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c1c176d91b
pytorch/test/test_cuda.py
Heungsub Hans Lee c1c176d91b record_stream() for shifted view tensors (#27371) 
Summary:
Issue: https://github.com/pytorch/pytorch/issues/27366

The address of a view tensor might be shifted from the head of the storage.

```python
>>> x = torch.rand(10, 10, device=0, requires_grad=True)
>>> y = x[2:]
>>> hex(x.data_ptr())
'0x7f1b15c00000'
>>> hex(y.data_ptr())
'0x7f1b15c00050'
```

Currently, `Tensor.record_stream()` silently ignores shifted view tensors, because `CUDACachingAllocator` cannot find the block from the shifted address.

```c++
void recordStream(void* ptr, cuda::CUDAStream stream)
{
  if (ptr) {
    std::lock_guard<std::recursive_mutex> lock(mutex);
    Block* block = find_allocated_block(ptr);
    if (block) {
      ...
    }
    // 'block' is nullptr if 'ptr' is shifted.
  }
}
```

So we cannot protect shifted view tensor which is used to compute or copy in an arbitrary stream against unexpected reallocation. Once we call `record_stream()` on a tensor, our intention is to protect the storage behind the tensor against reallocation until all works in the stream finish. This rule should be consistent regardless of the type of tensors including the view.

We can retrieve the head of the address from any types of tensors by `tensor.storage().data_ptr()`. Hence, I've thought it's better to pass to `recordStream()` rather than `tensor.data_ptr()` for consistent behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27371

Reviewed By: ezyang

Differential Revision: D17768558

Pulled By: albanD

fbshipit-source-id: 7705f52b0177625168edb6f71c07a029df471bc5
2019-10-08 12:31:26 -07:00

1871 lines
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import io
import tempfile
import unittest
import sys
from itertools import repeat
import os
from contextlib import contextmanager
import threading
if sys.version_info[0] == 3:
import queue
else:
import Queue as queue
import torch
import torch.cuda
import torch.cuda.comm as comm
from torch import multiprocessing as mp
from torch._six import inf, nan
from test_torch import _TestTorchMixin
from common_methods_invocations import tri_tests_args, tri_large_tests_args, \
_compare_trilu_indices, _compare_large_trilu_indices
from common_utils import TestCase, get_gpu_type, freeze_rng_state, run_tests, \
PY3, IS_WINDOWS, NO_MULTIPROCESSING_SPAWN, skipIfRocm, \
load_tests, slowTest, skipCUDANonDefaultStreamIf, default_floating_dtype
# load_tests from common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests
# We cannot import TEST_CUDA and TEST_MULTIGPU from common_cuda here,
# because if we do that, the TEST_CUDNN line from common_cuda will be executed
# multiple times as well during the execution of this test suite, and it will
# cause CUDA OOM error on Windows.
TEST_CUDA = torch.cuda.is_available()
TEST_MULTIGPU = TEST_CUDA and torch.cuda.device_count() >= 2
if not TEST_CUDA:
print('CUDA not available, skipping tests')
TestCase = object # noqa: F811
TEST_MAGMA = TEST_CUDA
TEST_LARGE_TENSOR = TEST_CUDA
TEST_MEDIUM_TENSOR = TEST_CUDA
if TEST_CUDA:
torch.ones(1).cuda() # has_magma shows up after cuda is initialized
TEST_MAGMA = torch.cuda.has_magma
TEST_LARGE_TENSOR = torch.cuda.get_device_properties(0).total_memory >= 12e9
TEST_MEDIUM_TENSOR = torch.cuda.get_device_properties(0).total_memory >= 6e9
types = [
torch.FloatTensor,
torch.DoubleTensor,
torch.LongTensor,
torch.IntTensor,
torch.ShortTensor,
torch.CharTensor,
torch.ByteTensor,
torch.HalfTensor,
]
def make_sparse_tensor(t, n, *sizes):
assert t.is_sparse
tensor = t()
i = tensor._indices()
i = i.new(len(sizes), n).copy_(
torch.cat([torch.LongTensor(1, n).random_(s) for s in sizes], 0))
v = tensor._values()
v = v.new(n).copy_(torch.randn(n))
return t(i, v, torch.Size(sizes))
_cycles_per_ms = None
def get_cycles_per_ms():
"""Approximate number of cycles per millisecond for torch.cuda._sleep"""
global _cycles_per_ms
if _cycles_per_ms is None:
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
torch.cuda._sleep(1000000)
end.record()
end.synchronize()
_cycles_per_ms = 1000000 / start.elapsed_time(end)
return _cycles_per_ms
class TestCuda(TestCase):
_do_cuda_memory_leak_check = True
_do_cuda_non_default_stream = True
FIFTY_MIL_CYCLES = 50000000
@staticmethod
def _test_memory_stats_generator(self, device=None, N=35):
if device is None:
device = torch.cuda.current_device()
m0 = torch.cuda.memory_allocated(device)
last_m_arr = [torch.cuda.memory_allocated(device)]
max_m_arr = [torch.cuda.max_memory_allocated(device)]
last_c_arr = [torch.cuda.memory_cached(device)]
max_c_arr = [torch.cuda.max_memory_cached(device)]
def alloc(*size):
with torch.cuda.device(device):
# NOTE: do **not** use methods that can have additional
# memory overhead, e.g., inplace random sampling methods.
# they can leave some memory occupied even after being
# deallocated, e.g., initialized RNG state, causing some
# memory checks below to fail.
return torch.cuda.FloatTensor(*size)
def assert_change(comp=1, empty_cache=False, reset_max_alloc=False, reset_max_cached=False):
# comp > 0: increased
# comp = 0: equal
# comp < 0: decreased
new_m = torch.cuda.memory_allocated(device)
new_max_m = torch.cuda.max_memory_allocated(device)
if comp > 0:
self.assertGreater(new_m, last_m_arr[0])
elif comp < 0:
self.assertLess(new_m, last_m_arr[0])
else:
self.assertEqual(new_m, last_m_arr[0])
self.assertLessEqual(new_m, new_max_m)
self.assertGreaterEqual(new_max_m, max_m_arr[0])
last_m_arr[0] = new_m
max_m_arr[0] = new_max_m
new_c = torch.cuda.memory_cached(device)
new_max_c = torch.cuda.max_memory_cached(device)
# emptying cache may happen (due to allocation or empty_cache), so
# we can't assert new_c >= last_c
self.assertLessEqual(new_c, new_max_c)
self.assertGreaterEqual(new_max_c, max_c_arr[0])
last_c_arr[0] = new_c
max_c_arr[0] = new_max_c
if empty_cache:
torch.cuda.empty_cache()
new_c = torch.cuda.memory_cached(device)
new_max_c = torch.cuda.max_memory_cached(device)
self.assertLessEqual(new_c, last_c_arr[0])
self.assertLessEqual(new_c, new_max_c)
self.assertEqual(new_max_c, max_c_arr[0])
last_c_arr[0] = new_c
if reset_max_alloc:
torch.cuda.reset_max_memory_allocated(device)
self.assertEqual(torch.cuda.memory_allocated(device), last_m_arr[0])
self.assertEqual(torch.cuda.max_memory_allocated(device), last_m_arr[0])
max_m_arr[0] = last_m_arr[0]
self.assertEqual(torch.cuda.memory_cached(device), last_c_arr[0])
self.assertEqual(torch.cuda.max_memory_cached(device), max_c_arr[0])
if reset_max_cached:
torch.cuda.reset_max_memory_cached(device)
self.assertEqual(torch.cuda.memory_allocated(device), last_m_arr[0])
self.assertEqual(torch.cuda.max_memory_allocated(device), max_m_arr[0])
self.assertEqual(torch.cuda.memory_cached(device), last_c_arr[0])
self.assertEqual(torch.cuda.max_memory_cached(device), last_c_arr[0])
max_c_arr[0] = last_c_arr[0]
assert_change(0)
assert_change(0, reset_max_alloc=True)
assert_change(0, empty_cache=True)
assert_change(0, reset_max_cached=True)
assert_change(0)
yield
tensors1 = [alloc(1), alloc(10, 20), alloc(200, 300, 2000)]
m1 = torch.cuda.memory_allocated(device)
assert_change(1)
yield
tensors2 = []
for i in range(1, int(N / 2) + 1):
# small ones
tensors2.append(alloc(i, i * 4))
assert_change(1)
yield
for i in range(5, int(N / 2) + 5):
# large ones
tensors2.append(alloc(i, i * 7, i * 9, i * 11))
assert_change(1, reset_max_alloc=(i % 2 == 0), reset_max_cached=(i % 2 == 1))
yield
tensors2.append(alloc(0, 0, 0))
assert_change(0)
yield
permute = []
for i in torch.randperm(len(tensors2)):
permute.append(tensors2[i])
assert_change(0)
yield
del tensors2
assert_change(0)
yield
tensors2 = permute
assert_change(0)
yield
del permute
assert_change(0, reset_max_alloc=True)
yield
for i in range(int(N / 2)):
x = tensors2[i].numel()
del tensors2[i]
assert_change(-x) # in case that tensors2[i] is empty
yield
for i in range(2, int(2 * N / 3) + 2):
tensors2.append(alloc(i, i * 3, i * 8))
assert_change(1)
yield
del tensors2
assert_change(-1, reset_max_cached=True)
assert_change(0)
self.assertEqual(torch.cuda.memory_allocated(device), m1)
yield True
del tensors1
assert_change(-1, reset_max_alloc=True)
self.assertEqual(torch.cuda.memory_allocated(device), m0)
# test empty_cache and reset_max_memory_*
assert_change(0, empty_cache=True)
assert_change(0, reset_max_cached=True)
assert_change(0, reset_max_alloc=True)
def test_memory_stats(self):
torch.cuda.empty_cache()
for _ in self._test_memory_stats_generator(self):
pass
def test_cuda_get_device_name(self):
# Testing the behaviour with None as an argument
current_device = torch.cuda.current_device()
current_device_name = torch.cuda.get_device_name(current_device)
device_name_None = torch.cuda.get_device_name(None)
self.assertEqual(current_device_name, device_name_None)
# Testing the behaviour for No argument
device_name_no_argument = torch.cuda.get_device_name()
self.assertEqual(current_device_name, device_name_no_argument)
def test_cuda_get_device_capability(self):
# Testing the behaviour with None as an argument
current_device = torch.cuda.current_device()
current_device_capability = torch.cuda.get_device_capability(current_device)
device_capability_None = torch.cuda.get_device_capability(None)
self.assertEqual(current_device_capability, device_capability_None)
# Testing the behaviour for No argument
device_capability_no_argument = torch.cuda.get_device_capability()
self.assertEqual(current_device_capability, device_capability_no_argument)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_memory_stats_multigpu(self):
# advance a generator with a end flag
def advance(gen, end):
if not end:
try:
next(gen)
except StopIteration:
end = True
return end
# interlace
torch.cuda.empty_cache()
gen0 = self._test_memory_stats_generator(self, device='cuda:0', N=35)
gen1 = self._test_memory_stats_generator(self, device=torch.device('cuda:1'), N=35)
end0 = end1 = False
while not (end0 and end1):
end0 = advance(gen0, end0)
end1 = advance(gen1, end1)
# semi-random order
torch.cuda.empty_cache()
gen0 = self._test_memory_stats_generator(self, device=0, N=35)
gen1 = self._test_memory_stats_generator(self, device=torch.device('cuda:1'), N=35)
end0 = end1 = False
while not (end0 and end1):
end0 = advance(gen0, end0)
if not end0:
gen1_max_times = torch.LongTensor(1).random_(0, 3)[0]
else:
gen1_max_times = inf
t = 0
while t < gen1_max_times and not end1:
end1 = advance(gen1, end1)
t += 1
def test_out_of_memory(self):
tensor = torch.zeros(1024, device='cuda')
with self.assertRaisesRegex(RuntimeError, "Tried to allocate 80.00 GiB"):
torch.empty(1024 * 1024 * 1024 * 80, dtype=torch.int8, device='cuda')
# ensure out of memory error doesn't disturb subsequent kernel
tensor.fill_(1)
self.assertTrue((tensor == 1).all())
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_autogpu(self):
x = torch.randn(5, 5).cuda()
y = torch.randn(5, 5).cuda()
self.assertEqual(x.get_device(), 0)
self.assertEqual(x.get_device(), 0)
with torch.cuda.device(1):
z = torch.randn(5, 5).cuda()
self.assertEqual(z.get_device(), 1)
q = x.add(y)
self.assertEqual(q.get_device(), 0)
w = torch.randn(5, 5).cuda()
self.assertEqual(w.get_device(), 1)
self.assertEqual(y.cuda().get_device(), 1)
z = z.cuda()
self.assertEqual(z.get_device(), 0)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_new(self):
x = torch.randn(3, 3).cuda()
self.assertEqual(x.new([0, 1, 2]).get_device(), 0)
self.assertEqual(x.new([0, 1, 2], device=1).get_device(), 1)
with torch.cuda.device(1):
self.assertEqual(x.new([0, 1, 2]).get_device(), 0)
self.assertEqual(x.new([0, 1, 2], device=1).get_device(), 1)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_copy_device(self):
x = torch.randn(5, 5).cuda()
with torch.cuda.device(1):
y = x.cuda()
self.assertEqual(y.get_device(), 1)
self.assertIs(y.cuda(), y)
z = y.cuda(0)
self.assertEqual(z.get_device(), 0)
self.assertIs(z.cuda(0), z)
x = torch.randn(5, 5)
with torch.cuda.device(1):
y = x.cuda()
self.assertEqual(y.get_device(), 1)
self.assertIs(y.cuda(), y)
z = y.cuda(0)
self.assertEqual(z.get_device(), 0)
self.assertIs(z.cuda(0), z)
def _test_copy_sync_current_stream(self, x, y):
x_plus_one = x + 1
s0 = torch.cuda.Stream(device=x.device)
s1 = torch.cuda.Stream(device=y.device)
s2 = torch.cuda.Stream(device=x.device)
s3 = torch.cuda.Stream(device=y.device)
# same dst stream different src streams
with torch.cuda.stream(s0):
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
with torch.cuda.stream(s1):
y.copy_(x_plus_one)
with torch.cuda.stream(s2), torch.cuda.stream(s1):
y.copy_(x)
s1.synchronize()
# The copy() is synchronized on the current streams of both src and dst.
# In the above test, the _sleep() op on s0 will not block the copy() on
# s2, but both copies are synchronized on s1 in the dst device. Hence,
# x is copied to y after x_plus_one is copied to y. If x and y are on
# the same device, both copy() ops are synchronized on s1.
self.assertEqual(y, x)
# same src stream different dst streams
with torch.cuda.stream(s1):
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
with torch.cuda.stream(s0):
y.copy_(x_plus_one)
with torch.cuda.stream(s3), torch.cuda.stream(s0):
y.copy_(x)
s0.synchronize()
# Similarly, both copy() ops are synchronized on s0.
self.assertEqual(y, x)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_copy_streams(self):
d0 = torch.device('cuda:0')
x0 = torch.zeros(5, 5, device=d0)
d1 = torch.device('cuda:1')
x1 = torch.zeros(5, 5, device=d1)
self._test_copy_sync_current_stream(x0, x1)
x2 = torch.zeros(5, 5, device=d0)
self._test_copy_sync_current_stream(x0, x2)
def test_copy_non_blocking(self):
def _test_copy_non_blocking(a, b):
event = torch.cuda.Event()
a.copy_(b, non_blocking=True)
event.record()
self.assertFalse(event.query())
event.synchronize()
self.assertEqual(a, b)
# 10MB copies
x = torch.ones(10000000, dtype=torch.uint8).cuda()
y = torch.zeros(10000000, dtype=torch.uint8).pin_memory()
_test_copy_non_blocking(x, y)
x = torch.zeros(10000000, dtype=torch.uint8).pin_memory()
y = torch.ones(10000000, dtype=torch.uint8).cuda()
_test_copy_non_blocking(x, y)
def test_serialization_array_with_storage(self):
x = torch.randn(5, 5).cuda()
y = torch.IntTensor(2, 5).fill_(0).cuda()
q = [x, y, x, y.storage()]
with tempfile.NamedTemporaryFile() as f:
torch.save(q, f)
f.seek(0)
q_copy = torch.load(f)
self.assertEqual(q_copy, q, 0)
q_copy[0].fill_(5)
self.assertEqual(q_copy[0], q_copy[2], 0)
self.assertTrue(isinstance(q_copy[0], torch.cuda.FloatTensor))
self.assertTrue(isinstance(q_copy[1], torch.cuda.IntTensor))
self.assertTrue(isinstance(q_copy[2], torch.cuda.FloatTensor))
self.assertTrue(isinstance(q_copy[3], torch.cuda.IntStorage))
q_copy[1].fill_(10)
self.assertTrue(q_copy[3], torch.cuda.IntStorage(10).fill_(10))
def test_type_conversions(self):
x = torch.randn(5, 5)
self.assertIsInstance(x.float(), torch.FloatTensor)
self.assertIsInstance(x.cuda().double(), torch.cuda.DoubleTensor)
self.assertIsInstance(x.cuda().float(), torch.cuda.FloatTensor)
self.assertIsInstance(x.cuda().float().cpu(), torch.FloatTensor)
self.assertIsInstance(x.cuda().float().cpu().int(), torch.IntTensor)
y = x.storage()
self.assertIsInstance(y.float(), torch.FloatStorage)
self.assertIsInstance(y.cuda().double(), torch.cuda.DoubleStorage)
self.assertIsInstance(y.cuda().float(), torch.cuda.FloatStorage)
self.assertIsInstance(y.cuda().float().cpu(), torch.FloatStorage)
self.assertIsInstance(y.cuda().float().cpu().int(), torch.IntStorage)
@unittest.skipIf(not TEST_LARGE_TENSOR, "not enough memory")
def test_arithmetic_large_tensor(self):
x = torch.empty(2**30, device='cuda')
x.fill_(1)
self.assertEqual(x.sum(), 2**30)
x += 1
self.assertEqual(x.sum(), 2**31)
x.fill_(1)
x -= 0.5
self.assertEqual(x.sum(), 2**29)
x.fill_(1)
x *= 2
self.assertEqual(x.sum(), 2**31)
x.fill_(1)
x /= 2
self.assertEqual(x.sum(), 2**29)
def _test_broadcast(self, input):
if not TEST_MULTIGPU:
raise unittest.SkipTest("only one GPU detected")
result = comm.broadcast(input, (0, 1))
for i, t in enumerate(result):
self.assertEqual(t.get_device(), i)
self.assertEqual(t, input)
if input.is_cuda and input.get_device() == i:
self.assertEqual(t.data_ptr(), input.data_ptr())
def test_broadcast_cpu(self):
self._test_broadcast(torch.randn(5, 5))
def test_broadcast_gpu(self):
self._test_broadcast(torch.randn(5, 5).cuda())
@staticmethod
def _test_broadcast_coalesced(self, tensors, buffer_size):
b_tensors = [comm.broadcast(t, (0, 1)) for t in tensors]
for (_, bt), t in zip(b_tensors, tensors):
self.assertEqual(bt.get_device(), 1)
self.assertEqual(bt, t)
self.assertIsInstance(bt, type(t))
bc_tensors = comm.broadcast_coalesced(tensors, (0, 1), buffer_size=buffer_size)
bc_tensors_t = list(zip(*bc_tensors))
self.assertEqual(b_tensors, bc_tensors_t)
for (_, bt), (_, bct) in zip(b_tensors, bc_tensors_t):
self.assertEqual(bt.get_device(), bct.get_device())
self.assertIsInstance(bct, type(bt))
# check that tensors on device[0] are returned as-is
for out_tensors in (b_tensors, bc_tensors_t):
for inp_t, (out_t, _) in zip(tensors, out_tensors):
self.assertIs(inp_t, out_t)
# check that the tensors not on device[0] have different version counters
# NOTE [ Version Counter in comm.*_coalesced ]
versions = [t._version for _, t in bc_tensors_t]
for old_version, (_, t) in zip(versions, bc_tensors_t):
self.assertEqual(t._version, old_version)
t.zero_()
self.assertEqual(t._version, old_version + 1)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
# Note: fails sometimes on the CI, passes on dual gfx906
def test_broadcast_coalesced(self):
numel = 5
num_bytes = numel * 8
tensors = [
make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 1, 2, 3),
torch.randn(numel).long().cuda(),
torch.randn(numel).cuda(),
make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 10, 2, 3),
make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 5, 2, 3),
make_sparse_tensor(torch.cuda.sparse.LongTensor, 7, 3, 3),
make_sparse_tensor(torch.cuda.sparse.FloatTensor, 2, 2, 3),
torch.randn(numel).long().cuda(),
torch.randn(numel).long().cuda(),
make_sparse_tensor(torch.cuda.sparse.LongTensor, 3, 2, 7),
torch.randn(numel * 2).int().cuda(), # int is 2x shorter
torch.randn(numel).cuda(),
]
self._test_broadcast_coalesced(self, tensors, num_bytes * 5 // 2)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_broadcast_coalesced_dense_only(self):
numel = 5
num_bytes = numel * 8
tensors = [
torch.randn(numel).long().cuda(),
torch.randn(numel).cuda(),
torch.randn(numel).long().cuda(),
torch.randn(numel).long().cuda(),
torch.randn(numel * 2).int().cuda(), # int is 2x shorter
torch.randn(numel).cuda(),
]
self._test_broadcast_coalesced(self, tensors, num_bytes * 5 // 2)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_reduce_add(self):
x = torch.randn(5, 5)
y = torch.randn(5, 5)
x_cuda = x.cuda(0)
y_cuda = y.cuda(1)
result = comm.reduce_add((x_cuda, y_cuda))
self.assertEqual(result.get_device(), 0)
self.assertEqual(result.cpu(), x + y)
@staticmethod
def _test_reduce_add_coalesced(self, tensors, buffer_size):
dup_tensors = [tensors, list(map(lambda t: t.cuda(1), tensors))]
r_tensors = list(map(comm.reduce_add, zip(*dup_tensors)))
for r, t in zip(r_tensors, tensors):
self.assertEqual(r.get_device(), t.get_device())
self.assertEqual(r, t * 2)
self.assertEqual(r.type(), t.type())
rc_tensors = comm.reduce_add_coalesced(dup_tensors, buffer_size=buffer_size)
self.assertEqual(r_tensors, rc_tensors)
for r, rc in zip(r_tensors, rc_tensors):
self.assertEqual(rc.get_device(), r.get_device())
self.assertEqual(rc.type(), r.type())
# Since we have both cuda:0 and cuda:1 inputs, the outputs must be new.
# We can check that they have different version counters.
# NOTE [ Version Counter in comm.*_coalesced ]
versions = [t._version for t in rc_tensors]
for old_version, t in zip(versions, rc_tensors):
self.assertEqual(t._version, old_version)
t.zero_()
self.assertEqual(t._version, old_version + 1)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_reduce_add_coalesced(self):
numel = 5
num_bytes = numel * 8
tensors = [
make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 1, 2, 3),
torch.randn(numel).long().cuda(),
torch.randn(numel).cuda(),
make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 10, 2, 3),
make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 5, 2, 3),
make_sparse_tensor(torch.cuda.sparse.LongTensor, 7, 3, 3),
make_sparse_tensor(torch.cuda.sparse.FloatTensor, 2, 2, 3),
torch.randn(numel).long().cuda(),
torch.randn(numel).long().cuda(),
make_sparse_tensor(torch.cuda.sparse.LongTensor, 3, 2, 7),
torch.randn(numel * 2).int().cuda(), # int is 2x shorter
torch.randn(numel).cuda(),
]
self._test_reduce_add_coalesced(self, tensors, num_bytes * 5 // 2)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_reduce_add_coalesced_dense_only(self):
numel = 5
num_bytes = numel * 8
tensors = [
torch.randn(numel).long().cuda(),
torch.randn(numel).cuda(),
torch.randn(numel).long().cuda(),
torch.randn(numel).long().cuda(),
torch.randn(numel * 2).int().cuda(), # int is 2x shorter
torch.randn(numel).cuda(),
]
self._test_reduce_add_coalesced(self, tensors, num_bytes * 5 // 2)
def _test_scatter(self, input, chunk_sizes=None, dim=0):
if not TEST_MULTIGPU:
raise unittest.SkipTest("only one GPU detected")
result = comm.scatter(input, (0, 1), chunk_sizes, dim)
self.assertEqual(len(result), 2)
if chunk_sizes is None:
chunk_sizes = tuple(repeat(input.size(dim) // 2, 2))
chunk_start = 0
for i, r in enumerate(result):
chunk_end = chunk_start + chunk_sizes[i]
index = [slice(None, None), slice(None, None)]
index[dim] = slice(chunk_start, chunk_end)
self.assertEqual(r, input[tuple(index)], 0)
chunk_start = chunk_end
def test_scatter_cpu(self):
self._test_scatter(torch.randn(4, 4), dim=0)
def test_scatter_cpu_dim(self):
self._test_scatter(torch.randn(4, 4), dim=1)
def test_scatter_cpu_neg_dim(self):
self._test_scatter(torch.randn(4, 4), dim=-2)
def test_scatter_cpu_sizes(self):
self._test_scatter(torch.randn(6, 4), chunk_sizes=(2, 4))
def test_scatter_gpu(self):
self._test_scatter(torch.randn(4, 4).cuda(), dim=0)
def test_scatter_gpu_dim(self):
self._test_scatter(torch.randn(4, 4).cuda(), dim=1)
def test_scatter_gpu_neg_dim(self):
self._test_scatter(torch.randn(4, 4).cuda(), dim=-2)
def test_scatter_gpu_sizes(self):
self._test_scatter(torch.randn(6, 4).cuda(), chunk_sizes=(2, 4))
def _test_gather(self, dim):
if not TEST_MULTIGPU:
raise unittest.SkipTest("only one GPU detected")
x = torch.randn(2, 5).cuda(0)
y = torch.randn(2, 5).cuda(1)
result = comm.gather((x, y), dim)
expected_size = list(x.size())
expected_size[dim] += y.size(dim)
expected_size = torch.Size(expected_size)
self.assertEqual(result.get_device(), 0)
self.assertEqual(result.size(), expected_size)
index = [slice(None, None), slice(None, None)]
index[dim] = slice(0, x.size(dim))
self.assertEqual(result[tuple(index)], x)
index[dim] = slice(x.size(dim), x.size(dim) + y.size(dim))
self.assertEqual(result[tuple(index)], y)
# Bool test case
t = torch.tensor([[False, True], [True, True]], device='cuda')
self.assertEqual(torch.gather(t, 1, torch.tensor([[0, 0], [1, 0]], device='cuda')),
torch.tensor([[False, False], [True, True]], device='cuda'))
def test_gather(self):
self._test_gather(0)
def test_gather_dim(self):
self._test_gather(1)
def test_torch_manual_seed_seeds_cuda_devices(self):
with freeze_rng_state():
x = torch.zeros(4, 4).float().cuda()
torch.manual_seed(2)
self.assertEqual(torch.cuda.initial_seed(), 2)
x.uniform_()
torch.manual_seed(2)
y = x.clone().uniform_()
self.assertEqual(x, y)
self.assertEqual(torch.cuda.initial_seed(), 2)
def test_manual_seed(self):
with freeze_rng_state():
x = torch.zeros(4, 4).float().cuda()
torch.cuda.manual_seed(2)
self.assertEqual(torch.cuda.initial_seed(), 2)
x.uniform_()
a = torch.bernoulli(torch.full_like(x, 0.5))
torch.cuda.manual_seed(2)
y = x.clone().uniform_()
b = torch.bernoulli(torch.full_like(x, 0.5))
self.assertEqual(x, y)
self.assertEqual(a, b)
self.assertEqual(torch.cuda.initial_seed(), 2)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_cat_autogpu(self):
x = torch.randn(4, 4).cuda(1)
y = torch.randn(4, 4).cuda(1)
z = torch.cat([x, y], 0)
self.assertEqual(z.get_device(), x.get_device())
def test_bernoulli(self):
_TestTorchMixin._test_bernoulli(self, torch.float32, torch.float64, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.float32, torch.float16, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.float16, torch.float64, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.float16, torch.float16, 'cuda')
# test that it works with integral tensors
_TestTorchMixin._test_bernoulli(self, torch.uint8, torch.float64, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.uint8, torch.float16, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.int64, torch.float64, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.int64, torch.float16, 'cuda')
# test that it works with bool tensors
_TestTorchMixin._test_bernoulli(self, torch.bool, torch.float16, 'cuda')
_TestTorchMixin._test_bernoulli(self, torch.int64, torch.float16, 'cuda')
@unittest.skipIf(torch.cuda.device_count() >= 10, "Loading a cuda:9 tensor")
@unittest.skipIf(not PY3, "Tensor was serialized with Python 3")
def test_load_nonexistent_device(self):
# Setup: create a serialized file object with a 'cuda:9' restore location
tensor = torch.randn(2, device='cuda')
buf = io.BytesIO()
torch.save(tensor, buf)
# NB: this might not work in the future if serialization changes
buf = io.BytesIO(buf.getvalue().replace(b'cuda:0', b'cuda:9'))
msg = r'Attempting to deserialize object on CUDA device 9'
with self.assertRaisesRegex(RuntimeError, msg):
_ = torch.load(buf)
def test_serialization_array_with_empty(self):
x = [torch.randn(4, 4).cuda(), torch.cuda.FloatTensor()]
with tempfile.NamedTemporaryFile() as f:
torch.save(x, f)
f.seek(0)
x_copy = torch.load(f)
for original, copy in zip(x, x_copy):
self.assertEqual(copy, original)
self.assertIs(type(copy), type(original))
self.assertEqual(copy.get_device(), original.get_device())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_multigpu_serialization_remap(self):
x = [torch.randn(4, 4).cuda(0), torch.randn(4, 4).cuda(1)]
def gpu_remap(storage, location):
if location == 'cuda:1':
return storage.cuda(0)
with tempfile.NamedTemporaryFile() as f:
torch.save(x, f)
f.seek(0)
x_copy = torch.load(f, map_location=gpu_remap)
for original, copy in zip(x, x_copy):
self.assertEqual(copy, original)
self.assertIs(type(copy), type(original))
self.assertEqual(copy.get_device(), 0)
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_multigpu_serialization_remap_dict(self):
x = [torch.randn(4, 4).cuda(0), torch.randn(4, 4).cuda(1)]
with tempfile.NamedTemporaryFile() as f:
torch.save(x, f)
f.seek(0)
x_copy = torch.load(f, map_location={'cuda:1': 'cuda:0'})
for original, copy in zip(x, x_copy):
self.assertEqual(copy, original)
self.assertIs(type(copy), type(original))
self.assertEqual(copy.get_device(), 0)
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_multigpu_storage_clone(self):
x = torch.randn(4, 4, device='cuda:1').storage()
y = x.clone()
self.assertEqual(x.get_device(), y.get_device())
for t in ['byte', 'char', 'short', 'int', 'long', 'half', 'double']:
self.assertEqual(getattr(x, t)().get_device(), x.get_device())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_cuda_set_device(self):
x = torch.randn(5, 5)
with torch.cuda.device(1):
self.assertEqual(x.cuda().get_device(), 1)
torch.cuda.set_device(0)
self.assertEqual(x.cuda().get_device(), 0)
with torch.cuda.device(1):
self.assertEqual(x.cuda().get_device(), 1)
self.assertEqual(x.cuda().get_device(), 0)
torch.cuda.set_device(1)
self.assertEqual(x.cuda().get_device(), 0)
def test_is_tensor(self):
for t in types:
tensor = get_gpu_type(t)()
self.assertTrue(torch.is_tensor(tensor))
self.assertTrue(torch.is_tensor(torch.cuda.HalfTensor()))
def test_cuda_synchronize(self):
torch.cuda.synchronize()
torch.cuda.synchronize('cuda')
torch.cuda.synchronize('cuda:0')
torch.cuda.synchronize(0)
torch.cuda.synchronize(torch.device('cuda:0'))
if TEST_MULTIGPU:
torch.cuda.synchronize('cuda:1')
torch.cuda.synchronize(1)
torch.cuda.synchronize(torch.device('cuda:1'))
with self.assertRaisesRegex(ValueError, "Expected a cuda device, but"):
torch.cuda.synchronize(torch.device("cpu"))
with self.assertRaisesRegex(ValueError, "Expected a cuda device, but"):
torch.cuda.synchronize("cpu")
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_current_stream(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
s0 = torch.cuda.current_stream()
s1 = torch.cuda.current_stream(device=1)
s2 = torch.cuda.current_stream(device=0)
self.assertEqual(d0, s0.device)
self.assertEqual(d1, s1.device)
self.assertEqual(d0, s2.device)
self.assertEqual(s0, s2)
with torch.cuda.device(d1):
s0 = torch.cuda.current_stream()
s1 = torch.cuda.current_stream(1)
s2 = torch.cuda.current_stream(d0)
self.assertEqual(d1, s0.device)
self.assertEqual(d1, s1.device)
self.assertEqual(d0, s2.device)
self.assertEqual(s0, s1)
with self.assertRaisesRegex(ValueError,
"Expected a cuda device, but got: cpu"):
torch.cuda.current_stream(torch.device('cpu'))
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
@skipCUDANonDefaultStreamIf(True)
def test_default_stream(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
with torch.cuda.device(d0):
s0 = torch.cuda.default_stream()
with torch.cuda.device(d1):
s1 = torch.cuda.default_stream()
s2 = torch.cuda.default_stream(device=0)
s3 = torch.cuda.default_stream(d1)
self.assertEqual(d0, s0.device)
self.assertEqual(d1, s1.device)
self.assertEqual(d0, s2.device)
self.assertEqual(d1, s3.device)
self.assertEqual(s0, s2)
self.assertEqual(s1, s3)
with torch.cuda.device(d0):
self.assertEqual(torch.cuda.current_stream(), s0)
with torch.cuda.device(d1):
self.assertEqual(torch.cuda.current_stream(), s1)
with self.assertRaisesRegex(ValueError,
"Expected a cuda device, but got: cpu"):
torch.cuda.default_stream(torch.device('cpu'))
@skipCUDANonDefaultStreamIf(True)
def test_streams(self):
default_stream = torch.cuda.current_stream()
user_stream = torch.cuda.Stream()
self.assertEqual(torch.cuda.current_stream(), default_stream)
self.assertNotEqual(default_stream, user_stream)
self.assertEqual(default_stream.cuda_stream, 0)
self.assertNotEqual(user_stream.cuda_stream, 0)
with torch.cuda.stream(user_stream):
self.assertEqual(torch.cuda.current_stream(), user_stream)
self.assertTrue(user_stream.query())
# copy 10 MB tensor from CPU-GPU which should take some time
tensor1 = torch.ByteTensor(10000000).pin_memory()
tensor2 = tensor1.cuda(non_blocking=True)
self.assertFalse(default_stream.query())
default_stream.synchronize()
self.assertTrue(default_stream.query())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_stream_event_device(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
e0 = torch.cuda.Event()
self.assertEqual(None, e0.device)
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
s0.record_event(e0)
with torch.cuda.device(d1):
s1 = torch.cuda.Stream()
e1 = s1.record_event()
self.assertEqual(s0.device, torch.device('cuda:0'))
self.assertEqual(e0.device, torch.device('cuda:0'))
self.assertEqual(s1.device, torch.device('cuda:1'))
self.assertEqual(e1.device, torch.device('cuda:1'))
def test_stream_event_repr(self):
s = torch.cuda.current_stream()
self.assertTrue("torch.cuda.Stream" in s.__repr__())
e = torch.cuda.Event()
self.assertTrue("torch.cuda.Event" in e.__repr__())
s.record_event(e)
self.assertTrue("torch.cuda.Event" in e.__repr__())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
# Note: fails sometimes on the CI, passes on dual gfx906
@skipIfRocm
def test_stream_context(self):
s0 = torch.cuda.current_stream()
s1 = torch.cuda.Stream(device=1)
s2 = torch.cuda.Stream(device=0)
with torch.cuda.device(s1.device):
prev_stream_on_cuda1 = torch.cuda.current_stream()
self.assertEqual(torch.cuda.current_stream(), s0)
self.assertEqual(0, torch.cuda.current_device())
with torch.cuda.stream(s1):
self.assertEqual(torch.cuda.current_stream(), s1)
self.assertEqual(1, torch.cuda.current_device())
with torch.cuda.stream(s2):
self.assertEqual(torch.cuda.current_stream(), s2)
self.assertEqual(0, torch.cuda.current_device())
with torch.cuda.stream(s0):
self.assertEqual(torch.cuda.current_stream(), s0)
self.assertEqual(0, torch.cuda.current_device())
self.assertEqual(torch.cuda.current_stream(), s2)
self.assertEqual(0, torch.cuda.current_device())
self.assertEqual(torch.cuda.current_stream(), s1)
self.assertEqual(1, torch.cuda.current_device())
with torch.cuda.device(s1.device):
self.assertEqual(prev_stream_on_cuda1, torch.cuda.current_stream())
self.assertEqual(torch.cuda.current_stream(), s0)
self.assertEqual(0, torch.cuda.current_device())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_streams_multi_gpu(self):
default_stream = torch.cuda.current_stream()
self.assertEqual(default_stream.device, torch.device('cuda:0'))
stream = torch.cuda.Stream(device=1)
self.assertEqual(stream.device, torch.device('cuda:1'))
with torch.cuda.device(1):
self.assertEqual(
torch.cuda.current_stream().device, torch.device('cuda:1'))
self.assertNotEqual(torch.cuda.current_stream(), default_stream)
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_streams_multi_gpu_query(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
torch.cuda.synchronize(d0)
torch.cuda.synchronize(d1)
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
with torch.cuda.device(d1):
s1 = torch.cuda.current_stream()
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
self.assertTrue(s0.query())
self.assertFalse(s1.query())
with torch.cuda.device(d0):
self.assertTrue(s0.query())
self.assertFalse(s1.query())
with torch.cuda.device(d1):
self.assertTrue(s0.query())
self.assertFalse(s1.query())
# deliberately using a different device
with torch.cuda.device(d0):
s1.synchronize()
self.assertTrue(s0.query())
self.assertTrue(s1.query())
with torch.cuda.device(d0):
self.assertTrue(s0.query())
self.assertTrue(s1.query())
with torch.cuda.device(d1):
self.assertTrue(s0.query())
self.assertTrue(s1.query())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_streams_multi_gpu_eq(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
s1 = torch.cuda.current_stream()
with torch.cuda.device(d1):
s2 = torch.cuda.current_stream()
s3 = torch.cuda.current_stream()
self.assertTrue(s0 == s0)
self.assertTrue(s0 == s1)
self.assertTrue(s2 == s2)
self.assertTrue(s2 == s3)
self.assertFalse(s0 == s2)
self.assertFalse(s1 == s3)
self.assertEqual(s0.device, s1.device)
self.assertEqual(s0.cuda_stream, s1.cuda_stream)
self.assertEqual(s2.device, s3.device)
self.assertEqual(s2.cuda_stream, s3.cuda_stream)
self.assertNotEqual(s0.device, s3.device)
self.assertEqual(hash(s0), hash(s1))
self.assertEqual(hash(s2), hash(s3))
self.assertNotEqual(hash(s0), hash(s3))
@unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
@skipIfRocm
def test_streams_priority(self):
low, high = torch.cuda.Stream.priority_range()
s0 = torch.cuda.Stream(device=0, priority=low)
self.assertEqual(low, s0.priority)
self.assertEqual(torch.device('cuda:0'), s0.device)
s1 = torch.cuda.Stream(device=1, priority=high)
self.assertEqual(high, s1.priority)
self.assertEqual(torch.device('cuda:1'), s1.device)
@unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
def test_tensor_device(self):
self.assertEqual(torch.cuda.FloatTensor(1).get_device(), 0)
self.assertEqual(torch.cuda.FloatTensor(1, device=1).get_device(), 1)
with torch.cuda.device(1):
self.assertEqual(torch.cuda.FloatTensor(1).get_device(), 1)
self.assertEqual(torch.cuda.FloatTensor(1, device=0).get_device(), 0)
self.assertEqual(torch.cuda.FloatTensor(1, device=None).get_device(), 1)
def test_events(self):
stream = torch.cuda.current_stream()
event = torch.cuda.Event(enable_timing=True)
self.assertTrue(event.query())
start_event = torch.cuda.Event(enable_timing=True)
stream.record_event(start_event)
torch.cuda._sleep(int(50 * get_cycles_per_ms()))
stream.record_event(event)
self.assertFalse(event.query())
event.synchronize()
self.assertTrue(event.query())
self.assertGreater(start_event.elapsed_time(event), 0)
@staticmethod
def _stream_synchronize(self, spin_time_cycles):
s = torch.cuda.current_stream()
e_tik = torch.cuda.Event(enable_timing=True)
e_tok = torch.cuda.Event(enable_timing=True)
e_tik.record(s)
torch.cuda._sleep(spin_time_cycles)
e_tok.record(s)
s.synchronize()
self.assertTrue(s.query())
# not necessary to check e_tik and e_tok, as elapsed_time would throw
# exception if otherwise.
return e_tik.elapsed_time(e_tok)
@staticmethod
def _event_synchronize(self, spin_time_cycles):
s = torch.cuda.current_stream()
e_tik = torch.cuda.Event(enable_timing=True)
e_tok = torch.cuda.Event(enable_timing=True)
e_tik.record(s)
torch.cuda._sleep(spin_time_cycles)
s.record_event(e_tok)
e_tok.synchronize()
self.assertTrue(s.query())
# not necessary to check e_tik and e_tok, as elapsed_time would throw
# exception if otherwise.
return e_tik.elapsed_time(e_tok)
@staticmethod
def _event_wait(self, spin_time_cycles):
s0 = torch.cuda.current_stream()
s1 = torch.cuda.Stream()
e_tik = torch.cuda.Event(blocking=True, enable_timing=True)
e_tok = torch.cuda.Event(blocking=True, enable_timing=True)
e_tik.record(s0)
torch.cuda._sleep(spin_time_cycles - 10)
e_sync = torch.cuda.Event(blocking=True)
e_sync.record()
e_sync.wait(s1)
with torch.cuda.stream(s1):
torch.cuda._sleep(10)
s1.synchronize()
s1.record_event(e_tok)
self.assertTrue(s0.query())
self.assertTrue(s1.query())
self.assertTrue(e_sync.query())
# not necessary to check e_tik and e_tok, as elapsed_time would throw
# exception if otherwise.
return e_tik.elapsed_time(e_tok)
@staticmethod
def _test_stream_event_nogil(self, sync_func, p2c, c2p):
with torch.cuda.device('cuda:1'):
c2p.put(0)
p2c.get()
c2p.put(sync_func(self, TestCuda.FIFTY_MIL_CYCLES))
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
# Flaky on the ROCm CI
@skipIfRocm
def test_stream_event_nogil(self):
for sync_func in [TestCuda._stream_synchronize,
TestCuda._event_synchronize,
TestCuda._event_wait]:
p2c = queue.Queue()
c2p = queue.Queue()
e_tik = torch.cuda.Event(enable_timing=True)
e_tok = torch.cuda.Event(enable_timing=True)
t = threading.Thread(
target=TestCuda._test_stream_event_nogil,
args=(self, sync_func, p2c, c2p))
t.daemon = True
t.start()
c2p.get()
with torch.cuda.device('cuda:0'):
e_tik.record()
p2c.put(0)
parent_time = sync_func(self, TestCuda.FIFTY_MIL_CYCLES)
child_time = c2p.get()
e_tok.record()
e_tok.synchronize()
total_time = e_tik.elapsed_time(e_tok)
# Without GIL, synchronizations in parent and child threads can
# overlap. The total execution time should be a little bit longer
# than spinning fifty million cycles and much shorter than twice of
# that. However, testing absolute execution time is not reliable as
# it may vary on different hardware in different environments.
# Therefore, this test uses relative comparisons, checking if the
# sum of parent and child threads execution time is greater than the
# real execution time by least 40%.
self.assertGreater(parent_time + child_time, total_time * 1.4)
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_events_wait(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
torch.cuda.synchronize(d0)
torch.cuda.synchronize(d1)
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
e0 = torch.cuda.Event()
s0.record_event(e0)
with torch.cuda.device(d1):
s1 = torch.cuda.current_stream()
self.assertFalse(s0.query())
self.assertTrue(s1.query())
s1.wait_event(e0)
s1.synchronize()
self.assertTrue(e0.query())
self.assertTrue(s0.query())
self.assertTrue(s1.query())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
def test_events_multi_gpu_query(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
e0 = s0.record_event()
s0.synchronize()
with torch.cuda.device(d1):
s1 = torch.cuda.current_stream()
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
e1 = s1.record_event()
self.assertTrue(e0.query())
self.assertFalse(e1.query())
with torch.cuda.device(d0):
self.assertTrue(e0.query())
self.assertFalse(e1.query())
with torch.cuda.device(d1):
self.assertTrue(e0.query())
self.assertFalse(e1.query())
# deliberately using a different device
with torch.cuda.device(d0):
e1.synchronize()
self.assertTrue(e0.query())
self.assertTrue(e1.query())
with torch.cuda.device(d0):
self.assertTrue(e0.query())
self.assertTrue(e1.query())
with torch.cuda.device(d1):
self.assertTrue(e0.query())
self.assertTrue(e1.query())
@unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
@skipIfRocm
def test_events_multi_gpu_elapsed_time(self):
d0 = torch.device('cuda:0')
d1 = torch.device('cuda:1')
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
e0 = torch.cuda.Event(enable_timing=True)
torch.cuda._sleep(10)
s0.record_event(e0)
with torch.cuda.device(d1):
s1 = torch.cuda.current_stream()
e1 = torch.cuda.Event(enable_timing=True)
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
s1.record_event(e1)
e0.synchronize()
e1.synchronize()
with torch.cuda.device(d0):
with self.assertRaises(RuntimeError):
self.assertGreater(e0.elapsed_time(e1), 0)
with torch.cuda.device(d1):
with self.assertRaises(RuntimeError):
self.assertGreater(e0.elapsed_time(e1), 0)
with torch.cuda.device(d0):
s0 = torch.cuda.current_stream()
e2 = torch.cuda.Event(enable_timing=True)
torch.cuda._sleep(TestCuda.FIFTY_MIL_CYCLES)
s0.record_event(e2)
s0.synchronize()
self.assertGreater(e0.elapsed_time(e2), 0)
# deliberately calling from a different device
with torch.cuda.device(d1):
self.assertGreater(e0.elapsed_time(e2), 0)
def test_record_stream(self):
cycles_per_ms = get_cycles_per_ms()
t = torch.FloatTensor([1, 2, 3, 4]).pin_memory()
result = torch.cuda.FloatTensor(t.size())
stream = torch.cuda.Stream()
ptr = [None]
# Performs the CPU->GPU copy in a background stream
def perform_copy():
with torch.cuda.stream(stream):
tmp = t.cuda(non_blocking=True)
ptr[0] = tmp.data_ptr()
torch.cuda.current_stream().wait_stream(stream)
tmp.record_stream(torch.cuda.current_stream())
torch.cuda._sleep(int(50 * cycles_per_ms)) # delay the copy
result.copy_(tmp)
perform_copy()
with torch.cuda.stream(stream):
tmp2 = torch.cuda.FloatTensor(t.size())
tmp2.zero_()
self.assertNotEqual(tmp2.data_ptr(), ptr[0], 'allocation re-used to soon')
self.assertEqual(result.tolist(), [1, 2, 3, 4])
# Check that the block will be re-used after the main stream finishes
torch.cuda.current_stream().synchronize()
with torch.cuda.stream(stream):
tmp3 = torch.cuda.FloatTensor(t.size())
self.assertEqual(tmp3.data_ptr(), ptr[0], 'allocation not re-used')
def test_record_stream_on_shifted_view(self):
# See issue #27366
# 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 = torch.cuda.Stream()
with torch.cuda.stream(stream_alloc):
base = torch.cuda.FloatTensor([10, 10])
# Record another stream on a shifted view tensor.
view = base[5:]
assert view.storage_offset() > 0
stream_record = torch.cuda.Stream()
with torch.cuda.stream(stream_record):
torch.cuda._sleep(int(50 * get_cycles_per_ms()))
view.record_stream(stream_record)
# Delete those tensors to make the block free soon.
data_ptr = base.data_ptr()
del base, view
# A new tensor should not be allocated to the block above.
stream_alloc.synchronize()
with torch.cuda.stream(stream_alloc):
try_realloc = torch.cuda.FloatTensor([10, 10])
self.assertNotEqual(try_realloc.data_ptr(), data_ptr)
def test_noncontiguous_pinned_memory(self):
# See issue #3266
x = torch.arange(0, 10).view((2, 5))
self.assertEqual(x.t(), x.t().pin_memory())
def test_caching_pinned_memory(self):
cycles_per_ms = get_cycles_per_ms()
# check that allocations are re-used after deletion
t = torch.FloatTensor([1]).pin_memory()
ptr = t.data_ptr()
del t
t = torch.FloatTensor([1]).pin_memory()
self.assertEqual(t.data_ptr(), ptr, 'allocation not reused')
# check that the allocation is not re-used if it's in-use by a copy
gpu_tensor = torch.cuda.FloatTensor([0])
torch.cuda._sleep(int(50 * cycles_per_ms)) # delay the copy
gpu_tensor.copy_(t, non_blocking=True)
del t
t = torch.FloatTensor([1]).pin_memory()
self.assertNotEqual(t.data_ptr(), ptr, 'allocation re-used too soon')
self.assertEqual(list(gpu_tensor), [1])
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_caching_pinned_memory_multi_gpu(self):
# checks that the events preventing pinned memory from being re-used
# too early are recorded on the correct GPU
cycles_per_ms = get_cycles_per_ms()
t = torch.FloatTensor([1]).pin_memory()
ptr = t.data_ptr()
gpu_tensor0 = torch.cuda.FloatTensor([0], device=0)
gpu_tensor1 = torch.cuda.FloatTensor([0], device=1)
with torch.cuda.device(1):
torch.cuda._sleep(int(50 * cycles_per_ms)) # delay the copy
gpu_tensor1.copy_(t, non_blocking=True)
del t
t = torch.FloatTensor([2]).pin_memory()
self.assertNotEqual(t.data_ptr(), ptr, 'allocation re-used too soon')
with torch.cuda.device(0):
gpu_tensor0.copy_(t, non_blocking=True)
self.assertEqual(gpu_tensor1[0], 1)
self.assertEqual(gpu_tensor0[0], 2)
def test_caching_allocator_record_stream_oom(self):
"""allocations delayed by a record_stream call should still be freed on
an out-of-memory in cuda_malloc_retry. see issue #19219"""
stream = torch.cuda.Stream()
with torch.cuda.stream(stream):
y = torch.zeros(40 * 1024 * 1024, device='cuda')
for _ in range(100):
x = torch.empty(40 * 1024 * 1024, device='cuda')
with torch.cuda.stream(stream):
y += x
# delays re-use of `x` until after all operations in `stream`
x.record_stream(stream)
del x
# we've made a mess by allocating up to the device capacity. free any
# cached blocks in case it affects future tests.
torch.cuda.empty_cache()
# Tests for historic illegal memory access, see #17040.
def test_reduction_gpu_memory_accessing(self):
x = torch.ones(512, 8, dtype=torch.float32, device='cuda')
torch.sum(x, 0)
def test_sum_fp16(self):
x = torch.zeros(10, device='cuda', dtype=torch.float16)
self.assertEqual(x.sum(), 0)
x = torch.ones(65504, device='cuda', dtype=torch.float16)
self.assertEqual(x.sum(), 65504)
self.assertEqual(x.sum(dtype=torch.float32), 65504)
x = torch.ones(65536, device='cuda', dtype=torch.float16)
self.assertEqual(x.sum(dtype=torch.float32), 65536)
a = torch.zeros(1203611).bernoulli_(0.0005)
x = a.to(device='cuda', dtype=torch.float16)
self.assertEqual(x.sum().item(), a.sum().item())
a = torch.zeros(100, 121, 80).bernoulli_(0.0005)
x = a.to(device='cuda', dtype=torch.float16)
self.assertEqual(x.sum((0, 2)).float().cpu(), a.sum((0, 2)))
def test_mean_fp16(self):
x = torch.ones(65536, device='cuda', dtype=torch.float16)
self.assertEqual(x.mean(), 1)
x = torch.ones(65536, device='cuda', dtype=torch.float16)
self.assertEqual(x.mean(dtype=torch.float32), 1)
def test_prod_large(self):
# tests global reduction (should_global_reduce = true) in case of non-zero identity element
x = torch.ones(240000, device='cuda', dtype=torch.float32)
self.assertEqual(x.prod(), 1)
@skipIfRocm
def test_fft_ifft_rfft_irfft(self):
_TestTorchMixin._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))
@contextmanager
def plan_cache_max_size(n, device=None):
if device is None:
plan_cache = torch.backends.cuda.cufft_plan_cache
else:
plan_cache = torch.backends.cuda.cufft_plan_cache[device]
original = plan_cache.max_size
plan_cache.max_size = n
yield
plan_cache.max_size = original
with plan_cache_max_size(max(1, torch.backends.cuda.cufft_plan_cache.size - 10)):
_TestTorchMixin._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))
with plan_cache_max_size(0):
_TestTorchMixin._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))
torch.backends.cuda.cufft_plan_cache.clear()
# check that stll works after clearing cache
with plan_cache_max_size(10):
_TestTorchMixin._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))
with self.assertRaisesRegex(RuntimeError, r"must be non-negative"):
torch.backends.cuda.cufft_plan_cache.max_size = -1
with self.assertRaisesRegex(RuntimeError, r"read-only property"):
torch.backends.cuda.cufft_plan_cache.size = -1
with self.assertRaisesRegex(RuntimeError, r"but got device with index"):
torch.backends.cuda.cufft_plan_cache[torch.cuda.device_count() + 10]
if TEST_MULTIGPU:
# Test that different GPU has different cache
x0 = torch.randn(2, 3, 3, device='cuda:0')
x1 = x0.cuda(1)
self.assertEqual(x0.rfft(2), x1.rfft(2))
# If a plan is used across different devices, the following line (or
# the assert above) would trigger illegal memory access. Other ways
# to trigger the error include
# (1) setting CUDA_LAUNCH_BLOCKING=1 (pytorch/pytorch#19224) and
# (2) printing a device 1 tensor.
x0.copy_(x1)
# Test that un-indexed `torch.backends.cuda.cufft_plan_cache` uses current device
with plan_cache_max_size(10, device='cuda:0'):
with plan_cache_max_size(11, device='cuda:1'):
self.assertEqual(torch.backends.cuda.cufft_plan_cache[0].max_size, 10)
self.assertEqual(torch.backends.cuda.cufft_plan_cache[1].max_size, 11)
self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 10) # default is cuda:0
with torch.cuda.device(1):
self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 11) # default is cuda:1
with torch.cuda.device(0):
self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 10) # default is cuda:0
self.assertEqual(torch.backends.cuda.cufft_plan_cache[0].max_size, 10)
with torch.cuda.device(1):
with plan_cache_max_size(11): # default is cuda:1
self.assertEqual(torch.backends.cuda.cufft_plan_cache[0].max_size, 10)
self.assertEqual(torch.backends.cuda.cufft_plan_cache[1].max_size, 11)
self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 11) # default is cuda:1
with torch.cuda.device(0):
self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 10) # default is cuda:0
self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 11) # default is cuda:1
def test_multinomial_ext(self):
# Test two corner cases from older PyTorch (Issue #4858)
freqs = torch.cuda.FloatTensor([
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.03178183361887932, 0.027680952101945877, 0.033176131546497345,
0.046052902936935425, 0.07742464542388916, 0.11543981730937958,
0.14148041605949402, 0.15784293413162231, 0.13180233538150787,
0.08271478116512299, 0.049702685326337814, 0.027557924389839172,
0.018125897273421288, 0.011851548217236996, 0.010252203792333603,
0.007422595750540495, 0.005372154992073774, 0.0045109698548913,
0.0036087757907807827, 0.0035267581697553396, 0.0018864056328311563,
0.0024605290964245796, 0.0022964938543736935, 0.0018453967059031129,
0.0010662291897460818, 0.0009842115687206388, 0.00045109697384759784,
0.0007791675161570311, 0.00020504408166743815, 0.00020504408166743815,
0.00020504408166743815, 0.00012302644609007984, 0.0,
0.00012302644609007984, 4.100881778867915e-05, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0])
torch.cuda.manual_seed(11042)
sample = torch.multinomial(freqs, 1000, True)
self.assertNotEqual(freqs[sample].min(), 0)
p = torch.zeros(3421, 2, device="cuda", dtype=torch.float)
p[:, 1] = 1
torch.cuda.manual_seed(5214)
r = torch.multinomial(p, 1)
self.assertNotEqual(r.min().item(), 0)
# test corner case from Issue #13867
torch.cuda.manual_seed(33)
probs = torch.randn(1000000, device='cuda').clamp(min=0) * 3e-5
samples = probs.multinomial(1000000, replacement=True)
self.assertGreater(probs[samples].min().item(), 0)
@staticmethod
def mute():
os.dup2(os.open(os.devnull, os.O_WRONLY), sys.stderr.fileno())
def _spawn_method(self, method, arg):
ctx = mp.get_context("spawn")
with ctx.Pool(1, initializer=self.mute) as pool:
errors = pool.map(method, [arg])
for e in errors:
if 'device-side assert triggered' not in str(e):
self.fail(e)
@staticmethod
def _test_multinomial_invalid_probs_cuda(probs):
try:
with torch.random.fork_rng(devices=[0]):
torch.multinomial(probs.to('cuda'), 2)
torch.cuda.synchronize()
return False # Should not be reached
except RuntimeError as e:
return e
@slowTest
@unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \
don't support multiprocessing with spawn start method")
@unittest.skipIf(IS_WINDOWS, 'FIXME: CUDA OOM error on Windows')
@unittest.skipIf(not PY3,
"spawn start method is not supported in Python 2, \
but we need it for creating another process with CUDA")
@skipIfRocm
def test_multinomial_invalid_probs_cuda(self):
test_method = TestCuda._test_multinomial_invalid_probs_cuda
self._spawn_method(test_method, torch.Tensor([1, -1, 1]))
self._spawn_method(test_method, torch.Tensor([1, inf, 1]))
self._spawn_method(test_method, torch.Tensor([1, -inf, 1]))
self._spawn_method(test_method, torch.Tensor([1, 1, nan]))
self._spawn_method(test_method, torch.Tensor([0, 1, 0]))
@slowTest
@unittest.skipIf(not TEST_LARGE_TENSOR, "not enough memory")
def test_huge_index(self):
src = torch.empty(15000000, 45, device='cuda', dtype=torch.long).random_(0, 2**22)
idx = torch.randperm(src.shape[0], device='cuda')
res = src[idx]
res_cpu = src.cpu()[idx.cpu()]
self.assertEqual(res.cpu(), res_cpu)
def test_tensor_gather(self):
_TestTorchMixin._test_gather(self, lambda t: t.cuda(), False)
def test_tensor_scatter(self):
_TestTorchMixin._test_scatter_base(self, lambda t: t.cuda(), 'scatter_', test_bounds=False)
def test_tensor_scatterAdd(self):
_TestTorchMixin._test_scatter_base(self, lambda t: t.cuda(), 'scatter_add_', test_bounds=False)
def test_tensor_scatterFill(self):
_TestTorchMixin._test_scatter_base(self, lambda t: t.cuda(), 'scatter_', True, test_bounds=False)
def test_min_max_inits(self):
# Testing if THC_reduceAll received the correct index initialization.
# This affects the result of THC_reduceAll operations at extreme values
x = torch.cuda.ByteTensor([0])
y = torch.cuda.ByteTensor([255])
expected = torch.cuda.LongTensor([0])[0]
_, v = x.max(dim=0)
self.assertEqual(v, expected)
_, v = y.min(dim=0)
self.assertEqual(v, expected)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_get_set_rng_state_all(self):
states = torch.cuda.get_rng_state_all()
before0 = torch.cuda.FloatTensor(100, device=0).normal_()
before1 = torch.cuda.FloatTensor(100, device=1).normal_()
torch.cuda.set_rng_state_all(states)
after0 = torch.cuda.FloatTensor(100, device=0).normal_()
after1 = torch.cuda.FloatTensor(100, device=1).normal_()
self.assertEqual(before0, after0, 0)
self.assertEqual(before1, after1, 0)
@skipIfRocm
def test_nvtx(self):
# Just making sure we can see the symbols
torch.cuda.nvtx.range_push("foo")
torch.cuda.nvtx.mark("bar")
torch.cuda.nvtx.range_pop()
@default_floating_dtype(torch.double)
def test_bincount_ext(self):
# ensure CUDA code coverage
input_size = (5000,)
w = torch.randn(input_size, device='cuda')
w_cpu = w.cpu()
# test shared memory impl
t = torch.randint(50, input_size, dtype=torch.int8, device='cuda')
self.assertEqual(t.cpu().bincount(), t.bincount())
self.assertEqual(t.cpu().bincount(w_cpu), t.bincount(w))
# test multi block memory impl
# see `THRESH_NUMBER_BINS_FOR_MULTI_BLOCK_MEM` in SummaryOps.cu
t = torch.randint(500, input_size, dtype=torch.int64, device='cuda')
self.assertEqual(t.cpu().bincount(), t.bincount())
self.assertEqual(t.cpu().bincount(w_cpu), t.bincount(w))
# test global memory impl
# see `THRESH_NUMBER_BINS_FOR_GLOBAL_MEM` in SummaryOps.cu
t = torch.randint(2000, input_size, dtype=torch.int64, device='cuda')
self.assertEqual(t.cpu().bincount(), t.bincount())
self.assertEqual(t.cpu().bincount(w_cpu), t.bincount(w))
t = torch.zeros([10], dtype=torch.int32, device='cuda')
# 35488 * 65536 as int32 would cause overflow to negative value
# giving negative bin offset
t[0] = 35488
counted = t.bincount(minlength=65536)
self.assertEqual(torch.sum(counted), 10)
def test_tiny_half_norm_(self):
a = torch.arange(25).cuda().float()
a /= 100000000
b = a.half()
self.assertGreater(b.norm().item(), 0)
def test_norm_type_conversion(self):
a = torch.ones(65536).cuda().half()
self.assertEqual(a.norm(p=0, dtype=torch.float32), 65536)
# Note: This test fails on ROCm CI gfx900 but passes on gfx906
@skipIfRocm
# Test that wrap_with_cuda_memory_check successfully detects leak
def test_cuda_memory_leak_detection(self):
l = []
@self.wrap_with_cuda_memory_check
def no_leak():
pass
@self.wrap_with_cuda_memory_check
def leak_gpu0():
l.append(torch.tensor(10, device=torch.device("cuda:0")))
no_leak()
with self.assertRaisesRegex(AssertionError, r"leaked \d+ bytes CUDA memory on device 0"):
leak_gpu0()
if TEST_MULTIGPU:
@self.wrap_with_cuda_memory_check
def leak_gpu1():
l.append(torch.tensor(10, device=torch.device("cuda:1")))
with self.assertRaisesRegex(AssertionError, r"leaked \d+ bytes CUDA memory on device 1"):
leak_gpu1()
def test_cuda_memory_leak_detection_propagates_errors(self):
with self.assertRaisesRegex(RuntimeError, r"The size of tensor a \(3\) must match"):
with self.assertLeaksNoCudaTensors():
x = torch.randn(3, 1, device='cuda')
y = torch.randn(2, 1, device='cuda')
z = x + y
def test_trilu_indices(self):
for test_args in tri_tests_args:
_compare_trilu_indices(self, *test_args, device='cuda')
# test default options
x = torch.ones(
3, 3, dtype=torch.long, device='cuda', layout=torch.strided)
self.assertEqual(
x.tril(0).nonzero().transpose(0, 1),
torch.tril_indices(3, 3, device='cuda'))
self.assertEqual(
x.triu(0).nonzero().transpose(0, 1),
torch.triu_indices(3, 3, device='cuda'))
def test_large_trilu_indices(self):
for test_args in tri_large_tests_args:
_compare_large_trilu_indices(self, *test_args, device='cuda')
@unittest.skipIf(not TEST_MEDIUM_TENSOR, "not enough memory")
def test_cuda_kernel_loop_overflow(self):
# Issue #24309: In extreme cases, the loop variable could overflow and continue
# the kernel loop with a negative index, causing a RuntimeError (invalid write):
x = torch.randn(1, 1, 1, 2**30 + 1, dtype=torch.float16, device="cuda")
expected = x[0, 0, 0, 2**30]
y = torch.nn.functional.avg_pool2d(x, kernel_size=1)
torch.cuda.synchronize()
self.assertEqual(y[0, 0, 0, 2**30], expected)
@unittest.skipIf(not TEST_LARGE_TENSOR, "not enough memory")
def test_cuda_kernel_loop_overflow_large(self):
# Make sure input.numel() > INT_MAX is handled:
x = torch.randn(1, 1, 1, 2**31, dtype=torch.float16, device="cuda")
with self.assertRaisesRegex(RuntimeError, "integer out of range"):
y = torch.nn.functional.avg_pool2d(x, kernel_size=1)
# Issue #24309: In extreme cases, the loop variable could overflow and continue
# the kernel loop with a negative index, causing a RuntimeError (invalid write):
x = torch.randn(1, 1, 1, 2**31 - 1, dtype=torch.float16, device="cuda")
expected = x[0, 0, 0, 2**31 - 2]
y = torch.nn.functional.avg_pool2d(x, kernel_size=1)
torch.cuda.synchronize()
self.assertEqual(y[0, 0, 0, 2**31 - 2], expected)
@skipCUDANonDefaultStreamIf(True)
def test_streaming_backwards_sync(self):
default_stream = torch.cuda.current_stream()
stream = torch.cuda.Stream()
class MultiplyInStream(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
return x * 2
@staticmethod
def backward(ctx, grad):
self.assertEqual(torch.cuda.current_stream(), stream)
# delays the operation in the the background stream
torch.cuda._sleep(1000 * 1000)
return grad * 2
x = torch.randn(5, 5, device='cuda', requires_grad=True)
with torch.cuda.stream(stream):
stream.wait_stream(default_stream)
output = MultiplyInStream.apply(x)
output.sum().backward()
self.assertEqual(x.grad, torch.ones_like(x) * 2)
self.assertEqual(torch.cuda.current_stream(), default_stream)
def test_streaming_backwards_multiple_streams(self):
class StreamModel(torch.nn.Module):
def __init__(self):
super(StreamModel, self).__init__()
self.event = torch.cuda.Event()
self.stream0 = torch.cuda.Stream()
self.stream1 = torch.cuda.Stream()
def forward(self, x):
x0 = x.clone()
torch._C._cuda_setStream(self.stream0._cdata)
y0 = x0 * 2
self.event.record(stream=torch.cuda.current_stream())
torch._C._cuda_setStream(self.stream1._cdata)
y1 = x * 3
self.stream1.wait_event(self.event)
return y0 + y1
stream = torch.cuda.Stream()
def accum_hook(grad):
self.assertEqual(torch.cuda.current_stream(), stream)
with torch.cuda.stream(stream):
x = torch.randn(5, 5, device='cuda', requires_grad=True)
x.register_hook(accum_hook)
torch.cuda.current_stream().wait_stream(stream)
model = StreamModel().cuda()
model(x).sum().backward()
self.assertEqual(x.grad, torch.ones_like(x) * 5)
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_cuda_init_race(self):
# See https://github.com/pytorch/pytorch/issues/16559
import subprocess
subprocess.check_call([sys.executable, '-c', """\
import torch
import threading
def worker(rank):
torch.tensor([1.]).cuda(rank)
t1 = threading.Thread(target=worker, args=(0,))
t2 = threading.Thread(target=worker, args=(1,))
t1.start()
t2.start()
"""])
if __name__ == '__main__':
run_tests()
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