Add validation for dygraph Transformer.

Add cross-attention cache for dygraph Transformer.
Add greedy search for dygraph Transformer.

dygraph/transformer/README.md

@@ -76,6 +76,7 @@ python -u train.py \
   --trg_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \
   --special_token '<s>' '<e>' '<unk>' \
   --training_file gen_data/wmt16_ende_data_bpe/train.tok.clean.bpe.32000.en-de \
+  --validation_file gen_data/wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de \
   --batch_size 4096
 ```
 
@@ -91,6 +92,7 @@ python -u train.py \
   --trg_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \
   --special_token '<s>' '<e>' '<unk>' \
   --training_file gen_data/wmt16_ende_data_bpe/train.tok.clean.bpe.32000.en-de \
+  --validation_file gen_data/wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de \
   --batch_size 4096 \
   --n_head 16 \
   --d_model 1024 \
@@ -121,10 +123,11 @@ Paddle动态图支持多进程多卡进行模型训练，启动训练的方式
 ```sh
 python -m paddle.distributed.launch --started_port 8999 --selected_gpus=0,1,2,3,4,5,6,7 --log_dir ./mylog train.py \
   --epoch 30 \
-  --src_vocab_fpath wmt16_ende_data_bpe/vocab_all.bpe.32000 \
-  --trg_vocab_fpath wmt16_ende_data_bpe/vocab_all.bpe.32000 \
+  --src_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \
+  --trg_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \
   --special_token '<s>' '<e>' '<unk>' \
-  --training_file wmt16_ende_data_bpe/train.tok.clean.bpe.32000.en-de \
+  --training_file gen_data/wmt16_ende_data_bpe/train.tok.clean.bpe.32000.en-de \
+  --validation_file gen_data/wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de \
   --batch_size 4096 \
   --print_step 100 \
   --use_cuda True \

dygraph/transformer/config.py

-# Copyright (c) 2019 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.
-
-
-class TrainTaskConfig(object):
-    """
-    TrainTaskConfig
-    """
-    # the epoch number to train.
-    pass_num = 20
-    # the number of sequences contained in a mini-batch.
-    # deprecated, set batch_size in args.
-    batch_size = 32
-    # the hyper parameters for Adam optimizer.
-    # This static learning_rate will be multiplied to the LearningRateScheduler
-    # derived learning rate the to get the final learning rate.
-    learning_rate = 2.0
-    beta1 = 0.9
-    beta2 = 0.997
-    eps = 1e-9
-    # the parameters for learning rate scheduling.
-    warmup_steps = 8000
-    # 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
-
-
-class InferTaskConfig(object):
-    # the number of examples in one run for sequence generation.
-    batch_size = 4
-    # the parameters for beam search.
-    beam_size = 4
-    alpha=0.6
-    # max decoded length, should be less than ModelHyperParams.max_length
-    max_out_len = 30
-
-
-
-class ModelHyperParams(object):
-    """
-    ModelHyperParams
-    """
-    # These following five vocabularies related configurations will be set
-    # automatically according to the passed vocabulary path and special tokens.
-    # 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 deciding the size of position encoding table.
-    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 = 2048
-    # the dimension that keys are projected to for dot-product attention.
-    d_key = 64
-    # the dimension that values are projected to for dot-product attention.
-    d_value = 64
-    # number of head used in multi-head attention.
-    n_head = 8
-    # number of sub-layers to be stacked in the encoder and decoder.
-    n_layer = 6
-    # dropout rates of different modules.
-    prepostprocess_dropout = 0.1
-    attention_dropout = 0.1
-    relu_dropout = 0.1
-    # to process before each sub-layer
-    preprocess_cmd = "n"  # layer normalization
-    # to process after each sub-layer
-    postprocess_cmd = "da"  # dropout + residual connection
-    # the flag indicating whether to share embedding and softmax weights.
-    # vocabularies in source and target should be same for weight sharing.
-    weight_sharing = False
-
-
-# The placeholder for batch_size in compile time. Must be -1 currently to be
-# consistent with some ops' infer-shape output in compile time, such as the
-# sequence_expand op used in beamsearch decoder.
-batch_size = -1
-# The placeholder for squence length in compile time.
-seq_len = ModelHyperParams.max_length
-# 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": [(batch_size, seq_len, 1), "int64", 2],
-    # The actual data shape of src_pos is:
-    # [batch_size, max_src_len_in_batch, 1]
-    "src_pos": [(batch_size, seq_len, 1), "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":
-    [(batch_size, ModelHyperParams.n_head, seq_len, seq_len), "float32"],
-    # The actual data shape of trg_word is:
-    # [batch_size, max_trg_len_in_batch, 1]
-    "trg_word": [(batch_size, seq_len, 1), "int64",
-                 2],  # lod_level is only used in fast decoder.
-    # The actual data shape of trg_pos is:
-    # [batch_size, max_trg_len_in_batch, 1]
-    "trg_pos": [(batch_size, seq_len, 1), "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":
-    [(batch_size, ModelHyperParams.n_head, seq_len, seq_len), "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":
-    [(batch_size, ModelHyperParams.n_head, seq_len, seq_len), "float32"],
-    # 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": [(batch_size, seq_len, ModelHyperParams.d_model), "float32"],
-    # The actual data shape of label_word is:
-    # [batch_size * max_trg_len_in_batch, 1]
-    "lbl_word": [(batch_size * seq_len, 1), "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": [(batch_size * seq_len, 1), "float32"],
-    # This input is used in beam-search decoder.
-    "init_score": [(batch_size, 1), "float32", 2],
-    # This input is used in beam-search decoder for the first gather
-    # (cell states updation)
-    "init_idx": [(batch_size, ), "int32"],
-}
-
-# Names of word embedding table which might be reused for weight sharing.
-word_emb_param_names = (
-    "src_word_emb_table",
-    "trg_word_emb_table",
-)
-# Names of position encoding table which will be initialized externally.
-pos_enc_param_names = (
-    "src_pos_enc_table",
-    "trg_pos_enc_table",
-)
-# separated inputs for different usages.
-encoder_data_input_fields = (
-    "src_word",
-    "src_pos",
-    "src_slf_attn_bias",
-)
-decoder_data_input_fields = (
-    "trg_word",
-    "trg_pos",
-    "trg_slf_attn_bias",
-    "trg_src_attn_bias",
-    "enc_output",
-)
-label_data_input_fields = (
-    "lbl_word",
-    "lbl_weight",
-)
-# In fast decoder, trg_pos (only containing the current time step) is generated
-# by ops and trg_slf_attn_bias is not needed.
-fast_decoder_data_input_fields = (
-    "trg_word",
-    # "init_score",
-    # "init_idx",
-    "trg_src_attn_bias",
-)
-
-
-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 Exception:  # for file path
-                    pass
-                setattr(g_cfg, key, value)
-                break

dygraph/transformer/model.py

@@ -18,7 +18,6 @@ import numpy as np
 
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
-from paddle.fluid.layers.utils import map_structure
 from paddle.fluid.dygraph import Embedding, LayerNorm, Linear, Layer, to_variable
 from paddle.fluid.dygraph.learning_rate_scheduler import LearningRateDecay
 
@@ -128,29 +127,46 @@ class MultiHeadAttention(Layer):
                               output_dim=d_model,
                               bias_attr=False)
 
-    def forward(self, queries, keys, values, attn_bias, cache=None):
-        # compute q ,k ,v
-        keys = queries if keys is None else keys
-        values = keys if values is None else values
+    def _prepare_qkv(self, queries, keys, values, cache=None):
+        if keys is None:  # self-attention
+            keys, values = queries, queries
+            static_kv = False
+        else:  # cross-attention
+            static_kv = True
 
         q = self.q_fc(queries)
-        k = self.k_fc(keys)
-        v = self.v_fc(values)
-
-        # split head
         q = layers.reshape(x=q, shape=[0, 0, self.n_head, self.d_key])
         q = layers.transpose(x=q, perm=[0, 2, 1, 3])
+
+        if cache is not None and static_kv and cache.has_key("static_k"):
+            # for encoder-decoder attention in inference and has cached
+            k = cache["static_k"]
+            v = cache["static_v"]
+        else:
+            k = self.k_fc(keys)
+            v = self.v_fc(values)
             k = layers.reshape(x=k, shape=[0, 0, self.n_head, self.d_key])
             k = layers.transpose(x=k, perm=[0, 2, 1, 3])
             v = layers.reshape(x=v, shape=[0, 0, self.n_head, self.d_value])
             v = layers.transpose(x=v, perm=[0, 2, 1, 3])
 
         if cache is not None:
+            if static_kv and not cache.has_key("static_k"):
+                # for encoder-decoder attention in inference and has not cached
+                cache["static_k"], cache["static_v"] = k, v
+            elif not static_kv:
+                # for decoder self-attention in inference
                 cache_k, cache_v = cache["k"], cache["v"]
                 k = layers.concat([cache_k, k], axis=2)
                 v = layers.concat([cache_v, v], axis=2)
                 cache["k"], cache["v"] = k, v
 
+        return q, k, v
+
+    def forward(self, queries, keys, values, attn_bias, cache=None):
+        # compute q ,k ,v
+        q, k, v = self._prepare_qkv(queries, keys, values, cache)
+
         # scale dot product attention
         product = layers.matmul(x=q,
                                 y=k,
@@ -381,7 +397,7 @@ class DecoderLayer(Layer):
 
         cross_attn_output = self.cross_attn(
             self.preprocesser2(self_attn_output), enc_output, enc_output,
-            cross_attn_bias)
+            cross_attn_bias, cache)
         cross_attn_output = self.postprocesser2(cross_attn_output,
                                                 self_attn_output)
 
@@ -810,6 +826,36 @@ class Transformer(Layer):
                      eos_id=1,
                      beam_size=4,
                      max_len=256):
+        if beam_size == 1:
+            return self._greedy_search(src_word,
+                                       src_pos,
+                                       src_slf_attn_bias,
+                                       trg_word,
+                                       trg_src_attn_bias,
+                                       bos_id=bos_id,
+                                       eos_id=eos_id,
+                                       max_len=max_len)
+        else:
+            return self._beam_search(src_word,
+                                     src_pos,
+                                     src_slf_attn_bias,
+                                     trg_word,
+                                     trg_src_attn_bias,
+                                     bos_id=bos_id,
+                                     eos_id=eos_id,
+                                     beam_size=beam_size,
+                                     max_len=max_len)
+
+    def _beam_search(self,
+                     src_word,
+                     src_pos,
+                     src_slf_attn_bias,
+                     trg_word,
+                     trg_src_attn_bias,
+                     bos_id=0,
+                     eos_id=1,
+                     beam_size=4,
+                     max_len=256):
         def expand_to_beam_size(tensor, beam_size):
             tensor = layers.reshape(tensor,
                                     [tensor.shape[0], 1] + tensor.shape[1:])
@@ -822,22 +868,30 @@ class Transformer(Layer):
                                   tensor.shape[2:])
 
         def split_batch_beams(tensor):
-            return fluid.layers.reshape(tensor,
+            return layers.reshape(tensor,
                                   shape=[-1, beam_size] +
                                   list(tensor.shape[1:]))
 
         def mask_probs(probs, finished, noend_mask_tensor):
             # TODO: use where_op
             finished = layers.cast(finished, dtype=probs.dtype)
-            probs = layers.elementwise_mul(
-                layers.expand(layers.unsqueeze(finished, [2]), [1, 1, self.trg_vocab_size]),
-                noend_mask_tensor, axis=-1) - layers.elementwise_mul(probs, (finished - 1), axis=0)
+            probs = layers.elementwise_mul(layers.expand(
+                layers.unsqueeze(finished, [2]), [1, 1, self.trg_vocab_size]),
+                                           noend_mask_tensor,
+                                           axis=-1) - layers.elementwise_mul(
+                                               probs, (finished - 1), axis=0)
             return probs
 
         def gather(x, indices, batch_pos):
-            topk_coordinates = fluid.layers.stack([batch_pos, indices], axis=2)
+            topk_coordinates = layers.stack([batch_pos, indices], axis=2)
             return layers.gather_nd(x, topk_coordinates)
 
+        def update_states(func, caches):
+            for cache in caches:  # no need to update static_kv
+                cache["k"] = func(cache["k"])
+                cache["v"] = func(cache["v"])
+            return caches
+
         # run encoder
         enc_output = self.encoder(src_word, src_pos, src_slf_attn_bias)
 
@@ -893,13 +947,13 @@ class Transformer(Layer):
             trg_pos = layers.fill_constant(shape=trg_word.shape,
                                            dtype="int64",
                                            value=i)
-            caches = map_structure(  # can not be reshaped since the 0 size
+            caches = update_states(  # can not be reshaped since the 0 size
                 lambda x: x if i == 0 else merge_batch_beams(x), caches)
             logits = self.decoder(trg_word, trg_pos, None, trg_src_attn_bias,
                                   enc_output, caches)
-            caches = map_structure(split_batch_beams, caches)
+            caches = update_states(split_batch_beams, caches)
             step_log_probs = split_batch_beams(
-                fluid.layers.log(fluid.layers.softmax(logits)))
+                layers.log(layers.softmax(logits)))
             step_log_probs = mask_probs(step_log_probs, finished,
                                         noend_mask_tensor)
             log_probs = layers.elementwise_add(x=step_log_probs,
@@ -908,15 +962,14 @@ class Transformer(Layer):
             log_probs = layers.reshape(log_probs,
                                        [-1, beam_size * self.trg_vocab_size])
             scores = log_probs
-            topk_scores, topk_indices = fluid.layers.topk(input=scores,
-                                                          k=beam_size)
-            beam_indices = fluid.layers.elementwise_floordiv(
+            topk_scores, topk_indices = layers.topk(input=scores, k=beam_size)
+            beam_indices = layers.elementwise_floordiv(
                 topk_indices, vocab_size_tensor)
-            token_indices = fluid.layers.elementwise_mod(
+            token_indices = layers.elementwise_mod(
                 topk_indices, vocab_size_tensor)
 
             # update states
-            caches = map_structure(lambda x: gather(x, beam_indices, batch_pos),
+            caches = update_states(lambda x: gather(x, beam_indices, batch_pos),
                                    caches)
             log_probs = gather(log_probs, topk_indices, batch_pos)
             finished = gather(finished, beam_indices, batch_pos)
@@ -937,3 +990,72 @@ class Transformer(Layer):
         finished_scores = topk_scores
 
         return finished_seq, finished_scores
+
+    def _greedy_search(self,
+                       src_word,
+                       src_pos,
+                       src_slf_attn_bias,
+                       trg_word,
+                       trg_src_attn_bias,
+                       bos_id=0,
+                       eos_id=1,
+                       max_len=256):
+        # run encoder
+        enc_output = self.encoder(src_word, src_pos, src_slf_attn_bias)
+
+        # constant number
+        batch_size = enc_output.shape[0]
+        max_len = (enc_output.shape[1] + 20) if max_len is None else max_len
+        end_token_tensor = layers.fill_constant(shape=[batch_size, 1],
+                                                dtype="int64",
+                                                value=eos_id)
+
+        predict_ids = []
+        log_probs = layers.fill_constant(shape=[batch_size, 1],
+                                         dtype="float32",
+                                         value=0)
+        trg_word = layers.fill_constant(shape=[batch_size, 1],
+                                        dtype="int64",
+                                        value=bos_id)
+
+        ## init states (caches) for transformer
+        caches = [{
+            "k":
+            layers.fill_constant(
+                shape=[batch_size, self.n_head, 0, self.d_key],
+                dtype=enc_output.dtype,
+                value=0),
+            "v":
+            layers.fill_constant(
+                shape=[batch_size, self.n_head, 0, self.d_value],
+                dtype=enc_output.dtype,
+                value=0),
+        } for i in range(self.n_layer)]
+
+        for i in range(max_len):
+            trg_pos = layers.fill_constant(shape=trg_word.shape,
+                                           dtype="int64",
+                                           value=i)
+            logits = self.decoder(trg_word, trg_pos, None, trg_src_attn_bias,
+                                  enc_output, caches)
+            step_log_probs = layers.log(layers.softmax(logits))
+            log_probs = layers.elementwise_add(x=step_log_probs,
+                                               y=log_probs,
+                                               axis=0)
+            scores = log_probs
+            topk_scores, topk_indices = layers.topk(input=scores, k=1)
+
+            finished = layers.equal(topk_indices, end_token_tensor)
+            trg_word = topk_indices
+            log_probs = topk_scores
+
+            predict_ids.append(topk_indices)
+
+            if layers.reduce_all(finished).numpy():
+                break
+
+        predict_ids = layers.stack(predict_ids, axis=0)
+        finished_seq = layers.transpose(predict_ids, [1, 2, 0])
+        finished_scores = topk_scores
+
+        return finished_seq, finished_scores

dygraph/transformer/train.py

@@ -57,6 +57,25 @@ def do_train(args):
                                      max_length=args.max_length,
                                      n_head=args.n_head)
     batch_generator = processor.data_generator(phase="train")
+    if args.validation_file:
+        val_processor = reader.DataProcessor(
+            fpattern=args.validation_file,
+            src_vocab_fpath=args.src_vocab_fpath,
+            trg_vocab_fpath=args.trg_vocab_fpath,
+            token_delimiter=args.token_delimiter,
+            use_token_batch=args.use_token_batch,
+            batch_size=args.batch_size,
+            device_count=trainer_count,
+            pool_size=args.pool_size,
+            sort_type=args.sort_type,
+            shuffle=False,
+            shuffle_batch=False,
+            start_mark=args.special_token[0],
+            end_mark=args.special_token[1],
+            unk_mark=args.special_token[2],
+            max_length=args.max_length,
+            n_head=args.n_head)
+        val_batch_generator = val_processor.data_generator(phase="train")
     if trainer_count > 1:  # for multi-process gpu training
         batch_generator = fluid.contrib.reader.distributed_batch_reader(
             batch_generator)
@@ -73,6 +92,9 @@ def do_train(args):
         # define data loader
         train_loader = fluid.io.DataLoader.from_generator(capacity=10)
         train_loader.set_batch_generator(batch_generator, places=place)
+        if args.validation_file:
+            val_loader = fluid.io.DataLoader.from_generator(capacity=10)
+            val_loader.set_batch_generator(val_batch_generator, places=place)
 
         # define model
         transformer = Transformer(
@@ -123,6 +145,7 @@ def do_train(args):
         # train loop
         for pass_id in range(args.epoch):
             pass_start_time = time.time()
+            avg_batch_time = time.time()
             batch_id = 0
             for input_data in train_loader():
                 (src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
@@ -155,7 +178,6 @@ def do_train(args):
                             (step_idx, pass_id, batch_id, total_avg_cost,
                             total_avg_cost - loss_normalizer,
                             np.exp([min(total_avg_cost, 100)])))
-                        avg_batch_time = time.time()
                     else:
                         logging.info(
                             "step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
@@ -164,12 +186,37 @@ def do_train(args):
                             total_avg_cost - loss_normalizer,
                             np.exp([min(total_avg_cost, 100)]),
                             args.print_step / (time.time() - avg_batch_time)))
-                        avg_batch_time = time.time()
 
-                if step_idx % args.save_step == 0 and step_idx != 0 and (
+
+                if step_idx % args.save_step == 0 and step_idx != 0:
+                    # validation
+                    if args.validation_file:
+                        transformer.eval()
+                        total_sum_cost = 0
+                        total_token_num = 0
+                        for input_data in val_loader():
+                            (src_word, src_pos, src_slf_attn_bias, trg_word,
+                             trg_pos, trg_slf_attn_bias, trg_src_attn_bias,
+                             lbl_word, lbl_weight) = input_data
+                            logits = transformer(src_word, src_pos,
+                                                 src_slf_attn_bias, trg_word,
+                                                 trg_pos, trg_slf_attn_bias,
+                                                 trg_src_attn_bias)
+                            sum_cost, avg_cost, token_num = criterion(
+                                logits, lbl_word, lbl_weight)
+                            total_sum_cost += sum_cost.numpy()
+                            total_token_num += token_num.numpy()
+                            total_avg_cost = total_sum_cost / total_token_num
+                        logging.info("validation, step_idx: %d, avg loss: %f, "
+                                     "normalized loss: %f, ppl: %f" %
+                                     (step_idx, total_avg_cost,
+                                      total_avg_cost - loss_normalizer,
+                                      np.exp([min(total_avg_cost, 100)])))
+                        transformer.train()
+
+                    if args.save_model and (
                             trainer_count == 1
                             or fluid.dygraph.parallel.Env().dev_id == 0):
-                    if args.save_model:
                         model_dir = os.path.join(args.save_model,
                                                  "step_" + str(step_idx))
                         if not os.path.exists(model_dir):
@@ -181,6 +228,7 @@ def do_train(args):
                             optimizer.state_dict(),
                             os.path.join(model_dir, "transformer"))
 
+                avg_batch_time = time.time()
                 batch_id += 1
                 step_idx += 1
 

dygraph/transformer/transformer.yaml

@@ -19,6 +19,8 @@ inference_model_dir: "infer_model"
 random_seed: None
 # The pattern to match training data files.
 training_file: "wmt16_ende_data_bpe/train.tok.clean.bpe.32000.en-de"
+# The pattern to match validation data files.
+validation_file: "wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de"
 # The pattern to match test data files.
 predict_file: "wmt16_ende_data_bpe/newstest2016.tok.bpe.32000.en-de"
 # The file to output the translation results of predict_file to.