# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import paddle.fluid as fluid from paddle.fluid.layers.device import get_places import paddle.fluid.profiler as profiler import numpy import six class BaseParallelForTest(unittest.TestCase): def run_test(self, callback, feed, fetch): """ Run the unittest for parallel.for Args: callback(callable): A callable function returns a generator. There are two yields in the generator function. The first yield returns the data layers, and the second yield returns the loss. The modified data variables will be sent back during the first yield. feed(dict): The executor feeding dictionary. fetch(list|basestr): The fetch name lists. Returns: None Raises: AssertionError when the computation of cpu, parallel.for in cpu, gpu, parallel.for in gpu are different. """ cpu = fluid.CPUPlace() result_cpu = self._run_test_impl_( callback=callback, feed=feed, fetch=fetch, place=cpu, use_parallel=False) result_cpu_parallel = self._run_test_impl_( callback=callback, feed=feed, fetch=fetch, place=cpu, use_parallel=True) if fluid.core.is_compiled_with_cuda(): gpu = fluid.CUDAPlace(0) result_gpu = self._run_test_impl_( callback=callback, feed=feed, fetch=fetch, place=gpu, use_parallel=False, use_gpu=True) result_gpu_parallel = self._run_test_impl_( callback=callback, feed=feed, fetch=fetch, place=gpu, use_parallel=True, use_gpu=True) result_gpu_nccl = self._run_test_impl_( callback=callback, feed=feed, fetch=fetch, place=gpu, use_parallel=True, use_nccl=True, use_gpu=True) self._assert_same_(fetch, result_cpu, result_cpu_parallel, result_gpu, result_gpu_parallel, result_gpu_nccl) else: self._assert_same_(fetch, result_cpu, result_cpu_parallel) def _run_test_impl_(self, callback, feed, fetch, place, use_parallel=False, use_nccl=False, use_gpu=False): """ Run a single test, returns the fetch values Args: place(Place): the computation place. use_parallel(bool): Whether use parallel.for or not. Returns: Fetched numpy arrays. """ if isinstance(fetch, six.string_types): fetch = [fetch] main = fluid.Program() startup = fluid.Program() # Fix seed main.random_seed = 10 startup.random_seed = 10 with fluid.program_guard(main, startup): generator = callback() # Automatically insert parallel do if use_parallel = True if use_parallel: thread_num = fluid.core.get_cuda_device_count( ) if use_gpu else 8 places = get_places(thread_num) pd = fluid.layers.ParallelDo(places, use_nccl=use_nccl) data = next(generator) if isinstance(data, fluid.framework.Variable): data = [data] with pd.do(): ins = list(map(pd.read_input, data)) if len(ins) == 1: ins = ins[0] loss = generator.send(ins) # patch input pd.write_output(loss) loss = pd() else: data = next(generator) loss = generator.send(data) self.assertIsNotNone(loss) avg_loss = fluid.layers.mean(loss) fluid.backward.append_backward(loss=avg_loss) exe = fluid.Executor(place) exe.run(startup) if use_gpu: profile_type = 'GPU' else: profile_type = 'CPU' with profiler.profiler(profile_type, 'total', '/tmp/profiler'): return exe.run(main, feed=feed, fetch_list=fetch) def _assert_same_(self, fetch, *args): """ Assert the return values of `run_test` are same. Args: fetch: Fetch list. Used for print error message *args: The fetch result lists of each situations. Returns: None Raises: AssertionError """ def _impl_(a, b, fetch_id, item_id): item_str = [ 'CPU', 'ParallelCPU', 'GPU', 'ParallelGPU', 'ParallelGPUNCCL' ] flag = numpy.allclose(a, b, rtol=0.1, atol=1e-3) self.assertTrue(flag, "The {0} are different in {1}, {2} vs {3}".format( fetch[fetch_id], item_str[item_id], a, b)) for i, items in enumerate(zip(*args)): self.assertGreater(len(items), 0) for j in range(1, len(items)): _impl_(items[0], items[j], fetch_id=i, item_id=j) class ParallelOpTest(BaseParallelForTest): @staticmethod def __network__(): x = fluid.layers.data(shape=[784], dtype='float32', name='img') x = yield x hidden = fluid.layers.fc(input=x, size=200, param_attr='fc1.w') hidden = fluid.layers.batch_norm(input=hidden) loss = fluid.layers.mean(hidden) yield loss def test_simple_fc(self): self.run_test( callback=self.__network__, feed={ 'img': numpy.random.random(size=(51, 784)).astype('float32') }, fetch=['fc1.w@GRAD']) def test_fc_with_tiny_data(self): self.run_test( callback=self.__network__, feed={'img': numpy.random.random(size=(1, 784)).astype('float32')}, fetch=['fc1.w@GRAD']) class ParallelOpTestMultipleInput(BaseParallelForTest): @staticmethod def __network__(): x = fluid.layers.data( shape=[784], dtype='float32', name='img1', stop_gradient=False) y = fluid.layers.data( shape=[784], dtype='float32', name='img2', stop_gradient=False) yield [x, y] x = x + y hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w') hidden2 = fluid.layers.fc(input=hidden1, size=200, param_attr='fc2.w') hidden3 = fluid.layers.fc(input=hidden2, size=200, param_attr='fc3.w') loss = fluid.layers.mean(hidden3) yield loss def test_simple_fc(self): self.run_test( callback=self.__network__, feed={ 'img1': numpy.random.random(size=(51, 784)).astype('float32'), 'img2': numpy.random.random(size=(51, 784)).astype('float32') }, fetch=['fc1.w@GRAD', 'fc2.w@GRAD', 'fc3.w@GRAD']) if __name__ == '__main__': unittest.main()