Change from width-first backward to deep-first backward process

test=develop

paddle/fluid/imperative/layer.cc

@@ -129,14 +129,14 @@ class Autograd {
     std::map<OpBase*, int> dep_counts = ComputeDepCounts(var->PreOp());
 
     while (!ready.empty()) {
-      OpBase* ready_op = ready.front();
-      ready.pop_front();
+      OpBase* ready_op = ready.back();
+      ready.pop_back();
       std::map<std::string, std::vector<VarBase*>> input_grads =
           ready_op->ApplyGrad();
 
       for (auto it : input_grads) {
         const std::vector<VarBase*>& ingrads = it.second;
-        for (size_t i = 0; i < ingrads.size(); ++i) {
+        for (int64_t i = ingrads.size() - 1; i >= 0; --i) {
           if (!ingrads[i]) continue;
           if (ready_op->input_vars_[it.first][i]->IsStopGradient()) {
             continue;

python/paddle/fluid/tests/unittests/test_imperative_transformer.py

@@ -106,7 +106,7 @@ class ModelHyperParams(object):
     # 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 = 1
+    n_layer = 6
     # dropout rates of different modules.
     prepostprocess_dropout = 0.1
     attention_dropout = 0.1
@@ -303,7 +303,7 @@ use_py_reader = False
 sync = False
 
 # how many batches we use
-batch_num = 1
+batch_num = 2
 
 np.random.seed = 1
 src_word_np = np.random.randint(
@@ -359,6 +359,59 @@ pos_inp2 = position_encoding_init(ModelHyperParams.max_length,
                                   ModelHyperParams.d_model)
 
 
+class PrePostProcessLayer(Layer):
+    def __init__(self, name_scope, process_cmd, shape_len=None):
+        super(PrePostProcessLayer, self).__init__(name_scope)
+        for cmd in process_cmd:
+            if cmd == "n":
+                self._layer_norm = LayerNorm(
+                    name_scope=self.full_name(),
+                    begin_norm_axis=shape_len - 1,
+                    param_attr=fluid.ParamAttr(
+                        initializer=fluid.initializer.Constant(1.)),
+                    bias_attr=fluid.ParamAttr(
+                        initializer=fluid.initializer.Constant(0.)))
+
+    def forward(self, prev_out, out, process_cmd, dropout_rate=0.):
+        for cmd in process_cmd:
+            if cmd == "a":  # add residual connection
+                out = out + prev_out if prev_out else out
+            elif cmd == "n":  # add layer normalization
+                out = self._layer_norm(out)
+            elif cmd == "d":  # add dropout
+                if dropout_rate:
+                    out = fluid.layers.dropout(
+                        out,
+                        dropout_prob=dropout_rate,
+                        seed=ModelHyperParams.dropout_seed,
+                        is_test=False)
+        return out
+
+
+class PositionwiseFeedForwardLayer(Layer):
+    def __init__(self, name_scope, d_inner_hid, d_hid, dropout_rate):
+        super(PositionwiseFeedForwardLayer, self).__init__(name_scope)
+        self._i2h = FC(name_scope=self.full_name(),
+                       size=d_inner_hid,
+                       num_flatten_dims=2,
+                       act="relu")
+        self._h2o = FC(name_scope=self.full_name(),
+                       size=d_hid,
+                       num_flatten_dims=2)
+        self._dropout_rate = dropout_rate
+
+    def forward(self, x):
+        hidden = self._i2h(x)
+        if self._dropout_rate:
+            hidden = fluid.layers.dropout(
+                hidden,
+                dropout_prob=self._dropout_rate,
+                seed=ModelHyperParams.dropout_seed,
+                is_test=False)
+        out = self._h2o(hidden)
+        return out
+
+
 class MultiHeadAttentionLayer(Layer):
     def __init__(self,
                  name_scope,
@@ -393,22 +446,11 @@ class MultiHeadAttentionLayer(Layer):
                            bias_attr=False,
                            num_flatten_dims=2)
 
-    def _mm(self, input):
-        input_shape = input.shape
-        param_shape = [
-            reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:], 1)
-        ] + [self._size]
-        self.x = self.create_parameter(
-            attr=None, shape=param_shape, dtype=self._dtype, is_bias=False)
-
     def forward(self, queries, keys, values, attn_bias):
         # compute q ,k ,v
         keys = queries if keys is None else keys
         values = keys if values is None else values
 
-        #  q = queries
-        #  k = keys
-        #  v = values
         q = self._q_fc(queries)
         k = self._k_fc(keys)
         v = self._v_fc(values)
@@ -453,38 +495,181 @@ class MultiHeadAttentionLayer(Layer):
             inplace=False)
 
         # fc to output
-        print(final_out.shape)
         proj_out = self._proj_fc(final_out)
         return proj_out
 
 
-class PrePostProcessLayer(Layer):
-    def __init__(self, name_scope, process_cmd, shape_len=None):
-        super(PrePostProcessLayer, self).__init__(name_scope)
-        for cmd in process_cmd:
-            if cmd == "n":
-                self._layer_norm = LayerNorm(
-                    name_scope=self.full_name(),
-                    begin_norm_axis=shape_len - 1,
-                    param_attr=fluid.ParamAttr(
-                        initializer=fluid.initializer.Constant(1.)),
-                    bias_attr=fluid.ParamAttr(
-                        initializer=fluid.initializer.Constant(0.)))
+class EncoderSubLayer(Layer):
+    def __init__(self,
+                 name_scope,
+                 n_head,
+                 d_key,
+                 d_value,
+                 d_model,
+                 d_inner_hid,
+                 prepostprocess_dropout,
+                 attention_dropout,
+                 relu_dropout,
+                 preprocess_cmd="n",
+                 postprocess_cmd="da"):
 
-    def forward(self, prev_out, out, process_cmd, dropout_rate=0.):
-        for cmd in process_cmd:
-            if cmd == "a":  # add residual connection
-                out = out + prev_out if prev_out else out
-            elif cmd == "n":  # add layer normalization
-                out = self._layer_norm(out)
-            elif cmd == "d":  # add dropout
-                if dropout_rate:
-                    out = fluid.layers.dropout(
-                        out,
-                        dropout_prob=dropout_rate,
-                        seed=ModelHyperParams.dropout_seed,
-                        is_test=False)
-        return out
+        super(EncoderSubLayer, self).__init__(name_scope)
+        self._preprocess_cmd = preprocess_cmd
+        self._postprocess_cmd = postprocess_cmd
+        self._prepostprocess_dropout = prepostprocess_dropout
+
+        self._preprocess_layer = PrePostProcessLayer(self.full_name(),
+                                                     self._preprocess_cmd, 3)
+        self._multihead_attention_layer = MultiHeadAttentionLayer(
+            self.full_name(), d_key, d_value, d_model, n_head,
+            attention_dropout)
+        self._postprocess_layer = PrePostProcessLayer(
+            self.full_name(), self._postprocess_cmd, None)
+        self._preprocess_layer2 = PrePostProcessLayer(self.full_name(),
+                                                      self._preprocess_cmd, 3)
+        self._positionwise_feed_forward = PositionwiseFeedForwardLayer(
+            self.full_name(), d_inner_hid, d_model, relu_dropout)
+        self._postprocess_layer2 = PrePostProcessLayer(
+            self.full_name(), self._postprocess_cmd, None)
+
+    def forward(self, enc_input, attn_bias):
+        pre_process_multihead = self._preprocess_layer(
+            None, enc_input, self._preprocess_cmd, self._prepostprocess_dropout)
+        attn_output = self._multihead_attention_layer(pre_process_multihead,
+                                                      None, None, attn_bias)
+        attn_output = self._postprocess_layer(enc_input, attn_output,
+                                              self._postprocess_cmd,
+                                              self._prepostprocess_dropout)
+        pre_process2_output = self._preprocess_layer2(
+            None, attn_output, self._preprocess_cmd,
+            self._prepostprocess_dropout)
+        ffd_output = self._positionwise_feed_forward(pre_process2_output)
+        return self._postprocess_layer2(attn_output, ffd_output,
+                                        self._postprocess_cmd,
+                                        self._prepostprocess_dropout)
+
+
+class EncoderLayer(Layer):
+    def __init__(self,
+                 name_scope,
+                 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(EncoderLayer, self).__init__(name_scope)
+        self._preprocess_cmd = preprocess_cmd
+        self._encoder_sublayers = list()
+        self._prepostprocess_dropout = prepostprocess_dropout
+        self._n_layer = n_layer
+        self._preprocess_layer = PrePostProcessLayer(self.full_name(),
+                                                     self._preprocess_cmd, 3)
+        for i in range(n_layer):
+            self._encoder_sublayers.append(
+                self.add_sublayer(
+                    'esl_%d' % i,
+                    EncoderSubLayer(
+                        self.full_name(), n_head, d_key, d_value, d_model,
+                        d_inner_hid, prepostprocess_dropout, attention_dropout,
+                        relu_dropout, preprocess_cmd, postprocess_cmd)))
+
+    def forward(self, enc_input, attn_bias):
+        for i in range(self._n_layer):
+            enc_output = self._encoder_sublayers[i](enc_input, attn_bias)
+            enc_input = enc_output
+
+        return self._preprocess_layer(None, enc_output, self._preprocess_cmd,
+                                      self._prepostprocess_dropout)
+
+
+class PrepareEncoderDecoderLayer(Layer):
+    def __init__(self,
+                 name_scope,
+                 src_vocab_size,
+                 src_emb_dim,
+                 src_max_len,
+                 dropout_rate,
+                 word_emb_param_name=None,
+                 pos_enc_param_name=None):
+        super(PrepareEncoderDecoderLayer, self).__init__(name_scope)
+        self._src_max_len = src_max_len
+        self._src_emb_dim = src_emb_dim
+        self._src_vocab_size = src_vocab_size
+        self._dropout_rate = dropout_rate
+        self._input_emb = Embedding(
+            name_scope=self.full_name(),
+            size=[src_vocab_size, src_emb_dim],
+            padding_idx=0,
+            param_attr=fluid.ParamAttr(
+                name=word_emb_param_name,
+                initializer=fluid.initializer.Normal(0., src_emb_dim**-0.5)))
+
+        if pos_enc_param_name is pos_enc_param_names[0]:
+            pos_inp = pos_inp1
+        else:
+            pos_inp = pos_inp2
+        self._pos_emb = Embedding(
+            name_scope=self.full_name(),
+            size=[self._src_max_len, src_emb_dim],
+            param_attr=fluid.ParamAttr(
+                name=pos_enc_param_name,
+                initializer=fluid.initializer.NumpyArrayInitializer(pos_inp),
+                trainable=False))
+
+        # use in dygraph_mode to fit different length batch
+        # self._pos_emb._w = to_variable(
+        #     position_encoding_init(self._src_max_len, self._src_emb_dim))
+
+    def forward(self, src_word, src_pos):
+        src_word_emb = self._input_emb(src_word)
+        src_word_emb = fluid.layers.scale(
+            x=src_word_emb, scale=self._src_emb_dim**0.5)
+        # # TODO change this to fit dynamic length input
+        src_pos_emb = self._pos_emb(src_pos)
+        src_pos_emb.stop_gradient = True
+        enc_input = src_word_emb + src_pos_emb
+        return fluid.layers.dropout(
+            enc_input,
+            dropout_prob=self._dropout_rate,
+            seed=ModelHyperParams.dropout_seed,
+            is_test=False) if self._dropout_rate else enc_input
+
+
+class WrapEncoderLayer(Layer):
+    def __init__(self, name_cope, 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, weight_sharing):
+        """
+        The wrapper assembles together all needed layers for the encoder.
+        """
+        super(WrapEncoderLayer, self).__init__(name_cope)
+
+        self._prepare_encoder_layer = PrepareEncoderDecoderLayer(
+            self.full_name(),
+            src_vocab_size,
+            d_model,
+            max_length,
+            prepostprocess_dropout,
+            word_emb_param_name=word_emb_param_names[0],
+            pos_enc_param_name=pos_enc_param_names[0])
+        self._encoder = EncoderLayer(
+            self.full_name(), n_layer, n_head, d_key, d_value, d_model,
+            d_inner_hid, prepostprocess_dropout, attention_dropout,
+            relu_dropout, preprocess_cmd, postprocess_cmd)
+
+    def forward(self, enc_inputs):
+        src_word, src_pos, src_slf_attn_bias = enc_inputs
+        enc_input = self._prepare_encoder_layer(src_word, src_pos)
+        enc_output = self._encoder(enc_input, src_slf_attn_bias)
+        return enc_output
 
 
 class DecoderSubLayer(Layer):
@@ -494,13 +679,20 @@ class DecoderSubLayer(Layer):
                  d_key,
                  d_value,
                  d_model,
+                 d_inner_hid,
+                 prepostprocess_dropout,
                  attention_dropout,
+                 relu_dropout,
+                 preprocess_cmd,
+                 postprocess_cmd,
                  cache=None,
-                 preprocess_cmd="n",
                  gather_idx=None):
         super(DecoderSubLayer, self).__init__(name_scope)
-        self._preprocess_layer = PrePostProcessLayer(self.full_name(),
-                                                     preprocess_cmd, 3)
+        self._postprocess_cmd = postprocess_cmd
+        self._preprocess_cmd = preprocess_cmd
+        self._prepostprcess_dropout = prepostprocess_dropout
+        self._pre_process_layer = PrePostProcessLayer(self.full_name(),
+                                                      preprocess_cmd, 3)
         self._multihead_attention_layer = MultiHeadAttentionLayer(
             self.full_name(),
             d_key,
@@ -510,42 +702,300 @@ class DecoderSubLayer(Layer):
             attention_dropout,
             cache=cache,
             gather_idx=gather_idx)
+        self._post_process_layer = PrePostProcessLayer(self.full_name(),
+                                                       postprocess_cmd, None)
+        self._pre_process_layer2 = PrePostProcessLayer(self.full_name(),
+                                                       preprocess_cmd, 3)
+        self._multihead_attention_layer2 = MultiHeadAttentionLayer(
+            self.full_name(),
+            d_key,
+            d_value,
+            d_model,
+            n_head,
+            attention_dropout,
+            cache=cache,
+            gather_idx=gather_idx,
+            static_kv=True)
+        self._post_process_layer2 = PrePostProcessLayer(self.full_name(),
+                                                        postprocess_cmd, None)
+        self._pre_process_layer3 = PrePostProcessLayer(self.full_name(),
+                                                       preprocess_cmd, 3)
+        self._positionwise_feed_forward_layer = PositionwiseFeedForwardLayer(
+            self.full_name(), d_inner_hid, d_model, relu_dropout)
+        self._post_process_layer3 = PrePostProcessLayer(self.full_name(),
+                                                        postprocess_cmd, None)
 
-    def forward(self, input, slf_attn_bias):
-        print(input.shape)
-        print(slf_attn_bias.shape)
-        y = self._preprocess_layer(None, input, "n", 0.1)
-        slf_attn_output = self._multihead_attention_layer(y, None, None,
-                                                          slf_attn_bias)
-        return slf_attn_output, y
+    def forward(self, dec_input, enc_output, slf_attn_bias, dec_enc_attn_bias):
+        pre_process_rlt = self._pre_process_layer(
+            None, dec_input, self._preprocess_cmd, self._prepostprcess_dropout)
+        slf_attn_output = self._multihead_attention_layer(pre_process_rlt, None,
+                                                          None, slf_attn_bias)
+        slf_attn_output_pp = self._post_process_layer(
+            dec_input, slf_attn_output, self._postprocess_cmd,
+            self._prepostprcess_dropout)
+        pre_process_rlt2 = self._pre_process_layer2(None, slf_attn_output_pp,
+                                                    self._preprocess_cmd,
+                                                    self._prepostprcess_dropout)
+        enc_attn_output_pp = self._multihead_attention_layer2(
+            pre_process_rlt2, enc_output, enc_output, dec_enc_attn_bias)
+        enc_attn_output = self._post_process_layer2(
+            slf_attn_output, enc_attn_output_pp, self._postprocess_cmd,
+            self._prepostprcess_dropout)
+        pre_process_rlt3 = self._pre_process_layer3(None, enc_attn_output,
+                                                    self._preprocess_cmd,
+                                                    self._prepostprcess_dropout)
+        ffd_output = self._positionwise_feed_forward_layer(pre_process_rlt3)
+        dec_output = self._post_process_layer3(enc_attn_output, ffd_output,
+                                               self._postprocess_cmd,
+                                               self._prepostprcess_dropout)
+        return dec_output
+
+
+class DecoderLayer(Layer):
+    def __init__(self,
+                 name_scope,
+                 n_layer,
+                 n_head,
+                 d_key,
+                 d_value,
+                 d_model,
+                 d_inner_hid,
+                 prepostprocess_dropout,
+                 attention_dropout,
+                 relu_dropout,
+                 preprocess_cmd,
+                 postprocess_cmd,
+                 caches=None,
+                 gather_idx=None):
+        super(DecoderLayer, self).__init__(name_scope)
+        self._pre_process_layer = PrePostProcessLayer(self.full_name(),
+                                                      preprocess_cmd, 3)
+        self._decoder_sub_layers = list()
+        self._n_layer = n_layer
+        self._preprocess_cmd = preprocess_cmd
+        self._prepostprocess_dropout = prepostprocess_dropout
+        for i in range(n_layer):
+            self._decoder_sub_layers.append(
+                self.add_sublayer(
+                    'dsl_%d' % i,
+                    DecoderSubLayer(
+                        self.full_name(),
+                        n_head,
+                        d_key,
+                        d_value,
+                        d_model,
+                        d_inner_hid,
+                        prepostprocess_dropout,
+                        attention_dropout,
+                        relu_dropout,
+                        preprocess_cmd,
+                        postprocess_cmd,
+                        cache=None if caches is None else caches[i],
+                        gather_idx=gather_idx)))
+
+    def forward(self, dec_input, enc_output, dec_slf_attn_bias,
+                dec_enc_attn_bias):
+        for i in range(self._n_layer):
+            tmp_dec_output = self._decoder_sub_layers[i](
+                dec_input, enc_output, dec_slf_attn_bias, dec_enc_attn_bias)
+            dec_input = tmp_dec_output
+
+        dec_output = self._pre_process_layer(None, tmp_dec_output,
+                                             self._preprocess_cmd,
+                                             self._prepostprocess_dropout)
+        return dec_output
+
+
+class WrapDecoderLayer(Layer):
+    def __init__(self,
+                 name_scope,
+                 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,
+                 caches=None,
+                 gather_idx=None):
+        """
+        The wrapper assembles together all needed layers for the encoder.
+        """
+        super(WrapDecoderLayer, self).__init__(name_scope)
+
+        self._prepare_decoder_layer = PrepareEncoderDecoderLayer(
+            self.full_name(),
+            trg_vocab_size,
+            d_model,
+            max_length,
+            prepostprocess_dropout,
+            word_emb_param_name=word_emb_param_names[1],
+            pos_enc_param_name=pos_enc_param_names[1])
+        self._decoder_layer = DecoderLayer(
+            self.full_name(),
+            n_layer,
+            n_head,
+            d_key,
+            d_value,
+            d_model,
+            d_inner_hid,
+            prepostprocess_dropout,
+            attention_dropout,
+            relu_dropout,
+            preprocess_cmd,
+            postprocess_cmd,
+            caches=caches,
+            gather_idx=gather_idx)
+        self._weight_sharing = weight_sharing
+        if not weight_sharing:
+            self._fc = FC(self.full_name(),
+                          size=trg_vocab_size,
+                          bias_attr=False)
+
+    def forward(self, dec_inputs=None, enc_output=None):
+        trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias = dec_inputs
+        dec_input = self._prepare_decoder_layer(trg_word, trg_pos)
+        dec_output = self._decoder_layer(dec_input, enc_output,
+                                         trg_slf_attn_bias, trg_src_attn_bias)
+
+        dec_output_reshape = fluid.layers.reshape(
+            dec_output, shape=[-1, dec_output.shape[-1]], inplace=False)
+
+        if self._weight_sharing:
+            predict = fluid.layers.matmul(
+                x=dec_output_reshape,
+                y=self._prepare_decoder_layer._input_emb._w,
+                transpose_y=True)
+        else:
+            predict = self._fc(dec_output_reshape)
+
+        if dec_inputs is None:
+            # Return probs for independent decoder program.
+            predict_out = fluid.layers.softmax(predict)
+            return predict_out
+        return predict
+
+
+class TransFormer(Layer):
+    def __init__(self,
+                 name_scope,
+                 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,
+                 label_smooth_eps,
+                 use_py_reader=False,
+                 is_test=False):
+        super(TransFormer, self).__init__(name_scope)
+        self._label_smooth_eps = label_smooth_eps
+        self._trg_vocab_size = trg_vocab_size
+        if weight_sharing:
+            assert src_vocab_size == trg_vocab_size, (
+                "Vocabularies in source and target should be same for weight sharing."
+            )
+        self._wrap_encoder_layer = WrapEncoderLayer(
+            self.full_name(), 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,
+            weight_sharing)
+        self._wrap_decoder_layer = WrapDecoderLayer(
+            self.full_name(), 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)
+
+        if weight_sharing:
+            self._wrap_decoder_layer._prepare_decoder_layer._input_emb._w = self._wrap_encoder_layer._prepare_encoder_layer._input_emb._w
+
+    def forward(self, enc_inputs, dec_inputs, label, weights):
+        enc_output = self._wrap_encoder_layer(enc_inputs)
+        predict = self._wrap_decoder_layer(dec_inputs, enc_output)
+        if self._label_smooth_eps:
+            label_out = fluid.layers.label_smooth(
+                label=fluid.layers.one_hot(
+                    input=label, depth=self._trg_vocab_size),
+                epsilon=self._label_smooth_eps)
+
+        cost = fluid.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 = fluid.layers.reduce_sum(weighted_cost)
+        token_num = fluid.layers.reduce_sum(weights)
+        token_num.stop_gradient = True
+        avg_cost = sum_cost / token_num
+        return sum_cost, avg_cost, predict, token_num
 
 
 class TestDygraphTransformer(unittest.TestCase):
     def test_transformer_float32(self):
         seed = 90
-        x1 = np.ones([32, 4, 512]).astype('float32')
-        x2 = np.ones([32, 8, 4, 4]).astype('float32')
-        with guard(place=fluid.CPUPlace()):
+        with guard():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
-            transformer = DecoderSubLayer(
-                'transformer', ModelHyperParams.n_head, ModelHyperParams.d_key,
-                ModelHyperParams.d_value, ModelHyperParams.d_model,
-                ModelHyperParams.attention_dropout)
-            optimizer = fluid.optimizer.SGD(learning_rate=0.003)
+            transformer = TransFormer(
+                'transformer',
+                ModelHyperParams.src_vocab_size,
+                ModelHyperParams.trg_vocab_size,
+                ModelHyperParams.max_length + 1,
+                ModelHyperParams.n_layer,
+                ModelHyperParams.n_head,
+                ModelHyperParams.d_key,
+                ModelHyperParams.d_value,
+                ModelHyperParams.d_model,
+                ModelHyperParams.d_inner_hid,
+                ModelHyperParams.prepostprocess_dropout,
+                ModelHyperParams.attention_dropout,
+                ModelHyperParams.relu_dropout,
+                ModelHyperParams.preprocess_cmd,
+                ModelHyperParams.postprocess_cmd,
+                ModelHyperParams.weight_sharing,
+                TrainTaskConfig.label_smooth_eps,
+                use_py_reader=use_py_reader,
+                is_test=False)
+            if sync:
+                lr_decay = fluid.layers.learning_rate_scheduler.noam_decay(
+                    ModelHyperParams.d_model, TrainTaskConfig.warmup_steps)
+                with fluid.default_main_program()._lr_schedule_guard():
+                    learning_rate = lr_decay * TrainTaskConfig.learning_rate
+                optimizer = fluid.optimizer.Adam(
+                    learning_rate=learning_rate,
+                    beta1=TrainTaskConfig.beta1,
+                    beta2=TrainTaskConfig.beta2,
+                    epsilon=TrainTaskConfig.eps)
+            else:
+                optimizer = fluid.optimizer.SGD(learning_rate=0.003)
             dy_param_init = dict()
             dy_param_updated = dict()
             for i in range(batch_num):
-                loss, y = transformer(to_variable(x1), to_variable(x2))
-                loss = fluid.layers.reduce_sum(loss)
-                print('dy los', loss.shape)
+                enc_inputs, dec_inputs, label, weights = create_data()
+                dy_sum_cost, dy_avg_cost, dy_predict, dy_token_num = transformer(
+                    enc_inputs, dec_inputs, label, weights)
                 if i == 0:
                     for param in transformer.parameters():
                         dy_param_init[param.name] = param._numpy()
 
-                loss._backward()
-                optimizer.minimize(loss)
-                dy_key_value = y._gradient()
+                dy_avg_cost._backward()
+                optimizer.minimize(dy_avg_cost)
                 transformer.clear_gradients()
                 if i == batch_num - 1:
                     for param in transformer.parameters():
@@ -554,60 +1004,92 @@ class TestDygraphTransformer(unittest.TestCase):
         with new_program_scope():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
-            transformer = DecoderSubLayer(
-                'transformer', ModelHyperParams.n_head, ModelHyperParams.d_key,
-                ModelHyperParams.d_value, ModelHyperParams.d_model,
-                ModelHyperParams.attention_dropout)
-            exe = fluid.Executor(fluid.CPUPlace())
+            transformer = TransFormer(
+                'transformer',
+                ModelHyperParams.src_vocab_size,
+                ModelHyperParams.trg_vocab_size,
+                ModelHyperParams.max_length + 1,
+                ModelHyperParams.n_layer,
+                ModelHyperParams.n_head,
+                ModelHyperParams.d_key,
+                ModelHyperParams.d_value,
+                ModelHyperParams.d_model,
+                ModelHyperParams.d_inner_hid,
+                ModelHyperParams.prepostprocess_dropout,
+                ModelHyperParams.attention_dropout,
+                ModelHyperParams.relu_dropout,
+                ModelHyperParams.preprocess_cmd,
+                ModelHyperParams.postprocess_cmd,
+                ModelHyperParams.weight_sharing,
+                TrainTaskConfig.label_smooth_eps,
+                use_py_reader=use_py_reader,
+                is_test=False)
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
             optimizer = fluid.optimizer.SGD(learning_rate=0.003)
 
-            data1 = fluid.layers.data(name='X', shape=[4, 512], dtype='float32')
-            data2 = fluid.layers.data(
-                name='Y', shape=[8, 4, 4], dtype='float32')
-            loss, y = transformer(data1, data2)
-            loss = fluid.layers.reduce_sum(loss)
-            print('loss hspae', loss.shape)
-
-            optimizer.minimize(loss)
+            data_input_names = encoder_data_input_fields + decoder_data_input_fields[:
+                                                                                     -1] + label_data_input_fields
+            all_inputs = make_all_inputs(data_input_names)
+            enc_inputs_len = len(encoder_data_input_fields)
+            dec_inputs_len = len(decoder_data_input_fields[:-1])
+            enc_inputs = all_inputs[0:enc_inputs_len]
+            dec_inputs = all_inputs[enc_inputs_len:enc_inputs_len +
+                                    dec_inputs_len]
+            label = all_inputs[-2]
+            weights = all_inputs[-1]
+            static_param_updated = dict()
+            static_param_init = dict()
+            static_param_name_list = list()
+            static_sum_cost, static_avg_cost, static_predict, static_token_num = transformer(
+                enc_inputs, dec_inputs, label, weights)
 
-            static_param_init = {}
-            static_param_name_list = []
-            static_param_updated = {}
+            optimizer.minimize(static_avg_cost)
             for param in transformer.parameters():
                 static_param_name_list.append(param.name)
             out = exe.run(fluid.default_startup_program(),
                           fetch_list=static_param_name_list)
-
             for i in range(len(static_param_name_list)):
                 static_param_init[static_param_name_list[i]] = out[i]
-
-            print(fluid.default_main_program())
+            static_sum_cost_value = None
+            static_avg_cost_value = None
+            static_predict_value = None
+            static_token_num_value = None
             for i in range(batch_num):
-                feed_dict = {"X": x1, "Y": x2}
+                feed_dict = create_feed_dict_list(create_data(True))
                 fetch_list = [
-                    "transformer/DecoderSubLayer_0/PrePostProcessLayer_0/LayerNorm_0.tmp_2@GRAD"
+                    static_sum_cost, static_avg_cost, static_predict,
+                    static_token_num
                 ]
                 fetch_list.extend(static_param_name_list)
 
                 out = exe.run(fluid.default_main_program(),
                               feed=feed_dict,
                               fetch_list=fetch_list)
+                static_sum_cost_value = out[0]
+                static_avg_cost_value = out[1]
+                static_predict_value = out[2]
+                static_token_num_value = out[3]
                 if i == batch_num - 1:
-                    static_key_value = out[0]
-                    for k in range(1, len(out)):
+                    for k in range(4, len(out)):
                         static_param_updated[static_param_name_list[k -
-                                                                    1]] = out[k]
+                                                                    4]] = out[k]
 
+        self.assertTrue(
+            np.allclose(static_avg_cost_value, dy_avg_cost._numpy()))
+        self.assertTrue(
+            np.allclose(static_sum_cost_value, dy_sum_cost._numpy()))
+        self.assertTrue(
+            np.allclose(
+                static_predict_value, dy_predict._numpy(), atol=1e-5))
+        self.assertTrue(
+            np.allclose(static_token_num_value, dy_token_num._numpy()))
         for key, value in six.iteritems(static_param_init):
-            self.assertTrue(np.array_equal(value, dy_param_init[key]))
+            self.assertTrue(np.allclose(value, dy_param_init[key]))
         for key, value in six.iteritems(static_param_updated):
-            if not (value == dy_param_updated[key]).all():
-                print(key)
-        if not np.array_equal(dy_key_value, static_key_value):
-            print("xxx", dy_key_value, static_key_value)
-            print("yyy")
-            print(dy_key_value - static_key_value)
-            print(np.where(dy_key_value - static_key_value))
+            self.assertTrue(
+                np.allclose(
+                    value, dy_param_updated[key], atol=1e-4))
 
 
 if __name__ == '__main__':