modeling_ernie.py

#   Copyright (c) 2018 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 division
from __future__ import absolute_import
from __future__ import print_function
from __future__ import unicode_literals

import sys
import os
import argparse
import json
import logging
import logging
from functools import partial
import six
if six.PY2:
    from pathlib2 import Path
else:
    from pathlib import Path

import paddle.fluid.dygraph as D
import paddle.fluid as F
import paddle.fluid.layers as L

from ernie.file_utils import _fetch_from_remote, add_docstring

log = logging.getLogger(__name__)

def _build_linear(n_in, n_out, name, init, act=None):
    return D.Linear(n_in, 
            n_out, 
            param_attr=F.ParamAttr(name='%s.w_0' % name if name is not None else None, initializer=init), 
            bias_attr='%s.b_0' % name if name is not None else None, act=act)


def _build_ln(n_in, name):
    return D.LayerNorm(normalized_shape=n_in, 
            param_attr=F.ParamAttr(name='%s_layer_norm_scale' % name if name is not None else None, 
                initializer=F.initializer.Constant(1.)),
            bias_attr=F.ParamAttr(name='%s_layer_norm_bias' % name if name is not None else None, 
                initializer=F.initializer.Constant(1.)),
        )


def append_name(name, postfix):
    if name is None:
        return None
    elif name == '':
        return postfix
    else:
        return '%s_%s' % (name, postfix)


class AttentionLayer(D.Layer):
    def __init__(self, cfg, name=None):
        super(AttentionLayer, self).__init__()
        initializer = F.initializer.TruncatedNormal(scale=cfg['initializer_range'])
        d_model = cfg['hidden_size']
        n_head = cfg['num_attention_heads']
        assert d_model % n_head == 0
        d_model_q = cfg.get('query_hidden_size_per_head', d_model // n_head) * n_head
        d_model_v = cfg.get('value_hidden_size_per_head', d_model // n_head) * n_head
        self.n_head = n_head
        self.d_key = d_model_q // n_head
        self.q = _build_linear(d_model, d_model_q, append_name(name, 'query_fc'), initializer)
        self.k = _build_linear(d_model, d_model_q, append_name(name, 'key_fc'), initializer)
        self.v = _build_linear(d_model, d_model_v, append_name(name, 'value_fc'), initializer)
        self.o = _build_linear(d_model_v, d_model, append_name(name, 'output_fc'), initializer)
        self.dropout = lambda i: L.dropout(i, dropout_prob=cfg['attention_probs_dropout_prob'], dropout_implementation="upscale_in_train",) if self.training else i

    def forward(self, queries, keys, values, attn_bias, past_cache):
        assert len(queries.shape) == len(keys.shape) == len(values.shape) == 3
        #bsz, q_len, q_dim = queries.shape
        #bsz, k_len, k_dim = keys.shape
        #bsz, v_len, v_dim = values.shape
        #assert k_len == v_len

        q = self.q(queries)
        k = self.k(keys)
        v = self.v(values)

        cache = (k, v)
        if past_cache is not None:
            cached_k, cached_v = past_cache
            k = L.concat([cached_k, k], 1)
            v = L.concat([cached_v, v], 1)

        q = L.transpose(L.reshape(q, [0, 0, self.n_head, q.shape[-1] // self.n_head]), [0, 2, 1, 3]) #[batch, head, seq, dim]
        k = L.transpose(L.reshape(k, [0, 0, self.n_head, k.shape[-1] // self.n_head]), [0, 2, 1, 3]) #[batch, head, seq, dim]
        v = L.transpose(L.reshape(v, [0, 0, self.n_head, v.shape[-1] // self.n_head]), [0, 2, 1, 3]) #[batch, head, seq, dim]


        q = L.scale(q, scale=self.d_key ** -0.5)
        score = L.matmul(q, k, transpose_y=True)
        if attn_bias is not None:
            score += attn_bias
        score = L.softmax(score, use_cudnn=True)
        score = self.dropout(score)

        out = L.matmul(score, v)
        out = L.transpose(out, [0, 2, 1, 3])
        out = L.reshape(out, [0, 0, out.shape[2] * out.shape[3]])

        out = self.o(out)
        return out, cache


class PositionwiseFeedForwardLayer(D.Layer):
    def __init__(self, cfg, name=None):
        super(PositionwiseFeedForwardLayer, self).__init__()
        initializer = F.initializer.TruncatedNormal(scale=cfg['initializer_range'])
        d_model = cfg['hidden_size']
        d_ffn = cfg.get('intermediate_size', 4 * d_model)
        assert cfg['hidden_act'] in ['relu', 'gelu']
        self.i = _build_linear(d_model, d_ffn, append_name(name, 'fc_0'), initializer, act=cfg['hidden_act'])
        self.o = _build_linear(d_ffn, d_model, append_name(name, 'fc_1'), initializer)
        prob = cfg.get('intermediate_dropout_prob', 0.)
        self.dropout = lambda i: L.dropout(i, dropout_prob=prob, dropout_implementation="upscale_in_train",) if self.training else i

    def forward(self, inputs):
        hidden = self.i(inputs)
        hidden = self.dropout(hidden)
        out = self.o(hidden)
        return out


class ErnieBlock(D.Layer):
    def __init__(self, cfg, name=None):
        super(ErnieBlock, self).__init__()
        d_model = cfg['hidden_size']
        initializer = F.initializer.TruncatedNormal(scale=cfg['initializer_range'])

        self.attn = AttentionLayer(cfg, name = append_name(name, 'multi_head_att'))
        self.ln1 = _build_ln(d_model, name = append_name(name, 'post_att'))
        self.ffn = PositionwiseFeedForwardLayer(cfg, name = append_name(name, 'ffn'))
        self.ln2 = _build_ln(d_model, name = append_name(name, 'post_ffn'))
        prob = cfg.get('intermediate_dropout_prob', cfg['hidden_dropout_prob'])
        self.dropout = lambda i: L.dropout(i, dropout_prob=prob, dropout_implementation="upscale_in_train",) if self.training else i

    def forward(self, inputs, attn_bias=None, past_cache=None):
        attn_out, cache = self.attn(inputs, inputs, inputs, attn_bias, past_cache=past_cache) #self attn
        attn_out = self.dropout(attn_out)
        hidden = attn_out + inputs 
        hidden = self.ln1(hidden) # dropout/ add/ norm

        ffn_out = self.ffn(hidden)
        ffn_out = self.dropout(ffn_out)
        hidden = ffn_out + hidden
        hidden = self.ln2(hidden)
        return hidden, cache

        
class ErnieEncoderStack(D.Layer):
    def __init__(self, cfg, name=None):
        super(ErnieEncoderStack, self).__init__()
        n_layers = cfg['num_hidden_layers']
        self.block = D.LayerList([ErnieBlock(cfg, append_name(name, 'layer_%d' % i)) for i in range(n_layers)])

    def forward(self, inputs, attn_bias=None, past_cache=None):
        if past_cache is not None:
            assert isinstance(past_cache, tuple), 'unknown type of `past_cache`, expect tuple or list. got %s' % repr(type(past_cache))
            past_cache = list(zip(*past_cache))
        else:
            past_cache = [None] * len(self.block)
        cache_list_k, cache_list_v, hidden_list = [], [], [inputs]

        for b, p in zip(self.block, past_cache):
            inputs, cache = b(inputs, attn_bias=attn_bias, past_cache=p)
            cache_k, cache_v = cache
            cache_list_k.append(cache_k)
            cache_list_v.append(cache_v)
            hidden_list.append(inputs)

        return inputs, hidden_list, (cache_list_k, cache_list_v)


class PretrainedModel(object):
    bce = 'https://ernie-github.cdn.bcebos.com/'
    resource_map = {
        'ernie-1.0': bce + 'model-ernie1.0.1.tar.gz',
        'ernie-2.0-en': bce + 'model-ernie2.0-en.1.tar.gz',
        'ernie-2.0-large-en':  bce + 'model-ernie2.0-large-en.1.tar.gz',
        'ernie-tiny': bce + 'model-ernie_tiny.1.tar.gz',
    }
    @classmethod
    def from_pretrained(cls, pretrain_dir_or_url, force_download=False, **kwargs):
        if not Path(pretrain_dir_or_url).exists() and pretrain_dir_or_url in cls.resource_map:
            url = cls.resource_map[pretrain_dir_or_url]
            log.info('get pretrain dir from %s' % url)
            pretrain_dir = _fetch_from_remote(url, force_download)
        else:
            log.info('pretrain dir %s not in %s, read from local' % (pretrain_dir_or_url, repr(cls.resource_map)))
            pretrain_dir = Path(pretrain_dir_or_url)

        if not pretrain_dir.exists():
            raise ValueError('pretrain dir not found: %s' % pretrain_dir)
        param_path = pretrain_dir /'params'
        state_dict_path = pretrain_dir / 'saved_weights'
        config_path = pretrain_dir / 'ernie_config.json'

        if not config_path.exists():
            raise ValueError('config path not found: %s' % config_path)
        name_prefix=kwargs.pop('name', None)
        cfg_dict = dict(json.loads(config_path.open().read()), **kwargs)
        model = cls(cfg_dict, name=name_prefix)
        
        log.info('loading pretrained model from %s' % pretrain_dir)

        #if os.path.exists(param_path):
        #    raise NotImplementedError()
        #    log.debug('load pretrained weight from program state')
        #    F.io.load_program_state(param_path) #buggy in dygraph.gurad, push paddle to fix
        if state_dict_path.with_suffix('.pdparams').exists():
            m, _ = D.load_dygraph(state_dict_path.as_posix())
            for k, v in model.state_dict().items():
                if k not in m:
                    log.warn('param:%s not set in pretrained model, skip' % k)
                    m[k] = v # FIXME: no need to do this in the future
            model.set_dict(m)
        else:
            raise ValueError('weight file not found in pretrain dir: %s' % pretrain_dir)
        return model


class ErnieModel(D.Layer, PretrainedModel):
    def __init__(self, cfg, name=None):
        """
        Fundamental pretrained Ernie model
        """
        log.debug('init ErnieModel with config: %s' % repr(cfg))
        D.Layer.__init__(self)
        d_model = cfg['hidden_size']
        d_emb = cfg.get('emb_size', cfg['hidden_size'])
        d_vocab = cfg['vocab_size']
        d_pos = cfg['max_position_embeddings']
        d_sent = cfg.get("sent_type_vocab_size") or cfg['type_vocab_size']
        self.n_head = cfg['num_attention_heads']
        self.return_additional_info = cfg.get('return_additional_info', False)
        initializer = F.initializer.TruncatedNormal(scale=cfg['initializer_range'])

        self.ln = _build_ln(d_model, name=append_name(name, 'pre_encoder'))
        self.word_emb = D.Embedding([d_vocab, d_emb], param_attr=F.ParamAttr(name=append_name(name, 'word_embedding'), initializer=initializer))
        self.pos_emb = D.Embedding([d_pos, d_emb], param_attr=F.ParamAttr(name=append_name(name, 'pos_embedding'), initializer=initializer))
        self.sent_emb = D.Embedding([d_sent, d_emb], param_attr=F.ParamAttr(name=append_name(name, 'sent_embedding'), initializer=initializer))
        prob = cfg['hidden_dropout_prob']
        self.dropout = lambda i: L.dropout(i, dropout_prob=prob, dropout_implementation="upscale_in_train",) if self.training else i

        self.encoder_stack = ErnieEncoderStack(cfg, append_name(name, 'encoder'))
        if cfg.get('has_pooler', True):
            self.pooler = _build_linear(cfg['hidden_size'], cfg['hidden_size'], append_name(name, 'pooled_fc'), initializer, act='tanh')
        else:
            self.pooler = None
        self.train()
    

    def eval(self):
        if F.in_dygraph_mode():
            super(ErnieModel, self).eval()
        self.training = False
        for l in self.sublayers():
            l.training = False

    def train(self):
        if F.in_dygraph_mode():
            super(ErnieModel, self).train()
        self.training = True
        for l in self.sublayers():
            l.training = True

    def forward(self, src_ids, sent_ids=None, pos_ids=None, input_mask=None, attn_bias=None, past_cache=None, use_causal_mask=False):
        """
        Args:
            src_ids (`Variable` of shape `[batch_size, seq_len]`): 
                Indices of input sequence tokens in the vocabulary.
            sent_ids (optional, `Variable` of shape `[batch_size, seq_len]`): 
                aka token_type_ids, Segment token indices to indicate first and second portions of the inputs.
                if None, assume all tokens come from `segment_a`
            pos_ids(optional, `Variable` of shape `[batch_size, seq_len]`): 
                Indices of positions of each input sequence tokens in the position embeddings.
            input_mask(optional `Variable` of shape `[batch_size, seq_len]`): 
                Mask to avoid performing attention on the padding token indices of the encoder input.
            attn_bias(optional, `Variable` of shape `[batch_size, seq_len, seq_len] or False`): 
                3D version of `input_mask`, if set, overrides `input_mask`; if set not False, will not apply attention mask
            past_cache(optional, tuple of two lists: cached key and cached value,
                each is a list of `Variable`s of shape `[batch_size, seq_len, hidden_size]`):
                cached key/value tensor that will be concated to generated key/value when performing self attention. 
                if set, `attn_bias` should not be None.

        Returns:
            pooled (`Variable` of shape `[batch_size, hidden_size]`):
                output logits of pooler classifier
            encoded(`Variable` of shape `[batch_size, seq_len, hidden_size]`):
                output logits of transformer stack
            info (Dictionary):
                addtional middle level info, inclues: all hidden stats, k/v caches.
        """
        #d_batch, d_seqlen = src_ids.shape
        assert len(src_ids.shape) == 2, 'expect src_ids.shape = [batch, sequecen], got %s' % (repr(src_ids.shape))
        assert attn_bias is not None if past_cache else True, 'if `past_cache` is specified; attn_bias should not be None'
        d_batch = L.shape(src_ids)[0]
        d_seqlen = L.shape(src_ids)[1]
        if pos_ids is None:
            pos_ids = L.reshape(L.range(0, d_seqlen, 1, dtype='int32'), [1, -1])
            pos_ids = L.cast(pos_ids, 'int64')
        if attn_bias is None:
            if input_mask is None:
                input_mask = L.cast(src_ids != 0, 'float32')
            assert len(input_mask.shape) == 2
            input_mask = L.unsqueeze(input_mask, axes=[-1])
            attn_bias = L.matmul(input_mask, input_mask, transpose_y=True)
            if use_causal_mask:
                sequence = L.reshape(L.range(0, d_seqlen, 1, dtype='float32') + 1., [1, 1, -1, 1])
                causal_mask = L.cast((L.matmul(sequence, 1. / sequence, transpose_y=True) >= 1.) , 'float32')
                attn_bias *= causal_mask
        else:
            assert len(attn_bias.shape) == 3, 'expect attn_bias tobe rank 3, got %r' % attn_bias.shape
        attn_bias = (1. - attn_bias) * -10000.0
        attn_bias = L.unsqueeze(attn_bias, [1])
        attn_bias = L.expand(attn_bias, [1, self.n_head, 1, 1]) # avoid broadcast =_=
        attn_bias.stop_gradient = True
            
        if sent_ids is None:
            sent_ids = L.zeros_like(src_ids)

        src_embedded = self.word_emb(src_ids)
        pos_embedded = self.pos_emb(pos_ids)
        sent_embedded = self.sent_emb(sent_ids)
        embedded = src_embedded + pos_embedded + sent_embedded

        embedded = self.dropout(self.ln(embedded))

        encoded, hidden_list, cache_list = self.encoder_stack(embedded, attn_bias, past_cache=past_cache)
        if self.pooler is not None:
            pooled = self.pooler(encoded[:, 0, :])
        else:
            pooled = None

        additional_info = {
            'hiddens': hidden_list,
            'caches': cache_list,
        }

        if self.return_additional_info:
            return pooled, encoded, additional_info
        else:
            return pooled, encoded
        

class ErnieModelForSequenceClassification(ErnieModel):
    """
    Ernie Model for text classfication or pointwise ranking tasks 
    """

    def __init__(self, cfg, name=None):
        super(ErnieModelForSequenceClassification, self).__init__(cfg, name=name)

        initializer = F.initializer.TruncatedNormal(scale=cfg['initializer_range'])
        self.classifier = _build_linear(cfg['hidden_size'], cfg['num_labels'], append_name(name, 'cls'), initializer)

        prob = cfg.get('classifier_dropout_prob', cfg['hidden_dropout_prob'])
        self.dropout = lambda i: L.dropout(i, dropout_prob=prob, dropout_implementation="upscale_in_train",) if self.training else i

    @add_docstring(ErnieModel.forward.__doc__)
    def forward(self, *args, **kwargs):
        """
        Args:
            labels (optional, `Variable` of shape [batch_size]): 
                ground truth label id for each sentence
        Returns:
            loss (`Variable` of shape []):
                Cross entropy loss mean over batch
                if labels not set, returns None
            logits (`Variable` of shape [batch_size, hidden_size]):
                output logits of classifier
        """
        labels = kwargs.pop('labels', None)
        pooled, encoded = super(ErnieModelForSequenceClassification, self).forward(*args, **kwargs)
        hidden = self.dropout(pooled)
        logits = self.classifier(hidden)

        if labels is not None:
            if len(labels.shape) == 1:
                labels = L.reshape(labels, [-1, 1])
            loss = L.softmax_with_cross_entropy(logits, labels)
            loss = L.reduce_mean(loss)
        else:
            loss = None
        return loss, logits


class ErnieModelForTokenClassification(ErnieModel):
    """
    Ernie Model for Named entity tasks(NER)
    """
    def __init__(self, cfg, name=None):
        super(ErnieModelForTokenClassification, self).__init__(cfg, name=name)

        initializer = F.initializer.TruncatedNormal(scale=cfg['initializer_range'])
        self.classifier = _build_linear(cfg['hidden_size'], cfg['num_labels'], append_name(name, 'cls'), initializer)

        prob = cfg.get('classifier_dropout_prob', cfg['hidden_dropout_prob'])
        self.dropout = lambda i: L.dropout(i, dropout_prob=prob, dropout_implementation="upscale_in_train",) if self.training else i

    @add_docstring(ErnieModel.forward.__doc__)
    def forward(self, *args, **kwargs):
        """
        Args:
            labels (optional, `Variable` of shape [batch_size, seq_len]): 
                ground truth label id for each token
        Returns:
            loss (`Variable` of shape []):
                Cross entropy loss mean over batch and time, ignore positions where label == -100
                if labels not set, returns None
            logits (`Variable` of shape [batch_size, seq_len, hidden_size]):
                output logits of classifier
            loss_weights (`Variable` of shape [batch_size, seq_len]):
                weigths of loss for each tokens.
            ignore_index (int):
                when label == `ignore_index`, this token will not contribute to loss
        """
        ignore_index = kwargs.pop('ignore_index', -100)
        labels = kwargs.pop('labels', None)
        loss_weights = kwargs.pop('loss_weights', None)
        pooled, encoded = super(ErnieModelForTokenClassification, self).forward(*args, **kwargs)
        hidden = self.dropout(encoded) # maybe not?
        logits = self.classifier(hidden)

        if labels is not None:
            if len(labels.shape) == 2:
                labels = L.unsqueeze(labels, axes=[-1])
            loss = L.softmax_with_cross_entropy(logits, labels, ignore_index=ignore_index)
            if loss_weights is not None:
                loss = L.squeeze(loss, [-1]) * loss_weights
            loss = L.reduce_mean(loss)
        else:
            loss = None
        return loss, logits


class ErnieModelForQuestionAnswering(ErnieModel):
    """
    Ernie model for reading comprehension tasks (SQuAD)
    """
    def __init__(self, cfg, name=None):
        super(ErnieModelForQuestionAnswering, self).__init__(cfg, name=name)

        initializer = F.initializer.TruncatedNormal(scale=cfg['initializer_range'])
        self.classifier = _build_linear(cfg['hidden_size'], 2, append_name(name, 'cls_mrc'), initializer)

        prob = cfg.get('classifier_dropout_prob', cfg['hidden_dropout_prob'])
        self.dropout = lambda i: L.dropout(i, dropout_prob=prob, dropout_implementation="upscale_in_train",) if self.training else i

    @add_docstring(ErnieModel.forward.__doc__)
    def forward(self, *args, **kwargs):
        """
        Args:
            start_pos (optional, `Variable` of shape [batch_size]): 
                token index of start of answer span in `context`
            end_pos (optional, `Variable` of shape [batch_size]): 
                token index of end of answer span in `context`
        Returns:
            loss (`Variable` of shape []):
                Cross entropy loss mean over batch and time, ignore positions where label == -100
                if labels not set, returns None
            start_logits (`Variable` of shape [batch_size, hidden_size]):
                output logits of start position, use argmax(start_logit) to get start index
            end_logits (`Variable` of shape [batch_size, hidden_size]):
                output logits of end position, use argmax(end_logit) to get end index
        """

        start_pos = kwargs.pop('start_pos', None)
        end_pos = kwargs.pop('end_pos', None)
        pooled, encoded = super(ErnieModelForQuestionAnswering, self).forward(*args, **kwargs)
        encoded = self.dropout(encoded)
        encoded = self.classifier(encoded)
        start_logit, end_logits = L.unstack(encoded, axis=-1)
        if start_pos is not None and end_pos is not None:
            if len(start_pos.shape) == 1:
                start_pos = L.unsqueeze(start_pos, axes=[-1])
            if len(end_pos.shape) == 1:
                end_pos = L.unsqueeze(end_pos, axes=[-1])
            start_loss = L.softmax_with_cross_entropy(start_logit, start_pos)
            end_loss = L.softmax_with_cross_entropy(end_logits, end_pos)
            loss = (L.reduce_mean(start_loss) + L.reduce_mean(end_loss)) / 2.
        else:
            loss = None
        return loss, start_logit, end_logits


class NSPHead(D.Layer):
    def __init__(self, cfg, name=None):
        super(NSPHead, self).__init__()
        initializer = F.initializer.TruncatedNormal(scale=cfg['initializer_range'])
        self.nsp = _build_linear(cfg['hidden_size'], 2, append_name(name, 'nsp_fc'), initializer)

    def forward(self, inputs, labels):
        """
        Args:
            start_pos (optional, `Variable` of shape [batch_size]): 
                token index of start of answer span in `context`
            end_pos (optional, `Variable` of shape [batch_size]): 
                token index of end of answer span in `context`
        Returns:
            loss (`Variable` of shape []):
                Cross entropy loss mean over batch and time, ignore positions where label == -100
                if labels not set, returns None
            start_logits (`Variable` of shape [batch_size, hidden_size]):
                output logits of start position
            end_logits (`Variable` of shape [batch_size, hidden_size]):
                output logits of end position
        """

        logits = self.nsp(inputs)
        loss = L.softmax_with_cross_entropy(logits, labels)
        loss = L.reduce_mean(loss)
        return loss


class ErnieModelForPretraining(ErnieModel):
    """
    Ernie Model for Masked Languate Model pretrain
    """
    def __init__(self, cfg, name=None):
        super(ErnieModelForPretraining, self).__init__(cfg, name=name)
        initializer = F.initializer.TruncatedNormal(scale=cfg['initializer_range'])
        d_model = cfg['hidden_size']
        d_vocab = cfg['vocab_size']

        self.pooler_heads = D.LayerList([NSPHead(cfg, name=name)])
        self.mlm = _build_linear(d_model, d_model, append_name(name, 'mask_lm_trans_fc'), initializer, act=cfg['hidden_act'])
        self.mlm_ln = _build_ln(d_model, name = append_name(name, 'mask_lm_trans'))
        self.mlm_bias = L.create_parameter(
                dtype='float32',
                shape=[d_vocab], 
                attr=F.ParamAttr(
                    name=append_name(name, 'mask_lm_out_fc.b_0'), 
                    initializer=F.initializer.Constant(value=0.0)
                    ),
                is_bias=True,
            )

    @add_docstring(ErnieModel.forward.__doc__)
    def forward(self, *args, **kwargs):
        """
        Args:
            nsp_labels (optional, `Variable` of shape [batch_size]): 
                labels for `next sentence prediction` tasks
            mlm_pos (optional, `Variable` of shape [n_mask, 2]): 
                index of mask_id in `src_ids`, can be obtained from `fluid.layers.where(src_ids==mask_id)`
            labels (optional, `Variable` of shape [n_mask]): 
                labels for `mask language model` tasks, the original token indices in masked position in `src_ids`
        Returns:
            loss (`Variable` of shape []):
                total_loss of `next sentence prediction` and `masked language model`
            mlm_loss (`Variable` of shape []):
                loss for `masked language model` task
            nsp_loss (`Variable` of shape []):
                loss for `next sentence prediction` task
        """

        mlm_labels = kwargs.pop('labels')
        mlm_pos = kwargs.pop('mlm_pos')
        nsp_labels = kwargs.pop('nsp_labels')
        pooled, encoded = super(ErnieModelForPretraining, self).forward(*args, **kwargs)
        if len(mlm_labels.shape) == 1:
            mlm_labels = L.reshape(mlm_labels, [-1, 1])
        if len(nsp_labels.shape) == 1:
            nsp_labels = L.reshape(nsp_labels, [-1, 1])

        nsp_loss = self.pooler_heads[0](pooled, nsp_labels)

        encoded_2d = L.gather_nd(encoded, mlm_pos)
        encoded_2d = self.mlm(encoded_2d)
        encoded_2d = self.mlm_ln(encoded_2d)
        logits_2d = L.matmul(encoded_2d, self.word_emb.weight, transpose_y=True) + self.mlm_bias
        mlm_loss = L.reduce_mean(L.softmax_with_cross_entropy(logits_2d, mlm_labels))
        total_loss = mlm_loss + nsp_loss
        return total_loss, mlm_loss, nsp_loss


class ErnieModelForGeneration(ErnieModel):
    """
    Ernie Model for sequence to sequence generation.
    """
    resource_map = {
        'ernie-gen-base-en': ErnieModel.bce + 'model-ernie-gen-base-en.1.tar.gz',
        'ernie-gen-large-en': ErnieModel.bce + 'model-ernie-gen-large-en.1.tar.gz',
        'ernie-gen-large-430g-en': ErnieModel.bce + 'model-ernie-gen-large-430g-en.1.tar.gz',
        'ernie-1.0': ErnieModel.bce + 'model-ernie1.0.1.tar.gz',
    }
    def __init__(self, cfg, name=None):
        cfg['return_additional_info'] = True
        cfg['has_pooler'] = False
        super(ErnieModelForGeneration, self).__init__(cfg, name=name)
        initializer = F.initializer.TruncatedNormal(scale=cfg['initializer_range'])
        d_model = cfg['hidden_size']
        d_vocab = cfg['vocab_size']

        self.mlm = _build_linear(d_model, d_model, append_name(name, 'mask_lm_trans_fc'), initializer, act=cfg['hidden_act'])
        self.mlm_ln = _build_ln(d_model, name = append_name(name, 'mask_lm_trans'))
        self.mlm_bias = L.create_parameter(
                dtype='float32',
                shape=[d_vocab], 
                attr=F.ParamAttr(
                    name=append_name(name, 'mask_lm_out_fc.b_0'), 
                    initializer=F.initializer.Constant(value=0.0)
                    ),
                is_bias=True,
            )

    @add_docstring(ErnieModel.forward.__doc__)
    def forward(self, *args, **kwargs):
        """
        Args
            tgt_labels(`Variable` of shape [batch_size, seqlen] or [batch, seqlen, vocab_size]):
                ground trouth target sequence id (hard label) or distribution (soft label)
            tgt_pos(`Variable` of shape [n_targets, 2]):
                index of tgt_labels in `src_ids`, can be obtained from `fluid.layers.where(src_ids==mask_id)`
            encoder_only(Bool):
                if set, will not return loss, logits_2d
        Returns:
            loss(`Variable` of shape []):
                cross entropy loss mean over every target label. if `encode_only`, returns None.
            logits(`Variable` of shape [n_targets, vocab_size]):
                logits for every targets. if `encode_only`, returns None.
            info(Dictionary): see `ErnieModel`
        """
        tgt_labels = kwargs.pop('tgt_labels', None)
        tgt_pos = kwargs.pop('tgt_pos', None)
        encode_only = kwargs.pop('encode_only', False)
        _, encoded, info = ErnieModel.forward(self, *args, **kwargs)
        if encode_only:
            return None, None, info
        elif tgt_labels is None or tgt_pos is None:
            encoded = self.mlm(encoded)
            encoded = self.mlm_ln(encoded)
            logits = L.matmul(encoded, self.word_emb.weight, transpose_y=True) + self.mlm_bias
            output_ids = L.argmax(logits, -1)
            return output_ids, logits, info
        else:
            encoded_2d = L.gather_nd(encoded, tgt_pos)
            encoded_2d = self.mlm(encoded_2d)
            encoded_2d = self.mlm_ln(encoded_2d)
            logits_2d = L.matmul(encoded_2d, self.word_emb.weight, transpose_y=True) + self.mlm_bias
            if len(tgt_labels.shape) == 1:
                tgt_labels = L.reshape(tgt_labels, [-1, 1])
            
            loss = L.reduce_mean(
                    L.softmax_with_cross_entropy(logits_2d, tgt_labels, soft_label=(tgt_labels.shape[-1] != 1))
                    )
            return loss, logits_2d, info