optimizer wmt14 dataset

demo/seqToseq/api_train_v2.py

-import os
-
 import paddle.v2 as paddle
 
-from seqToseq_net_v2 import seqToseq_net_v2
-
-# Data Definiation.
-# TODO:This code should be merged to dataset package.
-data_dir = "./data/pre-wmt14"
-src_lang_dict = os.path.join(data_dir, 'src.dict')
-trg_lang_dict = os.path.join(data_dir, 'trg.dict')
-
-source_dict_dim = len(open(src_lang_dict, "r").readlines())
-target_dict_dim = len(open(trg_lang_dict, "r").readlines())
-
-
-def read_to_dict(dict_path):
-    with open(dict_path, "r") as fin:
-        out_dict = {
-            line.strip(): line_count
-            for line_count, line in enumerate(fin)
-        }
-    return out_dict
-
-
-src_dict = read_to_dict(src_lang_dict)
-trg_dict = read_to_dict(trg_lang_dict)
-
-train_list = os.path.join(data_dir, 'train.list')
-test_list = os.path.join(data_dir, 'test.list')
-
-UNK_IDX = 2
-START = "<s>"
-END = "<e>"
 
-
-def _get_ids(s, dictionary):
-    words = s.strip().split()
-    return [dictionary[START]] + \
-           [dictionary.get(w, UNK_IDX) for w in words] + \
-           [dictionary[END]]
-
-
-def train_reader(file_name):
-    def reader():
-        with open(file_name, 'r') as f:
-            for line_count, line in enumerate(f):
-                line_split = line.strip().split('\t')
-                if len(line_split) != 2:
-                    continue
-                src_seq = line_split[0]  # one source sequence
-                src_ids = _get_ids(src_seq, src_dict)
-
-                trg_seq = line_split[1]  # one target sequence
-                trg_words = trg_seq.split()
-                trg_ids = [trg_dict.get(w, UNK_IDX) for w in trg_words]
-
-                # remove sequence whose length > 80 in training mode
-                if len(src_ids) > 80 or len(trg_ids) > 80:
-                    continue
-                trg_ids_next = trg_ids + [trg_dict[END]]
-                trg_ids = [trg_dict[START]] + trg_ids
-
-                yield src_ids, trg_ids, trg_ids_next
-
-    return reader
+def seqToseq_net(source_dict_dim, target_dict_dim):
+    ### Network Architecture
+    word_vector_dim = 512  # dimension of word vector
+    decoder_size = 512  # dimension of hidden unit in GRU Decoder network
+    encoder_size = 512  # dimension of hidden unit in GRU Encoder network
+
+    #### Encoder
+    src_word_id = paddle.layer.data(
+        name='source_language_word',
+        type=paddle.data_type.integer_value_sequence(source_dict_dim))
+    src_embedding = paddle.layer.embedding(
+        input=src_word_id,
+        size=word_vector_dim,
+        param_attr=paddle.attr.ParamAttr(name='_source_language_embedding'))
+    src_forward = paddle.networks.simple_gru(
+        input=src_embedding, size=encoder_size)
+    src_backward = paddle.networks.simple_gru(
+        input=src_embedding, size=encoder_size, reverse=True)
+    encoded_vector = paddle.layer.concat(input=[src_forward, src_backward])
+
+    #### Decoder
+    with paddle.layer.mixed(size=decoder_size) as encoded_proj:
+        encoded_proj += paddle.layer.full_matrix_projection(
+            input=encoded_vector)
+
+    backward_first = paddle.layer.first_seq(input=src_backward)
+
+    with paddle.layer.mixed(
+            size=decoder_size, act=paddle.activation.Tanh()) as decoder_boot:
+        decoder_boot += paddle.layer.full_matrix_projection(
+            input=backward_first)
+
+    def gru_decoder_with_attention(enc_vec, enc_proj, current_word):
+
+        decoder_mem = paddle.layer.memory(
+            name='gru_decoder', size=decoder_size, boot_layer=decoder_boot)
+
+        context = paddle.networks.simple_attention(
+            encoded_sequence=enc_vec,
+            encoded_proj=enc_proj,
+            decoder_state=decoder_mem)
+
+        with paddle.layer.mixed(size=decoder_size * 3) as decoder_inputs:
+            decoder_inputs += paddle.layer.full_matrix_projection(input=context)
+            decoder_inputs += paddle.layer.full_matrix_projection(
+                input=current_word)
+
+        gru_step = paddle.layer.gru_step(
+            name='gru_decoder',
+            input=decoder_inputs,
+            output_mem=decoder_mem,
+            size=decoder_size)
+
+        with paddle.layer.mixed(
+                size=target_dict_dim,
+                bias_attr=True,
+                act=paddle.activation.Softmax()) as out:
+            out += paddle.layer.full_matrix_projection(input=gru_step)
+        return out
+
+    decoder_group_name = "decoder_group"
+    group_input1 = paddle.layer.StaticInputV2(input=encoded_vector, is_seq=True)
+    group_input2 = paddle.layer.StaticInputV2(input=encoded_proj, is_seq=True)
+    group_inputs = [group_input1, group_input2]
+
+    trg_embedding = paddle.layer.embedding(
+        input=paddle.layer.data(
+            name='target_language_word',
+            type=paddle.data_type.integer_value_sequence(target_dict_dim)),
+        size=word_vector_dim,
+        param_attr=paddle.attr.ParamAttr(name='_target_language_embedding'))
+    group_inputs.append(trg_embedding)
+
+    # For decoder equipped with attention mechanism, in training,
+    # target embeding (the groudtruth) is the data input,
+    # while encoded source sequence is accessed to as an unbounded memory.
+    # Here, the StaticInput defines a read-only memory
+    # for the recurrent_group.
+    decoder = paddle.layer.recurrent_group(
+        name=decoder_group_name,
+        step=gru_decoder_with_attention,
+        input=group_inputs)
+
+    lbl = paddle.layer.data(
+        name='target_language_next_word',
+        type=paddle.data_type.integer_value_sequence(target_dict_dim))
+    cost = paddle.layer.classification_cost(input=decoder, label=lbl)
+
+    return cost
 
 
 def main():
     paddle.init(use_gpu=False, trainer_count=1)
 
+    # source and target dict dim.
+    dict_size = 30000
+    source_dict_dim = target_dict_dim = dict_size
+
     # define network topology
-    cost = seqToseq_net_v2(source_dict_dim, target_dict_dim)
+    cost = seqToseq_net(source_dict_dim, target_dict_dim)
     parameters = paddle.parameters.create(cost)
 
     # define optimize method and trainer
@@ -85,10 +115,9 @@ def main():
         'target_language_word': 1,
         'target_language_next_word': 2
     }
-
     wmt14_reader = paddle.reader.batched(
         paddle.reader.shuffle(
-            train_reader("data/pre-wmt14/train/train"), buf_size=8192),
+            paddle.dataset.wmt14.train(dict_size=dict_size), buf_size=8192),
         batch_size=5)
 
     # define event_handler callback

demo/seqToseq/seqToseq_net_v2.py

-import paddle.v2 as paddle
-
-
-def seqToseq_net_v2(source_dict_dim, target_dict_dim):
-    ### Network Architecture
-    word_vector_dim = 512  # dimension of word vector
-    decoder_size = 512  # dimension of hidden unit in GRU Decoder network
-    encoder_size = 512  # dimension of hidden unit in GRU Encoder network
-
-    #### Encoder
-    src_word_id = paddle.layer.data(
-        name='source_language_word',
-        type=paddle.data_type.integer_value_sequence(source_dict_dim))
-    src_embedding = paddle.layer.embedding(
-        input=src_word_id,
-        size=word_vector_dim,
-        param_attr=paddle.attr.ParamAttr(name='_source_language_embedding'))
-    src_forward = paddle.networks.simple_gru(
-        input=src_embedding, size=encoder_size)
-    src_backward = paddle.networks.simple_gru(
-        input=src_embedding, size=encoder_size, reverse=True)
-    encoded_vector = paddle.layer.concat(input=[src_forward, src_backward])
-
-    #### Decoder
-    with paddle.layer.mixed(size=decoder_size) as encoded_proj:
-        encoded_proj += paddle.layer.full_matrix_projection(
-            input=encoded_vector)
-
-    backward_first = paddle.layer.first_seq(input=src_backward)
-
-    with paddle.layer.mixed(
-            size=decoder_size, act=paddle.activation.Tanh()) as decoder_boot:
-        decoder_boot += paddle.layer.full_matrix_projection(
-            input=backward_first)
-
-    def gru_decoder_with_attention(enc_vec, enc_proj, current_word):
-
-        decoder_mem = paddle.layer.memory(
-            name='gru_decoder', size=decoder_size, boot_layer=decoder_boot)
-
-        context = paddle.networks.simple_attention(
-            encoded_sequence=enc_vec,
-            encoded_proj=enc_proj,
-            decoder_state=decoder_mem)
-
-        with paddle.layer.mixed(size=decoder_size * 3) as decoder_inputs:
-            decoder_inputs += paddle.layer.full_matrix_projection(input=context)
-            decoder_inputs += paddle.layer.full_matrix_projection(
-                input=current_word)
-
-        gru_step = paddle.layer.gru_step(
-            name='gru_decoder',
-            input=decoder_inputs,
-            output_mem=decoder_mem,
-            size=decoder_size)
-
-        with paddle.layer.mixed(
-                size=target_dict_dim,
-                bias_attr=True,
-                act=paddle.activation.Softmax()) as out:
-            out += paddle.layer.full_matrix_projection(input=gru_step)
-        return out
-
-    decoder_group_name = "decoder_group"
-    group_input1 = paddle.layer.StaticInputV2(input=encoded_vector, is_seq=True)
-    group_input2 = paddle.layer.StaticInputV2(input=encoded_proj, is_seq=True)
-    group_inputs = [group_input1, group_input2]
-
-    trg_embedding = paddle.layer.embedding(
-        input=paddle.layer.data(
-            name='target_language_word',
-            type=paddle.data_type.integer_value_sequence(target_dict_dim)),
-        size=word_vector_dim,
-        param_attr=paddle.attr.ParamAttr(name='_target_language_embedding'))
-    group_inputs.append(trg_embedding)
-
-    # For decoder equipped with attention mechanism, in training,
-    # target embeding (the groudtruth) is the data input,
-    # while encoded source sequence is accessed to as an unbounded memory.
-    # Here, the StaticInput defines a read-only memory
-    # for the recurrent_group.
-    decoder = paddle.layer.recurrent_group(
-        name=decoder_group_name,
-        step=gru_decoder_with_attention,
-        input=group_inputs)
-
-    lbl = paddle.layer.data(
-        name='target_language_next_word',
-        type=paddle.data_type.integer_value_sequence(target_dict_dim))
-    cost = paddle.layer.classification_cost(input=decoder, label=lbl)
-
-    return cost

python/paddle/v2/dataset/wmt14.py

@@ -14,129 +14,102 @@
 """
 wmt14 dataset
 """
-import paddle.v2.dataset.common
-import tarfile
+import os
 import os.path
-import itertools
+import tarfile
+
+import paddle.v2.dataset.common
+from wmt14_util import SeqToSeqDatasetCreater
 
 __all__ = ['train', 'test', 'build_dict']
 
 URL_DEV_TEST = 'http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz'
 MD5_DEV_TEST = '7d7897317ddd8ba0ae5c5fa7248d3ff5'
-URL_TRAIN = 'http://localhost:8000/train.tgz'
-MD5_TRAIN = '72de99da2830ea5a3a2c4eb36092bbc7'
-
-
-def word_count(f, word_freq=None):
-    add = paddle.v2.dataset.common.dict_add
-    if word_freq == None:
-        word_freq = {}
-
-    for l in f:
-        for w in l.strip().split():
-            add(word_freq, w)
-        add(word_freq, '<s>')
-        add(word_freq, '<e>')
-
-    return word_freq
-
-
-def get_word_dix(word_freq):
-    TYPO_FREQ = 50
-    word_freq = filter(lambda x: x[1] > TYPO_FREQ, word_freq.items())
-    word_freq_sorted = sorted(word_freq, key=lambda x: (-x[1], x[0]))
-    words, _ = list(zip(*word_freq_sorted))
-    word_idx = dict(zip(words, xrange(len(words))))
-    word_idx['<unk>'] = len(words)
-    return word_idx
-
-
-def get_word_freq(train, dev):
-    word_freq = word_count(train, word_count(dev))
-    if '<unk>' in word_freq:
-        # remove <unk> for now, since we will set it as last index
-        del word_freq['<unk>']
-    return word_freq
-
-
-def build_dict():
-    base_dir = './wmt14-data'
-    train_en_filename = base_dir + '/train/train.en'
-    train_fr_filename = base_dir + '/train/train.fr'
-    dev_en_filename = base_dir + '/dev/ntst1213.en'
-    dev_fr_filename = base_dir + '/dev/ntst1213.fr'
-
-    if not os.path.exists(train_en_filename) or not os.path.exists(
-            train_fr_filename):
+URL_TRAIN = 'http://localhost:8989/wmt14.tgz'
+MD5_TRAIN = '7373473f86016f1f48037c9c340a2d5b'
+
+START = "<s>"
+END = "<e>"
+UNK = "<unk>"
+UNK_IDX = 2
+
+DEFAULT_DATA_DIR = "./data"
+ORIGIN_DATA_DIR = "wmt14"
+INNER_DATA_DIR = "pre-wmt14"
+SRC_DICT = INNER_DATA_DIR + "/src.dict"
+TRG_DICT = INNER_DATA_DIR + "/trg.dict"
+TRAIN_FILE = INNER_DATA_DIR + "/train/train"
+
+
+def __process_data__(data_path, dict_size=None):
+    downloaded_data = os.path.join(data_path, ORIGIN_DATA_DIR)
+    if not os.path.exists(downloaded_data):
+        # 1. download and extract tgz.
         with tarfile.open(
                 paddle.v2.dataset.common.download(URL_TRAIN, 'wmt14',
                                                   MD5_TRAIN)) as tf:
-            tf.extractall(base_dir)
-
-    if not os.path.exists(dev_en_filename) or not os.path.exists(
-            dev_fr_filename):
-        with tarfile.open(
-                paddle.v2.dataset.common.download(URL_DEV_TEST, 'wmt14',
-                                                  MD5_DEV_TEST)) as tf:
-            tf.extractall(base_dir)
-
-    f_en = open(train_en_filename)
-    f_fr = open(train_fr_filename)
-    f_en_dev = open(dev_en_filename)
-    f_fr_dev = open(dev_fr_filename)
-
-    word_freq_en = get_word_freq(f_en, f_en_dev)
-    word_freq_fr = get_word_freq(f_fr, f_fr_dev)
-
-    f_en.close()
-    f_fr.close()
-    f_en_dev.close()
-    f_fr_dev.close()
-
-    return get_word_dix(word_freq_en), get_word_dix(word_freq_fr)
-
-
-def reader_creator(directory, path_en, path_fr, URL, MD5, dict_en, dict_fr):
-    def reader():
-        if not os.path.exists(path_en) or not os.path.exists(path_fr):
-            with tarfile.open(
-                    paddle.v2.dataset.common.download(URL, 'wmt14', MD5)) as tf:
-                tf.extractall(directory)
-
-        f_en = open(path_en)
-        f_fr = open(path_fr)
-        UNK_en = dict_en['<unk>']
-        UNK_fr = dict_fr['<unk>']
-
-        for en, fr in itertools.izip(f_en, f_fr):
-            src_ids = [dict_en.get(w, UNK_en) for w in en.strip().split()]
-            tar_ids = [
-                dict_fr.get(w, UNK_fr)
-                for w in ['<s>'] + fr.strip().split() + ['<e>']
+            tf.extractall(data_path)
+
+    # 2. process data file to intermediate format.
+    processed_data = os.path.join(data_path, INNER_DATA_DIR)
+    if not os.path.exists(processed_data):
+        dict_size = dict_size or -1
+        data_creator = SeqToSeqDatasetCreater(downloaded_data, processed_data)
+        data_creator.create_dataset(dict_size, mergeDict=False)
+
+
+def __read_to_dict__(dict_path, count):
+    with open(dict_path, "r") as fin:
+        out_dict = dict()
+        for line_count, line in enumerate(fin):
+            if line_count <= count:
+                out_dict[line.strip()] = line_count
+            else:
+                break
+    return out_dict
+
+
+def __reader__(file_name, src_dict, trg_dict):
+    with open(file_name, 'r') as f:
+        for line_count, line in enumerate(f):
+            line_split = line.strip().split('\t')
+            if len(line_split) != 2:
+                continue
+            src_seq = line_split[0]  # one source sequence
+            src_words = src_seq.split()
+            src_ids = [
+                src_dict.get(w, UNK_IDX) for w in [START] + src_words + [END]
             ]
 
+            trg_seq = line_split[1]  # one target sequence
+            trg_words = trg_seq.split()
+            trg_ids = [trg_dict.get(w, UNK_IDX) for w in trg_words]
+
             # remove sequence whose length > 80 in training mode
-            if len(src_ids) == 0 or len(tar_ids) <= 1 or len(
-                    src_ids) > 80 or len(tar_ids) > 80:
+            if len(src_ids) > 80 or len(trg_ids) > 80:
                 continue
+            trg_ids_next = trg_ids + [trg_dict[END]]
+            trg_ids = [trg_dict[START]] + trg_ids
+
+            yield src_ids, trg_ids, trg_ids_next
 
-            yield src_ids, tar_ids[:-1], tar_ids[1:]
 
-        f_en.close()
-        f_fr.close()
+def train(data_dir=None, dict_size=None):
+    data_dir = data_dir or DEFAULT_DATA_DIR
+    __process_data__(data_dir, dict_size)
+    src_lang_dict = os.path.join(data_dir, SRC_DICT)
+    trg_lang_dict = os.path.join(data_dir, TRG_DICT)
+    train_file_name = os.path.join(data_dir, TRAIN_FILE)
 
-    return reader
+    default_dict_size = len(open(src_lang_dict, "r").readlines())
 
+    if dict_size > default_dict_size:
+        raise ValueError("dict_dim should not be larger then the "
+                         "length of word dict")
 
-def train(dict_en, dict_fr):
-    directory = './wmt14-data'
-    return reader_creator(directory, directory + '/train/train.en',
-                          directory + '/train/train.fr', URL_TRAIN, MD5_TRAIN,
-                          dict_en, dict_fr)
+    real_dict_dim = dict_size or default_dict_size
 
+    src_dict = __read_to_dict__(src_lang_dict, real_dict_dim)
+    trg_dict = __read_to_dict__(trg_lang_dict, real_dict_dim)
 
-def test(dict_en, dict_fr):
-    directory = './wmt14-data'
-    return reader_creator(directory, directory + '/dev/ntst1213.en',
-                          directory + '/dev/ntst1213.fr', URL_DEV_TEST,
-                          MD5_DEV_TEST, dict_en, dict_fr)
+    return lambda: __reader__(train_file_name, src_dict, trg_dict)