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f050b16dd9
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/30445 Create distributed and rpc directories under caffe/test for better management of unit tests. Differential Revision: D18702786 fbshipit-source-id: e9daeed0cfb846ef68806f6decfcb57c0e0e3606
35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
r"""This file is allowed to initialize CUDA context when imported."""
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import torch
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import torch.cuda
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from torch.testing._internal.common_utils import TEST_WITH_ROCM, TEST_NUMBA
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TEST_CUDA = torch.cuda.is_available()
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TEST_MULTIGPU = TEST_CUDA and torch.cuda.device_count() >= 2
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CUDA_DEVICE = TEST_CUDA and torch.device("cuda:0")
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# note: if ROCm is targeted, TEST_CUDNN is code for TEST_MIOPEN
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TEST_CUDNN = TEST_CUDA and (TEST_WITH_ROCM or torch.backends.cudnn.is_acceptable(torch.tensor(1., device=CUDA_DEVICE)))
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TEST_CUDNN_VERSION = torch.backends.cudnn.version() if TEST_CUDNN else 0
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if TEST_NUMBA:
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import numba.cuda
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TEST_NUMBA_CUDA = numba.cuda.is_available()
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else:
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TEST_NUMBA_CUDA = False
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# Used below in `initialize_cuda_context_rng` to ensure that CUDA context and
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# RNG have been initialized.
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__cuda_ctx_rng_initialized = False
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# after this call, CUDA context and RNG must have been initialized on each GPU
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def initialize_cuda_context_rng():
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global __cuda_ctx_rng_initialized
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assert TEST_CUDA, 'CUDA must be available when calling initialize_cuda_context_rng'
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if not __cuda_ctx_rng_initialized:
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# initialize cuda context and rng for memory tests
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for i in range(torch.cuda.device_count()):
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torch.randn(1, device="cuda:{}".format(i))
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__cuda_ctx_rng_initialized = True
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