Add rnn_search_model

fluid/neural_machine_translation/rnn_search/README.md

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+The minimum PaddlePaddle version needed for the code sample in this directory is the lastest develop branch. If you are on a version of PaddlePaddle earlier than this, [please update your installation](http://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html).
+
+### RNN Search
+
+Training
+```
+python train.py
+```
+
+---
+### TODO
+
+This project is still under active development.

fluid/neural_machine_translation/rnn_search/config.py

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+class TrainConfig(object):
+    use_gpu = False
+    infer_only = False
+    parallel = False
+    batch_size = 16
+    pass_num = 5
+    learning_rate = 0.0002
+    buf_size = 100000
+
+
+class ModelConfig(object):
+    embedding_dim = 512
+    encoder_size = 512
+    decoder_size = 512
+    source_dict_dim = 10000
+    target_dict_dim = 10000
+    is_generating = False
+    beam_size = 3
+    max_length = 250

fluid/neural_machine_translation/rnn_search/model.py

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+"""seq2seq model for fluid."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle.fluid as fluid
+
+
+def lstm_step(x_t, hidden_t_prev, cell_t_prev, size):
+    def linear(inputs):
+        return fluid.layers.fc(input=inputs, size=size, bias_attr=True)
+
+    forget_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
+    input_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
+    output_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
+    cell_tilde = fluid.layers.tanh(x=linear([hidden_t_prev, x_t]))
+
+    cell_t = fluid.layers.sums(input=[
+        fluid.layers.elementwise_mul(
+            x=forget_gate, y=cell_t_prev), fluid.layers.elementwise_mul(
+                x=input_gate, y=cell_tilde)
+    ])
+
+    hidden_t = fluid.layers.elementwise_mul(
+        x=output_gate, y=fluid.layers.tanh(x=cell_t))
+
+    return hidden_t, cell_t
+
+
+def seq_to_seq_net(src_word_idx, trg_word_idx, label, embedding_dim,
+                   encoder_size, decoder_size, source_dict_dim, target_dict_dim,
+                   is_generating, beam_size, max_length):
+    """Construct a seq2seq network."""
+
+    def bi_lstm_encoder(input_seq, gate_size):
+        # Linear transformation part for input gate, output gate, forget gate
+        # and cell activation vectors need be done outside of dynamic_lstm.
+        # So the output size is 4 times of gate_size.
+        input_forward_proj = fluid.layers.fc(input=input_seq,
+                                             size=gate_size * 4,
+                                             act=None,
+                                             bias_attr=False)
+        forward, _ = fluid.layers.dynamic_lstm(
+            input=input_forward_proj, size=gate_size * 4, use_peepholes=False)
+        input_reversed_proj = fluid.layers.fc(input=input_seq,
+                                              size=gate_size * 4,
+                                              act=None,
+                                              bias_attr=False)
+        reversed, _ = fluid.layers.dynamic_lstm(
+            input=input_reversed_proj,
+            size=gate_size * 4,
+            is_reverse=True,
+            use_peepholes=False)
+        return forward, reversed
+
+    src_embedding = fluid.layers.embedding(
+        input=src_word_idx,
+        size=[source_dict_dim, embedding_dim],
+        dtype='float32')
+
+    src_forward, src_reversed = bi_lstm_encoder(
+        input_seq=src_embedding, gate_size=encoder_size)
+
+    encoded_vector = fluid.layers.concat(
+        input=[src_forward, src_reversed], axis=1)
+
+    encoded_proj = fluid.layers.fc(input=encoded_vector,
+                                   size=decoder_size,
+                                   bias_attr=False)
+
+    backward_first = fluid.layers.sequence_pool(
+        input=src_reversed, pool_type='first')
+
+    decoder_boot = fluid.layers.fc(input=backward_first,
+                                   size=decoder_size,
+                                   bias_attr=False,
+                                   act='tanh')
+
+    def lstm_decoder_with_attention(target_embedding, encoder_vec, encoder_proj,
+                                    decoder_boot, decoder_size):
+        def simple_attention(encoder_vec, encoder_proj, decoder_state):
+            decoder_state_proj = fluid.layers.fc(input=decoder_state,
+                                                 size=decoder_size,
+                                                 bias_attr=False)
+            decoder_state_expand = fluid.layers.sequence_expand(
+                x=decoder_state_proj, y=encoder_proj)
+            concated = fluid.layers.concat(
+                input=[encoder_proj, decoder_state_expand], axis=1)
+            attention_weights = fluid.layers.fc(input=concated,
+                                                size=1,
+                                                act='tanh',
+                                                bias_attr=False)
+            attention_weights = fluid.layers.sequence_softmax(attention_weights)
+            weigths_reshape = fluid.layers.reshape(
+                x=attention_weights, shape=[-1])
+            scaled = fluid.layers.elementwise_mul(
+                x=encoder_vec, y=weigths_reshape, axis=0)
+            context = fluid.layers.sequence_pool(input=scaled, pool_type='sum')
+            return context
+
+        rnn = fluid.layers.DynamicRNN()
+
+        cell_init = fluid.layers.fill_constant_batch_size_like(
+            input=decoder_boot,
+            value=0.0,
+            shape=[-1, decoder_size],
+            dtype='float32')
+        cell_init.stop_gradient = False
+
+        with rnn.block():
+            current_word = rnn.step_input(target_embedding)
+            encoder_vec = rnn.static_input(encoder_vec)
+            encoder_proj = rnn.static_input(encoder_proj)
+            hidden_mem = rnn.memory(init=decoder_boot, need_reorder=True)
+            cell_mem = rnn.memory(init=cell_init)
+            context = simple_attention(encoder_vec, encoder_proj, hidden_mem)
+            decoder_inputs = fluid.layers.concat(
+                input=[context, current_word], axis=1)
+            h, c = lstm_step(decoder_inputs, hidden_mem, cell_mem, decoder_size)
+            rnn.update_memory(hidden_mem, h)
+            rnn.update_memory(cell_mem, c)
+            out = fluid.layers.fc(input=h,
+                                  size=target_dict_dim,
+                                  bias_attr=True,
+                                  act='softmax')
+            rnn.output(out)
+        return rnn()
+
+    if not is_generating:
+        trg_embedding = fluid.layers.embedding(
+            input=trg_word_idx,
+            size=[target_dict_dim, embedding_dim],
+            dtype='float32')
+
+        prediction = lstm_decoder_with_attention(trg_embedding, encoded_vector,
+                                                 encoded_proj, decoder_boot,
+                                                 decoder_size)
+        cost = fluid.layers.cross_entropy(input=prediction, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+
+        return avg_cost

fluid/neural_machine_translation/rnn_search/train.py

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+"""seq2seq model for fluid."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import argparse
+import time
+
+import paddle.v2 as paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import paddle.fluid.framework as framework
+from paddle.fluid.executor import Executor
+
+from model import seq_to_seq_net
+from config import TrainConfig as train_conf
+from config import ModelConfig as model_conf
+
+
+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])
+    lod_t = core.LoDTensor()
+    lod_t.set(flattened_data, place)
+    lod_t.set_lod([lod])
+    return lod_t, lod[-1]
+
+
+def lodtensor_to_ndarray(lod_tensor):
+    dims = lod_tensor.get_dims()
+    ndarray = np.zeros(shape=dims).astype('float32')
+    for i in xrange(np.product(dims)):
+        ndarray.ravel()[i] = lod_tensor.get_float_element(i)
+    return ndarray
+
+
+def train():
+    src_word_idx = fluid.layers.data(
+        name='source_sequence', shape=[1], dtype='int64', lod_level=1)
+    trg_word_idx = fluid.layers.data(
+        name='target_sequence', shape=[1], dtype='int64', lod_level=1)
+    label = fluid.layers.data(
+        name='label_sequence', shape=[1], dtype='int64', lod_level=1)
+    if train_conf.parallel:
+        places = fluid.layers.get_places()
+        pd = fluid.layers.ParallelDo(places)
+        with pd.do():
+            src_word_idx_ = pd.read_input(src_word_idx)
+            trg_word_idx_ = pd.read_input(trg_word_idx)
+            label_ = pd.read_input(label)
+            avg_cost = seq_to_seq_net(
+                src_word_idx_,
+                trg_word_idx_,
+                label_,
+                embedding_dim=model_conf.embedding_dim,
+                encoder_size=model_conf.encoder_size,
+                decoder_size=model_conf.decoder_size,
+                source_dict_dim=model_conf.source_dict_dim,
+                target_dict_dim=model_conf.target_dict_dim,
+                is_generating=model_conf.is_generating,
+                beam_size=model_conf.beam_size,
+                max_length=model_conf.max_length)
+            pd.write_output(avg_cost)
+        avg_cost = pd()
+        avg_cost = fluid.layers.mean(x=avg_cost)
+    else:
+        avg_cost = seq_to_seq_net(
+            src_word_idx,
+            trg_word_idx,
+            label,
+            embedding_dim=model_conf.embedding_dim,
+            encoder_size=model_conf.encoder_size,
+            decoder_size=model_conf.decoder_size,
+            source_dict_dim=model_conf.source_dict_dim,
+            target_dict_dim=model_conf.target_dict_dim,
+            is_generating=model_conf.is_generating,
+            beam_size=model_conf.beam_size,
+            max_length=model_conf.max_length)
+
+    feeding_list = ["source_sequence", "target_sequence", "label_sequence"]
+    # clone from default main program
+    inference_program = fluid.default_main_program().clone()
+
+    optimizer = fluid.optimizer.Adam(learning_rate=train_conf.learning_rate)
+    optimizer.minimize(avg_cost)
+
+    train_batch_generator = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.wmt16.train(model_conf.source_dict_dim,
+                                       model_conf.target_dict_dim),
+            buf_size=train_conf.buf_size),
+        batch_size=train_conf.batch_size)
+
+    test_batch_generator = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.wmt16.test(model_conf.source_dict_dim,
+                                      model_conf.target_dict_dim),
+            buf_size=train_conf.buf_size),
+        batch_size=train_conf.batch_size)
+
+    place = core.CUDAPlace(0) if train_conf.use_gpu else core.CPUPlace()
+    exe = Executor(place)
+    exe.run(framework.default_startup_program())
+
+    def do_validation():
+        total_loss = 0.0
+        count = 0
+        for batch_id, data in enumerate(test_batch_generator()):
+            src_seq = to_lodtensor(map(lambda x: x[0], data), place)[0]
+            trg_seq = to_lodtensor(map(lambda x: x[1], data), place)[0]
+            lbl_seq = to_lodtensor(map(lambda x: x[2], data), place)[0]
+
+            fetch_outs = exe.run(inference_program,
+                                 feed={
+                                     feeding_list[0]: src_seq,
+                                     feeding_list[1]: trg_seq,
+                                     feeding_list[2]: lbl_seq
+                                 },
+                                 fetch_list=[avg_cost],
+                                 return_numpy=False)
+
+            total_loss += lodtensor_to_ndarray(fetch_outs[0])[0]
+            count += 1
+
+        return total_loss / count
+
+    for pass_id in xrange(train_conf.pass_num):
+        pass_start_time = time.time()
+        words_seen = 0
+        for batch_id, data in enumerate(train_batch_generator()):
+            src_seq, word_num = to_lodtensor(map(lambda x: x[0], data), place)
+            words_seen += word_num
+            trg_seq, word_num = to_lodtensor(map(lambda x: x[1], data), place)
+            words_seen += word_num
+            lbl_seq, _ = to_lodtensor(map(lambda x: x[2], data), place)
+
+            fetch_outs = exe.run(framework.default_main_program(),
+                                 feed={
+                                     feeding_list[0]: src_seq,
+                                     feeding_list[1]: trg_seq,
+                                     feeding_list[2]: lbl_seq
+                                 },
+                                 fetch_list=[avg_cost])
+
+            avg_cost_val = np.array(fetch_outs[0])
+            print('pass_id=%d, batch_id=%d, train_loss: %f' %
+                  (pass_id, batch_id, avg_cost_val))
+
+        pass_end_time = time.time()
+        test_loss = do_validation()
+        time_consumed = pass_end_time - pass_start_time
+        words_per_sec = words_seen / time_consumed
+        print("pass_id=%d, test_loss: %f, words/s: %f, sec/pass: %f" %
+              (pass_id, test_loss, words_per_sec, time_consumed))
+
+
+def infer():
+    pass
+
+
+if __name__ == '__main__':
+    if train_conf.infer_only:
+        infer()
+    else:
+        train()