Add reader, ParallelExecutor and refine for Transformer

Author: guoshengCS

fluid/neural_machine_translation/transformer/config.py

@@ -1,43 +1,49 @@
 class TrainTaskConfig(object):
-    use_gpu = False
+    use_gpu = True
     # the epoch number to train.
-    pass_num = 2
-
+    pass_num = 30
     # the number of sequences contained in a mini-batch.
-    batch_size = 64
-
+    batch_size = 32
     # the hyper parameters for Adam optimizer.
-    learning_rate = 0.001
+    # This learning_rate final learning rate.
+    learning_rate = 1
     beta1 = 0.9
     beta2 = 0.98
     eps = 1e-9
-
     # the parameters for learning rate scheduling.
     warmup_steps = 4000
-
     # the flag indicating to use average loss or sum loss when training.
-    use_avg_cost = False
-
+    use_avg_cost = True
+    # the weight used to mix up the ground-truth distribution and the fixed
+    # uniform distribution in label smoothing when training.
+    # Set this as zero if label smoothing is not wanted.
+    label_smooth_eps = 0.1
     # the directory for saving trained models.
     model_dir = "trained_models"
+    # the directory for saving checkpoints.
+    ckpt_dir = "trained_ckpts"
+    # the directory for loading checkpoint.
+    # If provided, continue training from the checkpoint.
+    ckpt_path = None
+    # the parameter to initialize the learning rate scheduler.
+    # It should be provided if use checkpoints, since the checkpoint doesn't
+    # include the training step counter currently.
+    start_step = 0
 
 
 class InferTaskConfig(object):
-    use_gpu = False
+    use_gpu = True
     # the number of examples in one run for sequence generation.
     batch_size = 10
-
     # the parameters for beam search.
     beam_size = 5
     max_length = 30
     # the number of decoded sentences to output.
     n_best = 1
-
     # the flags indicating whether to output the special tokens.
     output_bos = False
     output_eos = False
     output_unk = False
-
     # the directory for loading the trained model.
     model_path = "trained_models/pass_1.infer.model"
 
@@ -47,30 +53,24 @@ class ModelHyperParams(object):
     # <unk> token has alreay been added. As for the <pad> token, any token
     # included in dict can be used to pad, since the paddings' loss will be
     # masked out and make no effect on parameter gradients.
-
     # size of source word dictionary.
     src_vocab_size = 10000
-
     # size of target word dictionay
     trg_vocab_size = 10000
-
     # index for <bos> token
     bos_idx = 0
     # index for <eos> token
     eos_idx = 1
     # index for <unk> token
     unk_idx = 2
-
     # max length of sequences.
     # The size of position encoding table should at least plus 1, since the
     # sinusoid position encoding starts from 1 and 0 can be used as the 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
@@ -86,34 +86,116 @@ class ModelHyperParams(object):
     dropout = 0.1
 
 
+def merge_cfg_from_list(cfg_list, g_cfgs):
+    """
+    Set the above global configurations using the cfg_list. 
+    """
+    assert len(cfg_list) % 2 == 0
+    for key, value in zip(cfg_list[0::2], cfg_list[1::2]):
+        for g_cfg in g_cfgs:
+            if hasattr(g_cfg, key):
+                try:
+                    value = eval(value)
+                except SyntaxError:  # for file path
+                    pass
+                setattr(g_cfg, key, value)
+                break
+
+
+# Here list the data shapes and data types of all inputs.
+# The shapes here act as placeholder and are set to pass the infer-shape in
+# compile time.
+input_descs = {
+    # The actual data shape of src_word is:
+    # [batch_size * max_src_len_in_batch, 1]
+    "src_word": [(1 * (ModelHyperParams.max_length + 1), 1L), "int64"],
+    # The actual data shape of src_pos is:
+    # [batch_size * max_src_len_in_batch, 1]
+    "src_pos": [(1 * (ModelHyperParams.max_length + 1), 1L), "int64"],
+    # This input is used to remove attention weights on paddings in the
+    # encoder.
+    # The actual data shape of src_slf_attn_bias is:
+    # [batch_size, n_head, max_src_len_in_batch, max_src_len_in_batch]
+    "src_slf_attn_bias":
+    [(1, ModelHyperParams.n_head, (ModelHyperParams.max_length + 1),
+      (ModelHyperParams.max_length + 1)), "float32"],
+    # This shape input is used to reshape the output of embedding layer.
+    "src_data_shape": [(3L, ), "int32"],
+    # This shape input is used to reshape before softmax in self attention.
+    "src_slf_attn_pre_softmax_shape": [(2L, ), "int32"],
+    # This shape input is used to reshape after softmax in self attention.
+    "src_slf_attn_post_softmax_shape": [(4L, ), "int32"],
+    # The actual data shape of trg_word is:
+    # [batch_size * max_trg_len_in_batch, 1]
+    "trg_word": [(1 * (ModelHyperParams.max_length + 1), 1L), "int64"],
+    # The actual data shape of trg_pos is:
+    # [batch_size * max_trg_len_in_batch, 1]
+    "trg_pos": [(1 * (ModelHyperParams.max_length + 1), 1L), "int64"],
+    # This input is used to remove attention weights on paddings and
+    # subsequent words in the decoder.
+    # The actual data shape of trg_slf_attn_bias is:
+    # [batch_size, n_head, max_trg_len_in_batch, max_trg_len_in_batch]
+    "trg_slf_attn_bias": [(1, ModelHyperParams.n_head,
+                           (ModelHyperParams.max_length + 1),
+                           (ModelHyperParams.max_length + 1)), "float32"],
+    # This input is used to remove attention weights on paddings of the source
+    # input in the encoder-decoder attention.
+    # The actual data shape of trg_src_attn_bias is:
+    # [batch_size, n_head, max_trg_len_in_batch, max_src_len_in_batch]
+    "trg_src_attn_bias": [(1, ModelHyperParams.n_head,
+                           (ModelHyperParams.max_length + 1),
+                           (ModelHyperParams.max_length + 1)), "float32"],
+    # This shape input is used to reshape the output of embedding layer.
+    "trg_data_shape": [(3L, ), "int32"],
+    # This shape input is used to reshape before softmax in self attention.
+    "trg_slf_attn_pre_softmax_shape": [(2L, ), "int32"],
+    # This shape input is used to reshape after softmax in self attention.
+    "trg_slf_attn_post_softmax_shape": [(4L, ), "int32"],
+    # This shape input is used to reshape before softmax in encoder-decoder
+    # attention.
+    "trg_src_attn_pre_softmax_shape": [(2L, ), "int32"],
+    # This shape input is used to reshape after softmax in encoder-decoder
+    # attention.
+    "trg_src_attn_post_softmax_shape": [(4L, ), "int32"],
+    # This input is used in independent decoder program for inference.
+    # The actual data shape of enc_output is:
+    # [batch_size, max_src_len_in_batch, d_model]
+    "enc_output": [(1, (ModelHyperParams.max_length + 1),
+                    ModelHyperParams.d_model), "float32"],
+    # The actual data shape of label_word is:
+    # [batch_size * max_trg_len_in_batch, 1]
+    "lbl_word": [(1 * (ModelHyperParams.max_length + 1), 1L), "int64"],
+    # This input is used to mask out the loss of paddding tokens.
+    # The actual data shape of label_weight is:
+    # [batch_size * max_trg_len_in_batch, 1]
+    "lbl_weight": [(1 * (ModelHyperParams.max_length + 1), 1L), "float32"],
+}
+
 # Names of position encoding table which will be initialized externally.
 pos_enc_param_names = (
     "src_pos_enc_table",
     "trg_pos_enc_table", )
-
-# Names of all data layers in encoder listed in order.
-encoder_input_data_names = (
+# separated inputs for different usages.
+encoder_data_input_fields = (
     "src_word",
     "src_pos",
-    "src_slf_attn_bias",
+    "src_slf_attn_bias", )
+encoder_util_input_fields = (
     "src_data_shape",
     "src_slf_attn_pre_softmax_shape",
     "src_slf_attn_post_softmax_shape", )
-
-# Names of all data layers in decoder listed in order.
-decoder_input_data_names = (
+decoder_data_input_fields = (
     "trg_word",
     "trg_pos",
     "trg_slf_attn_bias",
     "trg_src_attn_bias",
+    "enc_output", )
+decoder_util_input_fields = (
     "trg_data_shape",
     "trg_slf_attn_pre_softmax_shape",
     "trg_slf_attn_post_softmax_shape",
     "trg_src_attn_pre_softmax_shape",
-    "trg_src_attn_post_softmax_shape",
-    "enc_output", )
-
-# Names of label related data layers listed in order.
-label_data_names = (
+    "trg_src_attn_post_softmax_shape", )
+label_data_input_fields = (
     "lbl_word",
     "lbl_weight", )

fluid/neural_machine_translation/transformer/infer.py

@@ -1,3 +1,4 @@
+import argparse
 import numpy as np
 
 import paddle
@@ -6,9 +7,52 @@
 import model
 from model import wrap_encoder as encoder
 from model import wrap_decoder as decoder
-from config import InferTaskConfig, ModelHyperParams, \
-        encoder_input_data_names, decoder_input_data_names
+from config import *
 from train import pad_batch_data
+import reader
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("Training for Transformer.")
+    parser.add_argument(
+        "--src_vocab_fpath",
+        type=str,
+        required=True,
+        help="The path of vocabulary file of source language.")
+    parser.add_argument(
+        "--trg_vocab_fpath",
+        type=str,
+        required=True,
+        help="The path of vocabulary file of target language.")
+    parser.add_argument(
+        "--test_file_pattern",
+        type=str,
+        required=True,
+        help="The pattern to match test data files.")
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=50,
+        help="The number of examples in one run for sequence generation.")
+    parser.add_argument(
+        "--pool_size",
+        type=int,
+        default=10000,
+        help="The buffer size to pool data.")
+    parser.add_argument(
+        "--special_token",
+        type=str,
+        default=["<s>", "<e>", "<unk>"],
+        nargs=3,
+        help="The <bos>, <eos> and <unk> tokens in the dictionary.")
+    parser.add_argument(
+        'opts',
+        help='See config.py for all options',
+        default=None,
+        nargs=argparse.REMAINDER)
+    args = parser.parse_args()
+    merge_cfg_from_list(args.opts, [InferTaskConfig, ModelHyperParams])
+    return args
 
 
 def translate_batch(exe,
@@ -243,7 +287,7 @@ def update_dec_in_data(dec_in_data, next_ids, active_beams, beam_inst_map):
     return seqs, scores[:, :n_best].tolist()
 
 
-def main():
+def infer(args):
     place = fluid.CUDAPlace(0) if InferTaskConfig.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
@@ -292,13 +336,23 @@ def main():
     decoder_program = fluid.io.get_inference_program(
         target_vars=[predict], main_program=decoder_program)
 
-    test_data = paddle.batch(
-        paddle.dataset.wmt16.test(ModelHyperParams.src_vocab_size,
-                                  ModelHyperParams.trg_vocab_size),
-        batch_size=InferTaskConfig.batch_size)
+    test_data = reader.DataReader(
+        src_vocab_fpath=args.src_vocab_fpath,
+        trg_vocab_fpath=args.trg_vocab_fpath,
+        fpattern=args.test_file_pattern,
+        batch_size=args.batch_size,
+        use_token_batch=False,
+        pool_size=args.pool_size,
+        sort_type=reader.SortType.NONE,
+        shuffle=False,
+        shuffle_batch=False,
+        start_mark=args.special_token[0],
+        end_mark=args.special_token[1],
+        unk_mark=args.special_token[2],
+        clip_last_batch=False)
 
-    trg_idx2word = paddle.dataset.wmt16.get_dict(
-        "de", dict_size=ModelHyperParams.trg_vocab_size, reverse=True)
+    trg_idx2word = test_data._load_dict(
+        dict_path=args.trg_vocab_fpath, reverse=True)
 
     def post_process_seq(seq,
                          bos_idx=ModelHyperParams.bos_idx,
@@ -320,15 +374,16 @@ def post_process_seq(seq,
                 (output_eos or idx != eos_idx),
             seq)
 
-    for batch_id, data in enumerate(test_data()):
+    for batch_id, data in enumerate(test_data.batch_generator()):
         batch_seqs, batch_scores = translate_batch(
             exe,
             [item[0] for item in data],
             encoder_program,
-            encoder_input_data_names,
+            encoder_data_input_fields + encoder_util_input_fields,
             [enc_output.name],
             decoder_program,
-            decoder_input_data_names,
+            decoder_data_input_fields[:-1] + decoder_util_input_fields +
+            (decoder_data_input_fields[-1], ),
             [predict.name],
             InferTaskConfig.beam_size,
             InferTaskConfig.max_length,
@@ -351,4 +406,5 @@ def post_process_seq(seq,
 
 
 if __name__ == "__main__":
-    main()
+    args = parse_args()
+    infer(args)