r"""
This package adds support for CUDA tensor types, that implement the same
function as CPU tensors, but they utilize GPUs for computation.
It is lazily initialized, so you can always import it, and use
:func:`is_available()` to determine if your system supports CUDA.
:ref:`cuda-semantics` has more details about working with CUDA.
"""
import contextlib
import platform
import ctypes
import os
import sys
import torch
import traceback
import warnings
from torch._six import raise_from
from subprocess import Popen, PIPE
from multiprocessing.util import register_after_fork as _register_after_fork
from ._utils import _get_device_index
_initialized = False
_queued_calls = [] # don't invoke these until initialization occurs
_in_bad_fork = False # this global is also used in torch.manual_seed
_original_pid = False
_cudart = None
def find_cuda_windows_lib():
# Override the default search process
# Fixes https://github.com/pytorch/pytorch/issues/20202
# The libary selection will be done in these directories one by one
# 1. [Package Root]\Lib
# That's where our libraries are in, which should be loaded first.
# 2. [Python Root]\Library\bin
# That's where `cudatoolkit` store the cuda libraries.
# 3. Default directories
# That is stored in the environment variable `PATH`.
test_env = os.environ.copy()
old_path = test_env['PATH']
py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
th_dll_path = os.path.join(os.path.dirname(
os.path.dirname(__file__)), 'lib')
test_env['PATH'] = ';'.join([th_dll_path, py_dll_path, old_path])
proc = Popen(['where', 'cudart64*.dll'], stdout=PIPE,
stderr=PIPE, stdin=PIPE, env=test_env)
out, err = proc.communicate()
out = out.decode().strip()
if len(out) > 0:
if out.find('\r\n') != -1:
out = out.split('\r\n')[0]
cuda_lib_name = os.path.basename(out)
cuda_lib = os.path.splitext(cuda_lib_name)[0]
cuda_lib = str(cuda_lib)
return ctypes.cdll.LoadLibrary(cuda_lib)
else:
return None
[docs]def is_available():
r"""Returns a bool indicating if CUDA is currently available."""
if (not hasattr(torch._C, '_cuda_isDriverSufficient') or
not torch._C._cuda_isDriverSufficient()):
return False
return torch._C._cuda_getDeviceCount() > 0
def _sleep(cycles):
torch._C._cuda_sleep(cycles)
def _load_cudart():
# First check the main program for CUDA symbols
if platform.system() == 'Windows':
lib = find_cuda_windows_lib()
else:
lib = ctypes.cdll.LoadLibrary(None)
if hasattr(lib, 'cudaGetErrorName'):
return lib
raise RuntimeError(
"couldn't find libcudart. Make sure CUDA libraries are installed in a "
"default location, or that they're in {}."
.format('DYLD_LIBRARY_PATH' if platform.system() == 'Darwin' else
'LD_LIBRARY_PATH'))
def _check_driver():
if not hasattr(torch._C, '_cuda_isDriverSufficient'):
raise AssertionError("Torch not compiled with CUDA enabled")
if not torch._C._cuda_isDriverSufficient():
if torch._C._cuda_getDriverVersion() == 0:
# found no NVIDIA driver on the system
raise AssertionError("""
Found no NVIDIA driver on your system. Please check that you
have an NVIDIA GPU and installed a driver from
http://www.nvidia.com/Download/index.aspx""")
else:
# TODO: directly link to the alternative bin that needs install
raise AssertionError("""
The NVIDIA driver on your system is too old (found version {}).
Please update your GPU driver by downloading and installing a new
version from the URL: http://www.nvidia.com/Download/index.aspx
Alternatively, go to: https://pytorch.org to install
a PyTorch version that has been compiled with your version
of the CUDA driver.""".format(str(torch._C._cuda_getDriverVersion())))
def _check_capability():
incorrect_binary_warn = """
Found GPU%d %s which requires CUDA_VERSION >= %d to
work properly, but your PyTorch was compiled
with CUDA_VERSION %d. Please install the correct PyTorch binary
using instructions from https://pytorch.org
"""
old_gpu_warn = """
Found GPU%d %s which is of cuda capability %d.%d.
PyTorch no longer supports this GPU because it is too old.
The minimum cuda capability that we support is 3.5.
"""
CUDA_VERSION = torch._C._cuda_getCompiledVersion()
for d in range(device_count()):
capability = get_device_capability(d)
major = capability[0]
minor = capability[1]
name = get_device_name(d)
if capability == (3, 0) or major < 3:
warnings.warn(old_gpu_warn % (d, name, major, capability[1]))
elif CUDA_VERSION <= 9000 and major >= 7 and minor >= 5:
warnings.warn(incorrect_binary_warn % (d, name, 10000, CUDA_VERSION))
def _lazy_call(callable):
if _initialized:
callable()
else:
# Don't store the actual traceback to avoid memory cycle
_queued_calls.append((callable, traceback.format_stack()))
_lazy_call(_check_capability)
class DeferredCudaCallError(Exception):
pass
[docs]def init():
r"""Initialize PyTorch's CUDA state. You may need to call
this explicitly if you are interacting with PyTorch via
its C API, as Python bindings for CUDA functionality will not
be until this initialization takes place. Ordinary users
should not need this, as all of PyTorch's CUDA methods
automatically initialize CUDA state on-demand.
Does nothing if the CUDA state is already initialized.
"""
_lazy_init()
def _lazy_init():
global _initialized, _cudart, _original_pid, _queued_calls
if _initialized:
return
if _in_bad_fork:
from sys import version_info
if version_info < (3, 4):
msg = ("To use CUDA with multiprocessing, you must use Python "
"3.4+ and the 'spawn' start method")
else:
msg = ("To use CUDA with multiprocessing, you must use the "
"'spawn' start method")
raise RuntimeError(
"Cannot re-initialize CUDA in forked subprocess. " + msg)
_check_driver()
torch._C._cuda_init()
_cudart = _load_cudart()
_cudart.cudaGetErrorName.restype = ctypes.c_char_p
_cudart.cudaGetErrorString.restype = ctypes.c_char_p
_original_pid = os.getpid()
_initialized = True
# Important to do this after _initialized, since some queued calls
# may themselves call _lazy_init()
for queued_call, orig_traceback in _queued_calls:
try:
queued_call()
except Exception as e:
msg = ("CUDA call failed lazily at initialization with error: {}\n\n"
"CUDA call was originally invoked at:\n\n{}").format(str(e), orig_traceback)
raise_from(DeferredCudaCallError(msg), e)
def _after_fork(arg):
global _initialized, _in_bad_fork
if _initialized and _original_pid != os.getpid():
_initialized = False
_in_bad_fork = True
_CudaBase.__new__ = _lazy_new
torch._C._cuda_set_run_yet_variable_to_false()
_register_after_fork(_after_fork, _after_fork)
def cudart():
_lazy_init()
return _cudart
class cudaStatus(object):
SUCCESS = 0
ERROR_NOT_READY = 34
class CudaError(RuntimeError):
def __init__(self, code):
msg = cudart().cudaGetErrorString(code).decode('utf-8')
super(CudaError, self).__init__('{0} ({1})'.format(msg, code))
def check_error(res):
if res != cudaStatus.SUCCESS:
raise CudaError(res)
[docs]class device(object):
r"""Context-manager that changes the selected device.
Arguments:
device (torch.device or int): device index to select. It's a no-op if
this argument is a negative integer or ``None``.
"""
def __init__(self, device):
self.idx = _get_device_index(device, optional=True)
self.prev_idx = -1
def __enter__(self):
if self.idx == -1:
return
self.prev_idx = torch._C._cuda_getDevice()
if self.prev_idx != self.idx:
torch._C._cuda_setDevice(self.idx)
_lazy_init()
def __exit__(self, *args):
if self.prev_idx != self.idx:
torch._C._cuda_setDevice(self.prev_idx)
return False
[docs]class device_of(device):
r"""Context-manager that changes the current device to that of given object.
You can use both tensors and storages as arguments. If a given object is
not allocated on a GPU, this is a no-op.
Arguments:
obj (Tensor or Storage): object allocated on the selected device.
"""
def __init__(self, obj):
idx = obj.get_device() if obj.is_cuda else -1
super(device_of, self).__init__(idx)
[docs]def set_device(device):
r"""Sets the current device.
Usage of this function is discouraged in favor of :any:`device`. In most
cases it's better to use ``CUDA_VISIBLE_DEVICES`` environmental variable.
Arguments:
device (torch.device or int): selected device. This function is a no-op
if this argument is negative.
"""
device = _get_device_index(device)
if device >= 0:
torch._C._cuda_setDevice(device)
[docs]def get_device_name(device=None):
r"""Gets the name of a device.
Arguments:
device (torch.device or int, optional): device for which to return the
name. This function is a no-op if this argument is a negative
integer. It uses the current device, given by :func:`~torch.cuda.current_device`,
if :attr:`device` is ``None`` (default).
"""
return get_device_properties(device).name
[docs]def get_device_capability(device=None):
r"""Gets the cuda capability of a device.
Arguments:
device (torch.device or int, optional): device for which to return the
device capability. This function is a no-op if this argument is
a negative integer. It uses the current device, given by
:func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
(default).
Returns:
tuple(int, int): the major and minor cuda capability of the device
"""
prop = get_device_properties(device)
return prop.major, prop.minor
def get_device_properties(device):
if not _initialized:
init() # will define _get_device_properties and _CudaDeviceProperties
device = _get_device_index(device, optional=True)
if device < 0 or device >= device_count():
raise AssertionError("Invalid device id")
return _get_device_properties(device)
[docs]@contextlib.contextmanager
def stream(stream):
r"""Context-manager that selects a given stream.
All CUDA kernels queued within its context will be enqueued on a selected
stream.
Arguments:
stream (Stream): selected stream. This manager is a no-op if it's
``None``.
.. note:: Streams are per-device. If the selected stream is not on the
current device, this function will also change the current device to
match the stream.
"""
if stream is None:
yield
return
src_prev_stream = current_stream()
if src_prev_stream.device != stream.device:
# The given stream is on a different device; have to restore the
# current_stream on that device on exit as well
with device(stream.device):
dst_prev_stream = current_stream()
torch._C._cuda_setStream(stream._cdata)
try:
yield
finally:
if src_prev_stream.device != stream.device:
torch._C._cuda_setStream(dst_prev_stream._cdata)
torch._C._cuda_setStream(src_prev_stream._cdata)
[docs]def device_count():
r"""Returns the number of GPUs available."""
if is_available():
return torch._C._cuda_getDeviceCount()
else:
return 0
[docs]def current_device():
r"""Returns the index of a currently selected device."""
_lazy_init()
return torch._C._cuda_getDevice()
[docs]def synchronize(device=None):
r"""Waits for all kernels in all streams on a CUDA device to complete.
Arguments:
device (torch.device or int, optional): device for which to synchronize.
It uses the current device, given by :func:`~torch.cuda.current_device`,
if :attr:`device` is ``None`` (default).
"""
_lazy_init()
with torch.cuda.device(device):
return torch._C._cuda_synchronize()
[docs]def ipc_collect():
r"""Force collects GPU memory after it has been released by CUDA IPC.
.. note::
Checks if any sent CUDA tensors could be cleaned from the memory. Force
closes shared memory file used for reference counting if there is no
active counters. Useful when the producer process stopped actively sending
tensors and want to release unused memory.
"""
_lazy_init()
return torch._C._cuda_ipc_collect()
[docs]def current_stream(device=None):
r"""Returns the currently selected :class:`Stream` for a given device.
Arguments:
device (torch.device or int, optional): selected device. Returns
the currently selected :class:`Stream` for the current device, given
by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
(default).
"""
_lazy_init()
return torch.cuda.Stream(_cdata=torch._C._cuda_getCurrentStream(
_get_device_index(device, optional=True)))
[docs]def default_stream(device=None):
r"""Returns the default :class:`Stream` for a given device.
Arguments:
device (torch.device or int, optional): selected device. Returns
the default :class:`Stream` for the current device, given by
:func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
(default).
"""
_lazy_init()
return torch.cuda.Stream(_cdata=torch._C._cuda_getDefaultStream(
_get_device_index(device, optional=True)))
[docs]def current_blas_handle():
r"""Returns cublasHandle_t pointer to current cuBLAS handle"""
_lazy_init()
return torch._C._cuda_getCurrentBlasHandle()
[docs]def empty_cache():
r"""Releases all unoccupied cached memory currently held by the caching
allocator so that those can be used in other GPU application and visible in
`nvidia-smi`.
.. note::
:func:`~torch.cuda.empty_cache` doesn't increase the amount of GPU
memory available for PyTorch. See :ref:`cuda-memory-management` for
more details about GPU memory management.
"""
if _initialized:
torch._C._cuda_emptyCache()
[docs]def memory_allocated(device=None):
r"""Returns the current GPU memory occupied by tensors in bytes for a given
device.
Arguments:
device (torch.device or int, optional): selected device. Returns
statistic for the current device, given by :func:`~torch.cuda.current_device`,
if :attr:`device` is ``None`` (default).
.. note::
This is likely less than the amount shown in `nvidia-smi` since some
unused memory can be held by the caching allocator and some context
needs to be created on GPU. See :ref:`cuda-memory-management` for more
details about GPU memory management.
"""
device = _get_device_index(device, optional=True)
return torch._C._cuda_memoryAllocated(device)
[docs]def max_memory_allocated(device=None):
r"""Returns the maximum GPU memory occupied by tensors in bytes for a given
device.
By default, this returns the peak allocated memory since the beginning of
this program. :func:`~torch.cuda.reset_max_memory_allocated` can be used to
reset the starting point in tracking this metric. For example, these two
functions can measure the peak allocated memory usage of each iteration in a
training loop.
Arguments:
device (torch.device or int, optional): selected device. Returns
statistic for the current device, given by :func:`~torch.cuda.current_device`,
if :attr:`device` is ``None`` (default).
.. note::
See :ref:`cuda-memory-management` for more details about GPU memory
management.
"""
device = _get_device_index(device, optional=True)
return torch._C._cuda_maxMemoryAllocated(device)
[docs]def reset_max_memory_allocated(device=None):
r"""Resets the starting point in tracking maximum GPU memory occupied by
tensors for a given device.
See :func:`~torch.cuda.max_memory_allocated` for details.
Arguments:
device (torch.device or int, optional): selected device. Returns
statistic for the current device, given by :func:`~torch.cuda.current_device`,
if :attr:`device` is ``None`` (default).
.. note::
See :ref:`cuda-memory-management` for more details about GPU memory
management.
"""
device = _get_device_index(device, optional=True)
return torch._C._cuda_resetMaxMemoryAllocated(device)
[docs]def memory_cached(device=None):
r"""Returns the current GPU memory managed by the caching allocator in bytes
for a given device.
Arguments:
device (torch.device or int, optional): selected device. Returns
statistic for the current device, given by :func:`~torch.cuda.current_device`,
if :attr:`device` is ``None`` (default).
.. note::
See :ref:`cuda-memory-management` for more details about GPU memory
management.
"""
device = _get_device_index(device, optional=True)
return torch._C._cuda_memoryCached(device)
[docs]def max_memory_cached(device=None):
r"""Returns the maximum GPU memory managed by the caching allocator in bytes
for a given device.
By default, this returns the peak cached memory since the beginning of this
program. :func:`~torch.cuda.reset_max_memory_cached` can be used to reset
the starting point in tracking this metric. For example, these two functions
can measure the peak cached memory amount of each iteration in a training
loop.
Arguments:
device (torch.device or int, optional): selected device. Returns
statistic for the current device, given by :func:`~torch.cuda.current_device`,
if :attr:`device` is ``None`` (default).
.. note::
See :ref:`cuda-memory-management` for more details about GPU memory
management.
"""
device = _get_device_index(device, optional=True)
return torch._C._cuda_maxMemoryCached(device)
[docs]def reset_max_memory_cached(device=None):
r"""Resets the starting point in tracking maximum GPU memory managed by the
caching allocator for a given device.
See :func:`~torch.cuda.max_memory_cached` for details.
Arguments:
device (torch.device or int, optional): selected device. Returns
statistic for the current device, given by :func:`~torch.cuda.current_device`,
if :attr:`device` is ``None`` (default).
.. note::
See :ref:`cuda-memory-management` for more details about GPU memory
management.
"""
device = _get_device_index(device, optional=True)
return torch._C._cuda_resetMaxMemoryCached(device)
def _host_allocator():
_lazy_init()
return torch._C._cuda_cudaHostAllocator()
@contextlib.contextmanager
def _free_mutex():
torch._C._cuda_lock_mutex()
try:
yield
finally:
torch._C._cuda_unlock_mutex()
from .random import *
################################################################################
# Define Storage and Tensor classes
################################################################################
from ..storage import _StorageBase
def _dummy_type(name):
def init_err(self):
class_name = self.__class__.__name__
raise RuntimeError(
"Tried to instantiate dummy base class {}".format(class_name))
return type(storage_name, (object,), {"__init__": init_err})
if not hasattr(torch._C, 'CudaDoubleStorageBase'):
# Define dummy base classes
for t in ['Double', 'Float', 'Long', 'Int', 'Short', 'Char', 'Byte', 'Half', 'Bool', 'BFloat16']:
storage_name = 'Cuda{0}StorageBase'.format(t)
tensor_name = 'Cuda{0}TensorBase'.format(t)
torch._C.__dict__[storage_name] = _dummy_type(storage_name)
torch._C.__dict__[tensor_name] = _dummy_type(tensor_name)
torch._C.__dict__['_CudaStreamBase'] = _dummy_type('CudaStreamBase')
torch._C.__dict__['_CudaEventBase'] = _dummy_type('CudaEventBase')
@staticmethod
def _lazy_new(cls, *args, **kwargs):
_lazy_init()
# We need this method only for lazy init, so we can remove it
del _CudaBase.__new__
return super(_CudaBase, cls).__new__(cls, *args, **kwargs)
class _CudaBase(object):
is_cuda = True
is_sparse = False
def type(self, *args, **kwargs):
with device(self.get_device()):
return super(_CudaBase, self).type(*args, **kwargs)
__new__ = _lazy_new
class DoubleStorage(_CudaBase, torch._C.CudaDoubleStorageBase, _StorageBase):
pass
class FloatStorage(_CudaBase, torch._C.CudaFloatStorageBase, _StorageBase):
pass
class LongStorage(_CudaBase, torch._C.CudaLongStorageBase, _StorageBase):
pass
class IntStorage(_CudaBase, torch._C.CudaIntStorageBase, _StorageBase):
pass
class ShortStorage(_CudaBase, torch._C.CudaShortStorageBase, _StorageBase):
pass
class CharStorage(_CudaBase, torch._C.CudaCharStorageBase, _StorageBase):
pass
class ByteStorage(_CudaBase, torch._C.CudaByteStorageBase, _StorageBase):
pass
class HalfStorage(_CudaBase, torch._C.CudaHalfStorageBase, _StorageBase):
pass
class BoolStorage(_CudaBase, torch._C.CudaBoolStorageBase, _StorageBase):
pass
class BFloat16Storage(_CudaBase, torch._C.CudaBFloat16StorageBase, _StorageBase):
pass
torch._storage_classes.add(DoubleStorage)
torch._storage_classes.add(FloatStorage)
torch._storage_classes.add(LongStorage)
torch._storage_classes.add(IntStorage)
torch._storage_classes.add(ShortStorage)
torch._storage_classes.add(CharStorage)
torch._storage_classes.add(ByteStorage)
torch._storage_classes.add(HalfStorage)
torch._storage_classes.add(BoolStorage)
torch._storage_classes.add(BFloat16Storage)
from . import sparse # noqa: F401
from . import profiler # noqa: F401
from . import nvtx # noqa: F401
from .streams import Stream, Event # noqa: F401
