wehub-resource-sync
261 lines
7.2 KiB
Python
261 lines
7.2 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import os
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import time
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import unittest
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from op_test import get_device, is_custom_device
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import paddle
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import paddle.incubate.multiprocessing as mp
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REPEAT = 20
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HAS_SHM_FILES = os.path.isdir('/dev/shm')
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def fill_tensor(queue, event):
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data = queue.get()
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with paddle.no_grad():
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data[0][:] = 5
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data[1][:] = 5
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event.set()
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def send_tensor(queue, event, device, dtype):
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tensor = paddle.ones([5, 5], dtype=dtype)
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queue.put(tensor)
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queue.put(tensor)
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event.wait()
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def send_parambase(queue, event, device, dtype):
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tensor = paddle.nn.Layer().create_parameter(
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[5, 5],
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dtype=dtype,
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default_initializer=paddle.nn.initializer.Constant(value=1.0),
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)
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queue.put(tensor)
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queue.put(tensor)
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event.wait()
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def check_ipc_tensor(event, ipc_metas):
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ground_truth1 = paddle.to_tensor([1, 2, 3])
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ground_truth2 = paddle.to_tensor([3, 4, 5])
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shared_ipc_tensor = paddle.to_tensor(
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paddle.base.core.DenseTensor._new_shared_cuda(ipc_metas)
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)
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paddle.cuda.ipc_collect()
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def tensor_equal(t1, t2):
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return (t1 == t2).all().item()
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# Step1: Check initial value of ipc tensor
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while not tensor_equal(ground_truth1, shared_ipc_tensor):
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time.sleep(0.1)
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event.set()
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# Step2: Check ipc tensor after update
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while not tensor_equal(ground_truth2, shared_ipc_tensor):
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time.sleep(0.1)
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event.set()
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class leak_checker:
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def __init__(self, test_case):
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self.checked_pids = [os.getpid()]
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self.test_case = test_case
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def __enter__(self):
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self.next_fds = self._get_next_fds(10)
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return self
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def __exit__(self, *args):
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if args[0] is None:
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self.test_case.assertFalse(self.has_shm_files())
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return False
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def check_pid(self, pid):
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self.checked_pids.append(pid)
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def _get_next_fds(self, n=1):
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fds = [os.dup(0) for i in range(n)]
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for fd in fds:
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os.close(fd)
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return fds
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def has_shm_files(self, wait=True):
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if not HAS_SHM_FILES:
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return False
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result = self._has_shm_files()
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if result and wait:
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time.sleep(0.5)
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return self._has_shm_files()
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return result
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def _has_shm_files(self):
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gc.collect()
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names = ['paddle_' + str(pid) for pid in self.checked_pids]
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for filename in os.listdir('/dev/shm'):
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for name in names:
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if filename.startswith(name):
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print("have", filename)
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return True
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return False
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class TestMultiprocessingBase(unittest.TestCase):
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def get_tensor(self, device="cpu"):
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self.device = device.lower()
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place = None
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tensor = paddle.zeros([5, 5], dtype="float32")
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return tensor
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def get_parameter(self):
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w = paddle.nn.Layer().create_parameter(
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[10, 10],
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default_initializer=paddle.nn.initializer.Constant(value=0.0),
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)
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return w
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def _test_empty(self, dtype="float32"):
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q = mp.Queue()
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empty = paddle.to_tensor([], dtype=dtype)
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q.put(empty)
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out = q.get(timeout=1)
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self.assertEqual(str(out), str(empty))
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def _test_sharing(
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self, ctx=mp, device='cpu', dtype="float32", repeat=1, param=False
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):
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def test_fill():
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if param:
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x = self.get_parameter()
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y = (x[:, 1]).detach()
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else:
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x = self.get_tensor()
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y = x[:, 1]
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data = [x, y]
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queue = ctx.Queue()
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event = ctx.Event()
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queue.put(data)
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process = ctx.Process(target=fill_tensor, args=(queue, event))
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process.daemon = True
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lc.check_pid(process.pid)
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process.start()
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event.wait(30)
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self.assertTrue(event.is_set())
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self.assertTrue(data[0].equal(5).all())
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self.assertTrue(data[1].equal(5).all())
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process.join(1 if device != get_device() else 10)
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self.assertFalse(process.is_alive())
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def test_receive():
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queue = ctx.Queue()
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event = ctx.Event()
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process = ctx.Process(
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target=send_parambase if param else send_tensor,
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args=(queue, event, device, dtype),
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)
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process.daemon = True
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lc.check_pid(process.pid)
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process.start()
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t1 = queue.get()
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t2 = queue.get()
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self.assertTrue(t1.equal(1).all())
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del t1, t2
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event.set()
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process.join(1 if device != get_device() else 10)
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self.assertFalse(process.is_alive())
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with leak_checker(self) as lc:
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for _ in range(repeat):
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test_fill()
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test_receive()
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class TestMultiprocessingCpu(TestMultiprocessingBase):
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def func_test_pass_tensor(self):
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paddle.set_device("cpu")
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self._test_sharing(repeat=REPEAT)
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def test_pass_tensor(self):
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self.func_test_pass_tensor()
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def func_test_pass_parambase(self):
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paddle.set_device("cpu")
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self._test_sharing(repeat=1, param=True)
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def test_pass_parambase(self):
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self.func_test_pass_parambase()
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def func_test_pass_empty(self):
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paddle.set_device("cpu")
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self._test_empty()
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def test_pass_empty(self):
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self.func_test_pass_empty()
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class TestMultiprocessingGpu(TestMultiprocessingBase):
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@unittest.skipIf(
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not (paddle.base.core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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def func_test_pass_tensor(self):
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paddle.set_device(get_device())
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self._test_sharing(mp.get_context("spawn"), get_device())
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def test_pass_tensor(self):
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self.func_test_pass_tensor()
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def test_ipc_tensor(self):
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paddle.device.set_device(get_device())
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initial_tensor = paddle.to_tensor([1, 2, 3])
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bonus = paddle.to_tensor([2])
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ipc_metas = initial_tensor.value().get_tensor()._share_cuda()
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ctx = mp.get_context("spawn")
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event = ctx.Event()
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process = ctx.Process(target=check_ipc_tensor, args=(event, ipc_metas))
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process.daemon = True
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process.start()
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# Step1: Check initial value of ipc tensor
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event.wait(30)
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self.assertTrue(event.is_set())
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# Step2: Check ipc tensor after update
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event.clear()
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initial_tensor.add_(bonus)
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event.wait(30)
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self.assertTrue(event.is_set())
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process.join(10)
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self.assertFalse(process.is_alive())
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if __name__ == "__main__":
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unittest.main()
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