diff --git a/python/paddle/fluid/tests/book/high-level-api/machine_translation/test_machine_translation.py b/python/paddle/fluid/tests/book/high-level-api/machine_translation/test_machine_translation.py index 7204c7b3c7648..1f85221a9d0ad 100644 --- a/python/paddle/fluid/tests/book/high-level-api/machine_translation/test_machine_translation.py +++ b/python/paddle/fluid/tests/book/high-level-api/machine_translation/test_machine_translation.py @@ -148,28 +148,6 @@ def decoder_decode(context, is_sparse): return translation_ids, translation_scores -def set_init_lod(data, lod, place): - res = fluid.LoDTensor() - res.set(data, place) - res.set_lod(lod) - return res - - -def to_lodtensor(data, place): - seq_lens = [len(seq) for seq in data] - cur_len = 0 - lod = [cur_len] - for l in seq_lens: - cur_len += l - lod.append(cur_len) - flattened_data = np.concatenate(data, axis=0).astype("int64") - flattened_data = flattened_data.reshape([len(flattened_data), 1]) - res = fluid.LoDTensor() - res.set(flattened_data, place) - res.set_lod([lod]) - return res - - def train_program(is_sparse): context = encoder(is_sparse) rnn_out = decoder_train(context, is_sparse) @@ -218,7 +196,6 @@ def event_handler(event): def decode_main(use_cuda, is_sparse): - if use_cuda and not fluid.core.is_compiled_with_cuda(): return place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() @@ -234,26 +211,32 @@ def decode_main(use_cuda, is_sparse): [1. for _ in range(batch_size)], dtype='float32') init_ids_data = init_ids_data.reshape((batch_size, 1)) init_scores_data = init_scores_data.reshape((batch_size, 1)) - init_lod = [i for i in range(batch_size)] + [batch_size] + init_lod = [1] * batch_size init_lod = [init_lod, init_lod] + init_ids = fluid.create_lod_tensor(init_ids_data, init_lod, place) + init_scores = fluid.create_lod_tensor(init_scores_data, init_lod, place) + train_data = paddle.batch( paddle.reader.shuffle( paddle.dataset.wmt14.train(dict_size), buf_size=1000), batch_size=batch_size) - for _, data in enumerate(train_data()): - init_ids = set_init_lod(init_ids_data, init_lod, place) - init_scores = set_init_lod(init_scores_data, init_lod, place) - src_word_data = to_lodtensor(map(lambda x: x[0], data), place) + feed_order = ['src_word_id'] + feed_list = [ + framework.default_main_program().global_block().var(var_name) + for var_name in feed_order + ] + feeder = fluid.DataFeeder(feed_list, place) + + for data in train_data(): + feed_dict = feeder.feed(map(lambda x: [x[0]], data)) + feed_dict['init_ids'] = init_ids + feed_dict['init_scores'] = init_scores result_ids, result_scores = exe.run( framework.default_main_program(), - feed={ - 'src_word_id': src_word_data, - 'init_ids': init_ids, - 'init_scores': init_scores - }, + feed=feed_dict, fetch_list=[translation_ids, translation_scores], return_numpy=False) print result_ids.lod() diff --git a/python/paddle/fluid/tests/book/test_machine_translation.py b/python/paddle/fluid/tests/book/test_machine_translation.py index e8a75f473f62d..23e5900f127a7 100644 --- a/python/paddle/fluid/tests/book/test_machine_translation.py +++ b/python/paddle/fluid/tests/book/test_machine_translation.py @@ -147,28 +147,6 @@ def decoder_decode(context, is_sparse): return translation_ids, translation_scores -def set_init_lod(data, lod, place): - res = fluid.LoDTensor() - res.set(data, place) - res.set_lod(lod) - return res - - -def to_lodtensor(data, place): - seq_lens = [len(seq) for seq in data] - cur_len = 0 - lod = [cur_len] - for l in seq_lens: - cur_len += l - lod.append(cur_len) - flattened_data = np.concatenate(data, axis=0).astype("int64") - flattened_data = flattened_data.reshape([len(flattened_data), 1]) - res = fluid.LoDTensor() - res.set(flattened_data, place) - res.set_lod([lod]) - return res - - def train_main(use_cuda, is_sparse, is_local=True): if use_cuda and not fluid.core.is_compiled_with_cuda(): return @@ -192,23 +170,25 @@ def train_main(use_cuda, is_sparse, is_local=True): paddle.dataset.wmt14.train(dict_size), buf_size=1000), batch_size=batch_size) + feed_order = [ + 'src_word_id', 'target_language_word', 'target_language_next_word' + ] + exe = Executor(place) def train_loop(main_program): exe.run(framework.default_startup_program()) + feed_list = [ + main_program.global_block().var(var_name) for var_name in feed_order + ] + feeder = fluid.DataFeeder(feed_list, place) + batch_id = 0 for pass_id in xrange(1): for data in train_data(): - word_data = to_lodtensor(map(lambda x: x[0], data), place) - trg_word = to_lodtensor(map(lambda x: x[1], data), place) - trg_word_next = to_lodtensor(map(lambda x: x[2], data), place) outs = exe.run(main_program, - feed={ - 'src_word_id': word_data, - 'target_language_word': trg_word, - 'target_language_next_word': trg_word_next - }, + feed=feeder.feed(data), fetch_list=[avg_cost]) avg_cost_val = np.array(outs[0]) print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) + @@ -258,26 +238,32 @@ def decode_main(use_cuda, is_sparse): [1. for _ in range(batch_size)], dtype='float32') init_ids_data = init_ids_data.reshape((batch_size, 1)) init_scores_data = init_scores_data.reshape((batch_size, 1)) - init_lod = [i for i in range(batch_size)] + [batch_size] + init_lod = [1] * batch_size init_lod = [init_lod, init_lod] + init_ids = fluid.create_lod_tensor(init_ids_data, init_lod, place) + init_scores = fluid.create_lod_tensor(init_scores_data, init_lod, place) + train_data = paddle.batch( paddle.reader.shuffle( paddle.dataset.wmt14.train(dict_size), buf_size=1000), batch_size=batch_size) - for _, data in enumerate(train_data()): - init_ids = set_init_lod(init_ids_data, init_lod, place) - init_scores = set_init_lod(init_scores_data, init_lod, place) - src_word_data = to_lodtensor(map(lambda x: x[0], data), place) + feed_order = ['src_word_id'] + feed_list = [ + framework.default_main_program().global_block().var(var_name) + for var_name in feed_order + ] + feeder = fluid.DataFeeder(feed_list, place) + + for data in train_data(): + feed_dict = feeder.feed(map(lambda x: [x[0]], data)) + feed_dict['init_ids'] = init_ids + feed_dict['init_scores'] = init_scores result_ids, result_scores = exe.run( framework.default_main_program(), - feed={ - 'src_word_id': src_word_data, - 'init_ids': init_ids, - 'init_scores': init_scores - }, + feed=feed_dict, fetch_list=[translation_ids, translation_scores], return_numpy=False) print result_ids.lod()