| # Owner(s): ["module: multiprocessing"] |
| |
| import contextlib |
| import gc |
| import os |
| import sys |
| import time |
| import unittest |
| import copy |
| from sys import platform |
| |
| import torch |
| import torch.cuda |
| import torch.multiprocessing as mp |
| import torch.utils.hooks |
| from torch.nn import Parameter |
| from torch.testing._internal.common_utils import (TestCase, run_tests, IS_WINDOWS, NO_MULTIPROCESSING_SPAWN, TEST_WITH_ASAN, |
| load_tests, slowTest, TEST_WITH_TSAN, TEST_WITH_TORCHDYNAMO, |
| TEST_WITH_ROCM, IS_MACOS) |
| |
| |
| # load_tests from common_utils is used to automatically filter tests for |
| # sharding on sandcastle. This line silences flake warnings |
| load_tests = load_tests |
| |
| TEST_REPEATS = 30 |
| HAS_SHM_FILES = os.path.isdir('/dev/shm') |
| MAX_WAITING_TIME_IN_SECONDS = 30 |
| TEST_CUDA_IPC = torch.cuda.is_available() and \ |
| sys.platform != 'darwin' and \ |
| sys.platform != 'win32' and \ |
| not TEST_WITH_ROCM # https://github.com/pytorch/pytorch/issues/90940 |
| TEST_MULTIGPU = TEST_CUDA_IPC and torch.cuda.device_count() > 1 |
| |
| if TEST_CUDA_IPC: |
| torch.cuda.memory._set_allocator_settings('expandable_segments:False') |
| |
| |
| class SubProcess(mp.Process): |
| def __init__(self, tensor): |
| super().__init__() |
| self.tensor = tensor |
| self.daemon = True |
| |
| def run(self): |
| self.tensor.add_(3) |
| |
| |
| def _test_cuda_ipc_deadlock_actor(queue, iterations): |
| for i in range(iterations): |
| if not queue.empty(): |
| queue.get() |
| time.sleep(.01) |
| |
| |
| def _test_cuda_ipc_deadlock_learner(queue, iterations): |
| net = torch.nn.LSTM(1, 1).cuda() |
| for i in range(iterations): |
| if not queue.full(): |
| queue.put(copy.deepcopy(net.state_dict())) |
| time.sleep(.01) |
| |
| |
| def simple_fill(queue, event): |
| data = queue.get() |
| data[0][:] = 4 |
| event.set() |
| |
| |
| def simple_pool_fill(tensor): |
| tensor.fill_(4) |
| return tensor.add(1) |
| |
| |
| def send_tensor(queue, event, device, dtype): |
| t = torch.ones(5, 5, device=device, dtype=dtype) |
| queue.put(t) |
| queue.put(t) |
| event.wait() |
| |
| |
| def send_and_delete_tensors(queue, event, device, dtype, count, size=5): |
| for i in range(count): |
| t = torch.full([size], i, device=device, dtype=dtype) |
| queue.put(t) |
| del t |
| event.wait() |
| |
| |
| def receive_and_send_sum(queue, out_queue, event, device, dtype, count, size=5): |
| s = torch.full([size], 0, device=device, dtype=dtype) |
| for i in range(count): |
| t = queue.get() |
| s += t |
| out_queue.put(s) |
| event.wait() |
| |
| |
| def receive_and_send(queue, out_queue, event, count): |
| for i in range(count): |
| t = queue.get() |
| out_queue.put(t.clone()) |
| event.wait() |
| |
| |
| def sum_tensors(inq, outq): |
| with torch.cuda.device(1): |
| tensors = inq.get() |
| for tensor in tensors: |
| outq.put((tensor.sum().item(), tensor.get_device(), |
| tensor.numel(), tensor.storage().size())) |
| |
| |
| def queue_get_exception(inqueue, outqueue): |
| os.close(2) # hide expected error message |
| try: |
| torch.zeros(5, 5).cuda() |
| except Exception as e: |
| outqueue.put(e) |
| else: |
| outqueue.put('no exception') |
| |
| |
| # Multiply by two in a separate stream |
| def cuda_multiply_two(queue, ready, done): |
| ready.set() |
| with torch.cuda.stream(torch.cuda.Stream()): |
| cuda_event, tensor = queue.get() |
| cuda_event.wait() |
| tensor.mul_(2) |
| cuda_event.record() |
| done.set() |
| del cuda_event |
| |
| |
| def requires_grad_variable_sharing(queue, ready): |
| var = queue.get() |
| ready.set() |
| queue.put(var.requires_grad) |
| |
| |
| def integer_parameter_serialization(iparam): |
| iparam + 1 |
| |
| |
| def autograd_sharing(queue, ready, master_modified, device, is_parameter): |
| var = queue.get() |
| ready.set() |
| master_modified.wait() |
| |
| expected_var = torch.arange(1., 26, device=device).view(5, 5) |
| expected_var[0, 0] = 1000 |
| is_ok = var.data.equal(expected_var) |
| var.data[:] = torch.ones(5, 5, device=device) |
| |
| is_ok &= var.grad is None |
| is_ok &= not var._backward_hooks |
| if is_parameter: |
| is_ok &= type(var) == Parameter |
| else: |
| is_ok &= type(var) == torch.Tensor |
| var._grad = torch.ones(5, 5, device=device) |
| |
| queue.put(is_ok) |
| |
| |
| def mixed_type_producer(queue, event): |
| for _ in range(10): |
| float_tensor = torch.ones(2, 2).float().cuda() |
| byte_tensor = torch.zeros(2, 2).byte().cuda() |
| |
| queue.put(float_tensor) |
| queue.put(byte_tensor) |
| event.wait() |
| event.clear() |
| |
| def simple_autograd_function(a=1): |
| torch.rand(3).requires_grad_(True).mean().backward() |
| return a ** 2 |
| |
| @contextlib.contextmanager |
| def fs_sharing(): |
| prev_strategy = mp.get_sharing_strategy() |
| mp.set_sharing_strategy('file_system') |
| try: |
| yield |
| finally: |
| mp.set_sharing_strategy(prev_strategy) |
| |
| |
| class leak_checker: |
| |
| def __init__(self, test_case): |
| self.checked_pids = [os.getpid()] |
| self.test_case = test_case |
| |
| def __enter__(self): |
| self.next_fds = self._get_next_fds(10) |
| return self |
| |
| def __exit__(self, *args): |
| if torch.cuda.is_available(): |
| torch.cuda.ipc_collect() |
| if args[0] is None: |
| # Check that the 10th available file-descriptor at the end of the |
| # test is no more than 4 higher than the 10th available at the |
| # start. This attempts to catch file descriptor leaks, but allows |
| # one-off initialization that may use up a file descriptor |
| # TODO: Disabled because this check is too flaky |
| # available_fds = self._get_next_fds(10) |
| # self.test_case.assertLessEqual( |
| # available_fds[-1] - self.next_fds[-1], 5) |
| self.test_case.assertFalse(self.has_shm_files()) |
| return False |
| |
| def check_pid(self, pid): |
| self.checked_pids.append(pid) |
| |
| def _get_next_fds(self, n=1): |
| # dup uses the lowest-numbered unused descriptor for the new descriptor |
| fds = [os.dup(0) for i in range(n)] |
| for fd in fds: |
| os.close(fd) |
| return fds |
| |
| def has_shm_files(self, wait=True): |
| if not HAS_SHM_FILES: |
| return False |
| |
| result = self._has_shm_files() |
| if not result or mp.get_sharing_strategy() != 'file_system' or not wait: |
| return result |
| |
| total_waiting_time = 0 |
| waiting_time = 0.5 |
| |
| while total_waiting_time <= MAX_WAITING_TIME_IN_SECONDS and result: |
| time.sleep(waiting_time) |
| total_waiting_time += waiting_time |
| result = self._has_shm_files() |
| |
| return result |
| |
| def _has_shm_files(self): |
| gc.collect() |
| names = ['torch_' + str(pid) for pid in self.checked_pids] |
| for filename in os.listdir('/dev/shm'): |
| for name in names: |
| if filename.startswith(name): |
| return True |
| return False |
| |
| |
| @unittest.skipIf(TEST_WITH_TSAN, "TSAN is not fork-safe since we're forking in a multi-threaded environment") |
| class TestMultiprocessing(TestCase): |
| |
| def tearDown(self): |
| # This will keep tests isolated from each-other |
| if torch.cuda.is_available(): |
| torch.cuda.ipc_collect() |
| |
| def _test_sharing(self, ctx=mp, device='cpu', dtype=torch.float, repeat=1): |
| def test_fill(): |
| x = torch.zeros(5, 5).to(device, dtype) |
| q = ctx.Queue() |
| e = ctx.Event() |
| |
| data = [x, x[:, 1]] |
| q.put(data) |
| |
| p = ctx.Process(target=simple_fill, args=(q, e)) |
| p.daemon = True |
| lc.check_pid(p.pid) |
| p.start() |
| |
| total_waiting_time = 0 |
| waiting_time = 0.5 |
| is_set = False |
| # Once the child process is done, it will set the event to notify the |
| # parent accordingly |
| while total_waiting_time <= MAX_WAITING_TIME_IN_SECONDS and not is_set: |
| time.sleep(waiting_time) |
| total_waiting_time += waiting_time |
| is_set = e.is_set() |
| |
| self.assertTrue(is_set) |
| self.assertTrue(data[0].eq(4).all()) |
| self.assertTrue(data[1].eq(4).all()) |
| |
| p.join(100) |
| self.assertFalse(p.is_alive()) |
| |
| def test_receive(): |
| q = ctx.Queue() |
| e = ctx.Event() |
| |
| p = ctx.Process(target=send_tensor, args=(q, e, device, dtype)) |
| p.daemon = True |
| lc.check_pid(p.pid) |
| p.start() |
| |
| t1 = q.get() |
| t2 = q.get() |
| self.assertTrue(t1.eq(1).all()) |
| s1 = t1.storage() |
| s2 = t2.storage() |
| self.assertEqual(type(s1), type(s2)) |
| self.assertEqual(s1.data_ptr(), s1.data_ptr()) |
| self.assertEqual(s1, s2) |
| |
| # We need to delete this tensors to allow producer (child process) |
| # collect them properly |
| del t1, t2 |
| |
| # Mark the event as done and join the process |
| e.set() |
| p.join(100) |
| self.assertFalse(p.is_alive()) |
| |
| with leak_checker(self) as lc: |
| for _ in range(repeat): |
| test_fill() |
| test_receive() |
| |
| def _test_preserve_sharing(self, ctx=mp, repeat=1): |
| def do_test(): |
| x = torch.randn(5, 5) |
| data = [x.storage(), x, x[2], x[:, 1]] |
| q = ctx.Queue() |
| q.put(data) |
| new_data = q.get(timeout=1) |
| self.assertEqual(new_data, data, atol=0, rtol=0) |
| storage_cdata = data[0]._cdata |
| self.assertEqual(new_data[0]._cdata, storage_cdata) |
| for t in new_data[1:]: |
| self.assertEqual(t.storage()._cdata, storage_cdata) |
| |
| with leak_checker(self): |
| for _ in range(repeat): |
| do_test() |
| |
| def _test_pool(self, ctx=mp, repeat=1): |
| def do_test(): |
| p = ctx.Pool(2) |
| for proc in p._pool: |
| lc.check_pid(proc.pid) |
| |
| buffers = [torch.zeros(2, 2) for i in range(4)] |
| results = p.map(simple_pool_fill, buffers, 1) |
| self.assertEqual(len(results), len(buffers)) |
| for r in results: |
| self.assertEqual(r, torch.ones(2, 2) * 5, atol=0, rtol=0) |
| for b in buffers: |
| self.assertEqual(b, torch.ones(2, 2) * 4, atol=0, rtol=0) |
| |
| p.close() |
| p.join() |
| |
| with leak_checker(self) as lc: |
| for _ in range(repeat): |
| do_test() |
| |
| @unittest.skipIf(platform == 'darwin', "file descriptor strategy is not supported on macOS") |
| @unittest.skipIf(TEST_WITH_ASAN, |
| "seems to hang with ASAN, see https://github.com/pytorch/pytorch/issues/5326") |
| def test_fd_sharing(self): |
| self._test_sharing(repeat=TEST_REPEATS) |
| |
| @unittest.skipIf(platform == 'darwin', "file descriptor strategy is not supported on macOS") |
| def test_fd_preserve_sharing(self): |
| self._test_preserve_sharing(repeat=TEST_REPEATS) |
| |
| @unittest.skipIf(platform == 'darwin', "file descriptor strategy is not supported on macOS") |
| def test_fd_pool(self): |
| self._test_pool(repeat=TEST_REPEATS) |
| |
| @unittest.skipIf(TEST_WITH_ASAN, |
| "seems to hang with ASAN, see https://github.com/pytorch/pytorch/issues/5326") |
| @unittest.skipIf(TEST_WITH_TORCHDYNAMO, |
| "Fail to clean up temporary /dev/shm/torch_* file, see https://github.com/pytorch/pytorch/issues/91467") |
| def test_fs_sharing(self): |
| with fs_sharing(): |
| # The test works but is very slow on MacOS, see https://github.com/pytorch/pytorch/pull/93183, |
| # so run it only once there. The delay is in waiting for the child process to terminate (join) |
| repeat = 1 if IS_MACOS else TEST_REPEATS |
| self._test_sharing(repeat=repeat) |
| |
| @unittest.skipIf(TEST_WITH_TORCHDYNAMO, |
| "Fail to clean up temporary /dev/shm/torch_* file, see https://github.com/pytorch/pytorch/issues/91467") |
| def test_fs_preserve_sharing(self): |
| with fs_sharing(): |
| self._test_preserve_sharing(repeat=TEST_REPEATS) |
| |
| @unittest.skipIf(TEST_WITH_TORCHDYNAMO, |
| "Fail to clean up temporary /dev/shm/torch_* file, see https://github.com/pytorch/pytorch/issues/91467") |
| def test_fs_pool(self): |
| with fs_sharing(): |
| self._test_pool(repeat=TEST_REPEATS) |
| |
| @unittest.skipIf(not HAS_SHM_FILES, "don't not how to check if shm files exist") |
| @unittest.skipIf(TEST_WITH_TORCHDYNAMO, |
| "Fail to clean up temporary /dev/shm/torch_* file, see https://github.com/pytorch/pytorch/issues/91467") |
| def test_fs(self): |
| def queue_put(): |
| x = torch.DoubleStorage(4) |
| q = mp.Queue() |
| self.assertFalse(lc.has_shm_files()) |
| q.put(x) |
| time.sleep(0.05) # queue serializes asynchronously |
| self.assertTrue(lc.has_shm_files(wait=False)) |
| q.get() |
| |
| with fs_sharing(), leak_checker(self) as lc: |
| for _ in range(TEST_REPEATS): |
| queue_put() |
| |
| def test_inherit_tensor(self): |
| t = torch.zeros(5, 5) |
| p = SubProcess(t.share_memory_()) |
| p.start() |
| p.join(2) |
| if p.exitcode is None: |
| print("test_inherit_tensor: SubProcess too slow") |
| else: |
| self.assertEqual(t, torch.ones(5, 5) * 3, atol=0, rtol=0) |
| |
| @unittest.skipIf(IS_WINDOWS, "Test needs to use fork multiprocessing") |
| def test_autograd_errors(self): |
| ctx = mp.get_context('fork') |
| simple_autograd_function() |
| # Autograd only uses thread when GPUs are involved |
| if torch.cuda.is_available() or torch.backends.mps.is_available(): |
| with self.assertRaisesRegex(RuntimeError, r'Unable to handle autograd'): |
| with ctx.Pool(3) as pool: |
| pool.map(simple_autograd_function, [1, 2, 3]) |
| else: |
| with ctx.Pool(3) as pool: |
| pool.map(simple_autograd_function, [1, 2, 3]) |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Test needs to use spawn multiprocessing") |
| def test_autograd_fine_with_spawn(self): |
| ctx = mp.get_context('spawn') |
| simple_autograd_function() |
| with ctx.Pool(3) as pool: |
| pool.map(simple_autograd_function, [1, 2, 3]) |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| def test_cuda_simple(self): |
| torch.cuda.FloatTensor([1]) # initialize CUDA outside of leak checker |
| self._test_sharing(mp.get_context('spawn'), 'cuda', torch.float) |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| def test_cuda_memory_allocation(self): |
| ctx = mp.get_context('spawn') |
| q = ctx.Queue() |
| e = ctx.Event() |
| p = ctx.Process(target=send_and_delete_tensors, args=(q, e, 'cuda', torch.int, 5)) |
| p.start() |
| t = [] |
| for _ in range(5): |
| t.append(q.get()) |
| self.assertEqual(t[0], torch.full([5], 0, dtype=torch.int32)) |
| del t |
| e.set() |
| p.join(1) |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| def test_cuda_ipc_deadlock(self): |
| ctx = mp.get_context('spawn') |
| queue = ctx.Queue(1) |
| processes = dict( |
| a=ctx.Process(target=_test_cuda_ipc_deadlock_actor, args=(queue, 100)), |
| l=ctx.Process(target=_test_cuda_ipc_deadlock_learner, args=(queue, 100))) |
| |
| for p in processes.values(): |
| p.start() |
| |
| for p in processes.values(): |
| p.join(10) |
| |
| for p in processes.values(): |
| self.assertFalse(p.is_alive()) |
| |
| |
| @slowTest |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| def test_cuda_send_many(self, name=None, size=5, count=100000): |
| ctx = mp.get_context('spawn') |
| q1 = ctx.Queue() |
| q2 = ctx.Queue() |
| q3 = ctx.Queue() |
| e1 = ctx.Event() |
| e2 = ctx.Event() |
| e3 = ctx.Event() |
| p1 = ctx.Process(target=send_and_delete_tensors, args=(q1, e1, 'cuda', torch.long, count, size)) |
| p2 = ctx.Process(target=receive_and_send, args=(q1, q2, e2, count)) |
| p3 = ctx.Process(target=receive_and_send_sum, args=(q2, q3, e3, 'cuda', torch.long, count, size)) |
| p1.start() |
| p2.start() |
| p3.start() |
| result = q3.get() |
| self.assertEqual(result[0], int(count * (count - 1) / 2)) |
| del result |
| e1.set() |
| e2.set() |
| e3.set() |
| p1.join(1) |
| p2.join(1) |
| p3.join(1) |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| @unittest.skipIf(not TEST_MULTIGPU, 'found only 1 GPU') |
| def test_cuda_small_tensors(self): |
| # Check multiple small tensors which will likely use the same |
| # underlying cached allocation |
| ctx = mp.get_context('spawn') |
| tensors = [] |
| for i in range(5): |
| device = i % 2 |
| tensors += [torch.arange(i * 5., (i + 1) * 5).cuda(device)] |
| |
| inq = ctx.Queue() |
| outq = ctx.Queue() |
| inq.put(tensors) |
| p = ctx.Process(target=sum_tensors, args=(inq, outq)) |
| p.start() |
| |
| results = [] |
| for _ in range(5): |
| results.append(outq.get()) |
| p.join() |
| |
| for i, _tensor in enumerate(tensors): |
| v, device, tensor_size, storage_size = results[i] |
| self.assertEqual(v, torch.arange(i * 5., (i + 1) * 5).sum()) |
| self.assertEqual(device, i % 2) |
| self.assertEqual(tensor_size, 5) |
| |
| # You might think this should be the case, but it's not! After |
| # data from the CUDA caching allocator goes through IPC, the |
| # size of the storage is the size of the *cached cudaMalloc for |
| # the entire memory block* of the storage, not just the storage. |
| # See Note [CUDA IPC and the caching allocator] for more info |
| # |
| # self.assertEqual(storage_size, 5) |
| |
| # Collect current process (producer) files, make sure nothing holds |
| # ref to the sent tensors |
| del _tensor |
| del tensors |
| |
| # We need to collect, as CUDA MP implementation holds one shared |
| # memory 'file' for performance reason |
| torch.cuda.ipc_collect() |
| |
| @unittest.skipIf(IS_WINDOWS, 'not applicable to Windows (only fails with fork)') |
| @unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available') |
| def test_cuda_bad_call(self): |
| # Initialize CUDA |
| t = torch.zeros(5, 5).cuda().cpu() |
| inq = mp.Queue() |
| outq = mp.Queue() |
| p = mp.Process(target=queue_get_exception, args=(inq, outq)) |
| p.start() |
| inq.put(t) |
| p.join() |
| self.assertIsInstance(outq.get(), RuntimeError) |
| |
| @unittest.skipIf(IS_WINDOWS, 'not applicable to Windows (only fails with fork)') |
| @unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available') |
| def test_wrong_cuda_fork(self): |
| stderr = TestCase.runWithPytorchAPIUsageStderr("""\ |
| import torch |
| from torch.multiprocessing import Process |
| def run(rank): |
| torch.cuda.set_device(rank) |
| if __name__ == "__main__": |
| size = 2 |
| processes = [] |
| for rank in range(size): |
| # it would work fine without the line below |
| x = torch.rand(20, 2).cuda() |
| p = Process(target=run, args=(rank,)) |
| p.start() |
| processes.append(p) |
| for p in processes: |
| p.join() |
| """) |
| self.assertRegex(stderr, "Cannot re-initialize CUDA in forked subprocess.") |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| def test_event(self): |
| ctx = mp.get_context('spawn') |
| queue = ctx.Queue() |
| ready = ctx.Event() |
| done = ctx.Event() |
| p = ctx.Process(target=cuda_multiply_two, args=(queue, ready, done)) |
| p.start() |
| |
| ready.wait() |
| with torch.cuda.stream(torch.cuda.Stream()): |
| tensor = torch.cuda.FloatTensor([1, 1, 1, 1]) |
| # Use a sleep kernel to test events. Without the event, the |
| # multiply happens before the add. |
| event = torch.cuda.Event(interprocess=True) |
| torch.cuda._sleep(20000000) # about 30 ms |
| tensor.add_(1) |
| event.record() |
| queue.put((event, tensor)) |
| done.wait() # must wait until subprocess records event |
| event.synchronize() |
| self.assertEqual(list(tensor), [4, 4, 4, 4]) |
| p.join() |
| |
| @staticmethod |
| def _test_event_multiprocess_child(event, p2c, c2p): |
| c2p.put(0) # notify parent child is ready |
| p2c.get() # wait for record in parent |
| event.synchronize() |
| c2p.put(1) # notify parent synchronization is done |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| def test_event_multiprocess(self): |
| event = torch.cuda.Event(enable_timing=False, interprocess=True) |
| self.assertTrue(event.query()) |
| |
| ctx = mp.get_context('spawn') |
| p2c = ctx.SimpleQueue() |
| c2p = ctx.SimpleQueue() |
| p = ctx.Process( |
| target=TestMultiprocessing._test_event_multiprocess_child, |
| args=(event, p2c, c2p)) |
| p.start() |
| |
| c2p.get() # wait for until child process is ready |
| torch.cuda._sleep(50000000) # spin for about 50 ms |
| event.record() |
| p2c.put(0) # notify child event is recorded |
| |
| self.assertFalse(event.query()) |
| c2p.get() # wait for synchronization in child |
| self.assertTrue(event.query()) |
| p.join() |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| @unittest.skipIf(not TEST_MULTIGPU, 'found only 1 GPU') |
| def test_event_handle_multi_gpu(self): |
| d0 = torch.device('cuda:0') |
| d1 = torch.device('cuda:1') |
| with torch.cuda.device(d0): |
| e0 = torch.cuda.Event(enable_timing=False, interprocess=True) |
| |
| with torch.cuda.device(d1): |
| # create handle on different device from un-recorded event |
| e0.ipc_handle() |
| |
| with torch.cuda.device(d0): |
| e1 = torch.cuda.Event(enable_timing=False, interprocess=True) |
| stream = torch.cuda.Stream() |
| torch.cuda._sleep(50000000) # spin for about 50 ms |
| e1.record(stream) |
| |
| with torch.cuda.device(d1): |
| # create handle on different device from recorded event |
| e1.ipc_handle() |
| |
| @staticmethod |
| def _test_event_handle_importer_consumer(handle, p2c, c2p): |
| e1 = torch.cuda.Event.from_ipc_handle(0, handle) |
| c2p.put(0) # notify parent child is ready |
| p2c.get() # wait for record in parent |
| e1.synchronize() |
| c2p.put(1) # notify synchronization is done in child |
| p2c.get() # wait for parent to finish before destructing child event |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| def test_event_handle_importer(self): |
| e0 = torch.cuda.Event(enable_timing=False, interprocess=True) |
| self.assertTrue(e0.query()) |
| |
| ctx = mp.get_context('spawn') |
| p2c = ctx.SimpleQueue() |
| c2p = ctx.SimpleQueue() |
| p = ctx.Process( |
| target=TestMultiprocessing._test_event_handle_importer_consumer, |
| args=(e0.ipc_handle(), p2c, c2p)) |
| p.start() |
| |
| c2p.get() # wait for child to become ready |
| torch.cuda._sleep(50000000) # spin for about 50 ms |
| e0.record() |
| p2c.put(0) # notify child event is recorded |
| |
| self.assertFalse(e0.query()) |
| c2p.get() # wait for synchronization in child |
| self.assertTrue(e0.query()) |
| p2c.put(1) # notify child that parent is done |
| p.join() |
| |
| @staticmethod |
| def _test_event_handle_exporter_consumer(handle, p2c, c2p): |
| stream = torch.cuda.Stream() |
| with torch.cuda.stream(stream): |
| e1 = torch.cuda.Event.from_ipc_handle( |
| torch.cuda.current_device(), handle) |
| torch.cuda._sleep(50000000) # spin for about 50 ms |
| e1.record() |
| c2p.put(0) |
| # wait for parent process finished synchronization before |
| # destructing e1 |
| p2c.get() |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| def test_event_handle_exporter(self): |
| e0 = torch.cuda.Event(enable_timing=False, interprocess=True) |
| |
| ctx = mp.get_context('spawn') |
| p2c = ctx.SimpleQueue() |
| c2p = ctx.SimpleQueue() |
| p = ctx.Process( |
| target=TestMultiprocessing._test_event_handle_exporter_consumer, |
| args=(e0.ipc_handle(), p2c, c2p)) |
| p.start() |
| # wait for event in child process is recorded |
| c2p.get() |
| |
| self.assertFalse(e0.query()) |
| e0.synchronize() |
| self.assertTrue(e0.query()) |
| p2c.put(0) |
| p.join() |
| |
| def _test_empty_tensor_sharing(self, dtype, device): |
| q = mp.Queue() |
| empty = torch.tensor([], dtype=dtype, device=device) |
| q.put(empty) |
| out = q.get(timeout=1) |
| self.assertEqual(out, empty) |
| |
| def test_empty_tensor_sharing(self): |
| self._test_empty_tensor_sharing(torch.float32, torch.device('cpu')) |
| self._test_empty_tensor_sharing(torch.int64, torch.device('cpu')) |
| |
| @unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available') |
| def test_empty_tensor_sharing_cuda(self): |
| self._test_empty_tensor_sharing(torch.float32, torch.device('cuda')) |
| self._test_empty_tensor_sharing(torch.int64, torch.device('cuda')) |
| |
| def _test_autograd_sharing(self, var, ctx=mp, is_parameter=False): |
| device = 'cuda' if var.is_cuda else 'cpu' |
| |
| ready = ctx.Event() |
| master_modified = ctx.Event() |
| queue = ctx.Queue() |
| p = ctx.Process(target=autograd_sharing, args=(queue, ready, master_modified, device, is_parameter)) |
| p.daemon = True |
| p.start() |
| |
| # This would cause an error if we tried to serialize the hooks, |
| # because it's a closure and pickle doesn't support closures. |
| @torch.utils.hooks.unserializable_hook |
| def hook(*unused): |
| pass |
| |
| if var.requires_grad: |
| var.register_hook(hook) |
| var._grad = torch.zeros(5, 5, device=device) |
| queue.put(var) |
| |
| ready.wait() |
| var.data[0, 0] = 1000 |
| var.grad.data[:] = torch.ones(5, 5, device=device) * 4 |
| master_modified.set() |
| |
| worker_ok = queue.get() |
| self.assertTrue(worker_ok) |
| |
| self.assertEqual(var.data, torch.ones(5, 5, device=device)) |
| self.assertEqual(var.grad.data, torch.ones(5, 5, device=device) * 4) |
| p.join(100) |
| self.assertFalse(p.is_alive()) |
| |
| # Check sharing a cudaMalloc allocation with different types of storage. |
| # (Issue #11422) |
| def _test_mixed_types_cuda_sharing(self, ctx=mp): |
| all_ones = torch.ones(2, 2).float() |
| all_zeros = torch.zeros(2, 2).byte() |
| queue = ctx.Queue() |
| event = ctx.Event() |
| |
| p = ctx.Process(target=mixed_type_producer, args=(queue, event)) |
| |
| p.start() |
| |
| for _ in range(10): |
| float_tensor = queue.get() |
| byte_tensor = queue.get() |
| self.assertEqual(float_tensor, all_ones) |
| self.assertEqual(byte_tensor, all_zeros) |
| del float_tensor, byte_tensor |
| event.set() |
| |
| time.sleep(5) |
| p.join() |
| |
| @unittest.skipIf(TEST_WITH_ASAN, |
| "non-deterministically hangs with ASAN https://github.com/pytorch/pytorch/issues/94024") |
| def test_variable_sharing(self): |
| for requires_grad in [True, False]: |
| var = torch.arange(1., 26).view(5, 5).requires_grad_(requires_grad) |
| self._test_autograd_sharing(var) |
| |
| # See https://github.com/pytorch/pytorch/issues/14997 |
| @unittest.skipIf(TEST_WITH_ASAN, |
| "non-deterministically hangs with ASAN") |
| def test_leaf_variable_sharing(self): |
| devices = ['cpu'] |
| if torch.cuda.is_available() and not NO_MULTIPROCESSING_SPAWN and TEST_CUDA_IPC: |
| devices.append('cuda') |
| for device in devices: |
| for requires_grad in [True, False]: |
| var = torch.arange(1., 26, device=device).view(5, 5).requires_grad_(requires_grad) |
| self.assertTrue(var.is_leaf) |
| ctx = mp.get_context('spawn') if device == 'cuda' else mp |
| ready = ctx.Event() |
| queue = ctx.Queue() |
| p = ctx.Process(target=requires_grad_variable_sharing, args=(queue, ready)) |
| p.daemon = True |
| p.start() |
| queue.put(var) |
| ready.wait() |
| worker_requires_grad = queue.get() |
| self.assertTrue(worker_requires_grad == requires_grad) |
| |
| def test_non_leaf_variable_sharing(self): |
| devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda'] |
| for device in devices: |
| var0 = torch.arange(1., 26, device=device).view(5, 5).requires_grad_(True) |
| var = var0 * 2 |
| # Don't use a regular Queue; it uses a background thread (which |
| # means we can't catch the exceptions) |
| queue = mp.SimpleQueue() |
| self.assertRaisesRegex(RuntimeError, r'requires_grad', lambda: queue.put(var)) |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| def test_cuda_variable_sharing(self): |
| for requires_grad in [True, False]: |
| var = torch.arange(1., 26, device='cuda').view(5, 5).requires_grad_(requires_grad) |
| self._test_autograd_sharing(var, mp.get_context('spawn')) |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| def test_mixed_types_cuda_sharing(self): |
| self._test_mixed_types_cuda_sharing(mp.get_context('spawn')) |
| |
| def test_parameter_sharing(self): |
| param = Parameter(torch.arange(1., 26).view(5, 5)) |
| self._test_autograd_sharing(param, is_parameter=True) |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| def test_cuda_parameter_sharing(self): |
| param = Parameter(torch.arange(1., 26, device='cuda').view(5, 5)) |
| self._test_autograd_sharing(param, mp.get_context('spawn'), is_parameter=True) |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| def test_integer_parameter_serialization_cpu(self): |
| self._test_integer_parameter_serialization(device='cpu') |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(not TEST_CUDA_IPC, 'CUDA IPC not available') |
| def test_integer_parameter_serialization_cuda(self): |
| self._test_integer_parameter_serialization(device='cuda') |
| |
| def _test_integer_parameter_serialization(self, device): |
| param = torch.nn.Parameter( |
| torch.tensor(0, dtype=torch.int64, device=device), |
| requires_grad=False |
| ) |
| |
| ctx = mp.get_context('spawn') |
| p = ctx.Process(target=integer_parameter_serialization, args=(param,)) |
| p.start() |
| p.join() |
| |
| self.assertEqual( |
| 0, p.exitcode, |
| msg=f'Failed to serialize successfully for "{device}" device!' |
| ) |
| |
| def test_empty_shared(self): |
| t = torch.tensor([]) |
| t.share_memory_() |
| |
| def _test_is_shared(self): |
| t = torch.randn(5, 5) |
| self.assertFalse(t.is_shared()) |
| t.share_memory_() |
| self.assertTrue(t.is_shared()) |
| |
| @unittest.skipIf(platform == 'darwin', "file descriptor strategy is not supported on macOS") |
| def test_is_shared(self): |
| self._test_is_shared() |
| |
| def test_fs_is_shared(self): |
| with fs_sharing(): |
| self._test_is_shared() |
| |
| @unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available') |
| def test_is_shared_cuda(self): |
| t = torch.randn(5, 5).cuda() |
| self.assertTrue(t.is_shared()) |
| |
| |
| if __name__ == '__main__': |
| run_tests() |
