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1f027ac02d
Summary: THCAllocator functionality is pretty obscure and it's hard to get it working with HIP because of how Caffe2/PyTorch rules are set up (see https://github.com/pytorch/pytorch/issues/39801). Let's just disable the test. Pull Request resolved: https://github.com/pytorch/pytorch/pull/39843 Reviewed By: zou3519 Differential Revision: D21998687 Pulled By: dzhulgakov fbshipit-source-id: cd12ba30cdfee658b98393ed3a72e83f4ecf1c9c
27 lines
1.1 KiB
Python
27 lines
1.1 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import unittest
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import torch
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from caffe2.python import core, workspace
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# This is a standalone test that doesn't use test_util as we're testing
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# initialization and thus we should be the ones calling GlobalInit
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@unittest.skipIf(not workspace.has_cuda_support,
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"THC pool testing is obscure and doesn't work on HIP yet")
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class TestGPUInit(unittest.TestCase):
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def testTHCAllocator(self):
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core.GlobalInit(['caffe2', '--caffe2_cuda_memory_pool=thc'])
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# just run one operator
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# it's importantant to not call anything here from Torch API
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# even torch.cuda.memory_allocated would initialize CUDA context
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workspace.RunOperatorOnce(core.CreateOperator(
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'ConstantFill', [], ["x"], shape=[5, 5], value=1.0,
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device_option=core.DeviceOption(workspace.GpuDeviceType)
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))
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# make sure we actually used THC allocator
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self.assertGreater(torch.cuda.memory_allocated(), 0)
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