mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
3a63a939d4
Test Plan: revert-hammer
Differential Revision:
D22517785 (288ece89e1)
Original commit changeset: 87334c893561
fbshipit-source-id: 0a0674f49c1bcfc98f7f88af5a8c7de93b76e458
35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
r"""This file is allowed to initialize CUDA context when imported."""
|
|
|
|
import torch
|
|
import torch.cuda
|
|
from torch.testing._internal.common_utils import TEST_NUMBA
|
|
|
|
|
|
TEST_CUDA = torch.cuda.is_available()
|
|
TEST_MULTIGPU = TEST_CUDA and torch.cuda.device_count() >= 2
|
|
CUDA_DEVICE = TEST_CUDA and torch.device("cuda:0")
|
|
# note: if ROCm is targeted, TEST_CUDNN is code for TEST_MIOPEN
|
|
TEST_CUDNN = TEST_CUDA and torch.backends.cudnn.is_acceptable(torch.tensor(1., device=CUDA_DEVICE))
|
|
TEST_CUDNN_VERSION = torch.backends.cudnn.version() if TEST_CUDNN else 0
|
|
|
|
if TEST_NUMBA:
|
|
import numba.cuda
|
|
TEST_NUMBA_CUDA = numba.cuda.is_available()
|
|
else:
|
|
TEST_NUMBA_CUDA = False
|
|
|
|
# Used below in `initialize_cuda_context_rng` to ensure that CUDA context and
|
|
# RNG have been initialized.
|
|
__cuda_ctx_rng_initialized = False
|
|
|
|
|
|
# after this call, CUDA context and RNG must have been initialized on each GPU
|
|
def initialize_cuda_context_rng():
|
|
global __cuda_ctx_rng_initialized
|
|
assert TEST_CUDA, 'CUDA must be available when calling initialize_cuda_context_rng'
|
|
if not __cuda_ctx_rng_initialized:
|
|
# initialize cuda context and rng for memory tests
|
|
for i in range(torch.cuda.device_count()):
|
|
torch.randn(1, device="cuda:{}".format(i))
|
|
__cuda_ctx_rng_initialized = True
|