add cls api

paddlepalm/backbone/bert.py

+# -*- coding: UTF-8 -*-
+#   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.
+"""v1.1 
+BERT model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from paddle import fluid
+from paddle.fluid import layers
+
+from paddlepalm.backbone.utils.transformer import pre_process_layer, encoder
+from paddlepalm.interface import backbone
+
+    
+class Model(backbone):
+    
+    def __init__(self, config, phase):
+
+        # self._is_training = phase == 'train' # backbone一般不用关心运行阶段，因为outputs在任何阶段基本不会变
+        self._emb_size = config["hidden_size"]
+        self._n_layer = config["num_hidden_layers"]
+        self._n_head = config["num_attention_heads"]
+        self._voc_size = config["vocab_size"]
+        self._max_position_seq_len = config["max_position_embeddings"]
+        self._sent_types = config["type_vocab_size"]
+        self._hidden_act = config["hidden_act"]
+        self._prepostprocess_dropout = config["hidden_dropout_prob"]
+        self._attention_dropout = config["attention_probs_dropout_prob"]
+
+        self._word_emb_name = "word_embedding"
+        self._pos_emb_name = "pos_embedding"
+        self._sent_emb_name = "sent_embedding"
+
+        # Initialize all weigths by truncated normal initializer, and all biases 
+        # will be initialized by constant zero by default.
+        self._param_initializer = fluid.initializer.TruncatedNormal(
+            scale=config["initializer_range"])
+
+    @property
+    def inputs_attr(self):
+        return {"token_ids": [[-1, -1, 1], 'int64'],
+                "position_ids": [[-1, -1, 1], 'int64'],
+                "segment_ids": [[-1, -1, 1], 'int64'],
+                "input_mask": [[-1, -1, 1], 'float32']}
+
+    @property
+    def outputs_attr(self):
+        return {"word_embedding": [[-1, -1, self._emb_size], 'float32'],
+                "embedding_table": [[-1, self._voc_size, self._emb_size], 'float32'],
+                "encoder_outputs": [[-1, -1, self._emb_size], 'float32'],
+                "sentence_embedding": [[-1, self._emb_size], 'float32'],
+                "sentence_pair_embedding": [[-1, self._emb_size], 'float32']}
+
+    def build(self, inputs, scope_name=""):
+        src_ids = inputs['token_ids']
+        pos_ids = inputs['position_ids']
+        sent_ids = inputs['segment_ids']
+        input_mask = inputs['input_mask']
+
+        self._emb_dtype = 'float32'
+        # padding id in vocabulary must be set to 0
+        emb_out = fluid.layers.embedding(
+            input=src_ids,
+            size=[self._voc_size, self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=fluid.ParamAttr(
+                name=scope_name+self._word_emb_name, initializer=self._param_initializer),
+            is_sparse=False)
+
+        # fluid.global_scope().find_var('backbone-word_embedding').get_tensor()
+        embedding_table = fluid.default_main_program().global_block().var(scope_name+self._word_emb_name)
+        
+        position_emb_out = fluid.layers.embedding(
+            input=pos_ids,
+            size=[self._max_position_seq_len, self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=fluid.ParamAttr(
+                name=scope_name+self._pos_emb_name, initializer=self._param_initializer))
+
+        sent_emb_out = fluid.layers.embedding(
+            sent_ids,
+            size=[self._sent_types, self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=fluid.ParamAttr(
+                name=scope_name+self._sent_emb_name, initializer=self._param_initializer))
+
+        emb_out = emb_out + position_emb_out
+        emb_out = emb_out + sent_emb_out
+
+        emb_out = pre_process_layer(
+            emb_out, 'nd', self._prepostprocess_dropout, name=scope_name+'pre_encoder')
+
+        self_attn_mask = fluid.layers.matmul(
+            x=input_mask, y=input_mask, transpose_y=True)
+
+        self_attn_mask = fluid.layers.scale(
+            x=self_attn_mask, scale=10000.0, bias=-1.0, bias_after_scale=False)
+        n_head_self_attn_mask = fluid.layers.stack(
+            x=[self_attn_mask] * self._n_head, axis=1)
+        n_head_self_attn_mask.stop_gradient = True
+
+        enc_out = encoder(
+            enc_input=emb_out,
+            attn_bias=n_head_self_attn_mask,
+            n_layer=self._n_layer,
+            n_head=self._n_head,
+            d_key=self._emb_size // self._n_head,
+            d_value=self._emb_size // self._n_head,
+            d_model=self._emb_size,
+            d_inner_hid=self._emb_size * 4,
+            prepostprocess_dropout=self._prepostprocess_dropout,
+            attention_dropout=self._attention_dropout,
+            relu_dropout=0,
+            hidden_act=self._hidden_act,
+            preprocess_cmd="",
+            postprocess_cmd="dan",
+            param_initializer=self._param_initializer,
+            name=scope_name+'encoder')
+
+        
+        next_sent_feat = fluid.layers.slice(
+            input=enc_out, axes=[1], starts=[0], ends=[1])
+        next_sent_feat = fluid.layers.reshape(next_sent_feat, [-1, next_sent_feat.shape[-1]])
+        next_sent_feat = fluid.layers.fc(
+            input=next_sent_feat,
+            size=self._emb_size,
+            act="tanh",
+            param_attr=fluid.ParamAttr(
+                name=scope_name+"pooled_fc.w_0", initializer=self._param_initializer),
+            bias_attr=scope_name+"pooled_fc.b_0")
+
+        return {'embedding_table': embedding_table,
+                'word_embedding': emb_out,
+                'encoder_outputs': enc_out,
+                'sentence_embedding': next_sent_feat,
+                'sentence_pair_embedding': next_sent_feat}
+
+    def postprocess(self, rt_outputs):
+        pass
+
+

paddlepalm/backbone/ernie.py

+# -*- coding: UTF-8 -*-
+#   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.
+"""Ernie model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+from __future__ import absolute_import
+
+from paddle import fluid
+from paddle.fluid import layers
+
+from paddlepalm.backbone.utils.transformer import pre_process_layer, encoder
+from paddlepalm.interface import backbone
+
+
+class Model(backbone):
+
+    def __init__(self,
+                 config,
+                 phase):
+
+        # self._is_training = phase == 'train' # backbone一般不用关心运行阶段，因为outputs在任何阶段基本不会变
+
+        self._emb_size = config['hidden_size']
+        self._n_layer = config['num_hidden_layers']
+        self._n_head = config['num_attention_heads']
+        self._voc_size = config['vocab_size']
+        self._max_position_seq_len = config['max_position_embeddings']
+        if config['sent_type_vocab_size']:
+            self._sent_types = config['sent_type_vocab_size']
+        else:
+            self._sent_types = config['type_vocab_size']
+
+        self._task_types = config['task_type_vocab_size']
+
+        self._hidden_act = config['hidden_act']
+        self._prepostprocess_dropout = config['hidden_dropout_prob']
+        self._attention_dropout = config['attention_probs_dropout_prob']
+
+        self._word_emb_name = "word_embedding"
+        self._pos_emb_name = "pos_embedding"
+        self._sent_emb_name = "sent_embedding"
+        self._task_emb_name = "task_embedding"
+        self._emb_dtype = "float32"
+
+        self._param_initializer = fluid.initializer.TruncatedNormal(
+            scale=config['initializer_range'])
+
+    @property
+    def inputs_attr(self):
+        return {"token_ids": [[-1, -1, 1], 'int64'],
+                "position_ids": [[-1, -1, 1], 'int64'],
+                "segment_ids": [[-1, -1, 1], 'int64'],
+                "input_mask": [[-1, -1, 1], 'float32'],
+                "task_ids": [[-1,-1, 1], 'int64']}
+
+    @property
+    def outputs_attr(self):
+        return {"word_embedding": [[-1, -1, self._emb_size], 'float32'],
+                "embedding_table": [[-1, self._voc_size, self._emb_size], 'float32'],
+                "encoder_outputs": [[-1, -1, self._emb_size], 'float32'],
+                "sentence_embedding": [[-1, self._emb_size], 'float32'],
+                "sentence_pair_embedding": [[-1, self._emb_size], 'float32']}
+
+    def build(self, inputs, scope_name=""):
+
+        src_ids = inputs['token_ids']
+        pos_ids = inputs['position_ids']
+        sent_ids = inputs['segment_ids']
+        input_mask = inputs['input_mask']
+        task_ids = inputs['task_ids']
+
+        # padding id in vocabulary must be set to 0
+        emb_out = fluid.layers.embedding(
+            input=src_ids,
+            size=[self._voc_size, self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=fluid.ParamAttr(
+                name=scope_name+self._word_emb_name, initializer=self._param_initializer),
+            is_sparse=False)
+
+        # fluid.global_scope().find_var('backbone-word_embedding').get_tensor()
+        embedding_table = fluid.default_main_program().global_block().var(scope_name+self._word_emb_name)
+        
+        position_emb_out = fluid.layers.embedding(
+            input=pos_ids,
+            size=[self._max_position_seq_len, self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=fluid.ParamAttr(
+                name=scope_name+self._pos_emb_name, initializer=self._param_initializer))
+
+        sent_emb_out = fluid.layers.embedding(
+            sent_ids,
+            size=[self._sent_types, self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=fluid.ParamAttr(
+                name=scope_name+self._sent_emb_name, initializer=self._param_initializer))
+
+        emb_out = emb_out + position_emb_out
+        emb_out = emb_out + sent_emb_out
+
+        task_emb_out = fluid.layers.embedding(
+            task_ids,
+            size=[self._task_types, self._emb_size],
+            dtype=self._emb_dtype,
+            param_attr=fluid.ParamAttr(
+                name=scope_name+self._task_emb_name,
+                initializer=self._param_initializer))
+
+        emb_out = emb_out + task_emb_out
+
+        emb_out = pre_process_layer(
+            emb_out, 'nd', self._prepostprocess_dropout, name=scope_name+'pre_encoder')
+
+        self_attn_mask = fluid.layers.matmul(
+            x=input_mask, y=input_mask, transpose_y=True)
+
+        self_attn_mask = fluid.layers.scale(
+            x=self_attn_mask, scale=10000.0, bias=-1.0, bias_after_scale=False)
+        n_head_self_attn_mask = fluid.layers.stack(
+            x=[self_attn_mask] * self._n_head, axis=1)
+        n_head_self_attn_mask.stop_gradient = True
+
+        enc_out = encoder(
+            enc_input=emb_out,
+            attn_bias=n_head_self_attn_mask,
+            n_layer=self._n_layer,
+            n_head=self._n_head,
+            d_key=self._emb_size // self._n_head,
+            d_value=self._emb_size // self._n_head,
+            d_model=self._emb_size,
+            d_inner_hid=self._emb_size * 4,
+            prepostprocess_dropout=self._prepostprocess_dropout,
+            attention_dropout=self._attention_dropout,
+            relu_dropout=0,
+            hidden_act=self._hidden_act,
+            preprocess_cmd="",
+            postprocess_cmd="dan",
+            param_initializer=self._param_initializer,
+            name=scope_name+'encoder')
+
+        
+        next_sent_feat = fluid.layers.slice(
+            input=enc_out, axes=[1], starts=[0], ends=[1])
+        next_sent_feat = fluid.layers.reshape(next_sent_feat, [-1, next_sent_feat.shape[-1]])
+        next_sent_feat = fluid.layers.fc(
+            input=next_sent_feat,
+            size=self._emb_size,
+            act="tanh",
+            param_attr=fluid.ParamAttr(
+                name=scope_name+"pooled_fc.w_0", initializer=self._param_initializer),
+            bias_attr=scope_name+"pooled_fc.b_0")
+
+        return {'embedding_table': embedding_table,
+                'word_embedding': emb_out,
+                'encoder_outputs': enc_out,
+                'sentence_embedding': next_sent_feat,
+                'sentence_pair_embedding': next_sent_feat}
+
+    def postprocess(self, rt_outputs):
+        pass

paddlepalm/backbone/utils/transformer.py

+# -*- coding: UTF-8 -*-
+#   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.
+"""Transformer encoder."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from functools import partial
+
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+
+from paddle.fluid.layer_helper import LayerHelper as LayerHelper
+from functools import reduce # py3
+def layer_norm(x, begin_norm_axis=1, epsilon=1e-6, param_attr=None, bias_attr=None):
+    helper = LayerHelper('layer_norm', **locals())
+    mean = layers.reduce_mean(x, dim=begin_norm_axis, keep_dim=True)
+    shift_x = layers.elementwise_sub(x=x, y=mean, axis=0)
+    variance = layers.reduce_mean(layers.square(shift_x), dim=begin_norm_axis, keep_dim=True)
+    r_stdev = layers.rsqrt(variance + epsilon)
+    norm_x = layers.elementwise_mul(x=shift_x, y=r_stdev, axis=0)
+
+    param_shape = [reduce(lambda x, y: x * y, norm_x.shape[begin_norm_axis:])]
+    param_dtype = norm_x.dtype
+    scale = helper.create_parameter(
+        attr=param_attr,
+        shape=param_shape,
+        dtype=param_dtype,
+        default_initializer=fluid.initializer.Constant(1.))
+    bias = helper.create_parameter(
+        attr=bias_attr,
+        shape=param_shape,
+        dtype=param_dtype,
+        is_bias=True,
+        default_initializer=fluid.initializer.Constant(0.))
+
+    out = layers.elementwise_mul(x=norm_x, y=scale, axis=-1)
+    out = layers.elementwise_add(x=out, y=bias, axis=-1)
+
+    return out
+
+
+def multi_head_attention(queries,
+                         keys,
+                         values,
+                         attn_bias,
+                         d_key,
+                         d_value,
+                         d_model,
+                         n_head=1,
+                         dropout_rate=0.,
+                         cache=None,
+                         param_initializer=None,
+                         name='multi_head_att'):
+    """
+    Multi-Head Attention. Note that attn_bias is added to the logit before
+    computing softmax activiation to mask certain selected positions so that
+    they will not considered in attention weights.
+    """
+    keys = queries if keys is None else keys
+    values = keys if values is None else values
+
+    if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3):
+        raise ValueError(
+            "Inputs: quries, keys and values should all be 3-D tensors.")
+
+    def __compute_qkv(queries, keys, values, n_head, d_key, d_value):
+        """
+        Add linear projection to queries, keys, and values.
+        """
+        q = layers.fc(input=queries,
+                      size=d_key * n_head,
+                      num_flatten_dims=2,
+                      param_attr=fluid.ParamAttr(
+                          name=name + '_query_fc.w_0',
+                          initializer=param_initializer),
+                      bias_attr=name + '_query_fc.b_0')
+        k = layers.fc(input=keys,
+                      size=d_key * n_head,
+                      num_flatten_dims=2,
+                      param_attr=fluid.ParamAttr(
+                          name=name + '_key_fc.w_0',
+                          initializer=param_initializer),
+                      bias_attr=name + '_key_fc.b_0')
+        v = layers.fc(input=values,
+                      size=d_value * n_head,
+                      num_flatten_dims=2,
+                      param_attr=fluid.ParamAttr(
+                          name=name + '_value_fc.w_0',
+                          initializer=param_initializer),
+                      bias_attr=name + '_value_fc.b_0')
+        return q, k, v
+
+    def __split_heads(x, n_head):
+        """
+        Reshape the last dimension of inpunt tensor x so that it becomes two
+        dimensions and then transpose. Specifically, input a tensor with shape
+        [bs, max_sequence_length, n_head * hidden_dim] then output a tensor
+        with shape [bs, n_head, max_sequence_length, hidden_dim].
+        """
+        hidden_size = x.shape[-1]
+        # The value 0 in shape attr means copying the corresponding dimension
+        # size of the input as the output dimension size.
+        reshaped = layers.reshape(
+            x=x, shape=[0, 0, n_head, hidden_size // n_head], inplace=True)
+
+        # permuate the dimensions into:
+        # [batch_size, n_head, max_sequence_len, hidden_size_per_head]
+        return layers.transpose(x=reshaped, perm=[0, 2, 1, 3])
+
+    def __combine_heads(x):
+        """
+        Transpose and then reshape the last two dimensions of inpunt tensor x
+        so that it becomes one dimension, which is reverse to __split_heads.
+        """
+        if len(x.shape) == 3: return x
+        if len(x.shape) != 4:
+            raise ValueError("Input(x) should be a 4-D Tensor.")
+
+        trans_x = layers.transpose(x, perm=[0, 2, 1, 3])
+        # The value 0 in shape attr means copying the corresponding dimension
+        # size of the input as the output dimension size.
+        return layers.reshape(
+            x=trans_x,
+            shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]],
+            inplace=True)
+
+    def scaled_dot_product_attention(q, k, v, attn_bias, d_key, dropout_rate):
+        """
+        Scaled Dot-Product Attention
+        """
+        scaled_q = layers.scale(x=q, scale=d_key**-0.5)
+        product = layers.matmul(x=scaled_q, y=k, transpose_y=True)
+        if attn_bias:
+            product += attn_bias
+        weights = layers.softmax(product)
+        if dropout_rate:
+            weights = layers.dropout(
+                weights,
+                dropout_prob=dropout_rate,
+                dropout_implementation="upscale_in_train",
+                is_test=False)
+        out = layers.matmul(weights, v)
+        return out
+
+    q, k, v = __compute_qkv(queries, keys, values, n_head, d_key, d_value)
+
+    if cache is not None:  # use cache and concat time steps
+        # Since the inplace reshape in __split_heads changes the shape of k and
+        # v, which is the cache input for next time step, reshape the cache
+        # input from the previous time step first.
+        k = cache["k"] = layers.concat(
+            [layers.reshape(
+                cache["k"], shape=[0, 0, d_model]), k], axis=1)
+        v = cache["v"] = layers.concat(
+            [layers.reshape(
+                cache["v"], shape=[0, 0, d_model]), v], axis=1)
+
+    q = __split_heads(q, n_head)
+    k = __split_heads(k, n_head)
+    v = __split_heads(v, n_head)
+
+    ctx_multiheads = scaled_dot_product_attention(q, k, v, attn_bias, d_key,
+                                                  dropout_rate)
+
+    out = __combine_heads(ctx_multiheads)
+
+    # Project back to the model size.
+    proj_out = layers.fc(input=out,
+                         size=d_model,
+                         num_flatten_dims=2,
+                         param_attr=fluid.ParamAttr(
+                             name=name + '_output_fc.w_0',
+                             initializer=param_initializer),
+                         bias_attr=name + '_output_fc.b_0')
+    return proj_out
+
+
+def positionwise_feed_forward(x,
+                              d_inner_hid,
+                              d_hid,
+                              dropout_rate,
+                              hidden_act,
+                              param_initializer=None,
+                              name='ffn'):
+    """
+    Position-wise Feed-Forward Networks.
+    This module consists of two linear transformations with a ReLU activation
+    in between, which is applied to each position separately and identically.
+    """
+    hidden = layers.fc(input=x,
+                       size=d_inner_hid,
+                       num_flatten_dims=2,
+                       act=hidden_act,
+                       param_attr=fluid.ParamAttr(
+                           name=name + '_fc_0.w_0',
+                           initializer=param_initializer),
+                       bias_attr=name + '_fc_0.b_0')
+    if dropout_rate:
+        hidden = layers.dropout(
+            hidden,
+            dropout_prob=dropout_rate,
+            dropout_implementation="upscale_in_train",
+            is_test=False)
+    out = layers.fc(input=hidden,
+                    size=d_hid,
+                    num_flatten_dims=2,
+                    param_attr=fluid.ParamAttr(
+                        name=name + '_fc_1.w_0', initializer=param_initializer),
+                    bias_attr=name + '_fc_1.b_0')
+    return out
+
+
+def pre_post_process_layer(prev_out, out, process_cmd, dropout_rate=0.,
+                           name=''):
+    """
+    Add residual connection, layer normalization and droput to the out tensor
+    optionally according to the value of process_cmd.
+    This will be used before or after multi-head attention and position-wise
+    feed-forward networks.
+    """
+    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_dtype = out.dtype
+            if out_dtype == fluid.core.VarDesc.VarType.FP16:
+                out = layers.cast(x=out, dtype="float32")
+            out = layer_norm(
+                out,
+                begin_norm_axis=len(out.shape) - 1,
+                param_attr=fluid.ParamAttr(
+                    name=name + '_layer_norm_scale',
+                    initializer=fluid.initializer.Constant(1.)),
+                bias_attr=fluid.ParamAttr(
+                    name=name + '_layer_norm_bias',
+                    initializer=fluid.initializer.Constant(0.)))
+            if out_dtype == fluid.core.VarDesc.VarType.FP16:
+                out = layers.cast(x=out, dtype="float16")
+        elif cmd == "d":  # add dropout
+            if dropout_rate:
+                out = layers.dropout(
+                    out,
+                    dropout_prob=dropout_rate,
+                    dropout_implementation="upscale_in_train",
+                    is_test=False)
+    return out
+
+
+pre_process_layer = partial(pre_post_process_layer, None)
+post_process_layer = pre_post_process_layer
+
+
+def encoder_layer(enc_input,
+                  attn_bias,
+                  n_head,
+                  d_key,
+                  d_value,
+                  d_model,
+                  d_inner_hid,
+                  prepostprocess_dropout,
+                  attention_dropout,
+                  relu_dropout,
+                  hidden_act,
+                  preprocess_cmd="n",
+                  postprocess_cmd="da",
+                  param_initializer=None,
+                  name=''):
+    """The encoder layers that can be stacked to form a deep encoder.
+    This module consits of a multi-head (self) attention followed by
+    position-wise feed-forward networks and both the two components companied
+    with the post_process_layer to add residual connection, layer normalization
+    and droput.
+    """
+    attn_output = multi_head_attention(
+        pre_process_layer(
+            enc_input,
+            preprocess_cmd,
+            prepostprocess_dropout,
+            name=name + '_pre_att'),
+        None,
+        None,
+        attn_bias,
+        d_key,
+        d_value,
+        d_model,
+        n_head,
+        attention_dropout,
+        param_initializer=param_initializer,
+        name=name + '_multi_head_att')
+    attn_output = post_process_layer(
+        enc_input,
+        attn_output,
+        postprocess_cmd,
+        prepostprocess_dropout,
+        name=name + '_post_att')
+    ffd_output = positionwise_feed_forward(
+        pre_process_layer(
+            attn_output,
+            preprocess_cmd,
+            prepostprocess_dropout,
+            name=name + '_pre_ffn'),
+        d_inner_hid,
+        d_model,
+        relu_dropout,
+        hidden_act,
+        param_initializer=param_initializer,
+        name=name + '_ffn')
+    return post_process_layer(
+        attn_output,
+        ffd_output,
+        postprocess_cmd,
+        prepostprocess_dropout,
+        name=name + '_post_ffn')
+
+
+def encoder(enc_input,
+            attn_bias,
+            n_layer,
+            n_head,
+            d_key,
+            d_value,
+            d_model,
+            d_inner_hid,
+            prepostprocess_dropout,
+            attention_dropout,
+            relu_dropout,
+            hidden_act,
+            preprocess_cmd="n",
+            postprocess_cmd="da",
+            param_initializer=None,
+            name=''):
+    """
+    The encoder is composed of a stack of identical layers returned by calling
+    encoder_layer.
+    """
+    for i in range(n_layer):
+        enc_output = encoder_layer(
+            enc_input,
+            attn_bias,
+            n_head,
+            d_key,
+            d_value,
+            d_model,
+            d_inner_hid,
+            prepostprocess_dropout,
+            attention_dropout,
+            relu_dropout,
+            hidden_act,
+            preprocess_cmd,
+            postprocess_cmd,
+            param_initializer=param_initializer,
+            name=name + '_layer_' + str(i))
+        enc_input = enc_output
+    enc_output = pre_process_layer(
+        enc_output, preprocess_cmd, prepostprocess_dropout, name="post_encoder")
+
+    return enc_output

paddlepalm/reader/cls.py

+# -*- coding: UTF-8 -*-
+#   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 paddlepalm.interface import reader
+from paddlepalm.reader.utils.reader4ernie import ClassifyReader
+
+def classify(data_path, vocab_path, batch_size, max_len, \
+             pred_batch_size=None, file_format='csv', tokenizer='wordpiece', \
+             lang='en', shuffle_train=True, seed=None, do_lower_case=False, \
+             seed=None, phase='train'):
+
+    assert lang.lower() in ['en', 'cn', 'english', 'chinese'], "supported language: en (English), cn (Chinese)."
+    assert phase in ['train', 'pred'], "supported phase: train, pred."
+    config = {
+        'train_file': data_path,
+        'pred_file': data_path,
+        'batch_size': batch_size,
+        'pred_batch_size': pred_batch_size,
+        'max_len': max_len,
+        'file_format': file_format,
+        'tokenizer': tokenizer,
+        'for_cn': lang.lower() == 'cn' or lang.lower() == 'chinese',
+        'shuffle_train': shuffle_train,
+        'do_lower_case': do_lower_case,
+        'seed': seed
+        }
+    if pred_batch_size is None:
+        del config['pred_batch_size']
+
+    return Reader(config, phase=phase)
+
+class Reader(reader):
+    
+    def __init__(self, config, phase='train', print_prefix=''):
+        """
+        Args:
+            phase: train, eval, pred
+            """
+
+        self._is_training = phase == 'train'
+
+        reader = ClassifyReader(config['vocab_path'],
+            max_seq_len=config['max_len'],
+            do_lower_case=config.get('do_lower_case', False),
+            for_cn=config.get('for_cn', False),
+            random_seed=config.get('seed', None))
+        self._reader = reader
+
+        self._batch_size = config['batch_size']
+        self._max_seq_len = config['max_len']
+
+        self._input_file = config['data_path']
+        if phase == 'train':
+            self._num_epochs = None # 防止iteartor终止
+            self._shuffle = config.get('shuffle_train', True)
+            # self._shuffle_buffer = config.get('shuffle_buffer', 5000)
+        elif phase == 'eval':
+            self._num_epochs = 1
+            self._shuffle = False
+            self._batch_size = config.get('pred_batch_size', self._batch_size)
+        elif phase == 'pred':
+            self._num_epochs = 1
+            self._shuffle = False
+            self._batch_size = config.get('pred_batch_size', self._batch_size)
+
+        self._phase = phase
+        # self._batch_size = 
+        self._print_first_n = config.get('print_first_n', 0)
+
+
+    @property
+    def outputs_attr(self):
+        if self._is_training:
+            return {"token_ids": [[-1, -1], 'int64'],
+                    "position_ids": [[-1, -1], 'int64'],
+                    "segment_ids": [[-1, -1], 'int64'],
+                    "input_mask": [[-1, -1, 1], 'float32'],
+                    "label_ids": [[-1], 'int64'],
+                    "task_ids": [[-1, -1], 'int64']
+                    }
+        else:
+            return {"token_ids": [[-1, -1], 'int64'],
+                    "position_ids": [[-1, -1], 'int64'],
+                    "segment_ids": [[-1, -1], 'int64'],
+                    "task_ids": [[-1, -1], 'int64'],
+                    "input_mask": [[-1, -1, 1], 'float32']
+                    }
+
+
+    def load_data(self):
+        self._data_generator = self._reader.data_generator(self._input_file, self._batch_size, self._num_epochs, shuffle=self._shuffle, phase=self._phase)
+
+    def iterator(self): 
+
+        def list_to_dict(x):
+            names = ['token_ids', 'segment_ids', 'position_ids', 'task_ids', 'input_mask', 
+                'label_ids', 'unique_ids']
+            outputs = {n: i for n,i in zip(names, x)}
+            del outputs['unique_ids']
+            if not self._is_training:
+                del outputs['label_ids']
+            return outputs
+
+        for batch in self._data_generator():
+            yield list_to_dict(batch)
+
+    def get_epoch_outputs(self):
+        return {'examples': self._reader.get_examples(self._phase),
+                'features': self._reader.get_features(self._phase)}
+
+    @property
+    def num_examples(self):
+        return self._reader.get_num_examples(phase=self._phase)
+

paddlepalm/reader/match.py

+# -*- coding: UTF-8 -*-
+#   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 paddlepalm.interface import reader
+from paddlepalm.reader.utils.reader4ernie import ClassifyReader
+
+class Reader(reader):
+    
+    def __init__(self, config, phase='train', dev_count=1, print_prefix=''):
+        """
+        Args:
+            phase: train, eval, pred
+            """
+
+        self._is_training = phase == 'train'
+
+        reader = ClassifyReader(config['vocab_path'],
+            max_seq_len=config['max_seq_len'],
+            do_lower_case=config.get('do_lower_case', True),
+            for_cn=config.get('for_cn', False),
+            random_seed=config.get('seed', None))
+        self._reader = reader
+        self._dev_count = dev_count
+
+        self._batch_size = config['batch_size']
+        self._max_seq_len = config['max_seq_len']
+        if phase == 'train':
+            self._input_file = config['train_file']
+            self._num_epochs = None # 防止iteartor终止
+            self._shuffle = config.get('shuffle', True)
+            self._shuffle_buffer = config.get('shuffle_buffer', 5000)
+        elif phase == 'eval':
+            self._input_file = config['dev_file']
+            self._num_epochs = 1
+            self._shuffle = False
+            self._batch_size = config.get('pred_batch_size', self._batch_size)
+        elif phase == 'pred':
+            self._input_file = config['pred_file']
+            self._num_epochs = 1
+            self._shuffle = False
+            self._batch_size = config.get('pred_batch_size', self._batch_size)
+
+        self._phase = phase
+        # self._batch_size = 
+        self._print_first_n = config.get('print_first_n', 1)
+
+
+    @property
+    def outputs_attr(self):
+        if self._is_training:
+            return {"token_ids": [[-1, -1], 'int64'],
+                    "position_ids": [[-1, -1], 'int64'],
+                    "segment_ids": [[-1, -1], 'int64'],
+                    "input_mask": [[-1, -1, 1], 'float32'],
+                    "label_ids": [[-1], 'int64'],
+                    "task_ids": [[-1, -1], 'int64']
+                    }
+        else:
+            return {"token_ids": [[-1, -1], 'int64'],
+                    "position_ids": [[-1, -1], 'int64'],
+                    "segment_ids": [[-1, -1], 'int64'],
+                    "task_ids": [[-1, -1], 'int64'],
+                    "input_mask": [[-1, -1, 1], 'float32']
+                    }
+
+
+    def load_data(self):
+        self._data_generator = self._reader.data_generator(self._input_file, self._batch_size, self._num_epochs, dev_count=self._dev_count, shuffle=self._shuffle, phase=self._phase)
+
+    def iterator(self): 
+
+        def list_to_dict(x):
+            names = ['token_ids', 'segment_ids', 'position_ids', 'task_ids', 'input_mask', 
+                'label_ids', 'unique_ids']
+            outputs = {n: i for n,i in zip(names, x)}
+            del outputs['unique_ids']
+            if not self._is_training:
+                del outputs['label_ids']
+            return outputs
+
+        for batch in self._data_generator():
+            yield list_to_dict(batch)
+
+    @property
+    def num_examples(self):
+        return self._reader.get_num_examples(phase=self._phase)
+

paddlepalm/reader/mlm.py

+# -*- coding: UTF-8 -*-
+#   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 paddlepalm.interface import reader
+from paddlepalm.reader.utils.reader4ernie import MaskLMReader
+import numpy as np
+
+class Reader(reader):
+    
+    def __init__(self, config, phase='train', dev_count=1, print_prefix=''):
+        """
+        Args:
+            phase: train, eval, pred
+            """
+
+        self._is_training = phase == 'train'
+
+        reader = MaskLMReader(config['vocab_path'],
+            max_seq_len=config['max_seq_len'],
+            do_lower_case=config.get('do_lower_case', False),
+            for_cn=config.get('for_cn', False),
+            random_seed=config.get('seed', None))
+        self._reader = reader
+        self._dev_count = dev_count
+
+        self._batch_size = config['batch_size']
+        self._max_seq_len = config['max_seq_len']
+        if phase == 'train':
+            self._input_file = config['train_file']
+            self._num_epochs = None # 防止iteartor终止
+            self._shuffle = config.get('shuffle', True)
+            self._shuffle_buffer = config.get('shuffle_buffer', 5000)
+        elif phase == 'eval':
+            self._input_file = config['dev_file']
+            self._num_epochs = 1
+            self._shuffle = False
+            self._batch_size = config.get('pred_batch_size', self._batch_size)
+        elif phase == 'pred':
+            self._input_file = config['pred_file']
+            self._num_epochs = 1
+            self._shuffle = False
+            self._batch_size = config.get('pred_batch_size', self._batch_size)
+
+        self._phase = phase
+        # self._batch_size = 
+        self._print_first_n = config.get('print_first_n', 1)
+
+
+    @property
+    def outputs_attr(self):
+        return {"token_ids": [[-1, -1], 'int64'],
+                "position_ids": [[-1, -1], 'int64'],
+                "segment_ids": [[-1, -1], 'int64'],
+                "input_mask": [[-1, -1, 1], 'float32'],
+                "task_ids": [[-1, -1], 'int64'],
+                "mask_label": [[-1], 'int64'],
+                "mask_pos": [[-1], 'int64'],
+                }
+
+
+    def load_data(self):
+        self._data_generator = self._reader.data_generator(self._input_file, self._batch_size, self._num_epochs, dev_count=self._dev_count, shuffle=self._shuffle, phase=self._phase)
+
+    def iterator(self): 
+
+        def list_to_dict(x):
+            names = ['token_ids', 'position_ids', 'segment_ids', 'input_mask', 
+                'task_ids', 'mask_label', 'mask_pos']
+            outputs = {n: i for n,i in zip(names, x)}
+            # outputs['batchsize_x_seqlen'] = [self._batch_size * len(outputs['token_ids'][0]) - 1]
+            return outputs
+
+        for batch in self._data_generator():
+            # print(np.shape(list_to_dict(batch)['token_ids']))
+            # print(list_to_dict(batch)['mask_label'].tolist())
+            yield list_to_dict(batch)
+
+    def get_epoch_outputs(self):
+        return {'examples': self._reader.get_examples(self._phase),
+                'features': self._reader.get_features(self._phase)}
+
+    @property
+    def num_examples(self):
+        return self._reader.get_num_examples(phase=self._phase)
+

paddlepalm/reader/mrc.py

+# -*- coding: UTF-8 -*-
+#   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 paddlepalm.interface import reader
+from paddlepalm.reader.utils.reader4ernie import MRCReader
+
+class Reader(reader):
+    
+    def __init__(self, config, phase='train', dev_count=1, print_prefix=''):
+        """
+        Args:
+            phase: train, eval, pred
+            """
+
+        self._is_training = phase == 'train'
+
+        reader = MRCReader(config['vocab_path'],
+            max_seq_len=config['max_seq_len'],
+            do_lower_case=config.get('do_lower_case', False),
+            tokenizer='FullTokenizer',
+            for_cn=config.get('for_cn', False),
+            doc_stride=config['doc_stride'],
+            remove_noanswer=config.get('remove_noanswer', True),
+            max_query_length=config['max_query_len'],
+            random_seed=config.get('seed', None))
+        self._reader = reader
+        self._dev_count = dev_count
+
+        self._batch_size = config['batch_size']
+        self._max_seq_len = config['max_seq_len']
+        if phase == 'train':
+            self._input_file = config['train_file']
+            # self._num_epochs = config['num_epochs']
+            self._num_epochs = None # 防止iteartor终止
+            self._shuffle = config.get('shuffle', True)
+            self._shuffle_buffer = config.get('shuffle_buffer', 5000)
+        if phase == 'eval':
+            self._input_file = config['dev_file']
+            self._num_epochs = 1
+            self._shuffle = False
+            self._batch_size = config.get('pred_batch_size', self._batch_size)
+        elif phase == 'pred':
+            self._input_file = config['pred_file']
+            self._num_epochs = 1
+            self._shuffle = False
+            self._batch_size = config.get('pred_batch_size', self._batch_size)
+
+        self._phase = phase
+        # self._batch_size = 
+        self._print_first_n = config.get('print_first_n', 1)
+
+        # TODO: without slide window version
+        self._with_slide_window = config.get('with_slide_window', False)
+
+
+    @property
+    def outputs_attr(self):
+        if self._is_training:
+            return {"token_ids": [[-1, -1], 'int64'],
+                    "position_ids": [[-1, -1], 'int64'],
+                    "segment_ids": [[-1, -1], 'int64'],
+                    "input_mask": [[-1, -1, 1], 'float32'],
+                    "start_positions": [[-1], 'int64'],
+                    "end_positions": [[-1], 'int64'],
+                    "task_ids": [[-1, -1], 'int64']
+                    }
+        else:
+            return {"token_ids": [[-1, -1], 'int64'],
+                    "position_ids": [[-1, -1], 'int64'],
+                    "segment_ids": [[-1, -1], 'int64'],
+                    "task_ids": [[-1, -1], 'int64'],
+                    "input_mask": [[-1, -1, 1], 'float32'],
+                    "unique_ids": [[-1], 'int64']
+                    }
+
+    @property
+    def epoch_outputs_attr(self):
+        if not self._is_training:
+            return {"examples": None,
+                    "features": None}
+
+    def load_data(self):
+        self._data_generator = self._reader.data_generator(self._input_file, self._batch_size, self._num_epochs, dev_count=self._dev_count, shuffle=self._shuffle, phase=self._phase)
+
+    def iterator(self): 
+
+        def list_to_dict(x):
+            names = ['token_ids', 'segment_ids', 'position_ids', 'task_ids', 'input_mask', 
+                'start_positions', 'end_positions', 'unique_ids']
+            outputs = {n: i for n,i in zip(names, x)}
+            if self._is_training:
+                del outputs['unique_ids']
+            else:
+                del outputs['start_positions']
+                del outputs['end_positions']
+            return outputs
+
+        for batch in self._data_generator():
+            yield list_to_dict(batch)
+
+    def get_epoch_outputs(self):
+        return {'examples': self._reader.get_examples(self._phase),
+                'features': self._reader.get_features(self._phase)}
+
+    @property
+    def num_examples(self):
+        return self._reader.get_num_examples(phase=self._phase)
+

paddlepalm/reader/utils/batching4bert.py

+# -*- coding: UTF-8 -*-
+#   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.
+"""Mask, padding and batching."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import numpy as np
+
+
+def mask(batch_tokens, total_token_num, vocab_size, CLS=1, SEP=2, MASK=3):
+    """
+    Add mask for batch_tokens, return out, mask_label, mask_pos;
+    Note: mask_pos responding the batch_tokens after padded;
+    """
+    max_len = max([len(sent) for sent in batch_tokens])
+    mask_label = []
+    mask_pos = []
+    prob_mask = np.random.rand(total_token_num)
+    # Note: the first token is [CLS], so [low=1]
+    replace_ids = np.random.randint(1, high=vocab_size, size=total_token_num)
+    pre_sent_len = 0
+    prob_index = 0
+    for sent_index, sent in enumerate(batch_tokens):
+        mask_flag = False
+        prob_index += pre_sent_len
+        for token_index, token in enumerate(sent):
+            prob = prob_mask[prob_index + token_index]
+            if prob > 0.15:
+                continue
+            elif 0.03 < prob <= 0.15:
+                # mask
+                if token != SEP and token != CLS:
+                    mask_label.append(sent[token_index])
+                    sent[token_index] = MASK
+                    mask_flag = True
+                    mask_pos.append(sent_index * max_len + token_index)
+            elif 0.015 < prob <= 0.03:
+                # random replace
+                if token != SEP and token != CLS:
+                    mask_label.append(sent[token_index])
+                    sent[token_index] = replace_ids[prob_index + token_index]
+                    mask_flag = True
+                    mask_pos.append(sent_index * max_len + token_index)
+            else:
+                # keep the original token
+                if token != SEP and token != CLS:
+                    mask_label.append(sent[token_index])
+                    mask_pos.append(sent_index * max_len + token_index)
+        pre_sent_len = len(sent)
+        # ensure at least mask one word in a sentence
+        while not mask_flag:
+            token_index = int(np.random.randint(1, high=len(sent) - 1, size=1))
+            if sent[token_index] != SEP and sent[token_index] != CLS:
+                mask_label.append(sent[token_index])
+                sent[token_index] = MASK
+                mask_flag = True
+                mask_pos.append(sent_index * max_len + token_index)
+    mask_label = np.array(mask_label).astype("int64").reshape([-1])
+    mask_pos = np.array(mask_pos).astype("int64").reshape([-1])
+    return batch_tokens, mask_label, mask_pos
+
+
+def prepare_batch_data(insts,
+                       total_token_num,
+                       max_len=None,
+                       voc_size=0,
+                       pad_id=None,
+                       cls_id=None,
+                       sep_id=None,
+                       mask_id=None,
+                       return_input_mask=True,
+                       return_max_len=True,
+                       return_num_token=False):
+    """
+    1. generate Tensor of data
+    2. generate Tensor of position
+    3. generate self attention mask, [shape: batch_size *  max_len * max_len]
+    """
+    batch_src_ids = [inst[0] for inst in insts]
+    batch_sent_ids = [inst[1] for inst in insts]
+    batch_pos_ids = [inst[2] for inst in insts]
+    labels_list = []
+    # compatible with mrqa, whose example includes start/end positions, 
+    # or unique id
+    for i in range(3, len(insts[0]), 1):
+        labels = [inst[i] for inst in insts]
+        labels = np.array(labels).astype("int64").reshape([-1])
+        labels_list.append(labels)
+    # First step: do mask without padding
+    if mask_id >= 0:
+        out, mask_label, mask_pos = mask(
+            batch_src_ids,
+            total_token_num,
+            vocab_size=voc_size,
+            CLS=cls_id,
+            SEP=sep_id,
+            MASK=mask_id)
+    else:
+        out = batch_src_ids
+    # Second step: padding
+    src_id, self_input_mask = pad_batch_data(
+        out, 
+        max_len=max_len,
+        pad_idx=pad_id, return_input_mask=True)
+    pos_id = pad_batch_data(
+        batch_pos_ids,
+        max_len=max_len,
+        pad_idx=pad_id,
+        return_pos=False,
+        return_input_mask=False)
+    sent_id = pad_batch_data(
+        batch_sent_ids,
+        max_len=max_len,
+        pad_idx=pad_id,
+        return_pos=False,
+        return_input_mask=False)
+    if mask_id >= 0:
+        return_list = [
+            src_id, pos_id, sent_id, self_input_mask, mask_label, mask_pos
+        ] + labels_list
+    else:
+        return_list = [src_id, pos_id, sent_id, self_input_mask] + labels_list
+    return return_list if len(return_list) > 1 else return_list[0]
+
+
+def pad_batch_data(insts,
+                   max_len=None,
+                   pad_idx=0,
+                   return_pos=False,
+                   return_input_mask=False,
+                   return_max_len=False,
+                   return_num_token=False):
+    """
+    Pad the instances to the max sequence length in batch, and generate the
+    corresponding position data and input mask.
+    """
+    return_list = []
+    if max_len is None:
+        max_len = max(len(inst) for inst in insts)
+    # Any token included in dict can be used to pad, since the paddings' loss
+    # will be masked out by weights and make no effect on parameter gradients.
+    inst_data = np.array([
+        list(inst) + list([pad_idx] * (max_len - len(inst))) for inst in insts
+    ])
+    return_list += [inst_data.astype("int64").reshape([-1, max_len])]
+    # position data
+    if return_pos:
+        inst_pos = np.array([
+            list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst))
+            for inst in insts
+        ])
+        return_list += [inst_pos.astype("int64").reshape([-1, max_len])]
+    if return_input_mask:
+        # This is used to avoid attention on paddings.
+        input_mask_data = np.array([[1] * len(inst) + [0] *
+                                    (max_len - len(inst)) for inst in insts])
+        input_mask_data = np.expand_dims(input_mask_data, axis=-1)
+        return_list += [input_mask_data.astype("float32")]
+    if return_max_len:
+        return_list += [max_len]
+    if return_num_token:
+        num_token = 0
+        for inst in insts:
+            num_token += len(inst)
+        return_list += [num_token]
+    return return_list if len(return_list) > 1 else return_list[0]
+
+
+if __name__ == "__main__":
+    pass
+
+

paddlepalm/reader/utils/batching4ernie.py

+# -*- coding: UTF-8 -*-
+#   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.
+"""Mask, padding and batching."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from six.moves import xrange
+
+
+def mask(batch_tokens,
+         seg_labels,
+         mask_word_tags,
+         total_token_num,
+         vocab_size,
+         CLS=1,
+         SEP=2,
+         MASK=3):
+    """
+    Add mask for batch_tokens, return out, mask_label, mask_pos;
+    Note: mask_pos responding the batch_tokens after padded;
+    """
+    max_len = max([len(sent) for sent in batch_tokens])
+    mask_label = []
+    mask_pos = []
+    prob_mask = np.random.rand(total_token_num)
+    # Note: the first token is [CLS], so [low=1]
+    replace_ids = np.random.randint(1, high=vocab_size, size=total_token_num)
+    pre_sent_len = 0
+    prob_index = 0
+    for sent_index, sent in enumerate(batch_tokens):
+        mask_flag = False
+        mask_word = mask_word_tags[sent_index]
+        prob_index += pre_sent_len
+        if mask_word:
+            beg = 0
+            for token_index, token in enumerate(sent):
+                seg_label = seg_labels[sent_index][token_index]
+                if seg_label == 1:
+                    continue
+                if beg == 0:
+                    if seg_label != -1:
+                        beg = token_index
+                    continue
+
+                prob = prob_mask[prob_index + beg]
+                if prob > 0.15:
+                    pass
+                else:
+                    for index in xrange(beg, token_index):
+                        prob = prob_mask[prob_index + index]
+                        base_prob = 1.0
+                        if index == beg:
+                            base_prob = 0.15
+                        if base_prob * 0.2 < prob <= base_prob:
+                            mask_label.append(sent[index])
+                            sent[index] = MASK
+                            mask_flag = True
+                            mask_pos.append(sent_index * max_len + index)
+                        elif base_prob * 0.1 < prob <= base_prob * 0.2:
+                            mask_label.append(sent[index])
+                            sent[index] = replace_ids[prob_index + index]
+                            mask_flag = True
+                            mask_pos.append(sent_index * max_len + index)
+                        else:
+                            mask_label.append(sent[index])
+                            mask_pos.append(sent_index * max_len + index)
+
+                if seg_label == -1:
+                    beg = 0
+                else:
+                    beg = token_index
+        else:
+            for token_index, token in enumerate(sent):
+                prob = prob_mask[prob_index + token_index]
+                if prob > 0.15:
+                    continue
+                elif 0.03 < prob <= 0.15:
+                    # mask
+                    if token != SEP and token != CLS:
+                        mask_label.append(sent[token_index])
+                        sent[token_index] = MASK
+                        mask_flag = True
+                        mask_pos.append(sent_index * max_len + token_index)
+                elif 0.015 < prob <= 0.03:
+                    # random replace
+                    if token != SEP and token != CLS:
+                        mask_label.append(sent[token_index])
+                        sent[token_index] = replace_ids[prob_index +
+                                                        token_index]
+                        mask_flag = True
+                        mask_pos.append(sent_index * max_len + token_index)
+                else:
+                    # keep the original token
+                    if token != SEP and token != CLS:
+                        mask_label.append(sent[token_index])
+                        mask_pos.append(sent_index * max_len + token_index)
+
+        pre_sent_len = len(sent)
+
+    mask_label = np.array(mask_label).astype("int64").reshape([-1])
+    mask_pos = np.array(mask_pos).astype("int64").reshape([-1])
+    return batch_tokens, mask_label, mask_pos
+
+
+def pad_batch_data(insts,
+                   pad_idx=0,
+                   return_pos=False,
+                   return_input_mask=False,
+                   return_max_len=False,
+                   return_num_token=False,
+                   return_seq_lens=False):
+    """
+    Pad the instances to the max sequence length in batch, and generate the
+    corresponding position data and attention bias.
+    """
+    return_list = []
+    max_len = max(len(inst) for inst in insts)
+    # Any token included in dict can be used to pad, since the paddings' loss
+    # will be masked out by weights and make no effect on parameter gradients.
+
+    inst_data = np.array(
+        [inst + list([pad_idx] * (max_len - len(inst))) for inst in insts])
+    return_list += [inst_data.astype("int64").reshape([-1, max_len])]
+
+    # position data
+    if return_pos:
+        inst_pos = np.array([
+            list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst))
+            for inst in insts
+        ])
+
+        return_list += [inst_pos.astype("int64").reshape([-1, max_len])]
+
+    if return_input_mask:
+        # This is used to avoid attention on paddings.
+        input_mask_data = np.array([[1] * len(inst) + [0] *
+                                    (max_len - len(inst)) for inst in insts])
+        input_mask_data = np.expand_dims(input_mask_data, axis=-1)
+        return_list += [input_mask_data.astype("float32")]
+
+    if return_max_len:
+        return_list += [max_len]
+
+    if return_num_token:
+        num_token = 0
+        for inst in insts:
+            num_token += len(inst)
+        return_list += [num_token]
+
+    if return_seq_lens:
+        seq_lens = np.array([len(inst) for inst in insts])
+        return_list += [seq_lens.astype("int64").reshape([-1])]
+
+    return return_list if len(return_list) > 1 else return_list[0]
+
+
+if __name__ == "__main__":
+
+    pass

paddlepalm/reader/utils/mlm_batching.py

+# -*- coding: UTF-8 -*-
+#   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.
+"""Mask, padding and batching."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import numpy as np
+
+
+def mask(batch_tokens, total_token_num, vocab_size, CLS=1, SEP=2, MASK=3):
+    """
+    Add mask for batch_tokens, return out, mask_label, mask_pos;
+    Note: mask_pos responding the batch_tokens after padded;
+    """
+    max_len = max([len(sent) for sent in batch_tokens])
+    mask_label = []
+    mask_pos = []
+    prob_mask = np.random.rand(total_token_num)
+    # Note: the first token is [CLS], so [low=1]
+    replace_ids = np.random.randint(1, high=vocab_size, size=total_token_num)
+    pre_sent_len = 0
+    prob_index = 0
+    for sent_index, sent in enumerate(batch_tokens):
+        mask_flag = False
+        prob_index += pre_sent_len
+        for token_index, token in enumerate(sent):
+            prob = prob_mask[prob_index + token_index]
+            if prob > 0.15:
+                continue
+            elif 0.03 < prob <= 0.15:
+                # mask
+                if token != SEP and token != CLS:
+                    mask_label.append(sent[token_index])
+                    sent[token_index] = MASK
+                    mask_flag = True
+                    mask_pos.append(sent_index * max_len + token_index)
+            elif 0.015 < prob <= 0.03:
+                # random replace
+                if token != SEP and token != CLS:
+                    mask_label.append(sent[token_index])
+                    sent[token_index] = replace_ids[prob_index + token_index]
+                    mask_flag = True
+                    mask_pos.append(sent_index * max_len + token_index)
+            else:
+                # keep the original token
+                if token != SEP and token != CLS:
+                    mask_label.append(sent[token_index])
+                    mask_pos.append(sent_index * max_len + token_index)
+        pre_sent_len = len(sent)
+        # ensure at least mask one word in a sentence
+        while not mask_flag:
+            token_index = int(np.random.randint(1, high=len(sent) - 1, size=1))
+            if sent[token_index] != SEP and sent[token_index] != CLS:
+                mask_label.append(sent[token_index])
+                sent[token_index] = MASK
+                mask_flag = True
+                mask_pos.append(sent_index * max_len + token_index)
+    mask_label = np.array(mask_label).astype("int64").reshape([-1])
+    mask_pos = np.array(mask_pos).astype("int64").reshape([-1])
+    return batch_tokens, mask_label, mask_pos
+
+
+def prepare_batch_data(insts,
+                       total_token_num,
+                       max_len=None,
+                       voc_size=0,
+                       pad_id=None,
+                       cls_id=None,
+                       sep_id=None,
+                       mask_id=None,
+                       task_id=0,
+                       return_input_mask=True,
+                       return_max_len=True,
+                       return_num_token=False):
+    """
+    1. generate Tensor of data
+    2. generate Tensor of position
+    3. generate self attention mask, [shape: batch_size *  max_len * max_len]
+    """
+    batch_src_ids = [inst[0] for inst in insts]
+    batch_sent_ids = [inst[1] for inst in insts]
+    batch_pos_ids = [inst[2] for inst in insts]
+
+    # 这里是否应该反过来？？？否则在task layer里展开后的word embedding是padding后的，这时候word的index是跟没有padding时的index对不上的？
+    # First step: do mask without padding
+    out, mask_label, mask_pos = mask(
+        batch_src_ids,
+        total_token_num,
+        vocab_size=voc_size,
+        CLS=cls_id,
+        SEP=sep_id,
+        MASK=mask_id)
+    # Second step: padding
+    src_id, self_input_mask = pad_batch_data(
+        out, 
+        max_len=max_len,
+        pad_idx=pad_id, return_input_mask=True)
+
+    pos_id = pad_batch_data(
+        batch_pos_ids,
+        max_len=max_len,
+        pad_idx=pad_id,
+        return_pos=False,
+        return_input_mask=False)
+    sent_id = pad_batch_data(
+        batch_sent_ids,
+        max_len=max_len,
+        pad_idx=pad_id,
+        return_pos=False,
+        return_input_mask=False)
+    task_ids = np.ones_like(
+        src_id, dtype="int64") * task_id
+    return_list = [
+        src_id, pos_id, sent_id, self_input_mask, task_ids, mask_label, mask_pos
+    ]
+    return return_list if len(return_list) > 1 else return_list[0]
+
+
+def pad_batch_data(insts,
+                   max_len=None,
+                   pad_idx=0,
+                   return_pos=False,
+                   return_input_mask=False,
+                   return_max_len=False,
+                   return_num_token=False):
+    """
+    Pad the instances to the max sequence length in batch, and generate the
+    corresponding position data and input mask.
+    """
+    return_list = []
+    if max_len is None:
+        max_len = max(len(inst) for inst in insts)
+    # Any token included in dict can be used to pad, since the paddings' loss
+    # will be masked out by weights and make no effect on parameter gradients.
+    inst_data = np.array([
+        list(inst) + list([pad_idx] * (max_len - len(inst))) for inst in insts
+    ])
+    return_list += [inst_data.astype("int64").reshape([-1, max_len])]
+    # position data
+    if return_pos:
+        inst_pos = np.array([
+            list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst))
+            for inst in insts
+        ])
+        return_list += [inst_pos.astype("int64").reshape([-1, max_len])]
+    if return_input_mask:
+        # This is used to avoid attention on paddings.
+        input_mask_data = np.array([[1] * len(inst) + [0] *
+                                    (max_len - len(inst)) for inst in insts])
+        input_mask_data = np.expand_dims(input_mask_data, axis=-1)
+        return_list += [input_mask_data.astype("float32")]
+    if return_max_len:
+        return_list += [max_len]
+    if return_num_token:
+        num_token = 0
+        for inst in insts:
+            num_token += len(inst)
+        return_list += [num_token]
+    return return_list if len(return_list) > 1 else return_list[0]
+
+
+if __name__ == "__main__":
+    pass
+
+

paddlepalm/reader/utils/mrqa_helper.py

+# -*- coding: UTF-8 -*-
+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+class MRQAExample(object):
+    """A single training/test example for simple sequence classification.
+
+     For examples without an answer, the start and end position are -1.
+  """
+
+    def __init__(self,
+                 qas_id,
+                 question_text,
+                 doc_tokens,
+                 orig_answer_text=None,
+                 start_position=None,
+                 end_position=None,
+                 is_impossible=False):
+        self.qas_id = qas_id
+        self.question_text = question_text
+        self.doc_tokens = doc_tokens
+        self.orig_answer_text = orig_answer_text
+        self.start_position = start_position
+        self.end_position = end_position
+        self.is_impossible = is_impossible
+
+    def __str__(self):
+        return self.__repr__()
+
+    def __repr__(self):
+        s = ""
+        s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
+        s += ", question_text: %s" % (
+            tokenization.printable_text(self.question_text))
+        s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
+        if self.start_position:
+            s += ", start_position: %d" % (self.start_position)
+        if self.start_position:
+            s += ", end_position: %d" % (self.end_position)
+        if self.start_position:
+            s += ", is_impossible: %r" % (self.is_impossible)
+        return s
+
+
+class MRQAFeature(object):
+    """A single set of features of data."""
+
+    def __init__(self,
+                 unique_id,
+                 example_index,
+                 doc_span_index,
+                 tokens,
+                 token_to_orig_map,
+                 token_is_max_context,
+                 input_ids,
+                 input_mask,
+                 segment_ids,
+                 start_position=None,
+                 end_position=None,
+                 is_impossible=None):
+        self.unique_id = unique_id
+        self.example_index = example_index
+        self.doc_span_index = doc_span_index
+        self.tokens = tokens
+        self.token_to_orig_map = token_to_orig_map
+        self.token_is_max_context = token_is_max_context
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.start_position = start_position
+        self.end_position = end_position
+        self.is_impossible = is_impossible
+

paddlepalm/task_paradigm/cls.py

+# -*- coding: UTF-8 -*-
+#   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.
+
+import paddle.fluid as fluid
+from paddle.fluid import layers
+from paddlepalm.base_task import base_task
+import numpy as np
+import os
+
+def classify(num_classes, input_dim, dropout_prob, pred_output_dir=None, param_initializer_range=0.02, phase='train'):
+
+    config = {
+        'num_classes': num_classes,
+        'hidden_size': input_dim,
+        'dropout_prob': dropout_prob,
+        'pred_output_dir': pred_output_dir,
+        'initializer_range': param_initializer_range
+    }
+
+    return Task(config, phase, config)
+
+
+class Task(base_task):
+    '''
+    classification
+    '''
+    def __init__(self, config, phase, backbone_config=None):
+        self._is_training = phase == 'train'
+        self._hidden_size = backbone_config['hidden_size']
+        self.num_classes = config['num_classes']
+    
+        if 'initializer_range' in config:
+            self._param_initializer = config['initializer_range']
+        else:
+            self._param_initializer = fluid.initializer.TruncatedNormal(
+                scale=backbone_config.get('initializer_range', 0.02))
+        if 'dropout_prob' in config:
+            self._dropout_prob = config['dropout_prob']
+        else:
+            self._dropout_prob = backbone_config.get('hidden_dropout_prob', 0.0)
+        self._pred_output_path = config.get('pred_output_dir', None)
+        self._preds = []
+
+    @property
+    def inputs_attrs(self):
+        if self._is_training:
+            reader = {"label_ids": [[-1], 'int64']}
+        else:
+            reader = {}
+        bb = {"sentence_embedding": [[-1, self._hidden_size], 'float32']}
+        return {'reader': reader, 'backbone': bb}
+
+    @property
+    def outputs_attrs(self):
+        if self._is_training:
+            return {'loss': [[1], 'float32']}
+        else:
+            return {'logits': [[-1, self.num_classes], 'float32']}
+
+    def build(self, inputs, scope_name=''):
+        sent_emb = inputs['backbone']['sentence_embedding']
+        if self._is_training:
+            label_ids = inputs['reader']['label_ids']
+            cls_feats = fluid.layers.dropout(
+                x=sent_emb,
+                dropout_prob=self._dropout_prob,
+                dropout_implementation="upscale_in_train")
+
+        logits = fluid.layers.fc(
+            input=sent_emb,
+            size=self.num_classes,
+            param_attr=fluid.ParamAttr(
+                name=scope_name+"cls_out_w",
+                initializer=self._param_initializer),
+            bias_attr=fluid.ParamAttr(
+                name=scope_name+"cls_out_b", initializer=fluid.initializer.Constant(0.)))
+
+        if self._is_training:
+            inputs = fluid.layers.softmax(logits)
+            loss = fluid.layers.cross_entropy(
+                input=inputs, label=label_ids)
+            loss = layers.mean(loss)
+            return {"loss": loss}
+        else:
+            return {"logits":logits}
+
+    def postprocess(self, rt_outputs):
+        if not self._is_training:
+            logits = rt_outputs['logits']
+            preds = np.argmax(logits, -1)
+            self._preds.extend(preds.tolist())
+
+    def epoch_postprocess(self, post_inputs):
+        # there is no post_inputs needed and not declared in epoch_inputs_attrs, hence no elements exist in post_inputs
+        if not self._is_training:
+            if self._pred_output_path is None:
+                raise ValueError('argument pred_output_path not found in config. Please add it into config dict/file.')
+            with open(os.path.join(self._pred_output_path, 'predictions.json'), 'w') as writer:
+                for p in self._preds:
+                    writer.write(str(p)+'\n')
+            print('Predictions saved at '+os.path.join(self._pred_output_path, 'predictions.json'))
+
+                

paddlepalm/task_paradigm/match.py

+# -*- coding: UTF-8 -*-
+#   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.
+
+import paddle.fluid as fluid
+from paddle.fluid import layers
+from paddlepalm.interface import task_paradigm
+import numpy as np
+import os
+
+class TaskParadigm(task_paradigm):
+    '''
+    matching
+    '''
+    def __init__(self, config, phase, backbone_config=None):
+        self._is_training = phase == 'train'
+        self._hidden_size = backbone_config['hidden_size']
+
+        if 'initializer_range' in config:
+            self._param_initializer = config['initializer_range']
+        else:
+            self._param_initializer = fluid.initializer.TruncatedNormal(
+                scale=backbone_config.get('initializer_range', 0.02))
+        if 'dropout_prob' in config:
+            self._dropout_prob = config['dropout_prob']
+        else:
+            self._dropout_prob = backbone_config.get('hidden_dropout_prob', 0.0)
+
+        self._pred_output_path = config.get('pred_output_path', None)
+        self._preds = []
+
+    
+    @property
+    def inputs_attrs(self):
+        if self._is_training:
+            reader = {"label_ids": [[-1, 1], 'int64']}
+        else:
+            reader = {}
+        bb = {"sentence_pair_embedding": [[-1, self._hidden_size], 'float32']}
+        return {'reader': reader, 'backbone': bb}
+
+    @property
+    def outputs_attrs(self):
+        if self._is_training:
+            return {"loss": [[1], 'float32']}
+        else:
+            return {"logits": [[-1, 2], 'float32']}
+
+    def build(self, inputs, scope_name=""):
+        if self._is_training:
+            labels = inputs["reader"]["label_ids"] 
+        cls_feats = inputs["backbone"]["sentence_pair_embedding"]
+
+        if self._is_training:
+            cls_feats = fluid.layers.dropout(
+                x=cls_feats,
+                dropout_prob=self._dropout_prob,
+                dropout_implementation="upscale_in_train")
+
+        logits = fluid.layers.fc(
+            input=cls_feats,
+            size=2,
+            param_attr=fluid.ParamAttr(
+                name=scope_name+"cls_out_w",
+                initializer=self._param_initializer),
+            bias_attr=fluid.ParamAttr(
+                name=scope_name+"cls_out_b",
+                initializer=fluid.initializer.Constant(0.)))
+
+        if self._is_training:
+            ce_loss, probs = fluid.layers.softmax_with_cross_entropy(
+                logits=logits, label=labels, return_softmax=True)
+            loss = fluid.layers.mean(x=ce_loss)
+            return {'loss': loss}
+        else:
+            return {'logits': logits}
+
+    def postprocess(self, rt_outputs):
+        if not self._is_training:
+            logits = rt_outputs['logits']
+            preds = np.argmax(logits, -1)
+            self._preds.extend(preds.tolist())
+
+    def epoch_postprocess(self, post_inputs):
+        # there is no post_inputs needed and not declared in epoch_inputs_attrs, hence no elements exist in post_inputs
+        if not self._is_training:
+            if self._pred_output_path is None:
+                raise ValueError('argument pred_output_path not found in config. Please add it into config dict/file.')
+            with open(os.path.join(self._pred_output_path, 'predictions.json'), 'w') as writer:
+                for p in self._preds:
+                    writer.write(str(p)+'\n')
+            print('Predictions saved at '+os.path.join(self._pred_output_path, 'predictions.json'))
+
+                

paddlepalm/task_paradigm/mlm.py

+# -*- coding: UTF-8 -*-
+#   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.
+
+import paddle.fluid as fluid
+from paddlepalm.interface import task_paradigm
+from paddle.fluid import layers
+from paddlepalm.backbone.utils.transformer import pre_process_layer
+
+class TaskParadigm(task_paradigm):
+    '''
+    matching
+    '''
+    def __init__(self, config, phase, backbone_config=None):
+        self._is_training = phase == 'train'
+        self._emb_size = backbone_config['hidden_size']
+        self._hidden_size = backbone_config['hidden_size']
+        self._vocab_size = backbone_config['vocab_size']
+        self._hidden_act = backbone_config['hidden_act']
+        self._initializer_range = backbone_config['initializer_range']
+    
+    @property
+    def inputs_attrs(self):
+        reader = {
+            "mask_label": [[-1, 1], 'int64'],
+            "mask_pos": [[-1, 1], 'int64']}
+        if not self._is_training:
+            del reader['mask_label']
+            del reader['batchsize_x_seqlen']
+        bb = {
+            "encoder_outputs": [[-1, -1, self._hidden_size], 'float32'],
+            "embedding_table": [[-1, self._vocab_size, self._emb_size], 'float32']}
+        return {'reader': reader, 'backbone': bb}
+
+    @property
+    def outputs_attrs(self):
+        if self._is_training:
+            return {"loss": [[1], 'float32']}
+        else:
+            return {"logits": [[-1], 'float32']}
+
+    def build(self, inputs, scope_name=""):
+        mask_pos = inputs["reader"]["mask_pos"]
+        if self._is_training:
+            mask_label = inputs["reader"]["mask_label"] 
+            max_position = inputs["reader"]["batchsize_x_seqlen"] - 1
+            mask_pos = fluid.layers.elementwise_min(mask_pos, max_position)
+            mask_pos.stop_gradient = True
+
+        word_emb = inputs["backbone"]["embedding_table"]
+        enc_out = inputs["backbone"]["encoder_outputs"]
+
+        emb_size = word_emb.shape[-1]
+
+        _param_initializer = fluid.initializer.TruncatedNormal(
+            scale=self._initializer_range)
+
+        reshaped_emb_out = fluid.layers.reshape(
+            x=enc_out, shape=[-1, emb_size])
+
+        # extract masked tokens' feature
+        mask_feat = fluid.layers.gather(input=reshaped_emb_out, index=mask_pos)
+
+        # transform: fc
+        mask_trans_feat = fluid.layers.fc(
+            input=mask_feat,
+            size=emb_size,
+            act=self._hidden_act,
+            param_attr=fluid.ParamAttr(
+                name=scope_name+'mask_lm_trans_fc.w_0',
+                initializer=_param_initializer),
+            bias_attr=fluid.ParamAttr(name=scope_name+'mask_lm_trans_fc.b_0'))
+        # transform: layer norm
+        mask_trans_feat = pre_process_layer(
+            mask_trans_feat, 'n', name=scope_name+'mask_lm_trans')
+
+        mask_lm_out_bias_attr = fluid.ParamAttr(
+            name=scope_name+"mask_lm_out_fc.b_0",
+            initializer=fluid.initializer.Constant(value=0.0))
+
+        fc_out = fluid.layers.matmul(
+            x=mask_trans_feat,
+            y=word_emb,
+            transpose_y=True)
+        fc_out += fluid.layers.create_parameter(
+            shape=[self._vocab_size],
+            dtype='float32',
+            attr=mask_lm_out_bias_attr,
+            is_bias=True)
+
+        if self._is_training:
+            mask_lm_loss = fluid.layers.softmax_with_cross_entropy(
+                logits=fc_out, label=mask_label)
+            loss = fluid.layers.mean(mask_lm_loss)
+            return {'loss': loss}
+        else:
+            return {'logits': fc_out}
+
+