diff --git a/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/__init__.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e775bd1f7c0f6af094f13b75cdc7bfc5f9b6241a Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/__init__.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/compat.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/compat.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f36dbd5bb086988b6a325501d975bb72e408bd6b Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/compat.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/core.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/core.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3ccaaab54201c916955cad33ae21708d6719857e Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/core.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/idnadata.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/idnadata.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..61d16ee3e68ecdb0cff8034a9ebbbe07b9bbc337 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/idnadata.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/intranges.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/intranges.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7de6e12f88fd26fd81147c50cd642f5955cf7508 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/intranges.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/package_data.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/package_data.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..19d995aaa8c659eb6e28f15266bf5c74a32c6b21 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/package_data.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/uts46data.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/uts46data.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ac1074c60a3688352a9a20d7088d25d9358565b9 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/idna/__pycache__/uts46data.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/idna/codec.py b/llmeval-env/lib/python3.10/site-packages/idna/codec.py new file mode 100644 index 0000000000000000000000000000000000000000..c855a4de6d781815acf2334306f41b7a036c45d4 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/idna/codec.py @@ -0,0 +1,118 @@ +from .core import encode, decode, alabel, ulabel, IDNAError +import codecs +import re +from typing import Any, Tuple, Optional + +_unicode_dots_re = re.compile('[\u002e\u3002\uff0e\uff61]') + +class Codec(codecs.Codec): + + def encode(self, data: str, errors: str = 'strict') -> Tuple[bytes, int]: + if errors != 'strict': + raise IDNAError('Unsupported error handling \"{}\"'.format(errors)) + + if not data: + return b"", 0 + + return encode(data), len(data) + + def decode(self, data: bytes, errors: str = 'strict') -> Tuple[str, int]: + if errors != 'strict': + raise IDNAError('Unsupported error handling \"{}\"'.format(errors)) + + if not data: + return '', 0 + + return decode(data), len(data) + +class IncrementalEncoder(codecs.BufferedIncrementalEncoder): + def _buffer_encode(self, data: str, errors: str, final: bool) -> Tuple[bytes, int]: + if errors != 'strict': + raise IDNAError('Unsupported error handling \"{}\"'.format(errors)) + + if not data: + return b'', 0 + + labels = _unicode_dots_re.split(data) + trailing_dot = b'' + if labels: + if not labels[-1]: + trailing_dot = b'.' + del labels[-1] + elif not final: + # Keep potentially unfinished label until the next call + del labels[-1] + if labels: + trailing_dot = b'.' + + result = [] + size = 0 + for label in labels: + result.append(alabel(label)) + if size: + size += 1 + size += len(label) + + # Join with U+002E + result_bytes = b'.'.join(result) + trailing_dot + size += len(trailing_dot) + return result_bytes, size + +class IncrementalDecoder(codecs.BufferedIncrementalDecoder): + def _buffer_decode(self, data: Any, errors: str, final: bool) -> Tuple[str, int]: + if errors != 'strict': + raise IDNAError('Unsupported error handling \"{}\"'.format(errors)) + + if not data: + return ('', 0) + + if not isinstance(data, str): + data = str(data, 'ascii') + + labels = _unicode_dots_re.split(data) + trailing_dot = '' + if labels: + if not labels[-1]: + trailing_dot = '.' + del labels[-1] + elif not final: + # Keep potentially unfinished label until the next call + del labels[-1] + if labels: + trailing_dot = '.' + + result = [] + size = 0 + for label in labels: + result.append(ulabel(label)) + if size: + size += 1 + size += len(label) + + result_str = '.'.join(result) + trailing_dot + size += len(trailing_dot) + return (result_str, size) + + +class StreamWriter(Codec, codecs.StreamWriter): + pass + + +class StreamReader(Codec, codecs.StreamReader): + pass + + +def search_function(name: str) -> Optional[codecs.CodecInfo]: + if name != 'idna2008': + return None + return codecs.CodecInfo( + name=name, + encode=Codec().encode, + decode=Codec().decode, + incrementalencoder=IncrementalEncoder, + incrementaldecoder=IncrementalDecoder, + streamwriter=StreamWriter, + streamreader=StreamReader, + ) + +codecs.register(search_function) diff --git a/llmeval-env/lib/python3.10/site-packages/idna/py.typed b/llmeval-env/lib/python3.10/site-packages/idna/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/llmeval-env/lib/python3.10/site-packages/multiprocess/context.py b/llmeval-env/lib/python3.10/site-packages/multiprocess/context.py new file mode 100644 index 0000000000000000000000000000000000000000..e8f29c65fe98b4c16598cdd3c93be195e1a84676 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/multiprocess/context.py @@ -0,0 +1,376 @@ +import os +import sys +import threading + +from . import process +from . import reduction + +__all__ = () + +# +# Exceptions +# + +class ProcessError(Exception): + pass + +class BufferTooShort(ProcessError): + pass + +class TimeoutError(ProcessError): + pass + +class AuthenticationError(ProcessError): + pass + +# +# Base type for contexts. Bound methods of an instance of this type are included in __all__ of __init__.py +# + +class BaseContext(object): + + ProcessError = ProcessError + BufferTooShort = BufferTooShort + TimeoutError = TimeoutError + AuthenticationError = AuthenticationError + + current_process = staticmethod(process.current_process) + parent_process = staticmethod(process.parent_process) + active_children = staticmethod(process.active_children) + + def cpu_count(self): + '''Returns the number of CPUs in the system''' + num = os.cpu_count() + if num is None: + raise NotImplementedError('cannot determine number of cpus') + else: + return num + + def Manager(self): + '''Returns a manager associated with a running server process + + The managers methods such as `Lock()`, `Condition()` and `Queue()` + can be used to create shared objects. + ''' + from .managers import SyncManager + m = SyncManager(ctx=self.get_context()) + m.start() + return m + + def Pipe(self, duplex=True): + '''Returns two connection object connected by a pipe''' + from .connection import Pipe + return Pipe(duplex) + + def Lock(self): + '''Returns a non-recursive lock object''' + from .synchronize import Lock + return Lock(ctx=self.get_context()) + + def RLock(self): + '''Returns a recursive lock object''' + from .synchronize import RLock + return RLock(ctx=self.get_context()) + + def Condition(self, lock=None): + '''Returns a condition object''' + from .synchronize import Condition + return Condition(lock, ctx=self.get_context()) + + def Semaphore(self, value=1): + '''Returns a semaphore object''' + from .synchronize import Semaphore + return Semaphore(value, ctx=self.get_context()) + + def BoundedSemaphore(self, value=1): + '''Returns a bounded semaphore object''' + from .synchronize import BoundedSemaphore + return BoundedSemaphore(value, ctx=self.get_context()) + + def Event(self): + '''Returns an event object''' + from .synchronize import Event + return Event(ctx=self.get_context()) + + def Barrier(self, parties, action=None, timeout=None): + '''Returns a barrier object''' + from .synchronize import Barrier + return Barrier(parties, action, timeout, ctx=self.get_context()) + + def Queue(self, maxsize=0): + '''Returns a queue object''' + from .queues import Queue + return Queue(maxsize, ctx=self.get_context()) + + def JoinableQueue(self, maxsize=0): + '''Returns a queue object''' + from .queues import JoinableQueue + return JoinableQueue(maxsize, ctx=self.get_context()) + + def SimpleQueue(self): + '''Returns a queue object''' + from .queues import SimpleQueue + return SimpleQueue(ctx=self.get_context()) + + def Pool(self, processes=None, initializer=None, initargs=(), + maxtasksperchild=None): + '''Returns a process pool object''' + from .pool import Pool + return Pool(processes, initializer, initargs, maxtasksperchild, + context=self.get_context()) + + def RawValue(self, typecode_or_type, *args): + '''Returns a shared object''' + from .sharedctypes import RawValue + return RawValue(typecode_or_type, *args) + + def RawArray(self, typecode_or_type, size_or_initializer): + '''Returns a shared array''' + from .sharedctypes import RawArray + return RawArray(typecode_or_type, size_or_initializer) + + def Value(self, typecode_or_type, *args, lock=True): + '''Returns a synchronized shared object''' + from .sharedctypes import Value + return Value(typecode_or_type, *args, lock=lock, + ctx=self.get_context()) + + def Array(self, typecode_or_type, size_or_initializer, *, lock=True): + '''Returns a synchronized shared array''' + from .sharedctypes import Array + return Array(typecode_or_type, size_or_initializer, lock=lock, + ctx=self.get_context()) + + def freeze_support(self): + '''Check whether this is a fake forked process in a frozen executable. + If so then run code specified by commandline and exit. + ''' + if sys.platform == 'win32' and getattr(sys, 'frozen', False): + from .spawn import freeze_support + freeze_support() + + def get_logger(self): + '''Return package logger -- if it does not already exist then + it is created. + ''' + from .util import get_logger + return get_logger() + + def log_to_stderr(self, level=None): + '''Turn on logging and add a handler which prints to stderr''' + from .util import log_to_stderr + return log_to_stderr(level) + + def allow_connection_pickling(self): + '''Install support for sending connections and sockets + between processes + ''' + # This is undocumented. In previous versions of multiprocessing + # its only effect was to make socket objects inheritable on Windows. + from . import connection + + def set_executable(self, executable): + '''Sets the path to a python.exe or pythonw.exe binary used to run + child processes instead of sys.executable when using the 'spawn' + start method. Useful for people embedding Python. + ''' + from .spawn import set_executable + set_executable(executable) + + def set_forkserver_preload(self, module_names): + '''Set list of module names to try to load in forkserver process. + This is really just a hint. + ''' + from .forkserver import set_forkserver_preload + set_forkserver_preload(module_names) + + def get_context(self, method=None): + if method is None: + return self + try: + ctx = _concrete_contexts[method] + except KeyError: + raise ValueError('cannot find context for %r' % method) from None + ctx._check_available() + return ctx + + def get_start_method(self, allow_none=False): + return self._name + + def set_start_method(self, method, force=False): + raise ValueError('cannot set start method of concrete context') + + @property + def reducer(self): + '''Controls how objects will be reduced to a form that can be + shared with other processes.''' + return globals().get('reduction') + + @reducer.setter + def reducer(self, reduction): + globals()['reduction'] = reduction + + def _check_available(self): + pass + +# +# Type of default context -- underlying context can be set at most once +# + +class Process(process.BaseProcess): + _start_method = None + @staticmethod + def _Popen(process_obj): + return _default_context.get_context().Process._Popen(process_obj) + + @staticmethod + def _after_fork(): + return _default_context.get_context().Process._after_fork() + +class DefaultContext(BaseContext): + Process = Process + + def __init__(self, context): + self._default_context = context + self._actual_context = None + + def get_context(self, method=None): + if method is None: + if self._actual_context is None: + self._actual_context = self._default_context + return self._actual_context + else: + return super().get_context(method) + + def set_start_method(self, method, force=False): + if self._actual_context is not None and not force: + raise RuntimeError('context has already been set') + if method is None and force: + self._actual_context = None + return + self._actual_context = self.get_context(method) + + def get_start_method(self, allow_none=False): + if self._actual_context is None: + if allow_none: + return None + self._actual_context = self._default_context + return self._actual_context._name + + def get_all_start_methods(self): + if sys.platform == 'win32': + return ['spawn'] + else: + methods = ['spawn', 'fork'] if sys.platform == 'darwin' else ['fork', 'spawn'] + if reduction.HAVE_SEND_HANDLE: + methods.append('forkserver') + return methods + + +# +# Context types for fixed start method +# + +if sys.platform != 'win32': + + class ForkProcess(process.BaseProcess): + _start_method = 'fork' + @staticmethod + def _Popen(process_obj): + from .popen_fork import Popen + return Popen(process_obj) + + class SpawnProcess(process.BaseProcess): + _start_method = 'spawn' + @staticmethod + def _Popen(process_obj): + from .popen_spawn_posix import Popen + return Popen(process_obj) + + @staticmethod + def _after_fork(): + # process is spawned, nothing to do + pass + + class ForkServerProcess(process.BaseProcess): + _start_method = 'forkserver' + @staticmethod + def _Popen(process_obj): + from .popen_forkserver import Popen + return Popen(process_obj) + + class ForkContext(BaseContext): + _name = 'fork' + Process = ForkProcess + + class SpawnContext(BaseContext): + _name = 'spawn' + Process = SpawnProcess + + class ForkServerContext(BaseContext): + _name = 'forkserver' + Process = ForkServerProcess + def _check_available(self): + if not reduction.HAVE_SEND_HANDLE: + raise ValueError('forkserver start method not available') + + _concrete_contexts = { + 'fork': ForkContext(), + 'spawn': SpawnContext(), + 'forkserver': ForkServerContext(), + } + if sys.platform == 'darwin': + # bpo-33725: running arbitrary code after fork() is no longer reliable + # on macOS since macOS 10.14 (Mojave). Use spawn by default instead. + _default_context = DefaultContext(_concrete_contexts['fork']) #FIXME: spawn + else: + _default_context = DefaultContext(_concrete_contexts['fork']) + +else: + + class SpawnProcess(process.BaseProcess): + _start_method = 'spawn' + @staticmethod + def _Popen(process_obj): + from .popen_spawn_win32 import Popen + return Popen(process_obj) + + @staticmethod + def _after_fork(): + # process is spawned, nothing to do + pass + + class SpawnContext(BaseContext): + _name = 'spawn' + Process = SpawnProcess + + _concrete_contexts = { + 'spawn': SpawnContext(), + } + _default_context = DefaultContext(_concrete_contexts['spawn']) + +# +# Force the start method +# + +def _force_start_method(method): + _default_context._actual_context = _concrete_contexts[method] + +# +# Check that the current thread is spawning a child process +# + +_tls = threading.local() + +def get_spawning_popen(): + return getattr(_tls, 'spawning_popen', None) + +def set_spawning_popen(popen): + _tls.spawning_popen = popen + +def assert_spawning(obj): + if get_spawning_popen() is None: + raise RuntimeError( + '%s objects should only be shared between processes' + ' through inheritance' % type(obj).__name__ + ) diff --git a/llmeval-env/lib/python3.10/site-packages/multiprocess/managers.py b/llmeval-env/lib/python3.10/site-packages/multiprocess/managers.py new file mode 100644 index 0000000000000000000000000000000000000000..50989cbe0a841dd91924e02df4eaaf50c9b5b2fe --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/multiprocess/managers.py @@ -0,0 +1,1378 @@ +# +# Module providing manager classes for dealing +# with shared objects +# +# multiprocessing/managers.py +# +# Copyright (c) 2006-2008, R Oudkerk +# Licensed to PSF under a Contributor Agreement. +# + +__all__ = [ 'BaseManager', 'SyncManager', 'BaseProxy', 'Token' ] + +# +# Imports +# + +import sys +import threading +import signal +import array +import queue +import time +import types +import os +from os import getpid + +from traceback import format_exc + +from . import connection +from .context import reduction, get_spawning_popen, ProcessError +from . import pool +from . import process +from . import util +from . import get_context +try: + from . import shared_memory +except ImportError: + HAS_SHMEM = False +else: + HAS_SHMEM = True + __all__.append('SharedMemoryManager') + +# +# Register some things for pickling +# + +def reduce_array(a): + return array.array, (a.typecode, a.tobytes()) +reduction.register(array.array, reduce_array) + +view_types = [type(getattr({}, name)()) for name in ('items','keys','values')] +if view_types[0] is not list: # only needed in Py3.0 + def rebuild_as_list(obj): + return list, (list(obj),) + for view_type in view_types: + reduction.register(view_type, rebuild_as_list) + +# +# Type for identifying shared objects +# + +class Token(object): + ''' + Type to uniquely identify a shared object + ''' + __slots__ = ('typeid', 'address', 'id') + + def __init__(self, typeid, address, id): + (self.typeid, self.address, self.id) = (typeid, address, id) + + def __getstate__(self): + return (self.typeid, self.address, self.id) + + def __setstate__(self, state): + (self.typeid, self.address, self.id) = state + + def __repr__(self): + return '%s(typeid=%r, address=%r, id=%r)' % \ + (self.__class__.__name__, self.typeid, self.address, self.id) + +# +# Function for communication with a manager's server process +# + +def dispatch(c, id, methodname, args=(), kwds={}): + ''' + Send a message to manager using connection `c` and return response + ''' + c.send((id, methodname, args, kwds)) + kind, result = c.recv() + if kind == '#RETURN': + return result + raise convert_to_error(kind, result) + +def convert_to_error(kind, result): + if kind == '#ERROR': + return result + elif kind in ('#TRACEBACK', '#UNSERIALIZABLE'): + if not isinstance(result, str): + raise TypeError( + "Result {0!r} (kind '{1}') type is {2}, not str".format( + result, kind, type(result))) + if kind == '#UNSERIALIZABLE': + return RemoteError('Unserializable message: %s\n' % result) + else: + return RemoteError(result) + else: + return ValueError('Unrecognized message type {!r}'.format(kind)) + +class RemoteError(Exception): + def __str__(self): + return ('\n' + '-'*75 + '\n' + str(self.args[0]) + '-'*75) + +# +# Functions for finding the method names of an object +# + +def all_methods(obj): + ''' + Return a list of names of methods of `obj` + ''' + temp = [] + for name in dir(obj): + func = getattr(obj, name) + if callable(func): + temp.append(name) + return temp + +def public_methods(obj): + ''' + Return a list of names of methods of `obj` which do not start with '_' + ''' + return [name for name in all_methods(obj) if name[0] != '_'] + +# +# Server which is run in a process controlled by a manager +# + +class Server(object): + ''' + Server class which runs in a process controlled by a manager object + ''' + public = ['shutdown', 'create', 'accept_connection', 'get_methods', + 'debug_info', 'number_of_objects', 'dummy', 'incref', 'decref'] + + def __init__(self, registry, address, authkey, serializer): + if not isinstance(authkey, bytes): + raise TypeError( + "Authkey {0!r} is type {1!s}, not bytes".format( + authkey, type(authkey))) + self.registry = registry + self.authkey = process.AuthenticationString(authkey) + Listener, Client = listener_client[serializer] + + # do authentication later + self.listener = Listener(address=address, backlog=16) + self.address = self.listener.address + + self.id_to_obj = {'0': (None, ())} + self.id_to_refcount = {} + self.id_to_local_proxy_obj = {} + self.mutex = threading.Lock() + + def serve_forever(self): + ''' + Run the server forever + ''' + self.stop_event = threading.Event() + process.current_process()._manager_server = self + try: + accepter = threading.Thread(target=self.accepter) + accepter.daemon = True + accepter.start() + try: + while not self.stop_event.is_set(): + self.stop_event.wait(1) + except (KeyboardInterrupt, SystemExit): + pass + finally: + if sys.stdout != sys.__stdout__: # what about stderr? + util.debug('resetting stdout, stderr') + sys.stdout = sys.__stdout__ + sys.stderr = sys.__stderr__ + sys.exit(0) + + def accepter(self): + while True: + try: + c = self.listener.accept() + except OSError: + continue + t = threading.Thread(target=self.handle_request, args=(c,)) + t.daemon = True + t.start() + + def _handle_request(self, c): + request = None + try: + connection.deliver_challenge(c, self.authkey) + connection.answer_challenge(c, self.authkey) + request = c.recv() + ignore, funcname, args, kwds = request + assert funcname in self.public, '%r unrecognized' % funcname + func = getattr(self, funcname) + except Exception: + msg = ('#TRACEBACK', format_exc()) + else: + try: + result = func(c, *args, **kwds) + except Exception: + msg = ('#TRACEBACK', format_exc()) + else: + msg = ('#RETURN', result) + + try: + c.send(msg) + except Exception as e: + try: + c.send(('#TRACEBACK', format_exc())) + except Exception: + pass + util.info('Failure to send message: %r', msg) + util.info(' ... request was %r', request) + util.info(' ... exception was %r', e) + + def handle_request(self, conn): + ''' + Handle a new connection + ''' + try: + self._handle_request(conn) + except SystemExit: + # Server.serve_client() calls sys.exit(0) on EOF + pass + finally: + conn.close() + + def serve_client(self, conn): + ''' + Handle requests from the proxies in a particular process/thread + ''' + util.debug('starting server thread to service %r', + threading.current_thread().name) + + recv = conn.recv + send = conn.send + id_to_obj = self.id_to_obj + + while not self.stop_event.is_set(): + + try: + methodname = obj = None + request = recv() + ident, methodname, args, kwds = request + try: + obj, exposed, gettypeid = id_to_obj[ident] + except KeyError as ke: + try: + obj, exposed, gettypeid = \ + self.id_to_local_proxy_obj[ident] + except KeyError: + raise ke + + if methodname not in exposed: + raise AttributeError( + 'method %r of %r object is not in exposed=%r' % + (methodname, type(obj), exposed) + ) + + function = getattr(obj, methodname) + + try: + res = function(*args, **kwds) + except Exception as e: + msg = ('#ERROR', e) + else: + typeid = gettypeid and gettypeid.get(methodname, None) + if typeid: + rident, rexposed = self.create(conn, typeid, res) + token = Token(typeid, self.address, rident) + msg = ('#PROXY', (rexposed, token)) + else: + msg = ('#RETURN', res) + + except AttributeError: + if methodname is None: + msg = ('#TRACEBACK', format_exc()) + else: + try: + fallback_func = self.fallback_mapping[methodname] + result = fallback_func( + self, conn, ident, obj, *args, **kwds + ) + msg = ('#RETURN', result) + except Exception: + msg = ('#TRACEBACK', format_exc()) + + except EOFError: + util.debug('got EOF -- exiting thread serving %r', + threading.current_thread().name) + sys.exit(0) + + except Exception: + msg = ('#TRACEBACK', format_exc()) + + try: + try: + send(msg) + except Exception: + send(('#UNSERIALIZABLE', format_exc())) + except Exception as e: + util.info('exception in thread serving %r', + threading.current_thread().name) + util.info(' ... message was %r', msg) + util.info(' ... exception was %r', e) + conn.close() + sys.exit(1) + + def fallback_getvalue(self, conn, ident, obj): + return obj + + def fallback_str(self, conn, ident, obj): + return str(obj) + + def fallback_repr(self, conn, ident, obj): + return repr(obj) + + fallback_mapping = { + '__str__':fallback_str, + '__repr__':fallback_repr, + '#GETVALUE':fallback_getvalue + } + + def dummy(self, c): + pass + + def debug_info(self, c): + ''' + Return some info --- useful to spot problems with refcounting + ''' + # Perhaps include debug info about 'c'? + with self.mutex: + result = [] + keys = list(self.id_to_refcount.keys()) + keys.sort() + for ident in keys: + if ident != '0': + result.append(' %s: refcount=%s\n %s' % + (ident, self.id_to_refcount[ident], + str(self.id_to_obj[ident][0])[:75])) + return '\n'.join(result) + + def number_of_objects(self, c): + ''' + Number of shared objects + ''' + # Doesn't use (len(self.id_to_obj) - 1) as we shouldn't count ident='0' + return len(self.id_to_refcount) + + def shutdown(self, c): + ''' + Shutdown this process + ''' + try: + util.debug('manager received shutdown message') + c.send(('#RETURN', None)) + except: + import traceback + traceback.print_exc() + finally: + self.stop_event.set() + + def create(self, c, typeid, /, *args, **kwds): + ''' + Create a new shared object and return its id + ''' + with self.mutex: + callable, exposed, method_to_typeid, proxytype = \ + self.registry[typeid] + + if callable is None: + if kwds or (len(args) != 1): + raise ValueError( + "Without callable, must have one non-keyword argument") + obj = args[0] + else: + obj = callable(*args, **kwds) + + if exposed is None: + exposed = public_methods(obj) + if method_to_typeid is not None: + if not isinstance(method_to_typeid, dict): + raise TypeError( + "Method_to_typeid {0!r}: type {1!s}, not dict".format( + method_to_typeid, type(method_to_typeid))) + exposed = list(exposed) + list(method_to_typeid) + + ident = '%x' % id(obj) # convert to string because xmlrpclib + # only has 32 bit signed integers + util.debug('%r callable returned object with id %r', typeid, ident) + + self.id_to_obj[ident] = (obj, set(exposed), method_to_typeid) + if ident not in self.id_to_refcount: + self.id_to_refcount[ident] = 0 + + self.incref(c, ident) + return ident, tuple(exposed) + + def get_methods(self, c, token): + ''' + Return the methods of the shared object indicated by token + ''' + return tuple(self.id_to_obj[token.id][1]) + + def accept_connection(self, c, name): + ''' + Spawn a new thread to serve this connection + ''' + threading.current_thread().name = name + c.send(('#RETURN', None)) + self.serve_client(c) + + def incref(self, c, ident): + with self.mutex: + try: + self.id_to_refcount[ident] += 1 + except KeyError as ke: + # If no external references exist but an internal (to the + # manager) still does and a new external reference is created + # from it, restore the manager's tracking of it from the + # previously stashed internal ref. + if ident in self.id_to_local_proxy_obj: + self.id_to_refcount[ident] = 1 + self.id_to_obj[ident] = \ + self.id_to_local_proxy_obj[ident] + obj, exposed, gettypeid = self.id_to_obj[ident] + util.debug('Server re-enabled tracking & INCREF %r', ident) + else: + raise ke + + def decref(self, c, ident): + if ident not in self.id_to_refcount and \ + ident in self.id_to_local_proxy_obj: + util.debug('Server DECREF skipping %r', ident) + return + + with self.mutex: + if self.id_to_refcount[ident] <= 0: + raise AssertionError( + "Id {0!s} ({1!r}) has refcount {2:n}, not 1+".format( + ident, self.id_to_obj[ident], + self.id_to_refcount[ident])) + self.id_to_refcount[ident] -= 1 + if self.id_to_refcount[ident] == 0: + del self.id_to_refcount[ident] + + if ident not in self.id_to_refcount: + # Two-step process in case the object turns out to contain other + # proxy objects (e.g. a managed list of managed lists). + # Otherwise, deleting self.id_to_obj[ident] would trigger the + # deleting of the stored value (another managed object) which would + # in turn attempt to acquire the mutex that is already held here. + self.id_to_obj[ident] = (None, (), None) # thread-safe + util.debug('disposing of obj with id %r', ident) + with self.mutex: + del self.id_to_obj[ident] + + +# +# Class to represent state of a manager +# + +class State(object): + __slots__ = ['value'] + INITIAL = 0 + STARTED = 1 + SHUTDOWN = 2 + +# +# Mapping from serializer name to Listener and Client types +# + +listener_client = { #XXX: register dill? + 'pickle' : (connection.Listener, connection.Client), + 'xmlrpclib' : (connection.XmlListener, connection.XmlClient) + } + +# +# Definition of BaseManager +# + +class BaseManager(object): + ''' + Base class for managers + ''' + _registry = {} + _Server = Server + + def __init__(self, address=None, authkey=None, serializer='pickle', + ctx=None): + if authkey is None: + authkey = process.current_process().authkey + self._address = address # XXX not final address if eg ('', 0) + self._authkey = process.AuthenticationString(authkey) + self._state = State() + self._state.value = State.INITIAL + self._serializer = serializer + self._Listener, self._Client = listener_client[serializer] + self._ctx = ctx or get_context() + + def get_server(self): + ''' + Return server object with serve_forever() method and address attribute + ''' + if self._state.value != State.INITIAL: + if self._state.value == State.STARTED: + raise ProcessError("Already started server") + elif self._state.value == State.SHUTDOWN: + raise ProcessError("Manager has shut down") + else: + raise ProcessError( + "Unknown state {!r}".format(self._state.value)) + return Server(self._registry, self._address, + self._authkey, self._serializer) + + def connect(self): + ''' + Connect manager object to the server process + ''' + Listener, Client = listener_client[self._serializer] + conn = Client(self._address, authkey=self._authkey) + dispatch(conn, None, 'dummy') + self._state.value = State.STARTED + + def start(self, initializer=None, initargs=()): + ''' + Spawn a server process for this manager object + ''' + if self._state.value != State.INITIAL: + if self._state.value == State.STARTED: + raise ProcessError("Already started server") + elif self._state.value == State.SHUTDOWN: + raise ProcessError("Manager has shut down") + else: + raise ProcessError( + "Unknown state {!r}".format(self._state.value)) + + if initializer is not None and not callable(initializer): + raise TypeError('initializer must be a callable') + + # pipe over which we will retrieve address of server + reader, writer = connection.Pipe(duplex=False) + + # spawn process which runs a server + self._process = self._ctx.Process( + target=type(self)._run_server, + args=(self._registry, self._address, self._authkey, + self._serializer, writer, initializer, initargs), + ) + ident = ':'.join(str(i) for i in self._process._identity) + self._process.name = type(self).__name__ + '-' + ident + self._process.start() + + # get address of server + writer.close() + self._address = reader.recv() + reader.close() + + # register a finalizer + self._state.value = State.STARTED + self.shutdown = util.Finalize( + self, type(self)._finalize_manager, + args=(self._process, self._address, self._authkey, + self._state, self._Client), + exitpriority=0 + ) + + @classmethod + def _run_server(cls, registry, address, authkey, serializer, writer, + initializer=None, initargs=()): + ''' + Create a server, report its address and run it + ''' + # bpo-36368: protect server process from KeyboardInterrupt signals + signal.signal(signal.SIGINT, signal.SIG_IGN) + + if initializer is not None: + initializer(*initargs) + + # create server + server = cls._Server(registry, address, authkey, serializer) + + # inform parent process of the server's address + writer.send(server.address) + writer.close() + + # run the manager + util.info('manager serving at %r', server.address) + server.serve_forever() + + def _create(self, typeid, /, *args, **kwds): + ''' + Create a new shared object; return the token and exposed tuple + ''' + assert self._state.value == State.STARTED, 'server not yet started' + conn = self._Client(self._address, authkey=self._authkey) + try: + id, exposed = dispatch(conn, None, 'create', (typeid,)+args, kwds) + finally: + conn.close() + return Token(typeid, self._address, id), exposed + + def join(self, timeout=None): + ''' + Join the manager process (if it has been spawned) + ''' + if self._process is not None: + self._process.join(timeout) + if not self._process.is_alive(): + self._process = None + + def _debug_info(self): + ''' + Return some info about the servers shared objects and connections + ''' + conn = self._Client(self._address, authkey=self._authkey) + try: + return dispatch(conn, None, 'debug_info') + finally: + conn.close() + + def _number_of_objects(self): + ''' + Return the number of shared objects + ''' + conn = self._Client(self._address, authkey=self._authkey) + try: + return dispatch(conn, None, 'number_of_objects') + finally: + conn.close() + + def __enter__(self): + if self._state.value == State.INITIAL: + self.start() + if self._state.value != State.STARTED: + if self._state.value == State.INITIAL: + raise ProcessError("Unable to start server") + elif self._state.value == State.SHUTDOWN: + raise ProcessError("Manager has shut down") + else: + raise ProcessError( + "Unknown state {!r}".format(self._state.value)) + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.shutdown() + + @staticmethod + def _finalize_manager(process, address, authkey, state, _Client): + ''' + Shutdown the manager process; will be registered as a finalizer + ''' + if process.is_alive(): + util.info('sending shutdown message to manager') + try: + conn = _Client(address, authkey=authkey) + try: + dispatch(conn, None, 'shutdown') + finally: + conn.close() + except Exception: + pass + + process.join(timeout=1.0) + if process.is_alive(): + util.info('manager still alive') + if hasattr(process, 'terminate'): + util.info('trying to `terminate()` manager process') + process.terminate() + process.join(timeout=1.0) + if process.is_alive(): + util.info('manager still alive after terminate') + + state.value = State.SHUTDOWN + try: + del BaseProxy._address_to_local[address] + except KeyError: + pass + + @property + def address(self): + return self._address + + @classmethod + def register(cls, typeid, callable=None, proxytype=None, exposed=None, + method_to_typeid=None, create_method=True): + ''' + Register a typeid with the manager type + ''' + if '_registry' not in cls.__dict__: + cls._registry = cls._registry.copy() + + if proxytype is None: + proxytype = AutoProxy + + exposed = exposed or getattr(proxytype, '_exposed_', None) + + method_to_typeid = method_to_typeid or \ + getattr(proxytype, '_method_to_typeid_', None) + + if method_to_typeid: + for key, value in list(method_to_typeid.items()): # isinstance? + assert type(key) is str, '%r is not a string' % key + assert type(value) is str, '%r is not a string' % value + + cls._registry[typeid] = ( + callable, exposed, method_to_typeid, proxytype + ) + + if create_method: + def temp(self, /, *args, **kwds): + util.debug('requesting creation of a shared %r object', typeid) + token, exp = self._create(typeid, *args, **kwds) + proxy = proxytype( + token, self._serializer, manager=self, + authkey=self._authkey, exposed=exp + ) + conn = self._Client(token.address, authkey=self._authkey) + dispatch(conn, None, 'decref', (token.id,)) + return proxy + temp.__name__ = typeid + setattr(cls, typeid, temp) + +# +# Subclass of set which get cleared after a fork +# + +class ProcessLocalSet(set): + def __init__(self): + util.register_after_fork(self, lambda obj: obj.clear()) + def __reduce__(self): + return type(self), () + +# +# Definition of BaseProxy +# + +class BaseProxy(object): + ''' + A base for proxies of shared objects + ''' + _address_to_local = {} + _mutex = util.ForkAwareThreadLock() + + def __init__(self, token, serializer, manager=None, + authkey=None, exposed=None, incref=True, manager_owned=False): + with BaseProxy._mutex: + tls_idset = BaseProxy._address_to_local.get(token.address, None) + if tls_idset is None: + tls_idset = util.ForkAwareLocal(), ProcessLocalSet() + BaseProxy._address_to_local[token.address] = tls_idset + + # self._tls is used to record the connection used by this + # thread to communicate with the manager at token.address + self._tls = tls_idset[0] + + # self._idset is used to record the identities of all shared + # objects for which the current process owns references and + # which are in the manager at token.address + self._idset = tls_idset[1] + + self._token = token + self._id = self._token.id + self._manager = manager + self._serializer = serializer + self._Client = listener_client[serializer][1] + + # Should be set to True only when a proxy object is being created + # on the manager server; primary use case: nested proxy objects. + # RebuildProxy detects when a proxy is being created on the manager + # and sets this value appropriately. + self._owned_by_manager = manager_owned + + if authkey is not None: + self._authkey = process.AuthenticationString(authkey) + elif self._manager is not None: + self._authkey = self._manager._authkey + else: + self._authkey = process.current_process().authkey + + if incref: + self._incref() + + util.register_after_fork(self, BaseProxy._after_fork) + + def _connect(self): + util.debug('making connection to manager') + name = process.current_process().name + if threading.current_thread().name != 'MainThread': + name += '|' + threading.current_thread().name + conn = self._Client(self._token.address, authkey=self._authkey) + dispatch(conn, None, 'accept_connection', (name,)) + self._tls.connection = conn + + def _callmethod(self, methodname, args=(), kwds={}): + ''' + Try to call a method of the referent and return a copy of the result + ''' + try: + conn = self._tls.connection + except AttributeError: + util.debug('thread %r does not own a connection', + threading.current_thread().name) + self._connect() + conn = self._tls.connection + + conn.send((self._id, methodname, args, kwds)) + kind, result = conn.recv() + + if kind == '#RETURN': + return result + elif kind == '#PROXY': + exposed, token = result + proxytype = self._manager._registry[token.typeid][-1] + token.address = self._token.address + proxy = proxytype( + token, self._serializer, manager=self._manager, + authkey=self._authkey, exposed=exposed + ) + conn = self._Client(token.address, authkey=self._authkey) + dispatch(conn, None, 'decref', (token.id,)) + return proxy + raise convert_to_error(kind, result) + + def _getvalue(self): + ''' + Get a copy of the value of the referent + ''' + return self._callmethod('#GETVALUE') + + def _incref(self): + if self._owned_by_manager: + util.debug('owned_by_manager skipped INCREF of %r', self._token.id) + return + + conn = self._Client(self._token.address, authkey=self._authkey) + dispatch(conn, None, 'incref', (self._id,)) + util.debug('INCREF %r', self._token.id) + + self._idset.add(self._id) + + state = self._manager and self._manager._state + + self._close = util.Finalize( + self, BaseProxy._decref, + args=(self._token, self._authkey, state, + self._tls, self._idset, self._Client), + exitpriority=10 + ) + + @staticmethod + def _decref(token, authkey, state, tls, idset, _Client): + idset.discard(token.id) + + # check whether manager is still alive + if state is None or state.value == State.STARTED: + # tell manager this process no longer cares about referent + try: + util.debug('DECREF %r', token.id) + conn = _Client(token.address, authkey=authkey) + dispatch(conn, None, 'decref', (token.id,)) + except Exception as e: + util.debug('... decref failed %s', e) + + else: + util.debug('DECREF %r -- manager already shutdown', token.id) + + # check whether we can close this thread's connection because + # the process owns no more references to objects for this manager + if not idset and hasattr(tls, 'connection'): + util.debug('thread %r has no more proxies so closing conn', + threading.current_thread().name) + tls.connection.close() + del tls.connection + + def _after_fork(self): + self._manager = None + try: + self._incref() + except Exception as e: + # the proxy may just be for a manager which has shutdown + util.info('incref failed: %s' % e) + + def __reduce__(self): + kwds = {} + if get_spawning_popen() is not None: + kwds['authkey'] = self._authkey + + if getattr(self, '_isauto', False): + kwds['exposed'] = self._exposed_ + return (RebuildProxy, + (AutoProxy, self._token, self._serializer, kwds)) + else: + return (RebuildProxy, + (type(self), self._token, self._serializer, kwds)) + + def __deepcopy__(self, memo): + return self._getvalue() + + def __repr__(self): + return '<%s object, typeid %r at %#x>' % \ + (type(self).__name__, self._token.typeid, id(self)) + + def __str__(self): + ''' + Return representation of the referent (or a fall-back if that fails) + ''' + try: + return self._callmethod('__repr__') + except Exception: + return repr(self)[:-1] + "; '__str__()' failed>" + +# +# Function used for unpickling +# + +def RebuildProxy(func, token, serializer, kwds): + ''' + Function used for unpickling proxy objects. + ''' + server = getattr(process.current_process(), '_manager_server', None) + if server and server.address == token.address: + util.debug('Rebuild a proxy owned by manager, token=%r', token) + kwds['manager_owned'] = True + if token.id not in server.id_to_local_proxy_obj: + server.id_to_local_proxy_obj[token.id] = \ + server.id_to_obj[token.id] + incref = ( + kwds.pop('incref', True) and + not getattr(process.current_process(), '_inheriting', False) + ) + return func(token, serializer, incref=incref, **kwds) + +# +# Functions to create proxies and proxy types +# + +def MakeProxyType(name, exposed, _cache={}): + ''' + Return a proxy type whose methods are given by `exposed` + ''' + exposed = tuple(exposed) + try: + return _cache[(name, exposed)] + except KeyError: + pass + + dic = {} + + for meth in exposed: + exec('''def %s(self, /, *args, **kwds): + return self._callmethod(%r, args, kwds)''' % (meth, meth), dic) + + ProxyType = type(name, (BaseProxy,), dic) + ProxyType._exposed_ = exposed + _cache[(name, exposed)] = ProxyType + return ProxyType + + +def AutoProxy(token, serializer, manager=None, authkey=None, + exposed=None, incref=True, manager_owned=False): + ''' + Return an auto-proxy for `token` + ''' + _Client = listener_client[serializer][1] + + if exposed is None: + conn = _Client(token.address, authkey=authkey) + try: + exposed = dispatch(conn, None, 'get_methods', (token,)) + finally: + conn.close() + + if authkey is None and manager is not None: + authkey = manager._authkey + if authkey is None: + authkey = process.current_process().authkey + + ProxyType = MakeProxyType('AutoProxy[%s]' % token.typeid, exposed) + proxy = ProxyType(token, serializer, manager=manager, authkey=authkey, + incref=incref, manager_owned=manager_owned) + proxy._isauto = True + return proxy + +# +# Types/callables which we will register with SyncManager +# + +class Namespace(object): + def __init__(self, /, **kwds): + self.__dict__.update(kwds) + def __repr__(self): + items = list(self.__dict__.items()) + temp = [] + for name, value in items: + if not name.startswith('_'): + temp.append('%s=%r' % (name, value)) + temp.sort() + return '%s(%s)' % (self.__class__.__name__, ', '.join(temp)) + +class Value(object): + def __init__(self, typecode, value, lock=True): + self._typecode = typecode + self._value = value + def get(self): + return self._value + def set(self, value): + self._value = value + def __repr__(self): + return '%s(%r, %r)'%(type(self).__name__, self._typecode, self._value) + value = property(get, set) + +def Array(typecode, sequence, lock=True): + return array.array(typecode, sequence) + +# +# Proxy types used by SyncManager +# + +class IteratorProxy(BaseProxy): + _exposed_ = ('__next__', 'send', 'throw', 'close') + def __iter__(self): + return self + def __next__(self, *args): + return self._callmethod('__next__', args) + def send(self, *args): + return self._callmethod('send', args) + def throw(self, *args): + return self._callmethod('throw', args) + def close(self, *args): + return self._callmethod('close', args) + + +class AcquirerProxy(BaseProxy): + _exposed_ = ('acquire', 'release') + def acquire(self, blocking=True, timeout=None): + args = (blocking,) if timeout is None else (blocking, timeout) + return self._callmethod('acquire', args) + def release(self): + return self._callmethod('release') + def __enter__(self): + return self._callmethod('acquire') + def __exit__(self, exc_type, exc_val, exc_tb): + return self._callmethod('release') + + +class ConditionProxy(AcquirerProxy): + _exposed_ = ('acquire', 'release', 'wait', 'notify', 'notify_all') + def wait(self, timeout=None): + return self._callmethod('wait', (timeout,)) + def notify(self, n=1): + return self._callmethod('notify', (n,)) + def notify_all(self): + return self._callmethod('notify_all') + def wait_for(self, predicate, timeout=None): + result = predicate() + if result: + return result + if timeout is not None: + endtime = getattr(time,'monotonic',time.time)() + timeout + else: + endtime = None + waittime = None + while not result: + if endtime is not None: + waittime = endtime - getattr(time,'monotonic',time.time)() + if waittime <= 0: + break + self.wait(waittime) + result = predicate() + return result + + +class EventProxy(BaseProxy): + _exposed_ = ('is_set', 'set', 'clear', 'wait') + def is_set(self): + return self._callmethod('is_set') + def set(self): + return self._callmethod('set') + def clear(self): + return self._callmethod('clear') + def wait(self, timeout=None): + return self._callmethod('wait', (timeout,)) + + +class BarrierProxy(BaseProxy): + _exposed_ = ('__getattribute__', 'wait', 'abort', 'reset') + def wait(self, timeout=None): + return self._callmethod('wait', (timeout,)) + def abort(self): + return self._callmethod('abort') + def reset(self): + return self._callmethod('reset') + @property + def parties(self): + return self._callmethod('__getattribute__', ('parties',)) + @property + def n_waiting(self): + return self._callmethod('__getattribute__', ('n_waiting',)) + @property + def broken(self): + return self._callmethod('__getattribute__', ('broken',)) + + +class NamespaceProxy(BaseProxy): + _exposed_ = ('__getattribute__', '__setattr__', '__delattr__') + def __getattr__(self, key): + if key[0] == '_': + return object.__getattribute__(self, key) + callmethod = object.__getattribute__(self, '_callmethod') + return callmethod('__getattribute__', (key,)) + def __setattr__(self, key, value): + if key[0] == '_': + return object.__setattr__(self, key, value) + callmethod = object.__getattribute__(self, '_callmethod') + return callmethod('__setattr__', (key, value)) + def __delattr__(self, key): + if key[0] == '_': + return object.__delattr__(self, key) + callmethod = object.__getattribute__(self, '_callmethod') + return callmethod('__delattr__', (key,)) + + +class ValueProxy(BaseProxy): + _exposed_ = ('get', 'set') + def get(self): + return self._callmethod('get') + def set(self, value): + return self._callmethod('set', (value,)) + value = property(get, set) + + __class_getitem__ = classmethod(types.GenericAlias) + + +BaseListProxy = MakeProxyType('BaseListProxy', ( + '__add__', '__contains__', '__delitem__', '__getitem__', '__len__', + '__mul__', '__reversed__', '__rmul__', '__setitem__', + 'append', 'count', 'extend', 'index', 'insert', 'pop', 'remove', + 'reverse', 'sort', '__imul__' + )) +class ListProxy(BaseListProxy): + def __iadd__(self, value): + self._callmethod('extend', (value,)) + return self + def __imul__(self, value): + self._callmethod('__imul__', (value,)) + return self + + +DictProxy = MakeProxyType('DictProxy', ( + '__contains__', '__delitem__', '__getitem__', '__iter__', '__len__', + '__setitem__', 'clear', 'copy', 'get', 'items', + 'keys', 'pop', 'popitem', 'setdefault', 'update', 'values' + )) +DictProxy._method_to_typeid_ = { + '__iter__': 'Iterator', + } + + +ArrayProxy = MakeProxyType('ArrayProxy', ( + '__len__', '__getitem__', '__setitem__' + )) + + +BasePoolProxy = MakeProxyType('PoolProxy', ( + 'apply', 'apply_async', 'close', 'imap', 'imap_unordered', 'join', + 'map', 'map_async', 'starmap', 'starmap_async', 'terminate', + )) +BasePoolProxy._method_to_typeid_ = { + 'apply_async': 'AsyncResult', + 'map_async': 'AsyncResult', + 'starmap_async': 'AsyncResult', + 'imap': 'Iterator', + 'imap_unordered': 'Iterator' + } +class PoolProxy(BasePoolProxy): + def __enter__(self): + return self + def __exit__(self, exc_type, exc_val, exc_tb): + self.terminate() + +# +# Definition of SyncManager +# + +class SyncManager(BaseManager): + ''' + Subclass of `BaseManager` which supports a number of shared object types. + + The types registered are those intended for the synchronization + of threads, plus `dict`, `list` and `Namespace`. + + The `multiprocess.Manager()` function creates started instances of + this class. + ''' + +SyncManager.register('Queue', queue.Queue) +SyncManager.register('JoinableQueue', queue.Queue) +SyncManager.register('Event', threading.Event, EventProxy) +SyncManager.register('Lock', threading.Lock, AcquirerProxy) +SyncManager.register('RLock', threading.RLock, AcquirerProxy) +SyncManager.register('Semaphore', threading.Semaphore, AcquirerProxy) +SyncManager.register('BoundedSemaphore', threading.BoundedSemaphore, + AcquirerProxy) +SyncManager.register('Condition', threading.Condition, ConditionProxy) +SyncManager.register('Barrier', threading.Barrier, BarrierProxy) +SyncManager.register('Pool', pool.Pool, PoolProxy) +SyncManager.register('list', list, ListProxy) +SyncManager.register('dict', dict, DictProxy) +SyncManager.register('Value', Value, ValueProxy) +SyncManager.register('Array', Array, ArrayProxy) +SyncManager.register('Namespace', Namespace, NamespaceProxy) + +# types returned by methods of PoolProxy +SyncManager.register('Iterator', proxytype=IteratorProxy, create_method=False) +SyncManager.register('AsyncResult', create_method=False) + +# +# Definition of SharedMemoryManager and SharedMemoryServer +# + +if HAS_SHMEM: + class _SharedMemoryTracker: + "Manages one or more shared memory segments." + + def __init__(self, name, segment_names=[]): + self.shared_memory_context_name = name + self.segment_names = segment_names + + def register_segment(self, segment_name): + "Adds the supplied shared memory block name to tracker." + util.debug(f"Register segment {segment_name!r} in pid {getpid()}") + self.segment_names.append(segment_name) + + def destroy_segment(self, segment_name): + """Calls unlink() on the shared memory block with the supplied name + and removes it from the list of blocks being tracked.""" + util.debug(f"Destroy segment {segment_name!r} in pid {getpid()}") + self.segment_names.remove(segment_name) + segment = shared_memory.SharedMemory(segment_name) + segment.close() + segment.unlink() + + def unlink(self): + "Calls destroy_segment() on all tracked shared memory blocks." + for segment_name in self.segment_names[:]: + self.destroy_segment(segment_name) + + def __del__(self): + util.debug(f"Call {self.__class__.__name__}.__del__ in {getpid()}") + self.unlink() + + def __getstate__(self): + return (self.shared_memory_context_name, self.segment_names) + + def __setstate__(self, state): + self.__init__(*state) + + + class SharedMemoryServer(Server): + + public = Server.public + \ + ['track_segment', 'release_segment', 'list_segments'] + + def __init__(self, *args, **kwargs): + Server.__init__(self, *args, **kwargs) + address = self.address + # The address of Linux abstract namespaces can be bytes + if isinstance(address, bytes): + address = os.fsdecode(address) + self.shared_memory_context = \ + _SharedMemoryTracker(f"shm_{address}_{getpid()}") + util.debug(f"SharedMemoryServer started by pid {getpid()}") + + def create(self, c, typeid, /, *args, **kwargs): + """Create a new distributed-shared object (not backed by a shared + memory block) and return its id to be used in a Proxy Object.""" + # Unless set up as a shared proxy, don't make shared_memory_context + # a standard part of kwargs. This makes things easier for supplying + # simple functions. + if hasattr(self.registry[typeid][-1], "_shared_memory_proxy"): + kwargs['shared_memory_context'] = self.shared_memory_context + return Server.create(self, c, typeid, *args, **kwargs) + + def shutdown(self, c): + "Call unlink() on all tracked shared memory, terminate the Server." + self.shared_memory_context.unlink() + return Server.shutdown(self, c) + + def track_segment(self, c, segment_name): + "Adds the supplied shared memory block name to Server's tracker." + self.shared_memory_context.register_segment(segment_name) + + def release_segment(self, c, segment_name): + """Calls unlink() on the shared memory block with the supplied name + and removes it from the tracker instance inside the Server.""" + self.shared_memory_context.destroy_segment(segment_name) + + def list_segments(self, c): + """Returns a list of names of shared memory blocks that the Server + is currently tracking.""" + return self.shared_memory_context.segment_names + + + class SharedMemoryManager(BaseManager): + """Like SyncManager but uses SharedMemoryServer instead of Server. + + It provides methods for creating and returning SharedMemory instances + and for creating a list-like object (ShareableList) backed by shared + memory. It also provides methods that create and return Proxy Objects + that support synchronization across processes (i.e. multi-process-safe + locks and semaphores). + """ + + _Server = SharedMemoryServer + + def __init__(self, *args, **kwargs): + if os.name == "posix": + # bpo-36867: Ensure the resource_tracker is running before + # launching the manager process, so that concurrent + # shared_memory manipulation both in the manager and in the + # current process does not create two resource_tracker + # processes. + from . import resource_tracker + resource_tracker.ensure_running() + BaseManager.__init__(self, *args, **kwargs) + util.debug(f"{self.__class__.__name__} created by pid {getpid()}") + + def __del__(self): + util.debug(f"{self.__class__.__name__}.__del__ by pid {getpid()}") + pass + + def get_server(self): + 'Better than monkeypatching for now; merge into Server ultimately' + if self._state.value != State.INITIAL: + if self._state.value == State.STARTED: + raise ProcessError("Already started SharedMemoryServer") + elif self._state.value == State.SHUTDOWN: + raise ProcessError("SharedMemoryManager has shut down") + else: + raise ProcessError( + "Unknown state {!r}".format(self._state.value)) + return self._Server(self._registry, self._address, + self._authkey, self._serializer) + + def SharedMemory(self, size): + """Returns a new SharedMemory instance with the specified size in + bytes, to be tracked by the manager.""" + with self._Client(self._address, authkey=self._authkey) as conn: + sms = shared_memory.SharedMemory(None, create=True, size=size) + try: + dispatch(conn, None, 'track_segment', (sms.name,)) + except BaseException as e: + sms.unlink() + raise e + return sms + + def ShareableList(self, sequence): + """Returns a new ShareableList instance populated with the values + from the input sequence, to be tracked by the manager.""" + with self._Client(self._address, authkey=self._authkey) as conn: + sl = shared_memory.ShareableList(sequence) + try: + dispatch(conn, None, 'track_segment', (sl.shm.name,)) + except BaseException as e: + sl.shm.unlink() + raise e + return sl diff --git a/llmeval-env/lib/python3.10/site-packages/multiprocess/shared_memory.py b/llmeval-env/lib/python3.10/site-packages/multiprocess/shared_memory.py new file mode 100644 index 0000000000000000000000000000000000000000..9a1e5aa17b87a232a831b5d250b8e0c2f15f574d --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/multiprocess/shared_memory.py @@ -0,0 +1,534 @@ +"""Provides shared memory for direct access across processes. + +The API of this package is currently provisional. Refer to the +documentation for details. +""" + + +__all__ = [ 'SharedMemory', 'ShareableList' ] + + +from functools import partial +import mmap +import os +import errno +import struct +import secrets +import types + +if os.name == "nt": + import _winapi + _USE_POSIX = False +else: + import _posixshmem + _USE_POSIX = True + +from . import resource_tracker + +_O_CREX = os.O_CREAT | os.O_EXCL + +# FreeBSD (and perhaps other BSDs) limit names to 14 characters. +_SHM_SAFE_NAME_LENGTH = 14 + +# Shared memory block name prefix +if _USE_POSIX: + _SHM_NAME_PREFIX = '/psm_' +else: + _SHM_NAME_PREFIX = 'wnsm_' + + +def _make_filename(): + "Create a random filename for the shared memory object." + # number of random bytes to use for name + nbytes = (_SHM_SAFE_NAME_LENGTH - len(_SHM_NAME_PREFIX)) // 2 + assert nbytes >= 2, '_SHM_NAME_PREFIX too long' + name = _SHM_NAME_PREFIX + secrets.token_hex(nbytes) + assert len(name) <= _SHM_SAFE_NAME_LENGTH + return name + + +class SharedMemory: + """Creates a new shared memory block or attaches to an existing + shared memory block. + + Every shared memory block is assigned a unique name. This enables + one process to create a shared memory block with a particular name + so that a different process can attach to that same shared memory + block using that same name. + + As a resource for sharing data across processes, shared memory blocks + may outlive the original process that created them. When one process + no longer needs access to a shared memory block that might still be + needed by other processes, the close() method should be called. + When a shared memory block is no longer needed by any process, the + unlink() method should be called to ensure proper cleanup.""" + + # Defaults; enables close() and unlink() to run without errors. + _name = None + _fd = -1 + _mmap = None + _buf = None + _flags = os.O_RDWR + _mode = 0o600 + _prepend_leading_slash = True if _USE_POSIX else False + + def __init__(self, name=None, create=False, size=0): + if not size >= 0: + raise ValueError("'size' must be a positive integer") + if create: + self._flags = _O_CREX | os.O_RDWR + if size == 0: + raise ValueError("'size' must be a positive number different from zero") + if name is None and not self._flags & os.O_EXCL: + raise ValueError("'name' can only be None if create=True") + + if _USE_POSIX: + + # POSIX Shared Memory + + if name is None: + while True: + name = _make_filename() + try: + self._fd = _posixshmem.shm_open( + name, + self._flags, + mode=self._mode + ) + except FileExistsError: + continue + self._name = name + break + else: + name = "/" + name if self._prepend_leading_slash else name + self._fd = _posixshmem.shm_open( + name, + self._flags, + mode=self._mode + ) + self._name = name + try: + if create and size: + os.ftruncate(self._fd, size) + stats = os.fstat(self._fd) + size = stats.st_size + self._mmap = mmap.mmap(self._fd, size) + except OSError: + self.unlink() + raise + + resource_tracker.register(self._name, "shared_memory") + + else: + + # Windows Named Shared Memory + + if create: + while True: + temp_name = _make_filename() if name is None else name + # Create and reserve shared memory block with this name + # until it can be attached to by mmap. + h_map = _winapi.CreateFileMapping( + _winapi.INVALID_HANDLE_VALUE, + _winapi.NULL, + _winapi.PAGE_READWRITE, + (size >> 32) & 0xFFFFFFFF, + size & 0xFFFFFFFF, + temp_name + ) + try: + last_error_code = _winapi.GetLastError() + if last_error_code == _winapi.ERROR_ALREADY_EXISTS: + if name is not None: + raise FileExistsError( + errno.EEXIST, + os.strerror(errno.EEXIST), + name, + _winapi.ERROR_ALREADY_EXISTS + ) + else: + continue + self._mmap = mmap.mmap(-1, size, tagname=temp_name) + finally: + _winapi.CloseHandle(h_map) + self._name = temp_name + break + + else: + self._name = name + # Dynamically determine the existing named shared memory + # block's size which is likely a multiple of mmap.PAGESIZE. + h_map = _winapi.OpenFileMapping( + _winapi.FILE_MAP_READ, + False, + name + ) + try: + p_buf = _winapi.MapViewOfFile( + h_map, + _winapi.FILE_MAP_READ, + 0, + 0, + 0 + ) + finally: + _winapi.CloseHandle(h_map) + try: + size = _winapi.VirtualQuerySize(p_buf) + finally: + _winapi.UnmapViewOfFile(p_buf) + self._mmap = mmap.mmap(-1, size, tagname=name) + + self._size = size + self._buf = memoryview(self._mmap) + + def __del__(self): + try: + self.close() + except OSError: + pass + + def __reduce__(self): + return ( + self.__class__, + ( + self.name, + False, + self.size, + ), + ) + + def __repr__(self): + return f'{self.__class__.__name__}({self.name!r}, size={self.size})' + + @property + def buf(self): + "A memoryview of contents of the shared memory block." + return self._buf + + @property + def name(self): + "Unique name that identifies the shared memory block." + reported_name = self._name + if _USE_POSIX and self._prepend_leading_slash: + if self._name.startswith("/"): + reported_name = self._name[1:] + return reported_name + + @property + def size(self): + "Size in bytes." + return self._size + + def close(self): + """Closes access to the shared memory from this instance but does + not destroy the shared memory block.""" + if self._buf is not None: + self._buf.release() + self._buf = None + if self._mmap is not None: + self._mmap.close() + self._mmap = None + if _USE_POSIX and self._fd >= 0: + os.close(self._fd) + self._fd = -1 + + def unlink(self): + """Requests that the underlying shared memory block be destroyed. + + In order to ensure proper cleanup of resources, unlink should be + called once (and only once) across all processes which have access + to the shared memory block.""" + if _USE_POSIX and self._name: + _posixshmem.shm_unlink(self._name) + resource_tracker.unregister(self._name, "shared_memory") + + +_encoding = "utf8" + +class ShareableList: + """Pattern for a mutable list-like object shareable via a shared + memory block. It differs from the built-in list type in that these + lists can not change their overall length (i.e. no append, insert, + etc.) + + Because values are packed into a memoryview as bytes, the struct + packing format for any storable value must require no more than 8 + characters to describe its format.""" + + # The shared memory area is organized as follows: + # - 8 bytes: number of items (N) as a 64-bit integer + # - (N + 1) * 8 bytes: offsets of each element from the start of the + # data area + # - K bytes: the data area storing item values (with encoding and size + # depending on their respective types) + # - N * 8 bytes: `struct` format string for each element + # - N bytes: index into _back_transforms_mapping for each element + # (for reconstructing the corresponding Python value) + _types_mapping = { + int: "q", + float: "d", + bool: "xxxxxxx?", + str: "%ds", + bytes: "%ds", + None.__class__: "xxxxxx?x", + } + _alignment = 8 + _back_transforms_mapping = { + 0: lambda value: value, # int, float, bool + 1: lambda value: value.rstrip(b'\x00').decode(_encoding), # str + 2: lambda value: value.rstrip(b'\x00'), # bytes + 3: lambda _value: None, # None + } + + @staticmethod + def _extract_recreation_code(value): + """Used in concert with _back_transforms_mapping to convert values + into the appropriate Python objects when retrieving them from + the list as well as when storing them.""" + if not isinstance(value, (str, bytes, None.__class__)): + return 0 + elif isinstance(value, str): + return 1 + elif isinstance(value, bytes): + return 2 + else: + return 3 # NoneType + + def __init__(self, sequence=None, *, name=None): + if name is None or sequence is not None: + sequence = sequence or () + _formats = [ + self._types_mapping[type(item)] + if not isinstance(item, (str, bytes)) + else self._types_mapping[type(item)] % ( + self._alignment * (len(item) // self._alignment + 1), + ) + for item in sequence + ] + self._list_len = len(_formats) + assert sum(len(fmt) <= 8 for fmt in _formats) == self._list_len + offset = 0 + # The offsets of each list element into the shared memory's + # data area (0 meaning the start of the data area, not the start + # of the shared memory area). + self._allocated_offsets = [0] + for fmt in _formats: + offset += self._alignment if fmt[-1] != "s" else int(fmt[:-1]) + self._allocated_offsets.append(offset) + _recreation_codes = [ + self._extract_recreation_code(item) for item in sequence + ] + requested_size = struct.calcsize( + "q" + self._format_size_metainfo + + "".join(_formats) + + self._format_packing_metainfo + + self._format_back_transform_codes + ) + + self.shm = SharedMemory(name, create=True, size=requested_size) + else: + self.shm = SharedMemory(name) + + if sequence is not None: + _enc = _encoding + struct.pack_into( + "q" + self._format_size_metainfo, + self.shm.buf, + 0, + self._list_len, + *(self._allocated_offsets) + ) + struct.pack_into( + "".join(_formats), + self.shm.buf, + self._offset_data_start, + *(v.encode(_enc) if isinstance(v, str) else v for v in sequence) + ) + struct.pack_into( + self._format_packing_metainfo, + self.shm.buf, + self._offset_packing_formats, + *(v.encode(_enc) for v in _formats) + ) + struct.pack_into( + self._format_back_transform_codes, + self.shm.buf, + self._offset_back_transform_codes, + *(_recreation_codes) + ) + + else: + self._list_len = len(self) # Obtains size from offset 0 in buffer. + self._allocated_offsets = list( + struct.unpack_from( + self._format_size_metainfo, + self.shm.buf, + 1 * 8 + ) + ) + + def _get_packing_format(self, position): + "Gets the packing format for a single value stored in the list." + position = position if position >= 0 else position + self._list_len + if (position >= self._list_len) or (self._list_len < 0): + raise IndexError("Requested position out of range.") + + v = struct.unpack_from( + "8s", + self.shm.buf, + self._offset_packing_formats + position * 8 + )[0] + fmt = v.rstrip(b'\x00') + fmt_as_str = fmt.decode(_encoding) + + return fmt_as_str + + def _get_back_transform(self, position): + "Gets the back transformation function for a single value." + + if (position >= self._list_len) or (self._list_len < 0): + raise IndexError("Requested position out of range.") + + transform_code = struct.unpack_from( + "b", + self.shm.buf, + self._offset_back_transform_codes + position + )[0] + transform_function = self._back_transforms_mapping[transform_code] + + return transform_function + + def _set_packing_format_and_transform(self, position, fmt_as_str, value): + """Sets the packing format and back transformation code for a + single value in the list at the specified position.""" + + if (position >= self._list_len) or (self._list_len < 0): + raise IndexError("Requested position out of range.") + + struct.pack_into( + "8s", + self.shm.buf, + self._offset_packing_formats + position * 8, + fmt_as_str.encode(_encoding) + ) + + transform_code = self._extract_recreation_code(value) + struct.pack_into( + "b", + self.shm.buf, + self._offset_back_transform_codes + position, + transform_code + ) + + def __getitem__(self, position): + position = position if position >= 0 else position + self._list_len + try: + offset = self._offset_data_start + self._allocated_offsets[position] + (v,) = struct.unpack_from( + self._get_packing_format(position), + self.shm.buf, + offset + ) + except IndexError: + raise IndexError("index out of range") + + back_transform = self._get_back_transform(position) + v = back_transform(v) + + return v + + def __setitem__(self, position, value): + position = position if position >= 0 else position + self._list_len + try: + item_offset = self._allocated_offsets[position] + offset = self._offset_data_start + item_offset + current_format = self._get_packing_format(position) + except IndexError: + raise IndexError("assignment index out of range") + + if not isinstance(value, (str, bytes)): + new_format = self._types_mapping[type(value)] + encoded_value = value + else: + allocated_length = self._allocated_offsets[position + 1] - item_offset + + encoded_value = (value.encode(_encoding) + if isinstance(value, str) else value) + if len(encoded_value) > allocated_length: + raise ValueError("bytes/str item exceeds available storage") + if current_format[-1] == "s": + new_format = current_format + else: + new_format = self._types_mapping[str] % ( + allocated_length, + ) + + self._set_packing_format_and_transform( + position, + new_format, + value + ) + struct.pack_into(new_format, self.shm.buf, offset, encoded_value) + + def __reduce__(self): + return partial(self.__class__, name=self.shm.name), () + + def __len__(self): + return struct.unpack_from("q", self.shm.buf, 0)[0] + + def __repr__(self): + return f'{self.__class__.__name__}({list(self)}, name={self.shm.name!r})' + + @property + def format(self): + "The struct packing format used by all currently stored items." + return "".join( + self._get_packing_format(i) for i in range(self._list_len) + ) + + @property + def _format_size_metainfo(self): + "The struct packing format used for the items' storage offsets." + return "q" * (self._list_len + 1) + + @property + def _format_packing_metainfo(self): + "The struct packing format used for the items' packing formats." + return "8s" * self._list_len + + @property + def _format_back_transform_codes(self): + "The struct packing format used for the items' back transforms." + return "b" * self._list_len + + @property + def _offset_data_start(self): + # - 8 bytes for the list length + # - (N + 1) * 8 bytes for the element offsets + return (self._list_len + 2) * 8 + + @property + def _offset_packing_formats(self): + return self._offset_data_start + self._allocated_offsets[-1] + + @property + def _offset_back_transform_codes(self): + return self._offset_packing_formats + self._list_len * 8 + + def count(self, value): + "L.count(value) -> integer -- return number of occurrences of value." + + return sum(value == entry for entry in self) + + def index(self, value): + """L.index(value) -> integer -- return first index of value. + Raises ValueError if the value is not present.""" + + for position, entry in enumerate(self): + if value == entry: + return position + else: + raise ValueError(f"{value!r} not in this container") + + __class_getitem__ = classmethod(types.GenericAlias) diff --git a/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/INSTALLER b/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/LICENSE.txt b/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..d226599fd2ec2f3353ffb0c434cd8be1b570ac95 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/LICENSE.txt @@ -0,0 +1,971 @@ +Copyright (c) 2005-2023, NumPy Developers. +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + + * Neither the name of the NumPy Developers nor the names of any + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +---- + +The NumPy repository and source distributions bundle several libraries that are +compatibly licensed. We list these here. + +Name: lapack-lite +Files: numpy/linalg/lapack_lite/* +License: BSD-3-Clause + For details, see numpy/linalg/lapack_lite/LICENSE.txt + +Name: tempita +Files: tools/npy_tempita/* +License: MIT + For details, see tools/npy_tempita/license.txt + +Name: dragon4 +Files: numpy/core/src/multiarray/dragon4.c +License: MIT + For license text, see numpy/core/src/multiarray/dragon4.c + +Name: libdivide +Files: numpy/core/include/numpy/libdivide/* +License: Zlib + For license text, see numpy/core/include/numpy/libdivide/LICENSE.txt + + +Note that the following files are vendored in the repository and sdist but not +installed in built numpy packages: + +Name: Meson +Files: vendored-meson/meson/* +License: Apache 2.0 + For license text, see vendored-meson/meson/COPYING + +Name: spin +Files: .spin/cmds.py +License: BSD-3 + For license text, see .spin/LICENSE + +---- + +This binary distribution of NumPy also bundles the following software: + + +Name: OpenBLAS +Files: numpy.libs/libopenblas*.so +Description: bundled as a dynamically linked library +Availability: https://github.com/OpenMathLib/OpenBLAS/ +License: BSD-3-Clause + Copyright (c) 2011-2014, The OpenBLAS Project + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + 3. Neither the name of the OpenBLAS project nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE + USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + +Name: LAPACK +Files: numpy.libs/libopenblas*.so +Description: bundled in OpenBLAS +Availability: https://github.com/OpenMathLib/OpenBLAS/ +License: BSD-3-Clause-Attribution + Copyright (c) 1992-2013 The University of Tennessee and The University + of Tennessee Research Foundation. All rights + reserved. + Copyright (c) 2000-2013 The University of California Berkeley. All + rights reserved. + Copyright (c) 2006-2013 The University of Colorado Denver. All rights + reserved. + + $COPYRIGHT$ + + Additional copyrights may follow + + $HEADER$ + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer listed + in this license in the documentation and/or other materials + provided with the distribution. + + - Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + + The copyright holders provide no reassurances that the source code + provided does not infringe any patent, copyright, or any other + intellectual property rights of third parties. The copyright holders + disclaim any liability to any recipient for claims brought against + recipient by any third party for infringement of that parties + intellectual property rights. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + +Name: GCC runtime library +Files: numpy.libs/libgfortran*.so +Description: dynamically linked to files compiled with gcc +Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libgfortran +License: GPL-3.0-with-GCC-exception + Copyright (C) 2002-2017 Free Software Foundation, Inc. + + Libgfortran is free software; you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation; either version 3, or (at your option) + any later version. + + Libgfortran is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + Under Section 7 of GPL version 3, you are granted additional + permissions described in the GCC Runtime Library Exception, version + 3.1, as published by the Free Software Foundation. + + You should have received a copy of the GNU General Public License and + a copy of the GCC Runtime Library Exception along with this program; + see the files COPYING3 and COPYING.RUNTIME respectively. If not, see + . + +---- + +Full text of license texts referred to above follows (that they are +listed below does not necessarily imply the conditions apply to the +present binary release): + +---- + +GCC RUNTIME LIBRARY EXCEPTION + +Version 3.1, 31 March 2009 + +Copyright (C) 2009 Free Software Foundation, Inc. + +Everyone is permitted to copy and distribute verbatim copies of this +license document, but changing it is not allowed. + +This GCC Runtime Library Exception ("Exception") is an additional +permission under section 7 of the GNU General Public License, version +3 ("GPLv3"). It applies to a given file (the "Runtime Library") that +bears a notice placed by the copyright holder of the file stating that +the file is governed by GPLv3 along with this Exception. + +When you use GCC to compile a program, GCC may combine portions of +certain GCC header files and runtime libraries with the compiled +program. The purpose of this Exception is to allow compilation of +non-GPL (including proprietary) programs to use, in this way, the +header files and runtime libraries covered by this Exception. + +0. Definitions. + +A file is an "Independent Module" if it either requires the Runtime +Library for execution after a Compilation Process, or makes use of an +interface provided by the Runtime Library, but is not otherwise based +on the Runtime Library. + +"GCC" means a version of the GNU Compiler Collection, with or without +modifications, governed by version 3 (or a specified later version) of +the GNU General Public License (GPL) with the option of using any +subsequent versions published by the FSF. + +"GPL-compatible Software" is software whose conditions of propagation, +modification and use would permit combination with GCC in accord with +the license of GCC. + +"Target Code" refers to output from any compiler for a real or virtual +target processor architecture, in executable form or suitable for +input to an assembler, loader, linker and/or execution +phase. Notwithstanding that, Target Code does not include data in any +format that is used as a compiler intermediate representation, or used +for producing a compiler intermediate representation. + +The "Compilation Process" transforms code entirely represented in +non-intermediate languages designed for human-written code, and/or in +Java Virtual Machine byte code, into Target Code. Thus, for example, +use of source code generators and preprocessors need not be considered +part of the Compilation Process, since the Compilation Process can be +understood as starting with the output of the generators or +preprocessors. + +A Compilation Process is "Eligible" if it is done using GCC, alone or +with other GPL-compatible software, or if it is done without using any +work based on GCC. For example, using non-GPL-compatible Software to +optimize any GCC intermediate representations would not qualify as an +Eligible Compilation Process. + +1. Grant of Additional Permission. + +You have permission to propagate a work of Target Code formed by +combining the Runtime Library with Independent Modules, even if such +propagation would otherwise violate the terms of GPLv3, provided that +all Target Code was generated by Eligible Compilation Processes. You +may then convey such a combination under terms of your choice, +consistent with the licensing of the Independent Modules. + +2. No Weakening of GCC Copyleft. + +The availability of this Exception does not imply any general +presumption that third-party software is unaffected by the copyleft +requirements of the license of GCC. + +---- + + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. We, the Free Software Foundation, use the +GNU General Public License for most of our software; it applies also to +any other work released this way by its authors. You can apply it to +your programs, too. + + When we speak of free software, we are referring to freedom, not +price. Our General Public Licenses are designed to make sure that you +have the freedom to distribute copies of free software (and charge for +them if you wish), that you receive source code or can get it if you +want it, that you can change the software or use pieces of it in new +free programs, and that you know you can do these things. + + To protect your rights, we need to prevent others from denying you +these rights or asking you to surrender the rights. Therefore, you have +certain responsibilities if you distribute copies of the software, or if +you modify it: responsibilities to respect the freedom of others. + + For example, if you distribute copies of such a program, whether +gratis or for a fee, you must pass on to the recipients the same +freedoms that you received. You must make sure that they, too, receive +or can get the source code. And you must show them these terms so they +know their rights. + + Developers that use the GNU GPL protect your rights with two steps: +(1) assert copyright on the software, and (2) offer you this License +giving you legal permission to copy, distribute and/or modify it. + + For the developers' and authors' protection, the GPL clearly explains +that there is no warranty for this free software. For both users' and +authors' sake, the GPL requires that modified versions be marked as +changed, so that their problems will not be attributed erroneously to +authors of previous versions. + + Some devices are designed to deny users access to install or run +modified versions of the software inside them, although the manufacturer +can do so. This is fundamentally incompatible with the aim of +protecting users' freedom to change the software. The systematic +pattern of such abuse occurs in the area of products for individuals to +use, which is precisely where it is most unacceptable. Therefore, we +have designed this version of the GPL to prohibit the practice for those +products. If such problems arise substantially in other domains, we +stand ready to extend this provision to those domains in future versions +of the GPL, as needed to protect the freedom of users. + + Finally, every program is threatened constantly by software patents. +States should not allow patents to restrict development and use of +software on general-purpose computers, but in those that do, we wish to +avoid the special danger that patents applied to a free program could +make it effectively proprietary. To prevent this, the GPL assures that +patents cannot be used to render the program non-free. + + The precise terms and conditions for copying, distribution and +modification follow. + + TERMS AND CONDITIONS + + 0. Definitions. + + "This License" refers to version 3 of the GNU General Public License. + + "Copyright" also means copyright-like laws that apply to other kinds of +works, such as semiconductor masks. + + "The Program" refers to any copyrightable work licensed under this +License. Each licensee is addressed as "you". "Licensees" and +"recipients" may be individuals or organizations. + + To "modify" a work means to copy from or adapt all or part of the work +in a fashion requiring copyright permission, other than the making of an +exact copy. The resulting work is called a "modified version" of the +earlier work or a work "based on" the earlier work. + + A "covered work" means either the unmodified Program or a work based +on the Program. + + To "propagate" a work means to do anything with it that, without +permission, would make you directly or secondarily liable for +infringement under applicable copyright law, except executing it on a +computer or modifying a private copy. Propagation includes copying, +distribution (with or without modification), making available to the +public, and in some countries other activities as well. + + To "convey" a work means any kind of propagation that enables other +parties to make or receive copies. Mere interaction with a user through +a computer network, with no transfer of a copy, is not conveying. + + An interactive user interface displays "Appropriate Legal Notices" +to the extent that it includes a convenient and prominently visible +feature that (1) displays an appropriate copyright notice, and (2) +tells the user that there is no warranty for the work (except to the +extent that warranties are provided), that licensees may convey the +work under this License, and how to view a copy of this License. If +the interface presents a list of user commands or options, such as a +menu, a prominent item in the list meets this criterion. + + 1. Source Code. + + The "source code" for a work means the preferred form of the work +for making modifications to it. "Object code" means any non-source +form of a work. + + A "Standard Interface" means an interface that either is an official +standard defined by a recognized standards body, or, in the case of +interfaces specified for a particular programming language, one that +is widely used among developers working in that language. + + The "System Libraries" of an executable work include anything, other +than the work as a whole, that (a) is included in the normal form of +packaging a Major Component, but which is not part of that Major +Component, and (b) serves only to enable use of the work with that +Major Component, or to implement a Standard Interface for which an +implementation is available to the public in source code form. A +"Major Component", in this context, means a major essential component +(kernel, window system, and so on) of the specific operating system +(if any) on which the executable work runs, or a compiler used to +produce the work, or an object code interpreter used to run it. + + The "Corresponding Source" for a work in object code form means all +the source code needed to generate, install, and (for an executable +work) run the object code and to modify the work, including scripts to +control those activities. However, it does not include the work's +System Libraries, or general-purpose tools or generally available free +programs which are used unmodified in performing those activities but +which are not part of the work. For example, Corresponding Source +includes interface definition files associated with source files for +the work, and the source code for shared libraries and dynamically +linked subprograms that the work is specifically designed to require, +such as by intimate data communication or control flow between those +subprograms and other parts of the work. + + The Corresponding Source need not include anything that users +can regenerate automatically from other parts of the Corresponding +Source. + + The Corresponding Source for a work in source code form is that +same work. + + 2. Basic Permissions. + + All rights granted under this License are granted for the term of +copyright on the Program, and are irrevocable provided the stated +conditions are met. This License explicitly affirms your unlimited +permission to run the unmodified Program. The output from running a +covered work is covered by this License only if the output, given its +content, constitutes a covered work. This License acknowledges your +rights of fair use or other equivalent, as provided by copyright law. + + You may make, run and propagate covered works that you do not +convey, without conditions so long as your license otherwise remains +in force. You may convey covered works to others for the sole purpose +of having them make modifications exclusively for you, or provide you +with facilities for running those works, provided that you comply with +the terms of this License in conveying all material for which you do +not control copyright. Those thus making or running the covered works +for you must do so exclusively on your behalf, under your direction +and control, on terms that prohibit them from making any copies of +your copyrighted material outside their relationship with you. + + Conveying under any other circumstances is permitted solely under +the conditions stated below. Sublicensing is not allowed; section 10 +makes it unnecessary. + + 3. Protecting Users' Legal Rights From Anti-Circumvention Law. + + No covered work shall be deemed part of an effective technological +measure under any applicable law fulfilling obligations under article +11 of the WIPO copyright treaty adopted on 20 December 1996, or +similar laws prohibiting or restricting circumvention of such +measures. + + When you convey a covered work, you waive any legal power to forbid +circumvention of technological measures to the extent such circumvention +is effected by exercising rights under this License with respect to +the covered work, and you disclaim any intention to limit operation or +modification of the work as a means of enforcing, against the work's +users, your or third parties' legal rights to forbid circumvention of +technological measures. + + 4. Conveying Verbatim Copies. + + You may convey verbatim copies of the Program's source code as you +receive it, in any medium, provided that you conspicuously and +appropriately publish on each copy an appropriate copyright notice; +keep intact all notices stating that this License and any +non-permissive terms added in accord with section 7 apply to the code; +keep intact all notices of the absence of any warranty; and give all +recipients a copy of this License along with the Program. + + You may charge any price or no price for each copy that you convey, +and you may offer support or warranty protection for a fee. + + 5. Conveying Modified Source Versions. + + You may convey a work based on the Program, or the modifications to +produce it from the Program, in the form of source code under the +terms of section 4, provided that you also meet all of these conditions: + + a) The work must carry prominent notices stating that you modified + it, and giving a relevant date. + + b) The work must carry prominent notices stating that it is + released under this License and any conditions added under section + 7. This requirement modifies the requirement in section 4 to + "keep intact all notices". + + c) You must license the entire work, as a whole, under this + License to anyone who comes into possession of a copy. This + License will therefore apply, along with any applicable section 7 + additional terms, to the whole of the work, and all its parts, + regardless of how they are packaged. This License gives no + permission to license the work in any other way, but it does not + invalidate such permission if you have separately received it. + + d) If the work has interactive user interfaces, each must display + Appropriate Legal Notices; however, if the Program has interactive + interfaces that do not display Appropriate Legal Notices, your + work need not make them do so. + + A compilation of a covered work with other separate and independent +works, which are not by their nature extensions of the covered work, +and which are not combined with it such as to form a larger program, +in or on a volume of a storage or distribution medium, is called an +"aggregate" if the compilation and its resulting copyright are not +used to limit the access or legal rights of the compilation's users +beyond what the individual works permit. Inclusion of a covered work +in an aggregate does not cause this License to apply to the other +parts of the aggregate. + + 6. Conveying Non-Source Forms. + + You may convey a covered work in object code form under the terms +of sections 4 and 5, provided that you also convey the +machine-readable Corresponding Source under the terms of this License, +in one of these ways: + + a) Convey the object code in, or embodied in, a physical product + (including a physical distribution medium), accompanied by the + Corresponding Source fixed on a durable physical medium + customarily used for software interchange. + + b) Convey the object code in, or embodied in, a physical product + (including a physical distribution medium), accompanied by a + written offer, valid for at least three years and valid for as + long as you offer spare parts or customer support for that product + model, to give anyone who possesses the object code either (1) a + copy of the Corresponding Source for all the software in the + product that is covered by this License, on a durable physical + medium customarily used for software interchange, for a price no + more than your reasonable cost of physically performing this + conveying of source, or (2) access to copy the + Corresponding Source from a network server at no charge. + + c) Convey individual copies of the object code with a copy of the + written offer to provide the Corresponding Source. This + alternative is allowed only occasionally and noncommercially, and + only if you received the object code with such an offer, in accord + with subsection 6b. + + d) Convey the object code by offering access from a designated + place (gratis or for a charge), and offer equivalent access to the + Corresponding Source in the same way through the same place at no + further charge. You need not require recipients to copy the + Corresponding Source along with the object code. If the place to + copy the object code is a network server, the Corresponding Source + may be on a different server (operated by you or a third party) + that supports equivalent copying facilities, provided you maintain + clear directions next to the object code saying where to find the + Corresponding Source. Regardless of what server hosts the + Corresponding Source, you remain obligated to ensure that it is + available for as long as needed to satisfy these requirements. + + e) Convey the object code using peer-to-peer transmission, provided + you inform other peers where the object code and Corresponding + Source of the work are being offered to the general public at no + charge under subsection 6d. + + A separable portion of the object code, whose source code is excluded +from the Corresponding Source as a System Library, need not be +included in conveying the object code work. + + A "User Product" is either (1) a "consumer product", which means any +tangible personal property which is normally used for personal, family, +or household purposes, or (2) anything designed or sold for incorporation +into a dwelling. In determining whether a product is a consumer product, +doubtful cases shall be resolved in favor of coverage. For a particular +product received by a particular user, "normally used" refers to a +typical or common use of that class of product, regardless of the status +of the particular user or of the way in which the particular user +actually uses, or expects or is expected to use, the product. A product +is a consumer product regardless of whether the product has substantial +commercial, industrial or non-consumer uses, unless such uses represent +the only significant mode of use of the product. + + "Installation Information" for a User Product means any methods, +procedures, authorization keys, or other information required to install +and execute modified versions of a covered work in that User Product from +a modified version of its Corresponding Source. The information must +suffice to ensure that the continued functioning of the modified object +code is in no case prevented or interfered with solely because +modification has been made. + + If you convey an object code work under this section in, or with, or +specifically for use in, a User Product, and the conveying occurs as +part of a transaction in which the right of possession and use of the +User Product is transferred to the recipient in perpetuity or for a +fixed term (regardless of how the transaction is characterized), the +Corresponding Source conveyed under this section must be accompanied +by the Installation Information. But this requirement does not apply +if neither you nor any third party retains the ability to install +modified object code on the User Product (for example, the work has +been installed in ROM). + + The requirement to provide Installation Information does not include a +requirement to continue to provide support service, warranty, or updates +for a work that has been modified or installed by the recipient, or for +the User Product in which it has been modified or installed. Access to a +network may be denied when the modification itself materially and +adversely affects the operation of the network or violates the rules and +protocols for communication across the network. + + Corresponding Source conveyed, and Installation Information provided, +in accord with this section must be in a format that is publicly +documented (and with an implementation available to the public in +source code form), and must require no special password or key for +unpacking, reading or copying. + + 7. Additional Terms. + + "Additional permissions" are terms that supplement the terms of this +License by making exceptions from one or more of its conditions. +Additional permissions that are applicable to the entire Program shall +be treated as though they were included in this License, to the extent +that they are valid under applicable law. If additional permissions +apply only to part of the Program, that part may be used separately +under those permissions, but the entire Program remains governed by +this License without regard to the additional permissions. + + When you convey a copy of a covered work, you may at your option +remove any additional permissions from that copy, or from any part of +it. (Additional permissions may be written to require their own +removal in certain cases when you modify the work.) You may place +additional permissions on material, added by you to a covered work, +for which you have or can give appropriate copyright permission. + + Notwithstanding any other provision of this License, for material you +add to a covered work, you may (if authorized by the copyright holders of +that material) supplement the terms of this License with terms: + + a) Disclaiming warranty or limiting liability differently from the + terms of sections 15 and 16 of this License; or + + b) Requiring preservation of specified reasonable legal notices or + author attributions in that material or in the Appropriate Legal + Notices displayed by works containing it; or + + c) Prohibiting misrepresentation of the origin of that material, or + requiring that modified versions of such material be marked in + reasonable ways as different from the original version; or + + d) Limiting the use for publicity purposes of names of licensors or + authors of the material; or + + e) Declining to grant rights under trademark law for use of some + trade names, trademarks, or service marks; or + + f) Requiring indemnification of licensors and authors of that + material by anyone who conveys the material (or modified versions of + it) with contractual assumptions of liability to the recipient, for + any liability that these contractual assumptions directly impose on + those licensors and authors. + + All other non-permissive additional terms are considered "further +restrictions" within the meaning of section 10. If the Program as you +received it, or any part of it, contains a notice stating that it is +governed by this License along with a term that is a further +restriction, you may remove that term. If a license document contains +a further restriction but permits relicensing or conveying under this +License, you may add to a covered work material governed by the terms +of that license document, provided that the further restriction does +not survive such relicensing or conveying. + + If you add terms to a covered work in accord with this section, you +must place, in the relevant source files, a statement of the +additional terms that apply to those files, or a notice indicating +where to find the applicable terms. + + Additional terms, permissive or non-permissive, may be stated in the +form of a separately written license, or stated as exceptions; +the above requirements apply either way. + + 8. Termination. + + You may not propagate or modify a covered work except as expressly +provided under this License. Any attempt otherwise to propagate or +modify it is void, and will automatically terminate your rights under +this License (including any patent licenses granted under the third +paragraph of section 11). + + However, if you cease all violation of this License, then your +license from a particular copyright holder is reinstated (a) +provisionally, unless and until the copyright holder explicitly and +finally terminates your license, and (b) permanently, if the copyright +holder fails to notify you of the violation by some reasonable means +prior to 60 days after the cessation. + + Moreover, your license from a particular copyright holder is +reinstated permanently if the copyright holder notifies you of the +violation by some reasonable means, this is the first time you have +received notice of violation of this License (for any work) from that +copyright holder, and you cure the violation prior to 30 days after +your receipt of the notice. + + Termination of your rights under this section does not terminate the +licenses of parties who have received copies or rights from you under +this License. If your rights have been terminated and not permanently +reinstated, you do not qualify to receive new licenses for the same +material under section 10. + + 9. Acceptance Not Required for Having Copies. + + You are not required to accept this License in order to receive or +run a copy of the Program. Ancillary propagation of a covered work +occurring solely as a consequence of using peer-to-peer transmission +to receive a copy likewise does not require acceptance. However, +nothing other than this License grants you permission to propagate or +modify any covered work. These actions infringe copyright if you do +not accept this License. Therefore, by modifying or propagating a +covered work, you indicate your acceptance of this License to do so. + + 10. Automatic Licensing of Downstream Recipients. + + Each time you convey a covered work, the recipient automatically +receives a license from the original licensors, to run, modify and +propagate that work, subject to this License. You are not responsible +for enforcing compliance by third parties with this License. + + An "entity transaction" is a transaction transferring control of an +organization, or substantially all assets of one, or subdividing an +organization, or merging organizations. If propagation of a covered +work results from an entity transaction, each party to that +transaction who receives a copy of the work also receives whatever +licenses to the work the party's predecessor in interest had or could +give under the previous paragraph, plus a right to possession of the +Corresponding Source of the work from the predecessor in interest, if +the predecessor has it or can get it with reasonable efforts. + + You may not impose any further restrictions on the exercise of the +rights granted or affirmed under this License. For example, you may +not impose a license fee, royalty, or other charge for exercise of +rights granted under this License, and you may not initiate litigation +(including a cross-claim or counterclaim in a lawsuit) alleging that +any patent claim is infringed by making, using, selling, offering for +sale, or importing the Program or any portion of it. + + 11. Patents. + + A "contributor" is a copyright holder who authorizes use under this +License of the Program or a work on which the Program is based. The +work thus licensed is called the contributor's "contributor version". + + A contributor's "essential patent claims" are all patent claims +owned or controlled by the contributor, whether already acquired or +hereafter acquired, that would be infringed by some manner, permitted +by this License, of making, using, or selling its contributor version, +but do not include claims that would be infringed only as a +consequence of further modification of the contributor version. For +purposes of this definition, "control" includes the right to grant +patent sublicenses in a manner consistent with the requirements of +this License. + + Each contributor grants you a non-exclusive, worldwide, royalty-free +patent license under the contributor's essential patent claims, to +make, use, sell, offer for sale, import and otherwise run, modify and +propagate the contents of its contributor version. + + In the following three paragraphs, a "patent license" is any express +agreement or commitment, however denominated, not to enforce a patent +(such as an express permission to practice a patent or covenant not to +sue for patent infringement). To "grant" such a patent license to a +party means to make such an agreement or commitment not to enforce a +patent against the party. + + If you convey a covered work, knowingly relying on a patent license, +and the Corresponding Source of the work is not available for anyone +to copy, free of charge and under the terms of this License, through a +publicly available network server or other readily accessible means, +then you must either (1) cause the Corresponding Source to be so +available, or (2) arrange to deprive yourself of the benefit of the +patent license for this particular work, or (3) arrange, in a manner +consistent with the requirements of this License, to extend the patent +license to downstream recipients. "Knowingly relying" means you have +actual knowledge that, but for the patent license, your conveying the +covered work in a country, or your recipient's use of the covered work +in a country, would infringe one or more identifiable patents in that +country that you have reason to believe are valid. + + If, pursuant to or in connection with a single transaction or +arrangement, you convey, or propagate by procuring conveyance of, a +covered work, and grant a patent license to some of the parties +receiving the covered work authorizing them to use, propagate, modify +or convey a specific copy of the covered work, then the patent license +you grant is automatically extended to all recipients of the covered +work and works based on it. + + A patent license is "discriminatory" if it does not include within +the scope of its coverage, prohibits the exercise of, or is +conditioned on the non-exercise of one or more of the rights that are +specifically granted under this License. You may not convey a covered +work if you are a party to an arrangement with a third party that is +in the business of distributing software, under which you make payment +to the third party based on the extent of your activity of conveying +the work, and under which the third party grants, to any of the +parties who would receive the covered work from you, a discriminatory +patent license (a) in connection with copies of the covered work +conveyed by you (or copies made from those copies), or (b) primarily +for and in connection with specific products or compilations that +contain the covered work, unless you entered into that arrangement, +or that patent license was granted, prior to 28 March 2007. + + Nothing in this License shall be construed as excluding or limiting +any implied license or other defenses to infringement that may +otherwise be available to you under applicable patent law. + + 12. No Surrender of Others' Freedom. + + If conditions are imposed on you (whether by court order, agreement or +otherwise) that contradict the conditions of this License, they do not +excuse you from the conditions of this License. If you cannot convey a +covered work so as to satisfy simultaneously your obligations under this +License and any other pertinent obligations, then as a consequence you may +not convey it at all. For example, if you agree to terms that obligate you +to collect a royalty for further conveying from those to whom you convey +the Program, the only way you could satisfy both those terms and this +License would be to refrain entirely from conveying the Program. + + 13. Use with the GNU Affero General Public License. + + Notwithstanding any other provision of this License, you have +permission to link or combine any covered work with a work licensed +under version 3 of the GNU Affero General Public License into a single +combined work, and to convey the resulting work. The terms of this +License will continue to apply to the part which is the covered work, +but the special requirements of the GNU Affero General Public License, +section 13, concerning interaction through a network will apply to the +combination as such. + + 14. Revised Versions of this License. + + The Free Software Foundation may publish revised and/or new versions of +the GNU General Public License from time to time. Such new versions will +be similar in spirit to the present version, but may differ in detail to +address new problems or concerns. + + Each version is given a distinguishing version number. If the +Program specifies that a certain numbered version of the GNU General +Public License "or any later version" applies to it, you have the +option of following the terms and conditions either of that numbered +version or of any later version published by the Free Software +Foundation. If the Program does not specify a version number of the +GNU General Public License, you may choose any version ever published +by the Free Software Foundation. + + If the Program specifies that a proxy can decide which future +versions of the GNU General Public License can be used, that proxy's +public statement of acceptance of a version permanently authorizes you +to choose that version for the Program. + + Later license versions may give you additional or different +permissions. However, no additional obligations are imposed on any +author or copyright holder as a result of your choosing to follow a +later version. + + 15. Disclaimer of Warranty. + + THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY +APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT +HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY +OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, +THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM +IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF +ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + + 16. Limitation of Liability. + + IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS +THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE +USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF +DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD +PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), +EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF +SUCH DAMAGES. + + 17. Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. + +Name: libquadmath +Files: numpy.libs/libquadmath*.so +Description: dynamically linked to files compiled with gcc +Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath +License: LGPL-2.1-or-later + + GCC Quad-Precision Math Library + Copyright (C) 2010-2019 Free Software Foundation, Inc. + Written by Francois-Xavier Coudert + + This file is part of the libquadmath library. + Libquadmath is free software; you can redistribute it and/or + modify it under the terms of the GNU Library General Public + License as published by the Free Software Foundation; either + version 2.1 of the License, or (at your option) any later version. + + Libquadmath is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU + Lesser General Public License for more details. + https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html diff --git a/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/METADATA b/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..8246dc4ed3bfc5e7ea1f402d2aff1b74ca62e16f --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/METADATA @@ -0,0 +1,1092 @@ +Metadata-Version: 2.1 +Name: numpy +Version: 1.26.4 +Summary: Fundamental package for array computing in Python +Home-page: https://numpy.org +Author: Travis E. Oliphant et al. +Maintainer-Email: NumPy Developers +License: Copyright (c) 2005-2023, NumPy Developers. + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + + * Neither the name of the NumPy Developers nor the names of any + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + ---- + + The NumPy repository and source distributions bundle several libraries that are + compatibly licensed. We list these here. + + Name: lapack-lite + Files: numpy/linalg/lapack_lite/* + License: BSD-3-Clause + For details, see numpy/linalg/lapack_lite/LICENSE.txt + + Name: tempita + Files: tools/npy_tempita/* + License: MIT + For details, see tools/npy_tempita/license.txt + + Name: dragon4 + Files: numpy/core/src/multiarray/dragon4.c + License: MIT + For license text, see numpy/core/src/multiarray/dragon4.c + + Name: libdivide + Files: numpy/core/include/numpy/libdivide/* + License: Zlib + For license text, see numpy/core/include/numpy/libdivide/LICENSE.txt + + + Note that the following files are vendored in the repository and sdist but not + installed in built numpy packages: + + Name: Meson + Files: vendored-meson/meson/* + License: Apache 2.0 + For license text, see vendored-meson/meson/COPYING + + Name: spin + Files: .spin/cmds.py + License: BSD-3 + For license text, see .spin/LICENSE + + ---- + + This binary distribution of NumPy also bundles the following software: + + + Name: OpenBLAS + Files: numpy.libs/libopenblas*.so + Description: bundled as a dynamically linked library + Availability: https://github.com/OpenMathLib/OpenBLAS/ + License: BSD-3-Clause + Copyright (c) 2011-2014, The OpenBLAS Project + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + 3. Neither the name of the OpenBLAS project nor the names of + its contributors may be used to endorse or promote products + derived from this software without specific prior written + permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE + USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + + Name: LAPACK + Files: numpy.libs/libopenblas*.so + Description: bundled in OpenBLAS + Availability: https://github.com/OpenMathLib/OpenBLAS/ + License: BSD-3-Clause-Attribution + Copyright (c) 1992-2013 The University of Tennessee and The University + of Tennessee Research Foundation. All rights + reserved. + Copyright (c) 2000-2013 The University of California Berkeley. All + rights reserved. + Copyright (c) 2006-2013 The University of Colorado Denver. All rights + reserved. + + $COPYRIGHT$ + + Additional copyrights may follow + + $HEADER$ + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer listed + in this license in the documentation and/or other materials + provided with the distribution. + + - Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + + The copyright holders provide no reassurances that the source code + provided does not infringe any patent, copyright, or any other + intellectual property rights of third parties. The copyright holders + disclaim any liability to any recipient for claims brought against + recipient by any third party for infringement of that parties + intellectual property rights. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + + Name: GCC runtime library + Files: numpy.libs/libgfortran*.so + Description: dynamically linked to files compiled with gcc + Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libgfortran + License: GPL-3.0-with-GCC-exception + Copyright (C) 2002-2017 Free Software Foundation, Inc. + + Libgfortran is free software; you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation; either version 3, or (at your option) + any later version. + + Libgfortran is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + Under Section 7 of GPL version 3, you are granted additional + permissions described in the GCC Runtime Library Exception, version + 3.1, as published by the Free Software Foundation. + + You should have received a copy of the GNU General Public License and + a copy of the GCC Runtime Library Exception along with this program; + see the files COPYING3 and COPYING.RUNTIME respectively. If not, see + . + + ---- + + Full text of license texts referred to above follows (that they are + listed below does not necessarily imply the conditions apply to the + present binary release): + + ---- + + GCC RUNTIME LIBRARY EXCEPTION + + Version 3.1, 31 March 2009 + + Copyright (C) 2009 Free Software Foundation, Inc. + + Everyone is permitted to copy and distribute verbatim copies of this + license document, but changing it is not allowed. + + This GCC Runtime Library Exception ("Exception") is an additional + permission under section 7 of the GNU General Public License, version + 3 ("GPLv3"). It applies to a given file (the "Runtime Library") that + bears a notice placed by the copyright holder of the file stating that + the file is governed by GPLv3 along with this Exception. + + When you use GCC to compile a program, GCC may combine portions of + certain GCC header files and runtime libraries with the compiled + program. The purpose of this Exception is to allow compilation of + non-GPL (including proprietary) programs to use, in this way, the + header files and runtime libraries covered by this Exception. + + 0. Definitions. + + A file is an "Independent Module" if it either requires the Runtime + Library for execution after a Compilation Process, or makes use of an + interface provided by the Runtime Library, but is not otherwise based + on the Runtime Library. + + "GCC" means a version of the GNU Compiler Collection, with or without + modifications, governed by version 3 (or a specified later version) of + the GNU General Public License (GPL) with the option of using any + subsequent versions published by the FSF. + + "GPL-compatible Software" is software whose conditions of propagation, + modification and use would permit combination with GCC in accord with + the license of GCC. + + "Target Code" refers to output from any compiler for a real or virtual + target processor architecture, in executable form or suitable for + input to an assembler, loader, linker and/or execution + phase. Notwithstanding that, Target Code does not include data in any + format that is used as a compiler intermediate representation, or used + for producing a compiler intermediate representation. + + The "Compilation Process" transforms code entirely represented in + non-intermediate languages designed for human-written code, and/or in + Java Virtual Machine byte code, into Target Code. Thus, for example, + use of source code generators and preprocessors need not be considered + part of the Compilation Process, since the Compilation Process can be + understood as starting with the output of the generators or + preprocessors. + + A Compilation Process is "Eligible" if it is done using GCC, alone or + with other GPL-compatible software, or if it is done without using any + work based on GCC. For example, using non-GPL-compatible Software to + optimize any GCC intermediate representations would not qualify as an + Eligible Compilation Process. + + 1. Grant of Additional Permission. + + You have permission to propagate a work of Target Code formed by + combining the Runtime Library with Independent Modules, even if such + propagation would otherwise violate the terms of GPLv3, provided that + all Target Code was generated by Eligible Compilation Processes. You + may then convey such a combination under terms of your choice, + consistent with the licensing of the Independent Modules. + + 2. No Weakening of GCC Copyleft. + + The availability of this Exception does not imply any general + presumption that third-party software is unaffected by the copyleft + requirements of the license of GCC. + + ---- + + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for + software and other kinds of works. + + The licenses for most software and other practical works are designed + to take away your freedom to share and change the works. By contrast, + the GNU General Public License is intended to guarantee your freedom to + share and change all versions of a program--to make sure it remains free + software for all its users. We, the Free Software Foundation, use the + GNU General Public License for most of our software; it applies also to + any other work released this way by its authors. You can apply it to + your programs, too. + + When we speak of free software, we are referring to freedom, not + price. Our General Public Licenses are designed to make sure that you + have the freedom to distribute copies of free software (and charge for + them if you wish), that you receive source code or can get it if you + want it, that you can change the software or use pieces of it in new + free programs, and that you know you can do these things. + + To protect your rights, we need to prevent others from denying you + these rights or asking you to surrender the rights. Therefore, you have + certain responsibilities if you distribute copies of the software, or if + you modify it: responsibilities to respect the freedom of others. + + For example, if you distribute copies of such a program, whether + gratis or for a fee, you must pass on to the recipients the same + freedoms that you received. You must make sure that they, too, receive + or can get the source code. And you must show them these terms so they + know their rights. + + Developers that use the GNU GPL protect your rights with two steps: + (1) assert copyright on the software, and (2) offer you this License + giving you legal permission to copy, distribute and/or modify it. + + For the developers' and authors' protection, the GPL clearly explains + that there is no warranty for this free software. For both users' and + authors' sake, the GPL requires that modified versions be marked as + changed, so that their problems will not be attributed erroneously to + authors of previous versions. + + Some devices are designed to deny users access to install or run + modified versions of the software inside them, although the manufacturer + can do so. This is fundamentally incompatible with the aim of + protecting users' freedom to change the software. The systematic + pattern of such abuse occurs in the area of products for individuals to + use, which is precisely where it is most unacceptable. Therefore, we + have designed this version of the GPL to prohibit the practice for those + products. If such problems arise substantially in other domains, we + stand ready to extend this provision to those domains in future versions + of the GPL, as needed to protect the freedom of users. + + Finally, every program is threatened constantly by software patents. + States should not allow patents to restrict development and use of + software on general-purpose computers, but in those that do, we wish to + avoid the special danger that patents applied to a free program could + make it effectively proprietary. To prevent this, the GPL assures that + patents cannot be used to render the program non-free. + + The precise terms and conditions for copying, distribution and + modification follow. + + TERMS AND CONDITIONS + + 0. Definitions. + + "This License" refers to version 3 of the GNU General Public License. + + "Copyright" also means copyright-like laws that apply to other kinds of + works, such as semiconductor masks. + + "The Program" refers to any copyrightable work licensed under this + License. Each licensee is addressed as "you". "Licensees" and + "recipients" may be individuals or organizations. + + To "modify" a work means to copy from or adapt all or part of the work + in a fashion requiring copyright permission, other than the making of an + exact copy. The resulting work is called a "modified version" of the + earlier work or a work "based on" the earlier work. + + A "covered work" means either the unmodified Program or a work based + on the Program. + + To "propagate" a work means to do anything with it that, without + permission, would make you directly or secondarily liable for + infringement under applicable copyright law, except executing it on a + computer or modifying a private copy. Propagation includes copying, + distribution (with or without modification), making available to the + public, and in some countries other activities as well. + + To "convey" a work means any kind of propagation that enables other + parties to make or receive copies. Mere interaction with a user through + a computer network, with no transfer of a copy, is not conveying. + + An interactive user interface displays "Appropriate Legal Notices" + to the extent that it includes a convenient and prominently visible + feature that (1) displays an appropriate copyright notice, and (2) + tells the user that there is no warranty for the work (except to the + extent that warranties are provided), that licensees may convey the + work under this License, and how to view a copy of this License. If + the interface presents a list of user commands or options, such as a + menu, a prominent item in the list meets this criterion. + + 1. Source Code. + + The "source code" for a work means the preferred form of the work + for making modifications to it. "Object code" means any non-source + form of a work. + + A "Standard Interface" means an interface that either is an official + standard defined by a recognized standards body, or, in the case of + interfaces specified for a particular programming language, one that + is widely used among developers working in that language. + + The "System Libraries" of an executable work include anything, other + than the work as a whole, that (a) is included in the normal form of + packaging a Major Component, but which is not part of that Major + Component, and (b) serves only to enable use of the work with that + Major Component, or to implement a Standard Interface for which an + implementation is available to the public in source code form. A + "Major Component", in this context, means a major essential component + (kernel, window system, and so on) of the specific operating system + (if any) on which the executable work runs, or a compiler used to + produce the work, or an object code interpreter used to run it. + + The "Corresponding Source" for a work in object code form means all + the source code needed to generate, install, and (for an executable + work) run the object code and to modify the work, including scripts to + control those activities. However, it does not include the work's + System Libraries, or general-purpose tools or generally available free + programs which are used unmodified in performing those activities but + which are not part of the work. For example, Corresponding Source + includes interface definition files associated with source files for + the work, and the source code for shared libraries and dynamically + linked subprograms that the work is specifically designed to require, + such as by intimate data communication or control flow between those + subprograms and other parts of the work. + + The Corresponding Source need not include anything that users + can regenerate automatically from other parts of the Corresponding + Source. + + The Corresponding Source for a work in source code form is that + same work. + + 2. Basic Permissions. + + All rights granted under this License are granted for the term of + copyright on the Program, and are irrevocable provided the stated + conditions are met. This License explicitly affirms your unlimited + permission to run the unmodified Program. The output from running a + covered work is covered by this License only if the output, given its + content, constitutes a covered work. This License acknowledges your + rights of fair use or other equivalent, as provided by copyright law. + + You may make, run and propagate covered works that you do not + convey, without conditions so long as your license otherwise remains + in force. You may convey covered works to others for the sole purpose + of having them make modifications exclusively for you, or provide you + with facilities for running those works, provided that you comply with + the terms of this License in conveying all material for which you do + not control copyright. Those thus making or running the covered works + for you must do so exclusively on your behalf, under your direction + and control, on terms that prohibit them from making any copies of + your copyrighted material outside their relationship with you. + + Conveying under any other circumstances is permitted solely under + the conditions stated below. Sublicensing is not allowed; section 10 + makes it unnecessary. + + 3. Protecting Users' Legal Rights From Anti-Circumvention Law. + + No covered work shall be deemed part of an effective technological + measure under any applicable law fulfilling obligations under article + 11 of the WIPO copyright treaty adopted on 20 December 1996, or + similar laws prohibiting or restricting circumvention of such + measures. + + When you convey a covered work, you waive any legal power to forbid + circumvention of technological measures to the extent such circumvention + is effected by exercising rights under this License with respect to + the covered work, and you disclaim any intention to limit operation or + modification of the work as a means of enforcing, against the work's + users, your or third parties' legal rights to forbid circumvention of + technological measures. + + 4. Conveying Verbatim Copies. + + You may convey verbatim copies of the Program's source code as you + receive it, in any medium, provided that you conspicuously and + appropriately publish on each copy an appropriate copyright notice; + keep intact all notices stating that this License and any + non-permissive terms added in accord with section 7 apply to the code; + keep intact all notices of the absence of any warranty; and give all + recipients a copy of this License along with the Program. + + You may charge any price or no price for each copy that you convey, + and you may offer support or warranty protection for a fee. + + 5. Conveying Modified Source Versions. + + You may convey a work based on the Program, or the modifications to + produce it from the Program, in the form of source code under the + terms of section 4, provided that you also meet all of these conditions: + + a) The work must carry prominent notices stating that you modified + it, and giving a relevant date. + + b) The work must carry prominent notices stating that it is + released under this License and any conditions added under section + 7. This requirement modifies the requirement in section 4 to + "keep intact all notices". + + c) You must license the entire work, as a whole, under this + License to anyone who comes into possession of a copy. This + License will therefore apply, along with any applicable section 7 + additional terms, to the whole of the work, and all its parts, + regardless of how they are packaged. This License gives no + permission to license the work in any other way, but it does not + invalidate such permission if you have separately received it. + + d) If the work has interactive user interfaces, each must display + Appropriate Legal Notices; however, if the Program has interactive + interfaces that do not display Appropriate Legal Notices, your + work need not make them do so. + + A compilation of a covered work with other separate and independent + works, which are not by their nature extensions of the covered work, + and which are not combined with it such as to form a larger program, + in or on a volume of a storage or distribution medium, is called an + "aggregate" if the compilation and its resulting copyright are not + used to limit the access or legal rights of the compilation's users + beyond what the individual works permit. Inclusion of a covered work + in an aggregate does not cause this License to apply to the other + parts of the aggregate. + + 6. Conveying Non-Source Forms. + + You may convey a covered work in object code form under the terms + of sections 4 and 5, provided that you also convey the + machine-readable Corresponding Source under the terms of this License, + in one of these ways: + + a) Convey the object code in, or embodied in, a physical product + (including a physical distribution medium), accompanied by the + Corresponding Source fixed on a durable physical medium + customarily used for software interchange. + + b) Convey the object code in, or embodied in, a physical product + (including a physical distribution medium), accompanied by a + written offer, valid for at least three years and valid for as + long as you offer spare parts or customer support for that product + model, to give anyone who possesses the object code either (1) a + copy of the Corresponding Source for all the software in the + product that is covered by this License, on a durable physical + medium customarily used for software interchange, for a price no + more than your reasonable cost of physically performing this + conveying of source, or (2) access to copy the + Corresponding Source from a network server at no charge. + + c) Convey individual copies of the object code with a copy of the + written offer to provide the Corresponding Source. This + alternative is allowed only occasionally and noncommercially, and + only if you received the object code with such an offer, in accord + with subsection 6b. + + d) Convey the object code by offering access from a designated + place (gratis or for a charge), and offer equivalent access to the + Corresponding Source in the same way through the same place at no + further charge. You need not require recipients to copy the + Corresponding Source along with the object code. If the place to + copy the object code is a network server, the Corresponding Source + may be on a different server (operated by you or a third party) + that supports equivalent copying facilities, provided you maintain + clear directions next to the object code saying where to find the + Corresponding Source. Regardless of what server hosts the + Corresponding Source, you remain obligated to ensure that it is + available for as long as needed to satisfy these requirements. + + e) Convey the object code using peer-to-peer transmission, provided + you inform other peers where the object code and Corresponding + Source of the work are being offered to the general public at no + charge under subsection 6d. + + A separable portion of the object code, whose source code is excluded + from the Corresponding Source as a System Library, need not be + included in conveying the object code work. + + A "User Product" is either (1) a "consumer product", which means any + tangible personal property which is normally used for personal, family, + or household purposes, or (2) anything designed or sold for incorporation + into a dwelling. In determining whether a product is a consumer product, + doubtful cases shall be resolved in favor of coverage. For a particular + product received by a particular user, "normally used" refers to a + typical or common use of that class of product, regardless of the status + of the particular user or of the way in which the particular user + actually uses, or expects or is expected to use, the product. A product + is a consumer product regardless of whether the product has substantial + commercial, industrial or non-consumer uses, unless such uses represent + the only significant mode of use of the product. + + "Installation Information" for a User Product means any methods, + procedures, authorization keys, or other information required to install + and execute modified versions of a covered work in that User Product from + a modified version of its Corresponding Source. The information must + suffice to ensure that the continued functioning of the modified object + code is in no case prevented or interfered with solely because + modification has been made. + + If you convey an object code work under this section in, or with, or + specifically for use in, a User Product, and the conveying occurs as + part of a transaction in which the right of possession and use of the + User Product is transferred to the recipient in perpetuity or for a + fixed term (regardless of how the transaction is characterized), the + Corresponding Source conveyed under this section must be accompanied + by the Installation Information. But this requirement does not apply + if neither you nor any third party retains the ability to install + modified object code on the User Product (for example, the work has + been installed in ROM). + + The requirement to provide Installation Information does not include a + requirement to continue to provide support service, warranty, or updates + for a work that has been modified or installed by the recipient, or for + the User Product in which it has been modified or installed. Access to a + network may be denied when the modification itself materially and + adversely affects the operation of the network or violates the rules and + protocols for communication across the network. + + Corresponding Source conveyed, and Installation Information provided, + in accord with this section must be in a format that is publicly + documented (and with an implementation available to the public in + source code form), and must require no special password or key for + unpacking, reading or copying. + + 7. Additional Terms. + + "Additional permissions" are terms that supplement the terms of this + License by making exceptions from one or more of its conditions. + Additional permissions that are applicable to the entire Program shall + be treated as though they were included in this License, to the extent + that they are valid under applicable law. If additional permissions + apply only to part of the Program, that part may be used separately + under those permissions, but the entire Program remains governed by + this License without regard to the additional permissions. + + When you convey a copy of a covered work, you may at your option + remove any additional permissions from that copy, or from any part of + it. (Additional permissions may be written to require their own + removal in certain cases when you modify the work.) You may place + additional permissions on material, added by you to a covered work, + for which you have or can give appropriate copyright permission. + + Notwithstanding any other provision of this License, for material you + add to a covered work, you may (if authorized by the copyright holders of + that material) supplement the terms of this License with terms: + + a) Disclaiming warranty or limiting liability differently from the + terms of sections 15 and 16 of this License; or + + b) Requiring preservation of specified reasonable legal notices or + author attributions in that material or in the Appropriate Legal + Notices displayed by works containing it; or + + c) Prohibiting misrepresentation of the origin of that material, or + requiring that modified versions of such material be marked in + reasonable ways as different from the original version; or + + d) Limiting the use for publicity purposes of names of licensors or + authors of the material; or + + e) Declining to grant rights under trademark law for use of some + trade names, trademarks, or service marks; or + + f) Requiring indemnification of licensors and authors of that + material by anyone who conveys the material (or modified versions of + it) with contractual assumptions of liability to the recipient, for + any liability that these contractual assumptions directly impose on + those licensors and authors. + + All other non-permissive additional terms are considered "further + restrictions" within the meaning of section 10. If the Program as you + received it, or any part of it, contains a notice stating that it is + governed by this License along with a term that is a further + restriction, you may remove that term. If a license document contains + a further restriction but permits relicensing or conveying under this + License, you may add to a covered work material governed by the terms + of that license document, provided that the further restriction does + not survive such relicensing or conveying. + + If you add terms to a covered work in accord with this section, you + must place, in the relevant source files, a statement of the + additional terms that apply to those files, or a notice indicating + where to find the applicable terms. + + Additional terms, permissive or non-permissive, may be stated in the + form of a separately written license, or stated as exceptions; + the above requirements apply either way. + + 8. Termination. + + You may not propagate or modify a covered work except as expressly + provided under this License. Any attempt otherwise to propagate or + modify it is void, and will automatically terminate your rights under + this License (including any patent licenses granted under the third + paragraph of section 11). + + However, if you cease all violation of this License, then your + license from a particular copyright holder is reinstated (a) + provisionally, unless and until the copyright holder explicitly and + finally terminates your license, and (b) permanently, if the copyright + holder fails to notify you of the violation by some reasonable means + prior to 60 days after the cessation. + + Moreover, your license from a particular copyright holder is + reinstated permanently if the copyright holder notifies you of the + violation by some reasonable means, this is the first time you have + received notice of violation of this License (for any work) from that + copyright holder, and you cure the violation prior to 30 days after + your receipt of the notice. + + Termination of your rights under this section does not terminate the + licenses of parties who have received copies or rights from you under + this License. If your rights have been terminated and not permanently + reinstated, you do not qualify to receive new licenses for the same + material under section 10. + + 9. Acceptance Not Required for Having Copies. + + You are not required to accept this License in order to receive or + run a copy of the Program. Ancillary propagation of a covered work + occurring solely as a consequence of using peer-to-peer transmission + to receive a copy likewise does not require acceptance. However, + nothing other than this License grants you permission to propagate or + modify any covered work. These actions infringe copyright if you do + not accept this License. Therefore, by modifying or propagating a + covered work, you indicate your acceptance of this License to do so. + + 10. Automatic Licensing of Downstream Recipients. + + Each time you convey a covered work, the recipient automatically + receives a license from the original licensors, to run, modify and + propagate that work, subject to this License. You are not responsible + for enforcing compliance by third parties with this License. + + An "entity transaction" is a transaction transferring control of an + organization, or substantially all assets of one, or subdividing an + organization, or merging organizations. If propagation of a covered + work results from an entity transaction, each party to that + transaction who receives a copy of the work also receives whatever + licenses to the work the party's predecessor in interest had or could + give under the previous paragraph, plus a right to possession of the + Corresponding Source of the work from the predecessor in interest, if + the predecessor has it or can get it with reasonable efforts. + + You may not impose any further restrictions on the exercise of the + rights granted or affirmed under this License. For example, you may + not impose a license fee, royalty, or other charge for exercise of + rights granted under this License, and you may not initiate litigation + (including a cross-claim or counterclaim in a lawsuit) alleging that + any patent claim is infringed by making, using, selling, offering for + sale, or importing the Program or any portion of it. + + 11. Patents. + + A "contributor" is a copyright holder who authorizes use under this + License of the Program or a work on which the Program is based. The + work thus licensed is called the contributor's "contributor version". + + A contributor's "essential patent claims" are all patent claims + owned or controlled by the contributor, whether already acquired or + hereafter acquired, that would be infringed by some manner, permitted + by this License, of making, using, or selling its contributor version, + but do not include claims that would be infringed only as a + consequence of further modification of the contributor version. For + purposes of this definition, "control" includes the right to grant + patent sublicenses in a manner consistent with the requirements of + this License. + + Each contributor grants you a non-exclusive, worldwide, royalty-free + patent license under the contributor's essential patent claims, to + make, use, sell, offer for sale, import and otherwise run, modify and + propagate the contents of its contributor version. + + In the following three paragraphs, a "patent license" is any express + agreement or commitment, however denominated, not to enforce a patent + (such as an express permission to practice a patent or covenant not to + sue for patent infringement). To "grant" such a patent license to a + party means to make such an agreement or commitment not to enforce a + patent against the party. + + If you convey a covered work, knowingly relying on a patent license, + and the Corresponding Source of the work is not available for anyone + to copy, free of charge and under the terms of this License, through a + publicly available network server or other readily accessible means, + then you must either (1) cause the Corresponding Source to be so + available, or (2) arrange to deprive yourself of the benefit of the + patent license for this particular work, or (3) arrange, in a manner + consistent with the requirements of this License, to extend the patent + license to downstream recipients. "Knowingly relying" means you have + actual knowledge that, but for the patent license, your conveying the + covered work in a country, or your recipient's use of the covered work + in a country, would infringe one or more identifiable patents in that + country that you have reason to believe are valid. + + If, pursuant to or in connection with a single transaction or + arrangement, you convey, or propagate by procuring conveyance of, a + covered work, and grant a patent license to some of the parties + receiving the covered work authorizing them to use, propagate, modify + or convey a specific copy of the covered work, then the patent license + you grant is automatically extended to all recipients of the covered + work and works based on it. + + A patent license is "discriminatory" if it does not include within + the scope of its coverage, prohibits the exercise of, or is + conditioned on the non-exercise of one or more of the rights that are + specifically granted under this License. You may not convey a covered + work if you are a party to an arrangement with a third party that is + in the business of distributing software, under which you make payment + to the third party based on the extent of your activity of conveying + the work, and under which the third party grants, to any of the + parties who would receive the covered work from you, a discriminatory + patent license (a) in connection with copies of the covered work + conveyed by you (or copies made from those copies), or (b) primarily + for and in connection with specific products or compilations that + contain the covered work, unless you entered into that arrangement, + or that patent license was granted, prior to 28 March 2007. + + Nothing in this License shall be construed as excluding or limiting + any implied license or other defenses to infringement that may + otherwise be available to you under applicable patent law. + + 12. No Surrender of Others' Freedom. + + If conditions are imposed on you (whether by court order, agreement or + otherwise) that contradict the conditions of this License, they do not + excuse you from the conditions of this License. If you cannot convey a + covered work so as to satisfy simultaneously your obligations under this + License and any other pertinent obligations, then as a consequence you may + not convey it at all. For example, if you agree to terms that obligate you + to collect a royalty for further conveying from those to whom you convey + the Program, the only way you could satisfy both those terms and this + License would be to refrain entirely from conveying the Program. + + 13. Use with the GNU Affero General Public License. + + Notwithstanding any other provision of this License, you have + permission to link or combine any covered work with a work licensed + under version 3 of the GNU Affero General Public License into a single + combined work, and to convey the resulting work. The terms of this + License will continue to apply to the part which is the covered work, + but the special requirements of the GNU Affero General Public License, + section 13, concerning interaction through a network will apply to the + combination as such. + + 14. Revised Versions of this License. + + The Free Software Foundation may publish revised and/or new versions of + the GNU General Public License from time to time. Such new versions will + be similar in spirit to the present version, but may differ in detail to + address new problems or concerns. + + Each version is given a distinguishing version number. If the + Program specifies that a certain numbered version of the GNU General + Public License "or any later version" applies to it, you have the + option of following the terms and conditions either of that numbered + version or of any later version published by the Free Software + Foundation. If the Program does not specify a version number of the + GNU General Public License, you may choose any version ever published + by the Free Software Foundation. + + If the Program specifies that a proxy can decide which future + versions of the GNU General Public License can be used, that proxy's + public statement of acceptance of a version permanently authorizes you + to choose that version for the Program. + + Later license versions may give you additional or different + permissions. However, no additional obligations are imposed on any + author or copyright holder as a result of your choosing to follow a + later version. + + 15. Disclaimer of Warranty. + + THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY + APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT + HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY + OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, + THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM + IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF + ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + + 16. Limitation of Liability. + + IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING + WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS + THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY + GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE + USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF + DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD + PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), + EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF + SUCH DAMAGES. + + 17. Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided + above cannot be given local legal effect according to their terms, + reviewing courts shall apply local law that most closely approximates + an absolute waiver of all civil liability in connection with the + Program, unless a warranty or assumption of liability accompanies a + copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest + possible use to the public, the best way to achieve this is to make it + free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest + to attach them to the start of each source file to most effectively + state the exclusion of warranty; and each file should have at least + the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + + Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short + notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + + The hypothetical commands `show w' and `show c' should show the appropriate + parts of the General Public License. Of course, your program's commands + might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, + if any, to sign a "copyright disclaimer" for the program, if necessary. + For more information on this, and how to apply and follow the GNU GPL, see + . + + The GNU General Public License does not permit incorporating your program + into proprietary programs. If your program is a subroutine library, you + may consider it more useful to permit linking proprietary applications with + the library. If this is what you want to do, use the GNU Lesser General + Public License instead of this License. But first, please read + . + + Name: libquadmath + Files: numpy.libs/libquadmath*.so + Description: dynamically linked to files compiled with gcc + Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath + License: LGPL-2.1-or-later + + GCC Quad-Precision Math Library + Copyright (C) 2010-2019 Free Software Foundation, Inc. + Written by Francois-Xavier Coudert + + This file is part of the libquadmath library. + Libquadmath is free software; you can redistribute it and/or + modify it under the terms of the GNU Library General Public + License as published by the Free Software Foundation; either + version 2.1 of the License, or (at your option) any later version. + + Libquadmath is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU + Lesser General Public License for more details. + https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Science/Research +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: BSD License +Classifier: Programming Language :: C +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Topic :: Software Development +Classifier: Topic :: Scientific/Engineering +Classifier: Typing :: Typed +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX +Classifier: Operating System :: Unix +Classifier: Operating System :: MacOS +Project-URL: Homepage, https://numpy.org +Project-URL: Documentation, https://numpy.org/doc/ +Project-URL: Source, https://github.com/numpy/numpy +Project-URL: Download, https://pypi.org/project/numpy/#files +Project-URL: Tracker, https://github.com/numpy/numpy/issues +Project-URL: Release notes, https://numpy.org/doc/stable/release +Requires-Python: >=3.9 +Description-Content-Type: text/markdown + +

+ +


+ + +[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)]( +https://numfocus.org) +[![PyPI Downloads](https://img.shields.io/pypi/dm/numpy.svg?label=PyPI%20downloads)]( +https://pypi.org/project/numpy/) +[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/numpy.svg?label=Conda%20downloads)]( +https://anaconda.org/conda-forge/numpy) +[![Stack Overflow](https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg)]( +https://stackoverflow.com/questions/tagged/numpy) +[![Nature Paper](https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue)]( +https://doi.org/10.1038/s41586-020-2649-2) +[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/numpy/numpy/badge)](https://api.securityscorecards.dev/projects/github.com/numpy/numpy) + + +NumPy is the fundamental package for scientific computing with Python. + +- **Website:** https://www.numpy.org +- **Documentation:** https://numpy.org/doc +- **Mailing list:** https://mail.python.org/mailman/listinfo/numpy-discussion +- **Source code:** https://github.com/numpy/numpy +- **Contributing:** https://www.numpy.org/devdocs/dev/index.html +- **Bug reports:** https://github.com/numpy/numpy/issues +- **Report a security vulnerability:** https://tidelift.com/docs/security + +It provides: + +- a powerful N-dimensional array object +- sophisticated (broadcasting) functions +- tools for integrating C/C++ and Fortran code +- useful linear algebra, Fourier transform, and random number capabilities + +Testing: + +NumPy requires `pytest` and `hypothesis`. Tests can then be run after installation with: + + python -c "import numpy, sys; sys.exit(numpy.test() is False)" + +Code of Conduct +---------------------- + +NumPy is a community-driven open source project developed by a diverse group of +[contributors](https://numpy.org/teams/). The NumPy leadership has made a strong +commitment to creating an open, inclusive, and positive community. Please read the +[NumPy Code of Conduct](https://numpy.org/code-of-conduct/) for guidance on how to interact +with others in a way that makes our community thrive. + +Call for Contributions +---------------------- + +The NumPy project welcomes your expertise and enthusiasm! + +Small improvements or fixes are always appreciated. If you are considering larger contributions +to the source code, please contact us through the [mailing +list](https://mail.python.org/mailman/listinfo/numpy-discussion) first. + +Writing code isn’t the only way to contribute to NumPy. You can also: +- review pull requests +- help us stay on top of new and old issues +- develop tutorials, presentations, and other educational materials +- maintain and improve [our website](https://github.com/numpy/numpy.org) +- develop graphic design for our brand assets and promotional materials +- translate website content +- help with outreach and onboard new contributors +- write grant proposals and help with other fundraising efforts + +For more information about the ways you can contribute to NumPy, visit [our website](https://numpy.org/contribute/). +If you’re unsure where to start or how your skills fit in, reach out! You can +ask on the mailing list or here, on GitHub, by opening a new issue or leaving a +comment on a relevant issue that is already open. + +Our preferred channels of communication are all public, but if you’d like to +speak to us in private first, contact our community coordinators at +numpy-team@googlegroups.com or on Slack (write numpy-team@googlegroups.com for +an invitation). + +We also have a biweekly community call, details of which are announced on the +mailing list. You are very welcome to join. + +If you are new to contributing to open source, [this +guide](https://opensource.guide/how-to-contribute/) helps explain why, what, +and how to successfully get involved. diff --git a/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/RECORD b/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..57927bb69cd1944f76cad7916efa8b929f9fb68c --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/RECORD @@ -0,0 +1,1406 @@ +../../../bin/f2py,sha256=ZCKHz6wOWZ-VlKQ194LiChfEU22BGIMIIFS0XIGKMhc,250 +numpy-1.26.4.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +numpy-1.26.4.dist-info/LICENSE.txt,sha256=EQewyDHpGNTx28KKMxkMdyFe8njUpMQAlXIIh3DUM0o,47721 +numpy-1.26.4.dist-info/METADATA,sha256=sJc0p_7UToS0yBYZNM5TLf8ed57Ggi1BVkTRF_Y4EHA,61041 +numpy-1.26.4.dist-info/RECORD,, +numpy-1.26.4.dist-info/WHEEL,sha256=sZM_NeUMz2G4fDenMf11eikcCxcLaQWiYRmjwQBavQs,137 +numpy-1.26.4.dist-info/entry_points.txt,sha256=zddyYJuUw9Uud7LeLfynXk62_ry0lGihDwCIgugBdZM,144 +numpy.libs/libgfortran-040039e1.so.5.0.0,sha256=FK-zEpsai1C8QKOwggx_EVLqm8EBIaqxUpQ_cFdHKIY,2686065 +numpy.libs/libopenblas64_p-r0-0cf96a72.3.23.dev.so,sha256=klTQhU3XYV4R3ijXca5AiHjKgSOnrCBPIeTMejdswuU,35123345 +numpy.libs/libquadmath-96973f99.so.0.0.0,sha256=k0wi3tDn0WnE1GeIdslgUa3z2UVF2pYvYLQWWbB12js,247609 +numpy/__config__.py,sha256=Q3bOwRWKFIXHUMLE9Y0f4dfmGx72-IFbQvjmVTRaT_w,4902 +numpy/__init__.cython-30.pxd,sha256=yk2a3etxRNlBgj5uLfIho2RYDYDzhRW8oagAG-wzbPI,36690 +numpy/__init__.pxd,sha256=Pa0VYRSeQRSFepQ6ROgZrNtGY5TzBXIddWsMHtK0OkM,35066 +numpy/__init__.py,sha256=Is0VNfoU10729FfMoUn_3ICHX0YL4xO4-JUnP3i8QC4,17005 +numpy/__init__.pyi,sha256=9kK465XL9oS_X3fJLv0Na29NEYnWvtdMhXPtrnF_cG8,154080 +numpy/__pycache__/__config__.cpython-310.pyc,, +numpy/__pycache__/__init__.cpython-310.pyc,, +numpy/__pycache__/_distributor_init.cpython-310.pyc,, +numpy/__pycache__/_globals.cpython-310.pyc,, +numpy/__pycache__/_pytesttester.cpython-310.pyc,, +numpy/__pycache__/conftest.cpython-310.pyc,, +numpy/__pycache__/ctypeslib.cpython-310.pyc,, +numpy/__pycache__/dtypes.cpython-310.pyc,, +numpy/__pycache__/exceptions.cpython-310.pyc,, +numpy/__pycache__/matlib.cpython-310.pyc,, +numpy/__pycache__/version.cpython-310.pyc,, +numpy/_core/__init__.py,sha256=C8_7wbHqUkB35JouY_XKsas1KLpRZ7JHWuZ7VGOPVpU,136 +numpy/_core/__init__.pyi,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/_core/__pycache__/__init__.cpython-310.pyc,, +numpy/_core/__pycache__/_dtype.cpython-310.pyc,, +numpy/_core/__pycache__/_dtype_ctypes.cpython-310.pyc,, +numpy/_core/__pycache__/_internal.cpython-310.pyc,, +numpy/_core/__pycache__/_multiarray_umath.cpython-310.pyc,, +numpy/_core/__pycache__/multiarray.cpython-310.pyc,, +numpy/_core/__pycache__/umath.cpython-310.pyc,, +numpy/_core/_dtype.py,sha256=vE16-yiwUSYsAIbq7FlEY1GbXZAp8wjADDxJg3eBX-U,126 +numpy/_core/_dtype_ctypes.py,sha256=i5EhoWPUhu4kla3Xu4ZvXF1lVLPiI6Zg4h6o8jaiamo,147 +numpy/_core/_internal.py,sha256=g5ugmqDgUhSlie5-onOctcm4p0gcMHSIRLHVYtFTk1M,135 +numpy/_core/_multiarray_umath.py,sha256=VPtoT2uHnyU3rKL0G27CgmNmB1WRHM0mtc7Y9L85C3U,159 +numpy/_core/multiarray.py,sha256=kZxC_7P3Jwz1RApzQU2QGmqSq4MAEvKmaJEYnAsbSOs,138 +numpy/_core/umath.py,sha256=YcV0cdbGcem6D5P3yX7cR9HGYBrT8VMoAgCBzGwPhgg,123 +numpy/_distributor_init.py,sha256=IKy2THwmu5UgBjtVbwbD9H-Ap8uaUJoPJ2btQ4Jatdo,407 +numpy/_globals.py,sha256=neEdcfLZoHLwber_1Xyrn26LcXy0MrSta03Ze7aKa6g,3094 +numpy/_pyinstaller/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/_pyinstaller/__pycache__/__init__.cpython-310.pyc,, +numpy/_pyinstaller/__pycache__/hook-numpy.cpython-310.pyc,, +numpy/_pyinstaller/__pycache__/pyinstaller-smoke.cpython-310.pyc,, +numpy/_pyinstaller/__pycache__/test_pyinstaller.cpython-310.pyc,, +numpy/_pyinstaller/hook-numpy.py,sha256=PUQ-mNWje6bFALB-mLVFRPkvbM4JpLXunB6sjBbTy5g,1409 +numpy/_pyinstaller/pyinstaller-smoke.py,sha256=6iL-eHMQaG3rxnS5EgcvrCqElm9aKL07Cjr1FZJSXls,1143 +numpy/_pyinstaller/test_pyinstaller.py,sha256=8K-7QxmfoXCG0NwR0bhIgCNrDjGlrTzWnrR1sR8btgU,1135 +numpy/_pytesttester.py,sha256=lQUTvKVz6kT8b4yiMV-uW-vG9KSv9UzqAmxaEMezTd8,6731 +numpy/_pytesttester.pyi,sha256=OtyXSiuSy8o_78w3QNQRjMLpvvNyEdC0aMsx6T-vRxU,489 +numpy/_typing/__init__.py,sha256=6w9E9V9VaT7vTM-veua8XcySv50Je5qSPJzK9HTocIg,7003 +numpy/_typing/__pycache__/__init__.cpython-310.pyc,, +numpy/_typing/__pycache__/_add_docstring.cpython-310.pyc,, +numpy/_typing/__pycache__/_array_like.cpython-310.pyc,, +numpy/_typing/__pycache__/_char_codes.cpython-310.pyc,, +numpy/_typing/__pycache__/_dtype_like.cpython-310.pyc,, +numpy/_typing/__pycache__/_extended_precision.cpython-310.pyc,, +numpy/_typing/__pycache__/_nbit.cpython-310.pyc,, +numpy/_typing/__pycache__/_nested_sequence.cpython-310.pyc,, +numpy/_typing/__pycache__/_scalars.cpython-310.pyc,, +numpy/_typing/__pycache__/_shape.cpython-310.pyc,, +numpy/_typing/__pycache__/setup.cpython-310.pyc,, +numpy/_typing/_add_docstring.py,sha256=xQhQX372aN_m3XN95CneMxOST2FdPcovR-MXM-9ep58,3922 +numpy/_typing/_array_like.py,sha256=L4gnx2KWG8yYcouz5b9boJIkkFNtOJV6QjcnGCrbnRY,4298 +numpy/_typing/_callable.pyi,sha256=Mf57BwohRn9ye6ixJqjNEnK0gKqnVPE9Gy8vK-6_zxo,11121 +numpy/_typing/_char_codes.py,sha256=LR51O5AUBDbCmJvlMoxyUvsfvb1p7WHrexgtTGtuWTc,5916 +numpy/_typing/_dtype_like.py,sha256=21Uxy0UgIawGM82xjDF_ifMq-nP-Bkhn_LpiK_HvWC4,5661 +numpy/_typing/_extended_precision.py,sha256=dGios-1k-QBGew7YFzONZTzVWxz-aYAaqlccl2_h5Bo,777 +numpy/_typing/_nbit.py,sha256=-EQOShHpB3r30b4RVEcruQRTcTaFAZwtqCJ4BsvpEzA,345 +numpy/_typing/_nested_sequence.py,sha256=5eNaVZAV9tZQLFWHYOuVs336JjoiaWxyZQ7cMKb6m1I,2566 +numpy/_typing/_scalars.py,sha256=eVP8PjlcTIlY7v0fRI3tFXPogWtpLJZ8nFvRRrLjDqs,980 +numpy/_typing/_shape.py,sha256=JPy7jJMkISGFTnkgiEifYM-4xTcjb7JMRkLIIjZLw08,211 +numpy/_typing/_ufunc.pyi,sha256=e74LtOP9e8kkRhvrIJ_RXz9Ua_L43Pd9IixwNwermnM,12638 +numpy/_typing/setup.py,sha256=SE0Q6HPqDjWUfceA4yXgkII8y3z7EiSF0Z-MNwOIyG4,337 +numpy/_utils/__init__.py,sha256=Hhetwsi3eTBe8HdWbG51zXmcrX1DiPLxkYSrslMLYcc,723 +numpy/_utils/__pycache__/__init__.cpython-310.pyc,, +numpy/_utils/__pycache__/_convertions.cpython-310.pyc,, +numpy/_utils/__pycache__/_inspect.cpython-310.pyc,, +numpy/_utils/__pycache__/_pep440.cpython-310.pyc,, +numpy/_utils/_convertions.py,sha256=0xMxdeLOziDmHsRM_8luEh4S-kQdMoMg6GxNDDas69k,329 +numpy/_utils/_inspect.py,sha256=8Ma7QBRwfSWKeK1ShJpFNc7CDhE6fkIE_wr1FxrG1A8,7447 +numpy/_utils/_pep440.py,sha256=Vr7B3QsijR5p6h8YAz2LjNGUyzHUJ5gZ4v26NpZAKDc,14069 +numpy/array_api/__init__.py,sha256=XtttWbDf6Yh0_m4zp-L_us4HKnV3oGwdlB6n-01Q9M8,10375 +numpy/array_api/__pycache__/__init__.cpython-310.pyc,, +numpy/array_api/__pycache__/_array_object.cpython-310.pyc,, +numpy/array_api/__pycache__/_constants.cpython-310.pyc,, +numpy/array_api/__pycache__/_creation_functions.cpython-310.pyc,, +numpy/array_api/__pycache__/_data_type_functions.cpython-310.pyc,, +numpy/array_api/__pycache__/_dtypes.cpython-310.pyc,, +numpy/array_api/__pycache__/_elementwise_functions.cpython-310.pyc,, +numpy/array_api/__pycache__/_indexing_functions.cpython-310.pyc,, +numpy/array_api/__pycache__/_manipulation_functions.cpython-310.pyc,, +numpy/array_api/__pycache__/_searching_functions.cpython-310.pyc,, +numpy/array_api/__pycache__/_set_functions.cpython-310.pyc,, +numpy/array_api/__pycache__/_sorting_functions.cpython-310.pyc,, +numpy/array_api/__pycache__/_statistical_functions.cpython-310.pyc,, +numpy/array_api/__pycache__/_typing.cpython-310.pyc,, +numpy/array_api/__pycache__/_utility_functions.cpython-310.pyc,, +numpy/array_api/__pycache__/linalg.cpython-310.pyc,, +numpy/array_api/__pycache__/setup.cpython-310.pyc,, +numpy/array_api/_array_object.py,sha256=rfCBzE6vUjk4HElQGTVwe6Tw2vxiUx7tmBpQEmm1iBk,43794 +numpy/array_api/_constants.py,sha256=AYayN2jf1Dp5rXZ7WPBdUhtPBo_JMCi-pD9oW5zmFkI,87 +numpy/array_api/_creation_functions.py,sha256=6SqHdzZqHOJFEyWFtqnj6KIKRivrGXxROlgnez_3Mt0,10050 +numpy/array_api/_data_type_functions.py,sha256=P57FOsNdXahNUriVtdldonbvBQrrZkVzxZbcqkR_8AA,6288 +numpy/array_api/_dtypes.py,sha256=kDU1NLvEQN-W2HPmJ2wGPx8jiNkFbrvTCD1T1RT8Pwo,4823 +numpy/array_api/_elementwise_functions.py,sha256=0kGuDX3Ur_Qp6tBMBWTO7LPUxzXNGAlA2SSJhdAp4DU,25992 +numpy/array_api/_indexing_functions.py,sha256=d-gzqzyvR45FQerRYJrbBzCWFnDsZWSI9pggA5QWRO4,715 +numpy/array_api/_manipulation_functions.py,sha256=qCoW5B5FXcFOWKPU9D9MXHdMeXIuzvnHUUvprNlwfjc,3317 +numpy/array_api/_searching_functions.py,sha256=mGZiqheYXGWiDK9rqXFiDKX0_B0mJ1OjdA-9FC2o5lA,1715 +numpy/array_api/_set_functions.py,sha256=ULpfK1zznW9joX1DXSiP0R3ahcDB_po7mZlpsRqi7Fs,2948 +numpy/array_api/_sorting_functions.py,sha256=7pszlxNN7-DNqEZlonGLFQrlXPP7evVA8jN31NShg00,2031 +numpy/array_api/_statistical_functions.py,sha256=HspfYteZWSa3InMs10KZz-sk3ZuW6teX6fNdo829T84,3584 +numpy/array_api/_typing.py,sha256=uKidRp6nYxgHnEPaqXXZsDDZ6tw1LshpbwLvy-09eeM,1347 +numpy/array_api/_utility_functions.py,sha256=HwycylbPAgRVz4nZvjvwqN3mQnJbqKA-NRMaAvIP-CE,824 +numpy/array_api/linalg.py,sha256=QPpG2tG1pZgzjrtTjjOu2GDu3cI6UpSsLrsG_o1jXYk,18411 +numpy/array_api/setup.py,sha256=Wx6qD7GU_APiqKolYPO0OHv4eHGYrjPZmDAgjWhOEhM,341 +numpy/array_api/tests/__init__.py,sha256=t_2GZ3lKcsu4ec4GMKPUDYaeMUJyDquBlQAcPgj7kFE,282 +numpy/array_api/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/array_api/tests/__pycache__/test_array_object.cpython-310.pyc,, +numpy/array_api/tests/__pycache__/test_creation_functions.cpython-310.pyc,, +numpy/array_api/tests/__pycache__/test_data_type_functions.cpython-310.pyc,, +numpy/array_api/tests/__pycache__/test_elementwise_functions.cpython-310.pyc,, +numpy/array_api/tests/__pycache__/test_indexing_functions.cpython-310.pyc,, +numpy/array_api/tests/__pycache__/test_manipulation_functions.cpython-310.pyc,, +numpy/array_api/tests/__pycache__/test_set_functions.cpython-310.pyc,, +numpy/array_api/tests/__pycache__/test_sorting_functions.cpython-310.pyc,, +numpy/array_api/tests/__pycache__/test_validation.cpython-310.pyc,, +numpy/array_api/tests/test_array_object.py,sha256=FQoAxP4CLDiv6iih8KKUDSLuYM6dtnDcB1f0pMHw4-M,17035 +numpy/array_api/tests/test_creation_functions.py,sha256=s3A1COWmXIAJdhzd8v7VtL-jbiSspskTqwYy0BTpmpw,5023 +numpy/array_api/tests/test_data_type_functions.py,sha256=qc8ktRlVXWC3PKhxPVWI_UF9f1zZtpmzHjdCtf3e16E,1018 +numpy/array_api/tests/test_elementwise_functions.py,sha256=CTj4LLwtusI51HkpzD0JPohP1ffNxogAVFz8WLuWFzM,3800 +numpy/array_api/tests/test_indexing_functions.py,sha256=AbuBGyEufEAf24b7fy8JQhdJtGPdP9XEIxPTJAfAFFo,627 +numpy/array_api/tests/test_manipulation_functions.py,sha256=wce25dSJjubrGhFxmiatzR_IpmNYp9ICJ9PZBBnZTOQ,1087 +numpy/array_api/tests/test_set_functions.py,sha256=D016G7v3ko49bND5sVERP8IqQXZiwr-2yrKbBPJ-oqg,546 +numpy/array_api/tests/test_sorting_functions.py,sha256=INPiYnuGBcsmWtYqdTTX3ENHmM4iUx4zs9KdwDaSmdA,602 +numpy/array_api/tests/test_validation.py,sha256=QUG9yWC3QhkPxNhbQeakwBbl-0Rr0iTuZ41_0sfVIGU,676 +numpy/compat/__init__.py,sha256=iAHrmsZWzouOMSyD9bdSE0APWMlRpqW92MQgF8y6x3E,448 +numpy/compat/__pycache__/__init__.cpython-310.pyc,, +numpy/compat/__pycache__/py3k.cpython-310.pyc,, +numpy/compat/__pycache__/setup.cpython-310.pyc,, +numpy/compat/py3k.py,sha256=Je74CVk_7qI_qX7pLbYcuQJsxlMq1poGIfRIrH99kZQ,3833 +numpy/compat/setup.py,sha256=36X1kF0C_NVROXfJ7w3SQeBm5AIDBuJbM5qT7cvSDgU,335 +numpy/compat/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/compat/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/compat/tests/__pycache__/test_compat.cpython-310.pyc,, +numpy/compat/tests/test_compat.py,sha256=YqV67pSN8nXPbXaEdjhmyaoVetNyFupVv57OMEgCwKA,579 +numpy/conftest.py,sha256=HZyWo_wJyrbgnyXxI8t05WOg_IrzNAMnEV7O8koHous,4623 +numpy/core/__init__.py,sha256=CNsO-Ab4ywM2Wz3AbqWOH3ig1q5Bno9PsUMrCv-HNS4,5780 +numpy/core/__init__.pyi,sha256=xtd9OFYza-ZG3jyEJrlzRPT-SkVoB_qYmVCe6FxRks0,126 +numpy/core/__pycache__/__init__.cpython-310.pyc,, +numpy/core/__pycache__/_add_newdocs.cpython-310.pyc,, +numpy/core/__pycache__/_add_newdocs_scalars.cpython-310.pyc,, +numpy/core/__pycache__/_asarray.cpython-310.pyc,, +numpy/core/__pycache__/_dtype.cpython-310.pyc,, +numpy/core/__pycache__/_dtype_ctypes.cpython-310.pyc,, +numpy/core/__pycache__/_exceptions.cpython-310.pyc,, +numpy/core/__pycache__/_internal.cpython-310.pyc,, +numpy/core/__pycache__/_machar.cpython-310.pyc,, +numpy/core/__pycache__/_methods.cpython-310.pyc,, +numpy/core/__pycache__/_string_helpers.cpython-310.pyc,, +numpy/core/__pycache__/_type_aliases.cpython-310.pyc,, +numpy/core/__pycache__/_ufunc_config.cpython-310.pyc,, +numpy/core/__pycache__/arrayprint.cpython-310.pyc,, +numpy/core/__pycache__/cversions.cpython-310.pyc,, +numpy/core/__pycache__/defchararray.cpython-310.pyc,, +numpy/core/__pycache__/einsumfunc.cpython-310.pyc,, +numpy/core/__pycache__/fromnumeric.cpython-310.pyc,, +numpy/core/__pycache__/function_base.cpython-310.pyc,, +numpy/core/__pycache__/getlimits.cpython-310.pyc,, +numpy/core/__pycache__/memmap.cpython-310.pyc,, +numpy/core/__pycache__/multiarray.cpython-310.pyc,, +numpy/core/__pycache__/numeric.cpython-310.pyc,, +numpy/core/__pycache__/numerictypes.cpython-310.pyc,, +numpy/core/__pycache__/overrides.cpython-310.pyc,, +numpy/core/__pycache__/records.cpython-310.pyc,, +numpy/core/__pycache__/shape_base.cpython-310.pyc,, +numpy/core/__pycache__/umath.cpython-310.pyc,, +numpy/core/__pycache__/umath_tests.cpython-310.pyc,, +numpy/core/_add_newdocs.py,sha256=39JFaeDPN2OQlSwfpY6_Jq9fO5vML8ZMF8J4ZTx_nrs,208972 +numpy/core/_add_newdocs_scalars.py,sha256=PF9v8POcSNH6ELYltkx9e07DWgMmft6NJy9zER3Jk44,12106 +numpy/core/_asarray.py,sha256=P2ddlZAsg1iGleRRfoQv_aKs2N7AGwpo5K4ZQv4Ujlk,3884 +numpy/core/_asarray.pyi,sha256=gNNxUVhToNU_F1QpgeEvUYddpUFN-AKP0QWa4gqcTGw,1086 +numpy/core/_dtype.py,sha256=SihUz41pHRB3Q2LiYYkug6LgMBKh6VV89MOpLxnXQdo,10606 +numpy/core/_dtype_ctypes.py,sha256=Vug4i7xKhznK2tdIjmn4ebclClpaCJwSZUlvEoYl0Eg,3673 +numpy/core/_exceptions.py,sha256=dZWKqfdLRvJvbAEG_fof_8ikEKxjakADMty1kLC_l_M,5379 +numpy/core/_internal.py,sha256=f9kNDuT-FGxF1EtVOVIxXWnH9gM9n-J5V2zwHMv4HEk,28348 +numpy/core/_internal.pyi,sha256=_mCTOX6Su8D4R9fV4HNeohPJx7515B-WOlv4uq6mry8,1032 +numpy/core/_machar.py,sha256=G3a3TXu8VDW_1EMxKKLnGMbvUShEIUEve3ealBlJJ3E,11565 +numpy/core/_methods.py,sha256=m31p0WjcFUGckbJiHnCpSaIQGqv-Lq5niIYkdd33YMo,8613 +numpy/core/_multiarray_tests.cpython-310-x86_64-linux-gnu.so,sha256=DeRvVJb8JhcVcq3CAMpUrF7kcD_EzZ9hc-OOsu4hwgs,175912 +numpy/core/_multiarray_umath.cpython-310-x86_64-linux-gnu.so,sha256=_l7-McVTJrByyPsjmiJYGSEYJstFzTx07QrwAw5w86E,7426841 +numpy/core/_operand_flag_tests.cpython-310-x86_64-linux-gnu.so,sha256=fSsrco1Kut3UaGZZ5f-w9gOw6iil4v5es4h0jjsgM0c,16856 +numpy/core/_rational_tests.cpython-310-x86_64-linux-gnu.so,sha256=JhmDykdI9J_hx_swy42UxeFjFGDF-MEj6u3jKL32770,59768 +numpy/core/_simd.cpython-310-x86_64-linux-gnu.so,sha256=sslr7CDjx6Wfj3izDn_VFC0BXkLyy9JyI8PoYsU-QRM,3527040 +numpy/core/_string_helpers.py,sha256=-fQM8z5s8_yX440PmgNEH3SUjEoXMPpPSysZwWZNbuo,2852 +numpy/core/_struct_ufunc_tests.cpython-310-x86_64-linux-gnu.so,sha256=CSlDZ5gi01I5v-N7jHvSfRLyOJSR9JMcIV2cIq5InGo,16960 +numpy/core/_type_aliases.py,sha256=qV6AZlsUWHMWTydmZya73xuBkKXiUKq_WXLj7q2CbZ0,7534 +numpy/core/_type_aliases.pyi,sha256=lguMSqMwvqAFHuRtm8YZSdKbikVz985BdKo_lo7GQCg,404 +numpy/core/_ufunc_config.py,sha256=-Twpe8dnd45ccXH-w-B9nvU8yCOd1E0e3Wpsts3g_bQ,13944 +numpy/core/_ufunc_config.pyi,sha256=-615enOVQMBhVx7Pln7DY_s4H6JjSgSnBy89YkpvuLg,1066 +numpy/core/_umath_tests.cpython-310-x86_64-linux-gnu.so,sha256=G_1pjqWX0wE-CBY1CeWdfvaHNGEfOREmgJkzFTNYX9Q,42272 +numpy/core/arrayprint.py,sha256=ySZj4TZFFVCa5yhMmJKFYQYhuQTabZTRBb1YoiCD-ac,63608 +numpy/core/arrayprint.pyi,sha256=21pOWjTSfJOBaKgOOPzRox1ERb3c9ydufqL0b11_P_Q,4428 +numpy/core/cversions.py,sha256=H_iNIpx9-hY1cQNxqjT2d_5SXZhJbMo_caq4_q6LB7I,347 +numpy/core/defchararray.py,sha256=G1LExk-dMeVTYRhtYgcCZEsHk5tkawk7giXcK4Q5KVM,73617 +numpy/core/defchararray.pyi,sha256=ib3aWFcM7F4KooU57mWUNi4GlosNjdfgrLKBVSIKDvU,9216 +numpy/core/einsumfunc.py,sha256=TrL6t79F0H0AQH0y5Cj7Tq0_pzk4fVFi-4q4jJmujYQ,51868 +numpy/core/einsumfunc.pyi,sha256=IJZNdHHG_soig8XvCbXZl43gMr3MMKl9dckTYWecqLs,4860 +numpy/core/fromnumeric.py,sha256=YMtxOBg51VMem39AHXFs-4_vOb1p48ei7njXdYTRJ_Q,128821 +numpy/core/fromnumeric.pyi,sha256=KATMFeFxUJ8YNRaC-jd_dTOt3opz2ng6lHgke5u5COk,23726 +numpy/core/function_base.py,sha256=tHg1qSHTz1eO_wHXNFRt3Q40uqVtPT2eyQdrWbIi4wQ,19836 +numpy/core/function_base.pyi,sha256=3ZYad3cdaGwNEyP8VwK97IYMqk2PDoVjpjQzhIYHjk0,4725 +numpy/core/getlimits.py,sha256=AopcTZDCUXMPcEKIZE1botc3mEhmLb2p1_ejlq1CLqY,25865 +numpy/core/getlimits.pyi,sha256=qeIXUEtognTHr_T-tv-VcZI7n8Z2VzAyIpIgKXzsLkc,82 +numpy/core/include/numpy/__multiarray_api.c,sha256=nPRzTez_Wy3YXy3zZNJNPMspAzxbLOdohqhXwouwMLM,12116 +numpy/core/include/numpy/__multiarray_api.h,sha256=ZM--FKMhIaSQS39cPW0hj5dx8ngNMmbcy6SbgXZBd8U,61450 +numpy/core/include/numpy/__ufunc_api.c,sha256=670Gcz-vhkF4taBDmktCpFRBrZ9CHJnPRx7ag7Z6HsI,1714 +numpy/core/include/numpy/__ufunc_api.h,sha256=0MBOl7dgO3ldqdDi-SdciEOuqGv1UNsmk7mp7tEy4AY,12456 +numpy/core/include/numpy/_dtype_api.h,sha256=4veCexGvx9KNWMIUuEUAVOfcsei9GqugohDY5ud16pA,16697 +numpy/core/include/numpy/_neighborhood_iterator_imp.h,sha256=s-Hw_l5WRwKtYvsiIghF0bg-mA_CgWnzFFOYVFJ-q4k,1857 +numpy/core/include/numpy/_numpyconfig.h,sha256=o0fV_jb-wgVtRxnVIWvUttiZafyrWYFm2ab9Uixz1Cw,855 +numpy/core/include/numpy/arrayobject.h,sha256=-BlWQ7kfVbzCqzHn0qaeMe0_08AbwliuG98XWG57lT8,282 +numpy/core/include/numpy/arrayscalars.h,sha256=C3vDRndZTZRbppiDyV5jp8sV3dRKsrwBIZcNlh9gSTA,3944 +numpy/core/include/numpy/experimental_dtype_api.h,sha256=tlehD5r_pYhHbGzIrUea6vtOgf6IQ8Txblnhx7455h8,15532 +numpy/core/include/numpy/halffloat.h,sha256=TRZfXgipa-dFppX2uNgkrjrPli-1BfJtadWjAembJ4s,1959 +numpy/core/include/numpy/ndarrayobject.h,sha256=PhY4NjRZDoU5Zbc8MW0swPEm81hwgWZ63gAU93bLVVI,10183 +numpy/core/include/numpy/ndarraytypes.h,sha256=EjWXv-J8C5JET4AlIbJRdctycL7-dyJZcnoWgnlCPc8,68009 +numpy/core/include/numpy/noprefix.h,sha256=d83l1QpCCVqMV2k29NMkL3Ld1qNjiC6hzOPWZAivEjQ,6830 +numpy/core/include/numpy/npy_1_7_deprecated_api.h,sha256=y0MJ8Qw7Bkt4H_4VxIzHzpkw5JqAdj5ECgtn08fZFrI,4327 +numpy/core/include/numpy/npy_3kcompat.h,sha256=SvN9yRA3i02O4JFMXxZz0Uq_vJ5ZpvC-pC2sfF56A5I,15883 +numpy/core/include/numpy/npy_common.h,sha256=apWBsCJeP8P5T0exgzhFcGohbASsUF8vtFdS2jc1VfU,37746 +numpy/core/include/numpy/npy_cpu.h,sha256=pcVRtj-Y6120C5kWB1VAiAjZoxkTPDEg0gGm5IAt3jM,4629 +numpy/core/include/numpy/npy_endian.h,sha256=we7X9fPeWzNpo_YTh09MPGDwdE0Rw_WDM4c9y4nBj5I,2786 +numpy/core/include/numpy/npy_interrupt.h,sha256=DQZIxi6FycLXD8drdHn2SSmLoRhIpo6osvPv13vowUA,1948 +numpy/core/include/numpy/npy_math.h,sha256=SbKRoc7O3gVuDl7HOZjk424O049I0zn-7i9GwBwNmmk,18945 +numpy/core/include/numpy/npy_no_deprecated_api.h,sha256=0yZrJcQEJ6MCHJInQk5TP9_qZ4t7EfBuoLOJ34IlJd4,678 +numpy/core/include/numpy/npy_os.h,sha256=hlQsg_7-RkvS3s8OM8KXy99xxyJbCm-W1AYVcdnO1cw,1256 +numpy/core/include/numpy/numpyconfig.h,sha256=Nr59kE3cXmen6y0UymIBaU7F1BSIuPwgKZ4gdV5Q5JU,5308 +numpy/core/include/numpy/old_defines.h,sha256=xuYQDDlMywu0Zsqm57hkgGwLsOFx6IvxzN2eiNF-gJY,6405 +numpy/core/include/numpy/random/LICENSE.txt,sha256=-8U59H0M-DvGE3gID7hz1cFGMBJsrL_nVANcOSbapew,1018 +numpy/core/include/numpy/random/bitgen.h,sha256=49AwKOR552r-NkhuSOF1usb_URiMSRMvD22JF5pKIng,488 +numpy/core/include/numpy/random/distributions.h,sha256=W5tOyETd0m1W0GdaZ5dJP8fKlBtsTpG23V2Zlmrlqpg,9861 +numpy/core/include/numpy/random/libdivide.h,sha256=ew9MNhPQd1LsCZiWiFmj9IZ7yOnA3HKOXffDeR9X1jw,80138 +numpy/core/include/numpy/ufuncobject.h,sha256=Xmnny_ulZo9VwxkfkXF-1HCTKDavIp9PV_H7XWhi0Z8,12070 +numpy/core/include/numpy/utils.h,sha256=wMNomSH3Dfj0q78PrjLVtFtN-FPo7UJ4o0ifCUO-6Es,1185 +numpy/core/lib/libnpymath.a,sha256=mb8EluEp8SLpEeCTQJ0VshL-CqeZfWxSbS5ItM-9POc,93960 +numpy/core/lib/npy-pkg-config/mlib.ini,sha256=_LsWV1eStNqwhdiYPa2538GL46dnfVwT4MrI1zbsoFw,147 +numpy/core/lib/npy-pkg-config/npymath.ini,sha256=kamUNrYKAmXqQa8BcNv7D5sLqHh6bnChM0_5rZCsTfY,360 +numpy/core/memmap.py,sha256=yWBJLeVClHsD8BYusnf9bdqypOMPrj3_zoO_lQ2zVMc,11771 +numpy/core/memmap.pyi,sha256=sxIQ7T5hPLG-RBNndAc8JPvrsKEX1amBSH2HGg48Obo,55 +numpy/core/multiarray.py,sha256=zXaWf_DSkFEWjUQqVRCGeevwsI6kjQ3x6_MUwA1Y8fk,56097 +numpy/core/multiarray.pyi,sha256=_0X4W90U5ZiKt2n-9OscK-pcQyV6oGK-8jwGy5k1qxA,24768 +numpy/core/numeric.py,sha256=DgajaCDXiiQR-zuW_rrx_QhApSsa5k5FONK3Uk9mfTs,77014 +numpy/core/numeric.pyi,sha256=oVQkI4ABayFl_ZzCiGH4DxfYASL-3aETi-3B93THnEQ,14315 +numpy/core/numerictypes.py,sha256=qIf9v1OpNjjVQzXnKpD-3V01y5Bj9huw5F-U5Wa4glc,18098 +numpy/core/numerictypes.pyi,sha256=dEqtq9MLrGaqqeAF1sdXBgnEwDWOzlK02A6MTg1PS5g,3267 +numpy/core/overrides.py,sha256=YUZFS8RCBvOJ27sH-jDRcyMjOCn9VigMyuQY4J21JBI,7093 +numpy/core/records.py,sha256=4mpIjUp2XtZxY5cD2S8mgfn8GCzQGGrrkqLBqAJwM-Q,37533 +numpy/core/records.pyi,sha256=uYwE6cAoGKgN6U4ryfGZx_3m-3sY006jytjWLrDRRy0,5692 +numpy/core/shape_base.py,sha256=RPMKxA7_FCAgg_CruExl0LehnczSTFaxA6hrcfrUzns,29743 +numpy/core/shape_base.pyi,sha256=Ilb4joJmbjkIZLzKww7NJeaxg2FP3AfFib3HtfOsrC0,2774 +numpy/core/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/core/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/core/tests/__pycache__/_locales.cpython-310.pyc,, +numpy/core/tests/__pycache__/test__exceptions.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_abc.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_api.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_argparse.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_array_coercion.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_array_interface.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_arraymethod.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_arrayprint.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_casting_floatingpoint_errors.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_casting_unittests.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_conversion_utils.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_cpu_dispatcher.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_cpu_features.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_custom_dtypes.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_cython.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_datetime.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_defchararray.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_deprecations.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_dlpack.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_dtype.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_einsum.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_errstate.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_extint128.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_function_base.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_getlimits.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_half.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_hashtable.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_indexerrors.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_indexing.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_item_selection.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_limited_api.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_longdouble.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_machar.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_mem_overlap.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_mem_policy.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_memmap.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_multiarray.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_nditer.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_nep50_promotions.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_numeric.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_numerictypes.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_numpy_2_0_compat.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_overrides.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_print.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_protocols.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_records.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_regression.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_scalar_ctors.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_scalar_methods.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_scalarbuffer.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_scalarinherit.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_scalarmath.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_scalarprint.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_shape_base.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_simd.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_simd_module.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_strings.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_ufunc.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_umath.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_umath_accuracy.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_umath_complex.cpython-310.pyc,, +numpy/core/tests/__pycache__/test_unicode.cpython-310.pyc,, +numpy/core/tests/_locales.py,sha256=S4x5soqF0oxpBYOE8J9Iky72O9J25IiZ8349m93pWC4,2206 +numpy/core/tests/data/astype_copy.pkl,sha256=lWSzCcvzRB_wpuRGj92spGIw-rNPFcd9hwJaRVvfWdk,716 +numpy/core/tests/data/generate_umath_validation_data.cpp,sha256=fyhQPNhIX9hzjeXujn6mhi1MVc133zELSV_hlSQ7BQU,5842 +numpy/core/tests/data/numpy_2_0_array.pkl,sha256=Vh02tdyCypa8Nb4QzdVhnDAiXEO2WQrcwcvOdDDFF5w,718 +numpy/core/tests/data/recarray_from_file.fits,sha256=NA0kliz31FlLnYxv3ppzeruONqNYkuEvts5wzXEeIc4,8640 +numpy/core/tests/data/umath-validation-set-README.txt,sha256=pxWwOaGGahaRd-AlAidDfocLyrAiDp0whf5hC7hYwqM,967 +numpy/core/tests/data/umath-validation-set-arccos.csv,sha256=W_aL99bjzVjlVyd5omfDUORag8jHzx6uctedPVZgOHQ,61365 +numpy/core/tests/data/umath-validation-set-arccosh.csv,sha256=Uko_d0kDXr1YlN-6Ii-fQQxUvbXAhRfC7Un4gJ23GJk,61365 +numpy/core/tests/data/umath-validation-set-arcsin.csv,sha256=15Aenze4WD2a2dF2aOBXpv9B7u3wwAeUVJdEm4TjOkQ,61339 +numpy/core/tests/data/umath-validation-set-arcsinh.csv,sha256=uDwx4PStpfV21IaPF8pmzQpul6i72g7zDwlfcynWaVQ,60289 +numpy/core/tests/data/umath-validation-set-arctan.csv,sha256=mw5tYze_BMs6ugGEZfg5mcXoInGYdn7fvSCYSUi9Bqw,60305 +numpy/core/tests/data/umath-validation-set-arctanh.csv,sha256=95l4Uu5RmZajljabfqlv5U34RVrifCMhhkop6iLeNBo,61339 +numpy/core/tests/data/umath-validation-set-cbrt.csv,sha256=v855MTZih-fZp_GuEDst2qaIsxU4a7vlAbeIJy2xKpc,60846 +numpy/core/tests/data/umath-validation-set-cos.csv,sha256=0PNnDqKkokZ7ERVDgbes8KNZc-ISJrZUlVZc5LkW18E,59122 +numpy/core/tests/data/umath-validation-set-cosh.csv,sha256=FGCNeUSUTAeASsb_j18iRSsCxXLxmzF-_C7tq1elVrQ,60869 +numpy/core/tests/data/umath-validation-set-exp.csv,sha256=BKg1_cyrKD2GXYMX_EB0DnXua8DI2O1KWODXf_BRhrk,17491 +numpy/core/tests/data/umath-validation-set-exp2.csv,sha256=f1b05MRXPOXihC9M-yi52udKBzVXalhbTuIcqoDAk-g,58624 +numpy/core/tests/data/umath-validation-set-expm1.csv,sha256=_ghc1xiUECNsBGrKCFUAy2lvu01_lkpeYJN0zDtCYWk,60299 +numpy/core/tests/data/umath-validation-set-log.csv,sha256=z9ej1ykKUoMRqYMUIJENWXbYi_A_x_RKs7K_GuXZJus,11692 +numpy/core/tests/data/umath-validation-set-log10.csv,sha256=RJgpruL16FVPgUT3-3xW4eppS_tn6o5yEW79KnITn48,68922 +numpy/core/tests/data/umath-validation-set-log1p.csv,sha256=IZZI-hi55HGCOvBat3vSBVha_8Nt-5alf2fqz6QeTG0,60303 +numpy/core/tests/data/umath-validation-set-log2.csv,sha256=HL2rOCsrEi378rNrbsXHPqlWlEGkXQq8R4e63YeTksU,68917 +numpy/core/tests/data/umath-validation-set-sin.csv,sha256=8PUjnQ_YfmxFb42XJrvpvmkeSpEOlEXSmNvIK4VgfAM,58611 +numpy/core/tests/data/umath-validation-set-sinh.csv,sha256=CYiibE8aX7MQnBatl__5k_PWc_9vHUifwS-sFZzzKk0,60293 +numpy/core/tests/data/umath-validation-set-tan.csv,sha256=Oq7gxMvblRVBrQ23kMxc8iT0bHnCWKg9EE4ZqzbJbOA,60299 +numpy/core/tests/data/umath-validation-set-tanh.csv,sha256=iolZF_MOyWRgYSa-SsD4df5mnyFK18zrICI740SWoTc,60299 +numpy/core/tests/examples/cython/__pycache__/setup.cpython-310.pyc,, +numpy/core/tests/examples/cython/checks.pyx,sha256=rKAhPSGHJ9oPK9Q_85YoUQyRTftEP1jcYOR5lSPB6oQ,662 +numpy/core/tests/examples/cython/meson.build,sha256=Qk4Q6OkpZ0xsLUkcGQVVrYkzb0ozoyL6YlSZ8_5tH1I,1088 +numpy/core/tests/examples/cython/setup.py,sha256=aAR-TvQabUabnCzuB6UdWdmRXaaPfIG7MzTIfMF-0tk,496 +numpy/core/tests/examples/limited_api/__pycache__/setup.cpython-310.pyc,, +numpy/core/tests/examples/limited_api/limited_api.c,sha256=mncE8TjjXmYpkwli433G0jB2zGQO_5NqWmGKdzRJZug,344 +numpy/core/tests/examples/limited_api/setup.py,sha256=p2w7F1ardi_GRXSrnNIR8W1oeH_pgmw_1P2wS0A2I6M,435 +numpy/core/tests/test__exceptions.py,sha256=QqxQSLXboPXEVwHz-TyE2JeIl_TC-rPugzfo25nbcns,2846 +numpy/core/tests/test_abc.py,sha256=FfgYA_HjYAi8XWGK_oOh6Zw86chB_KG_XoW_7ZlFp4c,2220 +numpy/core/tests/test_api.py,sha256=UMc7SvczAQ5ngHxE-NoXVvNpVzYRrn8oMwFNta1yMS0,22995 +numpy/core/tests/test_argparse.py,sha256=C0zBbwQ9xzzymXe_hHpWnnWQPwOi2ZdQB78gBAgJHvU,1969 +numpy/core/tests/test_array_coercion.py,sha256=zY4Pjlt4QZ0w71WxWGLHcrPnnhEF51yXYVLg5HMIy5c,34379 +numpy/core/tests/test_array_interface.py,sha256=8tGgj1Nzi76H_WF5GULkxqWL7Yu_Xf0lvTJZOwOBKsI,7774 +numpy/core/tests/test_arraymethod.py,sha256=VpjDYTmoMDTZcY7CsGzinBh0R_OICuwOykWCbmCRQZU,3244 +numpy/core/tests/test_arrayprint.py,sha256=cKaIoD9ZvsjJH0PHwZyOxmcRcBt1kN1WfFneqVqs0b8,40462 +numpy/core/tests/test_casting_floatingpoint_errors.py,sha256=W3Fgk0oKtXFv684fEZ7POwj6DHTYK0Jj_oGRLZ8UdyA,5063 +numpy/core/tests/test_casting_unittests.py,sha256=9-vkR0oXczQz8ED8DxGVPmalC8IZXe2jKgOCMGr8hIg,34298 +numpy/core/tests/test_conversion_utils.py,sha256=jNhbNNI-T8qtQnsIMEax7KFN30kjh0ICntLMwTyxJ5Q,6559 +numpy/core/tests/test_cpu_dispatcher.py,sha256=v_SlhUpENuoe7QYXizzYITLGXa7WfZ7jqcqmbSBg7JU,1542 +numpy/core/tests/test_cpu_features.py,sha256=mieGx7dxXFiyTYatbcCCjIjR67Un2hVcbJx4GEf2yFo,14892 +numpy/core/tests/test_custom_dtypes.py,sha256=JogRmttDLwfQ3PTbewEnGLKco9zV2Nu3yIfrMeCsx_I,9401 +numpy/core/tests/test_cython.py,sha256=t5-h4XSIFNLyw_9BIAQDYl8_80t_pH0SCfEa1Vf_3aI,3755 +numpy/core/tests/test_datetime.py,sha256=2vAGbrCQmsrWNXCVXOMZqUGZn2c-cQT-eZ1wTprYbcM,116211 +numpy/core/tests/test_defchararray.py,sha256=F88HUkByEP4H6cJ_ITvIe0a_T1BH2JOdRysMCu1XIn0,24997 +numpy/core/tests/test_deprecations.py,sha256=w2lhHb-W8hh7RoE_0Ftg8thpG86jvbFAJgior22DY2Q,31076 +numpy/core/tests/test_dlpack.py,sha256=cDlwFmTombb2rDeB8RHEAJ4eVMUiDbw8Oz5Jo1NQwk0,3522 +numpy/core/tests/test_dtype.py,sha256=J09pJF59v7UO6iNuJFISKP2DLPgdkQ_df5OAMDRLikU,75702 +numpy/core/tests/test_einsum.py,sha256=QzQAPIC-IjTV3Dxz97hBnvLBCmF8kpsBTBckThhgRjQ,53712 +numpy/core/tests/test_errstate.py,sha256=U3GT9I058jkF725mx4GdWUr9RoceCkGDV7Go79VA4wY,2219 +numpy/core/tests/test_extint128.py,sha256=gCZfAwPOb-F1TLsEEeDI0amQYwHk-60-OXi0ccZrrZ8,5643 +numpy/core/tests/test_function_base.py,sha256=Ibs6-WXZE5hsRx4VCnX-cZOWYKU-5PFXjouwAQzgnqQ,15595 +numpy/core/tests/test_getlimits.py,sha256=apdxr0zKkxaVHIUpLrqAvO39q54JKN14sV4xSbK2Ifs,6718 +numpy/core/tests/test_half.py,sha256=VYPyap9GYOWZuphsfFofcIRl-oa5Ufrtv83OTp6azdU,24593 +numpy/core/tests/test_hashtable.py,sha256=ZV8HL8NkDnoQZfnje7BP0fyIp4fSFqjKsQc40PaTggc,1011 +numpy/core/tests/test_indexerrors.py,sha256=kN9xLl6FVTzmI7fumn_cuZ3k0omXnTetgtCnPY44cvw,5130 +numpy/core/tests/test_indexing.py,sha256=x0ojWuhOwWD5MZuiJ9Ncim3CgkwI-GldWxrSCmjmFJM,54314 +numpy/core/tests/test_item_selection.py,sha256=kI30kiX8mIrZYPn0jw3lGGw1ruZF4PpE9zw-aai9EPA,6458 +numpy/core/tests/test_limited_api.py,sha256=5yO0nGmCKZ9b3S66QP7vY-HIgAoyOtHZmp8mvzKuOHI,1172 +numpy/core/tests/test_longdouble.py,sha256=jO8YMm_Hsz-XPKbmv6iMcOdHgTlIFkKTwAtxpy3Q1pE,13905 +numpy/core/tests/test_machar.py,sha256=_5_TDUVtAJvJI5jBfEFKpCZtAfKCsCFt7tXlWSkWzzc,1067 +numpy/core/tests/test_mem_overlap.py,sha256=QJ0unWD_LOoAGAo4ra0IvYenj56IYUtiz1fEJEmTY9Q,29086 +numpy/core/tests/test_mem_policy.py,sha256=CXa10FQw2Qj6MqJuaC8Fm4slsoipKFjCIpYF6c5IIAU,16801 +numpy/core/tests/test_memmap.py,sha256=tZ5lJs_4ZFsJmg392ZQ33fX0m8tdfZ8ZtY9Lq41LNtk,7477 +numpy/core/tests/test_multiarray.py,sha256=GPv4IJR9dijNG-icUsQsX2tBD2RdP3EhUehY4cxvVQU,380106 +numpy/core/tests/test_nditer.py,sha256=nVQ00aNxPHqf4ZcFs3e9AVDK64TCqlO0TzfocTAACZQ,130818 +numpy/core/tests/test_nep50_promotions.py,sha256=2TwtFvj1LBpYTtdR6NFe1RAAGXIJltLqwpA1vhQCVY4,8840 +numpy/core/tests/test_numeric.py,sha256=ZGNW5NKgShEjZC_TcPOtTuRaTM_GbuM21u82D205UPs,137294 +numpy/core/tests/test_numerictypes.py,sha256=f_xMjZJnyDwlc6XCrd71b6x1_6dAWOv-kZ3-NEq37hU,21687 +numpy/core/tests/test_numpy_2_0_compat.py,sha256=kVCTAXska7Xi5w_TYduWhid0nlCqI6Nvmt-gDnYsuKI,1630 +numpy/core/tests/test_overrides.py,sha256=t0gOZOzu7pevE58HA-npFYJqnInHR-LLBklnzKJWHqo,26080 +numpy/core/tests/test_print.py,sha256=ErZAWd88b0ygSEoYpd0BL2tFjkerMtn1vZ7dWvaNqTc,6837 +numpy/core/tests/test_protocols.py,sha256=fEXE9K9s22oiVWkX92BY-g00-uXCK-HxjZhZxxYAKFc,1168 +numpy/core/tests/test_records.py,sha256=pluit5x6jkWoPEIrHXM13L3xZuuSSiaxoXFsOdkakCU,20269 +numpy/core/tests/test_regression.py,sha256=SJo9cPTVr2SNjhgtW7boUMyNQlXxygsZ5g0oyqC8Eks,91595 +numpy/core/tests/test_scalar_ctors.py,sha256=qDIZV-tBukwAxNDhUmGtH3CemDXlS3xd_q3L52touuA,6115 +numpy/core/tests/test_scalar_methods.py,sha256=Uj-zU0zzzKAjMBdpkzsWZ3nSFj5gJkUlqi_euhOYdnU,7541 +numpy/core/tests/test_scalarbuffer.py,sha256=FSL94hriWX1_uV6Z33wB3ZXUrpmmX2-x87kNjIxUeBk,5580 +numpy/core/tests/test_scalarinherit.py,sha256=fMInDGKsiH3IS_2ejZtIcmJZ0Ry8c7kVsHx7wp5XDoM,2368 +numpy/core/tests/test_scalarmath.py,sha256=XZj_m2I2TLktJdFD1SWj2XtV8hT26VIxasDz3cAFvgA,43247 +numpy/core/tests/test_scalarprint.py,sha256=1599W5X0tjGhBnSQjalXkg6AY8eHXnr6PMqs4vYZQqs,18771 +numpy/core/tests/test_shape_base.py,sha256=D9haeuUVx3x3pOLmFQ9vUz7iU4T2bFTsPoI8HgSncFU,29723 +numpy/core/tests/test_simd.py,sha256=-L1UhIn9Eu_euLwaSU7bPRfYpWWOTb43qovoJS7Ws7w,48696 +numpy/core/tests/test_simd_module.py,sha256=OSpYhH_3QDxItyQcaW6SjXW57k2m-weRwpYOnJjCqN0,3902 +numpy/core/tests/test_strings.py,sha256=A9t1B65lFrYRLXgDJSg3mMDAe_hypIPcTMVOdAYIbU0,3835 +numpy/core/tests/test_ufunc.py,sha256=5pS2x3LACHn8GogYYad8LRAjByK7Gg9xTD9ik3d0Fm0,124907 +numpy/core/tests/test_umath.py,sha256=huHpclJqkO32k7BTflRHj8nImzg3p6yyryeS9LyHKWU,186482 +numpy/core/tests/test_umath_accuracy.py,sha256=mFcVdzXhhD9mqhzLDJVZsWfCHbjbFQ6XeEl5G8l-PTc,3897 +numpy/core/tests/test_umath_complex.py,sha256=WvZZZWeijo52RiOfx-G83bxzQOp_IJ3i9fEnUDVukLQ,23247 +numpy/core/tests/test_unicode.py,sha256=hUXIwMmoq89y_KXWzuXVyQaXvRwGjfY4TvKJsCbygEI,12775 +numpy/core/umath.py,sha256=JbT_SxnZ_3MEmjOI9UtX3CcAzX5Q-4RDlnnhDAEJ5Vo,2040 +numpy/core/umath_tests.py,sha256=TIzaDfrEHHgSc2J5kxFEibq8MOPhwSuyOZOUBsZNVSM,389 +numpy/ctypeslib.py,sha256=Po4XCWfxhwFQ1Q8x8DeayGiMCJLxREaCDkVyeladxBU,17247 +numpy/ctypeslib.pyi,sha256=A9te473aRO920iDVuyKypeVIQp-ueZK6EiI-qLSwJNg,7972 +numpy/distutils/__init__.py,sha256=BU1C21439HRo7yH1SsN9me6WCDPpOwRQ37ZpNwDMqCw,2074 +numpy/distutils/__init__.pyi,sha256=D8LRE6BNOmuBGO-oakJGnjT9UJTk9zSR5rxMfZzlX64,119 +numpy/distutils/__pycache__/__init__.cpython-310.pyc,, +numpy/distutils/__pycache__/_shell_utils.cpython-310.pyc,, +numpy/distutils/__pycache__/armccompiler.cpython-310.pyc,, +numpy/distutils/__pycache__/ccompiler.cpython-310.pyc,, +numpy/distutils/__pycache__/ccompiler_opt.cpython-310.pyc,, +numpy/distutils/__pycache__/conv_template.cpython-310.pyc,, +numpy/distutils/__pycache__/conv_template.cpython-310.pyc,sha256=xQspcWcISFYp-FhQdD5w9zCFa7QXMx1HO-rcwuaiGKw,8269 +numpy/distutils/__pycache__/core.cpython-310.pyc,, +numpy/distutils/__pycache__/cpuinfo.cpython-310.pyc,, +numpy/distutils/__pycache__/exec_command.cpython-310.pyc,, +numpy/distutils/__pycache__/extension.cpython-310.pyc,, +numpy/distutils/__pycache__/from_template.cpython-310.pyc,, +numpy/distutils/__pycache__/fujitsuccompiler.cpython-310.pyc,, +numpy/distutils/__pycache__/intelccompiler.cpython-310.pyc,, +numpy/distutils/__pycache__/lib2def.cpython-310.pyc,, +numpy/distutils/__pycache__/line_endings.cpython-310.pyc,, +numpy/distutils/__pycache__/log.cpython-310.pyc,, +numpy/distutils/__pycache__/mingw32ccompiler.cpython-310.pyc,, +numpy/distutils/__pycache__/misc_util.cpython-310.pyc,, +numpy/distutils/__pycache__/msvc9compiler.cpython-310.pyc,, +numpy/distutils/__pycache__/msvccompiler.cpython-310.pyc,, +numpy/distutils/__pycache__/npy_pkg_config.cpython-310.pyc,, +numpy/distutils/__pycache__/numpy_distribution.cpython-310.pyc,, +numpy/distutils/__pycache__/pathccompiler.cpython-310.pyc,, +numpy/distutils/__pycache__/setup.cpython-310.pyc,, +numpy/distutils/__pycache__/system_info.cpython-310.pyc,, +numpy/distutils/__pycache__/unixccompiler.cpython-310.pyc,, +numpy/distutils/_shell_utils.py,sha256=kMLOIoimB7PdFRgoVxCIyCFsIl1pP3d0hkm_s3E9XdA,2613 +numpy/distutils/armccompiler.py,sha256=8qUaYh8QHOJlz7MNvkuJNyYdCOCivuW0pbmf_2OPZu0,962 +numpy/distutils/ccompiler.py,sha256=6I-zQBLJCyZUZaYdmK23pmucM8MAn2OsvyzEdghPpW0,28618 +numpy/distutils/ccompiler_opt.py,sha256=diDOkSKj_j0xY08kP7-NoQsefxJgsN5clEirqXlavGY,100390 +numpy/distutils/checks/cpu_asimd.c,sha256=nXUsTLrSlhRL-UzDM8zMqn1uqJnR7TRlJi3Ixqw539w,818 +numpy/distutils/checks/cpu_asimddp.c,sha256=E4b9zT1IdSfGR2ACZJiQoR-BqaeDtzFqRNW8lBOXAaY,432 +numpy/distutils/checks/cpu_asimdfhm.c,sha256=6tXINVEpmA-lYRSbL6CrBu2ejNFmd9WONFGgg-JFXZE,529 +numpy/distutils/checks/cpu_asimdhp.c,sha256=SfwrEEA_091tmyI4vN3BNLs7ypUnrF_VbTg6gPl-ocs,379 +numpy/distutils/checks/cpu_avx.c,sha256=LuZW8o93VZZi7cYEP30dvKWTm7Mw1TLmCt5UaXDxCJg,779 +numpy/distutils/checks/cpu_avx2.c,sha256=jlDlea393op0JOiMJgmmPyKmyAXztLcObPOp9F9FaS0,749 +numpy/distutils/checks/cpu_avx512_clx.c,sha256=P-YHjj2XE4SithBkPwDgShOxGWnVSNUXg72h8O3kpbs,842 +numpy/distutils/checks/cpu_avx512_cnl.c,sha256=f_c2Z0xwAKTJeK3RYMIp1dgXYV8QyeOxUgKkMht4qko,948 +numpy/distutils/checks/cpu_avx512_icl.c,sha256=isI35-gm7Hqn2Qink5hP1XHWlh52a5vwKhEdW_CRviE,1004 +numpy/distutils/checks/cpu_avx512_knl.c,sha256=PVTkczTpHlXbTc7IQKlCFU9Cq4VGG-_JhVnT0_n-t1A,959 +numpy/distutils/checks/cpu_avx512_knm.c,sha256=eszPGr3XC9Js7mQUB0gFxlrNjQwfucQFz_UwFyNLjes,1132 +numpy/distutils/checks/cpu_avx512_skx.c,sha256=59VD8ebEJJHLlbY-4dakZV34bmq_lr9mBKz8BAcsdYc,1010 +numpy/distutils/checks/cpu_avx512_spr.c,sha256=i8DpADB8ZhIucKc8lt9JfYbQANRvR67u59oQf5winvg,904 +numpy/distutils/checks/cpu_avx512cd.c,sha256=Qfh5FJUv9ZWd_P5zxkvYYIkvqsPptgaDuKkeX_F8vyA,759 +numpy/distutils/checks/cpu_avx512f.c,sha256=d97NRcbJhqpvURnw7zyG0TOuEijKXvU0g4qOTWHbwxY,755 +numpy/distutils/checks/cpu_f16c.c,sha256=nzZzpUc8AfTtw-INR3KOxcjx9pyzVUM8OhsrdH2dO_w,868 +numpy/distutils/checks/cpu_fma3.c,sha256=YN6IDwuZALJHVVmpQ2tj-14HI_PcxH_giV8-XjzlmkU,817 +numpy/distutils/checks/cpu_fma4.c,sha256=qKdgTNNFg-n8vSB1Txco60HBLCcOi1aH23gZOX7yKqs,301 +numpy/distutils/checks/cpu_neon.c,sha256=Y0SjuVLzh3upcbY47igHjmKgjHbXxbvzncwB7acfjxw,600 +numpy/distutils/checks/cpu_neon_fp16.c,sha256=E7YOGyYP41u1sqiCHpCGGqjmo7Cs6yUkmJ46K7LZloc,251 +numpy/distutils/checks/cpu_neon_vfpv4.c,sha256=qFY1C_fQYz7M_a_8j0KTdn7vaE3NNVmWY2JGArDGM3w,609 +numpy/distutils/checks/cpu_popcnt.c,sha256=vRcXHVw2j1F9I_07eIZ_xzDX3fd3mqgiQXL1w3pULJk,1049 +numpy/distutils/checks/cpu_sse.c,sha256=6MHITtC76UpSR9uh0SiURpnkpPkLzT5tbrcXT4xBFxo,686 +numpy/distutils/checks/cpu_sse2.c,sha256=yUZzdjDtBS-vYlhfP-pEzj3m0UPmgZs-hA99TZAEACU,697 +numpy/distutils/checks/cpu_sse3.c,sha256=j5XRHumUuccgN9XPZyjWUUqkq8Nu8XCSWmvUhmJTJ08,689 +numpy/distutils/checks/cpu_sse41.c,sha256=y_k81P-1b-Hx8OeRVDE9V1O9JakS0zPvlFKJ3VbSmEw,675 +numpy/distutils/checks/cpu_sse42.c,sha256=3PXucdI2mII-txO7zFN99TlVveT_QUAETTGvRk-_hYw,692 +numpy/distutils/checks/cpu_ssse3.c,sha256=X6VWxIXMRpdSCBsHPXvot3yTZ4d5yK9Bi1ScQP3WC-Q,705 +numpy/distutils/checks/cpu_vsx.c,sha256=FVmR4iliKjcihzMCwloR1F2JYwSZK9P4f_hvIRLHSDQ,478 +numpy/distutils/checks/cpu_vsx2.c,sha256=yESs25Rt5ztb5-stuYbu3TbiyJKmllMpMLu01GOAHqE,263 +numpy/distutils/checks/cpu_vsx3.c,sha256=omC50tbEZNigsKMFPtE3zGRlIS2VuDTm3vZ9TBZWo4U,250 +numpy/distutils/checks/cpu_vsx4.c,sha256=ngezA1KuINqJkLAcMrZJR7bM0IeA25U6I-a5aISGXJo,305 +numpy/distutils/checks/cpu_vx.c,sha256=OpLU6jIfwvGJR4JPVVZLlUfvo7oAZ0YvsjafM2qtPlk,461 +numpy/distutils/checks/cpu_vxe.c,sha256=rYW_nKwXnlB0b8xCrJEr4TmvrEvS-NToxwyqqOHV8Bk,788 +numpy/distutils/checks/cpu_vxe2.c,sha256=Hv4wO23kwC2G6lqqercq4NE4K0nrvBxR7RIzr5HTXCc,624 +numpy/distutils/checks/cpu_xop.c,sha256=7uabsGeqvmVJQvuSEjs8-Sm8kpmvl6uZ9YHMF5h2opQ,234 +numpy/distutils/checks/extra_avx512bw_mask.c,sha256=pVPOhcu80yJVnIhOcHHXOlZ2proJ1MUf0XgccqhPoNk,636 +numpy/distutils/checks/extra_avx512dq_mask.c,sha256=nMfIvepISGFDexPrMYl5LWtdmt6Uy9TKPzF4BVayw2I,504 +numpy/distutils/checks/extra_avx512f_reduce.c,sha256=_NfbtfSAkm_A67umjR1oEb9yRnBL5EnTA76fvQIuNVk,1595 +numpy/distutils/checks/extra_vsx3_half_double.c,sha256=shHvIQZfR0o-sNefOt49BOh4WCmA0BpJvj4b7F9UdvQ,354 +numpy/distutils/checks/extra_vsx4_mma.c,sha256=GiQGZ9-6wYTgH42bJgSlXhWcTIrkjh5xv4uymj6rglk,499 +numpy/distutils/checks/extra_vsx_asm.c,sha256=BngiMVS9nyr22z6zMrOrHLeCloe_5luXhf5T5mYucgI,945 +numpy/distutils/checks/test_flags.c,sha256=uAIbhfAhyGe4nTdK_mZmoCefj9P0TGHNF9AUv_Cdx5A,16 +numpy/distutils/command/__init__.py,sha256=fW49zUB3syMFsKpf1oRBO0h8tmnTwRP3zUPrsB0R22M,1032 +numpy/distutils/command/__pycache__/__init__.cpython-310.pyc,, +numpy/distutils/command/__pycache__/autodist.cpython-310.pyc,, +numpy/distutils/command/__pycache__/bdist_rpm.cpython-310.pyc,, +numpy/distutils/command/__pycache__/build.cpython-310.pyc,, +numpy/distutils/command/__pycache__/build_clib.cpython-310.pyc,, +numpy/distutils/command/__pycache__/build_ext.cpython-310.pyc,, +numpy/distutils/command/__pycache__/build_py.cpython-310.pyc,, +numpy/distutils/command/__pycache__/build_scripts.cpython-310.pyc,, +numpy/distutils/command/__pycache__/build_src.cpython-310.pyc,, +numpy/distutils/command/__pycache__/config.cpython-310.pyc,, +numpy/distutils/command/__pycache__/config_compiler.cpython-310.pyc,, +numpy/distutils/command/__pycache__/develop.cpython-310.pyc,, +numpy/distutils/command/__pycache__/egg_info.cpython-310.pyc,, +numpy/distutils/command/__pycache__/install.cpython-310.pyc,, +numpy/distutils/command/__pycache__/install_clib.cpython-310.pyc,, +numpy/distutils/command/__pycache__/install_data.cpython-310.pyc,, +numpy/distutils/command/__pycache__/install_headers.cpython-310.pyc,, +numpy/distutils/command/__pycache__/sdist.cpython-310.pyc,, +numpy/distutils/command/autodist.py,sha256=8KWwr5mnjX20UpY4ITRDx-PreApyh9M7B92IwsEtTsQ,3718 +numpy/distutils/command/bdist_rpm.py,sha256=-tkZupIJr_jLqeX7xbRhE8-COXHRI0GoRpAKchVte54,709 +numpy/distutils/command/build.py,sha256=aj1SUGsDUTxs4Tch2ALLcPnuAVhaPjEPIZIobzMajm0,2613 +numpy/distutils/command/build_clib.py,sha256=TCuZDpRd8ZPZH6SRwIZcWZC3aoGc18Rll6FYcawS6qY,19317 +numpy/distutils/command/build_ext.py,sha256=UcyG8KKyrd5v1s6qDdKEkzwLwmoMlfHA893Lj-OOgl0,32983 +numpy/distutils/command/build_py.py,sha256=XiLZ2d_tmCE8uG5VAU5OK2zlzQayBfeY4l8FFEltbig,1144 +numpy/distutils/command/build_scripts.py,sha256=P2ytmZb3UpwfmbMXkFB2iMQk15tNUCynzMATllmp-Gs,1665 +numpy/distutils/command/build_src.py,sha256=sxsnfc8KBsnsSvI-8sKIKNo2KA2uvrrvW0WYZCqyjyk,31178 +numpy/distutils/command/config.py,sha256=SdN-Cxvwx3AD5k-Xx_VyS2WWpVGmflnYGiTIyruj_xM,20670 +numpy/distutils/command/config_compiler.py,sha256=Cp9RTpW72gg8XC_3-9dCTlLYr352pBfBRZA8YBWvOoY,4369 +numpy/distutils/command/develop.py,sha256=9SbbnFnVbSJVZxTFoV9pwlOcM1D30GnOWm2QonQDvHI,575 +numpy/distutils/command/egg_info.py,sha256=i-Zk4sftK5cMQVQ2jqSxTMpVI-gYyXN16-p5TvmjURc,921 +numpy/distutils/command/install.py,sha256=nkW2fl7OABcE3sUcoNM7iONkF64CBESdVlRjTLg3hVA,3073 +numpy/distutils/command/install_clib.py,sha256=1xv0_lPVu3g16GgICjjlh7T8zQ6PSlevCuq8Bocx5YM,1399 +numpy/distutils/command/install_data.py,sha256=Y59EBG61MWP_5C8XJvSCVfzYpMNVNVcH_Z6c0qgr9KA,848 +numpy/distutils/command/install_headers.py,sha256=tVpOGqkmh8AA_tam0K0SeCd4kvZj3UqSOjWKm6Kz4jY,919 +numpy/distutils/command/sdist.py,sha256=8Tsju1RwXNbPyQcjv8GRMFveFQqYlbNdSZh2X1OV-VU,733 +numpy/distutils/conv_template.py,sha256=F-4vkkfAjCb-fN79WYrXX3BMHMoiQO-W2u09q12OPuI,9536 +numpy/distutils/core.py,sha256=C-_z7rODE_12olz0dwtlKqwfaSLXEV3kZ1CyDJMsQh8,8200 +numpy/distutils/cpuinfo.py,sha256=XuNhsx_-tyrui_AOgn10yfZ9p4YBM68vW2_bGmKj07I,22639 +numpy/distutils/exec_command.py,sha256=0EGasX7tM47Q0k8yJA1q-BvIcjV_1UAC-zDmen-j6Lg,10283 +numpy/distutils/extension.py,sha256=YgeB8e2fVc2l_1etuRBv0P8c1NULOz4SaudHgsVBc30,3568 +numpy/distutils/fcompiler/__init__.py,sha256=DqfaiKGVagOFuL0v3VZxZZkRnWWvly0_lYHuLjaZTBo,40625 +numpy/distutils/fcompiler/__pycache__/__init__.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/absoft.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/arm.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/compaq.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/environment.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/fujitsu.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/g95.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/gnu.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/hpux.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/ibm.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/intel.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/lahey.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/mips.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/nag.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/none.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/nv.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/pathf95.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/pg.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/sun.cpython-310.pyc,, +numpy/distutils/fcompiler/__pycache__/vast.cpython-310.pyc,, +numpy/distutils/fcompiler/absoft.py,sha256=yPUHBNZHOr_gxnte16I_X85o1iL9FI4RLHjG9JOuyYU,5516 +numpy/distutils/fcompiler/arm.py,sha256=MCri346qo1bYwjlm32xHRyRl-bAINTlfVIubN6HDz68,2090 +numpy/distutils/fcompiler/compaq.py,sha256=sjU2GKHJGuChtRb_MhnouMqvkIOQflmowFE6ErCWZhE,3903 +numpy/distutils/fcompiler/environment.py,sha256=DOD2FtKDk6O9k6U0h9UKWQ-65wU8z1tSPn3gUlRwCso,3080 +numpy/distutils/fcompiler/fujitsu.py,sha256=yK3wdHoF5qq25UcnIM6FzTXsJGJxdfKa_f__t04Ne7M,1333 +numpy/distutils/fcompiler/g95.py,sha256=FH4uww6re50OUT_BfdoWSLCDUqk8LvmQ2_j5RhF5nLQ,1330 +numpy/distutils/fcompiler/gnu.py,sha256=ag8v_pp-fYpDPKJsVmNaFwN621b1MFQAxew0T1KdE_Y,20502 +numpy/distutils/fcompiler/hpux.py,sha256=gloUjWGo7MgJmukorDq7ZxDnnUKXx-C6AQfryQshVM4,1353 +numpy/distutils/fcompiler/ibm.py,sha256=Ts2PXg2ocrXtX9eguvcHeQ4JB2ktpd5isXtRTpU9F5Y,3534 +numpy/distutils/fcompiler/intel.py,sha256=XYF0GLVhJWjS8noEx4TJ704Eqt-JGBolRZEOkwgNItE,6570 +numpy/distutils/fcompiler/lahey.py,sha256=U63KMfN8zDAd_jnvMkS2N-dvP4UiSRB9Ces290qLNXw,1327 +numpy/distutils/fcompiler/mips.py,sha256=LAwT0DY5yqlYh20hNMYR1-OKu8A9GNw-TbUfI8pvglM,1714 +numpy/distutils/fcompiler/nag.py,sha256=9pQCMUlwjRVHGKwZxvwd4bW5p-9v7VXcflELEImHg1g,2777 +numpy/distutils/fcompiler/none.py,sha256=6RX2X-mV1HuhJZnVfQmDmLVhIUWseIT4P5wf3rdLq9Y,758 +numpy/distutils/fcompiler/nv.py,sha256=LGBQY417zibQ-fnPis5rNtP_I1Qk9OlhEFOnPvmwXHI,1560 +numpy/distutils/fcompiler/pathf95.py,sha256=MiHVar6-beUEYVEpqXORIX4f8G29I47D36kreltdfoQ,1061 +numpy/distutils/fcompiler/pg.py,sha256=NOB1stzrjvQMZS7bIPTgWTcAFe3cjNveA5-SztUZqD0,3568 +numpy/distutils/fcompiler/sun.py,sha256=mfS3RTj9uYT6K9Ikp8RjmsEPIWAtUTzMhX9sGjEyF6I,1577 +numpy/distutils/fcompiler/vast.py,sha256=Xuxa4sNraUPcQmt45SogAfN0kDHFb6C73uNZNmX3RBE,1667 +numpy/distutils/from_template.py,sha256=hpoFQortsLZdMSr_fJILzXzrIwFlZoFjsDSo6jNtvWs,7913 +numpy/distutils/fujitsuccompiler.py,sha256=JDuUUE-GyPahkNnDZLWNHyAmJ2lJPCnLuIUFfHkjMzA,834 +numpy/distutils/intelccompiler.py,sha256=N_pvWjlLORdlH34cs97oU4LBNr_s9r5ddsmme7XEvs4,4234 +numpy/distutils/lib2def.py,sha256=-3rDf9FXsDik3-Qpp-A6N_cYZKTlmVjVi4Jzyo-pSlY,3630 +numpy/distutils/line_endings.py,sha256=a8ZZECrPRffsbs0UygeR47_fOUlZppnx-QPssrIXtB0,2032 +numpy/distutils/log.py,sha256=m8caNBwPhIG7YTnD9iq9jjc6_yJOeU9FHuau2CSulds,2879 +numpy/distutils/mingw/gfortran_vs2003_hack.c,sha256=cbsN3Lk9Hkwzr9c-yOP2xEBg1_ml1X7nwAMDWxGjzc8,77 +numpy/distutils/mingw32ccompiler.py,sha256=4G8t_6plw7xqoF0icDaWGNSBgbyDaHQn3GB5l9gikEA,22067 +numpy/distutils/misc_util.py,sha256=2MxXE4rex_wSUhpLuwxOFeeor-WxZLjisVvXWycNaq4,89359 +numpy/distutils/msvc9compiler.py,sha256=FCtP7g34AVuMIaqQlH8AV1ZBdIUXbk5G7eBeeTSr1zE,2192 +numpy/distutils/msvccompiler.py,sha256=ILookUifVJF9tAtPJoVCqZ673m5od6MVKuAHuA3Rcfk,2647 +numpy/distutils/npy_pkg_config.py,sha256=fIFyWLTqRySO3hn-0i0FNdHeblRN_hDv-wc68-sa3hQ,12972 +numpy/distutils/numpy_distribution.py,sha256=10Urolg1aDAG0EHYfcvObzOgqRV0ARh2GhDklEg4vS0,634 +numpy/distutils/pathccompiler.py,sha256=KnJEA5H4cXg7SLrMjwWtidD24VSvOdu72d17votiY9E,713 +numpy/distutils/setup.py,sha256=l9ke_Bws431UdBfysaq7ZeGtZ8dix76oh9Huq5qqbkU,634 +numpy/distutils/system_info.py,sha256=SCk1ku0HnZNwConQBJN8FVidbeKVnrMxUyNWUVx73pY,114022 +numpy/distutils/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/distutils/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_build_ext.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_ccompiler_opt.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_ccompiler_opt_conf.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_exec_command.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_fcompiler.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_fcompiler_gnu.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_fcompiler_intel.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_fcompiler_nagfor.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_from_template.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_log.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_mingw32ccompiler.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_misc_util.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_npy_pkg_config.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_shell_utils.cpython-310.pyc,, +numpy/distutils/tests/__pycache__/test_system_info.cpython-310.pyc,, +numpy/distutils/tests/test_build_ext.py,sha256=RNrEi-YMTGQG5YDi5GWL8iJRkk_bQHBQKcqp43TdJco,2769 +numpy/distutils/tests/test_ccompiler_opt.py,sha256=N3pN-9gxPY1KvvMEjoXr7kLxTGN8aQOr8qo5gmlrm90,28778 +numpy/distutils/tests/test_ccompiler_opt_conf.py,sha256=maXytv39amuojbQIieIGIXMV4Cv-s0fsPMZeFEh9XyY,6347 +numpy/distutils/tests/test_exec_command.py,sha256=BK-hHfIIrkCep-jNmS5_Cwq5oESvsvX3V_0XDAkT1Ok,7395 +numpy/distutils/tests/test_fcompiler.py,sha256=mJXezTXDUbduhCwVGAfABHpEARWhnj8hLW9EOU3rn84,1277 +numpy/distutils/tests/test_fcompiler_gnu.py,sha256=nmfaFCVzbViIOQ2-MjgXt-bN8Uj674hCgiwr5Iol-_U,2136 +numpy/distutils/tests/test_fcompiler_intel.py,sha256=mxkfFD2rNfg8nn1pp_413S0uCdYXydPWBcz9ilgGkA0,1058 +numpy/distutils/tests/test_fcompiler_nagfor.py,sha256=CKEjik7YVfSJGL4abuctkmlkIUhAhv-x2aUcXiTR9b0,1102 +numpy/distutils/tests/test_from_template.py,sha256=SDYoe0XUpAayyEQDq7ZhrvEEz7U9upJDLYzhcdoVifc,1103 +numpy/distutils/tests/test_log.py,sha256=0tSM4q-00CjbMIRb9QOJzI4A7GHUiRGOG1SOOLz8dnM,868 +numpy/distutils/tests/test_mingw32ccompiler.py,sha256=rMC8-IyBOiuZVfAoklV_KnD9qVeB_hFVvb5dStxfk08,1609 +numpy/distutils/tests/test_misc_util.py,sha256=Qs96vTr8GZSyVCWuamzcNlVMRa15vt0Y-T2yZSUm_QA,3218 +numpy/distutils/tests/test_npy_pkg_config.py,sha256=apGrmViPcXoPCEOgDthJgL13C9N0qQMs392QjZDxJd4,2557 +numpy/distutils/tests/test_shell_utils.py,sha256=UKU_t5oIa_kVMv89Ys9KN6Z_Fy5beqPDUsDAWPmcoR8,2114 +numpy/distutils/tests/test_system_info.py,sha256=wMV7bH5oB0luLDR2tunHrLaUxsD_-sIhLnNpj1blQPs,11405 +numpy/distutils/unixccompiler.py,sha256=fN4-LH6JJp44SLE7JkdG2kKQlK4LC8zuUpVC-RtmJ-U,5426 +numpy/doc/__init__.py,sha256=OYmE-F6x0CD05PCDY2MiW1HLlwB6i9vhDpk-a3r4lHY,508 +numpy/doc/__pycache__/__init__.cpython-310.pyc,, +numpy/doc/__pycache__/constants.cpython-310.pyc,, +numpy/doc/__pycache__/ufuncs.cpython-310.pyc,, +numpy/doc/constants.py,sha256=PlXoj7b4A8Aa9nADbg83uzTBRJaX8dvJmEdbn4FDPPo,9155 +numpy/doc/ufuncs.py,sha256=i1alLg19mNyCFZ2LYSOZGm--RsRN1x63U_UYU-N3x60,5357 +numpy/dtypes.py,sha256=BuBztrPQRasUmVZhXr2_NgJujdUTNhNwd59pZZHk3lA,2229 +numpy/dtypes.pyi,sha256=tIHniAYP7ALg2iT7NgSXO67jvE-zRlDod3MazEmD4M8,1315 +numpy/exceptions.py,sha256=7j7tv8cwXGZYgldyMisGmnAxAl2s4YU0vexME81yYlA,7339 +numpy/exceptions.pyi,sha256=KsZqWNvyPUEXUGR9EhZCUQF2f9EVSpBRlJUlGqRT02k,600 +numpy/f2py/__init__.py,sha256=m-ty_WiJZ4GVfV5--kJ3MFJaLXestz5Eo-4H0FPscK4,5565 +numpy/f2py/__init__.pyi,sha256=eA7uYXZr0p0aaz5rBW-EypLx9RchrvqDYtSnkEJQsYw,1087 +numpy/f2py/__main__.py,sha256=6i2jVH2fPriV1aocTY_dUFvWK18qa-zjpnISA-OpF3w,130 +numpy/f2py/__pycache__/__init__.cpython-310.pyc,, +numpy/f2py/__pycache__/__main__.cpython-310.pyc,, +numpy/f2py/__pycache__/__version__.cpython-310.pyc,, +numpy/f2py/__pycache__/_isocbind.cpython-310.pyc,, +numpy/f2py/__pycache__/_src_pyf.cpython-310.pyc,, +numpy/f2py/__pycache__/auxfuncs.cpython-310.pyc,, +numpy/f2py/__pycache__/capi_maps.cpython-310.pyc,, +numpy/f2py/__pycache__/cb_rules.cpython-310.pyc,, +numpy/f2py/__pycache__/cfuncs.cpython-310.pyc,, +numpy/f2py/__pycache__/common_rules.cpython-310.pyc,, +numpy/f2py/__pycache__/crackfortran.cpython-310.pyc,, +numpy/f2py/__pycache__/diagnose.cpython-310.pyc,, +numpy/f2py/__pycache__/f2py2e.cpython-310.pyc,, +numpy/f2py/__pycache__/f90mod_rules.cpython-310.pyc,, +numpy/f2py/__pycache__/func2subr.cpython-310.pyc,, +numpy/f2py/__pycache__/rules.cpython-310.pyc,, +numpy/f2py/__pycache__/setup.cpython-310.pyc,, +numpy/f2py/__pycache__/symbolic.cpython-310.pyc,, +numpy/f2py/__pycache__/use_rules.cpython-310.pyc,, +numpy/f2py/__version__.py,sha256=7HHdjR82FCBmftwMRyrlhcEj-8mGQb6oCH-wlUPH4Nw,34 +numpy/f2py/_backends/__init__.py,sha256=7_bA7c_xDpLc4_8vPfH32-Lxn9fcUTgjQ25srdvwvAM,299 +numpy/f2py/_backends/__pycache__/__init__.cpython-310.pyc,, +numpy/f2py/_backends/__pycache__/_backend.cpython-310.pyc,, +numpy/f2py/_backends/__pycache__/_distutils.cpython-310.pyc,, +numpy/f2py/_backends/__pycache__/_meson.cpython-310.pyc,, +numpy/f2py/_backends/_backend.py,sha256=GKb9-UaFszT045vUgVukPs1n97iyyjqahrWKxLOKNYo,1187 +numpy/f2py/_backends/_distutils.py,sha256=pxh2YURFYYSykIOvBFwVvhoNX1oSk-c30IPPhzlko-0,2383 +numpy/f2py/_backends/_meson.py,sha256=gi-nbnPFDC38sumfAjg-Q5FPu6nNkyQXTjEuVf9W9Cc,6916 +numpy/f2py/_backends/meson.build.template,sha256=oTPNMAQzS4CJ_lfEzYv-oBeJTtQuThUYVN5R6ROWpNU,1579 +numpy/f2py/_isocbind.py,sha256=zaBgpfPNRmxVG3doUIlbZIiyB990MsXiwDabrSj9HnQ,2360 +numpy/f2py/_src_pyf.py,sha256=4t6TN4ZKWciC4f1z6fwaGrpIGhHKRiwHfcrNj4FIzCg,7654 +numpy/f2py/auxfuncs.py,sha256=dNs4b2KDIcG4M1hPBvD09-Vh7CDzlPIrFscOdvL3p1o,26539 +numpy/f2py/capi_maps.py,sha256=ENjYyeZ3CCJcLwJJgmKOSYrD1KPuhpwauXqeizdV55o,30563 +numpy/f2py/cb_rules.py,sha256=5TuHbJWGjsF6yVNzKuV2tAnwdLyhcWlmdsjYlDOZOv4,24992 +numpy/f2py/cfuncs.py,sha256=KJyW7mdjmFSmxssfeegGJs5NZyF3mZMgNvOxN9-vYHQ,51913 +numpy/f2py/common_rules.py,sha256=gHB76WypbkVmhaD_RWhy8Od4zDTgj8cbDOdUdIp6PIQ,5131 +numpy/f2py/crackfortran.py,sha256=ErLdkWP8MxeyW5vVPGXwyvrxZAwymlvIBC0th2rvK74,148553 +numpy/f2py/diagnose.py,sha256=0SRXBE2hJgKJN_Rf4Zn00oKXC_Tka3efPWM47zg6BoY,5197 +numpy/f2py/f2py2e.py,sha256=5t093ZQ4xs0_0UbyaYVd2yA2EVOaOAcuU29JI-IU2Ag,27717 +numpy/f2py/f90mod_rules.py,sha256=otm3_dmVIna0eBVHLu_693s3a_82lU3pqeqDacWI37s,9594 +numpy/f2py/func2subr.py,sha256=6d2R5awuHRT4xzgfUfwS7JHTqhhAieSXcENlssD_2c4,10298 +numpy/f2py/rules.py,sha256=B4FxSYEfZ_1j_z9GulQNZ1BNrPrUvlU3ybxwTkrIxjI,62727 +numpy/f2py/setup.cfg,sha256=Fpn4sjqTl5OT5sp8haqKIRnUcTPZNM6MIvUJBU7BIhg,48 +numpy/f2py/setup.py,sha256=MmAVspT8DDTqDuL8ZJhxK62g0lcso4vqI6QNQ9CsfoQ,2422 +numpy/f2py/src/fortranobject.c,sha256=g4BKDO1_9pCu6hithKXD2oH_Mt-HH1NTnP6leCqJrzc,46017 +numpy/f2py/src/fortranobject.h,sha256=neMKotYWbHvrhW9KXz4QzQ8fzPkiQXLHHjy82vLSeog,5835 +numpy/f2py/symbolic.py,sha256=jWBoAwECCxRdWczR9r7O6UERcYmH_GbdcAReNp7cmJY,53270 +numpy/f2py/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/f2py/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_abstract_interface.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_array_from_pyobj.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_assumed_shape.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_block_docstring.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_callback.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_character.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_common.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_compile_function.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_crackfortran.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_data.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_docs.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_f2cmap.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_f2py2e.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_isoc.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_kind.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_mixed.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_module_doc.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_parameter.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_pyf_src.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_quoted_character.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_regression.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_return_character.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_return_complex.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_return_integer.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_return_logical.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_return_real.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_semicolon_split.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_size.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_string.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_symbolic.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/test_value_attrspec.cpython-310.pyc,, +numpy/f2py/tests/__pycache__/util.cpython-310.pyc,, +numpy/f2py/tests/src/abstract_interface/foo.f90,sha256=JFU2w98cB_XNwfrqNtI0yDTmpEdxYO_UEl2pgI_rnt8,658 +numpy/f2py/tests/src/abstract_interface/gh18403_mod.f90,sha256=gvQJIzNtvacWE0dhysxn30-iUeI65Hpq7DiE9oRauz8,105 +numpy/f2py/tests/src/array_from_pyobj/wrapmodule.c,sha256=Ff5wHYV9-OJnZuelfFWcjAibRvDkEIlbTVczTyv6TG8,7299 +numpy/f2py/tests/src/assumed_shape/.f2py_f2cmap,sha256=But9r9m4iL7EGq_haMW8IiQ4VivH0TgUozxX4pPvdpE,29 +numpy/f2py/tests/src/assumed_shape/foo_free.f90,sha256=oBwbGSlbr9MkFyhVO2aldjc01dr9GHrMrSiRQek8U64,460 +numpy/f2py/tests/src/assumed_shape/foo_mod.f90,sha256=rfzw3QdI-eaDSl-hslCgGpd5tHftJOVhXvb21Y9Gf6M,499 +numpy/f2py/tests/src/assumed_shape/foo_use.f90,sha256=rmT9k4jP9Ru1PLcGqepw9Jc6P9XNXM0axY7o4hi9lUw,269 +numpy/f2py/tests/src/assumed_shape/precision.f90,sha256=r08JeTVmTTExA-hYZ6HzaxVwBn1GMbPAuuwBhBDtJUk,130 +numpy/f2py/tests/src/block_docstring/foo.f,sha256=y7lPCPu7_Fhs_Tf2hfdpDQo1bhtvNSKRaZAOpM_l3dg,97 +numpy/f2py/tests/src/callback/foo.f,sha256=C1hjfpRCQWiOVVzIHqnsYcnLrqQcixrnHCn8hd9GhVk,1254 +numpy/f2py/tests/src/callback/gh17797.f90,sha256=_Nrl0a2HgUbtymGU0twaJ--7rMa1Uco2A3swbWvHoMo,148 +numpy/f2py/tests/src/callback/gh18335.f90,sha256=NraOyKIXyvv_Y-3xGnmTjtNjW2Znsnlk8AViI8zfovc,506 +numpy/f2py/tests/src/callback/gh25211.f,sha256=a2sxlQhtDVbYn8KOKHUYqwc-aCFt7sDPSnJsXFG35uI,179 +numpy/f2py/tests/src/callback/gh25211.pyf,sha256=FWxo0JWQlw519BpZV8PoYeI_FZ_K6C-3Wk6gLrfBPlw,447 +numpy/f2py/tests/src/cli/gh_22819.pyf,sha256=5rvOfCv-wSosB354LC9pExJmMoSHnbGZGl_rtA2fogA,142 +numpy/f2py/tests/src/cli/hi77.f,sha256=ttyI6vAP3qLnDqy82V04XmoqrXNM6uhMvvLri2p0dq0,71 +numpy/f2py/tests/src/cli/hiworld.f90,sha256=QWOLPrTxYQu1yrEtyQMbM0fE9M2RmXe7c185KnD5x3o,51 +numpy/f2py/tests/src/common/block.f,sha256=GQ0Pd-VMX3H3a-__f2SuosSdwNXHpBqoGnQDjf8aG9g,224 +numpy/f2py/tests/src/common/gh19161.f90,sha256=BUejyhqpNVfHZHQ-QC7o7ZSo7lQ6YHyX08lSmQqs6YM,193 +numpy/f2py/tests/src/crackfortran/accesstype.f90,sha256=-5Din7YlY1TU7tUHD2p-_DSTxGBpDsWYNeT9WOwGhno,208 +numpy/f2py/tests/src/crackfortran/data_common.f,sha256=ZSUAh3uhn9CCF-cYqK5TNmosBGPfsuHBIEfudgysun4,193 +numpy/f2py/tests/src/crackfortran/data_multiplier.f,sha256=jYrJKZWF_59JF9EMOSALUjn0UupWvp1teuGpcL5s1Sc,197 +numpy/f2py/tests/src/crackfortran/data_stmts.f90,sha256=19YO7OGj0IksyBlmMLZGRBQLjoE3erfkR4tFvhznvvE,693 +numpy/f2py/tests/src/crackfortran/data_with_comments.f,sha256=hoyXw330VHh8duMVmAQZjr1lgLVF4zFCIuEaUIrupv0,175 +numpy/f2py/tests/src/crackfortran/foo_deps.f90,sha256=CaH7mnWTG7FcnJe2vXN_0zDbMadw6NCqK-JJ2HmDjK8,128 +numpy/f2py/tests/src/crackfortran/gh15035.f,sha256=jJly1AzF5L9VxbVQ0vr-sf4LaUo4eQzJguhuemFxnvg,375 +numpy/f2py/tests/src/crackfortran/gh17859.f,sha256=7K5dtOXGuBDAENPNCt-tAGJqTfNKz5OsqVSk16_e7Es,340 +numpy/f2py/tests/src/crackfortran/gh22648.pyf,sha256=qZHPRNQljIeYNwbqPLxREnOrSdVV14f3fnaHqB1M7c0,241 +numpy/f2py/tests/src/crackfortran/gh23533.f,sha256=w3tr_KcY3s7oSWGDmjfMHv5h0RYVGUpyXquNdNFOJQg,126 +numpy/f2py/tests/src/crackfortran/gh23598.f90,sha256=41W6Ire-5wjJTTg6oAo7O1WZfd1Ug9vvNtNgHS5MhEU,101 +numpy/f2py/tests/src/crackfortran/gh23598Warn.f90,sha256=1v-hMCT_K7prhhamoM20nMU9zILam84Hr-imck_dYYk,205 +numpy/f2py/tests/src/crackfortran/gh23879.f90,sha256=LWDJTYR3t9h1IsrKC8dVXZlBfWX7clLeU006X6Ow8oI,332 +numpy/f2py/tests/src/crackfortran/gh2848.f90,sha256=gPNasx98SIf7Z9ibk_DHiGKCvl7ERtsfoGXiFDT7FbM,282 +numpy/f2py/tests/src/crackfortran/operators.f90,sha256=-Fc-qjW1wBr3Dkvdd5dMTrt0hnjnV-1AYo-NFWcwFSo,1184 +numpy/f2py/tests/src/crackfortran/privatemod.f90,sha256=7bubZGMIn7iD31wDkjF1TlXCUM7naCIK69M9d0e3y-U,174 +numpy/f2py/tests/src/crackfortran/publicmod.f90,sha256=Pnwyf56Qd6W3FUH-ZMgnXEYkb7gn18ptNTdwmGan0Jo,167 +numpy/f2py/tests/src/crackfortran/pubprivmod.f90,sha256=eYpJwBYLKGOxVbKgEqfny1znib-b7uYhxcRXIf7uwXg,165 +numpy/f2py/tests/src/crackfortran/unicode_comment.f90,sha256=aINLh6GlfTwFewxvDoqnMqwuCNb4XAqi5Nj5vXguXYs,98 +numpy/f2py/tests/src/f2cmap/.f2py_f2cmap,sha256=iUOtfHd3OuT1Rz2-yiSgt4uPKGvCt5AzQ1iygJt_yjg,82 +numpy/f2py/tests/src/f2cmap/isoFortranEnvMap.f90,sha256=iJCD8a8MUTmuPuedbcmxW54Nr4alYuLhksBe1sHS4K0,298 +numpy/f2py/tests/src/isocintrin/isoCtests.f90,sha256=jcw-fzrFh0w5U66uJYfeUW4gv94L5MnWQ_NpsV9y0oI,998 +numpy/f2py/tests/src/kind/foo.f90,sha256=zIHpw1KdkWbTzbXb73hPbCg4N2Htj3XL8DIwM7seXpo,347 +numpy/f2py/tests/src/mixed/foo.f,sha256=90zmbSHloY1XQYcPb8B5d9bv9mCZx8Z8AMTtgDwJDz8,85 +numpy/f2py/tests/src/mixed/foo_fixed.f90,sha256=pxKuPzxF3Kn5khyFq9ayCsQiolxB3SaNtcWaK5j6Rv4,179 +numpy/f2py/tests/src/mixed/foo_free.f90,sha256=fIQ71wrBc00JUAVUj_r3QF9SdeNniBiMw6Ly7CGgPWU,139 +numpy/f2py/tests/src/module_data/mod.mod,sha256=EkjrU7NTZrOH68yKrz6C_eyJMSFSxGgC2yMQT9Zscek,412 +numpy/f2py/tests/src/module_data/module_data_docstring.f90,sha256=tDZ3fUlazLL8ThJm3VwNGJ75QIlLcW70NnMFv-JA4W0,224 +numpy/f2py/tests/src/negative_bounds/issue_20853.f90,sha256=fdOPhRi7ipygwYCXcda7p_dlrws5Hd2GlpF9EZ-qnck,157 +numpy/f2py/tests/src/parameter/constant_both.f90,sha256=-bBf2eqHb-uFxgo6Q7iAtVUUQzrGFqzhHDNaxwSICfQ,1939 +numpy/f2py/tests/src/parameter/constant_compound.f90,sha256=re7pfzcuaquiOia53UT7qNNrTYu2euGKOF4IhoLmT6g,469 +numpy/f2py/tests/src/parameter/constant_integer.f90,sha256=nEmMLitKoSAG7gBBEQLWumogN-KS3DBZOAZJWcSDnFw,612 +numpy/f2py/tests/src/parameter/constant_non_compound.f90,sha256=IcxESVLKJUZ1k9uYKoSb8Hfm9-O_4rVnlkiUU2diy8Q,609 +numpy/f2py/tests/src/parameter/constant_real.f90,sha256=quNbDsM1Ts2rN4WtPO67S9Xi_8l2cXabWRO00CPQSSQ,610 +numpy/f2py/tests/src/quoted_character/foo.f,sha256=WjC9D9171fe2f7rkUAZUvik9bkIf9adByfRGzh6V0cM,482 +numpy/f2py/tests/src/regression/gh25337/data.f90,sha256=9Uz8CHB9i3_mjC3cTOmkTgPAF5tWSwYacG3MUrU-SY0,180 +numpy/f2py/tests/src/regression/gh25337/use_data.f90,sha256=WATiDGAoCKnGgMzm_iMgmfVU0UKOQlk5Fm0iXCmPAkE,179 +numpy/f2py/tests/src/regression/inout.f90,sha256=CpHpgMrf0bqA1W3Ozo3vInDz0RP904S7LkpdAH6ODck,277 +numpy/f2py/tests/src/return_character/foo77.f,sha256=WzDNF3d_hUDSSZjtxd3DtE-bSx1ilOMEviGyYHbcFgM,980 +numpy/f2py/tests/src/return_character/foo90.f90,sha256=ULcETDEt7gXHRzmsMhPsGG4o3lGrcx-FEFaJsPGFKyA,1248 +numpy/f2py/tests/src/return_complex/foo77.f,sha256=8ECRJkfX82oFvGWKbIrCvKjf5QQQClx4sSEvsbkB6A8,973 +numpy/f2py/tests/src/return_complex/foo90.f90,sha256=c1BnrtWwL2dkrTr7wvlEqNDg59SeNMo3gyJuGdRwcDw,1238 +numpy/f2py/tests/src/return_integer/foo77.f,sha256=_8k1evlzBwvgZ047ofpdcbwKdF8Bm3eQ7VYl2Y8b5kA,1178 +numpy/f2py/tests/src/return_integer/foo90.f90,sha256=bzxbYtofivGRYH35Ang9ScnbNsVERN8-6ub5-eI-LGQ,1531 +numpy/f2py/tests/src/return_logical/foo77.f,sha256=FxiF_X0HkyXHzJM2rLyTubZJu4JB-ObLnVqfZwAQFl8,1188 +numpy/f2py/tests/src/return_logical/foo90.f90,sha256=9KmCe7yJYpi4ftkKOM3BCDnPOdBPTbUNrKxY3p37O14,1531 +numpy/f2py/tests/src/return_real/foo77.f,sha256=ZTrzb6oDrIDPlrVWP3Bmtkbz3ffHaaSQoXkfTGtCuFE,933 +numpy/f2py/tests/src/return_real/foo90.f90,sha256=gZuH5lj2lG6gqHlH766KQ3J4-Ero-G4WpOOo2MG3ohU,1194 +numpy/f2py/tests/src/size/foo.f90,sha256=IlFAQazwBRr3zyT7v36-tV0-fXtB1d7WFp6S1JVMstg,815 +numpy/f2py/tests/src/string/char.f90,sha256=ihr_BH9lY7eXcQpHHDQhFoKcbu7VMOX5QP2Tlr7xlaM,618 +numpy/f2py/tests/src/string/fixed_string.f90,sha256=5n6IkuASFKgYICXY9foCVoqndfAY0AQZFEK8L8ARBGM,695 +numpy/f2py/tests/src/string/gh24008.f,sha256=UA8Pr-_yplfOFmc6m4v9ryFQ8W9OulaglulefkFWD68,217 +numpy/f2py/tests/src/string/gh24662.f90,sha256=-Tp9Kd1avvM7AIr8ZukFA9RVr-wusziAnE8AvG9QQI4,197 +numpy/f2py/tests/src/string/gh25286.f90,sha256=2EpxvC-0_dA58MBfGQcLyHzpZgKcMf_W9c73C_Mqnok,304 +numpy/f2py/tests/src/string/gh25286.pyf,sha256=GjgWKh1fHNdPGRiX5ek60i1XSeZsfFalydWqjISPVV8,381 +numpy/f2py/tests/src/string/gh25286_bc.pyf,sha256=6Y9zU66NfcGhTXlFOdFjCSMSwKXpq5ZfAe3FwpkAsm4,384 +numpy/f2py/tests/src/string/scalar_string.f90,sha256=ACxV2i6iPDk-a6L_Bs4jryVKYJMEGUTitEIYTjbJes4,176 +numpy/f2py/tests/src/string/string.f,sha256=shr3fLVZaa6SyUJFYIF1OZuhff8v5lCwsVNBU2B-3pk,248 +numpy/f2py/tests/src/value_attrspec/gh21665.f90,sha256=JC0FfVXsnB2lZHb-nGbySnxv_9VHAyD0mKaLDowczFU,190 +numpy/f2py/tests/test_abstract_interface.py,sha256=C8-ly0_TqkmpQNZmwPHwo2IV2MBH0jQEjAhpqHrg8Y4,832 +numpy/f2py/tests/test_array_from_pyobj.py,sha256=Txff89VUeEhWqUCRVybIqsqH4YQvpk4Uyjmh_XjyMi0,24049 +numpy/f2py/tests/test_assumed_shape.py,sha256=FeaqtrWyBf5uyArcmI0D2e_f763aSMpgU3QmdDXe-tA,1466 +numpy/f2py/tests/test_block_docstring.py,sha256=SEpuq73T9oVtHhRVilFf1xF7nb683d4-Kv7V0kfL4AA,564 +numpy/f2py/tests/test_callback.py,sha256=cReSlVjgnoT74wmtNn-oEIZiJUTfRX7ljjlqJi716IQ,6494 +numpy/f2py/tests/test_character.py,sha256=3ugjM1liymMRbY8wub1eiap-jdyNYVHxlNZBqNoRLe4,21868 +numpy/f2py/tests/test_common.py,sha256=m7TTSJt5zUZKJF-MQUeTtCyxW7YwRBSETINXGPFu8S4,896 +numpy/f2py/tests/test_compile_function.py,sha256=9d_FZ8P2wbIlQ2qPDRrsFqPb4nMH8tiWqYZN-P_shCs,4186 +numpy/f2py/tests/test_crackfortran.py,sha256=y1x3U-jlQWD5rmTXz1I2RlTz7LEfbI6qxCDkR5fzPwY,13441 +numpy/f2py/tests/test_data.py,sha256=HFcmPYbiveKa-swJ8x8XlRR9sM0ESB9FEN-txZnHTok,2876 +numpy/f2py/tests/test_docs.py,sha256=jqtuHE5ZjxP4D8Of3Fkzz36F8_0qKbeS040_m0ac4v4,1662 +numpy/f2py/tests/test_f2cmap.py,sha256=p-Sylbr3ctdKT3UQV9FzpCuYPH5U7Vyn8weXFAjiI9o,391 +numpy/f2py/tests/test_f2py2e.py,sha256=eoswH-daMEBlueoVpxXrDloahCpr0RLzHbr3zBHOsjk,25423 +numpy/f2py/tests/test_isoc.py,sha256=_nPTPxNEEagiKriZBeFNesOattIlHDzaNKmj35xxDBY,1406 +numpy/f2py/tests/test_kind.py,sha256=aOMQSBoD_dw49acKN25_abEvQBLI27DsnWIb9CNpSAE,1671 +numpy/f2py/tests/test_mixed.py,sha256=Ctuw-H7DxhPjSt7wZdJ2xffawIoEBCPWc5F7PSkY4HY,848 +numpy/f2py/tests/test_module_doc.py,sha256=sjCXWIKrqMD1NQ1DUAzgQqkjS5w9h9gvM_Lj29Rdcrg,863 +numpy/f2py/tests/test_parameter.py,sha256=ADI7EV_CM4ztICpqHqeq8LI-WdB6cX0ttatdRdjbsUA,3941 +numpy/f2py/tests/test_pyf_src.py,sha256=eD0bZu_GWfoCq--wWqEKRf-F2h5AwoTyO6GMA9wJPr4,1135 +numpy/f2py/tests/test_quoted_character.py,sha256=cpjMdrHwimnkoJkXd_W_FSlh43oWytY5VHySW9oskO4,454 +numpy/f2py/tests/test_regression.py,sha256=v_6RDQr6IcMmbCMElfzRSLPgZhHnH5l99uztrbJAzqE,2532 +numpy/f2py/tests/test_return_character.py,sha256=18HJtiRwQ7a_2mdPUonD5forKWZJEapD-Vi1DsbTjVs,1493 +numpy/f2py/tests/test_return_complex.py,sha256=BZIIqQ1abdiPLgVmu03_q37yCtND0ijxGSMhGz2Wf-o,2397 +numpy/f2py/tests/test_return_integer.py,sha256=t--9UsdLF9flLTQv7a0KTSVoBuoDtTnmOG2QIFPINVc,1758 +numpy/f2py/tests/test_return_logical.py,sha256=XCmp8E8I6BOeNYF59HjSFAdv1hM9WaDvl8UDS10_05o,2017 +numpy/f2py/tests/test_return_real.py,sha256=ATek5AM7dCCPeIvoMOQIt5yFNFzKrFb1Kno8B4M0rn4,3235 +numpy/f2py/tests/test_semicolon_split.py,sha256=_Mdsi84lES18pPjl9J-QsbGttV4tPFFjZvJvejNcqPc,1635 +numpy/f2py/tests/test_size.py,sha256=q6YqQvcyqdXJeWbGijTiCbxyEG3EkPcvT8AlAW6RCMo,1164 +numpy/f2py/tests/test_string.py,sha256=5xZOfdReoHnId0950XfmtfduPPfBbtMkzBoXMtygvMk,2962 +numpy/f2py/tests/test_symbolic.py,sha256=28quk2kTKfWhKe56n4vINJ8G9weKBfc7HysMlE9J3_g,18341 +numpy/f2py/tests/test_value_attrspec.py,sha256=rWwJBfE2qGzqilZZurJ-7ucNoJDICye6lLetQSLFees,323 +numpy/f2py/tests/util.py,sha256=bEhG699c4bLVPR2WR8fV67avgX6kH5I74SicGb7Z7T4,11167 +numpy/f2py/use_rules.py,sha256=3pTDOPur6gbPHPtwuMJPQvpnUMw39Law1KFSH0coB_0,3527 +numpy/fft/__init__.py,sha256=HqjmF6s_dh0Ri4UZzUDtOKbNUyfAfJAWew3e3EL_KUk,8175 +numpy/fft/__init__.pyi,sha256=vD9Xzz5r13caF4AVL87Y4U9KOj9ic25Vci_wb3dmgpk,550 +numpy/fft/__pycache__/__init__.cpython-310.pyc,, +numpy/fft/__pycache__/_pocketfft.cpython-310.pyc,, +numpy/fft/__pycache__/helper.cpython-310.pyc,, +numpy/fft/_pocketfft.py,sha256=Xkm8wcP4JyBNMbp0ZoHIWhNDlgliX24RzrDuo29uRks,52897 +numpy/fft/_pocketfft.pyi,sha256=S6-ylUuHbgm8vNbh7tLru6K2R5SJzE81BC_Sllm6QrQ,2371 +numpy/fft/_pocketfft_internal.cpython-310-x86_64-linux-gnu.so,sha256=XzaFDzxI6s8RqdHjtZV6NrK33T630h9owQF8-rT9AAY,97008 +numpy/fft/helper.py,sha256=aNj1AcLvtfoX26RiLOwcR-k2QSMuBZkGj2Fu0CeFPJs,6154 +numpy/fft/helper.pyi,sha256=NLTEjy2Gz1aAMDZwCgssIyUne0ubjJqukfYkpsL3gXM,1176 +numpy/fft/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/fft/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/fft/tests/__pycache__/test_helper.cpython-310.pyc,, +numpy/fft/tests/__pycache__/test_pocketfft.cpython-310.pyc,, +numpy/fft/tests/test_helper.py,sha256=whgeaQ8PzFf3B1wkbXobGZ5sF4WxPp4gf1UPUVZest8,6148 +numpy/fft/tests/test_pocketfft.py,sha256=RdeCCvUQmJYVvccOJwToobTKDg9yzUL06o9MkPmRfmI,12895 +numpy/lib/__init__.py,sha256=XMPNJkG_mQ__xuvbf0OcpotgMbA9owt10ZHYVnYHq8E,2713 +numpy/lib/__init__.pyi,sha256=y5ANokFm7EkrlNoHdeQm1FsUhLFxkYtLuanCbsWrGio,5596 +numpy/lib/__pycache__/__init__.cpython-310.pyc,, +numpy/lib/__pycache__/_datasource.cpython-310.pyc,, +numpy/lib/__pycache__/_iotools.cpython-310.pyc,, +numpy/lib/__pycache__/_version.cpython-310.pyc,, +numpy/lib/__pycache__/arraypad.cpython-310.pyc,, +numpy/lib/__pycache__/arraysetops.cpython-310.pyc,, +numpy/lib/__pycache__/arrayterator.cpython-310.pyc,, +numpy/lib/__pycache__/format.cpython-310.pyc,, +numpy/lib/__pycache__/function_base.cpython-310.pyc,, +numpy/lib/__pycache__/histograms.cpython-310.pyc,, +numpy/lib/__pycache__/index_tricks.cpython-310.pyc,, +numpy/lib/__pycache__/mixins.cpython-310.pyc,, +numpy/lib/__pycache__/nanfunctions.cpython-310.pyc,, +numpy/lib/__pycache__/npyio.cpython-310.pyc,, +numpy/lib/__pycache__/polynomial.cpython-310.pyc,, +numpy/lib/__pycache__/recfunctions.cpython-310.pyc,, +numpy/lib/__pycache__/scimath.cpython-310.pyc,, +numpy/lib/__pycache__/setup.cpython-310.pyc,, +numpy/lib/__pycache__/shape_base.cpython-310.pyc,, +numpy/lib/__pycache__/stride_tricks.cpython-310.pyc,, +numpy/lib/__pycache__/twodim_base.cpython-310.pyc,, +numpy/lib/__pycache__/type_check.cpython-310.pyc,, +numpy/lib/__pycache__/ufunclike.cpython-310.pyc,, +numpy/lib/__pycache__/user_array.cpython-310.pyc,, +numpy/lib/__pycache__/utils.cpython-310.pyc,, +numpy/lib/_datasource.py,sha256=CDF3im6IxdY3Mu6fwRQmkSEBmXS3kQVInQ4plXsoX9c,22631 +numpy/lib/_iotools.py,sha256=Yg9HCfPg4tbhbdgLPcxSMiZXq1xDprvJKLebLwhDszY,30868 +numpy/lib/_version.py,sha256=6vK7czNSB_KrWx2rZJzJ1pyOc73Q07hAgfLB5ItUCnU,4855 +numpy/lib/_version.pyi,sha256=B572hyWrUWG-TAAAXrNNAT4AgyUAmJ4lvgpwMkDzunk,633 +numpy/lib/arraypad.py,sha256=bKP7ZS9NYFYzqSk8OnpFLFrMsua4m_hcqFsi7cGkrJE,31803 +numpy/lib/arraypad.pyi,sha256=ADXphtAORYl3EqvE5qs_u32B_TALKSOtF43jOLmoxRw,1728 +numpy/lib/arraysetops.py,sha256=GJ2RhkzIJmIbwyG6h3LOFTPXg62kM9tcV1a-7tdbVuU,33655 +numpy/lib/arraysetops.pyi,sha256=6X-5l5Yss_9y10LYyIsDLbGX77vt7PtVLDqxOlSRPfY,8372 +numpy/lib/arrayterator.py,sha256=BQ97S00zvfURUZfes0GZo-5hydYNRuvwX1I1bLzeRik,7063 +numpy/lib/arrayterator.pyi,sha256=f7Pwp83_6DiMYmJGUsffncM-FRAynB1iYGvhmHM_SZE,1537 +numpy/lib/format.py,sha256=T8qJMyG2DDVjjYNNpUvBgfA9tCo23IS0w9byRB6twwQ,34769 +numpy/lib/format.pyi,sha256=YWBxC3GdsZ7SKBN8I7nMwWeVuFD1aT9d-VJ8zE4-P-o,748 +numpy/lib/function_base.py,sha256=IhhgfSmYJE-dHoUOMXHPiGYXso-NdXPpLXF9y0gEA6I,189172 +numpy/lib/function_base.pyi,sha256=KWaC5UOBANU4hiIoN2eptE4HYsm4vgp_8BMFV1Y3JX4,16585 +numpy/lib/histograms.py,sha256=xsj_qpaZoI2Bv1FBpY8mIMPJrYRiuIBszn_6kO7YFRA,37778 +numpy/lib/histograms.pyi,sha256=hNwR2xYWkgJCP-nfRGxc-EgHLTD3qm4zmWXthZLt08M,995 +numpy/lib/index_tricks.py,sha256=4PEvXk6VFTkttMViYBVC4yDhyOiKIon6JpIm0d_CmNg,31346 +numpy/lib/index_tricks.pyi,sha256=D2nkNXOB9Vea1PfMaTn94OGBGayjTaQ-bKMsjDmYpak,4251 +numpy/lib/mixins.py,sha256=y6_MzQuiNjv-1EFVROqv2y2cAJi5X4rQYzbZCyUyXgw,7071 +numpy/lib/mixins.pyi,sha256=h9N1kbZsUntF0zjOxPYeD_rCB2dMiG35TYYPl9ymkI4,3117 +numpy/lib/nanfunctions.py,sha256=6EjzydZlugIzfiENKtC4ycZ2Nckt8ZQg5v6D6tX1SiU,65775 +numpy/lib/nanfunctions.pyi,sha256=oPqAfCinmBL85Ji7ko4QlzAzLAK9nZL0t2_CllEbCEU,606 +numpy/lib/npyio.py,sha256=NUjtFvAmPdTjwJQ-ia-xbCr849M_M6NilP5IHfkKaRg,97316 +numpy/lib/npyio.pyi,sha256=SUFWJh90vWZCdd6GCSGbfYeXKlWut0XY_SHvZJc8yqY,9728 +numpy/lib/polynomial.py,sha256=6Aw3_2vdbh4urERQ6NaPhf9a_T1o1o6cjm3fb5Z3_YE,44133 +numpy/lib/polynomial.pyi,sha256=GerIpQnf5LdtFMOy9AxhOTqUyfn57k4MxqEYrfdckWE,6958 +numpy/lib/recfunctions.py,sha256=-90AbWWvVFOqVUPLh9K9NYdKUHYIgSEyg2Y35MnOVUA,59423 +numpy/lib/scimath.py,sha256=T4ITysZgqhY1J8IxyXCtioHjMTg2ci-4i3mr9TBF2UA,15037 +numpy/lib/scimath.pyi,sha256=E2roKJzMFwWSyhLu8UPUr54WOpxF8jp_pyXYBgsUSQ8,2883 +numpy/lib/setup.py,sha256=0K5NJKuvKvNEWp-EX7j0ODi3ZQQgIMHobzSFJq3G7yM,405 +numpy/lib/shape_base.py,sha256=AhCO9DEyysE-P-QJF9ryUtJ1ghU4_0mORhAJ59poObU,38947 +numpy/lib/shape_base.pyi,sha256=bGJhLA_RvUpVTiDFgCV-1rUjV8e1qCh0gK_3PLgXA_U,5341 +numpy/lib/stride_tricks.py,sha256=brY5b-0YQJuIH2CavfpIinMolyTUv5k9DUvLoZ-imis,17911 +numpy/lib/stride_tricks.pyi,sha256=0pQ4DP9l6g21q2Ajv6dJFRWMr9auPGTNV9BmZUbogPY,1747 +numpy/lib/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/lib/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test__datasource.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test__iotools.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test__version.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_arraypad.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_arraysetops.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_arrayterator.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_financial_expired.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_format.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_function_base.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_histograms.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_index_tricks.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_io.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_loadtxt.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_mixins.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_nanfunctions.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_packbits.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_polynomial.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_recfunctions.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_regression.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_shape_base.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_stride_tricks.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_twodim_base.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_type_check.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_ufunclike.cpython-310.pyc,, +numpy/lib/tests/__pycache__/test_utils.cpython-310.pyc,, +numpy/lib/tests/data/py2-objarr.npy,sha256=F4cyUC-_TB9QSFLAo2c7c44rC6NUYIgrfGx9PqWPSKk,258 +numpy/lib/tests/data/py2-objarr.npz,sha256=xo13HBT0FbFZ2qvZz0LWGDb3SuQASSaXh7rKfVcJjx4,366 +numpy/lib/tests/data/py3-objarr.npy,sha256=pTTVh8ezp-lwAK3fkgvdKU8Arp5NMKznVD-M6Ex_uA0,341 +numpy/lib/tests/data/py3-objarr.npz,sha256=qQR0gS57e9ta16d_vCQjaaKM74gPdlwCPkp55P-qrdw,449 +numpy/lib/tests/data/python3.npy,sha256=X0ad3hAaLGXig9LtSHAo-BgOvLlFfPYMnZuVIxRmj-0,96 +numpy/lib/tests/data/win64python2.npy,sha256=agOcgHVYFJrV-nrRJDbGnUnF4ZTPYXuSeF-Mtg7GMpc,96 +numpy/lib/tests/test__datasource.py,sha256=65KXfUUvp8wXSqgQisuYlkhg-qHjBV5FXYetL8Ba-rc,10571 +numpy/lib/tests/test__iotools.py,sha256=HerCqvDE07JxjFQlWEfpZO7lC9z0Sbr3z20GSutoCPs,13743 +numpy/lib/tests/test__version.py,sha256=aO3YgkAohLsLzCNQ7vjIwdpFUMz0cPLbcuuxIkjuN74,1999 +numpy/lib/tests/test_arraypad.py,sha256=obohHbyM0gPYPUkd7iJSOSiDqyqtJsjDNtQX68NC4lM,54830 +numpy/lib/tests/test_arraysetops.py,sha256=5-T1MVhfIMivat8Z47GZw0ZaR811W_FskM1bAXnFyLU,35912 +numpy/lib/tests/test_arrayterator.py,sha256=AYs2SwV5ankgwnvKI9RSO1jZck118nu3SyZ4ngzZNso,1291 +numpy/lib/tests/test_financial_expired.py,sha256=yq5mqGMvqpkiiw9CuZhJgrYa7Squj1mXr_G-IvAFgwI,247 +numpy/lib/tests/test_format.py,sha256=xV0oi1eoRnVwAAhSOcPFQHQWF7TfsROtDYShQLPtdaA,41028 +numpy/lib/tests/test_function_base.py,sha256=DBKugIUEFTMP7g6iL1bk986E6ldCrcNdBCWOJbQla_Y,157830 +numpy/lib/tests/test_histograms.py,sha256=16_XJp-eFgsuM8B4mDQpQ4w_Ib29Hg0EPO-WFsdaFWA,32815 +numpy/lib/tests/test_index_tricks.py,sha256=Vjz25Y6H_ih0iEE2AG0kaxO9U8PwcXSrofzqnN4XBwI,20256 +numpy/lib/tests/test_io.py,sha256=3Tow1pucrQ7z7osNN4a2grBYUoBGNkQEhjmCjXT6Vag,107891 +numpy/lib/tests/test_loadtxt.py,sha256=gwcDJDJmLJRMLpg322yjQ1IzI505w9EqJoq4DmDPCdI,38560 +numpy/lib/tests/test_mixins.py,sha256=Wivwz3XBWsEozGzrzsyyvL3qAuE14t1BHk2LPm9Z9Zc,7030 +numpy/lib/tests/test_nanfunctions.py,sha256=01r_mmTCvKVdZuOGTEHNDZXrMS724us_jwZANzCd74A,47609 +numpy/lib/tests/test_packbits.py,sha256=OWGAd5g5GG0gl7WHqNfwkZ7G-2rrtLt2sI854PG4nnw,17546 +numpy/lib/tests/test_polynomial.py,sha256=URouxJpr8FQ5hiKybqhtOcLA7e-3hj4kWzjLBROByyA,11395 +numpy/lib/tests/test_recfunctions.py,sha256=6jzouPEQ7Uhtj8_-W5yTI6ymNp2nLgmdHzxdd74jVuM,44001 +numpy/lib/tests/test_regression.py,sha256=KzGFkhTcvEG97mymoOQ2hP2CEr2nPZou0Ztf4-WaXCs,8257 +numpy/lib/tests/test_shape_base.py,sha256=2iQCEFR6evVpF8woaenxUOzooHkfuMYkBaUj8ecyJ-E,26817 +numpy/lib/tests/test_stride_tricks.py,sha256=wprpWWH5eq07DY7rzG0WDv5fMtLxzRQz6fm6TZWlScQ,22849 +numpy/lib/tests/test_twodim_base.py,sha256=ll-72RhqCItIPB97nOWhH7H292h4nVIX_w1toKTPMUg,18841 +numpy/lib/tests/test_type_check.py,sha256=lxCH5aApWVYhhSoDQSLDTCHLVHuK2c-jBbnfnZUrOaA,15114 +numpy/lib/tests/test_ufunclike.py,sha256=4hSnXGlSC8HE-_pRRMzD8-HI4hGHqsAWu1pD0o2kPI0,2982 +numpy/lib/tests/test_utils.py,sha256=RVAxrzSFu6N3C4_jIgAlTDOWF_B7wr2v1Y20dX5upYM,6218 +numpy/lib/twodim_base.py,sha256=Mvzn_PyShIb9m7nJjJ4IetdxwmLYEsCPHvJoK7n2viU,32947 +numpy/lib/twodim_base.pyi,sha256=xFRcEVJdDj4mrXW_6iVP1lTMoJx4QJjYRD3o2_9f2eY,5370 +numpy/lib/type_check.py,sha256=_EOtB296nFYlNT7ztBYoC_yK9aycIb0KTmRjvzVdZNg,19954 +numpy/lib/type_check.pyi,sha256=LPvAvIxU-p5i_Qe-ic7hEvo4OTfSrNpplxMG7OAZe8Q,5571 +numpy/lib/ufunclike.py,sha256=_ceBGbGCMOd3u_h2UVzyaRK6ZY7ryoJ0GJB7zqcJG3w,6325 +numpy/lib/ufunclike.pyi,sha256=hLxcYfQprh1tTY_UO2QscA3Hd9Zd7cVGXIINZLhMFqY,1293 +numpy/lib/user_array.py,sha256=LE958--CMkBI2r3l1SQxmCHdCSw6HY6-RhWCnduzGA4,7721 +numpy/lib/utils.py,sha256=6NdleaELZiqARdj-ECZjxtwLf1bqklOcK43m9yoZefs,37804 +numpy/lib/utils.pyi,sha256=mVHVzWuc2-M3Oz60lFsbok0v8LH_HRHMjZpXwrtzF_c,2360 +numpy/linalg/__init__.py,sha256=mpdlEXWtTvpF7In776ONLwp6RIyo4U_GLPT1L1eIJnw,1813 +numpy/linalg/__init__.pyi,sha256=XBy4ocuypsRVflw_mbSTUhR4N5Roemu6w5SfeVwbkAc,620 +numpy/linalg/__pycache__/__init__.cpython-310.pyc,, +numpy/linalg/__pycache__/linalg.cpython-310.pyc,, +numpy/linalg/_umath_linalg.cpython-310-x86_64-linux-gnu.so,sha256=BYffhO2XUQkIZ6ShRB0sOUw5c1QgqjiLi_aYtyVfJL0,216793 +numpy/linalg/lapack_lite.cpython-310-x86_64-linux-gnu.so,sha256=zWHsImcTCxEkZbyF2rgrclRTDzz5E7poiDOwhOKaVRs,29849 +numpy/linalg/linalg.py,sha256=kDVK1GBxbUjlRgxXCoEfkRJm8yrNr1Iu7hMn2rKK8RE,90923 +numpy/linalg/linalg.pyi,sha256=zD9U5BUCB1uQggSxfZaTGX_uB2Hkp75sttGmZbCGgBI,7505 +numpy/linalg/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/linalg/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/linalg/tests/__pycache__/test_deprecations.cpython-310.pyc,, +numpy/linalg/tests/__pycache__/test_linalg.cpython-310.pyc,, +numpy/linalg/tests/__pycache__/test_regression.cpython-310.pyc,, +numpy/linalg/tests/test_deprecations.py,sha256=9p_SRmtxj2zc1doY9Ie3dyy5JzWy-tCQWFoajcAJUmM,640 +numpy/linalg/tests/test_linalg.py,sha256=rgvmK6Or70u8mN04puetL3FgSxZ8fJrOlI5ptTgCU5k,78085 +numpy/linalg/tests/test_regression.py,sha256=qbugUmrENybkEaM1GhfA01RXQUy8AkzalbrfzSIgUmM,5434 +numpy/ma/API_CHANGES.txt,sha256=F_4jW8X5cYBbzpcwteymkonTmvzgKKY2kGrHF1AtnrI,3405 +numpy/ma/LICENSE,sha256=BfO4g1GYjs-tEKvpLAxQ5YdcZFLVAJoAhMwpFVH_zKY,1593 +numpy/ma/README.rst,sha256=q-gCsZ4Cw_gUGGvEjog556sJUHIm8WTAwkFK5Qnz9XA,9872 +numpy/ma/__init__.py,sha256=dgP0WdnOpph28Fd6UiqoyDKhfrct0H6QWqbCcETsk6M,1404 +numpy/ma/__init__.pyi,sha256=ppCg_TS0POutNB3moJE4kBabWURnc0WGXyYPquXZxS4,6063 +numpy/ma/__pycache__/__init__.cpython-310.pyc,, +numpy/ma/__pycache__/core.cpython-310.pyc,, +numpy/ma/__pycache__/extras.cpython-310.pyc,, +numpy/ma/__pycache__/mrecords.cpython-310.pyc,, +numpy/ma/__pycache__/setup.cpython-310.pyc,, +numpy/ma/__pycache__/testutils.cpython-310.pyc,, +numpy/ma/__pycache__/timer_comparison.cpython-310.pyc,, +numpy/ma/core.py,sha256=4MglVRJtmQ9_iIVaQ2b-_Vmw1TjAhEsMJdtKOhyBFXQ,278213 +numpy/ma/core.pyi,sha256=YfgyuBuKxZ5v4I2JxZDvCLhnztOCRgzTeDg-JGTon_M,14305 +numpy/ma/extras.py,sha256=MC7QPS34PC4wxNbOp7pTy57dqF9B-L6L1KMI6rrfe2w,64383 +numpy/ma/extras.pyi,sha256=BBsiCZbaPpGCY506fkmqZdBkJNCXcglc3wcSBuAACNk,2646 +numpy/ma/mrecords.py,sha256=degd6dLaDEvEWNHmvSnUZXos1csIzaqjR_jAutm8JfI,27232 +numpy/ma/mrecords.pyi,sha256=r1a2I662ywnhGS6zvfcyK-9RHVvb4sHxiCx9Dhf5AE4,1934 +numpy/ma/setup.py,sha256=MqmMicr_xHkAGoG-T7NJ4YdUZIJLO4ZFp6AmEJDlyhw,418 +numpy/ma/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/ma/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/ma/tests/__pycache__/test_core.cpython-310.pyc,, +numpy/ma/tests/__pycache__/test_deprecations.cpython-310.pyc,, +numpy/ma/tests/__pycache__/test_extras.cpython-310.pyc,, +numpy/ma/tests/__pycache__/test_mrecords.cpython-310.pyc,, +numpy/ma/tests/__pycache__/test_old_ma.cpython-310.pyc,, +numpy/ma/tests/__pycache__/test_regression.cpython-310.pyc,, +numpy/ma/tests/__pycache__/test_subclassing.cpython-310.pyc,, +numpy/ma/tests/test_core.py,sha256=xd5S3oa0jObo8jnsJk0-o46d-KNC3RtgNRKinJeY_kE,215100 +numpy/ma/tests/test_deprecations.py,sha256=nq_wFVt2EBHcT3AHxattfKXx2JDf1K5D-QBzUU0_15A,2566 +numpy/ma/tests/test_extras.py,sha256=lX4cbdGDEXaBHzA3q8hJxve4635XCJw4AP7FO7zhOfk,74858 +numpy/ma/tests/test_mrecords.py,sha256=PsJhUlABgdpSsPUeijonfyFNqz5AfNSGQTtJUte7yts,19890 +numpy/ma/tests/test_old_ma.py,sha256=h4BncexBcBigqvZMA6RjDjpHPurWtt99A7KTag2rmOs,32690 +numpy/ma/tests/test_regression.py,sha256=foMpI0luAvwkkRpAfPDV_810h1URISXDZhmaNhxb50k,3287 +numpy/ma/tests/test_subclassing.py,sha256=HeTIE_n1I8atwzF8tpvNtGHp-0dmM8PT8AS4IDWbcso,16967 +numpy/ma/testutils.py,sha256=RQw0RyS7hOSVTk4KrCGleq0VHlnDqzwwaLtuZbRE4_I,10235 +numpy/ma/timer_comparison.py,sha256=pIGSZG-qYYYlRWSTgzPlyCAINbGKhXrZrDZBBjiM080,15658 +numpy/matlib.py,sha256=-54vTuGIgeTMg9ZUmElRPZ4Hr-XZ-om9xLzAsSoTvnc,10465 +numpy/matrixlib/__init__.py,sha256=BHBpQKoQv4EjT0UpWBA-Ck4L5OsMqTI2IuY24p-ucXk,242 +numpy/matrixlib/__init__.pyi,sha256=-t3ZuvbzRuRwWfZOeN4xlNWdm7gQEprhUsWzu8MRvUE,252 +numpy/matrixlib/__pycache__/__init__.cpython-310.pyc,, +numpy/matrixlib/__pycache__/defmatrix.cpython-310.pyc,, +numpy/matrixlib/__pycache__/setup.cpython-310.pyc,, +numpy/matrixlib/defmatrix.py,sha256=JXdJGm1LayOOXfKpp7OVZfb0pzzP4Lwh45sTJrleALc,30656 +numpy/matrixlib/defmatrix.pyi,sha256=lmBMRahKcMOl2PHDo79J67VRAZOkI54BzfDaTLpE0LI,451 +numpy/matrixlib/setup.py,sha256=1r7JRkSM4HyVorgtjoKJGWLcOcPO3wmvivpeEsVtAEg,426 +numpy/matrixlib/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/matrixlib/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/matrixlib/tests/__pycache__/test_defmatrix.cpython-310.pyc,, +numpy/matrixlib/tests/__pycache__/test_interaction.cpython-310.pyc,, +numpy/matrixlib/tests/__pycache__/test_masked_matrix.cpython-310.pyc,, +numpy/matrixlib/tests/__pycache__/test_matrix_linalg.cpython-310.pyc,, +numpy/matrixlib/tests/__pycache__/test_multiarray.cpython-310.pyc,, +numpy/matrixlib/tests/__pycache__/test_numeric.cpython-310.pyc,, +numpy/matrixlib/tests/__pycache__/test_regression.cpython-310.pyc,, +numpy/matrixlib/tests/test_defmatrix.py,sha256=8E_-y7VD2vsq1y8CcI8km37pp5qcAtkciO16xqf2UIs,14982 +numpy/matrixlib/tests/test_interaction.py,sha256=PpjmgjEKighDXvt38labKE6L7f2jP74UEmp3JRb_iOY,11875 +numpy/matrixlib/tests/test_masked_matrix.py,sha256=7YO_LCO8DOhW3CuXJuxH93rnmttfvHnU7El-MBzxzFw,8932 +numpy/matrixlib/tests/test_matrix_linalg.py,sha256=ObbSUXU4R2pWajH__xAdizADrU2kBKDDCxkDV-oVBXc,2059 +numpy/matrixlib/tests/test_multiarray.py,sha256=jB3XCBmAtcqf-Wb9PwBW6uIykPpMPthuXLJ0giTKzZE,554 +numpy/matrixlib/tests/test_numeric.py,sha256=MP70qUwgshTtThKZaZDp7_6U-Z66NIV1geVhasGXejQ,441 +numpy/matrixlib/tests/test_regression.py,sha256=8sHDtO8Zi8p3a1eQKEWxtCmKrXmHoD3qxlIokg2AIAU,927 +numpy/polynomial/__init__.py,sha256=braLh6zP2QwuNKRKAaZGdC_qKWZ-tJlc3BN83LeuE_0,6781 +numpy/polynomial/__init__.pyi,sha256=W8szYtVUy0RUi83jmFLK58BN8CKVSoHA2CW7IcdUl1c,701 +numpy/polynomial/__pycache__/__init__.cpython-310.pyc,, +numpy/polynomial/__pycache__/_polybase.cpython-310.pyc,, +numpy/polynomial/__pycache__/chebyshev.cpython-310.pyc,, +numpy/polynomial/__pycache__/hermite.cpython-310.pyc,, +numpy/polynomial/__pycache__/hermite_e.cpython-310.pyc,, +numpy/polynomial/__pycache__/laguerre.cpython-310.pyc,, +numpy/polynomial/__pycache__/legendre.cpython-310.pyc,, +numpy/polynomial/__pycache__/polynomial.cpython-310.pyc,, +numpy/polynomial/__pycache__/polyutils.cpython-310.pyc,, +numpy/polynomial/__pycache__/setup.cpython-310.pyc,, +numpy/polynomial/_polybase.py,sha256=YEnnQwlTgbn3dyD89ueraUx5nxx3x_pH6K6mmyEmhi8,39271 +numpy/polynomial/_polybase.pyi,sha256=J7yU9PPZW4W8mkqAltDfnL4ZNwljuM-bDEj4DPTJZpY,2321 +numpy/polynomial/chebyshev.py,sha256=NZCKjIblcX99foqZyp51i0_r8p0r1VKVGZFmQ1__kEk,62796 +numpy/polynomial/chebyshev.pyi,sha256=035CNdOas4dnb6lFLzRiBrYT_VnWh2T1-A3ibm_HYkI,1387 +numpy/polynomial/hermite.py,sha256=t5CFM-qE4tszYJiQZ301VcMn7IM67y2rUZPFPtnVRAc,52514 +numpy/polynomial/hermite.pyi,sha256=hdsvTULow8bIjnATudf0i6brpLHV7vbOoHzaMvbjMy0,1217 +numpy/polynomial/hermite_e.py,sha256=jRR3f8Oth8poV2Ix8c0eLEQR3UZary-2RupOrEAEUMY,52642 +numpy/polynomial/hermite_e.pyi,sha256=zV7msb9v9rV0iv_rnD3SjP-TGyc6pd3maCqiPCj3PbA,1238 +numpy/polynomial/laguerre.py,sha256=mcVw0ckWVX-kzJ1QIhdcuuxzPjuFmA3plQLkloQMOYM,50858 +numpy/polynomial/laguerre.pyi,sha256=Gxc9SLISNKMWrKdsVJ9fKFFFwfxxZzfF-Yc-2r__z5M,1178 +numpy/polynomial/legendre.py,sha256=wjtgFajmKEbYkSUk3vWSCveMHDP6UymK28bNUk4Ov0s,51550 +numpy/polynomial/legendre.pyi,sha256=9dmANwkxf7EbOHV3XQBPoaDtc56cCkf75Wo7FG9Zfj4,1178 +numpy/polynomial/polynomial.py,sha256=XsaZPHmLGJFqpJs7rPvO5E0loWQ1L3YHLIUybVu4dU8,49112 +numpy/polynomial/polynomial.pyi,sha256=bOPRnub4xXxsUwNGeiQLTT4PCfN1ysSrf6LBZIcAN2Y,1132 +numpy/polynomial/polyutils.py,sha256=Xy5qjdrjnRaqSlClG1ROmwWccLkAPC7IcHaNJLvhCf4,23237 +numpy/polynomial/polyutils.pyi,sha256=cFAyZ9Xzuw8Huhn9FEz4bhyD00m2Dp-2DiUSyogJwSo,264 +numpy/polynomial/setup.py,sha256=dXQfzVUMP9OcB6iKv5yo1GLEwFB3gJ48phIgo4N-eM0,373 +numpy/polynomial/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/polynomial/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/polynomial/tests/__pycache__/test_chebyshev.cpython-310.pyc,, +numpy/polynomial/tests/__pycache__/test_classes.cpython-310.pyc,, +numpy/polynomial/tests/__pycache__/test_hermite.cpython-310.pyc,, +numpy/polynomial/tests/__pycache__/test_hermite_e.cpython-310.pyc,, +numpy/polynomial/tests/__pycache__/test_laguerre.cpython-310.pyc,, +numpy/polynomial/tests/__pycache__/test_legendre.cpython-310.pyc,, +numpy/polynomial/tests/__pycache__/test_polynomial.cpython-310.pyc,, +numpy/polynomial/tests/__pycache__/test_polyutils.cpython-310.pyc,, +numpy/polynomial/tests/__pycache__/test_printing.cpython-310.pyc,, +numpy/polynomial/tests/__pycache__/test_symbol.cpython-310.pyc,, +numpy/polynomial/tests/test_chebyshev.py,sha256=6tMsFP1h7K8Zf72mNOta6Tv52_fVTlXknseuffj080c,20522 +numpy/polynomial/tests/test_classes.py,sha256=DFyY2IQBj3r2GZkvbRIeZO2EEY466xbuwc4PShAl4Sw,18331 +numpy/polynomial/tests/test_hermite.py,sha256=N9b2dx2UWPyja5v02dSoWYPnKvb6H-Ozgtrx-xjWz2k,18577 +numpy/polynomial/tests/test_hermite_e.py,sha256=_A3ohAWS4HXrQG06S8L47dImdZGTwYosCXnoyw7L45o,18911 +numpy/polynomial/tests/test_laguerre.py,sha256=BZOgs49VBXOFBepHopxuEDkIROHEvFBfWe4X73UZhn8,17511 +numpy/polynomial/tests/test_legendre.py,sha256=b_bblHs0F_BWw9ESuSq52ZsLKcQKFR5eqPf_SppWFqo,18673 +numpy/polynomial/tests/test_polynomial.py,sha256=4cuO8-5wdIxcz5CrucB5Ix7ySuMROokUF12F7ogQ_hc,20529 +numpy/polynomial/tests/test_polyutils.py,sha256=IxkbVfpcBqe5lOZluHFUPbLATLu1rwVg7ghLASpfYrY,3579 +numpy/polynomial/tests/test_printing.py,sha256=rfP4MaQbjGcO52faHmYrgsaarkm3Ndi3onwr6DDuapE,20525 +numpy/polynomial/tests/test_symbol.py,sha256=msTPv7B1niaKujU33kuZmdxJvLYvOjfl1oykmlL0dXo,5371 +numpy/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/random/LICENSE.md,sha256=EDFmtiuARDr7nrNIjgUuoGvgz_VmuQjxmeVh_eSa8Z8,3511 +numpy/random/__init__.pxd,sha256=9JbnX540aJNSothGs-7e23ozhilG6U8tINOUEp08M_k,431 +numpy/random/__init__.py,sha256=81Thnexg5umN5WZwD5TRyzNc2Yp-d14B6UC7NBgVKh8,7506 +numpy/random/__init__.pyi,sha256=RfW8mco48UaWDL1UC5ROv9vXiFZ9EGho62avhgEAHPc,2143 +numpy/random/__pycache__/__init__.cpython-310.pyc,, +numpy/random/__pycache__/_pickle.cpython-310.pyc,, +numpy/random/_bounded_integers.cpython-310-x86_64-linux-gnu.so,sha256=zACYhBZNDJi5rXbYmdgxjLJDbkyqoANLSLZge3Dvpa0,379216 +numpy/random/_bounded_integers.pxd,sha256=hcoucPH5hkFEM2nm12zYO-5O_Rt8RujEXT5YWuAzl1Q,1669 +numpy/random/_common.cpython-310-x86_64-linux-gnu.so,sha256=MaClzR2DAUNmBHlArlpsVGbr4NZRXS5eIxElj3SNFwY,276192 +numpy/random/_common.pxd,sha256=s2_IdIQ0MhNbogamulvXe-b93wbx882onmYkxqswwpo,4939 +numpy/random/_examples/cffi/__pycache__/extending.cpython-310.pyc,, +numpy/random/_examples/cffi/__pycache__/parse.cpython-310.pyc,, +numpy/random/_examples/cffi/extending.py,sha256=xSla3zWqxi6Hj48EvnYfD3WHfE189VvC4XsKu4_T_Iw,880 +numpy/random/_examples/cffi/parse.py,sha256=Bnb7t_6S_c5-3dZrQ-XX9EazOKhftUfcCejXXWyd1EU,1771 +numpy/random/_examples/cython/extending.pyx,sha256=4IE692pq1V53UhPZqQiQGcIHXDoNyqTx62x5a36puVg,2290 +numpy/random/_examples/cython/extending_distributions.pyx,sha256=oazFVWeemfE0eDzax7r7MMHNL1_Yofws2m-c_KT2Hbo,3870 +numpy/random/_examples/cython/meson.build,sha256=rXtugURMEo-ef4bPE1QIv4mzvWbeGjmcTdKCBvjxjtw,1443 +numpy/random/_examples/numba/__pycache__/extending.cpython-310.pyc,, +numpy/random/_examples/numba/__pycache__/extending_distributions.cpython-310.pyc,, +numpy/random/_examples/numba/extending.py,sha256=Ipyzel_h5iU_DMJ_vnXUgQC38uMDMn7adUpWSeEQLFE,1957 +numpy/random/_examples/numba/extending_distributions.py,sha256=Jnr9aWkHyIWygNbdae32GVURK-5T9BTGhuExRpvve98,2034 +numpy/random/_generator.cpython-310-x86_64-linux-gnu.so,sha256=YFpOzoKFPKyS6SDrmoptosTvc55vluPMG_Su1x_hTl8,976328 +numpy/random/_generator.pyi,sha256=zRvo_y6g0pWkE4fO1M9jLYUkxDfGdA6Enreb3U2AADM,22442 +numpy/random/_mt19937.cpython-310-x86_64-linux-gnu.so,sha256=0wvUt9mG_CmAC9WIxw0yIxQ5Mt3IAr3EbXIZf7wJbBE,120224 +numpy/random/_mt19937.pyi,sha256=_iZKaAmuKBQ4itSggfQvYYj_KjktcN4rt-YpE6bqFAM,724 +numpy/random/_pcg64.cpython-310-x86_64-linux-gnu.so,sha256=ZG1KBr34YGbShTmKifH2EGAddV-ad0slxzFtuexATqs,126400 +numpy/random/_pcg64.pyi,sha256=uxr5CbEJetN6lv9vBG21jlRhuzOK8SQnXrwqAQBxj_c,1091 +numpy/random/_philox.cpython-310-x86_64-linux-gnu.so,sha256=J4Bwk58gF6ftGukYM63WUlHm9w5yudN5ZOswvDg_awU,107384 +numpy/random/_philox.pyi,sha256=OKlaiIU-hj72Bp04zjNifwusOD_3-mYxIfvyuys8c_o,978 +numpy/random/_pickle.py,sha256=4NhdT-yk7C0m3tyZWmouYAs3ZGNPdPVNGfUIyuh8HDY,2318 +numpy/random/_sfc64.cpython-310-x86_64-linux-gnu.so,sha256=M57C1mIUFNGr30RKzEPM8qG2g6pB_t7sPcYLTD4nHfM,76712 +numpy/random/_sfc64.pyi,sha256=09afHTedVW-519493ZXtGcl-H-_zluj-B_yfEJG8MMs,709 +numpy/random/bit_generator.cpython-310-x86_64-linux-gnu.so,sha256=1O49cg-e7gVBVIUH3H_i8yPqSBzcM-N5Bv54vC6YGg0,246536 +numpy/random/bit_generator.pxd,sha256=lArpIXSgTwVnJMYc4XX0NGxegXq3h_QsUDK6qeZKbNc,1007 +numpy/random/bit_generator.pyi,sha256=aXv7a_hwa0nkjY8P2YENslwWp89UcFRn09woXh7Uoc0,3510 +numpy/random/c_distributions.pxd,sha256=7DE-mV3H_Dihk4OK4gMHHkyD4tPX1cAi4570zi5CI30,6344 +numpy/random/lib/libnpyrandom.a,sha256=xUcvOvieju5PThPQ8q0-uGJ5fjsCd5umnjIerIc85Sg,71926 +numpy/random/mtrand.cpython-310-x86_64-linux-gnu.so,sha256=uRfi8K2i7wCwzFCbdvFNi85IFeNkOr1YSBgDu7a1hf4,783752 +numpy/random/mtrand.pyi,sha256=3vAGOXsvyFFv0yZl34pVVPP7Dgt22COyfn4tUoi_hEQ,19753 +numpy/random/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/random/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/random/tests/__pycache__/test_direct.cpython-310.pyc,, +numpy/random/tests/__pycache__/test_extending.cpython-310.pyc,, +numpy/random/tests/__pycache__/test_generator_mt19937.cpython-310.pyc,, +numpy/random/tests/__pycache__/test_generator_mt19937_regressions.cpython-310.pyc,, +numpy/random/tests/__pycache__/test_random.cpython-310.pyc,, +numpy/random/tests/__pycache__/test_randomstate.cpython-310.pyc,, +numpy/random/tests/__pycache__/test_randomstate_regression.cpython-310.pyc,, +numpy/random/tests/__pycache__/test_regression.cpython-310.pyc,, +numpy/random/tests/__pycache__/test_seed_sequence.cpython-310.pyc,, +numpy/random/tests/__pycache__/test_smoke.cpython-310.pyc,, +numpy/random/tests/data/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/random/tests/data/__pycache__/__init__.cpython-310.pyc,, +numpy/random/tests/data/mt19937-testset-1.csv,sha256=Xkef402AVB-eZgYQkVtoxERHkxffCA9Jyt_oMbtJGwY,15844 +numpy/random/tests/data/mt19937-testset-2.csv,sha256=nsBEQNnff-aFjHYK4thjvUK4xSXDSfv5aTbcE59pOkE,15825 +numpy/random/tests/data/pcg64-testset-1.csv,sha256=xB00DpknGUTTCxDr9L6aNo9Hs-sfzEMbUSS4t11TTfE,23839 +numpy/random/tests/data/pcg64-testset-2.csv,sha256=NTdzTKvG2U7_WyU_IoQUtMzU3kEvDH39CgnR6VzhTkw,23845 +numpy/random/tests/data/pcg64dxsm-testset-1.csv,sha256=vNSUT-gXS_oEw_awR3O30ziVO4seNPUv1UIZ01SfVnI,23833 +numpy/random/tests/data/pcg64dxsm-testset-2.csv,sha256=uylS8PU2AIKZ185OC04RBr_OePweGRtvn-dE4YN0yYA,23839 +numpy/random/tests/data/philox-testset-1.csv,sha256=SedRaIy5zFadmk71nKrGxCFZ6BwKz8g1A9-OZp3IkkY,23852 +numpy/random/tests/data/philox-testset-2.csv,sha256=dWECt-sbfvaSiK8-Ygp5AqyjoN5i26VEOrXqg01rk3g,23838 +numpy/random/tests/data/sfc64-testset-1.csv,sha256=iHs6iX6KR8bxGwKk-3tedAdMPz6ZW8slDSUECkAqC8Q,23840 +numpy/random/tests/data/sfc64-testset-2.csv,sha256=FIDIDFCaPZfWUSxsJMAe58hPNmMrU27kCd9FhCEYt_k,23833 +numpy/random/tests/test_direct.py,sha256=6vLpCyeKnAWFEZei7l2YihVLQ0rSewO1hJBWt7A5fyQ,17779 +numpy/random/tests/test_extending.py,sha256=S3Wrzu3di4uBhr-Pxnx5dOPvlBY0FRdZqVX6CC1IN6s,4038 +numpy/random/tests/test_generator_mt19937.py,sha256=35LBwV6TtWPnxhefutxTQmhLzAQ5Ee4YiY8ziDXM-eQ,115477 +numpy/random/tests/test_generator_mt19937_regressions.py,sha256=xGkdz76BMX1EK0QPfabVxpNx9qQ9OC-1ZStWOs6N_M8,6387 +numpy/random/tests/test_random.py,sha256=kEkQs3i7zcpm9MozIRIz1FIx5B6fmXk0QqX0l6l-u_Y,70087 +numpy/random/tests/test_randomstate.py,sha256=DxF7rMUSxaAlL4h1qC3onHcHR7T_6rKWPbr0nJH84nE,85031 +numpy/random/tests/test_randomstate_regression.py,sha256=VucYWIjA7sAquWsalvZMnfkmYLM1O6ysyWnLl931-lA,7917 +numpy/random/tests/test_regression.py,sha256=trntK51UvajOVELiluEO85l64CKSw5nvBSc5SqYyr9w,5439 +numpy/random/tests/test_seed_sequence.py,sha256=GNRJ4jyzrtfolOND3gUWamnbvK6-b_p1bBK_RIG0sfU,3311 +numpy/random/tests/test_smoke.py,sha256=jjNz0aEGD1_oQl9a9UWt6Mz_298alG7KryLT1pgHljw,28183 +numpy/testing/__init__.py,sha256=InpVKoDAzMKO_l_HNcatziW_u1k9_JZze__t2nybrL0,595 +numpy/testing/__init__.pyi,sha256=AhK5NuOpdD-JjIzXOlssE8_iSLyFAAHzyGV_w1BT7vA,1674 +numpy/testing/__pycache__/__init__.cpython-310.pyc,, +numpy/testing/__pycache__/overrides.cpython-310.pyc,, +numpy/testing/__pycache__/print_coercion_tables.cpython-310.pyc,, +numpy/testing/__pycache__/setup.cpython-310.pyc,, +numpy/testing/_private/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/testing/_private/__pycache__/__init__.cpython-310.pyc,, +numpy/testing/_private/__pycache__/extbuild.cpython-310.pyc,, +numpy/testing/_private/__pycache__/utils.cpython-310.pyc,, +numpy/testing/_private/extbuild.py,sha256=nG2dwP4nUmQS3e5eIRinxt0s_f4sxxA1YfohCg-navo,8017 +numpy/testing/_private/utils.py,sha256=3FrSTMi0OdpDODBDoncgiDQzdo5NKA6YVfQ3uKRSQnc,85242 +numpy/testing/_private/utils.pyi,sha256=MMNrvwEeSTYzZFWawSSzHnTFYG-cSAIiID-1FuJ1f8U,10123 +numpy/testing/overrides.py,sha256=u6fcKSBC8HIzMPWKAbdyowU71h2Fx2ekDQxpG5NhIr8,2123 +numpy/testing/print_coercion_tables.py,sha256=ndxOsS4XfrZ4UY_9nqRTCnxhkzgdqcuUHL8nezd7Op4,6180 +numpy/testing/setup.py,sha256=GPKAtTTBRsNW4kmR7NjP6mmBR_GTdpaTvkTm10_VcLg,709 +numpy/testing/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/testing/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/testing/tests/__pycache__/test_utils.cpython-310.pyc,, +numpy/testing/tests/test_utils.py,sha256=IDOr-GXuNGlrsb-XzGSYUHXEqcGYJ78p60jOpBqyPM4,55740 +numpy/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/tests/__pycache__/test__all__.cpython-310.pyc,, +numpy/tests/__pycache__/test_ctypeslib.cpython-310.pyc,, +numpy/tests/__pycache__/test_lazyloading.cpython-310.pyc,, +numpy/tests/__pycache__/test_matlib.cpython-310.pyc,, +numpy/tests/__pycache__/test_numpy_config.cpython-310.pyc,, +numpy/tests/__pycache__/test_numpy_version.cpython-310.pyc,, +numpy/tests/__pycache__/test_public_api.cpython-310.pyc,, +numpy/tests/__pycache__/test_reloading.cpython-310.pyc,, +numpy/tests/__pycache__/test_scripts.cpython-310.pyc,, +numpy/tests/__pycache__/test_warnings.cpython-310.pyc,, +numpy/tests/test__all__.py,sha256=L3mCnYPTpzAgNfedVuq9g7xPWbc0c1Pot94k9jZ9NpI,221 +numpy/tests/test_ctypeslib.py,sha256=B06QKeFRgDIEbkEPBy_zYA1H5E2exuhTi7IDkzV8gfo,12257 +numpy/tests/test_lazyloading.py,sha256=YETrYiDLAqLX04K_u5_3NVxAfxDoeguxwkIRfz6qKcY,1162 +numpy/tests/test_matlib.py,sha256=gwhIXrJJo9DiecaGLCHLJBjhx2nVGl6yHq80AOUQSRM,1852 +numpy/tests/test_numpy_config.py,sha256=qHvepgi9oyAbQuZD06k7hpcCC2MYhdzcY6D1iQDPNMI,1241 +numpy/tests/test_numpy_version.py,sha256=A8cXFzp4k-p6J5zkOxlDfDvkoFMxDW2hpTFVXcaQRVo,1479 +numpy/tests/test_public_api.py,sha256=DTq7SO84uBjC2tKPoqX17xazc-SLkTAbQ2fLZwGM2jc,18170 +numpy/tests/test_reloading.py,sha256=QuVaPQulcNLg4Fl31Lw-O89L42KclYCK68n5GVy0PNQ,2354 +numpy/tests/test_scripts.py,sha256=jluCLfG94VM1cuX-5RcLFBli_yaJZpIvmVuMxRKRJrc,1645 +numpy/tests/test_warnings.py,sha256=ZEtXqHI1iyeVeLfVxDcMfN5qw67Ti2u54709hvBG4eY,2284 +numpy/typing/__init__.py,sha256=VoTILNDrUWvZx0LK9_97lBLQFKtSGmDt4QLOH8zYvlo,5234 +numpy/typing/__pycache__/__init__.cpython-310.pyc,, +numpy/typing/__pycache__/mypy_plugin.cpython-310.pyc,, +numpy/typing/__pycache__/setup.cpython-310.pyc,, +numpy/typing/mypy_plugin.py,sha256=24zVk4Ei3qH4Hc3SSz3v0XtIsycTo8HKoY6ilhB_7AQ,6376 +numpy/typing/setup.py,sha256=Cnz9q53w-vJNyE6vYxqYvQXx0pJbrG9quHyz9sqxfek,374 +numpy/typing/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy/typing/tests/__pycache__/__init__.cpython-310.pyc,, +numpy/typing/tests/__pycache__/test_isfile.cpython-310.pyc,, +numpy/typing/tests/__pycache__/test_runtime.cpython-310.pyc,, +numpy/typing/tests/__pycache__/test_typing.cpython-310.pyc,, +numpy/typing/tests/data/fail/arithmetic.pyi,sha256=4rY_ASCERAl8WCus1RakOe0Aw-8vvjilL29mgdD4lv0,3850 +numpy/typing/tests/data/fail/array_constructors.pyi,sha256=X9y_jUYS17WfYmXW5NwkVudyiR6ouUaAwEh0JRte42o,1089 +numpy/typing/tests/data/fail/array_like.pyi,sha256=OVAlEJZ5k8ZRKt0aGpZQwIjlUGpy0PzOOYqfI-IMqBQ,455 +numpy/typing/tests/data/fail/array_pad.pyi,sha256=57oK0Yp53rtKjjIrRFYLcxa-IfIGhtI-bEem7ggJKwI,132 +numpy/typing/tests/data/fail/arrayprint.pyi,sha256=-Fs9VnQfxyfak008Hq8kJWfB0snA6jGDXZz8ljQnwGE,549 +numpy/typing/tests/data/fail/arrayterator.pyi,sha256=FoU4ahHkJZ67dwWXer5FXLjjjesKKg-w2Jq1X1bHymA,480 +numpy/typing/tests/data/fail/bitwise_ops.pyi,sha256=GN9dVqk4_HFXn7zbRrHzJq_UGRFBccoYVUG1UuE7bXs,515 +numpy/typing/tests/data/fail/char.pyi,sha256=-vgN6EmfQ8VaA4SOZ5Ol9u4-Z7Q5I7G78LmaxZOuZ90,2615 +numpy/typing/tests/data/fail/chararray.pyi,sha256=jrNryZFpr8nxG2IHb9e0x3ranpvJpBy_RDex-WpT5rU,2296 +numpy/typing/tests/data/fail/comparisons.pyi,sha256=U4neWzwwtxG6QXsKlNGJuKXHBtwzYBQOa47_7SKF5Wg,888 +numpy/typing/tests/data/fail/constants.pyi,sha256=YSqNbXdhbdMmYbs7ntH0FCKbnm8IFeqsDlZBqcU43iw,286 +numpy/typing/tests/data/fail/datasource.pyi,sha256=PRT2hixR-mVxr2UILvHa99Dr54EF2h3snJXE-v3rWcc,395 +numpy/typing/tests/data/fail/dtype.pyi,sha256=OAGABqdXNB8gClJFEGMckoycuZcIasMaAlS2RkiKROI,334 +numpy/typing/tests/data/fail/einsumfunc.pyi,sha256=RS7GZqUCT_vEFJoyUx4gZlPO8GNFFNFWidxl-wLyRv0,539 +numpy/typing/tests/data/fail/false_positives.pyi,sha256=Q61qMsSsNCtmO0EMRxHj5Z7RYTyrELVpkzfJY5eK8Z0,366 +numpy/typing/tests/data/fail/flatiter.pyi,sha256=qLM4qm7gvJtEZ0rTHcyasUzoP5JbX4FREtqV3g1w6Lo,843 +numpy/typing/tests/data/fail/fromnumeric.pyi,sha256=FH2mjkgtCbA9soqlJRhYN7IIfRRrUL1i9mwqcbYKZSc,5591 +numpy/typing/tests/data/fail/histograms.pyi,sha256=yAPVt0rYTwtxnigoGT-u7hhKCE9iYxsXc24x2HGBrmA,367 +numpy/typing/tests/data/fail/index_tricks.pyi,sha256=moINir9iQoi6Q1ZuVg5BuSB9hSBtbg_uzv-Qm_lLYZk,509 +numpy/typing/tests/data/fail/lib_function_base.pyi,sha256=6y9T773CBLX-jUry1sCQGVuKVKM2wMuQ56Ni5V5j4Dw,2081 +numpy/typing/tests/data/fail/lib_polynomial.pyi,sha256=Ur7Y4iZX6WmoH5SDm0ePi8C8LPsuPs2Yr7g7P5O613g,899 +numpy/typing/tests/data/fail/lib_utils.pyi,sha256=VFpE6_DisvlDByyp1PiNPJEe5IcZp8cH0FlAJyoZipo,276 +numpy/typing/tests/data/fail/lib_version.pyi,sha256=7-ZJDZwDcB-wzpMN8TeYtZAgaqc7xnQ8Dnx2ISiX2Ts,158 +numpy/typing/tests/data/fail/linalg.pyi,sha256=yDd05aK1dI37RPt3pD2eJYo4dZFaT2yB1PEu3K0y9Tg,1322 +numpy/typing/tests/data/fail/memmap.pyi,sha256=HSTCQYNuW1Y6X1Woj361pN4rusSPs4oDCXywqk20yUo,159 +numpy/typing/tests/data/fail/modules.pyi,sha256=_ek4zKcdP-sIh_f-IDY0tP-RbLORKCSWelM9AOYxsyA,670 +numpy/typing/tests/data/fail/multiarray.pyi,sha256=XCdBxufNhR8ZtG8UMzk8nt9_NC5gJTKP9-xTqKO_K9I,1693 +numpy/typing/tests/data/fail/ndarray.pyi,sha256=YnjXy16RHs_esKelMjB07865CQ7gLyQnXhnitq5Kv5c,405 +numpy/typing/tests/data/fail/ndarray_misc.pyi,sha256=w-10xTDDWoff9Lq0dBO-jBeiBR-XjCz2qmes0dLx238,1372 +numpy/typing/tests/data/fail/nditer.pyi,sha256=w7emjnOxnf3NcvLktNLlke6Cuivn2gU3sVmGCfbG6rw,325 +numpy/typing/tests/data/fail/nested_sequence.pyi,sha256=em4GZwLDFE0QSxxg081wVwhh-Dmtkn8f7wThI0DiXVs,427 +numpy/typing/tests/data/fail/npyio.pyi,sha256=56QuHo9SvVR3Uhzl6gQZncCpX575Gy5wugjMICh20m0,620 +numpy/typing/tests/data/fail/numerictypes.pyi,sha256=fevH9x80CafYkiyBJ7LMLVl6GyTvQrZ34trBu6O8TtM,276 +numpy/typing/tests/data/fail/random.pyi,sha256=p5WsUGyOL-MGIeALh9Y0dVhYSRQLaUwMdjXc3G6C_7Q,2830 +numpy/typing/tests/data/fail/rec.pyi,sha256=Ws3TyesnoQjt7Q0wwtpShRDJmZCs2jjP17buFMomVGA,704 +numpy/typing/tests/data/fail/scalars.pyi,sha256=o91BwSfzPTczYVtbXsirqQUoUoYP1C_msGjc2GYsV04,2952 +numpy/typing/tests/data/fail/shape_base.pyi,sha256=Y_f4buHtX2Q2ZA4kaDTyR8LErlPXTzCB_-jBoScGh_Q,152 +numpy/typing/tests/data/fail/stride_tricks.pyi,sha256=IjA0Xrnx0lG3m07d1Hjbhtyo1Te5cXgjgr5fLUo4LYQ,315 +numpy/typing/tests/data/fail/testing.pyi,sha256=e7b5GKTWCtKGoB8z2a8edsW0Xjl1rMheALsvzEJjlCw,1370 +numpy/typing/tests/data/fail/twodim_base.pyi,sha256=ZqbRJfy5S_pW3fFLuomy4L5SBNqj6Nklexg9KDTo65c,899 +numpy/typing/tests/data/fail/type_check.pyi,sha256=CIyI0j0Buxv0QgCvNG2urjaKpoIZ-ZNawC2m6NzGlbo,379 +numpy/typing/tests/data/fail/ufunc_config.pyi,sha256=ukA0xwfJHLoGfoOIpWIN-91wj-DG8oaIjYbO72ymjg4,733 +numpy/typing/tests/data/fail/ufunclike.pyi,sha256=lbxjJyfARmt_QK1HxhxFxvwQTqCEZwJ9I53Wp8X3KIY,679 +numpy/typing/tests/data/fail/ufuncs.pyi,sha256=YaDTL7QLmGSUxE6JVMzpOlZTjHWrgbOo0UIlkX-6ZQk,1347 +numpy/typing/tests/data/fail/warnings_and_errors.pyi,sha256=PrbYDFI7IGN3Gf0OPBkVfefzQs4AXHwDQ495pvrX3RY,174 +numpy/typing/tests/data/misc/extended_precision.pyi,sha256=bS8bBeCFqjgtOiy-8_y39wfa7rwhdjLz2Vmo-RXAYD4,884 +numpy/typing/tests/data/mypy.ini,sha256=Ynv1VSx_kXTD2mFC3ZpgEFuCOg1F2VJXxPk0dxUnF2M,108 +numpy/typing/tests/data/pass/__pycache__/arithmetic.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/array_constructors.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/array_like.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/arrayprint.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/arrayterator.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/bitwise_ops.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/comparisons.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/dtype.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/einsumfunc.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/flatiter.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/fromnumeric.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/index_tricks.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/lib_utils.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/lib_version.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/literal.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/mod.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/modules.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/multiarray.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/ndarray_conversion.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/ndarray_misc.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/ndarray_shape_manipulation.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/numeric.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/numerictypes.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/random.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/scalars.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/simple.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/simple_py3.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/ufunc_config.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/ufunclike.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/ufuncs.cpython-310.pyc,, +numpy/typing/tests/data/pass/__pycache__/warnings_and_errors.cpython-310.pyc,, +numpy/typing/tests/data/pass/arithmetic.py,sha256=2z3dmuysQQmiPz8x0bg8SOOKW62mVJn97uMa9T0L7Vk,7455 +numpy/typing/tests/data/pass/array_constructors.py,sha256=3GrhfBcmWX53pJHD0NvhXjwr2-uNKREbR1I9WCcZ7rI,2419 +numpy/typing/tests/data/pass/array_like.py,sha256=ce_IVubBd7J6FkSpJmD7qMlRLuwmiidhOqhYfZb16Wo,916 +numpy/typing/tests/data/pass/arrayprint.py,sha256=y_KkuLz1uM7pv53qfq7GQOuud4LoXE3apK1wtARdVyM,766 +numpy/typing/tests/data/pass/arrayterator.py,sha256=FqcpKdUQBQ0FazHFxr9MsLEZG-jnJVGKWZX2owRr4DQ,393 +numpy/typing/tests/data/pass/bitwise_ops.py,sha256=UnmxVr9HwI8ifdrutGm_u3EZU4iOOPQhrOku7hTaH0c,970 +numpy/typing/tests/data/pass/comparisons.py,sha256=nTE-fvraLK6xTZcP4uPV02wOShzYKWDaoapx35AeDOY,2992 +numpy/typing/tests/data/pass/dtype.py,sha256=MqDKC6Ywv6jNkWsR8rdLuabzHUco5w1OylDHEdxve_I,1069 +numpy/typing/tests/data/pass/einsumfunc.py,sha256=eXj5L5MWPtQHgrHPsJ36qqrmBHqct9UoujjJCvHnF1k,1370 +numpy/typing/tests/data/pass/flatiter.py,sha256=0BnbuLMBC7MQlprNZ0QhNSscfYwPhEhXOhWoyiRACWU,174 +numpy/typing/tests/data/pass/fromnumeric.py,sha256=Xd_nJVVDoONdztUX8ddgo7EXJ2FD8AX51MO_Yujnmog,3742 +numpy/typing/tests/data/pass/index_tricks.py,sha256=oaFD9vY01_RI5OkrXt-xTk1n_dd-SpuPp-eZ58XR3c8,1492 +numpy/typing/tests/data/pass/lib_utils.py,sha256=sDQCjHVGUwct0RQqAtH5_16y241siSY4bXKZRsuJ8xA,434 +numpy/typing/tests/data/pass/lib_version.py,sha256=HnuGOx7tQA_bcxFIJ3dRoMAR0fockxg4lGqQ4g7LGIw,299 +numpy/typing/tests/data/pass/literal.py,sha256=DLzdWHD6ttW4S0NEvGQbsH_UEJjhZyhvO4OXJjoyvZQ,1331 +numpy/typing/tests/data/pass/mod.py,sha256=HB9aK4_wGJbc44tomaoroNy0foIL5cI9KIjknvMTbkk,1578 +numpy/typing/tests/data/pass/modules.py,sha256=t0KJxYWbrWd7HbbgIDFb3LAhJBiNNb6QPjjFDAgC2mU,576 +numpy/typing/tests/data/pass/multiarray.py,sha256=MxHax6l94yqlTVZleAqG77ILEbW6wU5osPcHzxJ85ns,1331 +numpy/typing/tests/data/pass/ndarray_conversion.py,sha256=yPgzXG6paY1uF_z-QyHYrcmrZvhX7qtvTUh7ANLseCA,1626 +numpy/typing/tests/data/pass/ndarray_misc.py,sha256=z3mucbn9fLM1gxmbUhWlp2lcrOv4zFjqZFze0caE2EA,2715 +numpy/typing/tests/data/pass/ndarray_shape_manipulation.py,sha256=37eYwMNqMLwanIW9-63hrokacnSz2K_qtPUlkdpsTjo,640 +numpy/typing/tests/data/pass/numeric.py,sha256=SdnsD5zv0wm8T2hnIylyS14ig2McSz6rG9YslckbNQ4,1490 +numpy/typing/tests/data/pass/numerictypes.py,sha256=r0_s-a0-H2MdWIn4U4P6W9RQO0V1xrDusgodHNZeIYM,750 +numpy/typing/tests/data/pass/random.py,sha256=uJCnzlsOn9hr_G1TpHLdsweJI4EdhUSEQ4dxROPjqAs,61881 +numpy/typing/tests/data/pass/scalars.py,sha256=En0adCZAwEigZrzdQ0JQwDEmrS0b-DMd1vvjkFcvwo8,3479 +numpy/typing/tests/data/pass/simple.py,sha256=HmAfCOdZBWQF211YaZFrIGisMgu5FzTELApKny08n3Y,2676 +numpy/typing/tests/data/pass/simple_py3.py,sha256=HuLrc5aphThQkLjU2_19KgGFaXwKOfSzXe0p2xMm8ZI,96 +numpy/typing/tests/data/pass/ufunc_config.py,sha256=_M8v-QWAeT1-2MkfSeAbNl_ZwyPvYfPTsLl6c1X8d_w,1204 +numpy/typing/tests/data/pass/ufunclike.py,sha256=Gve6cJ2AT3TAwOjUOQQDIUnqsRCGYq70_tv_sgODiiA,1039 +numpy/typing/tests/data/pass/ufuncs.py,sha256=xGuKuqPetUTS4io5YDHaki5nbYRu-wC29SGU32tzVIg,462 +numpy/typing/tests/data/pass/warnings_and_errors.py,sha256=Pcg-QWfY4PAhTKyehae8q6LhtbUABxa2Ye63-3h1f4w,150 +numpy/typing/tests/data/reveal/arithmetic.pyi,sha256=Ndmi_IFAl8z28RHsYTbOouf-B5FH91x_9ky-JwsdXVg,19765 +numpy/typing/tests/data/reveal/array_constructors.pyi,sha256=DcT8Z2rEpqYfjXySBejk8cGOUidUmizZGE5ZEy7r14E,10600 +numpy/typing/tests/data/reveal/arraypad.pyi,sha256=Q1pcU4B3eRsw5jsv-S0MsEfNUbp_4aMdO_o3n0rtA2A,776 +numpy/typing/tests/data/reveal/arrayprint.pyi,sha256=YyzzkL-wj4Rs-fdo3brpoaWtb5g3yk4Vn2HKu5KRo4w,876 +numpy/typing/tests/data/reveal/arraysetops.pyi,sha256=ApCFQcZzQ08zV32SJ86Xyv_7jazl3XKMmJmULtNquJ8,4155 +numpy/typing/tests/data/reveal/arrayterator.pyi,sha256=TF_1eneHoT0v9HqS9dKc5Xiv3iY3E330GR1RNcJ7s2Q,1111 +numpy/typing/tests/data/reveal/bitwise_ops.pyi,sha256=nRkyUGrBB_Es7TKyDxS_s3u2dFgBfzjocInI9Ea-J10,3919 +numpy/typing/tests/data/reveal/char.pyi,sha256=M_iTa9Pn8F7jQ1k6RN9KvbhEn00g7UYJZ5PV57ikcZM,7289 +numpy/typing/tests/data/reveal/chararray.pyi,sha256=O0EfwnKc3W1Fnx1c7Yotb1O84kVMuqJLlMBXd2duvjI,6093 +numpy/typing/tests/data/reveal/comparisons.pyi,sha256=huaf-seaF5ndTqfoaBfPtMMkOYovq7ibJl5-CRoQW7s,7468 +numpy/typing/tests/data/reveal/constants.pyi,sha256=P9vFEMkPpJ5KeUnzqPOuyHlh3zAFl9lzB4WxyB2od7A,1949 +numpy/typing/tests/data/reveal/ctypeslib.pyi,sha256=-Pk2rLEGCzz3B_y8Mu10JSVA8gPFztl5fV1dspPzqig,4727 +numpy/typing/tests/data/reveal/datasource.pyi,sha256=e8wjn60tO5EdnkBF34JrZT5XvdyW7kRWD2abtgr6qUg,671 +numpy/typing/tests/data/reveal/dtype.pyi,sha256=TKrYyxMu5IGobs0SDTIRcPuWsZ5X7zMYB4pmUlTTJxA,2872 +numpy/typing/tests/data/reveal/einsumfunc.pyi,sha256=pbtSfzIWUJRkDpe2riHBlvFlNSC3CqVM-SbYtBgX9H0,2044 +numpy/typing/tests/data/reveal/emath.pyi,sha256=-muNpWOv_niIn-zS3gUnFO4qBZAouNlVGue2x1L5Ris,2423 +numpy/typing/tests/data/reveal/false_positives.pyi,sha256=AplTmZV7TS7nivU8vegbstMN5MdMv4U0JJdZ4IeeA5M,482 +numpy/typing/tests/data/reveal/fft.pyi,sha256=ReQ9qn5frvJEy-g0RWpUGlPBntUS1cFSIu6WfPotHzE,1749 +numpy/typing/tests/data/reveal/flatiter.pyi,sha256=e1OQsVxQpgyfqMNw2puUTATl-w3swvdknlctAiWxf_E,882 +numpy/typing/tests/data/reveal/fromnumeric.pyi,sha256=PNtGQR1VmGk_xNbd0eP7k7B2oNCMBz2XOJ17-_SdE5M,12101 +numpy/typing/tests/data/reveal/getlimits.pyi,sha256=nUGOMFpWj3pMgqLy6ZbR7A4G2q7iLIl5zEFBGf-Qcfw,1592 +numpy/typing/tests/data/reveal/histograms.pyi,sha256=MxKWoa7UoJRRLim53H6OoyYfz87P3_9YUXGYPTknGVQ,1303 +numpy/typing/tests/data/reveal/index_tricks.pyi,sha256=HpD7lU7hcyDoLdZbeqskPXnX7KYwPtll7uJKYUzrlE8,3177 +numpy/typing/tests/data/reveal/lib_function_base.pyi,sha256=eSiSZUlmPXqVPKknM7GcEv76BDgj0IJRu3FXcZXpmqc,8318 +numpy/typing/tests/data/reveal/lib_polynomial.pyi,sha256=TOzOdMPDqveDv3vDKSjtq6RRvN-j_s2J7aud2ySDAB0,5986 +numpy/typing/tests/data/reveal/lib_utils.pyi,sha256=_zj7WGYGYMFXAHLK-F11aeFfDvjRvFARUjoXhbXn8V0,1049 +numpy/typing/tests/data/reveal/lib_version.pyi,sha256=UCioUeykot8-nWL6goKxZnKZxtgB4lFEi9wdN_xyF1U,672 +numpy/typing/tests/data/reveal/linalg.pyi,sha256=LPaY-RyYL7Xt3djCgNaWEgI8beI9Eo_XnvOwi6Y7-eo,4877 +numpy/typing/tests/data/reveal/matrix.pyi,sha256=ciJXsn5v2O1IZ3VEn5Ilp8-40NTQokfrOOgVXMFsvLo,2922 +numpy/typing/tests/data/reveal/memmap.pyi,sha256=A5PovMzjRp2zslF1vw3TdTQjj4Y0dIEJ__HDBV_svGM,842 +numpy/typing/tests/data/reveal/mod.pyi,sha256=-CNWft2jQGSdrO8dYRgwbl7OhL3a78Zo60JVmiY-gQI,5666 +numpy/typing/tests/data/reveal/modules.pyi,sha256=0WPq7A-aqWkJsV-IA1_7dFNCcxBacj1AWExaXbXErG4,1958 +numpy/typing/tests/data/reveal/multiarray.pyi,sha256=6MvfNKihK-oN6QwG9HFNelgheo4lnL0FCrmIF_qxdoA,5326 +numpy/typing/tests/data/reveal/nbit_base_example.pyi,sha256=DRUMGatQvQXTuovKEMF4dzazIU6it6FU53LkOEo2vNo,657 +numpy/typing/tests/data/reveal/ndarray_conversion.pyi,sha256=BfjQD8U756l4gOfY0LD47HhDRxbq0yCFfEFKvbXs7Rs,1791 +numpy/typing/tests/data/reveal/ndarray_misc.pyi,sha256=0EN-a47Msn4pZgKVdD-GrXCCmt-oxjlov5rszchBmOI,7126 +numpy/typing/tests/data/reveal/ndarray_shape_manipulation.pyi,sha256=QDQ9g6l-e73pTJp-Dosiynb-okbqi91D4KirjhIjcv4,1233 +numpy/typing/tests/data/reveal/nditer.pyi,sha256=VFXnT75BgWSUpb-dD-q5cZkfeOqsk-x9cH626g9FWT4,2021 +numpy/typing/tests/data/reveal/nested_sequence.pyi,sha256=IQyRlXduk-ZEakOtoliMLCqNgGbeg0mzZf-a-a3Gq_0,734 +numpy/typing/tests/data/reveal/npyio.pyi,sha256=YXagt2J-1suu5WXZ_si5NuJf7sHj_7NlaSLqQkam1Po,4209 +numpy/typing/tests/data/reveal/numeric.pyi,sha256=aJKnav-X45tjSFfgGD4iCetwEFcJXdNgU7valktjiCg,6160 +numpy/typing/tests/data/reveal/numerictypes.pyi,sha256=-YQRhwjBjsFJHjpGCRqzafNnKDdsmbBHbmPwccP0pLI,2487 +numpy/typing/tests/data/reveal/random.pyi,sha256=s6T074ZIpGAUqHnA-yAlozTLvt7PNBjCBqd-nGMqWGg,104091 +numpy/typing/tests/data/reveal/rec.pyi,sha256=DbRVk6lc7-3qPe-7Q26tUWpdaH9B4UVoQSYrRGJUo1Q,3858 +numpy/typing/tests/data/reveal/scalars.pyi,sha256=Qn3B3rsqSN397Jh25xs4odt2pfCQtWkoJe-e0-oX8d4,4790 +numpy/typing/tests/data/reveal/shape_base.pyi,sha256=YjiVukrK6OOydvopOaOmeAIIa0YQ2hn9_I_-FyYkHVU,2427 +numpy/typing/tests/data/reveal/stride_tricks.pyi,sha256=EBZR8gSP385nhotwJ3GH9DOUD2q5nUEYbXfhLo5xrPo,1542 +numpy/typing/tests/data/reveal/testing.pyi,sha256=_WOAj_t5SWYiqN0KG26Mza8RvaD3WAa7rFUlgksjLms,8611 +numpy/typing/tests/data/reveal/twodim_base.pyi,sha256=ZdNVo2HIJcx8iF9PA-z5W3Bs0hWM2nlVdbhLuAQlljM,3132 +numpy/typing/tests/data/reveal/type_check.pyi,sha256=yZSp50TtvPqv_PN7zmVcNOVUTUXMNYFGcguMNj25E9Y,3044 +numpy/typing/tests/data/reveal/ufunc_config.pyi,sha256=buwSvat3SVFAFl5k8TL6Mgpi32o6hHZYZ2Lpn6AHdEU,1327 +numpy/typing/tests/data/reveal/ufunclike.pyi,sha256=V_gLcZVrTXJ21VkUMwA0HyxUgA1r6OzjsdJegaKL2GE,1329 +numpy/typing/tests/data/reveal/ufuncs.pyi,sha256=VnwYr5KT_FLKfc0wV7dtNz7bNtaC9VIQt-oz56Hb5EE,2798 +numpy/typing/tests/data/reveal/warnings_and_errors.pyi,sha256=ImMlPt2PQBtX8Qf1EZFmLjNWm8fPE6IWQ_deaq_-85s,538 +numpy/typing/tests/test_isfile.py,sha256=BhKZs4-LrhFUfKjcG0yelySjE6ZITMxGIBYWGDHMRb8,864 +numpy/typing/tests/test_runtime.py,sha256=2qu8JEliITnZCBJ_QJpohacj_OQ08o73ixS2w2ooNXI,3275 +numpy/typing/tests/test_typing.py,sha256=Da1ZOFjtPh_Mvb5whpI-okBJdgLOAfJtJNyG6leGFoQ,8743 +numpy/version.py,sha256=OTLnSh0NGfWyL8VrnIj0Ndt_KZOTl1Z-kD9Cf-jRMmY,216 diff --git a/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/WHEEL b/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..4e4c38ae320920b8f083b87f408214cdecd350d2 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/WHEEL @@ -0,0 +1,6 @@ +Wheel-Version: 1.0 +Generator: meson +Root-Is-Purelib: false +Tag: cp310-cp310-manylinux_2_17_x86_64 +Tag: cp310-cp310-manylinux2014_x86_64 + diff --git a/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/entry_points.txt b/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/entry_points.txt new file mode 100644 index 0000000000000000000000000000000000000000..450d8ef27b9d42527fde6aced068dd7e13f2c9a3 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/numpy-1.26.4.dist-info/entry_points.txt @@ -0,0 +1,9 @@ +[array_api] +numpy = numpy.array_api + +[pyinstaller40] +hook-dirs = numpy:_pyinstaller_hooks_dir + +[console_scripts] +f2py = numpy.f2py.f2py2e:main + diff --git a/llmeval-env/lib/python3.10/site-packages/sacrebleu/metrics/__pycache__/bleu.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/sacrebleu/metrics/__pycache__/bleu.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5424b316fd82ec1d6e74c938cde791dc4ee4af49 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/sacrebleu/metrics/__pycache__/bleu.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/sacrebleu/metrics/__pycache__/chrf.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/sacrebleu/metrics/__pycache__/chrf.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7756ddb15225030e718d933a7c77bdb2c2caaca5 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/sacrebleu/metrics/__pycache__/chrf.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/sacrebleu/metrics/__pycache__/helpers.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/sacrebleu/metrics/__pycache__/helpers.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..03cf0c632e1b9cd01155ed3faa5ea03e34adf350 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/sacrebleu/metrics/__pycache__/helpers.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/__init__.py b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0ced3fed6af1a5b03a7ff7c8f9d1aa9254dd9ba5 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__init__.py @@ -0,0 +1,7 @@ +from .compiler import (CompiledKernel, ASTSource, compile, AttrsDescriptor) +from .errors import CompilationError + +__all__ = [ + "compile", "ASTSource", "AttrsDescriptor", "CompiledKernel", "CompilationError", "get_arch_default_num_warps", + "get_arch_default_num_stages" +] diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/__init__.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3d0a4b22ac6445112cd5c9134e3b6ccdda0bee53 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/__init__.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/code_generator.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/code_generator.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f84b7b56def1638edfb168d0297a8472c2dc2133 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/code_generator.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/compiler.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/compiler.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..19af09c56f5fefb33ff36278933d448bf1e367a6 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/compiler.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/errors.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/errors.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..be41fa6e6c88922dc8c3b492d0fa04d4424ba8bc Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/errors.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/make_launcher.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/make_launcher.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9dc84f9ab2342e834dfd11c04c78d74115f76af4 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/make_launcher.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/utils.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f4363a5e8348c4504780ad823bfe558b4fda0b32 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/compiler/__pycache__/utils.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/backends/__init__.py b/llmeval-env/lib/python3.10/site-packages/triton/compiler/backends/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/backends/__pycache__/__init__.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/compiler/backends/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..67e63381c92dc53d8c4d036d634537c9d9b42911 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/compiler/backends/__pycache__/__init__.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/backends/__pycache__/cuda.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/compiler/backends/__pycache__/cuda.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..40ef656b5c708120444f14c8feb1767b18d01ffd Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/compiler/backends/__pycache__/cuda.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/backends/cuda.py b/llmeval-env/lib/python3.10/site-packages/triton/compiler/backends/cuda.py new file mode 100644 index 0000000000000000000000000000000000000000..3c85ae8820df2f424b370b0f68ca29e612a2cf9a --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/compiler/backends/cuda.py @@ -0,0 +1,220 @@ +from triton.common.backend import BaseBackend +from dataclasses import dataclass +from ..._C.libtriton.triton import ClusterInfo, get_num_warps, TMAInfos, translate_triton_gpu_to_llvmir, get_shared_memory_size, translate_llvmir_to_ptx, compile_ptx_to_cubin, add_external_libs +from ...common.backend import get_cuda_version_key, path_to_ptxas +from ..._C.libtriton.triton import ir, runtime +import functools +from typing import Any +from ..utils import get_ids_of_tensormaps, parse_tma_info +from ..make_launcher import make_stub +import hashlib + + +def get_kernel_name(src: str, pattern: str) -> str: + ''' + Get kernel name from PTX code. + This Kernel name is required when launching the kernel. + ''' + # There is a name mangling in PTX codegen, so the original kernel names in Triton IR are not available in PTX/cubin. + assert src + for line in src.split('\n'): + line = line.strip() + if line.startswith(pattern): + return line.split()[-1] + + +@functools.lru_cache() +def ptx_get_version(cuda_version) -> int: + ''' + Get the highest PTX version supported by the current CUDA driver. + ''' + assert isinstance(cuda_version, str) + major, minor = map(int, cuda_version.split('.')) + if major == 12: + return 80 + minor + if major == 11: + return 70 + minor + if major == 10: + return 63 + minor + raise RuntimeError("Triton only support CUDA 10.0 or higher") + + +@dataclass(frozen=True) +class CUDAOptions: + num_warps: int = 4 + num_ctas: int = 1 + num_stages: int = 3 + cluster_dims: tuple = (1, 1, 1) + ptx_version: int = None + enable_warp_specialization: bool = False + enable_persistent: bool = False + optimize_epilogue: bool = False + enable_fp_fusion: bool = True + allow_fp8e4nv: bool = False + max_num_imprecise_acc_default: bool = None + extern_libs: dict = None + debug: bool = False + + def __post_init__(self): + # TODO: change API + if isinstance(self.extern_libs, dict): + extern_libs = tuple([(k, v) for k, v in self.extern_libs.items() if v]) + object.__setattr__(self, 'extern_libs', extern_libs) + assert self.num_warps > 0 and (self.num_warps & (self.num_warps - 1)) == 0, \ + "num_warps must be a power of 2" + + def hash(self): + key = '_'.join([f'{name}-{val}' for name, val in self.__dict__.items()]) + return hashlib.md5(key.encode("utf-8")).hexdigest() + + +class CUDABackend(BaseBackend): + + def __init__(self, device_type: tuple) -> None: + super().__init__(device_type) + self.capability = device_type[1] + assert isinstance(self.capability, int) + + def parse_options(self, opts) -> Any: + args = {k: opts[k] for k in CUDAOptions.__dataclass_fields__.keys() if k in opts} + args["allow_fp8e4nv"] = self.capability >= 89 + args["max_num_imprecise_acc_default"] = 0 if self.capability >= 89 else None + return CUDAOptions(**args) + + @staticmethod + def make_ttir(mod, metadata, opt): + pm = ir.pass_manager(mod.context) + pm.enable_debug() + pm.add_inliner_pass() + pm.add_triton_combine_pass() + pm.add_canonicalizer_pass() + pm.add_reorder_broadcast_pass() + pm.add_cse_pass() + pm.add_licm_pass() + pm.add_symbol_dce_pass() + pm.run(mod) + return mod + + @staticmethod + def make_ttgir(mod, metadata, opt, capability): + cluster_info = ClusterInfo() + if opt.cluster_dims is not None: + cluster_info.clusterDimX = opt.cluster_dims[0] + cluster_info.clusterDimY = opt.cluster_dims[1] + cluster_info.clusterDimZ = opt.cluster_dims[2] + # TTIR -> TTGIR + pm = ir.pass_manager(mod.context) + pm.enable_debug() + pm.add_convert_triton_to_tritongpu_pass(opt.num_warps, 32, opt.num_ctas, capability) + # optimize TTGIR + pm.add_tritongpu_coalesce_pass() + # TODO(Qingyi): Move PlanCTAPass to the front of CoalescePass + pm.add_plan_cta_pass(cluster_info) + pm.add_tritongpu_rewrite_tensor_pointer_pass(capability) + pm.add_plan_cta_pass(cluster_info) + pm.add_tritongpu_remove_layout_conversions_pass() + pm.add_tritongpu_accelerate_matmul_pass(capability) + pm.add_tritongpu_remove_layout_conversions_pass() + if opt.optimize_epilogue: + pm.add_tritongpu_optimize_epilogue_pass() + pm.add_tritongpu_optimize_dot_operands_pass() + pm.add_cse_pass() + ws_enabled = False + # `num_warps` does not mean the total number of warps of a CTA when + # warp specialization is enabled. + # it's the responsibility of the compiler to figure out the exact + # `num_warps` to use. + # TODO: support the case where `num_warps` from user is not 4. + if capability // 10 >= 9 and opt.enable_warp_specialization and opt.num_warps == 4: + pm.add_tritongpu_ws_feasibility_checking_pass(capability) + pm.run(mod) + ws_enabled = ir.is_ws_supported(mod) + pm = ir.pass_manager(mod.context) + pm.enable_debug() + if ws_enabled: + pm.add_tritongpu_wsdecomposing_pass(capability) + pm.add_tritongpu_wspipeline_pass(opt.num_stages, opt.num_warps, capability) + pm.add_tritongpu_wsmutex_pass(capability) + pm.add_tritongpu_wsmaterialization_pass(capability) + pm.add_licm_pass() + pm.add_cse_pass() + else: + pm.add_tritongpu_pipeline_pass(opt.num_stages, opt.num_warps, opt.num_ctas, capability) + pm.add_tritongpu_materialize_load_store_pass(opt.num_warps, capability) + if capability // 10 <= 8: + pm.add_tritongpu_prefetch_pass() + pm.add_tritongpu_optimize_dot_operands_pass() + pm.add_tritongpu_remove_layout_conversions_pass() + pm.add_tritongpu_decompose_conversions_pass() + pm.add_tritongpu_ws_fixup_missing_attrs_pass() + pm.add_tritongpu_reorder_instructions_pass() + pm.add_cse_pass() + pm.add_symbol_dce_pass() + if capability // 10 >= 9: + pm.add_tritongpu_fence_insertion_pass() + pm.add_tritongpu_ws_fixup_missing_attrs_pass() + pm.add_tritongpu_optimize_thread_locality_pass() + pm.add_canonicalizer_pass() + pm.run(mod) + metadata["cluster_dims"] = (cluster_info.clusterDimX, cluster_info.clusterDimY, cluster_info.clusterDimZ) + return mod + + @staticmethod + def make_llir(src, metadata, options, capability): + metadata["enable_warp_specialization"] = ir.is_ws_supported(src) + metadata["num_warps"] = get_num_warps(src) + tma_infos = TMAInfos() + # link libraries + if options.extern_libs: + names = [lib[0] for lib in options.extern_libs] + paths = [lib[1] for lib in options.extern_libs] + add_external_libs(src, names, paths) + # TritonGPU -> LLVM-IR + ret = translate_triton_gpu_to_llvmir(src, capability, tma_infos, runtime.TARGET.NVVM) + if len(tma_infos) > 0: + metadata["tensormaps_info"] = parse_tma_info(tma_infos, metadata["ids_of_folded_args"]) + for i, _ in enumerate(metadata["tensormaps_info"]): + metadata["tensormaps_info"][i].ids_of_folded_args = metadata["ids_of_folded_args"] + metadata["ids_of_tensormaps"] = get_ids_of_tensormaps(metadata.get("tensormaps_info", None)) + metadata["shared"] = get_shared_memory_size(src) + return ret + + @staticmethod + def make_ptx(src, metadata, opt, capability): + ptx_version = opt.ptx_version + if ptx_version is None: + _, cuda_version = path_to_ptxas() + ptx_version = ptx_get_version(cuda_version) + return translate_llvmir_to_ptx(src, capability, ptx_version, opt.enable_fp_fusion) + + @staticmethod + def make_cubin(src, metadata, opt, capability): + metadata["name"] = get_kernel_name(src, pattern='// .globl') + ptxas, _ = path_to_ptxas() + return compile_ptx_to_cubin(src, ptxas, capability, opt.enable_fp_fusion) + + def add_stages(self, stages, options): + stages["ttir"] = lambda src, metadata: self.make_ttir(src, metadata, options) + stages["ttgir"] = lambda src, metadata: self.make_ttgir(src, metadata, options, self.capability) + stages["llir"] = lambda src, metadata: self.make_llir(src, metadata, options, self.capability) + stages["ptx"] = lambda src, metadata: self.make_ptx(src, metadata, options, self.capability) + stages["cubin"] = lambda src, metadata: self.make_cubin(src, metadata, options, self.capability) + + def hash(self): + return f'{get_cuda_version_key()}-{self.capability}' + + def make_launcher_stub(self, src, metadata): + ids = { + "ids_of_tensormaps": metadata.get("ids_of_tensormaps", tuple()), "ids_of_folded_args": + metadata.get("ids_of_folded_args", + tuple()), "ids_of_const_exprs": src.fn.constexprs if hasattr(src, "fn") else tuple() + } + constants = src.constants if hasattr(src, "constants") else dict() + enable_warp_specialization = False + + # set constant + return make_stub(src.name, src.signature, constants, ids, enable_warp_specialization=enable_warp_specialization) + + @classmethod + def create_backend(cls, device_type: str): + return cls(device_type) diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/code_generator.py b/llmeval-env/lib/python3.10/site-packages/triton/compiler/code_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..e64c36acb0e4a4c20183c3acf83b6a1cc5a684c7 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/compiler/code_generator.py @@ -0,0 +1,1235 @@ +import ast +import inspect +import re +import sys +import warnings +from typing import Any, Callable, Dict, Optional, Tuple, Type, Union + +from .. import language +from .._C.libtriton.triton import ir +from ..language import constexpr, tensor +# ideally we wouldn't need any runtime component +from ..runtime import JITFunction +from .errors import (CompilationError, CompileTimeAssertionFailure, UnsupportedLanguageConstruct) + + +def mangle_ty(ty): + if ty.is_ptr(): + return 'P' + mangle_ty(ty.element_ty) + if ty.is_int(): + SIGNED = language.dtype.SIGNEDNESS.SIGNED + prefix = 'i' if ty.int_signedness == SIGNED else 'u' + return prefix + str(ty.int_bitwidth) + if ty.is_floating(): + return str(ty) + if ty.is_block(): + elt = mangle_ty(ty.scalar) + shape = '_'.join(map(str, ty.shape)) + return f'{elt}S{shape}S' + if ty.is_void(): + return 'V' + assert False, "Unsupported type" + + +def mangle_fn(name, arg_tys, constants): + # doesn't mangle ret type, which must be a function of arg tys + mangled_arg_names = '_'.join([mangle_ty(ty) for ty in arg_tys]) + mangled_constants = '_'.join([f'{i}c{repr(constants[i])}' for i in sorted(constants)]) + mangled_constants = mangled_constants.replace('.', '_d_') + mangled_constants = mangled_constants.replace("'", '_sq_') + # [ and ] are not allowed in LLVM identifiers + mangled_constants = mangled_constants.replace('[', '_').replace(']', '_') + ret = f'{name}__{mangled_arg_names}__{mangled_constants}' + return ret + + +def _is_triton_tensor(o: Any) -> bool: + return isinstance(o, tensor) + + +def _is_constexpr(o: Any) -> bool: + return isinstance(o, constexpr) + + +def _is_triton_scalar(o: Any) -> bool: + return _is_triton_tensor(o) and (not o.type.is_block() or o.type.numel == 1) + + +def _is_list_like(o: Any) -> bool: + return isinstance(o, (list, tuple)) + + +def _unwrap_if_constexpr(o: Any): + return o.value if isinstance(o, constexpr) else o + + +def _check_fn_args(node, fn, args): + if fn.noinline: + for idx, arg in enumerate(args): + if not _is_constexpr(arg) and not _is_triton_scalar(arg): + raise UnsupportedLanguageConstruct( + fn.src, node, + f'Function {fn.__name__} is marked noinline, but was called with non-scalar argument {fn.arg_names[idx]}:{arg}' + ) + + +def _get_fn_file_line(fn): + base_fn = fn + while not isinstance(base_fn, JITFunction): + base_fn = base_fn.fn + file_name = base_fn.fn.__code__.co_filename + lines, begin_line = inspect.getsourcelines(base_fn.fn) + # Match the following pattern: + # @triton.autotune(...) <- foo.__code__.co_firstlineno + # @triton.heuristics(...) + # @triton.jit + # def foo(...): <- this line is the first line + for idx, line in enumerate(lines): + if line.strip().startswith("def "): + begin_line += idx + break + return file_name, begin_line + + +_condition_types = {bool, int, type(None)} # Python types accepted for conditionals inside kernels + + +class enter_sub_region: + + def __init__(self, generator): + self.generator = generator + + def __enter__(self): + # record lscope & local_defs in the parent scope + self.liveins = self.generator.lscope.copy() + self.prev_defs = self.generator.local_defs.copy() + self.generator.local_defs = {} + self.insert_block = self.generator.builder.get_insertion_block() + self.insert_point = self.generator.builder.get_insertion_point() + return self.liveins, self.insert_block + + def __exit__(self, *args, **kwargs): + self.generator.builder.restore_insertion_point(self.insert_point) + self.generator.lscope = self.liveins + self.generator.local_defs = self.prev_defs + + +# Check if the given syntax node has an "early" return +class ContainsReturnChecker(ast.NodeVisitor): + + def __init__(self, gscope): + self.gscope = gscope + + def _visit_stmts(self, body) -> bool: + for s in body: + if self.visit(s): + return True + return False + + def _visit_function(self, fn) -> bool: + # Currently we only support JITFunctions defined in the global scope + if isinstance(fn, JITFunction) and not fn.noinline: + fn_node = fn.parse() + return ContainsReturnChecker(self.gscope).visit(fn_node) + return False + + def generic_visit(self, node) -> bool: + ret = False + for _, value in ast.iter_fields(node): + if isinstance(value, list): + for item in value: + if isinstance(item, ast.AST): + ret = ret or self.visit(item) + elif isinstance(value, ast.AST): + ret = ret or self.visit(value) + return ret + + def visit_Attribute(self, node: ast.Attribute) -> bool: + # If the left part is a name, it's possible that + # we call triton native function or a jit function from another module. + # If the left part is not a name, it must return a tensor or a constexpr + # whose methods do not contain return statements + # e.g., (tl.load(x)).to(y) + # So we only check if the expressions within value have return or not + if isinstance(node.value, ast.Name): + if node.value.id in self.gscope: + value = self.gscope[node.value.id] + fn = getattr(value, node.attr) + return self._visit_function(fn) + return False + return self.visit(node.value) + + def visit_Name(self, node: ast.Name) -> bool: + if type(node.ctx) == ast.Store: + return False + if node.id in self.gscope: + fn = self.gscope[node.id] + return self._visit_function(fn) + return False + + def visit_Return(self, node: ast.Return) -> bool: + return True + + def visit_Assign(self, node: ast.Assign) -> bool: + # There couldn't be an early return + # x = ... + return False + + def visit_AugAssign(self, node: ast.AugAssign) -> bool: + # There couldn't be an early return + # x += ... + return False + + def visit_Module(self, node: ast.Module) -> bool: + return self._visit_stmts(node.body) + + def visit_FunctionDef(self, node: ast.FunctionDef) -> bool: + return self._visit_stmts(node.body) + + def visit_If(self, node: ast.If) -> bool: + # TODO: optimize the following case in which we actually don't have + # a return when static_cond is false: + # if dynamic_cond + # if static_cond + # func_with_return + # else + # func_without_return + ret = self._visit_stmts(node.body) + if node.orelse: + ret = ret or self._visit_stmts(node.orelse) + return ret + + def visit_IfExp(self, node: ast.IfExp) -> bool: + return self.visit(node.body) or self.visit(node.orelse) + + def visit_Call(self, node: ast.Call) -> bool: + return self.visit(node.func) + + +class CodeGenerator(ast.NodeVisitor): + + def __init__(self, context, prototype, gscope, attributes, constants, function_name, options, debug=None, + module=None, is_kernel=False, function_types: Optional[Dict] = None, noinline=False, + file_name: Optional[str] = None, begin_line=0): + self.context = context + self.builder = ir.builder(context) + self.file_name = file_name + # node.lineno starts from 1, so we need to subtract 1 + self.begin_line = begin_line - 1 + self.builder.set_loc(file_name, begin_line, 0) + self.builder.options = options + self.module = self.builder.create_module() if module is None else module + self.function_ret_types = {} if function_types is None else function_types + self.prototype = prototype + self.gscope = gscope + self.lscope = dict() + self.attributes = attributes + self.constants = constants + self.function_name = function_name + self.is_kernel = is_kernel + self.last_node = None + self.debug = options.debug if debug is None else debug + self.noinline = noinline + self.scf_stack = [] + self.last_ret_type = None + # SSA-construction + # name => language.tensor + self.local_defs: Dict[str, tensor] = {} + self.global_uses: Dict[str, tensor] = {} + self.dereference_name: Callable[[str], Any] = self._define_name_lookup() + self.fn = None + + builtin_namespace: Dict[str, Any] = {_.__name__: _ for _ in (range, float, int, isinstance, getattr)} + builtin_namespace.update(( + ('print', language.core.device_print), + ('min', language.minimum), + )) + + def _define_name_lookup(self): + + def local_lookup(name: str, absent): + # this needs to be re-fetched from `self` every time, because it gets switched occasionally + value = self.lscope.get(name, absent) + if value is not absent and name not in self.local_defs: + self.global_uses[name] = value + return value + + absent_marker = object() + + def name_lookup(name: str) -> Any: + absent = absent_marker + for lookup_function in local_lookup, self.gscope.get, self.builtin_namespace.get: + value = lookup_function(name, absent) + if value is not absent: + return value + raise NameError(f'{name} is not defined') + + return name_lookup + + def set_value(self, name: str, value: Union[tensor, constexpr]) -> None: + ''' This function: + called by visit_Assign() & visit_FunctionDef() to store left value (lvalue) + 1. record local defined name (FIXME: should consider control flow) + 2. store tensor in self.lvalue + ''' + self.lscope[name] = value + self.local_defs[name] = value + + def _get_insertion_point_and_loc(self): + # XXX: this is a hack to get the location of the insertion point. + # The insertion point's location could be invalid sometimes, + # so we need to explicitly set the location + loc = self.builder.get_loc() + ip = self.builder.get_insertion_point() + return ip, loc + + def _set_insertion_point_and_loc(self, ip, loc): + self.builder.restore_insertion_point(ip) + self.builder.set_loc(loc) + + # + # AST visitor + # + def visit_compound_statement(self, stmts): + # Ensure that stmts is iterable + if not _is_list_like(stmts): + stmts = [stmts] + for stmt in stmts: + ret_type = self.visit(stmt) + if ret_type is not None and isinstance(stmt, ast.Return): + self.last_ret_type = ret_type + + def visit_Module(self, node): + ast.NodeVisitor.generic_visit(self, node) + + def visit_List(self, node): + ctx = self.visit(node.ctx) + assert ctx is None + elts = [self.visit(elt) for elt in node.elts] + return elts + + # By design, only non-kernel functions can return + def visit_Return(self, node): + ret_value = self.visit(node.value) + # ret_block = self.builder.create_block() + # post_ret_block = self.builder.create_block() + # self.builder.create_branch(ret_block) + # self.builder.set_insertion_point_to_end(ret_block) + if ret_value is None: + self.builder.ret([]) + ret_ty = None + elif isinstance(ret_value, tuple): + ret_values = [language.core._to_tensor(v, self.builder) for v in ret_value] + ret_types = [v.type for v in ret_values] + self.builder.ret([v.handle for v in ret_values]) + ret_ty = tuple(ret_types) + else: + ret = language.core._to_tensor(ret_value, self.builder) + self.builder.ret([ret.handle]) + ret_ty = ret.type + # self.builder.create_branch(post_ret_block) + # self.builder.set_insertion_point_to_end(post_ret_block) + return ret_ty + + def visit_FunctionDef(self, node): + arg_names, kwarg_names = self.visit(node.args) + if self.fn: + raise UnsupportedLanguageConstruct(None, node, "nested function definition is not supported.") + # initialize defaults + for i, default_value in enumerate(node.args.defaults): + arg_node = node.args.args[-i - 1] + annotation = arg_node.annotation + name = arg_node.arg + st_target = ast.Name(id=name, ctx=ast.Store()) + if annotation is None: + init_node = ast.Assign(targets=[st_target], value=default_value) + else: + init_node = ast.AnnAssign(target=st_target, value=default_value, annotation=annotation) + self.visit(init_node) + # initialize function + visibility = "public" if self.is_kernel else "private" + self.fn = self.builder.get_or_insert_function(self.module, self.function_name, + self.prototype.to_ir(self.builder), visibility, self.noinline) + self.module.push_back(self.fn) + entry = self.fn.add_entry_block() + arg_values = [] + idx = 0 + for i, arg_name in enumerate(arg_names): + if i in self.constants: + cst = self.constants[i] + if not _is_constexpr(cst): + cst = constexpr(self.constants[i]) + arg_values.append(cst) + continue + else: + if i in self.attributes: + for name, value in self.attributes[i]: + self.fn.set_arg_attr(idx, name, value) + arg_values.append(tensor(self.fn.args(idx), self.prototype.param_types[idx])) + idx += 1 + + insert_pt = self.builder.get_insertion_block() + for arg_name, arg_value in zip(arg_names, arg_values): + self.set_value(arg_name, arg_value) + self.builder.set_insertion_point_to_start(entry) + # visit function body + self.visit_compound_statement(node.body) + # finalize function + if self.last_ret_type is None: + self.builder.ret([]) + else: + # update return type + if isinstance(self.last_ret_type, tuple): + self.prototype.ret_types = list(self.last_ret_type) + self.fn.reset_type(self.prototype.to_ir(self.builder)) + else: + self.prototype.ret_types = [self.last_ret_type] + self.fn.reset_type(self.prototype.to_ir(self.builder)) + if insert_pt: + self.builder.set_insertion_point_to_end(insert_pt) + # Remove dead code + self.fn.finalize() + + def visit_arguments(self, node): + arg_names = [] + for arg in node.args: + arg_names += [self.visit(arg)] + kwarg_names = self.visit(node.kwarg) + return arg_names, kwarg_names + + def visit_arg(self, node): + ast.NodeVisitor.generic_visit(self, node) + return node.arg + + def visit_AnnAssign(self, node): + # extract attributes + annotation = self.visit(node.annotation) + target = self.visit(node.target) + value = self.visit(node.value) + # constexpr + if annotation == constexpr: + if target in self.lscope: + raise ValueError(f'{target} is already defined.' + f' constexpr cannot be reassigned.') + if not _is_constexpr(value): + value = constexpr(value) + self.lscope[target] = value + return self.lscope[target] + # default: call visit_Assign + return self.visit_Assign(node) + + def visit_Assign(self, node): + _names = [] + for target in node.targets: + _names += [self.visit(target)] + if len(_names) > 1: + raise UnsupportedLanguageConstruct(None, node, "simultaneous multiple assignment is not supported.") + names = _names[0] + values = self.visit(node.value) + if not _is_list_like(names): + names = [names] + if not _is_list_like(values): + values = [values] + native_nontensor_types = (language.dtype, ) + for name, value in zip(names, values): + # by default, constexpr are assigned into python variable + value = _unwrap_if_constexpr(value) + if value is not None and \ + not _is_triton_tensor(value) and \ + not isinstance(value, native_nontensor_types): + value = language.core._to_tensor(value, self.builder) + self.set_value(name, value) + + def visit_AugAssign(self, node): + name = node.target.id + lhs = ast.Name(id=name, ctx=ast.Load()) + rhs = ast.BinOp(lhs, node.op, node.value) + assign = ast.Assign(targets=[node.target], value=rhs) + self.visit(assign) + return self.dereference_name(name) + + def visit_Name(self, node): + if type(node.ctx) == ast.Store: + return node.id + return self.dereference_name(node.id) + + def visit_Store(self, node): + ast.NodeVisitor.generic_visit(self, node) + + def visit_Load(self, node): + ast.NodeVisitor.generic_visit(self, node) + + def visit_Tuple(self, node): + args = [self.visit(x) for x in node.elts] + return tuple(args) + + def _apply_binary_method(self, method_name, lhs, rhs): + # TODO: raise something meaningful if getattr fails below, esp for reverse method + if _is_triton_tensor(lhs): + return getattr(lhs, method_name)(rhs, _builder=self.builder) + if _is_triton_tensor(rhs): + reverse_method_name = re.sub(r"__(.*)__", r"__r\1__", method_name) + return getattr(rhs, reverse_method_name)(lhs, _builder=self.builder) + return getattr(lhs, method_name)(rhs) + + def visit_BinOp(self, node): + lhs = self.visit(node.left) + rhs = self.visit(node.right) + method_name = self._method_name_for_bin_op.get(type(node.op)) + if method_name is None: + raise UnsupportedLanguageConstruct( + None, node, "AST binary operator '{}' is not (currently) implemented.".format(node.op.__name__)) + return self._apply_binary_method(method_name, lhs, rhs) + + _method_name_for_bin_op: Dict[Type[ast.operator], str] = { + ast.Add: '__add__', + ast.Sub: '__sub__', + ast.Mult: '__mul__', + ast.Div: '__truediv__', + ast.FloorDiv: '__floordiv__', + ast.Mod: '__mod__', + ast.Pow: '__pow__', + ast.LShift: '__lshift__', + ast.RShift: '__rshift__', + ast.BitAnd: '__and__', + ast.BitOr: '__or__', + ast.BitXor: '__xor__', + } + + def visit_then_else_blocks(self, node, liveins, then_block, else_block): + # then block + self.builder.set_insertion_point_to_start(then_block) + self.visit_compound_statement(node.body) + then_block = self.builder.get_insertion_block() + then_defs = self.local_defs.copy() + # else block + else_defs = {} + if node.orelse: + self.builder.set_insertion_point_to_start(else_block) + self.lscope = liveins.copy() + self.local_defs = {} + self.visit_compound_statement(node.orelse) + else_defs = self.local_defs.copy() + else_block = self.builder.get_insertion_block() + + # update block arguments + names = [] + ret_types = [] + ir_ret_types = [] + # variables in livein whose value is updated in `if` + for name in liveins: + # check type + for defs, block_name in [(then_defs, 'then'), (else_defs, 'else')]: + if name in defs: + assert defs[name].type == liveins[name].type, \ + f'initial value for `{name}` is of type {liveins[name].type}, '\ + f'but the {block_name} block redefines it as {defs[name].type}' + if name in then_defs or name in else_defs: + names.append(name) + ret_types.append(then_defs[name].type if name in then_defs else else_defs[name].type) + ir_ret_types.append(then_defs[name].handle.get_type() if name in + then_defs else else_defs[name].handle.get_type()) + # variable defined in then but not in else + if name in then_defs and name not in else_defs: + else_defs[name] = liveins[name] + # variable defined in else but not in then + if name in else_defs and name not in then_defs: + then_defs[name] = liveins[name] + # variables that are both in then and else but not in liveins + # TODO: could probably be cleaned up + for name in then_defs.keys() & else_defs.keys(): + if name in names: + continue + then_ty = then_defs[name].type + else_ty = else_defs[name].type + assert then_ty == else_ty, \ + f'mismatched type for {name} between then block ({then_ty}) '\ + f'and else block ({else_ty})' + names.append(name) + ret_types.append(then_ty) + ir_ret_types.append(then_defs[name].handle.get_type()) + + return then_defs, else_defs, then_block, else_block, names, ret_types, ir_ret_types + + def visit_if_top_level(self, cond, node): + has_endif_block = True + with enter_sub_region(self) as sr: + liveins, ip_block = sr + then_block = self.builder.create_block() + else_block = self.builder.create_block() + # create basic-block after conditional + endif_block = self.builder.create_block() + # create branch + self.builder.set_insertion_point_to_end(ip_block) + self.builder.create_cond_branch(cond.handle, then_block, else_block) + # visit then and else blocks + then_defs, else_defs, then_block, else_block, names, ret_types, ir_ret_types = \ + self.visit_then_else_blocks(node, liveins, then_block, else_block) + # then terminator + self.builder.set_insertion_point_to_end(then_block) + if then_block.has_return() and else_block.has_return(): + has_endif_block = False + endif_block.erase() + if not then_block.has_terminator() and has_endif_block: + self.builder.create_branch(endif_block, [then_defs[n].handle for n in names]) + # else terminator + self.builder.set_insertion_point_to_end(else_block) + if not else_block.has_terminator() and has_endif_block: + self.builder.create_branch(endif_block, [else_defs[n].handle for n in names]) + if has_endif_block: + for ty in ir_ret_types: + endif_block.add_argument(ty) + if has_endif_block: + # change block + self.builder.set_insertion_point_to_start(endif_block) + # update value + for i, name in enumerate(names): + new_tensor = language.core.tensor(endif_block.arg(i), ret_types[i]) + self.set_value(name, new_tensor) + + # TODO: refactor + def visit_if_scf(self, cond, node): + with enter_sub_region(self) as sr: + liveins, _ = sr + ip, last_loc = self._get_insertion_point_and_loc() + then_block = self.builder.create_block() + else_block = self.builder.create_block() if node.orelse else None + then_defs, else_defs, then_block, else_block, names, ret_types, _ = \ + self.visit_then_else_blocks(node, liveins, then_block, else_block) + # create if op + self._set_insertion_point_and_loc(ip, last_loc) + if_op = self.builder.create_if_op([ty.to_ir(self.builder) for ty in ret_types], cond.handle, True) + then_block.merge_block_before(if_op.get_then_block()) + self.builder.set_insertion_point_to_end(if_op.get_then_block()) + if len(names) > 0: + self.builder.create_yield_op([then_defs[n].handle for n in names]) + if not node.orelse: + else_block = if_op.get_else_block() + else: + else_block.merge_block_before(if_op.get_else_block()) + self.builder.set_insertion_point_to_end(if_op.get_else_block()) + if len(names) > 0: + self.builder.create_yield_op([else_defs[n].handle for n in names]) + # update values + for i, name in enumerate(names): + new_tensor = language.core.tensor(if_op.get_result(i), ret_types[i]) + self.set_value(name, new_tensor) + + def visit_If(self, node): + cond = self.visit(node.test) + if _is_triton_tensor(cond): + cond = cond.to(language.int1, _builder=self.builder) + contains_return = ContainsReturnChecker(self.gscope).visit(node) + if self.scf_stack and contains_return: + raise UnsupportedLanguageConstruct( + None, node, "Cannot have `return` statements inside `while` or `for` statements in triton " + "(note that this also applies to `return` statements that are inside functions " + "transitively called from within `while`/`for` statements)") + elif self.scf_stack or not contains_return: + self.visit_if_scf(cond, node) + else: + self.visit_if_top_level(cond, node) + else: + cond = _unwrap_if_constexpr(cond) + # not isinstance - we insist the real thing, no subclasses and no ducks + if type(cond) not in _condition_types: + raise UnsupportedLanguageConstruct( + None, node, + "`if` conditionals can only accept values of type {{{}}}, not objects of type {}".format( + ', '.join(_.__name__ for _ in _condition_types), + type(cond).__name__)) + if cond: + self.visit_compound_statement(node.body) + else: + self.visit_compound_statement(node.orelse) + + def visit_IfExp(self, node): + cond = self.visit(node.test) + if _is_triton_tensor(cond): + cond = cond.to(language.int1, _builder=self.builder) + # TODO: Deal w/ more complicated return types (e.g tuple) + with enter_sub_region(self): + ip, last_loc = self._get_insertion_point_and_loc() + + then_block = self.builder.create_block() + self.builder.set_insertion_point_to_start(then_block) + then_val = language.core._to_tensor(self.visit(node.body), self.builder) + then_block = self.builder.get_insertion_block() + + else_block = self.builder.create_block() + self.builder.set_insertion_point_to_start(else_block) + # do not need to reset lscope since + # ternary expressions cannot define new variables + else_val = language.core._to_tensor(self.visit(node.orelse), self.builder) + else_block = self.builder.get_insertion_block() + + self._set_insertion_point_and_loc(ip, last_loc) + + assert then_val.type == else_val.type, \ + f'ternary expression with dynamic condition has inconsistent types {then_val.type} and {else_val.type}' + ret_type = then_val.type + + ret_type_ir = [ret_type.to_ir(self.builder)] if ret_type != language.void else [] + if_op = self.builder.create_if_op(ret_type_ir, cond.handle, True) + then_block.merge_block_before(if_op.get_then_block()) + if ret_type_ir: + self.builder.set_insertion_point_to_end(if_op.get_then_block()) + self.builder.create_yield_op([then_val.handle]) + + self.builder.set_insertion_point_to_end(if_op.get_then_block()) + else_block.merge_block_before(if_op.get_else_block()) + if ret_type_ir: + self.builder.set_insertion_point_to_end(if_op.get_else_block()) + self.builder.create_yield_op([else_val.handle]) + return language.core.tensor(if_op.get_result(0), ret_type) if ret_type_ir else None + else: + cond = _unwrap_if_constexpr(cond) + + # not isinstance - we insist the real thing, no subclasses and no ducks + if type(cond) not in _condition_types: + raise UnsupportedLanguageConstruct( + None, node, + "`if` conditionals can only accept values of type {{{}}}, not objects of type {}".format( + ', '.join(_.__name__ for _ in _condition_types), + type(cond).__name__)) + if cond: + return self.visit(node.body) + else: + return self.visit(node.orelse) + + def visit_Pass(self, node): + pass + + def visit_Compare(self, node): + if not (len(node.comparators) == 1 and len(node.ops) == 1): + raise UnsupportedLanguageConstruct(None, node, "simultaneous multiple comparison is not supported") + lhs = self.visit(node.left) + rhs = self.visit(node.comparators[0]) + lhs_value = _unwrap_if_constexpr(lhs) + rhs_value = _unwrap_if_constexpr(rhs) + if type(node.ops[0]) == ast.Is: + return constexpr(lhs_value is rhs_value) + if type(node.ops[0]) == ast.IsNot: + return constexpr(lhs_value is not rhs_value) + method_name = self._method_name_for_comp_op.get(type(node.ops[0])) + if method_name is None: + raise UnsupportedLanguageConstruct( + None, node, "AST comparison operator '{}' is not (currently) implemented.".format(node.ops[0].__name__)) + return self._apply_binary_method(method_name, lhs, rhs) + + _method_name_for_comp_op: Dict[Type[ast.cmpop], str] = { + ast.Eq: '__eq__', ast.NotEq: '__ne__', ast.Lt: '__lt__', ast.LtE: '__le__', ast.Gt: '__gt__', ast.GtE: '__ge__' + } + + def visit_UnaryOp(self, node): + op = self.visit(node.operand) + fn = self._method_name_for_unary_op.get(type(node.op)) + if fn is None: + raise UnsupportedLanguageConstruct( + None, node, "AST unary operator '{}' is not (currently) implemented.".format(node.op.__name__)) + if _is_triton_tensor(op): + return getattr(op, fn)(_builder=self.builder) + return getattr(op, fn)() + + _method_name_for_unary_op: Dict[Type[ast.unaryop], str] = { + ast.USub: '__neg__', ast.UAdd: '__pos__', ast.Not: '__not__', ast.Invert: '__invert__' + } + + def visit_While(self, node): + with enter_sub_region(self) as sr: + liveins, insert_block = sr + ip, last_loc = self._get_insertion_point_and_loc() + + # loop body (the after region) + # loop_block = self.builder.create_block() + dummy = self.builder.create_block() + self.builder.set_insertion_point_to_start(dummy) + self.scf_stack.append(node) + self.visit_compound_statement(node.body) + self.scf_stack.pop() + loop_defs = self.local_defs + dummy.erase() + + # collect loop-carried values + names = [] + ret_types = [] + init_args = [] + for name in loop_defs: + if name in liveins: + # We should not def new constexpr + assert _is_triton_tensor(loop_defs[name]), f'cannoe reassign constxpr {name} in the loop' + assert _is_triton_tensor(liveins[name]), f'cannot reasign constexpr {name} in the loop' + assert loop_defs[name].type == liveins[name].type, \ + f'Loop-carried variable {name} has initial type {liveins[name].type} '\ + f'but is re-assigned to {loop_defs[name].type} in loop! '\ + f'Please make sure that the type stays consistent.' + + # these are loop-carried values + names.append(name) + ret_types.append(loop_defs[name].type) + init_args.append(liveins[name]) + + self._set_insertion_point_and_loc(ip, last_loc) + while_op = self.builder.create_while_op([ty.to_ir(self.builder) for ty in ret_types], + [arg.handle for arg in init_args]) + # merge the condition region + before_block = self.builder.create_block_with_parent(while_op.get_before(), + [ty.to_ir(self.builder) for ty in ret_types]) + self.builder.set_insertion_point_to_start(before_block) + for i, name in enumerate(names): + self.lscope[name] = language.core.tensor(before_block.arg(i), ret_types[i]) + self.local_defs[name] = self.lscope[name] + cond = self.visit(node.test) + self.builder.set_insertion_point_to_end(before_block) + # create ConditionOp: e.g., scf.condition(%cond) %arg0, %arg1, ... + self.builder.create_condition_op(cond.handle, [before_block.arg(i) for i in range(len(init_args))]) + # merge the loop body + after_block = self.builder.create_block_with_parent(while_op.get_after(), + [ty.to_ir(self.builder) for ty in ret_types]) + + # generate loop body + self.builder.set_insertion_point_to_start(after_block) + for i, name in enumerate(names): + self.lscope[name] = language.core.tensor(after_block.arg(i), ret_types[i]) + self.local_defs[name] = self.lscope[name] + self.scf_stack.append(node) + self.visit_compound_statement(node.body) + self.scf_stack.pop() + loop_defs = self.local_defs + yields = [] + for name in loop_defs: + if name in liveins: + yields.append(loop_defs[name]) + self.builder.create_yield_op([y.handle for y in yields]) + + # WhileOp defines new values, update the symbol table (lscope, local_defs) + for i, name in enumerate(names): + new_def = language.core.tensor(while_op.get_result(i), ret_types[i]) + self.lscope[name] = new_def + self.local_defs[name] = new_def + + for stmt in node.orelse: + assert False, "Not implemented" + ast.NodeVisitor.generic_visit(self, stmt) + + def visit_Subscript(self, node): + assert node.ctx.__class__.__name__ == "Load" + lhs = self.visit(node.value) + slices = self.visit(node.slice) + if _is_triton_tensor(lhs): + return lhs.__getitem__(slices, _builder=self.builder) + return lhs[slices] + + def visit_ExtSlice(self, node): + return [self.visit(dim) for dim in node.dims] + + def visit_For(self, node): + IteratorClass = self.visit(node.iter.func) + iter_args = [self.visit(arg) for arg in node.iter.args] + if IteratorClass == language.static_range: + iterator = IteratorClass(*iter_args) + static_range = range(iterator.start.value, iterator.end.value, iterator.step.value) + for i in static_range: + self.lscope[node.target.id] = constexpr(i) + self.visit_compound_statement(node.body) + for stmt in node.orelse: + ast.NodeVisitor.generic_visit(self, stmt) + return + + if IteratorClass is not range: + raise RuntimeError('Only `range` and `static_range` iterators are currently supported') + + # visit iterator arguments + # note: only `range` iterator is supported now + # collect lower bound (lb), upper bound (ub), and step + lb = iter_args[0] if len(iter_args) > 1 else self.visit(ast.Num(0)) + ub = iter_args[1] if len(iter_args) > 1 else self.visit(node.iter.args[0]) + step = iter_args[2] if len(iter_args) > 2 else self.visit(ast.Num(1)) + # handle negative constant step (not supported by scf.for in MLIR) + negative_step = False + if _is_constexpr(step) and step.value < 0: + step = constexpr(-step.value) + negative_step = True + lb, ub = ub, lb + lb = language.core._to_tensor(lb, self.builder) + ub = language.core._to_tensor(ub, self.builder) + step = language.core._to_tensor(step, self.builder) + # induction variable type + if not lb.dtype.is_int() or not ub.dtype.is_int() or not step.dtype.is_int(): + raise TypeError(f"For loop bounds and step must all be ints, are ({lb.dtype}, {ub.dtype}, {step.dtype})") + iv_type = language.semantic.integer_promote_impl(lb.dtype, ub.dtype) + iv_type = language.semantic.integer_promote_impl(iv_type, step.dtype) + iv_ir_type = iv_type.to_ir(self.builder) + iv_is_signed = iv_type.int_signedness == language.core.dtype.SIGNEDNESS.SIGNED + # lb/ub/step might be constexpr, we need to cast them to tensor + lb = lb.handle + ub = ub.handle + step = step.handle + # ForOp can only accept IndexType as lb/ub/step. Cast integer to Index + lb = self.builder.create_int_cast(lb, iv_ir_type, iv_is_signed) + ub = self.builder.create_int_cast(ub, iv_ir_type, iv_is_signed) + step = self.builder.create_int_cast(step, iv_ir_type, iv_is_signed) + # Create placeholder for the loop induction variable + iv = self.builder.create_undef(iv_ir_type) + self.set_value(node.target.id, language.core.tensor(iv, iv_type)) + + with enter_sub_region(self) as sr: + liveins, insert_block = sr + ip, last_loc = self._get_insertion_point_and_loc() + + # create loop body block + block = self.builder.create_block() + self.builder.set_insertion_point_to_start(block) + # dry visit loop body + self.scf_stack.append(node) + self.visit_compound_statement(node.body) + self.scf_stack.pop() + block.erase() + + # If a variable (name) is defined in both its parent & itself, then it's + # a loop-carried variable. (They must be of the same type) + init_args = [] + yields = [] + names = [] + for name in self.local_defs: + if name in liveins: + assert _is_triton_tensor(self.local_defs[name]), f'{name} is not tensor' + assert _is_triton_tensor(liveins[name]) + assert self.local_defs[name].type == liveins[name].type, \ + f'Loop-carried variable {name} has initial type {liveins[name].type} '\ + f'but is re-assigned to {self.local_defs[name].type} in loop! '\ + f'Please make sure that the type stays consistent.' + + names.append(name) + init_args.append(language.core._to_tensor(liveins[name], self.builder)) + yields.append(language.core._to_tensor(self.local_defs[name], self.builder)) + + # create ForOp + self._set_insertion_point_and_loc(ip, last_loc) + for_op = self.builder.create_for_op(lb, ub, step, [arg.handle for arg in init_args]) + + self.scf_stack.append(node) + self.builder.set_insertion_point_to_start(for_op.get_body(0)) + for i, name in enumerate(names): + self.set_value(name, language.core.tensor(for_op.get_body(0).arg(i + 1), yields[i].type)) + self.visit_compound_statement(node.body) + self.scf_stack.pop() + yields = [] + for name in self.local_defs: + if name in liveins: + yields.append(language.core._to_tensor(self.local_defs[name], self.builder)) + + # create YieldOp + if len(yields) > 0: + self.builder.create_yield_op([y.handle for y in yields]) + for_op_region = for_op.get_body(0).get_parent() + assert for_op_region.size() == 1, "We use SCF, so the loop body should only have one block" + + # update induction variable with actual value, and replace all uses + self.builder.set_insertion_point_to_start(for_op.get_body(0)) + iv = for_op.get_induction_var() + if negative_step: + iv = self.builder.create_sub(ub, iv) + iv = self.builder.create_add(iv, lb) + self.lscope[node.target.id].handle.replace_all_uses_with(iv) + self.set_value(node.target.id, language.core.tensor(iv, iv_type)) + + # update lscope & local_defs (ForOp defines new values) + for i, name in enumerate(names): + self.set_value(name, language.core.tensor(for_op.get_result(i), yields[i].type)) + + for stmt in node.orelse: + assert False, "Don't know what to do with else after for" + ast.NodeVisitor.generic_visit(self, stmt) + + def visit_Slice(self, node): + lower = self.visit(node.lower) + upper = self.visit(node.upper) + step = self.visit(node.step) + return slice(lower, upper, step) + + def visit_Index(self, node): + return self.visit(node.value) + + def visit_keyword(self, node) -> Tuple[str, Any]: + return node.arg, self.visit(node.value) + + def visit_Assert(self, node) -> Any: + if not self.debug: + return + test = self.visit(node.test) + msg = self.visit(node.msg) + # Convert assert to triton's device_assert which happens on the device + return language.core.device_assert(test, msg, _builder=self.builder) + + def call_JitFunction(self, fn: JITFunction, args, kwargs): + args = inspect.getcallargs(fn.fn, *args, **kwargs) + args = [args[name] for name in fn.arg_names] + args = [arg if _is_triton_tensor(arg) else constexpr(arg) for arg in args] + # generate function def + attributes = dict() + constexprs = [i for i, arg in enumerate(args) if _is_constexpr(arg)] + constants = {i: args[i] for i in constexprs} + # generate call + args = [None if i in constexprs else arg for i, arg in enumerate(args)] + arg_vals = [arg.handle for arg in args if arg is not None] + arg_types = [arg.type for arg in args if arg is not None] + fn_name = mangle_fn(fn.__name__, arg_types, constants) + # generate function def if necessary + if not self.module.has_function(fn_name): + prototype = language.function_type([], arg_types) + gscope = sys.modules[fn.fn.__module__].__dict__ + # If the callee is not set, we use the same debug setting as the caller + file_name, begin_line = _get_fn_file_line(fn) + debug = self.debug if fn.debug is None else fn.debug + generator = CodeGenerator(self.context, prototype, gscope, attributes, constants, module=self.module, + function_name=fn_name, function_types=self.function_ret_types, + noinline=fn.noinline, file_name=file_name, begin_line=begin_line, + options=self.builder.options, debug=debug) + generator.visit(fn.parse()) + callee_ret_type = generator.last_ret_type + self.function_ret_types[fn_name] = callee_ret_type + else: + callee_ret_type = self.function_ret_types[fn_name] + symbol = self.module.get_function(fn_name) + call_op = self.builder.call(symbol, arg_vals) + if call_op.get_num_results() == 0 or callee_ret_type is None: + return None + elif call_op.get_num_results() == 1: + return tensor(call_op.get_result(0), callee_ret_type) + else: + # should return a tuple of tl.tensor + results = [] + for i in range(call_op.get_num_results()): + results.append(tensor(call_op.get_result(i), callee_ret_type[i])) + return tuple(results) + + def visit_Call(self, node): + fn = _unwrap_if_constexpr(self.visit(node.func)) + + static_implementation = self.statically_implemented_functions.get(fn) + if static_implementation is not None: + return static_implementation(self, node) + + kws = dict(self.visit(keyword) for keyword in node.keywords) + args = [self.visit(arg) for arg in node.args] + if fn is language.core.device_assert: # TODO: this should not be so hardcoded + if not self.debug: + return + if isinstance(fn, JITFunction): + _check_fn_args(node, fn, args) + return self.call_JitFunction(fn, args, kws) + if (hasattr(fn, '__self__') and _is_triton_tensor(fn.__self__)) or language.core.is_builtin(fn): + extra_kwargs = dict(_builder=self.builder) + sig = inspect.signature(fn) + if '_generator' in sig.parameters: + extra_kwargs['_generator'] = self + return fn(*args, **extra_kwargs, **kws) + if fn in self.builtin_namespace.values(): + args = map(_unwrap_if_constexpr, args) + return fn(*args, **kws) + + def visit_Constant(self, node): + return constexpr(node.value) + + def visit_BoolOp(self, node: ast.BoolOp): + if len(node.values) != 2: + raise UnsupportedLanguageConstruct( + None, node, + "chained boolean operators (A or B or C) are not supported; use parentheses to split the chain.") + lhs = self.visit(node.values[0]) + rhs = self.visit(node.values[1]) + method_name = self._method_name_for_bool_op.get(type(node.op)) + if method_name is None: + raise UnsupportedLanguageConstruct( + None, node, "AST boolean operator '{}' is not (currently) implemented.".format(node.op.__name__)) + return self._apply_binary_method(method_name, lhs, rhs) + + _method_name_for_bool_op: Dict[Type[ast.boolop], str] = {ast.And: 'logical_and', ast.Or: 'logical_or'} + + if sys.version_info < (3, 8): + + def visit_NameConstant(self, node): + return constexpr(node.value) + + def visit_Num(self, node): + return constexpr(node.n) + + def visit_Str(self, node): + return constexpr(ast.literal_eval(node)) + + def visit_Attribute(self, node): + lhs = self.visit(node.value) + if _is_triton_tensor(lhs): + if node.attr == "T": + return language.semantic.trans(lhs, builder=self.builder) + return getattr(lhs, node.attr) + + def visit_Expr(self, node): + ast.NodeVisitor.generic_visit(self, node) + + def visit_NoneType(self, node): + return None + + def visit_JoinedStr(self, node): + values = list(node.values) + for i, value in enumerate(values): + if isinstance(value, ast.Constant): + values[i] = str(value.value) + elif isinstance(value, ast.FormattedValue): + conversion_code = value.conversion + evaluated = self.visit(value.value) + if not _is_constexpr(evaluated): + raise UnsupportedLanguageConstruct( + None, node, + "Cannot evaluate f-string containing non-constexpr conversion values, found conversion of type " + + str(type(evaluated))) + values[i] = ("{}" if conversion_code < 0 else "{!" + chr(conversion_code) + "}").format(evaluated.value) + else: + raise AssertionError("encountered unexpected node of type {} in a JoinedStr node".format(type(value))) + return ''.join(values) + + def visit(self, node): + if node is None: + return + with warnings.catch_warnings(): + # The ast library added visit_Constant and deprecated some other + # methods but we can't move to that without breaking Python 3.6 and 3.7. + warnings.simplefilter("ignore", DeprecationWarning) # python 3.9 + warnings.simplefilter("ignore", PendingDeprecationWarning) # python 3.8 + self.last_node = node + last_loc = self.builder.get_loc() + if hasattr(node, 'lineno') and hasattr(node, 'col_offset'): + self.builder.set_loc(self.file_name, self.begin_line + node.lineno, node.col_offset) + last_loc = self.builder.get_loc() + ret = super().visit(node) + # Reset the location to the last one before the visit + if last_loc: + self.builder.set_loc(last_loc) + return ret + + def generic_visit(self, node): + raise UnsupportedLanguageConstruct(None, node, "unsupported AST node type: {}".format(type(node).__name__)) + + def execute_static_print(self, node: ast.Call) -> None: + # TODO: too simplistic? Perhaps do something else with non-constexpr + + kws = {name: _unwrap_if_constexpr(value) for name, value in (self.visit(keyword) for keyword in node.keywords)} + args = [_unwrap_if_constexpr(self.visit(arg)) for arg in node.args] + print(*args, **kws) + + def execute_static_assert(self, node: ast.Call) -> None: + arg_count = len(node.args) + if not (0 < arg_count <= 2) or len(node.keywords): + raise TypeError("`static_assert` requires one or two positional arguments only") + + passed = _unwrap_if_constexpr(self.visit(node.args[0])) + if not isinstance(passed, bool): + raise NotImplementedError( + "Assertion condition could not be determined at compile-time. Make sure that it depends only on `constexpr` values" + ) + if not passed: + if arg_count == 1: + message = "" + else: + try: + message = self.visit(node.args[1]) + except Exception as e: + message = "" + + raise CompileTimeAssertionFailure(None, node, _unwrap_if_constexpr(message)) + return None + + statically_implemented_functions: Dict[object, Callable[[ast.Call], Any]] = { + language.core.static_assert: execute_static_assert, + language.core.static_print: execute_static_print, + } + + +def str_to_ty(name): + if name[0] == "*": + ty = str_to_ty(name[1:]) + return language.pointer_type(ty) + tys = { + "fp8e4nv": language.float8e4nv, + "fp8e5": language.float8e5, + "fp8e4b15": language.float8e4b15, + "fp8e4b15x4": language.float8e4b15x4, + "fp16": language.float16, + "bf16": language.bfloat16, + "fp32": language.float32, + "fp64": language.float64, + "i1": language.int1, + "i8": language.int8, + "i16": language.int16, + "i32": language.int32, + "i64": language.int64, + "u8": language.uint8, + "u16": language.uint16, + "u32": language.uint32, + "u64": language.uint64, + "B": language.int1, + } + return tys[name] + + +def kernel_suffix(signature, specialization): + # suffix format: + # <'c' if equal to 1><'d' if divisible by 16><'e' if divisible by 8> + suffix = '' + for i, _ in enumerate(signature): + suffix += str(i) + if i in specialization.equal_to_1: + suffix += 'c' + if i in specialization.divisible_by_16: + suffix += 'd' + if i in specialization.divisible_by_8: + suffix += 'e' + return suffix + + +def ast_to_ttir(fn, specialization, options): + attrs = specialization.attrs + context = ir.context() + context.load_triton() + # create kernel prototype + cst_key = lambda i: fn.arg_names.index(i) if isinstance(i, str) else i + constants = {cst_key(key): value for key, value in specialization.constants.items()} + # visit kernel AST + gscope = fn.__globals__.copy() + function_name = '_'.join([fn.__name__, kernel_suffix(specialization.signature.values(), attrs)]) + tys = list(specialization.signature.values()) + new_constants = {k: True if k in tys and tys[k] == "i1" else 1 for k in attrs.equal_to_1} + new_attrs = {k: [("tt.divisibility", 16)] for k in attrs.divisible_by_16} + for k in attrs.divisible_by_8: + attr = new_attrs[k] if k in new_attrs else [] + if k in attrs.divisible_by_16: + attr.append(("tt.max_divisibility", 16)) + else: + attr.append(("tt.max_divisibility", 8)) + new_attrs[k] = attr + + all_constants = constants.copy() + all_constants.update(new_constants) + arg_types = [str_to_ty(v) for k, v in specialization.signature.items() if k not in constants] + file_name, begin_line = _get_fn_file_line(fn) + + prototype = language.function_type([], arg_types) + generator = CodeGenerator(context, prototype, gscope=gscope, constants=all_constants, function_name=function_name, + attributes=new_attrs, is_kernel=True, file_name=file_name, begin_line=begin_line, + options=options) + try: + generator.visit(fn.parse()) + except CompilationError as e: + if e.src is None: + e.set_source_code(fn.src) + raise + except Exception as e: + node = generator.last_node + if node is None: + raise + raise CompilationError(fn.src, node, repr(e)) from e + ret = generator.module + # module takes ownership of the context + ret.context = context + return ret diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/compiler.py b/llmeval-env/lib/python3.10/site-packages/triton/compiler/compiler.py new file mode 100644 index 0000000000000000000000000000000000000000..18b2346a61691f9b6632224841e040911c5a085d --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/compiler/compiler.py @@ -0,0 +1,270 @@ +from __future__ import annotations + +import hashlib +import json + +from .._C.libtriton.triton import (get_env_vars, ir) +# from ..runtime import driver, jit, JITFunction +# TODO: runtime.errors +from ..runtime.autotuner import OutOfResources +from ..runtime.cache import get_cache_manager +from ..runtime.driver import driver +from .utils import InfoFromBackendForTensorMap +from .backends.cuda import CUDABackend +from dataclasses import dataclass +from .code_generator import ast_to_ttir +from pathlib import Path +import re + + +@dataclass +class AttrsDescriptor: + divisible_by_16: set = None + equal_to_1: set = None + ids_of_folded_args: set = None + divisible_by_8: set = None + + def __post_init__(self): + if self.divisible_by_16 is None: + self.divisible_by_16 = set() + if self.equal_to_1 is None: + self.equal_to_1 = set() + if self.ids_of_folded_args is None: + self.ids_of_folded_args = set() + if self.divisible_by_8 is None: + self.divisible_by_8 = set() + + def hash(self): + key = str([sorted(x) for x in self.__dict__.values()]) + return hashlib.md5(key.encode("utf-8")).hexdigest() + + +# - ^\s*tt\.func\s+ : match the start of the string, any leading whitespace, the keyword func, +# and any following whitespace +# - (public\s+)? : optionally match the keyword public and any following whitespace +# - (@\w+) : match an @ symbol followed by one or more word characters +# (letters, digits, or underscores), and capture it as group 1 (the function name) +# - (\((?:%\w+: \S+(?: \{\S+ = \S+ : \S+\})?(?:, )?)*\)) : match a pair of parentheses enclosing +# zero or more arguments separated by commas, and capture it as group 2 (the argument list) +# - (attributes \{[\S\s]+\})? : optionally match attributes enclosed in braces and capture it as group 3 +mlir_prototype_pattern = r"^\s*tt\.func\s+(?:public\s+)?(@\w+)(\((?:%\w+: [\S\s]+(?: \{\S+ = \S+ : \S+\})?(?:, )?)*\))\s*(attributes \{[\S\s]+\})?\s+\{\s*$" +ptx_prototype_pattern = r"\.(?:visible|extern)\s+\.(?:entry|func)\s+(\w+)\s*\(([^)]*)\)" +prototype_pattern = { + "ttir": mlir_prototype_pattern, + "ttgir": mlir_prototype_pattern, + "ptx": ptx_prototype_pattern, +} + +mlir_arg_type_pattern = r'%\w+: ((?:[^,\s<]+|<[^>]+>)+),?' +ptx_arg_type_pattern = r"\.param\s+\.(\w+)" +arg_type_pattern = { + "ttir": mlir_arg_type_pattern, + "ttgir": mlir_arg_type_pattern, + "ptx": ptx_arg_type_pattern, +} + + +def convert_type_repr(x): + # Currently we only capture the pointer type and assume the pointer is on global memory. + # TODO: Capture and support shared memory space + match = re.search(r'!tt\.ptr<([^,]+)', x) + if match is not None: + return '*' + convert_type_repr(match.group(1)) + return x + + +def _get_num_warps_from_ir_str(src: str): + ttgir_num_warps_pattern = r'"triton_gpu.num-warps"\s?=\s?(\d+)\s?:' + # TODO(jlebar): Using a regex to get num-warps is a hack, and will break if + # e.g. someone has an instruction (not module) attribute named "num-warps". + num_warps_matches = re.findall(ttgir_num_warps_pattern, src) + assert len(num_warps_matches) == 1, "Expected exactly one match for num_warps" + num_warps = int(num_warps_matches[0]) + + # If warp specialization is enabled, the true number of warps from + # the perspective of e.g. CUDA is num-warps times the number of + # specialized groups. + num_warp_groups_matches = re.findall(r'"triton_gpu.num-warp-groups-per-cta"\s?=\s?(\d+)\s?:', src) + assert len(num_warp_groups_matches) == 0 or len(num_warp_groups_matches) == 1, \ + "Expected triton_gpu.num-warp-groups-per-cta attribute to appear 0 or 1 times" + if num_warp_groups_matches: + num_warps *= int(num_warp_groups_matches[0]) + + return num_warps + + +class ASTSource: + + def __init__(self, fn, signature, constants=None, attrs=None) -> None: + self.fn = fn + self.ext = "ttir" + self.name = fn.__name__ + self.signature = signature + self.constants = constants + self.attrs = attrs + if isinstance(self.signature, str): + self.signature = {k: v.strip() for k, v in enumerate(self.signature.split(","))} + if self.constants is None: + self.constants = dict() + if self.attrs is None: + self.attrs = AttrsDescriptor() + + def hash(self): + key = f"{self.fn.cache_key}-{self.attrs.hash()}-{self.signature.values()}-{self.constants}" + return hashlib.md5(key.encode("utf-8")).hexdigest() + + def make_ir(self, options): + return ast_to_ttir(self.fn, self, options=options) + + def metadata(self): + # TODO: remove once TMA support is cleaned up + return {"ids_of_folded_args": tuple([int(k) for k in self.attrs.ids_of_folded_args])} + + def parse_options(self): + return dict() + + +class IRSource: + + def __init__(self, path): + self.path = path + path = Path(path) + self.ext = path.suffix[1:] + self.src = path.read_text() + match = re.search(prototype_pattern[self.ext], self.src, re.MULTILINE) + self.name = match.group(1) + signature = match.group(2) + types = re.findall(arg_type_pattern[self.ext], signature) + self.signature = {k: convert_type_repr(ty) for k, ty in enumerate(types)} + + def hash(self): + return hashlib.md5(self.src.encode("utf-8")).hexdigest() + + def make_ir(self, options): + context = ir.context() + module = ir.parse_mlir_module(self.path, context) + module.context = context + return module + + def metadata(self): + return dict() + + def parse_options(self): + if self.ext == "ttgir": + return {'num_warps': _get_num_warps_from_ir_str(self.src)} + return dict() + + +def compile(src, target=None, options=None): + if target is None: + target = driver.get_current_target() + backend = CUDABackend(target) + # create backend + if not isinstance(src, ASTSource): + assert isinstance(src, str), "source must be either AST or a filepath" + src = IRSource(src) + extra_options = src.parse_options() + options = backend.parse_options(dict(options or dict(), **extra_options)) + # create cache manager + key = f"{src.hash()}-{backend.hash()}-{options.hash()}-{frozenset(sorted(get_env_vars().items()))}" + hash = hashlib.md5(key.encode("utf-8")).hexdigest() + fn_cache_manager = get_cache_manager(hash) + metadata_filename = f"{src.name}.json" + metadata_group = fn_cache_manager.get_group(metadata_filename) or {} + metadata_path = metadata_group.get(metadata_filename) + if metadata_path is not None: + # cache hit! + metadata = json.loads(Path(metadata_path).read_text()) + so_path = backend.make_launcher_stub(src, metadata) + return CompiledKernel(so_path, metadata_path) + # initialize metadata + metadata = { + "target": target, + **options.__dict__, + **get_env_vars(), + **src.metadata(), + } + # run compilation pipeline and populate metadata + stages = dict() + backend.add_stages(stages, options) + first_stage = list(stages.keys()).index(src.ext) + module = src.make_ir(options) + for ext, compile_ir in list(stages.items())[first_stage:]: + next_module = compile_ir(module, metadata) + metadata_group[f"{src.name}.{ext}"] = fn_cache_manager.put(next_module, f"{src.name}.{ext}") + module = next_module + # write-back metadata + metadata_group[metadata_filename] = fn_cache_manager.put(json.dumps(metadata, default=vars), metadata_filename, + binary=False) + fn_cache_manager.put_group(metadata_filename, metadata_group) + so_path = backend.make_launcher_stub(src, metadata) + # return handle to compiled kernel + return CompiledKernel(so_path, metadata_group.get(metadata_filename)) + + +class CompiledKernel: + + # Hooks for external tools to monitor the execution of triton kernels + # TODO: move out of this namespace since it's a runtime thing + launch_enter_hook = None + launch_exit_hook = None + + def __init__(self, so_path, metadata_path): + metadata_path = Path(metadata_path) + # initialize launcher + import importlib.util + spec = importlib.util.spec_from_file_location("__triton_launcher", so_path) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + self.run = getattr(mod, "launch") + # initialize metadata + self.metadata = json.loads(metadata_path.read_text()) + self.metadata['tensormaps_info'] = [InfoFromBackendForTensorMap(e) for e in self.metadata['tensormaps_info'] + ] if 'tensormaps_info' in self.metadata else [] + for i, _ in enumerate(self.metadata["tensormaps_info"]): + self.metadata["tensormaps_info"][i].ids_of_folded_args = tuple(self.metadata["ids_of_folded_args"]) + for key, val in self.metadata.items(): + setattr(self, key, val) + # stores the text of each level of IR that was generated during compilation + asm_files = [file for file in metadata_path.parent.glob(f'{metadata_path.stem}.*') if file.suffix != '.json'] + self.asm = { + file.suffix[1:]: file.read_bytes() if file.suffix[1:] == driver.binary_ext else file.read_text() + for file in asm_files + } + self.kernel = self.asm[driver.binary_ext] + # binaries are lazily initialized + # because it involves doing runtime things + # (e.g., checking amount of shared memory on current device) + self.module = None + self.function = None + + def _init_handles(self): + if self.module is not None: + return + device = driver.get_current_device() + # not enough shared memory to run the kernel + max_shared = driver.utils.get_device_properties(device)["max_shared_mem"] + if self.shared > max_shared: + raise OutOfResources(self.shared, max_shared, "shared memory") + # TODO: n_regs, n_spills should be metadata generated when calling `ptxas` + self.module, self.function, self.n_regs, self.n_spills = driver.utils.load_binary( + self.name, self.kernel, self.shared, device) + + def __getattribute__(self, name): + if name == 'run': + self._init_handles() + return super().__getattribute__(name) + + def __getitem__(self, grid): + self._init_handles() + + def runner(*args, stream=None): + args_expand = driver.assemble_tensormap_to_arg(self.tensormaps_info, args) + if stream is None: + device = driver.get_current_device() + stream = driver.get_current_stream(device) + self.run(grid[0], grid[1], grid[2], self.num_warps, self.num_ctas, self.cluster_dims[0], + self.cluster_dims[1], self.cluster_dims[2], self.shared, stream, self.function, + CompiledKernel.launch_enter_hook, CompiledKernel.launch_exit_hook, self, *args_expand) + + return runner diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/errors.py b/llmeval-env/lib/python3.10/site-packages/triton/compiler/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..2930117b51706426216d9c3815437b02c32c302b --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/compiler/errors.py @@ -0,0 +1,52 @@ +import ast +from typing import Optional, Union + + +class CompilationError(Exception): + source_line_count_max_in_message = 12 + + def _format_message(self) -> str: + node = self.node + if self.src is None: + source_excerpt = " " + else: + source_excerpt = self.src.split('\n')[:node.lineno][-self.source_line_count_max_in_message:] + if source_excerpt: + source_excerpt.append(' ' * node.col_offset + '^') + source_excerpt = '\n'.join(source_excerpt) + else: + source_excerpt = " " + + message = "at {}:{}:{}".format(node.lineno, node.col_offset, source_excerpt) + if self.error_message: + message += '\n' + self.error_message + return message + + def __init__(self, src: Optional[str], node: ast.AST, error_message: Union[str, None]): + self.src = src + self.node = node + self.error_message = error_message + self.message = self._format_message() + + def set_source_code(self, src: Optional[str]): + self.src = src + self.message = self._format_message() + + def __str__(self): + return self.message + + def __repr__(self): + return "{}({!r})".format(type(self).__name__, self.message) + + def __reduce__(self): + # this is necessary to make CompilationError picklable + return type(self), (self.src, self.node, self.error_message) + + +class CompileTimeAssertionFailure(CompilationError): + """Specific exception for failed tests in `static_assert` invocations""" + pass + + +class UnsupportedLanguageConstruct(CompilationError): + pass diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/make_launcher.py b/llmeval-env/lib/python3.10/site-packages/triton/compiler/make_launcher.py new file mode 100644 index 0000000000000000000000000000000000000000..52a8f74a11eb34ef8a571775ddc03db6e39577b3 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/compiler/make_launcher.py @@ -0,0 +1,297 @@ +import hashlib +import os +import tempfile + +from ..common import _build +from ..common.backend import get_cuda_version_key +from ..common.build import is_hip +from ..runtime.cache import get_cache_manager +from .utils import generate_cu_signature + +# ----- stub -------- + + +def make_so_cache_key(version_hash, signature, constants, ids, **kwargs): + # Get unique key for the compiled code + signature = {k: 'ptr' if v[0] == '*' else v for k, v in signature.items()} + key = f"{version_hash}-{''.join(signature.values())}-{constants}-{ids}" + for kw in kwargs: + key = f"{key}-{kwargs.get(kw)}" + key = hashlib.md5(key.encode("utf-8")).hexdigest() + return key + + +def make_stub(name, signature, constants, ids, **kwargs): + # name of files that are cached + so_cache_key = make_so_cache_key(get_cuda_version_key(), signature, constants, ids, **kwargs) + so_cache_manager = get_cache_manager(so_cache_key) + so_name = f"{name}.so" + # retrieve stub from cache if it exists + cache_path = so_cache_manager.get_file(so_name) + if cache_path is None: + with tempfile.TemporaryDirectory() as tmpdir: + src = generate_launcher(constants, signature, ids) + src_path = os.path.join(tmpdir, "main.c") + with open(src_path, "w") as f: + f.write(src) + so = _build(name, src_path, tmpdir) + with open(so, "rb") as f: + return so_cache_manager.put(f.read(), so_name, binary=True) + else: + return cache_path + + +# ----- source code generation -------- + + +def ty_to_cpp(ty): + if ty[0] == '*': + return "hipDeviceptr_t" if is_hip() else "CUdeviceptr" + return { + "i1": "int32_t", + "i8": "int8_t", + "i16": "int16_t", + "i32": "int32_t", + "i64": "int64_t", + "u32": "uint32_t", + "u64": "uint64_t", + "fp16": "float", + "bf16": "float", + "fp32": "float", + "f32": "float", + "fp64": "double", + }[ty] + + +def generate_launcher(constants, signature, ids): + # Record the end of regular arguments; + # subsequent arguments are architecture-specific descriptors, such as tensor descriptors for CUDA. + signature, desc_start_idx = generate_cu_signature(constants, signature, ids) + arg_decls = ', '.join(f"{ty_to_cpp(ty)} arg{i}" for i, ty in signature.items()) + + def _extracted_type(ty): + if ty[0] == '*': + return "PyObject*" + return { + 'i1': 'int32_t', + 'i32': 'int32_t', + 'i64': 'int64_t', + 'u32': 'uint32_t', + 'u64': 'uint64_t', + 'fp16': 'float', + 'bf16': 'float', + 'fp32': 'float', + 'f32': 'float', + 'fp64': 'double', + }[ty] + + def format_of(ty): + return { + "PyObject*": "O", + "float": "f", + "double": "d", + "long": "l", + "uint32_t": "I", + "int32_t": "i", + "uint64_t": "K", + "int64_t": "L", + }[ty] + + format = "iiiiiiiiiKKOOO" + ''.join([format_of(_extracted_type(ty)) for ty in signature.values()]) + + # generate glue code + folded_without_constexprs = [c for c in ids['ids_of_folded_args'] if c not in ids['ids_of_const_exprs']] + params = [ + i for i in signature.keys() + if i >= desc_start_idx or (i not in constants and i not in folded_without_constexprs) + ] + src = f""" +#include \"cuda.h\" +#include +#include +#include + +static inline void gpuAssert(CUresult code, const char *file, int line) +{{ + if (code != CUDA_SUCCESS) + {{ + const char* prefix = "Triton Error [CUDA]: "; + const char* str; + cuGetErrorString(code, &str); + char err[1024] = {{0}}; + strcat(err, prefix); + strcat(err, str); + PyGILState_STATE gil_state; + gil_state = PyGILState_Ensure(); + PyErr_SetString(PyExc_RuntimeError, err); + PyGILState_Release(gil_state); + }} +}} + +#define CUDA_CHECK(ans) {{ gpuAssert((ans), __FILE__, __LINE__); }} + +typedef CUresult (*cuLaunchKernelEx_t)(const CUlaunchConfig* config, CUfunction f, void** kernelParams, void** extra); + +static cuLaunchKernelEx_t getLaunchKernelExHandle() {{ + // Open the shared library + void* handle = dlopen("libcuda.so", RTLD_LAZY); + if (!handle) {{ + PyErr_SetString(PyExc_RuntimeError, "Failed to open libcuda.so"); + return NULL; + }} + // Clear any existing error + dlerror(); + cuLaunchKernelEx_t cuLaunchKernelExHandle = (cuLaunchKernelEx_t)dlsym(handle, "cuLaunchKernelEx"); + // Check for errors + const char *dlsym_error = dlerror(); + if (dlsym_error) {{ + PyErr_SetString(PyExc_RuntimeError, "Failed to retrieve cuLaunchKernelEx from libcuda.so"); + return NULL; + }} + return cuLaunchKernelExHandle; +}} + +static void _launch(int gridX, int gridY, int gridZ, int num_warps, int num_ctas, int clusterDimX, int clusterDimY, int clusterDimZ, int shared_memory, CUstream stream, CUfunction function{', ' + arg_decls if len(arg_decls) > 0 else ''}) {{ + void *params[] = {{ {', '.join(f"&arg{i}" for i in params)} }}; + if (gridX*gridY*gridZ > 0) {{ + if (num_ctas == 1) {{ + CUDA_CHECK(cuLaunchKernel(function, gridX, gridY, gridZ, 32*num_warps, 1, 1, shared_memory, stream, params, 0)); + }} else {{ + CUlaunchAttribute launchAttr[2]; + launchAttr[0].id = CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION; + launchAttr[0].value.clusterDim.x = clusterDimX; + launchAttr[0].value.clusterDim.y = clusterDimY; + launchAttr[0].value.clusterDim.z = clusterDimZ; + launchAttr[1].id = CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE; + launchAttr[1].value.clusterSchedulingPolicyPreference = CU_CLUSTER_SCHEDULING_POLICY_SPREAD; + CUlaunchConfig config; + config.gridDimX = gridX * clusterDimX; + config.gridDimY = gridY * clusterDimY; + config.gridDimZ = gridZ * clusterDimZ; + config.blockDimX = 32 * num_warps; + config.blockDimY = 1; + config.blockDimZ = 1; + config.sharedMemBytes = shared_memory; + config.hStream = stream; + config.attrs = launchAttr; + config.numAttrs = 2; + static cuLaunchKernelEx_t cuLaunchKernelExHandle = NULL; + if (cuLaunchKernelExHandle == NULL) {{ + cuLaunchKernelExHandle = getLaunchKernelExHandle(); + }} + CUDA_CHECK(cuLaunchKernelExHandle(&config, function, params, 0)); + }} + }} +}} + +typedef struct _DevicePtrInfo {{ + CUdeviceptr dev_ptr; + bool valid; +}} DevicePtrInfo; + +static inline DevicePtrInfo getPointer(PyObject *obj, int idx) {{ + DevicePtrInfo ptr_info; + ptr_info.dev_ptr = 0; + ptr_info.valid = true; + if (PyLong_Check(obj)) {{ + ptr_info.dev_ptr = PyLong_AsUnsignedLongLong(obj); + return ptr_info; + }} + if (obj == Py_None) {{ + // valid nullptr + return ptr_info; + }} + PyObject *ptr = PyObject_GetAttrString(obj, "data_ptr"); + if(ptr){{ + PyObject *empty_tuple = PyTuple_New(0); + PyObject *ret = PyObject_Call(ptr, empty_tuple, NULL); + Py_DECREF(empty_tuple); + Py_DECREF(ptr); + if (!PyLong_Check(ret)) {{ + PyErr_SetString(PyExc_TypeError, "data_ptr method of Pointer object must return 64-bit int"); + ptr_info.valid = false; + return ptr_info; + }} + ptr_info.dev_ptr = PyLong_AsUnsignedLongLong(ret); + if(!ptr_info.dev_ptr) + return ptr_info; + uint64_t dev_ptr; + int status = cuPointerGetAttribute(&dev_ptr, CU_POINTER_ATTRIBUTE_DEVICE_POINTER, ptr_info.dev_ptr); + if (status == CUDA_ERROR_INVALID_VALUE) {{ + PyErr_Format(PyExc_ValueError, + "Pointer argument (at %d) cannot be accessed from Triton (cpu tensor?)", idx); + ptr_info.valid = false; + }} + ptr_info.dev_ptr = dev_ptr; + Py_DECREF(ret); // Thanks ChatGPT! + return ptr_info; + }} + PyErr_SetString(PyExc_TypeError, "Pointer argument must be either uint64 or have data_ptr method"); + ptr_info.valid = false; + return ptr_info; +}} + +static PyObject* launch(PyObject* self, PyObject* args) {{ + int gridX, gridY, gridZ; + uint64_t _stream; + uint64_t _function; + int num_warps; + int num_ctas; + int clusterDimX; + int clusterDimY; + int clusterDimZ; + int shared_memory; + PyObject *launch_enter_hook = NULL; + PyObject *launch_exit_hook = NULL; + PyObject *compiled_kernel = NULL; + {' '.join([f"{_extracted_type(ty)} _arg{i}; " for i, ty in signature.items()])} + if(!PyArg_ParseTuple(args, \"{format}\", &gridX, &gridY, &gridZ, &num_warps, &num_ctas, &clusterDimX, &clusterDimY, &clusterDimZ, &shared_memory, &_stream, &_function, &launch_enter_hook, &launch_exit_hook, &compiled_kernel{', ' + ', '.join(f"&_arg{i}" for i, ty in signature.items()) if len(signature) > 0 else ''})) {{ + return NULL; + }} + + if (launch_enter_hook != Py_None && !PyObject_CallObject(launch_enter_hook, args)) {{ + return NULL; + }} + + + // raise exception asap + {"; ".join([f"DevicePtrInfo ptr_info{i} = getPointer(_arg{i}, {i}); if (!ptr_info{i}.valid) return NULL;" if ty[0] == "*" else "" for i, ty in signature.items()])}; + Py_BEGIN_ALLOW_THREADS; + _launch(gridX, gridY, gridZ, num_warps, num_ctas, clusterDimX, clusterDimY, clusterDimZ, shared_memory, (CUstream)_stream, (CUfunction)_function{', ' + ', '.join(f"ptr_info{i}.dev_ptr" if ty[0]=="*" else f"_arg{i}"for i, ty in signature.items()) if len(signature) > 0 else ''}); + Py_END_ALLOW_THREADS; + if (PyErr_Occurred()) {{ + return NULL; + }} + + if (launch_exit_hook != Py_None && !PyObject_CallObject(launch_exit_hook, args)) {{ + return NULL; + }} + + // return None + Py_INCREF(Py_None); + return Py_None; +}} + +static PyMethodDef ModuleMethods[] = {{ + {{"launch", launch, METH_VARARGS, "Entry point for all kernels with this signature"}}, + {{NULL, NULL, 0, NULL}} // sentinel +}}; + +static struct PyModuleDef ModuleDef = {{ + PyModuleDef_HEAD_INIT, + \"__triton_launcher\", + NULL, //documentation + -1, //size + ModuleMethods +}}; + +PyMODINIT_FUNC PyInit___triton_launcher(void) {{ + PyObject *m = PyModule_Create(&ModuleDef); + if(m == NULL) {{ + return NULL; + }} + PyModule_AddFunctions(m, ModuleMethods); + return m; +}} +""" + return src diff --git a/llmeval-env/lib/python3.10/site-packages/triton/compiler/utils.py b/llmeval-env/lib/python3.10/site-packages/triton/compiler/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..48233afedd4c0bf7bbb94d274060aae2eedf6d32 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/compiler/utils.py @@ -0,0 +1,282 @@ +# Copyright (c) 2023 NVIDIA Corporation & Affiliates. All rights reserved. +# +# Permission is hereby granted, free of charge, to any person obtaining +# a copy of this software and associated documentation files +# (the "Software"), to deal in the Software without restriction, +# including without limitation the rights to use, copy, modify, merge, +# publish, distribute, sublicense, and/or sell copies of the Software, +# and to permit persons to whom the Software is furnished to do so, +# subject to the following conditions: +# +# The above copyright notice and this permission notice shall be +# included in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +from __future__ import annotations + +from ..runtime import driver + + +def generate_cu_signature(constants, signature, ids): + # CUtensorMap*s are always the last arguments + num_regular_signatures = max(signature.keys()) + 1 if len(signature) > 0 else 0 + if ids["ids_of_tensormaps"] is not None: + for i, _ in enumerate(ids["ids_of_tensormaps"]): + signature[num_regular_signatures + i] = '*CUtensorMap' + return signature, num_regular_signatures + + +def dummy_tensormaps_info(n=2): + ret = [] + for i in range(n): + ret.append(InfoFromBackendForTensorMap(dummy=True)) + return ret + + +def parse_tma_info(infos, ids_of_folded_args): + ret = [] + for info in infos: + e = InfoFromBackendForTensorMap(infos=info) + e.ids_of_folded_args = ids_of_folded_args + ret.append(e) + return ret + + +def get_tma_mapping(tensormaps_info): + ret = {} + if tensormaps_info is not None: + for i, e in enumerate(tensormaps_info): + ret.update(e.get_address_tma_mapping()) + else: + ret = None + return ret + + +def get_ids_of_tensormaps(tensormaps_info): + ret = None + # order is not relevant + if tensormaps_info is not None: + ret = [e.get_id_of_tensormap() for e in tensormaps_info] + return ret + + +# decouple information for tensormap from backend +# please ignore the naming style, xx_yy is compiler.py style, xxYy is to comply with cuda tensormap style +# mixing style is for readability +class InfoFromBackendForTensorMap: + N = 2 + n = 0 + ntma = 0 + + def __init__(self, infos=None, dummy=False): + self.dummy = dummy + self.ids_of_folded_args = () + if not dummy and not isinstance(infos, dict): + self._extract_info_from_backend(infos) + elif not dummy and isinstance(infos, dict): + self._extract_info_from_dict(infos) + elif dummy: + self._dummy() + + def _dummy(self): + assert InfoFromBackendForTensorMap.n < InfoFromBackendForTensorMap.N + if InfoFromBackendForTensorMap.n == 0: + self.tensorDataType = driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT16"] + self.tensorRank = 4 + self.globalAddressArgIdx = 0 + self.globalStridesArgIdx = [7, 6, -1, -1] + self.globalDimsArgIdx = [5, 3, -1, -1] + self.boxDims = [16, 64, 1, 1] + self.elementStrides = [1, 1, 1, 1] + self.interleave = driver.utils.CUtensorMapInterleave["CU_TENSOR_MAP_INTERLEAVE_NONE"] + self.swizzle = driver.utils.CUtensorMapSwizzle["CU_TENSOR_MAP_SWIZZLE_32B"] + self.l2Promotion = driver.utils.CUtensorMapL2promotion["CU_TENSOR_MAP_L2_PROMOTION_L2_128B"] + self.TMADescArgIdx = 11 + self.oobFill = driver.utils.CUtensorMapFloatOOBfill["CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE"] + InfoFromBackendForTensorMap.n += 1 + return + if InfoFromBackendForTensorMap.n == 1: + self.tensorDataType = driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT16"] + self.tensorRank = 4 + self.globalAddressArgIdx = 1 + self.globalStridesArgIdx = [7, 6, -1, -1] + self.globalDimsArgIdx = [5, 3, -1, -1] + self.boxDims = [16, 64, 1, 1] + self.elementStrides = [1, 1, 1, 1] + self.interleave = driver.utils.CUtensorMapInterleave["CU_TENSOR_MAP_INTERLEAVE_NONE"] + self.swizzle = driver.utils.CUtensorMapSwizzle["CU_TENSOR_MAP_SWIZZLE_32B"] + self.l2Promotion = driver.utils.CUtensorMapL2promotion["CU_TENSOR_MAP_L2_PROMOTION_L2_128B"] + self.TMADescArgIdx = 12 + self.oobFill = driver.utils.CUtensorMapFloatOOBfill["CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE"] + InfoFromBackendForTensorMap.n += 1 + return + + def _extract_info_from_backend(self, infos): + self.tensorDataType = infos.tensorDataType + self.tensorRank = infos.tensorRank + self.globalAddressArgIdx = infos.globalAddressArgIdx + self.globalStridesArgIdx = infos.globalStridesArgIdx + self.globalDimsArgIdx = infos.globalDimsArgIdx + self.boxDims = infos.boxDims + self.elementStrides = infos.elementStrides + self.interleave = infos.interleave + self.swizzle = infos.swizzle + self.l2Promotion = infos.l2Promotion + self.oobFill = infos.oobFill + self.TMADescArgIdx = infos.TMADescArgIdx + + # dict could be from cached metadata json + def _extract_info_from_dict(self, infos: dict): + self.tensorDataType = infos['tensorDataType'] + self.tensorRank = infos['tensorRank'] + self.globalAddressArgIdx = infos['globalAddressArgIdx'] + self.globalStridesArgIdx = infos['globalStridesArgIdx'] + self.globalDimsArgIdx = infos['globalDimsArgIdx'] + self.boxDims = infos['boxDims'] + self.elementStrides = infos['elementStrides'] + self.interleave = infos['interleave'] + self.swizzle = infos['swizzle'] + self.l2Promotion = infos['l2Promotion'] + self.oobFill = infos['oobFill'] + self.TMADescArgIdx = infos['TMADescArgIdx'] + + def get_address_tma_mapping(self): + return {self.globalAddressArgIdx: self.TMADescArgIdx + len(self.ids_of_folded_args)} + + def get_id_of_tensormap(self): + return self.TMADescArgIdx + len(self.ids_of_folded_args) + + def getTMADescArgIdx(self): + return self.TMADescArgIdx + + # dtype:cuda.CUtensorMapDataType | int + def bytes_from_type(self, dtype): + return { + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_UINT8"]: 1, + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_UINT16"]: 2, + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_UINT32"]: 4, + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_INT32"]: 4, + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_UINT64"]: 8, + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_INT64"]: 8, + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT16"]: 2, + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT32"]: 4, + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT64"]: 8, + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_BFLOAT16"]: 2, + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ"]: 4, + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_TFLOAT32"]: 4, + driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ"]: 4 + }[dtype] + + def getTensorMapDataType(self): + return self.tensorDataType + + def getInterleave(self): + return self.interleave + + def getSwizzle(self): + return self.swizzle + + def getL2Promotion(self): + return self.l2Promotion + + def getOobFill(self): + return self.oobFill + + def getTensorRank(self): + return self.tensorRank + + def getBoxDims(self): + return self.boxDims + + def getElementStrides(self): + return self.elementStrides + + def getGlobalAddress(self, args): + idx = self.getOriginArgIdx(self.globalAddressArgIdx, args) + return args[idx] + + # args, captured kernel args in runtime + def getGlobalDims(self, args): + shape = [] + for e in self.globalDimsArgIdx: + t = 1 + # < 0 means folded arg or constant (-1 - value) + # -1 means extended dim which is 1, -2 means folded arg with constant 1 (-1 - value) + if e == -1: + t = 1 + elif e < 0 and e != -1: + t = -e - 1 + else: + idx = self.getOriginArgIdx(e, args) + t = args[idx] + shape.append(t) + return shape + + def getGlobalStrides(self, args): + t_globalDims = [int(e) for e in self.getGlobalDims(args)] + t_globalStridesArgIdx = self.globalStridesArgIdx.copy() + strides_in_elements = [] + # todo: get all stride from backend even in extended mode + for i in range(self.tensorRank): + t = 1 + if t_globalStridesArgIdx[i] == -1: + for ii in range(i): + t *= t_globalDims[ii] + # -2 means the sride in arguments is folded constant 1, we don't use 1 because it can not be distinguished from index 1 + elif t_globalStridesArgIdx[i] < 0: + t = -1 - t_globalStridesArgIdx[i] + else: + new_idx = self.getOriginArgIdx(t_globalStridesArgIdx[i], args) + t = args[new_idx] + + strides_in_elements.append(t) + + strides_in_elements = strides_in_elements[1:] + strides_in_bytes = [e * self.bytes_from_type(self.tensorDataType) for e in strides_in_elements] + return strides_in_bytes + + def getOriginArgIdx(self, idx, args): + if self.ids_of_folded_args: + ids_before_folding_arg = [i for i in range(len(args)) if i not in self.ids_of_folded_args] + return ids_before_folding_arg[idx] + else: + return idx + + def tensormap(self, args): + return driver.utils.cuTensorMapEncodeTiled( + self.getTensorMapDataType(), + self.getTensorRank(), + self.getGlobalAddress(args), + self.getGlobalDims(args), + self.getGlobalStrides(args), + self.getBoxDims(), + self.getElementStrides(), + self.getInterleave(), + self.getSwizzle(), + self.getL2Promotion(), + self.getOobFill(), + ) + + # make hashable to use as partial key in cache + def __hash__(self): + return hash((self.ids_of_folded_args, self.globalAddressArgIdx, tuple(self.globalDimsArgIdx), + tuple(self.globalStridesArgIdx), self.tensorDataType, self.tensorRank, tuple(self.boxDims), + tuple(self.elementStrides), self.interleave, self.swizzle, self.l2Promotion, self.oobFill)) + + def __eq__(self, other): + if not isinstance(other, self.__class__): + return False + return (self.ids_of_folded_args, self.globalAddressArgIdx, self.globalDimsArgIdx, self.globalStridesArgIdx, + self.tensorDataType, self.tensorRank, self.boxDims, self.elementStrides, self.interleave, self.swizzle, + self.l2Promotion, + self.oobFill) == (other.ids_of_folded_args, other.globalAddressArgIdx, other.globalDimsArgIdx, + other.globalStridesArgIdx, other.tensorDataType, other.tensorRank, other.boxDims, + other.elementStrides, other.interleave, other.swizzle, other.l2Promotion, + other.oobFill) diff --git a/llmeval-env/lib/python3.10/site-packages/triton/language/extra/__init__.py b/llmeval-env/lib/python3.10/site-packages/triton/language/extra/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2fd0ff3eeee37a0d4f7d44ef36f66b41895ff09f --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/language/extra/__init__.py @@ -0,0 +1,3 @@ +from . import cuda + +__all__ = ['cuda'] diff --git a/llmeval-env/lib/python3.10/site-packages/triton/language/extra/__pycache__/__init__.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/language/extra/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..29ce56e385dec0dbe4c7f6862e4125dccb4bf176 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/language/extra/__pycache__/__init__.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/language/extra/__pycache__/cuda.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/language/extra/__pycache__/cuda.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..771f0fba08af815ff19c37f734c4bbdbdebb69b7 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/language/extra/__pycache__/cuda.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/language/extra/cuda.py b/llmeval-env/lib/python3.10/site-packages/triton/language/extra/cuda.py new file mode 100644 index 0000000000000000000000000000000000000000..1cb494d9fa7fea9abafbb91bba149bd0461a3f4f --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/language/extra/cuda.py @@ -0,0 +1,18 @@ +from .. import core + + +@core.extern +def globaltimer(_builder=None): + return core.inline_asm_elementwise("mov.u64 $0, %globaltimer;", "=l", [], dtype=core.int64, is_pure=False, pack=1, + _builder=_builder) + + +@core.extern +def smid(_builder=None): + return core.inline_asm_elementwise("mov.u32 $0, %smid;", "=r", [], dtype=core.int32, is_pure=True, pack=1, + _builder=_builder) + + +@core.builtin +def num_threads(_builder=None): + return core.constexpr(_builder.options.num_warps * 32) diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/__init__.py b/llmeval-env/lib/python3.10/site-packages/triton/ops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6ceec8b56a00ba5ad66682aa3f8cdd7015a304a4 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/ops/__init__.py @@ -0,0 +1,14 @@ +# from .conv import _conv, conv +from . import blocksparse +from .cross_entropy import _cross_entropy, cross_entropy +from .flash_attention import attention +from .matmul import _matmul, matmul + +__all__ = [ + "blocksparse", + "_cross_entropy", + "cross_entropy", + "_matmul", + "matmul", + "attention", +] diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/__init__.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e349d5466160de12f15b029dc6faa7f193bee429 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/__init__.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/cross_entropy.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/cross_entropy.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..716b1100007046c0778775cbc6b31be3d337e1ae Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/cross_entropy.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/flash_attention.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/flash_attention.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8e069a3d941639dd763489fa4d7d9395c330460b Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/flash_attention.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/matmul.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/matmul.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3f474c371ae3f4f83448e4cfb61bd3413209378c Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/matmul.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/matmul_perf_model.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/matmul_perf_model.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..355d1b83e2279765a542f65035aa8ecab5827e5e Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/ops/__pycache__/matmul_perf_model.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/blocksparse/__pycache__/__init__.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/ops/blocksparse/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b648a65615f827ca39cff488a63e1732235c0058 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/ops/blocksparse/__pycache__/__init__.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/blocksparse/__pycache__/matmul.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/ops/blocksparse/__pycache__/matmul.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9903f0f11df4a1a2089583fbc8f31f8e204b6c5d Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/ops/blocksparse/__pycache__/matmul.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/blocksparse/matmul.py b/llmeval-env/lib/python3.10/site-packages/triton/ops/blocksparse/matmul.py new file mode 100644 index 0000000000000000000000000000000000000000..098e1543809e9dcea3a77c3a38538bcd34153eb8 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/ops/blocksparse/matmul.py @@ -0,0 +1,432 @@ +import torch + +from ... import cdiv, heuristics, jit +from ... import language as tl + +# ******************************************************** +# -------------------------------------------------------- +# Sparse = Dense x Dense (SDD) +# This operation uses super-blocking to make sure that +# it's done efficiently when small blocks can be grouped +# together +# -------------------------------------------------------- +# ******************************************************** + + +@heuristics({ + 'EVEN_K': lambda nargs: nargs['K'] % nargs['TILE_K'] == 0, +}) +@jit +def _sdd_kernel(A, B, C, # + stride_za, stride_ha, stride_ma, stride_ak, # + stride_zb, stride_hb, stride_bk, stride_nb, # + stride_zc, stride_hc, stride_mc, stride_nc, # + K, grid_offset, lut, # + TILE_M: tl.constexpr, TILE_N: tl.constexpr, TILE_K: tl.constexpr, # + BLOCK: tl.constexpr, EVEN_K: tl.constexpr # + ): + # ------------ # + # - Prologue - # + # ------------ # + block_id = tl.program_id(0) + grid_offset + lut += block_id * 3 + # offsets + off_z = tl.program_id(2) # batch + off_h = tl.load(lut + 0) # head + + # initialize pointers to A + start_am = tl.load(lut + 1) + offs_am = start_am * BLOCK + (tl.arange(0, TILE_M) % BLOCK) + offs_ak = tl.arange(0, TILE_K) + a_ptrs = A \ + + off_z * stride_za \ + + off_h * stride_ha \ + + offs_am[:, None] * stride_ma \ + + offs_ak[None, :] * stride_ak + # initialize pointers to B + start_bn = tl.load(lut + 2) + offs_bn = start_bn * BLOCK + (tl.arange(0, TILE_N) % BLOCK) + offs_bk = tl.arange(0, TILE_K) + b_ptrs = B \ + + off_z * stride_zb \ + + off_h * stride_hb \ + + offs_bn[None, :] * stride_nb \ + + offs_bk[:, None] * stride_bk + # ---------------- # + # Inner Loop # + # ---------------- # + acc = tl.zeros((TILE_M, TILE_N), dtype=tl.float32) + for k in range(K, 0, -TILE_K): + if EVEN_K: + a = tl.load(a_ptrs) + b = tl.load(b_ptrs) + else: + a = tl.load(a_ptrs, mask=offs_ak[None, :] < k, other=0.) + b = tl.load(b_ptrs, mask=offs_bk[:, None] < k, other=0.) + acc += tl.dot(a, b, out_dtype=tl.float32) + a_ptrs += TILE_K * stride_ak + b_ptrs += TILE_K * stride_bk + c = acc.to(C.dtype.element_ty) + # ---------------- # + # Epilogue # + # ---------------- # + offs_cm = tl.arange(0, TILE_M) % BLOCK + offs_cn = tl.arange(0, TILE_N) % BLOCK + pc = C \ + + off_z * stride_zc \ + + block_id * stride_hc \ + + offs_cm[:, None] * stride_mc \ + + offs_cn[None, :] * stride_nc + tl.store(pc, c, mask=True) + + +def sdd_matmul(a, b, trans_a, trans_b, trans_c, spdims, block, lut, widths, out=None): + if a.stride(2) != 1 and a.stride(3) != 1: + a = a.contiguous() + if b.stride(2) != 1 and b.stride(3) != 1: + b = b.contiguous() + # (A * B)^T = B^T * A^T + if trans_c: + a, b = b, a + trans_a, trans_b = not trans_b, not trans_a + # shape constraints + a_dim = -2 if trans_a else -1 + b_dim = -1 if trans_b else -2 + Ka, Kb = a.shape[a_dim], b.shape[b_dim] + if Ka != Kb: + raise ValueError(f"Inner dimension mismatch (A: {Ka} vs B: {Kb})") + # allocate output + if out is None: + c = torch.empty((a.shape[0], lut.shape[0], block, block), dtype=a.dtype, device=a.device) + else: + assert out.shape == (a.shape[0], lut.shape[0], block, block) + c = out + grid = [c.shape[1], 1, c.shape[0]] + _sdd_kernel[grid]( + a, b, c, # + a.stride(0), a.stride(1), a.stride(3 if trans_a else 2), a.stride(2 if trans_a else 3), # + b.stride(0), b.stride(1), b.stride(3 if trans_b else 2), b.stride(2 if trans_b else 3), # + c.stride(0), c.stride(1), c.stride(2), c.stride(3), # + Ka, 0, lut, # + TILE_M=block, TILE_N=block, TILE_K=32, BLOCK=block, num_stages=4, # + num_warps=4 # + ) + return c + + +def sdd_lut(layout, block, device): + lut = layout.nonzero(as_tuple=False).to(device).int() + lut = lut.contiguous() + return lut, None + + +# ----------------------------- +# Dense = Sparse x Dense (DSD) +# This operation uses a look-up table that contains pre-computed pointer increments +# in order to minimize computations in the inner loop of the matmul kernel. +# ----------------------------- + + +@jit +def _dsd_kernel(A, B, C, # + stride_az, stride_ha, stride_am, stride_ak, # + stride_zb, stride_hb, stride_bk, stride_bn, # + stride_zc, stride_hc, stride_cm, stride_cn, # + DS0, DS1, lut, # + TILE_M: tl.constexpr, TILE_N: tl.constexpr, TILE_K: tl.constexpr, # + GROUP_SIZE_M: tl.constexpr, BLOCK: tl.constexpr # + ): + # ------------ # + # - Prologue - # + # ------------ # + pid_m = tl.program_id(0) + pid_n = tl.program_id(1) + num_pid_m = tl.num_programs(0) + num_pid_n = tl.num_programs(1) + pid_n, pid_m = tl.swizzle2d(pid_n, pid_m, num_pid_n, num_pid_m, GROUP_SIZE_M) + pidz = tl.program_id(2) + header = lut + pid_n * 4 + offset = tl.load(header + 0) + K = tl.load(header + 1) + column = tl.load(header + 2) + off_h = tl.load(header + 3) + pinc = lut + offset + # initialize pointers to A (sparse) + block_id = tl.load(pinc + 1) + block_id = tl.multiple_of(block_id, 8) # compiler hint + offs_am = tl.arange(0, TILE_M) + offs_ak = tl.arange(0, TILE_K) + pa = A + pidz * stride_az \ + + block_id * stride_ha \ + + offs_am[:, None] * stride_am \ + + offs_ak[None, :] * stride_ak + # initialize pointers to B (dense) + offs_bn = pid_m * TILE_N + tl.arange(0, TILE_N) + offs_bn = tl.max_contiguous(tl.multiple_of(offs_bn % DS0, TILE_N), TILE_N) + start_bk = tl.load(pinc) + start_bk = tl.multiple_of(start_bk, 8) # compiler hint + offs_bk = start_bk + tl.arange(0, TILE_K) + pb = B + pidz * stride_zb \ + + off_h * stride_hb \ + + offs_bn[None, :] * stride_bn \ + + offs_bk[:, None] * stride_bk + # ---------------- # + # Inner Loop # + # ---------------- # + acc = tl.zeros((TILE_M, TILE_N), dtype=tl.float32) + pinc += 2 + inc_a = tl.load(pinc + 1) + inc_a = tl.multiple_of(inc_a, 8) + inc_b = tl.load(pinc) + inc_b = tl.multiple_of(inc_b, 8) + for k in range(K, 0, -TILE_K): + a = tl.load(pa) + b = tl.load(pb) + acc += tl.dot(a, b, out_dtype=tl.float32) + pa += inc_a + pb += inc_b * stride_bk + pinc += 2 + inc_a = tl.load(pinc + 1) + inc_a = tl.multiple_of(inc_a, 8) + inc_b = tl.load(pinc) + inc_b = tl.multiple_of(inc_b, 8) + c = acc.to(C.dtype.element_ty) + # initialize pointers to C + offs_cm = column * TILE_M + tl.arange(0, TILE_M) + offs_cn = pid_m * TILE_N + tl.arange(0, TILE_N) + pc = C \ + + off_h * stride_hc \ + + pidz * stride_zc \ + + offs_cm[:, None] * stride_cm \ + + offs_cn[None, :] * stride_cn + tl.store(pc, c, mask=offs_cn[None, :] < DS0) + + +def dsd_matmul(a, b, trans_a, trans_b, trans_c, spdims, block, lut, width, out=None): + if a.stride(2) != 1 and a.stride(3) != 1: + a = a.contiguous() + if b.stride(2) != 1 and b.stride(3) != 1: + b = b.contiguous() + # shapes / dtypes + AS1 = block * spdims[2 if trans_a else 1] + BS0 = b.size(0) + BS1 = b.size(1) + BS3 = b.size(2 if trans_b else 3) + dtype = a.dtype + # allocate output + CS0 = BS0 + CS1 = BS1 + CS2 = BS3 if trans_c else AS1 + CS3 = AS1 if trans_c else BS3 + if out is None: + c = torch.empty((CS0, CS1, CS2, CS3), dtype=dtype, device=a.device) + else: + assert out.shape == (CS0, CS1, CS2, CS3) + c = out + # meta-parameter heuristics + TILE_N = 128 + # compute output + grid = lambda meta: [cdiv(BS3, meta['TILE_N']), width, BS0] + _dsd_kernel[grid]( + a, b, c, # + a.stride(0), a.stride(1), a.stride(3 if trans_a else 2), a.stride(2 if trans_a else 3), # + b.stride(0), b.stride(1), b.stride(3 if trans_b else 2), b.stride(2 if trans_b else 3), # + c.stride(0), c.stride(1), c.stride(3 if trans_c else 2), c.stride(2 if trans_c else 3), # + BS3, AS1, lut, # + TILE_M=block, TILE_N=TILE_N, TILE_K=min(block, 32), BLOCK=block, num_stages=4, # + num_warps=4, GROUP_SIZE_M=4 # + ) + # exit() + return c + + +def dsd_lut(layout, block, step, trans, device): + """ + Generates the look-up table for incrementing pointers in the DSD/DDS matmul. + Example (BLOCK=32, STEP=16) + [[1, 0, 0, 1, 0], + [0, 1, 1, 0, 1], + [1, 0, 1, 0, 0]] + + Then the offsets for A are + [0 , 16, 32, 48] <- row 0 + \\----/ \\----/ + col=0 col=3 + [64, 80, 96, 112, 128, 144] <- row 1 + \\----/ \\----/ \\------/ + col=1 col=2 col=3 + [160, 176, 192, 208] + which leads to increments table + [0, 16, 16, 16, || 64, 16, 16, 16, 16, 16, || 160, 16, 16, 16] + + Because B is dense, the offsets are + [0, 16, 96, 112] <- row 0 + [32, 48, 64, 80] <- row 1 + [0, 16, 64, 80] <- row 2 + """ + sizes = torch.sum(layout, 2 if trans else 1) + head_id, col_id = torch.ones_like(sizes).nonzero(as_tuple=True) + sizes = sizes.flatten() + segments = sizes * step + # pointer increments + if trans: + nnz = layout.nonzero(as_tuple=False) + else: + nnz = layout.transpose(1, 2).nonzero(as_tuple=False) + num_blocks = nnz.size(0) + offsets = torch.zeros_like(sizes) + offsets[1:] = torch.cumsum(sizes[:-1], dim=0) + offsets = torch.min(offsets, (num_blocks - 1) * torch.ones_like(offsets)) + # ------------------------------- + # dense input pointer increments + # ------------------------------- + # Note that the inner loop matmul kernel may have a fixed step size (e.g., TILE_K) + # that is smaller than the block size, so we need to do a bit of extra work + # to handle this case + B_idx = nnz[:, 2] * block + B_incs = B_idx.clone() + B_incs[1:] -= B_idx[:-1] + div = block // step + B_incs = B_incs.view(-1, 1).repeat(1, div) + B_incs[:, 1:] = step + B_incs[:, 0] -= (div - 1) * step + # first increment for each reduction is actually the offset + B_incs[offsets[segments > 0], 0] = B_idx[offsets[segments > 0]] + B_incs = B_incs.view(-1) + # ------------------------------- + # sparse input pointer increments + # ------------------------------- + # same as above, except that the increments are in the sparse memory layout + if trans: + A_idx = torch.arange(num_blocks, device=layout.device) + else: + A_idx = torch.tensor([], dtype=torch.int64, device=layout.device) + current_offset = 0 + for z in range(layout.size(0)): + layoutw = layout[z, :, :].clone().long() + msum = layoutw.sum() + layoutw[layoutw > 0] = 1 + torch.arange(msum, device=layout.device) + A_idx = torch.cat((A_idx, current_offset + layoutw.T[layoutw.T > 0] - 1)) + current_offset += msum + A_incs = A_idx * block * block + A_incs[1:] -= A_idx[:-1] * block * block + A_incs = A_incs.view(-1, 1).repeat(1, div) + if trans: + A_incs[:, 1:] = step + A_incs[:, 0] -= (div - 1) * step + else: + A_incs[:, 1:] = step * block + A_incs[:, 0] -= (div - 1) * step * block + A_incs[offsets[segments > 0], 0] = A_idx[offsets[segments > 0]] + A_incs = A_incs.view(-1) + # create header + width = col_id.size(0) + offsets = offsets * 2 * div + 4 * width + segments = segments * div + header = torch.stack((offsets, segments, col_id, head_id), dim=1).view(-1).contiguous() + # create increments + incs = torch.stack((B_incs, A_incs), dim=1).view(-1).contiguous() + # pad by a factor 2*MAX_NUM_STAGES + # to accommodate pre-fetching inside the kernel + pad = torch.zeros(20, device=incs.device, dtype=incs.dtype) + incs = torch.cat((incs, pad)) + # create lut + lut = torch.cat((header, incs)) + lut = lut.type(torch.int32).to(device) + # create locks + return lut, width + + +# ----------------------------- +# Dense = Dense x Sparse (DDS) +# ----------------------------- +# AB = (B^T A^T)^T + + +def dds_matmul(a, b, trans_a, trans_b, trans_c, spdims, block, lut, width, out=None): + return dsd_matmul(b, a, not trans_b, not trans_a, not trans_c, spdims, block, lut, width, out=out) + + +############## +# MAIN API # +############## + + +class _matmul(torch.autograd.Function): + + fn = {'sdd': sdd_matmul, 'dsd': dsd_matmul, 'dds': dds_matmul} + + @staticmethod + def forward(ctx, a, b, trans_a, trans_b, trans_c, mode, spdims, block, c_lut, c_width, da_lut, da_width, db_lut, + db_width, out): + c = _matmul.fn[mode](a, b, trans_a, trans_b, trans_c, spdims, block, c_lut, c_width, out=out) + # save for backward + ctx.save_for_backward(a, b) + ctx.da_lut = da_lut + ctx.da_width = da_width + ctx.db_lut = db_lut + ctx.db_width = db_width + ctx.mode = mode + ctx.spdims = spdims + ctx.block = block + ctx.trans_a = trans_a + ctx.trans_b = trans_b + ctx.trans_c = trans_c + ctx.has_out = out is not None + return c + + @staticmethod + def backward(ctx, dc): + # saved for backward + a, b = ctx.saved_tensors + da, db = None, None + mode = ctx.mode + # gradients w.r.t. a + if ctx.needs_input_grad[0]: + mode_da = mode[1] + mode[0] + mode[2] + da = _matmul.fn[mode_da](dc, b, ctx.trans_c, not ctx.trans_b, ctx.trans_a, ctx.spdims, ctx.block, + ctx.da_lut, ctx.da_width) + # gradients w.r.t. b + if ctx.needs_input_grad[1]: + mode_db = mode[2] + mode[1] + mode[0] + db = _matmul.fn[mode_db](a, dc, not ctx.trans_a, ctx.trans_c, ctx.trans_b, ctx.spdims, ctx.block, + ctx.db_lut, ctx.db_width) + dout = dc if ctx.has_out else None + return da, db, None, None, None, \ + None, None, None, None, \ + None, None, None, None, None, dout + + +class matmul: + + def __init__(self, layout, block, mode, device, trans_a=False, trans_b=False, trans_c=False): + if mode not in ['sdd', 'dsd', 'dds']: + raise NotImplementedError('Supported modes are: sdd, dsd, dds') + self.block = block + self.mode = mode + self.trans_a = trans_a + self.trans_b = trans_b + self.trans_c = trans_c + self.layout = layout + self.spdims = layout.shape + step = min(block, 32) + if self.mode == 'sdd': + self.c_lut, self.c_width = sdd_lut(layout, block, device) + self.da_lut, self.da_width = dsd_lut(layout, block, step, True, device) + self.db_lut, self.db_width = dsd_lut(layout, block, step, False, device) + if self.mode == 'dsd': + self.c_lut, self.c_width = dsd_lut(layout, block, step, not self.trans_a, device) + self.da_lut, self.da_width = sdd_lut(layout, block, device) + self.db_lut, self.db_width = dsd_lut(layout, block, step, self.trans_a, device) + if self.mode == 'dds': + self.c_lut, self.c_width = dsd_lut(layout, block, step, self.trans_b, device) + self.da_lut, self.da_width = dsd_lut(layout, block, step, not self.trans_b, device) + self.db_lut, self.db_width = sdd_lut(layout, block, device) + + def __call__(self, a, b, out=None): + c = _matmul.apply(a, b, self.trans_a, self.trans_b, self.trans_c, self.mode, self.spdims, self.block, # + self.c_lut, self.c_width, # + self.da_lut, self.da_width, # + self.db_lut, self.db_width, # + out) + return c diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/blocksparse/softmax.py b/llmeval-env/lib/python3.10/site-packages/triton/ops/blocksparse/softmax.py new file mode 100644 index 0000000000000000000000000000000000000000..bcffff26bb515aa65f9e199d9e7c3b36bc6fe1c3 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/ops/blocksparse/softmax.py @@ -0,0 +1,228 @@ +import torch + +from ... import jit +from ... import language as tl +from ... import next_power_of_2 + + +def num_warps(n): + if n <= 128: + return 1 + if n <= 256: + return 2 + if n <= 512: + return 4 + if n <= 4096: + return 8 + return 16 + + +@jit +def _blocksparse_softmax_fwd(Out, A, stride_xz, LUT, # + R, extent, stride_zr, stride_hr, # relative attention + scale, is_causal, # + ROW_SIZE: tl.constexpr, # + BLOCK_SIZE: tl.constexpr, # + IS_DENSE: tl.constexpr # + ): + h = tl.program_id(0) + m = tl.program_id(1) + z = tl.program_id(2) + # create index ranges + hm = h * tl.num_programs(1) + m + lane_n = tl.arange(0, ROW_SIZE) % BLOCK_SIZE + block_n = tl.arange(0, ROW_SIZE) // BLOCK_SIZE + # extract information from LUT + header = LUT + (hm // BLOCK_SIZE) * 2 + size = tl.load(header + 0) + offset = tl.load(header + 1) + # pointer offset + off_a = z * stride_xz + off_a += (offset + block_n) * BLOCK_SIZE * BLOCK_SIZE # block indx + off_a += (m % BLOCK_SIZE) * BLOCK_SIZE # row indx + # do not need to read column indices in the dense case + if IS_DENSE: + ns = tl.arange(0, ROW_SIZE) + else: + off_lut = offset + 2 * tl.num_programs(0) * tl.num_programs(1) // BLOCK_SIZE + start_n = tl.load(LUT + off_lut + block_n, mask=block_n < size, other=0) + ns = start_n * BLOCK_SIZE + lane_n + # load X + mask = block_n < size + a = tl.load(A + off_a + lane_n, mask=mask, other=-float("inf")) + a = a.to(tl.float32) + # compute + out = a + out *= scale + # apply relative attention + if R is not None: + R += z * stride_zr + R += h * stride_hr + off_lo = (extent - m - 1) + ns + mask_lo = (off_lo >= 0) & (off_lo < extent) + rel_logits = tl.load(R + m * extent + off_lo, mask=mask_lo, other=0.0) + out += rel_logits + out = out.to(tl.float32) + # apply causal mask + out = tl.where((ns > m) & is_causal, -float("inf"), out) + # computation + out = tl.softmax(out) + # write-back + tl.store(Out + off_a + lane_n, out, mask=mask) + + +@jit +def _blocksparse_softmax_bwd(DA, stride_zdx, # + DOut, stride_zdout, # + Out, stride_zout, # + scale, # + LUT, # + DR, extent, stride_zr, stride_hr, stride_er, # + is_causal, # + ROW_SIZE: tl.constexpr, # + BLOCK_SIZE: tl.constexpr, # + IS_DENSE: tl.constexpr): + h = tl.program_id(0) + m = tl.program_id(1) + z = tl.program_id(2) + # create index ranges + hm = h * tl.num_programs(1) + m + lane_n = tl.arange(0, ROW_SIZE) % BLOCK_SIZE + block_n = tl.arange(0, ROW_SIZE) // BLOCK_SIZE + # extract information from LUT + header = LUT + (hm // BLOCK_SIZE) * 2 + size = tl.load(header + 0) + offset = tl.load(header + 1) + # row-col offset + off_mn = (offset + block_n) * BLOCK_SIZE * BLOCK_SIZE + off_mn += (m % BLOCK_SIZE) * BLOCK_SIZE + mask = block_n < size + # pointers + As = Out + z * stride_zout + off_mn + DOuts = DOut + z * stride_zdout + off_mn + # do not need to read column indices in the dense case + if IS_DENSE: + ns = tl.arange(0, ROW_SIZE) + else: + off_lut = offset + 2 * tl.num_programs(0) * tl.num_programs(1) // BLOCK_SIZE + start_n = tl.load(LUT + off_lut + block_n, mask=mask, other=0) + ns = start_n * BLOCK_SIZE + lane_n + # load data + a = tl.load(As + lane_n, mask=mask, other=0.0) + a = a.to(tl.float32) + dout = tl.load(DOuts + lane_n, mask=mask, other=0.0) + dout = dout.to(tl.float32) + # compute + a = tl.where((ns > m) & is_causal & (a == a), 0., a) + da = a * (dout - tl.sum(a * dout, 0)) + # apply relative attention + if DR is not None: + DR += z * stride_zr + DR += h * stride_hr + off_lo = (extent - m - 1) + ns + mask_lo = (off_lo >= 0) & (off_lo < extent) & mask + tl.store(DR + m * extent + off_lo, da, mask=mask_lo) + da = da * scale + # convert da + # write-back + DAs = DA + z * stride_zdx + off_mn + tl.store(DAs + lane_n, da, mask=mask) + + +class _softmax(torch.autograd.Function): + + @staticmethod + def make_lut(layout, block, device): + _empty = torch.tensor([], dtype=torch.int64, device=layout.device) + sizes = _empty.clone() + # sizes along rows + for h in range(layout.shape[0]): + sizes = torch.cat((sizes, layout[h, :, :].sum(-1))) + total_sizes = sizes * block + # offsets in block format + offsets = torch.zeros_like(sizes) + offsets[1:] = torch.cumsum(sizes[:-1], dim=0) + # block indices + columns = layout.nonzero(as_tuple=False)[:, 2] + header = torch.stack((sizes, offsets), dim=1).view(-1) + lut = torch.cat((header, columns)).type(torch.int32).to(device) + return lut, int(total_sizes.max()) + + @staticmethod + def forward(ctx, a, scale, rel_logits, is_causal, spdims, block, lut, maxlut, is_dense): + if scale is not None and isinstance(scale, torch.Tensor): + assert scale.device.type == "cpu" + scale = scale.item() + M = a.shape[0] + grid = [spdims[0], spdims[1] * block, M] + rel_shape = (1, 1, 1, 1) if rel_logits is None else rel_logits.shape + rel_strides = (1, 1, 1, 1) if rel_logits is None else rel_logits.stride() + # enqueue kernel + out = torch.empty_like(a) + _blocksparse_softmax_fwd[grid]( + out, a, a.stride(0), lut, # + rel_logits, rel_shape[-1], rel_strides[0], rel_strides[1], # relative attn# + scale, # + is_causal, # + BLOCK_SIZE=block, # + ROW_SIZE=next_power_of_2(maxlut), # + IS_DENSE=is_dense, # + num_warps=num_warps(maxlut) # + ) + # save to context + # ctx.mark_dirty(x) + ctx.save_for_backward(out, lut) + ctx.spdims = spdims + ctx.block = block + ctx.maxlut = maxlut + ctx.scale = scale + ctx.rel_shape = rel_shape + ctx.rel_strides = rel_strides + ctx.rel_dtype = a.dtype + ctx.is_dense = is_dense + ctx.is_causal = is_causal + return out + + @staticmethod + def backward(ctx, dout): + # retrieve from context + out, lut = ctx.saved_tensors + # relative logits gradients + dr = None + if ctx.needs_input_grad[3]: + dr = torch.zeros(ctx.rel_shape, dtype=ctx.rel_dtype, device=out.device) + # run kernel + M = out.shape[0] + grid = (ctx.spdims[0], ctx.spdims[1] * ctx.block, M) + da = torch.empty_like(dout) + _blocksparse_softmax_bwd[grid]( + da, da.stride(0), # + dout, dout.stride(0), # + out, out.stride(0), # + ctx.scale, # + lut, # + dr, ctx.rel_shape[-1], ctx.rel_strides[0], ctx.rel_strides[1], ctx.rel_strides[2], # + ctx.is_causal, # + BLOCK_SIZE=ctx.block, # + ROW_SIZE=next_power_of_2(ctx.maxlut), # + IS_DENSE=ctx.is_dense, # + num_warps=num_warps(ctx.maxlut) # + ) + return (da, None, None, dr, None, None, None, None, None, None, None, None, None, None, None, None, None, None) + + +class softmax: + + def __init__(self, layout, block, device, is_dense=False): + self.spdims = layout.shape + self.layout = layout + self.block = block + self.lut, self.maxlut = _softmax.make_lut(self.layout, self.block, device) + self.is_dense = is_dense + + def __call__(self, a, *, scale=1.0, rel_logits=None, is_causal=False): + if rel_logits is not None and rel_logits.dtype != a.dtype: + raise ValueError(f"relative position embedding must be {a.dtype}") + a = _softmax.apply(a, scale, rel_logits, is_causal, self.spdims, self.block, self.lut, self.maxlut, + self.is_dense) + return a diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/cross_entropy.py b/llmeval-env/lib/python3.10/site-packages/triton/ops/cross_entropy.py new file mode 100644 index 0000000000000000000000000000000000000000..88e8dae50db09ca17c1bb8464afee2975b3d77eb --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/ops/cross_entropy.py @@ -0,0 +1,96 @@ +import torch + +from .. import heuristics, jit +from .. import language as tl +from .. import next_power_of_2 + + +def num_warps(N): + if N < 2048: + return 4 + elif N < 8192: + return 8 + return 16 + + +@heuristics({'num_warps': lambda nargs: num_warps(nargs['N'])}) +@heuristics({'BLOCK': lambda nargs: next_power_of_2(nargs['N'])}) +@jit +def _forward(LOGITS, PROBS, IDX, LOSS, N, BLOCK: tl.constexpr): + row = tl.program_id(0) + cols = tl.arange(0, BLOCK) + idx = tl.load(IDX + row) + # pointers to logit and probs + LOGITS = LOGITS + row * N + cols + WRIT_PROBS = PROBS + row * N + cols + READ_PROBS = PROBS + row * N + idx + # write-back negative log-probs + logits = tl.load(LOGITS, mask=cols < N, other=-float('inf')) + logits = logits.to(tl.float32) + logits = logits - tl.max(logits, 0) + probs = tl.log(tl.sum(tl.exp(logits), 0)) - logits + tl.store(WRIT_PROBS, probs, mask=cols < N) + # There is a bug in the compiler, which fails to insert a barrier here. + # We add it explicitly for now. Will be fixed soon. + tl.debug_barrier() + # write-back loss + probs = tl.load(READ_PROBS) + tl.store(LOSS + row, probs) + + +@heuristics({'num_warps': lambda nargs: num_warps(nargs['N'])}) +@heuristics({'BLOCK': lambda nargs: next_power_of_2(nargs['N'])}) +@jit +def _backward(PROBS, IDX, DPROBS, N, BLOCK: tl.constexpr): + row = tl.program_id(0) + cols = tl.arange(0, BLOCK) + idx = tl.load(IDX + row) + # pointers to probs + PROBS = PROBS + row * N + cols + # We know d(-log(p[i])/dlogit[k] = -id_mat[i,k] + p[k] + # and we have -log(p[k]) stored in PROBS, so this is easy + probs = -tl.load(PROBS, mask=cols < N, other=float('inf')) + probs = tl.exp(probs.to(tl.float32)) + delta = cols == idx + # write result in-place in PROBS + dout = tl.load(DPROBS + row) + din = (probs - delta) * dout + tl.store(PROBS, din.to(PROBS.dtype.element_ty), mask=cols < N) + + +class _cross_entropy(torch.autograd.Function): + + @classmethod + def forward(cls, ctx, logits, indices): + # make sure we can use triton + assert (indices.dtype == torch.int64), "Indices are expected to be of type long." + # make kernel + device, dtype = logits.device, logits.dtype + n_cols = logits.shape[-1] + # run the kernel + result = torch.empty_like(indices, dtype=dtype, device=device) + neg_logprobs = torch.empty_like(logits, dtype=dtype, device=device) + grid = lambda opt: (logits.numel() // n_cols, ) + _forward[grid](logits, neg_logprobs, indices, result, n_cols) + # save for backward + ctx.save_for_backward(neg_logprobs, indices) + return result + + @classmethod + def backward(cls, ctx, dneg_logprobs): + """We know d(-log(p[i])/dlogit[k] = -id_mat[i,k] + p[k] + so we initialize the gradient as neg_logprobs, so we can just exponentiate + to get p[k], which is most of what we need... neg_logprobs will be + modified in place to become the gradient we want + """ + # load saved tensors + neg_logprobs, indices = ctx.saved_tensors + # run the kernel + # neg_logprobs will be modified in place to become our gradient: + n_cols = neg_logprobs.shape[-1] + grid = lambda opt: (neg_logprobs.numel() // n_cols, ) + _backward[grid](neg_logprobs, indices, dneg_logprobs, n_cols) + return neg_logprobs, None + + +cross_entropy = _cross_entropy.apply diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/flash_attention.py b/llmeval-env/lib/python3.10/site-packages/triton/ops/flash_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..d024ba7ab005f9566542768595aa632b1de1b8aa --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/ops/flash_attention.py @@ -0,0 +1,456 @@ +""" +Fused Attention +=============== +This is a Triton implementation of the Flash Attention algorithm +(see: Dao et al., https://arxiv.org/pdf/2205.14135v2.pdf; Rabe and Staats https://arxiv.org/pdf/2112.05682v2.pdf) + +Sequence Parallel implementation inspired by HazyResearch +(see https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attn_triton.py) +""" + +import torch + +from .. import cdiv, jit +from .. import language as tl + + +@jit +def _fwd_kernel(Q, K, V, sm_scale, # + L, # + Out, # + stride_qz, stride_qh, stride_qm, stride_qk, # + stride_kz, stride_kh, stride_kn, stride_kk, # + stride_vz, stride_vh, stride_vn, stride_vk, # + stride_oz, stride_oh, stride_om, stride_on, # + Z, H, N_CTX, # + Z_H_N_CTX, # + BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, # + BLOCK_N: tl.constexpr, # + IS_CAUSAL: tl.constexpr # + ): + start_m = tl.program_id(0) + off_hz = tl.program_id(1) + qvk_offset = off_hz * stride_qh + vk_offset = qvk_offset // stride_qm + + K_block_ptr = tl.make_block_ptr( + base=K, + shape=(BLOCK_DMODEL, Z_H_N_CTX), + strides=(stride_kk, stride_kn), + offsets=(0, vk_offset), + block_shape=(BLOCK_DMODEL, BLOCK_N), + order=(0, 1), + ) + V_block_ptr = tl.make_block_ptr( + base=V, + shape=(Z_H_N_CTX, BLOCK_DMODEL), + strides=(stride_vn, stride_vk), + offsets=(vk_offset, 0), + block_shape=(BLOCK_N, BLOCK_DMODEL), + order=(1, 0), + ) + # initialize offsets + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_n = tl.arange(0, BLOCK_N) + # initialize pointer to m and l + m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") + l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) + # credits to: Adam P. Goucher (https://github.com/apgoucher): + # scale sm_scale by 1/log_2(e) and use + # 2^x instead of exp in the loop because CSE and LICM + # don't work as expected with `exp` in the loop + qk_scale = sm_scale * 1.44269504 + # load q: it will stay in SRAM throughout + + offs_k = tl.arange(0, BLOCK_DMODEL) + Q_ptrs = Q + qvk_offset + offs_m[:, None] * stride_qm + offs_k[None, :] * stride_qk + q = tl.load(Q_ptrs) + + q = (q * qk_scale).to(K.dtype.element_ty) + lo = 0 + hi = (start_m + 1) * BLOCK_M if IS_CAUSAL else N_CTX + for start_n in range(lo, hi, BLOCK_N): + # -- load k, v -- + k = tl.load(K_block_ptr) + v = tl.load(V_block_ptr) + # -- compute qk --- + qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) + if IS_CAUSAL: + qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk, float("-inf")) + qk += tl.dot(q, k, allow_tf32=True) + # -- compute scaling constant --- + m_i_new = tl.maximum(m_i, tl.max(qk, 1)) + alpha = tl.math.exp2(m_i - m_i_new) + p = tl.math.exp2(qk - m_i_new[:, None]) + # -- scale and update acc -- + acc *= alpha[:, None] + acc += tl.dot(p.to(V.dtype.element_ty), v, allow_tf32=True) + # -- update m_i and l_i -- + l_i = l_i * alpha + tl.sum(p, 1) + m_i = m_i_new + # update pointers + K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N)) + V_block_ptr = tl.advance(V_block_ptr, (BLOCK_N, 0)) + # write back l and m + acc = acc / l_i[:, None] + l_ptrs = L + off_hz * N_CTX + offs_m + tl.store(l_ptrs, m_i + tl.math.log2(l_i)) + # write back O + O_block_ptr = tl.make_block_ptr( + base=Out, + shape=(Z_H_N_CTX, BLOCK_DMODEL), + strides=(stride_om, stride_on), + offsets=(vk_offset + start_m * BLOCK_M, 0), + block_shape=(BLOCK_M, BLOCK_DMODEL), + order=(1, 0), + ) + # O_ptrs = Out + qvk_offset + offs_m[:, None] * stride_qm + offs_k[None, :] * stride_qk + tl.store(O_block_ptr, acc.to(K.dtype.element_ty)) + + +@jit +def _bwd_preprocess( + Out, + DO, + Delta, + BLOCK_M: tl.constexpr, + D_HEAD: tl.constexpr, +): + off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M) + off_n = tl.arange(0, D_HEAD) + # load + o = tl.load(Out + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32) + do = tl.load(DO + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32) + # compute + delta = tl.sum(o * do, axis=1) + # write-back + tl.store(Delta + off_m, delta) + + +@jit +def _bwd_kernel_one_col_block(Q, K, V, sm_scale, qk_scale, # + Out, DO, # + DQ, DK, DV, # + L, # + D, # + Q_block_ptr, K_block_ptr, V_block_ptr, # + DO_block_ptr, DQ_block_ptr, DK_block_ptr, DV_block_ptr, # + stride_dqa, stride_qz, stride_qh, stride_qm, stride_qk, # + stride_kz, stride_kh, stride_kn, stride_kk, # + stride_vz, stride_vh, stride_vn, stride_vk, # + Z, H, N_CTX, # + off_h, off_z, off_hz, start_n, num_block, # + BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, # + BLOCK_N: tl.constexpr, # + SEQUENCE_PARALLEL: tl.constexpr, # + CAUSAL: tl.constexpr, # + MMA_V3: tl.constexpr # + ): + if CAUSAL: + lo = start_n * BLOCK_M + else: + lo = 0 + + Q_offset = (off_z * stride_qz + off_h * stride_qh) // stride_qm + DQ_offset = off_z * stride_qz + off_h * stride_qh + K_offset = (off_z * stride_kz + off_h * stride_kh) // stride_kn + V_offset = (off_z * stride_vz + off_h * stride_vh) // stride_vn + if SEQUENCE_PARALLEL: + DQ_offset += stride_dqa.to(tl.int64) * start_n + DQ_offset = DQ_offset // stride_qm + + Q_block_ptr = tl.advance(Q_block_ptr, (lo + Q_offset, 0)) + K_block_ptr = tl.advance(K_block_ptr, (start_n * BLOCK_M + K_offset, 0)) + V_block_ptr = tl.advance(V_block_ptr, (start_n * BLOCK_M + V_offset, 0)) + DO_block_ptr = tl.advance(DO_block_ptr, (lo + Q_offset, 0)) + DQ_block_ptr = tl.advance(DQ_block_ptr, (lo + DQ_offset, 0)) + DK_block_ptr = tl.advance(DK_block_ptr, (start_n * BLOCK_M + K_offset, 0)) + DV_block_ptr = tl.advance(DV_block_ptr, (start_n * BLOCK_M + V_offset, 0)) + + # initialize row/col offsets + offs_n = start_n * BLOCK_M + tl.arange(0, BLOCK_M) + offs_m = tl.arange(0, BLOCK_N) + # pointer to row-wise quantities in value-like data + D_ptrs = D + off_hz * N_CTX + l_ptrs = L + off_hz * N_CTX + # initialize dv amd dk + dv = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) + dk = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) + # k and v stay in SRAM throughout + k = tl.load(K_block_ptr) + v = tl.load(V_block_ptr) + # loop over rows + for start_m in range(lo, num_block * BLOCK_M, BLOCK_M): + offs_m_curr = start_m + offs_m + # load q, k, v, do on-chip + q = tl.load(Q_block_ptr) + # recompute p = softmax(qk, dim=-1).T + # NOTE: `do` is pre-divided by `l`; no normalization here + if CAUSAL: + qk = tl.where(offs_m_curr[:, None] >= (offs_n[None, :]), float(0.0), float("-inf")) + else: + qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) + qk += tl.dot(q, tl.trans(k)) + qk *= qk_scale + l_i = tl.load(l_ptrs + offs_m_curr) + p = tl.math.exp2(qk - l_i[:, None]) + # compute dv + do = tl.load(DO_block_ptr) + dv += tl.dot(tl.trans(p.to(Q.dtype.element_ty)), do, allow_tf32=True) + # compute dp = dot(v, do) + Di = tl.load(D_ptrs + offs_m_curr) + # dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) - Di[:, None] + dp = tl.dot(do, tl.trans(v), allow_tf32=True) + # compute ds = p * (dp - delta[:, None]) + ds = (p * (dp - Di[:, None]) * sm_scale).to(Q.dtype.element_ty) + # compute dk = dot(ds.T, q) + dk += tl.dot(tl.trans(ds), q, allow_tf32=True) + # compute dq + if not SEQUENCE_PARALLEL: + dq = tl.load(DQ_block_ptr) + dq += tl.dot(ds, k, allow_tf32=True) + tl.store(DQ_block_ptr, dq.to(Q.dtype.element_ty)) + elif SEQUENCE_PARALLEL: + if MMA_V3: + dq = tl.dot(ds, k, allow_tf32=True) + else: + # not work with mma v3, becuase M % 64 != 0 + dq = tl.trans(tl.dot(tl.trans(k), tl.trans(ds), allow_tf32=True)) + tl.store(DQ_block_ptr, dq.to(Q.dtype.element_ty)) + + # increment pointers + DQ_block_ptr = tl.advance(DQ_block_ptr, (BLOCK_M, 0)) + Q_block_ptr = tl.advance(Q_block_ptr, (BLOCK_M, 0)) + DO_block_ptr = tl.advance(DO_block_ptr, (BLOCK_M, 0)) + # write-back + tl.store(DV_block_ptr, dv.to(V.dtype.element_ty)) + tl.store(DK_block_ptr, dk.to(K.dtype.element_ty)) + + +@jit +def _bwd_kernel(Q, K, V, sm_scale, # + Out, DO, # + DQ, DK, DV, # + L, # + D, # + stride_dqa, stride_qz, stride_qh, stride_qm, stride_qk, # + stride_kz, stride_kh, stride_kn, stride_kk, # + stride_vz, stride_vh, stride_vn, stride_vk, # + Z, H, N_CTX, # + Z_H_N_CTX, # + SQ_Z_H_N_CTX, # + BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, # + BLOCK_N: tl.constexpr, # + SEQUENCE_PARALLEL: tl.constexpr, # + CAUSAL: tl.constexpr, # + MMA_V3: tl.constexpr # + ): + qk_scale = sm_scale * 1.44269504 + off_hz = tl.program_id(0) + off_z = off_hz // H + off_h = off_hz % H + + Q_block_ptr = tl.make_block_ptr( + base=Q, + shape=(Z_H_N_CTX, BLOCK_DMODEL), + strides=(stride_qm, stride_qk), + offsets=(0, 0), + block_shape=(BLOCK_M, BLOCK_DMODEL), + order=(1, 0), + ) + K_block_ptr = tl.make_block_ptr( + base=K, + shape=(Z_H_N_CTX, BLOCK_DMODEL), + strides=(stride_kn, stride_kk), + offsets=(0, 0), + block_shape=(BLOCK_M, BLOCK_DMODEL), + order=(1, 0), + ) + V_block_ptr = tl.make_block_ptr( + base=V, + shape=(Z_H_N_CTX, BLOCK_DMODEL), + strides=(stride_vn, stride_vk), + offsets=(0, 0), + block_shape=(BLOCK_M, BLOCK_DMODEL), + order=(1, 0), + ) + DO_block_ptr = tl.make_block_ptr( + base=DO, + shape=(Z_H_N_CTX, BLOCK_DMODEL), + strides=(stride_qm, stride_qk), + offsets=(0, 0), + block_shape=(BLOCK_M, BLOCK_DMODEL), + order=(1, 0), + ) + if SEQUENCE_PARALLEL: + DQ_block_ptr = tl.make_block_ptr( + base=DQ, + shape=(SQ_Z_H_N_CTX, BLOCK_DMODEL), + strides=(stride_qm, stride_qk), + offsets=(0, 0), + block_shape=(BLOCK_M, BLOCK_DMODEL), + order=(1, 0), + ) + else: + DQ_block_ptr = tl.make_block_ptr( + base=DQ, + shape=(Z_H_N_CTX, BLOCK_DMODEL), + strides=(stride_qm, stride_qk), + offsets=(0, 0), + block_shape=(BLOCK_M, BLOCK_DMODEL), + order=(1, 0), + ) + + DK_block_ptr = tl.make_block_ptr( + base=DK, + shape=(Z_H_N_CTX, BLOCK_DMODEL), + strides=(stride_kn, stride_kk), + offsets=(0, 0), + block_shape=(BLOCK_M, BLOCK_DMODEL), + order=(1, 0), + ) + DV_block_ptr = tl.make_block_ptr( + base=DV, + shape=(Z_H_N_CTX, BLOCK_DMODEL), + strides=(stride_vn, stride_vk), + offsets=(0, 0), + block_shape=(BLOCK_M, BLOCK_DMODEL), + order=(1, 0), + ) + + num_block_n = tl.cdiv(N_CTX, BLOCK_N) + if not SEQUENCE_PARALLEL: + for start_n in range(0, num_block_n): + _bwd_kernel_one_col_block(Q, K, V, sm_scale, qk_scale, Out, DO, # + DQ, DK, DV, # + L, # + D, # + Q_block_ptr, K_block_ptr, V_block_ptr, # + DO_block_ptr, DQ_block_ptr, DK_block_ptr, DV_block_ptr, # + stride_dqa, stride_qz, stride_qh, stride_qm, stride_qk, # + stride_kz, stride_kh, stride_kn, stride_kk, # + stride_vz, stride_vh, stride_vn, stride_vk, # + Z, H, N_CTX, # + off_h, off_z, off_hz, start_n, num_block_n, # + BLOCK_M=BLOCK_M, BLOCK_DMODEL=BLOCK_DMODEL, # + BLOCK_N=BLOCK_N, # + SEQUENCE_PARALLEL=SEQUENCE_PARALLEL, # + CAUSAL=CAUSAL, # + MMA_V3=MMA_V3 # + ) + else: + start_n = tl.program_id(1) + _bwd_kernel_one_col_block(Q, K, V, sm_scale, qk_scale, Out, DO, # + DQ, DK, DV, # + L, # + D, # + Q_block_ptr, K_block_ptr, V_block_ptr, # + DO_block_ptr, DQ_block_ptr, DK_block_ptr, DV_block_ptr, # + stride_dqa, stride_qz, stride_qh, stride_qm, stride_qk, # + stride_kz, stride_kh, stride_kn, stride_kk, # + stride_vz, stride_vh, stride_vn, stride_vk, # + Z, H, N_CTX, # + off_h, off_z, off_hz, start_n, num_block_n, # + BLOCK_M=BLOCK_M, BLOCK_DMODEL=BLOCK_DMODEL, # + BLOCK_N=BLOCK_N, # + SEQUENCE_PARALLEL=SEQUENCE_PARALLEL, # + CAUSAL=CAUSAL, # + MMA_V3=MMA_V3 # + ) + + +class _attention(torch.autograd.Function): + + @staticmethod + def forward(ctx, q, k, v, causal, sm_scale, sequence_parallel=False): + # only support for Ampere now + capability = torch.cuda.get_device_capability() + if capability[0] < 8: + raise RuntimeError("Flash attention currently only supported for compute capability >= 80") + BLOCK_M = 128 + BLOCK_N = 64 + # shape constraints + Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1] + assert Lq == Lk and Lk == Lv + assert Lk in {16, 32, 64, 128} + o = torch.empty_like(q) + grid = (cdiv(q.shape[2], BLOCK_M), q.shape[0] * q.shape[1], 1) + L = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32) + num_warps = 4 if Lk <= 64 else 8 + _fwd_kernel[grid]( + q, k, v, sm_scale, # + L, # + o, # + q.stride(0), q.stride(1), q.stride(2), q.stride(3), # + k.stride(0), k.stride(1), k.stride(2), k.stride(3), # + v.stride(0), v.stride(1), v.stride(2), v.stride(3), # + o.stride(0), o.stride(1), o.stride(2), o.stride(3), # + q.shape[0], q.shape[1], q.shape[2], # + q.shape[0] * q.shape[1] * q.shape[2], # + BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_DMODEL=Lk, # + IS_CAUSAL=causal, # + num_warps=num_warps, # + num_stages=4 # + ) + + ctx.save_for_backward(q, k, v, o, L) + ctx.grid = grid + ctx.sm_scale = sm_scale + ctx.BLOCK_DMODEL = Lk + ctx.causal = causal + ctx.sequence_parallel = sequence_parallel + return o + + @staticmethod + def backward(ctx, do): + capability = torch.cuda.get_device_capability() + MMA_V3 = capability[0] >= 9 + BLOCK = 128 + q, k, v, o, L = ctx.saved_tensors + sequence_parallel = ctx.sequence_parallel + seq_len_kv = k.shape[2] + do = do.contiguous() + if sequence_parallel: + replicas = cdiv(seq_len_kv, BLOCK) + new_dq_shape = (replicas, ) + q.shape + dq = torch.zeros(new_dq_shape, device=q.device, dtype=q.dtype) + else: + dq = torch.zeros_like(q, dtype=q.dtype) + dk = torch.empty_like(k) + dv = torch.empty_like(v) + delta = torch.empty_like(L) + _bwd_preprocess[(cdiv(q.shape[2], BLOCK) * ctx.grid[1], )]( + o, + do, + delta, + BLOCK_M=BLOCK, + D_HEAD=ctx.BLOCK_DMODEL, + ) + _bwd_kernel[(ctx.grid[1], cdiv(seq_len_kv, BLOCK) if sequence_parallel else 1)]( + q, k, v, ctx.sm_scale, # + o, do, # + dq, dk, dv, # + L, # + delta, # + o.numel(), q.stride(0), q.stride(1), q.stride(2), q.stride(3), # + k.stride(0), k.stride(1), k.stride(2), k.stride(3), # + v.stride(0), v.stride(1), v.stride(2), v.stride(3), # + q.shape[0], q.shape[1], q.shape[2], # + q.shape[0] * q.shape[1] * q.shape[2], # + cdiv(seq_len_kv, BLOCK) * q.shape[0] * q.shape[1] * q.shape[2], # + BLOCK_M=BLOCK, BLOCK_N=BLOCK, # + BLOCK_DMODEL=ctx.BLOCK_DMODEL, # + SEQUENCE_PARALLEL=sequence_parallel, # + CAUSAL=ctx.causal, # + MMA_V3=MMA_V3, # + num_warps=8, # + num_stages=1 # + ) + + if len(dq.shape) == 5: + dq = dq.sum(dim=0) + return dq, dk, dv, None, None, None + + +attention = _attention.apply diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/matmul.py b/llmeval-env/lib/python3.10/site-packages/triton/ops/matmul.py new file mode 100644 index 0000000000000000000000000000000000000000..832e52727f092f5f543730b16406c3c9d7f05d80 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/ops/matmul.py @@ -0,0 +1,204 @@ +import torch + +from .. import Config, autotune, cdiv, heuristics, jit +from .. import language as tl +from .matmul_perf_model import early_config_prune, estimate_matmul_time + +_ordered_datatypes = [torch.float16, torch.bfloat16, torch.float32] + + +def get_higher_dtype(a, b): + if a is b: + return a + + assert a in _ordered_datatypes + assert b in _ordered_datatypes + + for d in _ordered_datatypes: + if a is d: + return b + if b is d: + return a + + +def init_to_zero(name): + return lambda nargs: nargs[name].zero_() + + +def get_configs_io_bound(): + configs = [] + for num_stages in [2, 3, 4, 5, 6]: + for block_m in [16, 32]: + for block_k in [32, 64]: + for block_n in [32, 64, 128, 256]: + num_warps = 2 if block_n <= 64 else 4 + configs.append( + Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1}, + num_stages=num_stages, num_warps=num_warps)) + # split_k + for split_k in [2, 4, 8, 16]: + configs.append( + Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k}, + num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C'))) + return configs + + +@autotune( + configs=[ + # basic configs for compute-bound matmuls + Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8), + Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8), + Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4), + Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4), + Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4), + Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4), + Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4), + Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4), + Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2), + # good for int8 + Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8), + Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8), + Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4), + Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4), + Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4), + Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4), + Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4), + Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4), + Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2), + ] + get_configs_io_bound(), + key=['M', 'N', 'K'], + prune_configs_by={ + 'early_config_prune': early_config_prune, + 'perf_model': estimate_matmul_time, + 'top_k': 10, + }, +) +@heuristics({ + 'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0, +}) +@jit +def _kernel(A, B, C, M, N, K, # + stride_am, stride_ak, # + stride_bk, stride_bn, # + stride_cm, stride_cn, # + dot_out_dtype: tl.constexpr, # + allow_tf32: tl.constexpr, # + fp8_fast_accum: tl.constexpr, # + BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, # + GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr, AB_DTYPE: tl.constexpr # + ): + # matrix multiplication + pid = tl.program_id(0) + pid_z = tl.program_id(1) + grid_m = tl.cdiv(M, BLOCK_M) + grid_n = tl.cdiv(N, BLOCK_N) + # re-order program ID for better L2 performance + width = GROUP_M * grid_n + group_id = pid // width + group_size = min(grid_m - group_id * GROUP_M, GROUP_M) + pid_m = group_id * GROUP_M + (pid % group_size) + pid_n = (pid % width) // (group_size) + # do matrix multiplication + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) + rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) + rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K) + # pointers + A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak) + B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn) + acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=dot_out_dtype) + for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)): + if EVEN_K: + a = tl.load(A) + b = tl.load(B) + else: + k_remaining = K - k * (BLOCK_K * SPLIT_K) + _0 = tl.zeros((1, 1), dtype=C.dtype.element_ty) + a = tl.load(A, mask=rk[None, :] < k_remaining, other=_0) + b = tl.load(B, mask=rk[:, None] < k_remaining, other=_0) + if AB_DTYPE: + a = a.to(C.dtype.element_ty) + b = b.to(C.dtype.element_ty) + if fp8_fast_accum: + acc = tl.dot(a, b, acc, out_dtype=dot_out_dtype, allow_tf32=allow_tf32) + else: + acc += tl.dot(a, b, out_dtype=dot_out_dtype, allow_tf32=allow_tf32) + A += BLOCK_K * SPLIT_K * stride_ak + B += BLOCK_K * SPLIT_K * stride_bk + acc = acc.to(C.dtype.element_ty) + # rematerialize rm and rn to save registers + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn) + mask = (rm < M)[:, None] & (rn < N)[None, :] + # handles write-back with reduction-splitting + if SPLIT_K == 1: + tl.store(C, acc, mask=mask) + else: + tl.atomic_add(C, acc, mask=mask) + + +class _matmul(torch.autograd.Function): + kernel = _kernel + + _locks = {} + + @staticmethod + def _call(a, b, dot_out_dtype, allow_tf32, fp8_fast_accum): + device = a.device + # handle non-contiguous inputs if necessary + if a.stride(0) > 1 and a.stride(1) > 1: + a = a.contiguous() + if b.stride(0) > 1 and b.stride(1) > 1: + b = b.contiguous() + # checks constraints + assert a.shape[1] == b.shape[0], "incompatible dimensions" + M, K = a.shape + _, N = b.shape + # allocates output + if a.dtype in [tl.float8e4nv, tl.float8e4b15, tl.float8e5] or\ + b.dtype in [tl.float8e4nv, tl.float8e4b15, tl.float8e5]: + c_dtype = torch.float16 + elif a.dtype in [torch.int8] or b.dtype in [torch.int8]: + c_dtype = torch.int32 + else: + c_dtype = get_higher_dtype(a.dtype, b.dtype) + c = torch.empty((M, N), device=device, dtype=c_dtype) + if dot_out_dtype is None: + if c_dtype in [torch.float16, torch.float32, torch.bfloat16]: + dot_out_dtype = tl.float32 + else: + dot_out_dtype = tl.int32 + else: + assert isinstance(dot_out_dtype, torch.dtype), "dot_out_dtype must be a torch.dtype" + if dot_out_dtype == torch.float16: + dot_out_dtype = tl.float16 + elif dot_out_dtype in [torch.float32, torch.bfloat16]: + dot_out_dtype = tl.float32 + else: + dot_out_dtype = tl.int32 + ab_dtype = True + if a.dtype in [tl.float8e4nv, tl.float8e5] and b.dtype in [tl.float8e4nv, tl.float8e5]: + ab_dtype = False + if a.dtype in [torch.int8] and b.dtype in [torch.int8]: + ab_dtype = False + # launch kernel + grid = lambda META: (cdiv(M, META['BLOCK_M']) * cdiv(N, META['BLOCK_N']), META['SPLIT_K']) + _kernel[grid]( + a, b, c, M, N, K, # + a.stride(0), a.stride(1), # + b.stride(0), b.stride(1), # + c.stride(0), c.stride(1), # + dot_out_dtype=dot_out_dtype, # + allow_tf32=allow_tf32, # + fp8_fast_accum=fp8_fast_accum, # + GROUP_M=8, AB_DTYPE=ab_dtype) + return c + + @staticmethod + def forward(ctx, a, b, dot_out_dtype=None, allow_tf32=True, fp8_fast_accum=True): + return _matmul._call(a, b, dot_out_dtype=dot_out_dtype, allow_tf32=allow_tf32, fp8_fast_accum=fp8_fast_accum) + + +matmul = _matmul.apply diff --git a/llmeval-env/lib/python3.10/site-packages/triton/ops/matmul_perf_model.py b/llmeval-env/lib/python3.10/site-packages/triton/ops/matmul_perf_model.py new file mode 100644 index 0000000000000000000000000000000000000000..19e93268ec0b95dd61621c2e92dae8d99b4b32fc --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/ops/matmul_perf_model.py @@ -0,0 +1,162 @@ +import heapq + +import torch + +from .. import cdiv +from .._C.libtriton.triton import runtime +from ..runtime import driver +from ..testing import (get_dram_gbps, get_max_simd_tflops, get_max_tensorcore_tflops, nvsmi) + + +def get_tensorcore_tflops(backend, device, num_ctas, num_warps, dtype): + ''' return compute throughput in TOPS ''' + total_warps = num_ctas * min(num_warps, 4) + num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4 # on recent GPUs + cur_sm_clock = nvsmi(['clocks.max.sm'])[0] + tflops = min(num_subcores, total_warps) / num_subcores * get_max_tensorcore_tflops( + dtype, cur_sm_clock, backend, device) + return tflops + + +def get_simd_tflops(backend, device, num_ctas, num_warps, dtype): + ''' return compute throughput in TOPS ''' + total_warps = num_ctas * min(num_warps, 4) + num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4 # on recent GPUs + cur_sm_clock = nvsmi(['clocks.max.sm'])[0] + tflops = min(num_subcores, total_warps) / num_subcores * get_max_simd_tflops(dtype, cur_sm_clock, backend, device) + return tflops + + +def get_tflops(backend, device, num_ctas, num_warps, dtype): + capability = torch.cuda.get_device_capability(device) + if capability[0] < 8 and dtype == torch.float32: + return get_simd_tflops(backend, device, num_ctas, num_warps, dtype) + return get_tensorcore_tflops(backend, device, num_ctas, num_warps, dtype) + + +def estimate_matmul_time( + # backend, device, + num_warps, num_stages, # + A, B, C, # + M, N, K, # + BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, # + debug=False, **kwargs # +): + ''' return estimated running time in ms + = max(compute, loading) + store ''' + backend = runtime.backend.CUDA + device = torch.cuda.current_device() + dtype = A.dtype + dtsize = A.element_size() + + num_cta_m = cdiv(M, BLOCK_M) + num_cta_n = cdiv(N, BLOCK_N) + num_cta_k = SPLIT_K + num_ctas = num_cta_m * num_cta_n * num_cta_k + + # If the input is smaller than the block size + M, N = max(M, BLOCK_M), max(N, BLOCK_N) + + # time to compute + total_ops = 2 * M * N * K / (1024 * 1024 * 1024) # GOPS + tput = get_tflops(backend, device, num_ctas, num_warps, dtype) + compute_ms = total_ops / tput + + # time to load data + num_sm = driver.utils.get_device_properties(device)["multiprocessor_count"] + active_cta_ratio = min(1, num_ctas / num_sm) + active_cta_ratio_bw1 = min(1, num_ctas / 32) # 32 active ctas are enough to saturate + active_cta_ratio_bw2 = max(min(1, (num_ctas - 32) / (108 - 32)), 0) # 32-108, remaining 5% + dram_bw = get_dram_gbps(backend, device) * (active_cta_ratio_bw1 * 0.95 + active_cta_ratio_bw2 * 0.05) # in GB/s + l2_bw = dram_bw * 4 # rough estimation (should be 4.7 for A100?) + # assume 80% of (following) loads are in L2 cache + load_a_dram = M * K * dtsize * (1 + 0.2 * (num_cta_n - 1)) + load_a_l2 = M * K * dtsize * 0.8 * (num_cta_n - 1) + load_b_dram = N * K * dtsize * (1 + 0.2 * (num_cta_m - 1)) + load_b_l2 = N * K * dtsize * 0.8 * (num_cta_m - 1) + # total + total_dram = (load_a_dram + load_b_dram) / (1024 * 1024) # MB + total_l2 = (load_a_l2 + load_b_l2) / (1024 * 1024) + # loading time in ms + load_ms = total_dram / dram_bw + total_l2 / l2_bw + + # estimate storing time + store_bw = dram_bw * 0.6 # :o + store_c_dram = M * N * dtsize * SPLIT_K / (1024 * 1024) # MB + if SPLIT_K == 1: + store_ms = store_c_dram / store_bw + else: + reduce_bw = store_bw + store_ms = store_c_dram / reduce_bw + # c.zero_() + zero_ms = M * N * 2 / (1024 * 1024) / store_bw + store_ms += zero_ms + + total_time_ms = max(compute_ms, load_ms) + store_ms + if debug: + print(f'Total time: {total_time_ms}ms, compute time: {compute_ms}ms, ' + f'loading time: {load_ms}ms, store time: {store_ms}ms, ' + f'Activate CTAs: {active_cta_ratio*100}%') + return total_time_ms + + +def early_config_prune(configs, named_args): + device = torch.cuda.current_device() + capability = torch.cuda.get_device_capability() + # BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages + dtsize = named_args['A'].element_size() + dtype = named_args['A'].dtype + + # 1. make sure we have enough smem + pruned_configs = [] + for config in configs: + kw = config.kwargs + BLOCK_M, BLOCK_N, BLOCK_K, num_stages = \ + kw['BLOCK_M'], kw['BLOCK_N'], kw['BLOCK_K'], config.num_stages + + max_shared_memory = driver.utils.get_device_properties(device)["max_shared_mem"] + required_shared_memory = (BLOCK_M + BLOCK_N) * BLOCK_K * num_stages * dtsize + if required_shared_memory <= max_shared_memory: + pruned_configs.append(config) + configs = pruned_configs + + # Some dtypes do not allow atomic_add + if dtype not in [torch.float16, torch.float32]: + configs = [config for config in configs if config.kwargs['SPLIT_K'] == 1] + + # group configs by (BLOCK_M,_N,_K, SPLIT_K, num_warps) + configs_map = {} + for config in configs: + kw = config.kwargs + BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages = \ + kw['BLOCK_M'], kw['BLOCK_N'], kw['BLOCK_K'], kw['SPLIT_K'], config.num_warps, config.num_stages + + key = (BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps) + if key in configs_map: + configs_map[key].append((config, num_stages)) + else: + configs_map[key] = [(config, num_stages)] + + pruned_configs = [] + for k, v in configs_map.items(): + BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps = k + if capability[0] >= 8: + # compute cycles (only works for ampere GPUs) + mmas = BLOCK_M * BLOCK_N * BLOCK_K / (16 * 8 * 16) + mma_cycles = mmas / min(4, num_warps) * 8 + + ldgsts_latency = 300 # Does this matter? + optimal_num_stages = ldgsts_latency / mma_cycles + + # nearest stages, prefer large #stages + nearest = heapq.nsmallest( + 2, v, key=lambda x: 10 + abs(x[1] - optimal_num_stages) + if (x[1] - optimal_num_stages) < 0 else x[1] - optimal_num_stages) + + for n in nearest: + pruned_configs.append(n[0]) + else: # Volta & Turing only supports num_stages <= 2 + random_config = v[0][0] + random_config.num_stages = 2 + pruned_configs.append(random_config) + return pruned_configs diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/__init__.py b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e785018e0f9262b36101942c7c925d7bad5449d8 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__init__.py @@ -0,0 +1,18 @@ +from .autotuner import (Autotuner, Config, Heuristics, OutOfResources, autotune, heuristics) +from .driver import driver +from .jit import JITFunction, KernelInterface, MockTensor, TensorWrapper, reinterpret + +__all__ = [ + "driver", + "Config", + "Heuristics", + "autotune", + "heuristics", + "JITFunction", + "KernelInterface", + "reinterpret", + "TensorWrapper", + "OutOfResources", + "MockTensor", + "Autotuner", +] diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/__init__.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..dc475a5b372f9470932f32f6dc015ab37f3e3501 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/__init__.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/autotuner.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/autotuner.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..da4fd29326e6e60b5d33510da39cb0fee367d69f Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/autotuner.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/cache.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/cache.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..742b6744f51060c6ac92362b0d5438ae3c5926dd Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/cache.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/driver.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/driver.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..297ac0459441ba3e3ab544a51c5affe31c9e9a7c Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/driver.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/errors.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/errors.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..47e755aab5232b9a2196e54c9c68ca6a2cbf5467 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/errors.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/interpreter.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/interpreter.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b3876562bf6db83efd02480593d5bbdb762f22d6 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/interpreter.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/jit.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/jit.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8871ad614074c26b94ceff08bfb3b27c4e96e86b Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/runtime/__pycache__/jit.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/autotuner.py b/llmeval-env/lib/python3.10/site-packages/triton/runtime/autotuner.py new file mode 100644 index 0000000000000000000000000000000000000000..f46c5d30c2dc3622a0bf6d5a550dea4177f75a57 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/runtime/autotuner.py @@ -0,0 +1,328 @@ +from __future__ import annotations + +import builtins +import time +from typing import Dict + +from ..testing import do_bench +from .jit import KernelInterface + + +class OutOfResources(Exception): + + def __init__(self, required, limit, name): + self.message = (f"out of resource: {name}, Required: {required}, Hardware limit: {limit}. " + + "Reducing block sizes or `num_stages` may help.") + self.required = required + self.limit = limit + self.name = name + super().__init__(self.message) + + def __reduce__(self): + # this is necessary to make CompilationError picklable + return (type(self), (self.required, self.limit, self.name)) + + +class Autotuner(KernelInterface): + + def __init__( + self, + fn, + arg_names, + configs, + key, + reset_to_zero, + restore_value, + prune_configs_by: Dict = None, + warmup=25, + rep=100, + ): + """ + :param prune_configs_by: a dict of functions that are used to prune configs, fields: + 'perf_model': performance model used to predicate running time with different configs, returns running time + 'top_k': number of configs to bench + 'prune_num_stages_by'(optional): a function used to prune num_stages. It takes configs:List[Config] as its input, and returns pruned configs. + """ + if not configs: + self.configs = [Config({}, num_warps=4, num_stages=2, num_ctas=1)] + else: + self.configs = configs + self.key_idx = [arg_names.index(k) for k in key] + self.cache = {} + self.arg_names = arg_names + + # Reset to zero or restore values + self.reset_idx = [] + if reset_to_zero is not None: + self.reset_idx = [arg_names.index(k) for k in reset_to_zero] + self.restore_idx = [] + if restore_value is not None: + self.restore_idx = [arg_names.index(k) for k in restore_value] + + # Hook to reset or restore for required tensors + self.pre_hook = lambda args, reset_only=False: 0 + self.post_hook = lambda args: 0 + if len(self.reset_idx) > 0 or len(self.restore_idx) > 0: + + def _pre_hook(args, reset_only=False): + for i in self.reset_idx: + args[i].zero_() + if not reset_only: + self.restore_copies = [args[i].clone() for i in self.restore_idx] + + self.pre_hook = _pre_hook + if len(self.restore_idx) > 0: + + def _post_hook(args): + for i, j in enumerate(self.restore_idx): + args[j].copy_(self.restore_copies[i]) + self.restore_copies = [] + + self.post_hook = _post_hook + + self.perf_model = None + self.configs_top_k = 1.0 + self.early_config_prune = None + if prune_configs_by: + self.perf_model = prune_configs_by.get("perf_model", self.perf_model) + self.configs_top_k = prune_configs_by.get("top_k", self.configs_top_k) + self.early_config_prune = prune_configs_by.get("early_config_prune", self.early_config_prune) + + self.fn = fn + self.warmup = warmup + self.rep = rep + + def _bench(self, *args, config, **meta): + # check for conflicts, i.e. meta-parameters both provided + # as kwargs and by the autotuner + conflicts = meta.keys() & config.kwargs.keys() + if conflicts: + raise ValueError(f"Conflicting meta-parameters: {', '.join(conflicts)}." + " Make sure that you don't re-define auto-tuned symbols.") + # augment meta-parameters with tunable ones + current = dict(meta, **config.kwargs) + full_nargs = {**self.nargs, **current} + + def kernel_call(): + if config.pre_hook: + config.pre_hook(full_nargs) + self.pre_hook(args) + self.fn.run( + *args, + num_warps=config.num_warps, + num_stages=config.num_stages, + num_ctas=config.num_ctas, + enable_warp_specialization=config.enable_warp_specialization, + # enable_persistent=False, + **current, + ) + self.post_hook(args) + + try: + return do_bench(kernel_call, warmup=self.warmup, rep=self.rep, quantiles=(0.5, 0.2, 0.8)) + except OutOfResources: + return [float("inf"), float("inf"), float("inf")] + + def run(self, *args, **kwargs): + self.nargs = dict(zip(self.arg_names, args)) + if len(self.configs) > 1: + all_args = {**self.nargs, **kwargs} + _args = [] + for name in self.arg_names: + if name in all_args: + _args.append(all_args[name]) + key = [_args[i] for i in self.key_idx] + for arg in _args: + if hasattr(arg, "dtype"): + key.append(str(arg.dtype)) + key = tuple(key) + if key not in self.cache: + # prune configs + pruned_configs = self.prune_configs(kwargs) + bench_start = time.time() + timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs} + bench_end = time.time() + self.bench_time = bench_end - bench_start + self.cache[key] = builtins.min(timings, key=timings.get) + self.pre_hook(args, reset_only=True) + self.configs_timings = timings + config = self.cache[key] + else: + config = self.configs[0] + self.best_config = config + full_nargs = {**self.nargs, **kwargs, **self.best_config.kwargs} + if config.pre_hook is not None: + config.pre_hook(full_nargs) + ret = self.fn.run( + *args, + num_warps=config.num_warps, + num_stages=config.num_stages, + num_ctas=config.num_ctas, + enable_warp_specialization=config.enable_warp_specialization, + **kwargs, + **config.kwargs, + ) + self.nargs = None + return ret + + def prune_configs(self, kwargs): + pruned_configs = self.configs + if self.early_config_prune: + pruned_configs = self.early_config_prune(self.configs, self.nargs) + if self.perf_model: + top_k = self.configs_top_k + if isinstance(top_k, float) and top_k <= 1.0: + top_k = int(len(self.configs) * top_k) + if len(pruned_configs) > top_k: + est_timing = { + config: + self.perf_model( + **self.nargs, + **kwargs, + **config.kwargs, + num_stages=config.num_stages, + num_warps=config.num_warps, + num_ctas=config.num_ctas, + enable_warp_specialization=config.enable_warp_specialization, + enable_persistent=config.enable_persistent, + ) + for config in pruned_configs + } + pruned_configs = sorted(est_timing.keys(), key=lambda x: est_timing[x])[:top_k] + return pruned_configs + + def warmup(self, *args, **kwargs): + self.nargs = dict(zip(self.arg_names, args)) + for config in self.prune_configs(kwargs): + self.fn.warmup( + *args, + num_warps=config.num_warps, + num_ctas=config.num_ctas, + num_stages=config.num_stages, + enable_warp_specialization=config.enable_warp_specialization, + enable_persistent=config.enable_persistent, + **kwargs, + **config.kwargs, + ) + self.nargs = None + + +class Config: + """ + An object that represents a possible kernel configuration for the auto-tuner to try. + + :ivar meta: a dictionary of meta-parameters to pass to the kernel as keyword arguments. + :type meta: dict[Str, Any] + :ivar num_warps: the number of warps to use for the kernel when compiled for GPUs. For example, if + `num_warps=8`, then each kernel instance will be automatically parallelized to + cooperatively execute using `8 * 32 = 256` threads. + :type num_warps: int + :ivar num_stages: the number of stages that the compiler should use when software-pipelining loops. + Mostly useful for matrix multiplication workloads on SM80+ GPUs. + :type enable_warp_specialization: bool + :ivar enable_warp_specialization: enable specialization (spatial partitioning) or not. See https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#spatial-partitioning-also-known-as-warp-specialization + :ivar pre_hook: a function that will be called before the kernel is called. Parameters of this + function are args. + """ + + def __init__(self, kwargs, num_warps=4, num_stages=2, num_ctas=1, enable_warp_specialization=False, pre_hook=None): + self.kwargs = kwargs + self.num_warps = num_warps + self.num_ctas = num_ctas + self.num_stages = num_stages + self.enable_warp_specialization = enable_warp_specialization + # TODO[shuhaoj]: May make enable_persistent configurable in future if necessary. + self.enable_persistent = False + self.pre_hook = pre_hook + + def __str__(self): + res = [] + for k, v in self.kwargs.items(): + res.append(f"{k}: {v}") + res.append(f"num_warps: {self.num_warps}") + res.append(f"num_ctas: {self.num_ctas}") + res.append(f"num_stages: {self.num_stages}") + res.append(f"enable_warp_specialization: {self.enable_warp_specialization}") + res.append(f"enable_persistent: {self.enable_persistent}") + return ", ".join(res) + + +def autotune(configs, key, prune_configs_by=None, reset_to_zero=None, restore_value=None, warmup=25, rep=100): + """ + Decorator for auto-tuning a :code:`triton.jit`'d function. + + .. highlight:: python + .. code-block:: python + + @triton.autotune(configs=[ + triton.Config(meta={'BLOCK_SIZE': 128}, num_warps=4), + triton.Config(meta={'BLOCK_SIZE': 1024}, num_warps=8), + ], + key=['x_size'] # the two above configs will be evaluated anytime + # the value of x_size changes + ) + @triton.jit + def kernel(x_ptr, x_size, **META): + BLOCK_SIZE = META['BLOCK_SIZE'] + :note: When all the configurations are evaluated, the kernel will run multiple times. + This means that whatever value the kernel updates will be updated multiple times. + To avoid this undesired behavior, you can use the `reset_to_zero` argument, which + resets the value of the provided tensor to `zero` before running any configuration. + :param configs: a list of :code:`triton.Config` objects + :type configs: list[triton.Config] + :param key: a list of argument names whose change in value will trigger the evaluation of all provided configs. + :type key: list[str] + :param prune_configs_by: a dict of functions that are used to prune configs, fields: + 'perf_model': performance model used to predicate running time with different configs, returns running time + 'top_k': number of configs to bench + 'early_config_prune'(optional): a function used to do early prune (eg, num_stages). It takes configs:List[Config] as its input, and returns pruned configs. + :param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs. + :type reset_to_zero: list[str] + :param restore_value: a list of argument names whose value will be restored after evaluating any configs. + :type restore_value: list[str] + :param warmup: Warmup time (in ms) to pass to benchmarking, defaults to 25. + :type warmup: int + :param rep: Repetition time (in ms) to pass to benchmarking, defaults to 100. + :type rep: int + """ + + def decorator(fn): + return Autotuner(fn, fn.arg_names, configs, key, reset_to_zero, restore_value, prune_configs_by, warmup, rep) + + return decorator + + +class Heuristics(KernelInterface): + + def __init__(self, fn, arg_names, values) -> None: + self.fn = fn + self.values = values + self.arg_names = arg_names + + def run(self, *args, **kwargs): + for v, heur in self.values.items(): + kwargs[v] = heur({**dict(zip(self.arg_names, args)), **kwargs}) + return self.fn.run(*args, **kwargs) + + +def heuristics(values): + """ + Decorator for specifying how the values of certain meta-parameters may be computed. + This is useful for cases where auto-tuning is prohibitevely expensive, or just not applicable. + + .. highlight:: python + .. code-block:: python + + @triton.heuristics(values={'BLOCK_SIZE': lambda args: 2 ** int(math.ceil(math.log2(args[1])))}) + @triton.jit + def kernel(x_ptr, x_size, **META): + BLOCK_SIZE = META['BLOCK_SIZE'] # smallest power-of-two >= x_size + :param values: a dictionary of meta-parameter names and functions that compute the value of the meta-parameter. + each such function takes a list of positional arguments as input. + :type values: dict[str, Callable[[list[Any]], Any]] + """ + + def decorator(fn): + return Heuristics(fn, fn.arg_names, values) + + return decorator diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/backends/cuda.c b/llmeval-env/lib/python3.10/site-packages/triton/runtime/backends/cuda.c new file mode 100644 index 0000000000000000000000000000000000000000..928c8fc06e524e10e16eeb55cb4c02930e7bd404 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/runtime/backends/cuda.c @@ -0,0 +1,537 @@ +#include "cuda.h" +#include +#include +#define PY_SSIZE_T_CLEAN +#include + +// Raises a Python exception and returns false if code is not CUDA_SUCCESS. +static bool gpuAssert(CUresult code, const char *file, int line) { + if (code == CUDA_SUCCESS) + return true; + + const char *prefix = "Triton Error [CUDA]: "; + const char *str; + cuGetErrorString(code, &str); + char err[1024] = {0}; + strcat(err, prefix); + strcat(err, str); + PyGILState_STATE gil_state; + gil_state = PyGILState_Ensure(); + PyErr_SetString(PyExc_RuntimeError, err); + PyGILState_Release(gil_state); + return false; +} + +// To be used only *outside* a Py_{BEGIN,END}_ALLOW_THREADS block. +#define CUDA_CHECK_AND_RETURN_NULL(ans) \ + do { \ + if (!gpuAssert((ans), __FILE__, __LINE__)) \ + return NULL; \ + } while (0) + +// To be used inside a Py_{BEGIN,END}_ALLOW_THREADS block. +#define CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(ans) \ + do { \ + if (!gpuAssert((ans), __FILE__, __LINE__)) { \ + PyEval_RestoreThread(_save); \ + return NULL; \ + } \ + } while (0) + +#define ADD_ENUM_ITEM(value) \ + do { \ + PyObject *py_value = PyLong_FromLong(value); \ + PyDict_SetItemString(enum_dict, #value, py_value); \ + } while (0) + +#define ADD_ENUM_ITEM_0() +#define ADD_ENUM_ITEM_1(v1) ADD_ENUM_ITEM(v1) +#define ADD_ENUM_ITEM_2(v1, v2) \ + ADD_ENUM_ITEM(v1); \ + ADD_ENUM_ITEM(v2); +#define ADD_ENUM_ITEM_3(v1, v2, v3) \ + ADD_ENUM_ITEM(v1); \ + ADD_ENUM_ITEM(v2); \ + ADD_ENUM_ITEM(v3); +#define ADD_ENUM_ITEM_4(v1, v2, v3, v4) \ + ADD_ENUM_ITEM(v1); \ + ADD_ENUM_ITEM(v2); \ + ADD_ENUM_ITEM(v3); \ + ADD_ENUM_ITEM(v4); +#define ADD_ENUM_ITEM_5(v1, v2, v3, v4, v5) \ + ADD_ENUM_ITEM_2(v1, v2); \ + ADD_ENUM_ITEM_3(v3, v4, v5); +#define ADD_ENUM_ITEM_6(v1, v2, v3, v4, v5, v6) \ + ADD_ENUM_ITEM_2(v1, v2); \ + ADD_ENUM_ITEM_4(v3, v4, v5, v6); +#define ADD_ENUM_ITEM_7(v1, v2, v3, v4, v5, v6, v7) \ + ADD_ENUM_ITEM_3(v1, v2, v3); \ + ADD_ENUM_ITEM_4(v4, v5, v6, v7); +#define ADD_ENUM_ITEM_8(v1, v2, v3, v4, v5, v6, v7, v8) \ + ADD_ENUM_ITEM_4(v1, v2, v3, v4); \ + ADD_ENUM_ITEM_4(v5, v6, v7, v8); +#define ADD_ENUM_ITEM_9(v1, v2, v3, v4, v5, v6, v7, v8, v9) \ + ADD_ENUM_ITEM_5(v1, v2, v3, v4, v5); \ + ADD_ENUM_ITEM_4(v6, v7, v8, v9); +#define ADD_ENUM_ITEM_10(v1, v2, v3, v4, v5, v6, v7, v8, v9, v10) \ + ADD_ENUM_ITEM_5(v1, v2, v3, v4, v5); \ + ADD_ENUM_ITEM_5(v6, v7, v8, v9, v10); +#define ADD_ENUM_ITEM_11(v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11) \ + ADD_ENUM_ITEM_6(v1, v2, v3, v4, v5, v6); \ + ADD_ENUM_ITEM_5(v7, v8, v9, v10, v11); +#define ADD_ENUM_ITEM_12(v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12) \ + ADD_ENUM_ITEM_6(v1, v2, v3, v4, v5, v6); \ + ADD_ENUM_ITEM_6(v7, v8, v9, v10, v11, v12); +#define ADD_ENUM_ITEM_13(v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, \ + v13) \ + ADD_ENUM_ITEM_7(v1, v2, v3, v4, v5, v6, v7); \ + ADD_ENUM_ITEM_6(v8, v9, v10, v11, v12, v13); +#define ADD_ENUM_ITEM_14(v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, \ + v13, v14) \ + ADD_ENUM_ITEM_7(v1, v2, v3, v4, v5, v6, v7); \ + ADD_ENUM_ITEM_7(v8, v9, v10, v11, v12, v13, v14); + +#define DISPATCH_ARGS_N(_14, _13, _12, _11, _10, _9, _8, _7, _6, _5, _4, _3, \ + _2, _1, N, ...) \ + ADD_ENUM_ITEM_##N +#define DISPATCH_ARGS(...) \ + DISPATCH_ARGS_N(__VA_ARGS__, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, \ + 0) \ + (__VA_ARGS__) + +#define ADD_ENUM_TO_MODULE(module, enum_name, ...) \ + do { \ + PyObject *enum_dict = PyDict_New(); \ + DISPATCH_ARGS(__VA_ARGS__) \ + if (enum_dict != NULL) { \ + PyObject_SetAttrString(module, #enum_name, enum_dict); \ + } \ + } while (0) + +static void defineEnums(PyObject *self) { + ADD_ENUM_TO_MODULE( + self, CUtensorMapDataType, CU_TENSOR_MAP_DATA_TYPE_UINT8, + CU_TENSOR_MAP_DATA_TYPE_UINT16, CU_TENSOR_MAP_DATA_TYPE_UINT32, + CU_TENSOR_MAP_DATA_TYPE_INT32, CU_TENSOR_MAP_DATA_TYPE_UINT64, + CU_TENSOR_MAP_DATA_TYPE_INT64, CU_TENSOR_MAP_DATA_TYPE_FLOAT16, + CU_TENSOR_MAP_DATA_TYPE_FLOAT32, CU_TENSOR_MAP_DATA_TYPE_FLOAT64, + CU_TENSOR_MAP_DATA_TYPE_BFLOAT16, CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ, + CU_TENSOR_MAP_DATA_TYPE_TFLOAT32, CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ); + + ADD_ENUM_TO_MODULE(self, CUtensorMapInterleave, CU_TENSOR_MAP_INTERLEAVE_NONE, + CU_TENSOR_MAP_INTERLEAVE_16B, + CU_TENSOR_MAP_INTERLEAVE_32B); + + ADD_ENUM_TO_MODULE(self, CUtensorMapSwizzle, CU_TENSOR_MAP_SWIZZLE_NONE, + CU_TENSOR_MAP_SWIZZLE_32B, CU_TENSOR_MAP_SWIZZLE_64B, + CU_TENSOR_MAP_SWIZZLE_128B); + + ADD_ENUM_TO_MODULE( + self, CUtensorMapL2promotion, CU_TENSOR_MAP_L2_PROMOTION_NONE, + CU_TENSOR_MAP_L2_PROMOTION_L2_64B, CU_TENSOR_MAP_L2_PROMOTION_L2_128B, + CU_TENSOR_MAP_L2_PROMOTION_L2_256B); + + ADD_ENUM_TO_MODULE(self, CUtensorMapFloatOOBfill, + CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE, + CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA); +} + +typedef struct { + PyObject_HEAD cuuint32_t value; +} PyCUuint32; + +typedef struct { + PyObject_HEAD cuuint64_t value; +} PyCUuint64; + +#define DEFINE_CUUINT_CONSTRUCTOR(NAME, TYPE, FORMAT, VALUE_TYPE) \ + static PyObject *Py##NAME##_New(PyTypeObject *type, PyObject *args, \ + PyObject *kwds) { \ + Py##NAME *self; \ + VALUE_TYPE value; \ + if (!PyArg_ParseTuple(args, FORMAT, &value)) \ + return NULL; \ + self = (Py##NAME *)type->tp_alloc(type, 0); \ + if (self != NULL) { \ + self->value = (TYPE)value; \ + } \ + return (PyObject *)self; \ + } + +DEFINE_CUUINT_CONSTRUCTOR(CUuint32, cuuint32_t, "l", long) +DEFINE_CUUINT_CONSTRUCTOR(CUuint64, cuuint64_t, "L", long long) + +static PyTypeObject PyCUuint32_Type = { + PyVarObject_HEAD_INIT(NULL, 0).tp_name = "cuda_utils.cuuint32_t", + .tp_basicsize = sizeof(PyCUuint32), + .tp_flags = Py_TPFLAGS_DEFAULT, + .tp_new = PyCUuint32_New, +}; + +static PyTypeObject PyCUuint64_Type = { + PyVarObject_HEAD_INIT(NULL, 0).tp_name = "cuda_utils.cuuint64_t", + .tp_basicsize = sizeof(PyCUuint64), + .tp_flags = Py_TPFLAGS_DEFAULT, + .tp_new = PyCUuint64_New, +}; + +static void defineTypes(PyObject *self) { + if (PyType_Ready(&PyCUuint32_Type) < 0) { + PyErr_SetString(PyExc_TypeError, "Failed to ready cuuint32_t type"); + return; + } + Py_INCREF(&PyCUuint32_Type); + if (PyModule_AddObject(self, "cuuint32_t", (PyObject *)&PyCUuint32_Type) < + 0) { + PyErr_SetString(PyExc_RuntimeError, + "Failed to add cuuint32_t type to module"); + return; + } + + if (PyType_Ready(&PyCUuint64_Type) < 0) { + PyErr_SetString(PyExc_TypeError, "Failed to ready cuuint64_t type"); + return; + } + Py_INCREF(&PyCUuint64_Type); + if (PyModule_AddObject(self, "cuuint64_t", (PyObject *)&PyCUuint64_Type) < + 0) { + PyErr_SetString(PyExc_RuntimeError, + "Failed to add cuuint64_t type to module"); + return; + } +} + +static PyObject *getDeviceProperties(PyObject *self, PyObject *args) { + int device_id; + if (!PyArg_ParseTuple(args, "i", &device_id)) + return NULL; + // Get device handle + CUdevice device; + cuDeviceGet(&device, device_id); + + // create a struct to hold device properties + int max_shared_mem; + int multiprocessor_count; + int sm_clock_rate; + int mem_clock_rate; + int mem_bus_width; + CUDA_CHECK_AND_RETURN_NULL(cuDeviceGetAttribute( + &max_shared_mem, CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN, + device)); + CUDA_CHECK_AND_RETURN_NULL(cuDeviceGetAttribute( + &multiprocessor_count, CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT, device)); + CUDA_CHECK_AND_RETURN_NULL(cuDeviceGetAttribute( + &sm_clock_rate, CU_DEVICE_ATTRIBUTE_CLOCK_RATE, device)); + CUDA_CHECK_AND_RETURN_NULL(cuDeviceGetAttribute( + &mem_clock_rate, CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE, device)); + CUDA_CHECK_AND_RETURN_NULL(cuDeviceGetAttribute( + &mem_bus_width, CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH, device)); + + return Py_BuildValue("{s:i, s:i, s:i, s:i, s:i}", "max_shared_mem", + max_shared_mem, "multiprocessor_count", + multiprocessor_count, "sm_clock_rate", sm_clock_rate, + "mem_clock_rate", mem_clock_rate, "mem_bus_width", + mem_bus_width); +} + +static PyObject *loadBinary(PyObject *self, PyObject *args) { + const char *name; + const char *data; + Py_ssize_t data_size; + int shared; + int device; + if (!PyArg_ParseTuple(args, "ss#ii", &name, &data, &data_size, &shared, + &device)) { + return NULL; + } + CUfunction fun; + CUmodule mod; + int32_t n_regs = 0; + int32_t n_spills = 0; + // create driver handles + CUcontext pctx = 0; + + Py_BEGIN_ALLOW_THREADS; + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuCtxGetCurrent(&pctx)); + if (!pctx) { + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS( + cuDevicePrimaryCtxRetain(&pctx, device)); + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuCtxSetCurrent(pctx)); + } + + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuModuleLoadData(&mod, data)); + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS( + cuModuleGetFunction(&fun, mod, name)); + // get allocated registers and spilled registers from the function + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS( + cuFuncGetAttribute(&n_regs, CU_FUNC_ATTRIBUTE_NUM_REGS, fun)); + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS( + cuFuncGetAttribute(&n_spills, CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES, fun)); + n_spills /= 4; + // set dynamic shared memory if necessary + int shared_optin; + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuDeviceGetAttribute( + &shared_optin, CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN, + device)); + if (shared > 49152 && shared_optin > 49152) { + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS( + cuFuncSetCacheConfig(fun, CU_FUNC_CACHE_PREFER_SHARED)); + int shared_total, shared_static; + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuDeviceGetAttribute( + &shared_total, CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR, + device)); + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuFuncGetAttribute( + &shared_static, CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES, fun)); + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS( + cuFuncSetAttribute(fun, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, + shared_optin - shared_static)); + } + Py_END_ALLOW_THREADS; + + if (PyErr_Occurred()) { + return NULL; + } + return Py_BuildValue("(KKii)", (uint64_t)mod, (uint64_t)fun, n_regs, + n_spills); +} + +static PyObject *memAlloc(PyObject *self, PyObject *args) { + size_t bytesize; + CUdeviceptr dptr; + CUresult result; + + if (!PyArg_ParseTuple(args, "K", &bytesize)) { + return NULL; // Error parsing arguments + } + + Py_BEGIN_ALLOW_THREADS; + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuMemAlloc(&dptr, bytesize)); + Py_END_ALLOW_THREADS; + + return PyLong_FromUnsignedLongLong((unsigned long long)dptr); +} + +static PyObject *memcpyHtoD(PyObject *self, PyObject *args) { + unsigned long long dstDevicePtr, srcHostPtr; + size_t byteCount; + CUdeviceptr dstDevice; + const void *srcHost; + CUresult result; + + if (!PyArg_ParseTuple(args, "KKK", &dstDevicePtr, &srcHostPtr, &byteCount)) { + return NULL; // Error parsing arguments + } + + dstDevice = (CUdeviceptr)dstDevicePtr; + srcHost = (const void *)srcHostPtr; + + Py_BEGIN_ALLOW_THREADS; + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS( + cuMemcpyHtoD(dstDevice, srcHost, byteCount)); + Py_END_ALLOW_THREADS; + + Py_RETURN_NONE; +} + +static PyObject *memFree(PyObject *self, PyObject *args) { + CUdeviceptr dptr; + + if (!PyArg_ParseTuple(args, "K", &dptr)) { + return NULL; // Error parsing arguments + } + + Py_BEGIN_ALLOW_THREADS; + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuMemFree(dptr)); + Py_END_ALLOW_THREADS; + + Py_RETURN_NONE; +} + +// Helper function to convert a Python list to a cuuint64_t array +static cuuint64_t *list_to_cuuint64_array(PyObject *listObj) { + Py_ssize_t len = PyList_Size(listObj); + cuuint64_t *array = (cuuint64_t *)malloc(len * sizeof(cuuint64_t)); + for (Py_ssize_t i = 0; i < len; i++) { + PyObject *item = PyList_GetItem(listObj, i); + array[i] = (cuuint64_t)PyLong_AsUnsignedLongLong(item); + } + return array; +} + +// Helper function to convert a Python list to a cuuint32_t array +static cuuint32_t *list_to_cuuint32_array(PyObject *listObj) { + Py_ssize_t len = PyList_Size(listObj); + cuuint32_t *array = (cuuint32_t *)malloc(len * sizeof(cuuint32_t)); + for (Py_ssize_t i = 0; i < len; i++) { + PyObject *item = PyList_GetItem(listObj, i); + array[i] = (cuuint32_t)PyLong_AsUnsignedLong(item); + } + return array; +} + +typedef CUresult (*cuTensorMapEncodeTiled_t)( + CUtensorMap *tensorMap, CUtensorMapDataType tensorDataType, + cuuint32_t tensorRank, void *globalAddress, const cuuint64_t *globalDim, + const cuuint64_t *globalStrides, const cuuint32_t *boxDim, + const cuuint32_t *elementStrides, CUtensorMapInterleave interleave, + CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, + CUtensorMapFloatOOBfill oobFill); + +typedef CUresult (*cuOccupancyMaxActiveClusters_t)( + int *numClusters, CUfunction func, const CUlaunchConfig *config); + +#define defineGetFunctionHandle(name, symbolName) \ + static symbolName##_t name() { \ + /* Open the shared library */ \ + void *libHandle = dlopen("libcuda.so", RTLD_LAZY); \ + if (!libHandle) { \ + PyErr_SetString(PyExc_RuntimeError, "Failed to open libcuda.so"); \ + return NULL; \ + } \ + /* Clear any existing error */ \ + dlerror(); \ + symbolName##_t funcHandle = (symbolName##_t)dlsym(libHandle, #symbolName); \ + /* Check for errors */ \ + const char *err = dlerror(); \ + if (err) { \ + PyErr_SetString(PyExc_RuntimeError, \ + "Failed to retrieve " #symbolName " from libcuda.so"); \ + dlclose(libHandle); \ + return NULL; \ + } \ + return funcHandle; \ + } + +defineGetFunctionHandle(getCuTensorMapEncodeTiledHandle, + cuTensorMapEncodeTiled); +defineGetFunctionHandle(getCuOccupancyMaxActiveClustersHandle, + cuOccupancyMaxActiveClusters); + +static PyObject *tensorMapEncodeTiled(PyObject *self, PyObject *args) { + CUtensorMap *tensorMap = (CUtensorMap *)malloc(sizeof(CUtensorMap)); + CUtensorMapDataType tensorDataType; + cuuint32_t tensorRank; + void *globalAddress; + PyObject *globalDimObj, *globalStridesObj, *boxDimObj, *elementStridesObj; + CUtensorMapInterleave interleave; + CUtensorMapSwizzle swizzle; + CUtensorMapL2promotion l2Promotion; + CUtensorMapFloatOOBfill oobFill; + + // Parse arguments + if (!PyArg_ParseTuple(args, "iiKO!O!O!O!iiii", &tensorDataType, &tensorRank, + &globalAddress, &PyList_Type, &globalDimObj, + &PyList_Type, &globalStridesObj, &PyList_Type, + &boxDimObj, &PyList_Type, &elementStridesObj, + &interleave, &swizzle, &l2Promotion, &oobFill)) { + return NULL; // Error parsing arguments + } + + // Convert Python lists to C arrays + cuuint64_t *globalDim = list_to_cuuint64_array(globalDimObj); + cuuint64_t *globalStrides = list_to_cuuint64_array(globalStridesObj); + cuuint32_t *boxDim = list_to_cuuint32_array(boxDimObj); + cuuint32_t *elementStrides = list_to_cuuint32_array(elementStridesObj); + + static cuTensorMapEncodeTiled_t cuTensorMapEncodeTiledHandle = NULL; + if (cuTensorMapEncodeTiledHandle == NULL) { + cuTensorMapEncodeTiledHandle = getCuTensorMapEncodeTiledHandle(); + } + // Call the function + Py_BEGIN_ALLOW_THREADS; + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuTensorMapEncodeTiledHandle( + tensorMap, tensorDataType, tensorRank, globalAddress, globalDim, + globalStrides, boxDim, elementStrides, interleave, swizzle, l2Promotion, + oobFill)); + Py_END_ALLOW_THREADS; + + // Clean up + free(globalDim); + free(globalStrides); + free(boxDim); + free(elementStrides); + // Return the tensor map as a normal pointer + return PyLong_FromUnsignedLongLong((unsigned long long)tensorMap); +} + +static PyObject *occupancyMaxActiveClusters(PyObject *self, PyObject *args) { + int clusterDimX = -1, clusterDimY = -1, clusterDimZ = -1, + maxActiveClusters = -1; + int shared = 0; + CUfunction func; + + if (!PyArg_ParseTuple(args, "Kiiii", &func, &shared, &clusterDimX, + &clusterDimY, &clusterDimZ)) { + return NULL; + } + + // Let each SM have one block + int maxActiveBlocks = 1; + Py_BEGIN_ALLOW_THREADS; + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuFuncSetAttribute( + func, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, shared)); + Py_END_ALLOW_THREADS; + + CUlaunchAttribute launchAttr[1]; + launchAttr[0].id = CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION; + launchAttr[0].value.clusterDim.x = clusterDimX; + launchAttr[0].value.clusterDim.y = clusterDimY; + launchAttr[0].value.clusterDim.z = clusterDimZ; + CUlaunchConfig config; + config.gridDimX = clusterDimX; + config.gridDimY = maxActiveBlocks * clusterDimY; + config.gridDimZ = clusterDimZ; + config.blockDimX = 128; + config.blockDimY = 1; + config.blockDimZ = 1; + config.sharedMemBytes = shared; + config.hStream = 0; + config.numAttrs = 1; + config.attrs = launchAttr; + + static cuOccupancyMaxActiveClusters_t cuOccupancyMaxActiveClusters = NULL; + if (cuOccupancyMaxActiveClusters == NULL) { + cuOccupancyMaxActiveClusters = getCuOccupancyMaxActiveClustersHandle(); + } + + Py_BEGIN_ALLOW_THREADS; + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuFuncSetAttribute( + func, CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED, 1)); + CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS( + cuOccupancyMaxActiveClusters(&maxActiveClusters, func, &config)); + Py_END_ALLOW_THREADS; + return PyLong_FromLong(maxActiveClusters); +} + +static PyMethodDef ModuleMethods[] = { + {"load_binary", loadBinary, METH_VARARGS, + "Load provided cubin into CUDA driver"}, + {"get_device_properties", getDeviceProperties, METH_VARARGS, + "Get the properties for a given device"}, + {"cuMemAlloc", memAlloc, METH_VARARGS}, + {"cuMemcpyHtoD", memcpyHtoD, METH_VARARGS}, + {"cuMemFree", memFree, METH_VARARGS}, + {"cuTensorMapEncodeTiled", tensorMapEncodeTiled, METH_VARARGS, + "Python interface for cuTensorMapEncodeTiled function"}, + {"cuOccupancyMaxActiveClusters", occupancyMaxActiveClusters, METH_VARARGS, + "Python interface for cuOccupancyMaxActiveClusters function"}, + {NULL, NULL, 0, NULL} // sentinel +}; + +static struct PyModuleDef ModuleDef = {PyModuleDef_HEAD_INIT, "cuda_utils", + NULL, // documentation + -1, // size + ModuleMethods}; + +PyMODINIT_FUNC PyInit_cuda_utils(void) { + PyObject *m = PyModule_Create(&ModuleDef); + if (m == NULL) { + return NULL; + } + + defineEnums(m); + defineTypes(m); + PyModule_AddFunctions(m, ModuleMethods); + + return m; +} diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/backends/hip.c b/llmeval-env/lib/python3.10/site-packages/triton/runtime/backends/hip.c new file mode 100644 index 0000000000000000000000000000000000000000..c419132fe2405d8ad37d9858fdee7ad1e2f116e4 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/runtime/backends/hip.c @@ -0,0 +1,123 @@ +#define __HIP_PLATFORM_AMD__ +#include +#define PY_SSIZE_T_CLEAN +#include +#include +#include + +static inline void gpuAssert(hipError_t code, const char *file, int line) { + { + if (code != HIP_SUCCESS) { + { + const char *prefix = "Triton Error [HIP]: "; + const char *str = hipGetErrorString(code); + char err[1024] = {0}; + snprintf(err, 1024, "%s Code: %d, Messsage: %s", prefix, code, str); + PyGILState_STATE gil_state; + gil_state = PyGILState_Ensure(); + PyErr_SetString(PyExc_RuntimeError, err); + PyGILState_Release(gil_state); + } + } + } +} + +#define HIP_CHECK(ans) \ + { \ + gpuAssert((ans), __FILE__, __LINE__); \ + if (PyErr_Occurred()) \ + return NULL; \ + } + +static PyObject *getDeviceProperties(PyObject *self, PyObject *args) { + int device_id; + if (!PyArg_ParseTuple(args, "i", &device_id)) + return NULL; + + hipDeviceProp_t props; + HIP_CHECK(hipGetDeviceProperties(&props, device_id)); + + // create a struct to hold device properties + return Py_BuildValue("{s:i, s:i, s:i, s:i, s:i}", "max_shared_mem", + props.sharedMemPerBlock, "multiprocessor_count", + props.multiProcessorCount, "sm_clock_rate", + props.clockRate, "mem_clock_rate", props.memoryClockRate, + "mem_bus_width", props.memoryBusWidth); +} + +static PyObject *loadBinary(PyObject *self, PyObject *args) { + const char *name; + const char *data; + Py_ssize_t data_size; + int shared; + int device; + if (!PyArg_ParseTuple(args, "ss#ii", &name, &data, &data_size, &shared, + &device)) { + return NULL; + } + + // Open HSACO file + FILE *hsaco_file; + if ((hsaco_file = fopen(data, "rb")) == NULL) { + return NULL; + } + + // Read HSCAO file into Buffer + fseek(hsaco_file, 0L, SEEK_END); + size_t hsaco_file_size = ftell(hsaco_file); + unsigned char *hsaco = + (unsigned char *)malloc(hsaco_file_size * sizeof(unsigned char)); + rewind(hsaco_file); + fread(hsaco, sizeof(unsigned char), hsaco_file_size, hsaco_file); + fclose(hsaco_file); + + // set HIP options + hipJitOption opt[] = {hipJitOptionErrorLogBufferSizeBytes, + hipJitOptionErrorLogBuffer, + hipJitOptionInfoLogBufferSizeBytes, + hipJitOptionInfoLogBuffer, hipJitOptionLogVerbose}; + const unsigned int errbufsize = 8192; + const unsigned int logbufsize = 8192; + char _err[errbufsize]; + char _log[logbufsize]; + void *optval[] = {(void *)(uintptr_t)errbufsize, (void *)_err, + (void *)(uintptr_t)logbufsize, (void *)_log, (void *)1}; + + // launch HIP Binary + hipModule_t mod; + hipFunction_t fun; + hipModuleLoadDataEx(&mod, hsaco, 5, opt, optval); + hipModuleGetFunction(&fun, mod, name); + free(hsaco); + + // get allocated registers and spilled registers from the function + int n_regs = 0; + int n_spills = 0; + if (PyErr_Occurred()) { + return NULL; + } + return Py_BuildValue("(KKii)", (uint64_t)mod, (uint64_t)fun, n_regs, + n_spills); +} + +static PyMethodDef ModuleMethods[] = { + {"load_binary", loadBinary, METH_VARARGS, + "Load provided hsaco into HIP driver"}, + {"get_device_properties", getDeviceProperties, METH_VARARGS, + "Get the properties for a given device"}, + {NULL, NULL, 0, NULL} // sentinel +}; + +static struct PyModuleDef ModuleDef = {PyModuleDef_HEAD_INIT, "hip_utils", + NULL, // documentation + -1, // size + ModuleMethods}; + +PyMODINIT_FUNC PyInit_hip_utils(void) { + PyObject *m = PyModule_Create(&ModuleDef); + if (m == NULL) { + return NULL; + } + PyModule_AddFunctions(m, ModuleMethods); + return m; +} diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/cache.py b/llmeval-env/lib/python3.10/site-packages/triton/runtime/cache.py new file mode 100644 index 0000000000000000000000000000000000000000..43057e3937632273b129d54e763f08a442dfac99 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/runtime/cache.py @@ -0,0 +1,272 @@ +import importlib +import json +import os +import random +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Dict, List, Optional +import hashlib + + +def default_cache_dir(): + return os.path.join(Path.home(), ".triton", "cache") + + +def default_override_dir(): + return os.path.join(Path.home(), ".triton", "override") + + +def default_dump_dir(): + return os.path.join(Path.home(), ".triton", "dump") + + +class CacheManager(ABC): + + def __init__(self, key): + pass + + @abstractmethod + def get_file(self, filename) -> Optional[str]: + pass + + @abstractmethod + def put(self, data, filename, binary=True) -> str: + pass + + @abstractmethod + def get_group(self, filename: str) -> Optional[Dict[str, str]]: + pass + + @abstractmethod + def put_group(self, filename: str, group: Dict[str, str]): + pass + + +class FileCacheManager(CacheManager): + + def __init__(self, key, override=False, dump=False): + self.key = key + self.lock_path = None + if dump: + self.cache_dir = default_dump_dir() + self.cache_dir = os.path.join(self.cache_dir, self.key) + self.lock_path = os.path.join(self.cache_dir, "lock") + os.makedirs(self.cache_dir, exist_ok=True) + elif override: + self.cache_dir = default_override_dir() + self.cache_dir = os.path.join(self.cache_dir, self.key) + else: + # create cache directory if it doesn't exist + self.cache_dir = os.getenv("TRITON_CACHE_DIR", "").strip() or default_cache_dir() + if self.cache_dir: + self.cache_dir = os.path.join(self.cache_dir, self.key) + self.lock_path = os.path.join(self.cache_dir, "lock") + os.makedirs(self.cache_dir, exist_ok=True) + else: + raise RuntimeError("Could not create or locate cache dir") + + def _make_path(self, filename) -> str: + return os.path.join(self.cache_dir, filename) + + def _has_file(self, filename) -> bool: + if not self.cache_dir: + raise RuntimeError("Could not create or locate cache dir") + return os.path.exists(self._make_path(filename)) + + def get_file(self, filename) -> Optional[str]: + if self._has_file(filename): + return self._make_path(filename) + else: + return None + + def get_group(self, filename: str) -> Optional[Dict[str, str]]: + grp_filename = f"__grp__{filename}" + if not self._has_file(grp_filename): + return None + grp_filepath = self._make_path(grp_filename) + with open(grp_filepath) as f: + grp_data = json.load(f) + child_paths = grp_data.get("child_paths", None) + # Invalid group data. + if child_paths is None: + return None + result = {} + for c in child_paths: + p = self._make_path(c) + if os.path.exists(p): + result[c] = p + return result + + # Note a group of pushed files as being part of a group + def put_group(self, filename: str, group: Dict[str, str]) -> str: + if not self.cache_dir: + raise RuntimeError("Could not create or locate cache dir") + grp_contents = json.dumps({"child_paths": sorted(list(group.keys()))}) + grp_filename = f"__grp__{filename}" + return self.put(grp_contents, grp_filename, binary=False) + + def put(self, data, filename, binary=True) -> str: + if not self.cache_dir: + raise RuntimeError("Could not create or locate cache dir") + binary = isinstance(data, bytes) + if not binary: + data = str(data) + assert self.lock_path is not None + filepath = self._make_path(filename) + # Random ID to avoid any collisions + rnd_id = random.randint(0, 1000000) + # we use the PID incase a bunch of these around so we can see what PID made it + pid = os.getpid() + # use tempfile to be robust against program interruptions + temp_path = f"{filepath}.tmp.pid_{pid}_{rnd_id}" + mode = "wb" if binary else "w" + with open(temp_path, mode) as f: + f.write(data) + # Replace is guaranteed to be atomic on POSIX systems if it succeeds + # so filepath cannot see a partial write + os.replace(temp_path, filepath) + return filepath + + +class RemoteCacheBackend: + """ + A backend implementation for accessing a remote/distributed cache. + """ + + def __init__(self, key: str): + pass + + @abstractmethod + def get(self, filenames: List[str]) -> Dict[str, bytes]: + pass + + @abstractmethod + def put(self, filename: str, data: bytes): + pass + + +class RedisRemoteCacheBackend(RemoteCacheBackend): + + def __init__(self, key): + import redis + self._key = key + self._key_fmt = os.environ.get("TRITON_REDIS_KEY_FORMAT", "triton:{key}:{filename}") + self._redis = redis.Redis( + host=os.environ.get("TRITON_REDIS_HOST", "localhost"), + port=int(os.environ.get("TRITON_REDIS_PORT", 6379)), + ) + + def _get_key(self, filename: str) -> str: + return self._key_fmt.format(key=self._key, filename=filename) + + def get(self, filenames: List[str]) -> Dict[str, str]: + results = self._redis.mget([self._get_key(f) for f in filenames]) + return {filename: result for filename, result in zip(filenames, results) if result is not None} + + def put(self, filename: str, data: bytes) -> Dict[str, bytes]: + self._redis.set(self._get_key(filename), data) + + +class RemoteCacheManager(CacheManager): + + def __init__(self, key, override=False, dump=False): + # Setup backend pointed too by `TRITON_REMOTE_CACHE_BACKEND`. + remote_cache_manager = os.environ["TRITON_REMOTE_CACHE_BACKEND"] + module_path, clz_nme = remote_cache_manager.split(":") + module = importlib.import_module(module_path) + remote_cache_cls = getattr(module, clz_nme) + self._backend = remote_cache_cls(key) + + self._override = override + self._dump = dump + + # Use a `FileCacheManager` to materialize remote cache paths locally. + self._file_cache_manager = FileCacheManager(key, override=override, dump=dump) + + def _materialize(self, filename: str, data: bytes): + # We use a backing `FileCacheManager` to provide the materialized data. + return self._file_cache_manager.put(data, filename, binary=True) + + def get_file(self, filename: str) -> Optional[str]: + # We don't handle the dump/override cases. + if self._dump or self._override: + return self._file_cache_manager.get_file(filename) + + # We always check the remote cache backend -- even if our internal file- + # based cache has the item -- to make sure LRU accounting works as + # expected. + results = self._backend.get([filename]) + if len(results) == 0: + return None + (_, data), = results.items() + return self._materialize(filename, data) + + def put(self, data, filename: str, binary=True) -> str: + # We don't handle the dump/override cases. + if self._dump or self._override: + return self._file_cache_manager.put(data, filename, binary=binary) + + if not isinstance(data, bytes): + data = str(data).encode("utf-8") + self._backend.put(filename, data) + return self._materialize(filename, data) + + def get_group(self, filename: str) -> Optional[Dict[str, str]]: + # We don't handle the dump/override cases. + if self._dump or self._override: + return self._file_cache_manager.get_group(filename) + + grp_filename = f"__grp__{filename}" + grp_filepath = self.get_file(grp_filename) + if grp_filepath is None: + return None + with open(grp_filepath) as f: + grp_data = json.load(f) + child_paths = grp_data.get("child_paths", None) + + result = None + + # Found group data. + if child_paths is not None: + result = {} + for child_path, data in self._backend.get(child_paths).items(): + result[child_path] = self._materialize(child_path, data) + + return result + + def put_group(self, filename: str, group: Dict[str, str]): + # We don't handle the dump/override cases. + if self._dump or self._override: + return self._file_cache_manager.put_group(filename, group) + + grp_contents = json.dumps({"child_paths": sorted(list(group.keys()))}) + grp_filename = f"__grp__{filename}" + return self.put(grp_contents, grp_filename) + + +__cache_cls = FileCacheManager +__cache_cls_nme = "DEFAULT" + + +def get_cache_manager(key) -> CacheManager: + import os + + user_cache_manager = os.environ.get("TRITON_CACHE_MANAGER", None) + global __cache_cls + global __cache_cls_nme + + if user_cache_manager is not None and user_cache_manager != __cache_cls_nme: + module_path, clz_nme = user_cache_manager.split(":") + module = importlib.import_module(module_path) + __cache_cls = getattr(module, clz_nme) + __cache_cls_nme = user_cache_manager + + return __cache_cls(key) + + +def get_override_manager(key) -> CacheManager: + return __cache_cls(key, override=True) + + +def get_dump_manager(key) -> CacheManager: + return __cache_cls(key, dump=True) diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/driver.py b/llmeval-env/lib/python3.10/site-packages/triton/runtime/driver.py new file mode 100644 index 0000000000000000000000000000000000000000..21c76b88644a9f3ec94ad7c19dfd4600bb361e30 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/runtime/driver.py @@ -0,0 +1,244 @@ +import abc +import hashlib +import os +import tempfile +from pathlib import Path +import functools + +from ..common.build import _build +from .cache import get_cache_manager +from ..runtime import driver + + +class DriverBase(metaclass=abc.ABCMeta): + CUDA = 0 + HIP = 1 + + @staticmethod + def third_party_dir(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "third_party") + + def __init__(self) -> None: + pass + + +# ----------------------------- +# CUDA +# ----------------------------- + + +class CudaUtils(object): + + def __new__(cls): + if not hasattr(cls, "instance"): + cls.instance = super(CudaUtils, cls).__new__(cls) + return cls.instance + + def __init__(self): + dirname = os.path.dirname(os.path.realpath(__file__)) + src = Path(os.path.join(dirname, "backends", "cuda.c")).read_text() + key = hashlib.md5(src.encode("utf-8")).hexdigest() + cache = get_cache_manager(key) + fname = "cuda_utils.so" + cache_path = cache.get_file(fname) + if cache_path is None: + with tempfile.TemporaryDirectory() as tmpdir: + src_path = os.path.join(tmpdir, "main.c") + with open(src_path, "w") as f: + f.write(src) + so = _build("cuda_utils", src_path, tmpdir) + with open(so, "rb") as f: + cache_path = cache.put(f.read(), fname, binary=True) + import importlib.util + + spec = importlib.util.spec_from_file_location("cuda_utils", cache_path) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + self.load_binary = mod.load_binary + self.get_device_properties = mod.get_device_properties + self.CUtensorMapDataType = mod.CUtensorMapDataType + self.CUtensorMapInterleave = mod.CUtensorMapInterleave + self.CUtensorMapSwizzle = mod.CUtensorMapSwizzle + self.CUtensorMapL2promotion = mod.CUtensorMapL2promotion + self.CUtensorMapFloatOOBfill = mod.CUtensorMapFloatOOBfill + self.cuTensorMapEncodeTiled = mod.cuTensorMapEncodeTiled + self.cuMemAlloc = mod.cuMemAlloc + self.cuMemcpyHtoD = mod.cuMemcpyHtoD + self.cuMemFree = mod.cuMemFree + self.cuOccupancyMaxActiveClusters = mod.cuOccupancyMaxActiveClusters + + +class TensorMapManager: + + def __init__(self): + self.tensormaps_device = {} + + def __getitem__(self, key: tuple): + if key in self.tensormaps_device: + return int(self.tensormaps_device[key]) + else: + (e, args) = key + t_tensormap = e.tensormap(args) + TENSORMAP_SIZE_IN_BYTES = 128 + t_tensormap_device = driver.utils.cuMemAlloc(TENSORMAP_SIZE_IN_BYTES) + driver.utils.cuMemcpyHtoD(t_tensormap_device, t_tensormap, TENSORMAP_SIZE_IN_BYTES) + self.tensormaps_device[key] = t_tensormap_device + return int(self.tensormaps_device[key]) + + def __del__(self): + for _, v in self.tensormaps_device.items(): + driver.utils.cuMemFree(v) + + +class CudaDriver(DriverBase): + tensormap_manager = TensorMapManager() + + def __new__(cls): + if not hasattr(cls, "instance"): + cls.instance = super(CudaDriver, cls).__new__(cls) + return cls.instance + + def __init__(self): + self.utils = CudaUtils() + self.backend = self.CUDA + self.binary_ext = "cubin" + # TODO: support other frameworks than torch + import torch + self.get_device_capability = torch.cuda.get_device_capability + try: + from torch._C import _cuda_getCurrentRawStream + self.get_current_stream = _cuda_getCurrentRawStream + except ImportError: + self.get_current_stream = lambda idx: torch.cuda.current_stream(idx).cuda_stream + self.get_current_device = torch.cuda.current_device + self.set_current_device = torch.cuda.set_device + + @functools.lru_cache() + def get_current_target(self): + device = self.get_current_device() + capability = self.get_device_capability(device) + capability = capability[0] * 10 + capability[1] + return ("cuda", capability) + + def assemble_tensormap_to_arg(self, tensormaps_info, args): + args_with_tma = list(args) + if tensormaps_info is not None: + # tuple for hashable + args_ptr = tuple([arg.data_ptr() if hasattr(arg, 'data_ptr') else arg for arg in args]) + for i, e in enumerate(tensormaps_info): + args_with_tma.append(CudaDriver.tensormap_manager[(e, args_ptr)]) + return args_with_tma + + +# ----------------------------- +# HIP +# ----------------------------- + + +class HIPUtils(object): + + def __new__(cls): + if not hasattr(cls, "instance"): + cls.instance = super(HIPUtils, cls).__new__(cls) + return cls.instance + + def __init__(self): + dirname = os.path.dirname(os.path.realpath(__file__)) + src = Path(os.path.join(dirname, "backends", "hip.c")).read_text() + key = hashlib.md5(src.encode("utf-8")).hexdigest() + cache = get_cache_manager(key) + fname = "hip_utils.so" + cache_path = cache.get_file(fname) + if cache_path is None: + with tempfile.TemporaryDirectory() as tmpdir: + src_path = os.path.join(tmpdir, "main.c") + with open(src_path, "w") as f: + f.write(src) + so = _build("hip_utils", src_path, tmpdir) + with open(so, "rb") as f: + cache_path = cache.put(f.read(), fname, binary=True) + import importlib.util + + spec = importlib.util.spec_from_file_location("hip_utils", cache_path) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + self.load_binary = mod.load_binary + self.get_device_properties = mod.get_device_properties + + +class HIPDriver(DriverBase): + + def __new__(cls): + if not hasattr(cls, "instance"): + cls.instance = super(HIPDriver, cls).__new__(cls) + return cls.instance + + def __init__(self): + self.utils = HIPUtils() + self.backend = self.HIP + + +class UnsupportedDriver(DriverBase): + + def __new__(cls): + if not hasattr(cls, "instance"): + cls.instance = super(UnsupportedDriver, cls).__new__(cls) + return cls.instance + + def __init__(self): + self.utils = None + self.backend = None + + +# ----------------------------- +# Driver +# ----------------------------- + + +class LazyProxy: + + def __init__(self, init_fn): + self._init_fn = init_fn + self._obj = None + + def _initialize_obj(self): + if self._obj is None: + self._obj = self._init_fn() + + def __getattr__(self, name): + self._initialize_obj() + return getattr(self._obj, name) + + def __setattr__(self, name, value): + if name in ["_init_fn", "_obj"]: + super().__setattr__(name, value) + else: + self._initialize_obj() + setattr(self._obj, name, value) + + def __delattr__(self, name): + self._initialize_obj() + delattr(self._obj, name) + + def __repr__(self): + if self._obj is None: + return f"<{self.__class__.__name__} for {self._init_fn} not yet initialized>" + return repr(self._obj) + + def __str__(self): + self._initialize_obj() + return str(self._obj) + + +def initialize_driver(): + import torch + + if torch.version.hip is not None: + return HIPDriver() + elif torch.cuda.is_available(): + return CudaDriver() + else: + return UnsupportedDriver() + + +driver = LazyProxy(initialize_driver) diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/errors.py b/llmeval-env/lib/python3.10/site-packages/triton/runtime/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..a5d69aba685ec55c85853c7044724ae991ec5131 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/runtime/errors.py @@ -0,0 +1,13 @@ +class OutOfResources(Exception): + + def __init__(self, required, limit, name): + self.message = f"out of resource: {name}, " f"Required: {required}, " f"Hardware limit: {limit}" + self.message += ". Reducing block sizes or `num_stages` may help." + self.required = required + self.limit = limit + self.name = name + super().__init__(self.message) + + def __reduce__(self): + # this is necessary to make CompilationError picklable + return (type(self), (self.required, self.limit, self.name)) diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/interpreter.py b/llmeval-env/lib/python3.10/site-packages/triton/runtime/interpreter.py new file mode 100644 index 0000000000000000000000000000000000000000..f499252c64d5c31e000d2b910f7b2b3f5884d078 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/runtime/interpreter.py @@ -0,0 +1,544 @@ +import inspect + +import numpy as np + +import triton +import triton.language as tl +from .._C.libtriton.triton import interpreter as _interpreter + + +# TODO: duplicate +def str_to_ty(name): + language = tl + if name[0] == "*": + ty = str_to_ty(name[1:]) + return language.pointer_type(ty) + tys = { + "fp8e4nv": language.float8e4nv, + "fp8e5": language.float8e5, + "fp8e4b15": language.float8e4b15, + "fp8e4b15x4": language.float8e4b15x4, + "fp16": language.float16, + "bf16": language.bfloat16, + "fp32": language.float32, + "fp64": language.float64, + "i1": language.int1, + "i8": language.int8, + "i16": language.int16, + "i32": language.int32, + "i64": language.int64, + "u8": language.uint8, + "u16": language.uint16, + "u32": language.uint32, + "u64": language.uint64, + "B": language.int1, + } + return tys[name] + + +class TensorHandle: + + def __init__(self, data, dtype): + self.data = data + self.dtype = dtype + + def __bool__(self): + return bool(self.data.all()) + + +class BlockPointerHandle: + + def __init__(self, base, shape, strides, offsets, tensor_shape, order): + self.base = base + self.shape = shape + self.strides = strides + self.offsets = offsets + self.tensor_shape = tensor_shape + self.order = order + + def materialize_pointers(self, boundary_check): + dtype_tt = self.base.dtype.element_ty + n_bytes = dtype_tt.primitive_bitwidth // 8 + tensor_shape = self.tensor_shape + ptrs = np.broadcast_to(self.base.data, self.tensor_shape) + masks = np.ones(self.tensor_shape, dtype=bool) + for dim in range(len(tensor_shape)): + bcast_dims = [1] * len(tensor_shape) + bcast_dims[dim] = tensor_shape[dim] + off = (self.offsets[dim].data + np.arange(tensor_shape[dim])).reshape(bcast_dims) + ptrs = ptrs + (n_bytes * off * self.strides[dim].data).astype(np.uint64) + if dim in boundary_check: + masks = np.logical_and(masks, off < self.shape[dim].data) + ptrs = TensorHandle(ptrs, self.base.dtype) + return ptrs, masks + + +def wrap_ret(compute_ret_ty): + + def wrapper(fn): + + def wrapped(*args, **kwargs): + ret = fn(*args, **kwargs) + return TensorHandle(ret.data, compute_ret_ty(*args, **kwargs)) + + return wrapped + + return wrapper + + +class Builder: + + def __init__(self) -> None: + self.arch = None + # pass + + def set_grid_idx(self, x, y, z): + assert x < self.grid_dim[0] + assert y < self.grid_dim[1] + assert z < self.grid_dim[2] + self.grid_idx = (x, y, z) + + def set_grid_dim(self, nx, ny, nz): + self.grid_dim = (nx, ny, nz) + + def np_dtype(self, tt_dtype): + if isinstance(tt_dtype, tl.pointer_type): + return np.dtype(np.uint64) + np_types = { + tl.float16: np.dtype(np.float16), + tl.float32: np.dtype(np.float32), + tl.float64: np.dtype(np.float64), + tl.int8: np.dtype(np.int8), + tl.uint8: np.dtype(np.uint8), + tl.int16: np.dtype(np.int16), + tl.uint16: np.dtype(np.uint16), + tl.int32: np.dtype(np.int32), + tl.uint32: np.dtype(np.uint32), + tl.int64: np.dtype(np.int64), + tl.uint64: np.dtype(np.uint64), + } + return np_types[tt_dtype] + + # constants + def get_half_ty(self): + return tl.float16 + + def get_float_ty(self): + return tl.float32 + + def get_int64_ty(self): + return tl.int64 + + def get_ptr_ty(self, elt_ty, addr_space): + return tl.pointer_type(elt_ty, addr_space) + + def get_block_ty(self, dtype, shape): + return tl.tensor(shape, dtype) + + def get_int32(self, value): + return TensorHandle(np.array([value], dtype=np.int32), tl.int32) + + def get_int64(self, value): + return TensorHandle(np.array([value], dtype=np.int64), tl.int64) + + def get_fp16(self, value): + return TensorHandle(np.array([value], dtype=np.float16), tl.float16) + + def get_fp32(self, value): + return TensorHandle(np.array([value], dtype=np.float32), tl.float32) + + def get_null_value(self, type): + return TensorHandle(np.array([0], dtype=self.np_dtype(type)), type) + + # programming model + def create_get_program_id(self, axis): + assert self.grid_idx is not None + return TensorHandle(np.array([self.grid_idx[axis]], dtype=np.int32), tl.int32) + + def create_get_num_programs(self, axis): + return TensorHandle(np.array([self.grid_dim[axis]], dtype=np.int32), tl.int32) + + # memory ops + def create_load(self, ptr, _0, _1, is_volatile): + mask = TensorHandle(np.ones_like(ptr.data, dtype=bool), tl.int1) + other = None + return self.create_masked_load(ptr, mask, other, _0, _1, is_volatile) + + def create_store(self, ptr, val, _0, _1): + mask = TensorHandle(np.ones_like(ptr.data, dtype=bool), tl.int1) + return self.create_masked_store(ptr, val, mask, None, None) + + def create_masked_load(self, ptrs, mask, other, cache_modifier, eviction_policy, is_volatile): + dtype_tt = ptrs.dtype.element_ty + dtype_np = self.np_dtype(dtype_tt) + if other is None: + other = TensorHandle(np.ones_like(ptrs.data, dtype=dtype_np), dtype_tt) + ret = _interpreter.load(ptrs.data, mask.data, other.data, dtype_np) + return TensorHandle(ret, dtype_tt) + + def create_masked_store(self, ptrs, value, mask, cache_modifier, eviction_policy): + return _interpreter.store(ptrs.data, value.data, mask.data) + + # casting ops + def cast_impl(self, src, dst_type): + if isinstance(dst_type, tl.tensor): + dst_type = dst_type.dtype + return TensorHandle(src.data.astype(self.np_dtype(dst_type)), dst_type) + + create_si_to_fp = lambda self, src, dst_type: self.cast_impl(src, dst_type) + create_ui_to_fp = lambda self, src, dst_type: self.cast_impl(src, dst_type) + create_fp_to_si = lambda self, src, dst_type: self.cast_impl(src, dst_type) + create_fp_to_ui = lambda self, src, dst_type: self.cast_impl(src, dst_type) + create_fp_ext = lambda self, src, dst_type: self.cast_impl(src, dst_type) + create_fp_trunc = lambda self, src, dst_type: self.cast_impl(src, dst_type) + create_int_cast = lambda self, src, dst_type, is_signed: self.cast_impl(src, dst_type) + + def create_fp_to_fp(self, src, dst_type): + assert "float8 not NotImplemented yet" + + def create_bitcast(self, src, dst_type): + return TensorHandle(src.data.view(self.np_dtype(dst_type)), dst_type) + + # binary operators + def binary_op(self, lhs, rhs, op): + return TensorHandle(op(lhs.data, rhs.data), lhs.dtype) + + create_fadd = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.add) + create_fmul = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.multiply) + create_fdiv = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.divide) + create_frem = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.remainder) + create_fsub = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.subtract) + create_mul = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.multiply) + create_sdiv = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.floor_divide) + create_udiv = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.floor_divide) + create_srem = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.remainder) + create_urem = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.remainder) + create_add = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.add) + create_sub = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.subtract) + create_shl = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.left_shift) + create_lshr = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.right_shift) + create_ashr = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.right_shift) + create_minsi = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.minimum) + create_minui = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.minimum) + create_minf = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.minimum) + create_maxsi = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.maximum) + create_maxui = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.maximum) + create_maxf = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.maximum) + create_icmpSLE = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.less_equal) + create_icmpSLT = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.less) + create_icmpSGE = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.greater_equal) + create_icmpSGT = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.greater) + create_icmpULE = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.less_equal) + create_icmpULT = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.less) + create_icmpUGE = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.greater_equal) + create_icmpUGT = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.greater) + create_icmpEQ = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.equal) + create_icmpNE = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.not_equal) + create_fcmpOLT = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.less) + create_fcmpOGT = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.greater) + create_fcmpOLE = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.less_equal) + create_fcmpOGE = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.greater_equal) + create_fcmpOEQ = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.equal) + create_fcmpONE = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.not_equal) + create_fcmpULT = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.less) + create_fcmpUGT = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.greater) + create_fcmpULE = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.less_equal) + create_fcmpUGE = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.greater_equal) + create_fcmpUEQ = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.equal) + create_fcmpUNE = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.not_equal) + create_and = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.bitwise_and) + create_xor = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.bitwise_xor) + create_or = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.bitwise_or) + + # ternary functions + def ternary_op(self, lhs, rhs, other, op): + return TensorHandle(op(lhs.data, rhs.data, other.data), other.dtype) + + create_select = lambda self, cond, lhs, rhs: self.ternary_op(cond, lhs, rhs, np.where) + + # unary functions + def unary_op(self, arg, op): + return TensorHandle(op(arg.data), arg.dtype) + + create_exp = lambda self, arg: self.unary_op(arg, np.exp) + create_cos = lambda self, arg: self.unary_op(arg, np.cos) + create_sin = lambda self, arg: self.unary_op(arg, np.sin) + create_log = lambda self, arg: self.unary_op(arg, np.log) + create_sqrt = lambda self, arg: self.unary_op(arg, np.sqrt) + create_fabs = lambda self, arg: self.unary_op(arg, np.abs) + create_iabs = lambda self, arg: self.unary_op(arg, np.abs) + + # tensor operators + create_dot = lambda self, lhs, rhs: self.binary_op(lhs, rhs, np.dot) + create_reshape = lambda self, arg, shape, allowReorder: TensorHandle(arg.data.reshape(shape), arg.dtype) + create_trans = lambda self, arg: self.unary_op(arg, np.transpose) + + def create_dot(self, a, b, d, allow_tf32, maxNumImpreciseAcc): + return TensorHandle(np.dot(a.data, b.data) + d.data, a.dtype) + + def create_make_range(self, start, stop): + return TensorHandle(np.arange(start, stop, dtype=np.int32), tl.int32) + + # pointer arithmetic + + def create_addptr(self, ptr, offset): + dtype_tt = ptr.dtype.element_ty + return TensorHandle(ptr.data + (dtype_tt.primitive_bitwidth // 8) * offset.data.astype(np.uint64), ptr.dtype) + + def create_tensor_pointer_load(self, ptr, boundary_check, padding_option, cache_modifier, eviction_policy, + is_volatile): + ptrs, masks = ptr.materialize_pointers(boundary_check) + assert padding_option is None + other = None + return self.create_masked_load(ptrs, masks, other, cache_modifier, eviction_policy, is_volatile) + + def create_tensor_pointer_store(self, ptr, value, boundary_check, cache_modifier, eviction_policy): + ptrs, masks = ptr.materialize_pointers(boundary_check) + return self.create_masked_store(ptrs, value, masks, cache_modifier, eviction_policy) + + def create_expand_dims(self, arg, axis): + return TensorHandle(np.expand_dims(arg.data, axis), arg.dtype) + + def create_broadcast(self, arg, shape): + return TensorHandle(np.broadcast_to(arg.data, shape), arg.dtype) + + def create_int_to_ptr(self, val, dst_ty): + return TensorHandle(val.data.astype(np.uint64), dst_ty) + + # def create_cat(self, lhs, rhs): + # pass + + # def create_broadcast(self, arg, shape): + # pass + + def create_splat(self, arg, shape): + return TensorHandle(np.full(shape, arg.data[0], dtype=self.np_dtype(arg.dtype)), arg.dtype) + + # def create_atomic_cas(self, ptr, cmp, val, sem): + # pass + + # def create_atomic_rmw(self, rmwOp, ptr, val, mask, sem): + # pass + + # def create_extern_elementwise(self, libName, libPath, symbol, argList, retType, isPure): + # pass + + # def create_reduce(self, operands, axis): + # pass + + # def create_reduce_ret(self, args): + # pass + + # def create_scan(self, operands, axis): + # pass + + # def create_scan_ret(self, args): + # pass + + # def create_ptr_to_int(self, val, type): + # pass + + # def create_int_to_ptr(self, val, type): + # pass + + # def create_inline_asm(self, inlineAsm, constraints, values, type, isPure, pack): + # pass + + # def create_print(self, prefix, values): + # pass + + # def create_assert(self, condition, message, fileName, funcName, lineNo): + # pass + + # def create_undef(self, type): + # pass + + # def create_barrier(self): + # pass + + def create_make_block_ptr(self, base, shape, strides, offsets, tensor_shape, order): + return BlockPointerHandle(base, shape, strides, np.array(offsets), tensor_shape, order) + + def create_advance(self, ptr, offsets): + assert len(ptr.offsets) == len(offsets) + ret = BlockPointerHandle(ptr.base, ptr.shape, ptr.strides, ptr.offsets, ptr.tensor_shape, ptr.order) + for i in range(len(offsets)): + ret.offsets[i].data += offsets[i].data + return ret + + +def patch_attr(obj, name, member, builder): + new_member = lambda *args, member=member, **kwargs: (member(*args, ** + {k: v + for k, v in kwargs.items() + if k != "_builder"}, _builder=builder)) + setattr(obj, name, new_member) + + +def _patch_lang_tensor(tensor, builder): + for name, member in inspect.getmembers(tensor): + if tl.core.is_builtin(member): + patch_attr(tensor, name, member, builder) + tensor.__index__ = lambda self: int(self.handle.data) + tensor.__bool__ = lambda self: True + tensor.__str__ = lambda self: str(self.handle.data) + tensor.__getitem__ = lambda self, slices: self.handle.data.__getitem__(slices) + + +def _patch_lang_core(lang, builder): + for name, member in inspect.getmembers(lang): + if tl.core.is_builtin(member): + patch_attr(lang, name, member, builder) + # reduce is better off with a separate patch due to how + # the builder currently interfaces with custom functions + + def _new_reduce(input, axis, combine_fn): + fn = combine_fn.fn.__name__ + mapping = { + "maximum": np.max, + "_sum_combine": np.sum, + } + ret = mapping[fn](input.handle.data, axis=axis) + ret_type = tl.block_type(input.dtype, ret.shape) + return tl.core.tensor(TensorHandle(ret, input.dtype), ret_type) + + lang.reduce = _new_reduce + + +def _patch_lang_math(lang, builder): + math = lang.math + mapping = { + "abs": "abs", + "acos": "arccos", + "asin": "arcsin", + "exp2": "exp2", + "log2": "log2", + "max": "maximum", + } + + def make_numpy(name): + + def impl(*args, **kwargs): + ret_type = args[0].type # TODO: incorrect + ret_dtype = args[0].dtype # TODO: incorrect + args = [arg.handle.data for arg in args] + kwargs = {k: v.handle.data for k, v in kwargs.items()} + ret = getattr(np, mapping[name])(*args, **kwargs) + ret = tl.core.tensor(TensorHandle(ret, ret_dtype), ret_type) + return ret + + return impl + + def make_fallback(name): + + def fallback(*args, **kwargs): + raise NotImplementedError(f""" +{name} not supported in interpreter mode: no known numpy implementation. +If you think that {name} in fact does have a numpy implementation, please add it +to the mapping in python/triton/interpreter/new_interpreter.py:_patch_lang_math. +""") + + return fallback + + for name, member in inspect.getmembers(math): + if name in mapping: + setattr(math, name, make_numpy(name)) + else: + setattr(math, name, make_fallback(name)) + + +# TODO: wrap everything in triton tensors +def _implicit_cvt(arg): + if isinstance(arg, int): + ty = str_to_ty(triton.runtime.jit.JITFunction._type_of(triton.runtime.jit.JITFunction._key_of(arg))) + handle = TensorHandle(np.array([arg], dtype=np.int32), ty) + return tl.tensor(handle, ty) + if hasattr(arg, "data_ptr"): + ty = str_to_ty(triton.runtime.jit.JITFunction._type_of(triton.runtime.jit.JITFunction._key_of(arg))) + handle = TensorHandle(np.array([arg.data_ptr()], dtype=np.uint64), ty) + return tl.tensor(handle, ty) + return arg + + +def _unwrap(tensor): + if isinstance(tensor, triton.TensorWrapper): + return tensor.base + return tensor + + +builder = Builder() + +RESERVED_KWS = ["num_warps", "num_stages", "num_ctas", "enable_warp_specialization", "enable_fp_fusion"] + + +class GridExecutor: + + def __init__(self, fn, arg_names, grid): + from .jit import _normalize_ty # TODO: modularize + + self.fn = fn + self.arg_names = arg_names + self.grid = grid + __annotations__ = {name: _normalize_ty(ty) for name, ty in fn.__annotations__.items()} + self.constexprs = [name for name in arg_names if __annotations__.get(name) == "constexpr"] + + def _patch_lang(self, builder): + lang = [value for _, value in self.fn.__globals__.items() if value in [tl, tl.core]] + assert len(lang) == 1, "triton.language must be visible from within jit'd function" + _patch_lang_tensor(getattr(lang[0], "tensor"), builder) + _patch_lang_core(lang[0], builder) + _patch_lang_math(lang[0], builder) + + def __call__(self, *args_dev, **kwargs): + args_hst = [_unwrap(arg).cpu() if hasattr(arg, "data_ptr") else arg for arg in args_dev] + # removes reserved keywords from kwargs + kwargs = {k: v for k, v in kwargs.items() if k not in RESERVED_KWS} + # remaps core language functions to interpreted ones + self._patch_lang(builder) + # we need to copy arguments to the host for the interpreter + # implicitly convert tensor arguments to their base pointers + args = inspect.getcallargs(self.fn, *args_hst, **kwargs) + args = {name: arg if name in self.constexprs else _implicit_cvt(arg) for name, arg in args.items()} + # iterate through grid + grid = self.grid(args) if callable(self.grid) else self.grid + assert len(grid) <= 3 + grid = grid + (1, ) * (3 - len(grid)) + builder.set_grid_dim(*grid) + for x in range(grid[0]): + for y in range(grid[1]): + for z in range(grid[2]): + builder.set_grid_idx(x, y, z) + self.fn(**args) + # copy arguments back to propagate side-effects + for arg_dev, arg_hst in zip(args_dev, args_hst): + if hasattr(arg_dev, "data_ptr"): + _unwrap(arg_dev).copy_(arg_hst.to(arg_dev.device)) + + +class InterpretedFunction: + + def _patch_lang(self, builder): + lang = [value for _, value in self.fn.__globals__.items() if value in [tl, tl.core]] + assert len(lang) == 1, "triton.language must be visible from within jit'd function" + _patch_lang_tensor(getattr(lang[0], "tensor"), builder) + _patch_lang_core(lang[0], builder) + + def __init__(self, fn) -> None: + self.fn = fn + + def run(*args, **kwargs): + grid = kwargs["grid"] + kwargs = {k: v for k, v in kwargs.items() if k not in RESERVED_KWS + ["grid"]} + + return GridExecutor(self.fn, self.arg_names, grid)(*args, **kwargs) + + self.run = run + signature = inspect.signature(fn) + self.arg_names = [v.name for v in signature.parameters.values()] + + def __getitem__(self, grid): + return GridExecutor(self.fn, self.arg_names, grid) + + def __call__(self, *args, **kwargs): + self._patch_lang(builder) + return self.fn(*args, **kwargs) diff --git a/llmeval-env/lib/python3.10/site-packages/triton/runtime/jit.py b/llmeval-env/lib/python3.10/site-packages/triton/runtime/jit.py new file mode 100644 index 0000000000000000000000000000000000000000..e571166d288a9837b9e9a18938ed358c1571b0bc --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/runtime/jit.py @@ -0,0 +1,635 @@ +from __future__ import annotations, division + +import ast +import functools +import hashlib +import inspect +import os +import textwrap +from collections import defaultdict, namedtuple +from functools import cached_property +from typing import Callable, Generic, Iterable, List, Optional, TypeVar, Union, cast, overload + +from .._C.libtriton.triton import TMAInfos +from ..common.backend import get_backend, get_cuda_version_key +from .interpreter import InterpretedFunction +from ..runtime.driver import driver + +T = TypeVar("T") + +# ----------------------------------------------------------------------------- +# Dependencies Finder +# ----------------------------------------------------------------------------- + + +class DependenciesFinder(ast.NodeVisitor): + """ + This AST visitor is used to find dependencies of a JITFunction. This can + be used to invalidate a JITFunction's hash when its source code -- or + that of its dependencies -- changes. + """ + + def __init__(self, globals, src) -> None: + super().__init__() + self.ret = hashlib.sha1(src.encode("utf-8")).hexdigest() + self.globals = globals + + def visit_Name(self, node): + return self.globals.get(node.id, None) + + def visit_Attribute(self, node): + lhs = self.visit(node.value) + while isinstance(lhs, ast.Attribute): + lhs = self.visit(lhs.value) + if lhs is None or (getattr(lhs, "__name__", "") == "triton" + or getattr(lhs, "__name__", "").endswith(".triton")): + return None + return getattr(lhs, node.attr) + + def visit_Call(self, node): + func = self.visit(node.func) + if func is None: + return + if inspect.isbuiltin(func): + return + if func.__module__ and (func.__module__.startswith("triton.") or ".triton." in func.__module__): + return + assert isinstance( + func, JITFunction + ), f'Function "{func.__name__}" is being called from a Triton function but is not a Triton function itself. Decorate it with @triton.jit to fix this' + func_cache_key = func.cache_key + noinline = str(getattr(func, "noinline", False)) + self.ret = (self.ret + func_cache_key + noinline).encode("utf-8") + self.ret = hashlib.sha1(self.ret).hexdigest() + + +# ----------------------------------------------------------------------------- +# JITFunction +# ----------------------------------------------------------------------------- + + +def _normalize_ty(ty) -> str: + if isinstance(ty, type): + return ty.__name__ + elif isinstance(ty, str): + return ty + return repr(ty) + + +class KernelParam: + """Represents a parameter to a @jit'ed function. + + A parameter is just the name plus metadata; a parameter plus a value is a + KernelArg. + """ + + def __init__(self, num: int, param: inspect.Parameter, do_not_specialize: bool): + self.num = num + self._param = param + self.do_not_specialize = do_not_specialize + + @cached_property + def name(self): + return self._param.name + + @cached_property + def annotation(self): + if not self._param.annotation or self._param.annotation == inspect.Parameter.empty: + return "" + return _normalize_ty(self._param.annotation) + + @cached_property + def is_constexpr(self): + return "constexpr" in self.annotation + + @property + def default(self): + return self._param.default + + @property + def has_default(self): + return self._param.default != inspect.Parameter.empty + + +class KernelArg: + """Represents an argument to a @jit'ed function. + + An argument is a parameter plus a value. + """ + + def __init__(self, value, param): + self.value = value + self.param = param + + @property + def name(self): + return self.param.name + + def signature_key(self): + annotation = self.param.annotation + if "Tensor" in annotation: + return self.value.dtype + elif annotation == "bool": + return "i1" + elif annotation == "float": + return "fp32" + else: + return JITFunction._key_of(self.value) + + def specialization_key(self): + assert not self.param.do_not_specialize + + try: + return (self.value.data_ptr() % JITFunction.divisibility == 0, ) + except AttributeError: + pass + + if isinstance(self.value, int): + # bool is a subclass of int, so we don't check explicitly above. + return ( + self.value % JITFunction.divisibility == 0, + self.value % JITFunction.divisibility_8 == 0, + self.value == 1, + ) + + return (False, ) + + +class KernelInterface(Generic[T]): + run: T + + def __getitem__(self, grid) -> T: + """ + A JIT function is launched with: fn[grid](*args, **kwargs). + Hence JITFunction.__getitem__ returns a callable proxy that + memorizes the grid. + """ + return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs) + # return cast(T, functools.partial(cast(Callable, self.run), grid=grid)) + + +class JITFunction(KernelInterface[T]): + # Hook for inspecting compiled functions and modules + cache_hook = None + divisibility = 16 + # As Hopper TMA load and store primitive requires the tensor stride to be 16-byte aligned. + # And we only support WGMMA with float16 dtype on Hopper for now. + # So whether the LoadOp and StoreOp will lowering into TMA copy depend on whether the tensor stride is divisible by 8. + # TODO: Make it more reasonable to handle multiple dtypes. + divisibility_8 = 8 + + @staticmethod + def _key_of(arg): + if hasattr(arg, "dtype"): + return arg.dtype + elif isinstance(arg, bool): + return "i1" + elif isinstance(arg, int): + if -(2**31) <= arg and arg <= 2**31 - 1: + return "i32" + elif 2**63 <= arg and arg <= 2**64 - 1: + return "u64" + else: + return "i64" + elif isinstance(arg, float): + return "fp32" + elif arg is None: + return None + else: + raise TypeError(f"Unsupported type {type(arg)} for {arg}") + + @staticmethod + def _device_of(arg): + try: + return arg.device.type + except AttributeError: + return "" + + @staticmethod + def _pinned_memory_of(arg): + try: + return arg.is_pinned() + except (AttributeError, TypeError): + return False + + @staticmethod + def _spec_of(arg): + if hasattr(arg, "data_ptr"): + return arg.data_ptr() % JITFunction.divisibility == 0 + elif isinstance(arg, int): + return (arg % 16 == 0, arg == 1) + return (arg is None, ) + + # TODO(jlebar): Fold this into the KernelArg class. + def _get_config(self, *args): + from ..compiler import AttrsDescriptor + + def is_divisible_by_16(x): + if hasattr(x, "data_ptr"): + return x.data_ptr() % JITFunction.divisibility == 0 + elif isinstance(x, int): + return x % JITFunction.divisibility == 0 + if x is None: + return True + return False + + def is_divisible_by_8(x): + if isinstance(x, int): + return x % JITFunction.divisibility_8 == 0 + if x is None: + return True + return False + + divisible_by_16 = { + param.num + for param, arg in zip(self.params, args) + if is_divisible_by_16(arg) and not param.do_not_specialize + } + divisible_by_8 = { + param.num + for param, arg in zip(self.params, args) + if is_divisible_by_8(arg) and not param.do_not_specialize + } + equal_to_1 = { + param.num + for param, arg in zip(self.params, args) + if isinstance(arg, int) and not isinstance(arg, bool) and arg == 1 and not param.do_not_specialize + } + # folded equal_to_1 and None + # TODO: method to collect all folded args + none_args = {param.num for param, arg in zip(self.params, args) if arg is None and not param.do_not_specialize} + ids_of_folded_args = equal_to_1 | none_args + return AttrsDescriptor(tuple(divisible_by_16), tuple(equal_to_1), tuple(ids_of_folded_args), + tuple(divisible_by_8)) + # return _triton.code_gen.instance_descriptor(divisible_by_16, + # equal_to_1) + + @staticmethod + def _type_of(key): + # `None` is nullptr. Implicitly convert to *i8. + if key is None: + return "*i8" + dtype_str = str(key).split(".")[-1] + tys = { + "bool": "i1", + "float8e4nv": "fp8e4nv", + "float8e5": "fp8e5", + "float8e4b15": "fp8e4b15", + "float8e4b15x4": "fp8e4b15x4", + "float8_e4m3fn": "fp8e4nv", + "float8_e5m2": "fp8e5", + "float16": "fp16", + "bfloat16": "bf16", + "float32": "fp32", + "float64": "fp64", + "int8": "i8", + "int16": "i16", + "int32": "i32", + "int64": "i64", + "uint8": "u8", + "uint16": "u16", + "uint32": "u32", + "uint64": "u64", + } + # reinterpret can create triton type + for v in list(tys.values()): + tys[v] = v + return key if isinstance(key, str) else f"*{tys[dtype_str]}" + + def _make_constants(self, constexpr_key): + constants = dict(zip(self.constexprs, constexpr_key)) + return constants + + def _call_hook( + self, + key, + signature, + device, + constants, + num_warps, + num_ctas, + num_stages, + enable_warp_specialization, + enable_fp_fusion, + extern_libs, + configs, + ): + if JITFunction.cache_hook is None: + return False + + name = self.fn.__name__ + module = self.fn.__module__ + arg_reprs = ", ".join([f"{param.name}: {ty}" for param, ty in zip(self.params, key[1])]) + repr = f"{name}[num_warps={num_warps}, num_ctas={num_ctas}, num_stages={num_stages}, enable_warp_specialization={enable_warp_specialization}, enable_fp_fusion={enable_fp_fusion}]({arg_reprs})" + key = str(key) + + class LegacyCompiler: + + def __init__(self, module, name): + self.module = module + self.name = name + pass + + kwargs = dict( + signature=signature, + device=device, + constants=constants, + num_warps=num_warps, + num_ctas=num_ctas, + num_stages=num_stages, + enable_warp_specialization=enable_warp_specialization, + enable_fp_fusion=enable_fp_fusion, + extern_libs=extern_libs, + configs=configs, + ) + + return JITFunction.cache_hook( + key=key, + repr=repr, + fn=LegacyCompiler(module, name), + compile={"key": key, **kwargs}, + is_manual_warmup=False, + already_compiled=False, + ) + + def run(self, *args, grid, warmup, **kwargs): + from ..compiler import CompiledKernel, compile, ASTSource + from ..compiler.backends.cuda import CUDABackend + # deprecated arguments + assert "device_type" not in kwargs, "device_type option is deprecated; current target will be used" + assert "device" not in kwargs, "device option is deprecated; current device will be used" + assert "stream" not in kwargs, "stream option is deprecated; current stream will be used" + # parse options + device = driver.get_current_device() + stream = driver.get_current_stream(device) + target = driver.get_current_target() + backend = CUDABackend(target) + kwargs["debug"] = self.debug + options = backend.parse_options(kwargs) + # bind non-reserved keyword args and set defaults + kwargs = {k: v for k, v in kwargs.items() if not k in options.__dict__} + bound_args = self.signature.bind(*args, **kwargs) + bound_args.apply_defaults() + assert len(bound_args.arguments) == len(self.params) + # canonicalize grid + assert grid is not None + if callable(grid): + # Arguments are passed as a dict to `grid`, by contract. + # TODO(jlebar): In the new launch API, pass the compiler flags as a + # second parameter to `grid`. + grid = grid(dict(bound_args.arguments)) + grid_size = len(grid) + grid_0 = grid[0] + grid_1 = grid[1] if grid_size > 1 else 1 + grid_2 = grid[2] if grid_size > 2 else 1 + # compute cache key + args = [KernelArg(arg_value, param) for (_, arg_value), param in zip(bound_args.arguments.items(), self.params)] + sig_key = tuple(arg.signature_key() for arg in args if not arg.param.is_constexpr) + spec_key = tuple(arg.specialization_key() for arg in args if not arg.param.do_not_specialize) + constexpr_key = tuple(arg.value for arg in args if arg.param.is_constexpr) + key = (get_cuda_version_key(), sig_key, constexpr_key, spec_key, options) + # Kernel is not cached; we have to compile. + if key not in self.cache[device]: + configs = (self._get_config(*[arg.value for arg in args]), ) + constants = { + arg.param.num: arg.value + for arg in args + if arg.param.is_constexpr or arg.param.num in configs[0].equal_to_1 or arg.value is None + } + for i, arg in constants.items(): + if callable(arg): + raise TypeError(f"Callable constexpr at index {i} is not supported") + + # Build kernel signature -- doesn't include constexpr arguments. + signature = { + arg.param.num: self._type_of(self._key_of(arg.value)) + for arg in args + if not arg.param.is_constexpr + } + + if self._call_hook(key, signature, device, constants, options.num_warps, options.num_ctas, + options.num_stages, options.enable_warp_specialization, options.enable_fp_fusion, + options.extern_libs, configs): + return None + # compile the kernel + src = ASTSource(self, signature, constants, configs[0]) + self.cache[device][key] = compile( + src, + target=target, + options=options.__dict__, + ) + + kernel = self.cache[device][key] + if not warmup: + args = [arg.value for arg in args if not arg.param.is_constexpr] + kernel.run(grid_0, grid_1, grid_2, kernel.num_warps, kernel.num_ctas, # number of warps/ctas per instance + kernel.cluster_dims[0], kernel.cluster_dims[1], kernel.cluster_dims[2], # cluster + kernel.shared, stream, kernel.function, CompiledKernel.launch_enter_hook, + CompiledKernel.launch_exit_hook, kernel, + *driver.assemble_tensormap_to_arg(kernel.metadata["tensormaps_info"], args)) + return kernel + + def __init__(self, fn, version=None, do_not_specialize=None, debug=None, noinline=None): + do_not_specialize = do_not_specialize if do_not_specialize else [] + + self.fn = fn + self.module = fn.__module__ + self.version = version + self.signature = inspect.signature(fn) + self.do_not_specialize = do_not_specialize + self.starting_line_number = inspect.getsourcelines(fn)[1] + + self.params = [] + for i, param in enumerate(self.signature.parameters.values()): + dns = do_not_specialize and (i in do_not_specialize or param.name in do_not_specialize) + self.params.append(KernelParam(i, param, dns)) + + # function source code (without decorators) + self.src = textwrap.dedent(inspect.getsource(fn)) + self.src = self.src[self.src.find("def"):] + # cache of just-in-time compiled kernels + self.cache = defaultdict(dict) + self.hash = None + # JITFunction can be instantiated as kernel + # when called with a grid using __getitem__ + self.kernel = None + self.debug = True if os.environ.get("TRITON_DEBUG", "0") == "1" else debug + self.noinline = noinline + + # tma info + self.tensormaps_info = TMAInfos() + + # TODO(jlebar): Remove uses of these fields outside this file, then + # remove the fields here. + self.arg_names = [p.name for p in self.params] + self.constexprs = [p.num for p in self.params if p.is_constexpr] + + # re-use docs of wrapped function + self.__doc__ = fn.__doc__ + self.__name__ = fn.__name__ + self.__globals__ = fn.__globals__ + self.__module__ = fn.__module__ + + @property + def cache_key(self): + # TODO : hash should be attribute of `self` + if self.hash is None: + dependencies_finder = DependenciesFinder(globals=self.__globals__, src=self.src) + dependencies_finder.visit(self.parse()) + self.hash = dependencies_finder.ret + str(self.starting_line_number) + return self.hash + + def warmup(self, *args, grid, **kwargs): + return self.run(grid=grid, warmup=True, *map(MockTensor.wrap_dtype, args), **kwargs) + + # we do not parse `src` in the constructor because + # the user might want to monkey-patch self.src dynamically. + # Our unit tests do this, for example. + def parse(self): + tree = ast.parse(self.src) + assert isinstance(tree, ast.Module) + assert len(tree.body) == 1 + assert isinstance(tree.body[0], ast.FunctionDef) + return tree + + def __call__(self, *args, **kwargs): + raise RuntimeError("Cannot call @triton.jit'd outside of the scope of a kernel") + + def __setattr__(self, name, value): + super(JITFunction, self).__setattr__(name, value) + # - when `.src` attribute is set, cache path needs + # to be reinitialized + if name == "src": + self.hash = None + + def __repr__(self): + return f"JITFunction({self.module}:{self.fn.__name__})" + + +# ----------------------------------------------------------------------------- +# `jit` decorator +# ----------------------------------------------------------------------------- + + +@overload +def jit(fn: T) -> JITFunction[T]: + ... + + +@overload +def jit( + *, + version=None, + do_not_specialize: Optional[Iterable[int]] = None, + debug: Optional[bool] = None, + noinline: Optional[bool] = None, +) -> Callable[[T], JITFunction[T]]: + ... + + +def jit( + fn: Optional[T] = None, + *, + version=None, + do_not_specialize: Optional[Iterable[int]] = None, + debug: Optional[bool] = None, + noinline: Optional[bool] = None, +) -> Union[JITFunction[T], Callable[[T], JITFunction[T]]]: + """ + Decorator for JIT-compiling a function using the Triton compiler. + + :note: When a jit'd function is called, arguments are + implicitly converted to pointers if they have a :code:`.data_ptr()` method + and a `.dtype` attribute. + + :note: This function will be compiled and run on the GPU. It will only have access to: + + * python primitives, + * builtins within the triton package, + * arguments to this function, + * other jit'd functions + + :param fn: the function to be jit-compiled + :type fn: Callable + """ + + def decorator(fn: T) -> JITFunction[T]: + assert callable(fn) + if os.getenv("TRITON_INTERPRET", "0") == "1": + return InterpretedFunction(fn) + else: + return JITFunction( + fn, + version=version, + do_not_specialize=do_not_specialize, + debug=debug, + noinline=noinline, + ) + + if fn is not None: + return decorator(fn) + + else: + return decorator + + +# ----------------------------------------------------------------------------- +# Utilities for mocking tensors +# ----------------------------------------------------------------------------- + + +class MockTensor: + """ + Can be used in place of real tensors when calling: + kernel.warmup(MockTensor(torch.float32), ...) + """ + + @staticmethod + def wrap_dtype(arg): + if arg.__class__.__name__ == "dtype" and arg.__module__ == "torch": + return MockTensor(arg) + return arg + + def __init__(self, dtype): + self.dtype = dtype + + @staticmethod + def data_ptr(): + return 0 # optimistically assumes multiple of 16 + + +class TensorWrapper: + + def __init__(self, base, dtype): + self.dtype = dtype + self.base = base + self.is_cuda = base.is_cuda + self.device = base.device + self.shape = self.base.shape + + def data_ptr(self): + return self.base.data_ptr() + + def stride(self, i): + return self.base.stride(i) + + def __str__(self) -> str: + return f"TensorWrapper[{self.dtype}]({self.base})" + + def element_size(self): + return self.base.element_size() + + +def reinterpret(tensor, dtype): + if isinstance(tensor, TensorWrapper): + if dtype == tensor.base.dtype: + # Reinterpreting to the original interpretation; return the base. + return tensor.base + else: + # Reinterpreting a wrapped tensor to a different type. + return TensorWrapper(tensor.base, dtype) + elif hasattr(tensor, "data_ptr"): + # A new wrapper is needed around an unwrapped tensor. + return TensorWrapper(tensor, dtype) + else: + raise TypeError(f"Cannot reinterpret a {type(tensor)}.") diff --git a/llmeval-env/lib/python3.10/site-packages/triton/testing.py b/llmeval-env/lib/python3.10/site-packages/triton/testing.py new file mode 100644 index 0000000000000000000000000000000000000000..848db7c1b508bbe93636b10e88d88e23cbc35d2d --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/testing.py @@ -0,0 +1,493 @@ +import functools +import os +import subprocess +import sys +from contextlib import contextmanager +from typing import Any, Dict, List + +from . import language as tl +from ._C.libtriton.triton import runtime + + +def nvsmi(attrs): + attrs = ','.join(attrs) + cmd = ['nvidia-smi', '-i', '0', '--query-gpu=' + attrs, '--format=csv,noheader,nounits'] + out = subprocess.check_output(cmd) + ret = out.decode(sys.stdout.encoding).split(',') + ret = [int(x) for x in ret] + return ret + + +def do_bench_cudagraph(fn, rep=20, grad_to_none=None): + import torch + """ + Benchmark the runtime of the provided function. + + :param fn: Function to benchmark + :type fn: Callable + :param rep: Repetition time (in ms) + :type rep: int + :param grad_to_none: Reset the gradient of the provided tensor to None + :type grad_to_none: torch.tensor, optional + """ + if torch.cuda.current_stream() == torch.cuda.default_stream(): + raise RuntimeError("Cannot capture graph in default stream. Please use side stream in benchmark code.") + # warmup + fn() + # step 1 - we estimate the amount of time the kernel call takes + # NOTE: this estimate isn't super accurate because the GPU isn't warmed up at this point + # but it is probably good enough + if grad_to_none is not None: + for x in grad_to_none: + x.detach_() + x.requires_grad_(True) + x.grad = None + g = torch.cuda.CUDAGraph() + with torch.cuda.graph(g): + fn() + torch.cuda.synchronize() + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + start_event.record() + g.replay() + end_event.record() + torch.cuda.synchronize() + estimate_ms = start_event.elapsed_time(end_event) + n_repeat = max(1, int(rep / estimate_ms)) + # step 2 - construct a cuda graph with `n_repeat` unrolled function calls to minimize + # host overhead + g = torch.cuda.CUDAGraph() + with torch.cuda.graph(g): + for i in range(n_repeat): + if grad_to_none is not None: + for x in grad_to_none: + x.grad = None + fn() + torch.cuda.synchronize() + # measure time and return + ret = [] + n_retries = 10 + for i in range(n_retries): + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + start_event.record() + g.replay() + end_event.record() + torch.cuda.synchronize() + ret += [start_event.elapsed_time(end_event) / n_repeat] + return torch.mean(torch.tensor(ret)).item() + + +def do_bench(fn, warmup=25, rep=100, grad_to_none=None, quantiles=None, fast_flush=True, return_mode="mean"): + assert return_mode in ["min", "max", "mean", "median"] + import torch + """ + Benchmark the runtime of the provided function. By default, return the median runtime of :code:`fn` along with + the 20-th and 80-th performance percentile. + + :param fn: Function to benchmark + :type fn: Callable + :param warmup: Warmup time (in ms) + :type warmup: int + :param rep: Repetition time (in ms) + :type rep: int + :param grad_to_none: Reset the gradient of the provided tensor to None + :type grad_to_none: torch.tensor, optional + :param quantiles: Performance percentile to return in addition to the median. + :type quantiles: list[float] + :param fast_flush: Use faster kernel to flush L2 between measurements + :type fast_flush: bool + """ + + fn() + torch.cuda.synchronize() + + # We maintain a buffer of 256 MB that we clear + # before each kernel call to make sure that the L2 + # doesn't contain any input data before the run + if fast_flush: + cache = torch.empty(int(256e6 // 4), dtype=torch.int, device='cuda') + else: + cache = torch.empty(int(256e6), dtype=torch.int8, device='cuda') + + # Estimate the runtime of the function + start_event = torch.cuda.Event(enable_timing=True) + end_event = torch.cuda.Event(enable_timing=True) + start_event.record() + for _ in range(5): + cache.zero_() + fn() + end_event.record() + torch.cuda.synchronize() + estimate_ms = start_event.elapsed_time(end_event) / 5 + + # compute number of warmup and repeat + n_warmup = max(1, int(warmup / estimate_ms)) + n_repeat = max(1, int(rep / estimate_ms)) + start_event = [torch.cuda.Event(enable_timing=True) for i in range(n_repeat)] + end_event = [torch.cuda.Event(enable_timing=True) for i in range(n_repeat)] + # Warm-up + for _ in range(n_warmup): + fn() + # Benchmark + for i in range(n_repeat): + # we don't want `fn` to accumulate gradient values + # if it contains a backward pass. So we clear the + # provided gradients + if grad_to_none is not None: + for x in grad_to_none: + x.grad = None + # we clear the L2 cache before each run + cache.zero_() + # record time of `fn` + start_event[i].record() + fn() + end_event[i].record() + # Record clocks + torch.cuda.synchronize() + times = torch.tensor([s.elapsed_time(e) for s, e in zip(start_event, end_event)], dtype=torch.float) + if quantiles is not None: + ret = torch.quantile(times, torch.tensor(quantiles, dtype=torch.float)).tolist() + if len(ret) == 1: + ret = ret[0] + return ret + return getattr(torch, return_mode)(times).item() + + +def assert_close(x, y, atol=None, rtol=None, err_msg=''): + import numpy as np + import torch + + # canonicalize arguments to be tensors + if not isinstance(x, torch.Tensor): + x = torch.tensor(x) + if not isinstance(y, torch.Tensor): + y = torch.tensor(y) + # absolute tolerance + if atol is None: + atol = 1e-2 + atol = atol(x.dtype) if callable(atol) else atol + # relative tolerance hook + if rtol is None: + rtol = 0. + rtol = rtol(x.dtype) if callable(rtol) else rtol + # we use numpy instead of pytorch + # as it seems more memory efficient + # pytorch tends to oom on large tensors + if isinstance(x, torch.Tensor): + if x.dtype == torch.bfloat16: + x = x.float() + x = x.cpu().detach().numpy() + if isinstance(y, torch.Tensor): + if y.dtype == torch.bfloat16: + y = y.float() + y = y.cpu().detach().numpy() + # we handle size==1 case separately as we can + # provide better error message there + if x.size > 1 or y.size > 1: + np.testing.assert_allclose(x, y, atol=atol, rtol=rtol, equal_nan=True) + return + if not np.allclose(x, y, atol=atol, rtol=rtol): + raise AssertionError(f'{err_msg} {x} is not close to {y} (atol={atol}, rtol={rtol})') + + +class Benchmark: + """ + This class is used by the :code:`perf_report` function to generate line plots with a concise API. + """ + + def __init__( + self, + x_names: List[str], + x_vals: List[Any], + line_arg: str, + line_vals: List[Any], + line_names: List[str], + plot_name: str, + args: Dict[str, Any], + xlabel: str = '', + ylabel: str = '', + x_log: bool = False, + y_log: bool = False, + color=None, + styles=None, + ): + """ + Constructor. + x_vals can be a list of scalars or a list of tuples/lists. If x_vals is a list + of scalars and there are multiple x_names, all arguments will have the same value. + If x_vals is a list of tuples/lists, each element should have the same length as + x_names. + + :param x_names: Name of the arguments that should appear on the x axis of the plot. + :type x_names: List[str] + :param x_vals: List of values to use for the arguments in :code:`x_names`. + :type x_vals: List[Any] + :param line_arg: Argument name for which different values correspond to different lines in the plot. + :type line_arg: str + :param line_vals: List of values to use for the arguments in :code:`line_arg`. + :type line_vals: List[Any] + :param line_names: Label names for the different lines. + :type line_names: List[str] + :param plot_name: Name of the plot. + :type plot_name: str + :param args: Dictionary of keyword arguments to remain fixed throughout the benchmark. + :type args: Dict[str, Any] + :param xlabel: Label for the x axis of the plot. + :type xlabel: str, optional + :param ylabel: Label for the y axis of the plot. + :type ylabel: str, optional + :param x_log: Whether the x axis should be log scale. + :type x_log: bool, optional + :param y_log: Whether the y axis should be log scale. + :type y_log: bool, optional + """ + self.x_names = x_names + self.x_vals = x_vals + self.x_log = x_log + self.line_arg = line_arg + self.line_vals = line_vals + self.line_names = line_names + self.y_log = y_log + self.styles = styles + # plot info + self.xlabel = xlabel + self.ylabel = ylabel + self.plot_name = plot_name + self.args = args + + +class Mark: + + def __init__(self, fn, benchmarks): + self.fn = fn + self.benchmarks = benchmarks + + def _run(self, bench: Benchmark, save_path: str, show_plots: bool, print_data: bool, diff_col=False, **kwrags): + import os + + import matplotlib.pyplot as plt + import pandas as pd + y_mean = bench.line_names + y_min = [f'{x}-min' for x in bench.line_names] + y_max = [f'{x}-max' for x in bench.line_names] + x_names = list(bench.x_names) + df = pd.DataFrame(columns=x_names + y_mean + y_min + y_max) + for x in bench.x_vals: + # x can be a single value or a sequence of values. + if not isinstance(x, (list, tuple)): + x = [x for _ in x_names] + + if len(x) != len(x_names): + raise ValueError(f"Expected {len(x_names)} values, got {x}") + x_args = dict(zip(x_names, x)) + + row_mean, row_min, row_max = [], [], [] + for y in bench.line_vals: + ret = self.fn(**x_args, **{bench.line_arg: y}, **bench.args, **kwrags) + try: + y_mean, y_min, y_max = ret + except TypeError: + y_mean, y_min, y_max = ret, None, None + row_mean += [y_mean] + row_min += [y_min] + row_max += [y_max] + df.loc[len(df)] = list(x) + row_mean + row_min + row_max + + if bench.plot_name: + plt.figure() + ax = plt.subplot() + # Plot first x value on x axis if there are multiple. + first_x = x_names[0] + for i, y in enumerate(bench.line_names): + y_min, y_max = df[y + '-min'], df[y + '-max'] + col = bench.styles[i][0] if bench.styles else None + sty = bench.styles[i][1] if bench.styles else None + ax.plot(df[first_x], df[y], label=y, color=col, ls=sty) + if not y_min.isnull().all() and not y_max.isnull().all(): + y_min = y_min.astype(float) + y_max = y_max.astype(float) + ax.fill_between(df[first_x], y_min, y_max, alpha=0.15, color=col) + ax.legend() + ax.set_xlabel(bench.xlabel or first_x) + ax.set_ylabel(bench.ylabel) + # ax.set_title(bench.plot_name) + ax.set_xscale("log" if bench.x_log else "linear") + ax.set_yscale("log" if bench.y_log else "linear") + if show_plots: + plt.show() + if save_path: + plt.savefig(os.path.join(save_path, f"{bench.plot_name}.png")) + df = df[x_names + bench.line_names] + if diff_col and df.shape[1] == 2: + col0, col1 = df.columns.tolist() + df['Diff'] = df[col1] - df[col0] + + if print_data: + print(bench.plot_name + ':') + print(df) + if save_path: + df.to_csv(os.path.join(save_path, f"{bench.plot_name}.csv"), float_format='%.1f', index=False) + return df + + def run(self, show_plots=False, print_data=False, save_path='', return_df=False, **kwargs): + has_single_bench = isinstance(self.benchmarks, Benchmark) + benchmarks = [self.benchmarks] if has_single_bench else self.benchmarks + result_dfs = [] + if save_path: + html = open(os.path.join(save_path, "results.html"), "w") + html.write("\n") + for bench in benchmarks: + result_dfs.append(self._run(bench, save_path, show_plots, print_data, **kwargs)) + if save_path: + html.write(f"\n") + if save_path: + html.write("\n") + if return_df: + if has_single_bench: + return result_dfs[0] + else: + return result_dfs + return None + + +def perf_report(benchmarks): + """ + Mark a function for benchmarking. The benchmark can then be executed by using the :code:`.run` method on the return value. + + :param benchmarks: Benchmarking configurations. + :type benchmarks: List of :class:`Benchmark` + """ + wrapper = lambda fn: Mark(fn, benchmarks) + return wrapper + + +def get_dram_gbps(backend=None, device=None): + ''' return DRAM bandwidth in GB/s ''' + import torch + + from .runtime import driver + if not backend: + backend = runtime.backend.CUDA + if not device: + device = torch.cuda.current_device() + mem_clock_khz = driver.utils.get_device_properties(device)["mem_clock_rate"] # in kHz + bus_width = driver.utils.get_device_properties(device)["mem_bus_width"] + bw_gbps = mem_clock_khz * bus_width * 2 / 1e6 / 8 # In GB/s + return bw_gbps + + +def get_max_tensorcore_tflops(dtype, clock_rate, backend=None, device=None): + import torch + + from .runtime import driver + if not backend: + backend = runtime.backend.CUDA + if not device: + device = torch.cuda.current_device() + + num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4 + capability = torch.cuda.get_device_capability(device) + if capability[0] < 8: + assert dtype == torch.float16 + ops_per_sub_core = 256 # 2 4x4x4 Tensor Cores + else: + if dtype in [torch.float32, torch.int32]: + ops_per_sub_core = 256 + elif dtype in [torch.float16, torch.bfloat16, torch.int16]: + ops_per_sub_core = 512 + elif dtype in [torch.int8, tl.float8e4nv, tl.float8e4b15, tl.float8e5]: + ops_per_sub_core = 1024 + else: + raise RuntimeError("dtype not supported") + tflops = num_subcores * clock_rate * ops_per_sub_core * 1e-9 + return tflops + + +# create decorator that wraps test function into +# a cuda-memcheck system call + + +def cuda_memcheck(**target_kwargs): + + def decorator(test_fn): + + @functools.wraps(test_fn) + def wrapper(*args, **kwargs): + import psutil + ppid_name = psutil.Process(os.getppid()).name() + run_cuda_memcheck = target_kwargs.items() <= kwargs.items() + if run_cuda_memcheck and ppid_name != "cuda-memcheck": + path = os.path.realpath(test_fn.__globals__["__file__"]) + # get path of current file + env = {"PATH": os.environ["PATH"], "PYTORCH_NO_CUDA_MEMORY_CACHING": "1"} + assert 'request' in kwargs, "memcheck'ed test must have a (possibly unused) `request` fixture" + test_id = kwargs['request'].node.callspec.id + cmd = f"{path}::{test_fn.__name__}[{test_id}]" + out = subprocess.run(["cuda-memcheck", "pytest", "-vs", cmd], capture_output=True, env=env) + assert out.returncode == 0, "cuda-memcheck returned an error: bounds checking failed" + assert "ERROR SUMMARY: 0 errors" in str(out.stdout) + else: + test_fn(*args, **kwargs) + + return wrapper + + return decorator + + +@contextmanager +def set_gpu_clock(ref_sm_clock=1350, ref_mem_clock=1215): + try: + subprocess.check_output(["nvidia-smi", "-i", "0", "-pm", "1"]) + subprocess.check_output([ + "nvidia-smi", + "-i", + "0", + f"--lock-gpu-clocks={ref_sm_clock},{ref_sm_clock}", + ]) + subprocess.check_output([ + "nvidia-smi", + "-i", + "0", + f"--lock-memory-clocks={ref_mem_clock},{ref_mem_clock}", + ]) + cur_sm_clock = nvsmi(["clocks.current.sm"])[0] + cur_mem_clock = nvsmi(["clocks.current.memory"])[0] + assert abs(cur_sm_clock - ref_sm_clock) < 10, f"GPU SMs must run at {ref_sm_clock} MHz" + assert abs(cur_mem_clock - ref_mem_clock) < 10, f"GPU SMs must run at {ref_mem_clock} MHz" + tflops = 1e-6 * 2 * 108 * 4 * 256 * ref_sm_clock + gbps = 640 * 2 * ref_mem_clock * 1e-3 + yield tflops, gbps + finally: + subprocess.check_output(["nvidia-smi", "-i", "0", "-pm", "0"]) + subprocess.check_output(["nvidia-smi", "-i", "0", "-rgc"]) + subprocess.check_output(["nvidia-smi", "-i", "0", "-rmc"]) + + +def get_max_simd_tflops(dtype, clock_rate, backend=None, device=None): + import torch + + from .runtime import driver + if not backend: + backend = runtime.backend.CUDA + if not device: + device = torch.cuda.current_device() + + num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4 + capability = torch.cuda.get_device_capability() + if capability[0] < 8: + if dtype == torch.float32: + ops_per_sub_core = 32 # 2*16 + elif dtype == torch.float16: + ops_per_sub_core = 64 + else: + raise RuntimeError("dtype not supported") + else: + if dtype == torch.float32: + ops_per_sub_core = 32 + elif dtype in [torch.float16, torch.bfloat16]: + ops_per_sub_core = 64 + else: + raise RuntimeError("dtype not supported") + tflops = num_subcores * clock_rate * ops_per_sub_core * 1e-9 + return tflops diff --git a/llmeval-env/lib/python3.10/site-packages/triton/third_party/cuda/bin/cuobjdump b/llmeval-env/lib/python3.10/site-packages/triton/third_party/cuda/bin/cuobjdump new file mode 100644 index 0000000000000000000000000000000000000000..d3dd4b3ea3935a357ee5cad736bafc249d5107ef Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/third_party/cuda/bin/cuobjdump differ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/third_party/cuda/include/cuda.h b/llmeval-env/lib/python3.10/site-packages/triton/third_party/cuda/include/cuda.h new file mode 100644 index 0000000000000000000000000000000000000000..ec111be805af301204fb9041f76e9da5b5cf211c --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/triton/third_party/cuda/include/cuda.h @@ -0,0 +1,22119 @@ +/* + * Copyright 1993-2022 NVIDIA Corporation. All rights reserved. + * + * NOTICE TO LICENSEE: + * + * This source code and/or documentation ("Licensed Deliverables") are + * subject to NVIDIA intellectual property rights under U.S. and + * international Copyright laws. + * + * These Licensed Deliverables contained herein is PROPRIETARY and + * CONFIDENTIAL to NVIDIA and is being provided under the terms and + * conditions of a form of NVIDIA software license agreement by and + * between NVIDIA and Licensee ("License Agreement") or electronically + * accepted by Licensee. Notwithstanding any terms or conditions to + * the contrary in the License Agreement, reproduction or disclosure + * of the Licensed Deliverables to any third party without the express + * written consent of NVIDIA is prohibited. + * + * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE + * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE + * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS + * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND. + * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED + * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY, + * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. + * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE + * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY + * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY + * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, + * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS + * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE + * OF THESE LICENSED DELIVERABLES. + * + * U.S. Government End Users. These Licensed Deliverables are a + * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT + * 1995), consisting of "commercial computer software" and "commercial + * computer software documentation" as such terms are used in 48 + * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government + * only as a commercial end item. Consistent with 48 C.F.R.12.212 and + * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all + * U.S. Government End Users acquire the Licensed Deliverables with + * only those rights set forth herein. + * + * Any use of the Licensed Deliverables in individual and commercial + * software must include, in the user documentation and internal + * comments to the code, the above Disclaimer and U.S. Government End + * Users Notice. + */ + +#ifndef __cuda_cuda_h__ +#define __cuda_cuda_h__ + + + +#include +#ifdef _MSC_VER +typedef unsigned __int32 cuuint32_t; +typedef unsigned __int64 cuuint64_t; +#else +#include +typedef uint32_t cuuint32_t; +typedef uint64_t cuuint64_t; +#endif + +#if defined(__CUDA_API_VERSION_INTERNAL) || defined(__DOXYGEN_ONLY__) || defined(CUDA_ENABLE_DEPRECATED) +#define __CUDA_DEPRECATED +#elif defined(_MSC_VER) +#define __CUDA_DEPRECATED __declspec(deprecated) +#elif defined(__GNUC__) +#define __CUDA_DEPRECATED __attribute__((deprecated)) +#else +#define __CUDA_DEPRECATED +#endif + +#if defined(CUDA_FORCE_API_VERSION) +#error "CUDA_FORCE_API_VERSION is no longer supported." +#endif + +#if defined(__CUDA_API_VERSION_INTERNAL) || defined(CUDA_API_PER_THREAD_DEFAULT_STREAM) + #define __CUDA_API_PER_THREAD_DEFAULT_STREAM + #define __CUDA_API_PTDS(api) api ## _ptds + #define __CUDA_API_PTSZ(api) api ## _ptsz +#else + #define __CUDA_API_PTDS(api) api + #define __CUDA_API_PTSZ(api) api +#endif + +#define cuDeviceTotalMem cuDeviceTotalMem_v2 +#define cuCtxCreate cuCtxCreate_v2 +#define cuCtxCreate_v3 cuCtxCreate_v3 +#define cuModuleGetGlobal cuModuleGetGlobal_v2 +#define cuMemGetInfo cuMemGetInfo_v2 +#define cuMemAlloc cuMemAlloc_v2 +#define cuMemAllocPitch cuMemAllocPitch_v2 +#define cuMemFree cuMemFree_v2 +#define cuMemGetAddressRange cuMemGetAddressRange_v2 +#define cuMemAllocHost cuMemAllocHost_v2 +#define cuMemHostGetDevicePointer cuMemHostGetDevicePointer_v2 +#define cuMemcpyHtoD __CUDA_API_PTDS(cuMemcpyHtoD_v2) +#define cuMemcpyDtoH __CUDA_API_PTDS(cuMemcpyDtoH_v2) +#define cuMemcpyDtoD __CUDA_API_PTDS(cuMemcpyDtoD_v2) +#define cuMemcpyDtoA __CUDA_API_PTDS(cuMemcpyDtoA_v2) +#define cuMemcpyAtoD __CUDA_API_PTDS(cuMemcpyAtoD_v2) +#define cuMemcpyHtoA __CUDA_API_PTDS(cuMemcpyHtoA_v2) +#define cuMemcpyAtoH __CUDA_API_PTDS(cuMemcpyAtoH_v2) +#define cuMemcpyAtoA __CUDA_API_PTDS(cuMemcpyAtoA_v2) +#define cuMemcpyHtoAAsync __CUDA_API_PTSZ(cuMemcpyHtoAAsync_v2) +#define cuMemcpyAtoHAsync __CUDA_API_PTSZ(cuMemcpyAtoHAsync_v2) +#define cuMemcpy2D __CUDA_API_PTDS(cuMemcpy2D_v2) +#define cuMemcpy2DUnaligned __CUDA_API_PTDS(cuMemcpy2DUnaligned_v2) +#define cuMemcpy3D __CUDA_API_PTDS(cuMemcpy3D_v2) +#define cuMemcpyHtoDAsync __CUDA_API_PTSZ(cuMemcpyHtoDAsync_v2) +#define cuMemcpyDtoHAsync __CUDA_API_PTSZ(cuMemcpyDtoHAsync_v2) +#define cuMemcpyDtoDAsync __CUDA_API_PTSZ(cuMemcpyDtoDAsync_v2) +#define cuMemcpy2DAsync __CUDA_API_PTSZ(cuMemcpy2DAsync_v2) +#define cuMemcpy3DAsync __CUDA_API_PTSZ(cuMemcpy3DAsync_v2) +#define cuMemsetD8 __CUDA_API_PTDS(cuMemsetD8_v2) +#define cuMemsetD16 __CUDA_API_PTDS(cuMemsetD16_v2) +#define cuMemsetD32 __CUDA_API_PTDS(cuMemsetD32_v2) +#define cuMemsetD2D8 __CUDA_API_PTDS(cuMemsetD2D8_v2) +#define cuMemsetD2D16 __CUDA_API_PTDS(cuMemsetD2D16_v2) +#define cuMemsetD2D32 __CUDA_API_PTDS(cuMemsetD2D32_v2) +#define cuArrayCreate cuArrayCreate_v2 +#define cuArrayGetDescriptor cuArrayGetDescriptor_v2 +#define cuArray3DCreate cuArray3DCreate_v2 +#define cuArray3DGetDescriptor cuArray3DGetDescriptor_v2 +#define cuTexRefSetAddress cuTexRefSetAddress_v2 +#define cuTexRefGetAddress cuTexRefGetAddress_v2 +#define cuGraphicsResourceGetMappedPointer cuGraphicsResourceGetMappedPointer_v2 +#define cuCtxDestroy cuCtxDestroy_v2 +#define cuCtxPopCurrent cuCtxPopCurrent_v2 +#define cuCtxPushCurrent cuCtxPushCurrent_v2 +#define cuStreamDestroy cuStreamDestroy_v2 +#define cuEventDestroy cuEventDestroy_v2 +#define cuTexRefSetAddress2D cuTexRefSetAddress2D_v3 +#define cuLinkCreate cuLinkCreate_v2 +#define cuLinkAddData cuLinkAddData_v2 +#define cuLinkAddFile cuLinkAddFile_v2 +#define cuMemHostRegister cuMemHostRegister_v2 +#define cuGraphicsResourceSetMapFlags cuGraphicsResourceSetMapFlags_v2 +#define cuStreamBeginCapture __CUDA_API_PTSZ(cuStreamBeginCapture_v2) +#define cuDevicePrimaryCtxRelease cuDevicePrimaryCtxRelease_v2 +#define cuDevicePrimaryCtxReset cuDevicePrimaryCtxReset_v2 +#define cuDevicePrimaryCtxSetFlags cuDevicePrimaryCtxSetFlags_v2 +#define cuDeviceGetUuid_v2 cuDeviceGetUuid_v2 +#define cuIpcOpenMemHandle cuIpcOpenMemHandle_v2 + +#define cuGraphInstantiate cuGraphInstantiateWithFlags + +#define cuGraphExecUpdate cuGraphExecUpdate_v2 +#define cuGetProcAddress cuGetProcAddress_v2 +#define cuGraphAddKernelNode cuGraphAddKernelNode_v2 +#define cuGraphKernelNodeGetParams cuGraphKernelNodeGetParams_v2 +#define cuGraphKernelNodeSetParams cuGraphKernelNodeSetParams_v2 +#define cuGraphExecKernelNodeSetParams cuGraphExecKernelNodeSetParams_v2 + +#define cuStreamWriteValue32 __CUDA_API_PTSZ(cuStreamWriteValue32_v2) +#define cuStreamWaitValue32 __CUDA_API_PTSZ(cuStreamWaitValue32_v2) +#define cuStreamWriteValue64 __CUDA_API_PTSZ(cuStreamWriteValue64_v2) +#define cuStreamWaitValue64 __CUDA_API_PTSZ(cuStreamWaitValue64_v2) +#define cuStreamBatchMemOp __CUDA_API_PTSZ(cuStreamBatchMemOp_v2) +#define cuStreamGetCaptureInfo __CUDA_API_PTSZ(cuStreamGetCaptureInfo_v2) +#define cuStreamGetCaptureInfo_v2 __CUDA_API_PTSZ(cuStreamGetCaptureInfo_v2) + +#if defined(__CUDA_API_PER_THREAD_DEFAULT_STREAM) + #define cuMemcpy __CUDA_API_PTDS(cuMemcpy) + #define cuMemcpyAsync __CUDA_API_PTSZ(cuMemcpyAsync) + #define cuMemcpyPeer __CUDA_API_PTDS(cuMemcpyPeer) + #define cuMemcpyPeerAsync __CUDA_API_PTSZ(cuMemcpyPeerAsync) + #define cuMemcpy3DPeer __CUDA_API_PTDS(cuMemcpy3DPeer) + #define cuMemcpy3DPeerAsync __CUDA_API_PTSZ(cuMemcpy3DPeerAsync) + #define cuMemPrefetchAsync __CUDA_API_PTSZ(cuMemPrefetchAsync) + + #define cuMemsetD8Async __CUDA_API_PTSZ(cuMemsetD8Async) + #define cuMemsetD16Async __CUDA_API_PTSZ(cuMemsetD16Async) + #define cuMemsetD32Async __CUDA_API_PTSZ(cuMemsetD32Async) + #define cuMemsetD2D8Async __CUDA_API_PTSZ(cuMemsetD2D8Async) + #define cuMemsetD2D16Async __CUDA_API_PTSZ(cuMemsetD2D16Async) + #define cuMemsetD2D32Async __CUDA_API_PTSZ(cuMemsetD2D32Async) + + #define cuStreamGetPriority __CUDA_API_PTSZ(cuStreamGetPriority) + #define cuStreamGetId __CUDA_API_PTSZ(cuStreamGetId) + #define cuStreamGetFlags __CUDA_API_PTSZ(cuStreamGetFlags) + #define cuStreamGetCtx __CUDA_API_PTSZ(cuStreamGetCtx) + #define cuStreamWaitEvent __CUDA_API_PTSZ(cuStreamWaitEvent) + #define cuStreamEndCapture __CUDA_API_PTSZ(cuStreamEndCapture) + #define cuStreamIsCapturing __CUDA_API_PTSZ(cuStreamIsCapturing) + #define cuStreamUpdateCaptureDependencies __CUDA_API_PTSZ(cuStreamUpdateCaptureDependencies) + #define cuStreamAddCallback __CUDA_API_PTSZ(cuStreamAddCallback) + #define cuStreamAttachMemAsync __CUDA_API_PTSZ(cuStreamAttachMemAsync) + #define cuStreamQuery __CUDA_API_PTSZ(cuStreamQuery) + #define cuStreamSynchronize __CUDA_API_PTSZ(cuStreamSynchronize) + #define cuEventRecord __CUDA_API_PTSZ(cuEventRecord) + #define cuEventRecordWithFlags __CUDA_API_PTSZ(cuEventRecordWithFlags) + #define cuLaunchKernel __CUDA_API_PTSZ(cuLaunchKernel) + #define cuLaunchKernelEx __CUDA_API_PTSZ(cuLaunchKernelEx) + #define cuLaunchHostFunc __CUDA_API_PTSZ(cuLaunchHostFunc) + #define cuGraphicsMapResources __CUDA_API_PTSZ(cuGraphicsMapResources) + #define cuGraphicsUnmapResources __CUDA_API_PTSZ(cuGraphicsUnmapResources) + + #define cuLaunchCooperativeKernel __CUDA_API_PTSZ(cuLaunchCooperativeKernel) + + #define cuSignalExternalSemaphoresAsync __CUDA_API_PTSZ(cuSignalExternalSemaphoresAsync) + #define cuWaitExternalSemaphoresAsync __CUDA_API_PTSZ(cuWaitExternalSemaphoresAsync) + + #define cuGraphInstantiateWithParams __CUDA_API_PTSZ(cuGraphInstantiateWithParams) + #define cuGraphUpload __CUDA_API_PTSZ(cuGraphUpload) + #define cuGraphLaunch __CUDA_API_PTSZ(cuGraphLaunch) + #define cuStreamCopyAttributes __CUDA_API_PTSZ(cuStreamCopyAttributes) + #define cuStreamGetAttribute __CUDA_API_PTSZ(cuStreamGetAttribute) + #define cuStreamSetAttribute __CUDA_API_PTSZ(cuStreamSetAttribute) + #define cuMemMapArrayAsync __CUDA_API_PTSZ(cuMemMapArrayAsync) + + #define cuMemFreeAsync __CUDA_API_PTSZ(cuMemFreeAsync) + #define cuMemAllocAsync __CUDA_API_PTSZ(cuMemAllocAsync) + #define cuMemAllocFromPoolAsync __CUDA_API_PTSZ(cuMemAllocFromPoolAsync) +#endif + +/** + * \file cuda.h + * \brief Header file for the CUDA Toolkit application programming interface. + * + * \file cudaGL.h + * \brief Header file for the OpenGL interoperability functions of the + * low-level CUDA driver application programming interface. + * + * \file cudaD3D9.h + * \brief Header file for the Direct3D 9 interoperability functions of the + * low-level CUDA driver application programming interface. + */ + +/** + * \defgroup CUDA_TYPES Data types used by CUDA driver + * @{ + */ + +/** + * CUDA API version number + */ +#define CUDA_VERSION 12010 + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * CUDA device pointer + * CUdeviceptr is defined as an unsigned integer type whose size matches the size of a pointer on the target platform. + */ +#if defined(_WIN64) || defined(__LP64__) +typedef unsigned long long CUdeviceptr_v2; +#else +typedef unsigned int CUdeviceptr_v2; +#endif +typedef CUdeviceptr_v2 CUdeviceptr; /**< CUDA device pointer */ + +typedef int CUdevice_v1; /**< CUDA device */ +typedef CUdevice_v1 CUdevice; /**< CUDA device */ +typedef struct CUctx_st *CUcontext; /**< CUDA context */ +typedef struct CUmod_st *CUmodule; /**< CUDA module */ +typedef struct CUfunc_st *CUfunction; /**< CUDA function */ +typedef struct CUlib_st *CUlibrary; /**< CUDA library */ +typedef struct CUkern_st *CUkernel; /**< CUDA kernel */ +typedef struct CUarray_st *CUarray; /**< CUDA array */ +typedef struct CUmipmappedArray_st *CUmipmappedArray; /**< CUDA mipmapped array */ +typedef struct CUtexref_st *CUtexref; /**< CUDA texture reference */ +typedef struct CUsurfref_st *CUsurfref; /**< CUDA surface reference */ +typedef struct CUevent_st *CUevent; /**< CUDA event */ +typedef struct CUstream_st *CUstream; /**< CUDA stream */ +typedef struct CUgraphicsResource_st *CUgraphicsResource; /**< CUDA graphics interop resource */ +typedef unsigned long long CUtexObject_v1; /**< An opaque value that represents a CUDA texture object */ +typedef CUtexObject_v1 CUtexObject; /**< An opaque value that represents a CUDA texture object */ +typedef unsigned long long CUsurfObject_v1; /**< An opaque value that represents a CUDA surface object */ +typedef CUsurfObject_v1 CUsurfObject; /**< An opaque value that represents a CUDA surface object */ +typedef struct CUextMemory_st *CUexternalMemory; /**< CUDA external memory */ +typedef struct CUextSemaphore_st *CUexternalSemaphore; /**< CUDA external semaphore */ +typedef struct CUgraph_st *CUgraph; /**< CUDA graph */ +typedef struct CUgraphNode_st *CUgraphNode; /**< CUDA graph node */ +typedef struct CUgraphExec_st *CUgraphExec; /**< CUDA executable graph */ +typedef struct CUmemPoolHandle_st *CUmemoryPool; /**< CUDA memory pool */ +typedef struct CUuserObject_st *CUuserObject; /**< CUDA user object for graphs */ + +#ifndef CU_UUID_HAS_BEEN_DEFINED +#define CU_UUID_HAS_BEEN_DEFINED +typedef struct CUuuid_st { /**< CUDA definition of UUID */ + char bytes[16]; +} CUuuid; +#endif + +/** + * CUDA IPC handle size + */ +#define CU_IPC_HANDLE_SIZE 64 + +/** + * CUDA IPC event handle + */ +typedef struct CUipcEventHandle_st { + char reserved[CU_IPC_HANDLE_SIZE]; +} CUipcEventHandle_v1; +typedef CUipcEventHandle_v1 CUipcEventHandle; + +/** + * CUDA IPC mem handle + */ +typedef struct CUipcMemHandle_st { + char reserved[CU_IPC_HANDLE_SIZE]; +} CUipcMemHandle_v1; +typedef CUipcMemHandle_v1 CUipcMemHandle; + +/** + * CUDA Ipc Mem Flags + */ +typedef enum CUipcMem_flags_enum { + CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS = 0x1 /**< Automatically enable peer access between remote devices as needed */ +} CUipcMem_flags; + + +/** + * CUDA Mem Attach Flags + */ +typedef enum CUmemAttach_flags_enum { + CU_MEM_ATTACH_GLOBAL = 0x1, /**< Memory can be accessed by any stream on any device */ + CU_MEM_ATTACH_HOST = 0x2, /**< Memory cannot be accessed by any stream on any device */ + CU_MEM_ATTACH_SINGLE = 0x4 /**< Memory can only be accessed by a single stream on the associated device */ +} CUmemAttach_flags; + +/** + * Context creation flags + */ +typedef enum CUctx_flags_enum { + CU_CTX_SCHED_AUTO = 0x00, /**< Automatic scheduling */ + CU_CTX_SCHED_SPIN = 0x01, /**< Set spin as default scheduling */ + CU_CTX_SCHED_YIELD = 0x02, /**< Set yield as default scheduling */ + CU_CTX_SCHED_BLOCKING_SYNC = 0x04, /**< Set blocking synchronization as default scheduling */ + CU_CTX_BLOCKING_SYNC = 0x04, /**< Set blocking synchronization as default scheduling + * \deprecated This flag was deprecated as of CUDA 4.0 + * and was replaced with ::CU_CTX_SCHED_BLOCKING_SYNC. */ + CU_CTX_SCHED_MASK = 0x07, + CU_CTX_MAP_HOST = 0x08, /**< \deprecated This flag was deprecated as of CUDA 11.0 + * and it no longer has any effect. All contexts + * as of CUDA 3.2 behave as though the flag is enabled. */ + CU_CTX_LMEM_RESIZE_TO_MAX = 0x10, /**< Keep local memory allocation after launch */ + CU_CTX_COREDUMP_ENABLE = 0x20, /**< Trigger coredumps from exceptions in this context */ + CU_CTX_USER_COREDUMP_ENABLE= 0x40, /**< Enable user pipe to trigger coredumps in this context */ + CU_CTX_SYNC_MEMOPS = 0x80, /**< Force synchronous blocking on cudaMemcpy/cudaMemset */ + CU_CTX_FLAGS_MASK = 0xFF +} CUctx_flags; + +/** + * Event sched flags + */ +typedef enum CUevent_sched_flags_enum { + CU_EVENT_SCHED_AUTO = 0x00, /**< Automatic scheduling */ + CU_EVENT_SCHED_SPIN = 0x01, /**< Set spin as default scheduling */ + CU_EVENT_SCHED_YIELD = 0x02, /**< Set yield as default scheduling */ + CU_EVENT_SCHED_BLOCKING_SYNC = 0x04, /**< Set blocking synchronization as default scheduling */ +} CUevent_sched_flags; + +/** + * NVCL event scheduling flags + */ +typedef enum cl_event_flags_enum { + NVCL_EVENT_SCHED_AUTO = 0x00, /**< Automatic scheduling */ + NVCL_EVENT_SCHED_SPIN = 0x01, /**< Set spin as default scheduling */ + NVCL_EVENT_SCHED_YIELD = 0x02, /**< Set yield as default scheduling */ + NVCL_EVENT_SCHED_BLOCKING_SYNC = 0x04, /**< Set blocking synchronization as default scheduling */ +} cl_event_flags; + +/** + * NVCL context scheduling flags + */ +typedef enum cl_context_flags_enum { + NVCL_CTX_SCHED_AUTO = 0x00, /**< Automatic scheduling */ + NVCL_CTX_SCHED_SPIN = 0x01, /**< Set spin as default scheduling */ + NVCL_CTX_SCHED_YIELD = 0x02, /**< Set yield as default scheduling */ + NVCL_CTX_SCHED_BLOCKING_SYNC = 0x04, /**< Set blocking synchronization as default scheduling */ +} cl_context_flags; + + +/** + * Stream creation flags + */ +typedef enum CUstream_flags_enum { + CU_STREAM_DEFAULT = 0x0, /**< Default stream flag */ + CU_STREAM_NON_BLOCKING = 0x1 /**< Stream does not synchronize with stream 0 (the NULL stream) */ +} CUstream_flags; + +/** + * Legacy stream handle + * + * Stream handle that can be passed as a CUstream to use an implicit stream + * with legacy synchronization behavior. + * + * See details of the \link_sync_behavior + */ +#define CU_STREAM_LEGACY ((CUstream)0x1) + +/** + * Per-thread stream handle + * + * Stream handle that can be passed as a CUstream to use an implicit stream + * with per-thread synchronization behavior. + * + * See details of the \link_sync_behavior + */ +#define CU_STREAM_PER_THREAD ((CUstream)0x2) + +/** + * Event creation flags + */ +typedef enum CUevent_flags_enum { + CU_EVENT_DEFAULT = 0x0, /**< Default event flag */ + CU_EVENT_BLOCKING_SYNC = 0x1, /**< Event uses blocking synchronization */ + CU_EVENT_DISABLE_TIMING = 0x2, /**< Event will not record timing data */ + CU_EVENT_INTERPROCESS = 0x4 /**< Event is suitable for interprocess use. CU_EVENT_DISABLE_TIMING must be set */ +} CUevent_flags; + +/** + * Event record flags + */ +typedef enum CUevent_record_flags_enum { + CU_EVENT_RECORD_DEFAULT = 0x0, /**< Default event record flag */ + CU_EVENT_RECORD_EXTERNAL = 0x1 /**< When using stream capture, create an event record node + * instead of the default behavior. This flag is invalid + * when used outside of capture. */ +} CUevent_record_flags; + +/** + * Event wait flags + */ +typedef enum CUevent_wait_flags_enum { + CU_EVENT_WAIT_DEFAULT = 0x0, /**< Default event wait flag */ + CU_EVENT_WAIT_EXTERNAL = 0x1 /**< When using stream capture, create an event wait node + * instead of the default behavior. This flag is invalid + * when used outside of capture.*/ +} CUevent_wait_flags; + +/** + * Flags for ::cuStreamWaitValue32 and ::cuStreamWaitValue64 + */ +typedef enum CUstreamWaitValue_flags_enum { + CU_STREAM_WAIT_VALUE_GEQ = 0x0, /**< Wait until (int32_t)(*addr - value) >= 0 (or int64_t for 64 bit + values). Note this is a cyclic comparison which ignores wraparound. + (Default behavior.) */ + CU_STREAM_WAIT_VALUE_EQ = 0x1, /**< Wait until *addr == value. */ + CU_STREAM_WAIT_VALUE_AND = 0x2, /**< Wait until (*addr & value) != 0. */ + CU_STREAM_WAIT_VALUE_NOR = 0x3, /**< Wait until ~(*addr | value) != 0. Support for this operation can be + queried with ::cuDeviceGetAttribute() and + ::CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR.*/ + CU_STREAM_WAIT_VALUE_FLUSH = 1<<30 /**< Follow the wait operation with a flush of outstanding remote writes. This + means that, if a remote write operation is guaranteed to have reached the + device before the wait can be satisfied, that write is guaranteed to be + visible to downstream device work. The device is permitted to reorder + remote writes internally. For example, this flag would be required if + two remote writes arrive in a defined order, the wait is satisfied by the + second write, and downstream work needs to observe the first write. + Support for this operation is restricted to selected platforms and can be + queried with ::CU_DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES.*/ +} CUstreamWaitValue_flags; + +/** + * Flags for ::cuStreamWriteValue32 + */ +typedef enum CUstreamWriteValue_flags_enum { + CU_STREAM_WRITE_VALUE_DEFAULT = 0x0, /**< Default behavior */ + CU_STREAM_WRITE_VALUE_NO_MEMORY_BARRIER = 0x1 /**< Permits the write to be reordered with writes which were issued + before it, as a performance optimization. Normally, + ::cuStreamWriteValue32 will provide a memory fence before the + write, which has similar semantics to + __threadfence_system() but is scoped to the stream + rather than a CUDA thread. + This flag is not supported in the v2 API. */ +} CUstreamWriteValue_flags; + +/** + * Operations for ::cuStreamBatchMemOp + */ +typedef enum CUstreamBatchMemOpType_enum { + CU_STREAM_MEM_OP_WAIT_VALUE_32 = 1, /**< Represents a ::cuStreamWaitValue32 operation */ + CU_STREAM_MEM_OP_WRITE_VALUE_32 = 2, /**< Represents a ::cuStreamWriteValue32 operation */ + CU_STREAM_MEM_OP_WAIT_VALUE_64 = 4, /**< Represents a ::cuStreamWaitValue64 operation */ + CU_STREAM_MEM_OP_WRITE_VALUE_64 = 5, /**< Represents a ::cuStreamWriteValue64 operation */ + CU_STREAM_MEM_OP_BARRIER = 6, /**< Insert a memory barrier of the specified type */ + CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES = 3 /**< This has the same effect as ::CU_STREAM_WAIT_VALUE_FLUSH, but as a + standalone operation. */ +} CUstreamBatchMemOpType; + +/** + * Flags for ::cuStreamMemoryBarrier + */ +typedef enum CUstreamMemoryBarrier_flags_enum { + CU_STREAM_MEMORY_BARRIER_TYPE_SYS = 0x0, /**< System-wide memory barrier. */ + CU_STREAM_MEMORY_BARRIER_TYPE_GPU = 0x1 /**< Limit memory barrier scope to the GPU. */ +} CUstreamMemoryBarrier_flags; + +/** + * Per-operation parameters for ::cuStreamBatchMemOp + */ +typedef union CUstreamBatchMemOpParams_union { + CUstreamBatchMemOpType operation; + struct CUstreamMemOpWaitValueParams_st { + CUstreamBatchMemOpType operation; + CUdeviceptr address; + union { + cuuint32_t value; + cuuint64_t value64; + }; + unsigned int flags; + CUdeviceptr alias; /**< For driver internal use. Initial value is unimportant. */ + } waitValue; + struct CUstreamMemOpWriteValueParams_st { + CUstreamBatchMemOpType operation; + CUdeviceptr address; + union { + cuuint32_t value; + cuuint64_t value64; + }; + unsigned int flags; + CUdeviceptr alias; /**< For driver internal use. Initial value is unimportant. */ + } writeValue; + struct CUstreamMemOpFlushRemoteWritesParams_st { + CUstreamBatchMemOpType operation; + unsigned int flags; + } flushRemoteWrites; + struct CUstreamMemOpMemoryBarrierParams_st { /**< Only supported in the _v2 API */ + CUstreamBatchMemOpType operation; + unsigned int flags; + } memoryBarrier; + cuuint64_t pad[6]; +} CUstreamBatchMemOpParams_v1; +typedef CUstreamBatchMemOpParams_v1 CUstreamBatchMemOpParams; + +typedef struct CUDA_BATCH_MEM_OP_NODE_PARAMS_st { + CUcontext ctx; + unsigned int count; + CUstreamBatchMemOpParams *paramArray; + unsigned int flags; +} CUDA_BATCH_MEM_OP_NODE_PARAMS; + +/** + * Occupancy calculator flag + */ +typedef enum CUoccupancy_flags_enum { + CU_OCCUPANCY_DEFAULT = 0x0, /**< Default behavior */ + CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE = 0x1 /**< Assume global caching is enabled and cannot be automatically turned off */ +} CUoccupancy_flags; + +/** + * Flags for ::cuStreamUpdateCaptureDependencies + */ +typedef enum CUstreamUpdateCaptureDependencies_flags_enum { + CU_STREAM_ADD_CAPTURE_DEPENDENCIES = 0x0, /**< Add new nodes to the dependency set */ + CU_STREAM_SET_CAPTURE_DEPENDENCIES = 0x1 /**< Replace the dependency set with the new nodes */ +} CUstreamUpdateCaptureDependencies_flags; + +/** + * Array formats + */ +typedef enum CUarray_format_enum { + CU_AD_FORMAT_UNSIGNED_INT8 = 0x01, /**< Unsigned 8-bit integers */ + CU_AD_FORMAT_UNSIGNED_INT16 = 0x02, /**< Unsigned 16-bit integers */ + CU_AD_FORMAT_UNSIGNED_INT32 = 0x03, /**< Unsigned 32-bit integers */ + CU_AD_FORMAT_SIGNED_INT8 = 0x08, /**< Signed 8-bit integers */ + CU_AD_FORMAT_SIGNED_INT16 = 0x09, /**< Signed 16-bit integers */ + CU_AD_FORMAT_SIGNED_INT32 = 0x0a, /**< Signed 32-bit integers */ + CU_AD_FORMAT_HALF = 0x10, /**< 16-bit floating point */ + CU_AD_FORMAT_FLOAT = 0x20, /**< 32-bit floating point */ + CU_AD_FORMAT_NV12 = 0xb0, /**< 8-bit YUV planar format, with 4:2:0 sampling */ + CU_AD_FORMAT_UNORM_INT8X1 = 0xc0, /**< 1 channel unsigned 8-bit normalized integer */ + CU_AD_FORMAT_UNORM_INT8X2 = 0xc1, /**< 2 channel unsigned 8-bit normalized integer */ + CU_AD_FORMAT_UNORM_INT8X4 = 0xc2, /**< 4 channel unsigned 8-bit normalized integer */ + CU_AD_FORMAT_UNORM_INT16X1 = 0xc3, /**< 1 channel unsigned 16-bit normalized integer */ + CU_AD_FORMAT_UNORM_INT16X2 = 0xc4, /**< 2 channel unsigned 16-bit normalized integer */ + CU_AD_FORMAT_UNORM_INT16X4 = 0xc5, /**< 4 channel unsigned 16-bit normalized integer */ + CU_AD_FORMAT_SNORM_INT8X1 = 0xc6, /**< 1 channel signed 8-bit normalized integer */ + CU_AD_FORMAT_SNORM_INT8X2 = 0xc7, /**< 2 channel signed 8-bit normalized integer */ + CU_AD_FORMAT_SNORM_INT8X4 = 0xc8, /**< 4 channel signed 8-bit normalized integer */ + CU_AD_FORMAT_SNORM_INT16X1 = 0xc9, /**< 1 channel signed 16-bit normalized integer */ + CU_AD_FORMAT_SNORM_INT16X2 = 0xca, /**< 2 channel signed 16-bit normalized integer */ + CU_AD_FORMAT_SNORM_INT16X4 = 0xcb, /**< 4 channel signed 16-bit normalized integer */ + CU_AD_FORMAT_BC1_UNORM = 0x91, /**< 4 channel unsigned normalized block-compressed (BC1 compression) format */ + CU_AD_FORMAT_BC1_UNORM_SRGB = 0x92, /**< 4 channel unsigned normalized block-compressed (BC1 compression) format with sRGB encoding*/ + CU_AD_FORMAT_BC2_UNORM = 0x93, /**< 4 channel unsigned normalized block-compressed (BC2 compression) format */ + CU_AD_FORMAT_BC2_UNORM_SRGB = 0x94, /**< 4 channel unsigned normalized block-compressed (BC2 compression) format with sRGB encoding*/ + CU_AD_FORMAT_BC3_UNORM = 0x95, /**< 4 channel unsigned normalized block-compressed (BC3 compression) format */ + CU_AD_FORMAT_BC3_UNORM_SRGB = 0x96, /**< 4 channel unsigned normalized block-compressed (BC3 compression) format with sRGB encoding*/ + CU_AD_FORMAT_BC4_UNORM = 0x97, /**< 1 channel unsigned normalized block-compressed (BC4 compression) format */ + CU_AD_FORMAT_BC4_SNORM = 0x98, /**< 1 channel signed normalized block-compressed (BC4 compression) format */ + CU_AD_FORMAT_BC5_UNORM = 0x99, /**< 2 channel unsigned normalized block-compressed (BC5 compression) format */ + CU_AD_FORMAT_BC5_SNORM = 0x9a, /**< 2 channel signed normalized block-compressed (BC5 compression) format */ + CU_AD_FORMAT_BC6H_UF16 = 0x9b, /**< 3 channel unsigned half-float block-compressed (BC6H compression) format */ + CU_AD_FORMAT_BC6H_SF16 = 0x9c, /**< 3 channel signed half-float block-compressed (BC6H compression) format */ + CU_AD_FORMAT_BC7_UNORM = 0x9d, /**< 4 channel unsigned normalized block-compressed (BC7 compression) format */ + CU_AD_FORMAT_BC7_UNORM_SRGB = 0x9e /**< 4 channel unsigned normalized block-compressed (BC7 compression) format with sRGB encoding */ +} CUarray_format; + +/** + * Texture reference addressing modes + */ +typedef enum CUaddress_mode_enum { + CU_TR_ADDRESS_MODE_WRAP = 0, /**< Wrapping address mode */ + CU_TR_ADDRESS_MODE_CLAMP = 1, /**< Clamp to edge address mode */ + CU_TR_ADDRESS_MODE_MIRROR = 2, /**< Mirror address mode */ + CU_TR_ADDRESS_MODE_BORDER = 3 /**< Border address mode */ +} CUaddress_mode; + +/** + * Texture reference filtering modes + */ +typedef enum CUfilter_mode_enum { + CU_TR_FILTER_MODE_POINT = 0, /**< Point filter mode */ + CU_TR_FILTER_MODE_LINEAR = 1 /**< Linear filter mode */ +} CUfilter_mode; + +/** + * Device properties + */ +typedef enum CUdevice_attribute_enum { + CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK = 1, /**< Maximum number of threads per block */ + CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X = 2, /**< Maximum block dimension X */ + CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y = 3, /**< Maximum block dimension Y */ + CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z = 4, /**< Maximum block dimension Z */ + CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X = 5, /**< Maximum grid dimension X */ + CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y = 6, /**< Maximum grid dimension Y */ + CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z = 7, /**< Maximum grid dimension Z */ + CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK = 8, /**< Maximum shared memory available per block in bytes */ + CU_DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK = 8, /**< Deprecated, use CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK */ + CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY = 9, /**< Memory available on device for __constant__ variables in a CUDA C kernel in bytes */ + CU_DEVICE_ATTRIBUTE_WARP_SIZE = 10, /**< Warp size in threads */ + CU_DEVICE_ATTRIBUTE_MAX_PITCH = 11, /**< Maximum pitch in bytes allowed by memory copies */ + CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK = 12, /**< Maximum number of 32-bit registers available per block */ + CU_DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK = 12, /**< Deprecated, use CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK */ + CU_DEVICE_ATTRIBUTE_CLOCK_RATE = 13, /**< Typical clock frequency in kilohertz */ + CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT = 14, /**< Alignment requirement for textures */ + CU_DEVICE_ATTRIBUTE_GPU_OVERLAP = 15, /**< Device can possibly copy memory and execute a kernel concurrently. Deprecated. Use instead CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT. */ + CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT = 16, /**< Number of multiprocessors on device */ + CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT = 17, /**< Specifies whether there is a run time limit on kernels */ + CU_DEVICE_ATTRIBUTE_INTEGRATED = 18, /**< Device is integrated with host memory */ + CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY = 19, /**< Device can map host memory into CUDA address space */ + CU_DEVICE_ATTRIBUTE_COMPUTE_MODE = 20, /**< Compute mode (See ::CUcomputemode for details) */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH = 21, /**< Maximum 1D texture width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH = 22, /**< Maximum 2D texture width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT = 23, /**< Maximum 2D texture height */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH = 24, /**< Maximum 3D texture width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT = 25, /**< Maximum 3D texture height */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH = 26, /**< Maximum 3D texture depth */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH = 27, /**< Maximum 2D layered texture width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT = 28, /**< Maximum 2D layered texture height */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS = 29, /**< Maximum layers in a 2D layered texture */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH = 27, /**< Deprecated, use CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT = 28, /**< Deprecated, use CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES = 29, /**< Deprecated, use CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS */ + CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT = 30, /**< Alignment requirement for surfaces */ + CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS = 31, /**< Device can possibly execute multiple kernels concurrently */ + CU_DEVICE_ATTRIBUTE_ECC_ENABLED = 32, /**< Device has ECC support enabled */ + CU_DEVICE_ATTRIBUTE_PCI_BUS_ID = 33, /**< PCI bus ID of the device */ + CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID = 34, /**< PCI device ID of the device */ + CU_DEVICE_ATTRIBUTE_TCC_DRIVER = 35, /**< Device is using TCC driver model */ + CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE = 36, /**< Peak memory clock frequency in kilohertz */ + CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH = 37, /**< Global memory bus width in bits */ + CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE = 38, /**< Size of L2 cache in bytes */ + CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR = 39, /**< Maximum resident threads per multiprocessor */ + CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT = 40, /**< Number of asynchronous engines */ + CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING = 41, /**< Device shares a unified address space with the host */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH = 42, /**< Maximum 1D layered texture width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS = 43, /**< Maximum layers in a 1D layered texture */ + CU_DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER = 44, /**< Deprecated, do not use. */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH = 45, /**< Maximum 2D texture width if CUDA_ARRAY3D_TEXTURE_GATHER is set */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT = 46, /**< Maximum 2D texture height if CUDA_ARRAY3D_TEXTURE_GATHER is set */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE = 47, /**< Alternate maximum 3D texture width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE = 48, /**< Alternate maximum 3D texture height */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE = 49, /**< Alternate maximum 3D texture depth */ + CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID = 50, /**< PCI domain ID of the device */ + CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT = 51, /**< Pitch alignment requirement for textures */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH = 52, /**< Maximum cubemap texture width/height */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH = 53, /**< Maximum cubemap layered texture width/height */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS = 54, /**< Maximum layers in a cubemap layered texture */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH = 55, /**< Maximum 1D surface width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH = 56, /**< Maximum 2D surface width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT = 57, /**< Maximum 2D surface height */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH = 58, /**< Maximum 3D surface width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT = 59, /**< Maximum 3D surface height */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH = 60, /**< Maximum 3D surface depth */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH = 61, /**< Maximum 1D layered surface width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS = 62, /**< Maximum layers in a 1D layered surface */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH = 63, /**< Maximum 2D layered surface width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT = 64, /**< Maximum 2D layered surface height */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS = 65, /**< Maximum layers in a 2D layered surface */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH = 66, /**< Maximum cubemap surface width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH = 67, /**< Maximum cubemap layered surface width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS = 68, /**< Maximum layers in a cubemap layered surface */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH = 69, /**< Deprecated, do not use. Use cudaDeviceGetTexture1DLinearMaxWidth() or cuDeviceGetTexture1DLinearMaxWidth() instead. */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH = 70, /**< Maximum 2D linear texture width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT = 71, /**< Maximum 2D linear texture height */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH = 72, /**< Maximum 2D linear texture pitch in bytes */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH = 73, /**< Maximum mipmapped 2D texture width */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT = 74, /**< Maximum mipmapped 2D texture height */ + CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR = 75, /**< Major compute capability version number */ + CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR = 76, /**< Minor compute capability version number */ + CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH = 77, /**< Maximum mipmapped 1D texture width */ + CU_DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED = 78, /**< Device supports stream priorities */ + CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED = 79, /**< Device supports caching globals in L1 */ + CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED = 80, /**< Device supports caching locals in L1 */ + CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR = 81, /**< Maximum shared memory available per multiprocessor in bytes */ + CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR = 82, /**< Maximum number of 32-bit registers available per multiprocessor */ + CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY = 83, /**< Device can allocate managed memory on this system */ + CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD = 84, /**< Device is on a multi-GPU board */ + CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID = 85, /**< Unique id for a group of devices on the same multi-GPU board */ + CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED = 86, /**< Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)*/ + CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO = 87, /**< Ratio of single precision performance (in floating-point operations per second) to double precision performance */ + CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS = 88, /**< Device supports coherently accessing pageable memory without calling cudaHostRegister on it */ + CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS = 89, /**< Device can coherently access managed memory concurrently with the CPU */ + CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED = 90, /**< Device supports compute preemption. */ + CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM = 91, /**< Device can access host registered memory at the same virtual address as the CPU */ + CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS_V1 = 92, /**< Deprecated, along with v1 MemOps API, ::cuStreamBatchMemOp and related APIs are supported. */ + CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS_V1 = 93, /**< Deprecated, along with v1 MemOps API, 64-bit operations are supported in ::cuStreamBatchMemOp and related APIs. */ + CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR_V1 = 94, /**< Deprecated, along with v1 MemOps API, ::CU_STREAM_WAIT_VALUE_NOR is supported. */ + CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH = 95, /**< Device supports launching cooperative kernels via ::cuLaunchCooperativeKernel */ + CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH = 96, /**< Deprecated, ::cuLaunchCooperativeKernelMultiDevice is deprecated. */ + CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN = 97, /**< Maximum optin shared memory per block */ + CU_DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES = 98, /**< The ::CU_STREAM_WAIT_VALUE_FLUSH flag and the ::CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device. See \ref CUDA_MEMOP for additional details. */ + CU_DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED = 99, /**< Device supports host memory registration via ::cudaHostRegister. */ + CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES = 100, /**< Device accesses pageable memory via the host's page tables. */ + CU_DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST = 101, /**< The host can directly access managed memory on the device without migration. */ + CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED = 102, /**< Deprecated, Use CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED*/ + CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED = 102, /**< Device supports virtual memory management APIs like ::cuMemAddressReserve, ::cuMemCreate, ::cuMemMap and related APIs */ + CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR_SUPPORTED = 103, /**< Device supports exporting memory to a posix file descriptor with ::cuMemExportToShareableHandle, if requested via ::cuMemCreate */ + CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_HANDLE_SUPPORTED = 104, /**< Device supports exporting memory to a Win32 NT handle with ::cuMemExportToShareableHandle, if requested via ::cuMemCreate */ + CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_KMT_HANDLE_SUPPORTED = 105, /**< Device supports exporting memory to a Win32 KMT handle with ::cuMemExportToShareableHandle, if requested via ::cuMemCreate */ + CU_DEVICE_ATTRIBUTE_MAX_BLOCKS_PER_MULTIPROCESSOR = 106, /**< Maximum number of blocks per multiprocessor */ + CU_DEVICE_ATTRIBUTE_GENERIC_COMPRESSION_SUPPORTED = 107, /**< Device supports compression of memory */ + CU_DEVICE_ATTRIBUTE_MAX_PERSISTING_L2_CACHE_SIZE = 108, /**< Maximum L2 persisting lines capacity setting in bytes. */ + CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE = 109, /**< Maximum value of CUaccessPolicyWindow::num_bytes. */ + CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WITH_CUDA_VMM_SUPPORTED = 110, /**< Device supports specifying the GPUDirect RDMA flag with ::cuMemCreate */ + CU_DEVICE_ATTRIBUTE_RESERVED_SHARED_MEMORY_PER_BLOCK = 111, /**< Shared memory reserved by CUDA driver per block in bytes */ + CU_DEVICE_ATTRIBUTE_SPARSE_CUDA_ARRAY_SUPPORTED = 112, /**< Device supports sparse CUDA arrays and sparse CUDA mipmapped arrays */ + CU_DEVICE_ATTRIBUTE_READ_ONLY_HOST_REGISTER_SUPPORTED = 113, /**< Device supports using the ::cuMemHostRegister flag ::CU_MEMHOSTERGISTER_READ_ONLY to register memory that must be mapped as read-only to the GPU */ + CU_DEVICE_ATTRIBUTE_TIMELINE_SEMAPHORE_INTEROP_SUPPORTED = 114, /**< External timeline semaphore interop is supported on the device */ + CU_DEVICE_ATTRIBUTE_MEMORY_POOLS_SUPPORTED = 115, /**< Device supports using the ::cuMemAllocAsync and ::cuMemPool family of APIs */ + CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_SUPPORTED = 116, /**< Device supports GPUDirect RDMA APIs, like nvidia_p2p_get_pages (see https://docs.nvidia.com/cuda/gpudirect-rdma for more information) */ + CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_FLUSH_WRITES_OPTIONS = 117, /**< The returned attribute shall be interpreted as a bitmask, where the individual bits are described by the ::CUflushGPUDirectRDMAWritesOptions enum */ + CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WRITES_ORDERING = 118, /**< GPUDirect RDMA writes to the device do not need to be flushed for consumers within the scope indicated by the returned attribute. See ::CUGPUDirectRDMAWritesOrdering for the numerical values returned here. */ + CU_DEVICE_ATTRIBUTE_MEMPOOL_SUPPORTED_HANDLE_TYPES = 119, /**< Handle types supported with mempool based IPC */ + CU_DEVICE_ATTRIBUTE_CLUSTER_LAUNCH = 120, /**< Indicates device supports cluster launch */ + CU_DEVICE_ATTRIBUTE_DEFERRED_MAPPING_CUDA_ARRAY_SUPPORTED = 121, /**< Device supports deferred mapping CUDA arrays and CUDA mipmapped arrays */ + CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS = 122, /**< 64-bit operations are supported in ::cuStreamBatchMemOp and related MemOp APIs. */ + CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR = 123, /**< ::CU_STREAM_WAIT_VALUE_NOR is supported by MemOp APIs. */ + CU_DEVICE_ATTRIBUTE_DMA_BUF_SUPPORTED = 124, /**< Device supports buffer sharing with dma_buf mechanism. */ + CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED = 125, /**< Device supports IPC Events. */ + CU_DEVICE_ATTRIBUTE_MEM_SYNC_DOMAIN_COUNT = 126, /**< Number of memory domains the device supports. */ + CU_DEVICE_ATTRIBUTE_TENSOR_MAP_ACCESS_SUPPORTED = 127, /**< Device supports accessing memory using Tensor Map. */ + CU_DEVICE_ATTRIBUTE_UNIFIED_FUNCTION_POINTERS = 129, /**< Device supports unified function pointers. */ + CU_DEVICE_ATTRIBUTE_IS_NUMA_NODE = 130, + CU_DEVICE_ATTRIBUTE_NUMA_ID = 131, + CU_DEVICE_ATTRIBUTE_MULTICAST_SUPPORTED = 132, /**< Device supports switch multicast and reduction operations. */ + CU_DEVICE_ATTRIBUTE_MAX +} CUdevice_attribute; + +/** + * Legacy device properties + */ +typedef struct CUdevprop_st { + int maxThreadsPerBlock; /**< Maximum number of threads per block */ + int maxThreadsDim[3]; /**< Maximum size of each dimension of a block */ + int maxGridSize[3]; /**< Maximum size of each dimension of a grid */ + int sharedMemPerBlock; /**< Shared memory available per block in bytes */ + int totalConstantMemory; /**< Constant memory available on device in bytes */ + int SIMDWidth; /**< Warp size in threads */ + int memPitch; /**< Maximum pitch in bytes allowed by memory copies */ + int regsPerBlock; /**< 32-bit registers available per block */ + int clockRate; /**< Clock frequency in kilohertz */ + int textureAlign; /**< Alignment requirement for textures */ +} CUdevprop_v1; +typedef CUdevprop_v1 CUdevprop; + +/** + * Pointer information + */ +typedef enum CUpointer_attribute_enum { + CU_POINTER_ATTRIBUTE_CONTEXT = 1, /**< The ::CUcontext on which a pointer was allocated or registered */ + CU_POINTER_ATTRIBUTE_MEMORY_TYPE = 2, /**< The ::CUmemorytype describing the physical location of a pointer */ + CU_POINTER_ATTRIBUTE_DEVICE_POINTER = 3, /**< The address at which a pointer's memory may be accessed on the device */ + CU_POINTER_ATTRIBUTE_HOST_POINTER = 4, /**< The address at which a pointer's memory may be accessed on the host */ + CU_POINTER_ATTRIBUTE_P2P_TOKENS = 5, /**< A pair of tokens for use with the nv-p2p.h Linux kernel interface */ + CU_POINTER_ATTRIBUTE_SYNC_MEMOPS = 6, /**< Synchronize every synchronous memory operation initiated on this region */ + CU_POINTER_ATTRIBUTE_BUFFER_ID = 7, /**< A process-wide unique ID for an allocated memory region*/ + CU_POINTER_ATTRIBUTE_IS_MANAGED = 8, /**< Indicates if the pointer points to managed memory */ + CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL = 9, /**< A device ordinal of a device on which a pointer was allocated or registered */ + CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE = 10, /**< 1 if this pointer maps to an allocation that is suitable for ::cudaIpcGetMemHandle, 0 otherwise **/ + CU_POINTER_ATTRIBUTE_RANGE_START_ADDR = 11, /**< Starting address for this requested pointer */ + CU_POINTER_ATTRIBUTE_RANGE_SIZE = 12, /**< Size of the address range for this requested pointer */ + CU_POINTER_ATTRIBUTE_MAPPED = 13, /**< 1 if this pointer is in a valid address range that is mapped to a backing allocation, 0 otherwise **/ + CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES = 14, /**< Bitmask of allowed ::CUmemAllocationHandleType for this allocation **/ + CU_POINTER_ATTRIBUTE_IS_GPU_DIRECT_RDMA_CAPABLE = 15, /**< 1 if the memory this pointer is referencing can be used with the GPUDirect RDMA API **/ + CU_POINTER_ATTRIBUTE_ACCESS_FLAGS = 16, /**< Returns the access flags the device associated with the current context has on the corresponding memory referenced by the pointer given */ + CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE = 17 /**< Returns the mempool handle for the allocation if it was allocated from a mempool. Otherwise returns NULL. **/ + , + CU_POINTER_ATTRIBUTE_MAPPING_SIZE = 18, /**< Size of the actual underlying mapping that the pointer belongs to **/ + CU_POINTER_ATTRIBUTE_MAPPING_BASE_ADDR = 19, /**< The start address of the mapping that the pointer belongs to **/ + CU_POINTER_ATTRIBUTE_MEMORY_BLOCK_ID = 20 /**< A process-wide unique id corresponding to the physical allocation the pointer belongs to **/ +} CUpointer_attribute; + +/** + * Function properties + */ +typedef enum CUfunction_attribute_enum { + /** + * The maximum number of threads per block, beyond which a launch of the + * function would fail. This number depends on both the function and the + * device on which the function is currently loaded. + */ + CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK = 0, + + /** + * The size in bytes of statically-allocated shared memory required by + * this function. This does not include dynamically-allocated shared + * memory requested by the user at runtime. + */ + CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES = 1, + + /** + * The size in bytes of user-allocated constant memory required by this + * function. + */ + CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES = 2, + + /** + * The size in bytes of local memory used by each thread of this function. + */ + CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES = 3, + + /** + * The number of registers used by each thread of this function. + */ + CU_FUNC_ATTRIBUTE_NUM_REGS = 4, + + /** + * The PTX virtual architecture version for which the function was + * compiled. This value is the major PTX version * 10 + the minor PTX + * version, so a PTX version 1.3 function would return the value 13. + * Note that this may return the undefined value of 0 for cubins + * compiled prior to CUDA 3.0. + */ + CU_FUNC_ATTRIBUTE_PTX_VERSION = 5, + + /** + * The binary architecture version for which the function was compiled. + * This value is the major binary version * 10 + the minor binary version, + * so a binary version 1.3 function would return the value 13. Note that + * this will return a value of 10 for legacy cubins that do not have a + * properly-encoded binary architecture version. + */ + CU_FUNC_ATTRIBUTE_BINARY_VERSION = 6, + + /** + * The attribute to indicate whether the function has been compiled with + * user specified option "-Xptxas --dlcm=ca" set . + */ + CU_FUNC_ATTRIBUTE_CACHE_MODE_CA = 7, + + /** + * The maximum size in bytes of dynamically-allocated shared memory that can be used by + * this function. If the user-specified dynamic shared memory size is larger than this + * value, the launch will fail. + * See ::cuFuncSetAttribute, ::cuKernelSetAttribute + */ + CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES = 8, + + /** + * On devices where the L1 cache and shared memory use the same hardware resources, + * this sets the shared memory carveout preference, in percent of the total shared memory. + * Refer to ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR. + * This is only a hint, and the driver can choose a different ratio if required to execute the function. + * See ::cuFuncSetAttribute, ::cuKernelSetAttribute + */ + CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT = 9, + + /** + * If this attribute is set, the kernel must launch with a valid cluster + * size specified. + * See ::cuFuncSetAttribute, ::cuKernelSetAttribute + */ + CU_FUNC_ATTRIBUTE_CLUSTER_SIZE_MUST_BE_SET = 10, + + /** + * The required cluster width in blocks. The values must either all be 0 or + * all be positive. The validity of the cluster dimensions is otherwise + * checked at launch time. + * + * If the value is set during compile time, it cannot be set at runtime. + * Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED. + * See ::cuFuncSetAttribute, ::cuKernelSetAttribute + */ + CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_WIDTH = 11, + + /** + * The required cluster height in blocks. The values must either all be 0 or + * all be positive. The validity of the cluster dimensions is otherwise + * checked at launch time. + * + * If the value is set during compile time, it cannot be set at runtime. + * Setting it at runtime should return CUDA_ERROR_NOT_PERMITTED. + * See ::cuFuncSetAttribute, ::cuKernelSetAttribute + */ + CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_HEIGHT = 12, + + /** + * The required cluster depth in blocks. The values must either all be 0 or + * all be positive. The validity of the cluster dimensions is otherwise + * checked at launch time. + * + * If the value is set during compile time, it cannot be set at runtime. + * Setting it at runtime should return CUDA_ERROR_NOT_PERMITTED. + * See ::cuFuncSetAttribute, ::cuKernelSetAttribute + */ + CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_DEPTH = 13, + + /** + * Whether the function can be launched with non-portable cluster size. 1 is + * allowed, 0 is disallowed. A non-portable cluster size may only function + * on the specific SKUs the program is tested on. The launch might fail if + * the program is run on a different hardware platform. + * + * CUDA API provides cudaOccupancyMaxActiveClusters to assist with checking + * whether the desired size can be launched on the current device. + * + * Portable Cluster Size + * + * A portable cluster size is guaranteed to be functional on all compute + * capabilities higher than the target compute capability. The portable + * cluster size for sm_90 is 8 blocks per cluster. This value may increase + * for future compute capabilities. + * + * The specific hardware unit may support higher cluster sizes that’s not + * guaranteed to be portable. + * See ::cuFuncSetAttribute, ::cuKernelSetAttribute + */ + CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED = 14, + + /** + * The block scheduling policy of a function. The value type is + * CUclusterSchedulingPolicy / cudaClusterSchedulingPolicy. + * See ::cuFuncSetAttribute, ::cuKernelSetAttribute + */ + CU_FUNC_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE = 15, + + CU_FUNC_ATTRIBUTE_MAX +} CUfunction_attribute; + +/** + * Function cache configurations + */ +typedef enum CUfunc_cache_enum { + CU_FUNC_CACHE_PREFER_NONE = 0x00, /**< no preference for shared memory or L1 (default) */ + CU_FUNC_CACHE_PREFER_SHARED = 0x01, /**< prefer larger shared memory and smaller L1 cache */ + CU_FUNC_CACHE_PREFER_L1 = 0x02, /**< prefer larger L1 cache and smaller shared memory */ + CU_FUNC_CACHE_PREFER_EQUAL = 0x03 /**< prefer equal sized L1 cache and shared memory */ +} CUfunc_cache; + +/** + * Shared memory configurations + */ +typedef enum CUsharedconfig_enum { + CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE = 0x00, /**< set default shared memory bank size */ + CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE = 0x01, /**< set shared memory bank width to four bytes */ + CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE = 0x02 /**< set shared memory bank width to eight bytes */ +} CUsharedconfig; + +/** + * Shared memory carveout configurations. These may be passed to ::cuFuncSetAttribute or ::cuKernelSetAttribute + */ +typedef enum CUshared_carveout_enum { + CU_SHAREDMEM_CARVEOUT_DEFAULT = -1, /**< No preference for shared memory or L1 (default) */ + CU_SHAREDMEM_CARVEOUT_MAX_SHARED = 100, /**< Prefer maximum available shared memory, minimum L1 cache */ + CU_SHAREDMEM_CARVEOUT_MAX_L1 = 0 /**< Prefer maximum available L1 cache, minimum shared memory */ +} CUshared_carveout; + +/** + * Memory types + */ +typedef enum CUmemorytype_enum { + CU_MEMORYTYPE_HOST = 0x01, /**< Host memory */ + CU_MEMORYTYPE_DEVICE = 0x02, /**< Device memory */ + CU_MEMORYTYPE_ARRAY = 0x03, /**< Array memory */ + CU_MEMORYTYPE_UNIFIED = 0x04 /**< Unified device or host memory */ +} CUmemorytype; + +/** + * Compute Modes + */ +typedef enum CUcomputemode_enum { + CU_COMPUTEMODE_DEFAULT = 0, /**< Default compute mode (Multiple contexts allowed per device) */ + CU_COMPUTEMODE_PROHIBITED = 2, /**< Compute-prohibited mode (No contexts can be created on this device at this time) */ + CU_COMPUTEMODE_EXCLUSIVE_PROCESS = 3 /**< Compute-exclusive-process mode (Only one context used by a single process can be present on this device at a time) */ +} CUcomputemode; + +/** + * Memory advise values + */ +typedef enum CUmem_advise_enum { + CU_MEM_ADVISE_SET_READ_MOSTLY = 1, /**< Data will mostly be read and only occasionally be written to */ + CU_MEM_ADVISE_UNSET_READ_MOSTLY = 2, /**< Undo the effect of ::CU_MEM_ADVISE_SET_READ_MOSTLY */ + CU_MEM_ADVISE_SET_PREFERRED_LOCATION = 3, /**< Set the preferred location for the data as the specified device */ + CU_MEM_ADVISE_UNSET_PREFERRED_LOCATION = 4, /**< Clear the preferred location for the data */ + CU_MEM_ADVISE_SET_ACCESSED_BY = 5, /**< Data will be accessed by the specified device, so prevent page faults as much as possible */ + CU_MEM_ADVISE_UNSET_ACCESSED_BY = 6 /**< Let the Unified Memory subsystem decide on the page faulting policy for the specified device */ +} CUmem_advise; + +typedef enum CUmem_range_attribute_enum { + CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY = 1, /**< Whether the range will mostly be read and only occasionally be written to */ + CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION = 2, /**< The preferred location of the range */ + CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY = 3, /**< Memory range has ::CU_MEM_ADVISE_SET_ACCESSED_BY set for specified device */ + CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION = 4 /**< The last location to which the range was prefetched */ +} CUmem_range_attribute; + +/** + * Online compiler and linker options + */ +typedef enum CUjit_option_enum +{ + /** + * Max number of registers that a thread may use.\n + * Option type: unsigned int\n + * Applies to: compiler only + */ + CU_JIT_MAX_REGISTERS = 0, + + /** + * IN: Specifies minimum number of threads per block to target compilation + * for\n + * OUT: Returns the number of threads the compiler actually targeted. + * This restricts the resource utilization of the compiler (e.g. max + * registers) such that a block with the given number of threads should be + * able to launch based on register limitations. Note, this option does not + * currently take into account any other resource limitations, such as + * shared memory utilization.\n + * Cannot be combined with ::CU_JIT_TARGET.\n + * Option type: unsigned int\n + * Applies to: compiler only + */ + CU_JIT_THREADS_PER_BLOCK = 1, + + /** + * Overwrites the option value with the total wall clock time, in + * milliseconds, spent in the compiler and linker\n + * Option type: float\n + * Applies to: compiler and linker + */ + CU_JIT_WALL_TIME = 2, + + /** + * Pointer to a buffer in which to print any log messages + * that are informational in nature (the buffer size is specified via + * option ::CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES)\n + * Option type: char *\n + * Applies to: compiler and linker + */ + CU_JIT_INFO_LOG_BUFFER = 3, + + /** + * IN: Log buffer size in bytes. Log messages will be capped at this size + * (including null terminator)\n + * OUT: Amount of log buffer filled with messages\n + * Option type: unsigned int\n + * Applies to: compiler and linker + */ + CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES = 4, + + /** + * Pointer to a buffer in which to print any log messages that + * reflect errors (the buffer size is specified via option + * ::CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES)\n + * Option type: char *\n + * Applies to: compiler and linker + */ + CU_JIT_ERROR_LOG_BUFFER = 5, + + /** + * IN: Log buffer size in bytes. Log messages will be capped at this size + * (including null terminator)\n + * OUT: Amount of log buffer filled with messages\n + * Option type: unsigned int\n + * Applies to: compiler and linker + */ + CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES = 6, + + /** + * Level of optimizations to apply to generated code (0 - 4), with 4 + * being the default and highest level of optimizations.\n + * Option type: unsigned int\n + * Applies to: compiler only + */ + CU_JIT_OPTIMIZATION_LEVEL = 7, + + /** + * No option value required. Determines the target based on the current + * attached context (default)\n + * Option type: No option value needed\n + * Applies to: compiler and linker + */ + CU_JIT_TARGET_FROM_CUCONTEXT = 8, + + /** + * Target is chosen based on supplied ::CUjit_target. Cannot be + * combined with ::CU_JIT_THREADS_PER_BLOCK.\n + * Option type: unsigned int for enumerated type ::CUjit_target\n + * Applies to: compiler and linker + */ + CU_JIT_TARGET = 9, + + /** + * Specifies choice of fallback strategy if matching cubin is not found. + * Choice is based on supplied ::CUjit_fallback. This option cannot be + * used with cuLink* APIs as the linker requires exact matches.\n + * Option type: unsigned int for enumerated type ::CUjit_fallback\n + * Applies to: compiler only + */ + CU_JIT_FALLBACK_STRATEGY = 10, + + /** + * Specifies whether to create debug information in output (-g) + * (0: false, default)\n + * Option type: int\n + * Applies to: compiler and linker + */ + CU_JIT_GENERATE_DEBUG_INFO = 11, + + /** + * Generate verbose log messages (0: false, default)\n + * Option type: int\n + * Applies to: compiler and linker + */ + CU_JIT_LOG_VERBOSE = 12, + + /** + * Generate line number information (-lineinfo) (0: false, default)\n + * Option type: int\n + * Applies to: compiler only + */ + CU_JIT_GENERATE_LINE_INFO = 13, + + /** + * Specifies whether to enable caching explicitly (-dlcm) \n + * Choice is based on supplied ::CUjit_cacheMode_enum.\n + * Option type: unsigned int for enumerated type ::CUjit_cacheMode_enum\n + * Applies to: compiler only + */ + CU_JIT_CACHE_MODE = 14, + + /** + * \deprecated + * This jit option is deprecated and should not be used. + */ + CU_JIT_NEW_SM3X_OPT = 15, + + /** + * This jit option is used for internal purpose only. + */ + CU_JIT_FAST_COMPILE = 16, + + /** + * Array of device symbol names that will be relocated to the corresponding + * host addresses stored in ::CU_JIT_GLOBAL_SYMBOL_ADDRESSES.\n + * Must contain ::CU_JIT_GLOBAL_SYMBOL_COUNT entries.\n + * When loading a device module, driver will relocate all encountered + * unresolved symbols to the host addresses.\n + * It is only allowed to register symbols that correspond to unresolved + * global variables.\n + * It is illegal to register the same device symbol at multiple addresses.\n + * Option type: const char **\n + * Applies to: dynamic linker only + */ + CU_JIT_GLOBAL_SYMBOL_NAMES = 17, + + /** + * Array of host addresses that will be used to relocate corresponding + * device symbols stored in ::CU_JIT_GLOBAL_SYMBOL_NAMES.\n + * Must contain ::CU_JIT_GLOBAL_SYMBOL_COUNT entries.\n + * Option type: void **\n + * Applies to: dynamic linker only + */ + CU_JIT_GLOBAL_SYMBOL_ADDRESSES = 18, + + /** + * Number of entries in ::CU_JIT_GLOBAL_SYMBOL_NAMES and + * ::CU_JIT_GLOBAL_SYMBOL_ADDRESSES arrays.\n + * Option type: unsigned int\n + * Applies to: dynamic linker only + */ + CU_JIT_GLOBAL_SYMBOL_COUNT = 19, + + /** + * \deprecated + * Enable link-time optimization (-dlto) for device code (Disabled by default).\n + * This option is not supported on 32-bit platforms.\n + * Option type: int\n + * Applies to: compiler and linker + * + * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0 + */ + CU_JIT_LTO = 20, + + /** + * \deprecated + * Control single-precision denormals (-ftz) support (0: false, default). + * 1 : flushes denormal values to zero + * 0 : preserves denormal values + * Option type: int\n + * Applies to: link-time optimization specified with CU_JIT_LTO + * + * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0 + */ + CU_JIT_FTZ = 21, + + /** + * \deprecated + * Control single-precision floating-point division and reciprocals + * (-prec-div) support (1: true, default). + * 1 : Enables the IEEE round-to-nearest mode + * 0 : Enables the fast approximation mode + * Option type: int\n + * Applies to: link-time optimization specified with CU_JIT_LTO + * + * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0 + */ + CU_JIT_PREC_DIV = 22, + + /** + * \deprecated + * Control single-precision floating-point square root + * (-prec-sqrt) support (1: true, default). + * 1 : Enables the IEEE round-to-nearest mode + * 0 : Enables the fast approximation mode + * Option type: int\n + * Applies to: link-time optimization specified with CU_JIT_LTO + * + * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0 + */ + CU_JIT_PREC_SQRT = 23, + + /** + * \deprecated + * Enable/Disable the contraction of floating-point multiplies + * and adds/subtracts into floating-point multiply-add (-fma) + * operations (1: Enable, default; 0: Disable). + * Option type: int\n + * Applies to: link-time optimization specified with CU_JIT_LTO + * + * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0 + */ + CU_JIT_FMA = 24, + + /** + * \deprecated + * Array of kernel names that should be preserved at link time while others + * can be removed.\n + * Must contain ::CU_JIT_REFERENCED_KERNEL_COUNT entries.\n + * Note that kernel names can be mangled by the compiler in which case the + * mangled name needs to be specified.\n + * Wildcard "*" can be used to represent zero or more characters instead of + * specifying the full or mangled name.\n + * It is important to note that the wildcard "*" is also added implicitly. + * For example, specifying "foo" will match "foobaz", "barfoo", "barfoobaz" and + * thus preserve all kernels with those names. This can be avoided by providing + * a more specific name like "barfoobaz".\n + * Option type: const char **\n + * Applies to: dynamic linker only + * + * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0 + */ + CU_JIT_REFERENCED_KERNEL_NAMES = 25, + + /** + * \deprecated + * Number of entries in ::CU_JIT_REFERENCED_KERNEL_NAMES array.\n + * Option type: unsigned int\n + * Applies to: dynamic linker only + * + * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0 + */ + CU_JIT_REFERENCED_KERNEL_COUNT = 26, + + /** + * \deprecated + * Array of variable names (__device__ and/or __constant__) that should be + * preserved at link time while others can be removed.\n + * Must contain ::CU_JIT_REFERENCED_VARIABLE_COUNT entries.\n + * Note that variable names can be mangled by the compiler in which case the + * mangled name needs to be specified.\n + * Wildcard "*" can be used to represent zero or more characters instead of + * specifying the full or mangled name.\n + * It is important to note that the wildcard "*" is also added implicitly. + * For example, specifying "foo" will match "foobaz", "barfoo", "barfoobaz" and + * thus preserve all variables with those names. This can be avoided by providing + * a more specific name like "barfoobaz".\n + * Option type: const char **\n + * Applies to: link-time optimization specified with CU_JIT_LTO + * + * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0 + */ + CU_JIT_REFERENCED_VARIABLE_NAMES = 27, + + /** + * \deprecated + * Number of entries in ::CU_JIT_REFERENCED_VARIABLE_NAMES array.\n + * Option type: unsigned int\n + * Applies to: link-time optimization specified with CU_JIT_LTO + * + * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0 + */ + CU_JIT_REFERENCED_VARIABLE_COUNT = 28, + + /** + * \deprecated + * This option serves as a hint to enable the JIT compiler/linker + * to remove constant (__constant__) and device (__device__) variables + * unreferenced in device code (Disabled by default).\n + * Note that host references to constant and device variables using APIs like + * ::cuModuleGetGlobal() with this option specified may result in undefined behavior unless + * the variables are explicitly specified using ::CU_JIT_REFERENCED_VARIABLE_NAMES.\n + * Option type: int\n + * Applies to: link-time optimization specified with CU_JIT_LTO + * + * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0 + */ + CU_JIT_OPTIMIZE_UNUSED_DEVICE_VARIABLES = 29, + + /** + * Generate position independent code (0: false)\n + * Option type: int\n + * Applies to: compiler only + */ + CU_JIT_POSITION_INDEPENDENT_CODE = 30, + + CU_JIT_NUM_OPTIONS + +} CUjit_option; + +/* + * Indicates that compute device class supports accelerated features. + */ +#define CU_COMPUTE_ACCELERATED_TARGET_BASE 0x10000 + +/** + * Online compilation targets + */ +typedef enum CUjit_target_enum +{ + CU_TARGET_COMPUTE_30 = 30, /**< Compute device class 3.0 */ + CU_TARGET_COMPUTE_32 = 32, /**< Compute device class 3.2 */ + CU_TARGET_COMPUTE_35 = 35, /**< Compute device class 3.5 */ + CU_TARGET_COMPUTE_37 = 37, /**< Compute device class 3.7 */ + CU_TARGET_COMPUTE_50 = 50, /**< Compute device class 5.0 */ + CU_TARGET_COMPUTE_52 = 52, /**< Compute device class 5.2 */ + CU_TARGET_COMPUTE_53 = 53, /**< Compute device class 5.3 */ + CU_TARGET_COMPUTE_60 = 60, /**< Compute device class 6.0.*/ + CU_TARGET_COMPUTE_61 = 61, /**< Compute device class 6.1.*/ + CU_TARGET_COMPUTE_62 = 62, /**< Compute device class 6.2.*/ + CU_TARGET_COMPUTE_70 = 70, /**< Compute device class 7.0.*/ + CU_TARGET_COMPUTE_72 = 72, /**< Compute device class 7.2.*/ + CU_TARGET_COMPUTE_75 = 75, /**< Compute device class 7.5.*/ + CU_TARGET_COMPUTE_80 = 80, /**< Compute device class 8.0.*/ + CU_TARGET_COMPUTE_86 = 86, /**< Compute device class 8.6.*/ + CU_TARGET_COMPUTE_87 = 87, /**< Compute device class 8.7.*/ + CU_TARGET_COMPUTE_89 = 89, /**< Compute device class 8.9.*/ + CU_TARGET_COMPUTE_90 = 90, /**< Compute device class 9.0.*/ + + /**< Compute device class 9.0. with accelerated features.*/ + CU_TARGET_COMPUTE_90A = CU_COMPUTE_ACCELERATED_TARGET_BASE + CU_TARGET_COMPUTE_90 +} CUjit_target; + +/** + * Cubin matching fallback strategies + */ +typedef enum CUjit_fallback_enum +{ + CU_PREFER_PTX = 0, /**< Prefer to compile ptx if exact binary match not found */ + + CU_PREFER_BINARY /**< Prefer to fall back to compatible binary code if exact match not found */ + +} CUjit_fallback; + +/** + * Caching modes for dlcm + */ +typedef enum CUjit_cacheMode_enum +{ + CU_JIT_CACHE_OPTION_NONE = 0, /**< Compile with no -dlcm flag specified */ + CU_JIT_CACHE_OPTION_CG, /**< Compile with L1 cache disabled */ + CU_JIT_CACHE_OPTION_CA /**< Compile with L1 cache enabled */ +} CUjit_cacheMode; + +/** + * Device code formats + */ +typedef enum CUjitInputType_enum +{ + /** + * Compiled device-class-specific device code\n + * Applicable options: none + */ + CU_JIT_INPUT_CUBIN = 0, + + /** + * PTX source code\n + * Applicable options: PTX compiler options + */ + CU_JIT_INPUT_PTX = 1, + + /** + * Bundle of multiple cubins and/or PTX of some device code\n + * Applicable options: PTX compiler options, ::CU_JIT_FALLBACK_STRATEGY + */ + CU_JIT_INPUT_FATBINARY = 2, + + /** + * Host object with embedded device code\n + * Applicable options: PTX compiler options, ::CU_JIT_FALLBACK_STRATEGY + */ + CU_JIT_INPUT_OBJECT = 3, + + /** + * Archive of host objects with embedded device code\n + * Applicable options: PTX compiler options, ::CU_JIT_FALLBACK_STRATEGY + */ + CU_JIT_INPUT_LIBRARY = 4, + + /** + * \deprecated + * High-level intermediate code for link-time optimization\n + * Applicable options: NVVM compiler options, PTX compiler options + * + * Only valid with LTO-IR compiled with toolkits prior to CUDA 12.0 + */ + CU_JIT_INPUT_NVVM = 5, + + CU_JIT_NUM_INPUT_TYPES = 6 +} CUjitInputType; + +typedef struct CUlinkState_st *CUlinkState; + +/** + * Flags to register a graphics resource + */ +typedef enum CUgraphicsRegisterFlags_enum { + CU_GRAPHICS_REGISTER_FLAGS_NONE = 0x00, + CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY = 0x01, + CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD = 0x02, + CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST = 0x04, + CU_GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER = 0x08 +} CUgraphicsRegisterFlags; + +/** + * Flags for mapping and unmapping interop resources + */ +typedef enum CUgraphicsMapResourceFlags_enum { + CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE = 0x00, + CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY = 0x01, + CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD = 0x02 +} CUgraphicsMapResourceFlags; + +/** + * Array indices for cube faces + */ +typedef enum CUarray_cubemap_face_enum { + CU_CUBEMAP_FACE_POSITIVE_X = 0x00, /**< Positive X face of cubemap */ + CU_CUBEMAP_FACE_NEGATIVE_X = 0x01, /**< Negative X face of cubemap */ + CU_CUBEMAP_FACE_POSITIVE_Y = 0x02, /**< Positive Y face of cubemap */ + CU_CUBEMAP_FACE_NEGATIVE_Y = 0x03, /**< Negative Y face of cubemap */ + CU_CUBEMAP_FACE_POSITIVE_Z = 0x04, /**< Positive Z face of cubemap */ + CU_CUBEMAP_FACE_NEGATIVE_Z = 0x05 /**< Negative Z face of cubemap */ +} CUarray_cubemap_face; + +/** + * Limits + */ +typedef enum CUlimit_enum { + CU_LIMIT_STACK_SIZE = 0x00, /**< GPU thread stack size */ + CU_LIMIT_PRINTF_FIFO_SIZE = 0x01, /**< GPU printf FIFO size */ + CU_LIMIT_MALLOC_HEAP_SIZE = 0x02, /**< GPU malloc heap size */ + CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH = 0x03, /**< GPU device runtime launch synchronize depth */ + CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT = 0x04, /**< GPU device runtime pending launch count */ + CU_LIMIT_MAX_L2_FETCH_GRANULARITY = 0x05, /**< A value between 0 and 128 that indicates the maximum fetch granularity of L2 (in Bytes). This is a hint */ + CU_LIMIT_PERSISTING_L2_CACHE_SIZE = 0x06, /**< A size in bytes for L2 persisting lines cache size */ + CU_LIMIT_MAX +} CUlimit; + +/** + * Resource types + */ +typedef enum CUresourcetype_enum { + CU_RESOURCE_TYPE_ARRAY = 0x00, /**< Array resource */ + CU_RESOURCE_TYPE_MIPMAPPED_ARRAY = 0x01, /**< Mipmapped array resource */ + CU_RESOURCE_TYPE_LINEAR = 0x02, /**< Linear resource */ + CU_RESOURCE_TYPE_PITCH2D = 0x03 /**< Pitch 2D resource */ +} CUresourcetype; + +#ifdef _WIN32 +#define CUDA_CB __stdcall +#else +#define CUDA_CB +#endif + +/** + * CUDA host function + * \param userData Argument value passed to the function + */ +typedef void (CUDA_CB *CUhostFn)(void *userData); + +/** + * Specifies performance hint with ::CUaccessPolicyWindow for hitProp and missProp members. + */ +typedef enum CUaccessProperty_enum { + CU_ACCESS_PROPERTY_NORMAL = 0, /**< Normal cache persistence. */ + CU_ACCESS_PROPERTY_STREAMING = 1, /**< Streaming access is less likely to persit from cache. */ + CU_ACCESS_PROPERTY_PERSISTING = 2 /**< Persisting access is more likely to persist in cache.*/ +} CUaccessProperty; + +/** + * Specifies an access policy for a window, a contiguous extent of memory + * beginning at base_ptr and ending at base_ptr + num_bytes. + * num_bytes is limited by CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE. + * Partition into many segments and assign segments such that: + * sum of "hit segments" / window == approx. ratio. + * sum of "miss segments" / window == approx 1-ratio. + * Segments and ratio specifications are fitted to the capabilities of + * the architecture. + * Accesses in a hit segment apply the hitProp access policy. + * Accesses in a miss segment apply the missProp access policy. + */ +typedef struct CUaccessPolicyWindow_st { + void *base_ptr; /**< Starting address of the access policy window. CUDA driver may align it. */ + size_t num_bytes; /**< Size in bytes of the window policy. CUDA driver may restrict the maximum size and alignment. */ + float hitRatio; /**< hitRatio specifies percentage of lines assigned hitProp, rest are assigned missProp. */ + CUaccessProperty hitProp; /**< ::CUaccessProperty set for hit. */ + CUaccessProperty missProp; /**< ::CUaccessProperty set for miss. Must be either NORMAL or STREAMING */ +} CUaccessPolicyWindow_v1; +/** + * Access policy window + */ +typedef CUaccessPolicyWindow_v1 CUaccessPolicyWindow; + +/** + * GPU kernel node parameters + */ +typedef struct CUDA_KERNEL_NODE_PARAMS_st { + CUfunction func; /**< Kernel to launch */ + unsigned int gridDimX; /**< Width of grid in blocks */ + unsigned int gridDimY; /**< Height of grid in blocks */ + unsigned int gridDimZ; /**< Depth of grid in blocks */ + unsigned int blockDimX; /**< X dimension of each thread block */ + unsigned int blockDimY; /**< Y dimension of each thread block */ + unsigned int blockDimZ; /**< Z dimension of each thread block */ + unsigned int sharedMemBytes; /**< Dynamic shared-memory size per thread block in bytes */ + void **kernelParams; /**< Array of pointers to kernel parameters */ + void **extra; /**< Extra options */ +} CUDA_KERNEL_NODE_PARAMS_v1; + +/** + * GPU kernel node parameters + */typedef struct CUDA_KERNEL_NODE_PARAMS_v2_st { + CUfunction func; /**< Kernel to launch */ + unsigned int gridDimX; /**< Width of grid in blocks */ + unsigned int gridDimY; /**< Height of grid in blocks */ + unsigned int gridDimZ; /**< Depth of grid in blocks */ + unsigned int blockDimX; /**< X dimension of each thread block */ + unsigned int blockDimY; /**< Y dimension of each thread block */ + unsigned int blockDimZ; /**< Z dimension of each thread block */ + unsigned int sharedMemBytes; /**< Dynamic shared-memory size per thread block in bytes */ + void **kernelParams; /**< Array of pointers to kernel parameters */ + void **extra; /**< Extra options */ + CUkernel kern; /**< Kernel to launch, will only be referenced if func is NULL */ + CUcontext ctx; /**< Context for the kernel task to run in. The value NULL will indicate the current context should be used by the api. This field is ignored if func is set. */ +} CUDA_KERNEL_NODE_PARAMS_v2; +typedef CUDA_KERNEL_NODE_PARAMS_v2 CUDA_KERNEL_NODE_PARAMS; + +/** + * Memset node parameters + */ +typedef struct CUDA_MEMSET_NODE_PARAMS_st { + CUdeviceptr dst; /**< Destination device pointer */ + size_t pitch; /**< Pitch of destination device pointer. Unused if height is 1 */ + unsigned int value; /**< Value to be set */ + unsigned int elementSize; /**< Size of each element in bytes. Must be 1, 2, or 4. */ + size_t width; /**< Width of the row in elements */ + size_t height; /**< Number of rows */ +} CUDA_MEMSET_NODE_PARAMS_v1; +typedef CUDA_MEMSET_NODE_PARAMS_v1 CUDA_MEMSET_NODE_PARAMS; + +/** + * Host node parameters + */ +typedef struct CUDA_HOST_NODE_PARAMS_st { + CUhostFn fn; /**< The function to call when the node executes */ + void* userData; /**< Argument to pass to the function */ +} CUDA_HOST_NODE_PARAMS_v1; +typedef CUDA_HOST_NODE_PARAMS_v1 CUDA_HOST_NODE_PARAMS; + +/** + * Graph node types + */ +typedef enum CUgraphNodeType_enum { + CU_GRAPH_NODE_TYPE_KERNEL = 0, /**< GPU kernel node */ + CU_GRAPH_NODE_TYPE_MEMCPY = 1, /**< Memcpy node */ + CU_GRAPH_NODE_TYPE_MEMSET = 2, /**< Memset node */ + CU_GRAPH_NODE_TYPE_HOST = 3, /**< Host (executable) node */ + CU_GRAPH_NODE_TYPE_GRAPH = 4, /**< Node which executes an embedded graph */ + CU_GRAPH_NODE_TYPE_EMPTY = 5, /**< Empty (no-op) node */ + CU_GRAPH_NODE_TYPE_WAIT_EVENT = 6, /**< External event wait node */ + CU_GRAPH_NODE_TYPE_EVENT_RECORD = 7, /**< External event record node */ + CU_GRAPH_NODE_TYPE_EXT_SEMAS_SIGNAL = 8, /**< External semaphore signal node */ + CU_GRAPH_NODE_TYPE_EXT_SEMAS_WAIT = 9, /**< External semaphore wait node */ + CU_GRAPH_NODE_TYPE_MEM_ALLOC = 10,/**< Memory Allocation Node */ + CU_GRAPH_NODE_TYPE_MEM_FREE = 11,/**< Memory Free Node */ + CU_GRAPH_NODE_TYPE_BATCH_MEM_OP = 12 /**< Batch MemOp Node */ +} CUgraphNodeType; + +/** + * Graph instantiation results +*/ +typedef enum CUgraphInstantiateResult_enum +{ + CUDA_GRAPH_INSTANTIATE_SUCCESS = 0, /**< Instantiation succeeded */ + CUDA_GRAPH_INSTANTIATE_ERROR = 1, /**< Instantiation failed for an unexpected reason which is described in the return value of the function */ + CUDA_GRAPH_INSTANTIATE_INVALID_STRUCTURE = 2, /**< Instantiation failed due to invalid structure, such as cycles */ + CUDA_GRAPH_INSTANTIATE_NODE_OPERATION_NOT_SUPPORTED = 3, /**< Instantiation for device launch failed because the graph contained an unsupported operation */ + CUDA_GRAPH_INSTANTIATE_MULTIPLE_CTXS_NOT_SUPPORTED = 4 /**< Instantiation for device launch failed due to the nodes belonging to different contexts */ +} CUgraphInstantiateResult; + +/** + * Graph instantiation parameters + */ +typedef struct CUDA_GRAPH_INSTANTIATE_PARAMS_st +{ + cuuint64_t flags; /**< Instantiation flags */ + CUstream hUploadStream; /**< Upload stream */ + CUgraphNode hErrNode_out; /**< The node which caused instantiation to fail, if any */ + CUgraphInstantiateResult result_out; /**< Whether instantiation was successful. If it failed, the reason why */ +} CUDA_GRAPH_INSTANTIATE_PARAMS; + +typedef enum CUsynchronizationPolicy_enum { + CU_SYNC_POLICY_AUTO = 1, + CU_SYNC_POLICY_SPIN = 2, + CU_SYNC_POLICY_YIELD = 3, + CU_SYNC_POLICY_BLOCKING_SYNC = 4 +} CUsynchronizationPolicy; + +/** + * Cluster scheduling policies. These may be passed to ::cuFuncSetAttribute or ::cuKernelSetAttribute + */ +typedef enum CUclusterSchedulingPolicy_enum { + CU_CLUSTER_SCHEDULING_POLICY_DEFAULT = 0, /**< the default policy */ + CU_CLUSTER_SCHEDULING_POLICY_SPREAD = 1, /**< spread the blocks within a cluster to the SMs */ + CU_CLUSTER_SCHEDULING_POLICY_LOAD_BALANCING = 2 /**< allow the hardware to load-balance the blocks in a cluster to the SMs */ +} CUclusterSchedulingPolicy; + +typedef enum CUlaunchMemSyncDomain_enum { + CU_LAUNCH_MEM_SYNC_DOMAIN_DEFAULT = 0, + CU_LAUNCH_MEM_SYNC_DOMAIN_REMOTE = 1 +} CUlaunchMemSyncDomain; + +typedef struct CUlaunchMemSyncDomainMap_st { + unsigned char default_; + unsigned char remote; +} CUlaunchMemSyncDomainMap; + +typedef enum CUlaunchAttributeID_enum { + CU_LAUNCH_ATTRIBUTE_IGNORE = 0 /**< Ignored entry, for convenient composition */ + , CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW = 1 /**< Valid for streams, graph nodes, launches. */ + , CU_LAUNCH_ATTRIBUTE_COOPERATIVE = 2 /**< Valid for graph nodes, launches. */ + , CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY = 3 /**< Valid for streams. */ + , CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION = 4 /**< Valid for graph nodes, launches. */ + , CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE = 5 /**< Valid for graph nodes, launches. */ + , CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION = 6 /**< Valid for launches. Setting + programmaticStreamSerializationAllowed to non-0 + signals that the kernel will use programmatic + means to resolve its stream dependency, so that + the CUDA runtime should opportunistically allow + the grid's execution to overlap with the previous + kernel in the stream, if that kernel requests the + overlap. The dependent launches can choose to wait + on the dependency using the programmatic sync + (cudaGridDependencySynchronize() or equivalent PTX + instructions). */ + , CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT = 7 /**< Valid for launches. Event recorded through this + launch attribute is guaranteed to only trigger + after all block in the associated kernel trigger + the event. A block can trigger the event through + PTX launchdep.release or CUDA builtin function + cudaTriggerProgrammaticLaunchCompletion(). A + trigger can also be inserted at the beginning of + each block's execution if triggerAtBlockStart is + set to non-0. The dependent launches can choose to + wait on the dependency using the programmatic sync + (cudaGridDependencySynchronize() or equivalent PTX + instructions). Note that dependents (including the + CPU thread calling cuEventSynchronize()) are not + guaranteed to observe the release precisely when + it is released. For example, cuEventSynchronize() + may only observe the event trigger long after the + associated kernel has completed. This recording + type is primarily meant for establishing + programmatic dependency between device tasks. The + event supplied must not be an interprocess or + interop event. The event must disable timing (i.e. + created with ::CU_EVENT_DISABLE_TIMING flag set). + */ + , CU_LAUNCH_ATTRIBUTE_PRIORITY = 8 /**< Valid for streams, graph nodes, launches. */ + , CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP = 9 + , CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN = 10 +#ifdef __CUDA_API_VERSION_INTERNAL + , CU_LAUNCH_ATTRIBUTE_MAX +#endif +} CUlaunchAttributeID; + +typedef union CUlaunchAttributeValue_union { + char pad[64]; /**< Pad to 64 bytes */ + CUaccessPolicyWindow accessPolicyWindow; /**< Attribute ::CUaccessPolicyWindow. */ + int cooperative; /**< Nonzero indicates a cooperative kernel (see ::cuLaunchCooperativeKernel). */ + CUsynchronizationPolicy syncPolicy; /**< ::CUsynchronizationPolicy for work queued up in this stream */ + struct { + unsigned int x; + unsigned int y; + unsigned int z; + } clusterDim; /**< Cluster dimensions for the kernel node. */ + CUclusterSchedulingPolicy clusterSchedulingPolicyPreference; /**< Cluster scheduling policy preference for the kernel node. */ + int programmaticStreamSerializationAllowed; + struct { + CUevent event; + int flags; /* Does not accept ::CU_EVENT_RECORD_EXTERNAL */ + int triggerAtBlockStart; + } programmaticEvent; + int priority; /**< Execution priority of the kernel. */ + CUlaunchMemSyncDomainMap memSyncDomainMap; + CUlaunchMemSyncDomain memSyncDomain; +} CUlaunchAttributeValue; + +typedef struct CUlaunchAttribute_st { + CUlaunchAttributeID id; + char pad[8 - sizeof(CUlaunchAttributeID)]; + CUlaunchAttributeValue value; +} CUlaunchAttribute; + +typedef struct CUlaunchConfig_st { + unsigned int gridDimX; /**< Width of grid in blocks */ + unsigned int gridDimY; /**< Height of grid in blocks */ + unsigned int gridDimZ; /**< Depth of grid in blocks */ + unsigned int blockDimX; /**< X dimension of each thread block */ + unsigned int blockDimY; /**< Y dimension of each thread block */ + unsigned int blockDimZ; /**< Z dimension of each thread block */ + unsigned int sharedMemBytes; /**< Dynamic shared-memory size per thread block in bytes */ + CUstream hStream; /**< Stream identifier */ + CUlaunchAttribute *attrs; /**< nullable if numAttrs == 0 */ + unsigned int numAttrs; /**< number of attributes populated in attrs */ +} CUlaunchConfig; + +typedef CUlaunchAttributeID CUkernelNodeAttrID; +#define CU_KERNEL_NODE_ATTRIBUTE_ACCESS_POLICY_WINDOW CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW +#define CU_KERNEL_NODE_ATTRIBUTE_COOPERATIVE CU_LAUNCH_ATTRIBUTE_COOPERATIVE +#define CU_KERNEL_NODE_ATTRIBUTE_CLUSTER_DIMENSION CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION +#define CU_KERNEL_NODE_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE +#define CU_KERNEL_NODE_ATTRIBUTE_PRIORITY CU_LAUNCH_ATTRIBUTE_PRIORITY +#define CU_KERNEL_NODE_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP +#define CU_KERNEL_NODE_ATTRIBUTE_MEM_SYNC_DOMAIN CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN + +typedef CUlaunchAttributeValue CUkernelNodeAttrValue_v1; +typedef CUkernelNodeAttrValue_v1 CUkernelNodeAttrValue; + +/** + * Possible stream capture statuses returned by ::cuStreamIsCapturing + */ +typedef enum CUstreamCaptureStatus_enum { + CU_STREAM_CAPTURE_STATUS_NONE = 0, /**< Stream is not capturing */ + CU_STREAM_CAPTURE_STATUS_ACTIVE = 1, /**< Stream is actively capturing */ + CU_STREAM_CAPTURE_STATUS_INVALIDATED = 2 /**< Stream is part of a capture sequence that + has been invalidated, but not terminated */ +} CUstreamCaptureStatus; + +/** + * Possible modes for stream capture thread interactions. For more details see + * ::cuStreamBeginCapture and ::cuThreadExchangeStreamCaptureMode + */ +typedef enum CUstreamCaptureMode_enum { + CU_STREAM_CAPTURE_MODE_GLOBAL = 0, + CU_STREAM_CAPTURE_MODE_THREAD_LOCAL = 1, + CU_STREAM_CAPTURE_MODE_RELAXED = 2 +} CUstreamCaptureMode; + +typedef CUlaunchAttributeID CUstreamAttrID; +#define CU_STREAM_ATTRIBUTE_ACCESS_POLICY_WINDOW CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW +#define CU_STREAM_ATTRIBUTE_SYNCHRONIZATION_POLICY CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY +#define CU_STREAM_ATTRIBUTE_PRIORITY CU_LAUNCH_ATTRIBUTE_PRIORITY +#define CU_STREAM_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP +#define CU_STREAM_ATTRIBUTE_MEM_SYNC_DOMAIN CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN + +typedef CUlaunchAttributeValue CUstreamAttrValue_v1; +typedef CUstreamAttrValue_v1 CUstreamAttrValue; + +/** + * Flags to specify search options. For more details see ::cuGetProcAddress + */ +typedef enum CUdriverProcAddress_flags_enum { + CU_GET_PROC_ADDRESS_DEFAULT = 0, /**< Default search mode for driver symbols. */ + CU_GET_PROC_ADDRESS_LEGACY_STREAM = 1 << 0, /**< Search for legacy versions of driver symbols. */ + CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM = 1 << 1 /**< Search for per-thread versions of driver symbols. */ +} CUdriverProcAddress_flags; + +/** + * Flags to indicate search status. For more details see ::cuGetProcAddress + */ +typedef enum CUdriverProcAddressQueryResult_enum { + CU_GET_PROC_ADDRESS_SUCCESS = 0, /**< Symbol was succesfully found */ + CU_GET_PROC_ADDRESS_SYMBOL_NOT_FOUND = 1, /**< Symbol was not found in search */ + CU_GET_PROC_ADDRESS_VERSION_NOT_SUFFICIENT = 2 /**< Symbol was found but version supplied was not sufficient */ +} CUdriverProcAddressQueryResult; + +/** + * Execution Affinity Types + */ +typedef enum CUexecAffinityType_enum { + CU_EXEC_AFFINITY_TYPE_SM_COUNT = 0, /**< Create a context with limited SMs. */ + CU_EXEC_AFFINITY_TYPE_MAX +} CUexecAffinityType; + +/** + * Value for ::CU_EXEC_AFFINITY_TYPE_SM_COUNT + */ +typedef struct CUexecAffinitySmCount_st { + unsigned int val; /**< The number of SMs the context is limited to use. */ +} CUexecAffinitySmCount_v1; +typedef CUexecAffinitySmCount_v1 CUexecAffinitySmCount; + +/** + * Execution Affinity Parameters + */ +typedef struct CUexecAffinityParam_st { + CUexecAffinityType type; + union { + CUexecAffinitySmCount smCount; /** Value for ::CU_EXEC_AFFINITY_TYPE_SM_COUNT */ + } param; +} CUexecAffinityParam_v1; +/** + * Execution Affinity Parameters + */ +typedef CUexecAffinityParam_v1 CUexecAffinityParam; + +/** + * Library options to be specified with ::cuLibraryLoadData() or ::cuLibraryLoadFromFile() + */ +typedef enum CUlibraryOption_enum +{ + CU_LIBRARY_HOST_UNIVERSAL_FUNCTION_AND_DATA_TABLE = 0, + + /** + * Specifes that the argument \p code passed to ::cuLibraryLoadData() will be preserved. + * Specifying this option will let the driver know that \p code can be accessed at any point + * until ::cuLibraryUnload(). The default behavior is for the driver to allocate and + * maintain its own copy of \p code. Note that this is only a memory usage optimization + * hint and the driver can choose to ignore it if required. + * Specifying this option with ::cuLibraryLoadFromFile() is invalid and + * will return ::CUDA_ERROR_INVALID_VALUE. + */ + CU_LIBRARY_BINARY_IS_PRESERVED = 1, + + CU_LIBRARY_NUM_OPTIONS +} CUlibraryOption; + +typedef struct CUlibraryHostUniversalFunctionAndDataTable_st +{ + void *functionTable; + size_t functionWindowSize; + void *dataTable; + size_t dataWindowSize; +} CUlibraryHostUniversalFunctionAndDataTable; + +/** + * Error codes + */ +typedef enum cudaError_enum { + /** + * The API call returned with no errors. In the case of query calls, this + * also means that the operation being queried is complete (see + * ::cuEventQuery() and ::cuStreamQuery()). + */ + CUDA_SUCCESS = 0, + + /** + * This indicates that one or more of the parameters passed to the API call + * is not within an acceptable range of values. + */ + CUDA_ERROR_INVALID_VALUE = 1, + + /** + * The API call failed because it was unable to allocate enough memory to + * perform the requested operation. + */ + CUDA_ERROR_OUT_OF_MEMORY = 2, + + /** + * This indicates that the CUDA driver has not been initialized with + * ::cuInit() or that initialization has failed. + */ + CUDA_ERROR_NOT_INITIALIZED = 3, + + /** + * This indicates that the CUDA driver is in the process of shutting down. + */ + CUDA_ERROR_DEINITIALIZED = 4, + + /** + * This indicates profiler is not initialized for this run. This can + * happen when the application is running with external profiling tools + * like visual profiler. + */ + CUDA_ERROR_PROFILER_DISABLED = 5, + + /** + * \deprecated + * This error return is deprecated as of CUDA 5.0. It is no longer an error + * to attempt to enable/disable the profiling via ::cuProfilerStart or + * ::cuProfilerStop without initialization. + */ + CUDA_ERROR_PROFILER_NOT_INITIALIZED = 6, + + /** + * \deprecated + * This error return is deprecated as of CUDA 5.0. It is no longer an error + * to call cuProfilerStart() when profiling is already enabled. + */ + CUDA_ERROR_PROFILER_ALREADY_STARTED = 7, + + /** + * \deprecated + * This error return is deprecated as of CUDA 5.0. It is no longer an error + * to call cuProfilerStop() when profiling is already disabled. + */ + CUDA_ERROR_PROFILER_ALREADY_STOPPED = 8, + + /** + * This indicates that the CUDA driver that the application has loaded is a + * stub library. Applications that run with the stub rather than a real + * driver loaded will result in CUDA API returning this error. + */ + CUDA_ERROR_STUB_LIBRARY = 34, + + /** + * This indicates that requested CUDA device is unavailable at the current + * time. Devices are often unavailable due to use of + * ::CU_COMPUTEMODE_EXCLUSIVE_PROCESS or ::CU_COMPUTEMODE_PROHIBITED. + */ + CUDA_ERROR_DEVICE_UNAVAILABLE = 46, + + /** + * This indicates that no CUDA-capable devices were detected by the installed + * CUDA driver. + */ + CUDA_ERROR_NO_DEVICE = 100, + + /** + * This indicates that the device ordinal supplied by the user does not + * correspond to a valid CUDA device or that the action requested is + * invalid for the specified device. + */ + CUDA_ERROR_INVALID_DEVICE = 101, + + /** + * This error indicates that the Grid license is not applied. + */ + CUDA_ERROR_DEVICE_NOT_LICENSED = 102, + + /** + * This indicates that the device kernel image is invalid. This can also + * indicate an invalid CUDA module. + */ + CUDA_ERROR_INVALID_IMAGE = 200, + + /** + * This most frequently indicates that there is no context bound to the + * current thread. This can also be returned if the context passed to an + * API call is not a valid handle (such as a context that has had + * ::cuCtxDestroy() invoked on it). This can also be returned if a user + * mixes different API versions (i.e. 3010 context with 3020 API calls). + * See ::cuCtxGetApiVersion() for more details. + */ + CUDA_ERROR_INVALID_CONTEXT = 201, + + /** + * This indicated that the context being supplied as a parameter to the + * API call was already the active context. + * \deprecated + * This error return is deprecated as of CUDA 3.2. It is no longer an + * error to attempt to push the active context via ::cuCtxPushCurrent(). + */ + CUDA_ERROR_CONTEXT_ALREADY_CURRENT = 202, + + /** + * This indicates that a map or register operation has failed. + */ + CUDA_ERROR_MAP_FAILED = 205, + + /** + * This indicates that an unmap or unregister operation has failed. + */ + CUDA_ERROR_UNMAP_FAILED = 206, + + /** + * This indicates that the specified array is currently mapped and thus + * cannot be destroyed. + */ + CUDA_ERROR_ARRAY_IS_MAPPED = 207, + + /** + * This indicates that the resource is already mapped. + */ + CUDA_ERROR_ALREADY_MAPPED = 208, + + /** + * This indicates that there is no kernel image available that is suitable + * for the device. This can occur when a user specifies code generation + * options for a particular CUDA source file that do not include the + * corresponding device configuration. + */ + CUDA_ERROR_NO_BINARY_FOR_GPU = 209, + + /** + * This indicates that a resource has already been acquired. + */ + CUDA_ERROR_ALREADY_ACQUIRED = 210, + + /** + * This indicates that a resource is not mapped. + */ + CUDA_ERROR_NOT_MAPPED = 211, + + /** + * This indicates that a mapped resource is not available for access as an + * array. + */ + CUDA_ERROR_NOT_MAPPED_AS_ARRAY = 212, + + /** + * This indicates that a mapped resource is not available for access as a + * pointer. + */ + CUDA_ERROR_NOT_MAPPED_AS_POINTER = 213, + + /** + * This indicates that an uncorrectable ECC error was detected during + * execution. + */ + CUDA_ERROR_ECC_UNCORRECTABLE = 214, + + /** + * This indicates that the ::CUlimit passed to the API call is not + * supported by the active device. + */ + CUDA_ERROR_UNSUPPORTED_LIMIT = 215, + + /** + * This indicates that the ::CUcontext passed to the API call can + * only be bound to a single CPU thread at a time but is already + * bound to a CPU thread. + */ + CUDA_ERROR_CONTEXT_ALREADY_IN_USE = 216, + + /** + * This indicates that peer access is not supported across the given + * devices. + */ + CUDA_ERROR_PEER_ACCESS_UNSUPPORTED = 217, + + /** + * This indicates that a PTX JIT compilation failed. + */ + CUDA_ERROR_INVALID_PTX = 218, + + /** + * This indicates an error with OpenGL or DirectX context. + */ + CUDA_ERROR_INVALID_GRAPHICS_CONTEXT = 219, + + /** + * This indicates that an uncorrectable NVLink error was detected during the + * execution. + */ + CUDA_ERROR_NVLINK_UNCORRECTABLE = 220, + + /** + * This indicates that the PTX JIT compiler library was not found. + */ + CUDA_ERROR_JIT_COMPILER_NOT_FOUND = 221, + + /** + * This indicates that the provided PTX was compiled with an unsupported toolchain. + */ + + CUDA_ERROR_UNSUPPORTED_PTX_VERSION = 222, + + /** + * This indicates that the PTX JIT compilation was disabled. + */ + CUDA_ERROR_JIT_COMPILATION_DISABLED = 223, + + /** + * This indicates that the ::CUexecAffinityType passed to the API call is not + * supported by the active device. + */ + CUDA_ERROR_UNSUPPORTED_EXEC_AFFINITY = 224, + + /** + * This indicates that the code to be compiled by the PTX JIT contains + * unsupported call to cudaDeviceSynchronize. + */ + CUDA_ERROR_UNSUPPORTED_DEVSIDE_SYNC = 225, + + /** + * This indicates that the device kernel source is invalid. This includes + * compilation/linker errors encountered in device code or user error. + */ + CUDA_ERROR_INVALID_SOURCE = 300, + + /** + * This indicates that the file specified was not found. + */ + CUDA_ERROR_FILE_NOT_FOUND = 301, + + /** + * This indicates that a link to a shared object failed to resolve. + */ + CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND = 302, + + /** + * This indicates that initialization of a shared object failed. + */ + CUDA_ERROR_SHARED_OBJECT_INIT_FAILED = 303, + + /** + * This indicates that an OS call failed. + */ + CUDA_ERROR_OPERATING_SYSTEM = 304, + + /** + * This indicates that a resource handle passed to the API call was not + * valid. Resource handles are opaque types like ::CUstream and ::CUevent. + */ + CUDA_ERROR_INVALID_HANDLE = 400, + + /** + * This indicates that a resource required by the API call is not in a + * valid state to perform the requested operation. + */ + CUDA_ERROR_ILLEGAL_STATE = 401, + + /** + * This indicates that a named symbol was not found. Examples of symbols + * are global/constant variable names, driver function names, texture names, + * and surface names. + */ + CUDA_ERROR_NOT_FOUND = 500, + + /** + * This indicates that asynchronous operations issued previously have not + * completed yet. This result is not actually an error, but must be indicated + * differently than ::CUDA_SUCCESS (which indicates completion). Calls that + * may return this value include ::cuEventQuery() and ::cuStreamQuery(). + */ + CUDA_ERROR_NOT_READY = 600, + + /** + * While executing a kernel, the device encountered a + * load or store instruction on an invalid memory address. + * This leaves the process in an inconsistent state and any further CUDA work + * will return the same error. To continue using CUDA, the process must be terminated + * and relaunched. + */ + CUDA_ERROR_ILLEGAL_ADDRESS = 700, + + /** + * This indicates that a launch did not occur because it did not have + * appropriate resources. This error usually indicates that the user has + * attempted to pass too many arguments to the device kernel, or the + * kernel launch specifies too many threads for the kernel's register + * count. Passing arguments of the wrong size (i.e. a 64-bit pointer + * when a 32-bit int is expected) is equivalent to passing too many + * arguments and can also result in this error. + */ + CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES = 701, + + /** + * This indicates that the device kernel took too long to execute. This can + * only occur if timeouts are enabled - see the device attribute + * ::CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. + * This leaves the process in an inconsistent state and any further CUDA work + * will return the same error. To continue using CUDA, the process must be terminated + * and relaunched. + */ + CUDA_ERROR_LAUNCH_TIMEOUT = 702, + + /** + * This error indicates a kernel launch that uses an incompatible texturing + * mode. + */ + CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING = 703, + + /** + * This error indicates that a call to ::cuCtxEnablePeerAccess() is + * trying to re-enable peer access to a context which has already + * had peer access to it enabled. + */ + CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED = 704, + + /** + * This error indicates that ::cuCtxDisablePeerAccess() is + * trying to disable peer access which has not been enabled yet + * via ::cuCtxEnablePeerAccess(). + */ + CUDA_ERROR_PEER_ACCESS_NOT_ENABLED = 705, + + /** + * This error indicates that the primary context for the specified device + * has already been initialized. + */ + CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE = 708, + + /** + * This error indicates that the context current to the calling thread + * has been destroyed using ::cuCtxDestroy, or is a primary context which + * has not yet been initialized. + */ + CUDA_ERROR_CONTEXT_IS_DESTROYED = 709, + + /** + * A device-side assert triggered during kernel execution. The context + * cannot be used anymore, and must be destroyed. All existing device + * memory allocations from this context are invalid and must be + * reconstructed if the program is to continue using CUDA. + */ + CUDA_ERROR_ASSERT = 710, + + /** + * This error indicates that the hardware resources required to enable + * peer access have been exhausted for one or more of the devices + * passed to ::cuCtxEnablePeerAccess(). + */ + CUDA_ERROR_TOO_MANY_PEERS = 711, + + /** + * This error indicates that the memory range passed to ::cuMemHostRegister() + * has already been registered. + */ + CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED = 712, + + /** + * This error indicates that the pointer passed to ::cuMemHostUnregister() + * does not correspond to any currently registered memory region. + */ + CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED = 713, + + /** + * While executing a kernel, the device encountered a stack error. + * This can be due to stack corruption or exceeding the stack size limit. + * This leaves the process in an inconsistent state and any further CUDA work + * will return the same error. To continue using CUDA, the process must be terminated + * and relaunched. + */ + CUDA_ERROR_HARDWARE_STACK_ERROR = 714, + + /** + * While executing a kernel, the device encountered an illegal instruction. + * This leaves the process in an inconsistent state and any further CUDA work + * will return the same error. To continue using CUDA, the process must be terminated + * and relaunched. + */ + CUDA_ERROR_ILLEGAL_INSTRUCTION = 715, + + /** + * While executing a kernel, the device encountered a load or store instruction + * on a memory address which is not aligned. + * This leaves the process in an inconsistent state and any further CUDA work + * will return the same error. To continue using CUDA, the process must be terminated + * and relaunched. + */ + CUDA_ERROR_MISALIGNED_ADDRESS = 716, + + /** + * While executing a kernel, the device encountered an instruction + * which can only operate on memory locations in certain address spaces + * (global, shared, or local), but was supplied a memory address not + * belonging to an allowed address space. + * This leaves the process in an inconsistent state and any further CUDA work + * will return the same error. To continue using CUDA, the process must be terminated + * and relaunched. + */ + CUDA_ERROR_INVALID_ADDRESS_SPACE = 717, + + /** + * While executing a kernel, the device program counter wrapped its address space. + * This leaves the process in an inconsistent state and any further CUDA work + * will return the same error. To continue using CUDA, the process must be terminated + * and relaunched. + */ + CUDA_ERROR_INVALID_PC = 718, + + /** + * An exception occurred on the device while executing a kernel. Common + * causes include dereferencing an invalid device pointer and accessing + * out of bounds shared memory. Less common cases can be system specific - more + * information about these cases can be found in the system specific user guide. + * This leaves the process in an inconsistent state and any further CUDA work + * will return the same error. To continue using CUDA, the process must be terminated + * and relaunched. + */ + CUDA_ERROR_LAUNCH_FAILED = 719, + + /** + * This error indicates that the number of blocks launched per grid for a kernel that was + * launched via either ::cuLaunchCooperativeKernel or ::cuLaunchCooperativeKernelMultiDevice + * exceeds the maximum number of blocks as allowed by ::cuOccupancyMaxActiveBlocksPerMultiprocessor + * or ::cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors + * as specified by the device attribute ::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT. + */ + CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE = 720, + + /** + * This error indicates that the attempted operation is not permitted. + */ + CUDA_ERROR_NOT_PERMITTED = 800, + + /** + * This error indicates that the attempted operation is not supported + * on the current system or device. + */ + CUDA_ERROR_NOT_SUPPORTED = 801, + + /** + * This error indicates that the system is not yet ready to start any CUDA + * work. To continue using CUDA, verify the system configuration is in a + * valid state and all required driver daemons are actively running. + * More information about this error can be found in the system specific + * user guide. + */ + CUDA_ERROR_SYSTEM_NOT_READY = 802, + + /** + * This error indicates that there is a mismatch between the versions of + * the display driver and the CUDA driver. Refer to the compatibility documentation + * for supported versions. + */ + CUDA_ERROR_SYSTEM_DRIVER_MISMATCH = 803, + + /** + * This error indicates that the system was upgraded to run with forward compatibility + * but the visible hardware detected by CUDA does not support this configuration. + * Refer to the compatibility documentation for the supported hardware matrix or ensure + * that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES + * environment variable. + */ + CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE = 804, + + /** + * This error indicates that the MPS client failed to connect to the MPS control daemon or the MPS server. + */ + CUDA_ERROR_MPS_CONNECTION_FAILED = 805, + + /** + * This error indicates that the remote procedural call between the MPS server and the MPS client failed. + */ + CUDA_ERROR_MPS_RPC_FAILURE = 806, + + /** + * This error indicates that the MPS server is not ready to accept new MPS client requests. + * This error can be returned when the MPS server is in the process of recovering from a fatal failure. + */ + CUDA_ERROR_MPS_SERVER_NOT_READY = 807, + + /** + * This error indicates that the hardware resources required to create MPS client have been exhausted. + */ + CUDA_ERROR_MPS_MAX_CLIENTS_REACHED = 808, + + /** + * This error indicates the the hardware resources required to support device connections have been exhausted. + */ + CUDA_ERROR_MPS_MAX_CONNECTIONS_REACHED = 809, + + /** + * This error indicates that the MPS client has been terminated by the server. To continue using CUDA, the process must be terminated and relaunched. + */ + CUDA_ERROR_MPS_CLIENT_TERMINATED = 810, + + /** + * This error indicates that the module is using CUDA Dynamic Parallelism, but the current configuration, like MPS, does not support it. + */ + CUDA_ERROR_CDP_NOT_SUPPORTED = 811, + + /** + * This error indicates that a module contains an unsupported interaction between different versions of CUDA Dynamic Parallelism. + */ + CUDA_ERROR_CDP_VERSION_MISMATCH = 812, + + /** + * This error indicates that the operation is not permitted when + * the stream is capturing. + */ + CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED = 900, + + /** + * This error indicates that the current capture sequence on the stream + * has been invalidated due to a previous error. + */ + CUDA_ERROR_STREAM_CAPTURE_INVALIDATED = 901, + + /** + * This error indicates that the operation would have resulted in a merge + * of two independent capture sequences. + */ + CUDA_ERROR_STREAM_CAPTURE_MERGE = 902, + + /** + * This error indicates that the capture was not initiated in this stream. + */ + CUDA_ERROR_STREAM_CAPTURE_UNMATCHED = 903, + + /** + * This error indicates that the capture sequence contains a fork that was + * not joined to the primary stream. + */ + CUDA_ERROR_STREAM_CAPTURE_UNJOINED = 904, + + /** + * This error indicates that a dependency would have been created which + * crosses the capture sequence boundary. Only implicit in-stream ordering + * dependencies are allowed to cross the boundary. + */ + CUDA_ERROR_STREAM_CAPTURE_ISOLATION = 905, + + /** + * This error indicates a disallowed implicit dependency on a current capture + * sequence from cudaStreamLegacy. + */ + CUDA_ERROR_STREAM_CAPTURE_IMPLICIT = 906, + + /** + * This error indicates that the operation is not permitted on an event which + * was last recorded in a capturing stream. + */ + CUDA_ERROR_CAPTURED_EVENT = 907, + + /** + * A stream capture sequence not initiated with the ::CU_STREAM_CAPTURE_MODE_RELAXED + * argument to ::cuStreamBeginCapture was passed to ::cuStreamEndCapture in a + * different thread. + */ + CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD = 908, + + /** + * This error indicates that the timeout specified for the wait operation has lapsed. + */ + CUDA_ERROR_TIMEOUT = 909, + + /** + * This error indicates that the graph update was not performed because it included + * changes which violated constraints specific to instantiated graph update. + */ + CUDA_ERROR_GRAPH_EXEC_UPDATE_FAILURE = 910, + + /** + * This indicates that an async error has occurred in a device outside of CUDA. + * If CUDA was waiting for an external device's signal before consuming shared data, + * the external device signaled an error indicating that the data is not valid for + * consumption. This leaves the process in an inconsistent state and any further CUDA + * work will return the same error. To continue using CUDA, the process must be + * terminated and relaunched. + */ + CUDA_ERROR_EXTERNAL_DEVICE = 911, + + /** + * Indicates a kernel launch error due to cluster misconfiguration. + */ + CUDA_ERROR_INVALID_CLUSTER_SIZE = 912, + + /** + * This indicates that an unknown internal error has occurred. + */ + CUDA_ERROR_UNKNOWN = 999 +} CUresult; + +/** + * P2P Attributes + */ +typedef enum CUdevice_P2PAttribute_enum { + CU_DEVICE_P2P_ATTRIBUTE_PERFORMANCE_RANK = 0x01, /**< A relative value indicating the performance of the link between two devices */ + CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED = 0x02, /**< P2P Access is enable */ + CU_DEVICE_P2P_ATTRIBUTE_NATIVE_ATOMIC_SUPPORTED = 0x03, /**< Atomic operation over the link supported */ + CU_DEVICE_P2P_ATTRIBUTE_ACCESS_ACCESS_SUPPORTED = 0x04, /**< \deprecated use CU_DEVICE_P2P_ATTRIBUTE_CUDA_ARRAY_ACCESS_SUPPORTED instead */ + CU_DEVICE_P2P_ATTRIBUTE_CUDA_ARRAY_ACCESS_SUPPORTED = 0x04 /**< Accessing CUDA arrays over the link supported */ +} CUdevice_P2PAttribute; + +/** + * CUDA stream callback + * \param hStream The stream the callback was added to, as passed to ::cuStreamAddCallback. May be NULL. + * \param status ::CUDA_SUCCESS or any persistent error on the stream. + * \param userData User parameter provided at registration. + */ +typedef void (CUDA_CB *CUstreamCallback)(CUstream hStream, CUresult status, void *userData); + +/** + * Block size to per-block dynamic shared memory mapping for a certain + * kernel \param blockSize Block size of the kernel. + * + * \return The dynamic shared memory needed by a block. + */ +typedef size_t (CUDA_CB *CUoccupancyB2DSize)(int blockSize); + +/** + * If set, host memory is portable between CUDA contexts. + * Flag for ::cuMemHostAlloc() + */ +#define CU_MEMHOSTALLOC_PORTABLE 0x01 + +/** + * If set, host memory is mapped into CUDA address space and + * ::cuMemHostGetDevicePointer() may be called on the host pointer. + * Flag for ::cuMemHostAlloc() + */ +#define CU_MEMHOSTALLOC_DEVICEMAP 0x02 + +/** + * If set, host memory is allocated as write-combined - fast to write, + * faster to DMA, slow to read except via SSE4 streaming load instruction + * (MOVNTDQA). + * Flag for ::cuMemHostAlloc() + */ +#define CU_MEMHOSTALLOC_WRITECOMBINED 0x04 + +/** + * If set, host memory is portable between CUDA contexts. + * Flag for ::cuMemHostRegister() + */ +#define CU_MEMHOSTREGISTER_PORTABLE 0x01 + +/** + * If set, host memory is mapped into CUDA address space and + * ::cuMemHostGetDevicePointer() may be called on the host pointer. + * Flag for ::cuMemHostRegister() + */ +#define CU_MEMHOSTREGISTER_DEVICEMAP 0x02 + +/** + * If set, the passed memory pointer is treated as pointing to some + * memory-mapped I/O space, e.g. belonging to a third-party PCIe device. + * On Windows the flag is a no-op. + * On Linux that memory is marked as non cache-coherent for the GPU and + * is expected to be physically contiguous. It may return + * ::CUDA_ERROR_NOT_PERMITTED if run as an unprivileged user, + * ::CUDA_ERROR_NOT_SUPPORTED on older Linux kernel versions. + * On all other platforms, it is not supported and ::CUDA_ERROR_NOT_SUPPORTED + * is returned. + * Flag for ::cuMemHostRegister() + */ +#define CU_MEMHOSTREGISTER_IOMEMORY 0x04 + +/** +* If set, the passed memory pointer is treated as pointing to memory that is +* considered read-only by the device. On platforms without +* ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, this flag is +* required in order to register memory mapped to the CPU as read-only. Support +* for the use of this flag can be queried from the device attribute +* ::CU_DEVICE_ATTRIBUTE_READ_ONLY_HOST_REGISTER_SUPPORTED. Using this flag with +* a current context associated with a device that does not have this attribute +* set will cause ::cuMemHostRegister to error with ::CUDA_ERROR_NOT_SUPPORTED. +*/ +#define CU_MEMHOSTREGISTER_READ_ONLY 0x08 + +/** + * 2D memory copy parameters + */ +typedef struct CUDA_MEMCPY2D_st { + size_t srcXInBytes; /**< Source X in bytes */ + size_t srcY; /**< Source Y */ + + CUmemorytype srcMemoryType; /**< Source memory type (host, device, array) */ + const void *srcHost; /**< Source host pointer */ + CUdeviceptr srcDevice; /**< Source device pointer */ + CUarray srcArray; /**< Source array reference */ + size_t srcPitch; /**< Source pitch (ignored when src is array) */ + + size_t dstXInBytes; /**< Destination X in bytes */ + size_t dstY; /**< Destination Y */ + + CUmemorytype dstMemoryType; /**< Destination memory type (host, device, array) */ + void *dstHost; /**< Destination host pointer */ + CUdeviceptr dstDevice; /**< Destination device pointer */ + CUarray dstArray; /**< Destination array reference */ + size_t dstPitch; /**< Destination pitch (ignored when dst is array) */ + + size_t WidthInBytes; /**< Width of 2D memory copy in bytes */ + size_t Height; /**< Height of 2D memory copy */ +} CUDA_MEMCPY2D_v2; +typedef CUDA_MEMCPY2D_v2 CUDA_MEMCPY2D; + +/** + * 3D memory copy parameters + */ +typedef struct CUDA_MEMCPY3D_st { + size_t srcXInBytes; /**< Source X in bytes */ + size_t srcY; /**< Source Y */ + size_t srcZ; /**< Source Z */ + size_t srcLOD; /**< Source LOD */ + CUmemorytype srcMemoryType; /**< Source memory type (host, device, array) */ + const void *srcHost; /**< Source host pointer */ + CUdeviceptr srcDevice; /**< Source device pointer */ + CUarray srcArray; /**< Source array reference */ + void *reserved0; /**< Must be NULL */ + size_t srcPitch; /**< Source pitch (ignored when src is array) */ + size_t srcHeight; /**< Source height (ignored when src is array; may be 0 if Depth==1) */ + + size_t dstXInBytes; /**< Destination X in bytes */ + size_t dstY; /**< Destination Y */ + size_t dstZ; /**< Destination Z */ + size_t dstLOD; /**< Destination LOD */ + CUmemorytype dstMemoryType; /**< Destination memory type (host, device, array) */ + void *dstHost; /**< Destination host pointer */ + CUdeviceptr dstDevice; /**< Destination device pointer */ + CUarray dstArray; /**< Destination array reference */ + void *reserved1; /**< Must be NULL */ + size_t dstPitch; /**< Destination pitch (ignored when dst is array) */ + size_t dstHeight; /**< Destination height (ignored when dst is array; may be 0 if Depth==1) */ + + size_t WidthInBytes; /**< Width of 3D memory copy in bytes */ + size_t Height; /**< Height of 3D memory copy */ + size_t Depth; /**< Depth of 3D memory copy */ +} CUDA_MEMCPY3D_v2; +typedef CUDA_MEMCPY3D_v2 CUDA_MEMCPY3D; + +/** + * 3D memory cross-context copy parameters + */ +typedef struct CUDA_MEMCPY3D_PEER_st { + size_t srcXInBytes; /**< Source X in bytes */ + size_t srcY; /**< Source Y */ + size_t srcZ; /**< Source Z */ + size_t srcLOD; /**< Source LOD */ + CUmemorytype srcMemoryType; /**< Source memory type (host, device, array) */ + const void *srcHost; /**< Source host pointer */ + CUdeviceptr srcDevice; /**< Source device pointer */ + CUarray srcArray; /**< Source array reference */ + CUcontext srcContext; /**< Source context (ignored with srcMemoryType is ::CU_MEMORYTYPE_ARRAY) */ + size_t srcPitch; /**< Source pitch (ignored when src is array) */ + size_t srcHeight; /**< Source height (ignored when src is array; may be 0 if Depth==1) */ + + size_t dstXInBytes; /**< Destination X in bytes */ + size_t dstY; /**< Destination Y */ + size_t dstZ; /**< Destination Z */ + size_t dstLOD; /**< Destination LOD */ + CUmemorytype dstMemoryType; /**< Destination memory type (host, device, array) */ + void *dstHost; /**< Destination host pointer */ + CUdeviceptr dstDevice; /**< Destination device pointer */ + CUarray dstArray; /**< Destination array reference */ + CUcontext dstContext; /**< Destination context (ignored with dstMemoryType is ::CU_MEMORYTYPE_ARRAY) */ + size_t dstPitch; /**< Destination pitch (ignored when dst is array) */ + size_t dstHeight; /**< Destination height (ignored when dst is array; may be 0 if Depth==1) */ + + size_t WidthInBytes; /**< Width of 3D memory copy in bytes */ + size_t Height; /**< Height of 3D memory copy */ + size_t Depth; /**< Depth of 3D memory copy */ +} CUDA_MEMCPY3D_PEER_v1; +typedef CUDA_MEMCPY3D_PEER_v1 CUDA_MEMCPY3D_PEER; + +/** + * Array descriptor + */ +typedef struct CUDA_ARRAY_DESCRIPTOR_st +{ + size_t Width; /**< Width of array */ + size_t Height; /**< Height of array */ + + CUarray_format Format; /**< Array format */ + unsigned int NumChannels; /**< Channels per array element */ +} CUDA_ARRAY_DESCRIPTOR_v2; +typedef CUDA_ARRAY_DESCRIPTOR_v2 CUDA_ARRAY_DESCRIPTOR; + +/** + * 3D array descriptor + */ +typedef struct CUDA_ARRAY3D_DESCRIPTOR_st +{ + size_t Width; /**< Width of 3D array */ + size_t Height; /**< Height of 3D array */ + size_t Depth; /**< Depth of 3D array */ + + CUarray_format Format; /**< Array format */ + unsigned int NumChannels; /**< Channels per array element */ + unsigned int Flags; /**< Flags */ +} CUDA_ARRAY3D_DESCRIPTOR_v2; +typedef CUDA_ARRAY3D_DESCRIPTOR_v2 CUDA_ARRAY3D_DESCRIPTOR; + +/** + * Indicates that the layered sparse CUDA array or CUDA mipmapped array has a single mip tail region for all layers + */ +#define CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL 0x1 + +/** + * CUDA array sparse properties + */ +typedef struct CUDA_ARRAY_SPARSE_PROPERTIES_st { + struct { + unsigned int width; /**< Width of sparse tile in elements */ + unsigned int height; /**< Height of sparse tile in elements */ + unsigned int depth; /**< Depth of sparse tile in elements */ + } tileExtent; + + /** + * First mip level at which the mip tail begins. + */ + unsigned int miptailFirstLevel; + /** + * Total size of the mip tail. + */ + unsigned long long miptailSize; + /** + * Flags will either be zero or ::CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL + */ + unsigned int flags; + unsigned int reserved[4]; +} CUDA_ARRAY_SPARSE_PROPERTIES_v1; +typedef CUDA_ARRAY_SPARSE_PROPERTIES_v1 CUDA_ARRAY_SPARSE_PROPERTIES; + +/** + * CUDA array memory requirements + */ +typedef struct CUDA_ARRAY_MEMORY_REQUIREMENTS_st { + size_t size; /**< Total required memory size */ + size_t alignment; /**< alignment requirement */ + unsigned int reserved[4]; +} CUDA_ARRAY_MEMORY_REQUIREMENTS_v1; +typedef CUDA_ARRAY_MEMORY_REQUIREMENTS_v1 CUDA_ARRAY_MEMORY_REQUIREMENTS; + +/** + * CUDA Resource descriptor + */ +typedef struct CUDA_RESOURCE_DESC_st +{ + CUresourcetype resType; /**< Resource type */ + + union { + struct { + CUarray hArray; /**< CUDA array */ + } array; + struct { + CUmipmappedArray hMipmappedArray; /**< CUDA mipmapped array */ + } mipmap; + struct { + CUdeviceptr devPtr; /**< Device pointer */ + CUarray_format format; /**< Array format */ + unsigned int numChannels; /**< Channels per array element */ + size_t sizeInBytes; /**< Size in bytes */ + } linear; + struct { + CUdeviceptr devPtr; /**< Device pointer */ + CUarray_format format; /**< Array format */ + unsigned int numChannels; /**< Channels per array element */ + size_t width; /**< Width of the array in elements */ + size_t height; /**< Height of the array in elements */ + size_t pitchInBytes; /**< Pitch between two rows in bytes */ + } pitch2D; + struct { + int reserved[32]; + } reserved; + } res; + + unsigned int flags; /**< Flags (must be zero) */ +} CUDA_RESOURCE_DESC_v1; +typedef CUDA_RESOURCE_DESC_v1 CUDA_RESOURCE_DESC; + +/** + * Texture descriptor + */ +typedef struct CUDA_TEXTURE_DESC_st { + CUaddress_mode addressMode[3]; /**< Address modes */ + CUfilter_mode filterMode; /**< Filter mode */ + unsigned int flags; /**< Flags */ + unsigned int maxAnisotropy; /**< Maximum anisotropy ratio */ + CUfilter_mode mipmapFilterMode; /**< Mipmap filter mode */ + float mipmapLevelBias; /**< Mipmap level bias */ + float minMipmapLevelClamp; /**< Mipmap minimum level clamp */ + float maxMipmapLevelClamp; /**< Mipmap maximum level clamp */ + float borderColor[4]; /**< Border Color */ + int reserved[12]; +} CUDA_TEXTURE_DESC_v1; +typedef CUDA_TEXTURE_DESC_v1 CUDA_TEXTURE_DESC; + +/** + * Resource view format + */ +typedef enum CUresourceViewFormat_enum +{ + CU_RES_VIEW_FORMAT_NONE = 0x00, /**< No resource view format (use underlying resource format) */ + CU_RES_VIEW_FORMAT_UINT_1X8 = 0x01, /**< 1 channel unsigned 8-bit integers */ + CU_RES_VIEW_FORMAT_UINT_2X8 = 0x02, /**< 2 channel unsigned 8-bit integers */ + CU_RES_VIEW_FORMAT_UINT_4X8 = 0x03, /**< 4 channel unsigned 8-bit integers */ + CU_RES_VIEW_FORMAT_SINT_1X8 = 0x04, /**< 1 channel signed 8-bit integers */ + CU_RES_VIEW_FORMAT_SINT_2X8 = 0x05, /**< 2 channel signed 8-bit integers */ + CU_RES_VIEW_FORMAT_SINT_4X8 = 0x06, /**< 4 channel signed 8-bit integers */ + CU_RES_VIEW_FORMAT_UINT_1X16 = 0x07, /**< 1 channel unsigned 16-bit integers */ + CU_RES_VIEW_FORMAT_UINT_2X16 = 0x08, /**< 2 channel unsigned 16-bit integers */ + CU_RES_VIEW_FORMAT_UINT_4X16 = 0x09, /**< 4 channel unsigned 16-bit integers */ + CU_RES_VIEW_FORMAT_SINT_1X16 = 0x0a, /**< 1 channel signed 16-bit integers */ + CU_RES_VIEW_FORMAT_SINT_2X16 = 0x0b, /**< 2 channel signed 16-bit integers */ + CU_RES_VIEW_FORMAT_SINT_4X16 = 0x0c, /**< 4 channel signed 16-bit integers */ + CU_RES_VIEW_FORMAT_UINT_1X32 = 0x0d, /**< 1 channel unsigned 32-bit integers */ + CU_RES_VIEW_FORMAT_UINT_2X32 = 0x0e, /**< 2 channel unsigned 32-bit integers */ + CU_RES_VIEW_FORMAT_UINT_4X32 = 0x0f, /**< 4 channel unsigned 32-bit integers */ + CU_RES_VIEW_FORMAT_SINT_1X32 = 0x10, /**< 1 channel signed 32-bit integers */ + CU_RES_VIEW_FORMAT_SINT_2X32 = 0x11, /**< 2 channel signed 32-bit integers */ + CU_RES_VIEW_FORMAT_SINT_4X32 = 0x12, /**< 4 channel signed 32-bit integers */ + CU_RES_VIEW_FORMAT_FLOAT_1X16 = 0x13, /**< 1 channel 16-bit floating point */ + CU_RES_VIEW_FORMAT_FLOAT_2X16 = 0x14, /**< 2 channel 16-bit floating point */ + CU_RES_VIEW_FORMAT_FLOAT_4X16 = 0x15, /**< 4 channel 16-bit floating point */ + CU_RES_VIEW_FORMAT_FLOAT_1X32 = 0x16, /**< 1 channel 32-bit floating point */ + CU_RES_VIEW_FORMAT_FLOAT_2X32 = 0x17, /**< 2 channel 32-bit floating point */ + CU_RES_VIEW_FORMAT_FLOAT_4X32 = 0x18, /**< 4 channel 32-bit floating point */ + CU_RES_VIEW_FORMAT_UNSIGNED_BC1 = 0x19, /**< Block compressed 1 */ + CU_RES_VIEW_FORMAT_UNSIGNED_BC2 = 0x1a, /**< Block compressed 2 */ + CU_RES_VIEW_FORMAT_UNSIGNED_BC3 = 0x1b, /**< Block compressed 3 */ + CU_RES_VIEW_FORMAT_UNSIGNED_BC4 = 0x1c, /**< Block compressed 4 unsigned */ + CU_RES_VIEW_FORMAT_SIGNED_BC4 = 0x1d, /**< Block compressed 4 signed */ + CU_RES_VIEW_FORMAT_UNSIGNED_BC5 = 0x1e, /**< Block compressed 5 unsigned */ + CU_RES_VIEW_FORMAT_SIGNED_BC5 = 0x1f, /**< Block compressed 5 signed */ + CU_RES_VIEW_FORMAT_UNSIGNED_BC6H = 0x20, /**< Block compressed 6 unsigned half-float */ + CU_RES_VIEW_FORMAT_SIGNED_BC6H = 0x21, /**< Block compressed 6 signed half-float */ + CU_RES_VIEW_FORMAT_UNSIGNED_BC7 = 0x22 /**< Block compressed 7 */ +} CUresourceViewFormat; + +/** + * Resource view descriptor + */ +typedef struct CUDA_RESOURCE_VIEW_DESC_st +{ + CUresourceViewFormat format; /**< Resource view format */ + size_t width; /**< Width of the resource view */ + size_t height; /**< Height of the resource view */ + size_t depth; /**< Depth of the resource view */ + unsigned int firstMipmapLevel; /**< First defined mipmap level */ + unsigned int lastMipmapLevel; /**< Last defined mipmap level */ + unsigned int firstLayer; /**< First layer index */ + unsigned int lastLayer; /**< Last layer index */ + unsigned int reserved[16]; +} CUDA_RESOURCE_VIEW_DESC_v1; +typedef CUDA_RESOURCE_VIEW_DESC_v1 CUDA_RESOURCE_VIEW_DESC; + +/** + * Size of tensor map descriptor + */ +#define CU_TENSOR_MAP_NUM_QWORDS 16 + +/** + * Tensor map descriptor. Requires compiler support for aligning to 64 bytes. + */ +typedef struct CUtensorMap_st { +#if __cplusplus >= 201103L + alignas(64) +#elif __STDC_VERSION__ >= 201112L + _Alignas(64) +#endif + cuuint64_t opaque[CU_TENSOR_MAP_NUM_QWORDS]; +} CUtensorMap; + +/** + * Tensor map data type + */ +typedef enum CUtensorMapDataType_enum { + CU_TENSOR_MAP_DATA_TYPE_UINT8 = 0, + CU_TENSOR_MAP_DATA_TYPE_UINT16, + CU_TENSOR_MAP_DATA_TYPE_UINT32, + CU_TENSOR_MAP_DATA_TYPE_INT32, + CU_TENSOR_MAP_DATA_TYPE_UINT64, + CU_TENSOR_MAP_DATA_TYPE_INT64, + CU_TENSOR_MAP_DATA_TYPE_FLOAT16, + CU_TENSOR_MAP_DATA_TYPE_FLOAT32, + CU_TENSOR_MAP_DATA_TYPE_FLOAT64, + CU_TENSOR_MAP_DATA_TYPE_BFLOAT16, + CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ, + CU_TENSOR_MAP_DATA_TYPE_TFLOAT32, + CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ +} CUtensorMapDataType; + +/** + * Tensor map interleave layout type + */ +typedef enum CUtensorMapInterleave_enum { + CU_TENSOR_MAP_INTERLEAVE_NONE = 0, + CU_TENSOR_MAP_INTERLEAVE_16B, + CU_TENSOR_MAP_INTERLEAVE_32B +} CUtensorMapInterleave; + +/** + * Tensor map swizzling mode of shared memory banks + */ +typedef enum CUtensorMapSwizzle_enum { + CU_TENSOR_MAP_SWIZZLE_NONE = 0, + CU_TENSOR_MAP_SWIZZLE_32B, + CU_TENSOR_MAP_SWIZZLE_64B, + CU_TENSOR_MAP_SWIZZLE_128B +} CUtensorMapSwizzle; + +/** + * Tensor map L2 promotion type + */ +typedef enum CUtensorMapL2promotion_enum { + CU_TENSOR_MAP_L2_PROMOTION_NONE = 0, + CU_TENSOR_MAP_L2_PROMOTION_L2_64B, + CU_TENSOR_MAP_L2_PROMOTION_L2_128B, + CU_TENSOR_MAP_L2_PROMOTION_L2_256B +} CUtensorMapL2promotion; + +/** + * Tensor map out-of-bounds fill type + */ +typedef enum CUtensorMapFloatOOBfill_enum { + CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE = 0, + CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA +} CUtensorMapFloatOOBfill; + +/** + * GPU Direct v3 tokens + */ +typedef struct CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_st { + unsigned long long p2pToken; + unsigned int vaSpaceToken; +} CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_v1; +typedef CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_v1 CUDA_POINTER_ATTRIBUTE_P2P_TOKENS; + +/** +* Access flags that specify the level of access the current context's device has +* on the memory referenced. +*/ +typedef enum CUDA_POINTER_ATTRIBUTE_ACCESS_FLAGS_enum { + CU_POINTER_ATTRIBUTE_ACCESS_FLAG_NONE = 0x0, /**< No access, meaning the device cannot access this memory at all, thus must be staged through accessible memory in order to complete certain operations */ + CU_POINTER_ATTRIBUTE_ACCESS_FLAG_READ = 0x1, /**< Read-only access, meaning writes to this memory are considered invalid accesses and thus return error in that case. */ + CU_POINTER_ATTRIBUTE_ACCESS_FLAG_READWRITE = 0x3 /**< Read-write access, the device has full read-write access to the memory */ +} CUDA_POINTER_ATTRIBUTE_ACCESS_FLAGS; + +/** + * Kernel launch parameters + */ +typedef struct CUDA_LAUNCH_PARAMS_st { + CUfunction function; /**< Kernel to launch */ + unsigned int gridDimX; /**< Width of grid in blocks */ + unsigned int gridDimY; /**< Height of grid in blocks */ + unsigned int gridDimZ; /**< Depth of grid in blocks */ + unsigned int blockDimX; /**< X dimension of each thread block */ + unsigned int blockDimY; /**< Y dimension of each thread block */ + unsigned int blockDimZ; /**< Z dimension of each thread block */ + unsigned int sharedMemBytes; /**< Dynamic shared-memory size per thread block in bytes */ + CUstream hStream; /**< Stream identifier */ + void **kernelParams; /**< Array of pointers to kernel parameters */ +} CUDA_LAUNCH_PARAMS_v1; +typedef CUDA_LAUNCH_PARAMS_v1 CUDA_LAUNCH_PARAMS; + +/** + * External memory handle types + */ +typedef enum CUexternalMemoryHandleType_enum { + /** + * Handle is an opaque file descriptor + */ + CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_FD = 1, + /** + * Handle is an opaque shared NT handle + */ + CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32 = 2, + /** + * Handle is an opaque, globally shared handle + */ + CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT = 3, + /** + * Handle is a D3D12 heap object + */ + CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_HEAP = 4, + /** + * Handle is a D3D12 committed resource + */ + CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE = 5, + /** + * Handle is a shared NT handle to a D3D11 resource + */ + CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE = 6, + /** + * Handle is a globally shared handle to a D3D11 resource + */ + CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT = 7, + /** + * Handle is an NvSciBuf object + */ + CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF = 8 +} CUexternalMemoryHandleType; + +/** + * Indicates that the external memory object is a dedicated resource + */ +#define CUDA_EXTERNAL_MEMORY_DEDICATED 0x1 + +/** When the \p flags parameter of ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS + * contains this flag, it indicates that signaling an external semaphore object + * should skip performing appropriate memory synchronization operations over all + * the external memory objects that are imported as ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF, + * which otherwise are performed by default to ensure data coherency with other + * importers of the same NvSciBuf memory objects. + */ +#define CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC 0x01 + +/** When the \p flags parameter of ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS + * contains this flag, it indicates that waiting on an external semaphore object + * should skip performing appropriate memory synchronization operations over all + * the external memory objects that are imported as ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF, + * which otherwise are performed by default to ensure data coherency with other + * importers of the same NvSciBuf memory objects. + */ +#define CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC 0x02 + +/** + * When \p flags of ::cuDeviceGetNvSciSyncAttributes is set to this, + * it indicates that application needs signaler specific NvSciSyncAttr + * to be filled by ::cuDeviceGetNvSciSyncAttributes. + */ +#define CUDA_NVSCISYNC_ATTR_SIGNAL 0x1 + +/** + * When \p flags of ::cuDeviceGetNvSciSyncAttributes is set to this, + * it indicates that application needs waiter specific NvSciSyncAttr + * to be filled by ::cuDeviceGetNvSciSyncAttributes. + */ +#define CUDA_NVSCISYNC_ATTR_WAIT 0x2 +/** + * External memory handle descriptor + */ +typedef struct CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st { + /** + * Type of the handle + */ + CUexternalMemoryHandleType type; + union { + /** + * File descriptor referencing the memory object. Valid + * when type is + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_FD + */ + int fd; + /** + * Win32 handle referencing the semaphore object. Valid when + * type is one of the following: + * - ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32 + * - ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT + * - ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_HEAP + * - ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE + * - ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE + * - ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT + * Exactly one of 'handle' and 'name' must be non-NULL. If + * type is one of the following: + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT + * then 'name' must be NULL. + */ + struct { + /** + * Valid NT handle. Must be NULL if 'name' is non-NULL + */ + void *handle; + /** + * Name of a valid memory object. + * Must be NULL if 'handle' is non-NULL. + */ + const void *name; + } win32; + /** + * A handle representing an NvSciBuf Object. Valid when type + * is ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF + */ + const void *nvSciBufObject; + } handle; + /** + * Size of the memory allocation + */ + unsigned long long size; + /** + * Flags must either be zero or ::CUDA_EXTERNAL_MEMORY_DEDICATED + */ + unsigned int flags; + unsigned int reserved[16]; +} CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1; +typedef CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1 CUDA_EXTERNAL_MEMORY_HANDLE_DESC; + +/** + * External memory buffer descriptor + */ +typedef struct CUDA_EXTERNAL_MEMORY_BUFFER_DESC_st { + /** + * Offset into the memory object where the buffer's base is + */ + unsigned long long offset; + /** + * Size of the buffer + */ + unsigned long long size; + /** + * Flags reserved for future use. Must be zero. + */ + unsigned int flags; + unsigned int reserved[16]; +} CUDA_EXTERNAL_MEMORY_BUFFER_DESC_v1; +typedef CUDA_EXTERNAL_MEMORY_BUFFER_DESC_v1 CUDA_EXTERNAL_MEMORY_BUFFER_DESC; + +/** + * External memory mipmap descriptor + */ +typedef struct CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_st { + /** + * Offset into the memory object where the base level of the + * mipmap chain is. + */ + unsigned long long offset; + /** + * Format, dimension and type of base level of the mipmap chain + */ + CUDA_ARRAY3D_DESCRIPTOR arrayDesc; + /** + * Total number of levels in the mipmap chain + */ + unsigned int numLevels; + unsigned int reserved[16]; +} CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_v1; +typedef CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_v1 CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC; + +/** + * External semaphore handle types + */ +typedef enum CUexternalSemaphoreHandleType_enum { + /** + * Handle is an opaque file descriptor + */ + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD = 1, + /** + * Handle is an opaque shared NT handle + */ + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32 = 2, + /** + * Handle is an opaque, globally shared handle + */ + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT = 3, + /** + * Handle is a shared NT handle referencing a D3D12 fence object + */ + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE = 4, + /** + * Handle is a shared NT handle referencing a D3D11 fence object + */ + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE = 5, + /** + * Opaque handle to NvSciSync Object + */ + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC = 6, + /** + * Handle is a shared NT handle referencing a D3D11 keyed mutex object + */ + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX = 7, + /** + * Handle is a globally shared handle referencing a D3D11 keyed mutex object + */ + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT = 8, + /** + * Handle is an opaque file descriptor referencing a timeline semaphore + */ + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD = 9, + /** + * Handle is an opaque shared NT handle referencing a timeline semaphore + */ + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32 = 10 +} CUexternalSemaphoreHandleType; + +/** + * External semaphore handle descriptor + */ +typedef struct CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st { + /** + * Type of the handle + */ + CUexternalSemaphoreHandleType type; + union { + /** + * File descriptor referencing the semaphore object. Valid + * when type is one of the following: + * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD + * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD + */ + int fd; + /** + * Win32 handle referencing the semaphore object. Valid when + * type is one of the following: + * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32 + * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT + * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE + * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE + * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX + * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32 + * Exactly one of 'handle' and 'name' must be non-NULL. If + * type is one of the following: + * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT + * - ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT + * then 'name' must be NULL. + */ + struct { + /** + * Valid NT handle. Must be NULL if 'name' is non-NULL + */ + void *handle; + /** + * Name of a valid synchronization primitive. + * Must be NULL if 'handle' is non-NULL. + */ + const void *name; + } win32; + /** + * Valid NvSciSyncObj. Must be non NULL + */ + const void* nvSciSyncObj; + } handle; + /** + * Flags reserved for the future. Must be zero. + */ + unsigned int flags; + unsigned int reserved[16]; +} CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1; +typedef CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1 CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC; + +/** + * External semaphore signal parameters + */ +typedef struct CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st { + struct { + /** + * Parameters for fence objects + */ + struct { + /** + * Value of fence to be signaled + */ + unsigned long long value; + } fence; + union { + /** + * Pointer to NvSciSyncFence. Valid if ::CUexternalSemaphoreHandleType + * is of type ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC. + */ + void *fence; + unsigned long long reserved; + } nvSciSync; + /** + * Parameters for keyed mutex objects + */ + struct { + /** + * Value of key to release the mutex with + */ + unsigned long long key; + } keyedMutex; + unsigned int reserved[12]; + } params; + /** + * Only when ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS is used to + * signal a ::CUexternalSemaphore of type + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, the valid flag is + * ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC which indicates + * that while signaling the ::CUexternalSemaphore, no memory synchronization + * operations should be performed for any external memory object imported + * as ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF. + * For all other types of ::CUexternalSemaphore, flags must be zero. + */ + unsigned int flags; + unsigned int reserved[16]; +} CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1; +typedef CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1 CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS; + +/** + * External semaphore wait parameters + */ +typedef struct CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st { + struct { + /** + * Parameters for fence objects + */ + struct { + /** + * Value of fence to be waited on + */ + unsigned long long value; + } fence; + /** + * Pointer to NvSciSyncFence. Valid if CUexternalSemaphoreHandleType + * is of type CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC. + */ + union { + void *fence; + unsigned long long reserved; + } nvSciSync; + /** + * Parameters for keyed mutex objects + */ + struct { + /** + * Value of key to acquire the mutex with + */ + unsigned long long key; + /** + * Timeout in milliseconds to wait to acquire the mutex + */ + unsigned int timeoutMs; + } keyedMutex; + unsigned int reserved[10]; + } params; + /** + * Only when ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS is used to wait on + * a ::CUexternalSemaphore of type ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, + * the valid flag is ::CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC + * which indicates that while waiting for the ::CUexternalSemaphore, no memory + * synchronization operations should be performed for any external memory + * object imported as ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF. + * For all other types of ::CUexternalSemaphore, flags must be zero. + */ + unsigned int flags; + unsigned int reserved[16]; +} CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1; +typedef CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1 CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS; + +/** + * Semaphore signal node parameters + */ +typedef struct CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_st { + CUexternalSemaphore* extSemArray; /**< Array of external semaphore handles. */ + const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS* paramsArray; /**< Array of external semaphore signal parameters. */ + unsigned int numExtSems; /**< Number of handles and parameters supplied in extSemArray and paramsArray. */ +} CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1; +typedef CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1 CUDA_EXT_SEM_SIGNAL_NODE_PARAMS; + +/** + * Semaphore wait node parameters + */ +typedef struct CUDA_EXT_SEM_WAIT_NODE_PARAMS_st { + CUexternalSemaphore* extSemArray; /**< Array of external semaphore handles. */ + const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS* paramsArray; /**< Array of external semaphore wait parameters. */ + unsigned int numExtSems; /**< Number of handles and parameters supplied in extSemArray and paramsArray. */ +} CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1; +typedef CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1 CUDA_EXT_SEM_WAIT_NODE_PARAMS; + +typedef unsigned long long CUmemGenericAllocationHandle_v1; +typedef CUmemGenericAllocationHandle_v1 CUmemGenericAllocationHandle; + +/** + * Flags for specifying particular handle types + */ +typedef enum CUmemAllocationHandleType_enum { + CU_MEM_HANDLE_TYPE_NONE = 0x0, /**< Does not allow any export mechanism. > */ + CU_MEM_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR = 0x1, /**< Allows a file descriptor to be used for exporting. Permitted only on POSIX systems. (int) */ + CU_MEM_HANDLE_TYPE_WIN32 = 0x2, /**< Allows a Win32 NT handle to be used for exporting. (HANDLE) */ + CU_MEM_HANDLE_TYPE_WIN32_KMT = 0x4, /**< Allows a Win32 KMT handle to be used for exporting. (D3DKMT_HANDLE) */ + CU_MEM_HANDLE_TYPE_MAX = 0x7FFFFFFF +} CUmemAllocationHandleType; + +/** + * Specifies the memory protection flags for mapping. + */ +typedef enum CUmemAccess_flags_enum { + CU_MEM_ACCESS_FLAGS_PROT_NONE = 0x0, /**< Default, make the address range not accessible */ + CU_MEM_ACCESS_FLAGS_PROT_READ = 0x1, /**< Make the address range read accessible */ + CU_MEM_ACCESS_FLAGS_PROT_READWRITE = 0x3, /**< Make the address range read-write accessible */ + CU_MEM_ACCESS_FLAGS_PROT_MAX = 0x7FFFFFFF +} CUmemAccess_flags; + +/** + * Specifies the type of location + */ +typedef enum CUmemLocationType_enum { + CU_MEM_LOCATION_TYPE_INVALID = 0x0, + CU_MEM_LOCATION_TYPE_DEVICE = 0x1, /**< Location is a device location, thus id is a device ordinal */ + CU_MEM_LOCATION_TYPE_MAX = 0x7FFFFFFF +} CUmemLocationType; + +/** +* Defines the allocation types available +*/ +typedef enum CUmemAllocationType_enum { + CU_MEM_ALLOCATION_TYPE_INVALID = 0x0, + + /** This allocation type is 'pinned', i.e. cannot migrate from its current + * location while the application is actively using it + */ + CU_MEM_ALLOCATION_TYPE_PINNED = 0x1, + CU_MEM_ALLOCATION_TYPE_MAX = 0x7FFFFFFF +} CUmemAllocationType; + +/** +* Flag for requesting different optimal and required granularities for an allocation. +*/ +typedef enum CUmemAllocationGranularity_flags_enum { + CU_MEM_ALLOC_GRANULARITY_MINIMUM = 0x0, /**< Minimum required granularity for allocation */ + CU_MEM_ALLOC_GRANULARITY_RECOMMENDED = 0x1 /**< Recommended granularity for allocation for best performance */ +} CUmemAllocationGranularity_flags; + +/** +* Specifies the handle type for address range +*/ +typedef enum CUmemRangeHandleType_enum +{ + CU_MEM_RANGE_HANDLE_TYPE_DMA_BUF_FD = 0x1, + CU_MEM_RANGE_HANDLE_TYPE_MAX = 0x7FFFFFFF +} CUmemRangeHandleType; + +/** + * Sparse subresource types + */ +typedef enum CUarraySparseSubresourceType_enum { + CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SPARSE_LEVEL = 0, + CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL = 1 +} CUarraySparseSubresourceType; + +/** + * Memory operation types + */ +typedef enum CUmemOperationType_enum { + CU_MEM_OPERATION_TYPE_MAP = 1, + CU_MEM_OPERATION_TYPE_UNMAP = 2 +} CUmemOperationType; + +/** + * Memory handle types + */ +typedef enum CUmemHandleType_enum { + CU_MEM_HANDLE_TYPE_GENERIC = 0 +} CUmemHandleType; + +/** + * Specifies the CUDA array or CUDA mipmapped array memory mapping information + */ +typedef struct CUarrayMapInfo_st { + CUresourcetype resourceType; /**< Resource type */ + + union { + CUmipmappedArray mipmap; + CUarray array; + } resource; + + CUarraySparseSubresourceType subresourceType; /**< Sparse subresource type */ + + union { + struct { + unsigned int level; /**< For CUDA mipmapped arrays must a valid mipmap level. For CUDA arrays must be zero */ + unsigned int layer; /**< For CUDA layered arrays must be a valid layer index. Otherwise, must be zero */ + unsigned int offsetX; /**< Starting X offset in elements */ + unsigned int offsetY; /**< Starting Y offset in elements */ + unsigned int offsetZ; /**< Starting Z offset in elements */ + unsigned int extentWidth; /**< Width in elements */ + unsigned int extentHeight; /**< Height in elements */ + unsigned int extentDepth; /**< Depth in elements */ + } sparseLevel; + struct { + unsigned int layer; /**< For CUDA layered arrays must be a valid layer index. Otherwise, must be zero */ + unsigned long long offset; /**< Offset within mip tail */ + unsigned long long size; /**< Extent in bytes */ + } miptail; + } subresource; + + CUmemOperationType memOperationType; /**< Memory operation type */ + CUmemHandleType memHandleType; /**< Memory handle type */ + + union { + CUmemGenericAllocationHandle memHandle; + } memHandle; + + unsigned long long offset; /**< Offset within the memory */ + unsigned int deviceBitMask; /**< Device ordinal bit mask */ + unsigned int flags; /**< flags for future use, must be zero now. */ + unsigned int reserved[2]; /**< Reserved for future use, must be zero now. */ +} CUarrayMapInfo_v1; +typedef CUarrayMapInfo_v1 CUarrayMapInfo; + +/** + * Specifies a memory location. + */ +typedef struct CUmemLocation_st { + CUmemLocationType type; /**< Specifies the location type, which modifies the meaning of id. */ + int id; /**< identifier for a given this location's ::CUmemLocationType. */ +} CUmemLocation_v1; +typedef CUmemLocation_v1 CUmemLocation; + +/** + * Specifies compression attribute for an allocation. + */ +typedef enum CUmemAllocationCompType_enum { + CU_MEM_ALLOCATION_COMP_NONE = 0x0, /**< Allocating non-compressible memory */ + CU_MEM_ALLOCATION_COMP_GENERIC = 0x1 /**< Allocating compressible memory */ +} CUmemAllocationCompType; + +/** + * This flag if set indicates that the memory will be used as a tile pool. + */ +#define CU_MEM_CREATE_USAGE_TILE_POOL 0x1 + +/** +* Specifies the allocation properties for a allocation. +*/ +typedef struct CUmemAllocationProp_st { + /** Allocation type */ + CUmemAllocationType type; + /** requested ::CUmemAllocationHandleType */ + CUmemAllocationHandleType requestedHandleTypes; + /** Location of allocation */ + CUmemLocation location; + /** + * Windows-specific POBJECT_ATTRIBUTES required when + * ::CU_MEM_HANDLE_TYPE_WIN32 is specified. This object attributes structure + * includes security attributes that define + * the scope of which exported allocations may be transferred to other + * processes. In all other cases, this field is required to be zero. + */ + void *win32HandleMetaData; + struct { + /** + * Allocation hint for requesting compressible memory. + * On devices that support Compute Data Compression, compressible + * memory can be used to accelerate accesses to data with unstructured + * sparsity and other compressible data patterns. Applications are + * expected to query allocation property of the handle obtained with + * ::cuMemCreate using ::cuMemGetAllocationPropertiesFromHandle to + * validate if the obtained allocation is compressible or not. Note that + * compressed memory may not be mappable on all devices. + */ + unsigned char compressionType; + unsigned char gpuDirectRDMACapable; + /** Bitmask indicating intended usage for this allocation */ + unsigned short usage; + unsigned char reserved[4]; + } allocFlags; +} CUmemAllocationProp_v1; +typedef CUmemAllocationProp_v1 CUmemAllocationProp; + +/** +* Flags for querying different granularities for a multicast object +*/ +typedef enum CUmulticastGranularity_flags_enum { + CU_MULTICAST_GRANULARITY_MINIMUM = 0x0, /**< Minimum required granularity */ + CU_MULTICAST_GRANULARITY_RECOMMENDED = 0x1 /**< Recommended granularity for best performance */ +} CUmulticastGranularity_flags; + +/** +* Specifies the properties for a multicast object. +*/ +typedef struct CUmulticastObjectProp_st { + /** + * The number of devices in the multicast team that will bind memory to this + * object + */ + unsigned int numDevices; + /** + * The maximum amount of memory that can be bound to this multicast object + * per device + */ + size_t size; + /** + * Bitmask of exportable handle types (see ::CUmemAllocationHandleType) for + * this object + */ + unsigned long long handleTypes; + /** + * Flags for future use, must be zero now + */ + unsigned long long flags; +} CUmulticastObjectProp_v1; +typedef CUmulticastObjectProp_v1 CUmulticastObjectProp; + +/** + * Memory access descriptor + */ +typedef struct CUmemAccessDesc_st { + CUmemLocation location; /**< Location on which the request is to change it's accessibility */ + CUmemAccess_flags flags; /**< ::CUmemProt accessibility flags to set on the request */ +} CUmemAccessDesc_v1; +typedef CUmemAccessDesc_v1 CUmemAccessDesc; + +/** + * CUDA Graph Update error types + */ +typedef enum CUgraphExecUpdateResult_enum { + CU_GRAPH_EXEC_UPDATE_SUCCESS = 0x0, /**< The update succeeded */ + CU_GRAPH_EXEC_UPDATE_ERROR = 0x1, /**< The update failed for an unexpected reason which is described in the return value of the function */ + CU_GRAPH_EXEC_UPDATE_ERROR_TOPOLOGY_CHANGED = 0x2, /**< The update failed because the topology changed */ + CU_GRAPH_EXEC_UPDATE_ERROR_NODE_TYPE_CHANGED = 0x3, /**< The update failed because a node type changed */ + CU_GRAPH_EXEC_UPDATE_ERROR_FUNCTION_CHANGED = 0x4, /**< The update failed because the function of a kernel node changed (CUDA driver < 11.2) */ + CU_GRAPH_EXEC_UPDATE_ERROR_PARAMETERS_CHANGED = 0x5, /**< The update failed because the parameters changed in a way that is not supported */ + CU_GRAPH_EXEC_UPDATE_ERROR_NOT_SUPPORTED = 0x6, /**< The update failed because something about the node is not supported */ + CU_GRAPH_EXEC_UPDATE_ERROR_UNSUPPORTED_FUNCTION_CHANGE = 0x7, /**< The update failed because the function of a kernel node changed in an unsupported way */ + CU_GRAPH_EXEC_UPDATE_ERROR_ATTRIBUTES_CHANGED = 0x8 /**< The update failed because the node attributes changed in a way that is not supported */ +} CUgraphExecUpdateResult; + +/** + * Result information returned by cuGraphExecUpdate + */ +typedef struct CUgraphExecUpdateResultInfo_st { + /** + * Gives more specific detail when a cuda graph update fails. + */ + CUgraphExecUpdateResult result; + + /** + * The "to node" of the error edge when the topologies do not match. + * The error node when the error is associated with a specific node. + * NULL when the error is generic. + */ + CUgraphNode errorNode; + + /** + * The from node of error edge when the topologies do not match. Otherwise NULL. + */ + CUgraphNode errorFromNode; +} CUgraphExecUpdateResultInfo_v1; +typedef CUgraphExecUpdateResultInfo_v1 CUgraphExecUpdateResultInfo; + +/** + * CUDA memory pool attributes + */ +typedef enum CUmemPool_attribute_enum { + /** + * (value type = int) + * Allow cuMemAllocAsync to use memory asynchronously freed + * in another streams as long as a stream ordering dependency + * of the allocating stream on the free action exists. + * Cuda events and null stream interactions can create the required + * stream ordered dependencies. (default enabled) + */ + CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES = 1, + + /** + * (value type = int) + * Allow reuse of already completed frees when there is no dependency + * between the free and allocation. (default enabled) + */ + CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC, + + /** + * (value type = int) + * Allow cuMemAllocAsync to insert new stream dependencies + * in order to establish the stream ordering required to reuse + * a piece of memory released by cuFreeAsync (default enabled). + */ + CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES, + + /** + * (value type = cuuint64_t) + * Amount of reserved memory in bytes to hold onto before trying + * to release memory back to the OS. When more than the release + * threshold bytes of memory are held by the memory pool, the + * allocator will try to release memory back to the OS on the + * next call to stream, event or context synchronize. (default 0) + */ + CU_MEMPOOL_ATTR_RELEASE_THRESHOLD, + + /** + * (value type = cuuint64_t) + * Amount of backing memory currently allocated for the mempool. + */ + CU_MEMPOOL_ATTR_RESERVED_MEM_CURRENT, + + /** + * (value type = cuuint64_t) + * High watermark of backing memory allocated for the mempool since the + * last time it was reset. High watermark can only be reset to zero. + */ + CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH, + + /** + * (value type = cuuint64_t) + * Amount of memory from the pool that is currently in use by the application. + */ + CU_MEMPOOL_ATTR_USED_MEM_CURRENT, + + /** + * (value type = cuuint64_t) + * High watermark of the amount of memory from the pool that was in use by the application since + * the last time it was reset. High watermark can only be reset to zero. + */ + CU_MEMPOOL_ATTR_USED_MEM_HIGH +} CUmemPool_attribute; + +/** + * Specifies the properties of allocations made from the pool. + */ +typedef struct CUmemPoolProps_st { + CUmemAllocationType allocType; /**< Allocation type. Currently must be specified as CU_MEM_ALLOCATION_TYPE_PINNED */ + CUmemAllocationHandleType handleTypes; /**< Handle types that will be supported by allocations from the pool. */ + CUmemLocation location; /**< Location where allocations should reside. */ + /** + * Windows-specific LPSECURITYATTRIBUTES required when + * ::CU_MEM_HANDLE_TYPE_WIN32 is specified. This security attribute defines + * the scope of which exported allocations may be transferred to other + * processes. In all other cases, this field is required to be zero. + */ + void *win32SecurityAttributes; + unsigned char reserved[64]; /**< reserved for future use, must be 0 */ +} CUmemPoolProps_v1; +typedef CUmemPoolProps_v1 CUmemPoolProps; + +/** + * Opaque data for exporting a pool allocation + */ +typedef struct CUmemPoolPtrExportData_st { + unsigned char reserved[64]; +} CUmemPoolPtrExportData_v1; +typedef CUmemPoolPtrExportData_v1 CUmemPoolPtrExportData; + +/** + * Memory allocation node parameters + */ +typedef struct CUDA_MEM_ALLOC_NODE_PARAMS_st { + /** + * in: location where the allocation should reside (specified in ::location). + * ::handleTypes must be ::CU_MEM_HANDLE_TYPE_NONE. IPC is not supported. + */ + CUmemPoolProps poolProps; + const CUmemAccessDesc *accessDescs; /**< in: array of memory access descriptors. Used to describe peer GPU access */ + size_t accessDescCount; /**< in: number of memory access descriptors. Must not exceed the number of GPUs. */ + size_t bytesize; /**< in: size in bytes of the requested allocation */ + CUdeviceptr dptr; /**< out: address of the allocation returned by CUDA */ +} CUDA_MEM_ALLOC_NODE_PARAMS; + +typedef enum CUgraphMem_attribute_enum { + /** + * (value type = cuuint64_t) + * Amount of memory, in bytes, currently associated with graphs + */ + CU_GRAPH_MEM_ATTR_USED_MEM_CURRENT, + + /** + * (value type = cuuint64_t) + * High watermark of memory, in bytes, associated with graphs since the + * last time it was reset. High watermark can only be reset to zero. + */ + CU_GRAPH_MEM_ATTR_USED_MEM_HIGH, + + /** + * (value type = cuuint64_t) + * Amount of memory, in bytes, currently allocated for use by + * the CUDA graphs asynchronous allocator. + */ + CU_GRAPH_MEM_ATTR_RESERVED_MEM_CURRENT, + + /** + * (value type = cuuint64_t) + * High watermark of memory, in bytes, currently allocated for use by + * the CUDA graphs asynchronous allocator. + */ + CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH +} CUgraphMem_attribute; + +/** + * If set, each kernel launched as part of ::cuLaunchCooperativeKernelMultiDevice only + * waits for prior work in the stream corresponding to that GPU to complete before the + * kernel begins execution. + */ +#define CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_PRE_LAUNCH_SYNC 0x01 + +/** + * If set, any subsequent work pushed in a stream that participated in a call to + * ::cuLaunchCooperativeKernelMultiDevice will only wait for the kernel launched on + * the GPU corresponding to that stream to complete before it begins execution. + */ +#define CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_POST_LAUNCH_SYNC 0x02 + +/** + * If set, the CUDA array is a collection of layers, where each layer is either a 1D + * or a 2D array and the Depth member of CUDA_ARRAY3D_DESCRIPTOR specifies the number + * of layers, not the depth of a 3D array. + */ +#define CUDA_ARRAY3D_LAYERED 0x01 + +/** + * Deprecated, use CUDA_ARRAY3D_LAYERED + */ +#define CUDA_ARRAY3D_2DARRAY 0x01 + +/** + * This flag must be set in order to bind a surface reference + * to the CUDA array + */ +#define CUDA_ARRAY3D_SURFACE_LDST 0x02 + +/** + * If set, the CUDA array is a collection of six 2D arrays, representing faces of a cube. The + * width of such a CUDA array must be equal to its height, and Depth must be six. + * If ::CUDA_ARRAY3D_LAYERED flag is also set, then the CUDA array is a collection of cubemaps + * and Depth must be a multiple of six. + */ +#define CUDA_ARRAY3D_CUBEMAP 0x04 + +/** + * This flag must be set in order to perform texture gather operations + * on a CUDA array. + */ +#define CUDA_ARRAY3D_TEXTURE_GATHER 0x08 + +/** + * This flag if set indicates that the CUDA + * array is a DEPTH_TEXTURE. + */ +#define CUDA_ARRAY3D_DEPTH_TEXTURE 0x10 + +/** + * This flag indicates that the CUDA array may be bound as a color target + * in an external graphics API + */ +#define CUDA_ARRAY3D_COLOR_ATTACHMENT 0x20 + +/** + * This flag if set indicates that the CUDA array or CUDA mipmapped array + * is a sparse CUDA array or CUDA mipmapped array respectively + */ +#define CUDA_ARRAY3D_SPARSE 0x40 + +/** + * This flag if set indicates that the CUDA array or CUDA mipmapped array + * will allow deferred memory mapping + */ +#define CUDA_ARRAY3D_DEFERRED_MAPPING 0x80 + +/** + * Override the texref format with a format inferred from the array. + * Flag for ::cuTexRefSetArray() + */ +#define CU_TRSA_OVERRIDE_FORMAT 0x01 + +/** + * Read the texture as integers rather than promoting the values to floats + * in the range [0,1]. + * Flag for ::cuTexRefSetFlags() and ::cuTexObjectCreate() + */ +#define CU_TRSF_READ_AS_INTEGER 0x01 + +/** + * Use normalized texture coordinates in the range [0,1) instead of [0,dim). + * Flag for ::cuTexRefSetFlags() and ::cuTexObjectCreate() + */ +#define CU_TRSF_NORMALIZED_COORDINATES 0x02 + +/** + * Perform sRGB->linear conversion during texture read. + * Flag for ::cuTexRefSetFlags() and ::cuTexObjectCreate() + */ +#define CU_TRSF_SRGB 0x10 + + /** + * Disable any trilinear filtering optimizations. + * Flag for ::cuTexRefSetFlags() and ::cuTexObjectCreate() + */ +#define CU_TRSF_DISABLE_TRILINEAR_OPTIMIZATION 0x20 + +/** + * Enable seamless cube map filtering. + * Flag for ::cuTexObjectCreate() + */ +#define CU_TRSF_SEAMLESS_CUBEMAP 0x40 + +/** + * C++ compile time constant for CU_LAUNCH_PARAM_END + */ +#define CU_LAUNCH_PARAM_END_AS_INT 0x00 + +/** + * End of array terminator for the \p extra parameter to + * ::cuLaunchKernel + */ +#define CU_LAUNCH_PARAM_END ((void*)CU_LAUNCH_PARAM_END_AS_INT) + +/** + * C++ compile time constant for CU_LAUNCH_PARAM_BUFFER_POINTER + */ +#define CU_LAUNCH_PARAM_BUFFER_POINTER_AS_INT 0x01 + +/** + * Indicator that the next value in the \p extra parameter to + * ::cuLaunchKernel will be a pointer to a buffer containing all kernel + * parameters used for launching kernel \p f. This buffer needs to + * honor all alignment/padding requirements of the individual parameters. + * If ::CU_LAUNCH_PARAM_BUFFER_SIZE is not also specified in the + * \p extra array, then ::CU_LAUNCH_PARAM_BUFFER_POINTER will have no + * effect. + */ +#define CU_LAUNCH_PARAM_BUFFER_POINTER ((void*)CU_LAUNCH_PARAM_BUFFER_POINTER_AS_INT) + +/** + * C++ compile time constant for CU_LAUNCH_PARAM_BUFFER_SIZE + */ +#define CU_LAUNCH_PARAM_BUFFER_SIZE_AS_INT 0x02 + +/** + * Indicator that the next value in the \p extra parameter to + * ::cuLaunchKernel will be a pointer to a size_t which contains the + * size of the buffer specified with ::CU_LAUNCH_PARAM_BUFFER_POINTER. + * It is required that ::CU_LAUNCH_PARAM_BUFFER_POINTER also be specified + * in the \p extra array if the value associated with + * ::CU_LAUNCH_PARAM_BUFFER_SIZE is not zero. + */ +#define CU_LAUNCH_PARAM_BUFFER_SIZE ((void*)CU_LAUNCH_PARAM_BUFFER_SIZE_AS_INT) + +/** + * For texture references loaded into the module, use default texunit from + * texture reference. + */ +#define CU_PARAM_TR_DEFAULT -1 + +/** + * Device that represents the CPU + */ +#define CU_DEVICE_CPU ((CUdevice)-1) + +/** + * Device that represents an invalid device + */ +#define CU_DEVICE_INVALID ((CUdevice)-2) + +/** + * Bitmasks for ::CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_FLUSH_WRITES_OPTIONS + */ +typedef enum CUflushGPUDirectRDMAWritesOptions_enum { + CU_FLUSH_GPU_DIRECT_RDMA_WRITES_OPTION_HOST = 1<<0, /**< ::cuFlushGPUDirectRDMAWrites() and its CUDA Runtime API counterpart are supported on the device. */ + CU_FLUSH_GPU_DIRECT_RDMA_WRITES_OPTION_MEMOPS = 1<<1 /**< The ::CU_STREAM_WAIT_VALUE_FLUSH flag and the ::CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device. */ +} CUflushGPUDirectRDMAWritesOptions; + +/** + * Platform native ordering for GPUDirect RDMA writes + */ +typedef enum CUGPUDirectRDMAWritesOrdering_enum { + CU_GPU_DIRECT_RDMA_WRITES_ORDERING_NONE = 0, /**< The device does not natively support ordering of remote writes. ::cuFlushGPUDirectRDMAWrites() can be leveraged if supported. */ + CU_GPU_DIRECT_RDMA_WRITES_ORDERING_OWNER = 100, /**< Natively, the device can consistently consume remote writes, although other CUDA devices may not. */ + CU_GPU_DIRECT_RDMA_WRITES_ORDERING_ALL_DEVICES = 200 /**< Any CUDA device in the system can consistently consume remote writes to this device. */ +} CUGPUDirectRDMAWritesOrdering; + +/** + * The scopes for ::cuFlushGPUDirectRDMAWrites + */ +typedef enum CUflushGPUDirectRDMAWritesScope_enum { + CU_FLUSH_GPU_DIRECT_RDMA_WRITES_TO_OWNER = 100, /**< Blocks until remote writes are visible to the CUDA device context owning the data. */ + CU_FLUSH_GPU_DIRECT_RDMA_WRITES_TO_ALL_DEVICES = 200 /**< Blocks until remote writes are visible to all CUDA device contexts. */ +} CUflushGPUDirectRDMAWritesScope; + +/** + * The targets for ::cuFlushGPUDirectRDMAWrites + */ +typedef enum CUflushGPUDirectRDMAWritesTarget_enum { + CU_FLUSH_GPU_DIRECT_RDMA_WRITES_TARGET_CURRENT_CTX = 0 /**< Sets the target for ::cuFlushGPUDirectRDMAWrites() to the currently active CUDA device context. */ +} CUflushGPUDirectRDMAWritesTarget; + +/** + * The additional write options for ::cuGraphDebugDotPrint + */ +typedef enum CUgraphDebugDot_flags_enum { + CU_GRAPH_DEBUG_DOT_FLAGS_VERBOSE = 1<<0, /**< Output all debug data as if every debug flag is enabled */ + CU_GRAPH_DEBUG_DOT_FLAGS_RUNTIME_TYPES = 1<<1, /**< Use CUDA Runtime structures for output */ + CU_GRAPH_DEBUG_DOT_FLAGS_KERNEL_NODE_PARAMS = 1<<2, /**< Adds CUDA_KERNEL_NODE_PARAMS values to output */ + CU_GRAPH_DEBUG_DOT_FLAGS_MEMCPY_NODE_PARAMS = 1<<3, /**< Adds CUDA_MEMCPY3D values to output */ + CU_GRAPH_DEBUG_DOT_FLAGS_MEMSET_NODE_PARAMS = 1<<4, /**< Adds CUDA_MEMSET_NODE_PARAMS values to output */ + CU_GRAPH_DEBUG_DOT_FLAGS_HOST_NODE_PARAMS = 1<<5, /**< Adds CUDA_HOST_NODE_PARAMS values to output */ + CU_GRAPH_DEBUG_DOT_FLAGS_EVENT_NODE_PARAMS = 1<<6, /**< Adds CUevent handle from record and wait nodes to output */ + CU_GRAPH_DEBUG_DOT_FLAGS_EXT_SEMAS_SIGNAL_NODE_PARAMS = 1<<7, /**< Adds CUDA_EXT_SEM_SIGNAL_NODE_PARAMS values to output */ + CU_GRAPH_DEBUG_DOT_FLAGS_EXT_SEMAS_WAIT_NODE_PARAMS = 1<<8, /**< Adds CUDA_EXT_SEM_WAIT_NODE_PARAMS values to output */ + CU_GRAPH_DEBUG_DOT_FLAGS_KERNEL_NODE_ATTRIBUTES = 1<<9, /**< Adds CUkernelNodeAttrValue values to output */ + CU_GRAPH_DEBUG_DOT_FLAGS_HANDLES = 1<<10, /**< Adds node handles and every kernel function handle to output */ + CU_GRAPH_DEBUG_DOT_FLAGS_MEM_ALLOC_NODE_PARAMS = 1<<11, /**< Adds memory alloc node parameters to output */ + CU_GRAPH_DEBUG_DOT_FLAGS_MEM_FREE_NODE_PARAMS = 1<<12, /**< Adds memory free node parameters to output */ + CU_GRAPH_DEBUG_DOT_FLAGS_BATCH_MEM_OP_NODE_PARAMS = 1<<13 /**< Adds batch mem op node parameters to output */ + , CU_GRAPH_DEBUG_DOT_FLAGS_EXTRA_TOPO_INFO = 1<<14 /**< Adds edge numbering information */ +} CUgraphDebugDot_flags; + +/** + * Flags for user objects for graphs + */ +typedef enum CUuserObject_flags_enum { + CU_USER_OBJECT_NO_DESTRUCTOR_SYNC = 1 /**< Indicates the destructor execution is not synchronized by any CUDA handle. */ +} CUuserObject_flags; + +/** + * Flags for retaining user object references for graphs + */ +typedef enum CUuserObjectRetain_flags_enum { + CU_GRAPH_USER_OBJECT_MOVE = 1 /**< Transfer references from the caller rather than creating new references. */ +} CUuserObjectRetain_flags; + +/** + * Flags for instantiating a graph + */ +typedef enum CUgraphInstantiate_flags_enum { + CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH = 1 /**< Automatically free memory allocated in a graph before relaunching. */ + , CUDA_GRAPH_INSTANTIATE_FLAG_UPLOAD = 2 /**< Automatically upload the graph after instantiaton. */ + , CUDA_GRAPH_INSTANTIATE_FLAG_DEVICE_LAUNCH = 4 /**< Instantiate the graph to be launchable from the device. */ + , CUDA_GRAPH_INSTANTIATE_FLAG_USE_NODE_PRIORITY = 8 /**< Run the graph using the per-node priority attributes rather than the + priority of the stream it is launched into. */ +} CUgraphInstantiate_flags; + +typedef enum cuDeviceIsNumaNode_enum { + CU_DEVICE_NOT_NUMA_NODE = 0x01, + CU_DEVICE_IS_NUMA_NODE = 0x02, +} cuDeviceIsNumaNode; + +/** @} */ /* END CUDA_TYPES */ + +#if defined(__GNUC__) + #if defined(__CUDA_API_PUSH_VISIBILITY_DEFAULT) + #pragma GCC visibility push(default) + #endif +#endif + +#ifdef _WIN32 +#define CUDAAPI __stdcall +#else +#define CUDAAPI +#endif + +/** + * \defgroup CUDA_ERROR Error Handling + * + * ___MANBRIEF___ error handling functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the error handling functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** + * \brief Gets the string description of an error code + * + * Sets \p *pStr to the address of a NULL-terminated string description + * of the error code \p error. + * If the error code is not recognized, ::CUDA_ERROR_INVALID_VALUE + * will be returned and \p *pStr will be set to the NULL address. + * + * \param error - Error code to convert to string + * \param pStr - Address of the string pointer. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::CUresult, + * ::cudaGetErrorString + */ +CUresult CUDAAPI cuGetErrorString(CUresult error, const char **pStr); + +/** + * \brief Gets the string representation of an error code enum name + * + * Sets \p *pStr to the address of a NULL-terminated string representation + * of the name of the enum error code \p error. + * If the error code is not recognized, ::CUDA_ERROR_INVALID_VALUE + * will be returned and \p *pStr will be set to the NULL address. + * + * \param error - Error code to convert to string + * \param pStr - Address of the string pointer. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::CUresult, + * ::cudaGetErrorName + */ +CUresult CUDAAPI cuGetErrorName(CUresult error, const char **pStr); + +/** @} */ /* END CUDA_ERROR */ + +/** + * \defgroup CUDA_INITIALIZE Initialization + * + * ___MANBRIEF___ initialization functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the initialization functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** + * \brief Initialize the CUDA driver API + * Initializes the driver API and must be called before any other function from + * the driver API in the current process. Currently, the \p Flags parameter must be 0. If ::cuInit() + * has not been called, any function from the driver API will return + * ::CUDA_ERROR_NOT_INITIALIZED. + * + * \param Flags - Initialization flag for CUDA. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_SYSTEM_DRIVER_MISMATCH, + * ::CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE + * \notefnerr + */ +CUresult CUDAAPI cuInit(unsigned int Flags); + +/** @} */ /* END CUDA_INITIALIZE */ + +/** + * \defgroup CUDA_VERSION Version Management + * + * ___MANBRIEF___ version management functions of the low-level CUDA driver + * API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the version management functions of the low-level + * CUDA driver application programming interface. + * + * @{ + */ + +/** + * \brief Returns the latest CUDA version supported by driver + * + * Returns in \p *driverVersion the version of CUDA supported by + * the driver. The version is returned as + * (1000 × major + 10 × minor). For example, CUDA 9.2 + * would be represented by 9020. + * + * This function automatically returns ::CUDA_ERROR_INVALID_VALUE if + * \p driverVersion is NULL. + * + * \param driverVersion - Returns the CUDA driver version + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa + * ::cudaDriverGetVersion, + * ::cudaRuntimeGetVersion + */ +CUresult CUDAAPI cuDriverGetVersion(int *driverVersion); + +/** @} */ /* END CUDA_VERSION */ + +/** + * \defgroup CUDA_DEVICE Device Management + * + * ___MANBRIEF___ device management functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the device management functions of the low-level + * CUDA driver application programming interface. + * + * @{ + */ + +/** + * \brief Returns a handle to a compute device + * + * Returns in \p *device a device handle given an ordinal in the range [0, + * ::cuDeviceGetCount()-1]. + * + * \param device - Returned device handle + * \param ordinal - Device number to get handle for + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGetAttribute, + * ::cuDeviceGetCount, + * ::cuDeviceGetName, + * ::cuDeviceGetUuid, + * ::cuDeviceGetLuid, + * ::cuDeviceTotalMem, + * ::cuDeviceGetExecAffinitySupport + */ +CUresult CUDAAPI cuDeviceGet(CUdevice *device, int ordinal); + +/** + * \brief Returns the number of compute-capable devices + * + * Returns in \p *count the number of devices with compute capability greater + * than or equal to 2.0 that are available for execution. If there is no such + * device, ::cuDeviceGetCount() returns 0. + * + * \param count - Returned number of compute-capable devices + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa + * ::cuDeviceGetAttribute, + * ::cuDeviceGetName, + * ::cuDeviceGetUuid, + * ::cuDeviceGetLuid, + * ::cuDeviceGet, + * ::cuDeviceTotalMem, + * ::cuDeviceGetExecAffinitySupport, + * ::cudaGetDeviceCount + */ +CUresult CUDAAPI cuDeviceGetCount(int *count); + +/** + * \brief Returns an identifier string for the device + * + * Returns an ASCII string identifying the device \p dev in the NULL-terminated + * string pointed to by \p name. \p len specifies the maximum length of the + * string that may be returned. + * + * \param name - Returned identifier string for the device + * \param len - Maximum length of string to store in \p name + * \param dev - Device to get identifier string for + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGetAttribute, + * ::cuDeviceGetUuid, + * ::cuDeviceGetLuid, + * ::cuDeviceGetCount, + * ::cuDeviceGet, + * ::cuDeviceTotalMem, + * ::cuDeviceGetExecAffinitySupport, + * ::cudaGetDeviceProperties + */ +CUresult CUDAAPI cuDeviceGetName(char *name, int len, CUdevice dev); + +/** + * \brief Return an UUID for the device + * + * Note there is a later version of this API, ::cuDeviceGetUuid_v2. It will + * supplant this version in 12.0, which is retained for minor version compatibility. + * + * Returns 16-octets identifying the device \p dev in the structure + * pointed by the \p uuid. + * + * \param uuid - Returned UUID + * \param dev - Device to get identifier string for + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGetUuid_v2 + * ::cuDeviceGetAttribute, + * ::cuDeviceGetCount, + * ::cuDeviceGetName, + * ::cuDeviceGetLuid, + * ::cuDeviceGet, + * ::cuDeviceTotalMem, + * ::cuDeviceGetExecAffinitySupport, + * ::cudaGetDeviceProperties + */ +CUresult CUDAAPI cuDeviceGetUuid(CUuuid *uuid, CUdevice dev); + +/** + * \brief Return an UUID for the device (11.4+) + * + * Returns 16-octets identifying the device \p dev in the structure + * pointed by the \p uuid. If the device is in MIG mode, returns its + * MIG UUID which uniquely identifies the subscribed MIG compute instance. + * + * \param uuid - Returned UUID + * \param dev - Device to get identifier string for + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGetAttribute, + * ::cuDeviceGetCount, + * ::cuDeviceGetName, + * ::cuDeviceGetLuid, + * ::cuDeviceGet, + * ::cuDeviceTotalMem, + * ::cudaGetDeviceProperties + */ +CUresult CUDAAPI cuDeviceGetUuid_v2(CUuuid *uuid, CUdevice dev); + +/** + * \brief Return an LUID and device node mask for the device + * + * Return identifying information (\p luid and \p deviceNodeMask) to allow + * matching device with graphics APIs. + * + * \param luid - Returned LUID + * \param deviceNodeMask - Returned device node mask + * \param dev - Device to get identifier string for + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGetAttribute, + * ::cuDeviceGetCount, + * ::cuDeviceGetName, + * ::cuDeviceGet, + * ::cuDeviceTotalMem, + * ::cuDeviceGetExecAffinitySupport, + * ::cudaGetDeviceProperties + */ +CUresult CUDAAPI cuDeviceGetLuid(char *luid, unsigned int *deviceNodeMask, CUdevice dev); + +/** + * \brief Returns the total amount of memory on the device + * + * Returns in \p *bytes the total amount of memory available on the device + * \p dev in bytes. + * + * \param bytes - Returned memory available on device in bytes + * \param dev - Device handle + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGetAttribute, + * ::cuDeviceGetCount, + * ::cuDeviceGetName, + * ::cuDeviceGetUuid, + * ::cuDeviceGet, + * ::cuDeviceGetExecAffinitySupport, + * ::cudaMemGetInfo + */ +CUresult CUDAAPI cuDeviceTotalMem(size_t *bytes, CUdevice dev); + +/** + * \brief Returns the maximum number of elements allocatable in a 1D linear texture for a given texture element size. + * + * Returns in \p maxWidthInElements the maximum number of texture elements allocatable in a 1D linear texture + * for given \p format and \p numChannels. + * + * \param maxWidthInElements - Returned maximum number of texture elements allocatable for given \p format and \p numChannels. + * \param format - Texture format. + * \param numChannels - Number of channels per texture element. + * \param dev - Device handle. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGetAttribute, + * ::cuDeviceGetCount, + * ::cuDeviceGetName, + * ::cuDeviceGetUuid, + * ::cuDeviceGet, + * ::cudaMemGetInfo, + * ::cuDeviceTotalMem + */ +CUresult CUDAAPI cuDeviceGetTexture1DLinearMaxWidth(size_t *maxWidthInElements, CUarray_format format, unsigned numChannels, CUdevice dev); + +/** + * \brief Returns information about the device + * + * Returns in \p *pi the integer value of the attribute \p attrib on device + * \p dev. The supported attributes are: + * - ::CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK: Maximum number of threads per + * block; + * - ::CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X: Maximum x-dimension of a block + * - ::CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y: Maximum y-dimension of a block + * - ::CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z: Maximum z-dimension of a block + * - ::CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X: Maximum x-dimension of a grid + * - ::CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y: Maximum y-dimension of a grid + * - ::CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z: Maximum z-dimension of a grid + * - ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK: Maximum amount of + * shared memory available to a thread block in bytes + * - ::CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY: Memory available on device for + * __constant__ variables in a CUDA C kernel in bytes + * - ::CU_DEVICE_ATTRIBUTE_WARP_SIZE: Warp size in threads + * - ::CU_DEVICE_ATTRIBUTE_MAX_PITCH: Maximum pitch in bytes allowed by the + * memory copy functions that involve memory regions allocated through + * ::cuMemAllocPitch() + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH: Maximum 1D + * texture width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH: Maximum width + * for a 1D texture bound to linear memory + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH: Maximum + * mipmapped 1D texture width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH: Maximum 2D + * texture width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT: Maximum 2D + * texture height + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH: Maximum width + * for a 2D texture bound to linear memory + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT: Maximum height + * for a 2D texture bound to linear memory + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH: Maximum pitch + * in bytes for a 2D texture bound to linear memory + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH: Maximum + * mipmapped 2D texture width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT: Maximum + * mipmapped 2D texture height + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH: Maximum 3D + * texture width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT: Maximum 3D + * texture height + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH: Maximum 3D + * texture depth + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE: + * Alternate maximum 3D texture width, 0 if no alternate + * maximum 3D texture size is supported + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE: + * Alternate maximum 3D texture height, 0 if no alternate + * maximum 3D texture size is supported + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE: + * Alternate maximum 3D texture depth, 0 if no alternate + * maximum 3D texture size is supported + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH: + * Maximum cubemap texture width or height + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH: + * Maximum 1D layered texture width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS: + * Maximum layers in a 1D layered texture + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH: + * Maximum 2D layered texture width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT: + * Maximum 2D layered texture height + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS: + * Maximum layers in a 2D layered texture + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH: + * Maximum cubemap layered texture width or height + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS: + * Maximum layers in a cubemap layered texture + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH: + * Maximum 1D surface width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH: + * Maximum 2D surface width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT: + * Maximum 2D surface height + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH: + * Maximum 3D surface width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT: + * Maximum 3D surface height + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH: + * Maximum 3D surface depth + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH: + * Maximum 1D layered surface width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS: + * Maximum layers in a 1D layered surface + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH: + * Maximum 2D layered surface width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT: + * Maximum 2D layered surface height + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS: + * Maximum layers in a 2D layered surface + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH: + * Maximum cubemap surface width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH: + * Maximum cubemap layered surface width + * - ::CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS: + * Maximum layers in a cubemap layered surface + * - ::CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK: Maximum number of 32-bit + * registers available to a thread block + * - ::CU_DEVICE_ATTRIBUTE_CLOCK_RATE: The typical clock frequency in kilohertz + * - ::CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT: Alignment requirement; texture + * base addresses aligned to ::textureAlign bytes do not need an offset + * applied to texture fetches + * - ::CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT: Pitch alignment requirement + * for 2D texture references bound to pitched memory + * - ::CU_DEVICE_ATTRIBUTE_GPU_OVERLAP: 1 if the device can concurrently copy + * memory between host and device while executing a kernel, or 0 if not + * - ::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT: Number of multiprocessors on + * the device + * - ::CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT: 1 if there is a run time limit + * for kernels executed on the device, or 0 if not + * - ::CU_DEVICE_ATTRIBUTE_INTEGRATED: 1 if the device is integrated with the + * memory subsystem, or 0 if not + * - ::CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY: 1 if the device can map host + * memory into the CUDA address space, or 0 if not + * - ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE: Compute mode that device is currently + * in. Available modes are as follows: + * - ::CU_COMPUTEMODE_DEFAULT: Default mode - Device is not restricted and + * can have multiple CUDA contexts present at a single time. + * - ::CU_COMPUTEMODE_PROHIBITED: Compute-prohibited mode - Device is + * prohibited from creating new CUDA contexts. + * - ::CU_COMPUTEMODE_EXCLUSIVE_PROCESS: Compute-exclusive-process mode - Device + * can have only one context used by a single process at a time. + * - ::CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS: 1 if the device supports + * executing multiple kernels within the same context simultaneously, or 0 if + * not. It is not guaranteed that multiple kernels will be resident + * on the device concurrently so this feature should not be relied upon for + * correctness. + * - ::CU_DEVICE_ATTRIBUTE_ECC_ENABLED: 1 if error correction is enabled on the + * device, 0 if error correction is disabled or not supported by the device + * - ::CU_DEVICE_ATTRIBUTE_PCI_BUS_ID: PCI bus identifier of the device + * - ::CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID: PCI device (also known as slot) identifier + * of the device + * - ::CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID: PCI domain identifier of the device + * - ::CU_DEVICE_ATTRIBUTE_TCC_DRIVER: 1 if the device is using a TCC driver. TCC + * is only available on Tesla hardware running Windows Vista or later + * - ::CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE: Peak memory clock frequency in kilohertz + * - ::CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH: Global memory bus width in bits + * - ::CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE: Size of L2 cache in bytes. 0 if the device doesn't have L2 cache + * - ::CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR: Maximum resident threads per multiprocessor + * - ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING: 1 if the device shares a unified address space with + * the host, or 0 if not + * - ::CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR: Major compute capability version number + * - ::CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR: Minor compute capability version number + * - ::CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED: 1 if device supports caching globals + * in L1 cache, 0 if caching globals in L1 cache is not supported by the device + * - ::CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED: 1 if device supports caching locals + * in L1 cache, 0 if caching locals in L1 cache is not supported by the device + * - ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR: Maximum amount of + * shared memory available to a multiprocessor in bytes; this amount is shared + * by all thread blocks simultaneously resident on a multiprocessor + * - ::CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR: Maximum number of 32-bit + * registers available to a multiprocessor; this number is shared by all thread + * blocks simultaneously resident on a multiprocessor + * - ::CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY: 1 if device supports allocating managed memory + * on this system, 0 if allocating managed memory is not supported by the device on this system. + * - ::CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD: 1 if device is on a multi-GPU board, 0 if not. + * - ::CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID: Unique identifier for a group of devices + * associated with the same board. Devices on the same multi-GPU board will share the same identifier. + * - ::CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED: 1 if Link between the device and the host + * supports native atomic operations. + * - ::CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO: Ratio of single precision performance + * (in floating-point operations per second) to double precision performance. + * - ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS: Device supports coherently accessing + * pageable memory without calling cudaHostRegister on it. + * - ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS: Device can coherently access managed memory + * concurrently with the CPU. + * - ::CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED: Device supports Compute Preemption. + * - ::CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM: Device can access host registered + * memory at the same virtual address as the CPU. + * - ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN: The maximum per block shared memory size + * supported on this device. This is the maximum value that can be opted into when using the cuFuncSetAttribute() or cuKernelSetAttribute() call. + * For more details see ::CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES + * - ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES: Device accesses pageable memory via the host's + * page tables. + * - ::CU_DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST: The host can directly access managed memory on the device without migration. + * - ::CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED: Device supports virtual memory management APIs like ::cuMemAddressReserve, ::cuMemCreate, ::cuMemMap and related APIs + * - ::CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR_SUPPORTED: Device supports exporting memory to a posix file descriptor with ::cuMemExportToShareableHandle, if requested via ::cuMemCreate + * - ::CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_HANDLE_SUPPORTED: Device supports exporting memory to a Win32 NT handle with ::cuMemExportToShareableHandle, if requested via ::cuMemCreate + * - ::CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_KMT_HANDLE_SUPPORTED: Device supports exporting memory to a Win32 KMT handle with ::cuMemExportToShareableHandle, if requested via ::cuMemCreate + * - ::CU_DEVICE_ATTRIBUTE_MAX_BLOCKS_PER_MULTIPROCESSOR: Maximum number of thread blocks that can reside on a multiprocessor + * - ::CU_DEVICE_ATTRIBUTE_GENERIC_COMPRESSION_SUPPORTED: Device supports compressible memory allocation via ::cuMemCreate + * - ::CU_DEVICE_ATTRIBUTE_MAX_PERSISTING_L2_CACHE_SIZE: Maximum L2 persisting lines capacity setting in bytes + * - ::CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE: Maximum value of CUaccessPolicyWindow::num_bytes + * - ::CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WITH_CUDA_VMM_SUPPORTED: Device supports specifying the GPUDirect RDMA flag with ::cuMemCreate. + * - ::CU_DEVICE_ATTRIBUTE_RESERVED_SHARED_MEMORY_PER_BLOCK: Amount of shared memory per block reserved by CUDA driver in bytes + * - ::CU_DEVICE_ATTRIBUTE_SPARSE_CUDA_ARRAY_SUPPORTED: Device supports sparse CUDA arrays and sparse CUDA mipmapped arrays. + * - ::CU_DEVICE_ATTRIBUTE_READ_ONLY_HOST_REGISTER_SUPPORTED: Device supports using the ::cuMemHostRegister flag ::CU_MEMHOSTERGISTER_READ_ONLY to register memory that must be mapped as read-only to the GPU + * - ::CU_DEVICE_ATTRIBUTE_MEMORY_POOLS_SUPPORTED: Device supports using the ::cuMemAllocAsync and ::cuMemPool family of APIs + * - ::CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_SUPPORTED: Device supports GPUDirect RDMA APIs, like nvidia_p2p_get_pages (see https://docs.nvidia.com/cuda/gpudirect-rdma for more information) + * - ::CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_FLUSH_WRITES_OPTIONS: The returned attribute shall be interpreted as a bitmask, where the individual bits are described by the ::CUflushGPUDirectRDMAWritesOptions enum + * - ::CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WRITES_ORDERING: GPUDirect RDMA writes to the device do not need to be flushed for consumers within the scope indicated by the returned attribute. See ::CUGPUDirectRDMAWritesOrdering for the numerical values returned here. + * - ::CU_DEVICE_ATTRIBUTE_MEMPOOL_SUPPORTED_HANDLE_TYPES: Bitmask of handle types supported with mempool based IPC + * - ::CU_DEVICE_ATTRIBUTE_DEFERRED_MAPPING_CUDA_ARRAY_SUPPORTED: Device supports deferred mapping CUDA arrays and CUDA mipmapped arrays. + * + * \param pi - Returned device attribute value + * \param attrib - Device attribute to query + * \param dev - Device handle + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGetCount, + * ::cuDeviceGetName, + * ::cuDeviceGetUuid, + * ::cuDeviceGet, + * ::cuDeviceTotalMem, + * ::cuDeviceGetExecAffinitySupport, + * ::cudaDeviceGetAttribute, + * ::cudaGetDeviceProperties + */ +CUresult CUDAAPI cuDeviceGetAttribute(int *pi, CUdevice_attribute attrib, CUdevice dev); + +/** + * \brief Return NvSciSync attributes that this device can support. + * + * Returns in \p nvSciSyncAttrList, the properties of NvSciSync that + * this CUDA device, \p dev can support. The returned \p nvSciSyncAttrList + * can be used to create an NvSciSync object that matches this device's capabilities. + * + * If NvSciSyncAttrKey_RequiredPerm field in \p nvSciSyncAttrList is + * already set this API will return ::CUDA_ERROR_INVALID_VALUE. + * + * The applications should set \p nvSciSyncAttrList to a valid + * NvSciSyncAttrList failing which this API will return + * ::CUDA_ERROR_INVALID_HANDLE. + * + * The \p flags controls how applications intends to use + * the NvSciSync created from the \p nvSciSyncAttrList. The valid flags are: + * - ::CUDA_NVSCISYNC_ATTR_SIGNAL, specifies that the applications intends to + * signal an NvSciSync on this CUDA device. + * - ::CUDA_NVSCISYNC_ATTR_WAIT, specifies that the applications intends to + * wait on an NvSciSync on this CUDA device. + * + * At least one of these flags must be set, failing which the API + * returns ::CUDA_ERROR_INVALID_VALUE. Both the flags are orthogonal + * to one another: a developer may set both these flags that allows to + * set both wait and signal specific attributes in the same \p nvSciSyncAttrList. + * + * Note that this API updates the input \p nvSciSyncAttrList with values equivalent + * to the following public attribute key-values: + * NvSciSyncAttrKey_RequiredPerm is set to + * - NvSciSyncAccessPerm_SignalOnly if ::CUDA_NVSCISYNC_ATTR_SIGNAL is set in \p flags. + * - NvSciSyncAccessPerm_WaitOnly if ::CUDA_NVSCISYNC_ATTR_WAIT is set in \p flags. + * - NvSciSyncAccessPerm_WaitSignal if both ::CUDA_NVSCISYNC_ATTR_WAIT and + * ::CUDA_NVSCISYNC_ATTR_SIGNAL are set in \p flags. + * NvSciSyncAttrKey_PrimitiveInfo is set to + * - NvSciSyncAttrValPrimitiveType_SysmemSemaphore on any valid \p device. + * - NvSciSyncAttrValPrimitiveType_Syncpoint if \p device is a Tegra device. + * - NvSciSyncAttrValPrimitiveType_SysmemSemaphorePayload64b if \p device is GA10X+. + * NvSciSyncAttrKey_GpuId is set to the same UUID that is returned for this + * \p device from ::cuDeviceGetUuid. + * + * \param nvSciSyncAttrList - Return NvSciSync attributes supported. + * \param dev - Valid Cuda Device to get NvSciSync attributes for. + * \param flags - flags describing NvSciSync usage. + * + * \return + * + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_OUT_OF_MEMORY + * + * \sa + * ::cuImportExternalSemaphore, + * ::cuDestroyExternalSemaphore, + * ::cuSignalExternalSemaphoresAsync, + * ::cuWaitExternalSemaphoresAsync + */ +CUresult CUDAAPI cuDeviceGetNvSciSyncAttributes(void *nvSciSyncAttrList, CUdevice dev, int flags); + +/** + * \brief Sets the current memory pool of a device + * + * The memory pool must be local to the specified device. + * ::cuMemAllocAsync allocates from the current mempool of the provided stream's device. + * By default, a device's current memory pool is its default memory pool. + * + * \note Use ::cuMemAllocFromPoolAsync to specify asynchronous allocations from a device different + * than the one the stream runs on. + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuDeviceGetDefaultMemPool, ::cuDeviceGetMemPool, ::cuMemPoolCreate, ::cuMemPoolDestroy, ::cuMemAllocFromPoolAsync + */ +CUresult CUDAAPI cuDeviceSetMemPool(CUdevice dev, CUmemoryPool pool); + +/** + * \brief Gets the current mempool for a device + * + * Returns the last pool provided to ::cuDeviceSetMemPool for this device + * or the device's default memory pool if ::cuDeviceSetMemPool has never been called. + * By default the current mempool is the default mempool for a device. + * Otherwise the returned pool must have been set with ::cuDeviceSetMemPool. + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuDeviceGetDefaultMemPool, ::cuMemPoolCreate, ::cuDeviceSetMemPool + */ +CUresult CUDAAPI cuDeviceGetMemPool(CUmemoryPool *pool, CUdevice dev); + +/** + * \brief Returns the default mempool of a device + * + * The default mempool of a device contains device memory from that device. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_NOT_SUPPORTED + * \notefnerr + * + * \sa ::cuMemAllocAsync, ::cuMemPoolTrimTo, ::cuMemPoolGetAttribute, ::cuMemPoolSetAttribute, cuMemPoolSetAccess, ::cuDeviceGetMemPool, ::cuMemPoolCreate + */ +CUresult CUDAAPI cuDeviceGetDefaultMemPool(CUmemoryPool *pool_out, CUdevice dev); + +/** + * \brief Returns information about the execution affinity support of the device. + * + * Returns in \p *pi whether execution affinity type \p type is supported by device \p dev. + * The supported types are: + * - ::CU_EXEC_AFFINITY_TYPE_SM_COUNT: 1 if context with limited SMs is supported by the device, + * or 0 if not; + * + * \param pi - 1 if the execution affinity type \p type is supported by the device, or 0 if not + * \param type - Execution affinity type to query + * \param dev - Device handle + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGetAttribute, + * ::cuDeviceGetCount, + * ::cuDeviceGetName, + * ::cuDeviceGetUuid, + * ::cuDeviceGet, + * ::cuDeviceTotalMem + */ +CUresult CUDAAPI cuDeviceGetExecAffinitySupport(int *pi, CUexecAffinityType type, CUdevice dev); + +/** + * \brief Blocks until remote writes are visible to the specified scope + * + * Blocks until GPUDirect RDMA writes to the target context via mappings + * created through APIs like nvidia_p2p_get_pages (see + * https://docs.nvidia.com/cuda/gpudirect-rdma for more information), are + * visible to the specified scope. + * + * If the scope equals or lies within the scope indicated by + * ::CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WRITES_ORDERING, the call + * will be a no-op and can be safely omitted for performance. This can be + * determined by comparing the numerical values between the two enums, with + * smaller scopes having smaller values. + * + * Users may query support for this API via + * ::CU_DEVICE_ATTRIBUTE_FLUSH_FLUSH_GPU_DIRECT_RDMA_OPTIONS. + * + * \param target - The target of the operation, see ::CUflushGPUDirectRDMAWritesTarget + * \param scope - The scope of the operation, see ::CUflushGPUDirectRDMAWritesScope + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * \notefnerr + * + */ +CUresult CUDAAPI cuFlushGPUDirectRDMAWrites(CUflushGPUDirectRDMAWritesTarget target, CUflushGPUDirectRDMAWritesScope scope); + +/** @} */ /* END CUDA_DEVICE */ + +/** + * \defgroup CUDA_DEVICE_DEPRECATED Device Management [DEPRECATED] + * + * ___MANBRIEF___ deprecated device management functions of the low-level CUDA + * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the device management functions of the low-level + * CUDA driver application programming interface. + * + * @{ + */ + +/** + * \brief Returns properties for a selected device + * + * \deprecated + * + * This function was deprecated as of CUDA 5.0 and replaced by ::cuDeviceGetAttribute(). + * + * Returns in \p *prop the properties of device \p dev. The ::CUdevprop + * structure is defined as: + * + * \code + typedef struct CUdevprop_st { + int maxThreadsPerBlock; + int maxThreadsDim[3]; + int maxGridSize[3]; + int sharedMemPerBlock; + int totalConstantMemory; + int SIMDWidth; + int memPitch; + int regsPerBlock; + int clockRate; + int textureAlign + } CUdevprop; + * \endcode + * where: + * + * - ::maxThreadsPerBlock is the maximum number of threads per block; + * - ::maxThreadsDim[3] is the maximum sizes of each dimension of a block; + * - ::maxGridSize[3] is the maximum sizes of each dimension of a grid; + * - ::sharedMemPerBlock is the total amount of shared memory available per + * block in bytes; + * - ::totalConstantMemory is the total amount of constant memory available on + * the device in bytes; + * - ::SIMDWidth is the warp size; + * - ::memPitch is the maximum pitch allowed by the memory copy functions that + * involve memory regions allocated through ::cuMemAllocPitch(); + * - ::regsPerBlock is the total number of registers available per block; + * - ::clockRate is the clock frequency in kilohertz; + * - ::textureAlign is the alignment requirement; texture base addresses that + * are aligned to ::textureAlign bytes do not need an offset applied to + * texture fetches. + * + * \param prop - Returned properties of device + * \param dev - Device to get properties for + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGetAttribute, + * ::cuDeviceGetCount, + * ::cuDeviceGetName, + * ::cuDeviceGetUuid, + * ::cuDeviceGet, + * ::cuDeviceTotalMem + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuDeviceGetProperties(CUdevprop *prop, CUdevice dev); + +/** + * \brief Returns the compute capability of the device + * + * \deprecated + * + * This function was deprecated as of CUDA 5.0 and its functionality superseded + * by ::cuDeviceGetAttribute(). + * + * Returns in \p *major and \p *minor the major and minor revision numbers that + * define the compute capability of the device \p dev. + * + * \param major - Major revision number + * \param minor - Minor revision number + * \param dev - Device handle + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGetAttribute, + * ::cuDeviceGetCount, + * ::cuDeviceGetName, + * ::cuDeviceGetUuid, + * ::cuDeviceGet, + * ::cuDeviceTotalMem + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuDeviceComputeCapability(int *major, int *minor, CUdevice dev); + +/** @} */ /* END CUDA_DEVICE_DEPRECATED */ + +/** + * \defgroup CUDA_PRIMARY_CTX Primary Context Management + * + * ___MANBRIEF___ primary context management functions of the low-level CUDA driver + * API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the primary context management functions of the low-level + * CUDA driver application programming interface. + * + * The primary context is unique per device and shared with the CUDA runtime API. + * These functions allow integration with other libraries using CUDA. + * + * @{ + */ + +/** + * \brief Retain the primary context on the GPU + * + * Retains the primary context on the device. + * Once the user successfully retains the primary context, the primary context + * will be active and available to the user until the user releases it + * with ::cuDevicePrimaryCtxRelease() or resets it with ::cuDevicePrimaryCtxReset(). + * Unlike ::cuCtxCreate() the newly retained context is not pushed onto the stack. + * + * Retaining the primary context for the first time will fail with ::CUDA_ERROR_UNKNOWN + * if the compute mode of the device is ::CU_COMPUTEMODE_PROHIBITED. The function + * ::cuDeviceGetAttribute() can be used with ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE to + * determine the compute mode of the device. + * The nvidia-smi tool can be used to set the compute mode for + * devices. Documentation for nvidia-smi can be obtained by passing a + * -h option to it. + * + * Please note that the primary context always supports pinned allocations. Other + * flags can be specified by ::cuDevicePrimaryCtxSetFlags(). + * + * \param pctx - Returned context handle of the new context + * \param dev - Device for which primary context is requested + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa ::cuDevicePrimaryCtxRelease, + * ::cuDevicePrimaryCtxSetFlags, + * ::cuCtxCreate, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize + */ +CUresult CUDAAPI cuDevicePrimaryCtxRetain(CUcontext *pctx, CUdevice dev); + +/** + * \brief Release the primary context on the GPU + * + * Releases the primary context interop on the device. + * A retained context should always be released once the user is done using + * it. The context is automatically reset once the last reference to it is + * released. This behavior is different when the primary context was retained + * by the CUDA runtime from CUDA 4.0 and earlier. In this case, the primary + * context remains always active. + * + * Releasing a primary context that has not been previously retained will + * fail with ::CUDA_ERROR_INVALID_CONTEXT. + * + * Please note that unlike ::cuCtxDestroy() this method does not pop the context + * from stack in any circumstances. + * + * \param dev - Device which primary context is released + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_INVALID_CONTEXT + * \notefnerr + * + * \sa ::cuDevicePrimaryCtxRetain, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize + */ +CUresult CUDAAPI cuDevicePrimaryCtxRelease(CUdevice dev); + +/** + * \brief Set flags for the primary context + * + * Sets the flags for the primary context on the device overwriting perviously + * set ones. + * + * The three LSBs of the \p flags parameter can be used to control how the OS + * thread, which owns the CUDA context at the time of an API call, interacts + * with the OS scheduler when waiting for results from the GPU. Only one of + * the scheduling flags can be set when creating a context. + * + * - ::CU_CTX_SCHED_SPIN: Instruct CUDA to actively spin when waiting for + * results from the GPU. This can decrease latency when waiting for the GPU, + * but may lower the performance of CPU threads if they are performing work in + * parallel with the CUDA thread. + * + * - ::CU_CTX_SCHED_YIELD: Instruct CUDA to yield its thread when waiting for + * results from the GPU. This can increase latency when waiting for the GPU, + * but can increase the performance of CPU threads performing work in parallel + * with the GPU. + * + * - ::CU_CTX_SCHED_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a + * synchronization primitive when waiting for the GPU to finish work. + * + * - ::CU_CTX_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a + * synchronization primitive when waiting for the GPU to finish work.
+ * Deprecated: This flag was deprecated as of CUDA 4.0 and was + * replaced with ::CU_CTX_SCHED_BLOCKING_SYNC. + * + * - ::CU_CTX_SCHED_AUTO: The default value if the \p flags parameter is zero, + * uses a heuristic based on the number of active CUDA contexts in the + * process \e C and the number of logical processors in the system \e P. If + * \e C > \e P, then CUDA will yield to other OS threads when waiting for + * the GPU (::CU_CTX_SCHED_YIELD), otherwise CUDA will not yield while + * waiting for results and actively spin on the processor (::CU_CTX_SCHED_SPIN). + * Additionally, on Tegra devices, ::CU_CTX_SCHED_AUTO uses a heuristic based on + * the power profile of the platform and may choose ::CU_CTX_SCHED_BLOCKING_SYNC + * for low-powered devices. + * + * - ::CU_CTX_LMEM_RESIZE_TO_MAX: Instruct CUDA to not reduce local memory + * after resizing local memory for a kernel. This can prevent thrashing by + * local memory allocations when launching many kernels with high local + * memory usage at the cost of potentially increased memory usage.
+ * Deprecated: This flag is deprecated and the behavior enabled + * by this flag is now the default and cannot be disabled. + * + * - ::CU_CTX_COREDUMP_ENABLE: If GPU coredumps have not been enabled globally + * with ::cuCoredumpSetAttributeGlobal or environment variables, this flag can + * be set during context creation to instruct CUDA to create a coredump if + * this context raises an exception during execution. These environment variables + * are described in the CUDA-GDB user guide under the "GPU core dump support" + * section. + * The initial settings will be taken from the global settings at the time of + * context creation. The other settings that control coredump output can be + * modified by calling ::cuCoredumpSetAttribute from the created context after + * it becomes current. + * + * - ::CU_CTX_USER_COREDUMP_ENABLE: If user-triggered GPU coredumps have not + * been enabled globally with ::cuCoredumpSetAttributeGlobal or environment + * variables, this flag can be set during context creation to instruct CUDA to + * create a coredump if data is written to a certain pipe that is present in the + * OS space. These environment variables are described in the CUDA-GDB user + * guide under the "GPU core dump support" section. + * It is important to note that the pipe name *must* be set with + * ::cuCoredumpSetAttributeGlobal before creating the context if this flag is + * used. Setting this flag implies that ::CU_CTX_COREDUMP_ENABLE is set. + * The initial settings will be taken from the global settings at the time of + * context creation. The other settings that control coredump output can be + * modified by calling ::cuCoredumpSetAttribute from the created context after + * it becomes current. + * + * \param dev - Device for which the primary context flags are set + * \param flags - New flags for the device + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_INVALID_VALUE, + * \notefnerr + * + * \sa ::cuDevicePrimaryCtxRetain, + * ::cuDevicePrimaryCtxGetState, + * ::cuCtxCreate, + * ::cuCtxGetFlags, + * ::cuCtxSetFlags, + * ::cudaSetDeviceFlags + */ +CUresult CUDAAPI cuDevicePrimaryCtxSetFlags(CUdevice dev, unsigned int flags); + +/** + * \brief Get the state of the primary context + * + * Returns in \p *flags the flags for the primary context of \p dev, and in + * \p *active whether it is active. See ::cuDevicePrimaryCtxSetFlags for flag + * values. + * + * \param dev - Device to get primary context flags for + * \param flags - Pointer to store flags + * \param active - Pointer to store context state; 0 = inactive, 1 = active + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_INVALID_VALUE, + * \notefnerr + * + * \sa + * ::cuDevicePrimaryCtxSetFlags, + * ::cuCtxGetFlags, + * ::cuCtxSetFlags, + * ::cudaGetDeviceFlags + */ +CUresult CUDAAPI cuDevicePrimaryCtxGetState(CUdevice dev, unsigned int *flags, int *active); + +/** + * \brief Destroy all allocations and reset all state on the primary context + * + * Explicitly destroys and cleans up all resources associated with the current + * device in the current process. + * + * Note that it is responsibility of the calling function to ensure that no + * other module in the process is using the device any more. For that reason + * it is recommended to use ::cuDevicePrimaryCtxRelease() in most cases. + * However it is safe for other modules to call ::cuDevicePrimaryCtxRelease() + * even after resetting the device. + * Resetting the primary context does not release it, an application that has + * retained the primary context should explicitly release its usage. + * + * \param dev - Device for which primary context is destroyed + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE + * \notefnerr + * + * \sa ::cuDevicePrimaryCtxRetain, + * ::cuDevicePrimaryCtxRelease, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize, + * ::cudaDeviceReset + */ +CUresult CUDAAPI cuDevicePrimaryCtxReset(CUdevice dev); + +/** @} */ /* END CUDA_PRIMARY_CTX */ + +/** + * \defgroup CUDA_CTX Context Management + * + * ___MANBRIEF___ context management functions of the low-level CUDA driver + * API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the context management functions of the low-level + * CUDA driver application programming interface. + * + * Please note that some functions are described in + * \ref CUDA_PRIMARY_CTX "Primary Context Management" section. + * + * @{ + */ + +/** + * \brief Create a CUDA context + * + * \note In most cases it is recommended to use ::cuDevicePrimaryCtxRetain. + * + * Creates a new CUDA context and associates it with the calling thread. The + * \p flags parameter is described below. The context is created with a usage + * count of 1 and the caller of ::cuCtxCreate() must call ::cuCtxDestroy() + * when done using the context. If a context is already current to the thread, + * it is supplanted by the newly created context and may be restored by a subsequent + * call to ::cuCtxPopCurrent(). + * + * The three LSBs of the \p flags parameter can be used to control how the OS + * thread, which owns the CUDA context at the time of an API call, interacts + * with the OS scheduler when waiting for results from the GPU. Only one of + * the scheduling flags can be set when creating a context. + * + * - ::CU_CTX_SCHED_SPIN: Instruct CUDA to actively spin when waiting for + * results from the GPU. This can decrease latency when waiting for the GPU, + * but may lower the performance of CPU threads if they are performing work in + * parallel with the CUDA thread. + * + * - ::CU_CTX_SCHED_YIELD: Instruct CUDA to yield its thread when waiting for + * results from the GPU. This can increase latency when waiting for the GPU, + * but can increase the performance of CPU threads performing work in parallel + * with the GPU. + * + * - ::CU_CTX_SCHED_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a + * synchronization primitive when waiting for the GPU to finish work. + * + * - ::CU_CTX_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a + * synchronization primitive when waiting for the GPU to finish work.
+ * Deprecated: This flag was deprecated as of CUDA 4.0 and was + * replaced with ::CU_CTX_SCHED_BLOCKING_SYNC. + * + * - ::CU_CTX_SCHED_AUTO: The default value if the \p flags parameter is zero, + * uses a heuristic based on the number of active CUDA contexts in the + * process \e C and the number of logical processors in the system \e P. If + * \e C > \e P, then CUDA will yield to other OS threads when waiting for + * the GPU (::CU_CTX_SCHED_YIELD), otherwise CUDA will not yield while + * waiting for results and actively spin on the processor (::CU_CTX_SCHED_SPIN). + * Additionally, on Tegra devices, ::CU_CTX_SCHED_AUTO uses a heuristic based on + * the power profile of the platform and may choose ::CU_CTX_SCHED_BLOCKING_SYNC + * for low-powered devices. + * + * - ::CU_CTX_MAP_HOST: Instruct CUDA to support mapped pinned allocations. + * This flag must be set in order to allocate pinned host memory that is + * accessible to the GPU. + * + * - ::CU_CTX_LMEM_RESIZE_TO_MAX: Instruct CUDA to not reduce local memory + * after resizing local memory for a kernel. This can prevent thrashing by + * local memory allocations when launching many kernels with high local + * memory usage at the cost of potentially increased memory usage.
+ * Deprecated: This flag is deprecated and the behavior enabled + * by this flag is now the default and cannot be disabled. + * Instead, the per-thread stack size can be controlled with ::cuCtxSetLimit(). + * + * - ::CU_CTX_COREDUMP_ENABLE: If GPU coredumps have not been enabled globally + * with ::cuCoredumpSetAttributeGlobal or environment variables, this flag can + * be set during context creation to instruct CUDA to create a coredump if + * this context raises an exception during execution. These environment variables + * are described in the CUDA-GDB user guide under the "GPU core dump support" + * section. + * The initial attributes will be taken from the global attributes at the time of + * context creation. The other attributes that control coredump output can be + * modified by calling ::cuCoredumpSetAttribute from the created context after + * it becomes current. + * + * - ::CU_CTX_USER_COREDUMP_ENABLE: If user-triggered GPU coredumps have not + * been enabled globally with ::cuCoredumpSetAttributeGlobal or environment + * variables, this flag can be set during context creation to instruct CUDA to + * create a coredump if data is written to a certain pipe that is present in the + * OS space. These environment variables are described in the CUDA-GDB user + * guide under the "GPU core dump support" section. + * It is important to note that the pipe name *must* be set with + * ::cuCoredumpSetAttributeGlobal before creating the context if this flag is + * used. Setting this flag implies that ::CU_CTX_COREDUMP_ENABLE is set. + * The initial attributes will be taken from the global attributes at the time of + * context creation. The other attributes that control coredump output can be + * modified by calling ::cuCoredumpSetAttribute from the created context after + * it becomes current. + * Setting this flag on any context creation is equivalent to setting the + * ::CU_COREDUMP_ENABLE_USER_TRIGGER attribute to \p true globally. + * + * Context creation will fail with ::CUDA_ERROR_UNKNOWN if the compute mode of + * the device is ::CU_COMPUTEMODE_PROHIBITED. The function ::cuDeviceGetAttribute() + * can be used with ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE to determine the + * compute mode of the device. The nvidia-smi tool can be used to set + * the compute mode for * devices. + * Documentation for nvidia-smi can be obtained by passing a + * -h option to it. + * + * \param pctx - Returned context handle of the new context + * \param flags - Context creation flags + * \param dev - Device to create context on + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCoredumpSetAttributeGlobal, + * ::cuCoredumpSetAttribute, + * ::cuCtxSynchronize + */ +CUresult CUDAAPI cuCtxCreate(CUcontext *pctx, unsigned int flags, CUdevice dev); + +/** + * \brief Create a CUDA context with execution affinity + * + * Creates a new CUDA context with execution affinity and associates it with + * the calling thread. The \p paramsArray and \p flags parameter are described below. + * The context is created with a usage count of 1 and the caller of ::cuCtxCreate() must + * call ::cuCtxDestroy() when done using the context. If a context is already + * current to the thread, it is supplanted by the newly created context and may + * be restored by a subsequent call to ::cuCtxPopCurrent(). + * + * The type and the amount of execution resource the context can use is limited by \p paramsArray + * and \p numParams. The \p paramsArray is an array of \p CUexecAffinityParam and the \p numParams + * describes the size of the array. If two \p CUexecAffinityParam in the array have the same type, + * the latter execution affinity parameter overrides the former execution affinity parameter. + * The supported execution affinity types are: + * - ::CU_EXEC_AFFINITY_TYPE_SM_COUNT limits the portion of SMs that the context can use. The portion + * of SMs is specified as the number of SMs via \p CUexecAffinitySmCount. This limit will be internally + * rounded up to the next hardware-supported amount. Hence, it is imperative to query the actual execution + * affinity of the context via \p cuCtxGetExecAffinity after context creation. Currently, this attribute + * is only supported under Volta+ MPS. + * + * The three LSBs of the \p flags parameter can be used to control how the OS + * thread, which owns the CUDA context at the time of an API call, interacts + * with the OS scheduler when waiting for results from the GPU. Only one of + * the scheduling flags can be set when creating a context. + * + * - ::CU_CTX_SCHED_SPIN: Instruct CUDA to actively spin when waiting for + * results from the GPU. This can decrease latency when waiting for the GPU, + * but may lower the performance of CPU threads if they are performing work in + * parallel with the CUDA thread. + * + * - ::CU_CTX_SCHED_YIELD: Instruct CUDA to yield its thread when waiting for + * results from the GPU. This can increase latency when waiting for the GPU, + * but can increase the performance of CPU threads performing work in parallel + * with the GPU. + * + * - ::CU_CTX_SCHED_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a + * synchronization primitive when waiting for the GPU to finish work. + * + * - ::CU_CTX_BLOCKING_SYNC: Instruct CUDA to block the CPU thread on a + * synchronization primitive when waiting for the GPU to finish work.
+ * Deprecated: This flag was deprecated as of CUDA 4.0 and was + * replaced with ::CU_CTX_SCHED_BLOCKING_SYNC. + * + * - ::CU_CTX_SCHED_AUTO: The default value if the \p flags parameter is zero, + * uses a heuristic based on the number of active CUDA contexts in the + * process \e C and the number of logical processors in the system \e P. If + * \e C > \e P, then CUDA will yield to other OS threads when waiting for + * the GPU (::CU_CTX_SCHED_YIELD), otherwise CUDA will not yield while + * waiting for results and actively spin on the processor (::CU_CTX_SCHED_SPIN). + * Additionally, on Tegra devices, ::CU_CTX_SCHED_AUTO uses a heuristic based on + * the power profile of the platform and may choose ::CU_CTX_SCHED_BLOCKING_SYNC + * for low-powered devices. + * + * - ::CU_CTX_MAP_HOST: Instruct CUDA to support mapped pinned allocations. + * This flag must be set in order to allocate pinned host memory that is + * accessible to the GPU. + * + * - ::CU_CTX_LMEM_RESIZE_TO_MAX: Instruct CUDA to not reduce local memory + * after resizing local memory for a kernel. This can prevent thrashing by + * local memory allocations when launching many kernels with high local + * memory usage at the cost of potentially increased memory usage.
+ * Deprecated: This flag is deprecated and the behavior enabled + * by this flag is now the default and cannot be disabled. + * Instead, the per-thread stack size can be controlled with ::cuCtxSetLimit(). + * + * - ::CU_CTX_COREDUMP_ENABLE: If GPU coredumps have not been enabled globally + * with ::cuCoredumpSetAttributeGlobal or environment variables, this flag can + * be set during context creation to instruct CUDA to create a coredump if + * this context raises an exception during execution. These environment variables + * are described in the CUDA-GDB user guide under the "GPU core dump support" + * section. + * The initial attributes will be taken from the global attributes at the time of + * context creation. The other attributes that control coredump output can be + * modified by calling ::cuCoredumpSetAttribute from the created context after + * it becomes current. + * + * - ::CU_CTX_USER_COREDUMP_ENABLE: If user-triggered GPU coredumps have not + * been enabled globally with ::cuCoredumpSetAttributeGlobal or environment + * variables, this flag can be set during context creation to instruct CUDA to + * create a coredump if data is written to a certain pipe that is present in the + * OS space. These environment variables are described in the CUDA-GDB user + * guide under the "GPU core dump support" section. + * It is important to note that the pipe name *must* be set with + * ::cuCoredumpSetAttributeGlobal before creating the context if this flag is + * used. Setting this flag implies that ::CU_CTX_COREDUMP_ENABLE is set. + * The initial attributes will be taken from the global attributes at the time of + * context creation. The other attributes that control coredump output can be + * modified by calling ::cuCoredumpSetAttribute from the created context after + * it becomes current. + * Setting this flag on any context creation is equivalent to setting the + * ::CU_COREDUMP_ENABLE_USER_TRIGGER attribute to \p true globally. + * + * Context creation will fail with ::CUDA_ERROR_UNKNOWN if the compute mode of + * the device is ::CU_COMPUTEMODE_PROHIBITED. The function ::cuDeviceGetAttribute() + * can be used with ::CU_DEVICE_ATTRIBUTE_COMPUTE_MODE to determine the + * compute mode of the device. The nvidia-smi tool can be used to set + * the compute mode for * devices. + * Documentation for nvidia-smi can be obtained by passing a + * -h option to it. + * + * \param pctx - Returned context handle of the new context + * \param paramsArray - Execution affinity parameters + * \param numParams - Number of execution affinity parameters + * \param flags - Context creation flags + * \param dev - Device to create context on + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_UNSUPPORTED_EXEC_AFFINITY, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize, + * ::cuCoredumpSetAttributeGlobal, + * ::cuCoredumpSetAttribute, + * ::CUexecAffinityParam + */ +CUresult CUDAAPI cuCtxCreate_v3(CUcontext *pctx, CUexecAffinityParam *paramsArray, int numParams, unsigned int flags, CUdevice dev); + +/** + * \brief Destroy a CUDA context + * + * Destroys the CUDA context specified by \p ctx. The context \p ctx will be + * destroyed regardless of how many threads it is current to. + * It is the responsibility of the calling function to ensure that no API + * call issues using \p ctx while ::cuCtxDestroy() is executing. + * + * Destroys and cleans up all resources associated with the context. + * It is the caller's responsibility to ensure that the context or its resources + * are not accessed or passed in subsequent API calls and doing so will result in undefined behavior. + * These resources include CUDA types such as ::CUmodule, ::CUfunction, ::CUstream, ::CUevent, + * ::CUarray, ::CUmipmappedArray, ::CUtexObject, ::CUsurfObject, ::CUtexref, ::CUsurfref, + * ::CUgraphicsResource, ::CUlinkState, ::CUexternalMemory and ::CUexternalSemaphore. + * + * If \p ctx is current to the calling thread then \p ctx will also be + * popped from the current thread's context stack (as though ::cuCtxPopCurrent() + * were called). If \p ctx is current to other threads, then \p ctx will + * remain current to those threads, and attempting to access \p ctx from + * those threads will result in the error ::CUDA_ERROR_CONTEXT_IS_DESTROYED. + * + * \param ctx - Context to destroy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize + */ +CUresult CUDAAPI cuCtxDestroy(CUcontext ctx); + +/** + * \brief Pushes a context on the current CPU thread + * + * Pushes the given context \p ctx onto the CPU thread's stack of current + * contexts. The specified context becomes the CPU thread's current context, so + * all CUDA functions that operate on the current context are affected. + * + * The previous current context may be made current again by calling + * ::cuCtxDestroy() or ::cuCtxPopCurrent(). + * + * \param ctx - Context to push + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize + */ +CUresult CUDAAPI cuCtxPushCurrent(CUcontext ctx); + +/** + * \brief Pops the current CUDA context from the current CPU thread. + * + * Pops the current CUDA context from the CPU thread and passes back the + * old context handle in \p *pctx. That context may then be made current + * to a different CPU thread by calling ::cuCtxPushCurrent(). + * + * If a context was current to the CPU thread before ::cuCtxCreate() or + * ::cuCtxPushCurrent() was called, this function makes that context current to + * the CPU thread again. + * + * \param pctx - Returned popped context handle + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize + */ +CUresult CUDAAPI cuCtxPopCurrent(CUcontext *pctx); + +/** + * \brief Binds the specified CUDA context to the calling CPU thread + * + * Binds the specified CUDA context to the calling CPU thread. + * If \p ctx is NULL then the CUDA context previously bound to the + * calling CPU thread is unbound and ::CUDA_SUCCESS is returned. + * + * If there exists a CUDA context stack on the calling CPU thread, this + * will replace the top of that stack with \p ctx. + * If \p ctx is NULL then this will be equivalent to popping the top + * of the calling CPU thread's CUDA context stack (or a no-op if the + * calling CPU thread's CUDA context stack is empty). + * + * \param ctx - Context to bind to the calling CPU thread + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT + * \notefnerr + * + * \sa + * ::cuCtxGetCurrent, + * ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cudaSetDevice + */ +CUresult CUDAAPI cuCtxSetCurrent(CUcontext ctx); + +/** + * \brief Returns the CUDA context bound to the calling CPU thread. + * + * Returns in \p *pctx the CUDA context bound to the calling CPU thread. + * If no context is bound to the calling CPU thread then \p *pctx is + * set to NULL and ::CUDA_SUCCESS is returned. + * + * \param pctx - Returned context handle + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * \notefnerr + * + * \sa + * ::cuCtxSetCurrent, + * ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cudaGetDevice + */ +CUresult CUDAAPI cuCtxGetCurrent(CUcontext *pctx); + +/** + * \brief Returns the device ID for the current context + * + * Returns in \p *device the ordinal of the current context's device. + * + * \param device - Returned device ID for the current context + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize, + * ::cudaGetDevice + */ +CUresult CUDAAPI cuCtxGetDevice(CUdevice *device); + +/** + * \brief Returns the flags for the current context + * + * Returns in \p *flags the flags of the current context. See ::cuCtxCreate + * for flag values. + * + * \param flags - Pointer to store flags of current context + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetCurrent, + * ::cuCtxGetDevice, + * ::cuCtxGetLimit, + * ::cuCtxGetSharedMemConfig, + * ::cuCtxGetStreamPriorityRange, + * ::cuCtxSetFlags, + * ::cudaGetDeviceFlags + */ +CUresult CUDAAPI cuCtxGetFlags(unsigned int *flags); + +/** + * \brief Sets the flags for the current context + * + * \param flags - Flags to set on the current context + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetCurrent, + * ::cuCtxGetDevice, + * ::cuCtxGetLimit, + * ::cuCtxGetSharedMemConfig, + * ::cuCtxGetStreamPriorityRange, + * ::cuCtxGetFlags, + * ::cudaGetDeviceFlags, + * ::cuDevicePrimaryCtxSetFlags, + */ +CUresult CUDAAPI cuCtxSetFlags(unsigned int flags); + +/** + * \brief Returns the unique Id associated with the context supplied + * + * Returns in \p ctxId the unique Id which is associated with a given context. + * The Id is unique for the life of the program for this instance of CUDA. + * If context is supplied as NULL and there is one current, the Id of the + * current context is returned. + * + * \param ctx - Context for which to obtain the Id + * \param ctxId - Pointer to store the Id of the context + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_CONTEXT_IS_DESTROYED, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPushCurrent + */ +CUresult CUDAAPI cuCtxGetId(CUcontext ctx, unsigned long long *ctxId); + +/** + * \brief Block for a context's tasks to complete + * + * Blocks until the device has completed all preceding requested tasks. + * ::cuCtxSynchronize() returns an error if one of the preceding tasks failed. + * If the context was created with the ::CU_CTX_SCHED_BLOCKING_SYNC flag, the + * CPU thread will block until the GPU context has finished its work. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cudaDeviceSynchronize + */ +CUresult CUDAAPI cuCtxSynchronize(void); + +/** + * \brief Set resource limits + * + * Setting \p limit to \p value is a request by the application to update + * the current limit maintained by the context. The driver is free to + * modify the requested value to meet h/w requirements (this could be + * clamping to minimum or maximum values, rounding up to nearest element + * size, etc). The application can use ::cuCtxGetLimit() to find out exactly + * what the limit has been set to. + * + * Setting each ::CUlimit has its own specific restrictions, so each is + * discussed here. + * + * - ::CU_LIMIT_STACK_SIZE controls the stack size in bytes of each GPU thread. + * The driver automatically increases the per-thread stack size + * for each kernel launch as needed. This size isn't reset back to the + * original value after each launch. Setting this value will take effect + * immediately, and if necessary, the device will block until all preceding + * requested tasks are complete. + * + * - ::CU_LIMIT_PRINTF_FIFO_SIZE controls the size in bytes of the FIFO used + * by the ::printf() device system call. Setting ::CU_LIMIT_PRINTF_FIFO_SIZE + * must be performed before launching any kernel that uses the ::printf() + * device system call, otherwise ::CUDA_ERROR_INVALID_VALUE will be returned. + * + * - ::CU_LIMIT_MALLOC_HEAP_SIZE controls the size in bytes of the heap used + * by the ::malloc() and ::free() device system calls. Setting + * ::CU_LIMIT_MALLOC_HEAP_SIZE must be performed before launching any kernel + * that uses the ::malloc() or ::free() device system calls, otherwise + * ::CUDA_ERROR_INVALID_VALUE will be returned. + * + * - ::CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH controls the maximum nesting depth of + * a grid at which a thread can safely call ::cudaDeviceSynchronize(). Setting + * this limit must be performed before any launch of a kernel that uses the + * device runtime and calls ::cudaDeviceSynchronize() above the default sync + * depth, two levels of grids. Calls to ::cudaDeviceSynchronize() will fail + * with error code ::cudaErrorSyncDepthExceeded if the limitation is + * violated. This limit can be set smaller than the default or up the maximum + * launch depth of 24. When setting this limit, keep in mind that additional + * levels of sync depth require the driver to reserve large amounts of device + * memory which can no longer be used for user allocations. If these + * reservations of device memory fail, ::cuCtxSetLimit() will return + * ::CUDA_ERROR_OUT_OF_MEMORY, and the limit can be reset to a lower value. + * This limit is only applicable to devices of compute capability < 9.0. + * Attempting to set this limit on devices of other compute capability + * versions will result in the error ::CUDA_ERROR_UNSUPPORTED_LIMIT being + * returned. + * + * - ::CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT controls the maximum number of + * outstanding device runtime launches that can be made from the current + * context. A grid is outstanding from the point of launch up until the grid + * is known to have been completed. Device runtime launches which violate + * this limitation fail and return ::cudaErrorLaunchPendingCountExceeded when + * ::cudaGetLastError() is called after launch. If more pending launches than + * the default (2048 launches) are needed for a module using the device + * runtime, this limit can be increased. Keep in mind that being able to + * sustain additional pending launches will require the driver to reserve + * larger amounts of device memory upfront which can no longer be used for + * allocations. If these reservations fail, ::cuCtxSetLimit() will return + * ::CUDA_ERROR_OUT_OF_MEMORY, and the limit can be reset to a lower value. + * This limit is only applicable to devices of compute capability 3.5 and + * higher. Attempting to set this limit on devices of compute capability less + * than 3.5 will result in the error ::CUDA_ERROR_UNSUPPORTED_LIMIT being + * returned. + * + * - ::CU_LIMIT_MAX_L2_FETCH_GRANULARITY controls the L2 cache fetch granularity. + * Values can range from 0B to 128B. This is purely a performance hint and + * it can be ignored or clamped depending on the platform. + * + * - ::CU_LIMIT_PERSISTING_L2_CACHE_SIZE controls size in bytes available for + * persisting L2 cache. This is purely a performance hint and it can be + * ignored or clamped depending on the platform. + * + * \param limit - Limit to set + * \param value - Size of limit + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_UNSUPPORTED_LIMIT, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_INVALID_CONTEXT + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSynchronize, + * ::cudaDeviceSetLimit + */ +CUresult CUDAAPI cuCtxSetLimit(CUlimit limit, size_t value); + +/** + * \brief Returns resource limits + * + * Returns in \p *pvalue the current size of \p limit. The supported + * ::CUlimit values are: + * - ::CU_LIMIT_STACK_SIZE: stack size in bytes of each GPU thread. + * - ::CU_LIMIT_PRINTF_FIFO_SIZE: size in bytes of the FIFO used by the + * ::printf() device system call. + * - ::CU_LIMIT_MALLOC_HEAP_SIZE: size in bytes of the heap used by the + * ::malloc() and ::free() device system calls. + * - ::CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH: maximum grid depth at which a thread + * can issue the device runtime call ::cudaDeviceSynchronize() to wait on + * child grid launches to complete. + * - ::CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT: maximum number of outstanding + * device runtime launches that can be made from this context. + * - ::CU_LIMIT_MAX_L2_FETCH_GRANULARITY: L2 cache fetch granularity. + * - ::CU_LIMIT_PERSISTING_L2_CACHE_SIZE: Persisting L2 cache size in bytes + * + * \param limit - Limit to query + * \param pvalue - Returned size of limit + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_UNSUPPORTED_LIMIT + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize, + * ::cudaDeviceGetLimit + */ +CUresult CUDAAPI cuCtxGetLimit(size_t *pvalue, CUlimit limit); + +/** + * \brief Returns the preferred cache configuration for the current context. + * + * On devices where the L1 cache and shared memory use the same hardware + * resources, this function returns through \p pconfig the preferred cache configuration + * for the current context. This is only a preference. The driver will use + * the requested configuration if possible, but it is free to choose a different + * configuration if required to execute functions. + * + * This will return a \p pconfig of ::CU_FUNC_CACHE_PREFER_NONE on devices + * where the size of the L1 cache and shared memory are fixed. + * + * The supported cache configurations are: + * - ::CU_FUNC_CACHE_PREFER_NONE: no preference for shared memory or L1 (default) + * - ::CU_FUNC_CACHE_PREFER_SHARED: prefer larger shared memory and smaller L1 cache + * - ::CU_FUNC_CACHE_PREFER_L1: prefer larger L1 cache and smaller shared memory + * - ::CU_FUNC_CACHE_PREFER_EQUAL: prefer equal sized L1 cache and shared memory + * + * \param pconfig - Returned cache configuration + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize, + * ::cuFuncSetCacheConfig, + * ::cudaDeviceGetCacheConfig + */ +CUresult CUDAAPI cuCtxGetCacheConfig(CUfunc_cache *pconfig); + +/** + * \brief Sets the preferred cache configuration for the current context. + * + * On devices where the L1 cache and shared memory use the same hardware + * resources, this sets through \p config the preferred cache configuration for + * the current context. This is only a preference. The driver will use + * the requested configuration if possible, but it is free to choose a different + * configuration if required to execute the function. Any function preference + * set via ::cuFuncSetCacheConfig() or ::cuKernelSetCacheConfig() will be preferred over this context-wide + * setting. Setting the context-wide cache configuration to + * ::CU_FUNC_CACHE_PREFER_NONE will cause subsequent kernel launches to prefer + * to not change the cache configuration unless required to launch the kernel. + * + * This setting does nothing on devices where the size of the L1 cache and + * shared memory are fixed. + * + * Launching a kernel with a different preference than the most recent + * preference setting may insert a device-side synchronization point. + * + * The supported cache configurations are: + * - ::CU_FUNC_CACHE_PREFER_NONE: no preference for shared memory or L1 (default) + * - ::CU_FUNC_CACHE_PREFER_SHARED: prefer larger shared memory and smaller L1 cache + * - ::CU_FUNC_CACHE_PREFER_L1: prefer larger L1 cache and smaller shared memory + * - ::CU_FUNC_CACHE_PREFER_EQUAL: prefer equal sized L1 cache and shared memory + * + * \param config - Requested cache configuration + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize, + * ::cuFuncSetCacheConfig, + * ::cudaDeviceSetCacheConfig, + * ::cuKernelSetCacheConfig + */ +CUresult CUDAAPI cuCtxSetCacheConfig(CUfunc_cache config); + +/** + * \brief Returns the current shared memory configuration for the current context. + * + * This function will return in \p pConfig the current size of shared memory banks + * in the current context. On devices with configurable shared memory banks, + * ::cuCtxSetSharedMemConfig can be used to change this setting, so that all + * subsequent kernel launches will by default use the new bank size. When + * ::cuCtxGetSharedMemConfig is called on devices without configurable shared + * memory, it will return the fixed bank size of the hardware. + * + * The returned bank configurations can be either: + * - ::CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE: shared memory bank width is + * four bytes. + * - ::CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE: shared memory bank width will + * eight bytes. + * + * \param pConfig - returned shared memory configuration + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize, + * ::cuCtxGetSharedMemConfig, + * ::cuFuncSetCacheConfig, + * ::cudaDeviceGetSharedMemConfig + */ +CUresult CUDAAPI cuCtxGetSharedMemConfig(CUsharedconfig *pConfig); + +/** + * \brief Sets the shared memory configuration for the current context. + * + * On devices with configurable shared memory banks, this function will set + * the context's shared memory bank size which is used for subsequent kernel + * launches. + * + * Changed the shared memory configuration between launches may insert a device + * side synchronization point between those launches. + * + * Changing the shared memory bank size will not increase shared memory usage + * or affect occupancy of kernels, but may have major effects on performance. + * Larger bank sizes will allow for greater potential bandwidth to shared memory, + * but will change what kinds of accesses to shared memory will result in bank + * conflicts. + * + * This function will do nothing on devices with fixed shared memory bank size. + * + * The supported bank configurations are: + * - ::CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE: set bank width to the default initial + * setting (currently, four bytes). + * - ::CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE: set shared memory bank width to + * be natively four bytes. + * - ::CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE: set shared memory bank width to + * be natively eight bytes. + * + * \param config - requested shared memory configuration + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize, + * ::cuCtxGetSharedMemConfig, + * ::cuFuncSetCacheConfig, + * ::cudaDeviceSetSharedMemConfig + */ +CUresult CUDAAPI cuCtxSetSharedMemConfig(CUsharedconfig config); + +/** + * \brief Gets the context's API version. + * + * Returns a version number in \p version corresponding to the capabilities of + * the context (e.g. 3010 or 3020), which library developers can use to direct + * callers to a specific API version. If \p ctx is NULL, returns the API version + * used to create the currently bound context. + * + * Note that new API versions are only introduced when context capabilities are + * changed that break binary compatibility, so the API version and driver version + * may be different. For example, it is valid for the API version to be 3020 while + * the driver version is 4020. + * + * \param ctx - Context to check + * \param version - Pointer to version + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize + */ +CUresult CUDAAPI cuCtxGetApiVersion(CUcontext ctx, unsigned int *version); + +/** + * \brief Returns numerical values that correspond to the least and + * greatest stream priorities. + * + * Returns in \p *leastPriority and \p *greatestPriority the numerical values that correspond + * to the least and greatest stream priorities respectively. Stream priorities + * follow a convention where lower numbers imply greater priorities. The range of + * meaningful stream priorities is given by [\p *greatestPriority, \p *leastPriority]. + * If the user attempts to create a stream with a priority value that is + * outside the meaningful range as specified by this API, the priority is + * automatically clamped down or up to either \p *leastPriority or \p *greatestPriority + * respectively. See ::cuStreamCreateWithPriority for details on creating a + * priority stream. + * A NULL may be passed in for \p *leastPriority or \p *greatestPriority if the value + * is not desired. + * + * This function will return '0' in both \p *leastPriority and \p *greatestPriority if + * the current context's device does not support stream priorities + * (see ::cuDeviceGetAttribute). + * + * \param leastPriority - Pointer to an int in which the numerical value for least + * stream priority is returned + * \param greatestPriority - Pointer to an int in which the numerical value for greatest + * stream priority is returned + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \notefnerr + * + * \sa ::cuStreamCreateWithPriority, + * ::cuStreamGetPriority, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize, + * ::cudaDeviceGetStreamPriorityRange + */ +CUresult CUDAAPI cuCtxGetStreamPriorityRange(int *leastPriority, int *greatestPriority); + +/** + * \brief Resets all persisting lines in cache to normal status. + * + * ::cuCtxResetPersistingL2Cache Resets all persisting lines in cache to normal + * status. Takes effect on function return. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_SUPPORTED + * \notefnerr + * + * \sa + * ::CUaccessPolicyWindow + */ +CUresult CUDAAPI cuCtxResetPersistingL2Cache(void); + +/** + * \brief Returns the execution affinity setting for the current context. + * + * Returns in \p *pExecAffinity the current value of \p type. The supported + * ::CUexecAffinityType values are: + * - ::CU_EXEC_AFFINITY_TYPE_SM_COUNT: number of SMs the context is limited to use. + * + * \param type - Execution affinity type to query + * \param pExecAffinity - Returned execution affinity + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_UNSUPPORTED_EXEC_AFFINITY + * \notefnerr + * + * \sa + * ::CUexecAffinityParam + */ +CUresult CUDAAPI cuCtxGetExecAffinity(CUexecAffinityParam *pExecAffinity, CUexecAffinityType type); + + +/** @} */ /* END CUDA_CTX */ + +/** + * \defgroup CUDA_CTX_DEPRECATED Context Management [DEPRECATED] + * + * ___MANBRIEF___ deprecated context management functions of the low-level CUDA + * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the deprecated context management functions of the low-level + * CUDA driver application programming interface. + * + * @{ + */ + +/** + * \brief Increment a context's usage-count + * + * \deprecated + * + * Note that this function is deprecated and should not be used. + * + * Increments the usage count of the context and passes back a context handle + * in \p *pctx that must be passed to ::cuCtxDetach() when the application is + * done with the context. ::cuCtxAttach() fails if there is no context current + * to the thread. + * + * Currently, the \p flags parameter must be 0. + * + * \param pctx - Returned context handle of the current context + * \param flags - Context attach flags (must be 0) + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxDetach, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuCtxAttach(CUcontext *pctx, unsigned int flags); + +/** + * \brief Decrement a context's usage-count + * + * \deprecated + * + * Note that this function is deprecated and should not be used. + * + * Decrements the usage count of the context \p ctx, and destroys the context + * if the usage count goes to 0. The context must be a handle that was passed + * back by ::cuCtxCreate() or ::cuCtxAttach(), and must be current to the + * calling thread. + * + * \param ctx - Context to destroy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT + * \notefnerr + * + * \sa ::cuCtxCreate, + * ::cuCtxDestroy, + * ::cuCtxGetApiVersion, + * ::cuCtxGetCacheConfig, + * ::cuCtxGetDevice, + * ::cuCtxGetFlags, + * ::cuCtxGetLimit, + * ::cuCtxPopCurrent, + * ::cuCtxPushCurrent, + * ::cuCtxSetCacheConfig, + * ::cuCtxSetLimit, + * ::cuCtxSynchronize + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuCtxDetach(CUcontext ctx); + +/** @} */ /* END CUDA_CTX_DEPRECATED */ + + +/** + * \defgroup CUDA_MODULE Module Management + * + * ___MANBRIEF___ module management functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the module management functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** + * \brief Loads a compute module + * + * Takes a filename \p fname and loads the corresponding module \p module into + * the current context. The CUDA driver API does not attempt to lazily + * allocate the resources needed by a module; if the memory for functions and + * data (constant and global) needed by the module cannot be allocated, + * ::cuModuleLoad() fails. The file should be a \e cubin file as output by + * \b nvcc, or a \e PTX file either as output by \b nvcc or handwritten, or + * a \e fatbin file as output by \b nvcc from toolchain 4.0 or later. + * + * \param module - Returned module + * \param fname - Filename of module to load + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_PTX, + * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION, + * ::CUDA_ERROR_NOT_FOUND, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_FILE_NOT_FOUND, + * ::CUDA_ERROR_NO_BINARY_FOR_GPU, + * ::CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED, + * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND + * \notefnerr + * + * \sa ::cuModuleGetFunction, + * ::cuModuleGetGlobal, + * ::cuModuleGetTexRef, + * ::cuModuleLoadData, + * ::cuModuleLoadDataEx, + * ::cuModuleLoadFatBinary, + * ::cuModuleUnload + */ +CUresult CUDAAPI cuModuleLoad(CUmodule *module, const char *fname); + +/** + * \brief Load a module's data + * + * Takes a pointer \p image and loads the corresponding module \p module into + * the current context. The pointer may be obtained by mapping a \e cubin or + * \e PTX or \e fatbin file, passing a \e cubin or \e PTX or \e fatbin file + * as a NULL-terminated text string, or incorporating a \e cubin or \e fatbin + * object into the executable resources and using operating system calls such + * as Windows \c FindResource() to obtain the pointer. + * + * \param module - Returned module + * \param image - Module data to load + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_PTX, + * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_NO_BINARY_FOR_GPU, + * ::CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED, + * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND + * \notefnerr + * + * \sa ::cuModuleGetFunction, + * ::cuModuleGetGlobal, + * ::cuModuleGetTexRef, + * ::cuModuleLoad, + * ::cuModuleLoadDataEx, + * ::cuModuleLoadFatBinary, + * ::cuModuleUnload + */ +CUresult CUDAAPI cuModuleLoadData(CUmodule *module, const void *image); + +/** + * \brief Load a module's data with options + * + * Takes a pointer \p image and loads the corresponding module \p module into + * the current context. The pointer may be obtained by mapping a \e cubin or + * \e PTX or \e fatbin file, passing a \e cubin or \e PTX or \e fatbin file + * as a NULL-terminated text string, or incorporating a \e cubin or \e fatbin + * object into the executable resources and using operating system calls such + * as Windows \c FindResource() to obtain the pointer. Options are passed as + * an array via \p options and any corresponding parameters are passed in + * \p optionValues. The number of total options is supplied via \p numOptions. + * Any outputs will be returned via \p optionValues. + * + * \param module - Returned module + * \param image - Module data to load + * \param numOptions - Number of options + * \param options - Options for JIT + * \param optionValues - Option values for JIT + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_PTX, + * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_NO_BINARY_FOR_GPU, + * ::CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED, + * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND + * \notefnerr + * + * \sa ::cuModuleGetFunction, + * ::cuModuleGetGlobal, + * ::cuModuleGetTexRef, + * ::cuModuleLoad, + * ::cuModuleLoadData, + * ::cuModuleLoadFatBinary, + * ::cuModuleUnload + */ +CUresult CUDAAPI cuModuleLoadDataEx(CUmodule *module, const void *image, unsigned int numOptions, CUjit_option *options, void **optionValues); + +/** + * \brief Load a module's data + * + * Takes a pointer \p fatCubin and loads the corresponding module \p module + * into the current context. The pointer represents a fat binary object, + * which is a collection of different \e cubin and/or \e PTX files, all + * representing the same device code, but compiled and optimized for different + * architectures. + * + * Prior to CUDA 4.0, there was no documented API for constructing and using + * fat binary objects by programmers. Starting with CUDA 4.0, fat binary + * objects can be constructed by providing the -fatbin option to \b nvcc. + * More information can be found in the \b nvcc document. + * + * \param module - Returned module + * \param fatCubin - Fat binary to load + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_PTX, + * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION, + * ::CUDA_ERROR_NOT_FOUND, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_NO_BINARY_FOR_GPU, + * ::CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED, + * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND + * \notefnerr + * + * \sa ::cuModuleGetFunction, + * ::cuModuleGetGlobal, + * ::cuModuleGetTexRef, + * ::cuModuleLoad, + * ::cuModuleLoadData, + * ::cuModuleLoadDataEx, + * ::cuModuleUnload + */ +CUresult CUDAAPI cuModuleLoadFatBinary(CUmodule *module, const void *fatCubin); + +/** + * \brief Unloads a module + * + * Unloads a module \p hmod from the current context. + * + * \param hmod - Module to unload + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_destroy_ub + * + * \sa ::cuModuleGetFunction, + * ::cuModuleGetGlobal, + * ::cuModuleGetTexRef, + * ::cuModuleLoad, + * ::cuModuleLoadData, + * ::cuModuleLoadDataEx, + * ::cuModuleLoadFatBinary + */ +CUresult CUDAAPI cuModuleUnload(CUmodule hmod); + +/** + * CUDA Lazy Loading status + */ +typedef enum CUmoduleLoadingMode_enum { + CU_MODULE_EAGER_LOADING = 0x1, /**< Lazy Kernel Loading is not enabled */ + CU_MODULE_LAZY_LOADING = 0x2, /**< Lazy Kernel Loading is enabled */ +} CUmoduleLoadingMode; + +/** + * \brief Query lazy loading mode + * + * Returns lazy loading mode + * Module loading mode is controlled by CUDA_MODULE_LOADING env variable + * + * \param mode - Returns the lazy loading mode + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \notefnerr + * + * \sa + * ::cuModuleLoad, + */ +CUresult CUDAAPI cuModuleGetLoadingMode(CUmoduleLoadingMode *mode); + +/** + * \brief Returns a function handle + * + * Returns in \p *hfunc the handle of the function of name \p name located in + * module \p hmod. If no function of that name exists, ::cuModuleGetFunction() + * returns ::CUDA_ERROR_NOT_FOUND. + * + * \param hfunc - Returned function handle + * \param hmod - Module to retrieve function from + * \param name - Name of function to retrieve + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_FOUND + * \notefnerr + * + * \sa ::cuModuleGetGlobal, + * ::cuModuleGetTexRef, + * ::cuModuleLoad, + * ::cuModuleLoadData, + * ::cuModuleLoadDataEx, + * ::cuModuleLoadFatBinary, + * ::cuModuleUnload + */ +CUresult CUDAAPI cuModuleGetFunction(CUfunction *hfunc, CUmodule hmod, const char *name); + +/** + * \brief Returns a global pointer from a module + * + * Returns in \p *dptr and \p *bytes the base pointer and size of the + * global of name \p name located in module \p hmod. If no variable of that name + * exists, ::cuModuleGetGlobal() returns ::CUDA_ERROR_NOT_FOUND. + * One of the parameters \p dptr or \p bytes (not both) can be NULL in which + * case it is ignored. + * + * \param dptr - Returned global device pointer + * \param bytes - Returned global size in bytes + * \param hmod - Module to retrieve global from + * \param name - Name of global to retrieve + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_FOUND + * \notefnerr + * + * \sa ::cuModuleGetFunction, + * ::cuModuleGetTexRef, + * ::cuModuleLoad, + * ::cuModuleLoadData, + * ::cuModuleLoadDataEx, + * ::cuModuleLoadFatBinary, + * ::cuModuleUnload, + * ::cudaGetSymbolAddress, + * ::cudaGetSymbolSize + */ +CUresult CUDAAPI cuModuleGetGlobal(CUdeviceptr *dptr, size_t *bytes, CUmodule hmod, const char *name); + +/** + * \brief Creates a pending JIT linker invocation. + * + * If the call is successful, the caller owns the returned CUlinkState, which + * should eventually be destroyed with ::cuLinkDestroy. The + * device code machine size (32 or 64 bit) will match the calling application. + * + * Both linker and compiler options may be specified. Compiler options will + * be applied to inputs to this linker action which must be compiled from PTX. + * The options ::CU_JIT_WALL_TIME, + * ::CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES, and ::CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES + * will accumulate data until the CUlinkState is destroyed. + * + * \p optionValues must remain valid for the life of the CUlinkState if output + * options are used. No other references to inputs are maintained after this + * call returns. + * + * \note For LTO-IR input, only LTO-IR compiled with toolkits prior to CUDA 12.0 will be accepted + * + * \param numOptions Size of options arrays + * \param options Array of linker and compiler options + * \param optionValues Array of option values, each cast to void * + * \param stateOut On success, this will contain a CUlinkState to specify + * and complete this action + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND + * \notefnerr + * + * \sa ::cuLinkAddData, + * ::cuLinkAddFile, + * ::cuLinkComplete, + * ::cuLinkDestroy + */ +CUresult CUDAAPI +cuLinkCreate(unsigned int numOptions, CUjit_option *options, void **optionValues, CUlinkState *stateOut); + +/** + * \brief Add an input to a pending linker invocation + * + * Ownership of \p data is retained by the caller. No reference is retained to any + * inputs after this call returns. + * + * This method accepts only compiler options, which are used if the data must + * be compiled from PTX, and does not accept any of + * ::CU_JIT_WALL_TIME, ::CU_JIT_INFO_LOG_BUFFER, ::CU_JIT_ERROR_LOG_BUFFER, + * ::CU_JIT_TARGET_FROM_CUCONTEXT, or ::CU_JIT_TARGET. + * + * \note For LTO-IR input, only LTO-IR compiled with toolkits prior to CUDA 12.0 will be accepted + * + * \param state A pending linker action. + * \param type The type of the input data. + * \param data The input data. PTX must be NULL-terminated. + * \param size The length of the input data. + * \param name An optional name for this input in log messages. + * \param numOptions Size of options. + * \param options Options to be applied only for this input (overrides options from ::cuLinkCreate). + * \param optionValues Array of option values, each cast to void *. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_IMAGE, + * ::CUDA_ERROR_INVALID_PTX, + * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_NO_BINARY_FOR_GPU + * + * \sa ::cuLinkCreate, + * ::cuLinkAddFile, + * ::cuLinkComplete, + * ::cuLinkDestroy + */ +CUresult CUDAAPI +cuLinkAddData(CUlinkState state, CUjitInputType type, void *data, size_t size, const char *name, + unsigned int numOptions, CUjit_option *options, void **optionValues); + +/** + * \brief Add a file input to a pending linker invocation + * + * No reference is retained to any inputs after this call returns. + * + * This method accepts only compiler options, which are used if the input + * must be compiled from PTX, and does not accept any of + * ::CU_JIT_WALL_TIME, ::CU_JIT_INFO_LOG_BUFFER, ::CU_JIT_ERROR_LOG_BUFFER, + * ::CU_JIT_TARGET_FROM_CUCONTEXT, or ::CU_JIT_TARGET. + * + * This method is equivalent to invoking ::cuLinkAddData on the contents + * of the file. + * + * \note For LTO-IR input, only LTO-IR compiled with toolkits prior to CUDA 12.0 will be accepted + * + * \param state A pending linker action + * \param type The type of the input data + * \param path Path to the input file + * \param numOptions Size of options + * \param options Options to be applied only for this input (overrides options from ::cuLinkCreate) + * \param optionValues Array of option values, each cast to void * + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_FILE_NOT_FOUND + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_IMAGE, + * ::CUDA_ERROR_INVALID_PTX, + * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_NO_BINARY_FOR_GPU + * + * \sa ::cuLinkCreate, + * ::cuLinkAddData, + * ::cuLinkComplete, + * ::cuLinkDestroy + */ +CUresult CUDAAPI +cuLinkAddFile(CUlinkState state, CUjitInputType type, const char *path, + unsigned int numOptions, CUjit_option *options, void **optionValues); + +/** + * \brief Complete a pending linker invocation + * + * Completes the pending linker action and returns the cubin image for the linked + * device code, which can be used with ::cuModuleLoadData. The cubin is owned by + * \p state, so it should be loaded before \p state is destroyed via ::cuLinkDestroy. + * This call does not destroy \p state. + * + * \param state A pending linker invocation + * \param cubinOut On success, this will point to the output image + * \param sizeOut Optional parameter to receive the size of the generated image + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * + * \sa ::cuLinkCreate, + * ::cuLinkAddData, + * ::cuLinkAddFile, + * ::cuLinkDestroy, + * ::cuModuleLoadData + */ +CUresult CUDAAPI +cuLinkComplete(CUlinkState state, void **cubinOut, size_t *sizeOut); + +/** + * \brief Destroys state for a JIT linker invocation. + * + * \param state State object for the linker invocation + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_HANDLE + * + * \sa ::cuLinkCreate + */ +CUresult CUDAAPI +cuLinkDestroy(CUlinkState state); + +/** @} */ /* END CUDA_MODULE */ + +/** + * \defgroup CUDA_MODULE_DEPRECATED Module Management [DEPRECATED] + * + * ___MANBRIEF___ deprecated module management functions of the low-level CUDA + * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the deprecated module management functions of the low-level + * CUDA driver application programming interface. + * + * @{ + */ + +/** + * \brief Returns a handle to a texture reference + * + * \deprecated + * + * Returns in \p *pTexRef the handle of the texture reference of name \p name + * in the module \p hmod. If no texture reference of that name exists, + * ::cuModuleGetTexRef() returns ::CUDA_ERROR_NOT_FOUND. This texture reference + * handle should not be destroyed, since it will be destroyed when the module + * is unloaded. + * + * \param pTexRef - Returned texture reference + * \param hmod - Module to retrieve texture reference from + * \param name - Name of texture reference to retrieve + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_FOUND + * \notefnerr + * + * \sa + * ::cuModuleGetFunction, + * ::cuModuleGetGlobal, + * ::cuModuleGetSurfRef, + * ::cuModuleLoad, + * ::cuModuleLoadData, + * ::cuModuleLoadDataEx, + * ::cuModuleLoadFatBinary, + * ::cuModuleUnload + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuModuleGetTexRef(CUtexref *pTexRef, CUmodule hmod, const char *name); + +/** + * \brief Returns a handle to a surface reference + * + * \deprecated + * + * Returns in \p *pSurfRef the handle of the surface reference of name \p name + * in the module \p hmod. If no surface reference of that name exists, + * ::cuModuleGetSurfRef() returns ::CUDA_ERROR_NOT_FOUND. + * + * \param pSurfRef - Returned surface reference + * \param hmod - Module to retrieve surface reference from + * \param name - Name of surface reference to retrieve + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_FOUND + * \notefnerr + * + * \sa + * ::cuModuleGetFunction, + * ::cuModuleGetGlobal, + * ::cuModuleGetTexRef, + * ::cuModuleLoad, + * ::cuModuleLoadData, + * ::cuModuleLoadDataEx, + * ::cuModuleLoadFatBinary, + * ::cuModuleUnload + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuModuleGetSurfRef(CUsurfref *pSurfRef, CUmodule hmod, const char *name); + +/** @} */ /* END CUDA_MODULE_DEPRECATED */ + +/** + * \defgroup CUDA_LIBRARY Library Management + * + * ___MANBRIEF___ library management functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the library management functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** + * \brief Load a library with specified code and options + * + * Takes a pointer \p code and loads the corresponding library \p library into + * all contexts existent at the time of the call and future contexts at the time + * of creation until the library is unloaded with ::cuLibraryUnload(). + * + * The pointer may be obtained by mapping a \e cubin or \e PTX or \e fatbin file, + * passing a \e cubin or \e PTX or \e fatbin file as a NULL-terminated text string, or + * incorporating a \e cubin or \e fatbin object into the executable resources and + * using operating system calls such as Windows \c FindResource() to obtain the pointer. + * + * Options are passed as an array via \p jitOptions and any corresponding parameters are passed in + * \p jitOptionsValues. The number of total JIT options is supplied via \p numJitOptions. + * Any outputs will be returned via \p jitOptionsValues. + * + * Library load options are passed as an array via \p libraryOptions and any corresponding parameters are passed in + * \p libraryOptionValues. The number of total library load options is supplied via \p numLibraryOptions. + * + * \param library - Returned library + * \param code - Code to load + * \param jitOptions - Options for JIT + * \param jitOptionsValues - Option values for JIT + * \param numJitOptions - Number of options + * \param libraryOptions - Options for loading + * \param libraryOptionValues - Option values for loading + * \param numLibraryOptions - Number of options for loading + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_PTX, + * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_NO_BINARY_FOR_GPU, + * ::CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED, + * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND + * + * \sa ::cuLibraryLoadFromFile, + * ::cuLibraryUnload, + * ::cuModuleLoad, + * ::cuModuleLoadData, + * ::cuModuleLoadDataEx + */ +CUresult CUDAAPI cuLibraryLoadData(CUlibrary *library, const void *code, + CUjit_option *jitOptions, void **jitOptionsValues, unsigned int numJitOptions, + CUlibraryOption *libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions); + +/** + * \brief Load a library with specified file and options + * + * Takes a filename \p fileName and loads the corresponding library \p library into + * all contexts existent at the time of the call and future contexts at the time of + * creation until the library is unloaded with ::cuLibraryUnload(). + * + * The file should be a \e cubin file as output by \b nvcc, or a \e PTX file either + * as output by \b nvcc or handwritten, or a \e fatbin file as output by \b nvcc + * from toolchain 4.0 or later. + * + * Options are passed as an array via \p jitOptions and any corresponding parameters are + * passed in \p jitOptionsValues. The number of total options is supplied via \p numJitOptions. + * Any outputs will be returned via \p jitOptionsValues. + * + * Library load options are passed as an array via \p libraryOptions and any corresponding parameters are passed in + * \p libraryOptionValues. The number of total library load options is supplied via \p numLibraryOptions. + * + * \param library - Returned library + * \param fileName - File to load from + * \param jitOptions - Options for JIT + * \param jitOptionsValues - Option values for JIT + * \param numJitOptions - Number of options + * \param libraryOptions - Options for loading + * \param libraryOptionValues - Option values for loading + * \param numLibraryOptions - Number of options for loading + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_PTX, + * ::CUDA_ERROR_UNSUPPORTED_PTX_VERSION, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_NO_BINARY_FOR_GPU, + * ::CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED, + * ::CUDA_ERROR_JIT_COMPILER_NOT_FOUND + * + * \sa ::cuLibraryLoadData, + * ::cuLibraryUnload, + * ::cuModuleLoad, + * ::cuModuleLoadData, + * ::cuModuleLoadDataEx + */ +CUresult CUDAAPI cuLibraryLoadFromFile(CUlibrary *library, const char *fileName, + CUjit_option *jitOptions, void **jitOptionsValues, unsigned int numJitOptions, + CUlibraryOption *libraryOptions, void **libraryOptionValues, unsigned int numLibraryOptions); + +/** + * \brief Unloads a library + * + * Unloads the library specified with \p library + * + * \param library - Library to unload + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuLibraryLoadData, + * ::cuLibraryLoadFromFile, + * ::cuModuleUnload + */ +CUresult CUDAAPI cuLibraryUnload(CUlibrary library); + +/** + * \brief Returns a kernel handle + * + * Returns in \p pKernel the handle of the kernel with name \p name located in library \p library. + * If kernel handle is not found, the call returns ::CUDA_ERROR_NOT_FOUND. + * + * \param pKernel - Returned kernel handle + * \param library - Library to retrieve kernel from + * \param name - Name of kernel to retrieve + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_FOUND, + * + * \sa ::cuLibraryLoadData, + * ::cuLibraryLoadFromFile, + * ::cuLibraryUnload, + * ::cuKernelGetFunction, + * ::cuLibraryGetModule, + * ::cuModuleGetFunction + */ +CUresult CUDAAPI cuLibraryGetKernel(CUkernel *pKernel, CUlibrary library, const char *name); + +/** + * \brief Returns a module handle + * + * Returns in \p pMod the module handle associated with the current context located in + * library \p library. If module handle is not found, the call returns ::CUDA_ERROR_NOT_FOUND. + * + * \param pMod - Returned module handle + * \param library - Library to retrieve module from + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_FOUND, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_CONTEXT_IS_DESTROYED + * + * \sa ::cuLibraryLoadData, + * ::cuLibraryLoadFromFile, + * ::cuLibraryUnload, + * ::cuModuleGetFunction + */ +CUresult CUDAAPI cuLibraryGetModule(CUmodule *pMod, CUlibrary library); + +/** + * \brief Returns a function handle + * + * Returns in \p pFunc the handle of the function for the requested kernel \p kernel and + * the current context. If function handle is not found, the call returns ::CUDA_ERROR_NOT_FOUND. + * + * \param pFunc - Returned function handle + * \param kernel - Kernel to retrieve function for the requested context + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_FOUND, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_CONTEXT_IS_DESTROYED + * + * \sa ::cuLibraryLoadData, + * ::cuLibraryLoadFromFile, + * ::cuLibraryUnload, + * ::cuLibraryGetKernel, + * ::cuLibraryGetModule, + * ::cuModuleGetFunction + */ +CUresult CUDAAPI cuKernelGetFunction(CUfunction *pFunc, CUkernel kernel); + +/** + * \brief Returns a global device pointer + * + * Returns in \p *dptr and \p *bytes the base pointer and size of the global with + * name \p name for the requested library \p library and the current context. + * If no global for the requested name \p name exists, the call returns ::CUDA_ERROR_NOT_FOUND. + * One of the parameters \p dptr or \p bytes (not both) can be NULL in which + * case it is ignored. + * + * \param dptr - Returned global device pointer for the requested context + * \param bytes - Returned global size in bytes + * \param library - Library to retrieve global from + * \param name - Name of global to retrieve + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_FOUND, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_CONTEXT_IS_DESTROYED + * + * \sa ::cuLibraryLoadData, + * ::cuLibraryLoadFromFile, + * ::cuLibraryUnload, + * ::cuLibraryGetModule, + * cuModuleGetGlobal + */ +CUresult CUDAAPI cuLibraryGetGlobal(CUdeviceptr *dptr, size_t *bytes, CUlibrary library, const char *name); + +/** + * \brief Returns a pointer to managed memory + * + * Returns in \p *dptr and \p *bytes the base pointer and size of the managed memory with + * name \p name for the requested library \p library. If no managed memory with the + * requested name \p name exists, the call returns ::CUDA_ERROR_NOT_FOUND. One of the parameters + * \p dptr or \p bytes (not both) can be NULL in which case it is ignored. + * Note that managed memory for library \p library is shared across devices and is registered + * when the library is loaded into atleast one context. + * + * \note The API requires a CUDA context to be present and initialized on at least one device. + * If no context is present, the call returns ::CUDA_ERROR_NOT_FOUND. + * + * \param dptr - Returned pointer to the managed memory + * \param bytes - Returned memory size in bytes + * \param library - Library to retrieve managed memory from + * \param name - Name of managed memory to retrieve + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_FOUND, + * + * \sa ::cuLibraryLoadData, + * ::cuLibraryLoadFromFile, + * ::cuLibraryUnload, + */ +CUresult CUDAAPI cuLibraryGetManaged(CUdeviceptr *dptr, size_t *bytes, CUlibrary library, const char *name); + +/** + * \brief Returns a pointer to a unified function + * + * Returns in \p *fptr the function pointer to a unified function denoted by \p symbol. + * If no unified function with name \p symbol exists, the call returns ::CUDA_ERROR_NOT_FOUND. + * If there is no device with attribute ::CU_DEVICE_ATTRIBUTE_UNIFIED_FUNCTION_POINTERS present in the system, + * the call may return ::CUDA_ERROR_NOT_FOUND. + * + * \param fptr - Returned pointer to a unified function + * \param library - Library to retrieve function pointer memory from + * \param symbol - Name of function pointer to retrieve + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_FOUND, + * + * \sa ::cuLibraryLoadData, + * ::cuLibraryLoadFromFile, + * ::cuLibraryUnload, + */ +CUresult CUDAAPI cuLibraryGetUnifiedFunction(void **fptr, CUlibrary library, const char *symbol); + +/** + * \brief Returns information about a kernel + * + * Returns in \p *pi the integer value of the attribute \p attrib for the kernel + * \p kernel for the requested device \p dev. The supported attributes are: + * - ::CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK: The maximum number of threads + * per block, beyond which a launch of the kernel would fail. This number + * depends on both the kernel and the requested device. + * - ::CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES: The size in bytes of + * statically-allocated shared memory per block required by this kernel. + * This does not include dynamically-allocated shared memory requested by + * the user at runtime. + * - ::CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES: The size in bytes of user-allocated + * constant memory required by this kernel. + * - ::CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES: The size in bytes of local memory + * used by each thread of this kernel. + * - ::CU_FUNC_ATTRIBUTE_NUM_REGS: The number of registers used by each thread + * of this kernel. + * - ::CU_FUNC_ATTRIBUTE_PTX_VERSION: The PTX virtual architecture version for + * which the kernel was compiled. This value is the major PTX version * 10 + * + the minor PTX version, so a PTX version 1.3 function would return the + * value 13. Note that this may return the undefined value of 0 for cubins + * compiled prior to CUDA 3.0. + * - ::CU_FUNC_ATTRIBUTE_BINARY_VERSION: The binary architecture version for + * which the kernel was compiled. This value is the major binary + * version * 10 + the minor binary version, so a binary version 1.3 function + * would return the value 13. Note that this will return a value of 10 for + * legacy cubins that do not have a properly-encoded binary architecture + * version. + * - ::CU_FUNC_CACHE_MODE_CA: The attribute to indicate whether the kernel has + * been compiled with user specified option "-Xptxas --dlcm=ca" set. + * - ::CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES: The maximum size in bytes of + * dynamically-allocated shared memory. + * - ::CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT: Preferred shared memory-L1 + * cache split ratio in percent of total shared memory. + * - ::CU_FUNC_ATTRIBUTE_CLUSTER_SIZE_MUST_BE_SET: If this attribute is set, the + * kernel must launch with a valid cluster size specified. + * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_WIDTH: The required cluster width in + * blocks. + * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_HEIGHT: The required cluster height in + * blocks. + * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_DEPTH: The required cluster depth in + * blocks. + * - ::CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED: Indicates whether + * the function can be launched with non-portable cluster size. 1 is allowed, + * 0 is disallowed. A non-portable cluster size may only function on the + * specific SKUs the program is tested on. The launch might fail if the + * program is run on a different hardware platform. CUDA API provides + * cudaOccupancyMaxActiveClusters to assist with checking whether the desired + * size can be launched on the current device. A portable cluster size is + * guaranteed to be functional on all compute capabilities higher than the + * target compute capability. The portable cluster size for sm_90 is 8 blocks + * per cluster. This value may increase for future compute capabilities. The + * specific hardware unit may support higher cluster sizes that’s not + * guaranteed to be portable. + * - ::CU_FUNC_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE: The block + * scheduling policy of a function. The value type is CUclusterSchedulingPolicy. + * + * \note If another thread is trying to set the same attribute on the same device using + * ::cuKernelSetAttribute() simultaneously, the attribute query will give the old or new + * value depending on the interleavings chosen by the OS scheduler and memory consistency. + * + * \param pi - Returned attribute value + * \param attrib - Attribute requested + * \param kernel - Kernel to query attribute of + * \param dev - Device to query attribute of + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * + * \sa ::cuLibraryLoadData, + * ::cuLibraryLoadFromFile, + * ::cuLibraryUnload, + * ::cuKernelSetAttribute, + * ::cuLibraryGetKernel, + * ::cuLaunchKernel, + * ::cuKernelGetFunction, + * ::cuLibraryGetModule, + * ::cuModuleGetFunction, + * ::cuFuncGetAttribute + */ +CUresult CUDAAPI cuKernelGetAttribute(int *pi, CUfunction_attribute attrib, CUkernel kernel, CUdevice dev); + +/** + * \brief Sets information about a kernel + * + * This call sets the value of a specified attribute \p attrib on the kernel \p kernel + * for the requested device \p dev to an integer value specified by \p val. + * This function returns CUDA_SUCCESS if the new value of the attribute could be + * successfully set. If the set fails, this call will return an error. + * Not all attributes can have values set. Attempting to set a value on a read-only + * attribute will result in an error (CUDA_ERROR_INVALID_VALUE) + * + * Note that attributes set using ::cuFuncSetAttribute() will override the attribute + * set by this API irrespective of whether the call to ::cuFuncSetAttribute() is made + * before or after this API call. However, ::cuKernelGetAttribute() will always + * return the attribute value set by this API. + * + * Supported attributes are: + * - ::CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES: This is the maximum size in bytes of + * dynamically-allocated shared memory. The value should contain the requested + * maximum size of dynamically-allocated shared memory. The sum of this value and + * the function attribute ::CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES cannot exceed the + * device attribute ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN. + * The maximal size of requestable dynamic shared memory may differ by GPU + * architecture. + * - ::CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT: On devices where the L1 + * cache and shared memory use the same hardware resources, this sets the shared memory + * carveout preference, in percent of the total shared memory. + * See ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR + * This is only a hint, and the driver can choose a different ratio if required to execute the function. + * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_WIDTH: The required cluster width in + * blocks. The width, height, and depth values must either all be 0 or all be + * positive. The validity of the cluster dimensions is checked at launch time. + * If the value is set during compile time, it cannot be set at runtime. + * Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED. + * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_HEIGHT: The required cluster height in + * blocks. The width, height, and depth values must either all be 0 or all be + * positive. The validity of the cluster dimensions is checked at launch time. + * If the value is set during compile time, it cannot be set at runtime. + * Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED. + * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_DEPTH: The required cluster depth in + * blocks. The width, height, and depth values must either all be 0 or all be + * positive. The validity of the cluster dimensions is checked at launch time. + * If the value is set during compile time, it cannot be set at runtime. + * Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED. + * - ::CU_FUNC_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE: The block + * scheduling policy of a function. The value type is CUclusterSchedulingPolicy. + * + * \note The API has stricter locking requirements in comparison to its legacy counterpart + * ::cuFuncSetAttribute() due to device-wide semantics. If multiple threads are trying to + * set the same attribute on the same device simultaneously, the attribute setting will depend + * on the interleavings chosen by the OS scheduler and memory consistency. + * + * \param attrib - Attribute requested + * \param val - Value to set + * \param kernel - Kernel to set attribute of + * \param dev - Device to set attribute of + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * + * \sa ::cuLibraryLoadData, + * ::cuLibraryLoadFromFile, + * ::cuLibraryUnload, + * ::cuKernelGetAttribute, + * ::cuLibraryGetKernel, + * ::cuLaunchKernel, + * ::cuKernelGetFunction, + * ::cuLibraryGetModule, + * ::cuModuleGetFunction, + * ::cuFuncSetAttribute + */ +CUresult CUDAAPI cuKernelSetAttribute(CUfunction_attribute attrib, int val, CUkernel kernel, CUdevice dev); + +/** + * \brief Sets the preferred cache configuration for a device kernel. + * + * On devices where the L1 cache and shared memory use the same hardware + * resources, this sets through \p config the preferred cache configuration for + * the device kernel \p kernel on the requested device \p dev. This is only a preference. + * The driver will use the requested configuration if possible, but it is free to choose a different + * configuration if required to execute \p kernel. Any context-wide preference + * set via ::cuCtxSetCacheConfig() will be overridden by this per-kernel + * setting. + * + * Note that attributes set using ::cuFuncSetCacheConfig() will override the attribute + * set by this API irrespective of whether the call to ::cuFuncSetCacheConfig() is made + * before or after this API call. + * + * This setting does nothing on devices where the size of the L1 cache and + * shared memory are fixed. + * + * Launching a kernel with a different preference than the most recent + * preference setting may insert a device-side synchronization point. + * + * + * The supported cache configurations are: + * - ::CU_FUNC_CACHE_PREFER_NONE: no preference for shared memory or L1 (default) + * - ::CU_FUNC_CACHE_PREFER_SHARED: prefer larger shared memory and smaller L1 cache + * - ::CU_FUNC_CACHE_PREFER_L1: prefer larger L1 cache and smaller shared memory + * - ::CU_FUNC_CACHE_PREFER_EQUAL: prefer equal sized L1 cache and shared memory + * + * \note The API has stricter locking requirements in comparison to its legacy counterpart + * ::cuFuncSetCacheConfig() due to device-wide semantics. If multiple threads are trying to + * set a config on the same device simultaneously, the cache config setting will depend + * on the interleavings chosen by the OS scheduler and memory consistency. + * + * \param kernel - Kernel to configure cache for + * \param config - Requested cache configuration + * \param dev - Device to set attribute of + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * + * \sa ::cuLibraryLoadData, + * ::cuLibraryLoadFromFile, + * ::cuLibraryUnload, + * ::cuLibraryGetKernel, + * ::cuKernelGetFunction, + * ::cuLibraryGetModule, + * ::cuModuleGetFunction, + * ::cuFuncSetCacheConfig, + * ::cuCtxSetCacheConfig, + * ::cuLaunchKernel + */ +CUresult CUDAAPI cuKernelSetCacheConfig(CUkernel kernel, CUfunc_cache config, CUdevice dev); + +/** @} */ /* END CUDA_LIBRARY */ + +/** + * \defgroup CUDA_MEM Memory Management + * + * ___MANBRIEF___ memory management functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the memory management functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** + * \brief Gets free and total memory + * + * Returns in \p *total the total amount of memory available to the the current context. + * Returns in \p *free the amount of memory on the device that is free according to the OS. + * CUDA is not guaranteed to be able to allocate all of the memory that the OS reports as free. + * In a multi-tenet situation, free estimate returned is prone to race condition where + * a new allocation/free done by a different process or a different thread in the same + * process between the time when free memory was estimated and reported, will result in + * deviation in free value reported and actual free memory. + * + * The integrated GPU on Tegra shares memory with CPU and other component + * of the SoC. The free and total values returned by the API excludes + * the SWAP memory space maintained by the OS on some platforms. + * The OS may move some of the memory pages into swap area as the GPU or + * CPU allocate or access memory. See Tegra app note on how to calculate + * total and free memory on Tegra. + * + * \param free - Returned free memory in bytes + * \param total - Returned total memory in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemGetInfo + */ +CUresult CUDAAPI cuMemGetInfo(size_t *free, size_t *total); + +/** + * \brief Allocates device memory + * + * Allocates \p bytesize bytes of linear memory on the device and returns in + * \p *dptr a pointer to the allocated memory. The allocated memory is suitably + * aligned for any kind of variable. The memory is not cleared. If \p bytesize + * is 0, ::cuMemAlloc() returns ::CUDA_ERROR_INVALID_VALUE. + * + * \param dptr - Returned device pointer + * \param bytesize - Requested allocation size in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMalloc + */ +CUresult CUDAAPI cuMemAlloc(CUdeviceptr *dptr, size_t bytesize); + +/** + * \brief Allocates pitched device memory + * + * Allocates at least \p WidthInBytes * \p Height bytes of linear memory on + * the device and returns in \p *dptr a pointer to the allocated memory. The + * function may pad the allocation to ensure that corresponding pointers in + * any given row will continue to meet the alignment requirements for + * coalescing as the address is updated from row to row. \p ElementSizeBytes + * specifies the size of the largest reads and writes that will be performed + * on the memory range. \p ElementSizeBytes may be 4, 8 or 16 (since coalesced + * memory transactions are not possible on other data sizes). If + * \p ElementSizeBytes is smaller than the actual read/write size of a kernel, + * the kernel will run correctly, but possibly at reduced speed. The pitch + * returned in \p *pPitch by ::cuMemAllocPitch() is the width in bytes of the + * allocation. The intended usage of pitch is as a separate parameter of the + * allocation, used to compute addresses within the 2D array. Given the row + * and column of an array element of type \b T, the address is computed as: + * \code + T* pElement = (T*)((char*)BaseAddress + Row * Pitch) + Column; + * \endcode + * + * The pitch returned by ::cuMemAllocPitch() is guaranteed to work with + * ::cuMemcpy2D() under all circumstances. For allocations of 2D arrays, it is + * recommended that programmers consider performing pitch allocations using + * ::cuMemAllocPitch(). Due to alignment restrictions in the hardware, this is + * especially true if the application will be performing 2D memory copies + * between different regions of device memory (whether linear memory or CUDA + * arrays). + * + * The byte alignment of the pitch returned by ::cuMemAllocPitch() is guaranteed + * to match or exceed the alignment requirement for texture binding with + * ::cuTexRefSetAddress2D(). + * + * \param dptr - Returned device pointer + * \param pPitch - Returned pitch of allocation in bytes + * \param WidthInBytes - Requested allocation width in bytes + * \param Height - Requested allocation height in rows + * \param ElementSizeBytes - Size of largest reads/writes for range + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMallocPitch + */ +CUresult CUDAAPI cuMemAllocPitch(CUdeviceptr *dptr, size_t *pPitch, size_t WidthInBytes, size_t Height, unsigned int ElementSizeBytes); + +/** + * \brief Frees device memory + * + * Frees the memory space pointed to by \p dptr, which must have been returned + * by a previous call to one of the following memory allocation APIs - ::cuMemAlloc(), + * ::cuMemAllocPitch(), ::cuMemAllocManaged(), ::cuMemAllocAsync(), ::cuMemAllocFromPoolAsync() + * + * Note - This API will not perform any implict synchronization when the pointer was allocated with + * ::cuMemAllocAsync or ::cuMemAllocFromPoolAsync. Callers must ensure that all accesses to the + * pointer have completed before invoking ::cuMemFree. For best performance and memory reuse, users + * should use ::cuMemFreeAsync to free memory allocated via the stream ordered memory allocator. + * + * \param dptr - Pointer to memory to free + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemAllocManaged, ::cuMemAllocAsync, ::cuMemAllocFromPoolAsync, + * ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, ::cuMemcpy3D, ::cuMemcpy3DAsync, + * ::cuMemcpyAtoA, ::cuMemcpyAtoD, ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, + * ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, + * ::cuMemcpyHtoAAsync, ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, ::cuMemFreeAsync, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaFree + */ +CUresult CUDAAPI cuMemFree(CUdeviceptr dptr); + +/** + * \brief Get information on memory allocations + * + * Returns the base address in \p *pbase and size in \p *psize of the + * allocation by ::cuMemAlloc() or ::cuMemAllocPitch() that contains the input + * pointer \p dptr. Both parameters \p pbase and \p psize are optional. If one + * of them is NULL, it is ignored. + * + * \param pbase - Returned base address + * \param psize - Returned size of device memory allocation + * \param dptr - Device pointer to query + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_NOT_FOUND, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32 + */ +CUresult CUDAAPI cuMemGetAddressRange(CUdeviceptr *pbase, size_t *psize, CUdeviceptr dptr); + +/** + * \brief Allocates page-locked host memory + * + * Allocates \p bytesize bytes of host memory that is page-locked and + * accessible to the device. The driver tracks the virtual memory ranges + * allocated with this function and automatically accelerates calls to + * functions such as ::cuMemcpy(). Since the memory can be accessed directly by + * the device, it can be read or written with much higher bandwidth than + * pageable memory obtained with functions such as ::malloc(). Allocating + * excessive amounts of memory with ::cuMemAllocHost() may degrade system + * performance, since it reduces the amount of memory available to the system + * for paging. As a result, this function is best used sparingly to allocate + * staging areas for data exchange between host and device. + * + * Note all host memory allocated using ::cuMemHostAlloc() will automatically + * be immediately accessible to all contexts on all devices which support unified + * addressing (as may be queried using ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING). + * The device pointer that may be used to access this host memory from those + * contexts is always equal to the returned host pointer \p *pp. + * See \ref CUDA_UNIFIED for additional details. + * + * \param pp - Returned host pointer to page-locked memory + * \param bytesize - Requested allocation size in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMallocHost + */ +CUresult CUDAAPI cuMemAllocHost(void **pp, size_t bytesize); + +/** + * \brief Frees page-locked host memory + * + * Frees the memory space pointed to by \p p, which must have been returned by + * a previous call to ::cuMemAllocHost(). + * + * \param p - Pointer to memory to free + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaFreeHost + */ +CUresult CUDAAPI cuMemFreeHost(void *p); + +/** + * \brief Allocates page-locked host memory + * + * Allocates \p bytesize bytes of host memory that is page-locked and accessible + * to the device. The driver tracks the virtual memory ranges allocated with + * this function and automatically accelerates calls to functions such as + * ::cuMemcpyHtoD(). Since the memory can be accessed directly by the device, + * it can be read or written with much higher bandwidth than pageable memory + * obtained with functions such as ::malloc(). Allocating excessive amounts of + * pinned memory may degrade system performance, since it reduces the amount + * of memory available to the system for paging. As a result, this function is + * best used sparingly to allocate staging areas for data exchange between + * host and device. + * + * The \p Flags parameter enables different options to be specified that + * affect the allocation, as follows. + * + * - ::CU_MEMHOSTALLOC_PORTABLE: The memory returned by this call will be + * considered as pinned memory by all CUDA contexts, not just the one that + * performed the allocation. + * + * - ::CU_MEMHOSTALLOC_DEVICEMAP: Maps the allocation into the CUDA address + * space. The device pointer to the memory may be obtained by calling + * ::cuMemHostGetDevicePointer(). + * + * - ::CU_MEMHOSTALLOC_WRITECOMBINED: Allocates the memory as write-combined + * (WC). WC memory can be transferred across the PCI Express bus more + * quickly on some system configurations, but cannot be read efficiently by + * most CPUs. WC memory is a good option for buffers that will be written by + * the CPU and read by the GPU via mapped pinned memory or host->device + * transfers. + * + * All of these flags are orthogonal to one another: a developer may allocate + * memory that is portable, mapped and/or write-combined with no restrictions. + * + * The ::CU_MEMHOSTALLOC_DEVICEMAP flag may be specified on CUDA contexts for + * devices that do not support mapped pinned memory. The failure is deferred + * to ::cuMemHostGetDevicePointer() because the memory may be mapped into + * other CUDA contexts via the ::CU_MEMHOSTALLOC_PORTABLE flag. + * + * The memory allocated by this function must be freed with ::cuMemFreeHost(). + * + * Note all host memory allocated using ::cuMemHostAlloc() will automatically + * be immediately accessible to all contexts on all devices which support unified + * addressing (as may be queried using ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING). + * Unless the flag ::CU_MEMHOSTALLOC_WRITECOMBINED is specified, the device pointer + * that may be used to access this host memory from those contexts is always equal + * to the returned host pointer \p *pp. If the flag ::CU_MEMHOSTALLOC_WRITECOMBINED + * is specified, then the function ::cuMemHostGetDevicePointer() must be used + * to query the device pointer, even if the context supports unified addressing. + * See \ref CUDA_UNIFIED for additional details. + * + * \param pp - Returned host pointer to page-locked memory + * \param bytesize - Requested allocation size in bytes + * \param Flags - Flags for allocation request + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaHostAlloc + */ +CUresult CUDAAPI cuMemHostAlloc(void **pp, size_t bytesize, unsigned int Flags); + +/** + * \brief Passes back device pointer of mapped pinned memory + * + * Passes back the device pointer \p pdptr corresponding to the mapped, pinned + * host buffer \p p allocated by ::cuMemHostAlloc. + * + * ::cuMemHostGetDevicePointer() will fail if the ::CU_MEMHOSTALLOC_DEVICEMAP + * flag was not specified at the time the memory was allocated, or if the + * function is called on a GPU that does not support mapped pinned memory. + * + * For devices that have a non-zero value for the device attribute + * ::CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM, the memory + * can also be accessed from the device using the host pointer \p p. + * The device pointer returned by ::cuMemHostGetDevicePointer() may or may not + * match the original host pointer \p p and depends on the devices visible to the + * application. If all devices visible to the application have a non-zero value for the + * device attribute, the device pointer returned by ::cuMemHostGetDevicePointer() + * will match the original pointer \p p. If any device visible to the application + * has a zero value for the device attribute, the device pointer returned by + * ::cuMemHostGetDevicePointer() will not match the original host pointer \p p, + * but it will be suitable for use on all devices provided Unified Virtual Addressing + * is enabled. In such systems, it is valid to access the memory using either pointer + * on devices that have a non-zero value for the device attribute. Note however that + * such devices should access the memory using only one of the two pointers and not both. + * + * \p Flags provides for future releases. For now, it must be set to 0. + * + * \param pdptr - Returned device pointer + * \param p - Host pointer + * \param Flags - Options (must be 0) + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaHostGetDevicePointer + */ +CUresult CUDAAPI cuMemHostGetDevicePointer(CUdeviceptr *pdptr, void *p, unsigned int Flags); + +/** + * \brief Passes back flags that were used for a pinned allocation + * + * Passes back the flags \p pFlags that were specified when allocating + * the pinned host buffer \p p allocated by ::cuMemHostAlloc. + * + * ::cuMemHostGetFlags() will fail if the pointer does not reside in + * an allocation performed by ::cuMemAllocHost() or ::cuMemHostAlloc(). + * + * \param pFlags - Returned flags word + * \param p - Host pointer + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa + * ::cuMemAllocHost, + * ::cuMemHostAlloc, + * ::cudaHostGetFlags + */ +CUresult CUDAAPI cuMemHostGetFlags(unsigned int *pFlags, void *p); + +/** + * \brief Allocates memory that will be automatically managed by the Unified Memory system + * + * Allocates \p bytesize bytes of managed memory on the device and returns in + * \p *dptr a pointer to the allocated memory. If the device doesn't support + * allocating managed memory, ::CUDA_ERROR_NOT_SUPPORTED is returned. Support + * for managed memory can be queried using the device attribute + * ::CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY. The allocated memory is suitably + * aligned for any kind of variable. The memory is not cleared. If \p bytesize + * is 0, ::cuMemAllocManaged returns ::CUDA_ERROR_INVALID_VALUE. The pointer + * is valid on the CPU and on all GPUs in the system that support managed memory. + * All accesses to this pointer must obey the Unified Memory programming model. + * + * \p flags specifies the default stream association for this allocation. + * \p flags must be one of ::CU_MEM_ATTACH_GLOBAL or ::CU_MEM_ATTACH_HOST. If + * ::CU_MEM_ATTACH_GLOBAL is specified, then this memory is accessible from + * any stream on any device. If ::CU_MEM_ATTACH_HOST is specified, then the + * allocation should not be accessed from devices that have a zero value for the + * device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS; an explicit call to + * ::cuStreamAttachMemAsync will be required to enable access on such devices. + * + * If the association is later changed via ::cuStreamAttachMemAsync to + * a single stream, the default association as specified during ::cuMemAllocManaged + * is restored when that stream is destroyed. For __managed__ variables, the + * default association is always ::CU_MEM_ATTACH_GLOBAL. Note that destroying a + * stream is an asynchronous operation, and as a result, the change to default + * association won't happen until all work in the stream has completed. + * + * Memory allocated with ::cuMemAllocManaged should be released with ::cuMemFree. + * + * Device memory oversubscription is possible for GPUs that have a non-zero value for the + * device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. Managed memory on + * such GPUs may be evicted from device memory to host memory at any time by the Unified + * Memory driver in order to make room for other allocations. + * + * In a multi-GPU system where all GPUs have a non-zero value for the device attribute + * ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS, managed memory may not be populated when this + * API returns and instead may be populated on access. In such systems, managed memory can + * migrate to any processor's memory at any time. The Unified Memory driver will employ heuristics to + * maintain data locality and prevent excessive page faults to the extent possible. The application + * can also guide the driver about memory usage patterns via ::cuMemAdvise. The application + * can also explicitly migrate memory to a desired processor's memory via + * ::cuMemPrefetchAsync. + * + * In a multi-GPU system where all of the GPUs have a zero value for the device attribute + * ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS and all the GPUs have peer-to-peer support + * with each other, the physical storage for managed memory is created on the GPU which is active + * at the time ::cuMemAllocManaged is called. All other GPUs will reference the data at reduced + * bandwidth via peer mappings over the PCIe bus. The Unified Memory driver does not migrate + * memory among such GPUs. + * + * In a multi-GPU system where not all GPUs have peer-to-peer support with each other and + * where the value of the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS + * is zero for at least one of those GPUs, the location chosen for physical storage of managed + * memory is system-dependent. + * - On Linux, the location chosen will be device memory as long as the current set of active + * contexts are on devices that either have peer-to-peer support with each other or have a + * non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. + * If there is an active context on a GPU that does not have a non-zero value for that device + * attribute and it does not have peer-to-peer support with the other devices that have active + * contexts on them, then the location for physical storage will be 'zero-copy' or host memory. + * Note that this means that managed memory that is located in device memory is migrated to + * host memory if a new context is created on a GPU that doesn't have a non-zero value for + * the device attribute and does not support peer-to-peer with at least one of the other devices + * that has an active context. This in turn implies that context creation may fail if there is + * insufficient host memory to migrate all managed allocations. + * - On Windows, the physical storage is always created in 'zero-copy' or host memory. + * All GPUs will reference the data at reduced bandwidth over the PCIe bus. In these + * circumstances, use of the environment variable CUDA_VISIBLE_DEVICES is recommended to + * restrict CUDA to only use those GPUs that have peer-to-peer support. + * Alternatively, users can also set CUDA_MANAGED_FORCE_DEVICE_ALLOC to a + * non-zero value to force the driver to always use device memory for physical storage. + * When this environment variable is set to a non-zero value, all contexts created in + * that process on devices that support managed memory have to be peer-to-peer compatible + * with each other. Context creation will fail if a context is created on a device that + * supports managed memory and is not peer-to-peer compatible with any of the other + * managed memory supporting devices on which contexts were previously created, even if + * those contexts have been destroyed. These environment variables are described + * in the CUDA programming guide under the "CUDA environment variables" section. + * - On ARM, managed memory is not available on discrete gpu with Drive PX-2. + * + * \param dptr - Returned device pointer + * \param bytesize - Requested allocation size in bytes + * \param flags - Must be one of ::CU_MEM_ATTACH_GLOBAL or ::CU_MEM_ATTACH_HOST + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cuDeviceGetAttribute, ::cuStreamAttachMemAsync, + * ::cudaMallocManaged + */ +CUresult CUDAAPI cuMemAllocManaged(CUdeviceptr *dptr, size_t bytesize, unsigned int flags); + +/** + * \brief Returns a handle to a compute device + * + * Returns in \p *device a device handle given a PCI bus ID string. + * + * \param dev - Returned device handle + * + * \param pciBusId - String in one of the following forms: + * [domain]:[bus]:[device].[function] + * [domain]:[bus]:[device] + * [bus]:[device].[function] + * where \p domain, \p bus, \p device, and \p function are all hexadecimal values + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGet, + * ::cuDeviceGetAttribute, + * ::cuDeviceGetPCIBusId, + * ::cudaDeviceGetByPCIBusId + */ +CUresult CUDAAPI cuDeviceGetByPCIBusId(CUdevice *dev, const char *pciBusId); + +/** + * \brief Returns a PCI Bus Id string for the device + * + * Returns an ASCII string identifying the device \p dev in the NULL-terminated + * string pointed to by \p pciBusId. \p len specifies the maximum length of the + * string that may be returned. + * + * \param pciBusId - Returned identifier string for the device in the following format + * [domain]:[bus]:[device].[function] + * where \p domain, \p bus, \p device, and \p function are all hexadecimal values. + * pciBusId should be large enough to store 13 characters including the NULL-terminator. + * + * \param len - Maximum length of string to store in \p name + * + * \param dev - Device to get identifier string for + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuDeviceGet, + * ::cuDeviceGetAttribute, + * ::cuDeviceGetByPCIBusId, + * ::cudaDeviceGetPCIBusId + */ +CUresult CUDAAPI cuDeviceGetPCIBusId(char *pciBusId, int len, CUdevice dev); + +/** + * \brief Gets an interprocess handle for a previously allocated event + * + * Takes as input a previously allocated event. This event must have been + * created with the ::CU_EVENT_INTERPROCESS and ::CU_EVENT_DISABLE_TIMING + * flags set. This opaque handle may be copied into other processes and + * opened with ::cuIpcOpenEventHandle to allow efficient hardware + * synchronization between GPU work in different processes. + * + * After the event has been opened in the importing process, + * ::cuEventRecord, ::cuEventSynchronize, ::cuStreamWaitEvent and + * ::cuEventQuery may be used in either process. Performing operations + * on the imported event after the exported event has been freed + * with ::cuEventDestroy will result in undefined behavior. + * + * IPC functionality is restricted to devices with support for unified + * addressing on Linux and Windows operating systems. + * IPC functionality on Windows is restricted to GPUs in TCC mode + * Users can test their device for IPC functionality by calling + * ::cuapiDeviceGetAttribute with ::CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED + * + * \param pHandle - Pointer to a user allocated CUipcEventHandle + * in which to return the opaque event handle + * \param event - Event allocated with ::CU_EVENT_INTERPROCESS and + * ::CU_EVENT_DISABLE_TIMING flags. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_MAP_FAILED, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuEventCreate, + * ::cuEventDestroy, + * ::cuEventSynchronize, + * ::cuEventQuery, + * ::cuStreamWaitEvent, + * ::cuIpcOpenEventHandle, + * ::cuIpcGetMemHandle, + * ::cuIpcOpenMemHandle, + * ::cuIpcCloseMemHandle, + * ::cudaIpcGetEventHandle + */ +CUresult CUDAAPI cuIpcGetEventHandle(CUipcEventHandle *pHandle, CUevent event); + +/** + * \brief Opens an interprocess event handle for use in the current process + * + * Opens an interprocess event handle exported from another process with + * ::cuIpcGetEventHandle. This function returns a ::CUevent that behaves like + * a locally created event with the ::CU_EVENT_DISABLE_TIMING flag specified. + * This event must be freed with ::cuEventDestroy. + * + * Performing operations on the imported event after the exported event has + * been freed with ::cuEventDestroy will result in undefined behavior. + * + * IPC functionality is restricted to devices with support for unified + * addressing on Linux and Windows operating systems. + * IPC functionality on Windows is restricted to GPUs in TCC mode + * Users can test their device for IPC functionality by calling + * ::cuapiDeviceGetAttribute with ::CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED + * + * \param phEvent - Returns the imported event + * \param handle - Interprocess handle to open + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_MAP_FAILED, + * ::CUDA_ERROR_PEER_ACCESS_UNSUPPORTED, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuEventCreate, + * ::cuEventDestroy, + * ::cuEventSynchronize, + * ::cuEventQuery, + * ::cuStreamWaitEvent, + * ::cuIpcGetEventHandle, + * ::cuIpcGetMemHandle, + * ::cuIpcOpenMemHandle, + * ::cuIpcCloseMemHandle, + * ::cudaIpcOpenEventHandle + */ +CUresult CUDAAPI cuIpcOpenEventHandle(CUevent *phEvent, CUipcEventHandle handle); + +/** + * \brief Gets an interprocess memory handle for an existing device memory + * allocation + * + * Takes a pointer to the base of an existing device memory allocation created + * with ::cuMemAlloc and exports it for use in another process. This is a + * lightweight operation and may be called multiple times on an allocation + * without adverse effects. + * + * If a region of memory is freed with ::cuMemFree and a subsequent call + * to ::cuMemAlloc returns memory with the same device address, + * ::cuIpcGetMemHandle will return a unique handle for the + * new memory. + * + * IPC functionality is restricted to devices with support for unified + * addressing on Linux and Windows operating systems. + * IPC functionality on Windows is restricted to GPUs in TCC mode + * Users can test their device for IPC functionality by calling + * ::cuapiDeviceGetAttribute with ::CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED + * + * \param pHandle - Pointer to user allocated ::CUipcMemHandle to return + * the handle in. + * \param dptr - Base pointer to previously allocated device memory + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_MAP_FAILED, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuMemAlloc, + * ::cuMemFree, + * ::cuIpcGetEventHandle, + * ::cuIpcOpenEventHandle, + * ::cuIpcOpenMemHandle, + * ::cuIpcCloseMemHandle, + * ::cudaIpcGetMemHandle + */ +CUresult CUDAAPI cuIpcGetMemHandle(CUipcMemHandle *pHandle, CUdeviceptr dptr); + +/** + * \brief Opens an interprocess memory handle exported from another process + * and returns a device pointer usable in the local process. + * + * Maps memory exported from another process with ::cuIpcGetMemHandle into + * the current device address space. For contexts on different devices + * ::cuIpcOpenMemHandle can attempt to enable peer access between the + * devices as if the user called ::cuCtxEnablePeerAccess. This behavior is + * controlled by the ::CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS flag. + * ::cuDeviceCanAccessPeer can determine if a mapping is possible. + * + * Contexts that may open ::CUipcMemHandles are restricted in the following way. + * ::CUipcMemHandles from each ::CUdevice in a given process may only be opened + * by one ::CUcontext per ::CUdevice per other process. + * + * If the memory handle has already been opened by the current context, the + * reference count on the handle is incremented by 1 and the existing device pointer + * is returned. + * + * Memory returned from ::cuIpcOpenMemHandle must be freed with + * ::cuIpcCloseMemHandle. + * + * Calling ::cuMemFree on an exported memory region before calling + * ::cuIpcCloseMemHandle in the importing context will result in undefined + * behavior. + * + * IPC functionality is restricted to devices with support for unified + * addressing on Linux and Windows operating systems. + * IPC functionality on Windows is restricted to GPUs in TCC mode + * Users can test their device for IPC functionality by calling + * ::cuapiDeviceGetAttribute with ::CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED + * + * \param pdptr - Returned device pointer + * \param handle - ::CUipcMemHandle to open + * \param Flags - Flags for this operation. Must be specified as ::CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_MAP_FAILED, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_TOO_MANY_PEERS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \note No guarantees are made about the address returned in \p *pdptr. + * In particular, multiple processes may not receive the same address for the same \p handle. + * + * \sa + * ::cuMemAlloc, + * ::cuMemFree, + * ::cuIpcGetEventHandle, + * ::cuIpcOpenEventHandle, + * ::cuIpcGetMemHandle, + * ::cuIpcCloseMemHandle, + * ::cuCtxEnablePeerAccess, + * ::cuDeviceCanAccessPeer, + * ::cudaIpcOpenMemHandle + */ +CUresult CUDAAPI cuIpcOpenMemHandle(CUdeviceptr *pdptr, CUipcMemHandle handle, unsigned int Flags); + +/** + * \brief Attempts to close memory mapped with ::cuIpcOpenMemHandle + * + * Decrements the reference count of the memory returned by ::cuIpcOpenMemHandle by 1. + * When the reference count reaches 0, this API unmaps the memory. The original allocation + * in the exporting process as well as imported mappings in other processes + * will be unaffected. + * + * Any resources used to enable peer access will be freed if this is the + * last mapping using them. + * + * IPC functionality is restricted to devices with support for unified + * addressing on Linux and Windows operating systems. + * IPC functionality on Windows is restricted to GPUs in TCC mode + * Users can test their device for IPC functionality by calling + * ::cuapiDeviceGetAttribute with ::CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED + * + * \param dptr - Device pointer returned by ::cuIpcOpenMemHandle + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_MAP_FAILED, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE + * \sa + * ::cuMemAlloc, + * ::cuMemFree, + * ::cuIpcGetEventHandle, + * ::cuIpcOpenEventHandle, + * ::cuIpcGetMemHandle, + * ::cuIpcOpenMemHandle, + * ::cudaIpcCloseMemHandle + */ +CUresult CUDAAPI cuIpcCloseMemHandle(CUdeviceptr dptr); + +/** + * \brief Registers an existing host memory range for use by CUDA + * + * Page-locks the memory range specified by \p p and \p bytesize and maps it + * for the device(s) as specified by \p Flags. This memory range also is added + * to the same tracking mechanism as ::cuMemHostAlloc to automatically accelerate + * calls to functions such as ::cuMemcpyHtoD(). Since the memory can be accessed + * directly by the device, it can be read or written with much higher bandwidth + * than pageable memory that has not been registered. Page-locking excessive + * amounts of memory may degrade system performance, since it reduces the amount + * of memory available to the system for paging. As a result, this function is + * best used sparingly to register staging areas for data exchange between + * host and device. + * + * On systems where ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES + * is true, ::cuMemHostRegister will not page-lock the memory range specified + * by \p ptr but only populate unpopulated pages. + * + * The \p Flags parameter enables different options to be specified that + * affect the allocation, as follows. + * + * - ::CU_MEMHOSTREGISTER_PORTABLE: The memory returned by this call will be + * considered as pinned memory by all CUDA contexts, not just the one that + * performed the allocation. + * + * - ::CU_MEMHOSTREGISTER_DEVICEMAP: Maps the allocation into the CUDA address + * space. The device pointer to the memory may be obtained by calling + * ::cuMemHostGetDevicePointer(). + * + * - ::CU_MEMHOSTREGISTER_IOMEMORY: The pointer is treated as pointing to some + * I/O memory space, e.g. the PCI Express resource of a 3rd party device. + * + * - ::CU_MEMHOSTREGISTER_READ_ONLY: The pointer is treated as pointing to memory + * that is considered read-only by the device. On platforms without + * ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, this flag is + * required in order to register memory mapped to the CPU as read-only. Support + * for the use of this flag can be queried from the device attribute + * ::CU_DEVICE_ATTRIBUTE_READ_ONLY_HOST_REGISTER_SUPPORTED. Using this flag with + * a current context associated with a device that does not have this attribute + * set will cause ::cuMemHostRegister to error with CUDA_ERROR_NOT_SUPPORTED. + * + * All of these flags are orthogonal to one another: a developer may page-lock + * memory that is portable or mapped with no restrictions. + * + * The ::CU_MEMHOSTREGISTER_DEVICEMAP flag may be specified on CUDA contexts for + * devices that do not support mapped pinned memory. The failure is deferred + * to ::cuMemHostGetDevicePointer() because the memory may be mapped into + * other CUDA contexts via the ::CU_MEMHOSTREGISTER_PORTABLE flag. + * + * For devices that have a non-zero value for the device attribute + * ::CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM, the memory + * can also be accessed from the device using the host pointer \p p. + * The device pointer returned by ::cuMemHostGetDevicePointer() may or may not + * match the original host pointer \p ptr and depends on the devices visible to the + * application. If all devices visible to the application have a non-zero value for the + * device attribute, the device pointer returned by ::cuMemHostGetDevicePointer() + * will match the original pointer \p ptr. If any device visible to the application + * has a zero value for the device attribute, the device pointer returned by + * ::cuMemHostGetDevicePointer() will not match the original host pointer \p ptr, + * but it will be suitable for use on all devices provided Unified Virtual Addressing + * is enabled. In such systems, it is valid to access the memory using either pointer + * on devices that have a non-zero value for the device attribute. Note however that + * such devices should access the memory using only of the two pointers and not both. + * + * The memory page-locked by this function must be unregistered with + * ::cuMemHostUnregister(). + * + * \param p - Host pointer to memory to page-lock + * \param bytesize - Size in bytes of the address range to page-lock + * \param Flags - Flags for allocation request + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED, + * ::CUDA_ERROR_NOT_PERMITTED, + * ::CUDA_ERROR_NOT_SUPPORTED + * \notefnerr + * + * \sa + * ::cuMemHostUnregister, + * ::cuMemHostGetFlags, + * ::cuMemHostGetDevicePointer, + * ::cudaHostRegister + */ +CUresult CUDAAPI cuMemHostRegister(void *p, size_t bytesize, unsigned int Flags); + +/** + * \brief Unregisters a memory range that was registered with cuMemHostRegister. + * + * Unmaps the memory range whose base address is specified by \p p, and makes + * it pageable again. + * + * The base address must be the same one specified to ::cuMemHostRegister(). + * + * \param p - Host pointer to memory to unregister + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED, + * \notefnerr + * + * \sa + * ::cuMemHostRegister, + * ::cudaHostUnregister + */ +CUresult CUDAAPI cuMemHostUnregister(void *p); + +/** + * \brief Copies memory + * + * Copies data between two pointers. + * \p dst and \p src are base pointers of the destination and source, respectively. + * \p ByteCount specifies the number of bytes to copy. + * Note that this function infers the type of the transfer (host to host, host to + * device, device to device, or device to host) from the pointer values. This + * function is only allowed in contexts which support unified addressing. + * + * \param dst - Destination unified virtual address space pointer + * \param src - Source unified virtual address space pointer + * \param ByteCount - Size of memory copy in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * \note_memcpy + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemcpy, + * ::cudaMemcpyToSymbol, + * ::cudaMemcpyFromSymbol + */ +CUresult CUDAAPI cuMemcpy(CUdeviceptr dst, CUdeviceptr src, size_t ByteCount); + +/** + * \brief Copies device memory between two contexts + * + * Copies from device memory in one context to device memory in another + * context. \p dstDevice is the base device pointer of the destination memory + * and \p dstContext is the destination context. \p srcDevice is the base + * device pointer of the source memory and \p srcContext is the source pointer. + * \p ByteCount specifies the number of bytes to copy. + * + * \param dstDevice - Destination device pointer + * \param dstContext - Destination context + * \param srcDevice - Source device pointer + * \param srcContext - Source context + * \param ByteCount - Size of memory copy in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * + * \sa ::cuMemcpyDtoD, ::cuMemcpy3DPeer, ::cuMemcpyDtoDAsync, ::cuMemcpyPeerAsync, + * ::cuMemcpy3DPeerAsync, + * ::cudaMemcpyPeer + */ +CUresult CUDAAPI cuMemcpyPeer(CUdeviceptr dstDevice, CUcontext dstContext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount); + +/** + * \brief Copies memory from Host to Device + * + * Copies from host memory to device memory. \p dstDevice and \p srcHost are + * the base addresses of the destination and source, respectively. \p ByteCount + * specifies the number of bytes to copy. + * + * \param dstDevice - Destination device pointer + * \param srcHost - Source host pointer + * \param ByteCount - Size of memory copy in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * \note_memcpy + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemcpy, + * ::cudaMemcpyToSymbol + */ +CUresult CUDAAPI cuMemcpyHtoD(CUdeviceptr dstDevice, const void *srcHost, size_t ByteCount); + +/** + * \brief Copies memory from Device to Host + * + * Copies from device to host memory. \p dstHost and \p srcDevice specify the + * base pointers of the destination and source, respectively. \p ByteCount + * specifies the number of bytes to copy. + * + * \param dstHost - Destination host pointer + * \param srcDevice - Source device pointer + * \param ByteCount - Size of memory copy in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * \note_memcpy + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemcpy, + * ::cudaMemcpyFromSymbol + */ +CUresult CUDAAPI cuMemcpyDtoH(void *dstHost, CUdeviceptr srcDevice, size_t ByteCount); + +/** + * \brief Copies memory from Device to Device + * + * Copies from device memory to device memory. \p dstDevice and \p srcDevice + * are the base pointers of the destination and source, respectively. + * \p ByteCount specifies the number of bytes to copy. + * + * \param dstDevice - Destination device pointer + * \param srcDevice - Source device pointer + * \param ByteCount - Size of memory copy in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemcpy, + * ::cudaMemcpyToSymbol, + * ::cudaMemcpyFromSymbol + */ +CUresult CUDAAPI cuMemcpyDtoD(CUdeviceptr dstDevice, CUdeviceptr srcDevice, size_t ByteCount); + +/** + * \brief Copies memory from Device to Array + * + * Copies from device memory to a 1D CUDA array. \p dstArray and \p dstOffset + * specify the CUDA array handle and starting index of the destination data. + * \p srcDevice specifies the base pointer of the source. \p ByteCount + * specifies the number of bytes to copy. + * + * \param dstArray - Destination array + * \param dstOffset - Offset in bytes of destination array + * \param srcDevice - Source device pointer + * \param ByteCount - Size of memory copy in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemcpyToArray + */ +CUresult CUDAAPI cuMemcpyDtoA(CUarray dstArray, size_t dstOffset, CUdeviceptr srcDevice, size_t ByteCount); + +/** + * \brief Copies memory from Array to Device + * + * Copies from one 1D CUDA array to device memory. \p dstDevice specifies the + * base pointer of the destination and must be naturally aligned with the CUDA + * array elements. \p srcArray and \p srcOffset specify the CUDA array handle + * and the offset in bytes into the array where the copy is to begin. + * \p ByteCount specifies the number of bytes to copy and must be evenly + * divisible by the array element size. + * + * \param dstDevice - Destination device pointer + * \param srcArray - Source array + * \param srcOffset - Offset in bytes of source array + * \param ByteCount - Size of memory copy in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemcpyFromArray + */ +CUresult CUDAAPI cuMemcpyAtoD(CUdeviceptr dstDevice, CUarray srcArray, size_t srcOffset, size_t ByteCount); + +/** + * \brief Copies memory from Host to Array + * + * Copies from host memory to a 1D CUDA array. \p dstArray and \p dstOffset + * specify the CUDA array handle and starting offset in bytes of the destination + * data. \p pSrc specifies the base address of the source. \p ByteCount specifies + * the number of bytes to copy. + * + * \param dstArray - Destination array + * \param dstOffset - Offset in bytes of destination array + * \param srcHost - Source host pointer + * \param ByteCount - Size of memory copy in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * \note_memcpy + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemcpyToArray + */ +CUresult CUDAAPI cuMemcpyHtoA(CUarray dstArray, size_t dstOffset, const void *srcHost, size_t ByteCount); + +/** + * \brief Copies memory from Array to Host + * + * Copies from one 1D CUDA array to host memory. \p dstHost specifies the base + * pointer of the destination. \p srcArray and \p srcOffset specify the CUDA + * array handle and starting offset in bytes of the source data. + * \p ByteCount specifies the number of bytes to copy. + * + * \param dstHost - Destination device pointer + * \param srcArray - Source array + * \param srcOffset - Offset in bytes of source array + * \param ByteCount - Size of memory copy in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * \note_memcpy + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemcpyFromArray + */ +CUresult CUDAAPI cuMemcpyAtoH(void *dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount); + +/** + * \brief Copies memory from Array to Array + * + * Copies from one 1D CUDA array to another. \p dstArray and \p srcArray + * specify the handles of the destination and source CUDA arrays for the copy, + * respectively. \p dstOffset and \p srcOffset specify the destination and + * source offsets in bytes into the CUDA arrays. \p ByteCount is the number of + * bytes to be copied. The size of the elements in the CUDA arrays need not be + * the same format, but the elements must be the same size; and count must be + * evenly divisible by that size. + * + * \param dstArray - Destination array + * \param dstOffset - Offset in bytes of destination array + * \param srcArray - Source array + * \param srcOffset - Offset in bytes of source array + * \param ByteCount - Size of memory copy in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemcpyArrayToArray + */ +CUresult CUDAAPI cuMemcpyAtoA(CUarray dstArray, size_t dstOffset, CUarray srcArray, size_t srcOffset, size_t ByteCount); + +/** + * \brief Copies memory for 2D arrays + * + * Perform a 2D memory copy according to the parameters specified in \p pCopy. + * The ::CUDA_MEMCPY2D structure is defined as: + * + * \code + typedef struct CUDA_MEMCPY2D_st { + unsigned int srcXInBytes, srcY; + CUmemorytype srcMemoryType; + const void *srcHost; + CUdeviceptr srcDevice; + CUarray srcArray; + unsigned int srcPitch; + + unsigned int dstXInBytes, dstY; + CUmemorytype dstMemoryType; + void *dstHost; + CUdeviceptr dstDevice; + CUarray dstArray; + unsigned int dstPitch; + + unsigned int WidthInBytes; + unsigned int Height; + } CUDA_MEMCPY2D; + * \endcode + * where: + * - ::srcMemoryType and ::dstMemoryType specify the type of memory of the + * source and destination, respectively; ::CUmemorytype_enum is defined as: + * + * \code + typedef enum CUmemorytype_enum { + CU_MEMORYTYPE_HOST = 0x01, + CU_MEMORYTYPE_DEVICE = 0x02, + CU_MEMORYTYPE_ARRAY = 0x03, + CU_MEMORYTYPE_UNIFIED = 0x04 + } CUmemorytype; + * \endcode + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitch + * specify the (unified virtual address space) base address of the source data + * and the bytes per row to apply. ::srcArray is ignored. + * This value may be used only if unified addressing is supported in the calling + * context. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost and ::srcPitch + * specify the (host) base address of the source data and the bytes per row to + * apply. ::srcArray is ignored. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice and ::srcPitch + * specify the (device) base address of the source data and the bytes per row + * to apply. ::srcArray is ignored. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the + * handle of the source data. ::srcHost, ::srcDevice and ::srcPitch are + * ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch + * specify the (host) base address of the destination data and the bytes per + * row to apply. ::dstArray is ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitch + * specify the (unified virtual address space) base address of the source data + * and the bytes per row to apply. ::dstArray is ignored. + * This value may be used only if unified addressing is supported in the calling + * context. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch + * specify the (device) base address of the destination data and the bytes per + * row to apply. ::dstArray is ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_ARRAY, ::dstArray specifies the + * handle of the destination data. ::dstHost, ::dstDevice and ::dstPitch are + * ignored. + * + * - ::srcXInBytes and ::srcY specify the base address of the source data for + * the copy. + * + * \par + * For host pointers, the starting address is + * \code + void* Start = (void*)((char*)srcHost+srcY*srcPitch + srcXInBytes); + * \endcode + * + * \par + * For device pointers, the starting address is + * \code + CUdeviceptr Start = srcDevice+srcY*srcPitch+srcXInBytes; + * \endcode + * + * \par + * For CUDA arrays, ::srcXInBytes must be evenly divisible by the array + * element size. + * + * - ::dstXInBytes and ::dstY specify the base address of the destination data + * for the copy. + * + * \par + * For host pointers, the base address is + * \code + void* dstStart = (void*)((char*)dstHost+dstY*dstPitch + dstXInBytes); + * \endcode + * + * \par + * For device pointers, the starting address is + * \code + CUdeviceptr dstStart = dstDevice+dstY*dstPitch+dstXInBytes; + * \endcode + * + * \par + * For CUDA arrays, ::dstXInBytes must be evenly divisible by the array + * element size. + * + * - ::WidthInBytes and ::Height specify the width (in bytes) and height of + * the 2D copy being performed. + * - If specified, ::srcPitch must be greater than or equal to ::WidthInBytes + + * ::srcXInBytes, and ::dstPitch must be greater than or equal to + * ::WidthInBytes + dstXInBytes. + * + * \par + * ::cuMemcpy2D() returns an error if any pitch is greater than the maximum + * allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). ::cuMemAllocPitch() passes back + * pitches that always work with ::cuMemcpy2D(). On intra-device memory copies + * (device to device, CUDA array to device, CUDA array to CUDA array), + * ::cuMemcpy2D() may fail for pitches not computed by ::cuMemAllocPitch(). + * ::cuMemcpy2DUnaligned() does not have this restriction, but may run + * significantly slower in the cases where ::cuMemcpy2D() would have returned + * an error code. + * + * \param pCopy - Parameters for the memory copy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemcpy2D, + * ::cudaMemcpy2DToArray, + * ::cudaMemcpy2DFromArray + */ +CUresult CUDAAPI cuMemcpy2D(const CUDA_MEMCPY2D *pCopy); + +/** + * \brief Copies memory for 2D arrays + * + * Perform a 2D memory copy according to the parameters specified in \p pCopy. + * The ::CUDA_MEMCPY2D structure is defined as: + * + * \code + typedef struct CUDA_MEMCPY2D_st { + unsigned int srcXInBytes, srcY; + CUmemorytype srcMemoryType; + const void *srcHost; + CUdeviceptr srcDevice; + CUarray srcArray; + unsigned int srcPitch; + unsigned int dstXInBytes, dstY; + CUmemorytype dstMemoryType; + void *dstHost; + CUdeviceptr dstDevice; + CUarray dstArray; + unsigned int dstPitch; + unsigned int WidthInBytes; + unsigned int Height; + } CUDA_MEMCPY2D; + * \endcode + * where: + * - ::srcMemoryType and ::dstMemoryType specify the type of memory of the + * source and destination, respectively; ::CUmemorytype_enum is defined as: + * + * \code + typedef enum CUmemorytype_enum { + CU_MEMORYTYPE_HOST = 0x01, + CU_MEMORYTYPE_DEVICE = 0x02, + CU_MEMORYTYPE_ARRAY = 0x03, + CU_MEMORYTYPE_UNIFIED = 0x04 + } CUmemorytype; + * \endcode + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitch + * specify the (unified virtual address space) base address of the source data + * and the bytes per row to apply. ::srcArray is ignored. + * This value may be used only if unified addressing is supported in the calling + * context. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost and ::srcPitch + * specify the (host) base address of the source data and the bytes per row to + * apply. ::srcArray is ignored. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice and ::srcPitch + * specify the (device) base address of the source data and the bytes per row + * to apply. ::srcArray is ignored. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the + * handle of the source data. ::srcHost, ::srcDevice and ::srcPitch are + * ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitch + * specify the (unified virtual address space) base address of the source data + * and the bytes per row to apply. ::dstArray is ignored. + * This value may be used only if unified addressing is supported in the calling + * context. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch + * specify the (host) base address of the destination data and the bytes per + * row to apply. ::dstArray is ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch + * specify the (device) base address of the destination data and the bytes per + * row to apply. ::dstArray is ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_ARRAY, ::dstArray specifies the + * handle of the destination data. ::dstHost, ::dstDevice and ::dstPitch are + * ignored. + * + * - ::srcXInBytes and ::srcY specify the base address of the source data for + * the copy. + * + * \par + * For host pointers, the starting address is + * \code + void* Start = (void*)((char*)srcHost+srcY*srcPitch + srcXInBytes); + * \endcode + * + * \par + * For device pointers, the starting address is + * \code + CUdeviceptr Start = srcDevice+srcY*srcPitch+srcXInBytes; + * \endcode + * + * \par + * For CUDA arrays, ::srcXInBytes must be evenly divisible by the array + * element size. + * + * - ::dstXInBytes and ::dstY specify the base address of the destination data + * for the copy. + * + * \par + * For host pointers, the base address is + * \code + void* dstStart = (void*)((char*)dstHost+dstY*dstPitch + dstXInBytes); + * \endcode + * + * \par + * For device pointers, the starting address is + * \code + CUdeviceptr dstStart = dstDevice+dstY*dstPitch+dstXInBytes; + * \endcode + * + * \par + * For CUDA arrays, ::dstXInBytes must be evenly divisible by the array + * element size. + * + * - ::WidthInBytes and ::Height specify the width (in bytes) and height of + * the 2D copy being performed. + * - If specified, ::srcPitch must be greater than or equal to ::WidthInBytes + + * ::srcXInBytes, and ::dstPitch must be greater than or equal to + * ::WidthInBytes + dstXInBytes. + * + * \par + * ::cuMemcpy2D() returns an error if any pitch is greater than the maximum + * allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). ::cuMemAllocPitch() passes back + * pitches that always work with ::cuMemcpy2D(). On intra-device memory copies + * (device to device, CUDA array to device, CUDA array to CUDA array), + * ::cuMemcpy2D() may fail for pitches not computed by ::cuMemAllocPitch(). + * ::cuMemcpy2DUnaligned() does not have this restriction, but may run + * significantly slower in the cases where ::cuMemcpy2D() would have returned + * an error code. + * + * \param pCopy - Parameters for the memory copy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemcpy2D, + * ::cudaMemcpy2DToArray, + * ::cudaMemcpy2DFromArray + */ +CUresult CUDAAPI cuMemcpy2DUnaligned(const CUDA_MEMCPY2D *pCopy); + +/** + * \brief Copies memory for 3D arrays + * + * Perform a 3D memory copy according to the parameters specified in + * \p pCopy. The ::CUDA_MEMCPY3D structure is defined as: + * + * \code + typedef struct CUDA_MEMCPY3D_st { + + unsigned int srcXInBytes, srcY, srcZ; + unsigned int srcLOD; + CUmemorytype srcMemoryType; + const void *srcHost; + CUdeviceptr srcDevice; + CUarray srcArray; + unsigned int srcPitch; // ignored when src is array + unsigned int srcHeight; // ignored when src is array; may be 0 if Depth==1 + + unsigned int dstXInBytes, dstY, dstZ; + unsigned int dstLOD; + CUmemorytype dstMemoryType; + void *dstHost; + CUdeviceptr dstDevice; + CUarray dstArray; + unsigned int dstPitch; // ignored when dst is array + unsigned int dstHeight; // ignored when dst is array; may be 0 if Depth==1 + + unsigned int WidthInBytes; + unsigned int Height; + unsigned int Depth; + } CUDA_MEMCPY3D; + * \endcode + * where: + * - ::srcMemoryType and ::dstMemoryType specify the type of memory of the + * source and destination, respectively; ::CUmemorytype_enum is defined as: + * + * \code + typedef enum CUmemorytype_enum { + CU_MEMORYTYPE_HOST = 0x01, + CU_MEMORYTYPE_DEVICE = 0x02, + CU_MEMORYTYPE_ARRAY = 0x03, + CU_MEMORYTYPE_UNIFIED = 0x04 + } CUmemorytype; + * \endcode + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitch + * specify the (unified virtual address space) base address of the source data + * and the bytes per row to apply. ::srcArray is ignored. + * This value may be used only if unified addressing is supported in the calling + * context. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost, ::srcPitch and + * ::srcHeight specify the (host) base address of the source data, the bytes + * per row, and the height of each 2D slice of the 3D array. ::srcArray is + * ignored. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice, ::srcPitch and + * ::srcHeight specify the (device) base address of the source data, the bytes + * per row, and the height of each 2D slice of the 3D array. ::srcArray is + * ignored. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the + * handle of the source data. ::srcHost, ::srcDevice, ::srcPitch and + * ::srcHeight are ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitch + * specify the (unified virtual address space) base address of the source data + * and the bytes per row to apply. ::dstArray is ignored. + * This value may be used only if unified addressing is supported in the calling + * context. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch + * specify the (host) base address of the destination data, the bytes per row, + * and the height of each 2D slice of the 3D array. ::dstArray is ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch + * specify the (device) base address of the destination data, the bytes per + * row, and the height of each 2D slice of the 3D array. ::dstArray is ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_ARRAY, ::dstArray specifies the + * handle of the destination data. ::dstHost, ::dstDevice, ::dstPitch and + * ::dstHeight are ignored. + * + * - ::srcXInBytes, ::srcY and ::srcZ specify the base address of the source + * data for the copy. + * + * \par + * For host pointers, the starting address is + * \code + void* Start = (void*)((char*)srcHost+(srcZ*srcHeight+srcY)*srcPitch + srcXInBytes); + * \endcode + * + * \par + * For device pointers, the starting address is + * \code + CUdeviceptr Start = srcDevice+(srcZ*srcHeight+srcY)*srcPitch+srcXInBytes; + * \endcode + * + * \par + * For CUDA arrays, ::srcXInBytes must be evenly divisible by the array + * element size. + * + * - dstXInBytes, ::dstY and ::dstZ specify the base address of the + * destination data for the copy. + * + * \par + * For host pointers, the base address is + * \code + void* dstStart = (void*)((char*)dstHost+(dstZ*dstHeight+dstY)*dstPitch + dstXInBytes); + * \endcode + * + * \par + * For device pointers, the starting address is + * \code + CUdeviceptr dstStart = dstDevice+(dstZ*dstHeight+dstY)*dstPitch+dstXInBytes; + * \endcode + * + * \par + * For CUDA arrays, ::dstXInBytes must be evenly divisible by the array + * element size. + * + * - ::WidthInBytes, ::Height and ::Depth specify the width (in bytes), height + * and depth of the 3D copy being performed. + * - If specified, ::srcPitch must be greater than or equal to ::WidthInBytes + + * ::srcXInBytes, and ::dstPitch must be greater than or equal to + * ::WidthInBytes + dstXInBytes. + * - If specified, ::srcHeight must be greater than or equal to ::Height + + * ::srcY, and ::dstHeight must be greater than or equal to ::Height + ::dstY. + * + * \par + * ::cuMemcpy3D() returns an error if any pitch is greater than the maximum + * allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). + * + * The ::srcLOD and ::dstLOD members of the ::CUDA_MEMCPY3D structure must be + * set to 0. + * + * \param pCopy - Parameters for the memory copy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMemcpy3D + */ +CUresult CUDAAPI cuMemcpy3D(const CUDA_MEMCPY3D *pCopy); + +/** + * \brief Copies memory between contexts + * + * Perform a 3D memory copy according to the parameters specified in + * \p pCopy. See the definition of the ::CUDA_MEMCPY3D_PEER structure + * for documentation of its parameters. + * + * \param pCopy - Parameters for the memory copy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_sync + * + * \sa ::cuMemcpyDtoD, ::cuMemcpyPeer, ::cuMemcpyDtoDAsync, ::cuMemcpyPeerAsync, + * ::cuMemcpy3DPeerAsync, + * ::cudaMemcpy3DPeer + */ +CUresult CUDAAPI cuMemcpy3DPeer(const CUDA_MEMCPY3D_PEER *pCopy); + +/** + * \brief Copies memory asynchronously + * + * Copies data between two pointers. + * \p dst and \p src are base pointers of the destination and source, respectively. + * \p ByteCount specifies the number of bytes to copy. + * Note that this function infers the type of the transfer (host to host, host to + * device, device to device, or device to host) from the pointer values. This + * function is only allowed in contexts which support unified addressing. + * + * \param dst - Destination unified virtual address space pointer + * \param src - Source unified virtual address space pointer + * \param ByteCount - Size of memory copy in bytes + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * \note_async + * \note_null_stream + * \note_memcpy + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemcpyAsync, + * ::cudaMemcpyToSymbolAsync, + * ::cudaMemcpyFromSymbolAsync + */ +CUresult CUDAAPI cuMemcpyAsync(CUdeviceptr dst, CUdeviceptr src, size_t ByteCount, CUstream hStream); + +/** + * \brief Copies device memory between two contexts asynchronously. + * + * Copies from device memory in one context to device memory in another + * context. \p dstDevice is the base device pointer of the destination memory + * and \p dstContext is the destination context. \p srcDevice is the base + * device pointer of the source memory and \p srcContext is the source pointer. + * \p ByteCount specifies the number of bytes to copy. + * + * \param dstDevice - Destination device pointer + * \param dstContext - Destination context + * \param srcDevice - Source device pointer + * \param srcContext - Source context + * \param ByteCount - Size of memory copy in bytes + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * \note_async + * \note_null_stream + * + * \sa ::cuMemcpyDtoD, ::cuMemcpyPeer, ::cuMemcpy3DPeer, ::cuMemcpyDtoDAsync, + * ::cuMemcpy3DPeerAsync, + * ::cudaMemcpyPeerAsync + */ +CUresult CUDAAPI cuMemcpyPeerAsync(CUdeviceptr dstDevice, CUcontext dstContext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount, CUstream hStream); + +/** + * \brief Copies memory from Host to Device + * + * Copies from host memory to device memory. \p dstDevice and \p srcHost are + * the base addresses of the destination and source, respectively. \p ByteCount + * specifies the number of bytes to copy. + * + * \param dstDevice - Destination device pointer + * \param srcHost - Source host pointer + * \param ByteCount - Size of memory copy in bytes + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * \note_async + * \note_null_stream + * \note_memcpy + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemcpyAsync, + * ::cudaMemcpyToSymbolAsync + */ +CUresult CUDAAPI cuMemcpyHtoDAsync(CUdeviceptr dstDevice, const void *srcHost, size_t ByteCount, CUstream hStream); + +/** + * \brief Copies memory from Device to Host + * + * Copies from device to host memory. \p dstHost and \p srcDevice specify the + * base pointers of the destination and source, respectively. \p ByteCount + * specifies the number of bytes to copy. + * + * \param dstHost - Destination host pointer + * \param srcDevice - Source device pointer + * \param ByteCount - Size of memory copy in bytes + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * \note_async + * \note_null_stream + * \note_memcpy + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemcpyAsync, + * ::cudaMemcpyFromSymbolAsync + */ +CUresult CUDAAPI cuMemcpyDtoHAsync(void *dstHost, CUdeviceptr srcDevice, size_t ByteCount, CUstream hStream); + +/** + * \brief Copies memory from Device to Device + * + * Copies from device memory to device memory. \p dstDevice and \p srcDevice + * are the base pointers of the destination and source, respectively. + * \p ByteCount specifies the number of bytes to copy. + * + * \param dstDevice - Destination device pointer + * \param srcDevice - Source device pointer + * \param ByteCount - Size of memory copy in bytes + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * \note_async + * \note_null_stream + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemcpyAsync, + * ::cudaMemcpyToSymbolAsync, + * ::cudaMemcpyFromSymbolAsync + */ +CUresult CUDAAPI cuMemcpyDtoDAsync(CUdeviceptr dstDevice, CUdeviceptr srcDevice, size_t ByteCount, CUstream hStream); + +/** + * \brief Copies memory from Host to Array + * + * Copies from host memory to a 1D CUDA array. \p dstArray and \p dstOffset + * specify the CUDA array handle and starting offset in bytes of the + * destination data. \p srcHost specifies the base address of the source. + * \p ByteCount specifies the number of bytes to copy. + * + * \param dstArray - Destination array + * \param dstOffset - Offset in bytes of destination array + * \param srcHost - Source host pointer + * \param ByteCount - Size of memory copy in bytes + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * \note_async + * \note_null_stream + * \note_memcpy + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemcpyToArrayAsync + */ +CUresult CUDAAPI cuMemcpyHtoAAsync(CUarray dstArray, size_t dstOffset, const void *srcHost, size_t ByteCount, CUstream hStream); + +/** + * \brief Copies memory from Array to Host + * + * Copies from one 1D CUDA array to host memory. \p dstHost specifies the base + * pointer of the destination. \p srcArray and \p srcOffset specify the CUDA + * array handle and starting offset in bytes of the source data. + * \p ByteCount specifies the number of bytes to copy. + * + * \param dstHost - Destination pointer + * \param srcArray - Source array + * \param srcOffset - Offset in bytes of source array + * \param ByteCount - Size of memory copy in bytes + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * \note_async + * \note_null_stream + * \note_memcpy + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemcpyFromArrayAsync + */ +CUresult CUDAAPI cuMemcpyAtoHAsync(void *dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount, CUstream hStream); + +/** + * \brief Copies memory for 2D arrays + * + * Perform a 2D memory copy according to the parameters specified in \p pCopy. + * The ::CUDA_MEMCPY2D structure is defined as: + * + * \code + typedef struct CUDA_MEMCPY2D_st { + unsigned int srcXInBytes, srcY; + CUmemorytype srcMemoryType; + const void *srcHost; + CUdeviceptr srcDevice; + CUarray srcArray; + unsigned int srcPitch; + unsigned int dstXInBytes, dstY; + CUmemorytype dstMemoryType; + void *dstHost; + CUdeviceptr dstDevice; + CUarray dstArray; + unsigned int dstPitch; + unsigned int WidthInBytes; + unsigned int Height; + } CUDA_MEMCPY2D; + * \endcode + * where: + * - ::srcMemoryType and ::dstMemoryType specify the type of memory of the + * source and destination, respectively; ::CUmemorytype_enum is defined as: + * + * \code + typedef enum CUmemorytype_enum { + CU_MEMORYTYPE_HOST = 0x01, + CU_MEMORYTYPE_DEVICE = 0x02, + CU_MEMORYTYPE_ARRAY = 0x03, + CU_MEMORYTYPE_UNIFIED = 0x04 + } CUmemorytype; + * \endcode + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost and ::srcPitch + * specify the (host) base address of the source data and the bytes per row to + * apply. ::srcArray is ignored. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitch + * specify the (unified virtual address space) base address of the source data + * and the bytes per row to apply. ::srcArray is ignored. + * This value may be used only if unified addressing is supported in the calling + * context. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice and ::srcPitch + * specify the (device) base address of the source data and the bytes per row + * to apply. ::srcArray is ignored. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the + * handle of the source data. ::srcHost, ::srcDevice and ::srcPitch are + * ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitch + * specify the (unified virtual address space) base address of the source data + * and the bytes per row to apply. ::dstArray is ignored. + * This value may be used only if unified addressing is supported in the calling + * context. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch + * specify the (host) base address of the destination data and the bytes per + * row to apply. ::dstArray is ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch + * specify the (device) base address of the destination data and the bytes per + * row to apply. ::dstArray is ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_ARRAY, ::dstArray specifies the + * handle of the destination data. ::dstHost, ::dstDevice and ::dstPitch are + * ignored. + * + * - ::srcXInBytes and ::srcY specify the base address of the source data for + * the copy. + * + * \par + * For host pointers, the starting address is + * \code + void* Start = (void*)((char*)srcHost+srcY*srcPitch + srcXInBytes); + * \endcode + * + * \par + * For device pointers, the starting address is + * \code + CUdeviceptr Start = srcDevice+srcY*srcPitch+srcXInBytes; + * \endcode + * + * \par + * For CUDA arrays, ::srcXInBytes must be evenly divisible by the array + * element size. + * + * - ::dstXInBytes and ::dstY specify the base address of the destination data + * for the copy. + * + * \par + * For host pointers, the base address is + * \code + void* dstStart = (void*)((char*)dstHost+dstY*dstPitch + dstXInBytes); + * \endcode + * + * \par + * For device pointers, the starting address is + * \code + CUdeviceptr dstStart = dstDevice+dstY*dstPitch+dstXInBytes; + * \endcode + * + * \par + * For CUDA arrays, ::dstXInBytes must be evenly divisible by the array + * element size. + * + * - ::WidthInBytes and ::Height specify the width (in bytes) and height of + * the 2D copy being performed. + * - If specified, ::srcPitch must be greater than or equal to ::WidthInBytes + + * ::srcXInBytes, and ::dstPitch must be greater than or equal to + * ::WidthInBytes + dstXInBytes. + * - If specified, ::srcPitch must be greater than or equal to ::WidthInBytes + + * ::srcXInBytes, and ::dstPitch must be greater than or equal to + * ::WidthInBytes + dstXInBytes. + * - If specified, ::srcHeight must be greater than or equal to ::Height + + * ::srcY, and ::dstHeight must be greater than or equal to ::Height + ::dstY. + * + * \par + * ::cuMemcpy2DAsync() returns an error if any pitch is greater than the maximum + * allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). ::cuMemAllocPitch() passes back + * pitches that always work with ::cuMemcpy2D(). On intra-device memory copies + * (device to device, CUDA array to device, CUDA array to CUDA array), + * ::cuMemcpy2DAsync() may fail for pitches not computed by ::cuMemAllocPitch(). + * + * \param pCopy - Parameters for the memory copy + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * \note_async + * \note_null_stream + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemcpy2DAsync, + * ::cudaMemcpy2DToArrayAsync, + * ::cudaMemcpy2DFromArrayAsync + */ +CUresult CUDAAPI cuMemcpy2DAsync(const CUDA_MEMCPY2D *pCopy, CUstream hStream); + +/** + * \brief Copies memory for 3D arrays + * + * Perform a 3D memory copy according to the parameters specified in + * \p pCopy. The ::CUDA_MEMCPY3D structure is defined as: + * + * \code + typedef struct CUDA_MEMCPY3D_st { + + unsigned int srcXInBytes, srcY, srcZ; + unsigned int srcLOD; + CUmemorytype srcMemoryType; + const void *srcHost; + CUdeviceptr srcDevice; + CUarray srcArray; + unsigned int srcPitch; // ignored when src is array + unsigned int srcHeight; // ignored when src is array; may be 0 if Depth==1 + + unsigned int dstXInBytes, dstY, dstZ; + unsigned int dstLOD; + CUmemorytype dstMemoryType; + void *dstHost; + CUdeviceptr dstDevice; + CUarray dstArray; + unsigned int dstPitch; // ignored when dst is array + unsigned int dstHeight; // ignored when dst is array; may be 0 if Depth==1 + + unsigned int WidthInBytes; + unsigned int Height; + unsigned int Depth; + } CUDA_MEMCPY3D; + * \endcode + * where: + * - ::srcMemoryType and ::dstMemoryType specify the type of memory of the + * source and destination, respectively; ::CUmemorytype_enum is defined as: + * + * \code + typedef enum CUmemorytype_enum { + CU_MEMORYTYPE_HOST = 0x01, + CU_MEMORYTYPE_DEVICE = 0x02, + CU_MEMORYTYPE_ARRAY = 0x03, + CU_MEMORYTYPE_UNIFIED = 0x04 + } CUmemorytype; + * \endcode + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::srcDevice and ::srcPitch + * specify the (unified virtual address space) base address of the source data + * and the bytes per row to apply. ::srcArray is ignored. + * This value may be used only if unified addressing is supported in the calling + * context. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_HOST, ::srcHost, ::srcPitch and + * ::srcHeight specify the (host) base address of the source data, the bytes + * per row, and the height of each 2D slice of the 3D array. ::srcArray is + * ignored. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_DEVICE, ::srcDevice, ::srcPitch and + * ::srcHeight specify the (device) base address of the source data, the bytes + * per row, and the height of each 2D slice of the 3D array. ::srcArray is + * ignored. + * + * \par + * If ::srcMemoryType is ::CU_MEMORYTYPE_ARRAY, ::srcArray specifies the + * handle of the source data. ::srcHost, ::srcDevice, ::srcPitch and + * ::srcHeight are ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_UNIFIED, ::dstDevice and ::dstPitch + * specify the (unified virtual address space) base address of the source data + * and the bytes per row to apply. ::dstArray is ignored. + * This value may be used only if unified addressing is supported in the calling + * context. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_HOST, ::dstHost and ::dstPitch + * specify the (host) base address of the destination data, the bytes per row, + * and the height of each 2D slice of the 3D array. ::dstArray is ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_DEVICE, ::dstDevice and ::dstPitch + * specify the (device) base address of the destination data, the bytes per + * row, and the height of each 2D slice of the 3D array. ::dstArray is ignored. + * + * \par + * If ::dstMemoryType is ::CU_MEMORYTYPE_ARRAY, ::dstArray specifies the + * handle of the destination data. ::dstHost, ::dstDevice, ::dstPitch and + * ::dstHeight are ignored. + * + * - ::srcXInBytes, ::srcY and ::srcZ specify the base address of the source + * data for the copy. + * + * \par + * For host pointers, the starting address is + * \code + void* Start = (void*)((char*)srcHost+(srcZ*srcHeight+srcY)*srcPitch + srcXInBytes); + * \endcode + * + * \par + * For device pointers, the starting address is + * \code + CUdeviceptr Start = srcDevice+(srcZ*srcHeight+srcY)*srcPitch+srcXInBytes; + * \endcode + * + * \par + * For CUDA arrays, ::srcXInBytes must be evenly divisible by the array + * element size. + * + * - dstXInBytes, ::dstY and ::dstZ specify the base address of the + * destination data for the copy. + * + * \par + * For host pointers, the base address is + * \code + void* dstStart = (void*)((char*)dstHost+(dstZ*dstHeight+dstY)*dstPitch + dstXInBytes); + * \endcode + * + * \par + * For device pointers, the starting address is + * \code + CUdeviceptr dstStart = dstDevice+(dstZ*dstHeight+dstY)*dstPitch+dstXInBytes; + * \endcode + * + * \par + * For CUDA arrays, ::dstXInBytes must be evenly divisible by the array + * element size. + * + * - ::WidthInBytes, ::Height and ::Depth specify the width (in bytes), height + * and depth of the 3D copy being performed. + * - If specified, ::srcPitch must be greater than or equal to ::WidthInBytes + + * ::srcXInBytes, and ::dstPitch must be greater than or equal to + * ::WidthInBytes + dstXInBytes. + * - If specified, ::srcHeight must be greater than or equal to ::Height + + * ::srcY, and ::dstHeight must be greater than or equal to ::Height + ::dstY. + * + * \par + * ::cuMemcpy3DAsync() returns an error if any pitch is greater than the maximum + * allowed (::CU_DEVICE_ATTRIBUTE_MAX_PITCH). + * + * The ::srcLOD and ::dstLOD members of the ::CUDA_MEMCPY3D structure must be + * set to 0. + * + * \param pCopy - Parameters for the memory copy + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * \note_async + * \note_null_stream + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemcpy3DAsync + */ +CUresult CUDAAPI cuMemcpy3DAsync(const CUDA_MEMCPY3D *pCopy, CUstream hStream); + +/** + * \brief Copies memory between contexts asynchronously. + * + * Perform a 3D memory copy according to the parameters specified in + * \p pCopy. See the definition of the ::CUDA_MEMCPY3D_PEER structure + * for documentation of its parameters. + * + * \param pCopy - Parameters for the memory copy + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_async + * \note_null_stream + * + * \sa ::cuMemcpyDtoD, ::cuMemcpyPeer, ::cuMemcpyDtoDAsync, ::cuMemcpyPeerAsync, + * ::cuMemcpy3DPeerAsync, + * ::cudaMemcpy3DPeerAsync + */ +CUresult CUDAAPI cuMemcpy3DPeerAsync(const CUDA_MEMCPY3D_PEER *pCopy, CUstream hStream); + +/** + * \brief Initializes device memory + * + * Sets the memory range of \p N 8-bit values to the specified value + * \p uc. + * + * \param dstDevice - Destination device pointer + * \param uc - Value to set + * \param N - Number of elements + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_memset + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemset + */ +CUresult CUDAAPI cuMemsetD8(CUdeviceptr dstDevice, unsigned char uc, size_t N); + +/** + * \brief Initializes device memory + * + * Sets the memory range of \p N 16-bit values to the specified value + * \p us. The \p dstDevice pointer must be two byte aligned. + * + * \param dstDevice - Destination device pointer + * \param us - Value to set + * \param N - Number of elements + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_memset + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemset + */ +CUresult CUDAAPI cuMemsetD16(CUdeviceptr dstDevice, unsigned short us, size_t N); + +/** + * \brief Initializes device memory + * + * Sets the memory range of \p N 32-bit values to the specified value + * \p ui. The \p dstDevice pointer must be four byte aligned. + * + * \param dstDevice - Destination device pointer + * \param ui - Value to set + * \param N - Number of elements + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_memset + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32Async, + * ::cudaMemset + */ +CUresult CUDAAPI cuMemsetD32(CUdeviceptr dstDevice, unsigned int ui, size_t N); + +/** + * \brief Initializes device memory + * + * Sets the 2D memory range of \p Width 8-bit values to the specified value + * \p uc. \p Height specifies the number of rows to set, and \p dstPitch + * specifies the number of bytes between each row. This function performs + * fastest when the pitch is one that has been passed back by + * ::cuMemAllocPitch(). + * + * \param dstDevice - Destination device pointer + * \param dstPitch - Pitch of destination device pointer(Unused if \p Height is 1) + * \param uc - Value to set + * \param Width - Width of row + * \param Height - Number of rows + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_memset + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemset2D + */ +CUresult CUDAAPI cuMemsetD2D8(CUdeviceptr dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height); + +/** + * \brief Initializes device memory + * + * Sets the 2D memory range of \p Width 16-bit values to the specified value + * \p us. \p Height specifies the number of rows to set, and \p dstPitch + * specifies the number of bytes between each row. The \p dstDevice pointer + * and \p dstPitch offset must be two byte aligned. This function performs + * fastest when the pitch is one that has been passed back by + * ::cuMemAllocPitch(). + * + * \param dstDevice - Destination device pointer + * \param dstPitch - Pitch of destination device pointer(Unused if \p Height is 1) + * \param us - Value to set + * \param Width - Width of row + * \param Height - Number of rows + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_memset + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemset2D + */ +CUresult CUDAAPI cuMemsetD2D16(CUdeviceptr dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height); + +/** + * \brief Initializes device memory + * + * Sets the 2D memory range of \p Width 32-bit values to the specified value + * \p ui. \p Height specifies the number of rows to set, and \p dstPitch + * specifies the number of bytes between each row. The \p dstDevice pointer + * and \p dstPitch offset must be four byte aligned. This function performs + * fastest when the pitch is one that has been passed back by + * ::cuMemAllocPitch(). + * + * \param dstDevice - Destination device pointer + * \param dstPitch - Pitch of destination device pointer(Unused if \p Height is 1) + * \param ui - Value to set + * \param Width - Width of row + * \param Height - Number of rows + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_memset + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemset2D + */ +CUresult CUDAAPI cuMemsetD2D32(CUdeviceptr dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height); + +/** + * \brief Sets device memory + * + * Sets the memory range of \p N 8-bit values to the specified value + * \p uc. + * + * \param dstDevice - Destination device pointer + * \param uc - Value to set + * \param N - Number of elements + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_memset + * \note_null_stream + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemsetAsync + */ +CUresult CUDAAPI cuMemsetD8Async(CUdeviceptr dstDevice, unsigned char uc, size_t N, CUstream hStream); + +/** + * \brief Sets device memory + * + * Sets the memory range of \p N 16-bit values to the specified value + * \p us. The \p dstDevice pointer must be two byte aligned. + * + * \param dstDevice - Destination device pointer + * \param us - Value to set + * \param N - Number of elements + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_memset + * \note_null_stream + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemsetAsync + */ +CUresult CUDAAPI cuMemsetD16Async(CUdeviceptr dstDevice, unsigned short us, size_t N, CUstream hStream); + +/** + * \brief Sets device memory + * + * Sets the memory range of \p N 32-bit values to the specified value + * \p ui. The \p dstDevice pointer must be four byte aligned. + * + * \param dstDevice - Destination device pointer + * \param ui - Value to set + * \param N - Number of elements + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_memset + * \note_null_stream + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, ::cuMemsetD32, + * ::cudaMemsetAsync + */ +CUresult CUDAAPI cuMemsetD32Async(CUdeviceptr dstDevice, unsigned int ui, size_t N, CUstream hStream); + +/** + * \brief Sets device memory + * + * Sets the 2D memory range of \p Width 8-bit values to the specified value + * \p uc. \p Height specifies the number of rows to set, and \p dstPitch + * specifies the number of bytes between each row. This function performs + * fastest when the pitch is one that has been passed back by + * ::cuMemAllocPitch(). + * + * \param dstDevice - Destination device pointer + * \param dstPitch - Pitch of destination device pointer(Unused if \p Height is 1) + * \param uc - Value to set + * \param Width - Width of row + * \param Height - Number of rows + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_memset + * \note_null_stream + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemset2DAsync + */ +CUresult CUDAAPI cuMemsetD2D8Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height, CUstream hStream); + +/** + * \brief Sets device memory + * + * Sets the 2D memory range of \p Width 16-bit values to the specified value + * \p us. \p Height specifies the number of rows to set, and \p dstPitch + * specifies the number of bytes between each row. The \p dstDevice pointer + * and \p dstPitch offset must be two byte aligned. This function performs + * fastest when the pitch is one that has been passed back by + * ::cuMemAllocPitch(). + * + * \param dstDevice - Destination device pointer + * \param dstPitch - Pitch of destination device pointer(Unused if \p Height is 1) + * \param us - Value to set + * \param Width - Width of row + * \param Height - Number of rows + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_memset + * \note_null_stream + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D32, ::cuMemsetD2D32Async, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemset2DAsync + */ +CUresult CUDAAPI cuMemsetD2D16Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height, CUstream hStream); + +/** + * \brief Sets device memory + * + * Sets the 2D memory range of \p Width 32-bit values to the specified value + * \p ui. \p Height specifies the number of rows to set, and \p dstPitch + * specifies the number of bytes between each row. The \p dstDevice pointer + * and \p dstPitch offset must be four byte aligned. This function performs + * fastest when the pitch is one that has been passed back by + * ::cuMemAllocPitch(). + * + * \param dstDevice - Destination device pointer + * \param dstPitch - Pitch of destination device pointer(Unused if \p Height is 1) + * \param ui - Value to set + * \param Width - Width of row + * \param Height - Number of rows + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * \note_memset + * \note_null_stream + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D8Async, + * ::cuMemsetD2D16, ::cuMemsetD2D16Async, ::cuMemsetD2D32, + * ::cuMemsetD8, ::cuMemsetD8Async, ::cuMemsetD16, ::cuMemsetD16Async, + * ::cuMemsetD32, ::cuMemsetD32Async, + * ::cudaMemset2DAsync + */ +CUresult CUDAAPI cuMemsetD2D32Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height, CUstream hStream); + +/** + * \brief Creates a 1D or 2D CUDA array + * + * Creates a CUDA array according to the ::CUDA_ARRAY_DESCRIPTOR structure + * \p pAllocateArray and returns a handle to the new CUDA array in \p *pHandle. + * The ::CUDA_ARRAY_DESCRIPTOR is defined as: + * + * \code + typedef struct { + unsigned int Width; + unsigned int Height; + CUarray_format Format; + unsigned int NumChannels; + } CUDA_ARRAY_DESCRIPTOR; + * \endcode + * where: + * + * - \p Width, and \p Height are the width, and height of the CUDA array (in + * elements); the CUDA array is one-dimensional if height is 0, two-dimensional + * otherwise; + * - ::Format specifies the format of the elements; ::CUarray_format is + * defined as: + * \code + typedef enum CUarray_format_enum { + CU_AD_FORMAT_UNSIGNED_INT8 = 0x01, + CU_AD_FORMAT_UNSIGNED_INT16 = 0x02, + CU_AD_FORMAT_UNSIGNED_INT32 = 0x03, + CU_AD_FORMAT_SIGNED_INT8 = 0x08, + CU_AD_FORMAT_SIGNED_INT16 = 0x09, + CU_AD_FORMAT_SIGNED_INT32 = 0x0a, + CU_AD_FORMAT_HALF = 0x10, + CU_AD_FORMAT_FLOAT = 0x20 + } CUarray_format; + * \endcode + * - \p NumChannels specifies the number of packed components per CUDA array + * element; it may be 1, 2, or 4; + * + * Here are examples of CUDA array descriptions: + * + * Description for a CUDA array of 2048 floats: + * \code + CUDA_ARRAY_DESCRIPTOR desc; + desc.Format = CU_AD_FORMAT_FLOAT; + desc.NumChannels = 1; + desc.Width = 2048; + desc.Height = 1; + * \endcode + * + * Description for a 64 x 64 CUDA array of floats: + * \code + CUDA_ARRAY_DESCRIPTOR desc; + desc.Format = CU_AD_FORMAT_FLOAT; + desc.NumChannels = 1; + desc.Width = 64; + desc.Height = 64; + * \endcode + * + * Description for a \p width x \p height CUDA array of 64-bit, 4x16-bit + * float16's: + * \code + CUDA_ARRAY_DESCRIPTOR desc; + desc.Format = CU_AD_FORMAT_HALF; + desc.NumChannels = 4; + desc.Width = width; + desc.Height = height; + * \endcode + * + * Description for a \p width x \p height CUDA array of 16-bit elements, each + * of which is two 8-bit unsigned chars: + * \code + CUDA_ARRAY_DESCRIPTOR arrayDesc; + desc.Format = CU_AD_FORMAT_UNSIGNED_INT8; + desc.NumChannels = 2; + desc.Width = width; + desc.Height = height; + * \endcode + * + * \param pHandle - Returned array + * \param pAllocateArray - Array descriptor + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMallocArray + */ +CUresult CUDAAPI cuArrayCreate(CUarray *pHandle, const CUDA_ARRAY_DESCRIPTOR *pAllocateArray); + +/** + * \brief Get a 1D or 2D CUDA array descriptor + * + * Returns in \p *pArrayDescriptor a descriptor containing information on the + * format and dimensions of the CUDA array \p hArray. It is useful for + * subroutines that have been passed a CUDA array, but need to know the CUDA + * array parameters for validation or other purposes. + * + * \param pArrayDescriptor - Returned array descriptor + * \param hArray - Array to get descriptor of + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaArrayGetInfo + */ +CUresult CUDAAPI cuArrayGetDescriptor(CUDA_ARRAY_DESCRIPTOR *pArrayDescriptor, CUarray hArray); + +/** + * \brief Returns the layout properties of a sparse CUDA array + * + * Returns the layout properties of a sparse CUDA array in \p sparseProperties + * If the CUDA array is not allocated with flag ::CUDA_ARRAY3D_SPARSE + * ::CUDA_ERROR_INVALID_VALUE will be returned. + * + * If the returned value in ::CUDA_ARRAY_SPARSE_PROPERTIES::flags contains ::CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL, + * then ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize represents the total size of the array. Otherwise, it will be zero. + * Also, the returned value in ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailFirstLevel is always zero. + * Note that the \p array must have been allocated using ::cuArrayCreate or ::cuArray3DCreate. For CUDA arrays obtained + * using ::cuMipmappedArrayGetLevel, ::CUDA_ERROR_INVALID_VALUE will be returned. Instead, ::cuMipmappedArrayGetSparseProperties + * must be used to obtain the sparse properties of the entire CUDA mipmapped array to which \p array belongs to. + * + * \return + * ::CUDA_SUCCESS + * ::CUDA_ERROR_INVALID_VALUE + * + * \param[out] sparseProperties - Pointer to ::CUDA_ARRAY_SPARSE_PROPERTIES + * \param[in] array - CUDA array to get the sparse properties of + * \sa ::cuMipmappedArrayGetSparseProperties, ::cuMemMapArrayAsync + */ +CUresult CUDAAPI cuArrayGetSparseProperties(CUDA_ARRAY_SPARSE_PROPERTIES *sparseProperties, CUarray array); + +/** + * \brief Returns the layout properties of a sparse CUDA mipmapped array + * + * Returns the sparse array layout properties in \p sparseProperties + * If the CUDA mipmapped array is not allocated with flag ::CUDA_ARRAY3D_SPARSE + * ::CUDA_ERROR_INVALID_VALUE will be returned. + * + * For non-layered CUDA mipmapped arrays, ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize returns the + * size of the mip tail region. The mip tail region includes all mip levels whose width, height or depth + * is less than that of the tile. + * For layered CUDA mipmapped arrays, if ::CUDA_ARRAY_SPARSE_PROPERTIES::flags contains ::CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL, + * then ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize specifies the size of the mip tail of all layers combined. + * Otherwise, ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize specifies mip tail size per layer. + * The returned value of ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailFirstLevel is valid only if ::CUDA_ARRAY_SPARSE_PROPERTIES::miptailSize is non-zero. + * + * \return + * ::CUDA_SUCCESS + * ::CUDA_ERROR_INVALID_VALUE + * + * \param[out] sparseProperties - Pointer to ::CUDA_ARRAY_SPARSE_PROPERTIES + * \param[in] mipmap - CUDA mipmapped array to get the sparse properties of + * \sa ::cuArrayGetSparseProperties, ::cuMemMapArrayAsync + */ +CUresult CUDAAPI cuMipmappedArrayGetSparseProperties(CUDA_ARRAY_SPARSE_PROPERTIES *sparseProperties, CUmipmappedArray mipmap); + +/** + * \brief Returns the memory requirements of a CUDA array + * + * Returns the memory requirements of a CUDA array in \p memoryRequirements + * If the CUDA array is not allocated with flag ::CUDA_ARRAY3D_DEFERRED_MAPPING + * ::CUDA_ERROR_INVALID_VALUE will be returned. + * + * The returned value in ::CUDA_ARRAY_MEMORY_REQUIREMENTS::size + * represents the total size of the CUDA array. + * The returned value in ::CUDA_ARRAY_MEMORY_REQUIREMENTS::alignment + * represents the alignment necessary for mapping the CUDA array. + * + * \return + * ::CUDA_SUCCESS + * ::CUDA_ERROR_INVALID_VALUE + * + * \param[out] memoryRequirements - Pointer to ::CUDA_ARRAY_MEMORY_REQUIREMENTS + * \param[in] array - CUDA array to get the memory requirements of + * \param[in] device - Device to get the memory requirements for + * \sa ::cuMipmappedArrayGetMemoryRequirements, ::cuMemMapArrayAsync + */ +CUresult CUDAAPI cuArrayGetMemoryRequirements(CUDA_ARRAY_MEMORY_REQUIREMENTS *memoryRequirements, CUarray array, CUdevice device); + +/** + * \brief Returns the memory requirements of a CUDA mipmapped array + * + * Returns the memory requirements of a CUDA mipmapped array in \p memoryRequirements + * If the CUDA mipmapped array is not allocated with flag ::CUDA_ARRAY3D_DEFERRED_MAPPING + * ::CUDA_ERROR_INVALID_VALUE will be returned. + * + * The returned value in ::CUDA_ARRAY_MEMORY_REQUIREMENTS::size + * represents the total size of the CUDA mipmapped array. + * The returned value in ::CUDA_ARRAY_MEMORY_REQUIREMENTS::alignment + * represents the alignment necessary for mapping the CUDA mipmapped + * array. + * + * \return + * ::CUDA_SUCCESS + * ::CUDA_ERROR_INVALID_VALUE + * + * \param[out] memoryRequirements - Pointer to ::CUDA_ARRAY_MEMORY_REQUIREMENTS + * \param[in] mipmap - CUDA mipmapped array to get the memory requirements of + * \param[in] device - Device to get the memory requirements for + * \sa ::cuArrayGetMemoryRequirements, ::cuMemMapArrayAsync + */ +CUresult CUDAAPI cuMipmappedArrayGetMemoryRequirements(CUDA_ARRAY_MEMORY_REQUIREMENTS *memoryRequirements, CUmipmappedArray mipmap, CUdevice device); + +/** + * \brief Gets a CUDA array plane from a CUDA array + * + * Returns in \p pPlaneArray a CUDA array that represents a single format plane + * of the CUDA array \p hArray. + * + * If \p planeIdx is greater than the maximum number of planes in this array or if the array does + * not have a multi-planar format e.g: ::CU_AD_FORMAT_NV12, then ::CUDA_ERROR_INVALID_VALUE is returned. + * + * Note that if the \p hArray has format ::CU_AD_FORMAT_NV12, then passing in 0 for \p planeIdx returns + * a CUDA array of the same size as \p hArray but with one channel and ::CU_AD_FORMAT_UNSIGNED_INT8 as its format. + * If 1 is passed for \p planeIdx, then the returned CUDA array has half the height and width + * of \p hArray with two channels and ::CU_AD_FORMAT_UNSIGNED_INT8 as its format. + * + * \param pPlaneArray - Returned CUDA array referenced by the \p planeIdx + * \param hArray - Multiplanar CUDA array + * \param planeIdx - Plane index + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa + * ::cuArrayCreate, + * ::cudaArrayGetPlane + */ +CUresult CUDAAPI cuArrayGetPlane(CUarray *pPlaneArray, CUarray hArray, unsigned int planeIdx); + +/** + * \brief Destroys a CUDA array + * + * Destroys the CUDA array \p hArray. + * + * \param hArray - Array to destroy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_ARRAY_IS_MAPPED, + * ::CUDA_ERROR_CONTEXT_IS_DESTROYED + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaFreeArray + */ +CUresult CUDAAPI cuArrayDestroy(CUarray hArray); + +/** + * \brief Creates a 3D CUDA array + * + * Creates a CUDA array according to the ::CUDA_ARRAY3D_DESCRIPTOR structure + * \p pAllocateArray and returns a handle to the new CUDA array in \p *pHandle. + * The ::CUDA_ARRAY3D_DESCRIPTOR is defined as: + * + * \code + typedef struct { + unsigned int Width; + unsigned int Height; + unsigned int Depth; + CUarray_format Format; + unsigned int NumChannels; + unsigned int Flags; + } CUDA_ARRAY3D_DESCRIPTOR; + * \endcode + * where: + * + * - \p Width, \p Height, and \p Depth are the width, height, and depth of the + * CUDA array (in elements); the following types of CUDA arrays can be allocated: + * - A 1D array is allocated if \p Height and \p Depth extents are both zero. + * - A 2D array is allocated if only \p Depth extent is zero. + * - A 3D array is allocated if all three extents are non-zero. + * - A 1D layered CUDA array is allocated if only \p Height is zero and the + * ::CUDA_ARRAY3D_LAYERED flag is set. Each layer is a 1D array. The number + * of layers is determined by the depth extent. + * - A 2D layered CUDA array is allocated if all three extents are non-zero and + * the ::CUDA_ARRAY3D_LAYERED flag is set. Each layer is a 2D array. The number + * of layers is determined by the depth extent. + * - A cubemap CUDA array is allocated if all three extents are non-zero and the + * ::CUDA_ARRAY3D_CUBEMAP flag is set. \p Width must be equal to \p Height, and + * \p Depth must be six. A cubemap is a special type of 2D layered CUDA array, + * where the six layers represent the six faces of a cube. The order of the six + * layers in memory is the same as that listed in ::CUarray_cubemap_face. + * - A cubemap layered CUDA array is allocated if all three extents are non-zero, + * and both, ::CUDA_ARRAY3D_CUBEMAP and ::CUDA_ARRAY3D_LAYERED flags are set. + * \p Width must be equal to \p Height, and \p Depth must be a multiple of six. + * A cubemap layered CUDA array is a special type of 2D layered CUDA array that + * consists of a collection of cubemaps. The first six layers represent the first + * cubemap, the next six layers form the second cubemap, and so on. + * + * - ::Format specifies the format of the elements; ::CUarray_format is + * defined as: + * \code + typedef enum CUarray_format_enum { + CU_AD_FORMAT_UNSIGNED_INT8 = 0x01, + CU_AD_FORMAT_UNSIGNED_INT16 = 0x02, + CU_AD_FORMAT_UNSIGNED_INT32 = 0x03, + CU_AD_FORMAT_SIGNED_INT8 = 0x08, + CU_AD_FORMAT_SIGNED_INT16 = 0x09, + CU_AD_FORMAT_SIGNED_INT32 = 0x0a, + CU_AD_FORMAT_HALF = 0x10, + CU_AD_FORMAT_FLOAT = 0x20 + } CUarray_format; + * \endcode + * + * - \p NumChannels specifies the number of packed components per CUDA array + * element; it may be 1, 2, or 4; + * + * - ::Flags may be set to + * - ::CUDA_ARRAY3D_LAYERED to enable creation of layered CUDA arrays. If this flag is set, + * \p Depth specifies the number of layers, not the depth of a 3D array. + * - ::CUDA_ARRAY3D_SURFACE_LDST to enable surface references to be bound to the CUDA array. + * If this flag is not set, ::cuSurfRefSetArray will fail when attempting to bind the CUDA array + * to a surface reference. + * - ::CUDA_ARRAY3D_CUBEMAP to enable creation of cubemaps. If this flag is set, \p Width must be + * equal to \p Height, and \p Depth must be six. If the ::CUDA_ARRAY3D_LAYERED flag is also set, + * then \p Depth must be a multiple of six. + * - ::CUDA_ARRAY3D_TEXTURE_GATHER to indicate that the CUDA array will be used for texture gather. + * Texture gather can only be performed on 2D CUDA arrays. + * + * \p Width, \p Height and \p Depth must meet certain size requirements as listed in the following table. + * All values are specified in elements. Note that for brevity's sake, the full name of the device attribute + * is not specified. For ex., TEXTURE1D_WIDTH refers to the device attribute + * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH. + * + * Note that 2D CUDA arrays have different size requirements if the ::CUDA_ARRAY3D_TEXTURE_GATHER flag + * is set. \p Width and \p Height must not be greater than ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH + * and ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT respectively, in that case. + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + *
CUDA array typeValid extents that must always be met
{(width range in elements), (height range), + * (depth range)}		Valid extents with CUDA_ARRAY3D_SURFACE_LDST set
+ * {(width range in elements), (height range), (depth range)}
1D{ (1,TEXTURE1D_WIDTH), 0, 0 }{ (1,SURFACE1D_WIDTH), 0, 0 }
2D{ (1,TEXTURE2D_WIDTH), (1,TEXTURE2D_HEIGHT), 0 }{ (1,SURFACE2D_WIDTH), (1,SURFACE2D_HEIGHT), 0 }
3D{ (1,TEXTURE3D_WIDTH), (1,TEXTURE3D_HEIGHT), (1,TEXTURE3D_DEPTH) } + *
OR
{ (1,TEXTURE3D_WIDTH_ALTERNATE), (1,TEXTURE3D_HEIGHT_ALTERNATE), + * (1,TEXTURE3D_DEPTH_ALTERNATE) }		{ (1,SURFACE3D_WIDTH), (1,SURFACE3D_HEIGHT), + * (1,SURFACE3D_DEPTH) }
1D Layered{ (1,TEXTURE1D_LAYERED_WIDTH), 0, + * (1,TEXTURE1D_LAYERED_LAYERS) }{ (1,SURFACE1D_LAYERED_WIDTH), 0, + * (1,SURFACE1D_LAYERED_LAYERS) }
2D Layered{ (1,TEXTURE2D_LAYERED_WIDTH), (1,TEXTURE2D_LAYERED_HEIGHT), + * (1,TEXTURE2D_LAYERED_LAYERS) }{ (1,SURFACE2D_LAYERED_WIDTH), (1,SURFACE2D_LAYERED_HEIGHT), + * (1,SURFACE2D_LAYERED_LAYERS) }
Cubemap{ (1,TEXTURECUBEMAP_WIDTH), (1,TEXTURECUBEMAP_WIDTH), 6 }{ (1,SURFACECUBEMAP_WIDTH), + * (1,SURFACECUBEMAP_WIDTH), 6 }
Cubemap Layered{ (1,TEXTURECUBEMAP_LAYERED_WIDTH), (1,TEXTURECUBEMAP_LAYERED_WIDTH), + * (1,TEXTURECUBEMAP_LAYERED_LAYERS) }{ (1,SURFACECUBEMAP_LAYERED_WIDTH), (1,SURFACECUBEMAP_LAYERED_WIDTH), + * (1,SURFACECUBEMAP_LAYERED_LAYERS) }
+ * + * Here are examples of CUDA array descriptions: + * + * Description for a CUDA array of 2048 floats: + * \code + CUDA_ARRAY3D_DESCRIPTOR desc; + desc.Format = CU_AD_FORMAT_FLOAT; + desc.NumChannels = 1; + desc.Width = 2048; + desc.Height = 0; + desc.Depth = 0; + * \endcode + * + * Description for a 64 x 64 CUDA array of floats: + * \code + CUDA_ARRAY3D_DESCRIPTOR desc; + desc.Format = CU_AD_FORMAT_FLOAT; + desc.NumChannels = 1; + desc.Width = 64; + desc.Height = 64; + desc.Depth = 0; + * \endcode + * + * Description for a \p width x \p height x \p depth CUDA array of 64-bit, + * 4x16-bit float16's: + * \code + CUDA_ARRAY3D_DESCRIPTOR desc; + desc.Format = CU_AD_FORMAT_HALF; + desc.NumChannels = 4; + desc.Width = width; + desc.Height = height; + desc.Depth = depth; + * \endcode + * + * \param pHandle - Returned array + * \param pAllocateArray - 3D array descriptor + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa ::cuArray3DGetDescriptor, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaMalloc3DArray + */ +CUresult CUDAAPI cuArray3DCreate(CUarray *pHandle, const CUDA_ARRAY3D_DESCRIPTOR *pAllocateArray); + +/** + * \brief Get a 3D CUDA array descriptor + * + * Returns in \p *pArrayDescriptor a descriptor containing information on the + * format and dimensions of the CUDA array \p hArray. It is useful for + * subroutines that have been passed a CUDA array, but need to know the CUDA + * array parameters for validation or other purposes. + * + * This function may be called on 1D and 2D arrays, in which case the \p Height + * and/or \p Depth members of the descriptor struct will be set to 0. + * + * \param pArrayDescriptor - Returned 3D array descriptor + * \param hArray - 3D array to get descriptor of + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_CONTEXT_IS_DESTROYED + * \notefnerr + * + * \sa ::cuArray3DCreate, ::cuArrayCreate, + * ::cuArrayDestroy, ::cuArrayGetDescriptor, ::cuMemAlloc, ::cuMemAllocHost, + * ::cuMemAllocPitch, ::cuMemcpy2D, ::cuMemcpy2DAsync, ::cuMemcpy2DUnaligned, + * ::cuMemcpy3D, ::cuMemcpy3DAsync, ::cuMemcpyAtoA, ::cuMemcpyAtoD, + * ::cuMemcpyAtoH, ::cuMemcpyAtoHAsync, ::cuMemcpyDtoA, ::cuMemcpyDtoD, ::cuMemcpyDtoDAsync, + * ::cuMemcpyDtoH, ::cuMemcpyDtoHAsync, ::cuMemcpyHtoA, ::cuMemcpyHtoAAsync, + * ::cuMemcpyHtoD, ::cuMemcpyHtoDAsync, ::cuMemFree, ::cuMemFreeHost, + * ::cuMemGetAddressRange, ::cuMemGetInfo, ::cuMemHostAlloc, + * ::cuMemHostGetDevicePointer, ::cuMemsetD2D8, ::cuMemsetD2D16, + * ::cuMemsetD2D32, ::cuMemsetD8, ::cuMemsetD16, ::cuMemsetD32, + * ::cudaArrayGetInfo + */ +CUresult CUDAAPI cuArray3DGetDescriptor(CUDA_ARRAY3D_DESCRIPTOR *pArrayDescriptor, CUarray hArray); + +/** + * \brief Creates a CUDA mipmapped array + * + * Creates a CUDA mipmapped array according to the ::CUDA_ARRAY3D_DESCRIPTOR structure + * \p pMipmappedArrayDesc and returns a handle to the new CUDA mipmapped array in \p *pHandle. + * \p numMipmapLevels specifies the number of mipmap levels to be allocated. This value is + * clamped to the range [1, 1 + floor(log2(max(width, height, depth)))]. + * + * The ::CUDA_ARRAY3D_DESCRIPTOR is defined as: + * + * \code + typedef struct { + unsigned int Width; + unsigned int Height; + unsigned int Depth; + CUarray_format Format; + unsigned int NumChannels; + unsigned int Flags; + } CUDA_ARRAY3D_DESCRIPTOR; + * \endcode + * where: + * + * - \p Width, \p Height, and \p Depth are the width, height, and depth of the + * CUDA array (in elements); the following types of CUDA arrays can be allocated: + * - A 1D mipmapped array is allocated if \p Height and \p Depth extents are both zero. + * - A 2D mipmapped array is allocated if only \p Depth extent is zero. + * - A 3D mipmapped array is allocated if all three extents are non-zero. + * - A 1D layered CUDA mipmapped array is allocated if only \p Height is zero and the + * ::CUDA_ARRAY3D_LAYERED flag is set. Each layer is a 1D array. The number + * of layers is determined by the depth extent. + * - A 2D layered CUDA mipmapped array is allocated if all three extents are non-zero and + * the ::CUDA_ARRAY3D_LAYERED flag is set. Each layer is a 2D array. The number + * of layers is determined by the depth extent. + * - A cubemap CUDA mipmapped array is allocated if all three extents are non-zero and the + * ::CUDA_ARRAY3D_CUBEMAP flag is set. \p Width must be equal to \p Height, and + * \p Depth must be six. A cubemap is a special type of 2D layered CUDA array, + * where the six layers represent the six faces of a cube. The order of the six + * layers in memory is the same as that listed in ::CUarray_cubemap_face. + * - A cubemap layered CUDA mipmapped array is allocated if all three extents are non-zero, + * and both, ::CUDA_ARRAY3D_CUBEMAP and ::CUDA_ARRAY3D_LAYERED flags are set. + * \p Width must be equal to \p Height, and \p Depth must be a multiple of six. + * A cubemap layered CUDA array is a special type of 2D layered CUDA array that + * consists of a collection of cubemaps. The first six layers represent the first + * cubemap, the next six layers form the second cubemap, and so on. + * + * - ::Format specifies the format of the elements; ::CUarray_format is + * defined as: + * \code + typedef enum CUarray_format_enum { + CU_AD_FORMAT_UNSIGNED_INT8 = 0x01, + CU_AD_FORMAT_UNSIGNED_INT16 = 0x02, + CU_AD_FORMAT_UNSIGNED_INT32 = 0x03, + CU_AD_FORMAT_SIGNED_INT8 = 0x08, + CU_AD_FORMAT_SIGNED_INT16 = 0x09, + CU_AD_FORMAT_SIGNED_INT32 = 0x0a, + CU_AD_FORMAT_HALF = 0x10, + CU_AD_FORMAT_FLOAT = 0x20 + } CUarray_format; + * \endcode + * + * - \p NumChannels specifies the number of packed components per CUDA array + * element; it may be 1, 2, or 4; + * + * - ::Flags may be set to + * - ::CUDA_ARRAY3D_LAYERED to enable creation of layered CUDA mipmapped arrays. If this flag is set, + * \p Depth specifies the number of layers, not the depth of a 3D array. + * - ::CUDA_ARRAY3D_SURFACE_LDST to enable surface references to be bound to individual mipmap levels of + * the CUDA mipmapped array. If this flag is not set, ::cuSurfRefSetArray will fail when attempting to + * bind a mipmap level of the CUDA mipmapped array to a surface reference. + * - ::CUDA_ARRAY3D_CUBEMAP to enable creation of mipmapped cubemaps. If this flag is set, \p Width must be + * equal to \p Height, and \p Depth must be six. If the ::CUDA_ARRAY3D_LAYERED flag is also set, + * then \p Depth must be a multiple of six. + * - ::CUDA_ARRAY3D_TEXTURE_GATHER to indicate that the CUDA mipmapped array will be used for texture gather. + * Texture gather can only be performed on 2D CUDA mipmapped arrays. + * + * \p Width, \p Height and \p Depth must meet certain size requirements as listed in the following table. + * All values are specified in elements. Note that for brevity's sake, the full name of the device attribute + * is not specified. For ex., TEXTURE1D_MIPMAPPED_WIDTH refers to the device attribute + * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH. + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + * + *
CUDA array typeValid extents that must always be met
{(width range in elements), (height range), + * (depth range)}		Valid extents with CUDA_ARRAY3D_SURFACE_LDST set
+ * {(width range in elements), (height range), (depth range)}
1D{ (1,TEXTURE1D_MIPMAPPED_WIDTH), 0, 0 }{ (1,SURFACE1D_WIDTH), 0, 0 }
2D{ (1,TEXTURE2D_MIPMAPPED_WIDTH), (1,TEXTURE2D_MIPMAPPED_HEIGHT), 0 }{ (1,SURFACE2D_WIDTH), (1,SURFACE2D_HEIGHT), 0 }
3D{ (1,TEXTURE3D_WIDTH), (1,TEXTURE3D_HEIGHT), (1,TEXTURE3D_DEPTH) } + *
OR
{ (1,TEXTURE3D_WIDTH_ALTERNATE), (1,TEXTURE3D_HEIGHT_ALTERNATE), + * (1,TEXTURE3D_DEPTH_ALTERNATE) }		{ (1,SURFACE3D_WIDTH), (1,SURFACE3D_HEIGHT), + * (1,SURFACE3D_DEPTH) }
1D Layered{ (1,TEXTURE1D_LAYERED_WIDTH), 0, + * (1,TEXTURE1D_LAYERED_LAYERS) }{ (1,SURFACE1D_LAYERED_WIDTH), 0, + * (1,SURFACE1D_LAYERED_LAYERS) }
2D Layered{ (1,TEXTURE2D_LAYERED_WIDTH), (1,TEXTURE2D_LAYERED_HEIGHT), + * (1,TEXTURE2D_LAYERED_LAYERS) }{ (1,SURFACE2D_LAYERED_WIDTH), (1,SURFACE2D_LAYERED_HEIGHT), + * (1,SURFACE2D_LAYERED_LAYERS) }
Cubemap{ (1,TEXTURECUBEMAP_WIDTH), (1,TEXTURECUBEMAP_WIDTH), 6 }{ (1,SURFACECUBEMAP_WIDTH), + * (1,SURFACECUBEMAP_WIDTH), 6 }
Cubemap Layered{ (1,TEXTURECUBEMAP_LAYERED_WIDTH), (1,TEXTURECUBEMAP_LAYERED_WIDTH), + * (1,TEXTURECUBEMAP_LAYERED_LAYERS) }{ (1,SURFACECUBEMAP_LAYERED_WIDTH), (1,SURFACECUBEMAP_LAYERED_WIDTH), + * (1,SURFACECUBEMAP_LAYERED_LAYERS) }
+ * + * + * \param pHandle - Returned mipmapped array + * \param pMipmappedArrayDesc - mipmapped array descriptor + * \param numMipmapLevels - Number of mipmap levels + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa + * ::cuMipmappedArrayDestroy, + * ::cuMipmappedArrayGetLevel, + * ::cuArrayCreate, + * ::cudaMallocMipmappedArray + */ +CUresult CUDAAPI cuMipmappedArrayCreate(CUmipmappedArray *pHandle, const CUDA_ARRAY3D_DESCRIPTOR *pMipmappedArrayDesc, unsigned int numMipmapLevels); + +/** + * \brief Gets a mipmap level of a CUDA mipmapped array + * + * Returns in \p *pLevelArray a CUDA array that represents a single mipmap level + * of the CUDA mipmapped array \p hMipmappedArray. + * + * If \p level is greater than the maximum number of levels in this mipmapped array, + * ::CUDA_ERROR_INVALID_VALUE is returned. + * + * \param pLevelArray - Returned mipmap level CUDA array + * \param hMipmappedArray - CUDA mipmapped array + * \param level - Mipmap level + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa + * ::cuMipmappedArrayCreate, + * ::cuMipmappedArrayDestroy, + * ::cuArrayCreate, + * ::cudaGetMipmappedArrayLevel + */ +CUresult CUDAAPI cuMipmappedArrayGetLevel(CUarray *pLevelArray, CUmipmappedArray hMipmappedArray, unsigned int level); + +/** + * \brief Destroys a CUDA mipmapped array + * + * Destroys the CUDA mipmapped array \p hMipmappedArray. + * + * \param hMipmappedArray - Mipmapped array to destroy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_ARRAY_IS_MAPPED, + * ::CUDA_ERROR_CONTEXT_IS_DESTROYED + * \notefnerr + * + * \sa + * ::cuMipmappedArrayCreate, + * ::cuMipmappedArrayGetLevel, + * ::cuArrayCreate, + * ::cudaFreeMipmappedArray + */ +CUresult CUDAAPI cuMipmappedArrayDestroy(CUmipmappedArray hMipmappedArray); + +/** +* \brief Retrieve handle for an address range +* +* Get a handle of the specified type to an address range. The address range +* must have been obtained by a prior call to either ::cuMemAlloc or ::cuMemAddressReserve. +* If the address range was obtained via ::cuMemAddressReserve, it must also be fully mapped via ::cuMemMap. +* +* Users must ensure the \p dptr and \p size are aligned to the host page size. +* +* When requesting CUmemRangeHandleType::CU_MEM_RANGE_HANDLE_TYPE_DMA_BUF_FD, +* users are expected to query for dma_buf support for the platform +* by using ::CU_DEVICE_ATTRIBUTE_DMA_BUF_SUPPORTED device attribute before calling +* this API. The \p handle will be interpreted as a pointer to an integer to store the dma_buf file descriptor. +* Users must ensure the entire address range is backed and mapped when +* the address range is allocated by ::cuMemAddressReserve. All the physical +* allocations backing the address range must be resident on the same device and +* have identical allocation properties. Users are also expected to retrieve a +* new handle every time the underlying physical allocation(s) corresponding +* to a previously queried VA range are changed. +* +* \param[out] handle - Pointer to the location where the returned handle will be stored. +* \param[in] dptr - Pointer to a valid CUDA device allocation. Must be aligned to host page size. +* \param[in] size - Length of the address range. Must be aligned to host page size. +* \param[in] handleType - Type of handle requested (defines type and size of the \p handle output parameter) +* \param[in] flags - Reserved, must be zero +* +* \return +* CUDA_SUCCESS +* CUDA_ERROR_INVALID_VALUE +* CUDA_ERROR_NOT_SUPPORTED +*/ +CUresult CUDAAPI cuMemGetHandleForAddressRange(void *handle, CUdeviceptr dptr, size_t size, CUmemRangeHandleType handleType, unsigned long long flags); + +/** @} */ /* END CUDA_MEM */ + +/** + * \defgroup CUDA_VA Virtual Memory Management + * + * ___MANBRIEF___ virtual memory management functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the virtual memory management functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** +* \brief Allocate an address range reservation. +* +* Reserves a virtual address range based on the given parameters, giving +* the starting address of the range in \p ptr. This API requires a system that +* supports UVA. The size and address parameters must be a multiple of the +* host page size and the alignment must be a power of two or zero for default +* alignment. +* +* \param[out] ptr - Resulting pointer to start of virtual address range allocated +* \param[in] size - Size of the reserved virtual address range requested +* \param[in] alignment - Alignment of the reserved virtual address range requested +* \param[in] addr - Fixed starting address range requested +* \param[in] flags - Currently unused, must be zero +* \return +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_OUT_OF_MEMORY, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* +* \sa ::cuMemAddressFree +*/ +CUresult CUDAAPI cuMemAddressReserve(CUdeviceptr *ptr, size_t size, size_t alignment, CUdeviceptr addr, unsigned long long flags); + +/** +* \brief Free an address range reservation. +* +* Frees a virtual address range reserved by cuMemAddressReserve. The size +* must match what was given to memAddressReserve and the ptr given must +* match what was returned from memAddressReserve. +* +* \param[in] ptr - Starting address of the virtual address range to free +* \param[in] size - Size of the virtual address region to free +* \return +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* +* \sa ::cuMemAddressReserve +*/ +CUresult CUDAAPI cuMemAddressFree(CUdeviceptr ptr, size_t size); + +/** +* \brief Create a CUDA memory handle representing a memory allocation of a given size described by the given properties +* +* This creates a memory allocation on the target device specified through the +* \p prop structure. The created allocation will not have any device or host +* mappings. The generic memory \p handle for the allocation can be +* mapped to the address space of calling process via ::cuMemMap. This handle +* cannot be transmitted directly to other processes (see +* ::cuMemExportToShareableHandle). On Windows, the caller must also pass +* an LPSECURITYATTRIBUTE in \p prop to be associated with this handle which +* limits or allows access to this handle for a recipient process (see +* ::CUmemAllocationProp::win32HandleMetaData for more). The \p size of this +* allocation must be a multiple of the the value given via +* ::cuMemGetAllocationGranularity with the ::CU_MEM_ALLOC_GRANULARITY_MINIMUM +* flag. +* If ::CUmemAllocationProp::allocFlags::usage contains ::CU_MEM_CREATE_USAGE_TILE_POOL flag then +* the memory allocation is intended only to be used as backing tile pool for sparse CUDA arrays +* and sparse CUDA mipmapped arrays. +* (see ::cuMemMapArrayAsync). +* +* \param[out] handle - Value of handle returned. All operations on this allocation are to be performed using this handle. +* \param[in] size - Size of the allocation requested +* \param[in] prop - Properties of the allocation to create. +* \param[in] flags - flags for future use, must be zero now. +* \return +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_OUT_OF_MEMORY, +* ::CUDA_ERROR_INVALID_DEVICE, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* \notefnerr +* +* \sa ::cuMemRelease, ::cuMemExportToShareableHandle, ::cuMemImportFromShareableHandle +*/ +CUresult CUDAAPI cuMemCreate(CUmemGenericAllocationHandle *handle, size_t size, const CUmemAllocationProp *prop, unsigned long long flags); + +/** +* \brief Release a memory handle representing a memory allocation which was previously allocated through cuMemCreate. +* +* Frees the memory that was allocated on a device through cuMemCreate. +* +* The memory allocation will be freed when all outstanding mappings to the memory +* are unmapped and when all outstanding references to the handle (including it's +* shareable counterparts) are also released. The generic memory handle can be +* freed when there are still outstanding mappings made with this handle. Each +* time a recipient process imports a shareable handle, it needs to pair it with +* ::cuMemRelease for the handle to be freed. If \p handle is not a valid handle +* the behavior is undefined. +* +* \param[in] handle Value of handle which was returned previously by cuMemCreate. +* \return +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* \notefnerr +* +* \sa ::cuMemCreate +*/ +CUresult CUDAAPI cuMemRelease(CUmemGenericAllocationHandle handle); + +/** +* \brief Maps an allocation handle to a reserved virtual address range. +* +* Maps bytes of memory represented by \p handle starting from byte \p offset to +* \p size to address range [\p addr, \p addr + \p size]. This range must be an +* address reservation previously reserved with ::cuMemAddressReserve, and +* \p offset + \p size must be less than the size of the memory allocation. +* Both \p ptr, \p size, and \p offset must be a multiple of the value given via +* ::cuMemGetAllocationGranularity with the ::CU_MEM_ALLOC_GRANULARITY_MINIMUM flag. +* If \p handle represents a multicast object, \p ptr, \p size and \p offset must +* be aligned to the value returned by ::cuMulticastGetGranularity with the flag +* ::CU_MULTICAST_MINIMUM_GRANULARITY. For best performance however, it is +* recommended that \p ptr, \p size and \p offset be aligned to the value +* returned by ::cuMulticastGetGranularity with the flag +* ::CU_MULTICAST_RECOMMENDED_GRANULARITY. +* +* Please note calling ::cuMemMap does not make the address accessible, +* the caller needs to update accessibility of a contiguous mapped VA +* range by calling ::cuMemSetAccess. +* +* Once a recipient process obtains a shareable memory handle +* from ::cuMemImportFromShareableHandle, the process must +* use ::cuMemMap to map the memory into its address ranges before +* setting accessibility with ::cuMemSetAccess. +* +* ::cuMemMap can only create mappings on VA range reservations +* that are not currently mapped. +* +* \param[in] ptr - Address where memory will be mapped. +* \param[in] size - Size of the memory mapping. +* \param[in] offset - Offset into the memory represented by +* - \p handle from which to start mapping +* - Note: currently must be zero. +* \param[in] handle - Handle to a shareable memory +* \param[in] flags - flags for future use, must be zero now. +* \return +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_INVALID_DEVICE, +* ::CUDA_ERROR_OUT_OF_MEMORY, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* \notefnerr +* +* \sa ::cuMemUnmap, ::cuMemSetAccess, ::cuMemCreate, ::cuMemAddressReserve, ::cuMemImportFromShareableHandle +*/ +CUresult CUDAAPI cuMemMap(CUdeviceptr ptr, size_t size, size_t offset, CUmemGenericAllocationHandle handle, unsigned long long flags); + +/** + * \brief Maps or unmaps subregions of sparse CUDA arrays and sparse CUDA mipmapped arrays + * + * Performs map or unmap operations on subregions of sparse CUDA arrays and sparse CUDA mipmapped arrays. + * Each operation is specified by a ::CUarrayMapInfo entry in the \p mapInfoList array of size \p count. + * The structure ::CUarrayMapInfo is defined as follow: + \code + typedef struct CUarrayMapInfo_st { + CUresourcetype resourceType; + union { + CUmipmappedArray mipmap; + CUarray array; + } resource; + + CUarraySparseSubresourceType subresourceType; + union { + struct { + unsigned int level; + unsigned int layer; + unsigned int offsetX; + unsigned int offsetY; + unsigned int offsetZ; + unsigned int extentWidth; + unsigned int extentHeight; + unsigned int extentDepth; + } sparseLevel; + struct { + unsigned int layer; + unsigned long long offset; + unsigned long long size; + } miptail; + } subresource; + + CUmemOperationType memOperationType; + + CUmemHandleType memHandleType; + union { + CUmemGenericAllocationHandle memHandle; + } memHandle; + + unsigned long long offset; + unsigned int deviceBitMask; + unsigned int flags; + unsigned int reserved[2]; + } CUarrayMapInfo; + \endcode + * + * where ::CUarrayMapInfo::resourceType specifies the type of resource to be operated on. + * If ::CUarrayMapInfo::resourceType is set to ::CUresourcetype::CU_RESOURCE_TYPE_ARRAY then + * ::CUarrayMapInfo::resource::array must be set to a valid sparse CUDA array handle. + * The CUDA array must be either a 2D, 2D layered or 3D CUDA array and must have been allocated using + * ::cuArrayCreate or ::cuArray3DCreate with the flag ::CUDA_ARRAY3D_SPARSE + * or ::CUDA_ARRAY3D_DEFERRED_MAPPING. + * For CUDA arrays obtained using ::cuMipmappedArrayGetLevel, ::CUDA_ERROR_INVALID_VALUE will be returned. + * If ::CUarrayMapInfo::resourceType is set to ::CUresourcetype::CU_RESOURCE_TYPE_MIPMAPPED_ARRAY + * then ::CUarrayMapInfo::resource::mipmap must be set to a valid sparse CUDA mipmapped array handle. + * The CUDA mipmapped array must be either a 2D, 2D layered or 3D CUDA mipmapped array and must have been + * allocated using ::cuMipmappedArrayCreate with the flag ::CUDA_ARRAY3D_SPARSE + * or ::CUDA_ARRAY3D_DEFERRED_MAPPING. + * + * ::CUarrayMapInfo::subresourceType specifies the type of subresource within the resource. + * ::CUarraySparseSubresourceType_enum is defined as: + \code + typedef enum CUarraySparseSubresourceType_enum { + CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SPARSE_LEVEL = 0, + CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL = 1 + } CUarraySparseSubresourceType; + \endcode + * + * where ::CUarraySparseSubresourceType::CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SPARSE_LEVEL indicates a + * sparse-miplevel which spans at least one tile in every dimension. The remaining miplevels which + * are too small to span at least one tile in any dimension constitute the mip tail region as indicated by + * ::CUarraySparseSubresourceType::CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL subresource type. + * + * If ::CUarrayMapInfo::subresourceType is set to ::CUarraySparseSubresourceType::CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SPARSE_LEVEL + * then ::CUarrayMapInfo::subresource::sparseLevel struct must contain valid array subregion offsets and extents. + * The ::CUarrayMapInfo::subresource::sparseLevel::offsetX, ::CUarrayMapInfo::subresource::sparseLevel::offsetY + * and ::CUarrayMapInfo::subresource::sparseLevel::offsetZ must specify valid X, Y and Z offsets respectively. + * The ::CUarrayMapInfo::subresource::sparseLevel::extentWidth, ::CUarrayMapInfo::subresource::sparseLevel::extentHeight + * and ::CUarrayMapInfo::subresource::sparseLevel::extentDepth must specify valid width, height and depth extents respectively. + * These offsets and extents must be aligned to the corresponding tile dimension. + * For CUDA mipmapped arrays ::CUarrayMapInfo::subresource::sparseLevel::level must specify a valid mip level index. Otherwise, + * must be zero. + * For layered CUDA arrays and layered CUDA mipmapped arrays ::CUarrayMapInfo::subresource::sparseLevel::layer must specify a valid layer index. Otherwise, + * must be zero. + * ::CUarrayMapInfo::subresource::sparseLevel::offsetZ must be zero and ::CUarrayMapInfo::subresource::sparseLevel::extentDepth + * must be set to 1 for 2D and 2D layered CUDA arrays and CUDA mipmapped arrays. + * Tile extents can be obtained by calling ::cuArrayGetSparseProperties and ::cuMipmappedArrayGetSparseProperties + * + * If ::CUarrayMapInfo::subresourceType is set to ::CUarraySparseSubresourceType::CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL + * then ::CUarrayMapInfo::subresource::miptail struct must contain valid mip tail offset in + * ::CUarrayMapInfo::subresource::miptail::offset and size in ::CUarrayMapInfo::subresource::miptail::size. + * Both, mip tail offset and mip tail size must be aligned to the tile size. + * For layered CUDA mipmapped arrays which don't have the flag ::CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL set in ::CUDA_ARRAY_SPARSE_PROPERTIES::flags + * as returned by ::cuMipmappedArrayGetSparseProperties, ::CUarrayMapInfo::subresource::miptail::layer must specify a valid layer index. + * Otherwise, must be zero. + * + * If ::CUarrayMapInfo::resource::array or ::CUarrayMapInfo::resource::mipmap was created with ::CUDA_ARRAY3D_DEFERRED_MAPPING + * flag set the ::CUarrayMapInfo::subresourceType and the contents of ::CUarrayMapInfo::subresource will be ignored. + * + * ::CUarrayMapInfo::memOperationType specifies the type of operation. ::CUmemOperationType is defined as: + \code + typedef enum CUmemOperationType_enum { + CU_MEM_OPERATION_TYPE_MAP = 1, + CU_MEM_OPERATION_TYPE_UNMAP = 2 + } CUmemOperationType; + \endcode + * If ::CUarrayMapInfo::memOperationType is set to ::CUmemOperationType::CU_MEM_OPERATION_TYPE_MAP then the subresource + * will be mapped onto the tile pool memory specified by ::CUarrayMapInfo::memHandle at offset ::CUarrayMapInfo::offset. + * The tile pool allocation has to be created by specifying the ::CU_MEM_CREATE_USAGE_TILE_POOL flag when calling ::cuMemCreate. Also, + * ::CUarrayMapInfo::memHandleType must be set to ::CUmemHandleType::CU_MEM_HANDLE_TYPE_GENERIC. + * + * If ::CUarrayMapInfo::memOperationType is set to ::CUmemOperationType::CU_MEM_OPERATION_TYPE_UNMAP then an unmapping operation + * is performed. ::CUarrayMapInfo::memHandle must be NULL. + * + * ::CUarrayMapInfo::deviceBitMask specifies the list of devices that must map or unmap physical memory. + * Currently, this mask must have exactly one bit set, and the corresponding device must match the device associated with the stream. + * If ::CUarrayMapInfo::memOperationType is set to ::CUmemOperationType::CU_MEM_OPERATION_TYPE_MAP, the device must also match + * the device associated with the tile pool memory allocation as specified by ::CUarrayMapInfo::memHandle. + * + * ::CUarrayMapInfo::flags and ::CUarrayMapInfo::reserved[] are unused and must be set to zero. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * + * \param[in] mapInfoList - List of ::CUarrayMapInfo + * \param[in] count - Count of ::CUarrayMapInfo in \p mapInfoList + * \param[in] hStream - Stream identifier for the stream to use for map or unmap operations + * + * \sa ::cuMipmappedArrayCreate, ::cuArrayCreate, ::cuArray3DCreate, ::cuMemCreate, ::cuArrayGetSparseProperties, ::cuMipmappedArrayGetSparseProperties + */ +CUresult CUDAAPI cuMemMapArrayAsync(CUarrayMapInfo *mapInfoList, unsigned int count, CUstream hStream); + +/** +* \brief Unmap the backing memory of a given address range. +* +* The range must be the entire contiguous address range that was mapped to. In +* other words, ::cuMemUnmap cannot unmap a sub-range of an address range mapped +* by ::cuMemCreate / ::cuMemMap. Any backing memory allocations will be freed +* if there are no existing mappings and there are no unreleased memory handles. +* +* When ::cuMemUnmap returns successfully the address range is converted to an +* address reservation and can be used for a future calls to ::cuMemMap. Any new +* mapping to this virtual address will need to have access granted through +* ::cuMemSetAccess, as all mappings start with no accessibility setup. +* +* \param[in] ptr - Starting address for the virtual address range to unmap +* \param[in] size - Size of the virtual address range to unmap +* \returns +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* \notefnerr +* \note_sync +* +* \sa ::cuMemCreate, ::cuMemAddressReserve +*/ +CUresult CUDAAPI cuMemUnmap(CUdeviceptr ptr, size_t size); + +/** +* \brief Set the access flags for each location specified in \p desc for the given virtual address range +* +* Given the virtual address range via \p ptr and \p size, and the locations +* in the array given by \p desc and \p count, set the access flags for the +* target locations. The range must be a fully mapped address range +* containing all allocations created by ::cuMemMap / ::cuMemCreate. +* When setting the access flags for a virtual address range mapping a multicast +* object, \p ptr and \p size must be aligned to the value returned by +* ::cuMulticastGetGranularity with the flag ::CU_MULTICAST_MINIMUM_GRANULARITY. +* For best performance however, it is recommended that \p ptr and \p size be +* aligned to the value returned by ::cuMulticastGetGranularity with the flag +* ::CU_MULTICAST_RECOMMENDED_GRANULARITY. +* +* \param[in] ptr - Starting address for the virtual address range +* \param[in] size - Length of the virtual address range +* \param[in] desc - Array of ::CUmemAccessDesc that describe how to change the +* - mapping for each location specified +* \param[in] count - Number of ::CUmemAccessDesc in \p desc +* \returns +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_INVALID_DEVICE, +* ::CUDA_ERROR_NOT_SUPPORTED +* \notefnerr +* \note_sync +* +* \sa ::cuMemSetAccess, ::cuMemCreate, :cuMemMap +*/ +CUresult CUDAAPI cuMemSetAccess(CUdeviceptr ptr, size_t size, const CUmemAccessDesc *desc, size_t count); + +/** +* \brief Get the access \p flags set for the given \p location and \p ptr +* +* \param[out] flags - Flags set for this location +* \param[in] location - Location in which to check the flags for +* \param[in] ptr - Address in which to check the access flags for +* \returns +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_INVALID_DEVICE, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* +* \sa ::cuMemSetAccess +*/ +CUresult CUDAAPI cuMemGetAccess(unsigned long long *flags, const CUmemLocation *location, CUdeviceptr ptr); + +/** +* \brief Exports an allocation to a requested shareable handle type +* +* Given a CUDA memory handle, create a shareable memory +* allocation handle that can be used to share the memory with other +* processes. The recipient process can convert the shareable handle back into a +* CUDA memory handle using ::cuMemImportFromShareableHandle and map +* it with ::cuMemMap. The implementation of what this handle is and how it +* can be transferred is defined by the requested handle type in \p handleType +* +* Once all shareable handles are closed and the allocation is released, the allocated +* memory referenced will be released back to the OS and uses of the CUDA handle afterward +* will lead to undefined behavior. +* +* This API can also be used in conjunction with other APIs (e.g. Vulkan, OpenGL) +* that support importing memory from the shareable type +* +* \param[out] shareableHandle - Pointer to the location in which to store the requested handle type +* \param[in] handle - CUDA handle for the memory allocation +* \param[in] handleType - Type of shareable handle requested (defines type and size of the \p shareableHandle output parameter) +* \param[in] flags - Reserved, must be zero +* \returns +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* +* \sa ::cuMemImportFromShareableHandle +*/ +CUresult CUDAAPI cuMemExportToShareableHandle(void *shareableHandle, CUmemGenericAllocationHandle handle, CUmemAllocationHandleType handleType, unsigned long long flags); + +/** +* \brief Imports an allocation from a requested shareable handle type. +* +* If the current process cannot support the memory described by this shareable +* handle, this API will error as CUDA_ERROR_NOT_SUPPORTED. +* +* \note Importing shareable handles exported from some graphics APIs(VUlkan, OpenGL, etc) +* created on devices under an SLI group may not be supported, and thus this API will +* return CUDA_ERROR_NOT_SUPPORTED. +* There is no guarantee that the contents of \p handle will be the same CUDA memory handle +* for the same given OS shareable handle, or the same underlying allocation. +* +* \param[out] handle - CUDA Memory handle for the memory allocation. +* \param[in] osHandle - Shareable Handle representing the memory allocation that is to be imported. +* \param[in] shHandleType - handle type of the exported handle ::CUmemAllocationHandleType. +* \returns +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* +* \sa ::cuMemExportToShareableHandle, ::cuMemMap, ::cuMemRelease +*/ +CUresult CUDAAPI cuMemImportFromShareableHandle(CUmemGenericAllocationHandle *handle, void *osHandle, CUmemAllocationHandleType shHandleType); + +/** +* \brief Calculates either the minimal or recommended granularity +* +* Calculates either the minimal or recommended granularity +* for a given allocation specification and returns it in granularity. This +* granularity can be used as a multiple for alignment, size, or address mapping. +* +* \param[out] granularity Returned granularity. +* \param[in] prop Property for which to determine the granularity for +* \param[in] option Determines which granularity to return +* \returns +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* +* \sa ::cuMemCreate, ::cuMemMap +*/ +CUresult CUDAAPI cuMemGetAllocationGranularity(size_t *granularity, const CUmemAllocationProp *prop, CUmemAllocationGranularity_flags option); + +/** +* \brief Retrieve the contents of the property structure defining properties for this handle +* +* \param[out] prop - Pointer to a properties structure which will hold the information about this handle +* \param[in] handle - Handle which to perform the query on +* \returns +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* +* \sa ::cuMemCreate, ::cuMemImportFromShareableHandle +*/ +CUresult CUDAAPI cuMemGetAllocationPropertiesFromHandle(CUmemAllocationProp *prop, CUmemGenericAllocationHandle handle); + +/** +* \brief Given an address \p addr, returns the allocation handle of the backing memory allocation. +* +* The handle is guaranteed to be the same handle value used to map the memory. If the address +* requested is not mapped, the function will fail. The returned handle must be released with +* corresponding number of calls to ::cuMemRelease. +* +* \note The address \p addr, can be any address in a range previously mapped +* by ::cuMemMap, and not necessarily the start address. +* +* \param[out] handle CUDA Memory handle for the backing memory allocation. +* \param[in] addr Memory address to query, that has been mapped previously. +* \returns +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* +* \sa ::cuMemCreate, ::cuMemRelease, ::cuMemMap +*/ +CUresult CUDAAPI cuMemRetainAllocationHandle(CUmemGenericAllocationHandle *handle, void *addr); + +/** @} */ /* END CUDA_VA */ + +/** + * \defgroup CUDA_MALLOC_ASYNC Stream Ordered Memory Allocator + * + * ___MANBRIEF___ Functions for performing allocation and free operations in stream order. + * Functions for controlling the behavior of the underlying allocator. + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the stream ordered memory allocator exposed by the + * low-level CUDA driver application programming interface. + * + * @{ + * + * \section CUDA_MALLOC_ASYNC_overview overview + * + * The asynchronous allocator allows the user to allocate and free in stream order. + * All asynchronous accesses of the allocation must happen between + * the stream executions of the allocation and the free. If the memory is accessed + * outside of the promised stream order, a use before allocation / use after free error + * will cause undefined behavior. + * + * The allocator is free to reallocate the memory as long as it can guarantee + * that compliant memory accesses will not overlap temporally. + * The allocator may refer to internal stream ordering as well as inter-stream dependencies + * (such as CUDA events and null stream dependencies) when establishing the temporal guarantee. + * The allocator may also insert inter-stream dependencies to establish the temporal guarantee. + * + * \section CUDA_MALLOC_ASYNC_support Supported Platforms + * + * Whether or not a device supports the integrated stream ordered memory allocator + * may be queried by calling ::cuDeviceGetAttribute() with the device attribute + * ::CU_DEVICE_ATTRIBUTE_MEMORY_POOLS_SUPPORTED + */ + +/** + * \brief Frees memory with stream ordered semantics + * + * Inserts a free operation into \p hStream. + * The allocation must not be accessed after stream execution reaches the free. + * After this API returns, accessing the memory from any subsequent work launched on the GPU + * or querying its pointer attributes results in undefined behavior. + * + * \note During stream capture, this function results in the creation of a free node and + * must therefore be passed the address of a graph allocation. + * + * \param dptr - memory to free + * \param hStream - The stream establishing the stream ordering contract. + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT (default stream specified with no current context), + * ::CUDA_ERROR_NOT_SUPPORTED + */ +CUresult CUDAAPI cuMemFreeAsync(CUdeviceptr dptr, CUstream hStream); + +/** + * \brief Allocates memory with stream ordered semantics + * + * Inserts an allocation operation into \p hStream. + * A pointer to the allocated memory is returned immediately in *dptr. + * The allocation must not be accessed until the the allocation operation completes. + * The allocation comes from the memory pool current to the stream's device. + * + * \note The default memory pool of a device contains device memory from that device. + * \note Basic stream ordering allows future work submitted into the same stream to use the allocation. + * Stream query, stream synchronize, and CUDA events can be used to guarantee that the allocation + * operation completes before work submitted in a separate stream runs. + * \note During stream capture, this function results in the creation of an allocation node. In this case, + * the allocation is owned by the graph instead of the memory pool. The memory pool's properties + * are used to set the node's creation parameters. + * + * \param[out] dptr - Returned device pointer + * \param[in] bytesize - Number of bytes to allocate + * \param[in] hStream - The stream establishing the stream ordering contract and the memory pool to allocate from + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT (default stream specified with no current context), + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_OUT_OF_MEMORY + * + * \sa ::cuMemAllocFromPoolAsync, ::cuMemFreeAsync, ::cuDeviceSetMemPool, + * ::cuDeviceGetDefaultMemPool, ::cuDeviceGetMemPool, ::cuMemPoolCreate, + * ::cuMemPoolSetAccess, ::cuMemPoolSetAttribute + */ +CUresult CUDAAPI cuMemAllocAsync(CUdeviceptr *dptr, size_t bytesize, CUstream hStream); + +/** + * \brief Tries to release memory back to the OS + * + * Releases memory back to the OS until the pool contains fewer than minBytesToKeep + * reserved bytes, or there is no more memory that the allocator can safely release. + * The allocator cannot release OS allocations that back outstanding asynchronous allocations. + * The OS allocations may happen at different granularity from the user allocations. + * + * \note: Allocations that have not been freed count as outstanding. + * \note: Allocations that have been asynchronously freed but whose completion has + * not been observed on the host (eg. by a synchronize) can count as outstanding. + * + * \param[in] pool - The memory pool to trim + * \param[in] minBytesToKeep - If the pool has less than minBytesToKeep reserved, + * the TrimTo operation is a no-op. Otherwise the pool will be guaranteed to have + * at least minBytesToKeep bytes reserved after the operation. + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuMemAllocAsync, ::cuMemFreeAsync, ::cuDeviceGetDefaultMemPool, + * ::cuDeviceGetMemPool, ::cuMemPoolCreate + */ +CUresult CUDAAPI cuMemPoolTrimTo(CUmemoryPool pool, size_t minBytesToKeep); + +/** + * \brief Sets attributes of a memory pool + * + * Supported attributes are: + * - ::CU_MEMPOOL_ATTR_RELEASE_THRESHOLD: (value type = cuuint64_t) + * Amount of reserved memory in bytes to hold onto before trying + * to release memory back to the OS. When more than the release + * threshold bytes of memory are held by the memory pool, the + * allocator will try to release memory back to the OS on the + * next call to stream, event or context synchronize. (default 0) + * - ::CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES: (value type = int) + * Allow ::cuMemAllocAsync to use memory asynchronously freed + * in another stream as long as a stream ordering dependency + * of the allocating stream on the free action exists. + * Cuda events and null stream interactions can create the required + * stream ordered dependencies. (default enabled) + * - ::CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC: (value type = int) + * Allow reuse of already completed frees when there is no dependency + * between the free and allocation. (default enabled) + * - ::CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES: (value type = int) + * Allow ::cuMemAllocAsync to insert new stream dependencies + * in order to establish the stream ordering required to reuse + * a piece of memory released by ::cuMemFreeAsync (default enabled). + * - ::CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH: (value type = cuuint64_t) + * Reset the high watermark that tracks the amount of backing memory that was + * allocated for the memory pool. It is illegal to set this attribute to a non-zero value. + * - ::CU_MEMPOOL_ATTR_USED_MEM_HIGH: (value type = cuuint64_t) + * Reset the high watermark that tracks the amount of used memory that was + * allocated for the memory pool. + * + * \param[in] pool - The memory pool to modify + * \param[in] attr - The attribute to modify + * \param[in] value - Pointer to the value to assign + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuMemAllocAsync, ::cuMemFreeAsync, ::cuDeviceGetDefaultMemPool, + * ::cuDeviceGetMemPool, ::cuMemPoolCreate + */ +CUresult CUDAAPI cuMemPoolSetAttribute(CUmemoryPool pool, CUmemPool_attribute attr, void *value); + +/** + * \brief Gets attributes of a memory pool + * + * Supported attributes are: + * - ::CU_MEMPOOL_ATTR_RELEASE_THRESHOLD: (value type = cuuint64_t) + * Amount of reserved memory in bytes to hold onto before trying + * to release memory back to the OS. When more than the release + * threshold bytes of memory are held by the memory pool, the + * allocator will try to release memory back to the OS on the + * next call to stream, event or context synchronize. (default 0) + * - ::CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES: (value type = int) + * Allow ::cuMemAllocAsync to use memory asynchronously freed + * in another stream as long as a stream ordering dependency + * of the allocating stream on the free action exists. + * Cuda events and null stream interactions can create the required + * stream ordered dependencies. (default enabled) + * - ::CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC: (value type = int) + * Allow reuse of already completed frees when there is no dependency + * between the free and allocation. (default enabled) + * - ::CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES: (value type = int) + * Allow ::cuMemAllocAsync to insert new stream dependencies + * in order to establish the stream ordering required to reuse + * a piece of memory released by ::cuMemFreeAsync (default enabled). + * - ::CU_MEMPOOL_ATTR_RESERVED_MEM_CURRENT: (value type = cuuint64_t) + * Amount of backing memory currently allocated for the mempool + * - ::CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH: (value type = cuuint64_t) + * High watermark of backing memory allocated for the mempool since the + * last time it was reset. + * - ::CU_MEMPOOL_ATTR_USED_MEM_CURRENT: (value type = cuuint64_t) + * Amount of memory from the pool that is currently in use by the application. + * - ::CU_MEMPOOL_ATTR_USED_MEM_HIGH: (value type = cuuint64_t) + * High watermark of the amount of memory from the pool that was in use by the application. + * + * \param[in] pool - The memory pool to get attributes of + * \param[in] attr - The attribute to get + * \param[out] value - Retrieved value + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuMemAllocAsync, ::cuMemFreeAsync, ::cuDeviceGetDefaultMemPool, + * ::cuDeviceGetMemPool, ::cuMemPoolCreate + */ +CUresult CUDAAPI cuMemPoolGetAttribute(CUmemoryPool pool, CUmemPool_attribute attr, void *value); + +/** + * \brief Controls visibility of pools between devices + * + * \param[in] pool - The pool being modified + * \param[in] map - Array of access descriptors. Each descriptor instructs the access to enable for a single gpu. + * \param[in] count - Number of descriptors in the map array. + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuMemAllocAsync, ::cuMemFreeAsync, ::cuDeviceGetDefaultMemPool, + * ::cuDeviceGetMemPool, ::cuMemPoolCreate + */ +CUresult CUDAAPI cuMemPoolSetAccess(CUmemoryPool pool, const CUmemAccessDesc *map, size_t count); + +/** + * \brief Returns the accessibility of a pool from a device + * + * Returns the accessibility of the pool's memory from the specified location. + * + * \param[out] flags - the accessibility of the pool from the specified location + * \param[in] memPool - the pool being queried + * \param[in] location - the location accessing the pool + * + * \sa ::cuMemAllocAsync, ::cuMemFreeAsync, ::cuDeviceGetDefaultMemPool, + * ::cuDeviceGetMemPool, ::cuMemPoolCreate + */ +CUresult CUDAAPI cuMemPoolGetAccess(CUmemAccess_flags *flags, CUmemoryPool memPool, CUmemLocation *location); + +/** + * \brief Creates a memory pool + * + * Creates a CUDA memory pool and returns the handle in \p pool. The \p poolProps determines + * the properties of the pool such as the backing device and IPC capabilities. + * + * By default, the pool's memory will be accessible from the device it is allocated on. + * + * \note Specifying CU_MEM_HANDLE_TYPE_NONE creates a memory pool that will not support IPC. + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_NOT_SUPPORTED + * + * \sa ::cuDeviceSetMemPool, ::cuDeviceGetMemPool, ::cuDeviceGetDefaultMemPool, + * ::cuMemAllocFromPoolAsync, ::cuMemPoolExportToShareableHandle + */ +CUresult CUDAAPI cuMemPoolCreate(CUmemoryPool *pool, const CUmemPoolProps *poolProps); + +/** + * \brief Destroys the specified memory pool + * + * If any pointers obtained from this pool haven't been freed or + * the pool has free operations that haven't completed + * when ::cuMemPoolDestroy is invoked, the function will return immediately and the + * resources associated with the pool will be released automatically + * once there are no more outstanding allocations. + * + * Destroying the current mempool of a device sets the default mempool of + * that device as the current mempool for that device. + * + * \note A device's default memory pool cannot be destroyed. + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuMemFreeAsync, ::cuDeviceSetMemPool, ::cuDeviceGetMemPool, + * ::cuDeviceGetDefaultMemPool, ::cuMemPoolCreate + */ +CUresult CUDAAPI cuMemPoolDestroy(CUmemoryPool pool); + +/** + * \brief Allocates memory from a specified pool with stream ordered semantics. + * + * Inserts an allocation operation into \p hStream. + * A pointer to the allocated memory is returned immediately in *dptr. + * The allocation must not be accessed until the the allocation operation completes. + * The allocation comes from the specified memory pool. + * + * \note + * - The specified memory pool may be from a device different than that of the specified \p hStream. + * + * - Basic stream ordering allows future work submitted into the same stream to use the allocation. + * Stream query, stream synchronize, and CUDA events can be used to guarantee that the allocation + * operation completes before work submitted in a separate stream runs. + * + * \note During stream capture, this function results in the creation of an allocation node. In this case, + * the allocation is owned by the graph instead of the memory pool. The memory pool's properties + * are used to set the node's creation parameters. + * + * \param[out] dptr - Returned device pointer + * \param[in] bytesize - Number of bytes to allocate + * \param[in] pool - The pool to allocate from + * \param[in] hStream - The stream establishing the stream ordering semantic + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT (default stream specified with no current context), + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_OUT_OF_MEMORY + * + * \sa ::cuMemAllocAsync, ::cuMemFreeAsync, ::cuDeviceGetDefaultMemPool, + * ::cuDeviceGetMemPool, ::cuMemPoolCreate, ::cuMemPoolSetAccess, + * ::cuMemPoolSetAttribute + */ +CUresult CUDAAPI cuMemAllocFromPoolAsync(CUdeviceptr *dptr, size_t bytesize, CUmemoryPool pool, CUstream hStream); + +/** + * \brief Exports a memory pool to the requested handle type. + * + * Given an IPC capable mempool, create an OS handle to share the pool with another process. + * A recipient process can convert the shareable handle into a mempool with ::cuMemPoolImportFromShareableHandle. + * Individual pointers can then be shared with the ::cuMemPoolExportPointer and ::cuMemPoolImportPointer APIs. + * The implementation of what the shareable handle is and how it can be transferred is defined by the requested + * handle type. + * + * \note: To create an IPC capable mempool, create a mempool with a CUmemAllocationHandleType other than CU_MEM_HANDLE_TYPE_NONE. + * + * \param[out] handle_out - Returned OS handle + * \param[in] pool - pool to export + * \param[in] handleType - the type of handle to create + * \param[in] flags - must be 0 + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_OUT_OF_MEMORY + * + * \sa ::cuMemPoolImportFromShareableHandle, ::cuMemPoolExportPointer, + * ::cuMemPoolImportPointer, ::cuMemAllocAsync, ::cuMemFreeAsync, + * ::cuDeviceGetDefaultMemPool, ::cuDeviceGetMemPool, ::cuMemPoolCreate, + * ::cuMemPoolSetAccess, ::cuMemPoolSetAttribute + */ +CUresult CUDAAPI cuMemPoolExportToShareableHandle(void *handle_out, CUmemoryPool pool, CUmemAllocationHandleType handleType, unsigned long long flags); + +/** + * \brief imports a memory pool from a shared handle. + * + * Specific allocations can be imported from the imported pool with cuMemPoolImportPointer. + * + * \note Imported memory pools do not support creating new allocations. + * As such imported memory pools may not be used in cuDeviceSetMemPool + * or ::cuMemAllocFromPoolAsync calls. + * + * \param[out] pool_out - Returned memory pool + * \param[in] handle - OS handle of the pool to open + * \param[in] handleType - The type of handle being imported + * \param[in] flags - must be 0 + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_OUT_OF_MEMORY + * + * \sa ::cuMemPoolExportToShareableHandle, ::cuMemPoolExportPointer, ::cuMemPoolImportPointer + */ +CUresult CUDAAPI cuMemPoolImportFromShareableHandle( + CUmemoryPool *pool_out, + void *handle, + CUmemAllocationHandleType handleType, + unsigned long long flags); + +/** + * \brief Export data to share a memory pool allocation between processes. + * + * Constructs \p shareData_out for sharing a specific allocation from an already shared memory pool. + * The recipient process can import the allocation with the ::cuMemPoolImportPointer api. + * The data is not a handle and may be shared through any IPC mechanism. + * + * \param[out] shareData_out - Returned export data + * \param[in] ptr - pointer to memory being exported + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_OUT_OF_MEMORY + * + * \sa ::cuMemPoolExportToShareableHandle, ::cuMemPoolImportFromShareableHandle, ::cuMemPoolImportPointer + */ +CUresult CUDAAPI cuMemPoolExportPointer(CUmemPoolPtrExportData *shareData_out, CUdeviceptr ptr); + +/** + * \brief Import a memory pool allocation from another process. + * + * Returns in \p ptr_out a pointer to the imported memory. + * The imported memory must not be accessed before the allocation operation completes + * in the exporting process. The imported memory must be freed from all importing processes before + * being freed in the exporting process. The pointer may be freed with cuMemFree + * or cuMemFreeAsync. If cuMemFreeAsync is used, the free must be completed + * on the importing process before the free operation on the exporting process. + * + * \note The cuMemFreeAsync api may be used in the exporting process before + * the cuMemFreeAsync operation completes in its stream as long as the + * cuMemFreeAsync in the exporting process specifies a stream with + * a stream dependency on the importing process's cuMemFreeAsync. + * + * \param[out] ptr_out - pointer to imported memory + * \param[in] pool - pool from which to import + * \param[in] shareData - data specifying the memory to import + * + * \returns + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_OUT_OF_MEMORY + * + * \sa ::cuMemPoolExportToShareableHandle, ::cuMemPoolImportFromShareableHandle, ::cuMemPoolExportPointer + */ +CUresult CUDAAPI cuMemPoolImportPointer(CUdeviceptr *ptr_out, CUmemoryPool pool, CUmemPoolPtrExportData *shareData); + +/** @} */ /* END CUDA_MALLOC_ASYNC */ + +/** + * \defgroup CUDA_MULTICAST Multicast Object Management + * + * ___MANBRIEF___ Functions for creating multicast objects, adding devices to them and binding/unbinding memory + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the CUDA multicast object operations exposed by the + * low-level CUDA driver application programming interface. + * + * @{ + * + * \section CUDA_MULTICAST_overview overview + * + * A multicast object created via ::cuMulticastCreate enables certain memory + * operations to be broadcasted to a team of devices. Devices can be added to a + * multicast object via ::cuMulticastAddDevice. Memory can be bound on each + * participating device via either ::cuMulticastBindMem or ::cuMulticastBindAddr. + * Multicast objects can be mapped into a device's virtual address space using + * the virtual memmory management APIs (see ::cuMemMap and ::cuMemSetAccess). + * + * \section CUDA_MULTICAST_support Supported Platforms + * + * Support for multicast on a specific device can be queried using the device + * attribute ::CU_DEVICE_ATTRIBUTE_MULTICAST_SUPPORTED + */ + +/** + * \brief Create a generic allocation handle representing a multicast object described by the given properties. + * + * This creates a multicast object as described by \p prop. The number of + * participating devices is specified by ::CUmulticastObjectProp::numDevices. + * Devices can be added to the multicast object via ::cuMulticastAddDevice. + * All participating devices must be added to the multicast object before memory + * can be bound to it. Memory is bound to the multicast object via either + * ::cuMulticastBindMem or ::cuMulticastBindAddr, and can be unbound via + * ::cuMulticastUnbind. The total amount of memory that can be bound per device + * is specified by :CUmulticastObjectProp::size. This size must be a multiple of + * the value returned by ::cuMulticastGetGranularity with the flag + * ::CU_MULTICAST_GRANULARITY_MINIMUM. For best performance however, the size + * should be aligned to the value returned by ::cuMulticastGetGranularity with + * the flag ::CU_MULTICAST_GRANULARITY_RECOMMENDED. + * + * After all participating devices have been added, multicast objects can also + * be mapped to a device's virtual address space using the virtual memory + * management APIs (see ::cuMemMap and ::cuMemSetAccess). Multicast objects can + * also be shared with other processes by requesting a shareable handle via + * ::cuMemExportToShareableHandle. Note that the desired types of shareable + * handles must be specified in the bitmask ::CUmulticastObjectProp::handleTypes. + * Multicast objects can be released using the virtual memory management API + * ::cuMemRelease. + * + * \param[out] mcHandle Value of handle returned. + * \param[in] prop Properties of the multicast object to create. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_PERMITTED, + * ::CUDA_ERROR_NOT_SUPPORTED + * + * \sa ::cuMulticastAddDevice, ::cuMulticastBindMem, ::cuMulticastBindAddr, ::cuMulticastUnbind + * \sa ::cuMemCreate, ::cuMemRelease, ::cuMemExportToShareableHandle, ::cuMemImportFromShareableHandle + */ +CUresult CUDAAPI cuMulticastCreate(CUmemGenericAllocationHandle *mcHandle, const CUmulticastObjectProp *prop); + +/** + * \brief Associate a device to a multicast object. + * + * Associates a device to a multicast object. The added device will be a part of + * the multicast team of size specified by CUmulticastObjectProp::numDevices + * during ::cuMulticastCreate. + * The association of the device to the multicast object is permanent during + * the life time of the multicast object. + * All devices must be added to the multicast team before any memory can be + * bound to any device in the team. Any calls to ::cuMulticastBindMem or + * ::cuMulticastBindAddr will block until all devices have been added. + * Similarly all devices must be added to the multicast team before a virtual + * address range can be mapped to the multicast object. A call to ::cuMemMap + * will block until all devices have been added. + * + * \param[in] mcHandle Handle representing a multicast object. + * \param[in] dev Device that will be associated to the multicast + * object. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_PERMITTED, + * ::CUDA_ERROR_NOT_SUPPORTED + * + * \sa ::cuMulticastCreate, ::cuMulticastBindMem, ::cuMulticastBindAddr + */ +CUresult CUDAAPI cuMulticastAddDevice(CUmemGenericAllocationHandle mcHandle, CUdevice dev); + +/** + * \brief Bind a memory allocation represented by a handle to a multicast object. + * + * Binds a memory allocation specified by \p memHandle and created via + * ::cuMemCreate to a multicast object represented by \p mcHandle and created + * via ::cuMulticastCreate. The intended \p size of the bind, the offset in the + * multicast range \p mcOffset as well as the offset in the memory \p memOffset + * must be a multiple of the value returned by ::cuMulticastGetGranularity with + * the flag ::CU_MULTICAST_GRANULARITY_MINIMUM. For best performance however, + * \p size, \p mcOffset and \p memOffset should be aligned to the granularity of + * the memory allocation(see ::cuMemGetAllocationGranularity) or to the value + * returned by ::cuMulticastGetGranularity with the flag + * ::CU_MULTICAST_GRANULARITY_RECOMMENDED. + * + * The \p size + \p memOffset must be smaller than the size of the allocated + * memory. Similarly the \p size + \p mcOffset must be smaller than the size + * of the multicast object. + * The memory allocation must have beeen created on one of the devices + * that was added to the multicast team via ::cuMulticastAddDevice. + * Externally shareable as well as imported multicast objects can be bound only + * to externally shareable memory. + * Note that this call will return CUDA_ERROR_OUT_OF_MEMORY if there are + * insufficient resources required to perform the bind. This call may also + * return CUDA_ERROR_SYSTEM_NOT_READY if the necessary system software is not + * initialized or running. + * + * \param[in] mcHandle Handle representing a multicast object. + * \param[in] mcOffset Offset into the multicast object for attachment. + * \param[in] memHandle Handle representing a memory allocation. + * \param[in] memOffset Offset into the memory for attachment. + * \param[in] size Size of the memory that will be bound to the + * multicast object. + * \param[in] flags Flags for future use, must be zero for now. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_PERMITTED, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_SYSTEM_NOT_READY + * + * \sa ::cuMulticastCreate, ::cuMulticastAddDevice, ::cuMemCreate + */ +CUresult CUDAAPI cuMulticastBindMem(CUmemGenericAllocationHandle mcHandle, size_t mcOffset, CUmemGenericAllocationHandle memHandle, size_t memOffset, size_t size, unsigned long long flags); + +/** + * \brief Bind a memory allocation represented by a virtual address to a multicast object. + * + * Binds a memory allocation specified by its mapped address \p memptr to a + * multicast object represented by \p mcHandle. + * The memory must have been allocated via ::cuMemCreate or ::cudaMallocAsync. + * The intended \p size of the bind, the offset in the multicast range + * \p mcOffset and \p memptr must be a multiple of the value returned by + * ::cuMulticastGetGranularity with the flag ::CU_MULTICAST_GRANULARITY_MINIMUM. + * For best performance however, \p size, \p mcOffset and \p memptr should be + * aligned to the value returned by ::cuMulticastGetGranularity with the flag + * ::CU_MULTICAST_GRANULARITY_RECOMMENDED. + * + * The \p size must be smaller than the size of the allocated memory. + * Similarly the \p size + \p mcOffset must be smaller than the total size + * of the multicast object. + * The memory allocation must have beeen created on one of the devices + * that was added to the multicast team via ::cuMulticastAddDevice. + * Externally shareable as well as imported multicast objects can be bound only + * to externally shareable memory. + * Note that this call will return CUDA_ERROR_OUT_OF_MEMORY if there are + * insufficient resources required to perform the bind. This call may also + * return CUDA_ERROR_SYSTEM_NOT_READY if the necessary system software is not + * initialized or running. + * + * \param[in] mcHandle Handle representing a multicast object. + * \param[in] mcOffset Offset into multicast va range for attachment. + * \param[in] memptr Virtual address of the memory allocation. + * \param[in] size Size of memory that will be bound to the + * multicast object. + * \param[in] flags Flags for future use, must be zero now. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_PERMITTED, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_OUT_OF_MEMORY, + * ::CUDA_ERROR_SYSTEM_NOT_READY + * + * \sa ::cuMulticastCreate, ::cuMulticastAddDevice, ::cuMemCreate + */ +CUresult CUDAAPI cuMulticastBindAddr(CUmemGenericAllocationHandle mcHandle, size_t mcOffset, CUdeviceptr memptr, size_t size, unsigned long long flags); + +/** + * \brief Unbind any memory allocations bound to a multicast object at a given offset and upto a given size. + * + * Unbinds any memory allocations hosted on \p dev and bound to a multicast + * object at \p mcOffset and upto a given \p size. + * The intended \p size of the unbind and the offset in the multicast range + * ( \p mcOffset ) must be a multiple of the value returned by + * ::cuMulticastGetGranularity flag ::CU_MULTICAST_GRANULARITY_MINIMUM. + * The \p size + \p mcOffset must be smaller than the total size of the + * multicast object. + * + * \note + * Warning: + * The \p mcOffset and the \p size must match the corresponding values specified + * during the bind call. Any other values may result in undefined behavior. + * + * \param[in] mcHandle Handle representing a multicast object. + * \param[in] dev Device that hosts the memory allocation. + * \param[in] mcOffset Offset into the multicast object. + * \param[in] size Desired size to unbind. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_PERMITTED, + * ::CUDA_ERROR_NOT_SUPPORTED + * + * \sa ::cuMulticastBindMem, ::cuMulticastBindAddr + */ +CUresult CUDAAPI cuMulticastUnbind(CUmemGenericAllocationHandle mcHandle, CUdevice dev, size_t mcOffset, size_t size); + +/** +* \brief Calculates either the minimal or recommended granularity for multicast object +* +* Calculates either the minimal or recommended granularity for a given set of +* multicast object properties and returns it in granularity. This granularity +* can be used as a multiple for size, bind offsets and address mappings of the +* multicast object. +* +* \param[out] granularity Returned granularity. +* \param[in] prop Properties of the multicast object. +* \param[in] option Determines which granularity to return. +* +* \returns +* ::CUDA_SUCCESS, +* ::CUDA_ERROR_INVALID_VALUE, +* ::CUDA_ERROR_NOT_INITIALIZED, +* ::CUDA_ERROR_DEINITIALIZED, +* ::CUDA_ERROR_NOT_PERMITTED, +* ::CUDA_ERROR_NOT_SUPPORTED +* +* \sa ::cuMulticastCreate, ::cuMulticastBindMem, ::cuMulticastBindAddr, ::cuMulticastUnbind +*/ +CUresult CUDAAPI cuMulticastGetGranularity(size_t *granularity, const CUmulticastObjectProp *prop, CUmulticastGranularity_flags option); + +/** @} */ /* END CUDA_MULTICAST */ + +/** + * \defgroup CUDA_UNIFIED Unified Addressing + * + * ___MANBRIEF___ unified addressing functions of the low-level CUDA driver + * API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the unified addressing functions of the + * low-level CUDA driver application programming interface. + * + * @{ + * + * \section CUDA_UNIFIED_overview Overview + * + * CUDA devices can share a unified address space with the host. + * For these devices there is no distinction between a device + * pointer and a host pointer -- the same pointer value may be + * used to access memory from the host program and from a kernel + * running on the device (with exceptions enumerated below). + * + * \section CUDA_UNIFIED_support Supported Platforms + * + * Whether or not a device supports unified addressing may be + * queried by calling ::cuDeviceGetAttribute() with the device + * attribute ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING. + * + * Unified addressing is automatically enabled in 64-bit processes + * + * \section CUDA_UNIFIED_lookup Looking Up Information from Pointer Values + * + * It is possible to look up information about the memory which backs a + * pointer value. For instance, one may want to know if a pointer points + * to host or device memory. As another example, in the case of device + * memory, one may want to know on which CUDA device the memory + * resides. These properties may be queried using the function + * ::cuPointerGetAttribute() + * + * Since pointers are unique, it is not necessary to specify information + * about the pointers specified to the various copy functions in the + * CUDA API. The function ::cuMemcpy() may be used to perform a copy + * between two pointers, ignoring whether they point to host or device + * memory (making ::cuMemcpyHtoD(), ::cuMemcpyDtoD(), and ::cuMemcpyDtoH() + * unnecessary for devices supporting unified addressing). For + * multidimensional copies, the memory type ::CU_MEMORYTYPE_UNIFIED may be + * used to specify that the CUDA driver should infer the location of the + * pointer from its value. + * + * \section CUDA_UNIFIED_automaphost Automatic Mapping of Host Allocated Host Memory + * + * All host memory allocated in all contexts using ::cuMemAllocHost() and + * ::cuMemHostAlloc() is always directly accessible from all contexts on + * all devices that support unified addressing. This is the case regardless + * of whether or not the flags ::CU_MEMHOSTALLOC_PORTABLE and + * ::CU_MEMHOSTALLOC_DEVICEMAP are specified. + * + * The pointer value through which allocated host memory may be accessed + * in kernels on all devices that support unified addressing is the same + * as the pointer value through which that memory is accessed on the host, + * so it is not necessary to call ::cuMemHostGetDevicePointer() to get the device + * pointer for these allocations. + * + * Note that this is not the case for memory allocated using the flag + * ::CU_MEMHOSTALLOC_WRITECOMBINED, as discussed below. + * + * \section CUDA_UNIFIED_autopeerregister Automatic Registration of Peer Memory + * + * Upon enabling direct access from a context that supports unified addressing + * to another peer context that supports unified addressing using + * ::cuCtxEnablePeerAccess() all memory allocated in the peer context using + * ::cuMemAlloc() and ::cuMemAllocPitch() will immediately be accessible + * by the current context. The device pointer value through + * which any peer memory may be accessed in the current context + * is the same pointer value through which that memory may be + * accessed in the peer context. + * + * \section CUDA_UNIFIED_exceptions Exceptions, Disjoint Addressing + * + * Not all memory may be accessed on devices through the same pointer + * value through which they are accessed on the host. These exceptions + * are host memory registered using ::cuMemHostRegister() and host memory + * allocated using the flag ::CU_MEMHOSTALLOC_WRITECOMBINED. For these + * exceptions, there exists a distinct host and device address for the + * memory. The device address is guaranteed to not overlap any valid host + * pointer range and is guaranteed to have the same value across all + * contexts that support unified addressing. + * + * This device address may be queried using ::cuMemHostGetDevicePointer() + * when a context using unified addressing is current. Either the host + * or the unified device pointer value may be used to refer to this memory + * through ::cuMemcpy() and similar functions using the + * ::CU_MEMORYTYPE_UNIFIED memory type. + * + */ + +/** + * \brief Returns information about a pointer + * + * The supported attributes are: + * + * - ::CU_POINTER_ATTRIBUTE_CONTEXT: + * + * Returns in \p *data the ::CUcontext in which \p ptr was allocated or + * registered. + * The type of \p data must be ::CUcontext *. + * + * If \p ptr was not allocated by, mapped by, or registered with + * a ::CUcontext which uses unified virtual addressing then + * ::CUDA_ERROR_INVALID_VALUE is returned. + * + * - ::CU_POINTER_ATTRIBUTE_MEMORY_TYPE: + * + * Returns in \p *data the physical memory type of the memory that + * \p ptr addresses as a ::CUmemorytype enumerated value. + * The type of \p data must be unsigned int. + * + * If \p ptr addresses device memory then \p *data is set to + * ::CU_MEMORYTYPE_DEVICE. The particular ::CUdevice on which the + * memory resides is the ::CUdevice of the ::CUcontext returned by the + * ::CU_POINTER_ATTRIBUTE_CONTEXT attribute of \p ptr. + * + * If \p ptr addresses host memory then \p *data is set to + * ::CU_MEMORYTYPE_HOST. + * + * If \p ptr was not allocated by, mapped by, or registered with + * a ::CUcontext which uses unified virtual addressing then + * ::CUDA_ERROR_INVALID_VALUE is returned. + * + * If the current ::CUcontext does not support unified virtual + * addressing then ::CUDA_ERROR_INVALID_CONTEXT is returned. + * + * - ::CU_POINTER_ATTRIBUTE_DEVICE_POINTER: + * + * Returns in \p *data the device pointer value through which + * \p ptr may be accessed by kernels running in the current + * ::CUcontext. + * The type of \p data must be CUdeviceptr *. + * + * If there exists no device pointer value through which + * kernels running in the current ::CUcontext may access + * \p ptr then ::CUDA_ERROR_INVALID_VALUE is returned. + * + * If there is no current ::CUcontext then + * ::CUDA_ERROR_INVALID_CONTEXT is returned. + * + * Except in the exceptional disjoint addressing cases discussed + * below, the value returned in \p *data will equal the input + * value \p ptr. + * + * - ::CU_POINTER_ATTRIBUTE_HOST_POINTER: + * + * Returns in \p *data the host pointer value through which + * \p ptr may be accessed by by the host program. + * The type of \p data must be void **. + * If there exists no host pointer value through which + * the host program may directly access \p ptr then + * ::CUDA_ERROR_INVALID_VALUE is returned. + * + * Except in the exceptional disjoint addressing cases discussed + * below, the value returned in \p *data will equal the input + * value \p ptr. + * + * - ::CU_POINTER_ATTRIBUTE_P2P_TOKENS: + * + * Returns in \p *data two tokens for use with the nv-p2p.h Linux + * kernel interface. \p data must be a struct of type + * CUDA_POINTER_ATTRIBUTE_P2P_TOKENS. + * + * \p ptr must be a pointer to memory obtained from :cuMemAlloc(). + * Note that p2pToken and vaSpaceToken are only valid for the + * lifetime of the source allocation. A subsequent allocation at + * the same address may return completely different tokens. + * Querying this attribute has a side effect of setting the attribute + * ::CU_POINTER_ATTRIBUTE_SYNC_MEMOPS for the region of memory that + * \p ptr points to. + * + * - ::CU_POINTER_ATTRIBUTE_SYNC_MEMOPS: + * + * A boolean attribute which when set, ensures that synchronous memory operations + * initiated on the region of memory that \p ptr points to will always synchronize. + * See further documentation in the section titled "API synchronization behavior" + * to learn more about cases when synchronous memory operations can + * exhibit asynchronous behavior. + * + * - ::CU_POINTER_ATTRIBUTE_BUFFER_ID: + * + * Returns in \p *data a buffer ID which is guaranteed to be unique within the process. + * \p data must point to an unsigned long long. + * + * \p ptr must be a pointer to memory obtained from a CUDA memory allocation API. + * Every memory allocation from any of the CUDA memory allocation APIs will + * have a unique ID over a process lifetime. Subsequent allocations do not reuse IDs + * from previous freed allocations. IDs are only unique within a single process. + * + * + * - ::CU_POINTER_ATTRIBUTE_IS_MANAGED: + * + * Returns in \p *data a boolean that indicates whether the pointer points to + * managed memory or not. + * + * If \p ptr is not a valid CUDA pointer then ::CUDA_ERROR_INVALID_VALUE is returned. + * + * - ::CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL: + * + * Returns in \p *data an integer representing a device ordinal of a device against + * which the memory was allocated or registered. + * + * - ::CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE: + * + * Returns in \p *data a boolean that indicates if this pointer maps to + * an allocation that is suitable for ::cudaIpcGetMemHandle. + * + * - ::CU_POINTER_ATTRIBUTE_RANGE_START_ADDR: + * + * Returns in \p *data the starting address for the allocation referenced + * by the device pointer \p ptr. Note that this is not necessarily the + * address of the mapped region, but the address of the mappable address + * range \p ptr references (e.g. from ::cuMemAddressReserve). + * + * - ::CU_POINTER_ATTRIBUTE_RANGE_SIZE: + * + * Returns in \p *data the size for the allocation referenced by the device + * pointer \p ptr. Note that this is not necessarily the size of the mapped + * region, but the size of the mappable address range \p ptr references + * (e.g. from ::cuMemAddressReserve). To retrieve the size of the mapped + * region, see ::cuMemGetAddressRange + * + * - ::CU_POINTER_ATTRIBUTE_MAPPED: + * + * Returns in \p *data a boolean that indicates if this pointer is in a + * valid address range that is mapped to a backing allocation. + * + * - ::CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES: + * + * Returns a bitmask of the allowed handle types for an allocation that may + * be passed to ::cuMemExportToShareableHandle. + * + * - ::CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE: + * + * Returns in \p *data the handle to the mempool that the allocation was obtained from. + * + * \par + * + * Note that for most allocations in the unified virtual address space + * the host and device pointer for accessing the allocation will be the + * same. The exceptions to this are + * - user memory registered using ::cuMemHostRegister + * - host memory allocated using ::cuMemHostAlloc with the + * ::CU_MEMHOSTALLOC_WRITECOMBINED flag + * For these types of allocation there will exist separate, disjoint host + * and device addresses for accessing the allocation. In particular + * - The host address will correspond to an invalid unmapped device address + * (which will result in an exception if accessed from the device) + * - The device address will correspond to an invalid unmapped host address + * (which will result in an exception if accessed from the host). + * For these types of allocations, querying ::CU_POINTER_ATTRIBUTE_HOST_POINTER + * and ::CU_POINTER_ATTRIBUTE_DEVICE_POINTER may be used to retrieve the host + * and device addresses from either address. + * + * \param data - Returned pointer attribute value + * \param attribute - Pointer attribute to query + * \param ptr - Pointer + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuPointerSetAttribute, + * ::cuMemAlloc, + * ::cuMemFree, + * ::cuMemAllocHost, + * ::cuMemFreeHost, + * ::cuMemHostAlloc, + * ::cuMemHostRegister, + * ::cuMemHostUnregister, + * ::cudaPointerGetAttributes + */ +CUresult CUDAAPI cuPointerGetAttribute(void *data, CUpointer_attribute attribute, CUdeviceptr ptr); + +/** + * \brief Prefetches memory to the specified destination device + * + * Prefetches memory to the specified destination device. \p devPtr is the + * base device pointer of the memory to be prefetched and \p dstDevice is the + * destination device. \p count specifies the number of bytes to copy. \p hStream + * is the stream in which the operation is enqueued. The memory range must refer + * to managed memory allocated via ::cuMemAllocManaged or declared via __managed__ variables. + * + * Passing in CU_DEVICE_CPU for \p dstDevice will prefetch the data to host memory. If + * \p dstDevice is a GPU, then the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS + * must be non-zero. Additionally, \p hStream must be associated with a device that has a + * non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. + * + * The start address and end address of the memory range will be rounded down and rounded up + * respectively to be aligned to CPU page size before the prefetch operation is enqueued + * in the stream. + * + * If no physical memory has been allocated for this region, then this memory region + * will be populated and mapped on the destination device. If there's insufficient + * memory to prefetch the desired region, the Unified Memory driver may evict pages from other + * ::cuMemAllocManaged allocations to host memory in order to make room. Device memory + * allocated using ::cuMemAlloc or ::cuArrayCreate will not be evicted. + * + * By default, any mappings to the previous location of the migrated pages are removed and + * mappings for the new location are only setup on \p dstDevice. The exact behavior however + * also depends on the settings applied to this memory range via ::cuMemAdvise as described + * below: + * + * If ::CU_MEM_ADVISE_SET_READ_MOSTLY was set on any subset of this memory range, + * then that subset will create a read-only copy of the pages on \p dstDevice. + * + * If ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION was called on any subset of this memory + * range, then the pages will be migrated to \p dstDevice even if \p dstDevice is not the + * preferred location of any pages in the memory range. + * + * If ::CU_MEM_ADVISE_SET_ACCESSED_BY was called on any subset of this memory range, + * then mappings to those pages from all the appropriate processors are updated to + * refer to the new location if establishing such a mapping is possible. Otherwise, + * those mappings are cleared. + * + * Note that this API is not required for functionality and only serves to improve performance + * by allowing the application to migrate data to a suitable location before it is accessed. + * Memory accesses to this range are always coherent and are allowed even when the data is + * actively being migrated. + * + * Note that this function is asynchronous with respect to the host and all work + * on other devices. + * + * \param devPtr - Pointer to be prefetched + * \param count - Size in bytes + * \param dstDevice - Destination device to prefetch to + * \param hStream - Stream to enqueue prefetch operation + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * \note_async + * \note_null_stream + * + * \sa ::cuMemcpy, ::cuMemcpyPeer, ::cuMemcpyAsync, + * ::cuMemcpy3DPeerAsync, ::cuMemAdvise, + * ::cudaMemPrefetchAsync + */ +CUresult CUDAAPI cuMemPrefetchAsync(CUdeviceptr devPtr, size_t count, CUdevice dstDevice, CUstream hStream); + +/** + * \brief Advise about the usage of a given memory range + * + * Advise the Unified Memory subsystem about the usage pattern for the memory range + * starting at \p devPtr with a size of \p count bytes. The start address and end address of the memory + * range will be rounded down and rounded up respectively to be aligned to CPU page size before the + * advice is applied. The memory range must refer to managed memory allocated via ::cuMemAllocManaged + * or declared via __managed__ variables. The memory range could also refer to system-allocated pageable + * memory provided it represents a valid, host-accessible region of memory and all additional constraints + * imposed by \p advice as outlined below are also satisfied. Specifying an invalid system-allocated pageable + * memory range results in an error being returned. + * + * The \p advice parameter can take the following values: + * - ::CU_MEM_ADVISE_SET_READ_MOSTLY: This implies that the data is mostly going to be read + * from and only occasionally written to. Any read accesses from any processor to this region will create a + * read-only copy of at least the accessed pages in that processor's memory. Additionally, if ::cuMemPrefetchAsync + * is called on this region, it will create a read-only copy of the data on the destination processor. + * If any processor writes to this region, all copies of the corresponding page will be invalidated + * except for the one where the write occurred. The \p device argument is ignored for this advice. + * Note that for a page to be read-duplicated, the accessing processor must either be the CPU or a GPU + * that has a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. + * Also, if a context is created on a device that does not have the device attribute + * ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS set, then read-duplication will not occur until + * all such contexts are destroyed. + * If the memory region refers to valid system-allocated pageable memory, then the accessing device must + * have a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS for a read-only + * copy to be created on that device. Note however that if the accessing device also has a non-zero value for the + * device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, then setting this advice + * will not create a read-only copy when that device accesses this memory region. + * + * - ::CU_MEM_ADVISE_UNSET_READ_MOSTLY: Undoes the effect of ::CU_MEM_ADVISE_SET_READ_MOSTLY and also prevents the + * Unified Memory driver from attempting heuristic read-duplication on the memory range. Any read-duplicated + * copies of the data will be collapsed into a single copy. The location for the collapsed + * copy will be the preferred location if the page has a preferred location and one of the read-duplicated + * copies was resident at that location. Otherwise, the location chosen is arbitrary. + * + * - ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION: This advice sets the preferred location for the + * data to be the memory belonging to \p device. Passing in CU_DEVICE_CPU for \p device sets the + * preferred location as host memory. If \p device is a GPU, then it must have a non-zero value for the + * device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. Setting the preferred location + * does not cause data to migrate to that location immediately. Instead, it guides the migration policy + * when a fault occurs on that memory region. If the data is already in its preferred location and the + * faulting processor can establish a mapping without requiring the data to be migrated, then + * data migration will be avoided. On the other hand, if the data is not in its preferred location + * or if a direct mapping cannot be established, then it will be migrated to the processor accessing + * it. It is important to note that setting the preferred location does not prevent data prefetching + * done using ::cuMemPrefetchAsync. + * Having a preferred location can override the page thrash detection and resolution logic in the Unified + * Memory driver. Normally, if a page is detected to be constantly thrashing between for example host and device + * memory, the page may eventually be pinned to host memory by the Unified Memory driver. But + * if the preferred location is set as device memory, then the page will continue to thrash indefinitely. + * If ::CU_MEM_ADVISE_SET_READ_MOSTLY is also set on this memory region or any subset of it, then the + * policies associated with that advice will override the policies of this advice, unless read accesses from + * \p device will not result in a read-only copy being created on that device as outlined in description for + * the advice ::CU_MEM_ADVISE_SET_READ_MOSTLY. + * If the memory region refers to valid system-allocated pageable memory, then \p device must have a non-zero + * value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS. Additionally, if \p device has + * a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, + * then this call has no effect. Note however that this behavior may change in the future. + * + * - ::CU_MEM_ADVISE_UNSET_PREFERRED_LOCATION: Undoes the effect of ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION + * and changes the preferred location to none. + * + * - ::CU_MEM_ADVISE_SET_ACCESSED_BY: This advice implies that the data will be accessed by \p device. + * Passing in ::CU_DEVICE_CPU for \p device will set the advice for the CPU. If \p device is a GPU, then + * the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS must be non-zero. + * This advice does not cause data migration and has no impact on the location of the data per se. Instead, + * it causes the data to always be mapped in the specified processor's page tables, as long as the + * location of the data permits a mapping to be established. If the data gets migrated for any reason, + * the mappings are updated accordingly. + * This advice is recommended in scenarios where data locality is not important, but avoiding faults is. + * Consider for example a system containing multiple GPUs with peer-to-peer access enabled, where the + * data located on one GPU is occasionally accessed by peer GPUs. In such scenarios, migrating data + * over to the other GPUs is not as important because the accesses are infrequent and the overhead of + * migration may be too high. But preventing faults can still help improve performance, and so having + * a mapping set up in advance is useful. Note that on CPU access of this data, the data may be migrated + * to host memory because the CPU typically cannot access device memory directly. Any GPU that had the + * ::CU_MEM_ADVISE_SET_ACCESSED_BY flag set for this data will now have its mapping updated to point to the + * page in host memory. + * If ::CU_MEM_ADVISE_SET_READ_MOSTLY is also set on this memory region or any subset of it, then the + * policies associated with that advice will override the policies of this advice. Additionally, if the + * preferred location of this memory region or any subset of it is also \p device, then the policies + * associated with ::CU_MEM_ADVISE_SET_PREFERRED_LOCATION will override the policies of this advice. + * If the memory region refers to valid system-allocated pageable memory, then \p device must have a non-zero + * value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS. Additionally, if \p device has + * a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, + * then this call has no effect. + * + * - ::CU_MEM_ADVISE_UNSET_ACCESSED_BY: Undoes the effect of ::CU_MEM_ADVISE_SET_ACCESSED_BY. Any mappings to + * the data from \p device may be removed at any time causing accesses to result in non-fatal page faults. + * If the memory region refers to valid system-allocated pageable memory, then \p device must have a non-zero + * value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS. Additionally, if \p device has + * a non-zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES, + * then this call has no effect. + * + * \param devPtr - Pointer to memory to set the advice for + * \param count - Size in bytes of the memory range + * \param advice - Advice to be applied for the specified memory range + * \param device - Device to apply the advice for + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * \note_async + * \note_null_stream + * + * \sa ::cuMemcpy, ::cuMemcpyPeer, ::cuMemcpyAsync, + * ::cuMemcpy3DPeerAsync, ::cuMemPrefetchAsync, + * ::cudaMemAdvise + */ +CUresult CUDAAPI cuMemAdvise(CUdeviceptr devPtr, size_t count, CUmem_advise advice, CUdevice device); + +/** + * \brief Query an attribute of a given memory range + * + * Query an attribute about the memory range starting at \p devPtr with a size of \p count bytes. The + * memory range must refer to managed memory allocated via ::cuMemAllocManaged or declared via + * __managed__ variables. + * + * The \p attribute parameter can take the following values: + * - ::CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY: If this attribute is specified, \p data will be interpreted + * as a 32-bit integer, and \p dataSize must be 4. The result returned will be 1 if all pages in the given + * memory range have read-duplication enabled, or 0 otherwise. + * - ::CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION: If this attribute is specified, \p data will be + * interpreted as a 32-bit integer, and \p dataSize must be 4. The result returned will be a GPU device + * id if all pages in the memory range have that GPU as their preferred location, or it will be CU_DEVICE_CPU + * if all pages in the memory range have the CPU as their preferred location, or it will be CU_DEVICE_INVALID + * if either all the pages don't have the same preferred location or some of the pages don't have a + * preferred location at all. Note that the actual location of the pages in the memory range at the time of + * the query may be different from the preferred location. + * - ::CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY: If this attribute is specified, \p data will be interpreted + * as an array of 32-bit integers, and \p dataSize must be a non-zero multiple of 4. The result returned + * will be a list of device ids that had ::CU_MEM_ADVISE_SET_ACCESSED_BY set for that entire memory range. + * If any device does not have that advice set for the entire memory range, that device will not be included. + * If \p data is larger than the number of devices that have that advice set for that memory range, + * CU_DEVICE_INVALID will be returned in all the extra space provided. For ex., if \p dataSize is 12 + * (i.e. \p data has 3 elements) and only device 0 has the advice set, then the result returned will be + * { 0, CU_DEVICE_INVALID, CU_DEVICE_INVALID }. If \p data is smaller than the number of devices that have + * that advice set, then only as many devices will be returned as can fit in the array. There is no + * guarantee on which specific devices will be returned, however. + * - ::CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION: If this attribute is specified, \p data will be + * interpreted as a 32-bit integer, and \p dataSize must be 4. The result returned will be the last location + * to which all pages in the memory range were prefetched explicitly via ::cuMemPrefetchAsync. This will either be + * a GPU id or CU_DEVICE_CPU depending on whether the last location for prefetch was a GPU or the CPU + * respectively. If any page in the memory range was never explicitly prefetched or if all pages were not + * prefetched to the same location, CU_DEVICE_INVALID will be returned. Note that this simply returns the + * last location that the application requested to prefetch the memory range to. It gives no indication as to + * whether the prefetch operation to that location has completed or even begun. + * + * \param data - A pointers to a memory location where the result + * of each attribute query will be written to. + * \param dataSize - Array containing the size of data + * \param attribute - The attribute to query + * \param devPtr - Start of the range to query + * \param count - Size of the range to query + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * \note_async + * \note_null_stream + * + * \sa ::cuMemRangeGetAttributes, ::cuMemPrefetchAsync, + * ::cuMemAdvise, + * ::cudaMemRangeGetAttribute + */ +CUresult CUDAAPI cuMemRangeGetAttribute(void *data, size_t dataSize, CUmem_range_attribute attribute, CUdeviceptr devPtr, size_t count); + +/** + * \brief Query attributes of a given memory range. + * + * Query attributes of the memory range starting at \p devPtr with a size of \p count bytes. The + * memory range must refer to managed memory allocated via ::cuMemAllocManaged or declared via + * __managed__ variables. The \p attributes array will be interpreted to have \p numAttributes + * entries. The \p dataSizes array will also be interpreted to have \p numAttributes entries. + * The results of the query will be stored in \p data. + * + * The list of supported attributes are given below. Please refer to ::cuMemRangeGetAttribute for + * attribute descriptions and restrictions. + * + * - ::CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY + * - ::CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION + * - ::CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY + * - ::CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION + * + * \param data - A two-dimensional array containing pointers to memory + * locations where the result of each attribute query will be written to. + * \param dataSizes - Array containing the sizes of each result + * \param attributes - An array of attributes to query + * (numAttributes and the number of attributes in this array should match) + * \param numAttributes - Number of attributes to query + * \param devPtr - Start of the range to query + * \param count - Size of the range to query + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa ::cuMemRangeGetAttribute, ::cuMemAdvise, + * ::cuMemPrefetchAsync, + * ::cudaMemRangeGetAttributes + */ +CUresult CUDAAPI cuMemRangeGetAttributes(void **data, size_t *dataSizes, CUmem_range_attribute *attributes, size_t numAttributes, CUdeviceptr devPtr, size_t count); + +/** + * \brief Set attributes on a previously allocated memory region + * + * The supported attributes are: + * + * - ::CU_POINTER_ATTRIBUTE_SYNC_MEMOPS: + * + * A boolean attribute that can either be set (1) or unset (0). When set, + * the region of memory that \p ptr points to is guaranteed to always synchronize + * memory operations that are synchronous. If there are some previously initiated + * synchronous memory operations that are pending when this attribute is set, the + * function does not return until those memory operations are complete. + * See further documentation in the section titled "API synchronization behavior" + * to learn more about cases when synchronous memory operations can + * exhibit asynchronous behavior. + * \p value will be considered as a pointer to an unsigned integer to which this attribute is to be set. + * + * \param value - Pointer to memory containing the value to be set + * \param attribute - Pointer attribute to set + * \param ptr - Pointer to a memory region allocated using CUDA memory allocation APIs + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa ::cuPointerGetAttribute, + * ::cuPointerGetAttributes, + * ::cuMemAlloc, + * ::cuMemFree, + * ::cuMemAllocHost, + * ::cuMemFreeHost, + * ::cuMemHostAlloc, + * ::cuMemHostRegister, + * ::cuMemHostUnregister + */ +CUresult CUDAAPI cuPointerSetAttribute(const void *value, CUpointer_attribute attribute, CUdeviceptr ptr); + +/** + * \brief Returns information about a pointer. + * + * The supported attributes are (refer to ::cuPointerGetAttribute for attribute descriptions and restrictions): + * + * - ::CU_POINTER_ATTRIBUTE_CONTEXT + * - ::CU_POINTER_ATTRIBUTE_MEMORY_TYPE + * - ::CU_POINTER_ATTRIBUTE_DEVICE_POINTER + * - ::CU_POINTER_ATTRIBUTE_HOST_POINTER + * - ::CU_POINTER_ATTRIBUTE_SYNC_MEMOPS + * - ::CU_POINTER_ATTRIBUTE_BUFFER_ID + * - ::CU_POINTER_ATTRIBUTE_IS_MANAGED + * - ::CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL + * - ::CU_POINTER_ATTRIBUTE_RANGE_START_ADDR + * - ::CU_POINTER_ATTRIBUTE_RANGE_SIZE + * - ::CU_POINTER_ATTRIBUTE_MAPPED + * - ::CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE + * - ::CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES + * - ::CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE + * + * \param numAttributes - Number of attributes to query + * \param attributes - An array of attributes to query + * (numAttributes and the number of attributes in this array should match) + * \param data - A two-dimensional array containing pointers to memory + * locations where the result of each attribute query will be written to. + * \param ptr - Pointer to query + * + * Unlike ::cuPointerGetAttribute, this function will not return an error when the \p ptr + * encountered is not a valid CUDA pointer. Instead, the attributes are assigned default NULL values + * and CUDA_SUCCESS is returned. + * + * If \p ptr was not allocated by, mapped by, or registered with a ::CUcontext which uses UVA + * (Unified Virtual Addressing), ::CUDA_ERROR_INVALID_CONTEXT is returned. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuPointerGetAttribute, + * ::cuPointerSetAttribute, + * ::cudaPointerGetAttributes + */ +CUresult CUDAAPI cuPointerGetAttributes(unsigned int numAttributes, CUpointer_attribute *attributes, void **data, CUdeviceptr ptr); + +/** @} */ /* END CUDA_UNIFIED */ + +/** + * \defgroup CUDA_STREAM Stream Management + * + * ___MANBRIEF___ stream management functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the stream management functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** + * \brief Create a stream + * + * Creates a stream and returns a handle in \p phStream. The \p Flags argument + * determines behaviors of the stream. + * + * Valid values for \p Flags are: + * - ::CU_STREAM_DEFAULT: Default stream creation flag. + * - ::CU_STREAM_NON_BLOCKING: Specifies that work running in the created + * stream may run concurrently with work in stream 0 (the NULL stream), and that + * the created stream should perform no implicit synchronization with stream 0. + * + * \param phStream - Returned newly created stream + * \param Flags - Parameters for stream creation + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \notefnerr + * + * \sa ::cuStreamDestroy, + * ::cuStreamCreateWithPriority, + * ::cuStreamGetPriority, + * ::cuStreamGetFlags, + * ::cuStreamWaitEvent, + * ::cuStreamQuery, + * ::cuStreamSynchronize, + * ::cuStreamAddCallback, + * ::cudaStreamCreate, + * ::cudaStreamCreateWithFlags + */ +CUresult CUDAAPI cuStreamCreate(CUstream *phStream, unsigned int Flags); + +/** + * \brief Create a stream with the given priority + * + * Creates a stream with the specified priority and returns a handle in \p phStream. + * This API alters the scheduler priority of work in the stream. Work in a higher + * priority stream may preempt work already executing in a low priority stream. + * + * \p priority follows a convention where lower numbers represent higher priorities. + * '0' represents default priority. The range of meaningful numerical priorities can + * be queried using ::cuCtxGetStreamPriorityRange. If the specified priority is + * outside the numerical range returned by ::cuCtxGetStreamPriorityRange, + * it will automatically be clamped to the lowest or the highest number in the range. + * + * \param phStream - Returned newly created stream + * \param flags - Flags for stream creation. See ::cuStreamCreate for a list of + * valid flags + * \param priority - Stream priority. Lower numbers represent higher priorities. + * See ::cuCtxGetStreamPriorityRange for more information about + * meaningful stream priorities that can be passed. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \notefnerr + * + * \note Stream priorities are supported only on GPUs + * with compute capability 3.5 or higher. + * + * \note In the current implementation, only compute kernels launched in + * priority streams are affected by the stream's priority. Stream priorities have + * no effect on host-to-device and device-to-host memory operations. + * + * \sa ::cuStreamDestroy, + * ::cuStreamCreate, + * ::cuStreamGetPriority, + * ::cuCtxGetStreamPriorityRange, + * ::cuStreamGetFlags, + * ::cuStreamWaitEvent, + * ::cuStreamQuery, + * ::cuStreamSynchronize, + * ::cuStreamAddCallback, + * ::cudaStreamCreateWithPriority + */ +CUresult CUDAAPI cuStreamCreateWithPriority(CUstream *phStream, unsigned int flags, int priority); + + +/** + * \brief Query the priority of a given stream + * + * Query the priority of a stream created using ::cuStreamCreate or ::cuStreamCreateWithPriority + * and return the priority in \p priority. Note that if the stream was created with a + * priority outside the numerical range returned by ::cuCtxGetStreamPriorityRange, + * this function returns the clamped priority. + * See ::cuStreamCreateWithPriority for details about priority clamping. + * + * \param hStream - Handle to the stream to be queried + * \param priority - Pointer to a signed integer in which the stream's priority is returned + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \notefnerr + * + * \sa ::cuStreamDestroy, + * ::cuStreamCreate, + * ::cuStreamCreateWithPriority, + * ::cuCtxGetStreamPriorityRange, + * ::cuStreamGetFlags, + * ::cudaStreamGetPriority + */ +CUresult CUDAAPI cuStreamGetPriority(CUstream hStream, int *priority); + +/** + * \brief Query the flags of a given stream + * + * Query the flags of a stream created using ::cuStreamCreate or ::cuStreamCreateWithPriority + * and return the flags in \p flags. + * + * \param hStream - Handle to the stream to be queried + * \param flags - Pointer to an unsigned integer in which the stream's flags are returned + * The value returned in \p flags is a logical 'OR' of all flags that + * were used while creating this stream. See ::cuStreamCreate for the list + * of valid flags + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \notefnerr + * + * \sa ::cuStreamDestroy, + * ::cuStreamCreate, + * ::cuStreamGetPriority, + * ::cudaStreamGetFlags + */ +CUresult CUDAAPI cuStreamGetFlags(CUstream hStream, unsigned int *flags); + +/** + * \brief Returns the unique Id associated with the stream handle supplied + * + * Returns in \p streamId the unique Id which is associated with the given stream handle. + * The Id is unique for the life of the program. + * + * The stream handle \p hStream can refer to any of the following: + *
    + *
  • a stream created via any of the CUDA driver APIs such as ::cuStreamCreate + * and ::cuStreamCreateWithPriority, or their runtime API equivalents such as + * ::cudaStreamCreate, ::cudaStreamCreateWithFlags and ::cudaStreamCreateWithPriority. + * Passing an invalid handle will result in undefined behavior.
    • + *
  • any of the special streams such as the NULL stream, ::CU_STREAM_LEGACY and + * ::CU_STREAM_PER_THREAD. The runtime API equivalents of these are also accepted, + * which are NULL, ::cudaStreamLegacy and ::cudaStreamPerThread respectively.
    • + *
+ * + * \param hStream - Handle to the stream to be queried + * \param streamId - Pointer to store the Id of the stream + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa ::cuStreamDestroy, + * ::cuStreamCreate, + * ::cuStreamGetPriority, + * ::cudaStreamGetId + */ +CUresult CUDAAPI cuStreamGetId(CUstream hStream, unsigned long long *streamId); + +/** + * \brief Query the context associated with a stream + * + * Returns the CUDA context that the stream is associated with. + * + * The stream handle \p hStream can refer to any of the following: + *
    + *
  • a stream created via any of the CUDA driver APIs such as ::cuStreamCreate + * and ::cuStreamCreateWithPriority, or their runtime API equivalents such as + * ::cudaStreamCreate, ::cudaStreamCreateWithFlags and ::cudaStreamCreateWithPriority. + * The returned context is the context that was active in the calling thread when the + * stream was created. Passing an invalid handle will result in undefined behavior.
    • + *
  • any of the special streams such as the NULL stream, ::CU_STREAM_LEGACY and + * ::CU_STREAM_PER_THREAD. The runtime API equivalents of these are also accepted, + * which are NULL, ::cudaStreamLegacy and ::cudaStreamPerThread respectively. + * Specifying any of the special handles will return the context current to the + * calling thread. If no context is current to the calling thread, + * ::CUDA_ERROR_INVALID_CONTEXT is returned.
    • + *
+ * + * \param hStream - Handle to the stream to be queried + * \param pctx - Returned context associated with the stream + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * \notefnerr + * + * \sa ::cuStreamDestroy, + * ::cuStreamCreateWithPriority, + * ::cuStreamGetPriority, + * ::cuStreamGetFlags, + * ::cuStreamWaitEvent, + * ::cuStreamQuery, + * ::cuStreamSynchronize, + * ::cuStreamAddCallback, + * ::cudaStreamCreate, + * ::cudaStreamCreateWithFlags + */ +CUresult CUDAAPI cuStreamGetCtx(CUstream hStream, CUcontext *pctx); + +/** + * \brief Make a compute stream wait on an event + * + * Makes all future work submitted to \p hStream wait for all work captured in + * \p hEvent. See ::cuEventRecord() for details on what is captured by an event. + * The synchronization will be performed efficiently on the device when applicable. + * \p hEvent may be from a different context or device than \p hStream. + * + * flags include: + * - ::CU_EVENT_WAIT_DEFAULT: Default event creation flag. + * - ::CU_EVENT_WAIT_EXTERNAL: Event is captured in the graph as an external + * event node when performing stream capture. This flag is invalid outside + * of stream capture. + * + * \param hStream - Stream to wait + * \param hEvent - Event to wait on (may not be NULL) + * \param Flags - See ::CUevent_capture_flags + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * \note_null_stream + * \notefnerr + * + * \sa ::cuStreamCreate, + * ::cuEventRecord, + * ::cuStreamQuery, + * ::cuStreamSynchronize, + * ::cuStreamAddCallback, + * ::cuStreamDestroy, + * ::cudaStreamWaitEvent + */ +CUresult CUDAAPI cuStreamWaitEvent(CUstream hStream, CUevent hEvent, unsigned int Flags); + +/** + * \brief Add a callback to a compute stream + * + * \note This function is slated for eventual deprecation and removal. If + * you do not require the callback to execute in case of a device error, + * consider using ::cuLaunchHostFunc. Additionally, this function is not + * supported with ::cuStreamBeginCapture and ::cuStreamEndCapture, unlike + * ::cuLaunchHostFunc. + * + * Adds a callback to be called on the host after all currently enqueued + * items in the stream have completed. For each + * cuStreamAddCallback call, the callback will be executed exactly once. + * The callback will block later work in the stream until it is finished. + * + * The callback may be passed ::CUDA_SUCCESS or an error code. In the event + * of a device error, all subsequently executed callbacks will receive an + * appropriate ::CUresult. + * + * Callbacks must not make any CUDA API calls. Attempting to use a CUDA API + * will result in ::CUDA_ERROR_NOT_PERMITTED. Callbacks must not perform any + * synchronization that may depend on outstanding device work or other callbacks + * that are not mandated to run earlier. Callbacks without a mandated order + * (in independent streams) execute in undefined order and may be serialized. + * + * For the purposes of Unified Memory, callback execution makes a number of + * guarantees: + *
    + *
  • The callback stream is considered idle for the duration of the + * callback. Thus, for example, a callback may always use memory attached + * to the callback stream.
    • + *
  • The start of execution of a callback has the same effect as + * synchronizing an event recorded in the same stream immediately prior to + * the callback. It thus synchronizes streams which have been "joined" + * prior to the callback.
    • + *
  • Adding device work to any stream does not have the effect of making + * the stream active until all preceding host functions and stream callbacks + * have executed. Thus, for + * example, a callback might use global attached memory even if work has + * been added to another stream, if the work has been ordered behind the + * callback with an event.
    • + *
  • Completion of a callback does not cause a stream to become + * active except as described above. The callback stream will remain idle + * if no device work follows the callback, and will remain idle across + * consecutive callbacks without device work in between. Thus, for example, + * stream synchronization can be done by signaling from a callback at the + * end of the stream.
    • + *
+ * + * \param hStream - Stream to add callback to + * \param callback - The function to call once preceding stream operations are complete + * \param userData - User specified data to be passed to the callback function + * \param flags - Reserved for future use, must be 0 + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_SUPPORTED + * \note_null_stream + * \notefnerr + * + * \sa ::cuStreamCreate, + * ::cuStreamQuery, + * ::cuStreamSynchronize, + * ::cuStreamWaitEvent, + * ::cuStreamDestroy, + * ::cuMemAllocManaged, + * ::cuStreamAttachMemAsync, + * ::cuLaunchHostFunc, + * ::cudaStreamAddCallback + */ +CUresult CUDAAPI cuStreamAddCallback(CUstream hStream, CUstreamCallback callback, void *userData, unsigned int flags); + +/** + * \brief Begins graph capture on a stream + * + * Begin graph capture on \p hStream. When a stream is in capture mode, all operations + * pushed into the stream will not be executed, but will instead be captured into + * a graph, which will be returned via ::cuStreamEndCapture. Capture may not be initiated + * if \p stream is CU_STREAM_LEGACY. Capture must be ended on the same stream in which + * it was initiated, and it may only be initiated if the stream is not already in capture + * mode. The capture mode may be queried via ::cuStreamIsCapturing. A unique id + * representing the capture sequence may be queried via ::cuStreamGetCaptureInfo. + * + * If \p mode is not ::CU_STREAM_CAPTURE_MODE_RELAXED, ::cuStreamEndCapture must be + * called on this stream from the same thread. + * + * \param hStream - Stream in which to initiate capture + * \param mode - Controls the interaction of this capture sequence with other API + * calls that are potentially unsafe. For more details see + * ::cuThreadExchangeStreamCaptureMode. + * + * \note Kernels captured using this API must not use texture and surface references. + * Reading or writing through any texture or surface reference is undefined + * behavior. This restriction does not apply to texture and surface objects. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa + * ::cuStreamCreate, + * ::cuStreamIsCapturing, + * ::cuStreamEndCapture, + * ::cuThreadExchangeStreamCaptureMode + */ +CUresult CUDAAPI cuStreamBeginCapture(CUstream hStream, CUstreamCaptureMode mode); + +/** + * \brief Swaps the stream capture interaction mode for a thread + * + * Sets the calling thread's stream capture interaction mode to the value contained + * in \p *mode, and overwrites \p *mode with the previous mode for the thread. To + * facilitate deterministic behavior across function or module boundaries, callers + * are encouraged to use this API in a push-pop fashion: \code + CUstreamCaptureMode mode = desiredMode; + cuThreadExchangeStreamCaptureMode(&mode); + ... + cuThreadExchangeStreamCaptureMode(&mode); // restore previous mode + * \endcode + * + * During stream capture (see ::cuStreamBeginCapture), some actions, such as a call + * to ::cudaMalloc, may be unsafe. In the case of ::cudaMalloc, the operation is + * not enqueued asynchronously to a stream, and is not observed by stream capture. + * Therefore, if the sequence of operations captured via ::cuStreamBeginCapture + * depended on the allocation being replayed whenever the graph is launched, the + * captured graph would be invalid. + * + * Therefore, stream capture places restrictions on API calls that can be made within + * or concurrently to a ::cuStreamBeginCapture-::cuStreamEndCapture sequence. This + * behavior can be controlled via this API and flags to ::cuStreamBeginCapture. + * + * A thread's mode is one of the following: + * - \p CU_STREAM_CAPTURE_MODE_GLOBAL: This is the default mode. If the local thread has + * an ongoing capture sequence that was not initiated with + * \p CU_STREAM_CAPTURE_MODE_RELAXED at \p cuStreamBeginCapture, or if any other thread + * has a concurrent capture sequence initiated with \p CU_STREAM_CAPTURE_MODE_GLOBAL, + * this thread is prohibited from potentially unsafe API calls. + * - \p CU_STREAM_CAPTURE_MODE_THREAD_LOCAL: If the local thread has an ongoing capture + * sequence not initiated with \p CU_STREAM_CAPTURE_MODE_RELAXED, it is prohibited + * from potentially unsafe API calls. Concurrent capture sequences in other threads + * are ignored. + * - \p CU_STREAM_CAPTURE_MODE_RELAXED: The local thread is not prohibited from potentially + * unsafe API calls. Note that the thread is still prohibited from API calls which + * necessarily conflict with stream capture, for example, attempting ::cuEventQuery + * on an event that was last recorded inside a capture sequence. + * + * \param mode - Pointer to mode value to swap with the current mode + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa + * ::cuStreamBeginCapture + */ +CUresult CUDAAPI cuThreadExchangeStreamCaptureMode(CUstreamCaptureMode *mode); + +/** + * \brief Ends capture on a stream, returning the captured graph + * + * End capture on \p hStream, returning the captured graph via \p phGraph. + * Capture must have been initiated on \p hStream via a call to ::cuStreamBeginCapture. + * If capture was invalidated, due to a violation of the rules of stream capture, then + * a NULL graph will be returned. + * + * If the \p mode argument to ::cuStreamBeginCapture was not + * ::CU_STREAM_CAPTURE_MODE_RELAXED, this call must be from the same thread as + * ::cuStreamBeginCapture. + * + * \param hStream - Stream to query + * \param phGraph - The captured graph + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD + * \notefnerr + * + * \sa + * ::cuStreamCreate, + * ::cuStreamBeginCapture, + * ::cuStreamIsCapturing + */ +CUresult CUDAAPI cuStreamEndCapture(CUstream hStream, CUgraph *phGraph); + +/** + * \brief Returns a stream's capture status + * + * Return the capture status of \p hStream via \p captureStatus. After a successful + * call, \p *captureStatus will contain one of the following: + * - ::CU_STREAM_CAPTURE_STATUS_NONE: The stream is not capturing. + * - ::CU_STREAM_CAPTURE_STATUS_ACTIVE: The stream is capturing. + * - ::CU_STREAM_CAPTURE_STATUS_INVALIDATED: The stream was capturing but an error + * has invalidated the capture sequence. The capture sequence must be terminated + * with ::cuStreamEndCapture on the stream where it was initiated in order to + * continue using \p hStream. + * + * Note that, if this is called on ::CU_STREAM_LEGACY (the "null stream") while + * a blocking stream in the same context is capturing, it will return + * ::CUDA_ERROR_STREAM_CAPTURE_IMPLICIT and \p *captureStatus is unspecified + * after the call. The blocking stream capture is not invalidated. + * + * When a blocking stream is capturing, the legacy stream is in an + * unusable state until the blocking stream capture is terminated. The legacy + * stream is not supported for stream capture, but attempted use would have an + * implicit dependency on the capturing stream(s). + * + * \param hStream - Stream to query + * \param captureStatus - Returns the stream's capture status + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_STREAM_CAPTURE_IMPLICIT + * \notefnerr + * + * \sa + * ::cuStreamCreate, + * ::cuStreamBeginCapture, + * ::cuStreamEndCapture + */ +CUresult CUDAAPI cuStreamIsCapturing(CUstream hStream, CUstreamCaptureStatus *captureStatus); + +/** + * \brief Query a stream's capture state + * + * Query stream state related to stream capture. + * + * If called on ::CU_STREAM_LEGACY (the "null stream") while a stream not created + * with ::CU_STREAM_NON_BLOCKING is capturing, returns ::CUDA_ERROR_STREAM_CAPTURE_IMPLICIT. + * + * Valid data (other than capture status) is returned only if both of the following are true: + * - the call returns CUDA_SUCCESS + * - the returned capture status is ::CU_STREAM_CAPTURE_STATUS_ACTIVE + * + * \param hStream - The stream to query + * \param captureStatus_out - Location to return the capture status of the stream; required + * \param id_out - Optional location to return an id for the capture sequence, which is + * unique over the lifetime of the process + * \param graph_out - Optional location to return the graph being captured into. All + * operations other than destroy and node removal are permitted on the graph + * while the capture sequence is in progress. This API does not transfer + * ownership of the graph, which is transferred or destroyed at + * ::cuStreamEndCapture. Note that the graph handle may be invalidated before + * end of capture for certain errors. Nodes that are or become + * unreachable from the original stream at ::cuStreamEndCapture due to direct + * actions on the graph do not trigger ::CUDA_ERROR_STREAM_CAPTURE_UNJOINED. + * \param dependencies_out - Optional location to store a pointer to an array of nodes. + * The next node to be captured in the stream will depend on this set of nodes, + * absent operations such as event wait which modify this set. The array pointer + * is valid until the next API call which operates on the stream or until end of + * capture. The node handles may be copied out and are valid until they or the + * graph is destroyed. The driver-owned array may also be passed directly to + * APIs that operate on the graph (not the stream) without copying. + * \param numDependencies_out - Optional location to store the size of the array + * returned in dependencies_out. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_STREAM_CAPTURE_IMPLICIT + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuStreamBeginCapture, + * ::cuStreamIsCapturing, + * ::cuStreamUpdateCaptureDependencies + */ +CUresult CUDAAPI cuStreamGetCaptureInfo(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, + cuuint64_t *id_out, CUgraph *graph_out, const CUgraphNode **dependencies_out, size_t *numDependencies_out); + +/** + * \brief Update the set of dependencies in a capturing stream (11.3+) + * + * Modifies the dependency set of a capturing stream. The dependency set is the set + * of nodes that the next captured node in the stream will depend on. + * + * Valid flags are ::CU_STREAM_ADD_CAPTURE_DEPENDENCIES and + * ::CU_STREAM_SET_CAPTURE_DEPENDENCIES. These control whether the set passed to + * the API is added to the existing set or replaces it. A flags value of 0 defaults + * to ::CU_STREAM_ADD_CAPTURE_DEPENDENCIES. + * + * Nodes that are removed from the dependency set via this API do not result in + * ::CUDA_ERROR_STREAM_CAPTURE_UNJOINED if they are unreachable from the stream at + * ::cuStreamEndCapture. + * + * Returns ::CUDA_ERROR_ILLEGAL_STATE if the stream is not capturing. + * + * This API is new in CUDA 11.3. Developers requiring compatibility across minor + * versions to CUDA 11.0 should not use this API or provide a fallback. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_ILLEGAL_STATE + * + * \sa + * ::cuStreamBeginCapture, + * ::cuStreamGetCaptureInfo, + */ +CUresult CUDAAPI cuStreamUpdateCaptureDependencies(CUstream hStream, CUgraphNode *dependencies, size_t numDependencies, unsigned int flags); + +/** + * \brief Attach memory to a stream asynchronously + * + * Enqueues an operation in \p hStream to specify stream association of + * \p length bytes of memory starting from \p dptr. This function is a + * stream-ordered operation, meaning that it is dependent on, and will + * only take effect when, previous work in stream has completed. Any + * previous association is automatically replaced. + * + * \p dptr must point to one of the following types of memories: + * - managed memory declared using the __managed__ keyword or allocated with + * ::cuMemAllocManaged. + * - a valid host-accessible region of system-allocated pageable memory. This + * type of memory may only be specified if the device associated with the + * stream reports a non-zero value for the device attribute + * ::CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS. + * + * For managed allocations, \p length must be either zero or the entire + * allocation's size. Both indicate that the entire allocation's stream + * association is being changed. Currently, it is not possible to change stream + * association for a portion of a managed allocation. + * + * For pageable host allocations, \p length must be non-zero. + * + * The stream association is specified using \p flags which must be + * one of ::CUmemAttach_flags. + * If the ::CU_MEM_ATTACH_GLOBAL flag is specified, the memory can be accessed + * by any stream on any device. + * If the ::CU_MEM_ATTACH_HOST flag is specified, the program makes a guarantee + * that it won't access the memory on the device from any stream on a device that + * has a zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. + * If the ::CU_MEM_ATTACH_SINGLE flag is specified and \p hStream is associated with + * a device that has a zero value for the device attribute ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS, + * the program makes a guarantee that it will only access the memory on the device + * from \p hStream. It is illegal to attach singly to the NULL stream, because the + * NULL stream is a virtual global stream and not a specific stream. An error will + * be returned in this case. + * + * When memory is associated with a single stream, the Unified Memory system will + * allow CPU access to this memory region so long as all operations in \p hStream + * have completed, regardless of whether other streams are active. In effect, + * this constrains exclusive ownership of the managed memory region by + * an active GPU to per-stream activity instead of whole-GPU activity. + * + * Accessing memory on the device from streams that are not associated with + * it will produce undefined results. No error checking is performed by the + * Unified Memory system to ensure that kernels launched into other streams + * do not access this region. + * + * It is a program's responsibility to order calls to ::cuStreamAttachMemAsync + * via events, synchronization or other means to ensure legal access to memory + * at all times. Data visibility and coherency will be changed appropriately + * for all kernels which follow a stream-association change. + * + * If \p hStream is destroyed while data is associated with it, the association is + * removed and the association reverts to the default visibility of the allocation + * as specified at ::cuMemAllocManaged. For __managed__ variables, the default + * association is always ::CU_MEM_ATTACH_GLOBAL. Note that destroying a stream is an + * asynchronous operation, and as a result, the change to default association won't + * happen until all work in the stream has completed. + * + * \param hStream - Stream in which to enqueue the attach operation + * \param dptr - Pointer to memory (must be a pointer to managed memory or + * to a valid host-accessible region of system-allocated + * pageable memory) + * \param length - Length of memory + * \param flags - Must be one of ::CUmemAttach_flags + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_SUPPORTED + * \note_null_stream + * \notefnerr + * + * \sa ::cuStreamCreate, + * ::cuStreamQuery, + * ::cuStreamSynchronize, + * ::cuStreamWaitEvent, + * ::cuStreamDestroy, + * ::cuMemAllocManaged, + * ::cudaStreamAttachMemAsync + */ +CUresult CUDAAPI cuStreamAttachMemAsync(CUstream hStream, CUdeviceptr dptr, size_t length, unsigned int flags); + +/** + * \brief Determine status of a compute stream + * + * Returns ::CUDA_SUCCESS if all operations in the stream specified by + * \p hStream have completed, or ::CUDA_ERROR_NOT_READY if not. + * + * For the purposes of Unified Memory, a return value of ::CUDA_SUCCESS + * is equivalent to having called ::cuStreamSynchronize(). + * + * \param hStream - Stream to query status of + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_READY + * \note_null_stream + * \notefnerr + * + * \sa ::cuStreamCreate, + * ::cuStreamWaitEvent, + * ::cuStreamDestroy, + * ::cuStreamSynchronize, + * ::cuStreamAddCallback, + * ::cudaStreamQuery + */ +CUresult CUDAAPI cuStreamQuery(CUstream hStream); + +/** + * \brief Wait until a stream's tasks are completed + * + * Waits until the device has completed all operations in the stream specified + * by \p hStream. If the context was created with the + * ::CU_CTX_SCHED_BLOCKING_SYNC flag, the CPU thread will block until the + * stream is finished with all of its tasks. + * + * \param hStream - Stream to wait for + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE + + * \note_null_stream + * \notefnerr + * + * \sa ::cuStreamCreate, + * ::cuStreamDestroy, + * ::cuStreamWaitEvent, + * ::cuStreamQuery, + * ::cuStreamAddCallback, + * ::cudaStreamSynchronize + */ +CUresult CUDAAPI cuStreamSynchronize(CUstream hStream); + +/** + * \brief Destroys a stream + * + * Destroys the stream specified by \p hStream. + * + * In case the device is still doing work in the stream \p hStream + * when ::cuStreamDestroy() is called, the function will return immediately + * and the resources associated with \p hStream will be released automatically + * once the device has completed all work in \p hStream. + * + * \param hStream - Stream to destroy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa ::cuStreamCreate, + * ::cuStreamWaitEvent, + * ::cuStreamQuery, + * ::cuStreamSynchronize, + * ::cuStreamAddCallback, + * ::cudaStreamDestroy + */ +CUresult CUDAAPI cuStreamDestroy(CUstream hStream); + +/** + * \brief Copies attributes from source stream to destination stream. + * + * Copies attributes from source stream \p src to destination stream \p dst. + * Both streams must have the same context. + * + * \param[out] dst Destination stream + * \param[in] src Source stream + * For list of attributes see ::CUstreamAttrID + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa + * ::CUaccessPolicyWindow + */ +CUresult CUDAAPI cuStreamCopyAttributes(CUstream dst, CUstream src); + +/** + * \brief Queries stream attribute. + * + * Queries attribute \p attr from \p hStream and stores it in corresponding + * member of \p value_out. + * + * \param[in] hStream + * \param[in] attr + * \param[out] value_out + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa + * ::CUaccessPolicyWindow + */ +CUresult CUDAAPI cuStreamGetAttribute(CUstream hStream, CUstreamAttrID attr, + CUstreamAttrValue *value_out); + +/** + * \brief Sets stream attribute. + * + * Sets attribute \p attr on \p hStream from corresponding attribute of + * \p value. The updated attribute will be applied to subsequent work + * submitted to the stream. It will not affect previously submitted work. + * + * \param[out] hStream + * \param[in] attr + * \param[in] value + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa + * ::CUaccessPolicyWindow + */ +CUresult CUDAAPI cuStreamSetAttribute(CUstream hStream, CUstreamAttrID attr, + const CUstreamAttrValue *value); + +/** @} */ /* END CUDA_STREAM */ + + +/** + * \defgroup CUDA_EVENT Event Management + * + * ___MANBRIEF___ event management functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the event management functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** + * \brief Creates an event + * + * Creates an event *phEvent for the current context with the flags specified via + * \p Flags. Valid flags include: + * - ::CU_EVENT_DEFAULT: Default event creation flag. + * - ::CU_EVENT_BLOCKING_SYNC: Specifies that the created event should use blocking + * synchronization. A CPU thread that uses ::cuEventSynchronize() to wait on + * an event created with this flag will block until the event has actually + * been recorded. + * - ::CU_EVENT_DISABLE_TIMING: Specifies that the created event does not need + * to record timing data. Events created with this flag specified and + * the ::CU_EVENT_BLOCKING_SYNC flag not specified will provide the best + * performance when used with ::cuStreamWaitEvent() and ::cuEventQuery(). + * - ::CU_EVENT_INTERPROCESS: Specifies that the created event may be used as an + * interprocess event by ::cuIpcGetEventHandle(). ::CU_EVENT_INTERPROCESS must + * be specified along with ::CU_EVENT_DISABLE_TIMING. + * + * \param phEvent - Returns newly created event + * \param Flags - Event creation flags + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \notefnerr + * + * \sa + * ::cuEventRecord, + * ::cuEventQuery, + * ::cuEventSynchronize, + * ::cuEventDestroy, + * ::cuEventElapsedTime, + * ::cudaEventCreate, + * ::cudaEventCreateWithFlags + */ +CUresult CUDAAPI cuEventCreate(CUevent *phEvent, unsigned int Flags); + +/** + * \brief Records an event + * + * Captures in \p hEvent the contents of \p hStream at the time of this call. + * \p hEvent and \p hStream must be from the same context. + * Calls such as ::cuEventQuery() or ::cuStreamWaitEvent() will then + * examine or wait for completion of the work that was captured. Uses of + * \p hStream after this call do not modify \p hEvent. See note on default + * stream behavior for what is captured in the default case. + * + * ::cuEventRecord() can be called multiple times on the same event and + * will overwrite the previously captured state. Other APIs such as + * ::cuStreamWaitEvent() use the most recently captured state at the time + * of the API call, and are not affected by later calls to + * ::cuEventRecord(). Before the first call to ::cuEventRecord(), an + * event represents an empty set of work, so for example ::cuEventQuery() + * would return ::CUDA_SUCCESS. + * + * \param hEvent - Event to record + * \param hStream - Stream to record event for + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE + * \note_null_stream + * \notefnerr + * + * \sa ::cuEventCreate, + * ::cuEventQuery, + * ::cuEventSynchronize, + * ::cuStreamWaitEvent, + * ::cuEventDestroy, + * ::cuEventElapsedTime, + * ::cudaEventRecord, + * ::cuEventRecordWithFlags + */ +CUresult CUDAAPI cuEventRecord(CUevent hEvent, CUstream hStream); + +/** + * \brief Records an event + * + * Captures in \p hEvent the contents of \p hStream at the time of this call. + * \p hEvent and \p hStream must be from the same context. + * Calls such as ::cuEventQuery() or ::cuStreamWaitEvent() will then + * examine or wait for completion of the work that was captured. Uses of + * \p hStream after this call do not modify \p hEvent. See note on default + * stream behavior for what is captured in the default case. + * + * ::cuEventRecordWithFlags() can be called multiple times on the same event and + * will overwrite the previously captured state. Other APIs such as + * ::cuStreamWaitEvent() use the most recently captured state at the time + * of the API call, and are not affected by later calls to + * ::cuEventRecordWithFlags(). Before the first call to ::cuEventRecordWithFlags(), an + * event represents an empty set of work, so for example ::cuEventQuery() + * would return ::CUDA_SUCCESS. + * + * flags include: + * - ::CU_EVENT_RECORD_DEFAULT: Default event creation flag. + * - ::CU_EVENT_RECORD_EXTERNAL: Event is captured in the graph as an external + * event node when performing stream capture. This flag is invalid outside + * of stream capture. + * + * \param hEvent - Event to record + * \param hStream - Stream to record event for + * \param flags - See ::CUevent_capture_flags + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE + * \note_null_stream + * \notefnerr + * + * \sa ::cuEventCreate, + * ::cuEventQuery, + * ::cuEventSynchronize, + * ::cuStreamWaitEvent, + * ::cuEventDestroy, + * ::cuEventElapsedTime, + * ::cuEventRecord, + * ::cudaEventRecord + */ +CUresult CUDAAPI cuEventRecordWithFlags(CUevent hEvent, CUstream hStream, unsigned int flags); + +/** + * \brief Queries an event's status + * + * Queries the status of all work currently captured by \p hEvent. See + * ::cuEventRecord() for details on what is captured by an event. + * + * Returns ::CUDA_SUCCESS if all captured work has been completed, or + * ::CUDA_ERROR_NOT_READY if any captured work is incomplete. + * + * For the purposes of Unified Memory, a return value of ::CUDA_SUCCESS + * is equivalent to having called ::cuEventSynchronize(). + * + * \param hEvent - Event to query + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_READY + * \notefnerr + * + * \sa ::cuEventCreate, + * ::cuEventRecord, + * ::cuEventSynchronize, + * ::cuEventDestroy, + * ::cuEventElapsedTime, + * ::cudaEventQuery + */ +CUresult CUDAAPI cuEventQuery(CUevent hEvent); + +/** + * \brief Waits for an event to complete + * + * Waits until the completion of all work currently captured in \p hEvent. + * See ::cuEventRecord() for details on what is captured by an event. + * + * Waiting for an event that was created with the ::CU_EVENT_BLOCKING_SYNC + * flag will cause the calling CPU thread to block until the event has + * been completed by the device. If the ::CU_EVENT_BLOCKING_SYNC flag has + * not been set, then the CPU thread will busy-wait until the event has + * been completed by the device. + * + * \param hEvent - Event to wait for + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa ::cuEventCreate, + * ::cuEventRecord, + * ::cuEventQuery, + * ::cuEventDestroy, + * ::cuEventElapsedTime, + * ::cudaEventSynchronize + */ +CUresult CUDAAPI cuEventSynchronize(CUevent hEvent); + +/** + * \brief Destroys an event + * + * Destroys the event specified by \p hEvent. + * + * An event may be destroyed before it is complete (i.e., while + * ::cuEventQuery() would return ::CUDA_ERROR_NOT_READY). In this case, the + * call does not block on completion of the event, and any associated + * resources will automatically be released asynchronously at completion. + * + * \param hEvent - Event to destroy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa ::cuEventCreate, + * ::cuEventRecord, + * ::cuEventQuery, + * ::cuEventSynchronize, + * ::cuEventElapsedTime, + * ::cudaEventDestroy + */ +CUresult CUDAAPI cuEventDestroy(CUevent hEvent); + +/** + * \brief Computes the elapsed time between two events + * + * Computes the elapsed time between two events (in milliseconds with a + * resolution of around 0.5 microseconds). + * + * If either event was last recorded in a non-NULL stream, the resulting time + * may be greater than expected (even if both used the same stream handle). This + * happens because the ::cuEventRecord() operation takes place asynchronously + * and there is no guarantee that the measured latency is actually just between + * the two events. Any number of other different stream operations could execute + * in between the two measured events, thus altering the timing in a significant + * way. + * + * If ::cuEventRecord() has not been called on either event then + * ::CUDA_ERROR_INVALID_HANDLE is returned. If ::cuEventRecord() has been called + * on both events but one or both of them has not yet been completed (that is, + * ::cuEventQuery() would return ::CUDA_ERROR_NOT_READY on at least one of the + * events), ::CUDA_ERROR_NOT_READY is returned. If either event was created with + * the ::CU_EVENT_DISABLE_TIMING flag, then this function will return + * ::CUDA_ERROR_INVALID_HANDLE. + * + * \param pMilliseconds - Time between \p hStart and \p hEnd in ms + * \param hStart - Starting event + * \param hEnd - Ending event + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_READY, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa ::cuEventCreate, + * ::cuEventRecord, + * ::cuEventQuery, + * ::cuEventSynchronize, + * ::cuEventDestroy, + * ::cudaEventElapsedTime + */ +CUresult CUDAAPI cuEventElapsedTime(float *pMilliseconds, CUevent hStart, CUevent hEnd); + +/** @} */ /* END CUDA_EVENT */ + +/** + * \defgroup CUDA_EXTRES_INTEROP External Resource Interoperability + * + * ___MANBRIEF___ External resource interoperability functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the external resource interoperability functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + + /** + * \brief Imports an external memory object + * + * Imports an externally allocated memory object and returns + * a handle to that in \p extMem_out. + * + * The properties of the handle being imported must be described in + * \p memHandleDesc. The ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC structure + * is defined as follows: + * + * \code + typedef struct CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st { + CUexternalMemoryHandleType type; + union { + int fd; + struct { + void *handle; + const void *name; + } win32; + const void *nvSciBufObject; + } handle; + unsigned long long size; + unsigned int flags; + } CUDA_EXTERNAL_MEMORY_HANDLE_DESC; + * \endcode + * + * where ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type specifies the type + * of handle being imported. ::CUexternalMemoryHandleType is + * defined as: + * + * \code + typedef enum CUexternalMemoryHandleType_enum { + CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_FD = 1, + CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32 = 2, + CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT = 3, + CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_HEAP = 4, + CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE = 5, + CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE = 6, + CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT = 7, + CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF = 8 + } CUexternalMemoryHandleType; + * \endcode + * + * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_FD, then + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::fd must be a valid + * file descriptor referencing a memory object. Ownership of + * the file descriptor is transferred to the CUDA driver when the + * handle is imported successfully. Performing any operations on the + * file descriptor after it is imported results in undefined behavior. + * + * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32, then exactly one + * of ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle and + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name must not be + * NULL. If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle + * is not NULL, then it must represent a valid shared NT handle that + * references a memory object. Ownership of this handle is + * not transferred to CUDA after the import operation, so the + * application must release the handle using the appropriate system + * call. If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name + * is not NULL, then it must point to a NULL-terminated array of + * UTF-16 characters that refers to a memory object. + * + * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT, then + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle must + * be non-NULL and + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name + * must be NULL. The handle specified must be a globally shared KMT + * handle. This handle does not hold a reference to the underlying + * object, and thus will be invalid when all references to the + * memory object are destroyed. + * + * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_HEAP, then exactly one + * of ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle and + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name must not be + * NULL. If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle + * is not NULL, then it must represent a valid shared NT handle that + * is returned by ID3D12Device::CreateSharedHandle when referring to a + * ID3D12Heap object. This handle holds a reference to the underlying + * object. If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name + * is not NULL, then it must point to a NULL-terminated array of + * UTF-16 characters that refers to a ID3D12Heap object. + * + * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE, then exactly one + * of ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle and + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name must not be + * NULL. If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle + * is not NULL, then it must represent a valid shared NT handle that + * is returned by ID3D12Device::CreateSharedHandle when referring to a + * ID3D12Resource object. This handle holds a reference to the + * underlying object. If + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name + * is not NULL, then it must point to a NULL-terminated array of + * UTF-16 characters that refers to a ID3D12Resource object. + * + * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE, then + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle must + * represent a valid shared NT handle that is returned by + * IDXGIResource1::CreateSharedHandle when referring to a + * ID3D11Resource object. If + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name + * is not NULL, then it must point to a NULL-terminated array of + * UTF-16 characters that refers to a ID3D11Resource object. + * + * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT, then + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::handle must + * represent a valid shared KMT handle that is returned by + * IDXGIResource::GetSharedHandle when referring to a + * ID3D11Resource object and + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::win32::name + * must be NULL. + * + * If ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type is + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF, then + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::handle::nvSciBufObject must be non-NULL + * and reference a valid NvSciBuf object. + * If the NvSciBuf object imported into CUDA is also mapped by other drivers, then the + * application must use ::cuWaitExternalSemaphoresAsync or ::cuSignalExternalSemaphoresAsync + * as appropriate barriers to maintain coherence between CUDA and the other drivers. + * See ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC and ::CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC + * for memory synchronization. + * + * + * The size of the memory object must be specified in + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::size. + * + * Specifying the flag ::CUDA_EXTERNAL_MEMORY_DEDICATED in + * ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::flags indicates that the + * resource is a dedicated resource. The definition of what a + * dedicated resource is outside the scope of this extension. + * This flag must be set if ::CUDA_EXTERNAL_MEMORY_HANDLE_DESC::type + * is one of the following: + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT + * + * \param extMem_out - Returned handle to an external memory object + * \param memHandleDesc - Memory import handle descriptor + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_OPERATING_SYSTEM + * \notefnerr + * + * \note If the Vulkan memory imported into CUDA is mapped on the CPU then the + * application must use vkInvalidateMappedMemoryRanges/vkFlushMappedMemoryRanges + * as well as appropriate Vulkan pipeline barriers to maintain coherence between + * CPU and GPU. For more information on these APIs, please refer to "Synchronization + * and Cache Control" chapter from Vulkan specification. + * + * \sa ::cuDestroyExternalMemory, + * ::cuExternalMemoryGetMappedBuffer, + * ::cuExternalMemoryGetMappedMipmappedArray + */ +CUresult CUDAAPI cuImportExternalMemory(CUexternalMemory *extMem_out, const CUDA_EXTERNAL_MEMORY_HANDLE_DESC *memHandleDesc); + +/** + * \brief Maps a buffer onto an imported memory object + * + * Maps a buffer onto an imported memory object and returns a device + * pointer in \p devPtr. + * + * The properties of the buffer being mapped must be described in + * \p bufferDesc. The ::CUDA_EXTERNAL_MEMORY_BUFFER_DESC structure is + * defined as follows: + * + * \code + typedef struct CUDA_EXTERNAL_MEMORY_BUFFER_DESC_st { + unsigned long long offset; + unsigned long long size; + unsigned int flags; + } CUDA_EXTERNAL_MEMORY_BUFFER_DESC; + * \endcode + * + * where ::CUDA_EXTERNAL_MEMORY_BUFFER_DESC::offset is the offset in + * the memory object where the buffer's base address is. + * ::CUDA_EXTERNAL_MEMORY_BUFFER_DESC::size is the size of the buffer. + * ::CUDA_EXTERNAL_MEMORY_BUFFER_DESC::flags must be zero. + * + * The offset and size have to be suitably aligned to match the + * requirements of the external API. Mapping two buffers whose ranges + * overlap may or may not result in the same virtual address being + * returned for the overlapped portion. In such cases, the application + * must ensure that all accesses to that region from the GPU are + * volatile. Otherwise writes made via one address are not guaranteed + * to be visible via the other address, even if they're issued by the + * same thread. It is recommended that applications map the combined + * range instead of mapping separate buffers and then apply the + * appropriate offsets to the returned pointer to derive the + * individual buffers. + * + * The returned pointer \p devPtr must be freed using ::cuMemFree. + * + * \param devPtr - Returned device pointer to buffer + * \param extMem - Handle to external memory object + * \param bufferDesc - Buffer descriptor + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa ::cuImportExternalMemory, + * ::cuDestroyExternalMemory, + * ::cuExternalMemoryGetMappedMipmappedArray + */ +CUresult CUDAAPI cuExternalMemoryGetMappedBuffer(CUdeviceptr *devPtr, CUexternalMemory extMem, const CUDA_EXTERNAL_MEMORY_BUFFER_DESC *bufferDesc); + +/** + * \brief Maps a CUDA mipmapped array onto an external memory object + * + * Maps a CUDA mipmapped array onto an external object and returns a + * handle to it in \p mipmap. + * + * The properties of the CUDA mipmapped array being mapped must be + * described in \p mipmapDesc. The structure + * ::CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC is defined as follows: + * + * \code + typedef struct CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_st { + unsigned long long offset; + CUDA_ARRAY3D_DESCRIPTOR arrayDesc; + unsigned int numLevels; + } CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC; + * \endcode + * + * where ::CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::offset is the + * offset in the memory object where the base level of the mipmap + * chain is. + * ::CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::arrayDesc describes + * the format, dimensions and type of the base level of the mipmap + * chain. For further details on these parameters, please refer to the + * documentation for ::cuMipmappedArrayCreate. Note that if the mipmapped + * array is bound as a color target in the graphics API, then the flag + * ::CUDA_ARRAY3D_COLOR_ATTACHMENT must be specified in + * ::CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::arrayDesc::Flags. + * ::CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::numLevels specifies + * the total number of levels in the mipmap chain. + * + * If \p extMem was imported from a handle of type ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF, then + * ::CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC::numLevels must be equal to 1. + * + * The returned CUDA mipmapped array must be freed using ::cuMipmappedArrayDestroy. + * + * \param mipmap - Returned CUDA mipmapped array + * \param extMem - Handle to external memory object + * \param mipmapDesc - CUDA array descriptor + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa ::cuImportExternalMemory, + * ::cuDestroyExternalMemory, + * ::cuExternalMemoryGetMappedBuffer + */ +CUresult CUDAAPI cuExternalMemoryGetMappedMipmappedArray(CUmipmappedArray *mipmap, CUexternalMemory extMem, const CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC *mipmapDesc); + +/** + * \brief Destroys an external memory object. + * + * Destroys the specified external memory object. Any existing buffers + * and CUDA mipmapped arrays mapped onto this object must no longer be + * used and must be explicitly freed using ::cuMemFree and + * ::cuMipmappedArrayDestroy respectively. + * + * \param extMem - External memory object to be destroyed + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa ::cuImportExternalMemory, + * ::cuExternalMemoryGetMappedBuffer, + * ::cuExternalMemoryGetMappedMipmappedArray + */ +CUresult CUDAAPI cuDestroyExternalMemory(CUexternalMemory extMem); + +/** + * \brief Imports an external semaphore + * + * Imports an externally allocated synchronization object and returns + * a handle to that in \p extSem_out. + * + * The properties of the handle being imported must be described in + * \p semHandleDesc. The ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC is + * defined as follows: + * + * \code + typedef struct CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st { + CUexternalSemaphoreHandleType type; + union { + int fd; + struct { + void *handle; + const void *name; + } win32; + const void* NvSciSyncObj; + } handle; + unsigned int flags; + } CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC; + * \endcode + * + * where ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type specifies the type of + * handle being imported. ::CUexternalSemaphoreHandleType is defined + * as: + * + * \code + typedef enum CUexternalSemaphoreHandleType_enum { + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD = 1, + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32 = 2, + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT = 3, + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE = 4, + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE = 5, + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC = 6, + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX = 7, + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT = 8, + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD = 9, + CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32 = 10 + } CUexternalSemaphoreHandleType; + * \endcode + * + * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD, then + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::fd must be a valid + * file descriptor referencing a synchronization object. Ownership of + * the file descriptor is transferred to the CUDA driver when the + * handle is imported successfully. Performing any operations on the + * file descriptor after it is imported results in undefined behavior. + * + * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32, then exactly one + * of ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle and + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must not be + * NULL. If + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle + * is not NULL, then it must represent a valid shared NT handle that + * references a synchronization object. Ownership of this handle is + * not transferred to CUDA after the import operation, so the + * application must release the handle using the appropriate system + * call. If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name + * is not NULL, then it must name a valid synchronization object. + * + * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT, then + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle must + * be non-NULL and + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name + * must be NULL. The handle specified must be a globally shared KMT + * handle. This handle does not hold a reference to the underlying + * object, and thus will be invalid when all references to the + * synchronization object are destroyed. + * + * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE, then exactly one + * of ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle and + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must not be + * NULL. If + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle + * is not NULL, then it must represent a valid shared NT handle that + * is returned by ID3D12Device::CreateSharedHandle when referring to a + * ID3D12Fence object. This handle holds a reference to the underlying + * object. If + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name + * is not NULL, then it must name a valid synchronization object that + * refers to a valid ID3D12Fence object. + * + * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE, then + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle + * represents a valid shared NT handle that is returned by + * ID3D11Fence::CreateSharedHandle. If + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name + * is not NULL, then it must name a valid synchronization object that + * refers to a valid ID3D11Fence object. + * + * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, then + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::nvSciSyncObj + * represents a valid NvSciSyncObj. + * + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX, then + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle + * represents a valid shared NT handle that + * is returned by IDXGIResource1::CreateSharedHandle when referring to + * a IDXGIKeyedMutex object. If + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name + * is not NULL, then it must name a valid synchronization object that + * refers to a valid IDXGIKeyedMutex object. + * + * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT, then + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle + * represents a valid shared KMT handle that + * is returned by IDXGIResource::GetSharedHandle when referring to + * a IDXGIKeyedMutex object and + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must be NULL. + * + * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD, then + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::fd must be a valid + * file descriptor referencing a synchronization object. Ownership of + * the file descriptor is transferred to the CUDA driver when the + * handle is imported successfully. Performing any operations on the + * file descriptor after it is imported results in undefined behavior. + * + * If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::type is + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32, then exactly one + * of ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle and + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name must not be + * NULL. If + * ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::handle + * is not NULL, then it must represent a valid shared NT handle that + * references a synchronization object. Ownership of this handle is + * not transferred to CUDA after the import operation, so the + * application must release the handle using the appropriate system + * call. If ::CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC::handle::win32::name + * is not NULL, then it must name a valid synchronization object. + * + * \param extSem_out - Returned handle to an external semaphore + * \param semHandleDesc - Semaphore import handle descriptor + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_OPERATING_SYSTEM + * \notefnerr + * + * \sa ::cuDestroyExternalSemaphore, + * ::cuSignalExternalSemaphoresAsync, + * ::cuWaitExternalSemaphoresAsync + */ +CUresult CUDAAPI cuImportExternalSemaphore(CUexternalSemaphore *extSem_out, const CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC *semHandleDesc); + +/** + * \brief Signals a set of external semaphore objects + * + * Enqueues a signal operation on a set of externally allocated + * semaphore object in the specified stream. The operations will be + * executed when all prior operations in the stream complete. + * + * The exact semantics of signaling a semaphore depends on the type of + * the object. + * + * If the semaphore object is any one of the following types: + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD, + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32, + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT + * then signaling the semaphore will set it to the signaled state. + * + * If the semaphore object is any one of the following types: + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE, + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE, + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD, + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32 + * then the semaphore will be set to the value specified in + * ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS::params::fence::value. + * + * If the semaphore object is of the type ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC + * this API sets ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS::params::nvSciSync::fence + * to a value that can be used by subsequent waiters of the same NvSciSync object + * to order operations with those currently submitted in \p stream. Such an update + * will overwrite previous contents of + * ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS::params::nvSciSync::fence. By default, + * signaling such an external semaphore object causes appropriate memory synchronization + * operations to be performed over all external memory objects that are imported as + * ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF. This ensures that any subsequent accesses + * made by other importers of the same set of NvSciBuf memory object(s) are coherent. + * These operations can be skipped by specifying the flag + * ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC, which can be used as a + * performance optimization when data coherency is not required. But specifying this + * flag in scenarios where data coherency is required results in undefined behavior. + * Also, for semaphore object of the type ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, + * if the NvSciSyncAttrList used to create the NvSciSyncObj had not set the flags in + * ::cuDeviceGetNvSciSyncAttributes to CUDA_NVSCISYNC_ATTR_SIGNAL, this API will return + * CUDA_ERROR_NOT_SUPPORTED. + * NvSciSyncFence associated with semaphore object of the type + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC can be deterministic. For this the + * NvSciSyncAttrList used to create the semaphore object must have value of + * NvSciSyncAttrKey_RequireDeterministicFences key set to true. Deterministic fences + * allow users to enqueue a wait over the semaphore object even before corresponding + * signal is enqueued. For such a semaphore object, CUDA guarantees that each signal + * operation will increment the fence value by '1'. Users are expected to track count + * of signals enqueued on the semaphore object and insert waits accordingly. When such + * a semaphore object is signaled from multiple streams, due to concurrent stream + * execution, it is possible that the order in which the semaphore gets signaled is + * indeterministic. This could lead to waiters of the semaphore getting unblocked + * incorrectly. Users are expected to handle such situations, either by not using the + * same semaphore object with deterministic fence support enabled in different streams + * or by adding explicit dependency amongst such streams so that the semaphore is + * signaled in order. + * + * If the semaphore object is any one of the following types: + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX, + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT + * then the keyed mutex will be released with the key specified in + * ::CUDA_EXTERNAL_SEMAPHORE_PARAMS::params::keyedmutex::key. + * + * \param extSemArray - Set of external semaphores to be signaled + * \param paramsArray - Array of semaphore parameters + * \param numExtSems - Number of semaphores to signal + * \param stream - Stream to enqueue the signal operations in + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_SUPPORTED + * \notefnerr + * + * \sa ::cuImportExternalSemaphore, + * ::cuDestroyExternalSemaphore, + * ::cuWaitExternalSemaphoresAsync + */ +CUresult CUDAAPI cuSignalExternalSemaphoresAsync(const CUexternalSemaphore *extSemArray, const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS *paramsArray, unsigned int numExtSems, CUstream stream); + +/** + * \brief Waits on a set of external semaphore objects + * + * Enqueues a wait operation on a set of externally allocated + * semaphore object in the specified stream. The operations will be + * executed when all prior operations in the stream complete. + * + * The exact semantics of waiting on a semaphore depends on the type + * of the object. + * + * If the semaphore object is any one of the following types: + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD, + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32, + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT + * then waiting on the semaphore will wait until the semaphore reaches + * the signaled state. The semaphore will then be reset to the + * unsignaled state. Therefore for every signal operation, there can + * only be one wait operation. + * + * If the semaphore object is any one of the following types: + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE, + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE, + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD, + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32 + * then waiting on the semaphore will wait until the value of the + * semaphore is greater than or equal to + * ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS::params::fence::value. + * + * If the semaphore object is of the type ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC + * then, waiting on the semaphore will wait until the + * ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS::params::nvSciSync::fence is signaled by the + * signaler of the NvSciSyncObj that was associated with this semaphore object. + * By default, waiting on such an external semaphore object causes appropriate + * memory synchronization operations to be performed over all external memory objects + * that are imported as ::CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF. This ensures that + * any subsequent accesses made by other importers of the same set of NvSciBuf memory + * object(s) are coherent. These operations can be skipped by specifying the flag + * ::CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC, which can be used as a + * performance optimization when data coherency is not required. But specifying this + * flag in scenarios where data coherency is required results in undefined behavior. + * Also, for semaphore object of the type ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, + * if the NvSciSyncAttrList used to create the NvSciSyncObj had not set the flags in + * ::cuDeviceGetNvSciSyncAttributes to CUDA_NVSCISYNC_ATTR_WAIT, this API will return + * CUDA_ERROR_NOT_SUPPORTED. + * + * If the semaphore object is any one of the following types: + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX, + * ::CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT + * then the keyed mutex will be acquired when it is released with the key + * specified in ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS::params::keyedmutex::key + * or until the timeout specified by + * ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS::params::keyedmutex::timeoutMs + * has lapsed. The timeout interval can either be a finite value + * specified in milliseconds or an infinite value. In case an infinite + * value is specified the timeout never elapses. The windows INFINITE + * macro must be used to specify infinite timeout. + * + * \param extSemArray - External semaphores to be waited on + * \param paramsArray - Array of semaphore parameters + * \param numExtSems - Number of semaphores to wait on + * \param stream - Stream to enqueue the wait operations in + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_TIMEOUT + * \notefnerr + * + * \sa ::cuImportExternalSemaphore, + * ::cuDestroyExternalSemaphore, + * ::cuSignalExternalSemaphoresAsync + */ +CUresult CUDAAPI cuWaitExternalSemaphoresAsync(const CUexternalSemaphore *extSemArray, const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS *paramsArray, unsigned int numExtSems, CUstream stream); + +/** + * \brief Destroys an external semaphore + * + * Destroys an external semaphore object and releases any references + * to the underlying resource. Any outstanding signals or waits must + * have completed before the semaphore is destroyed. + * + * \param extSem - External semaphore to be destroyed + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa ::cuImportExternalSemaphore, + * ::cuSignalExternalSemaphoresAsync, + * ::cuWaitExternalSemaphoresAsync + */ +CUresult CUDAAPI cuDestroyExternalSemaphore(CUexternalSemaphore extSem); + +/** @} */ /* END CUDA_EXTRES_INTEROP */ + +/** + * \defgroup CUDA_MEMOP Stream Memory Operations + * + * ___MANBRIEF___ Stream memory operations of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the stream memory operations of the low-level CUDA + * driver application programming interface. + * + * Support for the ::CU_STREAM_WAIT_VALUE_NOR flag can be queried with + * ::CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR_V2. + * + * Support for the ::cuStreamWriteValue64() and ::cuStreamWaitValue64() + * functions, as well as for the ::CU_STREAM_MEM_OP_WAIT_VALUE_64 and + * ::CU_STREAM_MEM_OP_WRITE_VALUE_64 flags, can be queried with + * ::CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS. + * + * Support for both ::CU_STREAM_WAIT_VALUE_FLUSH and + * ::CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES requires dedicated platform + * hardware features and can be queried with ::cuDeviceGetAttribute() and + * ::CU_DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES. + * + * Note that all memory pointers passed as parameters to these operations + * are device pointers. Where necessary a device pointer should be + * obtained, for example with ::cuMemHostGetDevicePointer(). + * + * None of the operations accepts pointers to managed memory buffers + * (::cuMemAllocManaged). + * + * \note + * Warning: + * Improper use of these APIs may deadlock the application. Synchronization + * ordering established through these APIs is not visible to CUDA. CUDA tasks + * that are (even indirectly) ordered by these APIs should also have that order + * expressed with CUDA-visible dependencies such as events. This ensures that + * the scheduler does not serialize them in an improper order. + * + * @{ + */ + +/** + * \brief Wait on a memory location + * + * Enqueues a synchronization of the stream on the given memory location. Work + * ordered after the operation will block until the given condition on the + * memory is satisfied. By default, the condition is to wait for + * (int32_t)(*addr - value) >= 0, a cyclic greater-or-equal. + * Other condition types can be specified via \p flags. + * + * If the memory was registered via ::cuMemHostRegister(), the device pointer + * should be obtained with ::cuMemHostGetDevicePointer(). This function cannot + * be used with managed memory (::cuMemAllocManaged). + * + * Support for CU_STREAM_WAIT_VALUE_NOR can be queried with ::cuDeviceGetAttribute() and + * ::CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR_V2. + * + * \note + * Warning: + * Improper use of this API may deadlock the application. Synchronization + * ordering established through this API is not visible to CUDA. CUDA tasks + * that are (even indirectly) ordered by this API should also have that order + * expressed with CUDA-visible dependencies such as events. This ensures that + * the scheduler does not serialize them in an improper order. + * + * \param stream The stream to synchronize on the memory location. + * \param addr The memory location to wait on. + * \param value The value to compare with the memory location. + * \param flags See ::CUstreamWaitValue_flags. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_SUPPORTED + * \notefnerr + * + * \sa ::cuStreamWaitValue64, + * ::cuStreamWriteValue32, + * ::cuStreamWriteValue64, + * ::cuStreamBatchMemOp, + * ::cuMemHostRegister, + * ::cuStreamWaitEvent + */ +CUresult CUDAAPI cuStreamWaitValue32(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags); + +/** + * \brief Wait on a memory location + * + * Enqueues a synchronization of the stream on the given memory location. Work + * ordered after the operation will block until the given condition on the + * memory is satisfied. By default, the condition is to wait for + * (int64_t)(*addr - value) >= 0, a cyclic greater-or-equal. + * Other condition types can be specified via \p flags. + * + * If the memory was registered via ::cuMemHostRegister(), the device pointer + * should be obtained with ::cuMemHostGetDevicePointer(). + * + * Support for this can be queried with ::cuDeviceGetAttribute() and + * ::CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS. + * + * \note + * Warning: + * Improper use of this API may deadlock the application. Synchronization + * ordering established through this API is not visible to CUDA. CUDA tasks + * that are (even indirectly) ordered by this API should also have that order + * expressed with CUDA-visible dependencies such as events. This ensures that + * the scheduler does not serialize them in an improper order. + * + * \param stream The stream to synchronize on the memory location. + * \param addr The memory location to wait on. + * \param value The value to compare with the memory location. + * \param flags See ::CUstreamWaitValue_flags. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_SUPPORTED + * \notefnerr + * + * \sa ::cuStreamWaitValue32, + * ::cuStreamWriteValue32, + * ::cuStreamWriteValue64, + * ::cuStreamBatchMemOp, + * ::cuMemHostRegister, + * ::cuStreamWaitEvent + */ +CUresult CUDAAPI cuStreamWaitValue64(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags); + +/** + * \brief Write a value to memory + * + * Write a value to memory. + * + * If the memory was registered via ::cuMemHostRegister(), the device pointer + * should be obtained with ::cuMemHostGetDevicePointer(). This function cannot + * be used with managed memory (::cuMemAllocManaged). + * + * \param stream The stream to do the write in. + * \param addr The device address to write to. + * \param value The value to write. + * \param flags See ::CUstreamWriteValue_flags. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_SUPPORTED + * \notefnerr + * + * \sa ::cuStreamWriteValue64, + * ::cuStreamWaitValue32, + * ::cuStreamWaitValue64, + * ::cuStreamBatchMemOp, + * ::cuMemHostRegister, + * ::cuEventRecord + */ +CUresult CUDAAPI cuStreamWriteValue32(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags); + +/** + * \brief Write a value to memory + * + * Write a value to memory. + * + * If the memory was registered via ::cuMemHostRegister(), the device pointer + * should be obtained with ::cuMemHostGetDevicePointer(). + * + * Support for this can be queried with ::cuDeviceGetAttribute() and + * ::CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS. + * + * \param stream The stream to do the write in. + * \param addr The device address to write to. + * \param value The value to write. + * \param flags See ::CUstreamWriteValue_flags. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_SUPPORTED + * \notefnerr + * + * \sa ::cuStreamWriteValue32, + * ::cuStreamWaitValue32, + * ::cuStreamWaitValue64, + * ::cuStreamBatchMemOp, + * ::cuMemHostRegister, + * ::cuEventRecord + */ +CUresult CUDAAPI cuStreamWriteValue64(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags); + +/** + * \brief Batch operations to synchronize the stream via memory operations + * + * This is a batch version of ::cuStreamWaitValue32() and ::cuStreamWriteValue32(). + * Batching operations may avoid some performance overhead in both the API call + * and the device execution versus adding them to the stream in separate API + * calls. The operations are enqueued in the order they appear in the array. + * + * See ::CUstreamBatchMemOpType for the full set of supported operations, and + * ::cuStreamWaitValue32(), ::cuStreamWaitValue64(), ::cuStreamWriteValue32(), + * and ::cuStreamWriteValue64() for details of specific operations. + * + * See related APIs for details on querying support for specific operations. + * + * \note + * Warning: + * Improper use of this API may deadlock the application. Synchronization + * ordering established through this API is not visible to CUDA. CUDA tasks + * that are (even indirectly) ordered by this API should also have that order + * expressed with CUDA-visible dependencies such as events. This ensures that + * the scheduler does not serialize them in an improper order. For more + * information, see the Stream Memory Operations section in the programming + * guide(https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html). + * + * \param stream The stream to enqueue the operations in. + * \param count The number of operations in the array. Must be less than 256. + * \param paramArray The types and parameters of the individual operations. + * \param flags Reserved for future expansion; must be 0. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_SUPPORTED + * \notefnerr + * + * \sa ::cuStreamWaitValue32, + * ::cuStreamWaitValue64, + * ::cuStreamWriteValue32, + * ::cuStreamWriteValue64, + * ::cuMemHostRegister + */ +CUresult CUDAAPI cuStreamBatchMemOp(CUstream stream, unsigned int count, CUstreamBatchMemOpParams *paramArray, unsigned int flags); + +/** @} */ /* END CUDA_MEMOP */ + +/** + * \defgroup CUDA_EXEC Execution Control + * + * ___MANBRIEF___ execution control functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the execution control functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** + * \brief Returns information about a function + * + * Returns in \p *pi the integer value of the attribute \p attrib on the kernel + * given by \p hfunc. The supported attributes are: + * - ::CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK: The maximum number of threads + * per block, beyond which a launch of the function would fail. This number + * depends on both the function and the device on which the function is + * currently loaded. + * - ::CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES: The size in bytes of + * statically-allocated shared memory per block required by this function. + * This does not include dynamically-allocated shared memory requested by + * the user at runtime. + * - ::CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES: The size in bytes of user-allocated + * constant memory required by this function. + * - ::CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES: The size in bytes of local memory + * used by each thread of this function. + * - ::CU_FUNC_ATTRIBUTE_NUM_REGS: The number of registers used by each thread + * of this function. + * - ::CU_FUNC_ATTRIBUTE_PTX_VERSION: The PTX virtual architecture version for + * which the function was compiled. This value is the major PTX version * 10 + * + the minor PTX version, so a PTX version 1.3 function would return the + * value 13. Note that this may return the undefined value of 0 for cubins + * compiled prior to CUDA 3.0. + * - ::CU_FUNC_ATTRIBUTE_BINARY_VERSION: The binary architecture version for + * which the function was compiled. This value is the major binary + * version * 10 + the minor binary version, so a binary version 1.3 function + * would return the value 13. Note that this will return a value of 10 for + * legacy cubins that do not have a properly-encoded binary architecture + * version. + * - ::CU_FUNC_CACHE_MODE_CA: The attribute to indicate whether the function has + * been compiled with user specified option "-Xptxas --dlcm=ca" set . + * - ::CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES: The maximum size in bytes of + * dynamically-allocated shared memory. + * - ::CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT: Preferred shared memory-L1 + * cache split ratio in percent of total shared memory. + * - ::CU_FUNC_ATTRIBUTE_CLUSTER_SIZE_MUST_BE_SET: If this attribute is set, the + * kernel must launch with a valid cluster size specified. + * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_WIDTH: The required cluster width in + * blocks. + * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_HEIGHT: The required cluster height in + * blocks. + * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_DEPTH: The required cluster depth in + * blocks. + * - ::CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED: Indicates whether + * the function can be launched with non-portable cluster size. 1 is allowed, + * 0 is disallowed. A non-portable cluster size may only function on the + * specific SKUs the program is tested on. The launch might fail if the + * program is run on a different hardware platform. CUDA API provides + * cudaOccupancyMaxActiveClusters to assist with checking whether the desired + * size can be launched on the current device. A portable cluster size is + * guaranteed to be functional on all compute capabilities higher than the + * target compute capability. The portable cluster size for sm_90 is 8 blocks + * per cluster. This value may increase for future compute capabilities. The + * specific hardware unit may support higher cluster sizes that’s not + * guaranteed to be portable. + * - ::CU_FUNC_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE: The block + * scheduling policy of a function. The value type is CUclusterSchedulingPolicy. + * + * \param pi - Returned attribute value + * \param attrib - Attribute requested + * \param hfunc - Function to query attribute of + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuCtxGetCacheConfig, + * ::cuCtxSetCacheConfig, + * ::cuFuncSetCacheConfig, + * ::cuLaunchKernel, + * ::cudaFuncGetAttributes, + * ::cudaFuncSetAttribute, + * ::cuKernelGetAttribute + */ +CUresult CUDAAPI cuFuncGetAttribute(int *pi, CUfunction_attribute attrib, CUfunction hfunc); + +/** + * \brief Sets information about a function + * + * This call sets the value of a specified attribute \p attrib on the kernel given + * by \p hfunc to an integer value specified by \p val + * This function returns CUDA_SUCCESS if the new value of the attribute could be + * successfully set. If the set fails, this call will return an error. + * Not all attributes can have values set. Attempting to set a value on a read-only + * attribute will result in an error (CUDA_ERROR_INVALID_VALUE) + * + * Supported attributes for the cuFuncSetAttribute call are: + * - ::CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES: This maximum size in bytes of + * dynamically-allocated shared memory. The value should contain the requested + * maximum size of dynamically-allocated shared memory. The sum of this value and + * the function attribute ::CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES cannot exceed the + * device attribute ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN. + * The maximal size of requestable dynamic shared memory may differ by GPU + * architecture. + * - ::CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT: On devices where the L1 + * cache and shared memory use the same hardware resources, this sets the shared memory + * carveout preference, in percent of the total shared memory. + * See ::CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR + * This is only a hint, and the driver can choose a different ratio if required to execute the function. + * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_WIDTH: The required cluster width in + * blocks. The width, height, and depth values must either all be 0 or all be + * positive. The validity of the cluster dimensions is checked at launch time. + * If the value is set during compile time, it cannot be set at runtime. + * Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED. + * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_HEIGHT: The required cluster height in + * blocks. The width, height, and depth values must either all be 0 or all be + * positive. The validity of the cluster dimensions is checked at launch time. + * If the value is set during compile time, it cannot be set at runtime. + * Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED. + * - ::CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_DEPTH: The required cluster depth in + * blocks. The width, height, and depth values must either all be 0 or all be + * positive. The validity of the cluster dimensions is checked at launch time. + * If the value is set during compile time, it cannot be set at runtime. + * Setting it at runtime will return CUDA_ERROR_NOT_PERMITTED. + * - ::CU_FUNC_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE: The block + * scheduling policy of a function. The value type is CUclusterSchedulingPolicy. + * + * \param hfunc - Function to query attribute of + * \param attrib - Attribute requested + * \param value - The value to set + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuCtxGetCacheConfig, + * ::cuCtxSetCacheConfig, + * ::cuFuncSetCacheConfig, + * ::cuLaunchKernel, + * ::cudaFuncGetAttributes, + * ::cudaFuncSetAttribute, + * ::cuKernelSetAttribute + */ +CUresult CUDAAPI cuFuncSetAttribute(CUfunction hfunc, CUfunction_attribute attrib, int value); + +/** + * \brief Sets the preferred cache configuration for a device function + * + * On devices where the L1 cache and shared memory use the same hardware + * resources, this sets through \p config the preferred cache configuration for + * the device function \p hfunc. This is only a preference. The driver will use + * the requested configuration if possible, but it is free to choose a different + * configuration if required to execute \p hfunc. Any context-wide preference + * set via ::cuCtxSetCacheConfig() will be overridden by this per-function + * setting unless the per-function setting is ::CU_FUNC_CACHE_PREFER_NONE. In + * that case, the current context-wide setting will be used. + * + * This setting does nothing on devices where the size of the L1 cache and + * shared memory are fixed. + * + * Launching a kernel with a different preference than the most recent + * preference setting may insert a device-side synchronization point. + * + * + * The supported cache configurations are: + * - ::CU_FUNC_CACHE_PREFER_NONE: no preference for shared memory or L1 (default) + * - ::CU_FUNC_CACHE_PREFER_SHARED: prefer larger shared memory and smaller L1 cache + * - ::CU_FUNC_CACHE_PREFER_L1: prefer larger L1 cache and smaller shared memory + * - ::CU_FUNC_CACHE_PREFER_EQUAL: prefer equal sized L1 cache and shared memory + * + * \param hfunc - Kernel to configure cache for + * \param config - Requested cache configuration + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT + * \notefnerr + * + * \sa ::cuCtxGetCacheConfig, + * ::cuCtxSetCacheConfig, + * ::cuFuncGetAttribute, + * ::cuLaunchKernel, + * ::cudaFuncSetCacheConfig, + * ::cuKernelSetCacheConfig + */ +CUresult CUDAAPI cuFuncSetCacheConfig(CUfunction hfunc, CUfunc_cache config); + +/** + * \brief Sets the shared memory configuration for a device function. + * + * On devices with configurable shared memory banks, this function will + * force all subsequent launches of the specified device function to have + * the given shared memory bank size configuration. On any given launch of the + * function, the shared memory configuration of the device will be temporarily + * changed if needed to suit the function's preferred configuration. Changes in + * shared memory configuration between subsequent launches of functions, + * may introduce a device side synchronization point. + * + * Any per-function setting of shared memory bank size set via + * ::cuFuncSetSharedMemConfig will override the context wide setting set with + * ::cuCtxSetSharedMemConfig. + * + * Changing the shared memory bank size will not increase shared memory usage + * or affect occupancy of kernels, but may have major effects on performance. + * Larger bank sizes will allow for greater potential bandwidth to shared memory, + * but will change what kinds of accesses to shared memory will result in bank + * conflicts. + * + * This function will do nothing on devices with fixed shared memory bank size. + * + * The supported bank configurations are: + * - ::CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE: use the context's shared memory + * configuration when launching this function. + * - ::CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE: set shared memory bank width to + * be natively four bytes when launching this function. + * - ::CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE: set shared memory bank width to + * be natively eight bytes when launching this function. + * + * \param hfunc - kernel to be given a shared memory config + * \param config - requested shared memory configuration + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT + * \notefnerr + * + * \sa ::cuCtxGetCacheConfig, + * ::cuCtxSetCacheConfig, + * ::cuCtxGetSharedMemConfig, + * ::cuCtxSetSharedMemConfig, + * ::cuFuncGetAttribute, + * ::cuLaunchKernel, + * ::cudaFuncSetSharedMemConfig + */ +CUresult CUDAAPI cuFuncSetSharedMemConfig(CUfunction hfunc, CUsharedconfig config); + +/** + * \brief Returns a module handle + * + * Returns in \p *hmod the handle of the module that function \p hfunc + * is located in. The lifetime of the module corresponds to the lifetime of + * the context it was loaded in or until the module is explicitly unloaded. + * + * The CUDA runtime manages its own modules loaded into the primary context. + * If the handle returned by this API refers to a module loaded by the CUDA runtime, + * calling ::cuModuleUnload() on that module will result in undefined behavior. + * + * \param hmod - Returned module handle + * \param hfunc - Function to retrieve module for + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_FOUND + * \notefnerr + * + */ +CUresult CUDAAPI cuFuncGetModule(CUmodule *hmod, CUfunction hfunc); + +/** + * \brief Launches a CUDA function ::CUfunction or a CUDA kernel ::CUkernel + * + * Invokes the function ::CUfunction or the kernel ::CUkernel \p f + * on a \p gridDimX x \p gridDimY x \p gridDimZ grid of blocks. + * Each block contains \p blockDimX x \p blockDimY x + * \p blockDimZ threads. + * + * \p sharedMemBytes sets the amount of dynamic shared memory that will be + * available to each thread block. + * + * Kernel parameters to \p f can be specified in one of two ways: + * + * 1) Kernel parameters can be specified via \p kernelParams. If \p f + * has N parameters, then \p kernelParams needs to be an array of N + * pointers. Each of \p kernelParams[0] through \p kernelParams[N-1] + * must point to a region of memory from which the actual kernel + * parameter will be copied. The number of kernel parameters and their + * offsets and sizes do not need to be specified as that information is + * retrieved directly from the kernel's image. + * + * 2) Kernel parameters can also be packaged by the application into + * a single buffer that is passed in via the \p extra parameter. + * This places the burden on the application of knowing each kernel + * parameter's size and alignment/padding within the buffer. Here is + * an example of using the \p extra parameter in this manner: + * \code + size_t argBufferSize; + char argBuffer[256]; + + // populate argBuffer and argBufferSize + + void *config[] = { + CU_LAUNCH_PARAM_BUFFER_POINTER, argBuffer, + CU_LAUNCH_PARAM_BUFFER_SIZE, &argBufferSize, + CU_LAUNCH_PARAM_END + }; + status = cuLaunchKernel(f, gx, gy, gz, bx, by, bz, sh, s, NULL, config); + * \endcode + * + * The \p extra parameter exists to allow ::cuLaunchKernel to take + * additional less commonly used arguments. \p extra specifies a list of + * names of extra settings and their corresponding values. Each extra + * setting name is immediately followed by the corresponding value. The + * list must be terminated with either NULL or ::CU_LAUNCH_PARAM_END. + * + * - ::CU_LAUNCH_PARAM_END, which indicates the end of the \p extra + * array; + * - ::CU_LAUNCH_PARAM_BUFFER_POINTER, which specifies that the next + * value in \p extra will be a pointer to a buffer containing all + * the kernel parameters for launching kernel \p f; + * - ::CU_LAUNCH_PARAM_BUFFER_SIZE, which specifies that the next + * value in \p extra will be a pointer to a size_t containing the + * size of the buffer specified with ::CU_LAUNCH_PARAM_BUFFER_POINTER; + * + * The error ::CUDA_ERROR_INVALID_VALUE will be returned if kernel + * parameters are specified with both \p kernelParams and \p extra + * (i.e. both \p kernelParams and \p extra are non-NULL). + * + * Calling ::cuLaunchKernel() invalidates the persistent function state + * set through the following deprecated APIs: + * ::cuFuncSetBlockShape(), + * ::cuFuncSetSharedSize(), + * ::cuParamSetSize(), + * ::cuParamSeti(), + * ::cuParamSetf(), + * ::cuParamSetv(). + * + * Note that to use ::cuLaunchKernel(), the kernel \p f must either have + * been compiled with toolchain version 3.2 or later so that it will + * contain kernel parameter information, or have no kernel parameters. + * If either of these conditions is not met, then ::cuLaunchKernel() will + * return ::CUDA_ERROR_INVALID_IMAGE. + * + * Note that the API can also be used to launch context-less kernel ::CUkernel + * by querying the handle using ::cuLibraryGetKernel() and then passing it + * to the API by casting to ::CUfunction. Here, the context to launch + * the kernel on will either be taken from the specified stream \p hStream + * or the current context in case of NULL stream. + * + * \param f - Function ::CUfunction or Kernel ::CUkernel to launch + * \param gridDimX - Width of grid in blocks + * \param gridDimY - Height of grid in blocks + * \param gridDimZ - Depth of grid in blocks + * \param blockDimX - X dimension of each thread block + * \param blockDimY - Y dimension of each thread block + * \param blockDimZ - Z dimension of each thread block + * \param sharedMemBytes - Dynamic shared-memory size per thread block in bytes + * \param hStream - Stream identifier + * \param kernelParams - Array of pointers to kernel parameters + * \param extra - Extra options + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_IMAGE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_LAUNCH_FAILED, + * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES, + * ::CUDA_ERROR_LAUNCH_TIMEOUT, + * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED, + * ::CUDA_ERROR_NOT_FOUND + * \note_null_stream + * \notefnerr + * + * \sa ::cuCtxGetCacheConfig, + * ::cuCtxSetCacheConfig, + * ::cuFuncSetCacheConfig, + * ::cuFuncGetAttribute, + * ::cudaLaunchKernel, + * ::cuLibraryGetKernel, + * ::cuKernelSetCacheConfig, + * ::cuKernelGetAttribute, + * ::cuKernelSetAttribute + */ +CUresult CUDAAPI cuLaunchKernel(CUfunction f, + unsigned int gridDimX, + unsigned int gridDimY, + unsigned int gridDimZ, + unsigned int blockDimX, + unsigned int blockDimY, + unsigned int blockDimZ, + unsigned int sharedMemBytes, + CUstream hStream, + void **kernelParams, + void **extra); + +/** + * \brief Launches a CUDA function ::CUfunction or a CUDA kernel ::CUkernel with launch-time configuration + * + * Invokes the function ::CUfunction or the kernel ::CUkernel \p f with the specified launch-time configuration + * \p config. + * + * The ::CUlaunchConfig structure is defined as: + * \code + typedef struct CUlaunchConfig_st { + unsigned int gridDimX; + unsigned int gridDimY; + unsigned int gridDimZ; + unsigned int blockDimX; + unsigned int blockDimY; + unsigned int blockDimZ; + unsigned int sharedMemBytes; + CUstream hStream; + CUlaunchAttribute *attrs; + unsigned int numAttrs; + } CUlaunchConfig; + * \endcode + * where: + * - ::CUlaunchConfig::gridDimX is the width of the grid in blocks. + * - ::CUlaunchConfig::gridDimY is the height of the grid in blocks. + * - ::CUlaunchConfig::gridDimZ is the depth of the grid in blocks. + * - ::CUlaunchConfig::blockDimX is the X dimension of each thread block. + * - ::CUlaunchConfig::blockDimX is the Y dimension of each thread block. + * - ::CUlaunchConfig::blockDimZ is the Z dimension of each thread block. + * - ::CUlaunchConfig::sharedMemBytes is the dynamic shared-memory size per + * thread block in bytes. + * - ::CUlaunchConfig::hStream is the handle to the stream to perform the launch + * in. The CUDA context associated with this stream must match that associated + * with function f. + * - ::CUlaunchConfig::attrs is an array of ::CUlaunchConfig::numAttrs + * continguous ::CUlaunchAttribute elements. The value of this pointer is not + * considered if ::CUlaunchConfig::numAttrs is zero. However, in that case, it + * is recommended to set the pointer to NULL. + * - ::CUlaunchConfig::numAttrs is the numbers of attributes populating the + * first ::CUlaunchConfig::numAttrs positions of the ::CUlaunchConfig::attrs + * array. + * + * Launch-time configuration is specified by adding entries to + * ::CUlaunchConfig::attrs. Each entry is an attribute ID and a corresponding + * attribute value. + * + * The ::CUlaunchAttribute structure is defined as: + * \code + typedef struct CUlaunchAttribute_st { + CUlaunchAttributeID id; + CUlaunchAttributeValue value; + } CUlaunchAttribute; + * \endcode + * where: + * - ::CUlaunchAttribute::id is a unique enum identifying the attribute. + * - ::CUlaunchAttribute::value is a union that hold the attribute value. + * + * An example of using the \p config parameter: + * \code + CUlaunchAttribute coopAttr = {.id = CU_LAUNCH_ATTRIBUTE_COOPERATIVE, + .value = 1}; + CUlaunchConfig config = {... // set block and grid dimensions + .attrs = &coopAttr, + .numAttrs = 1}; + + cuLaunchKernelEx(&config, kernel, NULL, NULL); + * \endcode + * + * The ::CUlaunchAttributeID enum is defined as: + * \code + typedef enum CUlaunchAttributeID_enum { + CU_LAUNCH_ATTRIBUTE_IGNORE = 0, + CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW = 1, + CU_LAUNCH_ATTRIBUTE_COOPERATIVE = 2, + CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY = 3, + CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION = 4, + CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE = 5, + CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION = 6, + CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT = 7, + } CUlaunchAttributeID; + * \endcode + * + * and the corresponding ::CUlaunchAttributeValue union as : + * \code + typedef union CUlaunchAttributeValue_union { + cuuint64_t pad[8]; + CUaccessPolicyWindow accessPolicyWindow; + int cooperative; + CUsynchronizationPolicy syncPolicy; + struct { + unsigned int x; + unsigned int y; + unsigned int z; + } clusterDim; + CUclusterSchedulingPolicy clusterSchedulingPolicyPreference; + int programmaticStreamSerializationAllowed; + struct { + CUevent event; + int flags; + int triggerAtBlockStart; + } programmaticEvent; + } CUlaunchAttributeValue; + * \endcode + * + * Setting ::CU_LAUNCH_ATTRIBUTE_COOPERATIVE to a non-zero value causes the + * kernel launch to be a cooperative launch, with exactly the same usage and + * semantics of ::cuLaunchCooperativeKernel. + * + * Setting ::CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION to a non-zero + * values causes the kernel to use programmatic means to resolve its stream + * dependency -- enabling the CUDA runtime to opportunistically allow the grid's + * execution to overlap with the previous kernel in the stream, if that kernel + * requests the overlap. + * + * ::CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT records an event along with the + * kernel launch. Event recorded through this launch attribute is guaranteed to + * only trigger after all block in the associated kernel trigger the event. A + * block can trigger the event through PTX launchdep.release or CUDA builtin + * function cudaTriggerProgrammaticLaunchCompletion(). A trigger can also be + * inserted at the beginning of each block's execution if triggerAtBlockStart is + * set to non-0. Note that dependents (including the CPU thread calling + * cuEventSynchronize()) are not guaranteed to observe the release precisely + * when it is released. For example, cuEventSynchronize() may only observe the + * event trigger long after the associated kernel has completed. This recording + * type is primarily meant for establishing programmatic dependency between + * device tasks. The event supplied must not be an interprocess or interop + * event. The event must disable timing (i.e. created with + * ::CU_EVENT_DISABLE_TIMING flag set). + * + * The effect of other attributes is consistent with their effect when set via + * persistent APIs. + * + * See ::cuStreamSetAttribute for + * - ::CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW + * - ::CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY + * + * See ::cuFunctionSetAttribute for + * - ::CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION + * - ::CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE + * + * Kernel parameters to \p f can be specified in the same ways that they can be + * using ::cuLaunchKernel. + * + * Note that the API can also be used to launch context-less kernel ::CUkernel + * by querying the handle using ::cuLibraryGetKernel() and then passing it + * to the API by casting to ::CUfunction. Here, the context to launch + * the kernel on will either be taken from the specified stream ::CUlaunchConfig::hStream + * or the current context in case of NULL stream. + * + * \param config - Config to launch + * \param f - Function ::CUfunction or Kernel ::CUkernel to launch + * \param kernelParams - Array of pointers to kernel parameters + * \param extra - Extra options + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_IMAGE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_LAUNCH_FAILED, + * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES, + * ::CUDA_ERROR_LAUNCH_TIMEOUT, + * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING, + * ::CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED, + * ::CUDA_ERROR_NOT_FOUND + * \note_null_stream + * \notefnerr + * + * \sa ::cuCtxGetCacheConfig, + * ::cuCtxSetCacheConfig, + * ::cuFuncSetCacheConfig, + * ::cuFuncGetAttribute, + * ::cudaLaunchKernel, + * ::cudaLaunchKernelEx, + * ::cuLibraryGetKernel, + * ::cuKernelSetCacheConfig, + * ::cuKernelGetAttribute, + * ::cuKernelSetAttribute + */ +CUresult CUDAAPI cuLaunchKernelEx(const CUlaunchConfig *config, + CUfunction f, + void **kernelParams, + void **extra); + +/** + * \brief Launches a CUDA function ::CUfunction or a CUDA kernel ::CUkernel where thread blocks + * can cooperate and synchronize as they execute + * + * Invokes the function ::CUfunction or the kernel ::CUkernel \p f on a \p gridDimX x \p gridDimY x \p gridDimZ + * grid of blocks. Each block contains \p blockDimX x \p blockDimY x + * \p blockDimZ threads. + * + * Note that the API can also be used to launch context-less kernel ::CUkernel + * by querying the handle using ::cuLibraryGetKernel() and then passing it + * to the API by casting to ::CUfunction. Here, the context to launch + * the kernel on will either be taken from the specified stream \p hStream + * or the current context in case of NULL stream. + * + * \p sharedMemBytes sets the amount of dynamic shared memory that will be + * available to each thread block. + * + * The device on which this kernel is invoked must have a non-zero value for + * the device attribute ::CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH. + * + * The total number of blocks launched cannot exceed the maximum number of blocks per + * multiprocessor as returned by ::cuOccupancyMaxActiveBlocksPerMultiprocessor (or + * ::cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags) times the number of multiprocessors + * as specified by the device attribute ::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT. + * + * The kernel cannot make use of CUDA dynamic parallelism. + * + * Kernel parameters must be specified via \p kernelParams. If \p f + * has N parameters, then \p kernelParams needs to be an array of N + * pointers. Each of \p kernelParams[0] through \p kernelParams[N-1] + * must point to a region of memory from which the actual kernel + * parameter will be copied. The number of kernel parameters and their + * offsets and sizes do not need to be specified as that information is + * retrieved directly from the kernel's image. + * + * Calling ::cuLaunchCooperativeKernel() sets persistent function state that is + * the same as function state set through ::cuLaunchKernel API + * + * When the kernel \p f is launched via ::cuLaunchCooperativeKernel(), the previous + * block shape, shared size and parameter info associated with \p f + * is overwritten. + * + * Note that to use ::cuLaunchCooperativeKernel(), the kernel \p f must either have + * been compiled with toolchain version 3.2 or later so that it will + * contain kernel parameter information, or have no kernel parameters. + * If either of these conditions is not met, then ::cuLaunchCooperativeKernel() will + * return ::CUDA_ERROR_INVALID_IMAGE. + * + * Note that the API can also be used to launch context-less kernel ::CUkernel + * by querying the handle using ::cuLibraryGetKernel() and then passing it + * to the API by casting to ::CUfunction. Here, the context to launch + * the kernel on will either be taken from the specified stream \p hStream + * or the current context in case of NULL stream. + * + * \param f - Function ::CUfunction or Kernel ::CUkernel to launch + * \param gridDimX - Width of grid in blocks + * \param gridDimY - Height of grid in blocks + * \param gridDimZ - Depth of grid in blocks + * \param blockDimX - X dimension of each thread block + * \param blockDimY - Y dimension of each thread block + * \param blockDimZ - Z dimension of each thread block + * \param sharedMemBytes - Dynamic shared-memory size per thread block in bytes + * \param hStream - Stream identifier + * \param kernelParams - Array of pointers to kernel parameters + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_IMAGE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_LAUNCH_FAILED, + * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES, + * ::CUDA_ERROR_LAUNCH_TIMEOUT, + * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING, + * ::CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED, + * ::CUDA_ERROR_NOT_FOUND + * \note_null_stream + * \notefnerr + * + * \sa ::cuCtxGetCacheConfig, + * ::cuCtxSetCacheConfig, + * ::cuFuncSetCacheConfig, + * ::cuFuncGetAttribute, + * ::cuLaunchCooperativeKernelMultiDevice, + * ::cudaLaunchCooperativeKernel, + * ::cuLibraryGetKernel, + * ::cuKernelSetCacheConfig, + * ::cuKernelGetAttribute, + * ::cuKernelSetAttribute + */ +CUresult CUDAAPI cuLaunchCooperativeKernel(CUfunction f, + unsigned int gridDimX, + unsigned int gridDimY, + unsigned int gridDimZ, + unsigned int blockDimX, + unsigned int blockDimY, + unsigned int blockDimZ, + unsigned int sharedMemBytes, + CUstream hStream, + void **kernelParams); + +/** + * \brief Launches CUDA functions on multiple devices where thread blocks can cooperate and synchronize as they execute + * + * \deprecated This function is deprecated as of CUDA 11.3. + * + * Invokes kernels as specified in the \p launchParamsList array where each element + * of the array specifies all the parameters required to perform a single kernel launch. + * These kernels can cooperate and synchronize as they execute. The size of the array is + * specified by \p numDevices. + * + * No two kernels can be launched on the same device. All the devices targeted by this + * multi-device launch must be identical. All devices must have a non-zero value for the + * device attribute ::CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH. + * + * All kernels launched must be identical with respect to the compiled code. Note that + * any __device__, __constant__ or __managed__ variables present in the module that owns + * the kernel launched on each device, are independently instantiated on every device. + * It is the application's responsibility to ensure these variables are initialized and + * used appropriately. + * + * The size of the grids as specified in blocks, the size of the blocks themselves + * and the amount of shared memory used by each thread block must also match across + * all launched kernels. + * + * The streams used to launch these kernels must have been created via either ::cuStreamCreate + * or ::cuStreamCreateWithPriority. The NULL stream or ::CU_STREAM_LEGACY or ::CU_STREAM_PER_THREAD + * cannot be used. + * + * The total number of blocks launched per kernel cannot exceed the maximum number of blocks + * per multiprocessor as returned by ::cuOccupancyMaxActiveBlocksPerMultiprocessor (or + * ::cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags) times the number of multiprocessors + * as specified by the device attribute ::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT. Since the + * total number of blocks launched per device has to match across all devices, the maximum + * number of blocks that can be launched per device will be limited by the device with the + * least number of multiprocessors. + * + * The kernels cannot make use of CUDA dynamic parallelism. + * + * The ::CUDA_LAUNCH_PARAMS structure is defined as: + * \code + typedef struct CUDA_LAUNCH_PARAMS_st + { + CUfunction function; + unsigned int gridDimX; + unsigned int gridDimY; + unsigned int gridDimZ; + unsigned int blockDimX; + unsigned int blockDimY; + unsigned int blockDimZ; + unsigned int sharedMemBytes; + CUstream hStream; + void **kernelParams; + } CUDA_LAUNCH_PARAMS; + * \endcode + * where: + * - ::CUDA_LAUNCH_PARAMS::function specifies the kernel to be launched. All functions must + * be identical with respect to the compiled code. + * Note that you can also specify context-less kernel ::CUkernel by querying the handle + * using ::cuLibraryGetKernel() and then casting to ::CUfunction. In this case, the context to + * launch the kernel on be taken from the specified stream ::CUDA_LAUNCH_PARAMS::hStream. + * - ::CUDA_LAUNCH_PARAMS::gridDimX is the width of the grid in blocks. This must match across + * all kernels launched. + * - ::CUDA_LAUNCH_PARAMS::gridDimY is the height of the grid in blocks. This must match across + * all kernels launched. + * - ::CUDA_LAUNCH_PARAMS::gridDimZ is the depth of the grid in blocks. This must match across + * all kernels launched. + * - ::CUDA_LAUNCH_PARAMS::blockDimX is the X dimension of each thread block. This must match across + * all kernels launched. + * - ::CUDA_LAUNCH_PARAMS::blockDimX is the Y dimension of each thread block. This must match across + * all kernels launched. + * - ::CUDA_LAUNCH_PARAMS::blockDimZ is the Z dimension of each thread block. This must match across + * all kernels launched. + * - ::CUDA_LAUNCH_PARAMS::sharedMemBytes is the dynamic shared-memory size per thread block in bytes. + * This must match across all kernels launched. + * - ::CUDA_LAUNCH_PARAMS::hStream is the handle to the stream to perform the launch in. This cannot + * be the NULL stream or ::CU_STREAM_LEGACY or ::CU_STREAM_PER_THREAD. The CUDA context associated + * with this stream must match that associated with ::CUDA_LAUNCH_PARAMS::function. + * - ::CUDA_LAUNCH_PARAMS::kernelParams is an array of pointers to kernel parameters. If + * ::CUDA_LAUNCH_PARAMS::function has N parameters, then ::CUDA_LAUNCH_PARAMS::kernelParams + * needs to be an array of N pointers. Each of ::CUDA_LAUNCH_PARAMS::kernelParams[0] through + * ::CUDA_LAUNCH_PARAMS::kernelParams[N-1] must point to a region of memory from which the actual + * kernel parameter will be copied. The number of kernel parameters and their offsets and sizes + * do not need to be specified as that information is retrieved directly from the kernel's image. + * + * By default, the kernel won't begin execution on any GPU until all prior work in all the specified + * streams has completed. This behavior can be overridden by specifying the flag + * ::CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_PRE_LAUNCH_SYNC. When this flag is specified, each kernel + * will only wait for prior work in the stream corresponding to that GPU to complete before it begins + * execution. + * + * Similarly, by default, any subsequent work pushed in any of the specified streams will not begin + * execution until the kernels on all GPUs have completed. This behavior can be overridden by specifying + * the flag ::CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_POST_LAUNCH_SYNC. When this flag is specified, + * any subsequent work pushed in any of the specified streams will only wait for the kernel launched + * on the GPU corresponding to that stream to complete before it begins execution. + * + * Calling ::cuLaunchCooperativeKernelMultiDevice() sets persistent function state that is + * the same as function state set through ::cuLaunchKernel API when called individually for each + * element in \p launchParamsList. + * + * When kernels are launched via ::cuLaunchCooperativeKernelMultiDevice(), the previous + * block shape, shared size and parameter info associated with each ::CUDA_LAUNCH_PARAMS::function + * in \p launchParamsList is overwritten. + * + * Note that to use ::cuLaunchCooperativeKernelMultiDevice(), the kernels must either have + * been compiled with toolchain version 3.2 or later so that it will + * contain kernel parameter information, or have no kernel parameters. + * If either of these conditions is not met, then ::cuLaunchCooperativeKernelMultiDevice() will + * return ::CUDA_ERROR_INVALID_IMAGE. + * + * \param launchParamsList - List of launch parameters, one per device + * \param numDevices - Size of the \p launchParamsList array + * \param flags - Flags to control launch behavior + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_IMAGE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_LAUNCH_FAILED, + * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES, + * ::CUDA_ERROR_LAUNCH_TIMEOUT, + * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING, + * ::CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED + * \note_null_stream + * \notefnerr + * + * \sa ::cuCtxGetCacheConfig, + * ::cuCtxSetCacheConfig, + * ::cuFuncSetCacheConfig, + * ::cuFuncGetAttribute, + * ::cuLaunchCooperativeKernel, + * ::cudaLaunchCooperativeKernelMultiDevice + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuLaunchCooperativeKernelMultiDevice(CUDA_LAUNCH_PARAMS *launchParamsList, unsigned int numDevices, unsigned int flags); + +/** + * \brief Enqueues a host function call in a stream + * + * Enqueues a host function to run in a stream. The function will be called + * after currently enqueued work and will block work added after it. + * + * The host function must not make any CUDA API calls. Attempting to use a + * CUDA API may result in ::CUDA_ERROR_NOT_PERMITTED, but this is not required. + * The host function must not perform any synchronization that may depend on + * outstanding CUDA work not mandated to run earlier. Host functions without a + * mandated order (such as in independent streams) execute in undefined order + * and may be serialized. + * + * For the purposes of Unified Memory, execution makes a number of guarantees: + *
    + *
  • The stream is considered idle for the duration of the function's + * execution. Thus, for example, the function may always use memory attached + * to the stream it was enqueued in.
    • + *
  • The start of execution of the function has the same effect as + * synchronizing an event recorded in the same stream immediately prior to + * the function. It thus synchronizes streams which have been "joined" + * prior to the function.
    • + *
  • Adding device work to any stream does not have the effect of making + * the stream active until all preceding host functions and stream callbacks + * have executed. Thus, for + * example, a function might use global attached memory even if work has + * been added to another stream, if the work has been ordered behind the + * function call with an event.
    • + *
  • Completion of the function does not cause a stream to become + * active except as described above. The stream will remain idle + * if no device work follows the function, and will remain idle across + * consecutive host functions or stream callbacks without device work in + * between. Thus, for example, + * stream synchronization can be done by signaling from a host function at the + * end of the stream.
    • + *
+ * + * Note that, in contrast to ::cuStreamAddCallback, the function will not be + * called in the event of an error in the CUDA context. + * + * \param hStream - Stream to enqueue function call in + * \param fn - The function to call once preceding stream operations are complete + * \param userData - User-specified data to be passed to the function + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_SUPPORTED + * \note_null_stream + * \notefnerr + * + * \sa ::cuStreamCreate, + * ::cuStreamQuery, + * ::cuStreamSynchronize, + * ::cuStreamWaitEvent, + * ::cuStreamDestroy, + * ::cuMemAllocManaged, + * ::cuStreamAttachMemAsync, + * ::cuStreamAddCallback + */ +CUresult CUDAAPI cuLaunchHostFunc(CUstream hStream, CUhostFn fn, void *userData); + +/** @} */ /* END CUDA_EXEC */ + +/** + * \defgroup CUDA_EXEC_DEPRECATED Execution Control [DEPRECATED] + * + * ___MANBRIEF___ deprecated execution control functions of the low-level CUDA + * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the deprecated execution control functions of the + * low-level CUDA driver application programming interface. + * + * @{ + */ + +/** + * \brief Sets the block-dimensions for the function + * + * \deprecated + * + * Specifies the \p x, \p y, and \p z dimensions of the thread blocks that are + * created when the kernel given by \p hfunc is launched. + * + * \param hfunc - Kernel to specify dimensions of + * \param x - X dimension + * \param y - Y dimension + * \param z - Z dimension + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuFuncSetSharedSize, + * ::cuFuncSetCacheConfig, + * ::cuFuncGetAttribute, + * ::cuParamSetSize, + * ::cuParamSeti, + * ::cuParamSetf, + * ::cuParamSetv, + * ::cuLaunch, + * ::cuLaunchGrid, + * ::cuLaunchGridAsync, + * ::cuLaunchKernel + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuFuncSetBlockShape(CUfunction hfunc, int x, int y, int z); + +/** + * \brief Sets the dynamic shared-memory size for the function + * + * \deprecated + * + * Sets through \p bytes the amount of dynamic shared memory that will be + * available to each thread block when the kernel given by \p hfunc is launched. + * + * \param hfunc - Kernel to specify dynamic shared-memory size for + * \param bytes - Dynamic shared-memory size per thread in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuFuncSetBlockShape, + * ::cuFuncSetCacheConfig, + * ::cuFuncGetAttribute, + * ::cuParamSetSize, + * ::cuParamSeti, + * ::cuParamSetf, + * ::cuParamSetv, + * ::cuLaunch, + * ::cuLaunchGrid, + * ::cuLaunchGridAsync, + * ::cuLaunchKernel + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuFuncSetSharedSize(CUfunction hfunc, unsigned int bytes); + +/** + * \brief Sets the parameter size for the function + * + * \deprecated + * + * Sets through \p numbytes the total size in bytes needed by the function + * parameters of the kernel corresponding to \p hfunc. + * + * \param hfunc - Kernel to set parameter size for + * \param numbytes - Size of parameter list in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuFuncSetBlockShape, + * ::cuFuncSetSharedSize, + * ::cuFuncGetAttribute, + * ::cuParamSetf, + * ::cuParamSeti, + * ::cuParamSetv, + * ::cuLaunch, + * ::cuLaunchGrid, + * ::cuLaunchGridAsync, + * ::cuLaunchKernel + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuParamSetSize(CUfunction hfunc, unsigned int numbytes); + +/** + * \brief Adds an integer parameter to the function's argument list + * + * \deprecated + * + * Sets an integer parameter that will be specified the next time the + * kernel corresponding to \p hfunc will be invoked. \p offset is a byte offset. + * + * \param hfunc - Kernel to add parameter to + * \param offset - Offset to add parameter to argument list + * \param value - Value of parameter + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuFuncSetBlockShape, + * ::cuFuncSetSharedSize, + * ::cuFuncGetAttribute, + * ::cuParamSetSize, + * ::cuParamSetf, + * ::cuParamSetv, + * ::cuLaunch, + * ::cuLaunchGrid, + * ::cuLaunchGridAsync, + * ::cuLaunchKernel + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuParamSeti(CUfunction hfunc, int offset, unsigned int value); + +/** + * \brief Adds a floating-point parameter to the function's argument list + * + * \deprecated + * + * Sets a floating-point parameter that will be specified the next time the + * kernel corresponding to \p hfunc will be invoked. \p offset is a byte offset. + * + * \param hfunc - Kernel to add parameter to + * \param offset - Offset to add parameter to argument list + * \param value - Value of parameter + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuFuncSetBlockShape, + * ::cuFuncSetSharedSize, + * ::cuFuncGetAttribute, + * ::cuParamSetSize, + * ::cuParamSeti, + * ::cuParamSetv, + * ::cuLaunch, + * ::cuLaunchGrid, + * ::cuLaunchGridAsync, + * ::cuLaunchKernel + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuParamSetf(CUfunction hfunc, int offset, float value); + +/** + * \brief Adds arbitrary data to the function's argument list + * + * \deprecated + * + * Copies an arbitrary amount of data (specified in \p numbytes) from \p ptr + * into the parameter space of the kernel corresponding to \p hfunc. \p offset + * is a byte offset. + * + * \param hfunc - Kernel to add data to + * \param offset - Offset to add data to argument list + * \param ptr - Pointer to arbitrary data + * \param numbytes - Size of data to copy in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa ::cuFuncSetBlockShape, + * ::cuFuncSetSharedSize, + * ::cuFuncGetAttribute, + * ::cuParamSetSize, + * ::cuParamSetf, + * ::cuParamSeti, + * ::cuLaunch, + * ::cuLaunchGrid, + * ::cuLaunchGridAsync, + * ::cuLaunchKernel + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuParamSetv(CUfunction hfunc, int offset, void *ptr, unsigned int numbytes); + +/** + * \brief Launches a CUDA function + * + * \deprecated + * + * Invokes the kernel \p f on a 1 x 1 x 1 grid of blocks. The block + * contains the number of threads specified by a previous call to + * ::cuFuncSetBlockShape(). + * + * The block shape, dynamic shared memory size, and parameter information + * must be set using + * ::cuFuncSetBlockShape(), + * ::cuFuncSetSharedSize(), + * ::cuParamSetSize(), + * ::cuParamSeti(), + * ::cuParamSetf(), and + * ::cuParamSetv() + * prior to calling this function. + * + * Launching a function via ::cuLaunchKernel() invalidates the function's + * block shape, dynamic shared memory size, and parameter information. After + * launching via cuLaunchKernel, this state must be re-initialized prior to + * calling this function. Failure to do so results in undefined behavior. + * + * \param f - Kernel to launch + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_LAUNCH_FAILED, + * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES, + * ::CUDA_ERROR_LAUNCH_TIMEOUT, + * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED + * \notefnerr + * + * \sa ::cuFuncSetBlockShape, + * ::cuFuncSetSharedSize, + * ::cuFuncGetAttribute, + * ::cuParamSetSize, + * ::cuParamSetf, + * ::cuParamSeti, + * ::cuParamSetv, + * ::cuLaunchGrid, + * ::cuLaunchGridAsync, + * ::cuLaunchKernel + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuLaunch(CUfunction f); + +/** + * \brief Launches a CUDA function + * + * \deprecated + * + * Invokes the kernel \p f on a \p grid_width x \p grid_height grid of + * blocks. Each block contains the number of threads specified by a previous + * call to ::cuFuncSetBlockShape(). + * + * The block shape, dynamic shared memory size, and parameter information + * must be set using + * ::cuFuncSetBlockShape(), + * ::cuFuncSetSharedSize(), + * ::cuParamSetSize(), + * ::cuParamSeti(), + * ::cuParamSetf(), and + * ::cuParamSetv() + * prior to calling this function. + * + * Launching a function via ::cuLaunchKernel() invalidates the function's + * block shape, dynamic shared memory size, and parameter information. After + * launching via cuLaunchKernel, this state must be re-initialized prior to + * calling this function. Failure to do so results in undefined behavior. + * + * \param f - Kernel to launch + * \param grid_width - Width of grid in blocks + * \param grid_height - Height of grid in blocks + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_LAUNCH_FAILED, + * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES, + * ::CUDA_ERROR_LAUNCH_TIMEOUT, + * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED + * \notefnerr + * + * \sa ::cuFuncSetBlockShape, + * ::cuFuncSetSharedSize, + * ::cuFuncGetAttribute, + * ::cuParamSetSize, + * ::cuParamSetf, + * ::cuParamSeti, + * ::cuParamSetv, + * ::cuLaunch, + * ::cuLaunchGridAsync, + * ::cuLaunchKernel + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuLaunchGrid(CUfunction f, int grid_width, int grid_height); + +/** + * \brief Launches a CUDA function + * + * \deprecated + * + * Invokes the kernel \p f on a \p grid_width x \p grid_height grid of + * blocks. Each block contains the number of threads specified by a previous + * call to ::cuFuncSetBlockShape(). + * + * The block shape, dynamic shared memory size, and parameter information + * must be set using + * ::cuFuncSetBlockShape(), + * ::cuFuncSetSharedSize(), + * ::cuParamSetSize(), + * ::cuParamSeti(), + * ::cuParamSetf(), and + * ::cuParamSetv() + * prior to calling this function. + * + * Launching a function via ::cuLaunchKernel() invalidates the function's + * block shape, dynamic shared memory size, and parameter information. After + * launching via cuLaunchKernel, this state must be re-initialized prior to + * calling this function. Failure to do so results in undefined behavior. + * + * \param f - Kernel to launch + * \param grid_width - Width of grid in blocks + * \param grid_height - Height of grid in blocks + * \param hStream - Stream identifier + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_LAUNCH_FAILED, + * ::CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES, + * ::CUDA_ERROR_LAUNCH_TIMEOUT, + * ::CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING, + * ::CUDA_ERROR_SHARED_OBJECT_INIT_FAILED + * + * \note In certain cases where cubins are created with no ABI (i.e., using \p ptxas \p --abi-compile \p no), + * this function may serialize kernel launches. The CUDA driver retains asynchronous behavior by + * growing the per-thread stack as needed per launch and not shrinking it afterwards. + * + * \note_null_stream + * \notefnerr + * + * \sa ::cuFuncSetBlockShape, + * ::cuFuncSetSharedSize, + * ::cuFuncGetAttribute, + * ::cuParamSetSize, + * ::cuParamSetf, + * ::cuParamSeti, + * ::cuParamSetv, + * ::cuLaunch, + * ::cuLaunchGrid, + * ::cuLaunchKernel + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuLaunchGridAsync(CUfunction f, int grid_width, int grid_height, CUstream hStream); + + +/** + * \brief Adds a texture-reference to the function's argument list + * + * \deprecated + * + * Makes the CUDA array or linear memory bound to the texture reference + * \p hTexRef available to a device program as a texture. In this version of + * CUDA, the texture-reference must be obtained via ::cuModuleGetTexRef() and + * the \p texunit parameter must be set to ::CU_PARAM_TR_DEFAULT. + * + * \param hfunc - Kernel to add texture-reference to + * \param texunit - Texture unit (must be ::CU_PARAM_TR_DEFAULT) + * \param hTexRef - Texture-reference to add to argument list + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuParamSetTexRef(CUfunction hfunc, int texunit, CUtexref hTexRef); +/** @} */ /* END CUDA_EXEC_DEPRECATED */ + +/** + * \defgroup CUDA_GRAPH Graph Management + * + * ___MANBRIEF___ graph management functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the graph management functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** + * \brief Creates a graph + * + * Creates an empty graph, which is returned via \p phGraph. + * + * \param phGraph - Returns newly created graph + * \param flags - Graph creation flags, must be 0 + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddHostNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode, + * ::cuGraphInstantiate, + * ::cuGraphDestroy, + * ::cuGraphGetNodes, + * ::cuGraphGetRootNodes, + * ::cuGraphGetEdges, + * ::cuGraphClone + */ +CUresult CUDAAPI cuGraphCreate(CUgraph *phGraph, unsigned int flags); + +/** + * \brief Creates a kernel execution node and adds it to a graph + * + * Creates a new kernel execution node and adds it to \p hGraph with \p numDependencies + * dependencies specified via \p dependencies and arguments specified in \p nodeParams. + * It is possible for \p numDependencies to be 0, in which case the node will be placed + * at the root of the graph. \p dependencies may not have any duplicate entries. + * A handle to the new node will be returned in \p phGraphNode. + * + * The CUDA_KERNEL_NODE_PARAMS structure is defined as: + * + * \code + * typedef struct CUDA_KERNEL_NODE_PARAMS_st { + * CUfunction func; + * unsigned int gridDimX; + * unsigned int gridDimY; + * unsigned int gridDimZ; + * unsigned int blockDimX; + * unsigned int blockDimY; + * unsigned int blockDimZ; + * unsigned int sharedMemBytes; + * void **kernelParams; + * void **extra; + * } CUDA_KERNEL_NODE_PARAMS; + * \endcode + * + * When the graph is launched, the node will invoke kernel \p func on a (\p gridDimX x + * \p gridDimY x \p gridDimZ) grid of blocks. Each block contains + * (\p blockDimX x \p blockDimY x \p blockDimZ) threads. + * + * \p sharedMemBytes sets the amount of dynamic shared memory that will be + * available to each thread block. + * + * Kernel parameters to \p func can be specified in one of two ways: + * + * 1) Kernel parameters can be specified via \p kernelParams. If the kernel has N + * parameters, then \p kernelParams needs to be an array of N pointers. Each pointer, + * from \p kernelParams[0] to \p kernelParams[N-1], points to the region of memory from which the actual + * parameter will be copied. The number of kernel parameters and their offsets and sizes do not need + * to be specified as that information is retrieved directly from the kernel's image. + * + * 2) Kernel parameters for non-cooperative kernels can also be packaged by the application into a single + * buffer that is passed in via \p extra. This places the burden on the application of knowing each + * kernel parameter's size and alignment/padding within the buffer. The \p extra parameter exists + * to allow this function to take additional less commonly used arguments. \p extra specifies + * a list of names of extra settings and their corresponding values. Each extra setting name is + * immediately followed by the corresponding value. The list must be terminated with either NULL or + * CU_LAUNCH_PARAM_END. + * + * - ::CU_LAUNCH_PARAM_END, which indicates the end of the \p extra + * array; + * - ::CU_LAUNCH_PARAM_BUFFER_POINTER, which specifies that the next + * value in \p extra will be a pointer to a buffer + * containing all the kernel parameters for launching kernel + * \p func; + * - ::CU_LAUNCH_PARAM_BUFFER_SIZE, which specifies that the next + * value in \p extra will be a pointer to a size_t + * containing the size of the buffer specified with + * ::CU_LAUNCH_PARAM_BUFFER_POINTER; + * + * The error ::CUDA_ERROR_INVALID_VALUE will be returned if kernel parameters are specified with both + * \p kernelParams and \p extra (i.e. both \p kernelParams and \p extra are non-NULL). + * ::CUDA_ERROR_INVALID_VALUE will be returned if \p extra is used for a cooperative kernel. + * + * The \p kernelParams or \p extra array, as well as the argument values it points to, + * are copied during this call. + * + * \note Kernels launched using graphs must not use texture and surface references. Reading or + * writing through any texture or surface reference is undefined behavior. + * This restriction does not apply to texture and surface objects. + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * \param nodeParams - Parameters for the GPU execution node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuLaunchKernel, + * ::cuLaunchCooperativeKernel, + * ::cuGraphKernelNodeGetParams, + * ::cuGraphKernelNodeSetParams, + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddHostNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode + */ +CUresult CUDAAPI cuGraphAddKernelNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_KERNEL_NODE_PARAMS *nodeParams); + +/** + * \brief Returns a kernel node's parameters + * + * Returns the parameters of kernel node \p hNode in \p nodeParams. + * The \p kernelParams or \p extra array returned in \p nodeParams, + * as well as the argument values it points to, are owned by the node. + * This memory remains valid until the node is destroyed or its + * parameters are modified, and should not be modified + * directly. Use ::cuGraphKernelNodeSetParams to update the + * parameters of this node. + * + * The params will contain either \p kernelParams or \p extra, + * according to which of these was most recently set on the node. + * + * \param hNode - Node to get the parameters for + * \param nodeParams - Pointer to return the parameters + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuLaunchKernel, + * ::cuGraphAddKernelNode, + * ::cuGraphKernelNodeSetParams + */ +CUresult CUDAAPI cuGraphKernelNodeGetParams(CUgraphNode hNode, CUDA_KERNEL_NODE_PARAMS *nodeParams); + +/** + * \brief Sets a kernel node's parameters + * + * Sets the parameters of kernel node \p hNode to \p nodeParams. + * + * \param hNode - Node to set the parameters for + * \param nodeParams - Parameters to copy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuLaunchKernel, + * ::cuGraphAddKernelNode, + * ::cuGraphKernelNodeGetParams + */ +CUresult CUDAAPI cuGraphKernelNodeSetParams(CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS *nodeParams); + +/** + * \brief Creates a memcpy node and adds it to a graph + * + * Creates a new memcpy node and adds it to \p hGraph with \p numDependencies + * dependencies specified via \p dependencies. + * It is possible for \p numDependencies to be 0, in which case the node will be placed + * at the root of the graph. \p dependencies may not have any duplicate entries. + * A handle to the new node will be returned in \p phGraphNode. + * + * When the graph is launched, the node will perform the memcpy described by \p copyParams. + * See ::cuMemcpy3D() for a description of the structure and its restrictions. + * + * Memcpy nodes have some additional restrictions with regards to managed memory, if the + * system contains at least one device which has a zero value for the device attribute + * ::CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS. If one or more of the operands refer + * to managed memory, then using the memory type ::CU_MEMORYTYPE_UNIFIED is disallowed + * for those operand(s). The managed memory will be treated as residing on either the + * host or the device, depending on which memory type is specified. + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * \param copyParams - Parameters for the memory copy + * \param ctx - Context on which to run the node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuMemcpy3D, + * ::cuGraphMemcpyNodeGetParams, + * ::cuGraphMemcpyNodeSetParams, + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddHostNode, + * ::cuGraphAddMemsetNode + */ +CUresult CUDAAPI cuGraphAddMemcpyNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_MEMCPY3D *copyParams, CUcontext ctx); + +/** + * \brief Returns a memcpy node's parameters + * + * Returns the parameters of memcpy node \p hNode in \p nodeParams. + * + * \param hNode - Node to get the parameters for + * \param nodeParams - Pointer to return the parameters + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuMemcpy3D, + * ::cuGraphAddMemcpyNode, + * ::cuGraphMemcpyNodeSetParams + */ +CUresult CUDAAPI cuGraphMemcpyNodeGetParams(CUgraphNode hNode, CUDA_MEMCPY3D *nodeParams); + +/** + * \brief Sets a memcpy node's parameters + * + * Sets the parameters of memcpy node \p hNode to \p nodeParams. + * + * \param hNode - Node to set the parameters for + * \param nodeParams - Parameters to copy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuMemcpy3D, + * ::cuGraphAddMemcpyNode, + * ::cuGraphMemcpyNodeGetParams + */ +CUresult CUDAAPI cuGraphMemcpyNodeSetParams(CUgraphNode hNode, const CUDA_MEMCPY3D *nodeParams); + +/** + * \brief Creates a memset node and adds it to a graph + * + * Creates a new memset node and adds it to \p hGraph with \p numDependencies + * dependencies specified via \p dependencies. + * It is possible for \p numDependencies to be 0, in which case the node will be placed + * at the root of the graph. \p dependencies may not have any duplicate entries. + * A handle to the new node will be returned in \p phGraphNode. + * + * The element size must be 1, 2, or 4 bytes. + * When the graph is launched, the node will perform the memset described by \p memsetParams. + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * \param memsetParams - Parameters for the memory set + * \param ctx - Context on which to run the node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_CONTEXT + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuMemsetD2D32, + * ::cuGraphMemsetNodeGetParams, + * ::cuGraphMemsetNodeSetParams, + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddHostNode, + * ::cuGraphAddMemcpyNode + */ +CUresult CUDAAPI cuGraphAddMemsetNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_MEMSET_NODE_PARAMS *memsetParams, CUcontext ctx); + +/** + * \brief Returns a memset node's parameters + * + * Returns the parameters of memset node \p hNode in \p nodeParams. + * + * \param hNode - Node to get the parameters for + * \param nodeParams - Pointer to return the parameters + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuMemsetD2D32, + * ::cuGraphAddMemsetNode, + * ::cuGraphMemsetNodeSetParams + */ +CUresult CUDAAPI cuGraphMemsetNodeGetParams(CUgraphNode hNode, CUDA_MEMSET_NODE_PARAMS *nodeParams); + +/** + * \brief Sets a memset node's parameters + * + * Sets the parameters of memset node \p hNode to \p nodeParams. + * + * \param hNode - Node to set the parameters for + * \param nodeParams - Parameters to copy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuMemsetD2D32, + * ::cuGraphAddMemsetNode, + * ::cuGraphMemsetNodeGetParams + */ +CUresult CUDAAPI cuGraphMemsetNodeSetParams(CUgraphNode hNode, const CUDA_MEMSET_NODE_PARAMS *nodeParams); + +/** + * \brief Creates a host execution node and adds it to a graph + * + * Creates a new CPU execution node and adds it to \p hGraph with \p numDependencies + * dependencies specified via \p dependencies and arguments specified in \p nodeParams. + * It is possible for \p numDependencies to be 0, in which case the node will be placed + * at the root of the graph. \p dependencies may not have any duplicate entries. + * A handle to the new node will be returned in \p phGraphNode. + * + * When the graph is launched, the node will invoke the specified CPU function. + * Host nodes are not supported under MPS with pre-Volta GPUs. + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * \param nodeParams - Parameters for the host node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuLaunchHostFunc, + * ::cuGraphHostNodeGetParams, + * ::cuGraphHostNodeSetParams, + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode + */ +CUresult CUDAAPI cuGraphAddHostNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_HOST_NODE_PARAMS *nodeParams); + +/** + * \brief Returns a host node's parameters + * + * Returns the parameters of host node \p hNode in \p nodeParams. + * + * \param hNode - Node to get the parameters for + * \param nodeParams - Pointer to return the parameters + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuLaunchHostFunc, + * ::cuGraphAddHostNode, + * ::cuGraphHostNodeSetParams + */ +CUresult CUDAAPI cuGraphHostNodeGetParams(CUgraphNode hNode, CUDA_HOST_NODE_PARAMS *nodeParams); + +/** + * \brief Sets a host node's parameters + * + * Sets the parameters of host node \p hNode to \p nodeParams. + * + * \param hNode - Node to set the parameters for + * \param nodeParams - Parameters to copy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuLaunchHostFunc, + * ::cuGraphAddHostNode, + * ::cuGraphHostNodeGetParams + */ +CUresult CUDAAPI cuGraphHostNodeSetParams(CUgraphNode hNode, const CUDA_HOST_NODE_PARAMS *nodeParams); + +/** + * \brief Creates a child graph node and adds it to a graph + * + * Creates a new node which executes an embedded graph, and adds it to \p hGraph with + * \p numDependencies dependencies specified via \p dependencies. + * It is possible for \p numDependencies to be 0, in which case the node will be placed + * at the root of the graph. \p dependencies may not have any duplicate entries. + * A handle to the new node will be returned in \p phGraphNode. + * + * If \p hGraph contains allocation or free nodes, this call will return an error. + * + * The node executes an embedded child graph. The child graph is cloned in this call. + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * \param childGraph - The graph to clone into this node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphChildGraphNodeGetGraph, + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddHostNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode, + * ::cuGraphClone + */ +CUresult CUDAAPI cuGraphAddChildGraphNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUgraph childGraph); + +/** + * \brief Gets a handle to the embedded graph of a child graph node + * + * Gets a handle to the embedded graph in a child graph node. This call + * does not clone the graph. Changes to the graph will be reflected in + * the node, and the node retains ownership of the graph. + * + * Allocation and free nodes cannot be added to the returned graph. + * Attempting to do so will return an error. + * + * \param hNode - Node to get the embedded graph for + * \param phGraph - Location to store a handle to the graph + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddChildGraphNode, + * ::cuGraphNodeFindInClone + */ +CUresult CUDAAPI cuGraphChildGraphNodeGetGraph(CUgraphNode hNode, CUgraph *phGraph); + +/** + * \brief Creates an empty node and adds it to a graph + * + * Creates a new node which performs no operation, and adds it to \p hGraph with + * \p numDependencies dependencies specified via \p dependencies. + * It is possible for \p numDependencies to be 0, in which case the node will be placed + * at the root of the graph. \p dependencies may not have any duplicate entries. + * A handle to the new node will be returned in \p phGraphNode. + * + * An empty node performs no operation during execution, but can be used for + * transitive ordering. For example, a phased execution graph with 2 groups of n + * nodes with a barrier between them can be represented using an empty node and + * 2*n dependency edges, rather than no empty node and n^2 dependency edges. + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddHostNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode + */ +CUresult CUDAAPI cuGraphAddEmptyNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies); + +/** + * \brief Creates an event record node and adds it to a graph + * + * Creates a new event record node and adds it to \p hGraph with \p numDependencies + * dependencies specified via \p dependencies and event specified in \p event. + * It is possible for \p numDependencies to be 0, in which case the node will be placed + * at the root of the graph. \p dependencies may not have any duplicate entries. + * A handle to the new node will be returned in \p phGraphNode. + * + * Each launch of the graph will record \p event to capture execution of the + * node's dependencies. + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * \param event - Event for the node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddEventWaitNode, + * ::cuEventRecordWithFlags, + * ::cuStreamWaitEvent, + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode + */ +CUresult CUDAAPI cuGraphAddEventRecordNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUevent event); + +/** + * \brief Returns the event associated with an event record node + * + * Returns the event of event record node \p hNode in \p event_out. + * + * \param hNode - Node to get the event for + * \param event_out - Pointer to return the event + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddEventRecordNode, + * ::cuGraphEventRecordNodeSetEvent, + * ::cuGraphEventWaitNodeGetEvent, + * ::cuEventRecordWithFlags, + * ::cuStreamWaitEvent + */ +CUresult CUDAAPI cuGraphEventRecordNodeGetEvent(CUgraphNode hNode, CUevent *event_out); + +/** + * \brief Sets an event record node's event + * + * Sets the event of event record node \p hNode to \p event. + * + * \param hNode - Node to set the event for + * \param event - Event to use + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddEventRecordNode, + * ::cuGraphEventRecordNodeGetEvent, + * ::cuGraphEventWaitNodeSetEvent, + * ::cuEventRecordWithFlags, + * ::cuStreamWaitEvent + */ +CUresult CUDAAPI cuGraphEventRecordNodeSetEvent(CUgraphNode hNode, CUevent event); + +/** + * \brief Creates an event wait node and adds it to a graph + * + * Creates a new event wait node and adds it to \p hGraph with \p numDependencies + * dependencies specified via \p dependencies and event specified in \p event. + * It is possible for \p numDependencies to be 0, in which case the node will be placed + * at the root of the graph. \p dependencies may not have any duplicate entries. + * A handle to the new node will be returned in \p phGraphNode. + * + * The graph node will wait for all work captured in \p event. See ::cuEventRecord() + * for details on what is captured by an event. \p event may be from a different context + * or device than the launch stream. + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * \param event - Event for the node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddEventRecordNode, + * ::cuEventRecordWithFlags, + * ::cuStreamWaitEvent, + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode + */ +CUresult CUDAAPI cuGraphAddEventWaitNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUevent event); + +/** + * \brief Returns the event associated with an event wait node + * + * Returns the event of event wait node \p hNode in \p event_out. + * + * \param hNode - Node to get the event for + * \param event_out - Pointer to return the event + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddEventWaitNode, + * ::cuGraphEventWaitNodeSetEvent, + * ::cuGraphEventRecordNodeGetEvent, + * ::cuEventRecordWithFlags, + * ::cuStreamWaitEvent + */ +CUresult CUDAAPI cuGraphEventWaitNodeGetEvent(CUgraphNode hNode, CUevent *event_out); + +/** + * \brief Sets an event wait node's event + * + * Sets the event of event wait node \p hNode to \p event. + * + * \param hNode - Node to set the event for + * \param event - Event to use + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddEventWaitNode, + * ::cuGraphEventWaitNodeGetEvent, + * ::cuGraphEventRecordNodeSetEvent, + * ::cuEventRecordWithFlags, + * ::cuStreamWaitEvent + */ +CUresult CUDAAPI cuGraphEventWaitNodeSetEvent(CUgraphNode hNode, CUevent event); + +/** + * \brief Creates an external semaphore signal node and adds it to a graph + * + * Creates a new external semaphore signal node and adds it to \p hGraph with \p + * numDependencies dependencies specified via \p dependencies and arguments specified + * in \p nodeParams. It is possible for \p numDependencies to be 0, in which case the + * node will be placed at the root of the graph. \p dependencies may not have any + * duplicate entries. A handle to the new node will be returned in \p phGraphNode. + * + * Performs a signal operation on a set of externally allocated semaphore objects + * when the node is launched. The operation(s) will occur after all of the node's + * dependencies have completed. + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * \param nodeParams - Parameters for the node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphExternalSemaphoresSignalNodeGetParams, + * ::cuGraphExternalSemaphoresSignalNodeSetParams, + * ::cuGraphExecExternalSemaphoresSignalNodeSetParams, + * ::cuGraphAddExternalSemaphoresWaitNode, + * ::cuImportExternalSemaphore, + * ::cuSignalExternalSemaphoresAsync, + * ::cuWaitExternalSemaphoresAsync, + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddEventRecordNode, + * ::cuGraphAddEventWaitNode, + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode + */ +CUresult CUDAAPI cuGraphAddExternalSemaphoresSignalNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS *nodeParams); + +/** + * \brief Returns an external semaphore signal node's parameters + * + * Returns the parameters of an external semaphore signal node \p hNode in \p params_out. + * The \p extSemArray and \p paramsArray returned in \p params_out, + * are owned by the node. This memory remains valid until the node is destroyed or its + * parameters are modified, and should not be modified + * directly. Use ::cuGraphExternalSemaphoresSignalNodeSetParams to update the + * parameters of this node. + * + * \param hNode - Node to get the parameters for + * \param params_out - Pointer to return the parameters + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuLaunchKernel, + * ::cuGraphAddExternalSemaphoresSignalNode, + * ::cuGraphExternalSemaphoresSignalNodeSetParams, + * ::cuGraphAddExternalSemaphoresWaitNode, + * ::cuSignalExternalSemaphoresAsync, + * ::cuWaitExternalSemaphoresAsync + */ +CUresult CUDAAPI cuGraphExternalSemaphoresSignalNodeGetParams(CUgraphNode hNode, CUDA_EXT_SEM_SIGNAL_NODE_PARAMS *params_out); + +/** + * \brief Sets an external semaphore signal node's parameters + * + * Sets the parameters of an external semaphore signal node \p hNode to \p nodeParams. + * + * \param hNode - Node to set the parameters for + * \param nodeParams - Parameters to copy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddExternalSemaphoresSignalNode, + * ::cuGraphExternalSemaphoresSignalNodeSetParams, + * ::cuGraphAddExternalSemaphoresWaitNode, + * ::cuSignalExternalSemaphoresAsync, + * ::cuWaitExternalSemaphoresAsync + */ +CUresult CUDAAPI cuGraphExternalSemaphoresSignalNodeSetParams(CUgraphNode hNode, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS *nodeParams); + +/** + * \brief Creates an external semaphore wait node and adds it to a graph + * + * Creates a new external semaphore wait node and adds it to \p hGraph with \p numDependencies + * dependencies specified via \p dependencies and arguments specified in \p nodeParams. + * It is possible for \p numDependencies to be 0, in which case the node will be placed + * at the root of the graph. \p dependencies may not have any duplicate entries. A handle + * to the new node will be returned in \p phGraphNode. + * + * Performs a wait operation on a set of externally allocated semaphore objects + * when the node is launched. The node's dependencies will not be launched until + * the wait operation has completed. + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * \param nodeParams - Parameters for the node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphExternalSemaphoresWaitNodeGetParams, + * ::cuGraphExternalSemaphoresWaitNodeSetParams, + * ::cuGraphExecExternalSemaphoresWaitNodeSetParams, + * ::cuGraphAddExternalSemaphoresSignalNode, + * ::cuImportExternalSemaphore, + * ::cuSignalExternalSemaphoresAsync, + * ::cuWaitExternalSemaphoresAsync, + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddEventRecordNode, + * ::cuGraphAddEventWaitNode, + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode + */ +CUresult CUDAAPI cuGraphAddExternalSemaphoresWaitNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_EXT_SEM_WAIT_NODE_PARAMS *nodeParams); + +/** + * \brief Returns an external semaphore wait node's parameters + * + * Returns the parameters of an external semaphore wait node \p hNode in \p params_out. + * The \p extSemArray and \p paramsArray returned in \p params_out, + * are owned by the node. This memory remains valid until the node is destroyed or its + * parameters are modified, and should not be modified + * directly. Use ::cuGraphExternalSemaphoresSignalNodeSetParams to update the + * parameters of this node. + * + * \param hNode - Node to get the parameters for + * \param params_out - Pointer to return the parameters + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuLaunchKernel, + * ::cuGraphAddExternalSemaphoresWaitNode, + * ::cuGraphExternalSemaphoresWaitNodeSetParams, + * ::cuGraphAddExternalSemaphoresWaitNode, + * ::cuSignalExternalSemaphoresAsync, + * ::cuWaitExternalSemaphoresAsync + */ +CUresult CUDAAPI cuGraphExternalSemaphoresWaitNodeGetParams(CUgraphNode hNode, CUDA_EXT_SEM_WAIT_NODE_PARAMS *params_out); + +/** + * \brief Sets an external semaphore wait node's parameters + * + * Sets the parameters of an external semaphore wait node \p hNode to \p nodeParams. + * + * \param hNode - Node to set the parameters for + * \param nodeParams - Parameters to copy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddExternalSemaphoresWaitNode, + * ::cuGraphExternalSemaphoresWaitNodeSetParams, + * ::cuGraphAddExternalSemaphoresWaitNode, + * ::cuSignalExternalSemaphoresAsync, + * ::cuWaitExternalSemaphoresAsync + */ +CUresult CUDAAPI cuGraphExternalSemaphoresWaitNodeSetParams(CUgraphNode hNode, const CUDA_EXT_SEM_WAIT_NODE_PARAMS *nodeParams); + +/** + * \brief Creates a batch memory operation node and adds it to a graph + * + * Creates a new batch memory operation node and adds it to \p hGraph with \p + * numDependencies dependencies specified via \p dependencies and arguments specified in \p nodeParams. + * It is possible for \p numDependencies to be 0, in which case the node will be placed + * at the root of the graph. \p dependencies may not have any duplicate entries. + * A handle to the new node will be returned in \p phGraphNode. + * + * When the node is added, the paramArray inside \p nodeParams is copied and therefore it can be + * freed after the call returns. + * + * \note + * Warning: + * Improper use of this API may deadlock the application. Synchronization + * ordering established through this API is not visible to CUDA. CUDA tasks + * that are (even indirectly) ordered by this API should also have that order + * expressed with CUDA-visible dependencies such as events. This ensures that + * the scheduler does not serialize them in an improper order. For more + * information, see the Stream Memory Operations section in the programming + * guide(https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html). + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * \param nodeParams - Parameters for the node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuStreamBatchMemOp, + * ::cuStreamWaitValue32, + * ::cuStreamWriteValue32, + * ::cuStreamWaitValue64, + * ::cuStreamWriteValue64, + * ::cuGraphBatchMemOpNodeGetParams, + * ::cuGraphBatchMemOpNodeSetParams, + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode + */ +CUresult CUDAAPI cuGraphAddBatchMemOpNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams); + +/** + * \brief Returns a batch mem op node's parameters + * + * Returns the parameters of batch mem op node \p hNode in \p nodeParams_out. + * The \p paramArray returned in \p nodeParams_out is owned by the node. + * This memory remains valid until the node is destroyed or its + * parameters are modified, and should not be modified + * directly. Use ::cuGraphBatchMemOpNodeSetParams to update the + * parameters of this node. + * + * \param hNode - Node to get the parameters for + * \param nodeParams_out - Pointer to return the parameters + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuStreamBatchMemOp, + * ::cuGraphAddBatchMemOpNode, + * ::cuGraphBatchMemOpNodeSetParams + */ +CUresult CUDAAPI cuGraphBatchMemOpNodeGetParams(CUgraphNode hNode, CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams_out); + +/** + * \brief Sets a batch mem op node's parameters + * + * Sets the parameters of batch mem op node \p hNode to \p nodeParams. + * + * The paramArray inside \p nodeParams is copied and therefore it can be + * freed after the call returns. + * + * \param hNode - Node to set the parameters for + * \param nodeParams - Parameters to copy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuStreamBatchMemOp, + * ::cuGraphAddBatchMemOpNode, + * ::cuGraphBatchMemOpNodeGetParams + */ +CUresult CUDAAPI cuGraphBatchMemOpNodeSetParams(CUgraphNode hNode, const CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams); + +/** + * \brief Sets the parameters for a batch mem op node in the given graphExec + * + * Sets the parameters of a batch mem op node in an executable graph \p hGraphExec. + * The node is identified by the corresponding node \p hNode in the + * non-executable graph, from which the executable graph was instantiated. + * + * The following fields on operations may be modified on an executable graph: + * + * op.waitValue.address + * op.waitValue.value[64] + * op.waitValue.flags bits corresponding to wait type (i.e. CU_STREAM_WAIT_VALUE_FLUSH bit cannot be modified) + * op.writeValue.address + * op.writeValue.value[64] + * + * Other fields, such as the context, count or type of operations, and other types of operations such as membars, + * may not be modified. + * + * \p hNode must not have been removed from the original graph. + * + * The modifications only affect future launches of \p hGraphExec. Already + * enqueued or running launches of \p hGraphExec are not affected by this call. + * \p hNode is also not modified by this call. + * + * The paramArray inside \p nodeParams is copied and therefore it can be + * freed after the call returns. + * + * \param hGraphExec - The executable graph in which to set the specified node + * \param hNode - Batch mem op node from the graph from which graphExec was instantiated + * \param nodeParams - Updated Parameters to set + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuStreamBatchMemOp, + * ::cuGraphAddBatchMemOpNode, + * ::cuGraphBatchMemOpNodeGetParams, + * ::cuGraphBatchMemOpNodeSetParams, + * ::cuGraphInstantiate + */ +CUresult CUDAAPI cuGraphExecBatchMemOpNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_BATCH_MEM_OP_NODE_PARAMS *nodeParams); + +/** + * \brief Creates an allocation node and adds it to a graph + * + * Creates a new allocation node and adds it to \p hGraph with \p numDependencies + * dependencies specified via \p dependencies and arguments specified in \p nodeParams. + * It is possible for \p numDependencies to be 0, in which case the node will be placed + * at the root of the graph. \p dependencies may not have any duplicate entries. A handle + * to the new node will be returned in \p phGraphNode. + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * \param nodeParams - Parameters for the node + * + * When ::cuGraphAddMemAllocNode creates an allocation node, it returns the address of the allocation in + * \p nodeParams.dptr. The allocation's address remains fixed across instantiations and launches. + * + * If the allocation is freed in the same graph, by creating a free node using ::cuGraphAddMemFreeNode, + * the allocation can be accessed by nodes ordered after the allocation node but before the free node. + * These allocations cannot be freed outside the owning graph, and they can only be freed once in the + * owning graph. + * + * If the allocation is not freed in the same graph, then it can be accessed not only by nodes in the + * graph which are ordered after the allocation node, but also by stream operations ordered after the + * graph's execution but before the allocation is freed. + * + * Allocations which are not freed in the same graph can be freed by: + * - passing the allocation to ::cuMemFreeAsync or ::cuMemFree; + * - launching a graph with a free node for that allocation; or + * - specifying ::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH during instantiation, which makes + * each launch behave as though it called ::cuMemFreeAsync for every unfreed allocation. + * + * It is not possible to free an allocation in both the owning graph and another graph. If the allocation + * is freed in the same graph, a free node cannot be added to another graph. If the allocation is freed + * in another graph, a free node can no longer be added to the owning graph. + * + * The following restrictions apply to graphs which contain allocation and/or memory free nodes: + * - Nodes and edges of the graph cannot be deleted. + * - The graph cannot be used in a child node. + * - Only one instantiation of the graph may exist at any point in time. + * - The graph cannot be cloned. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddMemFreeNode, + * ::cuGraphMemAllocNodeGetParams, + * ::cuDeviceGraphMemTrim, + * ::cuDeviceGetGraphMemAttribute, + * ::cuDeviceSetGraphMemAttribute, + * ::cuMemAllocAsync, + * ::cuMemFreeAsync, + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddEventRecordNode, + * ::cuGraphAddEventWaitNode, + * ::cuGraphAddExternalSemaphoresSignalNode, + * ::cuGraphAddExternalSemaphoresWaitNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode + */ +CUresult CUDAAPI cuGraphAddMemAllocNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUDA_MEM_ALLOC_NODE_PARAMS *nodeParams); + +/** + * \brief Returns a memory alloc node's parameters + * + * Returns the parameters of a memory alloc node \p hNode in \p params_out. + * The \p poolProps and \p accessDescs returned in \p params_out, are owned by the + * node. This memory remains valid until the node is destroyed. The returned + * parameters must not be modified. + * + * \param hNode - Node to get the parameters for + * \param params_out - Pointer to return the parameters + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddMemAllocNode, + * ::cuGraphMemFreeNodeGetParams + */ +CUresult CUDAAPI cuGraphMemAllocNodeGetParams(CUgraphNode hNode, CUDA_MEM_ALLOC_NODE_PARAMS *params_out); + +/** + * \brief Creates a memory free node and adds it to a graph + * + * Creates a new memory free node and adds it to \p hGraph with \p numDependencies + * dependencies specified via \p dependencies and arguments specified in \p nodeParams. + * It is possible for \p numDependencies to be 0, in which case the node will be placed + * at the root of the graph. \p dependencies may not have any duplicate entries. A handle + * to the new node will be returned in \p phGraphNode. + * + * \param phGraphNode - Returns newly created node + * \param hGraph - Graph to which to add the node + * \param dependencies - Dependencies of the node + * \param numDependencies - Number of dependencies + * \param dptr - Address of memory to free + * + * ::cuGraphAddMemFreeNode will return ::CUDA_ERROR_INVALID_VALUE if the user attempts to free: + * - an allocation twice in the same graph. + * - an address that was not returned by an allocation node. + * - an invalid address. + * + * The following restrictions apply to graphs which contain allocation and/or memory free nodes: + * - Nodes and edges of the graph cannot be deleted. + * - The graph cannot be used in a child node. + * - Only one instantiation of the graph may exist at any point in time. + * - The graph cannot be cloned. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_NOT_SUPPORTED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddMemAllocNode, + * ::cuGraphMemFreeNodeGetParams, + * ::cuDeviceGraphMemTrim, + * ::cuDeviceGetGraphMemAttribute, + * ::cuDeviceSetGraphMemAttribute, + * ::cuMemAllocAsync, + * ::cuMemFreeAsync, + * ::cuGraphCreate, + * ::cuGraphDestroyNode, + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddEventRecordNode, + * ::cuGraphAddEventWaitNode, + * ::cuGraphAddExternalSemaphoresSignalNode, + * ::cuGraphAddExternalSemaphoresWaitNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode + */ +CUresult CUDAAPI cuGraphAddMemFreeNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, CUdeviceptr dptr); + +/** + * \brief Returns a memory free node's parameters + * + * Returns the address of a memory free node \p hNode in \p dptr_out. + * + * \param hNode - Node to get the parameters for + * \param dptr_out - Pointer to return the device address + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddMemFreeNode, + * ::cuGraphMemAllocNodeGetParams + */ +CUresult CUDAAPI cuGraphMemFreeNodeGetParams(CUgraphNode hNode, CUdeviceptr *dptr_out); + +/** + * \brief Free unused memory that was cached on the specified device for use with graphs back to the OS. + * + * Blocks which are not in use by a graph that is either currently executing or scheduled to execute are + * freed back to the operating system. + * + * \param device - The device for which cached memory should be freed. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_DEVICE + * + * \sa + * ::cuGraphAddMemAllocNode, + * ::cuGraphAddMemFreeNode, + * ::cuDeviceSetGraphMemAttribute, + * ::cuDeviceGetGraphMemAttribute + */ +CUresult CUDAAPI cuDeviceGraphMemTrim(CUdevice device); + +/** + * \brief Query asynchronous allocation attributes related to graphs + * + * Valid attributes are: + * + * - ::CU_GRAPH_MEM_ATTR_USED_MEM_CURRENT: Amount of memory, in bytes, currently associated with graphs + * - ::CU_GRAPH_MEM_ATTR_USED_MEM_HIGH: High watermark of memory, in bytes, associated with graphs since the + * last time it was reset. High watermark can only be reset to zero. + * - ::CU_GRAPH_MEM_ATTR_RESERVED_MEM_CURRENT: Amount of memory, in bytes, currently allocated for use by + * the CUDA graphs asynchronous allocator. + * - ::CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH: High watermark of memory, in bytes, currently allocated for use by + * the CUDA graphs asynchronous allocator. + * + * \param device - Specifies the scope of the query + * \param attr - attribute to get + * \param value - retrieved value + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_DEVICE + * + * \sa + * ::cuDeviceSetGraphMemAttribute, + * ::cuGraphAddMemAllocNode, + * ::cuGraphAddMemFreeNode + */ +CUresult CUDAAPI cuDeviceGetGraphMemAttribute(CUdevice device, CUgraphMem_attribute attr, void* value); + +/** + * \brief Set asynchronous allocation attributes related to graphs + * + * Valid attributes are: + * + * - ::CU_GRAPH_MEM_ATTR_USED_MEM_HIGH: High watermark of memory, in bytes, associated with graphs since the + * last time it was reset. High watermark can only be reset to zero. + * - ::CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH: High watermark of memory, in bytes, currently allocated for use by + * the CUDA graphs asynchronous allocator. + * + * \param device - Specifies the scope of the query + * \param attr - attribute to get + * \param value - pointer to value to set + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_DEVICE + * + * \sa + * ::cuDeviceGetGraphMemAttribute, + * ::cuGraphAddMemAllocNode, + * ::cuGraphAddMemFreeNode + */ +CUresult CUDAAPI cuDeviceSetGraphMemAttribute(CUdevice device, CUgraphMem_attribute attr, void* value); + +/** + * \brief Clones a graph + * + * This function creates a copy of \p originalGraph and returns it in \p phGraphClone. + * All parameters are copied into the cloned graph. The original graph may be modified + * after this call without affecting the clone. + * + * Child graph nodes in the original graph are recursively copied into the clone. + * + * \param phGraphClone - Returns newly created cloned graph + * \param originalGraph - Graph to clone + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OUT_OF_MEMORY + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphCreate, + * ::cuGraphNodeFindInClone + */ +CUresult CUDAAPI cuGraphClone(CUgraph *phGraphClone, CUgraph originalGraph); + +/** + * \brief Finds a cloned version of a node + * + * This function returns the node in \p hClonedGraph corresponding to \p hOriginalNode + * in the original graph. + * + * \p hClonedGraph must have been cloned from \p hOriginalGraph via ::cuGraphClone. + * \p hOriginalNode must have been in \p hOriginalGraph at the time of the call to + * ::cuGraphClone, and the corresponding cloned node in \p hClonedGraph must not have + * been removed. The cloned node is then returned via \p phClonedNode. + * + * \param phNode - Returns handle to the cloned node + * \param hOriginalNode - Handle to the original node + * \param hClonedGraph - Cloned graph to query + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphClone + */ +CUresult CUDAAPI cuGraphNodeFindInClone(CUgraphNode *phNode, CUgraphNode hOriginalNode, CUgraph hClonedGraph); + +/** + * \brief Returns a node's type + * + * Returns the node type of \p hNode in \p type. + * + * \param hNode - Node to query + * \param type - Pointer to return the node type + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphGetNodes, + * ::cuGraphGetRootNodes, + * ::cuGraphChildGraphNodeGetGraph, + * ::cuGraphKernelNodeGetParams, + * ::cuGraphKernelNodeSetParams, + * ::cuGraphHostNodeGetParams, + * ::cuGraphHostNodeSetParams, + * ::cuGraphMemcpyNodeGetParams, + * ::cuGraphMemcpyNodeSetParams, + * ::cuGraphMemsetNodeGetParams, + * ::cuGraphMemsetNodeSetParams + */ +CUresult CUDAAPI cuGraphNodeGetType(CUgraphNode hNode, CUgraphNodeType *type); + +/** + * \brief Returns a graph's nodes + * + * Returns a list of \p hGraph's nodes. \p nodes may be NULL, in which case this + * function will return the number of nodes in \p numNodes. Otherwise, + * \p numNodes entries will be filled in. If \p numNodes is higher than the actual + * number of nodes, the remaining entries in \p nodes will be set to NULL, and the + * number of nodes actually obtained will be returned in \p numNodes. + * + * \param hGraph - Graph to query + * \param nodes - Pointer to return the nodes + * \param numNodes - See description + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphCreate, + * ::cuGraphGetRootNodes, + * ::cuGraphGetEdges, + * ::cuGraphNodeGetType, + * ::cuGraphNodeGetDependencies, + * ::cuGraphNodeGetDependentNodes + */ +CUresult CUDAAPI cuGraphGetNodes(CUgraph hGraph, CUgraphNode *nodes, size_t *numNodes); + +/** + * \brief Returns a graph's root nodes + * + * Returns a list of \p hGraph's root nodes. \p rootNodes may be NULL, in which case this + * function will return the number of root nodes in \p numRootNodes. Otherwise, + * \p numRootNodes entries will be filled in. If \p numRootNodes is higher than the actual + * number of root nodes, the remaining entries in \p rootNodes will be set to NULL, and the + * number of nodes actually obtained will be returned in \p numRootNodes. + * + * \param hGraph - Graph to query + * \param rootNodes - Pointer to return the root nodes + * \param numRootNodes - See description + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphCreate, + * ::cuGraphGetNodes, + * ::cuGraphGetEdges, + * ::cuGraphNodeGetType, + * ::cuGraphNodeGetDependencies, + * ::cuGraphNodeGetDependentNodes + */ +CUresult CUDAAPI cuGraphGetRootNodes(CUgraph hGraph, CUgraphNode *rootNodes, size_t *numRootNodes); + +/** + * \brief Returns a graph's dependency edges + * + * Returns a list of \p hGraph's dependency edges. Edges are returned via corresponding + * indices in \p from and \p to; that is, the node in \p to[i] has a dependency on the + * node in \p from[i]. \p from and \p to may both be NULL, in which + * case this function only returns the number of edges in \p numEdges. Otherwise, + * \p numEdges entries will be filled in. If \p numEdges is higher than the actual + * number of edges, the remaining entries in \p from and \p to will be set to NULL, and + * the number of edges actually returned will be written to \p numEdges. + * + * \param hGraph - Graph to get the edges from + * \param from - Location to return edge endpoints + * \param to - Location to return edge endpoints + * \param numEdges - See description + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphGetNodes, + * ::cuGraphGetRootNodes, + * ::cuGraphAddDependencies, + * ::cuGraphRemoveDependencies, + * ::cuGraphNodeGetDependencies, + * ::cuGraphNodeGetDependentNodes + */ +CUresult CUDAAPI cuGraphGetEdges(CUgraph hGraph, CUgraphNode *from, CUgraphNode *to, size_t *numEdges); + +/** + * \brief Returns a node's dependencies + * + * Returns a list of \p node's dependencies. \p dependencies may be NULL, in which case this + * function will return the number of dependencies in \p numDependencies. Otherwise, + * \p numDependencies entries will be filled in. If \p numDependencies is higher than the actual + * number of dependencies, the remaining entries in \p dependencies will be set to NULL, and the + * number of nodes actually obtained will be returned in \p numDependencies. + * + * \param hNode - Node to query + * \param dependencies - Pointer to return the dependencies + * \param numDependencies - See description + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphNodeGetDependentNodes, + * ::cuGraphGetNodes, + * ::cuGraphGetRootNodes, + * ::cuGraphGetEdges, + * ::cuGraphAddDependencies, + * ::cuGraphRemoveDependencies + */ +CUresult CUDAAPI cuGraphNodeGetDependencies(CUgraphNode hNode, CUgraphNode *dependencies, size_t *numDependencies); + +/** + * \brief Returns a node's dependent nodes + * + * Returns a list of \p node's dependent nodes. \p dependentNodes may be NULL, in which + * case this function will return the number of dependent nodes in \p numDependentNodes. + * Otherwise, \p numDependentNodes entries will be filled in. If \p numDependentNodes is + * higher than the actual number of dependent nodes, the remaining entries in + * \p dependentNodes will be set to NULL, and the number of nodes actually obtained will + * be returned in \p numDependentNodes. + * + * \param hNode - Node to query + * \param dependentNodes - Pointer to return the dependent nodes + * \param numDependentNodes - See description + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphNodeGetDependencies, + * ::cuGraphGetNodes, + * ::cuGraphGetRootNodes, + * ::cuGraphGetEdges, + * ::cuGraphAddDependencies, + * ::cuGraphRemoveDependencies + */ +CUresult CUDAAPI cuGraphNodeGetDependentNodes(CUgraphNode hNode, CUgraphNode *dependentNodes, size_t *numDependentNodes); + +/** + * \brief Adds dependency edges to a graph + * + * The number of dependencies to be added is defined by \p numDependencies + * Elements in \p from and \p to at corresponding indices define a dependency. + * Each node in \p from and \p to must belong to \p hGraph. + * + * If \p numDependencies is 0, elements in \p from and \p to will be ignored. + * Specifying an existing dependency will return an error. + * + * \param hGraph - Graph to which dependencies are added + * \param from - Array of nodes that provide the dependencies + * \param to - Array of dependent nodes + * \param numDependencies - Number of dependencies to be added + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphRemoveDependencies, + * ::cuGraphGetEdges, + * ::cuGraphNodeGetDependencies, + * ::cuGraphNodeGetDependentNodes + */ +CUresult CUDAAPI cuGraphAddDependencies(CUgraph hGraph, const CUgraphNode *from, const CUgraphNode *to, size_t numDependencies); + +/** + * \brief Removes dependency edges from a graph + * + * The number of \p dependencies to be removed is defined by \p numDependencies. + * Elements in \p from and \p to at corresponding indices define a dependency. + * Each node in \p from and \p to must belong to \p hGraph. + * + * If \p numDependencies is 0, elements in \p from and \p to will be ignored. + * Specifying a non-existing dependency will return an error. + * + * Dependencies cannot be removed from graphs which contain allocation or free nodes. + * Any attempt to do so will return an error. + * + * \param hGraph - Graph from which to remove dependencies + * \param from - Array of nodes that provide the dependencies + * \param to - Array of dependent nodes + * \param numDependencies - Number of dependencies to be removed + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddDependencies, + * ::cuGraphGetEdges, + * ::cuGraphNodeGetDependencies, + * ::cuGraphNodeGetDependentNodes + */ +CUresult CUDAAPI cuGraphRemoveDependencies(CUgraph hGraph, const CUgraphNode *from, const CUgraphNode *to, size_t numDependencies); + +/** + * \brief Remove a node from the graph + * + * Removes \p hNode from its graph. This operation also severs any dependencies of other nodes + * on \p hNode and vice versa. + * + * Nodes which belong to a graph which contains allocation or free nodes cannot be destroyed. + * Any attempt to do so will return an error. + * + * \param hNode - Node to remove + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddChildGraphNode, + * ::cuGraphAddEmptyNode, + * ::cuGraphAddKernelNode, + * ::cuGraphAddHostNode, + * ::cuGraphAddMemcpyNode, + * ::cuGraphAddMemsetNode + */ +CUresult CUDAAPI cuGraphDestroyNode(CUgraphNode hNode); + +/** + * \brief Creates an executable graph from a graph + * + * Instantiates \p hGraph as an executable graph. The graph is validated for any + * structural constraints or intra-node constraints which were not previously + * validated. If instantiation is successful, a handle to the instantiated graph + * is returned in \p phGraphExec. + * + * The \p flags parameter controls the behavior of instantiation and subsequent + * graph launches. Valid flags are: + * + * - ::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH, which configures a + * graph containing memory allocation nodes to automatically free any + * unfreed memory allocations before the graph is relaunched. + * + * - ::CUDA_GRAPH_INSTANTIATE_FLAG_DEVICE_LAUNCH, which configures the graph for launch + * from the device. If this flag is passed, the executable graph handle returned can be + * used to launch the graph from both the host and device. This flag can only be used + * on platforms which support unified addressing. This flag cannot be used in + * conjunction with ::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH. + * + * - ::CUDA_GRAPH_INSTANTIATE_FLAG_USE_NODE_PRIORITY, which causes the graph + * to use the priorities from the per-node attributes rather than the priority + * of the launch stream during execution. Note that priorities are only available + * on kernel nodes, and are copied from stream priority during stream capture. + * + * If \p hGraph contains any allocation or free nodes, there can be at most one + * executable graph in existence for that graph at a time. An attempt to instantiate + * a second executable graph before destroying the first with ::cuGraphExecDestroy + * will result in an error. + * + * If \p hGraph contains kernels which call device-side cudaGraphLaunch() from multiple + * contexts, this will result in an error. + * + * Graphs instantiated for launch on the device have additional restrictions which do not + * apply to host graphs: + * + * - The graph's nodes must reside on a single context. + * - The graph can only contain kernel nodes, memcpy nodes, memset nodes, and child graph nodes. + * Operation-specific restrictions are outlined below. + * - Kernel nodes: + * - Use of CUDA Dynamic Parallelism is not permitted. + * - Cooperative launches are permitted as long as MPS is not in use. + * - Memcpy nodes: + * - Only copies involving device memory and/or pinned device-mapped host memory are permitted. + * - Copies involving CUDA arrays are not permitted. + * - Both operands must be accessible from the current context, and the current context must + * match the context of other nodes in the graph. + * + * \param phGraphExec - Returns instantiated graph + * \param hGraph - Graph to instantiate + * \param flags - Flags to control instantiation. See ::CUgraphInstantiate_flags. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphInstantiate, + * ::cuGraphCreate, + * ::cuGraphUpload, + * ::cuGraphLaunch, + * ::cuGraphExecDestroy + */ +CUresult CUDAAPI cuGraphInstantiate(CUgraphExec *phGraphExec, CUgraph hGraph, unsigned long long flags); + +/** + * \brief Creates an executable graph from a graph + * + * Instantiates \p hGraph as an executable graph according to the \p instantiateParams structure. + * The graph is validated for any structural constraints or intra-node constraints + * which were not previously validated. If instantiation is successful, a handle to + * the instantiated graph is returned in \p phGraphExec. + * + * \p instantiateParams controls the behavior of instantiation and subsequent + * graph launches, as well as returning more detailed information in the event of an error. + * ::CUDA_GRAPH_INSTANTIATE_PARAMS is defined as: + * + * \code + typedef struct { + cuuint64_t flags; + CUstream hUploadStream; + CUgraphNode hErrNode_out; + CUgraphInstantiateResult result_out; + } CUDA_GRAPH_INSTANTIATE_PARAMS; + * \endcode + * + * The \p flags field controls the behavior of instantiation and subsequent + * graph launches. Valid flags are: + * + * - ::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH, which configures a + * graph containing memory allocation nodes to automatically free any + * unfreed memory allocations before the graph is relaunched. + * + * - ::CUDA_GRAPH_INSTANTIATE_FLAG_UPLOAD, which will perform an upload of the graph + * into \p hUploadStream once the graph has been instantiated. + * + * - ::CUDA_GRAPH_INSTANTIATE_FLAG_DEVICE_LAUNCH, which configures the graph for launch + * from the device. If this flag is passed, the executable graph handle returned can be + * used to launch the graph from both the host and device. This flag can only be used + * on platforms which support unified addressing. This flag cannot be used in + * conjunction with ::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH. + * + * - ::CUDA_GRAPH_INSTANTIATE_FLAG_USE_NODE_PRIORITY, which causes the graph + * to use the priorities from the per-node attributes rather than the priority + * of the launch stream during execution. Note that priorities are only available + * on kernel nodes, and are copied from stream priority during stream capture. + * + * If \p hGraph contains any allocation or free nodes, there can be at most one + * executable graph in existence for that graph at a time. An attempt to instantiate a + * second executable graph before destroying the first with ::cuGraphExecDestroy will + * result in an error. + * + * If \p hGraph contains kernels which call device-side cudaGraphLaunch() from multiple + * contexts, this will result in an error. + * + * Graphs instantiated for launch on the device have additional restrictions which do not + * apply to host graphs: + * + * - The graph's nodes must reside on a single context. + * - The graph can only contain kernel nodes, memcpy nodes, memset nodes, and child graph nodes. + * Operation-specific restrictions are outlined below. + * - Kernel nodes: + * - Use of CUDA Dynamic Parallelism is not permitted. + * - Cooperative launches are permitted as long as MPS is not in use. + * - Memcpy nodes: + * - Only copies involving device memory and/or pinned device-mapped host memory are permitted. + * - Copies involving CUDA arrays are not permitted. + * - Both operands must be accessible from the current context, and the current context must + * match the context of other nodes in the graph. + * + * In the event of an error, the \p result_out and \p hErrNode_out fields will contain more + * information about the nature of the error. Possible error reporting includes: + * + * - ::CUDA_GRAPH_INSTANTIATE_ERROR, if passed an invalid value or if an unexpected error occurred + * which is described by the return value of the function. \p hErrNode_out will be set to NULL. + * - ::CUDA_GRAPH_INSTANTIATE_INVALID_STRUCTURE, if the graph structure is invalid. \p hErrNode_out + * will be set to one of the offending nodes. + * - ::CUDA_GRAPH_INSTANTIATE_NODE_OPERATION_NOT_SUPPORTED, if the graph is instantiated for device + * launch but contains a node of an unsupported node type, or a node which performs unsupported + * operations, such as use of CUDA dynamic parallelism within a kernel node. \p hErrNode_out will + * be set to this node. + * - ::CUDA_GRAPH_INSTANTIATE_MULTIPLE_CTXS_NOT_SUPPORTED, if the graph is instantiated for device + * launch but a node’s context differs from that of another node. This error can also be returned + * if a graph is not instantiated for device launch and it contains kernels which call device-side + * cudaGraphLaunch() from multiple contexts. \p hErrNode_out will be set to this node. + * + * If instantiation is successful, \p result_out will be set to ::CUDA_GRAPH_INSTANTIATE_SUCCESS, + * and \p hErrNode_out will be set to NULL. + * + * \param phGraphExec - Returns instantiated graph + * \param hGraph - Graph to instantiate + * \param instantiateParams - Instantiation parameters + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphCreate, + * ::cuGraphInstantiate, + * ::cuGraphExecDestroy + */ +CUresult CUDAAPI cuGraphInstantiateWithParams(CUgraphExec *phGraphExec, CUgraph hGraph, CUDA_GRAPH_INSTANTIATE_PARAMS *instantiateParams); + +/** + * \brief Query the instantiation flags of an executable graph + * + * Returns the flags that were passed to instantiation for the given executable graph. + * ::CUDA_GRAPH_INSTANTIATE_FLAG_UPLOAD will not be returned by this API as it does + * not affect the resulting executable graph. + * + * \param hGraphExec - The executable graph to query + * \param flags - Returns the instantiation flags + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphInstantiate, + * ::cuGraphInstantiateWithParams + */ +CUresult CUDAAPI cuGraphExecGetFlags(CUgraphExec hGraphExec, cuuint64_t *flags); + +/** + * \brief Sets the parameters for a kernel node in the given graphExec + * + * Sets the parameters of a kernel node in an executable graph \p hGraphExec. + * The node is identified by the corresponding node \p hNode in the + * non-executable graph, from which the executable graph was instantiated. + * + * \p hNode must not have been removed from the original graph. All \p nodeParams + * fields may change, but the following restrictions apply to \p func updates: + * + * - The owning context of the function cannot change. + * - A node whose function originally did not use CUDA dynamic parallelism cannot be updated + * to a function which uses CDP + * - If \p hGraphExec was not instantiated for device launch, a node whose function originally + * did not use device-side cudaGraphLaunch() cannot be updated to a function which uses + * device-side cudaGraphLaunch() unless the node resides on the same context as nodes which + * contained such calls at instantiate-time. If no such calls were present at instantiation, + * these updates cannot be performed at all. + * + * The modifications only affect future launches of \p hGraphExec. Already + * enqueued or running launches of \p hGraphExec are not affected by this call. + * \p hNode is also not modified by this call. + * + * \param hGraphExec - The executable graph in which to set the specified node + * \param hNode - kernel node from the graph from which graphExec was instantiated + * \param nodeParams - Updated Parameters to set + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddKernelNode, + * ::cuGraphKernelNodeSetParams, + * ::cuGraphExecMemcpyNodeSetParams, + * ::cuGraphExecMemsetNodeSetParams, + * ::cuGraphExecHostNodeSetParams, + * ::cuGraphExecChildGraphNodeSetParams, + * ::cuGraphExecEventRecordNodeSetEvent, + * ::cuGraphExecEventWaitNodeSetEvent, + * ::cuGraphExecExternalSemaphoresSignalNodeSetParams, + * ::cuGraphExecExternalSemaphoresWaitNodeSetParams, + * ::cuGraphExecUpdate, + * ::cuGraphInstantiate + */ +CUresult CUDAAPI cuGraphExecKernelNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS *nodeParams); + +/** + * \brief Sets the parameters for a memcpy node in the given graphExec. + * + * Updates the work represented by \p hNode in \p hGraphExec as though \p hNode had + * contained \p copyParams at instantiation. hNode must remain in the graph which was + * used to instantiate \p hGraphExec. Changed edges to and from hNode are ignored. + * + * The source and destination memory in \p copyParams must be allocated from the same + * contexts as the original source and destination memory. Both the instantiation-time + * memory operands and the memory operands in \p copyParams must be 1-dimensional. + * Zero-length operations are not supported. + * + * The modifications only affect future launches of \p hGraphExec. Already enqueued + * or running launches of \p hGraphExec are not affected by this call. hNode is also + * not modified by this call. + * + * Returns CUDA_ERROR_INVALID_VALUE if the memory operands' mappings changed or + * either the original or new memory operands are multidimensional. + * + * \param hGraphExec - The executable graph in which to set the specified node + * \param hNode - Memcpy node from the graph which was used to instantiate graphExec + * \param copyParams - The updated parameters to set + * \param ctx - Context on which to run the node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddMemcpyNode, + * ::cuGraphMemcpyNodeSetParams, + * ::cuGraphExecKernelNodeSetParams, + * ::cuGraphExecMemsetNodeSetParams, + * ::cuGraphExecHostNodeSetParams, + * ::cuGraphExecChildGraphNodeSetParams, + * ::cuGraphExecEventRecordNodeSetEvent, + * ::cuGraphExecEventWaitNodeSetEvent, + * ::cuGraphExecExternalSemaphoresSignalNodeSetParams, + * ::cuGraphExecExternalSemaphoresWaitNodeSetParams, + * ::cuGraphExecUpdate, + * ::cuGraphInstantiate + */ +CUresult CUDAAPI cuGraphExecMemcpyNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_MEMCPY3D *copyParams, CUcontext ctx); + +/** + * \brief Sets the parameters for a memset node in the given graphExec. + * + * Updates the work represented by \p hNode in \p hGraphExec as though \p hNode had + * contained \p memsetParams at instantiation. hNode must remain in the graph which was + * used to instantiate \p hGraphExec. Changed edges to and from hNode are ignored. + * + * The destination memory in \p memsetParams must be allocated from the same + * contexts as the original destination memory. Both the instantiation-time + * memory operand and the memory operand in \p memsetParams must be 1-dimensional. + * Zero-length operations are not supported. + * + * The modifications only affect future launches of \p hGraphExec. Already enqueued + * or running launches of \p hGraphExec are not affected by this call. hNode is also + * not modified by this call. + * + * Returns CUDA_ERROR_INVALID_VALUE if the memory operand's mappings changed or + * either the original or new memory operand are multidimensional. + * + * \param hGraphExec - The executable graph in which to set the specified node + * \param hNode - Memset node from the graph which was used to instantiate graphExec + * \param memsetParams - The updated parameters to set + * \param ctx - Context on which to run the node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddMemsetNode, + * ::cuGraphMemsetNodeSetParams, + * ::cuGraphExecKernelNodeSetParams, + * ::cuGraphExecMemcpyNodeSetParams, + * ::cuGraphExecHostNodeSetParams, + * ::cuGraphExecChildGraphNodeSetParams, + * ::cuGraphExecEventRecordNodeSetEvent, + * ::cuGraphExecEventWaitNodeSetEvent, + * ::cuGraphExecExternalSemaphoresSignalNodeSetParams, + * ::cuGraphExecExternalSemaphoresWaitNodeSetParams, + * ::cuGraphExecUpdate, + * ::cuGraphInstantiate + */ +CUresult CUDAAPI cuGraphExecMemsetNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_MEMSET_NODE_PARAMS *memsetParams, CUcontext ctx); + +/** + * \brief Sets the parameters for a host node in the given graphExec. + * + * Updates the work represented by \p hNode in \p hGraphExec as though \p hNode had + * contained \p nodeParams at instantiation. hNode must remain in the graph which was + * used to instantiate \p hGraphExec. Changed edges to and from hNode are ignored. + * + * The modifications only affect future launches of \p hGraphExec. Already enqueued + * or running launches of \p hGraphExec are not affected by this call. hNode is also + * not modified by this call. + * + * \param hGraphExec - The executable graph in which to set the specified node + * \param hNode - Host node from the graph which was used to instantiate graphExec + * \param nodeParams - The updated parameters to set + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddHostNode, + * ::cuGraphHostNodeSetParams, + * ::cuGraphExecKernelNodeSetParams, + * ::cuGraphExecMemcpyNodeSetParams, + * ::cuGraphExecMemsetNodeSetParams, + * ::cuGraphExecChildGraphNodeSetParams, + * ::cuGraphExecEventRecordNodeSetEvent, + * ::cuGraphExecEventWaitNodeSetEvent, + * ::cuGraphExecExternalSemaphoresSignalNodeSetParams, + * ::cuGraphExecExternalSemaphoresWaitNodeSetParams, + * ::cuGraphExecUpdate, + * ::cuGraphInstantiate + */ +CUresult CUDAAPI cuGraphExecHostNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_HOST_NODE_PARAMS *nodeParams); + +/** + * \brief Updates node parameters in the child graph node in the given graphExec. + * + * Updates the work represented by \p hNode in \p hGraphExec as though the nodes contained + * in \p hNode's graph had the parameters contained in \p childGraph's nodes at instantiation. + * \p hNode must remain in the graph which was used to instantiate \p hGraphExec. + * Changed edges to and from \p hNode are ignored. + * + * The modifications only affect future launches of \p hGraphExec. Already enqueued + * or running launches of \p hGraphExec are not affected by this call. \p hNode is also + * not modified by this call. + * + * The topology of \p childGraph, as well as the node insertion order, must match that + * of the graph contained in \p hNode. See ::cuGraphExecUpdate() for a list of restrictions + * on what can be updated in an instantiated graph. The update is recursive, so child graph + * nodes contained within the top level child graph will also be updated. + * + * \param hGraphExec - The executable graph in which to set the specified node + * \param hNode - Host node from the graph which was used to instantiate graphExec + * \param childGraph - The graph supplying the updated parameters + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddChildGraphNode, + * ::cuGraphChildGraphNodeGetGraph, + * ::cuGraphExecKernelNodeSetParams, + * ::cuGraphExecMemcpyNodeSetParams, + * ::cuGraphExecMemsetNodeSetParams, + * ::cuGraphExecHostNodeSetParams, + * ::cuGraphExecEventRecordNodeSetEvent, + * ::cuGraphExecEventWaitNodeSetEvent, + * ::cuGraphExecExternalSemaphoresSignalNodeSetParams, + * ::cuGraphExecExternalSemaphoresWaitNodeSetParams, + * ::cuGraphExecUpdate, + * ::cuGraphInstantiate + */ +CUresult CUDAAPI cuGraphExecChildGraphNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, CUgraph childGraph); + +/** + * \brief Sets the event for an event record node in the given graphExec + * + * Sets the event of an event record node in an executable graph \p hGraphExec. + * The node is identified by the corresponding node \p hNode in the + * non-executable graph, from which the executable graph was instantiated. + * + * The modifications only affect future launches of \p hGraphExec. Already + * enqueued or running launches of \p hGraphExec are not affected by this call. + * \p hNode is also not modified by this call. + * + * \param hGraphExec - The executable graph in which to set the specified node + * \param hNode - event record node from the graph from which graphExec was instantiated + * \param event - Updated event to use + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddEventRecordNode, + * ::cuGraphEventRecordNodeGetEvent, + * ::cuGraphEventWaitNodeSetEvent, + * ::cuEventRecordWithFlags, + * ::cuStreamWaitEvent, + * ::cuGraphExecKernelNodeSetParams, + * ::cuGraphExecMemcpyNodeSetParams, + * ::cuGraphExecMemsetNodeSetParams, + * ::cuGraphExecHostNodeSetParams, + * ::cuGraphExecChildGraphNodeSetParams, + * ::cuGraphExecEventWaitNodeSetEvent, + * ::cuGraphExecExternalSemaphoresSignalNodeSetParams, + * ::cuGraphExecExternalSemaphoresWaitNodeSetParams, + * ::cuGraphExecUpdate, + * ::cuGraphInstantiate + */ +CUresult CUDAAPI cuGraphExecEventRecordNodeSetEvent(CUgraphExec hGraphExec, CUgraphNode hNode, CUevent event); + +/** + * \brief Sets the event for an event wait node in the given graphExec + * + * Sets the event of an event wait node in an executable graph \p hGraphExec. + * The node is identified by the corresponding node \p hNode in the + * non-executable graph, from which the executable graph was instantiated. + * + * The modifications only affect future launches of \p hGraphExec. Already + * enqueued or running launches of \p hGraphExec are not affected by this call. + * \p hNode is also not modified by this call. + * + * \param hGraphExec - The executable graph in which to set the specified node + * \param hNode - event wait node from the graph from which graphExec was instantiated + * \param event - Updated event to use + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddEventWaitNode, + * ::cuGraphEventWaitNodeGetEvent, + * ::cuGraphEventRecordNodeSetEvent, + * ::cuEventRecordWithFlags, + * ::cuStreamWaitEvent, + * ::cuGraphExecKernelNodeSetParams, + * ::cuGraphExecMemcpyNodeSetParams, + * ::cuGraphExecMemsetNodeSetParams, + * ::cuGraphExecHostNodeSetParams, + * ::cuGraphExecChildGraphNodeSetParams, + * ::cuGraphExecEventRecordNodeSetEvent, + * ::cuGraphExecExternalSemaphoresSignalNodeSetParams, + * ::cuGraphExecExternalSemaphoresWaitNodeSetParams, + * ::cuGraphExecUpdate, + * ::cuGraphInstantiate + */ +CUresult CUDAAPI cuGraphExecEventWaitNodeSetEvent(CUgraphExec hGraphExec, CUgraphNode hNode, CUevent event); + +/** + * \brief Sets the parameters for an external semaphore signal node in the given graphExec + * + * Sets the parameters of an external semaphore signal node in an executable graph \p hGraphExec. + * The node is identified by the corresponding node \p hNode in the + * non-executable graph, from which the executable graph was instantiated. + * + * \p hNode must not have been removed from the original graph. + * + * The modifications only affect future launches of \p hGraphExec. Already + * enqueued or running launches of \p hGraphExec are not affected by this call. + * \p hNode is also not modified by this call. + * + * Changing \p nodeParams->numExtSems is not supported. + * + * \param hGraphExec - The executable graph in which to set the specified node + * \param hNode - semaphore signal node from the graph from which graphExec was instantiated + * \param nodeParams - Updated Parameters to set + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddExternalSemaphoresSignalNode, + * ::cuImportExternalSemaphore, + * ::cuSignalExternalSemaphoresAsync, + * ::cuWaitExternalSemaphoresAsync, + * ::cuGraphExecKernelNodeSetParams, + * ::cuGraphExecMemcpyNodeSetParams, + * ::cuGraphExecMemsetNodeSetParams, + * ::cuGraphExecHostNodeSetParams, + * ::cuGraphExecChildGraphNodeSetParams, + * ::cuGraphExecEventRecordNodeSetEvent, + * ::cuGraphExecEventWaitNodeSetEvent, + * ::cuGraphExecExternalSemaphoresWaitNodeSetParams, + * ::cuGraphExecUpdate, + * ::cuGraphInstantiate + */ +CUresult CUDAAPI cuGraphExecExternalSemaphoresSignalNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS *nodeParams); + +/** + * \brief Sets the parameters for an external semaphore wait node in the given graphExec + * + * Sets the parameters of an external semaphore wait node in an executable graph \p hGraphExec. + * The node is identified by the corresponding node \p hNode in the + * non-executable graph, from which the executable graph was instantiated. + * + * \p hNode must not have been removed from the original graph. + * + * The modifications only affect future launches of \p hGraphExec. Already + * enqueued or running launches of \p hGraphExec are not affected by this call. + * \p hNode is also not modified by this call. + * + * Changing \p nodeParams->numExtSems is not supported. + * + * \param hGraphExec - The executable graph in which to set the specified node + * \param hNode - semaphore wait node from the graph from which graphExec was instantiated + * \param nodeParams - Updated Parameters to set + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphAddExternalSemaphoresWaitNode, + * ::cuImportExternalSemaphore, + * ::cuSignalExternalSemaphoresAsync, + * ::cuWaitExternalSemaphoresAsync, + * ::cuGraphExecKernelNodeSetParams, + * ::cuGraphExecMemcpyNodeSetParams, + * ::cuGraphExecMemsetNodeSetParams, + * ::cuGraphExecHostNodeSetParams, + * ::cuGraphExecChildGraphNodeSetParams, + * ::cuGraphExecEventRecordNodeSetEvent, + * ::cuGraphExecEventWaitNodeSetEvent, + * ::cuGraphExecExternalSemaphoresSignalNodeSetParams, + * ::cuGraphExecUpdate, + * ::cuGraphInstantiate + */ +CUresult CUDAAPI cuGraphExecExternalSemaphoresWaitNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_EXT_SEM_WAIT_NODE_PARAMS *nodeParams); + +/** + * \brief Enables or disables the specified node in the given graphExec + * + * Sets \p hNode to be either enabled or disabled. Disabled nodes are functionally equivalent + * to empty nodes until they are reenabled. Existing node parameters are not affected by + * disabling/enabling the node. + * + * The node is identified by the corresponding node \p hNode in the non-executable + * graph, from which the executable graph was instantiated. + * + * \p hNode must not have been removed from the original graph. + * + * The modifications only affect future launches of \p hGraphExec. Already + * enqueued or running launches of \p hGraphExec are not affected by this call. + * \p hNode is also not modified by this call. + * + * \note Currently only kernel, memset and memcpy nodes are supported. + * + * \param hGraphExec - The executable graph in which to set the specified node + * \param hNode - Node from the graph from which graphExec was instantiated + * \param isEnabled - Node is enabled if != 0, otherwise the node is disabled + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphNodeGetEnabled, + * ::cuGraphExecUpdate, + * ::cuGraphInstantiate + * ::cuGraphLaunch + */ +CUresult CUDAAPI cuGraphNodeSetEnabled(CUgraphExec hGraphExec, CUgraphNode hNode, unsigned int isEnabled); + +/** + * \brief Query whether a node in the given graphExec is enabled + * + * Sets isEnabled to 1 if \p hNode is enabled, or 0 if \p hNode is disabled. + * + * The node is identified by the corresponding node \p hNode in the non-executable + * graph, from which the executable graph was instantiated. + * + * \p hNode must not have been removed from the original graph. + * + * \note Currently only kernel, memset and memcpy nodes are supported. + * + * \param hGraphExec - The executable graph in which to set the specified node + * \param hNode - Node from the graph from which graphExec was instantiated + * \param isEnabled - Location to return the enabled status of the node + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphNodeSetEnabled, + * ::cuGraphExecUpdate, + * ::cuGraphInstantiate + * ::cuGraphLaunch + */ +CUresult CUDAAPI cuGraphNodeGetEnabled(CUgraphExec hGraphExec, CUgraphNode hNode, unsigned int *isEnabled); + +/** + * \brief Uploads an executable graph in a stream + * + * Uploads \p hGraphExec to the device in \p hStream without executing it. Uploads of + * the same \p hGraphExec will be serialized. Each upload is ordered behind both any + * previous work in \p hStream and any previous launches of \p hGraphExec. + * Uses memory cached by \p stream to back the allocations owned by \p hGraphExec. + * + * \param hGraphExec - Executable graph to upload + * \param hStream - Stream in which to upload the graph + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphInstantiate, + * ::cuGraphLaunch, + * ::cuGraphExecDestroy + */ +CUresult CUDAAPI cuGraphUpload(CUgraphExec hGraphExec, CUstream hStream); + +/** + * \brief Launches an executable graph in a stream + * + * Executes \p hGraphExec in \p hStream. Only one instance of \p hGraphExec may be executing + * at a time. Each launch is ordered behind both any previous work in \p hStream + * and any previous launches of \p hGraphExec. To execute a graph concurrently, it must be + * instantiated multiple times into multiple executable graphs. + * + * If any allocations created by \p hGraphExec remain unfreed (from a previous launch) and + * \p hGraphExec was not instantiated with ::CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH, + * the launch will fail with ::CUDA_ERROR_INVALID_VALUE. + * + * \param hGraphExec - Executable graph to launch + * \param hStream - Stream in which to launch the graph + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphInstantiate, + * ::cuGraphUpload, + * ::cuGraphExecDestroy + */ +CUresult CUDAAPI cuGraphLaunch(CUgraphExec hGraphExec, CUstream hStream); + +/** + * \brief Destroys an executable graph + * + * Destroys the executable graph specified by \p hGraphExec, as well + * as all of its executable nodes. If the executable graph is + * in-flight, it will not be terminated, but rather freed + * asynchronously on completion. + * + * \param hGraphExec - Executable graph to destroy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphInstantiate, + * ::cuGraphUpload, + * ::cuGraphLaunch + */ +CUresult CUDAAPI cuGraphExecDestroy(CUgraphExec hGraphExec); + +/** + * \brief Destroys a graph + * + * Destroys the graph specified by \p hGraph, as well as all of its nodes. + * + * \param hGraph - Graph to destroy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_VALUE + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphCreate + */ +CUresult CUDAAPI cuGraphDestroy(CUgraph hGraph); + +/** + * \brief Check whether an executable graph can be updated with a graph and perform the update if possible + * + * Updates the node parameters in the instantiated graph specified by \p hGraphExec with the + * node parameters in a topologically identical graph specified by \p hGraph. + * + * Limitations: + * + * - Kernel nodes: + * - The owning context of the function cannot change. + * - A node whose function originally did not use CUDA dynamic parallelism cannot be updated + * to a function which uses CDP. + * - A cooperative node cannot be updated to a non-cooperative node, and vice-versa. + * - If the graph was instantiated with CUDA_GRAPH_INSTANTIATE_FLAG_USE_NODE_PRIORITY, the + * priority attribute cannot change. Equality is checked on the originally requested + * priority values, before they are clamped to the device's supported range. + * - If \p hGraphExec was not instantiated for device launch, a node whose function originally + * did not use device-side cudaGraphLaunch() cannot be updated to a function which uses + * device-side cudaGraphLaunch() unless the node resides on the same context as nodes which + * contained such calls at instantiate-time. If no such calls were present at instantiation, + * these updates cannot be performed at all. + * - Memset and memcpy nodes: + * - The CUDA device(s) to which the operand(s) was allocated/mapped cannot change. + * - The source/destination memory must be allocated from the same contexts as the original + * source/destination memory. + * - Only 1D memsets can be changed. + * - Additional memcpy node restrictions: + * - Changing either the source or destination memory type(i.e. CU_MEMORYTYPE_DEVICE, + * CU_MEMORYTYPE_ARRAY, etc.) is not supported. + * - External semaphore wait nodes and record nodes: + * - Changing the number of semaphores is not supported. + * + * Note: The API may add further restrictions in future releases. The return code should always be checked. + * + * cuGraphExecUpdate sets the result member of \p resultInfo to CU_GRAPH_EXEC_UPDATE_ERROR_TOPOLOGY_CHANGED + * under the following conditions: + * - The count of nodes directly in \p hGraphExec and \p hGraph differ, in which case resultInfo->errorNode + * is set to NULL. + * - \p hGraph has more exit nodes than \p hGraph, in which case resultInfo->errorNode is set to one of + * the exit nodes in hGraph. + * - A node in \p hGraph has a different number of dependencies than the node from \p hGraphExec it is paired with, + * in which case resultInfo->errorNode is set to the node from \p hGraph. + * - A node in \p hGraph has a dependency that does not match with the corresponding dependency of the paired node + * from \p hGraphExec. resultInfo->errorNode will be set to the node from \p hGraph. resultInfo->errorFromNode + * will be set to the mismatched dependency. The dependencies are paired based on edge order and a dependency + * does not match when the nodes are already paired based on other edges examined in the graph. + * + * cuGraphExecUpdate sets the result member of \p resultInfo to: + * - CU_GRAPH_EXEC_UPDATE_ERROR if passed an invalid value. + * - CU_GRAPH_EXEC_UPDATE_ERROR_TOPOLOGY_CHANGED if the graph topology changed + * - CU_GRAPH_EXEC_UPDATE_ERROR_NODE_TYPE_CHANGED if the type of a node changed, in which case + * \p hErrorNode_out is set to the node from \p hGraph. + * - CU_GRAPH_EXEC_UPDATE_ERROR_UNSUPPORTED_FUNCTION_CHANGE if the function changed in an unsupported + * way(see note above), in which case \p hErrorNode_out is set to the node from \p hGraph + * - CU_GRAPH_EXEC_UPDATE_ERROR_PARAMETERS_CHANGED if any parameters to a node changed in a way + * that is not supported, in which case \p hErrorNode_out is set to the node from \p hGraph. + * - CU_GRAPH_EXEC_UPDATE_ERROR_ATTRIBUTES_CHANGED if any attributes of a node changed in a way + * that is not supported, in which case \p hErrorNode_out is set to the node from \p hGraph. + * - CU_GRAPH_EXEC_UPDATE_ERROR_NOT_SUPPORTED if something about a node is unsupported, like + * the node's type or configuration, in which case \p hErrorNode_out is set to the node from \p hGraph + * + * If the update fails for a reason not listed above, the result member of \p resultInfo will be set + * to CU_GRAPH_EXEC_UPDATE_ERROR. If the update succeeds, the result member will be set to CU_GRAPH_EXEC_UPDATE_SUCCESS. + * + * cuGraphExecUpdate returns CUDA_SUCCESS when the updated was performed successfully. It returns + * CUDA_ERROR_GRAPH_EXEC_UPDATE_FAILURE if the graph update was not performed because it included + * changes which violated constraints specific to instantiated graph update. + * + * \param hGraphExec The instantiated graph to be updated + * \param hGraph The graph containing the updated parameters + * \param resultInfo the error info structure + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_GRAPH_EXEC_UPDATE_FAILURE, + * \note_graph_thread_safety + * \notefnerr + * + * \sa + * ::cuGraphInstantiate + */ +CUresult CUDAAPI cuGraphExecUpdate(CUgraphExec hGraphExec, CUgraph hGraph, CUgraphExecUpdateResultInfo *resultInfo); + +/** + * \brief Copies attributes from source node to destination node. + * + * Copies attributes from source node \p src to destination node \p dst. + * Both node must have the same context. + * + * \param[out] dst Destination node + * \param[in] src Source node + * For list of attributes see ::CUkernelNodeAttrID + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa + * ::CUaccessPolicyWindow + */ +CUresult CUDAAPI cuGraphKernelNodeCopyAttributes(CUgraphNode dst, CUgraphNode src); + +/** + * \brief Queries node attribute. + * + * Queries attribute \p attr from node \p hNode and stores it in corresponding + * member of \p value_out. + * + * \param[in] hNode + * \param[in] attr + * \param[out] value_out + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa + * ::CUaccessPolicyWindow + */ +CUresult CUDAAPI cuGraphKernelNodeGetAttribute(CUgraphNode hNode, CUkernelNodeAttrID attr, + CUkernelNodeAttrValue *value_out); + +/** + * \brief Sets node attribute. + * + * Sets attribute \p attr on node \p hNode from corresponding attribute of + * \p value. + * + * \param[out] hNode + * \param[in] attr + * \param[out] value + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE + * \notefnerr + * + * \sa + * ::CUaccessPolicyWindow + */ +CUresult CUDAAPI cuGraphKernelNodeSetAttribute(CUgraphNode hNode, CUkernelNodeAttrID attr, + const CUkernelNodeAttrValue *value); + +/** + * \brief Write a DOT file describing graph structure + * + * Using the provided \p hGraph, write to \p path a DOT formatted description of the graph. + * By default this includes the graph topology, node types, node id, kernel names and memcpy direction. + * \p flags can be specified to write more detailed information about each node type such as + * parameter values, kernel attributes, node and function handles. + * + * \param hGraph - The graph to create a DOT file from + * \param path - The path to write the DOT file to + * \param flags - Flags from CUgraphDebugDot_flags for specifying which additional node information to write + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_OPERATING_SYSTEM + */ +CUresult CUDAAPI cuGraphDebugDotPrint(CUgraph hGraph, const char *path, unsigned int flags); + +/** + * \brief Create a user object + * + * Create a user object with the specified destructor callback and initial reference count. The + * initial references are owned by the caller. + * + * Destructor callbacks cannot make CUDA API calls and should avoid blocking behavior, as they + * are executed by a shared internal thread. Another thread may be signaled to perform such + * actions, if it does not block forward progress of tasks scheduled through CUDA. + * + * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects. + * + * \param object_out - Location to return the user object handle + * \param ptr - The pointer to pass to the destroy function + * \param destroy - Callback to free the user object when it is no longer in use + * \param initialRefcount - The initial refcount to create the object with, typically 1. The + * initial references are owned by the calling thread. + * \param flags - Currently it is required to pass ::CU_USER_OBJECT_NO_DESTRUCTOR_SYNC, + * which is the only defined flag. This indicates that the destroy + * callback cannot be waited on by any CUDA API. Users requiring + * synchronization of the callback should signal its completion + * manually. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuUserObjectRetain, + * ::cuUserObjectRelease, + * ::cuGraphRetainUserObject, + * ::cuGraphReleaseUserObject, + * ::cuGraphCreate + */ +CUresult CUDAAPI cuUserObjectCreate(CUuserObject *object_out, void *ptr, CUhostFn destroy, + unsigned int initialRefcount, unsigned int flags); + +/** + * \brief Retain a reference to a user object + * + * Retains new references to a user object. The new references are owned by the caller. + * + * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects. + * + * \param object - The object to retain + * \param count - The number of references to retain, typically 1. Must be nonzero + * and not larger than INT_MAX. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuUserObjectCreate, + * ::cuUserObjectRelease, + * ::cuGraphRetainUserObject, + * ::cuGraphReleaseUserObject, + * ::cuGraphCreate + */ +CUresult CUDAAPI cuUserObjectRetain(CUuserObject object, unsigned int count); + +/** + * \brief Release a reference to a user object + * + * Releases user object references owned by the caller. The object's destructor is invoked if + * the reference count reaches zero. + * + * It is undefined behavior to release references not owned by the caller, or to use a user + * object handle after all references are released. + * + * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects. + * + * \param object - The object to release + * \param count - The number of references to release, typically 1. Must be nonzero + * and not larger than INT_MAX. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuUserObjectCreate, + * ::cuUserObjectRetain, + * ::cuGraphRetainUserObject, + * ::cuGraphReleaseUserObject, + * ::cuGraphCreate + */ +CUresult CUDAAPI cuUserObjectRelease(CUuserObject object, unsigned int count); + +/** + * \brief Retain a reference to a user object from a graph + * + * Creates or moves user object references that will be owned by a CUDA graph. + * + * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects. + * + * \param graph - The graph to associate the reference with + * \param object - The user object to retain a reference for + * \param count - The number of references to add to the graph, typically 1. Must be + * nonzero and not larger than INT_MAX. + * \param flags - The optional flag ::CU_GRAPH_USER_OBJECT_MOVE transfers references + * from the calling thread, rather than create new references. Pass 0 + * to create new references. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuUserObjectCreate, + * ::cuUserObjectRetain, + * ::cuUserObjectRelease, + * ::cuGraphReleaseUserObject, + * ::cuGraphCreate + */ +CUresult CUDAAPI cuGraphRetainUserObject(CUgraph graph, CUuserObject object, unsigned int count, unsigned int flags); + +/** + * \brief Release a user object reference from a graph + * + * Releases user object references owned by a graph. + * + * See CUDA User Objects in the CUDA C++ Programming Guide for more information on user objects. + * + * \param graph - The graph that will release the reference + * \param object - The user object to release a reference for + * \param count - The number of references to release, typically 1. Must be nonzero + * and not larger than INT_MAX. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuUserObjectCreate, + * ::cuUserObjectRetain, + * ::cuUserObjectRelease, + * ::cuGraphRetainUserObject, + * ::cuGraphCreate + */ +CUresult CUDAAPI cuGraphReleaseUserObject(CUgraph graph, CUuserObject object, unsigned int count); + +/** @} */ /* END CUDA_GRAPH */ + +/** + * \defgroup CUDA_OCCUPANCY Occupancy + * + * ___MANBRIEF___ occupancy calculation functions of the low-level CUDA driver + * API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the occupancy calculation functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** + * \brief Returns occupancy of a function + * + * Returns in \p *numBlocks the number of the maximum active blocks per + * streaming multiprocessor. + * + * \param numBlocks - Returned occupancy + * \param func - Kernel for which occupancy is calculated + * \param blockSize - Block size the kernel is intended to be launched with + * \param dynamicSMemSize - Per-block dynamic shared memory usage intended, in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa + * ::cudaOccupancyMaxActiveBlocksPerMultiprocessor + */ +CUresult CUDAAPI cuOccupancyMaxActiveBlocksPerMultiprocessor(int *numBlocks, CUfunction func, int blockSize, size_t dynamicSMemSize); + +/** + * \brief Returns occupancy of a function + * + * Returns in \p *numBlocks the number of the maximum active blocks per + * streaming multiprocessor. + * + * The \p Flags parameter controls how special cases are handled. The + * valid flags are: + * + * - ::CU_OCCUPANCY_DEFAULT, which maintains the default behavior as + * ::cuOccupancyMaxActiveBlocksPerMultiprocessor; + * + * - ::CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE, which suppresses the + * default behavior on platform where global caching affects + * occupancy. On such platforms, if caching is enabled, but + * per-block SM resource usage would result in zero occupancy, the + * occupancy calculator will calculate the occupancy as if caching + * is disabled. Setting ::CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE makes + * the occupancy calculator to return 0 in such cases. More information + * can be found about this feature in the "Unified L1/Texture Cache" + * section of the Maxwell tuning guide. + * + * \param numBlocks - Returned occupancy + * \param func - Kernel for which occupancy is calculated + * \param blockSize - Block size the kernel is intended to be launched with + * \param dynamicSMemSize - Per-block dynamic shared memory usage intended, in bytes + * \param flags - Requested behavior for the occupancy calculator + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa + * ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags + */ +CUresult CUDAAPI cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int *numBlocks, CUfunction func, int blockSize, size_t dynamicSMemSize, unsigned int flags); + +/** + * \brief Suggest a launch configuration with reasonable occupancy + * + * Returns in \p *blockSize a reasonable block size that can achieve + * the maximum occupancy (or, the maximum number of active warps with + * the fewest blocks per multiprocessor), and in \p *minGridSize the + * minimum grid size to achieve the maximum occupancy. + * + * If \p blockSizeLimit is 0, the configurator will use the maximum + * block size permitted by the device / function instead. + * + * If per-block dynamic shared memory allocation is not needed, the + * user should leave both \p blockSizeToDynamicSMemSize and \p + * dynamicSMemSize as 0. + * + * If per-block dynamic shared memory allocation is needed, then if + * the dynamic shared memory size is constant regardless of block + * size, the size should be passed through \p dynamicSMemSize, and \p + * blockSizeToDynamicSMemSize should be NULL. + * + * Otherwise, if the per-block dynamic shared memory size varies with + * different block sizes, the user needs to provide a unary function + * through \p blockSizeToDynamicSMemSize that computes the dynamic + * shared memory needed by \p func for any given block size. \p + * dynamicSMemSize is ignored. An example signature is: + * + * \code + * // Take block size, returns dynamic shared memory needed + * size_t blockToSmem(int blockSize); + * \endcode + * + * \param minGridSize - Returned minimum grid size needed to achieve the maximum occupancy + * \param blockSize - Returned maximum block size that can achieve the maximum occupancy + * \param func - Kernel for which launch configuration is calculated + * \param blockSizeToDynamicSMemSize - A function that calculates how much per-block dynamic shared memory \p func uses based on the block size + * \param dynamicSMemSize - Dynamic shared memory usage intended, in bytes + * \param blockSizeLimit - The maximum block size \p func is designed to handle + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa + * ::cudaOccupancyMaxPotentialBlockSize + */ +CUresult CUDAAPI cuOccupancyMaxPotentialBlockSize(int *minGridSize, int *blockSize, CUfunction func, CUoccupancyB2DSize blockSizeToDynamicSMemSize, size_t dynamicSMemSize, int blockSizeLimit); + +/** + * \brief Suggest a launch configuration with reasonable occupancy + * + * An extended version of ::cuOccupancyMaxPotentialBlockSize. In + * addition to arguments passed to ::cuOccupancyMaxPotentialBlockSize, + * ::cuOccupancyMaxPotentialBlockSizeWithFlags also takes a \p Flags + * parameter. + * + * The \p Flags parameter controls how special cases are handled. The + * valid flags are: + * + * - ::CU_OCCUPANCY_DEFAULT, which maintains the default behavior as + * ::cuOccupancyMaxPotentialBlockSize; + * + * - ::CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE, which suppresses the + * default behavior on platform where global caching affects + * occupancy. On such platforms, the launch configurations that + * produces maximal occupancy might not support global + * caching. Setting ::CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE + * guarantees that the the produced launch configuration is global + * caching compatible at a potential cost of occupancy. More information + * can be found about this feature in the "Unified L1/Texture Cache" + * section of the Maxwell tuning guide. + * + * \param minGridSize - Returned minimum grid size needed to achieve the maximum occupancy + * \param blockSize - Returned maximum block size that can achieve the maximum occupancy + * \param func - Kernel for which launch configuration is calculated + * \param blockSizeToDynamicSMemSize - A function that calculates how much per-block dynamic shared memory \p func uses based on the block size + * \param dynamicSMemSize - Dynamic shared memory usage intended, in bytes + * \param blockSizeLimit - The maximum block size \p func is designed to handle + * \param flags - Options + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa + * ::cudaOccupancyMaxPotentialBlockSizeWithFlags + */ +CUresult CUDAAPI cuOccupancyMaxPotentialBlockSizeWithFlags(int *minGridSize, int *blockSize, CUfunction func, CUoccupancyB2DSize blockSizeToDynamicSMemSize, size_t dynamicSMemSize, int blockSizeLimit, unsigned int flags); + +/** + * \brief Returns dynamic shared memory available per block when launching \p numBlocks blocks on SM + * + * Returns in \p *dynamicSmemSize the maximum size of dynamic shared memory to allow \p numBlocks blocks per SM. + * + * \param dynamicSmemSize - Returned maximum dynamic shared memory + * \param func - Kernel function for which occupancy is calculated + * \param numBlocks - Number of blocks to fit on SM + * \param blockSize - Size of the blocks + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + */ +CUresult CUDAAPI cuOccupancyAvailableDynamicSMemPerBlock(size_t *dynamicSmemSize, CUfunction func, int numBlocks, int blockSize); + +/** + * \brief Given the kernel function (\p func) and launch configuration + * (\p config), return the maximum cluster size in \p *clusterSize. + * + * The cluster dimensions in \p config are ignored. If func has a required + * cluster size set (see ::cudaFuncGetAttributes / ::cuFuncGetAttribute),\p + * *clusterSize will reflect the required cluster size. + * + * By default this function will always return a value that's portable on + * future hardware. A higher value may be returned if the kernel function + * allows non-portable cluster sizes. + * + * This function will respect the compile time launch bounds. + * + * \param clusterSize - Returned maximum cluster size that can be launched + * for the given kernel function and launch configuration + * \param func - Kernel function for which maximum cluster + * size is calculated + * \param config - Launch configuration for the given kernel function + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa + * ::cudaFuncGetAttributes, + * ::cuFuncGetAttribute + */ +CUresult CUDAAPI cuOccupancyMaxPotentialClusterSize(int *clusterSize, CUfunction func, const CUlaunchConfig *config); + +/** + * \brief Given the kernel function (\p func) and launch configuration + * (\p config), return the maximum number of clusters that could co-exist + * on the target device in \p *numClusters. + * + * If the function has required cluster size already set (see + * ::cudaFuncGetAttributes / ::cuFuncGetAttribute), the cluster size + * from config must either be unspecified or match the required size. + * Without required sizes, the cluster size must be specified in config, + * else the function will return an error. + * + * Note that various attributes of the kernel function may affect occupancy + * calculation. Runtime environment may affect how the hardware schedules + * the clusters, so the calculated occupancy is not guaranteed to be achievable. + * + * \param numClusters - Returned maximum number of clusters that + * could co-exist on the target device + * \param func - Kernel function for which maximum number + * of clusters are calculated + * \param config - Launch configuration for the given kernel function + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_CLUSTER_SIZE, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa + * ::cudaFuncGetAttributes, + * ::cuFuncGetAttribute + */ +CUresult CUDAAPI cuOccupancyMaxActiveClusters(int *numClusters, CUfunction func, const CUlaunchConfig *config); +/** @} */ /* END CUDA_OCCUPANCY */ + +/** + * \defgroup CUDA_TEXREF_DEPRECATED Texture Reference Management [DEPRECATED] + * + * ___MANBRIEF___ deprecated texture reference management functions of the + * low-level CUDA driver API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the deprecated texture reference management + * functions of the low-level CUDA driver application programming interface. + * + * @{ + */ + +/** + * \brief Binds an array as a texture reference + * + * \deprecated + * + * Binds the CUDA array \p hArray to the texture reference \p hTexRef. Any + * previous address or CUDA array state associated with the texture reference + * is superseded by this function. \p Flags must be set to + * ::CU_TRSA_OVERRIDE_FORMAT. Any CUDA array previously bound to \p hTexRef is + * unbound. + * + * \param hTexRef - Texture reference to bind + * \param hArray - Array to bind + * \param Flags - Options (must be ::CU_TRSA_OVERRIDE_FORMAT) + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetArray(CUtexref hTexRef, CUarray hArray, unsigned int Flags); + +/** + * \brief Binds a mipmapped array to a texture reference + * + * \deprecated + * + * Binds the CUDA mipmapped array \p hMipmappedArray to the texture reference \p hTexRef. + * Any previous address or CUDA array state associated with the texture reference + * is superseded by this function. \p Flags must be set to ::CU_TRSA_OVERRIDE_FORMAT. + * Any CUDA array previously bound to \p hTexRef is unbound. + * + * \param hTexRef - Texture reference to bind + * \param hMipmappedArray - Mipmapped array to bind + * \param Flags - Options (must be ::CU_TRSA_OVERRIDE_FORMAT) + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetMipmappedArray(CUtexref hTexRef, CUmipmappedArray hMipmappedArray, unsigned int Flags); + +/** + * \brief Binds an address as a texture reference + * + * \deprecated + * + * Binds a linear address range to the texture reference \p hTexRef. Any + * previous address or CUDA array state associated with the texture reference + * is superseded by this function. Any memory previously bound to \p hTexRef + * is unbound. + * + * Since the hardware enforces an alignment requirement on texture base + * addresses, ::cuTexRefSetAddress() passes back a byte offset in + * \p *ByteOffset that must be applied to texture fetches in order to read from + * the desired memory. This offset must be divided by the texel size and + * passed to kernels that read from the texture so they can be applied to the + * ::tex1Dfetch() function. + * + * If the device memory pointer was returned from ::cuMemAlloc(), the offset + * is guaranteed to be 0 and NULL may be passed as the \p ByteOffset parameter. + * + * The total number of elements (or texels) in the linear address range + * cannot exceed ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH. + * The number of elements is computed as (\p bytes / bytesPerElement), + * where bytesPerElement is determined from the data format and number of + * components set using ::cuTexRefSetFormat(). + * + * \param ByteOffset - Returned byte offset + * \param hTexRef - Texture reference to bind + * \param dptr - Device pointer to bind + * \param bytes - Size of memory to bind in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetAddress(size_t *ByteOffset, CUtexref hTexRef, CUdeviceptr dptr, size_t bytes); + +/** + * \brief Binds an address as a 2D texture reference + * + * \deprecated + * + * Binds a linear address range to the texture reference \p hTexRef. Any + * previous address or CUDA array state associated with the texture reference + * is superseded by this function. Any memory previously bound to \p hTexRef + * is unbound. + * + * Using a ::tex2D() function inside a kernel requires a call to either + * ::cuTexRefSetArray() to bind the corresponding texture reference to an + * array, or ::cuTexRefSetAddress2D() to bind the texture reference to linear + * memory. + * + * Function calls to ::cuTexRefSetFormat() cannot follow calls to + * ::cuTexRefSetAddress2D() for the same texture reference. + * + * It is required that \p dptr be aligned to the appropriate hardware-specific + * texture alignment. You can query this value using the device attribute + * ::CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT. If an unaligned \p dptr is + * supplied, ::CUDA_ERROR_INVALID_VALUE is returned. + * + * \p Pitch has to be aligned to the hardware-specific texture pitch alignment. + * This value can be queried using the device attribute + * ::CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT. If an unaligned \p Pitch is + * supplied, ::CUDA_ERROR_INVALID_VALUE is returned. + * + * Width and Height, which are specified in elements (or texels), cannot exceed + * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH and + * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT respectively. + * \p Pitch, which is specified in bytes, cannot exceed + * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH. + * + * \param hTexRef - Texture reference to bind + * \param desc - Descriptor of CUDA array + * \param dptr - Device pointer to bind + * \param Pitch - Line pitch in bytes + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddress, + * ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetAddress2D(CUtexref hTexRef, const CUDA_ARRAY_DESCRIPTOR *desc, CUdeviceptr dptr, size_t Pitch); + +/** + * \brief Sets the format for a texture reference + * + * \deprecated + * + * Specifies the format of the data to be read by the texture reference + * \p hTexRef. \p fmt and \p NumPackedComponents are exactly analogous to the + * ::Format and ::NumChannels members of the ::CUDA_ARRAY_DESCRIPTOR structure: + * They specify the format of each component and the number of components per + * array element. + * + * \param hTexRef - Texture reference + * \param fmt - Format to set + * \param NumPackedComponents - Number of components per array element + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat, + * ::cudaCreateChannelDesc + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetFormat(CUtexref hTexRef, CUarray_format fmt, int NumPackedComponents); + +/** + * \brief Sets the addressing mode for a texture reference + * + * \deprecated + * + * Specifies the addressing mode \p am for the given dimension \p dim of the + * texture reference \p hTexRef. If \p dim is zero, the addressing mode is + * applied to the first parameter of the functions used to fetch from the + * texture; if \p dim is 1, the second, and so on. ::CUaddress_mode is defined + * as: + * \code + typedef enum CUaddress_mode_enum { + CU_TR_ADDRESS_MODE_WRAP = 0, + CU_TR_ADDRESS_MODE_CLAMP = 1, + CU_TR_ADDRESS_MODE_MIRROR = 2, + CU_TR_ADDRESS_MODE_BORDER = 3 + } CUaddress_mode; + * \endcode + * + * Note that this call has no effect if \p hTexRef is bound to linear memory. + * Also, if the flag, ::CU_TRSF_NORMALIZED_COORDINATES, is not set, the only + * supported address mode is ::CU_TR_ADDRESS_MODE_CLAMP. + * + * \param hTexRef - Texture reference + * \param dim - Dimension + * \param am - Addressing mode to set + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetArray, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetAddressMode(CUtexref hTexRef, int dim, CUaddress_mode am); + +/** + * \brief Sets the filtering mode for a texture reference + * + * \deprecated + * + * Specifies the filtering mode \p fm to be used when reading memory through + * the texture reference \p hTexRef. ::CUfilter_mode_enum is defined as: + * + * \code + typedef enum CUfilter_mode_enum { + CU_TR_FILTER_MODE_POINT = 0, + CU_TR_FILTER_MODE_LINEAR = 1 + } CUfilter_mode; + * \endcode + * + * Note that this call has no effect if \p hTexRef is bound to linear memory. + * + * \param hTexRef - Texture reference + * \param fm - Filtering mode to set + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetFilterMode(CUtexref hTexRef, CUfilter_mode fm); + +/** + * \brief Sets the mipmap filtering mode for a texture reference + * + * \deprecated + * + * Specifies the mipmap filtering mode \p fm to be used when reading memory through + * the texture reference \p hTexRef. ::CUfilter_mode_enum is defined as: + * + * \code + typedef enum CUfilter_mode_enum { + CU_TR_FILTER_MODE_POINT = 0, + CU_TR_FILTER_MODE_LINEAR = 1 + } CUfilter_mode; + * \endcode + * + * Note that this call has no effect if \p hTexRef is not bound to a mipmapped array. + * + * \param hTexRef - Texture reference + * \param fm - Filtering mode to set + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetMipmapFilterMode(CUtexref hTexRef, CUfilter_mode fm); + +/** + * \brief Sets the mipmap level bias for a texture reference + * + * \deprecated + * + * Specifies the mipmap level bias \p bias to be added to the specified mipmap level when + * reading memory through the texture reference \p hTexRef. + * + * Note that this call has no effect if \p hTexRef is not bound to a mipmapped array. + * + * \param hTexRef - Texture reference + * \param bias - Mipmap level bias + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetMipmapLevelBias(CUtexref hTexRef, float bias); + +/** + * \brief Sets the mipmap min/max mipmap level clamps for a texture reference + * + * \deprecated + * + * Specifies the min/max mipmap level clamps, \p minMipmapLevelClamp and \p maxMipmapLevelClamp + * respectively, to be used when reading memory through the texture reference + * \p hTexRef. + * + * Note that this call has no effect if \p hTexRef is not bound to a mipmapped array. + * + * \param hTexRef - Texture reference + * \param minMipmapLevelClamp - Mipmap min level clamp + * \param maxMipmapLevelClamp - Mipmap max level clamp + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetMipmapLevelClamp(CUtexref hTexRef, float minMipmapLevelClamp, float maxMipmapLevelClamp); + +/** + * \brief Sets the maximum anisotropy for a texture reference + * + * \deprecated + * + * Specifies the maximum anisotropy \p maxAniso to be used when reading memory through + * the texture reference \p hTexRef. + * + * Note that this call has no effect if \p hTexRef is bound to linear memory. + * + * \param hTexRef - Texture reference + * \param maxAniso - Maximum anisotropy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetMaxAnisotropy(CUtexref hTexRef, unsigned int maxAniso); + +/** + * \brief Sets the border color for a texture reference + * + * \deprecated + * + * Specifies the value of the RGBA color via the \p pBorderColor to the texture reference + * \p hTexRef. The color value supports only float type and holds color components in + * the following sequence: + * pBorderColor[0] holds 'R' component + * pBorderColor[1] holds 'G' component + * pBorderColor[2] holds 'B' component + * pBorderColor[3] holds 'A' component + * + * Note that the color values can be set only when the Address mode is set to + * CU_TR_ADDRESS_MODE_BORDER using ::cuTexRefSetAddressMode. + * Applications using integer border color values have to "reinterpret_cast" their values to float. + * + * \param hTexRef - Texture reference + * \param pBorderColor - RGBA color + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddressMode, + * ::cuTexRefGetAddressMode, ::cuTexRefGetBorderColor + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetBorderColor(CUtexref hTexRef, float *pBorderColor); + +/** + * \brief Sets the flags for a texture reference + * + * \deprecated + * + * Specifies optional flags via \p Flags to specify the behavior of data + * returned through the texture reference \p hTexRef. The valid flags are: + * + * - ::CU_TRSF_READ_AS_INTEGER, which suppresses the default behavior of + * having the texture promote integer data to floating point data in the + * range [0, 1]. Note that texture with 32-bit integer format + * would not be promoted, regardless of whether or not this + * flag is specified; + * - ::CU_TRSF_NORMALIZED_COORDINATES, which suppresses the + * default behavior of having the texture coordinates range + * from [0, Dim) where Dim is the width or height of the CUDA + * array. Instead, the texture coordinates [0, 1.0) reference + * the entire breadth of the array dimension; + * - ::CU_TRSF_DISABLE_TRILINEAR_OPTIMIZATION, which disables any trilinear + * filtering optimizations. Trilinear optimizations improve texture filtering + * performance by allowing bilinear filtering on textures in scenarios where + * it can closely approximate the expected results. + * + * \param hTexRef - Texture reference + * \param Flags - Optional flags to set + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefSetFlags(CUtexref hTexRef, unsigned int Flags); + +/** + * \brief Gets the address associated with a texture reference + * + * \deprecated + * + * Returns in \p *pdptr the base address bound to the texture reference + * \p hTexRef, or returns ::CUDA_ERROR_INVALID_VALUE if the texture reference + * is not bound to any device memory range. + * + * \param pdptr - Returned device address + * \param hTexRef - Texture reference + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetAddress(CUdeviceptr *pdptr, CUtexref hTexRef); + +/** + * \brief Gets the array bound to a texture reference + * + * \deprecated + * + * Returns in \p *phArray the CUDA array bound to the texture reference + * \p hTexRef, or returns ::CUDA_ERROR_INVALID_VALUE if the texture reference + * is not bound to any CUDA array. + * + * \param phArray - Returned array + * \param hTexRef - Texture reference + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetArray(CUarray *phArray, CUtexref hTexRef); + +/** + * \brief Gets the mipmapped array bound to a texture reference + * + * \deprecated + * + * Returns in \p *phMipmappedArray the CUDA mipmapped array bound to the texture + * reference \p hTexRef, or returns ::CUDA_ERROR_INVALID_VALUE if the texture reference + * is not bound to any CUDA mipmapped array. + * + * \param phMipmappedArray - Returned mipmapped array + * \param hTexRef - Texture reference + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetMipmappedArray(CUmipmappedArray *phMipmappedArray, CUtexref hTexRef); + +/** + * \brief Gets the addressing mode used by a texture reference + * + * \deprecated + * + * Returns in \p *pam the addressing mode corresponding to the + * dimension \p dim of the texture reference \p hTexRef. Currently, the only + * valid value for \p dim are 0 and 1. + * + * \param pam - Returned addressing mode + * \param hTexRef - Texture reference + * \param dim - Dimension + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetAddressMode(CUaddress_mode *pam, CUtexref hTexRef, int dim); + +/** + * \brief Gets the filter-mode used by a texture reference + * + * \deprecated + * + * Returns in \p *pfm the filtering mode of the texture reference + * \p hTexRef. + * + * \param pfm - Returned filtering mode + * \param hTexRef - Texture reference + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetFilterMode(CUfilter_mode *pfm, CUtexref hTexRef); + +/** + * \brief Gets the format used by a texture reference + * + * \deprecated + * + * Returns in \p *pFormat and \p *pNumChannels the format and number + * of components of the CUDA array bound to the texture reference \p hTexRef. + * If \p pFormat or \p pNumChannels is NULL, it will be ignored. + * + * \param pFormat - Returned format + * \param pNumChannels - Returned number of components + * \param hTexRef - Texture reference + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetFormat(CUarray_format *pFormat, int *pNumChannels, CUtexref hTexRef); + +/** + * \brief Gets the mipmap filtering mode for a texture reference + * + * \deprecated + * + * Returns the mipmap filtering mode in \p pfm that's used when reading memory through + * the texture reference \p hTexRef. + * + * \param pfm - Returned mipmap filtering mode + * \param hTexRef - Texture reference + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetMipmapFilterMode(CUfilter_mode *pfm, CUtexref hTexRef); + +/** + * \brief Gets the mipmap level bias for a texture reference + * + * \deprecated + * + * Returns the mipmap level bias in \p pBias that's added to the specified mipmap + * level when reading memory through the texture reference \p hTexRef. + * + * \param pbias - Returned mipmap level bias + * \param hTexRef - Texture reference + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetMipmapLevelBias(float *pbias, CUtexref hTexRef); + +/** + * \brief Gets the min/max mipmap level clamps for a texture reference + * + * \deprecated + * + * Returns the min/max mipmap level clamps in \p pminMipmapLevelClamp and \p pmaxMipmapLevelClamp + * that's used when reading memory through the texture reference \p hTexRef. + * + * \param pminMipmapLevelClamp - Returned mipmap min level clamp + * \param pmaxMipmapLevelClamp - Returned mipmap max level clamp + * \param hTexRef - Texture reference + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetMipmapLevelClamp(float *pminMipmapLevelClamp, float *pmaxMipmapLevelClamp, CUtexref hTexRef); + +/** + * \brief Gets the maximum anisotropy for a texture reference + * + * \deprecated + * + * Returns the maximum anisotropy in \p pmaxAniso that's used when reading memory through + * the texture reference \p hTexRef. + * + * \param pmaxAniso - Returned maximum anisotropy + * \param hTexRef - Texture reference + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFlags, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetMaxAnisotropy(int *pmaxAniso, CUtexref hTexRef); + +/** + * \brief Gets the border color used by a texture reference + * + * \deprecated + * + * Returns in \p pBorderColor, values of the RGBA color used by + * the texture reference \p hTexRef. + * The color value is of type float and holds color components in + * the following sequence: + * pBorderColor[0] holds 'R' component + * pBorderColor[1] holds 'G' component + * pBorderColor[2] holds 'B' component + * pBorderColor[3] holds 'A' component + * + * \param hTexRef - Texture reference + * \param pBorderColor - Returned Type and Value of RGBA color + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefSetAddressMode, + * ::cuTexRefSetAddressMode, ::cuTexRefSetBorderColor + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetBorderColor(float *pBorderColor, CUtexref hTexRef); + +/** + * \brief Gets the flags used by a texture reference + * + * \deprecated + * + * Returns in \p *pFlags the flags of the texture reference \p hTexRef. + * + * \param pFlags - Returned flags + * \param hTexRef - Texture reference + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefSetAddress, + * ::cuTexRefSetAddress2D, ::cuTexRefSetAddressMode, ::cuTexRefSetArray, + * ::cuTexRefSetFilterMode, ::cuTexRefSetFlags, ::cuTexRefSetFormat, + * ::cuTexRefGetAddress, ::cuTexRefGetAddressMode, ::cuTexRefGetArray, + * ::cuTexRefGetFilterMode, ::cuTexRefGetFormat + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefGetFlags(unsigned int *pFlags, CUtexref hTexRef); + +/** + * \brief Creates a texture reference + * + * \deprecated + * + * Creates a texture reference and returns its handle in \p *pTexRef. Once + * created, the application must call ::cuTexRefSetArray() or + * ::cuTexRefSetAddress() to associate the reference with allocated memory. + * Other texture reference functions are used to specify the format and + * interpretation (addressing, filtering, etc.) to be used when the memory is + * read through this texture reference. + * + * \param pTexRef - Returned texture reference + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefDestroy + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefCreate(CUtexref *pTexRef); + +/** + * \brief Destroys a texture reference + * + * \deprecated + * + * Destroys the texture reference specified by \p hTexRef. + * + * \param hTexRef - Texture reference to destroy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuTexRefCreate + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuTexRefDestroy(CUtexref hTexRef); + +/** @} */ /* END CUDA_TEXREF_DEPRECATED */ + + +/** + * \defgroup CUDA_SURFREF_DEPRECATED Surface Reference Management [DEPRECATED] + * + * ___MANBRIEF___ surface reference management functions of the low-level CUDA + * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the surface reference management functions of the + * low-level CUDA driver application programming interface. + * + * @{ + */ + +/** + * \brief Sets the CUDA array for a surface reference. + * + * \deprecated + * + * Sets the CUDA array \p hArray to be read and written by the surface reference + * \p hSurfRef. Any previous CUDA array state associated with the surface + * reference is superseded by this function. \p Flags must be set to 0. + * The ::CUDA_ARRAY3D_SURFACE_LDST flag must have been set for the CUDA array. + * Any CUDA array previously bound to \p hSurfRef is unbound. + + * \param hSurfRef - Surface reference handle + * \param hArray - CUDA array handle + * \param Flags - set to 0 + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuModuleGetSurfRef, + * ::cuSurfRefGetArray + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuSurfRefSetArray(CUsurfref hSurfRef, CUarray hArray, unsigned int Flags); + +/** + * \brief Passes back the CUDA array bound to a surface reference. + * + * \deprecated + * + * Returns in \p *phArray the CUDA array bound to the surface reference + * \p hSurfRef, or returns ::CUDA_ERROR_INVALID_VALUE if the surface reference + * is not bound to any CUDA array. + + * \param phArray - Surface reference handle + * \param hSurfRef - Surface reference handle + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa ::cuModuleGetSurfRef, ::cuSurfRefSetArray + */ +__CUDA_DEPRECATED CUresult CUDAAPI cuSurfRefGetArray(CUarray *phArray, CUsurfref hSurfRef); + +/** @} */ /* END CUDA_SURFREF_DEPRECATED */ + +/** + * \defgroup CUDA_TEXOBJECT Texture Object Management + * + * ___MANBRIEF___ texture object management functions of the low-level CUDA + * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the texture object management functions of the + * low-level CUDA driver application programming interface. The texture + * object API is only supported on devices of compute capability 3.0 or higher. + * + * @{ + */ + +/** + * \brief Creates a texture object + * + * Creates a texture object and returns it in \p pTexObject. \p pResDesc describes + * the data to texture from. \p pTexDesc describes how the data should be sampled. + * \p pResViewDesc is an optional argument that specifies an alternate format for + * the data described by \p pResDesc, and also describes the subresource region + * to restrict access to when texturing. \p pResViewDesc can only be specified if + * the type of resource is a CUDA array or a CUDA mipmapped array. + * + * Texture objects are only supported on devices of compute capability 3.0 or higher. + * Additionally, a texture object is an opaque value, and, as such, should only be + * accessed through CUDA API calls. + * + * The ::CUDA_RESOURCE_DESC structure is defined as: + * \code + typedef struct CUDA_RESOURCE_DESC_st + { + CUresourcetype resType; + + union { + struct { + CUarray hArray; + } array; + struct { + CUmipmappedArray hMipmappedArray; + } mipmap; + struct { + CUdeviceptr devPtr; + CUarray_format format; + unsigned int numChannels; + size_t sizeInBytes; + } linear; + struct { + CUdeviceptr devPtr; + CUarray_format format; + unsigned int numChannels; + size_t width; + size_t height; + size_t pitchInBytes; + } pitch2D; + } res; + + unsigned int flags; + } CUDA_RESOURCE_DESC; + + * \endcode + * where: + * - ::CUDA_RESOURCE_DESC::resType specifies the type of resource to texture from. + * CUresourceType is defined as: + * \code + typedef enum CUresourcetype_enum { + CU_RESOURCE_TYPE_ARRAY = 0x00, + CU_RESOURCE_TYPE_MIPMAPPED_ARRAY = 0x01, + CU_RESOURCE_TYPE_LINEAR = 0x02, + CU_RESOURCE_TYPE_PITCH2D = 0x03 + } CUresourcetype; + * \endcode + * + * \par + * If ::CUDA_RESOURCE_DESC::resType is set to ::CU_RESOURCE_TYPE_ARRAY, ::CUDA_RESOURCE_DESC::res::array::hArray + * must be set to a valid CUDA array handle. + * + * \par + * If ::CUDA_RESOURCE_DESC::resType is set to ::CU_RESOURCE_TYPE_MIPMAPPED_ARRAY, ::CUDA_RESOURCE_DESC::res::mipmap::hMipmappedArray + * must be set to a valid CUDA mipmapped array handle. + * + * \par + * If ::CUDA_RESOURCE_DESC::resType is set to ::CU_RESOURCE_TYPE_LINEAR, ::CUDA_RESOURCE_DESC::res::linear::devPtr + * must be set to a valid device pointer, that is aligned to ::CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT. + * ::CUDA_RESOURCE_DESC::res::linear::format and ::CUDA_RESOURCE_DESC::res::linear::numChannels + * describe the format of each component and the number of components per array element. ::CUDA_RESOURCE_DESC::res::linear::sizeInBytes + * specifies the size of the array in bytes. The total number of elements in the linear address range cannot exceed + * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH. The number of elements is computed as (sizeInBytes / (sizeof(format) * numChannels)). + * + * \par + * If ::CUDA_RESOURCE_DESC::resType is set to ::CU_RESOURCE_TYPE_PITCH2D, ::CUDA_RESOURCE_DESC::res::pitch2D::devPtr + * must be set to a valid device pointer, that is aligned to ::CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT. + * ::CUDA_RESOURCE_DESC::res::pitch2D::format and ::CUDA_RESOURCE_DESC::res::pitch2D::numChannels + * describe the format of each component and the number of components per array element. ::CUDA_RESOURCE_DESC::res::pitch2D::width + * and ::CUDA_RESOURCE_DESC::res::pitch2D::height specify the width and height of the array in elements, and cannot exceed + * ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH and ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT respectively. + * ::CUDA_RESOURCE_DESC::res::pitch2D::pitchInBytes specifies the pitch between two rows in bytes and has to be aligned to + * ::CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT. Pitch cannot exceed ::CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH. + * + * - ::flags must be set to zero. + * + * + * The ::CUDA_TEXTURE_DESC struct is defined as + * \code + typedef struct CUDA_TEXTURE_DESC_st { + CUaddress_mode addressMode[3]; + CUfilter_mode filterMode; + unsigned int flags; + unsigned int maxAnisotropy; + CUfilter_mode mipmapFilterMode; + float mipmapLevelBias; + float minMipmapLevelClamp; + float maxMipmapLevelClamp; + } CUDA_TEXTURE_DESC; + * \endcode + * where + * - ::CUDA_TEXTURE_DESC::addressMode specifies the addressing mode for each dimension of the texture data. ::CUaddress_mode is defined as: + * \code + typedef enum CUaddress_mode_enum { + CU_TR_ADDRESS_MODE_WRAP = 0, + CU_TR_ADDRESS_MODE_CLAMP = 1, + CU_TR_ADDRESS_MODE_MIRROR = 2, + CU_TR_ADDRESS_MODE_BORDER = 3 + } CUaddress_mode; + * \endcode + * This is ignored if ::CUDA_RESOURCE_DESC::resType is ::CU_RESOURCE_TYPE_LINEAR. Also, if the flag, ::CU_TRSF_NORMALIZED_COORDINATES + * is not set, the only supported address mode is ::CU_TR_ADDRESS_MODE_CLAMP. + * + * - ::CUDA_TEXTURE_DESC::filterMode specifies the filtering mode to be used when fetching from the texture. CUfilter_mode is defined as: + * \code + typedef enum CUfilter_mode_enum { + CU_TR_FILTER_MODE_POINT = 0, + CU_TR_FILTER_MODE_LINEAR = 1 + } CUfilter_mode; + * \endcode + * This is ignored if ::CUDA_RESOURCE_DESC::resType is ::CU_RESOURCE_TYPE_LINEAR. + * + * - ::CUDA_TEXTURE_DESC::flags can be any combination of the following: + * - ::CU_TRSF_READ_AS_INTEGER, which suppresses the default behavior of + * having the texture promote integer data to floating point data in the + * range [0, 1]. Note that texture with 32-bit integer format would not be + * promoted, regardless of whether or not this flag is specified. + * - ::CU_TRSF_NORMALIZED_COORDINATES, which suppresses the default behavior + * of having the texture coordinates range from [0, Dim) where Dim is the + * width or height of the CUDA array. Instead, the texture coordinates + * [0, 1.0) reference the entire breadth of the array dimension; Note that + * for CUDA mipmapped arrays, this flag has to be set. + * - ::CU_TRSF_DISABLE_TRILINEAR_OPTIMIZATION, which disables any trilinear + * filtering optimizations. Trilinear optimizations improve texture filtering + * performance by allowing bilinear filtering on textures in scenarios where + * it can closely approximate the expected results. + * - ::CU_TRSF_SEAMLESS_CUBEMAP, which enables seamless cube map filtering. + * This flag can only be specified if the underlying resource is a CUDA array + * or a CUDA mipmapped array that was created with the flag ::CUDA_ARRAY3D_CUBEMAP. + * When seamless cube map filtering is enabled, texture address modes specified + * by ::CUDA_TEXTURE_DESC::addressMode are ignored. Instead, if the ::CUDA_TEXTURE_DESC::filterMode + * is set to ::CU_TR_FILTER_MODE_POINT the address mode ::CU_TR_ADDRESS_MODE_CLAMP + * will be applied for all dimensions. If the ::CUDA_TEXTURE_DESC::filterMode is + * set to ::CU_TR_FILTER_MODE_LINEAR seamless cube map filtering will be performed + * when sampling along the cube face borders. + * + * - ::CUDA_TEXTURE_DESC::maxAnisotropy specifies the maximum anisotropy ratio to be used when doing anisotropic filtering. This value will be + * clamped to the range [1,16]. + * + * - ::CUDA_TEXTURE_DESC::mipmapFilterMode specifies the filter mode when the calculated mipmap level lies between two defined mipmap levels. + * + * - ::CUDA_TEXTURE_DESC::mipmapLevelBias specifies the offset to be applied to the calculated mipmap level. + * + * - ::CUDA_TEXTURE_DESC::minMipmapLevelClamp specifies the lower end of the mipmap level range to clamp access to. + * + * - ::CUDA_TEXTURE_DESC::maxMipmapLevelClamp specifies the upper end of the mipmap level range to clamp access to. + * + * + * The ::CUDA_RESOURCE_VIEW_DESC struct is defined as + * \code + typedef struct CUDA_RESOURCE_VIEW_DESC_st + { + CUresourceViewFormat format; + size_t width; + size_t height; + size_t depth; + unsigned int firstMipmapLevel; + unsigned int lastMipmapLevel; + unsigned int firstLayer; + unsigned int lastLayer; + } CUDA_RESOURCE_VIEW_DESC; + * \endcode + * where: + * - ::CUDA_RESOURCE_VIEW_DESC::format specifies how the data contained in the CUDA array or CUDA mipmapped array should + * be interpreted. Note that this can incur a change in size of the texture data. If the resource view format is a block + * compressed format, then the underlying CUDA array or CUDA mipmapped array has to have a base of format ::CU_AD_FORMAT_UNSIGNED_INT32. + * with 2 or 4 channels, depending on the block compressed format. For ex., BC1 and BC4 require the underlying CUDA array to have + * a format of ::CU_AD_FORMAT_UNSIGNED_INT32 with 2 channels. The other BC formats require the underlying resource to have the same base + * format but with 4 channels. + * + * - ::CUDA_RESOURCE_VIEW_DESC::width specifies the new width of the texture data. If the resource view format is a block + * compressed format, this value has to be 4 times the original width of the resource. For non block compressed formats, + * this value has to be equal to that of the original resource. + * + * - ::CUDA_RESOURCE_VIEW_DESC::height specifies the new height of the texture data. If the resource view format is a block + * compressed format, this value has to be 4 times the original height of the resource. For non block compressed formats, + * this value has to be equal to that of the original resource. + * + * - ::CUDA_RESOURCE_VIEW_DESC::depth specifies the new depth of the texture data. This value has to be equal to that of the + * original resource. + * + * - ::CUDA_RESOURCE_VIEW_DESC::firstMipmapLevel specifies the most detailed mipmap level. This will be the new mipmap level zero. + * For non-mipmapped resources, this value has to be zero.::CUDA_TEXTURE_DESC::minMipmapLevelClamp and ::CUDA_TEXTURE_DESC::maxMipmapLevelClamp + * will be relative to this value. For ex., if the firstMipmapLevel is set to 2, and a minMipmapLevelClamp of 1.2 is specified, + * then the actual minimum mipmap level clamp will be 3.2. + * + * - ::CUDA_RESOURCE_VIEW_DESC::lastMipmapLevel specifies the least detailed mipmap level. For non-mipmapped resources, this value + * has to be zero. + * + * - ::CUDA_RESOURCE_VIEW_DESC::firstLayer specifies the first layer index for layered textures. This will be the new layer zero. + * For non-layered resources, this value has to be zero. + * + * - ::CUDA_RESOURCE_VIEW_DESC::lastLayer specifies the last layer index for layered textures. For non-layered resources, + * this value has to be zero. + * + * + * \param pTexObject - Texture object to create + * \param pResDesc - Resource descriptor + * \param pTexDesc - Texture descriptor + * \param pResViewDesc - Resource view descriptor + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexObjectDestroy, + * ::cudaCreateTextureObject + */ +CUresult CUDAAPI cuTexObjectCreate(CUtexObject *pTexObject, const CUDA_RESOURCE_DESC *pResDesc, const CUDA_TEXTURE_DESC *pTexDesc, const CUDA_RESOURCE_VIEW_DESC *pResViewDesc); + +/** + * \brief Destroys a texture object + * + * Destroys the texture object specified by \p texObject. + * + * \param texObject - Texture object to destroy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexObjectCreate, + * ::cudaDestroyTextureObject + */ +CUresult CUDAAPI cuTexObjectDestroy(CUtexObject texObject); + +/** + * \brief Returns a texture object's resource descriptor + * + * Returns the resource descriptor for the texture object specified by \p texObject. + * + * \param pResDesc - Resource descriptor + * \param texObject - Texture object + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexObjectCreate, + * ::cudaGetTextureObjectResourceDesc, + */ +CUresult CUDAAPI cuTexObjectGetResourceDesc(CUDA_RESOURCE_DESC *pResDesc, CUtexObject texObject); + +/** + * \brief Returns a texture object's texture descriptor + * + * Returns the texture descriptor for the texture object specified by \p texObject. + * + * \param pTexDesc - Texture descriptor + * \param texObject - Texture object + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexObjectCreate, + * ::cudaGetTextureObjectTextureDesc + */ +CUresult CUDAAPI cuTexObjectGetTextureDesc(CUDA_TEXTURE_DESC *pTexDesc, CUtexObject texObject); + +/** + * \brief Returns a texture object's resource view descriptor + * + * Returns the resource view descriptor for the texture object specified by \p texObject. + * If no resource view was set for \p texObject, the ::CUDA_ERROR_INVALID_VALUE is returned. + * + * \param pResViewDesc - Resource view descriptor + * \param texObject - Texture object + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTexObjectCreate, + * ::cudaGetTextureObjectResourceViewDesc + */ +CUresult CUDAAPI cuTexObjectGetResourceViewDesc(CUDA_RESOURCE_VIEW_DESC *pResViewDesc, CUtexObject texObject); + +/** @} */ /* END CUDA_TEXOBJECT */ + +/** + * \defgroup CUDA_SURFOBJECT Surface Object Management + * + * ___MANBRIEF___ surface object management functions of the low-level CUDA + * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the surface object management functions of the + * low-level CUDA driver application programming interface. The surface + * object API is only supported on devices of compute capability 3.0 or higher. + * + * @{ + */ + +/** + * \brief Creates a surface object + * + * Creates a surface object and returns it in \p pSurfObject. \p pResDesc describes + * the data to perform surface load/stores on. ::CUDA_RESOURCE_DESC::resType must be + * ::CU_RESOURCE_TYPE_ARRAY and ::CUDA_RESOURCE_DESC::res::array::hArray + * must be set to a valid CUDA array handle. ::CUDA_RESOURCE_DESC::flags must be set to zero. + * + * Surface objects are only supported on devices of compute capability 3.0 or higher. + * Additionally, a surface object is an opaque value, and, as such, should only be + * accessed through CUDA API calls. + * + * \param pSurfObject - Surface object to create + * \param pResDesc - Resource descriptor + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuSurfObjectDestroy, + * ::cudaCreateSurfaceObject + */ +CUresult CUDAAPI cuSurfObjectCreate(CUsurfObject *pSurfObject, const CUDA_RESOURCE_DESC *pResDesc); + +/** + * \brief Destroys a surface object + * + * Destroys the surface object specified by \p surfObject. + * + * \param surfObject - Surface object to destroy + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuSurfObjectCreate, + * ::cudaDestroySurfaceObject + */ +CUresult CUDAAPI cuSurfObjectDestroy(CUsurfObject surfObject); + +/** + * \brief Returns a surface object's resource descriptor + * + * Returns the resource descriptor for the surface object specified by \p surfObject. + * + * \param pResDesc - Resource descriptor + * \param surfObject - Surface object + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuSurfObjectCreate, + * ::cudaGetSurfaceObjectResourceDesc + */ +CUresult CUDAAPI cuSurfObjectGetResourceDesc(CUDA_RESOURCE_DESC *pResDesc, CUsurfObject surfObject); + +/** @} */ /* END CUDA_SURFOBJECT */ + +/** + * \defgroup CUDA_TENSOR_MEMORY Tensor Core Managment + * + * ___MANBRIEF___ tensor core management functions of the low-level CUDA + * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the tensor core management functions of the + * low-level CUDA driver application programming interface. The tensor + * core API is only supported on devices of compute capability 9.0 or higher. + * + * @{ + */ + +/** + * \brief Create a tensor map descriptor object representing tiled memory region + * + * Creates a descriptor for Tensor Memory Access (TMA) object specified + * by the parameters describing a tiled region and returns it in \p tensorMap. + * + * Tensor map objects are only supported on devices of compute capability 9.0 or higher. + * Additionally, a tensor map object is an opaque value, and, as such, should only be + * accessed through CUDA API calls. + * + * The parameters passed are bound to the following requirements: + * + * - \p tensorMap address must be aligned to 64 bytes. + * + * - \p tensorDataType has to be an enum from ::CUtensorMapDataType which is defined as: + * \code + typedef enum CUtensorMapDataType_enum { + CU_TENSOR_MAP_DATA_TYPE_UINT8 = 0, // 1 byte + CU_TENSOR_MAP_DATA_TYPE_UINT16, // 2 bytes + CU_TENSOR_MAP_DATA_TYPE_UINT32, // 4 bytes + CU_TENSOR_MAP_DATA_TYPE_INT32, // 4 bytes + CU_TENSOR_MAP_DATA_TYPE_UINT64, // 8 bytes + CU_TENSOR_MAP_DATA_TYPE_INT64, // 8 bytes + CU_TENSOR_MAP_DATA_TYPE_FLOAT16, // 2 bytes + CU_TENSOR_MAP_DATA_TYPE_FLOAT32, // 4 bytes + CU_TENSOR_MAP_DATA_TYPE_FLOAT64, // 8 bytes + CU_TENSOR_MAP_DATA_TYPE_BFLOAT16, // 2 bytes + CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ, // 4 bytes + CU_TENSOR_MAP_DATA_TYPE_TFLOAT32, // 4 bytes + CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ // 4 bytes + } CUtensorMapDataType; + * \endcode + * + * - \p tensorRank must be non-zero and less than or equal to the maximum supported dimensionality of 5. If \p interleave is not + * ::CU_TENSOR_MAP_INTERLEAVE_NONE, then \p tensorRank must additionally be greater than or equal to 3. + * + * - \p globalAddress, which specifies the starting address of the memory region described, must be 32 byte aligned when \p interleave is + * ::CU_TENSOR_MAP_INTERLEAVE_32B and 16 byte aligned otherwise. + * + * - \p globalDim array, which specifies tensor size of each of the \p tensorRank dimensions, must be non-zero and less than or + * equal to 2^32. + * + * - \p globalStrides array, which specifies tensor stride of each of the lower \p tensorRank - 1 dimensions in bytes, must be a + * multiple of 16 and less than 2^40. Additionally, the stride must be a multiple of 32 when \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_32B. + * Each following dimension specified includes previous dimension stride: + * \code + globalStrides[0] = globalDim[0] * elementSizeInBytes(tensorDataType) + padding[0]; + for (i = 1; i < tensorRank - 1; i++) + globalStrides[i] = globalStrides[i – 1] * globalStrides[i] + padding[i]; + assert(globalStrides[i] >= globalDim[i]); + * \endcode + * + * - \p boxDim array, which specifies number of elements to be traversed along each of the \p tensorRank dimensions, must be less + * than or equal to 8. + * When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_NONE, { \p boxDim[0] * elementSizeInBytes( \p tensorDataType ) } must be a multiple + * of 16 bytes. + * + * - \p elementStrides array, which specifies the iteration step along each of the \p tensorRank dimensions, must be non-zero and less + * than or equal to 8. Note that when \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_NONE, the first element of this array is ignored since + * TMA doesn’t support the stride for dimension zero. + * When all elemets of \p elementStrides array is one, \p boxDim specifies the number of elements to load. However, if the \p elementStrides[i] + * is not equal to one, then TMA loads ceil( \p boxDim[i] / \p elementStrides[i]) number of elements along i-th dimension. To load N elements along + * i-th dimension, \p boxDim[i] must be set to N * \p elementStrides[i]. + * + * - \p interleave specifies the interleaved layout of type ::CUtensorMapInterleave, which is defined as: + * \code + typedef enum CUtensorMapInterleave_enum { + CU_TENSOR_MAP_INTERLEAVE_NONE = 0, + CU_TENSOR_MAP_INTERLEAVE_16B, + CU_TENSOR_MAP_INTERLEAVE_32B + } CUtensorMapInterleave; + * \endcode + * TMA supports interleaved layouts like NC/8HWC8 where C8 utilizes 16 bytes in memory assuming 2 byte per channel or NC/16HWC16 where C16 + * uses 32 bytes. + * When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_NONE and \p swizzle is not ::CU_TENSOR_MAP_SWIZZLE_NONE, the bounding box inner dimension + * (computed as \p boxDim[0] multiplied by element size derived from \p tensorDataType) must be less than or equal to the swizzle size. + * - CU_TENSOR_MAP_SWIZZLE_32B implies the bounding box inner dimension will be <= 32. + * - CU_TENSOR_MAP_SWIZZLE_64B implies the bounding box inner dimension will be <= 64. + * - CU_TENSOR_MAP_SWIZZLE_128B implies the bounding box inner dimension will be <= 128. + * + * - \p swizzle, which specifies the shared memory bank swizzling pattern, has to be of type ::CUtensorMapSwizzle which is defined as: + * \code + typedef enum CUtensorMapSwizzle_enum { + CU_TENSOR_MAP_SWIZZLE_NONE = 0, + CU_TENSOR_MAP_SWIZZLE_32B, + CU_TENSOR_MAP_SWIZZLE_64B, + CU_TENSOR_MAP_SWIZZLE_128B + } CUtensorMapSwizzle; + * \endcode + * Data is organized in specific order in global memory; however, it may not match the order in which data are accessed by application in + * the shared memory. This difference in data organization may cause bank conflicts when shared memory is accessed. In order to avoid this + * problem, data can be loaded to shard memory with shuffling across shared memory banks. + * Note that it’s expected that when \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_32B, \p swizzle should be ::CU_TENSOR_MAP_SWIZZLE_32B mode. + * Other interleave modes can have any swizzling patterns. + * + * - \p l2Promotion specifies L2 fetch size which indicates the byte granurality at which L2 requests is filled from DRAM. It must be of + * type ::CUtensorMapL2promotion, which is defined as: + * \code + typedef enum CUtensorMapL2promotion_enum { + CU_TENSOR_MAP_L2_PROMOTION_NONE = 0, + CU_TENSOR_MAP_L2_PROMOTION_L2_64B, + CU_TENSOR_MAP_L2_PROMOTION_L2_128B, + CU_TENSOR_MAP_L2_PROMOTION_L2_256B + } CUtensorMapL2promotion; + * \endcode + * + * - \p oobFill, which indicates whether zero or a special NaN constant should be used to fill out-of-bound elements, must be of type + * ::CUtensorMapFloatOOBfill which is defined as: + * \code + typedef enum CUtensorMapFloatOOBfill_enum { + CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE = 0, + CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA + } CUtensorMapFloatOOBfill; + * \endcode + * Note that ::CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA can only be used when \p tensorDataType represents a floating data type. + * + * \param tensorMap - Tensor map object to create + * \param tensorDataType - Tensor data type + * \param tensorRank - Dimensionality of tensor + * \param globalAddress - Starting address of memory region described by tensor + * \param globalDim - Array containing tensor size (number of elements) along each of the \p tensorRank dimensions + * \param globalStrides - Array containing stride size (in bytes) along each of the \p tensorRank - 1 dimensions + * \param boxDim - Array containing traversal box size (number of elments) along each of the \p tensorRank dimensions. Specifies how many elements to be traversed along each tensor dimension. + * \param elementStrides - Array containing traversal stride in each of the \p tensorRank dimensions + * \param interleave - Type of interleaved layout the tensor addresses + * \param swizzle - Bank swizzling pattern inside shared memory + * \param l2Promotion - L2 promotion size + * \param oobFill - Indicate whether zero or special NaN constant must be used to fill out-of-bound elements + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTensorMapEncodeIm2col, + * ::cuTensorMapReplaceAddress + */ +CUresult CUDAAPI cuTensorMapEncodeTiled(CUtensorMap *tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void *globalAddress, const cuuint64_t *globalDim, const cuuint64_t *globalStrides, const cuuint32_t *boxDim, const cuuint32_t *elementStrides, CUtensorMapInterleave interleave, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill); + + +/** + * \brief Create a tensor map descriptor object representing im2col memory region + * + * Creates a descriptor for Tensor Memory Access (TMA) object specified + * by the parameters describing a im2col memory layout and returns it in \p tensorMap. + * + * Tensor map objects are only supported on devices of compute capability 9.0 or higher. + * Additionally, a tensor map object is an opaque value, and, as such, should only be + * accessed through CUDA API calls. + * + * The parameters passed are bound to the following requirements: + * + * - \p tensorMap address must be aligned to 64 bytes. + * + * - \p tensorDataType has to be an enum from ::CUtensorMapDataType which is defined as: + * \code + typedef enum CUtensorMapDataType_enum { + CU_TENSOR_MAP_DATA_TYPE_UINT8 = 0, // 1 byte + CU_TENSOR_MAP_DATA_TYPE_UINT16, // 2 bytes + CU_TENSOR_MAP_DATA_TYPE_UINT32, // 4 bytes + CU_TENSOR_MAP_DATA_TYPE_INT32, // 4 bytes + CU_TENSOR_MAP_DATA_TYPE_UINT64, // 8 bytes + CU_TENSOR_MAP_DATA_TYPE_INT64, // 8 bytes + CU_TENSOR_MAP_DATA_TYPE_FLOAT16, // 2 bytes + CU_TENSOR_MAP_DATA_TYPE_FLOAT32, // 4 bytes + CU_TENSOR_MAP_DATA_TYPE_FLOAT64, // 8 bytes + CU_TENSOR_MAP_DATA_TYPE_BFLOAT16, // 2 bytes + CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ, // 4 bytes + CU_TENSOR_MAP_DATA_TYPE_TFLOAT32, // 4 bytes + CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ // 4 bytes + } CUtensorMapDataType; + * \endcode + * + * - \p tensorRank must be one of dimensions 3, 4, or 5. + * + * - \p globalAddress, which specifies the starting address of the memory region described, must be 32 byte aligned when \p interleave is + * ::CU_TENSOR_MAP_INTERLEAVE_32B and 16 byte aligned otherwise. + * + * - \p globalDim array, which specifies tensor size of each of the \p tensorRank dimensions, must be non-zero and less than or + * equal to 2^32. + * + * - \p globalStrides array, which specifies tensor stride of each of the lower \p tensorRank - 1 dimensions in bytes, must be a + * multiple of 16 and less than 2^40. Additionally, the stride must be a multiple of 32 when \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_32B. + * Each following dimension specified includes previous dimension stride: + * \code + globalStrides[0] = globalDim[0] * elementSizeInBytes(tensorDataType) + padding[0]; + for (i = 1; i < tensorRank - 1; i++) + globalStrides[i] = globalStrides[i – 1] * globalStrides[i] + padding[i]; + assert(globalStrides[i] >= globalDim[i]); + * \endcode + * + * - \p pixelBoxLowerCorner array specifies the coordinate offsets {D, H, W} of the bounding box from top/left/front corner. The number of + * offsets and their precision depends on the tensor dimensionality: + * - When \p tensorRank is 3, one signed offset within range [-32768, 32767] is supported. + * - When \p tensorRank is 4, two signed offsets each within range [-128, 127] are supported. + * - When \p tensorRank is 5, three offsets each within range [-16, 15] are supported. + * + * - \p pixelBoxUpperCorner array specifies the coordinate offsets {D, H, W} of the bounding box from bottom/right/back corner. The number of + * offsets and their precision depends on the tensor dimensionality: + * - When \p tensorRank is 3, one signed offset within range [-32768, 32767] is supported. + * - When \p tensorRank is 4, two signed offsets each within range [-128, 127] are supported. + * - When \p tensorRank is 5, three offsets each within range [-16, 15] are supported. + * The bounding box specified by \p pixelBoxLowerCorner and \p pixelBoxUpperCorner must have non-zero area. + * + * - \p channelsPerPixel, which specifies the number of elements which must be accessed along C dimension, must be less than or equal to 256. + * + * - \p pixelsPerColumn, which specifies the number of elements that must be accessed along the {N, D, H, W} dimensions, must be less than or + * equal to 1024. + * + * - \p elementStrides array, which specifies the iteration step along each of the \p tensorRank dimensions, must be non-zero and less + * than or equal to 8. Note that when \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_NONE, the first element of this array is ignored since + * TMA doesn’t support the stride for dimension zero. + * When all elemets of \p elementStrides array is one, \p boxDim specifies the number of elements to load. However, if the \p elementStrides[i] + * is not equal to one, then TMA loads ceil( \p boxDim[i] / \p elementStrides[i]) number of elements along i-th dimension. To load N elements along + * i-th dimension, \p boxDim[i] must be set to N * \p elementStrides[i]. + * + * - \p interleave specifies the interleaved layout of type ::CUtensorMapInterleave, which is defined as: + * \code + typedef enum CUtensorMapInterleave_enum { + CU_TENSOR_MAP_INTERLEAVE_NONE = 0, + CU_TENSOR_MAP_INTERLEAVE_16B, + CU_TENSOR_MAP_INTERLEAVE_32B + } CUtensorMapInterleave; + * \endcode + * TMA supports interleaved layouts like NC/8HWC8 where C8 utilizes 16 bytes in memory assuming 2 byte per channel or NC/16HWC16 where C16 + * uses 32 bytes. + * When \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_NONE and \p swizzle is not ::CU_TENSOR_MAP_SWIZZLE_NONE, the bounding box inner dimension + * (computed as \p boxDim[0] multiplied by element size derived from \p tensorDataType) must be less than or equal to the swizzle size. + * - CU_TENSOR_MAP_SWIZZLE_32B implies the bounding box inner dimension will be <= 32. + * - CU_TENSOR_MAP_SWIZZLE_64B implies the bounding box inner dimension will be <= 64. + * - CU_TENSOR_MAP_SWIZZLE_128B implies the bounding box inner dimension will be <= 128. + * + * - \p swizzle, which specifies the shared memory bank swizzling pattern, has to be of type ::CUtensorMapSwizzle which is defined as: + * \code + typedef enum CUtensorMapSwizzle_enum { + CU_TENSOR_MAP_SWIZZLE_NONE = 0, + CU_TENSOR_MAP_SWIZZLE_32B, + CU_TENSOR_MAP_SWIZZLE_64B, + CU_TENSOR_MAP_SWIZZLE_128B + } CUtensorMapSwizzle; + * \endcode + * Data is organized in specific order in global memory; however, it may not match the order in which data are accessed by application in + * the shared memory. This difference in data organization may cause bank conflicts when shared memory is accessed. In order to avoid this + * problem, data can be loaded to shard memory with shuffling across shared memory banks. + * Note that it’s expected that when \p interleave is ::CU_TENSOR_MAP_INTERLEAVE_32B, \p swizzle should be ::CU_TENSOR_MAP_SWIZZLE_32B mode. + * Other interleave modes can have any swizzling patterns. + * + * - \p l2Promotion specifies L2 fetch size which indicates the byte granurality at which L2 requests is filled from DRAM. It must be of + * type ::CUtensorMapL2promotion, which is defined as: + * \code + typedef enum CUtensorMapL2promotion_enum { + CU_TENSOR_MAP_L2_PROMOTION_NONE = 0, + CU_TENSOR_MAP_L2_PROMOTION_L2_64B, + CU_TENSOR_MAP_L2_PROMOTION_L2_128B, + CU_TENSOR_MAP_L2_PROMOTION_L2_256B + } CUtensorMapL2promotion; + * \endcode + * + * - \p oobFill, which indicates whether zero or a special NaN constant should be used to fill out-of-bound elements, must be of type + * ::CUtensorMapFloatOOBfill which is defined as: + * \code + typedef enum CUtensorMapFloatOOBfill_enum { + CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE = 0, + CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA + } CUtensorMapFloatOOBfill; + * \endcode + * Note that ::CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA can only be used when \p tensorDataType represents a floating data type. + * + * \param tensorMap - Tensor map object to create + * \param tensorDataType - Tensor data type + * \param tensorRank - Dimensionality of tensor, needs to be at least of dimension 3 + * \param globalAddress - Starting address of memory region described by tensor + * \param globalDim - Array containing tensor size (number of elements) along each of the \p tensorRank dimensions + * \param globalStrides - Array containing stride size (in bytes) along each of the \p tensorRank - 1 dimensions + * \param pixelBoxLowerCorner - Array containing DHW dimentions of lower box corner + * \param pixelBoxUpperCorner - Array containing DHW dimentions of upper box corner + * \param channelsPerPixel - Number of channels per pixel + * \param pixelsPerColumn - Number of pixels per column + * \param elementStrides - Array containing traversal stride in each of the \p tensorRank dimensions + * \param interleave - Type of interleaved layout the tensor addresses + * \param swizzle - Bank swizzling pattern inside shared memory + * \param l2Promotion - L2 promotion size + * \param oobFill - Indicate whether zero or special NaN constant must be used to fill out-of-bound elements + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTensorMapEncodeTiled, + * ::cuTensorMapReplaceAddress + */ +CUresult CUDAAPI cuTensorMapEncodeIm2col(CUtensorMap *tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void *globalAddress, const cuuint64_t *globalDim, const cuuint64_t *globalStrides, const int *pixelBoxLowerCorner, const int *pixelBoxUpperCorner, cuuint32_t channelsPerPixel, cuuint32_t pixelsPerColumn, const cuuint32_t *elementStrides, CUtensorMapInterleave interleave, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill); + +/** + * \brief Modify an existing tensor map descriptor with an updated global address + * + * Modifies the descriptor for Tensor Memory Access (TMA) object passed in \p tensorMap with + * an updated \p globalAddress. + * + * Tensor map objects are only supported on devices of compute capability 9.0 or higher. + * Additionally, a tensor map object is an opaque value, and, as such, should only be + * accessed through CUDA API calls. + * + * \param tensorMap - Tensor map object to modify + * \param globalAddress - Starting address of memory region described by tensor, must follow previous alignment requirements + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuTensorMapEncodeTiled, + * ::cuTensorMapEncodeIm2col + */ +CUresult CUDAAPI cuTensorMapReplaceAddress(CUtensorMap *tensorMap, void *globalAddress); + +/** @} */ +/* END CUDA_TENSOR_MEMORY */ + +/** + * \defgroup CUDA_PEER_ACCESS Peer Context Memory Access + * + * ___MANBRIEF___ direct peer context memory access functions of the low-level + * CUDA driver API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the direct peer context memory access functions + * of the low-level CUDA driver application programming interface. + * + * @{ + */ + +/** + * \brief Queries if a device may directly access a peer device's memory. + * + * Returns in \p *canAccessPeer a value of 1 if contexts on \p dev are capable of + * directly accessing memory from contexts on \p peerDev and 0 otherwise. + * If direct access of \p peerDev from \p dev is possible, then access may be + * enabled on two specific contexts by calling ::cuCtxEnablePeerAccess(). + * + * \param canAccessPeer - Returned access capability + * \param dev - Device from which allocations on \p peerDev are to + * be directly accessed. + * \param peerDev - Device on which the allocations to be directly accessed + * by \p dev reside. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_DEVICE + * \notefnerr + * + * \sa + * ::cuCtxEnablePeerAccess, + * ::cuCtxDisablePeerAccess, + * ::cudaDeviceCanAccessPeer + */ +CUresult CUDAAPI cuDeviceCanAccessPeer(int *canAccessPeer, CUdevice dev, CUdevice peerDev); + +/** + * \brief Enables direct access to memory allocations in a peer context. + * + * If both the current context and \p peerContext are on devices which support unified + * addressing (as may be queried using ::CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING) and same + * major compute capability, then on success all allocations from \p peerContext will + * immediately be accessible by the current context. See \ref CUDA_UNIFIED for additional + * details. + * + * Note that access granted by this call is unidirectional and that in order to access + * memory from the current context in \p peerContext, a separate symmetric call + * to ::cuCtxEnablePeerAccess() is required. + * + * Note that there are both device-wide and system-wide limitations per system + * configuration, as noted in the CUDA Programming Guide under the section + * "Peer-to-Peer Memory Access". + * + * Returns ::CUDA_ERROR_PEER_ACCESS_UNSUPPORTED if ::cuDeviceCanAccessPeer() indicates + * that the ::CUdevice of the current context cannot directly access memory + * from the ::CUdevice of \p peerContext. + * + * Returns ::CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED if direct access of + * \p peerContext from the current context has already been enabled. + * + * Returns ::CUDA_ERROR_TOO_MANY_PEERS if direct peer access is not possible + * because hardware resources required for peer access have been exhausted. + * + * Returns ::CUDA_ERROR_INVALID_CONTEXT if there is no current context, \p peerContext + * is not a valid context, or if the current context is \p peerContext. + * + * Returns ::CUDA_ERROR_INVALID_VALUE if \p Flags is not 0. + * + * \param peerContext - Peer context to enable direct access to from the current context + * \param Flags - Reserved for future use and must be set to 0 + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED, + * ::CUDA_ERROR_TOO_MANY_PEERS, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_PEER_ACCESS_UNSUPPORTED, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa + * ::cuDeviceCanAccessPeer, + * ::cuCtxDisablePeerAccess, + * ::cudaDeviceEnablePeerAccess + */ +CUresult CUDAAPI cuCtxEnablePeerAccess(CUcontext peerContext, unsigned int Flags); + +/** + * \brief Disables direct access to memory allocations in a peer context and + * unregisters any registered allocations. + * + Returns ::CUDA_ERROR_PEER_ACCESS_NOT_ENABLED if direct peer access has + * not yet been enabled from \p peerContext to the current context. + * + * Returns ::CUDA_ERROR_INVALID_CONTEXT if there is no current context, or if + * \p peerContext is not a valid context. + * + * \param peerContext - Peer context to disable direct access to + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_PEER_ACCESS_NOT_ENABLED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * \notefnerr + * + * \sa + * ::cuDeviceCanAccessPeer, + * ::cuCtxEnablePeerAccess, + * ::cudaDeviceDisablePeerAccess + */ +CUresult CUDAAPI cuCtxDisablePeerAccess(CUcontext peerContext); + +/** + * \brief Queries attributes of the link between two devices. + * + * Returns in \p *value the value of the requested attribute \p attrib of the + * link between \p srcDevice and \p dstDevice. The supported attributes are: + * - ::CU_DEVICE_P2P_ATTRIBUTE_PERFORMANCE_RANK: A relative value indicating the + * performance of the link between two devices. + * - ::CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED P2P: 1 if P2P Access is enable. + * - ::CU_DEVICE_P2P_ATTRIBUTE_NATIVE_ATOMIC_SUPPORTED: 1 if Atomic operations over + * the link are supported. + * - ::CU_DEVICE_P2P_ATTRIBUTE_CUDA_ARRAY_ACCESS_SUPPORTED: 1 if cudaArray can + * be accessed over the link. + * + * Returns ::CUDA_ERROR_INVALID_DEVICE if \p srcDevice or \p dstDevice are not valid + * or if they represent the same device. + * + * Returns ::CUDA_ERROR_INVALID_VALUE if \p attrib is not valid or if \p value is + * a null pointer. + * + * \param value - Returned value of the requested attribute + * \param attrib - The requested attribute of the link between \p srcDevice and \p dstDevice. + * \param srcDevice - The source device of the target link. + * \param dstDevice - The destination device of the target link. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_DEVICE, + * ::CUDA_ERROR_INVALID_VALUE + * \notefnerr + * + * \sa + * ::cuCtxEnablePeerAccess, + * ::cuCtxDisablePeerAccess, + * ::cuDeviceCanAccessPeer, + * ::cudaDeviceGetP2PAttribute + */ +CUresult CUDAAPI cuDeviceGetP2PAttribute(int* value, CUdevice_P2PAttribute attrib, CUdevice srcDevice, CUdevice dstDevice); + +/** @} */ /* END CUDA_PEER_ACCESS */ + +/** + * \defgroup CUDA_GRAPHICS Graphics Interoperability + * + * ___MANBRIEF___ graphics interoperability functions of the low-level CUDA + * driver API (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the graphics interoperability functions of the + * low-level CUDA driver application programming interface. + * + * @{ + */ + +/** + * \brief Unregisters a graphics resource for access by CUDA + * + * Unregisters the graphics resource \p resource so it is not accessible by + * CUDA unless registered again. + * + * If \p resource is invalid then ::CUDA_ERROR_INVALID_HANDLE is + * returned. + * + * \param resource - Resource to unregister + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_UNKNOWN + * \notefnerr + * + * \sa + * ::cuGraphicsD3D9RegisterResource, + * ::cuGraphicsD3D10RegisterResource, + * ::cuGraphicsD3D11RegisterResource, + * ::cuGraphicsGLRegisterBuffer, + * ::cuGraphicsGLRegisterImage, + * ::cudaGraphicsUnregisterResource + */ +CUresult CUDAAPI cuGraphicsUnregisterResource(CUgraphicsResource resource); + +/** + * \brief Get an array through which to access a subresource of a mapped graphics resource. + * + * Returns in \p *pArray an array through which the subresource of the mapped + * graphics resource \p resource which corresponds to array index \p arrayIndex + * and mipmap level \p mipLevel may be accessed. The value set in \p *pArray may + * change every time that \p resource is mapped. + * + * If \p resource is not a texture then it cannot be accessed via an array and + * ::CUDA_ERROR_NOT_MAPPED_AS_ARRAY is returned. + * If \p arrayIndex is not a valid array index for \p resource then + * ::CUDA_ERROR_INVALID_VALUE is returned. + * If \p mipLevel is not a valid mipmap level for \p resource then + * ::CUDA_ERROR_INVALID_VALUE is returned. + * If \p resource is not mapped then ::CUDA_ERROR_NOT_MAPPED is returned. + * + * \param pArray - Returned array through which a subresource of \p resource may be accessed + * \param resource - Mapped resource to access + * \param arrayIndex - Array index for array textures or cubemap face + * index as defined by ::CUarray_cubemap_face for + * cubemap textures for the subresource to access + * \param mipLevel - Mipmap level for the subresource to access + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_MAPPED, + * ::CUDA_ERROR_NOT_MAPPED_AS_ARRAY + * \notefnerr + * + * \sa + * ::cuGraphicsResourceGetMappedPointer, + * ::cudaGraphicsSubResourceGetMappedArray + */ +CUresult CUDAAPI cuGraphicsSubResourceGetMappedArray(CUarray *pArray, CUgraphicsResource resource, unsigned int arrayIndex, unsigned int mipLevel); + +/** + * \brief Get a mipmapped array through which to access a mapped graphics resource. + * + * Returns in \p *pMipmappedArray a mipmapped array through which the mapped graphics + * resource \p resource. The value set in \p *pMipmappedArray may change every time + * that \p resource is mapped. + * + * If \p resource is not a texture then it cannot be accessed via a mipmapped array and + * ::CUDA_ERROR_NOT_MAPPED_AS_ARRAY is returned. + * If \p resource is not mapped then ::CUDA_ERROR_NOT_MAPPED is returned. + * + * \param pMipmappedArray - Returned mipmapped array through which \p resource may be accessed + * \param resource - Mapped resource to access + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_MAPPED, + * ::CUDA_ERROR_NOT_MAPPED_AS_ARRAY + * \notefnerr + * + * \sa + * ::cuGraphicsResourceGetMappedPointer, + * ::cudaGraphicsResourceGetMappedMipmappedArray + */ +CUresult CUDAAPI cuGraphicsResourceGetMappedMipmappedArray(CUmipmappedArray *pMipmappedArray, CUgraphicsResource resource); + +/** + * \brief Get a device pointer through which to access a mapped graphics resource. + * + * Returns in \p *pDevPtr a pointer through which the mapped graphics resource + * \p resource may be accessed. + * Returns in \p pSize the size of the memory in bytes which may be accessed from that pointer. + * The value set in \p pPointer may change every time that \p resource is mapped. + * + * If \p resource is not a buffer then it cannot be accessed via a pointer and + * ::CUDA_ERROR_NOT_MAPPED_AS_POINTER is returned. + * If \p resource is not mapped then ::CUDA_ERROR_NOT_MAPPED is returned. + * * + * \param pDevPtr - Returned pointer through which \p resource may be accessed + * \param pSize - Returned size of the buffer accessible starting at \p *pPointer + * \param resource - Mapped resource to access + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_MAPPED, + * ::CUDA_ERROR_NOT_MAPPED_AS_POINTER + * \notefnerr + * + * \sa + * ::cuGraphicsMapResources, + * ::cuGraphicsSubResourceGetMappedArray, + * ::cudaGraphicsResourceGetMappedPointer + */ +CUresult CUDAAPI cuGraphicsResourceGetMappedPointer(CUdeviceptr *pDevPtr, size_t *pSize, CUgraphicsResource resource); + +/** + * \brief Set usage flags for mapping a graphics resource + * + * Set \p flags for mapping the graphics resource \p resource. + * + * Changes to \p flags will take effect the next time \p resource is mapped. + * The \p flags argument may be any of the following: + + * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE: Specifies no hints about how this + * resource will be used. It is therefore assumed that this resource will be + * read from and written to by CUDA kernels. This is the default value. + * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_READONLY: Specifies that CUDA kernels which + * access this resource will not write to this resource. + * - ::CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITEDISCARD: Specifies that CUDA kernels + * which access this resource will not read from this resource and will + * write over the entire contents of the resource, so none of the data + * previously stored in the resource will be preserved. + * + * If \p resource is presently mapped for access by CUDA then + * ::CUDA_ERROR_ALREADY_MAPPED is returned. + * If \p flags is not one of the above values then ::CUDA_ERROR_INVALID_VALUE is returned. + * + * \param resource - Registered resource to set flags for + * \param flags - Parameters for resource mapping + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_ALREADY_MAPPED + * \notefnerr + * + * \sa + * ::cuGraphicsMapResources, + * ::cudaGraphicsResourceSetMapFlags + */ +CUresult CUDAAPI cuGraphicsResourceSetMapFlags(CUgraphicsResource resource, unsigned int flags); + +/** + * \brief Map graphics resources for access by CUDA + * + * Maps the \p count graphics resources in \p resources for access by CUDA. + * + * The resources in \p resources may be accessed by CUDA until they + * are unmapped. The graphics API from which \p resources were registered + * should not access any resources while they are mapped by CUDA. If an + * application does so, the results are undefined. + * + * This function provides the synchronization guarantee that any graphics calls + * issued before ::cuGraphicsMapResources() will complete before any subsequent CUDA + * work issued in \p stream begins. + * + * If \p resources includes any duplicate entries then ::CUDA_ERROR_INVALID_HANDLE is returned. + * If any of \p resources are presently mapped for access by CUDA then ::CUDA_ERROR_ALREADY_MAPPED is returned. + * + * \param count - Number of resources to map + * \param resources - Resources to map for CUDA usage + * \param hStream - Stream with which to synchronize + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_ALREADY_MAPPED, + * ::CUDA_ERROR_UNKNOWN + * \note_null_stream + * \notefnerr + * + * \sa + * ::cuGraphicsResourceGetMappedPointer, + * ::cuGraphicsSubResourceGetMappedArray, + * ::cuGraphicsUnmapResources, + * ::cudaGraphicsMapResources + */ +CUresult CUDAAPI cuGraphicsMapResources(unsigned int count, CUgraphicsResource *resources, CUstream hStream); + +/** + * \brief Unmap graphics resources. + * + * Unmaps the \p count graphics resources in \p resources. + * + * Once unmapped, the resources in \p resources may not be accessed by CUDA + * until they are mapped again. + * + * This function provides the synchronization guarantee that any CUDA work issued + * in \p stream before ::cuGraphicsUnmapResources() will complete before any + * subsequently issued graphics work begins. + * + * + * If \p resources includes any duplicate entries then ::CUDA_ERROR_INVALID_HANDLE is returned. + * If any of \p resources are not presently mapped for access by CUDA then ::CUDA_ERROR_NOT_MAPPED is returned. + * + * \param count - Number of resources to unmap + * \param resources - Resources to unmap + * \param hStream - Stream with which to synchronize + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_INVALID_HANDLE, + * ::CUDA_ERROR_NOT_MAPPED, + * ::CUDA_ERROR_UNKNOWN + * \note_null_stream + * \notefnerr + * + * \sa + * ::cuGraphicsMapResources, + * ::cudaGraphicsUnmapResources + */ +CUresult CUDAAPI cuGraphicsUnmapResources(unsigned int count, CUgraphicsResource *resources, CUstream hStream); + +/** @} */ /* END CUDA_GRAPHICS */ + +/** + * \defgroup CUDA_DRIVER_ENTRY_POINT Driver Entry Point Access + * + * ___MANBRIEF___ driver entry point access functions of the low-level CUDA driver API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the driver entry point access functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** + * \brief Returns the requested driver API function pointer + * + * Returns in \p **pfn the address of the CUDA driver function for the requested + * CUDA version and flags. + * + * The CUDA version is specified as (1000 * major + 10 * minor), so CUDA 11.2 + * should be specified as 11020. For a requested driver symbol, if the specified + * CUDA version is greater than or equal to the CUDA version in which the driver symbol + * was introduced, this API will return the function pointer to the corresponding + * versioned function. + * + * The pointer returned by the API should be cast to a function pointer matching the + * requested driver function's definition in the API header file. The function pointer + * typedef can be picked up from the corresponding typedefs header file. For example, + * cudaTypedefs.h consists of function pointer typedefs for driver APIs defined in cuda.h. + * + * The API will return ::CUDA_SUCCESS and set the returned \p pfn to NULL if the + * requested driver function is not supported on the platform, no ABI + * compatible driver function exists for the specified \p cudaVersion or if the + * driver symbol is invalid. + * + * It will also set the optional \p symbolStatus to one of the values in + * ::CUdriverProcAddressQueryResult with the following meanings: + * - ::CU_GET_PROC_ADDRESS_SUCCESS - The requested symbol was succesfully found based + * on input arguments and \p pfn is valid + * - ::CU_GET_PROC_ADDRESS_SYMBOL_NOT_FOUND - The requested symbol was not found + * - ::CU_GET_PROC_ADDRESS_VERSION_NOT_SUFFICIENT - The requested symbol was found but is + * not supported by cudaVersion specified + * + * The requested flags can be: + * - ::CU_GET_PROC_ADDRESS_DEFAULT: This is the default mode. This is equivalent to + * ::CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM if the code is compiled with + * --default-stream per-thread compilation flag or the macro CUDA_API_PER_THREAD_DEFAULT_STREAM + * is defined; ::CU_GET_PROC_ADDRESS_LEGACY_STREAM otherwise. + * - ::CU_GET_PROC_ADDRESS_LEGACY_STREAM: This will enable the search for all driver symbols + * that match the requested driver symbol name except the corresponding per-thread versions. + * - ::CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM: This will enable the search for all + * driver symbols that match the requested driver symbol name including the per-thread + * versions. If a per-thread version is not found, the API will return the legacy version + * of the driver function. + * + * \param symbol - The base name of the driver API function to look for. As an example, + * for the driver API ::cuMemAlloc_v2, \p symbol would be cuMemAlloc and + * \p cudaVersion would be the ABI compatible CUDA version for the _v2 variant. + * \param pfn - Location to return the function pointer to the requested driver function + * \param cudaVersion - The CUDA version to look for the requested driver symbol + * \param flags - Flags to specify search options. + * \param symbolStatus - Optional location to store the status of the search for + * \p symbol based on \p cudaVersion. See ::CUdriverProcAddressQueryResult + * for possible values. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_SUPPORTED + * \note_version_mixing + * + * \sa + * ::cudaGetDriverEntryPoint + */ +CUresult CUDAAPI cuGetProcAddress(const char *symbol, void **pfn, int cudaVersion, cuuint64_t flags, CUdriverProcAddressQueryResult *symbolStatus); + +/** @} */ /* END CUDA_DRIVER_ENTRY_POINT */ + +/** + * \defgroup CUDA_COREDUMP Coredump Attributes Control API + * + * ___MANBRIEF___ coredump attribute control functions for the low-level CUDA API + * (___CURRENT_FILE___) ___ENDMANBRIEF___ + * + * This section describes the coredump attribute control functions of the low-level CUDA + * driver application programming interface. + * + * @{ + */ + +/** + * Flags for choosing a coredump attribute to get/set + */ +typedef enum CUcoredumpSettings_enum { + CU_COREDUMP_ENABLE_ON_EXCEPTION = 1, + CU_COREDUMP_TRIGGER_HOST, + CU_COREDUMP_LIGHTWEIGHT, + CU_COREDUMP_ENABLE_USER_TRIGGER, + CU_COREDUMP_FILE, + CU_COREDUMP_PIPE, + CU_COREDUMP_MAX +} CUcoredumpSettings; + +/** + * \brief Allows caller to fetch a coredump attribute value for the current context + * + * Returns in \p *value the requested value specified by \p attrib. It is up to the caller + * to ensure that the data type and size of \p *value matches the request. + * + * If the caller calls this function with \p *value equal to NULL, the size of the memory + * region (in bytes) expected for \p attrib will be placed in \p size. + * + * The supported attributes are: + * - ::CU_COREDUMP_ENABLE_ON_EXCEPTION: Bool where ::true means that GPU exceptions from + * this context will create a coredump at the location specified by ::CU_COREDUMP_FILE. + * The default value is ::false unless set to ::true globally or locally, or the + * CU_CTX_USER_COREDUMP_ENABLE flag was set during context creation. + * - ::CU_COREDUMP_TRIGGER_HOST: Bool where ::true means that the host CPU will + * also create a coredump. The default value is ::true unless set to ::false globally or + * or locally. + * - ::CU_COREDUMP_LIGHTWEIGHT: Bool where ::true means that any resulting coredumps + * will not have a dump of GPU memory or non-reloc ELF images. The default value is + * ::false unless set to ::true globally or locally. + * - ::CU_COREDUMP_ENABLE_USER_TRIGGER: Bool where ::true means that a coredump can be + * created by writing to the system pipe specified by ::CU_COREDUMP_PIPE. The default + * value is ::false unless set to ::true globally or locally. + * - ::CU_COREDUMP_FILE: String of up to 1023 characters that defines the location where + * any coredumps generated by this context will be written. The default value is + * ::core.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine running + * the CUDA applications and ::PID is the process ID of the CUDA application. + * - ::CU_COREDUMP_PIPE: String of up to 1023 characters that defines the name of the pipe + * that will be monitored if user-triggered coredumps are enabled. The default value is + * ::corepipe.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine running + * the CUDA application and ::PID is the process ID of the CUDA application. + * + * \param attrib - The enum defining which value to fetch. + * \param value - void* containing the requested data. + * \param size - The size of the memory region \p value points to. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_PERMITTED, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_CONTEXT_IS_DESTROYED + * + * \sa + * ::cuCoredumpGetAttributeGlobal, + * ::cuCoredumpSetAttribute, + * ::cuCoredumpSetAttributeGlobal + */ +CUresult CUDAAPI cuCoredumpGetAttribute(CUcoredumpSettings attrib, void* value, size_t *size); + +/** + * \brief Allows caller to fetch a coredump attribute value for the entire application + * + * Returns in \p *value the requested value specified by \p attrib. It is up to the caller + * to ensure that the data type and size of \p *value matches the request. + * + * If the caller calls this function with \p *value equal to NULL, the size of the memory + * region (in bytes) expected for \p attrib will be placed in \p size. + * + * The supported attributes are: + * - ::CU_COREDUMP_ENABLE_ON_EXCEPTION: Bool where ::true means that GPU exceptions from + * this context will create a coredump at the location specified by ::CU_COREDUMP_FILE. + * The default value is ::false. + * - ::CU_COREDUMP_TRIGGER_HOST: Bool where ::true means that the host CPU will + * also create a coredump. The default value is ::true. + * - ::CU_COREDUMP_LIGHTWEIGHT: Bool where ::true means that any resulting coredumps + * will not have a dump of GPU memory or non-reloc ELF images. The default value is + * ::false. + * - ::CU_COREDUMP_ENABLE_USER_TRIGGER: Bool where ::true means that a coredump can be + * created by writing to the system pipe specified by ::CU_COREDUMP_PIPE. The default + * value is ::false. + * - ::CU_COREDUMP_FILE: String of up to 1023 characters that defines the location where + * any coredumps generated by this context will be written. The default value is + * ::core.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine running + * the CUDA applications and ::PID is the process ID of the CUDA application. + * - ::CU_COREDUMP_PIPE: String of up to 1023 characters that defines the name of the pipe + * that will be monitored if user-triggered coredumps are enabled. The default value is + * ::corepipe.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine running + * the CUDA application and ::PID is the process ID of the CUDA application. + * + * \param attrib - The enum defining which value to fetch. + * \param value - void* containing the requested data. + * \param size - The size of the memory region \p value points to. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE + * + * \sa + * ::cuCoredumpGetAttribute, + * ::cuCoredumpSetAttribute, + * ::cuCoredumpSetAttributeGlobal + */ +CUresult CUDAAPI cuCoredumpGetAttributeGlobal(CUcoredumpSettings attrib, void *value, size_t *size); + +/** + * \brief Allows caller to set a coredump attribute value for the current context + * + * This function should be considered an alternate interface to the CUDA-GDB environment + * variables defined in this document: https://docs.nvidia.com/cuda/cuda-gdb/index.html#gpu-coredump + * + * An important design decision to note is that any coredump environment variable values + * set before CUDA initializes will take permanent precedence over any values set with this + * this function. This decision was made to ensure no change in behavior for any users that + * may be currently using these variables to get coredumps. + * + * \p *value shall contain the requested value specified by \p set. It is up to the caller + * to ensure that the data type and size of \p *value matches the request. + * + * If the caller calls this function with \p *value equal to NULL, the size of the memory + * region (in bytes) expected for \p set will be placed in \p size. + * + * ::CU_COREDUMP_ENABLE_USER_TRIGGER and ::CU_COREDUMP_PIPE cannot be set on a per-context basis. + * + * The supported attributes are: + * - ::CU_COREDUMP_ENABLE_ON_EXCEPTION: Bool where ::true means that GPU exceptions from + * this context will create a coredump at the location specified by ::CU_COREDUMP_FILE. + * The default value is ::false. + * - ::CU_COREDUMP_TRIGGER_HOST: Bool where ::true means that the host CPU will + * also create a coredump. The default value is ::true. + * - ::CU_COREDUMP_LIGHTWEIGHT: Bool where ::true means that any resulting coredumps + * will not have a dump of GPU memory or non-reloc ELF images. The default value is + * ::false. + * - ::CU_COREDUMP_FILE: String of up to 1023 characters that defines the location where + * any coredumps generated by this context will be written. The default value is + * ::core.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine running + * the CUDA applications and ::PID is the process ID of the CUDA application. + * + * \param attrib - The enum defining which value to set. + * \param value - void* containing the requested data. + * \param size - The size of the memory region \p value points to. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_PERMITTED, + * ::CUDA_ERROR_DEINITIALIZED, + * ::CUDA_ERROR_NOT_INITIALIZED, + * ::CUDA_ERROR_INVALID_CONTEXT, + * ::CUDA_ERROR_CONTEXT_IS_DESTROYED + * + * \sa + * ::cuCoredumpGetAttributeGlobal, + * ::cuCoredumpGetAttribute, + * ::cuCoredumpSetAttributeGlobal + */ +CUresult CUDAAPI cuCoredumpSetAttribute(CUcoredumpSettings attrib, void* value, size_t *size); + +/** + * \brief Allows caller to set a coredump attribute value globally + * + * This function should be considered an alternate interface to the CUDA-GDB environment + * variables defined in this document: https://docs.nvidia.com/cuda/cuda-gdb/index.html#gpu-coredump + * + * An important design decision to note is that any coredump environment variable values + * set before CUDA initializes will take permanent precedence over any values set with this + * this function. This decision was made to ensure no change in behavior for any users that + * may be currently using these variables to get coredumps. + * + * \p *value shall contain the requested value specified by \p set. It is up to the caller + * to ensure that the data type and size of \p *value matches the request. + * + * If the caller calls this function with \p *value equal to NULL, the size of the memory + * region (in bytes) expected for \p set will be placed in \p size. + * + * The supported attributes are: + * - ::CU_COREDUMP_ENABLE_ON_EXCEPTION: Bool where ::true means that GPU exceptions from + * this context will create a coredump at the location specified by ::CU_COREDUMP_FILE. + * The default value is ::false. + * - ::CU_COREDUMP_TRIGGER_HOST: Bool where ::true means that the host CPU will + * also create a coredump. The default value is ::true. + * - ::CU_COREDUMP_LIGHTWEIGHT: Bool where ::true means that any resulting coredumps + * will not have a dump of GPU memory or non-reloc ELF images. The default value is + * ::false. + * - ::CU_COREDUMP_ENABLE_USER_TRIGGER: Bool where ::true means that a coredump can be + * created by writing to the system pipe specified by ::CU_COREDUMP_PIPE. The default + * value is ::false. + * - ::CU_COREDUMP_FILE: String of up to 1023 characters that defines the location where + * any coredumps generated by this context will be written. The default value is + * ::core.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine running + * the CUDA applications and ::PID is the process ID of the CUDA application. + * - ::CU_COREDUMP_PIPE: String of up to 1023 characters that defines the name of the pipe + * that will be monitored if user-triggered coredumps are enabled. This value may not be + * changed after ::CU_COREDUMP_ENABLE_USER_TRIGGER is set to ::true. The default + * value is ::corepipe.cuda.HOSTNAME.PID where ::HOSTNAME is the host name of the machine + * running the CUDA application and ::PID is the process ID of the CUDA application. + * + * \param attrib - The enum defining which value to set. + * \param value - void* containing the requested data. + * \param size - The size of the memory region \p value points to. + * + * \return + * ::CUDA_SUCCESS, + * ::CUDA_ERROR_INVALID_VALUE, + * ::CUDA_ERROR_NOT_PERMITTED + * + * \sa + * ::cuCoredumpGetAttribute, + * ::cuCoredumpGetAttributeGlobal, + * ::cuCoredumpSetAttribute + */ +CUresult CUDAAPI cuCoredumpSetAttributeGlobal(CUcoredumpSettings attrib, void *value, size_t *size); + +/** @} */ /* END CUDA_COREDUMP */ + +CUresult CUDAAPI cuGetExportTable(const void **ppExportTable, const CUuuid *pExportTableId); + +/** + * CUDA API versioning support + */ +#if defined(__CUDA_API_VERSION_INTERNAL) + #undef cuMemHostRegister + #undef cuGraphicsResourceSetMapFlags + #undef cuLinkCreate + #undef cuLinkAddData + #undef cuLinkAddFile + #undef cuDeviceTotalMem + #undef cuCtxCreate + #undef cuModuleGetGlobal + #undef cuMemGetInfo + #undef cuMemAlloc + #undef cuMemAllocPitch + #undef cuMemFree + #undef cuMemGetAddressRange + #undef cuMemAllocHost + #undef cuMemHostGetDevicePointer + #undef cuMemcpyHtoD + #undef cuMemcpyDtoH + #undef cuMemcpyDtoD + #undef cuMemcpyDtoA + #undef cuMemcpyAtoD + #undef cuMemcpyHtoA + #undef cuMemcpyAtoH + #undef cuMemcpyAtoA + #undef cuMemcpyHtoAAsync + #undef cuMemcpyAtoHAsync + #undef cuMemcpy2D + #undef cuMemcpy2DUnaligned + #undef cuMemcpy3D + #undef cuMemcpyHtoDAsync + #undef cuMemcpyDtoHAsync + #undef cuMemcpyDtoDAsync + #undef cuMemcpy2DAsync + #undef cuMemcpy3DAsync + #undef cuMemsetD8 + #undef cuMemsetD16 + #undef cuMemsetD32 + #undef cuMemsetD2D8 + #undef cuMemsetD2D16 + #undef cuMemsetD2D32 + #undef cuArrayCreate + #undef cuArrayGetDescriptor + #undef cuArray3DCreate + #undef cuArray3DGetDescriptor + #undef cuTexRefSetAddress + #undef cuTexRefSetAddress2D + #undef cuTexRefGetAddress + #undef cuGraphicsResourceGetMappedPointer + #undef cuCtxDestroy + #undef cuCtxPopCurrent + #undef cuCtxPushCurrent + #undef cuStreamDestroy + #undef cuEventDestroy + #undef cuMemcpy + #undef cuMemcpyAsync + #undef cuMemcpyPeer + #undef cuMemcpyPeerAsync + #undef cuMemcpy3DPeer + #undef cuMemcpy3DPeerAsync + #undef cuMemsetD8Async + #undef cuMemsetD16Async + #undef cuMemsetD32Async + #undef cuMemsetD2D8Async + #undef cuMemsetD2D16Async + #undef cuMemsetD2D32Async + #undef cuStreamGetPriority + #undef cuStreamGetId + #undef cuStreamGetFlags + #undef cuStreamGetCtx + #undef cuStreamWaitEvent + #undef cuStreamAddCallback + #undef cuStreamAttachMemAsync + #undef cuStreamQuery + #undef cuStreamSynchronize + #undef cuEventRecord + #undef cuEventRecordWithFlags + #undef cuLaunchKernel + #undef cuLaunchKernelEx + #undef cuLaunchHostFunc + #undef cuGraphicsMapResources + #undef cuGraphicsUnmapResources + #undef cuStreamWriteValue32 + #undef cuStreamWaitValue32 + #undef cuStreamWriteValue64 + #undef cuStreamWaitValue64 + #undef cuStreamBatchMemOp + #undef cuStreamWriteValue32_v2 + #undef cuStreamWaitValue32_v2 + #undef cuStreamWriteValue64_v2 + #undef cuStreamWaitValue64_v2 + #undef cuStreamBatchMemOp_v2 + #undef cuMemPrefetchAsync + #undef cuLaunchCooperativeKernel + #undef cuSignalExternalSemaphoresAsync + #undef cuWaitExternalSemaphoresAsync + #undef cuStreamBeginCapture + #undef cuStreamEndCapture + #undef cuStreamIsCapturing + #undef cuStreamGetCaptureInfo + #undef cuStreamGetCaptureInfo_v2 + #undef cuGraphInstantiateWithParams + #undef cuGraphExecUpdate + #undef cuGraphUpload + #undef cuGraphLaunch + #undef cuDevicePrimaryCtxRelease + #undef cuDevicePrimaryCtxReset + #undef cuDevicePrimaryCtxSetFlags + #undef cuIpcOpenMemHandle + #undef cuStreamCopyAttributes + #undef cuStreamSetAttribute + #undef cuStreamGetAttribute + #undef cuGraphInstantiate + #undef cuGraphAddKernelNode + #undef cuGraphKernelNodeGetParams + #undef cuGraphKernelNodeSetParams + #undef cuGraphExecKernelNodeSetParams + #undef cuMemMapArrayAsync + #undef cuMemFreeAsync + #undef cuMemAllocAsync + #undef cuMemAllocFromPoolAsync + #undef cuStreamUpdateCaptureDependencies + #undef cuGetProcAddress + + CUresult CUDAAPI cuMemHostRegister(void *p, size_t bytesize, unsigned int Flags); + CUresult CUDAAPI cuGraphicsResourceSetMapFlags(CUgraphicsResource resource, unsigned int flags); + CUresult CUDAAPI cuLinkCreate(unsigned int numOptions, CUjit_option *options, void **optionValues, CUlinkState *stateOut); + CUresult CUDAAPI cuLinkAddData(CUlinkState state, CUjitInputType type, void *data, size_t size, const char *name, + unsigned int numOptions, CUjit_option *options, void **optionValues); + CUresult CUDAAPI cuLinkAddFile(CUlinkState state, CUjitInputType type, const char *path, + unsigned int numOptions, CUjit_option *options, void **optionValues); + CUresult CUDAAPI cuTexRefSetAddress2D_v2(CUtexref hTexRef, const CUDA_ARRAY_DESCRIPTOR *desc, CUdeviceptr dptr, size_t Pitch); + + typedef unsigned int CUdeviceptr_v1; + + typedef struct CUDA_MEMCPY2D_v1_st + { + unsigned int srcXInBytes; /**< Source X in bytes */ + unsigned int srcY; /**< Source Y */ + CUmemorytype srcMemoryType; /**< Source memory type (host, device, array) */ + const void *srcHost; /**< Source host pointer */ + CUdeviceptr_v1 srcDevice; /**< Source device pointer */ + CUarray srcArray; /**< Source array reference */ + unsigned int srcPitch; /**< Source pitch (ignored when src is array) */ + + unsigned int dstXInBytes; /**< Destination X in bytes */ + unsigned int dstY; /**< Destination Y */ + CUmemorytype dstMemoryType; /**< Destination memory type (host, device, array) */ + void *dstHost; /**< Destination host pointer */ + CUdeviceptr_v1 dstDevice; /**< Destination device pointer */ + CUarray dstArray; /**< Destination array reference */ + unsigned int dstPitch; /**< Destination pitch (ignored when dst is array) */ + + unsigned int WidthInBytes; /**< Width of 2D memory copy in bytes */ + unsigned int Height; /**< Height of 2D memory copy */ + } CUDA_MEMCPY2D_v1; + + typedef struct CUDA_MEMCPY3D_v1_st + { + unsigned int srcXInBytes; /**< Source X in bytes */ + unsigned int srcY; /**< Source Y */ + unsigned int srcZ; /**< Source Z */ + unsigned int srcLOD; /**< Source LOD */ + CUmemorytype srcMemoryType; /**< Source memory type (host, device, array) */ + const void *srcHost; /**< Source host pointer */ + CUdeviceptr_v1 srcDevice; /**< Source device pointer */ + CUarray srcArray; /**< Source array reference */ + void *reserved0; /**< Must be NULL */ + unsigned int srcPitch; /**< Source pitch (ignored when src is array) */ + unsigned int srcHeight; /**< Source height (ignored when src is array; may be 0 if Depth==1) */ + + unsigned int dstXInBytes; /**< Destination X in bytes */ + unsigned int dstY; /**< Destination Y */ + unsigned int dstZ; /**< Destination Z */ + unsigned int dstLOD; /**< Destination LOD */ + CUmemorytype dstMemoryType; /**< Destination memory type (host, device, array) */ + void *dstHost; /**< Destination host pointer */ + CUdeviceptr_v1 dstDevice; /**< Destination device pointer */ + CUarray dstArray; /**< Destination array reference */ + void *reserved1; /**< Must be NULL */ + unsigned int dstPitch; /**< Destination pitch (ignored when dst is array) */ + unsigned int dstHeight; /**< Destination height (ignored when dst is array; may be 0 if Depth==1) */ + + unsigned int WidthInBytes; /**< Width of 3D memory copy in bytes */ + unsigned int Height; /**< Height of 3D memory copy */ + unsigned int Depth; /**< Depth of 3D memory copy */ + } CUDA_MEMCPY3D_v1; + + typedef struct CUDA_ARRAY_DESCRIPTOR_v1_st + { + unsigned int Width; /**< Width of array */ + unsigned int Height; /**< Height of array */ + + CUarray_format Format; /**< Array format */ + unsigned int NumChannels; /**< Channels per array element */ + } CUDA_ARRAY_DESCRIPTOR_v1; + + typedef struct CUDA_ARRAY3D_DESCRIPTOR_v1_st + { + unsigned int Width; /**< Width of 3D array */ + unsigned int Height; /**< Height of 3D array */ + unsigned int Depth; /**< Depth of 3D array */ + + CUarray_format Format; /**< Array format */ + unsigned int NumChannels; /**< Channels per array element */ + unsigned int Flags; /**< Flags */ + } CUDA_ARRAY3D_DESCRIPTOR_v1; + + CUresult CUDAAPI cuDeviceTotalMem(unsigned int *bytes, CUdevice dev); + CUresult CUDAAPI cuCtxCreate(CUcontext *pctx, unsigned int flags, CUdevice dev); + CUresult CUDAAPI cuModuleGetGlobal(CUdeviceptr_v1 *dptr, unsigned int *bytes, CUmodule hmod, const char *name); + CUresult CUDAAPI cuMemGetInfo(unsigned int *free, unsigned int *total); + CUresult CUDAAPI cuMemAlloc(CUdeviceptr_v1 *dptr, unsigned int bytesize); + CUresult CUDAAPI cuMemAllocPitch(CUdeviceptr_v1 *dptr, unsigned int *pPitch, unsigned int WidthInBytes, unsigned int Height, unsigned int ElementSizeBytes); + CUresult CUDAAPI cuMemFree(CUdeviceptr_v1 dptr); + CUresult CUDAAPI cuMemGetAddressRange(CUdeviceptr_v1 *pbase, unsigned int *psize, CUdeviceptr_v1 dptr); + CUresult CUDAAPI cuMemAllocHost(void **pp, unsigned int bytesize); + CUresult CUDAAPI cuMemHostGetDevicePointer(CUdeviceptr_v1 *pdptr, void *p, unsigned int Flags); + CUresult CUDAAPI cuMemcpyHtoD(CUdeviceptr_v1 dstDevice, const void *srcHost, unsigned int ByteCount); + CUresult CUDAAPI cuMemcpyDtoH(void *dstHost, CUdeviceptr_v1 srcDevice, unsigned int ByteCount); + CUresult CUDAAPI cuMemcpyDtoD(CUdeviceptr_v1 dstDevice, CUdeviceptr_v1 srcDevice, unsigned int ByteCount); + CUresult CUDAAPI cuMemcpyDtoA(CUarray dstArray, unsigned int dstOffset, CUdeviceptr_v1 srcDevice, unsigned int ByteCount); + CUresult CUDAAPI cuMemcpyAtoD(CUdeviceptr_v1 dstDevice, CUarray srcArray, unsigned int srcOffset, unsigned int ByteCount); + CUresult CUDAAPI cuMemcpyHtoA(CUarray dstArray, unsigned int dstOffset, const void *srcHost, unsigned int ByteCount); + CUresult CUDAAPI cuMemcpyAtoH(void *dstHost, CUarray srcArray, unsigned int srcOffset, unsigned int ByteCount); + CUresult CUDAAPI cuMemcpyAtoA(CUarray dstArray, unsigned int dstOffset, CUarray srcArray, unsigned int srcOffset, unsigned int ByteCount); + CUresult CUDAAPI cuMemcpyHtoAAsync(CUarray dstArray, unsigned int dstOffset, const void *srcHost, unsigned int ByteCount, CUstream hStream); + CUresult CUDAAPI cuMemcpyAtoHAsync(void *dstHost, CUarray srcArray, unsigned int srcOffset, unsigned int ByteCount, CUstream hStream); + CUresult CUDAAPI cuMemcpy2D(const CUDA_MEMCPY2D_v1 *pCopy); + CUresult CUDAAPI cuMemcpy2DUnaligned(const CUDA_MEMCPY2D_v1 *pCopy); + CUresult CUDAAPI cuMemcpy3D(const CUDA_MEMCPY3D_v1 *pCopy); + CUresult CUDAAPI cuMemcpyHtoDAsync(CUdeviceptr_v1 dstDevice, const void *srcHost, unsigned int ByteCount, CUstream hStream); + CUresult CUDAAPI cuMemcpyDtoHAsync(void *dstHost, CUdeviceptr_v1 srcDevice, unsigned int ByteCount, CUstream hStream); + CUresult CUDAAPI cuMemcpyDtoDAsync(CUdeviceptr_v1 dstDevice, CUdeviceptr_v1 srcDevice, unsigned int ByteCount, CUstream hStream); + CUresult CUDAAPI cuMemcpy2DAsync(const CUDA_MEMCPY2D_v1 *pCopy, CUstream hStream); + CUresult CUDAAPI cuMemcpy3DAsync(const CUDA_MEMCPY3D_v1 *pCopy, CUstream hStream); + CUresult CUDAAPI cuMemsetD8(CUdeviceptr_v1 dstDevice, unsigned char uc, unsigned int N); + CUresult CUDAAPI cuMemsetD16(CUdeviceptr_v1 dstDevice, unsigned short us, unsigned int N); + CUresult CUDAAPI cuMemsetD32(CUdeviceptr_v1 dstDevice, unsigned int ui, unsigned int N); + CUresult CUDAAPI cuMemsetD2D8(CUdeviceptr_v1 dstDevice, unsigned int dstPitch, unsigned char uc, unsigned int Width, unsigned int Height); + CUresult CUDAAPI cuMemsetD2D16(CUdeviceptr_v1 dstDevice, unsigned int dstPitch, unsigned short us, unsigned int Width, unsigned int Height); + CUresult CUDAAPI cuMemsetD2D32(CUdeviceptr_v1 dstDevice, unsigned int dstPitch, unsigned int ui, unsigned int Width, unsigned int Height); + CUresult CUDAAPI cuArrayCreate(CUarray *pHandle, const CUDA_ARRAY_DESCRIPTOR_v1 *pAllocateArray); + CUresult CUDAAPI cuArrayGetDescriptor(CUDA_ARRAY_DESCRIPTOR_v1 *pArrayDescriptor, CUarray hArray); + CUresult CUDAAPI cuArray3DCreate(CUarray *pHandle, const CUDA_ARRAY3D_DESCRIPTOR_v1 *pAllocateArray); + CUresult CUDAAPI cuArray3DGetDescriptor(CUDA_ARRAY3D_DESCRIPTOR_v1 *pArrayDescriptor, CUarray hArray); + CUresult CUDAAPI cuTexRefSetAddress(unsigned int *ByteOffset, CUtexref hTexRef, CUdeviceptr_v1 dptr, unsigned int bytes); + CUresult CUDAAPI cuTexRefSetAddress2D(CUtexref hTexRef, const CUDA_ARRAY_DESCRIPTOR_v1 *desc, CUdeviceptr_v1 dptr, unsigned int Pitch); + CUresult CUDAAPI cuTexRefGetAddress(CUdeviceptr_v1 *pdptr, CUtexref hTexRef); + CUresult CUDAAPI cuGraphicsResourceGetMappedPointer(CUdeviceptr_v1 *pDevPtr, unsigned int *pSize, CUgraphicsResource resource); + + CUresult CUDAAPI cuCtxDestroy(CUcontext ctx); + CUresult CUDAAPI cuCtxPopCurrent(CUcontext *pctx); + CUresult CUDAAPI cuCtxPushCurrent(CUcontext ctx); + CUresult CUDAAPI cuStreamDestroy(CUstream hStream); + CUresult CUDAAPI cuEventDestroy(CUevent hEvent); + CUresult CUDAAPI cuDevicePrimaryCtxRelease(CUdevice dev); + CUresult CUDAAPI cuDevicePrimaryCtxReset(CUdevice dev); + CUresult CUDAAPI cuDevicePrimaryCtxSetFlags(CUdevice dev, unsigned int flags); + + CUresult CUDAAPI cuMemcpyHtoD_v2(CUdeviceptr dstDevice, const void *srcHost, size_t ByteCount); + CUresult CUDAAPI cuMemcpyDtoH_v2(void *dstHost, CUdeviceptr srcDevice, size_t ByteCount); + CUresult CUDAAPI cuMemcpyDtoD_v2(CUdeviceptr dstDevice, CUdeviceptr srcDevice, size_t ByteCount); + CUresult CUDAAPI cuMemcpyDtoA_v2(CUarray dstArray, size_t dstOffset, CUdeviceptr srcDevice, size_t ByteCount); + CUresult CUDAAPI cuMemcpyAtoD_v2(CUdeviceptr dstDevice, CUarray srcArray, size_t srcOffset, size_t ByteCount); + CUresult CUDAAPI cuMemcpyHtoA_v2(CUarray dstArray, size_t dstOffset, const void *srcHost, size_t ByteCount); + CUresult CUDAAPI cuMemcpyAtoH_v2(void *dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount); + CUresult CUDAAPI cuMemcpyAtoA_v2(CUarray dstArray, size_t dstOffset, CUarray srcArray, size_t srcOffset, size_t ByteCount); + CUresult CUDAAPI cuMemcpyHtoAAsync_v2(CUarray dstArray, size_t dstOffset, const void *srcHost, size_t ByteCount, CUstream hStream); + CUresult CUDAAPI cuMemcpyAtoHAsync_v2(void *dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount, CUstream hStream); + CUresult CUDAAPI cuMemcpy2D_v2(const CUDA_MEMCPY2D *pCopy); + CUresult CUDAAPI cuMemcpy2DUnaligned_v2(const CUDA_MEMCPY2D *pCopy); + CUresult CUDAAPI cuMemcpy3D_v2(const CUDA_MEMCPY3D *pCopy); + CUresult CUDAAPI cuMemcpyHtoDAsync_v2(CUdeviceptr dstDevice, const void *srcHost, size_t ByteCount, CUstream hStream); + CUresult CUDAAPI cuMemcpyDtoHAsync_v2(void *dstHost, CUdeviceptr srcDevice, size_t ByteCount, CUstream hStream); + CUresult CUDAAPI cuMemcpyDtoDAsync_v2(CUdeviceptr dstDevice, CUdeviceptr srcDevice, size_t ByteCount, CUstream hStream); + CUresult CUDAAPI cuMemcpy2DAsync_v2(const CUDA_MEMCPY2D *pCopy, CUstream hStream); + CUresult CUDAAPI cuMemcpy3DAsync_v2(const CUDA_MEMCPY3D *pCopy, CUstream hStream); + CUresult CUDAAPI cuMemsetD8_v2(CUdeviceptr dstDevice, unsigned char uc, size_t N); + CUresult CUDAAPI cuMemsetD16_v2(CUdeviceptr dstDevice, unsigned short us, size_t N); + CUresult CUDAAPI cuMemsetD32_v2(CUdeviceptr dstDevice, unsigned int ui, size_t N); + CUresult CUDAAPI cuMemsetD2D8_v2(CUdeviceptr dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height); + CUresult CUDAAPI cuMemsetD2D16_v2(CUdeviceptr dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height); + CUresult CUDAAPI cuMemsetD2D32_v2(CUdeviceptr dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height); + CUresult CUDAAPI cuMemcpy(CUdeviceptr dst, CUdeviceptr src, size_t ByteCount); + CUresult CUDAAPI cuMemcpyAsync(CUdeviceptr dst, CUdeviceptr src, size_t ByteCount, CUstream hStream); + CUresult CUDAAPI cuMemcpyPeer(CUdeviceptr dstDevice, CUcontext dstContext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount); + CUresult CUDAAPI cuMemcpyPeerAsync(CUdeviceptr dstDevice, CUcontext dstContext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount, CUstream hStream); + CUresult CUDAAPI cuMemcpy3DPeer(const CUDA_MEMCPY3D_PEER *pCopy); + CUresult CUDAAPI cuMemcpy3DPeerAsync(const CUDA_MEMCPY3D_PEER *pCopy, CUstream hStream); + + CUresult CUDAAPI cuMemsetD8Async(CUdeviceptr dstDevice, unsigned char uc, size_t N, CUstream hStream); + CUresult CUDAAPI cuMemsetD16Async(CUdeviceptr dstDevice, unsigned short us, size_t N, CUstream hStream); + CUresult CUDAAPI cuMemsetD32Async(CUdeviceptr dstDevice, unsigned int ui, size_t N, CUstream hStream); + CUresult CUDAAPI cuMemsetD2D8Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height, CUstream hStream); + CUresult CUDAAPI cuMemsetD2D16Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height, CUstream hStream); + CUresult CUDAAPI cuMemsetD2D32Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height, CUstream hStream); + + CUresult CUDAAPI cuStreamGetPriority(CUstream hStream, int *priority); + CUresult CUDAAPI cuStreamGetId(CUstream hStream, unsigned long long *streamId); + CUresult CUDAAPI cuStreamGetFlags(CUstream hStream, unsigned int *flags); + CUresult CUDAAPI cuStreamGetCtx(CUstream hStream, CUcontext *pctx); + CUresult CUDAAPI cuStreamWaitEvent(CUstream hStream, CUevent hEvent, unsigned int Flags); + CUresult CUDAAPI cuStreamAddCallback(CUstream hStream, CUstreamCallback callback, void *userData, unsigned int flags); + CUresult CUDAAPI cuStreamAttachMemAsync(CUstream hStream, CUdeviceptr dptr, size_t length, unsigned int flags); + CUresult CUDAAPI cuStreamQuery(CUstream hStream); + CUresult CUDAAPI cuStreamSynchronize(CUstream hStream); + CUresult CUDAAPI cuEventRecord(CUevent hEvent, CUstream hStream); + CUresult CUDAAPI cuEventRecordWithFlags(CUevent hEvent, CUstream hStream, unsigned int flags); + CUresult CUDAAPI cuLaunchKernel(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void **kernelParams, void **extra); + CUresult CUDAAPI cuLaunchKernelEx(const CUlaunchConfig *config, CUfunction f, void **kernelParams, void **extra); + CUresult CUDAAPI cuLaunchHostFunc(CUstream hStream, CUhostFn fn, void *userData); + CUresult CUDAAPI cuGraphicsMapResources(unsigned int count, CUgraphicsResource *resources, CUstream hStream); + CUresult CUDAAPI cuGraphicsUnmapResources(unsigned int count, CUgraphicsResource *resources, CUstream hStream); + CUresult CUDAAPI cuStreamWriteValue32(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags); + CUresult CUDAAPI cuStreamWaitValue32(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags); + CUresult CUDAAPI cuStreamWriteValue64(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags); + CUresult CUDAAPI cuStreamWaitValue64(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags); + CUresult CUDAAPI cuStreamBatchMemOp(CUstream stream, unsigned int count, CUstreamBatchMemOpParams *paramArray, unsigned int flags); + + CUresult CUDAAPI cuStreamWriteValue32_ptsz(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags); + CUresult CUDAAPI cuStreamWaitValue32_ptsz(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags); + CUresult CUDAAPI cuStreamWriteValue64_ptsz(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags); + CUresult CUDAAPI cuStreamWaitValue64_ptsz(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags); + CUresult CUDAAPI cuStreamBatchMemOp_ptsz(CUstream stream, unsigned int count, CUstreamBatchMemOpParams *paramArray, unsigned int flags); + + CUresult CUDAAPI cuStreamWriteValue32_v2(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags); + CUresult CUDAAPI cuStreamWaitValue32_v2(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags); + CUresult CUDAAPI cuStreamWriteValue64_v2(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags); + CUresult CUDAAPI cuStreamWaitValue64_v2(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags); + CUresult CUDAAPI cuStreamBatchMemOp_v2(CUstream stream, unsigned int count, CUstreamBatchMemOpParams *paramArray, unsigned int flags); + CUresult CUDAAPI cuMemPrefetchAsync(CUdeviceptr devPtr, size_t count, CUdevice dstDevice, CUstream hStream); + CUresult CUDAAPI cuLaunchCooperativeKernel(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void **kernelParams); + CUresult CUDAAPI cuSignalExternalSemaphoresAsync(const CUexternalSemaphore *extSemArray, const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS *paramsArray, unsigned int numExtSems, CUstream stream); + CUresult CUDAAPI cuWaitExternalSemaphoresAsync(const CUexternalSemaphore *extSemArray, const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS *paramsArray, unsigned int numExtSems, CUstream stream); + CUresult CUDAAPI cuStreamBeginCapture(CUstream hStream); + CUresult CUDAAPI cuStreamBeginCapture_ptsz(CUstream hStream); + CUresult CUDAAPI cuStreamBeginCapture_v2(CUstream hStream, CUstreamCaptureMode mode); + CUresult CUDAAPI cuStreamEndCapture(CUstream hStream, CUgraph *phGraph); + CUresult CUDAAPI cuStreamIsCapturing(CUstream hStream, CUstreamCaptureStatus *captureStatus); + CUresult CUDAAPI cuStreamGetCaptureInfo(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out); + CUresult CUDAAPI cuStreamGetCaptureInfo_ptsz(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out); + CUresult CUDAAPI cuStreamGetCaptureInfo_v2(CUstream hStream, CUstreamCaptureStatus *captureStatus_out, cuuint64_t *id_out, CUgraph *graph_out, const CUgraphNode **dependencies_out, size_t *numDependencies_out); + CUresult CUDAAPI cuGraphAddKernelNode(CUgraphNode *phGraphNode, CUgraph hGraph, const CUgraphNode *dependencies, size_t numDependencies, const CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams); + CUresult CUDAAPI cuGraphKernelNodeGetParams(CUgraphNode hNode, CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams); + CUresult CUDAAPI cuGraphKernelNodeSetParams(CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams); + CUresult CUDAAPI cuGraphExecKernelNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS_v1 *nodeParams); + CUresult CUDAAPI cuGraphInstantiateWithParams(CUgraphExec *phGraphExec, CUgraph hGraph, CUDA_GRAPH_INSTANTIATE_PARAMS *instantiateParams); + CUresult CUDAAPI cuGraphExecUpdate(CUgraphExec hGraphExec, CUgraph hGraph, CUgraphNode *hErrorNode_out, CUgraphExecUpdateResult *updateResult_out); + CUresult CUDAAPI cuGraphUpload(CUgraphExec hGraph, CUstream hStream); + CUresult CUDAAPI cuGraphLaunch(CUgraphExec hGraph, CUstream hStream); + CUresult CUDAAPI cuStreamCopyAttributes(CUstream dstStream, CUstream srcStream); + CUresult CUDAAPI cuStreamGetAttribute(CUstream hStream, CUstreamAttrID attr, CUstreamAttrValue *value); + CUresult CUDAAPI cuStreamSetAttribute(CUstream hStream, CUstreamAttrID attr, const CUstreamAttrValue *param); + + CUresult CUDAAPI cuIpcOpenMemHandle(CUdeviceptr *pdptr, CUipcMemHandle handle, unsigned int Flags); + CUresult CUDAAPI cuGraphInstantiate(CUgraphExec *phGraphExec, CUgraph hGraph, CUgraphNode *phErrorNode, char *logBuffer, size_t bufferSize); + CUresult CUDAAPI cuGraphInstantiate_v2(CUgraphExec *phGraphExec, CUgraph hGraph, CUgraphNode *phErrorNode, char *logBuffer, size_t bufferSize); + + CUresult CUDAAPI cuMemMapArrayAsync(CUarrayMapInfo *mapInfoList, unsigned int count, CUstream hStream); + + CUresult CUDAAPI cuMemFreeAsync(CUdeviceptr dptr, CUstream hStream); + CUresult CUDAAPI cuMemAllocAsync(CUdeviceptr *dptr, size_t bytesize, CUstream hStream); + CUresult CUDAAPI cuMemAllocFromPoolAsync(CUdeviceptr *dptr, size_t bytesize, CUmemoryPool pool, CUstream hStream); + + CUresult CUDAAPI cuStreamUpdateCaptureDependencies(CUstream hStream, CUgraphNode *dependencies, size_t numDependencies, unsigned int flags); + CUresult CUDAAPI cuGetProcAddress(const char *symbol, void **pfn, int cudaVersion, cuuint64_t flags); + +#elif defined(__CUDA_API_PER_THREAD_DEFAULT_STREAM) +static inline CUresult cuGetProcAddress_v2_ptsz(const char *symbol, void **funcPtr, int driverVersion, cuuint64_t flags, CUdriverProcAddressQueryResult *symbolStatus) { + const int procAddressMask = (CU_GET_PROC_ADDRESS_LEGACY_STREAM| + CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM); + if ((flags & procAddressMask) == 0) { + flags |= CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM; + } + return cuGetProcAddress_v2(symbol, funcPtr, driverVersion, flags, symbolStatus); +} +#define cuGetProcAddress_v2 cuGetProcAddress_v2_ptsz +#endif + +#ifdef __cplusplus +} +#endif + +#if defined(__GNUC__) + #if defined(__CUDA_API_PUSH_VISIBILITY_DEFAULT) + #pragma GCC visibility pop + #endif +#endif + +#undef __CUDA_DEPRECATED + +#endif /* __cuda_cuda_h__ */ diff --git a/llmeval-env/lib/python3.10/site-packages/triton/third_party/cuda/lib/libdevice.10.bc b/llmeval-env/lib/python3.10/site-packages/triton/third_party/cuda/lib/libdevice.10.bc new file mode 100644 index 0000000000000000000000000000000000000000..b2c75a5026df628a34c4095ec05806af50fe0d87 Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/triton/third_party/cuda/lib/libdevice.10.bc differ