Merge pull request #10895 from nickyfantasy/high_level_api_machine_translation

Simplify Machine Translation demo by using Trainer API

python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt

@@ -13,3 +13,4 @@ add_subdirectory(understand_sentiment)
 add_subdirectory(label_semantic_roles)
 add_subdirectory(word2vec)
 add_subdirectory(recommender_system)
+add_subdirectory(machine_translation)

python/paddle/fluid/tests/book/high-level-api/machine_translation/CMakeLists.txt

+file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
+string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+# default test
+foreach(src ${TEST_OPS})
+    py_test(${src} SRCS ${src}.py)
+endforeach()

python/paddle/fluid/tests/book/high-level-api/machine_translation/test_machine_translation.py

+#   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 contextlib
+
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.framework as framework
+import paddle.fluid.layers as pd
+from paddle.fluid.executor import Executor
+from functools import partial
+import unittest
+import os
+
+dict_size = 30000
+source_dict_dim = target_dict_dim = dict_size
+hidden_dim = 32
+word_dim = 16
+batch_size = 2
+max_length = 8
+topk_size = 50
+trg_dic_size = 10000
+beam_size = 2
+
+decoder_size = hidden_dim
+
+
+def encoder(is_sparse):
+    # encoder
+    src_word_id = pd.data(
+        name="src_word_id", shape=[1], dtype='int64', lod_level=1)
+    src_embedding = pd.embedding(
+        input=src_word_id,
+        size=[dict_size, word_dim],
+        dtype='float32',
+        is_sparse=is_sparse,
+        param_attr=fluid.ParamAttr(name='vemb'))
+
+    fc1 = pd.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
+    lstm_hidden0, lstm_0 = pd.dynamic_lstm(input=fc1, size=hidden_dim * 4)
+    encoder_out = pd.sequence_last_step(input=lstm_hidden0)
+    return encoder_out
+
+
+def decoder_train(context, is_sparse):
+    # decoder
+    trg_language_word = pd.data(
+        name="target_language_word", shape=[1], dtype='int64', lod_level=1)
+    trg_embedding = pd.embedding(
+        input=trg_language_word,
+        size=[dict_size, word_dim],
+        dtype='float32',
+        is_sparse=is_sparse,
+        param_attr=fluid.ParamAttr(name='vemb'))
+
+    rnn = pd.DynamicRNN()
+    with rnn.block():
+        current_word = rnn.step_input(trg_embedding)
+        pre_state = rnn.memory(init=context)
+        current_state = pd.fc(input=[current_word, pre_state],
+                              size=decoder_size,
+                              act='tanh')
+
+        current_score = pd.fc(input=current_state,
+                              size=target_dict_dim,
+                              act='softmax')
+        rnn.update_memory(pre_state, current_state)
+        rnn.output(current_score)
+
+    return rnn()
+
+
+def decoder_decode(context, is_sparse):
+    init_state = context
+    array_len = pd.fill_constant(shape=[1], dtype='int64', value=max_length)
+    counter = pd.zeros(shape=[1], dtype='int64', force_cpu=True)
+
+    # fill the first element with init_state
+    state_array = pd.create_array('float32')
+    pd.array_write(init_state, array=state_array, i=counter)
+
+    # ids, scores as memory
+    ids_array = pd.create_array('int64')
+    scores_array = pd.create_array('float32')
+
+    init_ids = pd.data(name="init_ids", shape=[1], dtype="int64", lod_level=2)
+    init_scores = pd.data(
+        name="init_scores", shape=[1], dtype="float32", lod_level=2)
+
+    pd.array_write(init_ids, array=ids_array, i=counter)
+    pd.array_write(init_scores, array=scores_array, i=counter)
+
+    cond = pd.less_than(x=counter, y=array_len)
+
+    while_op = pd.While(cond=cond)
+    with while_op.block():
+        pre_ids = pd.array_read(array=ids_array, i=counter)
+        pre_state = pd.array_read(array=state_array, i=counter)
+        pre_score = pd.array_read(array=scores_array, i=counter)
+
+        # expand the lod of pre_state to be the same with pre_score
+        pre_state_expanded = pd.sequence_expand(pre_state, pre_score)
+
+        pre_ids_emb = pd.embedding(
+            input=pre_ids,
+            size=[dict_size, word_dim],
+            dtype='float32',
+            is_sparse=is_sparse)
+
+        # use rnn unit to update rnn
+        current_state = pd.fc(input=[pre_state_expanded, pre_ids_emb],
+                              size=decoder_size,
+                              act='tanh')
+        current_state_with_lod = pd.lod_reset(x=current_state, y=pre_score)
+        # use score to do beam search
+        current_score = pd.fc(input=current_state_with_lod,
+                              size=target_dict_dim,
+                              act='softmax')
+        topk_scores, topk_indices = pd.topk(current_score, k=topk_size)
+        selected_ids, selected_scores = pd.beam_search(
+            pre_ids, topk_indices, topk_scores, beam_size, end_id=10, level=0)
+
+        pd.increment(x=counter, value=1, in_place=True)
+
+        # update the memories
+        pd.array_write(current_state, array=state_array, i=counter)
+        pd.array_write(selected_ids, array=ids_array, i=counter)
+        pd.array_write(selected_scores, array=scores_array, i=counter)
+
+        pd.less_than(x=counter, y=array_len, cond=cond)
+
+    translation_ids, translation_scores = pd.beam_search_decode(
+        ids=ids_array, scores=scores_array)
+
+    # return init_ids, init_scores
+
+    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)
+    label = pd.data(
+        name="target_language_next_word", shape=[1], dtype='int64', lod_level=1)
+    cost = pd.cross_entropy(input=rnn_out, label=label)
+    avg_cost = pd.mean(cost)
+    return avg_cost
+
+
+def train(use_cuda, is_sparse, is_local=True):
+    EPOCH_NUM = 1
+
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            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'
+    ]
+
+    def event_handler(event):
+        if isinstance(event, fluid.EndStepEvent):
+            print('pass_id=' + str(event.epoch) + ' batch=' + str(event.step))
+            if event.step == 10:
+                trainer.stop()
+
+    trainer = fluid.Trainer(
+        train_func=partial(train_program, is_sparse),
+        optimizer=fluid.optimizer.Adagrad(
+            learning_rate=1e-4,
+            regularization=fluid.regularizer.L2DecayRegularizer(
+                regularization_coeff=0.1)),
+        place=place)
+
+    trainer.train(
+        reader=train_reader,
+        num_epochs=EPOCH_NUM,
+        event_handler=event_handler,
+        feed_order=feed_order)
+
+
+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()
+
+    context = encoder(is_sparse)
+    translation_ids, translation_scores = decoder_decode(context, is_sparse)
+
+    exe = Executor(place)
+    exe.run(framework.default_startup_program())
+
+    init_ids_data = np.array([1 for _ in range(batch_size)], dtype='int64')
+    init_scores_data = np.array(
+        [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 = [init_lod, init_lod]
+
+    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)
+
+        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
+            },
+            fetch_list=[translation_ids, translation_scores],
+            return_numpy=False)
+        print result_ids.lod()
+        break
+
+
+class TestMachineTranslation(unittest.TestCase):
+    pass
+
+
+@contextlib.contextmanager
+def scope_prog_guard():
+    prog = fluid.Program()
+    startup_prog = fluid.Program()
+    scope = fluid.core.Scope()
+    with fluid.scope_guard(scope):
+        with fluid.program_guard(prog, startup_prog):
+            yield
+
+
+def inject_test_train(use_cuda, is_sparse):
+    f_name = 'test_{0}_{1}_train'.format('cuda' if use_cuda else 'cpu', 'sparse'
+                                         if is_sparse else 'dense')
+
+    def f(*args):
+        with scope_prog_guard():
+            train(use_cuda, is_sparse)
+
+    setattr(TestMachineTranslation, f_name, f)
+
+
+def inject_test_decode(use_cuda, is_sparse, decorator=None):
+    f_name = 'test_{0}_{1}_decode'.format('cuda'
+                                          if use_cuda else 'cpu', 'sparse'
+                                          if is_sparse else 'dense')
+
+    def f(*args):
+        with scope_prog_guard():
+            decode_main(use_cuda, is_sparse)
+
+    if decorator is not None:
+        f = decorator(f)
+
+    setattr(TestMachineTranslation, f_name, f)
+
+
+for _use_cuda_ in (False, True):
+    for _is_sparse_ in (False, True):
+        inject_test_train(_use_cuda_, _is_sparse_)
+
+for _use_cuda_ in (False, True):
+    for _is_sparse_ in (False, True):
+
+        _decorator_ = None
+        if _use_cuda_:
+            _decorator_ = unittest.skip(
+                reason='Beam Search does not support CUDA!')
+
+        inject_test_decode(
+            is_sparse=_is_sparse_, use_cuda=_use_cuda_, decorator=_decorator_)
+
+if __name__ == '__main__':
+    unittest.main()