diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_transformer.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..604b24439a2a25dd881b553ea1c5f666fcfb99e9
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_transformer.py
@@ -0,0 +1,235 @@
+# Copyright (c) 2020 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 logging
+import numpy as np
+import time
+import os
+import unittest
+
+import paddle.fluid as fluid
+
+import transformer_util as util
+from transformer_dygraph_model import Transformer
+from transformer_dygraph_model import CrossEntropyCriterion
+
+trainer_count = 1
+place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() else fluid.CPUPlace(
+)
+SEED = 10
+
+
+def train_static(args, batch_generator):
+    train_prog = fluid.default_main_program()
+    startup_prog = fluid.default_startup_program()
+    train_prog.random_seed = SEED
+    startup_prog.random_seed = SEED
+    with fluid.program_guard(train_prog, startup_prog):
+        with fluid.unique_name.guard():
+            # define input and reader
+            input_field_names = util.encoder_data_input_fields + \
+                                util.decoder_data_input_fields[:-1] + util.label_data_input_fields
+            input_descs = util.get_input_descs(args)
+            input_slots = [{
+                "name": name,
+                "shape": input_descs[name][0],
+                "dtype": input_descs[name][1]
+            } for name in input_field_names]
+            input_field = util.InputField(input_slots)
+            # Define DataLoader
+            data_loader = fluid.io.DataLoader.from_generator(
+                input_field.feed_list, capacity=60)
+            data_loader.set_batch_generator(batch_generator, places=place)
+            # define model
+            transformer = Transformer(
+                args.src_vocab_size, args.trg_vocab_size, args.max_length + 1,
+                args.n_layer, args.n_head, args.d_key, args.d_value,
+                args.d_model, args.d_inner_hid, args.prepostprocess_dropout,
+                args.attention_dropout, args.relu_dropout, args.preprocess_cmd,
+                args.postprocess_cmd, args.weight_sharing, args.bos_idx,
+                args.eos_idx)
+            logits = transformer(*input_field.feed_list[:7])
+            # define loss
+            criterion = CrossEntropyCriterion(args.label_smooth_eps)
+            lbl_word, lbl_weight = input_field.feed_list[7:]
+            sum_cost, avg_cost, token_num = criterion(logits, lbl_word,
+                                                      lbl_weight)
+            # define optimizer
+            learning_rate = fluid.layers.learning_rate_scheduler.noam_decay(
+                args.d_model, args.warmup_steps, args.learning_rate)
+            optimizer = fluid.optimizer.Adam(
+                learning_rate=learning_rate,
+                beta1=args.beta1,
+                beta2=args.beta2,
+                epsilon=float(args.eps))
+            optimizer.minimize(avg_cost)
+            # the best cross-entropy value with label smoothing
+            loss_normalizer = -((1. - args.label_smooth_eps) * np.log(
+                (1. - args.label_smooth_eps)) + args.label_smooth_eps * np.log(
+                    args.label_smooth_eps / (args.trg_vocab_size - 1) + 1e-20))
+    step_idx = 0
+    total_batch_num = 0
+    avg_loss = []
+    exe = fluid.Executor(place)
+    exe.run(startup_prog)
+    for pass_id in range(args.epoch):
+        batch_id = 0
+        for feed_dict in data_loader:
+            outs = exe.run(program=train_prog,
+                           feed=feed_dict,
+                           fetch_list=[sum_cost.name, token_num.name])
+            if step_idx % args.print_step == 0:
+                sum_cost_val, token_num_val = np.array(outs[0]), np.array(outs[
+                    1])
+                total_sum_cost = sum_cost_val.sum()
+                total_token_num = token_num_val.sum()
+                total_avg_cost = total_sum_cost / total_token_num
+                avg_loss.append(total_avg_cost)
+                if step_idx == 0:
+                    logging.info(
+                        "step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
+                        "normalized loss: %f, ppl: %f" %
+                        (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, "
+                        "normalized loss: %f, ppl: %f, speed: %.2f step/s" %
+                        (step_idx, pass_id, batch_id, total_avg_cost,
+                         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()
+            batch_id += 1
+            step_idx += 1
+            total_batch_num = total_batch_num + 1
+            if step_idx == 10:
+                if args.save_model:
+                    model_path = os.path.join(
+                        args.save_model, "step_" + str(step_idx), "transformer")
+                    fluid.save(train_prog, model_path)
+                break
+    return np.array(avg_loss)
+
+
+def train_dygraph(args, batch_generator):
+    with fluid.dygraph.guard(place):
+        if SEED is not None:
+            fluid.default_main_program().random_seed = SEED
+            fluid.default_startup_program().random_seed = SEED
+        # define data loader
+        train_loader = fluid.io.DataLoader.from_generator(capacity=10)
+        train_loader.set_batch_generator(batch_generator, places=place)
+        # define model
+        transformer = Transformer(
+            args.src_vocab_size, args.trg_vocab_size, args.max_length + 1,
+            args.n_layer, args.n_head, args.d_key, args.d_value, args.d_model,
+            args.d_inner_hid, args.prepostprocess_dropout,
+            args.attention_dropout, args.relu_dropout, args.preprocess_cmd,
+            args.postprocess_cmd, args.weight_sharing, args.bos_idx,
+            args.eos_idx)
+        # define loss
+        criterion = CrossEntropyCriterion(args.label_smooth_eps)
+        # define optimizer
+        learning_rate = fluid.layers.learning_rate_scheduler.noam_decay(
+            args.d_model, args.warmup_steps, args.learning_rate)
+        # define optimizer
+        optimizer = fluid.optimizer.Adam(
+            learning_rate=learning_rate,
+            beta1=args.beta1,
+            beta2=args.beta2,
+            epsilon=float(args.eps),
+            parameter_list=transformer.parameters())
+        # the best cross-entropy value with label smoothing
+        loss_normalizer = -(
+            (1. - args.label_smooth_eps) * np.log(
+                (1. - args.label_smooth_eps)) + args.label_smooth_eps *
+            np.log(args.label_smooth_eps / (args.trg_vocab_size - 1) + 1e-20))
+        ce_time = []
+        ce_ppl = []
+        avg_loss = []
+        step_idx = 0
+        for pass_id in range(args.epoch):
+            pass_start_time = time.time()
+            batch_id = 0
+            for input_data in train_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)
+                avg_cost.backward()
+                optimizer.minimize(avg_cost)
+                transformer.clear_gradients()
+                if step_idx % args.print_step == 0:
+                    total_avg_cost = avg_cost.numpy() * trainer_count
+                    avg_loss.append(total_avg_cost[0])
+                    if step_idx == 0:
+                        logging.info(
+                            "step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
+                            "normalized loss: %f, ppl: %f" %
+                            (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, "
+                            "normalized loss: %f, ppl: %f, speed: %.2f step/s" %
+                            (step_idx, pass_id, batch_id, total_avg_cost,
+                             total_avg_cost - loss_normalizer,
+                             np.exp([min(total_avg_cost, 100)]),
+                             args.print_step / (time.time() - avg_batch_time)))
+                        ce_ppl.append(np.exp([min(total_avg_cost, 100)]))
+                        avg_batch_time = time.time()
+                batch_id += 1
+                step_idx += 1
+                if step_idx == 10:
+                    if args.save_model:
+                        model_dir = os.path.join(args.save_model + '_dygraph',
+                                                 "step_" + str(step_idx))
+                        if not os.path.exists(model_dir):
+                            os.makedirs(model_dir)
+                        fluid.save_dygraph(
+                            transformer.state_dict(),
+                            os.path.join(model_dir, "transformer"))
+                        fluid.save_dygraph(
+                            optimizer.state_dict(),
+                            os.path.join(model_dir, "transformer"))
+                    break
+            time_consumed = time.time() - pass_start_time
+            ce_time.append(time_consumed)
+        return np.array(avg_loss)
+
+
+class TestTransformer(unittest.TestCase):
+    def prepare(self, mode='train'):
+        args = util.ModelHyperParams()
+        batch_generator = util.get_feed_data_reader(args, mode)
+        return args, batch_generator
+
+    def test_train(self):
+        args, batch_generator = self.prepare(mode='train')
+        static_avg_loss = train_static(args, batch_generator)
+        dygraph_avg_loss = train_dygraph(args, batch_generator)
+        self.assertTrue(np.allclose(static_avg_loss, dygraph_avg_loss))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7ce8740d1889370678365359cf655daefc75bf2
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py
@@ -0,0 +1,488 @@
+# 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.
+
+from __future__ import print_function
+
+import numpy as np
+
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+from paddle.fluid.dygraph import Embedding, LayerNorm, Linear, Layer
+from paddle.fluid.dygraph.jit import dygraph_to_static_func
+
+
+def position_encoding_init(n_position, d_pos_vec):
+    """
+    Generate the initial values for the sinusoid position encoding table.
+    """
+    channels = d_pos_vec
+    position = np.arange(n_position)
+    num_timescales = channels // 2
+    log_timescale_increment = (np.log(float(1e4) / float(1)) /
+                               (num_timescales - 1))
+    inv_timescales = np.exp(np.arange(
+        num_timescales)) * -log_timescale_increment
+    scaled_time = np.expand_dims(position, 1) * np.expand_dims(inv_timescales,
+                                                               0)
+    signal = np.concatenate([np.sin(scaled_time), np.cos(scaled_time)], axis=1)
+    signal = np.pad(signal, [[0, 0], [0, np.mod(channels, 2)]], 'constant')
+    position_enc = signal
+    return position_enc.astype("float32")
+
+
+class PrePostProcessLayer(Layer):
+    def __init__(self, process_cmd, d_model, dropout_rate):
+        super(PrePostProcessLayer, self).__init__()
+        self.process_cmd = process_cmd
+        self.functors = []
+        for cmd in self.process_cmd:
+            if cmd == "a":  # add residual connection
+                self.functors.append(lambda x, y: x + y if y else x)
+            elif cmd == "n":  # add layer normalization
+                self.functors.append(
+                    self.add_sublayer(
+                        "layer_norm_%d" % len(
+                            self.sublayers(include_sublayers=False)),
+                        LayerNorm(
+                            normalized_shape=d_model,
+                            param_attr=fluid.ParamAttr(
+                                initializer=fluid.initializer.Constant(1.)),
+                            bias_attr=fluid.ParamAttr(
+                                initializer=fluid.initializer.Constant(0.)))))
+            elif cmd == "d":  # add dropout
+                if dropout_rate:
+                    self.functors.append(lambda x: layers.dropout(
+                        x, dropout_prob=dropout_rate, is_test=False))
+
+    @dygraph_to_static_func
+    def forward(self, x, residual=None):
+        for i, cmd in enumerate(self.process_cmd):
+            if cmd == "a":
+                x = self.functors[i](x, residual)
+            else:
+                x = self.functors[i](x)
+        return x
+
+
+class MultiHeadAttention(Layer):
+    def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0.):
+        super(MultiHeadAttention, self).__init__()
+        self.n_head = n_head
+        self.d_key = d_key
+        self.d_value = d_value
+        self.d_model = d_model
+        self.dropout_rate = dropout_rate
+        self.q_fc = Linear(
+            input_dim=d_model, output_dim=d_key * n_head, bias_attr=False)
+        self.k_fc = Linear(
+            input_dim=d_model, output_dim=d_key * n_head, bias_attr=False)
+        self.v_fc = Linear(
+            input_dim=d_model, output_dim=d_value * n_head, bias_attr=False)
+        self.proj_fc = Linear(
+            input_dim=d_value * n_head, output_dim=d_model, bias_attr=False)
+
+    @dygraph_to_static_func
+    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
+        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])
+        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:
+            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
+        # scale dot product attention
+        product = layers.matmul(
+            x=q, y=k, transpose_y=True, alpha=self.d_model**-0.5)
+        if attn_bias:
+            product += attn_bias
+        weights = layers.softmax(product)
+        if self.dropout_rate:
+            weights = layers.dropout(
+                weights, dropout_prob=self.dropout_rate, is_test=False)
+            out = layers.matmul(weights, v)
+        out = layers.transpose(out, perm=[0, 2, 1, 3])
+        out = layers.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])
+        out = self.proj_fc(out)
+        return out
+
+
+class FFN(Layer):
+    def __init__(self, d_inner_hid, d_model, dropout_rate):
+        super(FFN, self).__init__()
+        self.dropout_rate = dropout_rate
+        self.fc1 = Linear(input_dim=d_model, output_dim=d_inner_hid, act="relu")
+        self.fc2 = Linear(input_dim=d_inner_hid, output_dim=d_model)
+
+    @dygraph_to_static_func
+    def forward(self, x):
+        hidden = self.fc1(x)
+        if self.dropout_rate:
+            hidden = layers.dropout(
+                hidden, dropout_prob=self.dropout_rate, is_test=False)
+        out = self.fc2(hidden)
+        return out
+
+
+class EncoderLayer(Layer):
+    def __init__(self,
+                 n_head,
+                 d_key,
+                 d_value,
+                 d_model,
+                 d_inner_hid,
+                 prepostprocess_dropout,
+                 attention_dropout,
+                 relu_dropout,
+                 preprocess_cmd="n",
+                 postprocess_cmd="da"):
+
+        super(EncoderLayer, self).__init__()
+
+        self.preprocesser1 = PrePostProcessLayer(preprocess_cmd, d_model,
+                                                 prepostprocess_dropout)
+        self.self_attn = MultiHeadAttention(d_key, d_value, d_model, n_head,
+                                            attention_dropout)
+        self.postprocesser1 = PrePostProcessLayer(postprocess_cmd, d_model,
+                                                  prepostprocess_dropout)
+
+        self.preprocesser2 = PrePostProcessLayer(preprocess_cmd, d_model,
+                                                 prepostprocess_dropout)
+        self.ffn = FFN(d_inner_hid, d_model, relu_dropout)
+        self.postprocesser2 = PrePostProcessLayer(postprocess_cmd, d_model,
+                                                  prepostprocess_dropout)
+
+    @dygraph_to_static_func
+    def forward(self, enc_input, attn_bias):
+        attn_output = self.self_attn(
+            self.preprocesser1(enc_input), None, None, attn_bias)
+        attn_output = self.postprocesser1(attn_output, enc_input)
+        ffn_output = self.ffn(self.preprocesser2(attn_output))
+        ffn_output = self.postprocesser2(ffn_output, attn_output)
+        return ffn_output
+
+
+class Encoder(Layer):
+    def __init__(self,
+                 n_layer,
+                 n_head,
+                 d_key,
+                 d_value,
+                 d_model,
+                 d_inner_hid,
+                 prepostprocess_dropout,
+                 attention_dropout,
+                 relu_dropout,
+                 preprocess_cmd="n",
+                 postprocess_cmd="da"):
+
+        super(Encoder, self).__init__()
+
+        self.encoder_layers = list()
+        for i in range(n_layer):
+            self.encoder_layers.append(
+                self.add_sublayer(
+                    "layer_%d" % i,
+                    EncoderLayer(n_head, d_key, d_value, d_model, d_inner_hid,
+                                 prepostprocess_dropout, attention_dropout,
+                                 relu_dropout, preprocess_cmd,
+                                 postprocess_cmd)))
+        self.processer = PrePostProcessLayer(preprocess_cmd, d_model,
+                                             prepostprocess_dropout)
+
+    @dygraph_to_static_func
+    def forward(self, enc_input, attn_bias):
+        for encoder_layer in self.encoder_layers:
+            enc_output = encoder_layer(enc_input, attn_bias)
+            enc_input = enc_output
+
+        return self.processer(enc_output)
+
+
+class Embedder(Layer):
+    def __init__(self, vocab_size, emb_dim, bos_idx=0):
+        super(Embedder, self).__init__()
+        self.word_embedder = Embedding(
+            size=[vocab_size, emb_dim],
+            padding_idx=bos_idx,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Normal(0., emb_dim**-0.5)))
+
+    @dygraph_to_static_func
+    def forward(self, word):
+        word_emb = self.word_embedder(word)
+        return word_emb
+
+
+class WrapEncoder(Layer):
+    def __init__(self, src_vocab_size, max_length, n_layer, n_head, d_key,
+                 d_value, d_model, d_inner_hid, prepostprocess_dropout,
+                 attention_dropout, relu_dropout, preprocess_cmd,
+                 postprocess_cmd, word_embedder):
+        super(WrapEncoder, self).__init__()
+        self.emb_dropout = prepostprocess_dropout
+        self.emb_dim = d_model
+        self.word_embedder = word_embedder
+        self.pos_encoder = Embedding(
+            size=[max_length, self.emb_dim],
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.NumpyArrayInitializer(
+                    position_encoding_init(max_length, self.emb_dim)),
+                trainable=False))
+        self.encoder = Encoder(n_layer, n_head, d_key, d_value, d_model,
+                               d_inner_hid, prepostprocess_dropout,
+                               attention_dropout, relu_dropout, preprocess_cmd,
+                               postprocess_cmd)
+
+    @dygraph_to_static_func
+    def forward(self, src_word, src_pos, src_slf_attn_bias):
+        word_emb = self.word_embedder(src_word)
+        word_emb = layers.scale(x=word_emb, scale=self.emb_dim**0.5)
+        pos_enc = self.pos_encoder(src_pos)
+        pos_enc.stop_gradient = True
+        emb = word_emb + pos_enc
+        enc_input = layers.dropout(
+            emb, dropout_prob=self.emb_dropout,
+            is_test=False) if self.emb_dropout else emb
+        enc_output = self.encoder(enc_input, src_slf_attn_bias)
+        return enc_output
+
+
+class DecoderLayer(Layer):
+    def __init__(self,
+                 n_head,
+                 d_key,
+                 d_value,
+                 d_model,
+                 d_inner_hid,
+                 prepostprocess_dropout,
+                 attention_dropout,
+                 relu_dropout,
+                 preprocess_cmd="n",
+                 postprocess_cmd="da"):
+        super(DecoderLayer, self).__init__()
+
+        self.preprocesser1 = PrePostProcessLayer(preprocess_cmd, d_model,
+                                                 prepostprocess_dropout)
+        self.self_attn = MultiHeadAttention(d_key, d_value, d_model, n_head,
+                                            attention_dropout)
+        self.postprocesser1 = PrePostProcessLayer(postprocess_cmd, d_model,
+                                                  prepostprocess_dropout)
+        self.preprocesser2 = PrePostProcessLayer(preprocess_cmd, d_model,
+                                                 prepostprocess_dropout)
+        self.cross_attn = MultiHeadAttention(d_key, d_value, d_model, n_head,
+                                             attention_dropout)
+        self.postprocesser2 = PrePostProcessLayer(postprocess_cmd, d_model,
+                                                  prepostprocess_dropout)
+        self.preprocesser3 = PrePostProcessLayer(preprocess_cmd, d_model,
+                                                 prepostprocess_dropout)
+        self.ffn = FFN(d_inner_hid, d_model, relu_dropout)
+        self.postprocesser3 = PrePostProcessLayer(postprocess_cmd, d_model,
+                                                  prepostprocess_dropout)
+
+    @dygraph_to_static_func
+    def forward(self,
+                dec_input,
+                enc_output,
+                self_attn_bias,
+                cross_attn_bias,
+                cache=None):
+        self_attn_output = self.self_attn(
+            self.preprocesser1(dec_input), None, None, self_attn_bias, cache)
+        self_attn_output = self.postprocesser1(self_attn_output, dec_input)
+        cross_attn_output = self.cross_attn(
+            self.preprocesser2(self_attn_output), enc_output, enc_output,
+            cross_attn_bias)
+        cross_attn_output = self.postprocesser2(cross_attn_output,
+                                                self_attn_output)
+        ffn_output = self.ffn(self.preprocesser3(cross_attn_output))
+        ffn_output = self.postprocesser3(ffn_output, cross_attn_output)
+        return ffn_output
+
+
+class Decoder(Layer):
+    def __init__(self, n_layer, n_head, d_key, d_value, d_model, d_inner_hid,
+                 prepostprocess_dropout, attention_dropout, relu_dropout,
+                 preprocess_cmd, postprocess_cmd):
+        super(Decoder, self).__init__()
+
+        self.decoder_layers = list()
+        for i in range(n_layer):
+            self.decoder_layers.append(
+                self.add_sublayer(
+                    "layer_%d" % i,
+                    DecoderLayer(n_head, d_key, d_value, d_model, d_inner_hid,
+                                 prepostprocess_dropout, attention_dropout,
+                                 relu_dropout, preprocess_cmd,
+                                 postprocess_cmd)))
+        self.processer = PrePostProcessLayer(preprocess_cmd, d_model,
+                                             prepostprocess_dropout)
+
+    @dygraph_to_static_func
+    def forward(self,
+                dec_input,
+                enc_output,
+                self_attn_bias,
+                cross_attn_bias,
+                caches=None):
+        for i, decoder_layer in enumerate(self.decoder_layers):
+            dec_output = decoder_layer(dec_input, enc_output, self_attn_bias,
+                                       cross_attn_bias, None
+                                       if caches is None else caches[i])
+            dec_input = dec_output
+        return self.processer(dec_output)
+
+
+class WrapDecoder(Layer):
+    def __init__(self, trg_vocab_size, max_length, n_layer, n_head, d_key,
+                 d_value, d_model, d_inner_hid, prepostprocess_dropout,
+                 attention_dropout, relu_dropout, preprocess_cmd,
+                 postprocess_cmd, share_input_output_embed, word_embedder):
+        super(WrapDecoder, self).__init__()
+
+        self.emb_dropout = prepostprocess_dropout
+        self.emb_dim = d_model
+        self.word_embedder = word_embedder
+        self.pos_encoder = Embedding(
+            size=[max_length, self.emb_dim],
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.NumpyArrayInitializer(
+                    position_encoding_init(max_length, self.emb_dim)),
+                trainable=False))
+        self.decoder = Decoder(n_layer, n_head, d_key, d_value, d_model,
+                               d_inner_hid, prepostprocess_dropout,
+                               attention_dropout, relu_dropout, preprocess_cmd,
+                               postprocess_cmd)
+        if share_input_output_embed:
+            self.linear = lambda x: layers.matmul(x=x,
+                                                  y=self.word_embedder.
+                                                  word_embedder.weight,
+                                                  transpose_y=True)
+        else:
+            self.linear = Linear(
+                input_dim=d_model, output_dim=trg_vocab_size, bias_attr=False)
+
+    @dygraph_to_static_func
+    def forward(self,
+                trg_word,
+                trg_pos,
+                trg_slf_attn_bias,
+                trg_src_attn_bias,
+                enc_output,
+                caches=None):
+        word_emb = self.word_embedder(trg_word)
+        word_emb = layers.scale(x=word_emb, scale=self.emb_dim**0.5)
+        pos_enc = self.pos_encoder(trg_pos)
+        pos_enc.stop_gradient = True
+        emb = word_emb + pos_enc
+        dec_input = layers.dropout(
+            emb, dropout_prob=self.emb_dropout,
+            is_test=False) if self.emb_dropout else emb
+        dec_output = self.decoder(dec_input, enc_output, trg_slf_attn_bias,
+                                  trg_src_attn_bias, caches)
+        dec_output = layers.reshape(
+            dec_output,
+            shape=[-1, dec_output.shape[-1]], )
+        logits = self.linear(dec_output)
+        return logits
+
+
+class CrossEntropyCriterion(object):
+    def __init__(self, label_smooth_eps):
+        self.label_smooth_eps = label_smooth_eps
+
+    @dygraph_to_static_func
+    def __call__(self, predict, label, weights):
+        if self.label_smooth_eps:
+            label_out = layers.label_smooth(
+                label=layers.one_hot(
+                    input=label, depth=predict.shape[-1]),
+                epsilon=self.label_smooth_eps)
+
+        cost = layers.softmax_with_cross_entropy(
+            logits=predict,
+            label=label_out,
+            soft_label=True if self.label_smooth_eps else False)
+        weighted_cost = cost * weights
+        sum_cost = layers.reduce_sum(weighted_cost)
+        token_num = layers.reduce_sum(weights)
+        token_num.stop_gradient = True
+        avg_cost = sum_cost / token_num
+        return sum_cost, avg_cost, token_num
+
+
+class Transformer(Layer):
+    def __init__(self,
+                 src_vocab_size,
+                 trg_vocab_size,
+                 max_length,
+                 n_layer,
+                 n_head,
+                 d_key,
+                 d_value,
+                 d_model,
+                 d_inner_hid,
+                 prepostprocess_dropout,
+                 attention_dropout,
+                 relu_dropout,
+                 preprocess_cmd,
+                 postprocess_cmd,
+                 weight_sharing,
+                 bos_id=0,
+                 eos_id=1):
+        super(Transformer, self).__init__()
+        src_word_embedder = Embedder(
+            vocab_size=src_vocab_size, emb_dim=d_model, bos_idx=bos_id)
+        self.encoder = WrapEncoder(
+            src_vocab_size, max_length, n_layer, n_head, d_key, d_value,
+            d_model, d_inner_hid, prepostprocess_dropout, attention_dropout,
+            relu_dropout, preprocess_cmd, postprocess_cmd, src_word_embedder)
+        if weight_sharing:
+            assert src_vocab_size == trg_vocab_size, (
+                "Vocabularies in source and target should be same for weight sharing."
+            )
+            trg_word_embedder = src_word_embedder
+        else:
+            trg_word_embedder = Embedder(
+                vocab_size=trg_vocab_size, emb_dim=d_model, bos_idx=bos_id)
+        self.decoder = WrapDecoder(
+            trg_vocab_size, max_length, n_layer, n_head, d_key, d_value,
+            d_model, d_inner_hid, prepostprocess_dropout, attention_dropout,
+            relu_dropout, preprocess_cmd, postprocess_cmd, weight_sharing,
+            trg_word_embedder)
+
+        self.trg_vocab_size = trg_vocab_size
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.d_key = d_key
+        self.d_value = d_value
+
+    @dygraph_to_static_func
+    def forward(self, src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
+                trg_slf_attn_bias, trg_src_attn_bias):
+        enc_output = self.encoder(src_word, src_pos, src_slf_attn_bias)
+        predict = self.decoder(trg_word, trg_pos, trg_slf_attn_bias,
+                               trg_src_attn_bias, enc_output)
+        return predict
diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_util.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2e00d85c73e2f58fe52ab61d14f5030f3d53c28
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_util.py
@@ -0,0 +1,276 @@
+# Copyright (c) 2020 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 pickle
+import warnings
+import six
+from functools import partial
+import numpy as np
+
+import paddle
+import paddle.fluid as fluid
+import paddle.dataset.wmt16 as wmt16
+
+
+def get_input_descs(args):
+
+    batch_size = args.batch_size  # TODO None(before)
+    seq_len = None
+    n_head = getattr(args, "n_head", 8)
+    d_model = getattr(args, "d_model", 512)
+    input_descs = {
+        "src_word": [(batch_size, seq_len), "int64", 2],
+        "src_pos": [(batch_size, seq_len), "int64"],
+        "src_slf_attn_bias":
+        [(batch_size, n_head, seq_len, seq_len), "float32"],
+        "trg_word": [(batch_size, seq_len), "int64", 2],
+        "trg_pos": [(batch_size, seq_len), "int64"],
+        "trg_slf_attn_bias":
+        [(batch_size, n_head, seq_len, seq_len), "float32"],
+        "trg_src_attn_bias": [(batch_size, n_head, seq_len, seq_len), "float32"
+                              ],  # TODO: 1 for predict, seq_len for train
+        "enc_output": [(batch_size, seq_len, d_model), "float32"],
+        "lbl_word": [(None, 1), "int64"],
+        "lbl_weight": [(None, 1), "float32"],
+        "init_score": [(batch_size, 1), "float32", 2],
+        "init_idx": [(batch_size, ), "int32"],
+    }
+    return input_descs
+
+
+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", )
+fast_decoder_data_input_fields = (
+    "trg_word",
+    "trg_src_attn_bias", )
+
+
+class ModelHyperParams(object):
+    print_step = 2
+    init_from_params = "trained_models/step_10/"
+    save_model = "trained_models"
+    inference_model_dir = "infer_model"
+    output_file = "predict.txt"
+    batch_size = 5
+    epoch = 1
+    learning_rate = 2.0
+    beta1 = 0.9
+    beta2 = 0.997
+    eps = 1e-9
+    warmup_steps = 8000
+    label_smooth_eps = 0.1
+    beam_size = 5
+    max_out_len = 256
+    n_best = 1
+    src_vocab_size = 10000
+    trg_vocab_size = 10000
+    bos_idx = 0  # index for <bos> token
+    eos_idx = 1  # index for <eos> token
+    unk_idx = 2  # index for <unk> token
+    max_length = 256
+    d_model = 512
+    d_inner_hid = 2048
+    d_key = 64
+    d_value = 64
+    n_head = 8
+    n_layer = 6
+    prepostprocess_dropout = 0.1
+    attention_dropout = 0.1
+    relu_dropout = 0.1
+    preprocess_cmd = "n"  # layer normalization
+    postprocess_cmd = "da"  # dropout + residual connection
+    weight_sharing = True
+
+
+def pad_batch_data(insts,
+                   pad_idx,
+                   n_head,
+                   is_target=False,
+                   is_label=False,
+                   return_attn_bias=True,
+                   return_max_len=True,
+                   return_num_token=False):
+    return_list = []
+    max_len = max(len(inst) for inst in insts)
+    inst_data = np.array(
+        [inst + [pad_idx] * (max_len - len(inst)) for inst in insts])
+    return_list += [inst_data.astype("int64").reshape([-1, 1])]
+    if is_label:  # label weight
+        inst_weight = np.array([[1.] * len(inst) + [0.] * (max_len - len(inst))
+                                for inst in insts])
+        return_list += [inst_weight.astype("float32").reshape([-1, 1])]
+    else:  # position data
+        inst_pos = np.array([
+            list(range(0, len(inst))) + [0] * (max_len - len(inst))
+            for inst in insts
+        ])
+        return_list += [inst_pos.astype("int64").reshape([-1, 1])]
+    if return_attn_bias:
+        if is_target:
+            slf_attn_bias_data = np.ones((inst_data.shape[0], max_len, max_len))
+            slf_attn_bias_data = np.triu(slf_attn_bias_data,
+                                         1).reshape([-1, 1, max_len, max_len])
+            slf_attn_bias_data = np.tile(slf_attn_bias_data,
+                                         [1, n_head, 1, 1]) * [-1e9]
+        else:
+            slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] *
+                                           (max_len - len(inst))
+                                           for inst in insts])
+            slf_attn_bias_data = np.tile(
+                slf_attn_bias_data.reshape([-1, 1, 1, max_len]),
+                [1, n_head, max_len, 1])
+        return_list += [slf_attn_bias_data.astype("float32")]
+    if return_max_len:
+        return_list += [max_len]
+    if return_num_token:
+        num_token = 0
+        for inst in insts:
+            num_token += len(inst)
+        return_list += [num_token]
+    return return_list if len(return_list) > 1 else return_list[0]
+
+
+def prepare_train_input(insts, src_pad_idx, trg_pad_idx, n_head):
+    src_word, src_pos, src_slf_attn_bias, src_max_len = pad_batch_data(
+        [inst[0] for inst in insts], src_pad_idx, n_head, is_target=False)
+    src_word = src_word.reshape(-1, src_max_len)
+    src_pos = src_pos.reshape(-1, src_max_len)
+    trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = pad_batch_data(
+        [inst[1] for inst in insts], trg_pad_idx, n_head, is_target=True)
+    trg_word = trg_word.reshape(-1, trg_max_len)
+    trg_pos = trg_pos.reshape(-1, trg_max_len)
+
+    trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
+                                [1, 1, trg_max_len, 1]).astype("float32")
+
+    lbl_word, lbl_weight, num_token = pad_batch_data(
+        [inst[2] for inst in insts],
+        trg_pad_idx,
+        n_head,
+        is_target=False,
+        is_label=True,
+        return_attn_bias=False,
+        return_max_len=False,
+        return_num_token=True)
+    lbl_word = lbl_word.reshape(-1, 1)
+    lbl_weight = lbl_weight.reshape(-1, 1)
+
+    data_inputs = [
+        src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
+        trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
+    ]
+
+    return data_inputs
+
+
+def prepare_infer_input(insts, src_pad_idx, bos_idx, n_head):
+    src_word, src_pos, src_slf_attn_bias, src_max_len = pad_batch_data(
+        [inst[0] for inst in insts], src_pad_idx, n_head, is_target=False)
+    # start tokens
+    trg_word = np.asarray([[bos_idx]] * len(insts), dtype="int64")
+    trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
+                                [1, 1, 1, 1]).astype("float32")
+    trg_word = trg_word.reshape(-1, 1)
+    src_word = src_word.reshape(-1, src_max_len)
+    src_pos = src_pos.reshape(-1, src_max_len)
+
+    data_inputs = [
+        src_word, src_pos, src_slf_attn_bias, trg_word, trg_src_attn_bias
+    ]
+    return data_inputs
+
+
+def get_feed_data_reader(args, mode='train'):
+    def __for_train__():
+        train_reader = paddle.batch(
+            wmt16.train(args.src_vocab_size, args.trg_vocab_size),
+            batch_size=args.batch_size)
+        for batch in train_reader():
+            tensors = prepare_train_input(batch, args.eos_idx, args.eos_idx,
+                                          args.n_head)
+            yield tensors
+
+    def __for_test__():
+        test_reader = paddle.batch(
+            wmt16.train(args.src_vocab_size, args.trg_vocab_size),
+            batch_size=args.batch_size)
+        for batch in test_reader():
+            tensors = prepare_infer_input(batch, args.eos_idx, args.eos_idx,
+                                          args.n_head)
+            yield tensors
+
+    return __for_train__ if mode == 'train' else __for_test__
+
+
+class InputField(object):
+    def __init__(self, input_slots):
+        self.feed_list = []
+        for slot in input_slots:
+            self.feed_list.append(
+                fluid.layers.data(
+                    name=slot['name'],
+                    shape=slot['shape'],
+                    dtype=slot['dtype'],
+                    lod_level=slot.get('lod_level', 0),
+                    append_batch_size=False))
+
+
+def load(program, model_path, executor=None, var_list=None):
+    """
+    To load python2 saved models in python3.
+    """
+    try:
+        fluid.load(program, model_path, executor, var_list)
+    except UnicodeDecodeError:
+        warnings.warn(
+            "An UnicodeDecodeError is catched, which might be caused by loading "
+            "a python2 saved model. Encoding of pickle.load would be set and "
+            "load again automatically.")
+        if six.PY3:
+            load_bak = pickle.load
+            pickle.load = partial(load_bak, encoding="latin1")
+            fluid.load(program, model_path, executor, var_list)
+            pickle.load = load_bak
+
+
+def load_dygraph(model_path, keep_name_table=False):
+    """
+    To load python2 saved models in python3.
+    """
+    try:
+        para_dict, opti_dict = fluid.load_dygraph(model_path, keep_name_table)
+        return para_dict, opti_dict
+    except UnicodeDecodeError:
+        warnings.warn(
+            "An UnicodeDecodeError is catched, which might be caused by loading "
+            "a python2 saved model. Encoding of pickle.load would be set and "
+            "load again automatically.")
+        if six.PY3:
+            load_bak = pickle.load
+            pickle.load = partial(load_bak, encoding="latin1")
+            para_dict, opti_dict = fluid.load_dygraph(model_path,
+                                                      keep_name_table)
+            pickle.load = load_bak
+            return para_dict, opti_dict