Source code for torch.cuda
# mypy: allow-untyped-defs
r"""
This package adds support for CUDA tensor types.
It implements 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 importlib
import os
import threading
import traceback
import warnings
from functools import lru_cache
from typing import Any, Callable, cast, List, Optional, Tuple, Union
import torch
import torch._C
from torch import device as _device
from torch._utils import _dummy_type, _LazySeedTracker, classproperty
from torch.types import Device
from . import gds
from ._utils import _get_device_index
from .graphs import (
CUDAGraph,
graph,
graph_pool_handle,
is_current_stream_capturing,
make_graphed_callables,
)
from .streams import Event, ExternalStream, Stream
try:
from torch._C import _cudart # type: ignore[attr-defined]
except ImportError:
_cudart = None
_initialized = False
_tls = threading.local()
_initialization_lock = threading.Lock()
_queued_calls: List[
Tuple[Callable[[], None], List[str]]
] = [] # don't invoke these until initialization occurs
_is_in_bad_fork = getattr(torch._C, "_cuda_isInBadFork", lambda: False)
_device_t = Union[_device, str, int, None]
_HAS_PYNVML = False
_PYNVML_ERR = None
try:
from torch import version as _version
try:
if not _version.hip:
import pynvml # type: ignore[import]
else:
import amdsmi # type: ignore[import]
_HAS_PYNVML = True
except ModuleNotFoundError:
pass
finally:
del _version
except ImportError as err:
_PYNVML_ERR = err # sometimes a lib is installed but the import fails for some other reason, so we log the error for later
_lazy_seed_tracker = _LazySeedTracker()
# Define dummy _CudaDeviceProperties type if PyTorch was compiled without CUDA
if hasattr(torch._C, "_CudaDeviceProperties"):
_CudaDeviceProperties = torch._C._CudaDeviceProperties
else:
_CudaDeviceProperties = _dummy_type("_CudaDeviceProperties") # type: ignore[assignment, misc]
if hasattr(torch._C, "_cuda_exchangeDevice"):
_exchange_device = torch._C._cuda_exchangeDevice
else:
def _exchange_device(device: int) -> int:
if device < 0:
return -1
raise RuntimeError("PyTorch was compiled without CUDA support")
if hasattr(torch._C, "_cuda_maybeExchangeDevice"):
_maybe_exchange_device = torch._C._cuda_maybeExchangeDevice
else:
def _maybe_exchange_device(device: int) -> int:
if device < 0:
return -1
raise RuntimeError("PyTorch was compiled without CUDA support")
has_half: bool = True
has_magma: bool = torch._C._has_magma
default_generators: Tuple[torch._C.Generator] = () # type: ignore[assignment]
def _is_compiled() -> bool:
r"""Return true if compile with CUDA support."""
return hasattr(torch._C, "_cuda_getDeviceCount")
def _nvml_based_avail() -> bool:
return os.getenv("PYTORCH_NVML_BASED_CUDA_CHECK") == "1"
[docs]def is_available() -> bool:
r"""Return a bool indicating if CUDA is currently available."""
if not _is_compiled():
return False
if _nvml_based_avail():
# The user has set an env variable to request this availability check that attempts to avoid fork poisoning by
# using NVML at the cost of a weaker CUDA availability assessment. Note that if NVML discovery/initialization
# fails, this assessment falls back to the default CUDA Runtime API assessment (`cudaGetDeviceCount`)
return device_count() > 0
else:
# The default availability inspection never throws and returns 0 if the driver is missing or can't
# be initialized. This uses the CUDA Runtime API `cudaGetDeviceCount` which in turn initializes the CUDA Driver
# API via `cuInit`
return torch._C._cuda_getDeviceCount() > 0
def is_bf16_supported(including_emulation: bool = True):
r"""Return a bool indicating if the current CUDA/ROCm device supports dtype bfloat16."""
# Check for ROCm, if true return true, no ROCM_VERSION check required,
# since it is supported on AMD GPU archs.
if torch.version.hip:
return True
# If CUDA is not available, than it does not support bf16 either
if not is_available():
return False
device = torch.cuda.current_device()
# Check for CUDA version and device compute capability.
# This is a fast way to check for it.
cuda_version = torch.version.cuda
if (
cuda_version is not None
and int(cuda_version.split(".")[0]) >= 11
and torch.cuda.get_device_properties(device).major >= 8
):
return True
if not including_emulation:
return False
# Finally try to create a bfloat16 device.
return _check_bf16_tensor_supported(device)
@lru_cache(maxsize=16)
def _check_bf16_tensor_supported(device: _device_t):
try:
torch.tensor([1.0], dtype=torch.bfloat16, device=device)
return True
except Exception:
return False
def _sleep(cycles):
torch._C._cuda_sleep(cycles)
def _extract_arch_version(arch_string: str):
"""Extracts the architecture string from a CUDA version"""
base = arch_string.split("_")[1]
if base.endswith("a"):
base = base[:-1]
return int(base)
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 supported by this library is %d.%d.
"""
if torch.version.cuda is not None: # on ROCm we don't want this check
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)
current_arch = major * 10 + minor
min_arch = min(
(_extract_arch_version(arch) for arch in torch.cuda.get_arch_list()),
default=35,
)
if current_arch < min_arch:
warnings.warn(
old_gpu_warn
% (d, name, major, minor, min_arch // 10, min_arch % 10)
)
def _check_cubins():
incompatible_device_warn = """
{} with CUDA capability sm_{} is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities {}.
If you want to use the {} GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/
"""
if torch.version.cuda is None: # on ROCm we don't want this check
return
arch_list = get_arch_list()
if len(arch_list) == 0:
return
supported_sm = [_extract_arch_version(arch) for arch in arch_list if "sm_" in arch]
for idx in range(device_count()):
cap_major, cap_minor = get_device_capability(idx)
# NVIDIA GPU compute architectures are backward compatible within major version
supported = any(sm // 10 == cap_major for sm in supported_sm)
if not supported:
device_name = get_device_name(idx)
capability = cap_major * 10 + cap_minor
warnings.warn(
incompatible_device_warn.format(
device_name, capability, " ".join(arch_list), device_name
)
)
[docs]def is_initialized():
r"""Return whether PyTorch's CUDA state has been initialized."""
return _initialized and not _is_in_bad_fork()
def _lazy_call(callable, **kwargs):
if is_initialized():
callable()
else:
# TODO(torch_deploy): this accesses linecache, which attempts to read the
# file system to get traceback info. Patch linecache or do something
# else here if this ends up being important.
global _lazy_seed_tracker
if kwargs.get("seed_all", False):
_lazy_seed_tracker.queue_seed_all(callable, traceback.format_stack())
elif kwargs.get("seed", False):
_lazy_seed_tracker.queue_seed(callable, traceback.format_stack())
else:
# Don't store the actual traceback to avoid memory cycle
_queued_calls.append((callable, traceback.format_stack()))
_lazy_call(_check_capability)
_lazy_call(_check_cubins)
class DeferredCudaCallError(Exception):
pass
OutOfMemoryError = torch._C.OutOfMemoryError
[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 available 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, _queued_calls
if is_initialized() or hasattr(_tls, "is_initializing"):
return
with _initialization_lock:
# We be double-checked locking, boys! This is OK because
# the above test was GIL protected anyway. The inner test
# is for when a thread blocked on some other thread which was
# doing the initialization; when they get the lock, they will
# find there is nothing left to do.
if is_initialized():
return
# It is important to prevent other threads from entering _lazy_init
# immediately, while we are still guaranteed to have the GIL, because some
# of the C calls we make below will release the GIL
if _is_in_bad_fork():
raise RuntimeError(
"Cannot re-initialize CUDA in forked subprocess. To use CUDA with "
"multiprocessing, you must use the 'spawn' start method"
)
if not hasattr(torch._C, "_cuda_getDeviceCount"):
raise AssertionError("Torch not compiled with CUDA enabled")
if _cudart is None:
raise AssertionError(
"libcudart functions unavailable. It looks like you have a broken build?"
)
# This function throws if there's a driver initialization error, no GPUs
# are found or any other error occurs
if "CUDA_MODULE_LOADING" not in os.environ:
os.environ["CUDA_MODULE_LOADING"] = "LAZY"
torch._C._cuda_init()
# Some of the queued calls may reentrantly call _lazy_init();
# we need to just return without initializing in that case.
# However, we must not let any *other* threads in!
_tls.is_initializing = True
for calls in _lazy_seed_tracker.get_calls():
if calls:
_queued_calls.append(calls)
try:
for queued_call, orig_traceback in _queued_calls:
try:
queued_call()
except Exception as e:
msg = (
f"CUDA call failed lazily at initialization with error: {str(e)}\n\n"
f"CUDA call was originally invoked at:\n\n{''.join(orig_traceback)}"
)
raise DeferredCudaCallError(msg) from e
finally:
delattr(_tls, "is_initializing")
_initialized = True
[docs]def cudart():
r"""Retrieves the CUDA runtime API module.
This function initializes the CUDA runtime environment if it is not already
initialized and returns the CUDA runtime API module (_cudart). The CUDA
runtime API module provides access to various CUDA runtime functions.
Args:
``None``
Returns:
module: The CUDA runtime API module (_cudart).
Raises:
RuntimeError: If CUDA cannot be re-initialized in a forked subprocess.
AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable.
Example of CUDA operations with profiling:
>>> import torch
>>> from torch.cuda import cudart, check_error
>>> import os
>>>
>>> os.environ['CUDA_PROFILE'] = '1'
>>>
>>> def perform_cuda_operations_with_streams():
>>> stream = torch.cuda.Stream()
>>> with torch.cuda.stream(stream):
>>> x = torch.randn(100, 100, device='cuda')
>>> y = torch.randn(100, 100, device='cuda')
>>> z = torch.mul(x, y)
>>> return z
>>>
>>> torch.cuda.synchronize()
>>> print("====== Start nsys profiling ======")
>>> check_error(cudart().cudaProfilerStart())
>>> with torch.autograd.profiler.emit_nvtx():
>>> result = perform_cuda_operations_with_streams()
>>> print("CUDA operations completed.")
>>> check_error(torch.cuda.cudart().cudaProfilerStop())
>>> print("====== End nsys profiling ======")
To run this example and save the profiling information, execute:
>>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
This command profiles the CUDA operations in the provided script and saves
the profiling information to a file named `trace_name.prof`.
The `--profile-from-start off` option ensures that profiling starts only
after the `cudaProfilerStart` call in the script.
The `--csv` and `--print-summary` options format the profiling output as a
CSV file and print a summary, respectively.
The `-o` option specifies the output file name, and the `-f` option forces the
overwrite of the output file if it already exists.
"""
_lazy_init()
return _cudart
class cudaStatus:
SUCCESS: int = 0
ERROR_NOT_READY: int = 34
class CudaError(RuntimeError):
def __init__(self, code: int) -> None:
msg = _cudart.cudaGetErrorString(_cudart.cudaError(code))
super().__init__(f"{msg} ({code})")
def check_error(res: int) -> None:
if res != _cudart.cudaError.success:
raise CudaError(res)
class _DeviceGuard:
def __init__(self, index: int):
self.idx = index
self.prev_idx = -1
def __enter__(self):
self.prev_idx = torch.cuda._exchange_device(self.idx)
def __exit__(self, type: Any, value: Any, traceback: Any):
self.idx = torch.cuda._maybe_exchange_device(self.prev_idx)
return False
[docs]class device:
r"""Context-manager that changes the selected device.
Args:
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: Any):
self.idx = _get_device_index(device, optional=True)
self.prev_idx = -1
def __enter__(self):
self.prev_idx = torch.cuda._exchange_device(self.idx)
def __exit__(self, type: Any, value: Any, traceback: Any):
self.idx = torch.cuda._maybe_exchange_device(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.
Args:
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().__init__(idx)
[docs]def set_device(device: _device_t) -> None:
r"""Set 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.
Args:
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: Optional[_device_t] = None) -> str:
r"""Get the name of a device.
Args:
device (torch.device or int or str, 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).
Returns:
str: the name of the device
"""
return get_device_properties(device).name
[docs]def get_device_capability(device: Optional[_device_t] = None) -> Tuple[int, int]:
r"""Get the cuda capability of a device.
Args:
device (torch.device or int or str, 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
[docs]def get_device_properties(device: _device_t) -> _CudaDeviceProperties:
r"""Get the properties of a device.
Args:
device (torch.device or int or str): device for which to return the
properties of the device.
Returns:
_CudaDeviceProperties: the properties of the device
"""
_lazy_init() # will define _get_device_properties
device = _get_device_index(device, optional=True)
if device < 0 or device >= device_count():
raise AssertionError("Invalid device id")
return _get_device_properties(device) # type: ignore[name-defined]
[docs]def can_device_access_peer(device: _device_t, peer_device: _device_t) -> bool:
r"""Check if peer access between two devices is possible."""
_lazy_init()
device = _get_device_index(device, optional=True)
peer_device = _get_device_index(peer_device)
if device < 0 or device >= device_count():
raise AssertionError("Invalid device id")
if peer_device < 0 or peer_device >= device_count():
raise AssertionError("Invalid peer device id")
return torch._C._cuda_canDeviceAccessPeer(device, peer_device)
[docs]class StreamContext:
r"""Context-manager that selects a given stream.
All CUDA kernels queued within its context will be enqueued on a selected
stream.
Args:
Stream (Stream): selected stream. This manager is a no-op if it's
``None``.
.. note:: Streams are per-device.
"""
cur_stream: Optional["torch.cuda.Stream"]
def __init__(self, stream: Optional["torch.cuda.Stream"]):
self.stream = stream
self.idx = _get_device_index(None, True)
if not torch.jit.is_scripting():
if self.idx is None:
self.idx = -1
self.src_prev_stream = (
None if not torch.jit.is_scripting() else torch.cuda.default_stream(None)
)
self.dst_prev_stream = (
None if not torch.jit.is_scripting() else torch.cuda.default_stream(None)
)
def __enter__(self):
# Local cur_stream variable for type refinement
cur_stream = self.stream
# Return if stream is None or CUDA device not available
if cur_stream is None or self.idx == -1:
return
self.src_prev_stream = torch.cuda.current_stream(None)
# If the stream is not on the current device, then
# set the current stream on the device
if self.src_prev_stream.device != cur_stream.device:
with device(cur_stream.device):
self.dst_prev_stream = torch.cuda.current_stream(cur_stream.device)
torch.cuda.set_stream(cur_stream)
def __exit__(self, type: Any, value: Any, traceback: Any):
# Local cur_stream variable for type refinement
cur_stream = self.stream
# If stream is None or no CUDA device available, return
if cur_stream is None or self.idx == -1:
return
# Reset the stream on the original device
# and destination device
if self.src_prev_stream.device != cur_stream.device: # type: ignore[union-attr]
torch.cuda.set_stream(self.dst_prev_stream) # type: ignore[arg-type]
torch.cuda.set_stream(self.src_prev_stream) # type: ignore[arg-type]
[docs]def stream(stream: Optional["torch.cuda.Stream"]) -> StreamContext:
r"""Wrap around the Context-manager StreamContext that selects a given stream.
Arguments:
stream (Stream): selected stream. This manager is a no-op if it's
``None``.
..Note:: In eager mode stream is of type Stream class while in JIT it is
an object of the custom class ``torch.classes.cuda.Stream``.
"""
return StreamContext(stream)
def _set_stream_by_id(stream_id, device_index, device_type):
r"""set stream specified by the stream id, device index and
device type
Args: stream_id (int): stream id in stream pool
device_index (int): device index in topo
device_type (int): enum device type
"""
torch._C._cuda_setStream(
stream_id=stream_id,
device_index=device_index,
device_type=device_type,
)
[docs]def set_stream(stream: Stream):
r"""Set the current stream.This is a wrapper API to set the stream.
Usage of this function is discouraged in favor of the ``stream``
context manager.
Args:
stream (Stream): selected stream. This function is a no-op
if this argument is ``None``.
"""
if stream is None:
return
_set_stream_by_id(
stream_id=stream.stream_id,
device_index=stream.device_index,
device_type=stream.device_type,
)
def _parse_visible_devices() -> Union[List[int], List[str]]:
r"""Parse CUDA_VISIBLE_DEVICES environment variable."""
var = os.getenv("CUDA_VISIBLE_DEVICES")
if torch.version.hip:
hip_devices = os.getenv("HIP_VISIBLE_DEVICES")
if hip_devices is not None:
var = hip_devices
if var is None:
return list(range(64))
def _strtoul(s: str) -> int:
"""Return -1 or positive integer sequence string starts with."""
if not s:
return -1
for idx, c in enumerate(s):
if not (c.isdigit() or (idx == 0 and c in "+-")):
break
if idx + 1 == len(s):
idx += 1
return int(s[:idx]) if idx > 0 else -1
def parse_list_with_prefix(lst: str, prefix: str) -> List[str]:
rcs: List[str] = []
for elem in lst.split(","):
# Repeated id results in empty set
if elem in rcs:
return cast(List[str], [])
# Anything other but prefix is ignored
if not elem.startswith(prefix):
break
rcs.append(elem)
return rcs
if var.startswith("GPU-"):
return parse_list_with_prefix(var, "GPU-")
if var.startswith("MIG-"):
return parse_list_with_prefix(var, "MIG-")
# CUDA_VISIBLE_DEVICES uses something like strtoul
# which makes `1gpu2,2ampere` is equivalent to `1,2`
rc: List[int] = []
for elem in var.split(","):
x = _strtoul(elem.strip())
# Repeated ordinal results in empty set
if x in rc:
return cast(List[int], [])
# Negative value aborts the sequence
if x < 0:
break
rc.append(x)
return rc
def _raw_device_count_amdsmi() -> int:
if not _HAS_PYNVML: # If amdsmi is not available
return -1
try:
amdsmi.amdsmi_init()
except amdsmi.AmdSmiException as e:
warnings.warn(f"Can't initialize amdsmi - Error code: {e.err_code}")
return -1
socket_handles = amdsmi.amdsmi_get_processor_handles()
return len(socket_handles)
def _raw_device_count_nvml() -> int:
r"""Return number of devices as reported by NVML or negative value if NVML discovery/initialization failed."""
from ctypes import byref, c_int, CDLL
nvml_h = CDLL("libnvidia-ml.so.1")
rc = nvml_h.nvmlInit()
if rc != 0:
warnings.warn("Can't initialize NVML")
return -1
dev_count = c_int(-1)
rc = nvml_h.nvmlDeviceGetCount_v2(byref(dev_count))
if rc != 0:
warnings.warn("Can't get nvml device count")
return -1
del nvml_h
return dev_count.value
def _raw_device_uuid_amdsmi() -> Optional[List[str]]:
from ctypes import byref, c_int, c_void_p, CDLL, create_string_buffer
if not _HAS_PYNVML: # If amdsmi is not available
return None
try:
amdsmi.amdsmi_init()
except amdsmi.AmdSmiException:
warnings.warn("Can't initialize amdsmi")
return None
try:
socket_handles = amdsmi.amdsmi_get_processor_handles()
dev_count = len(socket_handles)
except amdsmi.AmdSmiException:
warnings.warn("Can't get amdsmi device count")
return None
uuids: List[str] = []
for idx in range(dev_count):
try:
handler = amdsmi.amdsmi_get_processor_handles()[idx]
except amdsmi.AmdSmiException:
warnings.warn("Cannot get amd device handler")
return None
try:
uuid = amdsmi.amdsmi_get_gpu_device_uuid(handler)
except amdsmi.AmdSmiException:
warnings.warn("Cannot get uuid for amd device")
return None
uuids.append(str(uuid))
return uuids
def _raw_device_uuid_nvml() -> Optional[List[str]]:
r"""Return list of device UUID as reported by NVML or None if NVM discovery/initialization failed."""
from ctypes import byref, c_int, c_void_p, CDLL, create_string_buffer
nvml_h = CDLL("libnvidia-ml.so.1")
rc = nvml_h.nvmlInit()
if rc != 0:
warnings.warn("Can't initialize NVML")
return None
dev_count = c_int(-1)
rc = nvml_h.nvmlDeviceGetCount_v2(byref(dev_count))
if rc != 0:
warnings.warn("Can't get nvml device count")
return None
uuids: List[str] = []
for idx in range(dev_count.value):
dev_id = c_void_p()
rc = nvml_h.nvmlDeviceGetHandleByIndex_v2(idx, byref(dev_id))
if rc != 0:
warnings.warn("Can't get device handle")
return None
buf_len = 96
buf = create_string_buffer(buf_len)
rc = nvml_h.nvmlDeviceGetUUID(dev_id, buf, buf_len)
if rc != 0:
warnings.warn("Can't get device UUID")
return None
uuids.append(buf.raw.decode("ascii").strip("\0"))
del nvml_h
return uuids
def _transform_uuid_to_ordinals(candidates: List[str], uuids: List[str]) -> List[int]:
r"""Given the set of partial uuids and list of known uuids builds a set of ordinals excluding ambiguous partials IDs."""
def uuid_to_orinal(candidate: str, uuids: List[str]) -> int:
best_match = -1
for idx, uuid in enumerate(uuids):
if not uuid.startswith(candidate):
continue
# Ambiguous candidate
if best_match != -1:
return -1
best_match = idx
return best_match
rc: List[int] = []
for candidate in candidates:
idx = uuid_to_orinal(candidate, uuids)
# First invalid ordinal stops parsing
if idx < 0:
break
# Duplicates result in empty set
if idx in rc:
return cast(List[int], [])
rc.append(idx)
return rc
def _device_count_amdsmi() -> int:
visible_devices = _parse_visible_devices()
if not visible_devices:
return 0
try:
if type(visible_devices[0]) is str:
return -1
else:
raw_cnt = _raw_device_count_amdsmi()
if raw_cnt <= 0:
return raw_cnt
# Trim the list up to a maximum available device
for idx, val in enumerate(visible_devices):
if cast(int, val) >= raw_cnt:
return idx
except OSError:
return -1
except AttributeError:
return -1
return len(visible_devices)
def _device_count_nvml() -> int:
r"""Return number of devices as reported by NVML taking CUDA_VISIBLE_DEVICES into account.
Negative value is returned if NVML discovery or initialization has failed.
"""
visible_devices = _parse_visible_devices()
if not visible_devices:
return 0
try:
if type(visible_devices[0]) is str:
# Skip MIG parsing
if visible_devices[0].startswith("MIG-"):
return -1
uuids = _raw_device_uuid_nvml()
if uuids is None:
return -1
visible_devices = _transform_uuid_to_ordinals(
cast(List[str], visible_devices), uuids
)
else:
raw_cnt = _raw_device_count_nvml()
if raw_cnt <= 0:
return raw_cnt
# Trim the list up to a maximum available device
for idx, val in enumerate(visible_devices):
if cast(int, val) >= raw_cnt:
return idx
except OSError:
return -1
except AttributeError:
return -1
return len(visible_devices)
def _get_nvml_device_index(device: Optional[Union[int, Device]]) -> int:
r"""Return the NVML index of the device, taking CUDA_VISIBLE_DEVICES into account."""
idx = _get_device_index(device, optional=True)
visible_devices = _parse_visible_devices()
if type(visible_devices[0]) is str:
uuids = _raw_device_uuid_nvml()
if uuids is None:
raise RuntimeError("Can't get device UUIDs")
visible_devices = _transform_uuid_to_ordinals(
cast(List[str], visible_devices), uuids
)
visible_devices = cast(List[int], visible_devices)
if idx < 0 or idx >= len(visible_devices):
raise RuntimeError(
f"device {idx} is not visible (CUDA_VISIBLE_DEVICES={visible_devices})"
)
return visible_devices[idx]
_cached_device_count: Optional[int] = None
[docs]def device_count() -> int:
r"""Return the number of GPUs available."""
global _cached_device_count
if not _is_compiled():
return 0
if _cached_device_count is not None:
return _cached_device_count
# bypass _device_count_nvml() if rocm (not supported)
nvml_count = _device_count_amdsmi() if torch.version.hip else _device_count_nvml()
r = torch._C._cuda_getDeviceCount() if nvml_count < 0 else nvml_count
# NB: Do not cache the device count prior to CUDA initialization, because
# the number of devices can change due to changes to CUDA_VISIBLE_DEVICES
# setting prior to CUDA initialization.
if _initialized:
_cached_device_count = r
return r
[docs]def get_arch_list() -> List[str]:
r"""Return list CUDA architectures this library was compiled for."""
if not is_available():
return []
arch_flags = torch._C._cuda_getArchFlags()
if arch_flags is None:
return []
return arch_flags.split()
[docs]def get_gencode_flags() -> str:
r"""Return NVCC gencode flags this library was compiled with."""
arch_list = get_arch_list()
if len(arch_list) == 0:
return ""
arch_list_ = [arch.split("_") for arch in arch_list]
return " ".join(
[
f"-gencode compute=compute_{arch},code={kind}_{arch}"
for (kind, arch) in arch_list_
]
)
[docs]def current_device() -> int:
r"""Return the index of a currently selected device."""
_lazy_init()
return torch._C._cuda_getDevice()
[docs]def synchronize(device: _device_t = None) -> None:
r"""Wait for all kernels in all streams on a CUDA device to complete.
Args:
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: Optional[_device_t] = None) -> Stream:
r"""Return the currently selected :class:`Stream` for a given device.
Args:
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()
streamdata = torch._C._cuda_getCurrentStream(
_get_device_index(device, optional=True)
)
return Stream(
stream_id=streamdata[0], device_index=streamdata[1], device_type=streamdata[2]
)
[docs]def default_stream(device: Optional[_device_t] = None) -> Stream:
r"""Return the default :class:`Stream` for a given device.
Args:
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()
streamdata = torch._C._cuda_getDefaultStream(
_get_device_index(device, optional=True)
)
return Stream(
stream_id=streamdata[0], device_index=streamdata[1], device_type=streamdata[2]
)
[docs]def current_blas_handle():
r"""Return cublasHandle_t pointer to current cuBLAS handle"""
_lazy_init()
return torch._C._cuda_getCurrentBlasHandle()
[docs]def set_sync_debug_mode(debug_mode: Union[int, str]) -> None:
r"""Set the debug mode for cuda synchronizing operations.
Args:
debug_mode(str or int): if "default" or 0, don't error or warn on synchronizing operations,
if "warn" or 1, warn on synchronizing operations, if "error" or 2, error out synchronizing operations.
Warning:
This is an experimental feature, and not all synchronizing operations will trigger warning or error. In
particular, operations in torch.distributed and torch.sparse namespaces are not covered yet.
"""
_lazy_init()
if isinstance(debug_mode, str):
if debug_mode == "default":
debug_mode = 0
elif debug_mode == "warn":
debug_mode = 1
elif debug_mode == "error":
debug_mode = 2
else:
raise RuntimeError(
"invalid value of debug_mode, expected one of `default`, `warn`, `error`"
)
torch._C._cuda_set_sync_debug_mode(debug_mode)
[docs]def get_sync_debug_mode() -> int:
r"""Return current value of debug mode for cuda synchronizing operations."""
_lazy_init()
return torch._C._cuda_get_sync_debug_mode()
def _get_pynvml_handler(device: Optional[Union[Device, int]] = None):
if not _HAS_PYNVML:
raise ModuleNotFoundError(
"pynvml does not seem to be installed or it can't be imported."
) from _PYNVML_ERR
from pynvml import NVMLError_DriverNotLoaded
try:
pynvml.nvmlInit()
except NVMLError_DriverNotLoaded as e:
raise RuntimeError("cuda driver can't be loaded, is cuda enabled?") from e
device = _get_nvml_device_index(device)
handle = pynvml.nvmlDeviceGetHandleByIndex(device)
return handle
def _get_amdsmi_handler(device: Optional[Union[Device, int]] = None):
if not _HAS_PYNVML:
raise ModuleNotFoundError(
"amdsmi does not seem to be installed or it can't be imported."
) from _PYNVML_ERR
try:
amdsmi.amdsmi_init()
except amdsmi.AmdSmiException as e:
raise RuntimeError(
"amdsmi driver can't be loaded, requires >=ROCm5.6 installation"
) from e
device = _get_amdsmi_device_index(device)
handle = amdsmi.amdsmi_get_processor_handles()[device]
return handle
def _get_amdsmi_device_index(device: Optional[Union[int, Device]]) -> int:
r"""Return the amdsmi index of the device, taking visible_devices into account."""
idx = _get_device_index(device, optional=True)
visible_devices = _parse_visible_devices()
if type(visible_devices[0]) is str:
raise RuntimeError("HIP_VISIBLE_DEVICES should be indices and not strings")
idx_map = dict(enumerate(cast(List[int], visible_devices)))
if idx not in idx_map:
raise RuntimeError(
f"device {idx} is not visible (HIP_VISIBLE_DEVICES={visible_devices})"
)
return idx_map[idx]
def _get_amdsmi_memory_usage(device: Optional[Union[Device, int]] = None) -> int:
handle = _get_amdsmi_handler()
device = _get_amdsmi_device_index(device)
return amdsmi.amdsmi_get_gpu_vram_usage(handle)["vram_used"]
def _get_amdsmi_utilization(device: Optional[Union[Device, int]] = None) -> int:
handle = _get_amdsmi_handler()
device = _get_amdsmi_device_index(device)
handle = amdsmi.amdsmi_get_processor_handles()[device]
return amdsmi.amdsmi_get_gpu_activity(handle)["gfx_activity"]
def _get_amdsmi_temperature(device: Optional[Union[Device, int]] = None) -> int:
handle = _get_amdsmi_handler(device)
return amdsmi.amdsmi_get_temp_metric(
handle,
amdsmi.AmdSmiTemperatureType.JUNCTION,
amdsmi.AmdSmiTemperatureMetric.CURRENT,
)
def _get_amdsmi_power_draw(device: Optional[Union[Device, int]] = None) -> int:
handle = _get_amdsmi_handler(device)
socket_power = amdsmi.amdsmi_get_power_info(handle)["average_socket_power"]
if socket_power != "N/A":
return socket_power
else:
return amdsmi.amdsmi_get_power_info(handle)["current_socket_power"]
def _get_amdsmi_clock_rate(device: Optional[Union[Device, int]] = None) -> int:
handle = _get_amdsmi_handler(device)
clock_info = amdsmi.amdsmi_get_clock_info(handle, amdsmi.AmdSmiClkType.GFX)
if "cur_clk" in clock_info: # ROCm 6.2 deprecation
return clock_info["cur_clk"]
else:
return clock_info["clk"]
[docs]def memory_usage(device: Optional[Union[Device, int]] = None) -> int:
r"""Return the percent of time over the past sample period during which global (device)
memory was being read or written as given by `nvidia-smi`.
Args:
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).
Warning: Each sample period may be between 1 second and 1/6 second,
depending on the product being queried.
"""
if not torch.version.hip:
handle = _get_pynvml_handler()
device = _get_nvml_device_index(device)
handle = pynvml.nvmlDeviceGetHandleByIndex(device)
return pynvml.nvmlDeviceGetUtilizationRates(handle).memory
else:
return _get_amdsmi_memory_usage(device)
[docs]def utilization(device: Optional[Union[Device, int]] = None) -> int:
r"""Return the percent of time over the past sample period during which one or
more kernels was executing on the GPU as given by `nvidia-smi`.
Args:
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).
Warning: Each sample period may be between 1 second and 1/6 second,
depending on the product being queried.
"""
if not torch.version.hip:
handle = _get_pynvml_handler(device)
device = _get_nvml_device_index(device)
handle = pynvml.nvmlDeviceGetHandleByIndex(device)
return pynvml.nvmlDeviceGetUtilizationRates(handle).gpu
else:
return _get_amdsmi_utilization(device)
[docs]def temperature(device: Optional[Union[Device, int]] = None) -> int:
r"""Return the average temperature of the GPU sensor in Degrees C (Centigrades).
The average temperature is computed based on past sample period as given by `nvidia-smi`.
Args:
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).
Warning: Each sample period may be between 1 second and 1/6 second,
depending on the product being queried.
"""
if not torch.version.hip:
handle = _get_pynvml_handler(device)
# 0 refers to the temperature sensor for the GPU die.
return pynvml.nvmlDeviceGetTemperature(handle, 0)
else:
return _get_amdsmi_temperature(device)
[docs]def power_draw(device: Optional[Union[Device, int]] = None) -> int:
r"""Return the average power draw of the GPU sensor in mW (MilliWatts)
over the past sample period as given by `nvidia-smi` for Fermi or newer fully supported devices.
Args:
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).
Warning: Each sample period may be between 1 second and 1/6 second,
depending on the product being queried.
"""
if not torch.version.hip:
handle = _get_pynvml_handler(device)
return pynvml.nvmlDeviceGetPowerUsage(handle)
else:
return _get_amdsmi_power_draw(device)
[docs]def clock_rate(device: Optional[Union[Device, int]] = None) -> int:
r"""Return the clock speed of the GPU SM in Hz Hertz over the past sample period as given by `nvidia-smi`.
Args:
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).
Warning: Each sample period may be between 1 second and 1/6 second,
depending on the product being queried.
"""
if not torch.version.hip:
handle = _get_pynvml_handler(device)
return pynvml.nvmlDeviceGetClockInfo(handle, 1)
else:
return _get_amdsmi_clock_rate(device)
def _get_device(device: Union[int, str, torch.device]) -> torch.device:
r"""Return the torch.device type object from the passed in device.
Args:
device (torch.device or int): selected device.
"""
if isinstance(device, str):
device = torch.device(device)
elif isinstance(device, int):
device = torch.device("cuda", device)
return device
def _get_generator(device: torch.device) -> torch._C.Generator:
r"""Return the CUDA Generator object for the given device.
Args:
device (torch.device): selected device.
"""
idx = device.index
if idx is None:
idx = current_device()
return torch.cuda.default_generators[idx]
def _set_rng_state_offset(
offset: int, device: Union[int, str, torch.device] = "cuda"
) -> None:
r"""Set the random number generator state offset of the specified GPU.
Args:
offset (int): The desired offset
device (torch.device or int, optional): The device to set the RNG state.
Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).
"""
final_device = _get_device(device)
def cb():
default_generator = _get_generator(final_device)
default_generator.set_offset(offset)
_lazy_call(cb)
def _get_rng_state_offset(device: Union[int, str, torch.device] = "cuda") -> int:
r"""Return the random number generator state offset of the specified GPU.
Args:
device (torch.device or int, optional): The device to return the RNG state offset of.
Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).
.. warning::
This function eagerly initializes CUDA.
"""
_lazy_init()
final_device = _get_device(device)
default_generator = _get_generator(final_device)
return default_generator.get_offset()
from .memory import * # noqa: F403
from .random import * # noqa: F403
################################################################################
# Define Storage and Tensor classes
################################################################################
@staticmethod # type: ignore[misc]
def _lazy_new(cls, *args, **kwargs):
_lazy_init()
# We may need to call lazy init again if we are a forked child
# del _CudaBase.__new__
return super(_CudaBase, cls).__new__(cls, *args, **kwargs)
class _CudaBase:
is_cuda = True
is_sparse = False
def type(self, *args, **kwargs):
# We could use a Protocol here to tell mypy that self has `get_device` method
# but it is only available in the typing module on Python >= 3.8
# or on typing_extensions module on Python >= 3.6
with device(self.get_device()): # type: ignore[attr-defined]
return super().type(*args, **kwargs) # type: ignore[misc]
__new__ = _lazy_new
from torch.storage import _LegacyStorage, _warn_typed_storage_removal
class _CudaLegacyStorage(_LegacyStorage):
@classmethod
def from_buffer(cls, *args, **kwargs):
_warn_typed_storage_removal()
raise RuntimeError("from_buffer: Not available for CUDA storage")
@classmethod
def _new_with_weak_ptr(cls, *args, **kwargs):
raise RuntimeError("_new_with_weak_ptr: Not available for CUDA storage")
@classmethod
def _new_shared_filename(cls, manager, obj, size, *, device=None, dtype=None):
raise RuntimeError("_new_shared_filename: Not available for CUDA storage")
class ByteStorage(_CudaLegacyStorage):
@classproperty
def dtype(self):
_warn_typed_storage_removal()
return self._dtype
@classproperty
def _dtype(self):
return torch.uint8
class DoubleStorage(_CudaLegacyStorage):
@classproperty
def dtype(self):
_warn_typed_storage_removal()
return self._dtype
@classproperty
def _dtype(self):
return torch.double
class FloatStorage(_CudaLegacyStorage):
@classproperty
def dtype(self):
_warn_typed_storage_removal()
return self._dtype
@classproperty
def _dtype(self):
return torch.float
class HalfStorage(_CudaLegacyStorage):
@classproperty
def dtype(self):
_warn_typed_storage_removal()
return self._dtype
@classproperty
def _dtype(self):
return torch.half
class LongStorage(_CudaLegacyStorage):
@classproperty
def dtype(self):
_warn_typed_storage_removal()
return self._dtype
@classproperty
def _dtype(self):
return torch.long
class IntStorage(_CudaLegacyStorage):
@classproperty
def dtype(self):
_warn_typed_storage_removal()
return self._dtype
@classproperty
def _dtype(self):
return torch.int
class ShortStorage(_CudaLegacyStorage):
@classproperty
def dtype(self):
_warn_typed_storage_removal()
return self._dtype
@classproperty
def _dtype(self):
return torch.short
class CharStorage(_CudaLegacyStorage):
@classproperty
def dtype(self):
_warn_typed_storage_removal()
return self._dtype
@classproperty
def _dtype(self):
return torch.int8
class BoolStorage(_CudaLegacyStorage):
@classproperty
def dtype(self):
_warn_typed_storage_removal()
return self._dtype
@classproperty
def _dtype(self):
return torch.bool
class BFloat16Storage(_CudaLegacyStorage):
@classproperty
def dtype(self):
_warn_typed_storage_removal()
return self._dtype
@classproperty
def _dtype(self):
return torch.bfloat16
class ComplexDoubleStorage(_CudaLegacyStorage):
@classproperty
def dtype(self):
_warn_typed_storage_removal()
return self._dtype
@classproperty
def _dtype(self):
return torch.cdouble
class ComplexFloatStorage(_CudaLegacyStorage):
@classproperty
def dtype(self):
_warn_typed_storage_removal()
return self._dtype
@classproperty
def _dtype(self):
return torch.cfloat
del _LegacyStorage
del _CudaLegacyStorage
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)
torch._storage_classes.add(ComplexDoubleStorage)
torch._storage_classes.add(ComplexFloatStorage)
class _WrappedTritonKernel:
"""Just a simple wrapper to store some metadata for testing purposes."""
def __init__(self, kernel):
self.kernel = kernel
self.kernel_invoked = False
def __call__(self, *args, **kwargs):
res = self.kernel(*args, **kwargs)
self.kernel_invoked = True
return res
def _register_triton_kernels():
if torch._running_with_deploy():
return
@_WrappedTritonKernel
def kernel_impl(*args, **kwargs):
from torch.sparse._triton_ops import bsr_dense_mm
return bsr_dense_mm(*args, skip_checks=True, **kwargs)
@_WrappedTritonKernel
def addmm_kernel_impl(*args, **kwargs):
from torch.sparse._triton_ops import bsr_dense_addmm
return bsr_dense_addmm(*args, skip_checks=True, **kwargs)
has_triton = importlib.util.find_spec("triton") is not None
if has_triton:
torch._TritonLibrary.registerOp(
"_triton_bsr_dense_mm_out",
"_triton_bsr_dense_mm_out(Tensor bsr, Tensor dense, *, Tensor(a!) out) -> Tensor(a!)",
kernel_impl,
"SparseCsrCUDA",
)
torch._TritonLibrary.registerOp(
"_triton_bsr_dense_addmm_out",
(
"_triton_bsr_dense_addmm_out(Tensor input, Tensor bsr, Tensor dense,"
" *, Scalar beta, Scalar alpha, Tensor(a!) out) -> Tensor(a!)"
),
addmm_kernel_impl,
"SparseCsrCUDA",
)
_lazy_call(_register_triton_kernels)
from . import amp, jiterator, nvtx, profiler, sparse, tunable
__all__ = [
# Typed storage and tensors
"BFloat16Storage",
"BFloat16Tensor",
"BoolStorage",
"BoolTensor",
"ByteStorage",
"ByteTensor",
"CharStorage",
"CharTensor",
"ComplexDoubleStorage",
"ComplexFloatStorage",
"DoubleStorage",
"DoubleTensor",
"FloatStorage",
"FloatTensor",
"HalfStorage",
"HalfTensor",
"IntStorage",
"IntTensor",
"LongStorage",
"LongTensor",
"ShortStorage",
"ShortTensor",
"CUDAGraph",
"CudaError",
"DeferredCudaCallError",
"Event",
"ExternalStream",
"Stream",
"StreamContext",
"amp",
"caching_allocator_alloc",
"caching_allocator_delete",
"can_device_access_peer",
"check_error",
"cudaStatus",
"cudart",
"current_blas_handle",
"current_device",
"current_stream",
"default_generators",
"default_stream",
"device",
"device_count",
"device_of",
"empty_cache",
"get_allocator_backend",
"CUDAPluggableAllocator",
"change_current_allocator",
"get_arch_list",
"get_device_capability",
"get_device_name",
"get_device_properties",
"get_gencode_flags",
"get_rng_state",
"get_rng_state_all",
"get_sync_debug_mode",
"graph",
"graph_pool_handle",
"graphs",
"has_half",
"has_magma",
"init",
"initial_seed",
"ipc_collect",
"is_available",
"is_bf16_supported",
"is_current_stream_capturing",
"is_initialized",
"jiterator",
"list_gpu_processes",
"make_graphed_callables",
"manual_seed",
"manual_seed_all",
"max_memory_allocated",
"max_memory_cached",
"max_memory_reserved",
"mem_get_info",
"memory",
"memory_allocated",
"memory_cached",
"memory_reserved",
"memory_snapshot",
"memory_stats",
"memory_stats_as_nested_dict",
"memory_summary",
"memory_usage",
"MemPool",
"MemPoolContext",
"use_mem_pool",
"temperature",
"power_draw",
"clock_rate",
"nccl",
"nvtx",
"profiler",
"random",
"reset_accumulated_memory_stats",
"reset_max_memory_allocated",
"reset_max_memory_cached",
"reset_peak_memory_stats",
"seed",
"seed_all",
"set_device",
"set_per_process_memory_fraction",
"set_rng_state",
"set_rng_state_all",
"set_stream",
"set_sync_debug_mode",
"sparse",
"stream",
"streams",
"synchronize",
"tunable",
"utilization",
]
