# 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 numpy import unittest import paddle.fluid as fluid import paddle import paddle.dataset.mnist as mnist import paddle.dataset.wmt16 as wmt16 def simple_fc_net(use_feed): if use_feed: img = fluid.layers.data(name='image', shape=[784], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') else: reader = fluid.layers.open_files( filenames=['./mnist.recordio'], shapes=[[-1, 784], [-1, 1]], lod_levels=[0, 0], dtypes=['float32', 'int64'], thread_num=1, for_parallel=True) reader = fluid.layers.io.double_buffer(reader) img, label = fluid.layers.read_file(reader) hidden = img for _ in xrange(4): hidden = fluid.layers.fc( hidden, size=200, act='tanh', bias_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=1.0))) prediction = fluid.layers.fc(hidden, size=10, act='softmax') loss = fluid.layers.cross_entropy(input=prediction, label=label) loss = fluid.layers.mean(loss) return loss def fc_with_batchnorm(use_feed): if use_feed: img = fluid.layers.data(name='image', shape=[784], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') else: reader = fluid.layers.open_files( filenames=['mnist.recordio'], shapes=[[-1, 784], [-1, 1]], lod_levels=[0, 0], dtypes=['float32', 'int64'], thread_num=1, for_parallel=True) reader = fluid.layers.io.double_buffer(reader) img, label = fluid.layers.read_file(reader) hidden = img for _ in xrange(1): hidden = fluid.layers.fc( hidden, size=200, act='tanh', bias_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=1.0))) hidden = fluid.layers.batch_norm(input=hidden) prediction = fluid.layers.fc(hidden, size=10, act='softmax') loss = fluid.layers.cross_entropy(input=prediction, label=label) loss = fluid.layers.mean(loss) return loss def squeeze_excitation(input, num_channels, reduction_ratio): # pool = fluid.layers.pool2d( # input=input, pool_size=0, pool_type='avg', global_pooling=True) conv = input shape = conv.shape reshape = fluid.layers.reshape( x=conv, shape=[-1, shape[1], shape[2] * shape[3]]) pool = fluid.layers.reduce_mean(input=reshape, dim=2) squeeze = fluid.layers.fc(input=pool, size=num_channels / reduction_ratio, act='relu') excitation = fluid.layers.fc(input=squeeze, size=num_channels, act='sigmoid') scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0) return scale def conv_bn_layer(input, num_filters, filter_size, stride=1, groups=1, act=None): conv = fluid.layers.conv2d( input=input, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=(filter_size - 1) / 2, groups=groups, act=None, bias_attr=False) return fluid.layers.batch_norm(input=conv, act=act, momentum=0.1) def shortcut(input, ch_out, stride): ch_in = input.shape[1] if ch_in != ch_out: if stride == 1: filter_size = 1 else: filter_size = 3 return conv_bn_layer(input, ch_out, filter_size, stride) else: return input def bottleneck_block(input, num_filters, stride, cardinality, reduction_ratio): # The number of first 1x1 convolutional channels for each bottleneck build block # was halved to reduce the compution cost. conv0 = conv_bn_layer( input=input, num_filters=num_filters, filter_size=1, act='relu') conv1 = conv_bn_layer( input=conv0, num_filters=num_filters * 2, filter_size=3, stride=stride, groups=cardinality, act='relu') conv2 = conv_bn_layer( input=conv1, num_filters=num_filters * 2, filter_size=1, act=None) scale = squeeze_excitation( input=conv2, num_channels=num_filters * 2, reduction_ratio=reduction_ratio) short = shortcut(input, num_filters * 2, stride) return fluid.layers.elementwise_add(x=short, y=scale, act='relu') def SE_ResNeXt50Small(batch_size=2, use_feed=False): assert not use_feed, "SE_ResNeXt doesn't support feed yet" img = fluid.layers.fill_constant( shape=[batch_size, 3, 224, 224], dtype='float32', value=0.0) label = fluid.layers.fill_constant( shape=[batch_size, 1], dtype='int64', value=0.0) conv = conv_bn_layer( input=img, num_filters=16, filter_size=3, stride=2, act='relu') conv = conv_bn_layer( input=conv, num_filters=16, filter_size=3, stride=1, act='relu') conv = conv_bn_layer( input=conv, num_filters=16, filter_size=3, stride=1, act='relu') conv = fluid.layers.pool2d( input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max') cardinality = 32 reduction_ratio = 16 depth = [3, 4, 6, 3] num_filters = [128, 256, 512, 1024] for block in range(len(depth)): for i in range(depth[block]): conv = bottleneck_block( input=conv, num_filters=num_filters[block], stride=2 if i == 0 and block != 0 else 1, cardinality=cardinality, reduction_ratio=reduction_ratio) shape = conv.shape reshape = fluid.layers.reshape( x=conv, shape=[-1, shape[1], shape[2] * shape[3]]) pool = fluid.layers.reduce_mean(input=reshape, dim=2) dropout = fluid.layers.dropout(x=pool, dropout_prob=0.2) # Classifier layer: prediction = fluid.layers.fc(input=dropout, size=1000, act='softmax') loss = fluid.layers.cross_entropy(input=prediction, label=label) loss = fluid.layers.mean(loss) return loss import time class TestParallelExecutorBase(unittest.TestCase): def check_network_convergence(self, method, memory_opt=True, iter=50, batch_size=None, allow_op_delay=False, feed_dict=None, seed=None, use_parallel_executor=True): def run_executor(exe, feed, fetch_list, program=None): if isinstance(exe, fluid.ParallelExecutor): res = exe.run(fetch_list=fetch_list, feed=feed) elif isinstance(exe, fluid.Executor): if program is None: program = fluid.default_main_program() res = exe.run(program=program, feed=feed, fetch_list=fetch_list) else: raise ValueError('Unkown type exe') return res main = fluid.Program() startup = fluid.Program() startup.random_seed = 1 # Fix random seed with fluid.program_guard(main, startup): if seed is not None: startup.random_seed = seed loss = method(use_feed=feed_dict is not None) adam = fluid.optimizer.Adam() adam.minimize(loss) if memory_opt: fluid.memory_optimize(main) place = fluid.CUDAPlace(0) startup_exe = fluid.Executor(place) startup_exe.run(startup) if use_parallel_executor: exe = fluid.ParallelExecutor( True, loss_name=loss.name, allow_op_delay=allow_op_delay) else: exe = fluid.Executor(place=place) if batch_size is not None: batch_size *= fluid.core.get_cuda_device_count() begin = time.time() first_loss, = run_executor( exe=exe, feed=feed_dict, fetch_list=[loss.name]) first_loss = numpy.array(first_loss) for i in xrange(iter): run_executor(exe=exe, feed=feed_dict, fetch_list=[]) last_loss, = run_executor( exe=exe, feed=feed_dict, fetch_list=[loss.name]) end = time.time() if batch_size is not None: print "%.4f Instance per second" % ( (batch_size * iter + 2) / (end - begin)) last_loss = numpy.array(last_loss) print first_loss, last_loss # self.assertGreater(first_loss[0], last_loss[0]) return first_loss, last_loss class TestMNIST(TestParallelExecutorBase): @classmethod def setUpClass(cls): # Convert mnist to recordio file with fluid.program_guard(fluid.Program(), fluid.Program()): reader = paddle.batch(mnist.train(), batch_size=4) feeder = fluid.DataFeeder( feed_list=[ # order is image and label fluid.layers.data( name='image', shape=[784]), fluid.layers.data( name='label', shape=[1], dtype='int64'), ], place=fluid.CPUPlace()) fluid.recordio_writer.convert_reader_to_recordio_file( './mnist.recordio', reader, feeder) def check_simple_fc_convergence(self): self.check_network_convergence(simple_fc_net) self.check_network_convergence(simple_fc_net, allow_op_delay=True) img = numpy.zeros(shape=[32, 784], dtype='float32') label = numpy.ones(shape=[32, 1], dtype='int64') self.check_network_convergence( simple_fc_net, feed_dict={"image": img, "label": label}) def test_simple_fc(self): self.check_simple_fc_convergence() def check_simple_fc_parallel_accuracy(self): img = numpy.zeros(shape=[32, 784], dtype='float32') label = numpy.ones(shape=[32, 1], dtype='int64') single_first_loss, single_last_loss = self.check_network_convergence( method=simple_fc_net, seed=1000, feed_dict={"image": img, "label": label}, use_parallel_executor=False) parallel_first_loss, parallel_last_loss = self.check_network_convergence( method=simple_fc_net, seed=1000, feed_dict={"image": img, "label": label}, use_parallel_executor=True) for p_f in parallel_first_loss: self.assertAlmostEquals(p_f, single_first_loss[0], delta=1e-6) for p_l in parallel_last_loss: self.assertAlmostEquals(p_l, single_last_loss[0], delta=1e-6) def test_simple_fc_parallel_accuracy(self): self.check_simple_fc_parallel_accuracy() def check_batchnorm_fc_convergence(self): self.check_network_convergence(fc_with_batchnorm) img = numpy.zeros(shape=[32, 784], dtype='float32') label = numpy.ones(shape=[32, 1], dtype='int64') self.check_network_convergence( fc_with_batchnorm, feed_dict={"image": img, "label": label}) def test_batchnorm_fc(self): self.check_batchnorm_fc_convergence() class TestResnet(TestParallelExecutorBase): # @classmethod # def setUpClass(cls): # # import os # # if os.path.exists('./flowers.recordio'): # # return # with fluid.program_guard(fluid.Program(), fluid.Program()): # reader = paddle.batch(flowers.train(), batch_size=4) # feeder = fluid.DataFeeder( # feed_list=[ # fluid.layers.data( # name='image', shape=[3, 224, 224]), # fluid.layers.data( # name='label', shape=[1], dtype='int64'), # ], # place=fluid.CPUPlace()) # fluid.recordio_writer.convert_reader_to_recordio_file( # "./flowers.recordio", reader, feeder, compressor=fluid.core.RecordIOWriter.Compressor.NoCompress) def check_resnet_convergence(self): import functools batch_size = 2 self.check_network_convergence( functools.partial( SE_ResNeXt50Small, batch_size=batch_size), iter=20, batch_size=batch_size) def test_resnet(self): self.check_resnet_convergence() class ModelHyperParams(object): # Dictionary size for source and target language. This model directly uses # paddle.dataset.wmt16 in which , and token has # alreay been added, but the token is not added. Transformer requires # sequences in a mini-batch are padded to have the same length. A token is # added into the original dictionary in paddle.dateset.wmt16. # size of source word dictionary. src_vocab_size = 10000 # index for token in source language. src_pad_idx = src_vocab_size # size of target word dictionay trg_vocab_size = 10000 # index for token in target language. trg_pad_idx = trg_vocab_size # position value corresponding to the token. pos_pad_idx = 0 # max length of sequences. It should plus 1 to include position # padding token for position encoding. max_length = 50 # the dimension for word embeddings, which is also the last dimension of # the input and output of multi-head attention, position-wise feed-forward # networks, encoder and decoder. d_model = 512 # size of the hidden layer in position-wise feed-forward networks. d_inner_hid = 1024 # the dimension that keys are projected to for dot-product attention. d_key = 64 # the dimension that values are projected to for dot-product attention. d_value = 64 # number of head used in multi-head attention. n_head = 8 # number of sub-layers to be stacked in the encoder and decoder. n_layer = 6 # dropout rate used by all dropout layers. dropout = 0.1 import numpy as np def prepare_batch_input(insts, src_pad_idx, trg_pad_idx, n_head): """ Pad the instances to the max sequence length in batch, and generate the corresponding position data and attention bias. Then, convert the numpy data to tensors and return a dict mapping names to tensors. """ def __pad_batch_data(insts, pad_idx, is_target=False, return_pos=True, return_attn_bias=True, return_max_len=True): """ Pad the instances to the max sequence length in batch, and generate the corresponding position data and attention bias. """ return_list = [] max_len = max(len(inst) for inst in insts) inst_data = np.array( [inst + [pad_idx] * (max_len - len(inst)) for inst in insts]) return_list += [inst_data.astype("int64").reshape([-1, 1])] if return_pos: inst_pos = np.array([[ pos_i + 1 if w_i != pad_idx else 0 for pos_i, w_i in enumerate(inst) ] for inst in inst_data]) return_list += [inst_pos.astype("int64").reshape([-1, 1])] if return_attn_bias: if is_target: # This is used to avoid attention on paddings and subsequent # words. slf_attn_bias_data = np.ones((inst_data.shape[0], max_len, max_len)) slf_attn_bias_data = np.triu(slf_attn_bias_data, 1).reshape( [-1, 1, max_len, max_len]) slf_attn_bias_data = np.tile(slf_attn_bias_data, [1, n_head, 1, 1]) * [-1e9] else: # This is used to avoid attention on paddings. slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] * (max_len - len(inst)) for inst in insts]) slf_attn_bias_data = np.tile( slf_attn_bias_data.reshape([-1, 1, 1, max_len]), [1, n_head, max_len, 1]) return_list += [slf_attn_bias_data.astype("float32")] if return_max_len: return_list += [max_len] return return_list if len(return_list) > 1 else return_list[0] def data_to_tensor(data_list, name_list, input_dict, place): assert len(data_list) == len(name_list) for i in range(len(name_list)): tensor = fluid.LoDTensor() tensor.set(data_list[i], place) input_dict[name_list[i]] = tensor src_word, src_pos, src_slf_attn_bias, src_max_len = __pad_batch_data( [inst[0] for inst in insts], src_pad_idx, is_target=False) trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = __pad_batch_data( [inst[1] for inst in insts], trg_pad_idx, is_target=True) trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :], [1, 1, trg_max_len, 1]).astype("float32") lbl_word = __pad_batch_data([inst[2] for inst in insts], trg_pad_idx, False, False, False, False) lbl_weight = (lbl_word != trg_pad_idx).astype("float32").reshape([-1, 1]) return [ src_word, src_pos, trg_word, trg_pos, src_slf_attn_bias, trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight ] import transformer_model def transformer(use_feed): assert not use_feed, "transfomer doesn't support feed yet" return transformer_model.transformer( ModelHyperParams.src_vocab_size + 1, ModelHyperParams.trg_vocab_size + 1, ModelHyperParams.max_length + 1, ModelHyperParams.n_layer, ModelHyperParams.n_head, ModelHyperParams.d_key, ModelHyperParams.d_value, ModelHyperParams.d_model, ModelHyperParams.d_inner_hid, ModelHyperParams.dropout, ModelHyperParams.src_pad_idx, ModelHyperParams.trg_pad_idx, ModelHyperParams.pos_pad_idx) class TestTransformer(TestParallelExecutorBase): @classmethod def setUpClass(cls): reader = paddle.batch( wmt16.train(ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size), batch_size=transformer_model.batch_size) with fluid.recordio_writer.create_recordio_writer( "./wmt16.recordio") as writer: for batch in reader(): for tensor in prepare_batch_input( batch, ModelHyperParams.src_pad_idx, ModelHyperParams.trg_pad_idx, ModelHyperParams.n_head): t = fluid.LoDTensor() t.set(tensor, fluid.CPUPlace()) writer.append_tensor(t) writer.complete_append_tensor() @unittest.skip("transformer is buggy in multi gpu") def test_main(self): self.check_network_convergence(transformer) class ParallelExecutorTestingDuringTraining(unittest.TestCase): def check_network_convergence(self): main = fluid.Program() startup = fluid.Program() with fluid.program_guard(main, startup): loss = simple_fc_net(True) test_program = main.clone(for_test=True) opt = fluid.optimizer.SGD(learning_rate=0.001) opt.minimize(loss) batch_size = 32 image = numpy.random.normal(size=(batch_size, 784)).astype('float32') label = numpy.random.randint(0, 10, (batch_size, 1), dtype="int64") place = fluid.CUDAPlace(0) exe = fluid.Executor(place) exe.run(startup) feed_dict = {'image': image, 'label': label} train_exe = fluid.ParallelExecutor( use_cuda=True, loss_name=loss.name, main_program=main) test_exe = fluid.ParallelExecutor( use_cuda=True, main_program=test_program, share_vars_from=train_exe) for i in xrange(5): test_loss, = test_exe.run([loss.name], feed=feed_dict) test_loss = numpy.array(test_loss) train_loss, = train_exe.run([loss.name], feed=feed_dict) train_loss = numpy.array(train_loss) self.assertTrue( numpy.allclose( train_loss, test_loss, atol=1e-8), "Train loss: " + str(train_loss) + "\n Test loss:" + str(test_loss)) def test_parallel(self): self.check_network_convergence() import paddle.dataset.conll05 as conll05 import paddle.fluid as fluid word_dict, verb_dict, label_dict = conll05.get_dict() word_dict_len = len(word_dict) label_dict_len = len(label_dict) pred_dict_len = len(verb_dict) mark_dict_len = 2 word_dim = 32 mark_dim = 5 hidden_dim = 512 depth = 8 mix_hidden_lr = 1e-3 embedding_name = 'emb' def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, is_sparse, **ignored): # 8 features predicate_embedding = fluid.layers.embedding( input=predicate, is_sparse=is_sparse, size=[pred_dict_len, word_dim], dtype='float32', param_attr='vemb') mark_embedding = fluid.layers.embedding( input=mark, is_sparse=is_sparse, size=[mark_dict_len, mark_dim], dtype='float32') word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2] emb_layers = [ fluid.layers.embedding( size=[word_dict_len, word_dim], is_sparse=is_sparse, input=x, param_attr=fluid.ParamAttr( name=embedding_name, trainable=False)) for x in word_input ] emb_layers.append(predicate_embedding) emb_layers.append(mark_embedding) hidden_0_layers = [ fluid.layers.fc(input=emb, size=hidden_dim, act='tanh') for emb in emb_layers ] hidden_0 = fluid.layers.sums(input=hidden_0_layers) lstm_0 = fluid.layers.dynamic_lstm( input=hidden_0, size=hidden_dim, candidate_activation='relu', gate_activation='sigmoid', cell_activation='sigmoid') # stack L-LSTM and R-LSTM with direct edges input_tmp = [hidden_0, lstm_0] for i in range(1, depth): mix_hidden = fluid.layers.sums(input=[ fluid.layers.fc(input=input_tmp[0], size=hidden_dim, act='tanh'), fluid.layers.fc(input=input_tmp[1], size=hidden_dim, act='tanh') ]) lstm = fluid.layers.dynamic_lstm( input=mix_hidden, size=hidden_dim, candidate_activation='relu', gate_activation='sigmoid', cell_activation='sigmoid', is_reverse=((i % 2) == 1)) input_tmp = [mix_hidden, lstm] feature_out = fluid.layers.sums(input=[ fluid.layers.fc(input=input_tmp[0], size=label_dict_len, act='tanh'), fluid.layers.fc(input=input_tmp[1], size=label_dict_len, act='tanh') ]) return feature_out class TestCRFModel(unittest.TestCase): def check_network_convergence(self, is_sparse): main = fluid.Program() startup = fluid.Program() with fluid.program_guard(main, startup): word = fluid.layers.data( name='word_data', shape=[1], dtype='int64', lod_level=1) predicate = fluid.layers.data( name='verb_data', shape=[1], dtype='int64', lod_level=1) ctx_n2 = fluid.layers.data( name='ctx_n2_data', shape=[1], dtype='int64', lod_level=1) ctx_n1 = fluid.layers.data( name='ctx_n1_data', shape=[1], dtype='int64', lod_level=1) ctx_0 = fluid.layers.data( name='ctx_0_data', shape=[1], dtype='int64', lod_level=1) ctx_p1 = fluid.layers.data( name='ctx_p1_data', shape=[1], dtype='int64', lod_level=1) ctx_p2 = fluid.layers.data( name='ctx_p2_data', shape=[1], dtype='int64', lod_level=1) mark = fluid.layers.data( name='mark_data', shape=[1], dtype='int64', lod_level=1) feature_out = db_lstm(**locals()) target = fluid.layers.data( name='target', shape=[1], dtype='int64', lod_level=1) crf_cost = fluid.layers.linear_chain_crf( input=feature_out, label=target, param_attr=fluid.ParamAttr( name='crfw', learning_rate=1e-1)) avg_cost = fluid.layers.mean(crf_cost) sgd_optimizer = fluid.optimizer.SGD( learning_rate=fluid.layers.exponential_decay( learning_rate=0.01, decay_steps=100000, decay_rate=0.5, staircase=True)) sgd_optimizer.minimize(avg_cost) train_data = paddle.batch( paddle.reader.shuffle( paddle.dataset.conll05.test(), buf_size=8192), batch_size=16) place = fluid.CUDAPlace(0) exe = fluid.Executor(place) exe.run(startup) pe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name) feeder = fluid.DataFeeder( feed_list=[ word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, predicate, mark, target ], place=fluid.CPUPlace()) data = train_data() for i in xrange(10): cur_batch = next(data) print map(numpy.array, pe.run(feed=feeder.feed(cur_batch), fetch_list=[avg_cost.name]))[0] def test_update_sparse_parameter(self): self.check_network_convergence(is_sparse=True) def test_update_dense_parameter(self): self.check_network_convergence(is_sparse=False)