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@ -12,7 +12,7 @@
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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 numpy
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import numpy as np
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import unittest
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import paddle.fluid as fluid
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@ -243,7 +243,7 @@ class TestParallelExecutorBase(unittest.TestCase):
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begin = time.time()
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first_loss, = run_executor(
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exe=exe, feed=feed_dict, fetch_list=[loss.name])
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first_loss = numpy.array(first_loss)
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first_loss = np.array(first_loss)
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for i in xrange(iter):
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run_executor(exe=exe, feed=feed_dict, fetch_list=[])
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@ -256,7 +256,7 @@ class TestParallelExecutorBase(unittest.TestCase):
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print "%.4f Instance per second" % (
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(batch_size * iter + 2) / (end - begin))
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last_loss = numpy.array(last_loss)
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last_loss = np.array(last_loss)
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print first_loss, last_loss
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# self.assertGreater(first_loss[0], last_loss[0])
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@ -284,8 +284,8 @@ class TestMNIST(TestParallelExecutorBase):
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self.check_network_convergence(simple_fc_net)
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self.check_network_convergence(simple_fc_net, allow_op_delay=True)
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img = numpy.zeros(shape=[32, 784], dtype='float32')
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label = numpy.ones(shape=[32, 1], dtype='int64')
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img = np.zeros(shape=[32, 784], dtype='float32')
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label = np.ones(shape=[32, 1], dtype='int64')
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self.check_network_convergence(
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simple_fc_net, feed_dict={"image": img,
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"label": label})
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@ -294,8 +294,8 @@ class TestMNIST(TestParallelExecutorBase):
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self.check_simple_fc_convergence()
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def check_simple_fc_parallel_accuracy(self):
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img = numpy.zeros(shape=[32, 784], dtype='float32')
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label = numpy.ones(shape=[32, 1], dtype='int64')
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img = np.zeros(shape=[32, 784], dtype='float32')
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label = np.ones(shape=[32, 1], dtype='int64')
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single_first_loss, single_last_loss = self.check_network_convergence(
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method=simple_fc_net,
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seed=1000,
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@ -319,8 +319,8 @@ class TestMNIST(TestParallelExecutorBase):
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def check_batchnorm_fc_convergence(self):
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self.check_network_convergence(fc_with_batchnorm)
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img = numpy.zeros(shape=[32, 784], dtype='float32')
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label = numpy.ones(shape=[32, 1], dtype='int64')
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img = np.zeros(shape=[32, 784], dtype='float32')
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label = np.ones(shape=[32, 1], dtype='int64')
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self.check_network_convergence(
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fc_with_batchnorm, feed_dict={"image": img,
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"label": label})
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@ -404,9 +404,6 @@ class ModelHyperParams(object):
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dropout = 0.1
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import numpy as np
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def prepare_batch_input(insts, src_pad_idx, trg_pad_idx, n_head):
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"""
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Pad the instances to the max sequence length in batch, and generate the
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@ -533,9 +530,8 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
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opt.minimize(loss)
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batch_size = 32
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image = numpy.random.normal(size=(batch_size,
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784)).astype('float32')
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label = numpy.random.randint(0, 10, (batch_size, 1), dtype="int64")
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image = np.random.normal(size=(batch_size, 784)).astype('float32')
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label = np.random.randint(0, 10, (batch_size, 1), dtype="int64")
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place = fluid.CUDAPlace(0)
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exe = fluid.Executor(place)
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@ -552,12 +548,12 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
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for i in xrange(5):
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test_loss, = test_exe.run([loss.name], feed=feed_dict)
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test_loss = numpy.array(test_loss)
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test_loss = np.array(test_loss)
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train_loss, = train_exe.run([loss.name], feed=feed_dict)
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train_loss = numpy.array(train_loss)
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train_loss = np.array(train_loss)
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self.assertTrue(
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numpy.allclose(
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np.allclose(
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train_loss, test_loss, atol=1e-8),
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"Train loss: " + str(train_loss) + "\n Test loss:" +
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str(test_loss))
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@ -712,7 +708,7 @@ class TestCRFModel(unittest.TestCase):
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data = train_data()
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for i in xrange(10):
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cur_batch = next(data)
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print map(numpy.array,
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print map(np.array,
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pe.run(feed=feeder.feed(cur_batch),
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fetch_list=[avg_cost.name]))[0]
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@ -721,3 +717,84 @@ class TestCRFModel(unittest.TestCase):
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def test_update_dense_parameter(self):
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self.check_network_convergence(is_sparse=False)
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# test fetch all the variables of global_block
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import paddle.dataset.flowers as flowers
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import math
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def Lenet(data, class_dim):
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conv1 = fluid.layers.conv2d(data, 32, 5, 1, act=None)
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bn1 = fluid.layers.batch_norm(conv1, act='relu')
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pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2)
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conv2 = fluid.layers.conv2d(pool1, 50, 5, 1, act=None)
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bn2 = fluid.layers.batch_norm(conv2, act='relu')
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pool2 = fluid.layers.pool2d(bn2, 2, 'max', 2)
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fc1 = fluid.layers.fc(pool2, size=500, act='relu')
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fc2 = fluid.layers.fc(fc1, size=class_dim, act='softmax')
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return fc2
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class TestFetchOp(unittest.TestCase):
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def parallel_exe(self, train_inputs, seed):
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main = fluid.Program()
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startup = fluid.Program()
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startup.random_seed = seed
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with fluid.program_guard(main, startup):
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data = fluid.layers.data(
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name='image', shape=[3, 224, 224], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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out = Lenet(data, class_dim=102)
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loss = fluid.layers.cross_entropy(input=out, label=label)
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loss = fluid.layers.mean(loss)
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opt = fluid.optimizer.Momentum(
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learning_rate=0.1,
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momentum=0.9,
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regularization=fluid.regularizer.L2Decay(1e-4))
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opt.minimize(loss)
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# TODO(zcd): I found that onece the memory optimizer is open,
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# parallel_exe doesn't fetch some variable, such as conv2d_0.b_0@GRAD,
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# conv2d_1.b_0@GRAD. Those variables should not be pruned.
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# fluid.memory_optimize(main)
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place = fluid.CUDAPlace(0)
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exe = fluid.Executor(place)
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exe.run(startup)
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feeder = fluid.DataFeeder(place=place, feed_list=[data, label])
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pe = fluid.ParallelExecutor(
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use_cuda=True, loss_name=loss.name, main_program=main)
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fetch_list = []
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all_vars = main.global_block().vars
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for k, v in all_vars.iteritems():
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if 'tmp' not in k and k[0] is not '_' or v.persistable:
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fetch_list.append(k)
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for data in train_inputs:
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ret = pe.run(fetch_list, feed=feeder.feed(data))
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for i in range(len(fetch_list)):
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assert not math.isnan(np.sum(ret[i])) and \
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not math.isinf(np.sum(ret[i]))
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def test_update_sparse_parameter(self):
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tst_reader = paddle.batch(flowers.test(use_xmap=False), batch_size=16)
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tst_reader_iter = tst_reader()
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iters = 3
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train_inputs = []
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for i in range(iters):
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train_inputs.append(tst_reader_iter.next())
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self.parallel_exe(train_inputs, seed=1)
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if __name__ == '__main__':
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unittest.main()
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Yao Cheng
7 years ago
