacy/__init__.py new file mode 100644 index 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and b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/__pycache__/translate.cpython-311.pyc differ diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/functionalization.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/functionalization.py new file mode 100644 index 0000000000000000000000000000000000000000..cc492588e60fdfe3edba051288c83527203252fd --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/functionalization.py @@ -0,0 +1,199 @@ +from typing import List, Optional + +from torchgen.api import dispatcher +from torchgen.api.types import ( + BaseCppType, + BaseCType, + Binding, + boolT, + ConstRefCType, + CType, + longT, + NamedCType, + tensorT, +) +from torchgen.model import ( + Argument, + BaseTy, + BaseType, + FunctionSchema, + NativeFunction, + NativeFunctionsViewGroup, +) + + +# This file describes the translation of JIT schema to API's used +# when creating view lambdas that are used by the functionalization pass. +# There are two types of lambdas: forward lambdas and reverse lambdas. +# These API's mostly follow the dispatcher API, with a few quirks: +# - The lambda capture has to convert reference types to value types +# - While the forward lambda just directly calls into the at::_ops API +# (following the dispatcher convention), the logic here for the reverse lambda +# is responsible for generating both the call-site, and the declarations +# (which are implemented manually in the at::functionalization::impl namespace). + +# The lambdas generated for each view op in the functionalization pass are of the form +# [capture_arguments](outer_arguments) -> returns_type { +# return name(inner_arguments); +# } + +# Define some specific lambda input arguments. +base_binding = Binding( + name="base", + nctype=NamedCType(name="base", type=ConstRefCType(BaseCType(tensorT))), + argument=Argument( + name="base", type=BaseType(BaseTy.Tensor), default=None, annotation=None + ), + default=None, +) +mutated_view_binding = Binding( + name="mutated_view", + nctype=NamedCType(name="mutated_view", type=ConstRefCType(BaseCType(tensorT))), + argument=Argument( + name="base", type=BaseType(BaseTy.Tensor), default=None, annotation=None + ), + default=None, +) +mutated_view_idx_binding = Binding( + name="mutated_view_idx", + nctype=NamedCType(name="mutated_view_idx", type=BaseCType(longT)), + argument=Argument( + name="base", type=BaseType(BaseTy.Tensor), default=None, annotation=None + ), + default=None, +) +reapply_views_binding = Binding( + name="reapply_views", + nctype=NamedCType(name="reapply_views", type=BaseCType(boolT)), + argument=Argument( + name="reapply_views", type=BaseType(BaseTy.bool), default=None, annotation=None + ), + default=None, +) + +InverseReturnModeT = BaseCppType("at::functionalization", "InverseReturnMode") +inverse_return_mode_binding = Binding( + name="inverse_return_mode", + nctype=NamedCType(name="inverse_return_mode", type=BaseCType(InverseReturnModeT)), + argument=Argument( + name="inverse_return_mode", + # NB: not actually a bool but it doesn't matter because this isn't used + type=BaseType(BaseTy.bool), + default=None, + annotation=None, + ), + default=None, +) + + +# The lambda capture itself doesn't have a name. +# The name returned here corresponds to the name of the inner function called by the lambda. +def name( + g: NativeFunctionsViewGroup, + *, + is_reverse: bool, + include_namespace: bool, + reapply_views: Optional[bool] = None, +) -> str: + if reapply_views is None: + # reapply_views is only important for the fwd lambda, + # since we always plumb the runtime "reapply_views" argument into the reverse function. + assert is_reverse + if is_reverse: + return reverse_name(g.view, include_namespace) + # in the forward case, we just directly call into the at::_ops API (so we always need the namespace) + assert include_namespace + assert g.view_copy is not None + api_name = ( + g.view.func.name.unambiguous_name() + if reapply_views + else g.view_copy.func.name.unambiguous_name() + ) + return f"at::_ops::{api_name}::call" + + +def reverse_name(f: NativeFunction, include_namespace: bool) -> str: + # for the reverse: we plumb the "reapply_views" flag into that function and support + # both copy and non-copy variants. (We could avoid doing that, but that would require + # writing out twice as many view inverse functions). + api_name = f.func.name.unambiguous_name() + # in the reverse case, we codegen both the call-sites (which need the full namespace) and the declarations (which don't) + if include_namespace: + return f"at::functionalization::FunctionalInverses::{api_name}_inverse" + else: + return f"{api_name}_inverse" + + +def capture_arguments(func: FunctionSchema, *, is_reverse: bool) -> List[Binding]: + # capture arguments include all arguments except `self`. + # Importantly, they don't include any C++ reference types (or else we'll get a dangling reference in the capture), + # So any reference types (IntArrayRef) need to be converted to value types (vector) + args = func.arguments.flat_all + assert args[0].type == BaseType(BaseTy.Tensor) + non_self_args = args[1:] + non_self_value_bindings = [ + dispatcher.argument(a, remove_non_owning_ref_types=True) for a in non_self_args + ] + + all_bindings = [ + inverse_return_mode_binding if is_reverse else reapply_views_binding + ] + all_bindings.extend(non_self_value_bindings) + return all_bindings + + +def returns_type(func: FunctionSchema) -> CType: + # Assertion: all view ops return tensor-like outputs + assert len(func.returns) >= 1 + for ret in func.returns: + assert ret.type.is_tensor_like() + # However, the return type of the lambda is always an individual tensor. + # For multi-tensor outputs, each tensor needs to be tracked individually. + return BaseCType(tensorT) + + +def outer_arguments(*, is_reverse: bool) -> List[Binding]: + if is_reverse: + return [base_binding, mutated_view_binding, mutated_view_idx_binding] + else: + return [base_binding, mutated_view_idx_binding] + + +def inner_call_index(func: FunctionSchema) -> Optional[Binding]: + # For view ops that return multiple tensors (like `split`), we generate a separate lambda for each output. + # When we replay a view op that returns multiple tensors, we need to index into the output appropriately + if len(func.returns) > 1 or ( + len(func.returns) == 1 and func.returns[0].type.is_list_like() + ): + return mutated_view_idx_binding + return None + + +def inner_arguments(func: FunctionSchema, is_reverse: bool) -> List[Binding]: + args = func.arguments.flat_all + assert args[0].type == BaseType(BaseTy.Tensor) + non_self_args = args[1:] + # The forward lambda calls the at::_ops API, while the reverse lambda calls the view inverse API. + # Both of these follow the dispatcher API. + non_self_bindings = [dispatcher.argument(a) for a in non_self_args] + if not is_reverse: + # the forward lambda swaps out the original tensor argument with the lambd arg "base" + return [base_binding] + non_self_bindings + else: + # the reverse lambda does the same, but with an additional "mutated_view" arg + # additionally, we have a calling convention: for view ops that return multiple tensor outputs + # their corresponding view_inverse function takes in an additional index argument. + index_binding = inner_call_index(func) + if index_binding is not None: + return [ + base_binding, + mutated_view_binding, + inverse_return_mode_binding, + index_binding, + ] + non_self_bindings + else: + return [ + base_binding, + mutated_view_binding, + inverse_return_mode_binding, + ] + non_self_bindings diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/python.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/python.py new file mode 100644 index 0000000000000000000000000000000000000000..1a3b4505d9df6324462ee5dc4632505c4d658118 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/python.py @@ -0,0 +1,1509 @@ +from dataclasses import dataclass +from typing import Dict, List, Optional, Sequence, Set, Tuple, Union + +from torchgen.api import cpp + +from torchgen.api.types import Binding, CppSignature, CppSignatureGroup +from torchgen.gen import pythonify_default +from torchgen.model import ( + Argument, + BaseTy, + BaseType, + FunctionSchema, + ListType, + NativeFunction, + OptionalType, + Return, + Type, + Variant, +) + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# +# Data Models +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# +# [Notes] python binding codegen +# +# The Python binding codegen produces code that takes the input list of +# PyObjects, finds the matching ATen C++ function using PythonArgParser, +# converts the PyObjects into C++ types and calls the ATen C++ function: +# +# +--------+ parsing +------------------------+ binding +-----------------------+ +# | PyObjs | ---------> | PythonArgParser Output | ---------> | Cpp Function Dispatch | +# +--------+ +------------------------+ +-----------------------+ +# +# The following examples demonstrate the data models the Python binding +# codegen needs to deal with and the tasks it needs to accomplish. It +# helps understand the purpose of the new data types we introduced below. +# +# - Function Schema (source of truth) +# +# aten::empty.names(int[] size, *, Dimname[]? names, +# ScalarType? dtype=None, Layout? layout=None, +# Device? device=None, bool? pin_memory=None, +# MemoryFormat? memory_format=None) -> Tensor +# +# - Python Signature +# +# It's used to generate input schema string for PythonArgParser. +# Note: TensorOptions fields are reordered and the additional +# 'requires_grad' field is added: +# +# empty(IntArrayRef size, *, DimnameList? names, +# MemoryFormat? memory_format=None, ScalarType dtype=None, +# Layout layout=torch.strided, Device device=None, +# bool pin_memory=False, bool requires_grad=False) +# +# - C++ Signature +# +# It's used to generate C++ lambda formals & dispatch call. +# Note: the scattered TensorOptions fields are packed into 'options'. +# +# auto dispatch_empty = +# [](IntArrayRef size, c10::optional names, +# const TensorOptions & options, +# c10::optional memory_format) -> Tensor { +# pybind11::gil_scoped_release no_gil; +# return torch::empty(size, names, options, memory_format); +# }; +# +# - Binding between Python Arguments and C++ Arguments +# +# Given a set of Python Arguments in scope, we need produce the +# binding expressions that translate the Python API into C++ API: +# +# Python Args Cpp Args Binding Exprs +# ----------------------------------------------------------------- +# 0: size size '_r.intlist(0)' +# 1: names names 'names' [special init] +# 2: memory_format -------+ +# 3: dtype -----+-|--> options 'options' [special packing] +# 4: layout / | +# 5: device / +--> memory_format '_r.memoryformatOptional(2)' +# 6: pin_memory / +# 7: requires_grad -+ +# +# So the full dispatch expression would look like: +# +# dispatch_empty(_r.intlist(0), names, options, +# _r.memoryformatOptional(2)) +# +# Where does 'names' come from? It involves special local init: +# +# auto __names = _r.toDimnameListOptional(1); +# c10::optional names = +# __names ? c10::make_optional(DimnameList(__names.value())) +# : c10::nullopt; +# +# Where does 'options' come from? It involves special local init +# for TensorOptions. Note that Python side has the additional +# 'requires_grad' field: +# +# const auto options = TensorOptions() +# .dtype(_r.scalartype(3)) +# .device(_r.device(5)) +# .layout(_r.layoutOptional(4)) +# .requires_grad(_r.toBool(7)) +# .pinned_memory(_r.toBool(6)); +# +# In some other cases one Python Argument can map to multiple C++ +# Arguments. For example: +# +# aten::max.names_dim(Tensor self, Dimname dim, bool keepdim=False) +# -> (Tensor values, Tensor indices) +# +# Python Args Cpp Args Binding Exprs +# --------------------------------------------------------------------- +# +----> max 'out[0]' +# /-----> max_values 'out[1] +# 0: input / self '_r.tensor(0)' +# 1: dim / dim '_r.dimname(1)' +# 2: keepdim / keepdim '_r.toBool(2)' +# 3: out -----+ [local init] out '_r.tensorlist_n<2>(3)' +# +# As demonstrated above, the binding can involve reordering, +# packing, unpacking and special local inits. +# +# +# Let's look at a concrete example: +# +# static PythonArgParser parser({ +# "abs(Tensor input, *, Tensor out=None)", +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ^ +# +--- Python Schema, represented by PythonSignature and PythonArgument +# +# }, /*traceable=*/true); +# +# ParsedArgs<2> parsed_args; +# auto _r = parser.parse(nullptr, args, kwargs, parsed_args); +# +# ... +# +# if (_r.isNone(1)) { +# ~~~~~~~~~~~~ <--- Scattered PythonArgParser output (arg name = 'out') +# represented by PythonArgParserOutputExpr +# +# // aten::abs(Tensor self) -> Tensor +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ^ +# +--- NativeFunction schema, base version +# +# auto dispatch_abs = [](const Tensor & self) -> Tensor { +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ^ +# +--- dispatch_lambda_args / dispatch_lambda_return_str +# generated from NativeFunction / CppSignature +# (deprecated PythonSignature is special) +# arguments are represented by DispatchLambdaArgument +# +# pybind11::gil_scoped_release no_gil; +# return self.abs(); +# ~~~~~~~~~~~ <--- cpp_dispatch_target / cpp_dispatch_exprs +# generated from NativeFunction / CppSignature +# }; +# return wrap(dispatch_abs(_r.tensor(0))); +# ~~~~~~~~~~~~~ +# ^ +# +--- dispatch_lambda_exprs +# binding PythonArgParserOutputExpr (python args) +# and DispatchLambdaArgument (c++ args) +# +# } else { +# // aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# ^ +# +--- NativeFunction schema, out-variant +# +# auto dispatch_abs_out = [](Tensor out, const Tensor & self) -> Tensor { +# pybind11::gil_scoped_release no_gil; +# return at::abs_out(out, self); +# }; +# return wrap(dispatch_abs_out(_r.tensor(1), _r.tensor(0))); +# } +# +# +# [Notes] python interface codegen +# The python dataclasses below are used used to generate both python binding code +# and pyi type hint signatures. +# In theory these two should look very similar, but there are number of differences +# in how pyi signatures vs. python_arg_parser signatures are generated. +# These differences have been encapsulated in signature_str() vs. signature_str_pyi() +# to display the full signatures, and argument_str() vs argument_str_pyi() to display arguments. +# For examples, only pyi signatures include return types. + + +@dataclass(frozen=True) +class PythonReturns: + returns: Tuple[Return, ...] + + +@dataclass(frozen=True) +class PythonArgument: + name: str + type: Type + default: Optional[str] + + # Used to generate the default init expr for some PythonArgParser outputs, e.g.: + # + # _r.layoutWithDefault(3, layout_from_backend(self.options().backend()))) + # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + # ^ + # +--- default_init str + default_init: Optional[str] + + # Compute argument formal for python argument parsing. + # Needs to be consistent with torch/csrc/utils/python_arg_parser.h. + def argument_str(self, *, method: bool = False, symint: bool = True) -> str: + type_str = ( + argument_type_str(self.type, symint=symint) + .replace("const ", "") + .replace(" &", "") + ) + + name = self.name + # s/self/input/ outside method bindings + # [old codegen] TODO: remove this? doesn't rename in codegen, it's just + # for the parse string + if name == "self" and type_str in ["Tensor", "Number"] and not method: + name = "input" + + # add default + if self.default is not None: + default = { + "nullptr": "None", + "c10::nullopt": "None", + "{}": "None", + }.get(self.default, self.default) + return f"{type_str} {name}={default}" + else: + return f"{type_str} {name}" + + def argument_str_pyi( + self, *, method: bool = False, deprecated: bool = False + ) -> str: + type_str = argument_type_str_pyi(self.type) + + name = self.name + # s/self/input/ outside method bindings + # [old codegen] TODO: remove this? doesn't rename in codegen, it's just + # for the parse string + if name == "self" and type_str == "Tensor" and not method and not deprecated: + name = "input" + + if name == "from": # from is a Python keyword... + name += "_" + + # pyi merges the _out and functional variants into the same signature, with an optional out arg + if name == "out" and type_str == "Tensor" and not deprecated: + type_str = "Optional[" + type_str + "]" + + # pyi deprecated signatures don't get defaults for their out arg + treat_as_no_default = ( + deprecated + and isinstance(self, PythonOutArgument) + and self.default == "None" + ) + + # add default + if self.default is not None and not treat_as_no_default: + if ( + isinstance(self.type, ListType) + and self.type.elem == BaseType(BaseTy.int) + and self.default.startswith("{") + and self.default.endswith("}") + ): + default = "(" + self.default[1:-1] + ")" + else: + default = { + "nullptr": "None", + "c10::nullopt": "None", + "{}": "None", + "MemoryFormat::Contiguous": "contiguous_format", + "QScheme::PER_TENSOR_AFFINE": "per_tensor_affine", + }.get(self.default, self.default) + return f"{name}: {type_str} = {default}" + else: + return f"{name}: {type_str}" + + +@dataclass(frozen=True) +class PythonOutArgument(PythonArgument): + # In Python signature multiple output fields are packed into one 'out' argument. + # When binding to C++, it's first binded to a local 'out' variable: + # 'auto out = _r.tensorlist_n<2>(2);', + # then binded to scattered C++ output arguments as 'out[0]', 'out[1]', and etc. + # TODO: maybe don't need keep scattered out fields for python signature? + outputs: Tuple[PythonArgument, ...] + + @staticmethod + def from_outputs( + outputs: Tuple[PythonArgument, ...] + ) -> Optional["PythonOutArgument"]: + if not outputs: + return None + + size = len(outputs) + if size == 1: + return PythonOutArgument( + name=outputs[0].name, + type=outputs[0].type, + default="None", + default_init=None, + outputs=outputs, + ) + elif size > 1: + if any(not a.type.is_tensor_like() for a in outputs): + raise RuntimeError(f"Unsupported output type: {outputs}") + return PythonOutArgument( + name="out", + # TODO: shouldn't this be OptionalType[ListType[...]], since it defaults to None? + type=ListType(BaseType(BaseTy.Tensor), size), + default="None", + default_init=None, + outputs=outputs, + ) + raise AssertionError(r"Unexpected PythonOutArgument size") + + +@dataclass(frozen=True) +class PythonSignature: + # Base operator name, without inplace/outplace suffix. + name: str + + # Positional arguments. + # TODO: create a dedicated SelfArgument type for 'self'? + input_args: Tuple[PythonArgument, ...] + + # Keyword arguments excluding the 'out' argument and scattered kwargs belonging + # to TensorOptions (dtype, layout, device, pin_memory, requires_grad, etc). + input_kwargs: Tuple[PythonArgument, ...] + + output_args: Optional[PythonOutArgument] + + # Return types, which are only used by pyi + returns: PythonReturns + + # These are scattered kwargs arguments belonging to TensorOptions. + # When binding to C++, they are packed into a TensorOptions object 'options'. + # It's possible that the C++ signature doesn't take TensorOptions object (e.g. + # for out variant), in which case they will be used as scattered fields without + # being packed into 'options'. + # TODO: maybe create a PythonTensorOptionsArgument? + tensor_options_args: Tuple[PythonArgument, ...] + + # method or function signature? + method: bool + + @property + def deprecated(self) -> bool: + return False + + def arguments( + self, *, skip_outputs: bool = False, skip_tensor_options: bool = False + ) -> Tuple[Union[PythonArgument, PythonOutArgument], ...]: + result: List[Union[PythonArgument, PythonOutArgument]] = [] + result.extend(self.input_args) + result.extend(self.input_kwargs) + if self.output_args is not None and not skip_outputs: + result.append(self.output_args) + if not skip_tensor_options: + result.extend(self.tensor_options_args) + return tuple(result) + + def arguments_count(self) -> int: + return len(self.arguments()) + + def output_idx(self) -> int: + return len(self.input_args) + len(self.input_kwargs) + + # [old codegen] Compute the Python function signature for argument parsing, + # as specified in torch/csrc/utils/python_arg_parser.h. WARNING: + # this is NOT the same type signature as specified by PEP 484 + # as understood by mypy; our format was independently developed + # and has some quirks to make it more suitable specifically + # for error parsing. + # + # For a translation to mypy-valid type signatures, see + # signature_str_pyi(). + def signature_str(self, *, skip_outputs: bool = False, symint: bool = True) -> str: + args = self.arguments(skip_outputs=skip_outputs) + schema_formals: List[str] = [ + a.argument_str(method=self.method, symint=symint) for a in args + ] + positional_argc = len(self.input_args) + if len(schema_formals) > positional_argc: + schema_formals.insert(positional_argc, "*") + + return f'{self.name}({", ".join(schema_formals)})' + + def signature_str_pyi(self, *, skip_outputs: bool = False) -> str: + args = self.arguments(skip_outputs=skip_outputs) + schema_formals: List[str] = [ + a.argument_str_pyi(method=self.method) for a in args + ] + positional_argc = len(self.input_args) + if len(schema_formals) > positional_argc: + schema_formals.insert(positional_argc, "*") + + # only pyi signatures include returns + returns_str = returns_str_pyi(self) + # pyi also includes self (with no typing/defaults) for methods + if self.method: + schema_formals.insert(0, "self") + return f'def {self.name}({", ".join(schema_formals)}) -> {returns_str}: ...' + + def signature_str_pyi_vararg(self, *, skip_outputs: bool = False) -> Optional[str]: + # only pyi uses vararg signatures + args = self.arguments(skip_outputs=skip_outputs) + schema_formals: List[str] = [ + a.argument_str_pyi(method=self.method) for a in args + ] + # vararg only applies to pyi signatures. vararg variants are not generated for all signatures + num_args = self.arguments_count() + num_positionalargs = len(self.input_args) + + have_vararg_version = False + if num_args > 0: + vararg_type = args[0].type + if ( + isinstance(vararg_type, ListType) + and str(vararg_type.elem) in ["int", "SymInt"] + and num_positionalargs == 1 + ): + have_vararg_version = True + + if not have_vararg_version: + return None + # Below are the major changes in vararg vs. regular pyi signatures + # vararg signatures also omit the asterix + schema_formals[0] = "*" + args[0].name + ": _int" + + returns_str = returns_str_pyi(self) + # pyi also includes self (with no typing/defaults) for methods + if self.method: + schema_formals.insert(0, "self") + return f'def {self.name}({", ".join(schema_formals)}) -> {returns_str}: ...' + + +# The deprecated python signature involves some special logic, so create a +# dedicated data model to store these extra properties. +@dataclass(frozen=True) +class PythonSignatureDeprecated(PythonSignature): + # Schema for the deprecated function + deprecated_schema: FunctionSchema + + # The deprecated signature might miss some arguments that the corresponding + # C++ signature expects. We need store the constant default values to pass in. + # For example: + # [deprecate signature]: addmm(Scalar beta, Tensor self, Tensor mat1, Tensor mat2) + # [func schema]: aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor + # [func call]: self.addmm(mat1, mat2, beta, 1) + # We store ['self', 'mat1', 'mat2', 'beta', '1'] in this case. + deprecated_args_exprs: Tuple[str, ...] + + @property + def deprecated(self) -> bool: + return True + + def signature_str(self, *, skip_outputs: bool = False, symint: bool = True) -> str: + return ( + PythonSignature.signature_str( + self, skip_outputs=skip_outputs, symint=symint + ) + + "|deprecated" + ) + + def signature_str_pyi(self, *, skip_outputs: bool = False) -> str: + args = self.arguments(skip_outputs=skip_outputs) + schema_formals: List[str] = [ + a.argument_str_pyi(method=self.method, deprecated=True) for a in args + ] + positional_argc = len(self.input_args) + if len(schema_formals) > positional_argc: + schema_formals.insert(positional_argc, "*") + + returns_str = returns_str_pyi(self) + return f'def {self.name}({", ".join(schema_formals)}) -> {returns_str}: ...' + + def signature_str_pyi_vararg(self, *, skip_outputs: bool = False) -> Optional[str]: + # the codegen doesn't include vararg variants for deprecated signatures + return None + + +# This struct is used to hold the PythonSignature and its corresponding +# NativeFunction BEFORE grouping base and out-variant functions. +# Why not store NativeFunction in PythonSignature or construct PythonSignature +# from NativeFunction? Because they are not 1-1 mapped. +# One native function could have both deprecated and non-deprecated python +# signatures - NativeFunction doesn't contain information to construct the +# deprecated python signature. +# One python signature is used to handle both the base and the out-variant +# function - see 'PythonSignatureGroup'. +@dataclass(frozen=True) +class PythonSignatureNativeFunctionPair: + signature: PythonSignature + function: NativeFunction + + +# We merge pairs of functions with signatures that are equivalent mod +# output arguments, and use a single entry in the python_arg_parser sig +# list for both (output arguments become optional). +@dataclass(frozen=True) +class PythonSignatureGroup: + # The signature used for Python argument parsing. The outplace signature + # is preferred if exists, because it can be used to parse inputs for both + # the out-place variant and the base version (with output omitted). + signature: PythonSignature + + # The regular ATen declaration (e.g. conv2d) + base: NativeFunction + + # The out variant (e.g. conv2d_out) + outplace: Optional[NativeFunction] + + @classmethod + def from_pairs( + cls, + functional: PythonSignatureNativeFunctionPair, + out: Optional[PythonSignatureNativeFunctionPair], + ) -> "PythonSignatureGroup": + if out is None: + return PythonSignatureGroup( + signature=functional.signature, + base=functional.function, + outplace=None, + ) + + # prefer the signature with optional out=... arguments because it's the + # superset that can be used to parse input for both base and outplace. + signature_kwargs = out.signature.__dict__.copy() + + # Out overloads in C++ don't have TensorOptions arguments, + # so take these from the functional variant + signature_kwargs[ + "tensor_options_args" + ] = functional.signature.tensor_options_args + + return PythonSignatureGroup( + signature=type(out.signature)(**signature_kwargs), + base=functional.function, + outplace=out.function, + ) + + +# C++ function dispatch is wrapped in a lambda function. The lambda function +# has almost the same signature as the C++ function, only with some small +# variants - see details below. +# This data model is used to represent arguments of the lambda function +# signature. +@dataclass(frozen=True) +class DispatchLambdaArgument: + name: str + type_str: str + is_out_arg: bool + + +# To pass PyObjects arguments to C++ function (via the lambda wrapper), +# we need first convert PyObjects into simple C++ objects. This work +# is done by PythonArgParser. +# This data model is used to represent the output of PythonArgParser. +# It has 1-1 mapping with PythonArgument in PythonSignature. +@dataclass(frozen=True) +class PythonArgParserOutputExpr: + # argument name + name: str + + # RHS expression to reference PythonArgParser output. + expr: str + + # In some special cases we need create different expr, e.g.: + # '_r.isNone(1)' instead of '_r.tensor(1)'. + index: int + + # The python argument it maps to. + argument: PythonArgument + + @property + def is_none_expr(self) -> str: + return f"_r.isNone({self.index})" + + +# To pass PythonArgParser output to the lambda wrapper, we need bind +# PythonArgParserOutputExpr to DispatchLambdaArgument. +# They are not always 1-1 mapped, e.g. scattered TensorOptions fields +# need be packed into a TensorOptions object, which is the argument +# that the lambda function wrapper takes. +@dataclass(frozen=True) +class DispatchLambdaArgumentExprs: + # The exprs that provide the binding for lambda arguments, e.g.: + # + # 'self' -> '_r.tensor(0)' + # 'min' -> 'out[0]' / 'min_indices' -> 'out[1]' + # 'options' -> 'options' + # + # It has 1-1 mapping with DispatchLambdaArgument. + exprs: Sequence[str] + + # Special local inits, which might introduce new variables that + # the 'exprs' above reference, e.g.: + # + # 'auto out = _r.tensorlist_n<2>(2);' + # + inits: Sequence[str] + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# +# Helper Functions +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # + + +def _cpp_signature(f: NativeFunction, *, method: bool = False) -> CppSignature: + return CppSignatureGroup.from_native_function(f, method=method).signature + + +def has_tensor_options(f: NativeFunction) -> bool: + return f.func.arguments.tensor_options is not None + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# +# Python Signature +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # + + +# 'simple_type' was introduced by the old codegen, which is slightly +# different from the python schema type, e.g.: doesn't have '?' suffix +# for optional Tensor/TensorList; doesn't have '[size]' suffix for list type. +def argument_type_str( + t: Type, *, simple_type: bool = False, symint: bool = True +) -> str: + if isinstance(t, BaseType): + if t.name == BaseTy.Tensor: + return "Tensor" + elif t.name == BaseTy.int: + return "int64_t" + elif t.name == BaseTy.float: + return "double" + elif t.name == BaseTy.str: + return "c10::string_view" + elif t.name in [ + BaseTy.bool, + BaseTy.QScheme, + BaseTy.Scalar, + BaseTy.ScalarType, + BaseTy.Generator, + BaseTy.Storage, + BaseTy.Layout, + BaseTy.Device, + BaseTy.DeviceIndex, + BaseTy.MemoryFormat, + BaseTy.Dimname, + BaseTy.Stream, + BaseTy.ConstQuantizerPtr, + BaseTy.SymInt, + ]: + # These python schema type names line up with their function schema names + return t.name.name + + elif isinstance(t, OptionalType): + if str(t.elem) == "Tensor": + # Is it desired to keep '?' for simple_type with new style dispatcher? + return "Tensor?" + elem = argument_type_str(t.elem, simple_type=simple_type, symint=symint) + return f"{elem}?" + elif isinstance(t, ListType): + size = t.size if not simple_type else None + if str(t.elem) == "bool": + assert t.size is not None + return f"::std::array" + elif str(t.elem) == "int": + return f"IntArrayRef[{size}]" if size is not None else "IntArrayRef" + elif str(t.elem) == "SymInt": + if symint: + return ( + f"SymIntArrayRef[{size}]" if size is not None else "SymIntArrayRef" + ) + else: + return f"IntArrayRef[{size}]" if size is not None else "IntArrayRef" + elif str(t.elem) == "Tensor": + return f"TensorList[{size}]" if size is not None else "TensorList" + elif str(t.elem) == "Scalar": + return f"ScalarList[{size}]" if size is not None else "ScalarList" + elif str(t.elem) == "Tensor?": + if simple_type: + return "c10::List>" + else: + return "const c10::List> &" + elif str(t.elem) == "Dimname": + return f"DimnameList[{size}]" if size is not None else "DimnameList" + elem = argument_type_str(t.elem, simple_type=simple_type, symint=symint) + return f"ArrayRef<{elem}>" + + raise RuntimeError(f"unrecognized type {repr(t)}") + + +def argument_type_size(t: Type) -> Optional[int]: + l = t.is_list_like() + if l is not None and str(l.elem) != "bool": + return l.size + else: + return None + + +def argument(a: Argument) -> PythonArgument: + return PythonArgument( + name=a.name, + type=a.type, + # TODO: directly translate a.default to python default + default=str( + pythonify_default(cpp.default_expr(a.default, a.type, symint=False)) + ) + if a.default is not None + else None, + default_init=None, + ) + + +# Generates a PythonSignature that can be used for either .pyi or PythonArgParser codegen +def signature( + f: NativeFunction, *, method: bool = False, pyi: bool = False +) -> PythonSignature: + return signature_from_schema( + f.func, category_override=f.category_override, method=method, pyi=pyi + ) + + +def signature_from_schema( + func: FunctionSchema, + *, + category_override: Optional[str], + method: bool = False, + pyi: bool = False, +) -> PythonSignature: + args: List[Argument] = [] + args.extend(func.arguments.pre_self_positional) + # Skip SelfArgument if this is method. + if not method and func.arguments.self_arg is not None: + args.append(func.arguments.self_arg.argument) + args.extend(func.arguments.post_self_positional) + args.extend(func.arguments.pre_tensor_options_kwarg_only) + # Skip TensorOptionsArguments. Python side TensorOptions + # arguments are created based on different rules - see below. + args.extend(func.arguments.post_tensor_options_kwarg_only) + args.extend(func.arguments.out) + + input_arg_set = {a.name for a in func.arguments.flat_positional} + kwarg_only_set = {a.name for a in func.arguments.flat_kwarg_only} + out_arg_set = {a.name for a in func.arguments.out} + + input_args = tuple(map(argument, filter(lambda a: a.name in input_arg_set, args))) + input_kwargs = tuple( + map(argument, filter(lambda a: a.name in kwarg_only_set, args)) + ) + outputs = tuple(map(argument, filter(lambda a: a.name in out_arg_set, args))) + + # Reintroduce the scattered fields of TensorOptions for Python. + # Compared to the cpp counterpart, the python arguments have new property + # (default_init) and a new argument 'requires_grad', which require some + # special handlings. + # [old codegen] TODO: because these aren't guaranteed to be 100% faithful + # to the original versions in the yaml, this recreation is a potential + # source of drift between eager and JIT. Pull this logic out to a shared place. + + has_tensor_input_arg = any( + a.type.is_tensor_like() for a in func.arguments.flat_non_out + ) + if any(a.name == "requires_grad" for a in func.schema_order_arguments()): + raise ValueError( + "argument named requires_grad is reserved, should not explicitly add it in the schema" + ) + + # [old codegen] this probably won't work if one of the returns is not a tensor, + # but it will produce a compile-time error that is obvious. + has_tensor_return = any(r.type.is_tensor_like() for r in func.returns) + + name: str = cpp.name(func) + is_factory_function = category_override == "factory" or ( + has_tensor_return and not has_tensor_input_arg + ) + is_like_or_new_function = ( + category_override in ("new", "like") + or name.startswith("new_") + or name.endswith("_like") + ) + is_dummy_function = category_override == "dummy" + + tensor_options_args: List[PythonArgument] = [] + if (is_factory_function or is_like_or_new_function) and not is_dummy_function: + + def topt_default_init(name: str) -> Optional[str]: + topt_args = func.arguments.tensor_options + if topt_args is None: + return None + a = getattr(topt_args, name) + if a.default is None or a.default == "None": + return None + return cpp.default_expr(a.default, a.type, symint=False) + + tensor_options_args.append( + PythonArgument( + name="dtype", + type=OptionalType(BaseType(BaseTy.ScalarType)), + default="None", + default_init=( + None if is_like_or_new_function else topt_default_init("dtype") + ), + ) + ) + tensor_options_args.append( + PythonArgument( + name="layout", + type=OptionalType(BaseType(BaseTy.Layout)), + default="None", + default_init=( + None if is_like_or_new_function else topt_default_init("layout") + ), + ) + ) + tensor_options_args.append( + PythonArgument( + name="device", + type=OptionalType(BaseType(BaseTy.Device)), + default="None", + default_init=( + None + if is_like_or_new_function + else ( + topt_default_init("device") + or "torch::tensors::get_default_device()" + ) + ), + ) + ) + tensor_options_args.append( + PythonArgument( + name="pin_memory", + type=OptionalType(BaseType(BaseTy.bool)), + default="False", + default_init=None, + ) + ) + tensor_options_args.append( + PythonArgument( + name="requires_grad", + type=OptionalType(BaseType(BaseTy.bool)), + default="False", + default_init=None, + ) + ) + + returns = PythonReturns(returns=func.returns) + + return PythonSignature( + name=str(func.name.name), + input_args=input_args, + input_kwargs=input_kwargs, + output_args=PythonOutArgument.from_outputs(outputs), + tensor_options_args=tuple(tensor_options_args), + returns=returns, + method=method, + ) + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# +# Python Interface +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # + + +def structseq_fieldnames(returns: Tuple[Return, ...]) -> List[str]: + if len(returns) <= 1 or all(r.name is None for r in returns): + return [] + else: + if any(r.name is None for r in returns): + # When building on Windows, `PyStructSequence_UnnamedField` could not be + # resolved by the linker for some reason, which cause error in building: + # + # python_nn_functions.cpp.obj : error LNK2001: unresolved external symbol + # PyStructSequence_UnnamedField + # + # Thus, at this point in time, we do not support unnamed + # fields in structseq; you must either name all fields, + # or none of them. + raise ValueError("Unnamed field is not supported by codegen") + + return [str(r.name) for r in returns] + + +def argument_type_str_pyi(t: Type) -> str: + add_optional = False + if isinstance(t, OptionalType): + t = t.elem + add_optional = True + + if isinstance(t, BaseType): + if t.name in [BaseTy.int, BaseTy.DeviceIndex]: + ret = "_int" + if t.name == BaseTy.SymInt: + ret = "Union[_int, SymInt]" + elif t.name == BaseTy.float: + ret = "_float" + elif t.name == BaseTy.str: + ret = "str" + elif t.name == BaseTy.Scalar: + ret = "Union[Number, _complex]" + elif t.name == BaseTy.ScalarType: + ret = "_dtype" + elif t.name == BaseTy.bool: + ret = "_bool" + elif t.name == BaseTy.QScheme: + ret = "_qscheme" + elif t.name == BaseTy.Layout: + ret = "_layout" + elif t.name == BaseTy.Device: + ret = "Optional[DeviceLikeType]" + elif t.name == BaseTy.MemoryFormat: + ret = "memory_format" + elif t.name == BaseTy.Dimname: + ret = "Union[str, ellipsis, None]" + elif t.name == BaseTy.Storage: + ret = "Union[Storage, UntypedStorage]" + elif t.name in [BaseTy.Tensor, BaseTy.Generator, BaseTy.Stream]: + # These python schema type names line up with their function schema names + ret = t.name.name + + elif isinstance(t, ListType): + if str(t.elem) == "int": + ret = "Union[_int, _size]" if t.size is not None else "_size" + elif t.is_tensor_like(): + # TODO: this doesn't seem right... + # Tensor?[] currently translates to Optional[Union[Tuple[Tensor, ...], List[Tensor]]] + # It should probably translate to Union[Tuple[Optional[Tensor], ...], List[Optional[Tensor]]] + if isinstance(t.elem, OptionalType): + add_optional = True + ret = ( + "Union[Tensor, Tuple[Tensor, ...], List[Tensor]]" + if t.size is not None + else "Union[Tuple[Tensor, ...], List[Tensor]]" + ) + elif str(t.elem) == "float": + ret = "Sequence[_float]" + elif str(t.elem) == "SymInt" and t.size is not None: + elem = argument_type_str_pyi(t.elem) + ret = f"Union[{elem}, Sequence[{elem}]]" + else: + elem = argument_type_str_pyi(t.elem) + ret = f"Sequence[{elem}]" + + else: + raise RuntimeError(f"unrecognized type {repr(t)}") + + if add_optional: + ret = "Optional[" + ret + "]" + + return ret + + +def return_type_str_pyi(t: Type) -> str: + # Where arguments are open to accepting Union, return types should return + # concrete types + + if isinstance(t, OptionalType): + inner = return_type_str_pyi(t.elem) + return f"Optional[{inner}]" + + if isinstance(t, BaseType): + if t.name == BaseTy.Device: + return "_device" + elif t.name == BaseTy.Dimname: + ret = "Optional[str]" + else: + return argument_type_str_pyi(t) + + if isinstance(t, ListType): + inner = return_type_str_pyi(t.elem) + return f"Tuple[{inner}, ...]" + + return argument_type_str_pyi(t) + + +def returns_structseq_pyi(signature: PythonSignature) -> Optional[Tuple[str, str]]: + python_returns = [return_type_str_pyi(r.type) for r in signature.returns.returns] + structseq_name = signature.name + field_names = structseq_fieldnames(signature.returns.returns) + if field_names: + # These types are structseq objects which act like named NamedTuples, but + # the constructor acts like the constructor of tuple. Using typing.NamedTuple + # does not allow us to override __init__. + field_names_str = ", ".join(repr(name) for name in field_names) + seq_type = f"Tuple[{', '.join(python_returns)}]" + structseq_def_lines = [ + f"class {structseq_name}({seq_type}):", + ] + for name, typ in zip(field_names, python_returns): + structseq_def_lines.extend( + [ + " @property", + f" def {name}(self) -> {typ}: ...", + ] + ) + structseq_def_lines.extend( + [ + f" def __new__(cls, sequence: {seq_type}): ...", + f" n_fields: _int = {len(field_names)}", + f" n_sequeunce_fields: _int = {len(field_names)}", + " n_unnamed_fields: _int = 0", + " def __init_subclass__(cls) -> NoReturn: ... # prohibit subclassing", + "", # add an extra newline + ] + ) + structseq_def = "\n".join(structseq_def_lines) + # Example: + # structseq_def = ( + # "class max(Tuple[Tensor, Tensor]):\n" + # " @property\n" + # " def values(self) -> Tensor: ...\n" + # " @property\n" + # " def indices(self) -> Tensor: ...\n" + # " def __new__(cls, sequence: Tuple[Tensor, Tensor]): ...\n" + # " n_fields: _int = 2", + # " n_sequeunce_fields: _int = 2", + # " n_unnamed_fields: _int = 0", + # " def __init_subclass__(cls) -> NoReturn: ... # prohibit subclassing", + # ) + return structseq_name, structseq_def + return None + + +def returns_str_pyi(signature: PythonSignature) -> str: + field_names = structseq_fieldnames(signature.returns.returns) + if field_names: + return f"torch.return_types.{signature.name}" + + python_returns = [return_type_str_pyi(r.type) for r in signature.returns.returns] + if len(python_returns) > 1: + return "Tuple[" + ", ".join(python_returns) + "]" + if len(python_returns) == 1: + return python_returns[0] + return "None" + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# +# C++ Function Dispatch +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# This section provides APIs to generate the code that does C++ function +# dispatch. The C++ function call is wrapped by a lambda function. +# For example: +# +# // aten::selu_(Tensor(a!) self) -> Tensor(a!) +# auto dispatch_selu_ = [](Tensor self) -> Tensor { +# pybind11::gil_scoped_release no_gil; +# return at::selu_(self); +# }; +# +# The lambda function's signature follows the C++ signature in common +# cases, e.g.: +# +# // aten::add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor +# [](const Tensor & self, const Tensor & other, Scalar alpha) -> Tensor +# +# For out variant the 'out' argument's type is changed from 'Tensor &' +# to 'Tensor'. It's because when calling the lambda it passes in the +# PythonArgParser output '_r.tensor(3)', which is stack allocated object +# and needs to pass by value. Also see comments in 'dispatch_lambda_return_str()'. +# +# // aten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) +# [](Tensor out, const Tensor & self, const Tensor & other, Scalar alpha) -> Tensor +# +# For multi-output case it can keep using reference type because the +# PythonArgParser output has been unpacked to local variables, e.g.: +# +# // aten::max.names_dim_max(Tensor self, Dimname dim, bool keepdim=False, *, +# // Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices) +# [](Tensor & max, Tensor & max_values, const Tensor & self, Dimname dim, bool keepdim) -> std::tuple +# +# For deprecated python signature, it should follow deprecated python arg order. +# TODO: This is to keep same byte-for-byte result as the old codegen - maybe unnecessary? + + +def dispatch_lambda_args( + ps: PythonSignature, f: NativeFunction, symint: bool = True +) -> Tuple[DispatchLambdaArgument, ...]: + if isinstance(ps, PythonSignatureDeprecated): + schema = ps.deprecated_schema + else: + schema = f.func + + # Start with cpp arguments - dispatch lambda signature always include 'self' + cpp_args = cpp.arguments( + arguments=schema.arguments, + faithful=False, + symint=symint, + method=False, + cpp_no_default_args=f.cpp_no_default_args, + ) + out_args: Set[str] = {a.name for a in schema.arguments.out} + + # Convert from cpp argument to lambda argument + def dispatch_lambda_arg(cpp_arg: Binding) -> DispatchLambdaArgument: + type_str = cpp_arg.type + is_out_arg = cpp_arg.name in out_args + if ps.method and cpp_arg.name == "self": + # For method's 'self', we can use 'const Tensor &' and simply ignore mutability! + type_str = "const at::Tensor &" + else: + # For other cases we need prevent dangling refs to temps (unless it's + # unpacked scattered output) + # The reason is explained in the comments above and in 'dispatch_lambda_return_str()'. + # TODO: avoid this special handling? + ensure_temp_safe = len(out_args) <= 1 or not is_out_arg + if ensure_temp_safe: + type_str = { + "at::Tensor &": "at::Tensor", + }.get(type_str, type_str) + return DispatchLambdaArgument( + name=cpp_arg.name, + type_str=type_str, + is_out_arg=is_out_arg, + ) + + return tuple(map(dispatch_lambda_arg, cpp_args)) + + +# [old codegen] XXX: if you got here because of an assertion failure, it doesn't mean +# it's enough to just extend the list here. Before you do this, make sure +# to add an appropriate wrap() overload in torch/csrc/autograd/utils/wrap_outputs.h. +SUPPORTED_RETURN_TYPES = { + "at::Tensor", + "::std::tuple", + "::std::tuple", + "::std::tuple", + "::std::tuple", + "::std::tuple", + "::std::tuple", + "::std::tuple", + "::std::tuple", + "::std::tuple", + "::std::tuple", + "::std::tuple>", + "::std::vector", + # Needed for flash attention forw/backward + "::std::tuple", + "at::Scalar", + "bool", + "int64_t", + "void*", + "void", + "at::QScheme", + "double", + "at::IntArrayRef", + "at::ScalarType", + "at::Stream", +} + + +def dispatch_lambda_return_str(f: NativeFunction) -> str: + # [old codegen] Remove type annotation (e.g. 'Tensor' rather than 'Tensor &') + # because the dispatch lambdas take mutable arguments *by value*, not + # by reference. If you then return a reference to such an argument, you + # will now have a pointer to a dangling stack entry. Not good. + # + # You want: + # + # auto dispatch_selu_ = [](Tensor self) -> Tensor { ...; return at::selu_(self); }; + # ^^^^^^ + # + # *not* + # + # auto dispatch_selu_ = [](Tensor self) -> Tensor& { ...; return at::selu_(self); }; + # ^^^^^^^ + # + # (NB: We can't make dispatch_selu_ take Tensor&, because the enclosing + # codegen looks like dispatch_selu_(_r.tensor(0)), and you can't take a + # mutable reference to temporary. Maybe we could assign it to a + # variable itself.) + returns_without_annotation = tuple( + Return(r.name, r.type, None) for r in f.func.returns + ) + return_str = cpp.returns_type(returns_without_annotation, symint=True).cpp_type() + if return_str not in SUPPORTED_RETURN_TYPES: + raise RuntimeError(f"{f.func.name} returns unsupported type {return_str}") + return return_str + + +def cpp_dispatch_target(f: NativeFunction) -> str: + symint = f.func.has_symint() + name = cpp.name(f.func, symint_overload=symint) + if Variant.method in f.variants: + return f"self.{name}" + if Variant.function in f.variants: + if has_tensor_options(f) or f.func.name.name.base.endswith("_like"): + namespace = "torch" + else: + namespace = "at" + return f"{namespace}::{name}" + raise RuntimeError(f"could not dispatch, neither function nor method: {f.func}") + + +def cpp_dispatch_exprs( + f: NativeFunction, + *, + python_signature: Optional[PythonSignature] = None, +) -> Tuple[str, ...]: + cpp_args: Sequence[Binding] = _cpp_signature(f, method=False).arguments() + + exprs: Tuple[str, ...] = tuple() + if not isinstance(python_signature, PythonSignatureDeprecated): + # By default the exprs are consistent with the C++ signature. + exprs = tuple(a.name for a in cpp_args) + else: + # For deprecated python signature we may need fill in some constants. + exprs = tuple( + filter( + lambda n: n != "out" or f.func.is_out_fn(), + python_signature.deprecated_args_exprs, + ) + ) + + if Variant.method in f.variants: + exprs = tuple(filter("self".__ne__, exprs)) + + return exprs + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# +# Python / C++ Args Binding +# +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # + + +# We explicitly enumerate the PythonArgParser unpacking methods for all +# supported types. This might be more verbose than necessary, partially +# because of the irregularity of unpacking method naming, partially +# because we want to mimic the old codegen behavior - to reject +# unexpected and/or unsupported cases which the old codegen rejects. +# For certain cases it is intentionally more restrictive than necessary, +# e.g.: it doesn't accepts doublelist with definite size. +def arg_parser_unpack_method( + t: Type, default: Optional[str], default_init: Optional[str], *, symint: bool = True +) -> str: + has_default_init = default_init is not None + if has_default_init and str(t) not in ( + "ScalarType?", + "ScalarType", + "Device", + "Device?", + "Layout", + "Layout?", + "bool", + "bool?", + ): + raise RuntimeError(f"type '{t}' does not supported unpacking with default") + + if isinstance(t, BaseType): + if t.name in [ + BaseTy.Tensor, + BaseTy.Stream, + BaseTy.Storage, + BaseTy.Scalar, + BaseTy.Dimname, + ]: + # These unpack methods line up with their schema names + return t.name.name.lower() + elif t.name == BaseTy.ScalarType: + return "scalartypeWithDefault" if has_default_init else "scalartype" + elif t.name == BaseTy.Device: + return "deviceWithDefault" if has_default_init else "device" + elif t.name == BaseTy.DeviceIndex: + return "toInt64" + elif t.name == BaseTy.int: + return "toInt64" + elif t.name == BaseTy.SymInt: + return "toSymInt" if symint else "toInt64" + elif t.name == BaseTy.bool: + return "toBoolWithDefault" if has_default_init else "toBool" + elif t.name == BaseTy.float: + return "toDouble" + elif t.name == BaseTy.str: + return "stringView" + elif t.name == BaseTy.Layout: + return "layoutWithDefault" if has_default_init else "layout" + elif t.name == BaseTy.MemoryFormat: + return "memoryformat" + + elif isinstance(t, OptionalType): + if str(t.elem) == "Tensor": + return "optionalTensor" + elif str(t.elem) == "Generator": + return "generator" + elif str(t.elem) == "Dimname[]": + return "toDimnameListOptional" + elif not has_default_init and default in (None, "None", "c10::nullopt"): + # If default is None: append 'Optional' to elem's unpacking method + return ( + arg_parser_unpack_method(t.elem, None, None, symint=symint) + "Optional" + ) + else: + # Otherwise, load as underlying type with default + return arg_parser_unpack_method( + t.elem, default, default_init, symint=symint + ) + + elif isinstance(t, ListType): + if str(t.elem) == "Tensor": + # accept and use definite size + return f"tensorlist_n<{t.size}>" if t.size is not None else "tensorlist" + elif str(t.elem) == "Tensor?": + return "list_of_optional_tensors" + elif str(t.elem) == "Dimname": + # accept definite size + return "dimnamelist" + elif str(t.elem) == "int": + # accept definite size + return "intlist" + elif str(t.elem) == "float": + return "doublelist" + elif str(t.elem) == "SymInt": + # accept definite size + return "symintlist" if symint else "intlist" + elif str(t.elem) == "Scalar": + return "scalarlist" + raise RuntimeError(f"type '{t}' is not supported by PythonArgParser") + + +# Return RHS expression for python argument using PythonArgParser output. +# e.g. for arg name 'foo', arg type 'bool', arg_index = 2, returns '_r.toBool(2)' +def arg_parser_output_expr( + arg_index: int, a: PythonArgument, *, symint: bool = True +) -> PythonArgParserOutputExpr: + has_default = a.default_init is not None + unpack_method = arg_parser_unpack_method( + t=a.type, default=a.default, default_init=a.default_init, symint=symint + ) + default = f", {a.default_init}" if has_default else "" + expr = f"_r.{unpack_method}({arg_index}{default})" + + return PythonArgParserOutputExpr( + name=a.name, + expr=expr, + index=arg_index, + argument=a, + ) + + +# Returns a map with key = arg_name and value = PythonArgParserOutputExpr. +def arg_parser_output_exprs( + ps: PythonSignature, f: NativeFunction, *, symint: bool = True +) -> Dict[str, PythonArgParserOutputExpr]: + return { + e.name: e + for i, a in enumerate(ps.arguments()) + for e in (arg_parser_output_expr(i, a, symint=symint),) + } + + +# argument name to type for scattered tensor options fields +TENSOR_OPTIONS_FIELDS = { + "dtype": "ScalarType?", + "device": "Device?", + "layout": "Layout?", + "pin_memory": "bool?", + "requires_grad": "bool?", +} + + +# bind arg parser outputs (python args) with dispatch lambda arguments (c++ args). +def dispatch_lambda_exprs( + ps: PythonSignature, f: NativeFunction, *, symint: bool = True +) -> DispatchLambdaArgumentExprs: + # This method is to bind 'arg_parser_outputs' and 'lambda_args' by producing + # 'inits' and 'lambda_args_exprs' for each lambda argument using arg parser + # outputs. + arg_parser_outputs = arg_parser_output_exprs(ps, f, symint=symint) + lambda_args = dispatch_lambda_args(ps, f, symint=symint) + inits: List[str] = [] + lambda_args_exprs: Dict[str, str] = {} + + has_toptions = has_tensor_options(f) + + # 1. special inits/unpacking to provide binding exprs for lambda arguments. + for a in ps.arguments(skip_tensor_options=True): + name = a.name + arg_parser_expr = arg_parser_outputs[a.name].expr + + if has_toptions and name == "self": + # TODO: why this needs to be special case? + inits.extend( + [ + f"auto self = {arg_parser_expr};", + ] + ) + lambda_args_exprs[name] = name + elif ( + isinstance(a, PythonOutArgument) + and len(a.outputs) > 1 + and f.func.is_out_fn() + ): + inits.extend( + [ + f"auto out = {arg_parser_expr};", + ] + ) + for i, out_arg in enumerate(a.outputs): + lambda_args_exprs[out_arg.name] = f"out[{i}]" + elif str(a.type) == "Dimname[]?": + # [old codegen] + # TODO: make this part of something more general, or get rid of it. + # optional> are special. The PythonArgParser returns an + # optional>, which cannot be implicitly converted to + # optional>. One needs to unwrap the optional and rewrap. + inits.extend( + [ + f"auto __{name} = {arg_parser_expr};", + f"c10::optional {name} = __{name} ? c10::make_optional(DimnameList(__{name}.value())) : c10::nullopt;", # noqa: B950 + ] + ) + lambda_args_exprs[name] = name + else: + # default case - directly using PythonArgParser output expr + lambda_args_exprs[name] = arg_parser_expr + + # method's self is passed directly to python binding, rather than parsed + if ps.method: + lambda_args_exprs["self"] = "self" + + # 2. special packing/checking for TensorOptions. + tensor_options_args_names = [a.name for a in ps.tensor_options_args] + if has_toptions: + if f.func.is_out_fn(): + raise RuntimeError(f"{f.func}: tensor options with output arg") + for a in ps.tensor_options_args: + if a.name not in TENSOR_OPTIONS_FIELDS: + raise RuntimeError( + f"{f.func}: unrecognized tensor options field '{a.name}' in python binding arguments" + ) + if str(a.type) != TENSOR_OPTIONS_FIELDS.get(a.name): + raise RuntimeError( + f"{f.func}: unrecognized type '{str(a.type)}' for tensor options field '{a.name}'" + ) + if not all( + a in tensor_options_args_names for a in TENSOR_OPTIONS_FIELDS.keys() + ): + raise RuntimeError( + f"{f.func}: incomplete tensor options args: {tensor_options_args_names}" + ) + + inits.append( + f"""\ +const auto options = TensorOptions() + .dtype({arg_parser_outputs['dtype'].expr}) + .device({arg_parser_outputs['device'].expr}) + .layout({arg_parser_outputs['layout'].expr}) + .requires_grad({arg_parser_outputs['requires_grad'].expr}) + .pinned_memory({arg_parser_outputs['pin_memory'].expr}); +torch::utils::maybe_initialize_device(options); +""" + ) + lambda_args_exprs["options"] = "options" + + # 3. special case - access scattered TensorOptions fields without packing + # TODO: maybe move to the generator side as it's not related to binding. + if not has_toptions and tensor_options_args_names: + if "dtype" in tensor_options_args_names: + # we're an output-arg variant, check these args against output tensor + if not f.func.is_out_fn(): + raise RuntimeError( + f"{f.func}: dtype in tensor_options_args without output arg" + ) + if not all(a in tensor_options_args_names for a in ("layout", "device")): + raise RuntimeError( + f"{f.func}: incomplete tensor options for output check" + ) + + inits.append( + f"""\ +check_out_type_matches({arg_parser_outputs['out'].expr}, {arg_parser_outputs['dtype'].expr}, + {arg_parser_outputs['dtype'].is_none_expr}, {arg_parser_outputs['layout'].expr}, + {arg_parser_outputs['device'].expr}, {arg_parser_outputs['device'].is_none_expr}); +""" + ) + # we'll set requires_grad on outgoing tensor + if "requires_grad" not in tensor_options_args_names: + raise RuntimeError( + f'{f.func}: expected "requires_grad" in tensor_options_args absent, but found [{tensor_options_args_names}]' + ) + + return DispatchLambdaArgumentExprs( + exprs=tuple(lambda_args_exprs[a.name] for a in lambda_args), + inits=inits, + ) diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/__init__.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d3e2f9a431b45c7ff1b0357dcb0e24a508a38a87 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/__init__.py @@ -0,0 +1,3 @@ +from .types import * +from .types_base import * +from .signatures import * # isort:skip diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/__pycache__/signatures.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/__pycache__/signatures.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e4c93b283f8f4ff57e879cd49313b1439c68e725 Binary files /dev/null and b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/__pycache__/signatures.cpython-311.pyc differ diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/__pycache__/types_base.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/__pycache__/types_base.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bc3d77421b2f216648979a259c4b87a64d240543 Binary files /dev/null and b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/__pycache__/types_base.cpython-311.pyc differ diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/signatures.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/signatures.py new file mode 100644 index 0000000000000000000000000000000000000000..f21ab29178e5ca46777b873fc5c1ef12f32f6443 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/signatures.py @@ -0,0 +1,423 @@ +from dataclasses import dataclass + +from typing import Iterator, List, Optional, Sequence, Set, Tuple, Union + +from torchgen.model import ( + BackendIndex, + FunctionSchema, + NativeFunction, + NativeFunctionsGroup, + NativeFunctionsViewGroup, +) + +from .types_base import Binding, CType, Expr + + +@dataclass(frozen=True) +class CppSignature: + """ + A CppSignature represents a single overload in the C++ API. For + any given function schema, there may be multiple CppSignatures + corresponding to it, based on how we desugar to C++. See also + CppSignatureGroup. + """ + + # The schema this signature is derived from + func: FunctionSchema + + # Is this a C++ signature for a method, i.e. Tensor::my_op(...)? + method: bool + + # Is this a faithful C++ signature (i.e. following the JIT schema) or a convenience API + # (i.e. with a potential TensorOptions argument and out arguments in the front) + faithful: bool + + # Is this a symint C++ signature. For BC reasons, functions that take + # SymInts still present as int64_t in C++, and the SymInt variant is + # offered at a different overload name + # + # NB: If a function RETURNS a SymInt, this is ALWAYS false + symint: bool + + # The set of C++ arguments which should not have defaults applied to them + cpp_no_default_args: Set[str] + + # Is this a fallback C++ binding? Fallback bindings are enabled by + # manual_cpp_binding: True and are alternate, non-public API that + # lets manual C++ binding implementors access the binding that would + # have been automatically generated + fallback_binding: bool = False + + # Return the unpacked argument structure of this signature, + # discarding information about which arguments are semantically + # related to each other. + def arguments(self) -> Sequence[Binding]: + return cpp.arguments( + self.func.arguments, + faithful=self.faithful, + symint=self.symint, + method=self.method, + cpp_no_default_args=self.cpp_no_default_args, + ) + + def name(self, *, suppress_symint_suffix: bool = False) -> str: + n = cpp.name( + self.func, + faithful_name_for_out_overloads=self.faithful, + symint_overload=False if suppress_symint_suffix else self.symint, + ) + if self.fallback_binding: + n = f"__dispatch_{n}" + return n + + # Render the C++ declaration for this signature + def decl( + self, + *, + name: Optional[str] = None, + prefix: str = "", + is_redispatching_fn: bool = False, + suppress_symint_suffix: bool = False, + ) -> str: + returns_type = cpp.returns_type( + self.func.returns, symint=self.symint + ).cpp_type() + cpp_args = [a.decl() for a in self.arguments()] + if is_redispatching_fn: + cpp_args = ["c10::DispatchKeySet dispatchKeySet"] + cpp_args + cpp_args_str = ", ".join(cpp_args) + if name is None: + name = prefix + self.name(suppress_symint_suffix=suppress_symint_suffix) + return f"{returns_type} {name}({cpp_args_str})" + + # Render the C++ definition for this signature, not including + # the body (with curly braces) + def defn( + self, + *, + name: Optional[str] = None, + prefix: str = "", + is_redispatching_fn: bool = False, + ) -> str: + returns_type = cpp.returns_type( + self.func.returns, symint=self.symint + ).cpp_type() + cpp_args = [a.defn() for a in self.arguments()] + if is_redispatching_fn: + cpp_args = ["c10::DispatchKeySet dispatchKeySet"] + cpp_args + cpp_args_str = ", ".join(cpp_args) + if name is None: + name = prefix + self.name() + return f"{returns_type} {name}({cpp_args_str})" + + def ptr_type(self) -> str: + args_types_str = ", ".join(a.type for a in self.arguments()) + return f"{cpp.returns_type(self.func.returns, symint=self.symint).cpp_type()} (*)({args_types_str})" + + # Return the C++ function type, e.g., something like int(bool) + def type(self) -> str: + args_types_str = ", ".join(a.type for a in self.arguments()) + return f"{cpp.returns_type(self.func.returns, symint=self.symint).cpp_type()} ({args_types_str})" + + +# Represents group of all CppSignatures associated with a +# FunctionSchema. Right now, that's the regular, user-visible +# signature, as well as a "faithful" signature which doesn't +# have grouping. +@dataclass(frozen=True) +class CppSignatureGroup: + func: FunctionSchema + signature: CppSignature + faithful_signature: Optional[CppSignature] + symint_signature: Optional[CppSignature] + symint_faithful_signature: Optional[CppSignature] + + def most_faithful_signature(self) -> CppSignature: + if self.faithful_signature: + return self.faithful_signature + else: + return self.signature + + def signatures(self, *, symint: bool = True) -> Iterator[CppSignature]: + yield self.signature + if self.faithful_signature: + yield self.faithful_signature + if symint: + if self.symint_signature: + yield self.symint_signature + if self.symint_faithful_signature: + yield self.symint_faithful_signature + + @staticmethod + def from_native_function( + f: NativeFunction, *, method: bool, fallback_binding: bool = False + ) -> "CppSignatureGroup": + func = f.func + + def make_sig(*, faithful: bool, symint: bool) -> CppSignature: + return CppSignature( + func=func, + faithful=faithful, + symint=symint, + method=method, + fallback_binding=fallback_binding, + cpp_no_default_args=f.cpp_no_default_args, + ) + + def make_sigs(*, symint: bool) -> Tuple[CppSignature, Optional[CppSignature]]: + faithful_signature: Optional[CppSignature] = None + if func.arguments.tensor_options is not None or len(func.arguments.out) > 0: + faithful_signature = make_sig(faithful=True, symint=symint) + signature = make_sig(faithful=False, symint=symint) + return signature, faithful_signature + + signature, faithful_signature = make_sigs(symint=False) + symint_signature: Optional[CppSignature] = None + symint_faithful_signature: Optional[CppSignature] = None + if func.has_symint(): + symint_signature, symint_faithful_signature = make_sigs(symint=True) + + return CppSignatureGroup( + func=func, + signature=signature, + faithful_signature=faithful_signature, + symint_signature=symint_signature, + symint_faithful_signature=symint_faithful_signature, + ) + + +@dataclass(frozen=True) +class DispatcherSignature: + # The schema this signature is derived from + func: FunctionSchema + + # Allows you to prepend an arbitrary prefix to the signature name. + # This is useful for parts of the codegen that generate wrappers around kernels, + # and need to avoid naming collisions. + prefix: str = "" + + symint: bool = True + + def arguments(self) -> List[Binding]: + return dispatcher.arguments(self.func, symint=self.symint) + + def name(self) -> str: + return self.prefix + dispatcher.name(self.func) + + def decl(self, name: Optional[str] = None) -> str: + args_str = ", ".join(a.decl() for a in self.arguments()) + if name is None: + name = self.name() + return f"{self.returns_type().cpp_type()} {name}({args_str})" + + def defn( + self, name: Optional[str] = None, *, is_redispatching_fn: bool = False + ) -> str: + args = [a.defn() for a in self.arguments()] + if is_redispatching_fn: + args = ["c10::DispatchKeySet dispatchKeySet"] + args + args_str = ", ".join(args) + if name is None: + name = self.name() + return f"{self.returns_type().cpp_type()} {name}({args_str})" + + def exprs(self) -> List[Expr]: + return [Expr(a.name, a.nctype) for a in self.arguments()] + + def returns_type(self) -> CType: + return dispatcher.returns_type(self.func.returns, symint=self.symint) + + def ptr_type(self) -> str: + dispatcher_args_types_str = ", ".join(a.type for a in self.arguments()) + return f"{self.returns_type().cpp_type()} (*)({dispatcher_args_types_str})" + + # Return the C++ function type, e.g., something like int(bool) + def type(self) -> str: + dispatcher_args_types_str = ", ".join(a.type for a in self.arguments()) + return f"{self.returns_type().cpp_type()} ({dispatcher_args_types_str})" + + @staticmethod + def from_schema( + func: FunctionSchema, *, prefix: str = "", symint: bool = True + ) -> "DispatcherSignature": + return DispatcherSignature(func, prefix, symint) + + +@dataclass(frozen=True) +class NativeSignature: + # The schema this signature is derived from + func: FunctionSchema + + symint: bool + + prefix: str = "" + + def name(self) -> str: + return self.prefix + native.name(self.func) + + def decl(self, name: Optional[str] = None) -> str: + args_str = ", ".join(a.decl() for a in self.arguments()) + if name is None: + name = self.name() + return f"{native.returns_type(self.func.returns, symint=self.symint).cpp_type()} {name}({args_str})" + + def defn(self, name: Optional[str] = None) -> str: + args_str = ", ".join(a.defn() for a in self.arguments()) + if name is None: + name = self.name() + return f"{native.returns_type(self.func.returns, symint=self.symint).cpp_type()} {name}({args_str})" + + def ptr_type(self) -> str: + # don't include defaults in type signature! + args_str = ", ".join(a.defn() for a in self.arguments()) + return f"{native.returns_type(self.func.returns, symint=self.symint).cpp_type()} (*)({args_str})" + + def arguments(self) -> List[Binding]: + return native.arguments(self.func, symint=self.symint) + + def returns_type(self) -> CType: + return native.returns_type(self.func.returns, symint=self.symint) + + def dispatcher_exprs(self) -> List[Expr]: + return translate.translate( + self.arguments(), dispatcher.arguments(self.func), method=False + ) + + +@dataclass(frozen=True) +class ViewInverseSignature: + g: NativeFunctionsViewGroup + + def name(self) -> str: + return functionalization.reverse_name(self.g.view, include_namespace=False) + + def decl(self) -> str: + return_type = functionalization.returns_type(self.g.view.func) + decls = [ + a.decl() + for a in functionalization.inner_arguments( + self.g.view.func, is_reverse=True + ) + ] + return f"static {return_type.cpp_type()} {self.name()}({', '.join(decls)});" + + +@dataclass(frozen=True) +class FunctionalizationLambda: + g: NativeFunctionsViewGroup + + # are we generating the forward lambda or the reverse lambda? + is_reverse: bool + + def captures(self) -> List[Expr]: + # The lambda lives inside of a kernel following the dispatcher API, so its outer context is the dispatcher arguments + # We also need to read the "reapply views" TLS at the time that the functionalization kernel was executed, + # and plumb it into the lambda. + outer_ctx = dispatcher.arguments(self.g.view.func) + [ + functionalization.reapply_views_binding, + functionalization.inverse_return_mode_binding, + ] + capture_bindings = functionalization.capture_arguments( + self.g.view.func, is_reverse=self.is_reverse + ) + # allow_expensive_conversions is set because we want to convert + # some reference types (IntArrayRef) to value types (vector). + capture_exprs = translate.translate( + outer_ctx, capture_bindings, method=False, allow_expensive_conversions=True + ) + return capture_exprs + + def decl(self) -> str: + return_type = functionalization.returns_type(self.g.view.func) + capture_str = ", ".join( + f"{val.type.name} = {val.expr}" for val in self.captures() + ) + decls = [ + a.decl() + for a in functionalization.outer_arguments(is_reverse=self.is_reverse) + ] + return f"[{capture_str}]({', '.join(decls)}) -> {return_type.cpp_type()}" + + def inner_call(self, *, reapply_views: Optional[bool] = None) -> str: + inner_call_name = functionalization.name( + self.g, + is_reverse=self.is_reverse, + include_namespace=True, + reapply_views=reapply_views, + ) + + arg_ctx = functionalization.outer_arguments(is_reverse=self.is_reverse) + capture_ctx = functionalization.capture_arguments( + self.g.view.func, is_reverse=self.is_reverse + ) + full_ctx = arg_ctx + capture_ctx + + assert self.g.view_copy is not None + call_bindings = functionalization.inner_arguments( + self.g.view_copy.func, is_reverse=self.is_reverse + ) + maybe_index = functionalization.inner_call_index(self.g.view_copy.func) + call_exprs = [ + e.expr for e in translate.translate(full_ctx, call_bindings, method=False) + ] + if not self.is_reverse and maybe_index is not None: + return f'{inner_call_name}({", ".join(call_exprs)})[{maybe_index.name}];' + else: + return f'{inner_call_name}({", ".join(call_exprs)});' + + @staticmethod + def from_func( + g: NativeFunctionsViewGroup, *, is_reverse: bool + ) -> "FunctionalizationLambda": + return FunctionalizationLambda(g, is_reverse) + + +@dataclass(frozen=True) +class StructuredImplSignature: + g: NativeFunctionsGroup + name: str + + def defn(self, name: Optional[str] = None) -> str: + args_str = ", ".join(a.defn() for a in self.arguments()) + return f"TORCH_IMPL_FUNC({self.name})({args_str})" + + def arguments(self) -> List[Binding]: + return structured.impl_arguments(self.g) + + +# Helper functions + + +def kernel_signature( + f: NativeFunction, backend_index: BackendIndex, *, prefix: str = "" +) -> Union["NativeSignature", "DispatcherSignature"]: + # Note [External Backends Follow Dispatcher API] + # Kernel signatures for in-tree backends follow the "native" API, + # while kernels for out-of-tree backends follow the dispatcher API. + # See the comments in `native.py` for details, but historically there have been + # some small differences in schema convention between them and the Dispatcher API. + # Any differences that require translating between the two will results in a runtime cost, + # so we'd like to keep the differences as small as possible. + # With external backends, we'd like to enforce that they write their kernels with schemas + # that match the Dispatcher API directly, if they can. + meta = backend_index.get_kernel(f) + symint = meta is not None and meta.supports_symint() + if symint: + assert ( + f.func.has_symint() + ), f"attempted to define symint kernel for {backend_index.dispatch_key} without SymInt in schema" + if backend_index.external: + return DispatcherSignature.from_schema(f.func, prefix=prefix, symint=symint) + else: + return NativeSignature(f.func, prefix=prefix, symint=symint) + + +# Functions only, no types +from torchgen.api import ( + cpp, + dispatcher, + functionalization, + native, + structured, + translate, +) diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/types_base.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/types_base.py new file mode 100644 index 0000000000000000000000000000000000000000..2f8561e49abe6bc4818ed388c882e07243c665cb --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/types/types_base.py @@ -0,0 +1,270 @@ +""" +Where should I add a new type? `types_base.py` vs `types.py` + +This file defines data model classes for torchgen typing system, as well as some base types such as int32_t. + +`types.py` defines ATen Tensor type and some c10 types, along with signatures that use these types. + +The difference between these two files, is `types_base.py` should be implementation-agnostic, meaning it shouldn't +contain any type definition that is tight to a specific C++ library (e.g., ATen), so that it can be easily reused +if we want to generate code for another C++ library. + +Add new types to `types.py` if these types are ATen/c10 related. +Add new types to `types_base.py` if they are basic and not attached to ATen/c10. +""" +from abc import ABC, abstractmethod +from dataclasses import dataclass +from enum import auto, Enum +from typing import List, Optional, Union + +from torchgen.model import Argument, SelfArgument, TensorOptionsArguments + +# An ArgName is just the str name of the argument in schema; +# but in some special circumstances, we may add a little extra +# context. The Enum SpecialArgName covers all of these cases; +# grep for their construction sites to see when they can occur. + + +class SpecialArgName(Enum): + possibly_redundant_memory_format = auto() + + +ArgName = Union[str, SpecialArgName] + + +# This class shouldn't be created directly; instead, use/create one of the singletons below. +@dataclass(frozen=True) +class BaseCppType: + ns: Optional[str] + name: str + + def __str__(self) -> str: + if self.ns is None or self.ns == "": + return self.name + return f"{self.ns}::{self.name}" + + +# The set of all non-templated, valid, fully-qualified names of C++ types that are used in the codegen. +# Templated types get their own dataclass, mainly to make namespace parsing easier. +byteT = BaseCppType("", "uint8_t") +charT = BaseCppType("", "int8_t") +shortT = BaseCppType("", "int16_t") +# It would be more symmetric for this to be called intT, but it easy to mix +# this up with JIT int (which is int64_t in C++), so we intentionally don't +# define intT to make it obvious when you've stuffed it up +int32T = BaseCppType("", "int32_t") +longT = BaseCppType("", "int64_t") +doubleT = BaseCppType("", "double") +floatT = BaseCppType("", "float") +boolT = BaseCppType("", "bool") +voidT = BaseCppType("", "void") + + +class CType(ABC): + @abstractmethod + def cpp_type(self, *, strip_ref: bool = False) -> str: + raise NotImplementedError + + @abstractmethod + def cpp_type_registration_declarations(self) -> str: + raise NotImplementedError + + @abstractmethod + def remove_const_ref(self) -> "CType": + return self + + +@dataclass(frozen=True) +class BaseCType(CType): + type: BaseCppType + + def cpp_type(self, *, strip_ref: bool = False) -> str: + return str(self.type) + + # For BC reasons, we don't want to introduce at:: namespaces to RegistrationDeclarations.yaml + # TODO: Kill this when we eventually remove it! + def cpp_type_registration_declarations(self) -> str: + return str(self.type).replace("at::", "") + + def remove_const_ref(self) -> "CType": + return self + + +@dataclass(frozen=True) +class ConstRefCType(CType): + elem: "CType" + + def cpp_type(self, *, strip_ref: bool = False) -> str: + if strip_ref: + return self.elem.cpp_type(strip_ref=strip_ref) + return f"const {self.elem.cpp_type()} &" + + def cpp_type_registration_declarations(self) -> str: + return f"const {self.elem.cpp_type_registration_declarations()} &" + + def remove_const_ref(self) -> "CType": + return self.elem.remove_const_ref() + + +@dataclass(frozen=True) +class VectorCType(CType): + elem: "CType" + + def cpp_type(self, *, strip_ref: bool = False) -> str: + # Do not pass `strip_ref` recursively. + return f"::std::vector<{self.elem.cpp_type()}>" + + def cpp_type_registration_declarations(self) -> str: + return f"::std::vector<{self.elem.cpp_type_registration_declarations()}>" + + def remove_const_ref(self) -> "CType": + return VectorCType(self.elem.remove_const_ref()) + + +@dataclass(frozen=True) +class ArrayCType(CType): + elem: "CType" + size: int + + def cpp_type(self, *, strip_ref: bool = False) -> str: + # Do not pass `strip_ref` recursively. + return f"::std::array<{self.elem.cpp_type()},{self.size}>" + + def cpp_type_registration_declarations(self) -> str: + return f"::std::array<{self.elem.cpp_type_registration_declarations()},{self.size}>" + + def remove_const_ref(self) -> "CType": + return ArrayCType(self.elem.remove_const_ref(), self.size) + + +@dataclass(frozen=True) +class TupleCType(CType): + elems: List["CType"] + + def cpp_type(self, *, strip_ref: bool = False) -> str: + # Do not pass `strip_ref` recursively. + return f'::std::tuple<{",".join([e.cpp_type() for e in self.elems])}>' + + def cpp_type_registration_declarations(self) -> str: + return f'::std::tuple<{",".join([e.cpp_type_registration_declarations() for e in self.elems])}>' + + def remove_const_ref(self) -> "CType": + return TupleCType([e.remove_const_ref() for e in self.elems]) + + +@dataclass(frozen=True) +class MutRefCType(CType): + elem: "CType" + + def cpp_type(self, *, strip_ref: bool = False) -> str: + if strip_ref: + return self.elem.cpp_type(strip_ref=strip_ref) + return f"{self.elem.cpp_type()} &" + + def cpp_type_registration_declarations(self) -> str: + return f"{self.elem.cpp_type_registration_declarations()} &" + + def remove_const_ref(self) -> "CType": + return self.elem.remove_const_ref() + + +# A NamedCType is short for Named C++ semantic type. A NamedCType represents a C++ type, plus +# semantic information about what it represents. For example, consider the +# argument "bool pin_memory"; its normal C++ type is "bool", but its C++ +# semantic type also keeps track that this represents a "pin_memory"; you can't +# just use a random other boolean in a context where you need a "pin_memory"! +# + + +@dataclass(frozen=True) +class NamedCType: + name: ArgName + type: CType + + def cpp_type(self, *, strip_ref: bool = False) -> str: + return self.type.cpp_type(strip_ref=strip_ref) + + # For BC reasons, we don't want to introduce at:: namespaces to RegistrationDeclarations.yaml + # TODO: Kill this when we eventually remove it! + def cpp_type_registration_declarations(self) -> str: + return self.type.cpp_type_registration_declarations() + + def remove_const_ref(self) -> "NamedCType": + return NamedCType(self.name, self.type.remove_const_ref()) + + def with_name(self, name: str) -> "NamedCType": + return NamedCType(name, self.type) + + +# A binding represents any C++ binding site for a formal parameter. +# We don't distinguish between binding sites for different APIs; +# instead, all of the important distinctions are encoded in CType, +# which you can use to figure out if a given Binding is appropriate +# for use in another context. (See torchgen.api.translate) + + +@dataclass(frozen=True) +class Binding: + name: str + nctype: NamedCType + argument: Union[Argument, TensorOptionsArguments, SelfArgument] + # TODO: maybe don't represent default here + default: Optional[str] = None + + def rename(self, name: str) -> "Binding": + return Binding( + name=name, + nctype=self.nctype, + argument=self.argument, + default=self.default, + ) + + @property + def type(self) -> str: + return self.nctype.cpp_type() + + def no_default(self) -> "Binding": + return Binding( + name=self.name, + nctype=self.nctype, + default=None, + argument=self.argument, + ) + + def decl(self, *, func_ptr_cast: bool = False) -> str: + mb_default = "" + if self.default is not None: + mb_default = f"={self.default}" + + # casting only needs to know the type + if func_ptr_cast: + return f"{self.type}" + else: + return f"{self.type} {self.name}{mb_default}" + + # For BC reasons, we don't want to introduce at:: namespaces to RegistrationDeclarations.yaml + # TODO: Kill this when we eventually remove it! + def decl_registration_declarations(self) -> str: + type_s = self.nctype.cpp_type_registration_declarations() + mb_default = "" + if self.default is not None: + mb_default = f"={self.default}" + return f"{type_s} {self.name}{mb_default}" + + def defn(self) -> str: + return f"{self.type} {self.name}" + + def with_name(self, name: str) -> "Binding": + return Binding( + name=name, nctype=self.nctype, argument=self.argument, default=self.default + ) + + +# An Expr is a C++ expression. It has a C++ string representing its syntax, +# as well as a CType saying what it provides. + + +@dataclass(frozen=True) +class Expr: + expr: str + type: NamedCType diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/ufunc.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/ufunc.py new file mode 100644 index 0000000000000000000000000000000000000000..7f044706068cf9af126070d8fa39cdca7da83b8b --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/ufunc.py @@ -0,0 +1,209 @@ +from dataclasses import dataclass +from typing import List, Optional + +import torchgen.api.types as api_types + +from torchgen.api import cpp, structured +from torchgen.api.types import ( + ArgName, + BaseCppType, + BaseCType, + Binding, + ConstRefCType, + CType, + NamedCType, + scalarT, +) +from torchgen.model import ( + Argument, + BaseTy, + BaseType, + DispatchKey, + FunctionSchema, + NativeFunctionsGroup, + Type, +) + + +def schema_kernel_name(func: FunctionSchema, dispatch_key: DispatchKey) -> str: + assert func.is_out_fn(), "ufunc.kernel_name should only be invoked on out schemas" + return f"ufunc_{func.name.name}_{dispatch_key}" + + +def kernel_name(g: NativeFunctionsGroup, dispatch_key: DispatchKey) -> str: + return schema_kernel_name(g.out.func, dispatch_key) + + +# Tensors are omitted (as they are stored in TensorIterator), everything else is +# passed along (technically, we can pass tensors along too, it just wastes +# argument registers) +# +# NB: used for CPU only +def dispatchstub_type(t: Type, *, binds: ArgName) -> Optional[NamedCType]: + # Dispatch stubs are always plain ints + r = cpp.valuetype_type(t, binds=binds, symint=False) + if r is not None: + return r + + if t == BaseType(BaseTy.Scalar): + return NamedCType(binds, ConstRefCType(BaseCType(scalarT))) + elif t == BaseType(BaseTy.Tensor): + return None + else: + raise AssertionError(f"unrecognized type {repr(t)}") + + +def opmath_type(scalar_t: BaseCppType) -> BaseCppType: + if scalar_t == api_types.scalar_t: + return api_types.opmath_t + raise NotImplementedError + + +# NB: Tensors in constructor are stored in opmath_t, not scalar_t +# because Tensor in constructor = its a scalar tensor partially applied = +# it can be higher precision and we want to compute in that higher precision +# +# NB: CUDA only +def ufunctor_ctor_type(t: Type, *, binds: ArgName, scalar_t: BaseCppType) -> NamedCType: + r = cpp.valuetype_type(t, binds=binds, symint=False) + if r is not None: + return r + + if t == BaseType(BaseTy.Scalar): + return NamedCType(binds, BaseCType(opmath_type(scalar_t))) + elif t == BaseType(BaseTy.Tensor): + return NamedCType(binds, BaseCType(opmath_type(scalar_t))) + else: + raise AssertionError(f"unrecognized type {repr(t)}") + + +# Only Tensors ever get passed directly to operator() +# +# NB: CUDA only +# (Actually, this works for CPU too) +def ufunctor_apply_type( + t: Type, *, binds: ArgName, scalar_t: BaseCppType +) -> NamedCType: + if t == BaseType(BaseTy.Tensor): + return NamedCType(binds, BaseCType(scalar_t)) + else: + raise AssertionError(f"unrecognized type {repr(t)}") + + +# The actual ufunc template function the user writes. Everything here +# is done in the computation type. compute_t is opmath_t in CUDA and scalar_t +# in CPU +def ufunc_type(t: Type, *, binds: ArgName, compute_t: CType) -> NamedCType: + r = cpp.valuetype_type(t, binds=binds, symint=False) + if r is not None: + return r + + if t == BaseType(BaseTy.Scalar): + return NamedCType(binds, compute_t) + elif t == BaseType(BaseTy.Tensor): + return NamedCType(binds, compute_t) + else: + raise AssertionError(f"unrecognized type {repr(t)}") + + +def ufunctor_ctor_argument(a: Argument, scalar_t: BaseCppType) -> Binding: + return Binding( + nctype=ufunctor_ctor_type(a.type, binds=a.name, scalar_t=scalar_t), + name=a.name, + default=None, + argument=a, + ) + + +def ufunctor_apply_argument(a: Argument, scalar_t: BaseCppType) -> Binding: + return Binding( + nctype=ufunctor_apply_type(a.type, binds=a.name, scalar_t=scalar_t), + name=a.name, + default=None, + argument=a, + ) + + +def ufunc_argument(a: Argument, compute_t: CType) -> Binding: + return Binding( + nctype=ufunc_type(a.type, binds=a.name, compute_t=compute_t), + name=a.name, + default=None, + argument=a, + ) + + +@dataclass(frozen=True) +class UfunctorBindings: + ctor: List[Binding] + apply: List[Binding] + + +# ufunctors are a CUDA-only concept representing functors that take some of +# their arguments on a host-side constructor, and the rest in the device-side +# apply. E.g., +# +# template +# struct CUDAFunctorOnSelf_add { +# using opmath_t = at::opmath_type; +# opmath_t other_; +# opmath_t alpha_; +# CUDAFunctorOnSelf_add(opmath_t other, opmath_t alpha) : other_(other), alpha_(alpha) {} +# __device__ scalar_t operator()(scalar_t self) { +# return ufunc::add(static_cast(self), other_, alpha_); +# } +# }; +# +# The ctor refers to the constructor CUDAFunctorOnSelf_add, while apply refers +# to the operator() definition +def ufunctor_arguments( + g: NativeFunctionsGroup, *, scalar_tensor_idx: Optional[int], scalar_t: BaseCppType +) -> UfunctorBindings: + ctor = [] + apply = [] + for a in g.functional.func.arguments.flat_non_out: + if a.type.is_tensor_like(): + if scalar_tensor_idx == 0: + # put it in the ctor anyway + ctor.append(ufunctor_ctor_argument(a, scalar_t=scalar_t)) + scalar_tensor_idx = None + else: + if scalar_tensor_idx is not None: + scalar_tensor_idx -= 1 + apply.append(ufunctor_apply_argument(a, scalar_t=scalar_t)) + else: + ctor.append(ufunctor_ctor_argument(a, scalar_t=scalar_t)) + assert scalar_tensor_idx is None + return UfunctorBindings(ctor=ctor, apply=apply) + + +# ufuncs are the inner loop template functions that you wrote in ufunc/add.h +# which do the actual computation in question. E.g., +# +# template +# C10_HOST_DEVICE T add(T self, T other, T alpha) __ubsan_ignore_undefined__ { +# return self + alpha * other; +# } +# +# In this file, we refer to T as compute_t which is bound by caller +def ufunc_arguments(g: NativeFunctionsGroup, *, compute_t: CType) -> List[Binding]: + return [ + ufunc_argument(a, compute_t=compute_t) + for a in g.functional.func.arguments.flat_non_out + ] + + +# Stubs are the DispatchStub trampolines that CPU kernels use to get to their +# vectorized versions. E.g., +# +# using structured_binary_fn_alpha = void(*)(TensorIteratorBase&, const Scalar& alpha); +# DECLARE_DISPATCH(structured_binary_fn_alpha, add_stub); +def stub_arguments(g: NativeFunctionsGroup) -> List[Binding]: + # stubs drop all tensor arguments (they are implicit in the TensorIterator + # argument and keep everything else) + return [ + r + for a in g.out.func.arguments.flat_non_out + if not a.type.is_tensor_like() + for r in structured.argument(a) + ] diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/unboxing.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/unboxing.py new file mode 100644 index 0000000000000000000000000000000000000000..df4430c49b745753dc83b2115a7f4d8c000190d0 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/api/unboxing.py @@ -0,0 +1,248 @@ +from typing import List, Tuple + +from torchgen.api import cpp +from torchgen.api.types import Binding, CppSignatureGroup, CType +from torchgen.model import ( + Argument, + BaseTy, + BaseType, + ListType, + NativeFunction, + OptionalType, + Type, +) + +# This file generates the code for unboxing wrappers, i.e., the glue logic to unbox a boxed operator and convert the +# ivalues from stack to correct arguments to the unboxed kernel, based on corresponding JIT schema. This codegen is +# an alternative way to generate unboxing wrappers similar to the existing C++ metaprogramming approach but gets the +# job done statically. These generated unboxing wrappers will be useful under the scenario where we need to register +# a fixed set of operators known at compile time and thus can save some time in runtime initialization phase. +# +# Here's an example on how the codegen works: +# +# - Function Schema (source of truth) +# +# aten::empty.names(int[] size, *, Dimname[]? names, +# ScalarType? dtype=None, Layout? layout=None, +# Device? device=None, bool? pin_memory=None, +# MemoryFormat? memory_format=None) -> Tensor +# - Argument Conversion +# Generates C++ code to convert an ivalue (from stack) to its underlying C++ type. +# - int[] size +# ```cpp +# const c10::List size_list_in = (std::move(peek(stack, 0, 7))).toList(); +# +# std::vector size_vec; +# for (c10::IValue size_elem: size_list_in) { +# int64_t size_base = size_elem.to(); +# size_vec.push_back(size_base); +# } +# at::ArrayRef size_list_out(size_vec); +# ~~~~~~~~~~~~~ <-- The converted argument from ivalues in the stack. +# Will be passed to unboxed kernel. +# ``` +# - Dimname[]? names +# ```cpp +# c10::optional names_opt = (std::move(peek(stack, 1, 7))).toOptional(); +# c10::optional> names_opt_out; +# if (names_opt.has_value()) { +# ~~~~~~~~~~~ <-- Unwrapping optional shell +# const c10::IValue names_opt_in = names_opt.value(); +# const c10::List names_list_in = names_opt_in.toList(); +# +# std::vector names_vec; +# for (c10::IValue names_elem: names_list_in) { +# ~~~~~~~~~~~~~~~~~~~~~~~~~ <-- Unrolling list, then convert elements one by one. +# at::Dimname names_base = names_elem.to(); +# names_vec.push_back(names_base); +# } +# at::ArrayRef names_list_out(names_vec); +# +# names_opt_out = c10::optional>(names_list_out); +# } else { +# names_opt_out = c10::optional>(); +# } +# ``` +# - ScalarType? dtype (similarly for the rest of the arguments) +# ```cpp +# c10::optional dtype_opt = (std::move(peek(stack, 2, 7))).toOptional(); +# c10::optional dtype_opt_out; +# if (dtype_opt.has_value()) { +# const c10::IValue dtype_opt_in = dtype_opt.value(); +# at::ScalarType dtype_base = dtype_opt_in.to(); +# ~~~~~~~~~~~~~~~~~~~~ <-- For base types, convert ivalue to it +# directly using ".to()" API. +# dtype_opt_out = c10::optional(dtype_base); +# } else { +# dtype_opt_out = c10::optional(); +# } +# ``` +# +# - Unboxed Kernel Call +# ```cpp +# auto result_ = torch::empty( +# size_list_out, +# names_opt_out, +# options, +# memory_format_opt_out +# ); +# ``` +# +# - Push Result Back to Stack +# ```cpp +# drop(stack, 7); +# pack(stack, std::move(result_)); +# ``` +connector = "\n\t" + + +# Return unboxing function name for a NativeFunction +def name(f: NativeFunction) -> str: + return f.func.name.unambiguous_name() + + +# Convert all the arguments in a NativeFunction to C++ code +def convert_arguments(f: NativeFunction) -> Tuple[List[Binding], List[str]]: + # we need the 'self' argument so method needs to be False + args = ( + CppSignatureGroup.from_native_function(f, method=False) + .most_faithful_signature() + .arguments() + ) + code_list = [ + f"c10::IValue {args[i].name} = std::move(peek(stack, {i}, {len(args)}));" + for i in range(len(args)) + ] + [""] + binding_list = [] + for arg in args: + # expecting only Argument + if not isinstance(arg.argument, Argument): + raise Exception( + f"Unexpected argument type, expecting `Argument` but got {arg}" + ) + argument: Argument = arg.argument + unboxed_name, _, code, decl = argumenttype_ivalue_convert( + argument.type, + argument.name, + mutable=argument.is_write, + ) + code_list.extend(decl) + code_list.extend(code) + binding_list.append(arg.with_name(unboxed_name)) + return binding_list, code_list + + +# Takes in the type, name and mutability corresponding to an argument, and generates a tuple of: +# (1) the C++ code necessary to unbox the argument +# (2) A Binding corresponding to the newly created unboxed variable, including variable name and its CType +def argumenttype_ivalue_convert( + t: Type, arg_name: str, *, mutable: bool = False +) -> Tuple[str, CType, List[str], List[str]]: + # Unboxing is for mobile, which doesn't care about SymInts + ctype = cpp.argumenttype_type( + t=t, mutable=mutable, binds=arg_name, symint=False + ).type + + if isinstance(t, BaseType): + out_name = f"{arg_name}_base" + code, decl = _gen_code_base_type( + arg_name=arg_name, out_name=out_name, ctype=ctype + ) + elif isinstance(t, OptionalType): + out_name = f"{arg_name}_opt_out" + code, decl = _gen_code_optional_type( + arg_name=arg_name, + out_name=out_name, + t=t, + ctype=ctype, + ) + elif isinstance(t, ListType): + out_name = f"{arg_name}_list_out" + code, decl = _gen_code_list_type( + arg_name=arg_name, + out_name=out_name, + t=t, + ctype=ctype, + ) + else: + raise Exception(f"Cannot handle type {t}. arg_name: {arg_name}") + return out_name, ctype, code, decl + + +def _gen_code_base_type( + arg_name: str, out_name: str, ctype: CType +) -> Tuple[List[str], List[str]]: + return [ + f"{ctype.cpp_type(strip_ref=True)} {out_name} = {arg_name}.to<{ctype.cpp_type(strip_ref=True)}>();" + ], [] + + +def _gen_code_optional_type( + arg_name: str, out_name: str, t: OptionalType, ctype: CType +) -> Tuple[List[str], List[str]]: + in_name = f"{arg_name}_opt_in" + res_name, _, res_code, decl = argumenttype_ivalue_convert(t.elem, in_name) + return ( + f""" +c10::optional {arg_name}_opt = {arg_name}.toOptional(); +{ctype.cpp_type(strip_ref=True)} {out_name}; +if ({arg_name}_opt.has_value()) {{ + const c10::IValue {in_name} = {arg_name}_opt.value(); + {connector.join(res_code)} + {out_name} = {ctype.cpp_type(strip_ref=True)}({res_name}); +}} else {{ + {out_name} = {ctype.cpp_type(strip_ref=True)}(); +}} + """.split( + "\n" + ), + decl, + ) + + +def _gen_code_list_type( + arg_name: str, out_name: str, t: ListType, ctype: CType +) -> Tuple[List[str], List[str]]: + in_name = f"{arg_name}_list_in" + elem_name = f"{arg_name}_elem" + code = [f"const c10::List {in_name} = {arg_name}.toList();"] + res_name, res_ctype, res_code, decl = argumenttype_ivalue_convert(t.elem, elem_name) + # handle list type with size, e.g., bool[4] + if isinstance(t.elem, BaseType) and t.elem.name == BaseTy.bool and t.size: + code.extend( + f""" +{ctype.cpp_type(strip_ref=True)} {out_name} = as_array<{res_ctype.cpp_type(strip_ref=True)}, {t.size}>({in_name}); + """.split( + "\n" + ) + ) + # we have to use c10::List for optional element. e.g., Tensor?[] -> c10::List> + elif isinstance(t.elem, OptionalType): + code.extend( + f""" +{ctype.cpp_type(strip_ref=True)} {out_name}; +for (c10::IValue {elem_name}: {in_name}) {{ + {connector.join(res_code)} + {out_name}.push_back({res_name}); +}} + """.split( + "\n" + ) + ) + else: + # use ArrayRef as default. + vec_name = arg_name + "_vec" + # need to bring vector instantiation out of scope so that ArrayRef has valid data + decl.append(f"std::vector<{res_ctype.cpp_type(strip_ref=True)}> {vec_name};") + code.extend( + f""" +for (c10::IValue {elem_name}: {in_name}) {{ + {connector.join(res_code)} + {vec_name}.push_back({res_name}); +}} +{ctype.cpp_type(strip_ref=True)} {out_name}({vec_name}); + """.split( + "\n" + ) + ) + return code, decl diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/DispatchKeyNativeFunctions.h b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/DispatchKeyNativeFunctions.h new file mode 100644 index 0000000000000000000000000000000000000000..b45a17b5922f8a0b76e0237616914ce9969efca5 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/DispatchKeyNativeFunctions.h @@ -0,0 +1,19 @@ +#pragma once + +// an external backend might generate file within its code tree +// and check all the source files within the tree with clang-format. +// so, disable it since the backend might have a different config. +// clang-format off + +// ${generated_comment} + +#include + +${namespace_prologue} + +struct ${class_name} { + +${dispatch_declarations} + +}; +${namespace_epilogue} diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/Function.h b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/Function.h new file mode 100644 index 0000000000000000000000000000000000000000..5bbd742aae0ad0933d22790715599b4309efca8a --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/Function.h @@ -0,0 +1,26 @@ +#pragma once + +// ${generated_comment} + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +${static_dispatch_ops_headers} + +${operator_includes} + +namespace at { + +${function_definitions} + +} diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/NativeFunction.h b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/NativeFunction.h new file mode 100644 index 0000000000000000000000000000000000000000..4f70db62a4c6429ee8e782fb13fb0ae6ffc5d957 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/NativeFunction.h @@ -0,0 +1,17 @@ +#pragma once + +// ${generated_comment} + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +${extra_includes} + +${native_function_declarations} diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/NativeMetaFunctions.h b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/NativeMetaFunctions.h new file mode 100644 index 0000000000000000000000000000000000000000..89989e2121c9aa34a4583205c3541a04edd36700 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/NativeMetaFunctions.h @@ -0,0 +1,19 @@ +#pragma once + +// ${generated_comment} + +#include +#include +#include +#include + +${NativeMetaFunctions_includes} + +namespace at { + +namespace meta { + +${NativeMetaFunctions_declarations} + +} // namespace meta +} // namespace at diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/Operators.h b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/Operators.h new file mode 100644 index 0000000000000000000000000000000000000000..e74b96ef3d5c6b6d50fe63eac4dca51f0655daa5 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/Operators.h @@ -0,0 +1,74 @@ +#pragma once + +// ${generated_comment} + +#ifdef TORCH_ASSERT_NO_OPERATORS +#error This change adds a dependency on native_functions.yaml, \ + meaning the file will need to be re-compiled every time an operator \ + is changed or added. Consider if your change would be better placed in \ + another file, or if a more specific header might achieve the same goal. \ + See NOTE: [Tensor vs. TensorBase] +#endif + +#if defined(AT_PER_OPERATOR_HEADERS) && defined(TORCH_ASSERT_ONLY_METHOD_OPERATORS) +#error This change adds a dependency on all pytorch operators, meaning the \ + file will need to be re-compiled every time an operator is changed or added. \ + Consider including a specific operator from \ + and see NOTE [TORCH_ASSERT_ONLY_METHOD_OPERATORS]. +#endif + +#include +#include +#include +#include +#include +#include +#include +#include + +${Operators_includes} + +// Extension writers: do you write wrapper functions? Are you frustrated with +// resolving overloads of operators? Are you frustrated with dealing with +// pointer-to-methods and resolving overloads of pointer-to-methods?? Look no +// further, this is the utility for you. +// +// Given an operator schema: aten::op.overload(... +// +// Use ATEN_FN2(op, overload) to get a *function* version of the operator +// that is guaranteed to not be overloaded. This means that you can safely +// decltype(&ATEN_FN2(op, overload)) it. NB: the 2 means this macro takes 2 args. +// +// Given an operator schema without an overload name: aten::op(... +// +// Use ATEN_FN(op) to get an unambiguous *function* version of the operator. +// +// There is some interesting behavior for out= operations. +// ATEN_FN2(sin, out) gives a function that is *faithful* to the schema; +// that is, the order of arguments is exactly what it looks like in the schema. + +#define ATEN_FN2(op_name, overload) at::_ops::op_name##_##overload::call +#define ATEN_FN(op_name) at::_ops::op_name::call + +// Separately, ATEN_OP(op) and ATEN_OP2(op, overload) define a class containing compile-time +// metadata about a given aten operator. +// Notable data on the class includes: +// - ATEN_OP2(add, Tensor)::name // returns the string name: "add" +// - ATEN_OP2(add, Tensor)::overload_name // returns the string overload name: "Tensor" +// - ATEN_OP2(add, Tensor)::schema // returns the C++ schema type: at::Tensor (const at::Tensor &, const at::Tensor &, const at::Scalar &) +// - ATEN_OP2(add, Tensor)::schema_str // returns the string jit type: "add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor" + +#define ATEN_OP2(op_name, overload) at::_ops::op_name##_##overload +#define ATEN_OP(op_name) at::_ops::op_name + +// WARNING: Please do not call any of the ops in the _ops namespace directly. +// Use the ATEN_FN macros. We do not guarantee stability of the naming +// scheme for the functions in at::_ops + +// See Note [The ATen Operators API] for details of the at::_ops namespace + +namespace at { +namespace _ops { +${Operators_declarations} +} // namespace _ops +} // namespace at diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/RedispatchFunctions.cpp b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/RedispatchFunctions.cpp new file mode 100644 index 0000000000000000000000000000000000000000..58102bd97fca4eaef477818b0b0a92b7995e38b1 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/RedispatchFunctions.cpp @@ -0,0 +1,15 @@ +// ${generated_comment} + +#include +#include + +#include +#include + +namespace at { + +namespace redispatch { + ${function_redispatch_definitions} +} // namespace redispatch + +} // namespace at diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/RegisterDispatchDefinitions.ini b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/RegisterDispatchDefinitions.ini new file mode 100644 index 0000000000000000000000000000000000000000..3bf7f9b1bb32112a126e88a2e23e47c91e58dd9c --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/RegisterDispatchDefinitions.ini @@ -0,0 +1,24 @@ +${ns_prologue} + +// NB: TORCH_LIBRARY_IMPL must be in an anonymous namespace to avoid +// ambiguity with conflicting identifiers that may have been defined in +// at namespace already. +namespace { + +${dispatch_helpers} + +${dispatch_anonymous_definitions} + +${static_init_dispatch_registrations} + +} // anonymous namespace + +${deferred_dispatch_registrations} + +namespace ${dispatch_namespace} { + +${dispatch_namespaced_definitions} + +} // namespace ${dispatch_namespace} + +${ns_epilogue} diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/TensorBody.h b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/TensorBody.h new file mode 100644 index 0000000000000000000000000000000000000000..010f12d4cfbce98804d42ed7028e686ce7ba1174 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/TensorBody.h @@ -0,0 +1,753 @@ +#pragma once + +#ifdef TORCH_ASSERT_NO_OPERATORS +#error This change adds a dependency on native_functions.yaml, \ + meaning the file will need to be re-compiled every time an operator \ + is changed or added. Consider if your change would be better placed in \ + another file, or if a more specific header might achieve the same goal. \ + See NOTE: [Tensor vs. TensorBase] +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +#include + +namespace c10{ +template class List; +template class IListRef; +} +namespace at { +struct Generator; +struct Type; +class DeprecatedTypeProperties; +class Tensor; +} // namespace at +namespace at { +namespace indexing { +struct TensorIndex; +} // namespace indexing +} // namespace at + +namespace torch { namespace autograd { + +struct Node; + +}} // namespace torch::autograd + +namespace at { + +class OptionalTensorRef; +class TensorRef; +class Tensor; +using TensorList = ArrayRef; +using ITensorList = c10::IListRef; + +using Stream = c10::Stream; + +// Tensor is a "generic" object holding a pointer to the underlying TensorImpl object, which +// has an embedded reference count. In this way, Tensor is similar to boost::intrusive_ptr. +// +// For example: +// +// void func(Tensor a) { +// Tensor b = a; +// ... +// } +// +// In this example, when we say Tensor b = a, we are creating a new object that points to the +// same underlying TensorImpl, and bumps its reference count. When b goes out of scope, the +// destructor decrements the reference count by calling release() on the TensorImpl it points to. +// The existing constructors, operator overloads, etc. take care to implement the correct semantics. +// +// Note that Tensor can also be NULL, i.e. it is not associated with any underlying TensorImpl, and +// special care must be taken to handle this. +class TORCH_API Tensor: public TensorBase { + protected: + // Create a Tensor with a +0 reference count. Special care must be + // taken to avoid decrementing this reference count at destruction + // time. Intended to support MaybeOwnedTraits. + explicit Tensor(unsafe_borrow_t, const TensorBase& rhs): TensorBase(unsafe_borrow_t{}, rhs) {} + friend MaybeOwnedTraits; + friend OptionalTensorRef; + friend TensorRef; + + public: + Tensor() = default; + // This constructor should not be used by end users and is an implementation + // detail invoked by autogenerated code. + explicit Tensor( + c10::intrusive_ptr tensor_impl) + : TensorBase(std::move(tensor_impl)) {} + Tensor(const Tensor &tensor) = default; + Tensor(Tensor &&tensor) = default; + + // Implicitly move-constructible from TensorBase, but must be explicit to increase refcount + explicit Tensor(const TensorBase &base): TensorBase(base) {} + /*implicit*/ Tensor(TensorBase &&base): TensorBase(std::move(base)) {} + + // Creates a new wrapper from TensorImpl. Intentionally a free method because + // it should be used with care. Checks necessary invariants + static Tensor wrap_tensor_impl( + c10::intrusive_ptr tensor_impl) { + return TensorBase::wrap_tensor_impl(std::move(tensor_impl)); + } + + Tensor contiguous(MemoryFormat memory_format=MemoryFormat::Contiguous) const { + return TensorBase::contiguous(memory_format); + } + + Tensor conj() const { + if (!this->is_complex()) { + return *this; + } + + switch (this->layout()) { + case at::kSparse: + case at::kSparseCsr: + case at::kSparseCsc: + case at::kSparseBsr: + case at::kSparseBsc: + return this->conj_physical(); + default: + return this->_conj(); + } + } + + // Aliased by Dimname overloads, so need explicit using + using TensorBase::size; + using TensorBase::sym_size; + using TensorBase::stride; + + /// Should be used if *this can reasonably be expected to be contiguous and + /// performance is important. + /// Compared to contiguous, it saves a reference count + /// increment/decrement if *this is already contiguous, at the cost + /// in all cases of an extra pointer of stack usage, an extra branch + /// to access, and an extra branch at destruction time. + c10::MaybeOwned expect_contiguous(MemoryFormat memory_format=MemoryFormat::Contiguous) const &; + + // Use .contiguous() instead. Trying to borrow from a prvalue Tensor + // will only lead to trouble and dangling references. + c10::MaybeOwned expect_contiguous(MemoryFormat memory_format=MemoryFormat::Contiguous) && = delete; + + // The following overloads are very intruiging. Consider the following + // program: + // + // x[1] = 3; + // + // We would expect that the first entry of x is written to 3. But how can we + // actually achieve this? x[1] evaluates to a tensor... + // + // The answer is, using a ref-qualifier. x[1] is an rvalue, which cannot be + // (profitably) assigned to in the traditional sense, so we overload + // assignment to mean, "Actually, copy 3 into the tensor data." This is done + // with an rvalue-reference ref-qualified overload (the methods with && at the + // end of their type.) + // + // There's one more fly in the ointment: We also want + // + // Tensor x = y; + // + // to work, and we want it NOT to copy. So we need a traditional operator= + // overload. But we MUST specify a mutable lvalue ref-qualifier, to + // disambiguate the traditional overload from the rvalue-reference + // ref-qualified overload. Otherwise, it will be ambiguous, because + // a non ref-qualified method is eligible for all situations. + + // Unfortunately, we have to write these constructors out manually + // to work around an MSVC bug: + // error C2580: 'at::Tensor &at::Tensor::operator =(const at::Tensor &) &': + // multiple versions of a defaulted special member functions are not allowed + // Tensor& operator=(const Tensor&) & = default; + // Tensor& operator=(Tensor&&) & = default; + + // Also MSVC will wrongly issue the following warning with the aforementioned fix + // warning C4522: 'at::Tensor': multiple assignment operators specified + // Let's just skip the warning. + // + // TODO: temporarily disabled + + Tensor& operator=(const TensorBase& x) & { + impl_ = x.getIntrusivePtr(); + return *this; + } + Tensor& operator=(TensorBase&& x) & noexcept { + impl_ = x.unsafeReleaseIntrusivePtr(); + return *this; + } + + Tensor& operator=(const Tensor &x) & { + return operator=(static_cast(x)); + } + Tensor& operator=(Tensor &&x) & noexcept { + return operator=(static_cast(x)); + } + + Tensor& operator=(const Scalar &v) && { + return fill_(v); + } + Tensor& operator=(const Tensor &rhs) && { + return copy_(rhs); + } + Tensor& operator=(Tensor&& rhs) && { + return copy_(rhs); + } + + C10_DEPRECATED_MESSAGE("Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device().") + DeprecatedTypeProperties & type() const { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + dispatchKeyToBackend(legacyExtractDispatchKey(key_set())), + scalar_type()); + } + + Tensor toType(ScalarType t) const { + return to(options().dtype(t), /*non_blocking*/ false, /*copy*/ false); + } + + // TODO: Deprecate me + Tensor toBackend(Backend b) const { + return to(options().device(backendToDeviceType(b)).layout(layout_from_backend(b)), /*non_blocking*/ false, /*copy*/ false); + } + + C10_DEPRECATED_MESSAGE("Tensor.is_variable() is deprecated; everything is a variable now. (If you want to assert that variable has been appropriately handled already, use at::impl::variable_excluded_from_dispatch())") + bool is_variable() const noexcept { + return !at::impl::variable_excluded_from_dispatch(); + } + + template + C10_DEPRECATED_MESSAGE("Tensor.data() is deprecated. Please use Tensor.data_ptr() instead.") + T * data() const { + return data_ptr(); + } + + template + T item() const; + + template class PtrTraits = DefaultPtrTraits, typename index_t = int64_t> + C10_DEPRECATED_MESSAGE("packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead") + GenericPackedTensorAccessor packed_accessor() const & { + return generic_packed_accessor(); + } + template class PtrTraits = DefaultPtrTraits, typename index_t = int64_t> + C10_DEPRECATED_MESSAGE("packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead") + GenericPackedTensorAccessor packed_accessor() && = delete; + + Tensor operator~() const { + return bitwise_not(); + } + Tensor operator-() const { + return neg(); + } + Tensor& operator+=(const Tensor & other) { + return add_(other); + } + Tensor& operator+=(const Scalar & other) { + return add_(other); + } + Tensor& operator-=(const Tensor & other) { + return sub_(other); + } + Tensor& operator-=(const Scalar & other) { + return sub_(other); + } + Tensor& operator*=(const Tensor & other) { + return mul_(other); + } + Tensor& operator*=(const Scalar & other) { + return mul_(other); + } + Tensor& operator/=(const Tensor & other) { + return div_(other); + } + Tensor& operator/=(const Scalar & other) { + return div_(other); + } + Tensor& operator&=(const Tensor & other) { + return bitwise_and_(other); + } + Tensor& operator|=(const Tensor & other) { + return bitwise_or_(other); + } + Tensor& operator^=(const Tensor & other) { + return bitwise_xor_(other); + } + Tensor operator[](const Scalar & index) const { + if (!index.isIntegral(false)) { + TORCH_CHECK_INDEX(false, "Can only index tensors with integral scalars"); + } + return this->operator[](index.toLong()); + } + Tensor operator[](const Tensor & index) const { + // These properties are checked in the Scalar constructor, but we already + // check them here to provide more useful diagnostics for the user. + if (!index.defined()) { + TORCH_CHECK_INDEX(false, "Can only index with tensors that are defined"); + } + if (index.dim() != 0) { + TORCH_CHECK_INDEX(false, + "Can only index with tensors that are scalars (zero-dim)"); + } + // The Scalar(Tensor) constructor is explicit, so we need to call it. + return this->operator[](index.item()); + } + Tensor operator[](int64_t index) const { + return select(0, index); + } + + Tensor index(ArrayRef indices) const; + Tensor index(std::initializer_list indices) const; + + Tensor & index_put_(ArrayRef indices, Tensor const & rhs); + Tensor & index_put_(ArrayRef indices, const Scalar& v); + Tensor & index_put_(std::initializer_list indices, Tensor const & rhs); + Tensor & index_put_(std::initializer_list indices, const Scalar& v); + + Tensor cpu() const { + return to(options().device(c10::DeviceType::CPU), /*non_blocking*/ false, /*copy*/ false); + } + + // TODO: The Python version also accepts arguments + Tensor cuda() const { + return to(options().device(c10::DeviceType::CUDA), /*non_blocking*/ false, /*copy*/ false); + } + + Tensor hip() const { + return to(options().device(c10::DeviceType::HIP), /*non_blocking*/ false, /*copy*/ false); + } + + Tensor ve() const { + return to(options().device(c10::DeviceType::VE), /*non_blocking*/ false, /*copy*/ false); + } + + Tensor vulkan() const { + return to(options().device(c10::DeviceType::Vulkan), /*non_blocking*/ false, /*copy*/ false); + } + + Tensor metal() const { + return to(options().device(c10::DeviceType::Metal), /*non_blocking*/ false, /*copy*/ false); + } + + Tensor meta() const { + return to(options().device(c10::DeviceType::Meta), /*non_blocking*/ false, /*copy*/ false); + } + + // ~~~~~ Autograd API ~~~~~ + + /// \fn bool is_leaf() const; + /// + /// All Tensors that have `requires_grad()` which is ``false`` will be leaf Tensors by convention. + /// + /// For Tensors that have `requires_grad()` which is ``true``, they will be leaf Tensors if they were + /// created by the user. This means that they are not the result of an operation and so + /// `grad_fn()` is `nullptr`. + /// + /// Only leaf Tensors will have their `grad()` populated during a call to `backward()`. + /// To get `grad()` populated for non-leaf Tensors, you can use `retain_grad()`. + /// + /// Example: + /// @code + /// auto a = torch::rand(10, torch::requires_grad()); + /// std::cout << a.is_leaf() << std::endl; // prints `true` + /// + /// auto b = torch::rand(10, torch::requires_grad()).to(torch::kCUDA); + /// std::cout << b.is_leaf() << std::endl; // prints `false` + /// // b was created by the operation that cast a cpu Tensor into a cuda Tensor + /// + /// auto c = torch::rand(10, torch::requires_grad()) + 2; + /// std::cout << c.is_leaf() << std::endl; // prints `false` + /// // c was created by the addition operation + /// + /// auto d = torch::rand(10).cuda(); + /// std::cout << d.is_leaf() << std::endl; // prints `true` + /// // d does not require gradients and so has no operation creating it (that is tracked by the autograd engine) + /// + /// auto e = torch::rand(10).cuda().requires_grad_(); + /// std::cout << e.is_leaf() << std::endl; // prints `true` + /// // e requires gradients and has no operations creating it + /// + /// auto f = torch::rand(10, torch::device(torch::kCUDA).requires_grad(true)); + /// std::cout << f.is_leaf() << std::endl; // prints `true` + /// // f requires grad, has no operation creating it + /// @endcode + + /// \fn void backward(const Tensor & gradient={}, c10::optional retain_graph=c10::nullopt, bool create_graph=false, c10::optional inputs=c10::nullopt) const; + /// + /// Computes the gradient of current tensor with respect to graph leaves. + /// + /// The graph is differentiated using the chain rule. If the tensor is + /// non-scalar (i.e. its data has more than one element) and requires + /// gradient, the function additionally requires specifying ``gradient``. + /// It should be a tensor of matching type and location, that contains + /// the gradient of the differentiated function w.r.t. this Tensor. + /// + /// This function accumulates gradients in the leaves - you might need to + /// zero them before calling it. + /// + /// \param gradient Gradient w.r.t. the + /// tensor. If it is a tensor, it will be automatically converted + /// to a Tensor that does not require grad unless ``create_graph`` is True. + /// None values can be specified for scalar Tensors or ones that + /// don't require grad. If a None value would be acceptable then + /// this argument is optional. + /// \param retain_graph If ``false``, the graph used to compute + /// the grads will be freed. Note that in nearly all cases setting + /// this option to True is not needed and often can be worked around + /// in a much more efficient way. Defaults to the value of + /// ``create_graph``. + /// \param create_graph If ``true``, graph of the derivative will + /// be constructed, allowing to compute higher order derivative + /// products. Defaults to ``false``. + /// \param inputs Inputs w.r.t. which the gradient will be accumulated into + /// ``at::Tensor::grad``. All other Tensors will be ignored. If not + /// provided, the gradient is accumulated into all the leaf Tensors + /// that were used to compute the current tensor. + /// When inputs are provided and a given input is not a leaf, + /// the current implementation will call its grad_fn (even though it is not strictly needed to get this gradients). + /// It is an implementation detail on which the user should not rely. + /// See https://github.com/pytorch/pytorch/pull/60521#issuecomment-867061780 for more details. + void backward(const Tensor & gradient={}, c10::optional retain_graph=c10::nullopt, bool create_graph=false, c10::optional inputs=c10::nullopt) const { + // NB: Adding this wrapper to _backward here because we'd like our + // 'backwards' api to accept the 'inputs' argument optionally. Since code gen + // currently does not support optional of TensorList our approach is to replace + // backward in native_functions.yaml with _backward and call it here instead. + if (inputs.has_value()) { + TORCH_CHECK(inputs.value().size() > 0, "'inputs' argument to backward cannot be empty") + this->_backward(inputs.value(), gradient, retain_graph, create_graph); + } else { + this->_backward({}, gradient, retain_graph, create_graph); + } + } + + /// \fn Tensor detach() const; + /// + /// Returns a new Tensor, detached from the current graph. + /// The result will never require gradient. + + /// \fn Tensor & detach_() const; + /// + /// Detaches the Tensor from the graph that created it, making it a leaf. + /// Views cannot be detached in-place. + + /// \fn void retain_grad() const; + /// + /// Enables this Tensor to have their :attr:`grad` populated during + /// :func:`backward`. This is a no-op for leaf tensors. + + /// \fn bool retains_grad() const; + /// + /// Is ``true`` if this Tensor is non-leaf and its :attr:`grad` is enabled to be + /// populated during :func:`backward`, ``false`` otherwise. + + const Tensor& set_requires_grad(bool requires_grad) const { + TensorBase::set_requires_grad(requires_grad); + return *this; + } + + /// Return a mutable reference to the gradient. This is conventionally + /// used as `t.grad() = x` to set a gradient to a completely new tensor. + /// Note that this function work with a non-const Tensor and is not + /// thread safe. + Tensor& mutable_grad() const { + return impl_->mutable_grad(); + } + + /// This function returns an undefined tensor by default and returns a defined tensor + /// the first time a call to `backward()` computes gradients for this Tensor. + /// The attribute will then contain the gradients computed and future calls + /// to `backward()` will accumulate (add) gradients into it. + const Tensor& grad() const { + const Tensor& maybe_grad = impl_->grad(); + if (!is_leaf() && !retains_grad() && !maybe_grad.defined()) { + TORCH_WARN( + "The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad " + "attribute won't be populated during autograd.backward(). If you indeed want the .grad " + "field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. " + "If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor " + "instead. See github.com/pytorch/pytorch/pull/30531 for more informations."); + } + return maybe_grad; + } + + // The Forward AD API functions below are low level and are not to be used by end + // users who should use the API provided in torch/csrc/autograd.h + + /// This function returns the forward gradient for this Tensor at the given level. + const Tensor& _fw_grad(uint64_t level) const { + return impl_->_fw_grad(level, *this); + } + + /// This function can be used to set the value of the forward grad. + /// Note that the given new_grad might not be used directly if it has different + /// metadata (size/stride/storage offset) compared to this Tensor. In that case, + /// new_grad content will be copied into a new Tensor + void _set_fw_grad(const TensorBase& new_grad, uint64_t level, bool is_inplace_op) const { + impl_->_set_fw_grad(new_grad, *this, level, is_inplace_op); + } + + + // STOP. Thinking of adding a method here, which only makes use + // of other ATen methods? Define it in native_functions.yaml. + + //example + //Tensor * add(Tensor & b); + ${tensor_method_declarations} + + // Special C++ only overloads for std()-like functions (See gh-40287) + // These are needed because int -> bool conversion takes precedence over int -> IntArrayRef + // So, for example std(0) would select the std(unbiased=False) overload + + Tensor var(int dim) const { + return var(IntArrayRef{dim}); + } + + Tensor std(int dim) const { + return std(IntArrayRef{dim}); + } + + // We changed .dtype() to return a TypeMeta in #12766. Ideally, we want the + // at::kDouble and its friends to be TypeMeta's, but that hasn't happened yet. + // Before that change, we make this method to maintain BC for C++ usage like + // `x.to(y.dtype)`. + // TODO: remove following two after at::kDouble and its friends are TypeMeta's. + inline Tensor to(caffe2::TypeMeta type_meta, bool non_blocking=false, bool copy=false) const { + return this->to(/*scalar_type=*/typeMetaToScalarType(type_meta), non_blocking, copy); + } + inline Tensor to(Device device, caffe2::TypeMeta type_meta, bool non_blocking=false, bool copy=false) const { + return this->to(device, /*scalar_type=*/typeMetaToScalarType(type_meta), non_blocking, copy); + } + + template + decltype(auto) m(F func, Args&&... params) const { + return func(*this, std::forward(params)...); + } + + /// NOTE: This is similar to the legacy `.data()` function on `Variable`, and is intended + /// to be used from functions that need to access the `Variable`'s equivalent `Tensor` + /// (i.e. `Tensor` that shares the same storage and tensor metadata with the `Variable`). + /// + /// One notable difference with the legacy `.data()` function is that changes to the + /// returned `Tensor`'s tensor metadata (e.g. sizes / strides / storage / storage_offset) + /// will not update the original `Variable`, due to the fact that this function + /// shallow-copies the `Variable`'s underlying TensorImpl. + at::Tensor tensor_data() const { + return TensorBase::tensor_data(); + } + + /// NOTE: `var.variable_data()` in C++ has the same semantics as `tensor.data` + /// in Python, which create a new `Variable` that shares the same storage and + /// tensor metadata with the original `Variable`, but with a completely new + /// autograd history. + /// + /// NOTE: If we change the tensor metadata (e.g. sizes / strides / + /// storage / storage_offset) of a variable created from `var.variable_data()`, those + /// changes will not update the original variable `var`. In `.variable_data()`, we set + /// `allow_tensor_metadata_change_` to false to make such changes explicitly illegal, + /// in order to prevent users from changing metadata of `var.variable_data()` + /// and expecting the original variable `var` to also be updated. + at::Tensor variable_data() const { + return TensorBase::variable_data(); + } + + // Hooks + //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + template + using hook_return_void_t = std::enable_if_t>::value, unsigned>; + template + using hook_return_var_t = std::enable_if_t, Tensor>::value, unsigned>; + + /// Registers a backward hook. + /// + /// The hook will be called every time a gradient with respect to the Tensor is computed. + /// The hook should have one of the following signature: + /// ``` + /// hook(Tensor grad) -> Tensor + /// ``` + /// ``` + /// hook(Tensor grad) -> void + /// ``` + /// The hook should not modify its argument, but it can optionally return a new gradient + /// which will be used in place of `grad`. + /// + /// This function returns the index of the hook in the list which can be used to remove hook. + /// + /// Example: + /// @code + /// auto v = torch::tensor({0., 0., 0.}, torch::requires_grad()); + /// auto h = v.register_hook([](torch::Tensor grad){ return grad * 2; }); // double the gradient + /// v.backward(torch::tensor({1., 2., 3.})); + /// // This prints: + /// // ``` + /// // 2 + /// // 4 + /// // 6 + /// // [ CPUFloatType{3} ] + /// // ``` + /// std::cout << v.grad() << std::endl; + /// v.remove_hook(h); // removes the hook + /// @endcode + template + hook_return_void_t register_hook(T&& hook) const; + template + hook_return_var_t register_hook(T&& hook) const; + + // Variable methods + //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + Tensor data() const { + return TensorBase::data(); + } + + void _backward(TensorList inputs, const c10::optional& gradient, c10::optional keep_graph, bool create_graph) const; + + const Tensor& requires_grad_(bool _requires_grad=true) const { + TensorBase::requires_grad_(_requires_grad); + return *this; + } +}; + +namespace detail { +// Helper creator for Tensor class which doesn't requires the users to pass +// in an intrusive_ptr instead it just converts the argument passed to +// requested intrusive_ptr type. +template +Tensor make_tensor(Args&&... args) { + return Tensor(c10::make_intrusive(std::forward(args)...)); +} + +} // namespace detail + +} // namespace at + + +namespace at { +${tensor_method_definitions} +} // namespace at + + +namespace c10 { +template <> +struct MaybeOwnedTraits { + using owned_type = at::Tensor; + using borrow_type = at::Tensor; + + static borrow_type createBorrow(const owned_type& from) { + // NOTE: this can be implemented without the special + // unsafe_borrow_t Tensor constructor as + // + // return borrow_type(c10::intrusive_ptr::reclaim(from.unsafeGetTensorImpl())); + // + // but that hurts inlining due to the nullptr check in the + // Tensor(c10::intrusive_ptr<...>) constructor. We already know + // that from.impl_ isn't null because from is a valid Tensor, so + // we needn't do the check again. (using __builtin_assume can + // avoid this, but wouldn't be portable to MSVC.) + return borrow_type(borrow_type::unsafe_borrow_t{}, from); + } + + static void assignBorrow(borrow_type& lhs, const borrow_type& rhs) { + lhs.unsafeReleaseTensorImpl(); + // See above note: this can be implemented with public API + // similarly to createBorrow(), but that would hurt inlining. + lhs = borrow_type(borrow_type::unsafe_borrow_t{}, rhs); + } + + static void destroyBorrow(borrow_type& toDestroy) { + toDestroy.unsafeReleaseTensorImpl(); // "leak" it, but it was already +0. + } + + static const owned_type& referenceFromBorrow(const borrow_type& borrow) { + return borrow; + } + + static const owned_type* pointerFromBorrow(const borrow_type& borrow) { + return &borrow; + } + + static bool debugBorrowIsValid(const borrow_type& /*borrow*/) { + return true; + } +}; + +template <> +struct ExclusivelyOwnedTraits { + using repr_type = at::Tensor; + using pointer_type = at::Tensor*; + using const_pointer_type = const at::Tensor*; + + static repr_type nullRepr() { + return at::Tensor(); + } + + template + static repr_type createInPlace(Args&&... args) { + return at::Tensor(std::forward(args)...); + } + + static repr_type moveToRepr(at::Tensor&& x) { + return std::move(x); + } + + static void destroyOwned(at::Tensor& x) { + return ExclusivelyOwnedTraits::destroyOwned(x); + } + + static at::Tensor take(at::Tensor& x) { + return std::move(x); + } + + static pointer_type getImpl(repr_type& x) { + return &x; + } + + static const_pointer_type getImpl(const repr_type& x) { + return &x; + } +}; +} // namespace c10 + +namespace at { + +inline c10::MaybeOwned borrow_from_optional_tensor( + const c10::optional& opt) { + return opt.has_value() + ? c10::MaybeOwned::borrowed(*opt) + : c10::MaybeOwned::owned(std::in_place); +} + +inline c10::MaybeOwned Tensor::expect_contiguous(MemoryFormat memory_format) const & { + if (is_contiguous(memory_format)) { + return c10::MaybeOwned::borrowed(*this); + } else { + return c10::MaybeOwned::owned(__dispatch_contiguous(memory_format)); + } +} +} // namespace at diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/TensorMethods.cpp b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/TensorMethods.cpp new file mode 100644 index 0000000000000000000000000000000000000000..76439040eda45ec34f627298260e7bf081fd728c --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/ATen/templates/TensorMethods.cpp @@ -0,0 +1,61 @@ +#include +#include + +#include + +namespace at { + +namespace { + +// Verifies the requested type is the same as the Tensor's type. +void check_type(const TensorBase& tensor, ScalarType type, c10::string_view type_name) { + TORCH_CHECK( + tensor.scalar_type() == type + || (isQIntType(tensor.scalar_type()) + && toUnderlying(tensor.scalar_type()) == type), + "expected scalar type ", type_name, " but found ", tensor.scalar_type()); +} + +} // namespace + +#define DEFINE_CAST(T, name) \ + template <> \ + TORCH_API const T* TensorBase::const_data_ptr() const { \ + check_type(*this, ScalarType::name, #name); \ + return this->unsafeGetTensorImpl()->data_ptr_impl(); \ + } \ + \ + template <> \ + TORCH_API const T* TensorBase::const_data_ptr() const { \ + check_type(*this, ScalarType::name, #name); \ + return this->unsafeGetTensorImpl()->data_ptr_impl>(); \ + } \ + \ + template <> \ + TORCH_API T* TensorBase::mutable_data_ptr() const { \ + check_type(*this, ScalarType::name, #name); \ + return this->unsafeGetTensorImpl()->mutable_data_ptr_impl(); \ + } \ + \ + template <> \ + TORCH_API T* TensorBase::data_ptr() const { \ + return mutable_data_ptr(); \ + } \ + + AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(DEFINE_CAST) + AT_FORALL_QINT_TYPES(DEFINE_CAST) + DEFINE_CAST(uint16_t, UInt16) + DEFINE_CAST(uint32_t, UInt32) + DEFINE_CAST(uint64_t, UInt64) + #undef DEFINE_CAST + + #define DEFINE_ITEM(T, name) \ + template <> \ + TORCH_API T Tensor::item() const { \ + return item().to##name(); \ + } + + AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(DEFINE_ITEM) + #undef DEFINE_ITEM + + } //namespace at diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/BUILD.bazel b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/BUILD.bazel new file mode 100644 index 0000000000000000000000000000000000000000..d1a0db360d230fe0f027c19869c6307f17010503 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/BUILD.bazel @@ -0,0 +1,4 @@ +load("//:tools/bazel.bzl", "rules") +load(":build.bzl", "define_targets") + +define_targets(rules = rules) diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/__init__.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..90f3669f4c393913b2786596e1b73c32a3f53e0c Binary files /dev/null and b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/__init__.cpython-311.pyc differ diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/context.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/context.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fc2201d40720978d2ef82007c729c35ee8629504 Binary files /dev/null and b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/context.cpython-311.pyc differ diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_autograd.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_autograd.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6b817d65f3037bffc4a2096a0c7bad7b6ffb850d Binary files /dev/null and b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_autograd.cpython-311.pyc differ diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_variable_factories.cpython-311.pyc b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_variable_factories.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e6e51fee10ef986515cc0573131cb118a92d9913 Binary files /dev/null and b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_variable_factories.cpython-311.pyc differ diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/deprecated.yaml b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/deprecated.yaml new file mode 100644 index 0000000000000000000000000000000000000000..52f7ec50b6ea15dae1c3308358997950d295c924 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/torchgen/packaged/autograd/deprecated.yaml @@ -0,0 +1,134 @@ +# Deprecated function signatures. These are exposed in Python, but not included +# in the error message suggestions. + +- name: add(Tensor self, Scalar alpha, Tensor other) -> Tensor + aten: add(self, other, alpha) + +- name: add_(Tensor(a!) self, Scalar alpha, Tensor other) -> Tensor(a!) + aten: add_(self, other, alpha) + +- name: add(Tensor self, Scalar alpha, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + aten: add_out(out, self, other, alpha) + +- name: addbmm(Scalar beta, Tensor self, Scalar alpha, Tensor batch1, Tensor batch2) -> Tensor + aten: addbmm(self, batch1, batch2, beta, alpha) + +- name: addbmm_(Scalar beta, Tensor(a!) self, Scalar alpha, Tensor batch1, Tensor batch2) -> Tensor(a!) + aten: addbmm_(self, batch1, batch2, beta, alpha) + +- name: addbmm(Scalar beta, Tensor self, Scalar alpha, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor(a!) + aten: addbmm_out(out, self, batch1, batch2, beta, alpha) + +- name: addbmm(Scalar beta, Tensor self, Tensor batch1, Tensor batch2) -> Tensor + aten: addbmm(self, batch1, batch2, beta, 1) + +- name: addbmm_(Scalar beta, Tensor(a!) self, Tensor batch1, Tensor batch2) -> Tensor(a!) + aten: addbmm_(self, batch1, batch2, beta, 1) + +- name: addbmm(Scalar beta, Tensor self, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor(a!) + aten: addbmm_out(out, self, batch1, batch2, beta, 1) + +- name: addcdiv(Tensor self, Scalar value, Tensor tensor1, Tensor tensor2) -> Tensor + aten: addcdiv(self, tensor1, tensor2, value) + +- name: addcdiv_(Tensor(a!) self, Scalar value, Tensor tensor1, Tensor tensor2) -> Tensor(a!) + aten: addcdiv_(self, tensor1, tensor2, value) + +- name: addcdiv(Tensor self, Scalar value, Tensor tensor1, Tensor tensor2, *, Tensor(a!) out) -> Tensor(a!) + aten: addcdiv_out(out, self, tensor1, tensor2, value) + +- name: addcmul(Tensor self, Scalar value, Tensor tensor1, Tensor tensor2) -> Tensor + aten: addcmul(self, tensor1, tensor2, value) + +- name: addcmul_(Tensor(a!) self, Scalar value, Tensor tensor1, Tensor tensor2) -> Tensor(a!) + aten: addcmul_(self, tensor1, tensor2, value) + +- name: addcmul(Tensor self, Scalar value, Tensor tensor1, Tensor tensor2, *, Tensor(a!) out) -> Tensor(a!) + aten: addcmul_out(out, self, tensor1, tensor2, value) + +- name: addmm(Scalar beta, Tensor self, Scalar alpha, Tensor mat1, Tensor mat2) -> Tensor + aten: addmm(self, mat1, mat2, beta, alpha) + +- name: addmm_(Scalar beta, Tensor(a!) self, Scalar alpha, Tensor mat1, Tensor mat2) -> Tensor(a!) + aten: addmm_(self, mat1, mat2, beta, alpha) + +- name: addmm(Scalar beta, Tensor self, Scalar alpha, Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) + aten: addmm_out(out, self, mat1, mat2, beta, alpha) + +- name: addmm(Scalar beta, Tensor self, Tensor mat1, Tensor mat2) -> Tensor + aten: addmm(self, mat1, mat2, beta, 1) + +- name: addmm_(Scalar beta, Tensor(a!) self, Tensor mat1, Tensor mat2) -> Tensor(a!) + aten: addmm_(self, mat1, mat2, beta, 1) + +- name: addmm(Scalar beta, Tensor self, Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) + aten: addmm_out(out, self, mat1, mat2, beta, 1) + +- name: sspaddmm(Scalar beta, Tensor self, Scalar alpha, Tensor mat1, Tensor mat2) -> Tensor + aten: sspaddmm(self, mat1, mat2, beta, alpha) + +- name: sspaddmm(Scalar beta, Tensor self, Tensor mat1, Tensor mat2) -> Tensor + aten: sspaddmm(self, mat1, mat2, beta, 1) + +- name: addmv(Scalar beta, Tensor self, Scalar alpha, Tensor mat, Tensor vec) -> Tensor + aten: addmv(self, mat, vec, beta, alpha) + +- name: addmv_(Scalar beta, Tensor(a!) self, Scalar alpha, Tensor mat, Tensor vec) -> Tensor(a!) + aten: addmv_(self, mat, vec, beta, alpha) + +- name: addmv(Scalar beta, Tensor self, Scalar alpha, Tensor mat, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) + aten: addmv_out(out, self, mat, vec, beta, alpha) + +- name: addmv(Scalar beta, Tensor self, Tensor mat, Tensor vec) -> Tensor + aten: addmv(self, mat, vec, beta, 1) + +- name: addmv_(Scalar beta, Tensor(a!) self, Tensor mat, Tensor vec) -> Tensor(a!) + aten: addmv_(self, mat, vec, beta, 1) + +- name: addmv(Scalar beta, Tensor self, Tensor mat, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) + aten: addmv_out(out, self, mat, vec, beta, 1) + +- name: addr(Scalar beta, Tensor self, Scalar alpha, Tensor vec1, Tensor vec2) -> Tensor + aten: addr(self, vec1, vec2, beta, alpha) + +- name: addr_(Scalar beta, Tensor(a!) self, Scalar alpha, Tensor vec1, Tensor vec2) -> Tensor(a!) + aten: addr_(self, vec1, vec2, beta, alpha) + +- name: addr(Scalar beta, Tensor self, Scalar alpha, Tensor vec1, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) + aten: addr_out(out, self, vec1, vec2, beta, alpha) + +- name: addr(Scalar beta, Tensor self, Tensor vec1, Tensor vec2) -> Tensor + aten: addr(self, vec1, vec2, beta, 1) + +- name: addr_(Scalar beta, Tensor(a!) self, Tensor vec1, Tensor vec2) -> Tensor(a!) + aten: addr_(self, vec1, vec2, beta, 1) + +- name: addr(Scalar beta, Tensor self, Tensor vec1, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) + aten: addr_out(out, self, vec1, vec2, beta, 1) + +- name: baddbmm(Scalar beta, Tensor self, Scalar alpha, Tensor batch1, Tensor batch2) -> Tensor + aten: baddbmm(self, batch1, batch2, beta, alpha) + +- name: baddbmm_(Scalar beta, Tensor(a!) self, Scalar alpha, Tensor batch1, Tensor batch2) -> Tensor(a!) + aten: baddbmm_(self, batch1, batch2, beta, alpha) + +- name: baddbmm(Scalar beta, Tensor self, Scalar alpha, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor(a!) + aten: baddbmm_out(out, self, batch1, batch2, beta, alpha) + +- name: baddbmm(Scalar beta, Tensor self, Tensor batch1, Tensor batch2) -> Tensor + aten: baddbmm(self, batch1, batch2, beta, 1) + +- name: baddbmm_(Scalar beta, Tensor(a!) self, Tensor batch1, Tensor batch2) -> Tensor(a!) + aten: baddbmm_(self, batch1, batch2, beta, 1) + +- name: baddbmm(Scalar beta, Tensor self, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor(a!) + aten: baddbmm_out(out, self, batch1, batch2, beta, 1) + +- name: sub(Tensor self, Scalar alpha, Tensor other) -> Tensor + aten: sub(self, other, alpha) + +- name: sub_(Tensor(a!) self, Scalar alpha, Tensor other) -> Tensor(a!) + aten: sub_(self, other, alpha) + +- name: sub(Tensor self, Scalar alpha, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + aten: sub_out(out, self, other, alpha) diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/wheel/__main__.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/wheel/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..0be74537494dc2cf18c2e3b318ffd22b886aef6b --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/wheel/__main__.py @@ -0,0 +1,23 @@ +""" +Wheel command line tool (enable python -m wheel syntax) +""" + +from __future__ import annotations + +import sys + + +def main(): # needed for console script + if __package__ == "": + # To be able to run 'python wheel-0.9.whl/wheel': + import os.path + + path = os.path.dirname(os.path.dirname(__file__)) + sys.path[0:0] = [path] + import wheel.cli + + sys.exit(wheel.cli.main()) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tuning-competition-baseline/.venv/lib/python3.11/site-packages/wheel/macosx_libfile.py b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/wheel/macosx_libfile.py new file mode 100644 index 0000000000000000000000000000000000000000..abdfc9eda1d4c1f33270c155e1610fe73bd54263 --- /dev/null +++ b/tuning-competition-baseline/.venv/lib/python3.11/site-packages/wheel/macosx_libfile.py @@ -0,0 +1,482 @@ +""" +This module contains function to analyse dynamic library +headers to extract system information + +Currently only for MacOSX + +Library file on macosx system starts with Mach-O or Fat field. +This can be distinguish by first 32 bites and it is called magic number. +Proper value of magic number is with suffix _MAGIC. Suffix _CIGAM means +reversed bytes order. +Both fields can occur in two types: 32 and 64 bytes. + +FAT field inform that this library contains few version of library +(typically for different types version). It contains +information where Mach-O headers starts. + +Each section started with Mach-O header contains one library +(So if file starts with this field it contains only one version). + +After filed Mach-O there are section fields. +Each of them starts with two fields: +cmd - magic number for this command +cmdsize - total size occupied by this section information. + +In this case only sections LC_VERSION_MIN_MACOSX (for macosx 10.13 and earlier) +and LC_BUILD_VERSION (for macosx 10.14 and newer) are interesting, +because them contains information about minimal system version. + +Important remarks: +- For fat files this implementation looks for maximum number version. + It not check if it is 32 or 64 and do not compare it with currently built package. + So it is possible to false report higher version that needed. +- All structures signatures are taken form macosx header files. +- I think that binary format will be more stable than `otool` output. + and if apple introduce some changes both implementation will need to be updated. +- The system compile will set the deployment target no lower than + 11.0 for arm64 builds. For "Universal 2" builds use the x86_64 deployment + target when the arm64 target is 11.0. +""" + +from __future__ import annotations + +import ctypes +import os +import sys +from io import BufferedIOBase +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from typing import Union + + StrPath = Union[str, os.PathLike[str]] + +"""here the needed const and struct from mach-o header files""" + +FAT_MAGIC = 0xCAFEBABE +FAT_CIGAM = 0xBEBAFECA +FAT_MAGIC_64 = 0xCAFEBABF +FAT_CIGAM_64 = 0xBFBAFECA +MH_MAGIC = 0xFEEDFACE +MH_CIGAM = 0xCEFAEDFE +MH_MAGIC_64 = 0xFEEDFACF +MH_CIGAM_64 = 0xCFFAEDFE + +LC_VERSION_MIN_MACOSX = 0x24 +LC_BUILD_VERSION = 0x32 + +CPU_TYPE_ARM64 = 0x0100000C + +mach_header_fields = [ + ("magic", ctypes.c_uint32), + ("cputype", ctypes.c_int), + ("cpusubtype", ctypes.c_int), + ("filetype", ctypes.c_uint32), + ("ncmds", ctypes.c_uint32), + ("sizeofcmds", ctypes.c_uint32), + ("flags", ctypes.c_uint32), +] +""" +struct mach_header { + uint32_t magic; /* mach magic number identifier */ + cpu_type_t cputype; /* cpu specifier */ + cpu_subtype_t cpusubtype; /* machine specifier */ + uint32_t filetype; /* type of file */ + uint32_t ncmds; /* number of load commands */ + uint32_t sizeofcmds; /* the size of all the load commands */ + uint32_t flags; /* flags */ +}; +typedef integer_t cpu_type_t; +typedef integer_t cpu_subtype_t; +""" + +mach_header_fields_64 = mach_header_fields + [("reserved", ctypes.c_uint32)] +""" +struct mach_header_64 { + uint32_t magic; /* mach magic number identifier */ + cpu_type_t cputype; /* cpu specifier */ + cpu_subtype_t cpusubtype; /* machine specifier */ + uint32_t filetype; /* type of file */ + uint32_t ncmds; /* number of load commands */ + uint32_t sizeofcmds; /* the size of all the load commands */ + uint32_t flags; /* flags */ + uint32_t reserved; /* reserved */ +}; +""" + +fat_header_fields = [("magic", ctypes.c_uint32), ("nfat_arch", ctypes.c_uint32)] +""" +struct fat_header { + uint32_t magic; /* FAT_MAGIC or FAT_MAGIC_64 */ + uint32_t nfat_arch; /* number of structs that follow */ +}; +""" + +fat_arch_fields = [ + ("cputype", ctypes.c_int), + ("cpusubtype", ctypes.c_int), + ("offset", ctypes.c_uint32), + ("size", ctypes.c_uint32), + ("align", ctypes.c_uint32), +] +""" +struct fat_arch { + cpu_type_t cputype; /* cpu specifier (int) */ + cpu_subtype_t cpusubtype; /* machine specifier (int) */ + uint32_t offset; /* file offset to this object file */ + uint32_t size; /* size of this object file */ + uint32_t align; /* alignment as a power of 2 */ +}; +""" + +fat_arch_64_fields = [ + ("cputype", ctypes.c_int), + ("cpusubtype", ctypes.c_int), + ("offset", ctypes.c_uint64), + ("size", ctypes.c_uint64), + ("align", ctypes.c_uint32), + ("reserved", ctypes.c_uint32), +] +""" +struct fat_arch_64 { + cpu_type_t cputype; /* cpu specifier (int) */ + cpu_subtype_t cpusubtype; /* machine specifier (int) */ + uint64_t offset; /* file offset to this object file */ + uint64_t size; /* size of this object file */ + uint32_t align; /* alignment as a power of 2 */ + uint32_t reserved; /* reserved */ +}; +""" + +segment_base_fields = [("cmd", ctypes.c_uint32), ("cmdsize", ctypes.c_uint32)] +"""base for reading segment info""" + +segment_command_fields = [ + ("cmd", ctypes.c_uint32), + ("cmdsize", ctypes.c_uint32), + ("segname", ctypes.c_char * 16), + ("vmaddr", ctypes.c_uint32), + ("vmsize", ctypes.c_uint32), + ("fileoff", ctypes.c_uint32), + ("filesize", ctypes.c_uint32), + ("maxprot", ctypes.c_int), + ("initprot", ctypes.c_int), + ("nsects", ctypes.c_uint32), + ("flags", ctypes.c_uint32), +] +""" +struct segment_command { /* for 32-bit architectures */ + uint32_t cmd; /* LC_SEGMENT */ + uint32_t cmdsize; /* includes sizeof section structs */ + char segname[16]; /* segment name */ + uint32_t vmaddr; /* memory address of this segment */ + uint32_t vmsize; /* memory size of this segment */ + uint32_t fileoff; /* file offset of this segment */ + uint32_t filesize; /* amount to map from the file */ + vm_prot_t maxprot; /* maximum VM protection */ + vm_prot_t initprot; /* initial VM protection */ + uint32_t nsects; /* number of sections in segment */ + uint32_t flags; /* flags */ +}; +typedef int vm_prot_t; +""" + +segment_command_fields_64 = [ + ("cmd", ctypes.c_uint32), + ("cmdsize", ctypes.c_uint32), + ("segname", ctypes.c_char * 16), + ("vmaddr", ctypes.c_uint64), + ("vmsize", ctypes.c_uint64), + ("fileoff", ctypes.c_uint64), + ("filesize", ctypes.c_uint64), + ("maxprot", ctypes.c_int), + ("initprot", ctypes.c_int), + ("nsects", ctypes.c_uint32), + ("flags", ctypes.c_uint32), +] +""" +struct segment_command_64 { /* for 64-bit architectures */ + uint32_t cmd; /* LC_SEGMENT_64 */ + uint32_t cmdsize; /* includes sizeof section_64 structs */ + char segname[16]; /* segment name */ + uint64_t vmaddr; /* memory address of this segment */ + uint64_t vmsize; /* memory size of this segment */ + uint64_t fileoff; /* file offset of this segment */ + uint64_t filesize; /* amount to map from the file */ + vm_prot_t maxprot; /* maximum VM protection */ + vm_prot_t initprot; /* initial VM protection */ + uint32_t nsects; /* number of sections in segment */ + uint32_t flags; /* flags */ +}; +""" + +version_min_command_fields = segment_base_fields + [ + ("version", ctypes.c_uint32), + ("sdk", ctypes.c_uint32), +] +""" +struct version_min_command { + uint32_t cmd; /* LC_VERSION_MIN_MACOSX or + LC_VERSION_MIN_IPHONEOS or + LC_VERSION_MIN_WATCHOS or + LC_VERSION_MIN_TVOS */ + uint32_t cmdsize; /* sizeof(struct min_version_command) */ + uint32_t version; /* X.Y.Z is encoded in nibbles xxxx.yy.zz */ + uint32_t sdk; /* X.Y.Z is encoded in nibbles xxxx.yy.zz */ +}; +""" + +build_version_command_fields = segment_base_fields + [ + ("platform", ctypes.c_uint32), + ("minos", ctypes.c_uint32), + ("sdk", ctypes.c_uint32), + ("ntools", ctypes.c_uint32), +] +""" +struct build_version_command { + uint32_t cmd; /* LC_BUILD_VERSION */ + uint32_t cmdsize; /* sizeof(struct build_version_command) plus */ + /* ntools * sizeof(struct build_tool_version) */ + uint32_t platform; /* platform */ + uint32_t minos; /* X.Y.Z is encoded in nibbles xxxx.yy.zz */ + uint32_t sdk; /* X.Y.Z is encoded in nibbles xxxx.yy.zz */ + uint32_t ntools; /* number of tool entries following this */ +}; +""" + + +def swap32(x: int) -> int: + return ( + ((x << 24) & 0xFF000000) + | ((x << 8) & 0x00FF0000) + | ((x >> 8) & 0x0000FF00) + | ((x >> 24) & 0x000000FF) + ) + + +def get_base_class_and_magic_number( + lib_file: BufferedIOBase, + seek: int | None = None, +) -> tuple[type[ctypes.Structure], int]: + if seek is None: + seek = lib_file.tell() + else: + lib_file.seek(seek) + magic_number = ctypes.c_uint32.from_buffer_copy( + lib_file.read(ctypes.sizeof(ctypes.c_uint32)) + ).value + + # Handle wrong byte order + if magic_number in [FAT_CIGAM, FAT_CIGAM_64, MH_CIGAM, MH_CIGAM_64]: + if sys.byteorder == "little": + BaseClass = ctypes.BigEndianStructure + else: + BaseClass = ctypes.LittleEndianStructure + + magic_number = swap32(magic_number) + else: + BaseClass = ctypes.Structure + + lib_file.seek(seek) + return BaseClass, magic_number + + +def read_data(struct_class: type[ctypes.Structure], lib_file: BufferedIOBase): + return struct_class.from_buffer_copy(lib_file.read(ctypes.sizeof(struct_class))) + + +def extract_macosx_min_system_version(path_to_lib: str): + with open(path_to_lib, "rb") as lib_file: + BaseClass, magic_number = get_base_class_and_magic_number(lib_file, 0) + if magic_number not in [FAT_MAGIC, FAT_MAGIC_64, MH_MAGIC, MH_MAGIC_64]: + return + + if magic_number in [FAT_MAGIC, FAT_CIGAM_64]: + + class FatHeader(BaseClass): + _fields_ = fat_header_fields + + fat_header = read_data(FatHeader, lib_file) + if magic_number == FAT_MAGIC: + + class FatArch(BaseClass): + _fields_ = fat_arch_fields + + else: + + class FatArch(BaseClass): + _fields_ = fat_arch_64_fields + + fat_arch_list = [ + read_data(FatArch, lib_file) for _ in range(fat_header.nfat_arch) + ] + + versions_list: list[tuple[int, int, int]] = [] + for el in fat_arch_list: + try: + version = read_mach_header(lib_file, el.offset) + if version is not None: + if el.cputype == CPU_TYPE_ARM64 and len(fat_arch_list) != 1: + # Xcode will not set the deployment target below 11.0.0 + # for the arm64 architecture. Ignore the arm64 deployment + # in fat binaries when the target is 11.0.0, that way + # the other architectures can select a lower deployment + # target. + # This is safe because there is no arm64 variant for + # macOS 10.15 or earlier. + if version == (11, 0, 0): + continue + versions_list.append(version) + except ValueError: + pass + + if len(versions_list) > 0: + return max(versions_list) + else: + return None + + else: + try: + return read_mach_header(lib_file, 0) + except ValueError: + """when some error during read library files""" + return None + + +def read_mach_header( + lib_file: BufferedIOBase, + seek: int | None = None, +) -> tuple[int, int, int] | None: + """ + This function parses a Mach-O header and extracts + information about the minimal macOS version. + + :param lib_file: reference to opened library file with pointer + """ + base_class, magic_number = get_base_class_and_magic_number(lib_file, seek) + arch = "32" if magic_number == MH_MAGIC else "64" + + class SegmentBase(base_class): + _fields_ = segment_base_fields + + if arch == "32": + + class MachHeader(base_class): + _fields_ = mach_header_fields + + else: + + class MachHeader(base_class): + _fields_ = mach_header_fields_64 + + mach_header = read_data(MachHeader, lib_file) + for _i in range(mach_header.ncmds): + pos = lib_file.tell() + segment_base = read_data(SegmentBase, lib_file) + lib_file.seek(pos) + if segment_base.cmd == LC_VERSION_MIN_MACOSX: + + class VersionMinCommand(base_class): + _fields_ = version_min_command_fields + + version_info = read_data(VersionMinCommand, lib_file) + return parse_version(version_info.version) + elif segment_base.cmd == LC_BUILD_VERSION: + + class VersionBuild(base_class): + _fields_ = build_version_command_fields + + version_info = read_data(VersionBuild, lib_file) + return parse_version(version_info.minos) + else: + lib_file.seek(pos + segment_base.cmdsize) + continue + + +def parse_version(version: int) -> tuple[int, int, int]: + x = (version & 0xFFFF0000) >> 16 + y = (version & 0x0000FF00) >> 8 + z = version & 0x000000FF + return x, y, z + + +def calculate_macosx_platform_tag(archive_root: StrPath, platform_tag: str) -> str: + """ + Calculate proper macosx platform tag basing on files which are included to wheel + + Example platform tag `macosx-10.14-x86_64` + """ + prefix, base_version, suffix = platform_tag.split("-") + base_version = tuple(int(x) for x in base_version.split(".")) + base_version = base_version[:2] + if base_version[0] > 10: + base_version = (base_version[0], 0) + assert len(base_version) == 2 + if "MACOSX_DEPLOYMENT_TARGET" in os.environ: + deploy_target = tuple( + int(x) for x in os.environ["MACOSX_DEPLOYMENT_TARGET"].split(".") + ) + deploy_target = deploy_target[:2] + if deploy_target[0] > 10: + deploy_target = (deploy_target[0], 0) + if deploy_target < base_version: + sys.stderr.write( + "[WARNING] MACOSX_DEPLOYMENT_TARGET is set to a lower value ({}) than " + "the version on which the Python interpreter was compiled ({}), and " + "will be ignored.\n".format( + ".".join(str(x) for x in deploy_target), + ".".join(str(x) for x in base_version), + ) + ) + else: + base_version = deploy_target + + assert len(base_version) == 2 + start_version = base_version + versions_dict: dict[str, tuple[int, int]] = {} + for dirpath, _dirnames, filenames in os.walk(archive_root): + for filename in filenames: + if filename.endswith(".dylib") or filename.endswith(".so"): + lib_path = os.path.join(dirpath, filename) + min_ver = extract_macosx_min_system_version(lib_path) + if min_ver is not None: + min_ver = min_ver[0:2] + if min_ver[0] > 10: + min_ver = (min_ver[0], 0) + versions_dict[lib_path] = min_ver + + if len(versions_dict) > 0: + base_version = max(base_version, max(versions_dict.values())) + + # macosx platform tag do not support minor bugfix release + fin_base_version = "_".join([str(x) for x in base_version]) + if start_version < base_version: + problematic_files = [k for k, v in versions_dict.items() if v > start_version] + problematic_files = "\n".join(problematic_files) + if len(problematic_files) == 1: + files_form = "this file" + else: + files_form = "these files" + error_message = ( + "[WARNING] This wheel needs a higher macOS version than {} " + "To silence this warning, set MACOSX_DEPLOYMENT_TARGET to at least " + + fin_base_version + + " or recreate " + + files_form + + " with lower " + "MACOSX_DEPLOYMENT_TARGET: \n" + problematic_files + ) + + if "MACOSX_DEPLOYMENT_TARGET" in os.environ: + error_message = error_message.format( + "is set in MACOSX_DEPLOYMENT_TARGET variable." + ) + else: + error_message = error_message.format( + "the version your Python interpreter is compiled against." + ) + + sys.stderr.write(error_message) + + platform_tag = prefix + "_" + fin_base_version + "_" + suffix + return platform_tag