def setUp(self):
self.pool = memory.MemoryAsyncPool()
self.unit = memory._allocation_unit_size
self.stream = stream_module.Stream()
self.stream_ident = self.stream.ptr
cupy.get_default_memory_pool().free_all_blocks()
cupy.cuda.Device().synchronize()
def setUp(self):
if cupy.cuda.runtime.deviceGetAttribute(
cupy.cuda.runtime.cudaDevAttrMemoryPoolsSupported, 0) == 0:
pytest.skip('malloc_async is not supported on device 0')
self.pool = memory.MemoryAsyncPool()
self.unit = memory._allocation_unit_size
self.stream = stream_module.Stream()
self.stream_ident = self.stream.ptr
cupy.get_default_memory_pool().free_all_blocks()
cupy.cuda.Device().synchronize()
c = cupy.random.random([extent[i] for i in mode_c])
a = a.astype(dtype)
c = c.astype(dtype)
desc_a = cutensor.create_tensor_descriptor(a)
desc_c = cutensor.create_tensor_descriptor(c)
alpha = 1.0
beta = 0.1
# rehearsal
c = cutensor.reduction(alpha, a, desc_a, mode_a, beta, c, desc_c, mode_c)
ev_start = stream.Event()
ev_end = stream.Event()
st = stream.Stream()
with st:
# measurement
ev_start.record()
c = cutensor.reduction(alpha, a, desc_a, mode_a, beta, c, desc_c, mode_c)
ev_end.record()
st.synchronize()
elapsed_ms = stream.get_elapsed_time(ev_start, ev_end)
transfer_byte = a.size * a.itemsize + c.size * c.itemsize
if beta != 0.0:
transfer_byte += c.size * c.itemsize
gbs = transfer_byte / elapsed_ms / 1e6
print('dtype: {}'.format(numpy.dtype(dtype).name))
print('time (ms): {}'.format(elapsed_ms))
def setUp(self):
self.pool = memory.SingleDeviceMemoryPool(allocator=mock_alloc)
self.unit = memory._allocation_unit_size
self.stream = stream_module.Stream()
self.stream_ptr = self.stream.ptr
def setUp(self):
self.stream = stream_module.Stream() if self.use_streams else None
def _get_stream(strm):
if strm is None:
return stream.Stream(null=True)
else:
return strm
