add layer norm to Layers, add transformer test in imperative mode (#16092)

* add layer norm to Layers, add transformer prepare encoding

* little change

* finish encoder part

* add decoder part

* finish model part

* add test case and part of data feed

* add transformer test

* add to_parameter, add remove in set_attr

* test=develop, fix pos encoding bug, create_parameter with stantard name

* test=develop, rm dropout test in imperative

* test=develop, fix cpu error

* test=develop, fix minize bug

* test=develop, fix one hot not stop gradient

* test=develop, fix one hot not stop gradient

* test=develop, refine parameter name

* test=develop, fix transformer test in imperative mode

* test=develop, fix transformer test in imperative mode

* test=develop, fix boost and mkl download error

* test=develop, fix boost and mkl download error

* test=develop, fix ci and refine code

* test=develop, fix ci and refine code

paddle/fluid/imperative/layer.cc

@@ -315,6 +315,9 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
       for (size_t i = 0; i < outputs.size(); ++i) {
         framework::Variable* grad = outputs[i]->var_;
         framework::Variable* orig_grad = origin_outputs[i]->var_;
+        VLOG(3) << "AddTo Called with orig_grad is: "
+                << origin_outputs[i]->name_ << " Grad to be added is "
+                << outputs[i]->name_;
         AddTo(grad, orig_grad, place_);
         delete grad;
       }

paddle/fluid/imperative/tracer.cc

@@ -277,6 +277,7 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
           VarBase* var = current_vars_map[var_it->second];
           InitGrad(var, prepared_op.GetDeviceContext());
           grad_out_vars.push_back(var->grads_);
+          VLOG(3) << "grads output var name: " << var->name_;
         }
       }
     }

python/paddle/dataset/wmt14.py

@@ -15,7 +15,7 @@
 WMT14 dataset.
 The original WMT14 dataset is too large and a small set of data for set is
 provided. This module will download dataset from
-http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz and
+http://paddlepaddle.bj.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz and
 parse training set and test set into paddle reader creators.
 
 """

python/paddle/fluid/imperative/base.py

@@ -44,7 +44,7 @@ def guard(place=None):
                     yield
 
 
-def to_variable(value, block=None):
+def to_variable(value, block=None, name=None):
     if isinstance(value, np.ndarray):
         assert enabled(), "to_variable could only be called in imperative mode"
 
@@ -53,7 +53,7 @@ def to_variable(value, block=None):
         py_var = framework.Variable(
             block,
             type=core.VarDesc.VarType.LOD_TENSOR,
-            name=None,
+            name=name,
             shape=value.shape,
             dtype=value.dtype)
         var = py_var._ivar.value()

python/paddle/fluid/imperative/layer_object_helper.py

@@ -105,6 +105,7 @@ class LayerObjectHelper(LayerHelperBase):
 
         Returns dtype of the input
         """
+        inputs_in = inputs_in if (inputs_in is not None) else []
         inputs = self._multiple_input(inputs_in)
         dtype = None
         for each in inputs:

python/paddle/fluid/imperative/layers.py

@@ -17,10 +17,12 @@ import contextlib
 import sys
 import numpy as np
 import collections
+import six
 from .. import unique_name
 from paddle.fluid import core
 from .layer_object_helper import LayerObjectHelper
 from paddle.fluid import framework
+from ..param_attr import ParamAttr
 
 __all__ = ['Layer', 'PyLayer']
 
@@ -72,6 +74,10 @@ class Layer(core.Layer):
 
         Returns created parameter Variable.
         """
+        if isinstance(attr, ParamAttr) and (attr.name is not None):
+            attr.name = ".".join([self._full_name, attr.name])
+        elif isinstance(attr, six.string_types):
+            attr = ".".join([self._full_name, attr])
         return self._helper.create_parameter(attr, shape, dtype, is_bias,
                                              default_initializer)
 
@@ -164,6 +170,7 @@ class Layer(core.Layer):
             the sublayer passed in.
         """
         assert isinstance(sublayer, core.Layer)
+
         self._sub_layers[name] = sublayer
         return sublayer
 

python/paddle/fluid/imperative/nn.py

@@ -20,10 +20,12 @@ from .. import core
 from ..layers import utils
 from . import layers
 from ..framework import Variable, OpProtoHolder
+from ..layers import layer_function_generator
 from ..param_attr import ParamAttr
 from ..initializer import Normal, Constant
-
-__all__ = ['Conv2D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding', 'GRUUnit']
+__all__ = [
+    'Conv2D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding', 'GRUUnit', 'LayerNorm'
+]
 
 
 class Conv2D(layers.Layer):
@@ -438,7 +440,6 @@ class Embedding(layers.Layer):
         self._size = size
         self._is_sparse = is_sparse
         self._is_distributed = is_distributed
-
         self._padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
             size[0] + padding_idx)
 
@@ -471,6 +472,131 @@ class Embedding(layers.Layer):
         return out
 
 
+class LayerNorm(layers.Layer):
+    def __init__(self,
+                 name_scope,
+                 scale=True,
+                 shift=True,
+                 begin_norm_axis=1,
+                 epsilon=1e-05,
+                 param_attr=None,
+                 bias_attr=None,
+                 act=None):
+        """
+        ${comment}
+
+        The formula is as follows:
+
+        ..  math::
+
+            \\mu & = \\frac{1}{H}\\sum_{i=1}^{H} a_i
+
+            \\sigma & = \\sqrt{\\frac{1}{H}\sum_{i=1}^{H}(a_i - \\mu)^2}
+
+            h & = f(\\frac{g}{\\sigma}(a - \\mu) + b)
+
+        * :math:`a`: the vector representation of the summed inputs to the neurons
+        in that layer.
+
+        * :math:`H`: the number of hidden units in a layers
+
+        * :math:`g`: the trainable scale parameter.
+
+        * :math:`b`: the trainable bias parameter.
+
+        Args:
+            input(Variable): The input tensor variable.
+            scale(bool): Whether to learn the adaptive gain :math:`g` after
+                normalization. Default True.
+            shift(bool): Whether to learn the adaptive bias :math:`b` after
+                normalization. Default True.
+            begin_norm_axis(int): The normalization will be performed along
+                dimensions from :attr:`begin_norm_axis` to :attr:`rank(input)`.
+                Default 1.
+            epsilon(float): The small value added to the variance to prevent
+                division by zero. Default 1e-05.
+            param_attr(ParamAttr|None): The parameter attribute for the learnable
+                gain :math:`g`. If :attr:`scale` is False, :attr:`param_attr` is
+                omitted. If :attr:`scale` is True and :attr:`param_attr` is None,
+                a default :code:`ParamAttr` would be added as scale. The
+                :attr:`param_attr` is initialized as 1 if it is added. Default None.
+            bias_attr(ParamAttr|None): The parameter attribute for the learnable
+                bias :math:`b`. If :attr:`shift` is False, :attr:`bias_attr` is
+                omitted. If :attr:`shift` is True and :attr:`param_attr` is None,
+                a default :code:`ParamAttr` would be added as bias. The
+                :attr:`bias_attr` is initialized as 0 if it is added. Default None.
+            act(str): Activation to be applied to the output of layer normalizaiton.
+                      Default None.
+        Returns:
+            ${y_comment}
+
+        Examples:
+
+            >>> data = fluid.layers.data(name='data', shape=[3, 32, 32],
+            >>>                          dtype='float32')
+            >>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
+        """
+
+        super(LayerNorm, self).__init__(name_scope)
+        self._scale = scale
+        self._shift = shift
+        self._begin_norm_axis = begin_norm_axis
+        self._epsilon = epsilon
+        self._param_attr = param_attr
+        self._bias_attr = bias_attr
+        self._act = act
+
+    def _build_once(self, input):
+        self._dtype = self._helper.input_dtype(input)
+        input_shape = input.shape
+        param_shape = [
+            reduce(lambda x, y: x * y, input_shape[self._begin_norm_axis:])
+        ]
+        if self._scale:
+            self._scale_w = self.create_parameter(
+                attr=self._param_attr,
+                shape=param_shape,
+                dtype=self._dtype,
+                default_initializer=Constant(1.0))
+        if self._shift:
+            assert self._bias_attr is not False
+            self._bias_w = self.create_parameter(
+                attr=self._bias_attr,
+                shape=param_shape,
+                dtype=self._dtype,
+                is_bias=True)
+
+    def forward(self, input):
+        inputs = dict()
+        inputs['X'] = input
+        if self._scale:
+            inputs['Scale'] = self._scale_w
+        if self._shift:
+            inputs['Bias'] = self._bias_w
+        # create output
+        mean_out = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype, stop_gradient=True)
+        variance_out = self._helper.create_variable_for_type_inference(
+            dtype=self._dtype, stop_gradient=True)
+        layer_norm_out = self._helper.create_variable_for_type_inference(
+            self._dtype)
+
+        self._helper.append_op(
+            type="layer_norm",
+            inputs=inputs,
+            outputs={
+                "Y": layer_norm_out,
+                "Mean": mean_out,
+                "Variance": variance_out,
+            },
+            attrs={
+                "epsilon": self._epsilon,
+                "begin_norm_axis": self._begin_norm_axis
+            })
+
+        return self._helper.append_activation(layer_norm_out)
+
+
 class GRUUnit(layers.Layer):
     """
     **GRU unit layer**

python/paddle/fluid/layer_helper_base.py

@@ -268,11 +268,9 @@ class LayerHelperBase(object):
         """
         # Deepcopy the attr so that parameters can be shared in program
         attr = copy.deepcopy(attr)
-        if attr is None:
-            attr = ParamAttr._to_attr(attr)
+        attr = ParamAttr._to_attr(attr)
         if not attr:
             return None
-
         assert isinstance(attr, ParamAttr)
         suffix = 'b' if is_bias else 'w'
         if attr.name is None:

python/paddle/fluid/layers/nn.py

@@ -6206,7 +6206,8 @@ def one_hot(input, depth):
         type="one_hot",
         inputs={'X': input},
         attrs={'depth': depth},
-        outputs={'Out': one_hot_out})
+        outputs={'Out': one_hot_out},
+        stop_gradient=True)
     return one_hot_out
 
 

python/paddle/fluid/optimizer.py

@@ -397,13 +397,14 @@ class Optimizer(object):
             for param in parameters:
                 if not param.trainable:
                     continue
-                # create gradient variable
-                grad_var = Variable(
-                    block=loss.block,
-                    name=param._ivar._grad_name(),
-                    stop_gradient=True,
-                    ivar=param._ivar._grad_ivar())
-                params_grads.append((param, grad_var))
+                if param._ivar._grad_ivar() is not None:
+                    # create gradient variable
+                    grad_var = Variable(
+                        block=loss.block,
+                        name=param._ivar._grad_name(),
+                        stop_gradient=True,
+                        ivar=param._ivar._grad_ivar())
+                    params_grads.append((param, grad_var))
             with program_guard(framework.default_main_program(),
                                framework.default_startup_program()):
                 optimize_ops = self._create_optimization_pass(params_grads)

python/paddle/fluid/tests/unittests/test_imperative_transformer.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.
+
+from __future__ import print_function
+
+import unittest
+import paddle.fluid as fluid
+from paddle.fluid.imperative import Embedding, LayerNorm, FC, to_variable, Layer, guard
+from test_imperative_base import new_program_scope
+from paddle.fluid import core
+import numpy as np
+import six
+np.set_printoptions(suppress=True)
+
+
+# Copy from models
+class TrainTaskConfig(object):
+    # support both CPU and GPU now.
+    use_gpu = True
+    # the epoch number to train.
+    pass_num = 30
+    # 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
+    # the directory for saving trained models.
+    model_dir = "trained_models"
+    # the directory for saving checkpoints.
+    ckpt_dir = "trained_ckpts"
+    # the directory for loading checkpoint.
+    # If provided, continue training from the checkpoint.
+    ckpt_path = None
+    # the parameter to initialize the learning rate scheduler.
+    # It should be provided if use checkpoints, since the checkpoint doesn't
+    # include the training step counter currently.
+    start_step = 0
+    # the frequency to save trained models.
+    save_freq = 10000
+
+
+class InferTaskConfig(object):
+    use_gpu = True
+    # the number of examples in one run for sequence generation.
+    batch_size = 10
+    # the parameters for beam search.
+    beam_size = 5
+    max_out_len = 256
+    # the number of decoded sentences to output.
+    n_best = 1
+    # the flags indicating whether to output the special tokens.
+    output_bos = False
+    output_eos = False
+    output_unk = True
+    # the directory for loading the trained model.
+    model_path = "trained_models/pass_1.infer.model"
+
+
+class ModelHyperParams(object):
+    # 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 = 4
+    # 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
+    # random seed used in dropout for CE.
+    dropout_seed = 1
+    # the flag indicating whether to share embedding and softmax weights.
+    # vocabularies in source and target should be same for weight sharing.
+    weight_sharing = True
+
+
+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
+
+
+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")
+
+
+def create_data(is_static=False):
+    if is_static:
+        return [
+            src_word_np, src_pos_np, src_slf_attn_bias_np, trg_word_np,
+            trg_pos_np, trg_slf_attn_bias_np, trg_src_attn_bias_np, lbl_word_np,
+            lbl_weight_np
+        ]
+    else:
+        enc_inputs = [
+            to_variable(src_word_np), to_variable(src_pos_np),
+            to_variable(src_slf_attn_bias_np)
+        ]
+        dec_inputs = [
+            to_variable(trg_word_np), to_variable(trg_pos_np),
+            to_variable(trg_slf_attn_bias_np), to_variable(trg_src_attn_bias_np)
+        ]
+        label = to_variable(lbl_word_np)
+        weight = to_variable(lbl_weight_np)
+        return enc_inputs, dec_inputs, label, weight
+
+
+def create_feed_dict_list(data, init=False):
+    if init:
+        data_input_names = encoder_data_input_fields + \
+                           decoder_data_input_fields[:-1] + label_data_input_fields + pos_enc_param_names
+    else:
+        data_input_names = encoder_data_input_fields + \
+                           decoder_data_input_fields[:-1] + label_data_input_fields
+    feed_dict_list = dict()
+    for i in range(len(data_input_names)):
+        feed_dict_list[data_input_names[i]] = data[i]
+    return feed_dict_list
+
+
+def make_all_inputs(input_fields):
+    """
+    Define the input data layers for the transformer model.
+    """
+    inputs = []
+    for input_field in input_fields:
+        input_var = fluid.layers.data(
+            name=input_field,
+            shape=input_descs[input_field][0],
+            dtype=input_descs[input_field][1],
+            lod_level=input_descs[input_field][2]
+            if len(input_descs[input_field]) == 3 else 0,
+            append_batch_size=False)
+        inputs.append(input_var)
+    return inputs
+
+
+# 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 = 32
+# 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", )
+# if we use py_reader
+use_py_reader = False
+
+# if we run sync mode
+sync = False
+
+# how many batches we use
+batch_num = 2
+
+np.random.seed = 1
+src_word_np = np.random.randint(
+    1,
+    ModelHyperParams.src_vocab_size - 1,
+    size=(batch_size, seq_len, 1),
+    dtype='int64')
+src_pos_np = np.random.randint(
+    1, seq_len, size=(batch_size, seq_len, 1), dtype='int64')
+src_slf_attn_bias_np = np.random.randn(batch_size, ModelHyperParams.n_head,
+                                       seq_len, seq_len).astype('float32')
+
+trg_word_np = np.random.randint(
+    1,
+    ModelHyperParams.src_vocab_size - 1,
+    size=(batch_size, seq_len, 1),
+    dtype='int64')
+trg_pos_np = np.random.randint(
+    1, seq_len, size=(batch_size, seq_len, 1), dtype='int64')
+trg_slf_attn_bias_np = np.random.randn(batch_size, ModelHyperParams.n_head,
+                                       seq_len, seq_len).astype('float32')
+trg_src_attn_bias_np = np.random.randn(batch_size, ModelHyperParams.n_head,
+                                       seq_len, seq_len).astype('float32')
+
+lbl_word_np = np.random.randint(
+    1,
+    ModelHyperParams.src_vocab_size - 1,
+    size=(batch_size * seq_len, 1),
+    dtype='int64')
+lbl_weight_np = np.random.randn(batch_size * seq_len, 1).astype('float32')
+
+# np.random.seed = 1
+# src_word_np = np.arange(0, 10).reshape([batch_size, seq_len, 1]).astype('int64')
+# src_pos_np = np.random.randint(
+#     1, seq_len, size=(batch_size, seq_len, 1), dtype='int64')
+# src_slf_attn_bias_np = np.random.randn(batch_size, ModelHyperParams.n_head,
+#                                        seq_len, seq_len).astype('float32')
+#
+# trg_word_np =  np.arange(0, 10).reshape([batch_size, seq_len, 1]).astype('int64')
+# trg_pos_np = np.random.randint(
+#     1, seq_len, size=(batch_size, seq_len, 1), dtype='int64')
+# trg_slf_attn_bias_np = np.random.randn(batch_size, ModelHyperParams.n_head,
+#                                        seq_len, seq_len).astype('float32')
+# trg_src_attn_bias_np = np.random.randn(batch_size, ModelHyperParams.n_head,
+#                                        seq_len, seq_len).astype('float32')
+#
+# lbl_word_np =  np.arange(0, 10).reshape([batch_size * seq_len, 1]).astype('int64')
+# lbl_weight_np = np.random.randn(batch_size * seq_len, 1).astype('float32')
+#
+pos_inp1 = position_encoding_init(ModelHyperParams.max_length,
+                                  ModelHyperParams.d_model)
+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,
+                 d_key,
+                 d_value,
+                 d_model,
+                 n_head=1,
+                 dropout_rate=0.,
+                 cache=None,
+                 gather_idx=None,
+                 static_kv=False):
+        super(MultiHeadAttentionLayer, self).__init__(name_scope)
+        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 = FC(name_scope=self.full_name(),
+                        size=d_key * n_head,
+                        bias_attr=False,
+                        num_flatten_dims=2)
+        self._k_fc = FC(name_scope=self.full_name(),
+                        size=d_key * n_head,
+                        bias_attr=False,
+                        num_flatten_dims=2)
+        self._v_fc = FC(name_scope=self.full_name(),
+                        size=d_value * n_head,
+                        bias_attr=False,
+                        num_flatten_dims=2)
+        self._proj_fc = FC(name_scope=self.full_name(),
+                           size=self._d_model,
+                           bias_attr=False,
+                           num_flatten_dims=2)
+
+    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 = self._q_fc(queries)
+        k = self._k_fc(keys)
+        v = self._v_fc(values)
+
+        # split head
+        reshaped_q = fluid.layers.reshape(
+            x=q, shape=[0, 0, self._n_head, self._d_key], inplace=False)
+        transpose_q = fluid.layers.transpose(x=reshaped_q, perm=[0, 2, 1, 3])
+        reshaped_k = fluid.layers.reshape(
+            x=k, shape=[0, 0, self._n_head, self._d_key], inplace=False)
+        transpose_k = fluid.layers.transpose(x=reshaped_k, perm=[0, 2, 1, 3])
+        reshaped_v = fluid.layers.reshape(
+            x=v, shape=[0, 0, self._n_head, self._d_value], inplace=False)
+        transpose_v = fluid.layers.transpose(x=reshaped_v, perm=[0, 2, 1, 3])
+
+        #scale dot product attention
+        product = fluid.layers.matmul(
+            x=transpose_q,
+            y=transpose_k,
+            transpose_y=True,
+            alpha=self._d_model**-0.5)
+        if attn_bias:
+            product += attn_bias
+        weights = fluid.layers.softmax(product)
+        if self._dropout_rate:
+            weights_droped = fluid.layers.dropout(
+                weights,
+                dropout_prob=self._dropout_rate,
+                seed=ModelHyperParams.dropout_seed,
+                is_test=False)
+            out = fluid.layers.matmul(weights_droped, transpose_v)
+        else:
+            out = fluid.layers.matmul(weights, transpose_v)
+
+        # combine heads
+        if len(out.shape) != 4:
+            raise ValueError("Input(x) should be a 4-D Tensor.")
+        trans_x = fluid.layers.transpose(out, perm=[0, 2, 1, 3])
+        final_out = fluid.layers.reshape(
+            x=trans_x,
+            shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]],
+            inplace=False)
+
+        # fc to output
+        proj_out = self._proj_fc(final_out)
+        return proj_out
+
+
+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"):
+
+        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 imperative_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):
+    def __init__(self,
+                 name_scope,
+                 n_head,
+                 d_key,
+                 d_value,
+                 d_model,
+                 d_inner_hid,
+                 prepostprocess_dropout,
+                 attention_dropout,
+                 relu_dropout,
+                 preprocess_cmd,
+                 postprocess_cmd,
+                 cache=None,
+                 gather_idx=None):
+        super(DecoderSubLayer, self).__init__(name_scope)
+        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,
+            d_value,
+            d_model,
+            n_head,
+            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, 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 TestImperativeTransformer(unittest.TestCase):
+    def test_transformer_float32(self):
+        seed = 90
+        with guard():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+            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):
+                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()
+
+                dy_avg_cost._backward()
+                optimizer.minimize(dy_avg_cost)
+                transformer.clear_gradients()
+                if i == batch_num - 1:
+                    for param in transformer.parameters():
+                        dy_param_updated[param.name] = param._numpy()
+
+        with new_program_scope():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+            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)
+
+            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)
+
+            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]
+            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 = create_feed_dict_list(create_data(True))
+                fetch_list = [
+                    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:
+                    for k in range(4, len(out)):
+                        static_param_updated[static_param_name_list[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.allclose(value, dy_param_init[key]))
+        for key, value in six.iteritems(static_param_updated):
+            self.assertTrue(
+                np.allclose(
+                    value, dy_param_updated[key], atol=1e-4))
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -70,6 +70,34 @@ class LayerTest(unittest.TestCase):
 
 
 class TestLayer(LayerTest):
+    def test_layer_norm(self):
+        inp = np.ones([3, 32, 32], dtype='float32')
+        with self.static_graph():
+            t = layers.data(
+                name='data',
+                shape=[3, 32, 32],
+                dtype='float32',
+                append_batch_size=False)
+            ret = layers.layer_norm(t)
+            static_ret = self.get_static_graph_result(
+                feed={'data': inp}, fetch_list=[ret])[0]
+        with self.static_graph():
+            t = layers.data(
+                name='data',
+                shape=[3, 32, 32],
+                dtype='float32',
+                append_batch_size=False)
+            lm = nn.LayerNorm('layer_norm')
+            ret = lm(t)
+            static_ret2 = self.get_static_graph_result(
+                feed={'data': inp}, fetch_list=[ret])[0]
+        with self.dynamic_graph():
+            lm = nn.LayerNorm('layer_norm')
+            dy_ret = lm(base.to_variable(inp))
+
+        self.assertTrue(np.allclose(static_ret, static_ret2))
+        self.assertTrue(np.allclose(dy_ret._numpy(), static_ret2))
+
     def test_relu(self):
         with self.static_graph():
             t = layers.data(name='t', shape=[3, 3], dtype='float32')