def test_record_stream_cuda(self, cuda_sleep):
# This test detects unexpected block reallocation. For reliable test,
# the stream to allocate tensors is isolated. The allocator will not
# reuse free blocks which were allocated from another stream.
stream_alloc = new_stream(torch.device("cuda"))
with torch.cuda.stream(stream_alloc):
x = torch.rand(1, device=torch.device("cuda"))
stream = new_stream(torch.device("cuda"))
record_stream(x, stream)
with use_stream(stream):
cuda_sleep(0.5)
# 'x' is deleted at Python's perspective. But the block of 'x' is still
# required for 'stream'. 'y' shouldn't be allocated to the block.
data_ptr = x.data_ptr()
del x
stream_alloc.synchronize()
with torch.cuda.stream(stream_alloc):
y = torch.rand(1, device=torch.device("cuda"))
assert y.data_ptr() != data_ptr
# Pause Python until 'stream' finishes tasks queued. Now the block of
# 'x' is free to be reallocated.
wait_stream(CPUStream, stream)
with torch.cuda.stream(stream_alloc):
z = torch.rand(1, device=torch.device("cuda"))
assert z.data_ptr() == data_ptr
def test_record_stream_shifted_view(self, cuda_sleep):
# Issue: https://github.com/pytorch/pytorch/issues/27366
stream_alloc = new_stream(torch.device("cuda"))
with torch.cuda.stream(stream_alloc):
x = torch.rand(2, device=torch.device("cuda"))
y = x[1:]
assert y.data_ptr() > x.data_ptr()
stream = new_stream(torch.device("cuda"))
with use_stream(stream):
cuda_sleep(0.5)
record_stream(y, stream)
data_ptr = x.data_ptr()
del x, y
stream_alloc.synchronize()
with torch.cuda.stream(stream_alloc):
z = torch.rand(2, device=torch.device("cuda"))
assert z.data_ptr() != data_ptr
def test_copy_wait_cuda_cuda(cuda_sleep):
prev_stream = current_stream(torch.device("cuda"))
next_stream = new_stream(torch.device("cuda"))
_test_copy_wait(prev_stream, next_stream, cuda_sleep)
def test_use_stream_cuda(self):
stream = new_stream(torch.device("cuda"))
with use_stream(stream):
assert current_stream(torch.device("cuda")) == stream
def test_new_stream_cuda(self):
stream = new_stream(torch.device("cuda"))
assert isinstance(stream, torch.cuda.Stream)
assert stream != torch.cuda.default_stream()
def test_new_stream_cpu(self):
stream = new_stream(torch.device("cpu"))
assert stream is CPUStream
def test_wait_stream_cuda_cuda(self, cuda_sleep):
source = current_stream(torch.device("cuda"))
target = new_stream(torch.device("cuda"))
self._test_wait_stream(source, target, cuda_sleep)
def test_wait_stream_cuda_cpu(self, cuda_sleep):
source = new_stream(torch.device("cuda"))
target = CPUStream
self._test_wait_stream(source, target, cuda_sleep)
