modeling_xlnet.py

# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
""" PyTorch XLNet model.
"""
from __future__ import absolute_import, division, print_function, unicode_literals

import json
import logging
import math
import os
import sys
from io import open

import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import CrossEntropyLoss, MSELoss

from .modeling_utils import (CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig, PreTrainedModel,
                             SequenceSummary, PoolerAnswerClass, PoolerEndLogits, PoolerStartLogits,
                             add_start_docstrings)


logger = logging.getLogger(__name__)

XLNET_PRETRAINED_MODEL_ARCHIVE_MAP = {
    'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-pytorch_model.bin",
    'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-pytorch_model.bin",
}
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-config.json",
    'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-config.json",
}


def build_tf_xlnet_to_pytorch_map(model, config, tf_weights=None):
    """ A map of modules from TF to PyTorch.
        I use a map to keep the PyTorch model as
        identical to the original PyTorch model as possible.
    """

    tf_to_pt_map = {}

    if hasattr(model, 'transformer'):
        if hasattr(model, 'lm_loss'):
            # We will load also the output bias
            tf_to_pt_map['model/lm_loss/bias'] = model.lm_loss.bias
        if hasattr(model, 'sequence_summary') and 'model/sequnece_summary/summary/kernel' in tf_weights:
            # We will load also the sequence summary
            tf_to_pt_map['model/sequnece_summary/summary/kernel'] = model.sequence_summary.summary.weight
            tf_to_pt_map['model/sequnece_summary/summary/bias'] = model.sequence_summary.summary.bias
        if hasattr(model, 'logits_proj') and config.finetuning_task is not None \
                and 'model/regression_{}/logit/kernel'.format(config.finetuning_task) in tf_weights:
            tf_to_pt_map['model/regression_{}/logit/kernel'.format(config.finetuning_task)] = model.logits_proj.weight
            tf_to_pt_map['model/regression_{}/logit/bias'.format(config.finetuning_task)] = model.logits_proj.bias

        # Now load the rest of the transformer
        model = model.transformer

    # Embeddings and output
    tf_to_pt_map.update({'model/transformer/word_embedding/lookup_table': model.word_embedding.weight,
                         'model/transformer/mask_emb/mask_emb': model.mask_emb})

    # Transformer blocks
    for i, b in enumerate(model.layer):
        layer_str = "model/transformer/layer_%d/" % i
        tf_to_pt_map.update({
            layer_str + "rel_attn/LayerNorm/gamma": b.rel_attn.layer_norm.weight,
            layer_str + "rel_attn/LayerNorm/beta": b.rel_attn.layer_norm.bias,
            layer_str + "rel_attn/o/kernel": b.rel_attn.o,
            layer_str + "rel_attn/q/kernel": b.rel_attn.q,
            layer_str + "rel_attn/k/kernel": b.rel_attn.k,
            layer_str + "rel_attn/r/kernel": b.rel_attn.r,
            layer_str + "rel_attn/v/kernel": b.rel_attn.v,
            layer_str + "ff/LayerNorm/gamma": b.ff.layer_norm.weight,
            layer_str + "ff/LayerNorm/beta": b.ff.layer_norm.bias,
            layer_str + "ff/layer_1/kernel": b.ff.layer_1.weight,
            layer_str + "ff/layer_1/bias": b.ff.layer_1.bias,
            layer_str + "ff/layer_2/kernel": b.ff.layer_2.weight,
            layer_str + "ff/layer_2/bias": b.ff.layer_2.bias,
        })

    # Relative positioning biases
    if config.untie_r:
        r_r_list = []
        r_w_list = []
        r_s_list = []
        seg_embed_list = []
        for b in model.layer:
            r_r_list.append(b.rel_attn.r_r_bias)
            r_w_list.append(b.rel_attn.r_w_bias)
            r_s_list.append(b.rel_attn.r_s_bias)
            seg_embed_list.append(b.rel_attn.seg_embed)
    else:
        r_r_list = [model.r_r_bias]
        r_w_list = [model.r_w_bias]
        r_s_list = [model.r_s_bias]
        seg_embed_list = [model.seg_embed]
    tf_to_pt_map.update({
        'model/transformer/r_r_bias': r_r_list,
        'model/transformer/r_w_bias': r_w_list,
        'model/transformer/r_s_bias': r_s_list,
        'model/transformer/seg_embed': seg_embed_list})
    return tf_to_pt_map

def load_tf_weights_in_xlnet(model, config, tf_path):
    """ Load tf checkpoints in a pytorch model
    """
    try:
        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
            "https://www.tensorflow.org/install/ for installation instructions.")
        raise
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    tf_weights = {}
    for name, shape in init_vars:
        logger.info("Loading TF weight {} with shape {}".format(name, shape))
        array = tf.train.load_variable(tf_path, name)
        tf_weights[name] = array

    # Build TF to PyTorch weights loading map
    tf_to_pt_map = build_tf_xlnet_to_pytorch_map(model, config, tf_weights)

    for name, pointer in tf_to_pt_map.items():
        logger.info("Importing {}".format(name))
        if name not in tf_weights:
            logger.info("{} not in tf pre-trained weights, skipping".format(name))
            continue
        array = tf_weights[name]
        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
        # which are not required for using pretrained model
        if 'kernel' in name and ('ff' in name or 'summary' in name or 'logit' in name):
            logger.info("Transposing")
            array = np.transpose(array)
        if isinstance(pointer, list):
            # Here we will split the TF weigths
            assert len(pointer) == array.shape[0]
            for i, p_i in enumerate(pointer):
                arr_i = array[i, ...]
                try:
                    assert p_i.shape == arr_i.shape
                except AssertionError as e:
                    e.args += (p_i.shape, arr_i.shape)
                    raise
                logger.info("Initialize PyTorch weight {} for layer {}".format(name, i))
                p_i.data = torch.from_numpy(arr_i)
        else:
            try:
                assert pointer.shape == array.shape
            except AssertionError as e:
                e.args += (pointer.shape, array.shape)
                raise
            logger.info("Initialize PyTorch weight {}".format(name))
            pointer.data = torch.from_numpy(array)
        tf_weights.pop(name, None)
        tf_weights.pop(name + '/Adam', None)
        tf_weights.pop(name + '/Adam_1', None)

    logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
    return model


def gelu(x):
    """ Implementation of the gelu activation function.
        XLNet is using OpenAI GPT's gelu (not exactly the same as BERT)
        Also see https://arxiv.org/abs/1606.08415
    """
    cdf = 0.5 * (1.0 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
    return x * cdf


def swish(x):
    return x * torch.sigmoid(x)


ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}


class XLNetConfig(PretrainedConfig):
    """Configuration class to store the configuration of a ``XLNetModel``.

    Args:
        vocab_size_or_config_json_file: Vocabulary size of ``inputs_ids`` in ``XLNetModel``.
        d_model: Size of the encoder layers and the pooler layer.
        n_layer: Number of hidden layers in the Transformer encoder.
        n_head: Number of attention heads for each attention layer in
            the Transformer encoder.
        d_inner: The size of the "intermediate" (i.e., feed-forward)
            layer in the Transformer encoder.
        ff_activation: The non-linear activation function (function or string) in the
            encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
        untie_r: untie relative position biases
        attn_type: 'bi' for XLNet, 'uni' for Transformer-XL

        dropout: The dropout probabilitiy for all fully connected
            layers in the embeddings, encoder, and pooler.
        dropatt: The dropout ratio for the attention
            probabilities.
        initializer_range: The sttdev of the truncated_normal_initializer for
            initializing all weight matrices.
        layer_norm_eps: The epsilon used by LayerNorm.

        dropout: float, dropout rate.
        dropatt: float, dropout rate on attention probabilities.
        init: str, the initialization scheme, either "normal" or "uniform".
        init_range: float, initialize the parameters with a uniform distribution
            in [-init_range, init_range]. Only effective when init="uniform".
        init_std: float, initialize the parameters with a normal distribution
            with mean 0 and stddev init_std. Only effective when init="normal".
        mem_len: int, the number of tokens to cache.
        reuse_len: int, the number of tokens in the currect batch to be cached
            and reused in the future.
        bi_data: bool, whether to use bidirectional input pipeline.
            Usually set to True during pretraining and False during finetuning.
        clamp_len: int, clamp all relative distances larger than clamp_len.
            -1 means no clamping.
        same_length: bool, whether to use the same attention length for each token.
        finetuning_task: name of the glue task on which the model was fine-tuned if any
    """
    pretrained_config_archive_map = XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP

    def __init__(self,
                 vocab_size_or_config_json_file=32000,
                 d_model=1024,
                 n_layer=24,
                 n_head=16,
                 d_inner=4096,
                 ff_activation="gelu",
                 untie_r=True,
                 attn_type="bi",

                 initializer_range=0.02,
                 layer_norm_eps=1e-12,

                 dropout=0.1,
                 mem_len=None,
                 reuse_len=None,
                 bi_data=False,
                 clamp_len=-1,
                 same_length=False,

                 finetuning_task=None,
                 num_labels=2,
                 summary_type='last',
                 summary_use_proj=True,
                 summary_activation='tanh',
                 summary_last_dropout=0.1,
                 start_n_top=5,
                 end_n_top=5,
                 **kwargs):
        """Constructs XLNetConfig.
        """
        super(XLNetConfig, self).__init__(**kwargs)

        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
                        and isinstance(vocab_size_or_config_json_file, unicode)):
            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
                json_config = json.loads(reader.read())
            for key, value in json_config.items():
                self.__dict__[key] = value
        elif isinstance(vocab_size_or_config_json_file, int):
            self.n_token = vocab_size_or_config_json_file
            self.d_model = d_model
            self.n_layer = n_layer
            self.n_head = n_head
            assert d_model % n_head == 0
            self.d_head = d_model // n_head
            self.ff_activation = ff_activation
            self.d_inner = d_inner
            self.untie_r = untie_r
            self.attn_type = attn_type

            self.initializer_range = initializer_range
            self.layer_norm_eps = layer_norm_eps

            self.dropout = dropout
            self.mem_len = mem_len
            self.reuse_len = reuse_len
            self.bi_data = bi_data
            self.clamp_len = clamp_len
            self.same_length = same_length

            self.finetuning_task = finetuning_task
            self.num_labels = num_labels
            self.summary_type = summary_type
            self.summary_use_proj = summary_use_proj
            self.summary_activation = summary_activation
            self.summary_last_dropout = summary_last_dropout
            self.start_n_top = start_n_top
            self.end_n_top = end_n_top
        else:
            raise ValueError("First argument must be either a vocabulary size (int)"
                             " or the path to a pretrained model config file (str)")

    @property
    def max_position_embeddings(self):
        return -1

    @property
    def vocab_size(self):
        return self.n_token

    @vocab_size.setter
    def vocab_size(self, value):
        self.n_token = value

    @property
    def hidden_size(self):
        return self.d_model

    @property
    def num_attention_heads(self):
        return self.n_head

    @property
    def num_hidden_layers(self):
        return self.n_layer


try:
    from apex.normalization.fused_layer_norm import FusedLayerNorm as XLNetLayerNorm
except (ImportError, AttributeError) as e:
    logger.info("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .")
    class XLNetLayerNorm(nn.Module):
        def __init__(self, d_model, eps=1e-12):
            """Construct a layernorm module in the TF style (epsilon inside the square root).
            """
            super(XLNetLayerNorm, self).__init__()
            self.weight = nn.Parameter(torch.ones(d_model))
            self.bias = nn.Parameter(torch.zeros(d_model))
            self.variance_epsilon = eps

        def forward(self, x):
            u = x.mean(-1, keepdim=True)
            s = (x - u).pow(2).mean(-1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.variance_epsilon)
            return self.weight * x + self.bias

class XLNetRelativeAttention(nn.Module):
    def __init__(self, config):
        super(XLNetRelativeAttention, self).__init__()
        self.output_attentions = config.output_attentions

        if config.d_model % config.n_head != 0:
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (config.d_model, config.n_head))

        self.n_head = config.n_head
        self.d_head = config.d_head
        self.d_model = config.d_model
        self.scale = 1 / (config.d_head ** 0.5)

        self.q = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
        self.k = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
        self.v = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
        self.o = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))
        self.r = nn.Parameter(torch.FloatTensor(config.d_model, self.n_head, self.d_head))

        self.r_r_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
        self.r_s_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
        self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
        self.seg_embed = nn.Parameter(torch.FloatTensor(2, self.n_head, self.d_head))

        self.layer_norm = XLNetLayerNorm(config.d_model, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.dropout)

    def prune_heads(self, heads):
        raise NotImplementedError

    @staticmethod
    def rel_shift(x, klen=-1):
        """perform relative shift to form the relative attention score."""
        x_size = x.shape

        x = x.reshape(x_size[1], x_size[0], x_size[2], x_size[3])
        x = x[1:, ...]
        x = x.reshape(x_size[0], x_size[1] - 1, x_size[2], x_size[3])
        # x = x[:, 0:klen, :, :]
        x = torch.index_select(x, 1, torch.arange(klen, device=x.device, dtype=torch.long))

        return x

    def rel_attn_core(self, q_head, k_head_h, v_head_h, k_head_r, seg_mat=None, attn_mask=None, head_mask=None):
        """Core relative positional attention operations."""

        # content based attention score
        ac = torch.einsum('ibnd,jbnd->ijbn', q_head + self.r_w_bias, k_head_h)

        # position based attention score
        bd = torch.einsum('ibnd,jbnd->ijbn', q_head + self.r_r_bias, k_head_r)
        bd = self.rel_shift(bd, klen=ac.shape[1])

        # segment based attention score
        if seg_mat is None:
            ef = 0
        else:
            ef = torch.einsum('ibnd,snd->ibns', q_head + self.r_s_bias, self.seg_embed)
            ef = torch.einsum('ijbs,ibns->ijbn', seg_mat, ef)

        # merge attention scores and perform masking
        attn_score = (ac + bd + ef) * self.scale
        if attn_mask is not None:
            # attn_score = attn_score * (1 - attn_mask) - 1e30 * attn_mask
            if attn_mask.dtype == torch.float16:
                attn_score = attn_score - 65500 * attn_mask
            else:
                attn_score = attn_score - 1e30 * attn_mask

        # attention probability
        attn_prob = F.softmax(attn_score, dim=1)
        attn_prob = self.dropout(attn_prob)

        # Mask heads if we want to
        if head_mask is not None:
            attn_prob = attn_prob * head_mask

        # attention output
        attn_vec = torch.einsum('ijbn,jbnd->ibnd', attn_prob, v_head_h)

        if self.output_attentions:
            return attn_vec, attn_prob

        return attn_vec

    def post_attention(self, h, attn_vec, residual=True):
        """Post-attention processing."""
        # post-attention projection (back to `d_model`)
        attn_out = torch.einsum('ibnd,hnd->ibh', attn_vec, self.o)

        attn_out = self.dropout(attn_out)
        if residual:
            attn_out = attn_out + h
        output = self.layer_norm(attn_out)

        return output

    def forward(self, h, g,
                      attn_mask_h, attn_mask_g,
                      r, seg_mat,
                      mems=None, target_mapping=None, head_mask=None):
        if g is not None:
            ###### Two-stream attention with relative positional encoding.
            # content based attention score
            if mems is not None and mems.dim() > 1:
                cat = torch.cat([mems, h], dim=0)
            else:
                cat = h

            # content-based key head
            k_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.k)

            # content-based value head
            v_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.v)

            # position-based key head
            k_head_r = torch.einsum('ibh,hnd->ibnd', r, self.r)

            ##### h-stream
            # content-stream query head
            q_head_h = torch.einsum('ibh,hnd->ibnd', h, self.q)

            # core attention ops
            attn_vec_h = self.rel_attn_core(
                q_head_h, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_h, head_mask=head_mask)

            if self.output_attentions:
                attn_vec_h, attn_prob_h = attn_vec_h

            # post processing
            output_h = self.post_attention(h, attn_vec_h)

            ##### g-stream
            # query-stream query head
            q_head_g = torch.einsum('ibh,hnd->ibnd', g, self.q)

            # core attention ops
            if target_mapping is not None:
                q_head_g = torch.einsum('mbnd,mlb->lbnd', q_head_g, target_mapping)
                attn_vec_g = self.rel_attn_core(
                    q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g, head_mask=head_mask)

                if self.output_attentions:
                    attn_vec_g, attn_prob_g = attn_vec_g

                attn_vec_g = torch.einsum('lbnd,mlb->mbnd', attn_vec_g, target_mapping)
            else:
                attn_vec_g = self.rel_attn_core(
                    q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g, head_mask=head_mask)

                if self.output_attentions:
                    attn_vec_g, attn_prob_g = attn_vec_g

            # post processing
            output_g = self.post_attention(g, attn_vec_g)

            if self.output_attentions:
                attn_prob = attn_prob_h, attn_prob_g

        else:
            ###### Multi-head attention with relative positional encoding
            if mems is not None and mems.dim() > 1:
                cat = torch.cat([mems, h], dim=0)
            else:
                cat = h

            # content heads
            q_head_h = torch.einsum('ibh,hnd->ibnd', h, self.q)
            k_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.k)
            v_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.v)

            # positional heads
            k_head_r = torch.einsum('ibh,hnd->ibnd', r, self.r)

            # core attention ops
            attn_vec = self.rel_attn_core(
                q_head_h, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_h, head_mask=head_mask)

            if self.output_attentions:
                attn_vec, attn_prob = attn_vec

            # post processing
            output_h = self.post_attention(h, attn_vec)
            output_g = None

        outputs = (output_h, output_g)
        if self.output_attentions:
            outputs = outputs + (attn_prob,)
        return outputs

class XLNetFeedForward(nn.Module):
    def __init__(self, config):
        super(XLNetFeedForward, self).__init__()
        self.layer_norm = XLNetLayerNorm(config.d_model, eps=config.layer_norm_eps)
        self.layer_1 = nn.Linear(config.d_model, config.d_inner)
        self.layer_2 = nn.Linear(config.d_inner, config.d_model)
        self.dropout = nn.Dropout(config.dropout)
        if isinstance(config.ff_activation, str) or \
                (sys.version_info[0] == 2 and isinstance(config.ff_activation, unicode)):
            self.activation_function = ACT2FN[config.ff_activation]
        else:
            self.activation_function = config.ff_activation

    def forward(self, inp):
        output = inp
        output = self.layer_1(output)
        output = self.activation_function(output)
        output = self.dropout(output)
        output = self.layer_2(output)
        output = self.dropout(output)
        output = self.layer_norm(output + inp)
        return output

class XLNetLayer(nn.Module):
    def __init__(self, config):
        super(XLNetLayer, self).__init__()
        self.rel_attn = XLNetRelativeAttention(config)
        self.ff = XLNetFeedForward(config)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, output_h, output_g,
                attn_mask_h, attn_mask_g,
                r, seg_mat, mems=None, target_mapping=None, head_mask=None):
        outputs = self.rel_attn(output_h, output_g, attn_mask_h, attn_mask_g,
                                r, seg_mat, mems=mems, target_mapping=target_mapping,
                                head_mask=head_mask)
        output_h, output_g = outputs[:2]

        if output_g is not None:
            output_g = self.ff(output_g)
        output_h = self.ff(output_h)

        outputs = (output_h, output_g) + outputs[2:]  # Add again attentions if there are there
        return outputs


class XLNetPreTrainedModel(PreTrainedModel):
    """ An abstract class to handle weights initialization and
        a simple interface for dowloading and loading pretrained models.
    """
    config_class = XLNetConfig
    pretrained_model_archive_map = XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
    load_tf_weights = load_tf_weights_in_xlnet
    base_model_prefix = "transformer"

    def __init__(self, *inputs, **kwargs):
        super(XLNetPreTrainedModel, self).__init__(*inputs, **kwargs)

    def init_weights(self, module):
        """ Initialize the weights.
        """
        if isinstance(module, (nn.Linear, nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if isinstance(module, nn.Linear) and module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, XLNetLayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, XLNetRelativeAttention):
            for param in [module.q, module.k, module.v, module.o, module.r,
                          module.r_r_bias, module.r_s_bias, module.r_w_bias,
                          module.seg_embed]:
                param.data.normal_(mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, XLNetModel):
                module.mask_emb.data.normal_(mean=0.0, std=self.config.initializer_range)


XLNET_START_DOCSTRING = r"""    The XLNet model was proposed in
    `XLNet: Generalized Autoregressive Pretraining for Language Understanding`_
    by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
    XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method
    to learn bidirectional contexts by maximizing the expected likelihood over all permutations
    of the input sequence factorization order.

    The specific attention pattern can be controlled at training and test time using the `perm_mask` input.

    Do to the difficulty of training a fully auto-regressive model over various factorization order,
    XLNet is pretrained using only a sub-set of the output tokens as target which are selected
    with the `target_mapping` input.

    To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and
    `target_mapping` inputs to control the attention span and outputs (see examples in `examples/run_generation.py`)

    This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
    refer to the PyTorch documentation for all matter related to general usage and behavior.

    .. _`XLNet: Generalized Autoregressive Pretraining for Language Understanding`:
        http://arxiv.org/abs/1906.08237

    .. _`torch.nn.Module`:
        https://pytorch.org/docs/stable/nn.html#module

    Parameters:
        config (:class:`~pytorch_transformers.XLNetConfig`): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the configuration.
            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""

XLNET_INPUTS_DOCSTRING = r"""
    Inputs:
        **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            Indices of input sequence tokens in the vocabulary.
            XLNet is a model with relative position embeddings so you can either pad the inputs on
            the right or on the left.
            Indices can be obtained using :class:`pytorch_transformers.XLNetTokenizer`.
            See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
            :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
        **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            A parallel sequence of tokens (can be used to indicate various portions of the inputs).
            The embeddings from these tokens will be summed with the respective token embeddings.
            Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices).
        **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
        **input_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
            Mask to avoid performing attention on padding token indices.
            Negative of `attention_mask`, i.e. with 0 for real tokens and 1 for padding.
            Kept for compatibility with the original code base.
            You can only uses one of `input_mask` and `attention_mask`
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are MASKED, ``0`` for tokens that are NOT MASKED.
        **mems**: (`optional`)
            list of ``torch.FloatTensor`` (one for each layer):
            that contains pre-computed hidden-states (key and values in the attention blocks) as output by the model
            (see `mems` output below). Can be used to speed up sequential decoding and attend to longer context.
            To activate mems you need to set up config.mem_len to a positive value which will be the max number of tokens in
            the memory output by the model. E.g. `model = XLNetModel.from_pretrained('xlnet-base-case, mem_len=1024)` will
            instantiate a model which can use up to 1024 tokens of memory (in addition to the input it self).
        **perm_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, sequence_length)``:
            Mask to indicate the attention pattern for each input token with values selected in ``[0, 1]``:
            If ``perm_mask[k, i, j] = 0``, i attend to j in batch k;
            if ``perm_mask[k, i, j] = 1``, i does not attend to j in batch k.
            If None, each token attends to all the others (full bidirectional attention).
            Only used during pretraining (to define factorization order) or for sequential decoding (generation).
        **target_mapping**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, num_predict, sequence_length)``:
            Mask to indicate the output tokens to use.
            If ``target_mapping[k, i, j] = 1``, the i-th predict in batch k is on the j-th token.
            Only used during pretraining for partial prediction or for sequential decoding (generation).
        **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:
            ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""

@add_start_docstrings("The bare XLNet Model transformer outputing raw hidden-states without any specific head on top.",
                      XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class XLNetModel(XLNetPreTrainedModel):
    r"""
    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
            Sequence of hidden-states at the last layer of the model.
        **mems**:
            list of ``torch.FloatTensor`` (one for each layer):
            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
            See details in the docstring of the `mems` input above.
        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
            of shape ``(batch_size, sequence_length, hidden_size)``:
            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
        model = XLNetModel.from_pretrained('xlnet-large-cased')
        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
        outputs = model(input_ids)
        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple

    """
    def __init__(self, config):
        super(XLNetModel, self).__init__(config)
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states

        self.mem_len = config.mem_len
        self.reuse_len = config.reuse_len
        self.d_model = config.d_model
        self.same_length = config.same_length
        self.attn_type = config.attn_type
        self.bi_data = config.bi_data
        self.clamp_len = config.clamp_len
        self.n_layer = config.n_layer

        self.word_embedding = nn.Embedding(config.n_token, config.d_model)
        self.mask_emb = nn.Parameter(torch.FloatTensor(1, 1, config.d_model))
        self.layer = nn.ModuleList([XLNetLayer(config) for _ in range(config.n_layer)])
        self.dropout = nn.Dropout(config.dropout)

        self.apply(self.init_weights)

    def _resize_token_embeddings(self, new_num_tokens):
        self.word_embedding = self._get_resized_embeddings(self.word_embedding, new_num_tokens)
        return self.word_embedding

    def _prune_heads(self, heads_to_prune):
        raise NotImplementedError

    def create_mask(self, qlen, mlen):
        """
        Creates causal attention mask. Float mask where 1.0 indicates masked, 0.0 indicates not-masked.

        Args:
            qlen: TODO Lysandre didn't fill
            mlen: TODO Lysandre didn't fill

        ::

                  same_length=False:      same_length=True:
                  <mlen > <  qlen >       <mlen > <  qlen >
               ^ [0 0 0 0 0 1 1 1 1]     [0 0 0 0 0 1 1 1 1]
                 [0 0 0 0 0 0 1 1 1]     [1 0 0 0 0 0 1 1 1]
            qlen [0 0 0 0 0 0 0 1 1]     [1 1 0 0 0 0 0 1 1]
                 [0 0 0 0 0 0 0 0 1]     [1 1 1 0 0 0 0 0 1]
               v [0 0 0 0 0 0 0 0 0]     [1 1 1 1 0 0 0 0 0]

        """
        attn_mask = torch.ones([qlen, qlen])
        mask_up = torch.triu(attn_mask, diagonal=1)
        attn_mask_pad = torch.zeros([qlen, mlen])
        ret = torch.cat([attn_mask_pad, mask_up], dim=1)
        if self.same_length:
            mask_lo = torch.tril(attn_mask, diagonal=-1)
            ret = torch.cat([ret[:, :qlen] + mask_lo, ret[:, qlen:]], dim=1)

        ret = ret.to(next(self.parameters()))
        return ret

    def cache_mem(self, curr_out, prev_mem):
        """cache hidden states into memory."""
        if self.mem_len is None or self.mem_len == 0:
            return None
        else:
            if self.reuse_len is not None and self.reuse_len > 0:
                curr_out = curr_out[:self.reuse_len]

            if prev_mem is None:
                new_mem = curr_out[-self.mem_len:]
            else:
                new_mem = torch.cat([prev_mem, curr_out], dim=0)[-self.mem_len:]

        return new_mem.detach()

    @staticmethod
    def positional_embedding(pos_seq, inv_freq, bsz=None):
        sinusoid_inp = torch.einsum('i,d->id', pos_seq, inv_freq)
        pos_emb = torch.cat([torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)], dim=-1)
        pos_emb = pos_emb[:, None, :]

        if bsz is not None:
            pos_emb = pos_emb.expand(-1, bsz, -1)

        return pos_emb

    def relative_positional_encoding(self, qlen, klen, bsz=None):
        """create relative positional encoding."""
        freq_seq = torch.arange(0, self.d_model, 2.0, dtype=torch.float)
        inv_freq = 1 / torch.pow(10000, (freq_seq / self.d_model))

        if self.attn_type == 'bi':
            # beg, end = klen - 1, -qlen
            beg, end = klen, -qlen
        elif self.attn_type == 'uni':
            # beg, end = klen - 1, -1
            beg, end = klen, -1
        else:
            raise ValueError('Unknown `attn_type` {}.'.format(self.attn_type))

        if self.bi_data:
            fwd_pos_seq = torch.arange(beg, end, -1.0, dtype=torch.float)
            bwd_pos_seq = torch.arange(-beg, -end, 1.0, dtype=torch.float)

            if self.clamp_len > 0:
                fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
                bwd_pos_seq = bwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)

            if bsz is not None:
                fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz//2)
                bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq, bsz//2)
            else:
                fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq)
                bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq)

            pos_emb = torch.cat([fwd_pos_emb, bwd_pos_emb], dim=1)
        else:
            fwd_pos_seq = torch.arange(beg, end, -1.0)
            if self.clamp_len > 0:
                fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
            pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz)

        pos_emb = pos_emb.to(next(self.parameters()))
        return pos_emb

    def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
                mems=None, perm_mask=None, target_mapping=None, head_mask=None):
        # the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end
        # but we want a unified interface in the library with the batch size on the first dimension
        # so we move here the first dimension (batch) to the end
        input_ids = input_ids.transpose(0, 1).contiguous()
        token_type_ids = token_type_ids.transpose(0, 1).contiguous() if token_type_ids is not None else None
        input_mask = input_mask.transpose(0, 1).contiguous() if input_mask is not None else None
        attention_mask = attention_mask.transpose(0, 1).contiguous() if attention_mask is not None else None
        perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None
        target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None

        qlen, bsz = input_ids.shape[0], input_ids.shape[1]
        mlen = mems[0].shape[0] if mems is not None and mems[0] is not None else 0
        klen = mlen + qlen

        dtype_float = next(self.parameters()).dtype
        device = next(self.parameters()).device

        ##### Attention mask
        # causal attention mask
        if self.attn_type == 'uni':
            attn_mask = self.create_mask(qlen, mlen)
            attn_mask = attn_mask[:, :, None, None]
        elif self.attn_type == 'bi':
            attn_mask = None
        else:
            raise ValueError('Unsupported attention type: {}'.format(self.attn_type))

        # data mask: input mask & perm mask
        assert input_mask is None or attention_mask is None, "You can only use one of input_mask (uses 1 for padding) "
        "or attention_mask (uses 0 for padding, added for compatbility with BERT). Please choose one."
        if input_mask is None and attention_mask is not None:
            input_mask = 1.0 - attention_mask
        if input_mask is not None and perm_mask is not None:
            data_mask = input_mask[None] + perm_mask
        elif input_mask is not None and perm_mask is None:
            data_mask = input_mask[None]
        elif input_mask is None and perm_mask is not None:
            data_mask = perm_mask
        else:
            data_mask = None

        if data_mask is not None:
            # all mems can be attended to
            mems_mask = torch.zeros([data_mask.shape[0], mlen, bsz]).to(data_mask)
            data_mask = torch.cat([mems_mask, data_mask], dim=1)
            if attn_mask is None:
                attn_mask = data_mask[:, :, :, None]
            else:
                attn_mask += data_mask[:, :, :, None]

        if attn_mask is not None:
            attn_mask = (attn_mask > 0).to(dtype_float)

        if attn_mask is not None:
            non_tgt_mask = -torch.eye(qlen).to(attn_mask)
            non_tgt_mask = torch.cat([torch.zeros([qlen, mlen]).to(attn_mask), non_tgt_mask], dim=-1)
            non_tgt_mask = ((attn_mask + non_tgt_mask[:, :, None, None]) > 0).to(attn_mask)
        else:
            non_tgt_mask = None

        ##### Word embeddings and prepare h & g hidden states
        word_emb_k = self.word_embedding(input_ids)
        output_h = self.dropout(word_emb_k)
        if target_mapping is not None:
            word_emb_q = self.mask_emb.expand(target_mapping.shape[0], bsz, -1)
        # else:  # We removed the inp_q input which was same as target mapping
        #     inp_q_ext = inp_q[:, :, None]
        #     word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k
            output_g = self.dropout(word_emb_q)
        else:
            output_g = None

        ##### Segment embedding
        if token_type_ids is not None:
            # Convert `token_type_ids` to one-hot `seg_mat`
            mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device)
            cat_ids = torch.cat([mem_pad, token_type_ids], dim=0)

            # `1` indicates not in the same segment [qlen x klen x bsz]
            seg_mat = (token_type_ids[:, None] != cat_ids[None, :]).long()
            seg_mat = F.one_hot(seg_mat, num_classes=2).to(dtype_float)
        else:
            seg_mat = None

        ##### Positional encoding
        pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz)
        pos_emb = self.dropout(pos_emb)

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] (a head_mask for each layer)
        # and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head]
        if head_mask is not None:
            if head_mask.dim() == 1:
                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0)
                head_mask = head_mask.expand(self.n_layer, -1, -1, -1, -1)
            elif head_mask.dim() == 2:
                head_mask = head_mask.unsqueeze(1).unsqueeze(1).unsqueeze(1)
            head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
        else:
            head_mask = [None] * self.n_layer

        new_mems = ()
        if mems is None:
            mems = [None] * len(self.layer)

        attentions = []
        hidden_states = []
        for i, layer_module in enumerate(self.layer):
            # cache new mems
            new_mems = new_mems + (self.cache_mem(output_h, mems[i]),)
            if self.output_hidden_states:
                hidden_states.append((output_h, output_g) if output_g is not None else output_h)

            outputs = layer_module(output_h, output_g, attn_mask_h=non_tgt_mask, attn_mask_g=attn_mask,
                                   r=pos_emb, seg_mat=seg_mat, mems=mems[i], target_mapping=target_mapping,
                                   head_mask=head_mask[i])
            output_h, output_g = outputs[:2]
            if self.output_attentions:
                attentions.append(outputs[2])

        # Add last hidden state
        if self.output_hidden_states:
            hidden_states.append((output_h, output_g) if output_g is not None else output_h)

        output = self.dropout(output_g if output_g is not None else output_h)

        # Prepare outputs, we transpose back here to shape [bsz, len, hidden_dim] (cf. beginning of forward() method)
        outputs = (output.permute(1, 0, 2).contiguous(), new_mems)
        if self.output_hidden_states:
            if output_g is not None:
                hidden_states = tuple(h.permute(1, 0, 2).contiguous() for hs in hidden_states for h in hs)
            else:
                hidden_states = tuple(hs.permute(1, 0, 2).contiguous() for hs in hidden_states)
            outputs = outputs + (hidden_states,)
        if self.output_attentions:
            attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions)
            outputs = outputs + (attentions,)

        return outputs  # outputs, new_mems, (hidden_states), (attentions)


@add_start_docstrings("""XLNet Model with a language modeling head on top
    (linear layer with weights tied to the input embeddings). """,
    XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class XLNetLMHeadModel(XLNetPreTrainedModel):
    r"""
        **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            Labels for language modeling.
            Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
            Indices are selected in ``[-1, 0, ..., config.vocab_size]``
            All labels set to ``-1`` are ignored (masked), the loss is only
            computed for labels in ``[0, ..., config.vocab_size]``

    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Language modeling loss.
        **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        **mems**:
            list of ``torch.FloatTensor`` (one for each layer):
            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
            See details in the docstring of the `mems` input above.
        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
            of shape ``(batch_size, sequence_length, hidden_size)``:
            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
        model = XLNetLMHeadModel.from_pretrained('xlnet-large-cased')
        # We show how to setup inputs to predict a next token using a bi-directional context.
        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is very <mask>")).unsqueeze(0)  # We will predict the masked token
        perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float)
        perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
        target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float)  # Shape [1, 1, seq_length] => let's predict one token
        target_mapping[0, 0, -1] = 1.0  # Our first (and only) prediction will be the last token of the sequence (the masked token)
        outputs = model(input_ids, perm_mask=perm_mask, target_mapping=target_mapping)
        next_token_logits = outputs[0]  # Output has shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size]

    """
    def __init__(self, config):
        super(XLNetLMHeadModel, self).__init__(config)
        self.attn_type = config.attn_type
        self.same_length = config.same_length

        self.transformer = XLNetModel(config)
        self.lm_loss = nn.Linear(config.d_model, config.n_token, bias=True)

        self.apply(self.init_weights)
        self.tie_weights()

    def tie_weights(self):
        """ Make sure we are sharing the embeddings
        """
        self._tie_or_clone_weights(self.lm_loss, self.transformer.word_embedding)

    def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
                mems=None, perm_mask=None, target_mapping=None,
                labels=None, head_mask=None):
        transformer_outputs = self.transformer(input_ids, token_type_ids=token_type_ids,
                                               input_mask=input_mask, attention_mask=attention_mask,
                                               mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,
                                               head_mask=head_mask)

        logits = self.lm_loss(transformer_outputs[0])

        outputs = (logits,) + transformer_outputs[1:]  # Keep mems, hidden states, attentions if there are in it

        if labels is not None:
            # Flatten the tokens
            loss_fct = CrossEntropyLoss(ignore_index=-1)
            loss = loss_fct(logits.view(-1, logits.size(-1)),
                            labels.view(-1))
            outputs = (loss,) + outputs

        return outputs  # return (loss), logits, mems, (hidden states), (attentions)


@add_start_docstrings("""XLNet Model with a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """,
    XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class XLNetForSequenceClassification(XLNetPreTrainedModel):
    r"""
        **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
            Labels for computing the sequence classification/regression loss.
            Indices should be in ``[0, ..., config.num_labels - 1]``.
            If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
            If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).

    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Classification (or regression if config.num_labels==1) loss.
        **logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        **mems**:
            list of ``torch.FloatTensor`` (one for each layer):
            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
            See details in the docstring of the `mems` input above.
        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
            of shape ``(batch_size, sequence_length, hidden_size)``:
            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
        model = XLNetForSequenceClassification.from_pretrained('xlnet-large-cased')
        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
        labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
        outputs = model(input_ids, labels=labels)
        loss, logits = outputs[:2]

    """
    def __init__(self, config):
        super(XLNetForSequenceClassification, self).__init__(config)
        self.num_labels = config.num_labels

        self.transformer = XLNetModel(config)
        self.sequence_summary = SequenceSummary(config)
        self.logits_proj = nn.Linear(config.d_model, config.num_labels)

        self.apply(self.init_weights)

    def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
                mems=None, perm_mask=None, target_mapping=None,
                labels=None, head_mask=None):
        transformer_outputs = self.transformer(input_ids, token_type_ids=token_type_ids,
                                               input_mask=input_mask, attention_mask=attention_mask,
                                               mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,
                                               head_mask=head_mask)
        output = transformer_outputs[0]

        output = self.sequence_summary(output)
        logits = self.logits_proj(output)

        outputs = (logits,) + transformer_outputs[1:]  # Keep mems, hidden states, attentions if there are in it

        if labels is not None:
            if self.num_labels == 1:
                #  We are doing regression
                loss_fct = MSELoss()
                loss = loss_fct(logits.view(-1), labels.view(-1))
            else:
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            outputs = (loss,) + outputs

        return outputs  # return (loss), logits, mems, (hidden states), (attentions)


@add_start_docstrings("""XLNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
    the hidden-states output to compute `span start logits` and `span end logits`). """,
    XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
class XLNetForQuestionAnswering(XLNetPreTrainedModel):
    r"""
        **start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`).
            Position outside of the sequence are not taken into account for computing the loss.
        **end_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`).
            Position outside of the sequence are not taken into account for computing the loss.
        **is_impossible**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
            Labels whether a question has an answer or no answer (SQuAD 2.0)
        **cls_index**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
            Labels for position (index) of the classification token to use as input for computing plausibility of the answer.
        **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
            Optional mask of tokens which can't be in answers (e.g. [CLS], [PAD], ...).
            1.0 means token should be masked. 0.0 mean token is not masked.

    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
        **start_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
            ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``
            Log probabilities for the top config.start_n_top start token possibilities (beam-search).
        **start_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
            ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``
            Indices for the top config.start_n_top start token possibilities (beam-search).
        **end_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
            ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
            Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
        **end_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
            ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
            Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
        **cls_logits**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
            ``torch.FloatTensor`` of shape ``(batch_size,)``
            Log probabilities for the ``is_impossible`` label of the answers.
        **mems**:
            list of ``torch.FloatTensor`` (one for each layer):
            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
            See details in the docstring of the `mems` input above.
        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
            of shape ``(batch_size, sequence_length, hidden_size)``:
            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
        model = XLMForQuestionAnswering.from_pretrained('xlnet-large-cased')
        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
        start_positions = torch.tensor([1])
        end_positions = torch.tensor([3])
        outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
        loss, start_scores, end_scores = outputs[:2]

    """
    def __init__(self, config):
        super(XLNetForQuestionAnswering, self).__init__(config)
        self.start_n_top = config.start_n_top
        self.end_n_top = config.end_n_top

        self.transformer = XLNetModel(config)
        self.start_logits = PoolerStartLogits(config)
        self.end_logits = PoolerEndLogits(config)
        self.answer_class = PoolerAnswerClass(config)

        self.apply(self.init_weights)

    def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
                mems=None, perm_mask=None, target_mapping=None,
                start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None,
                head_mask=None):
        transformer_outputs = self.transformer(input_ids, token_type_ids=token_type_ids,
                                               input_mask=input_mask, attention_mask=attention_mask,
                                               mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,
                                               head_mask=head_mask)
        hidden_states = transformer_outputs[0]
        start_logits = self.start_logits(hidden_states, p_mask=p_mask)

        outputs = transformer_outputs[1:]  # Keep mems, hidden states, attentions if there are in it

        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, let's remove the dimension added by batch splitting
            for x in (start_positions, end_positions, cls_index, is_impossible):
                if x is not None and x.dim() > 1:
                    x.squeeze_(-1)

            # during training, compute the end logits based on the ground truth of the start position
            end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)

            loss_fct = CrossEntropyLoss()
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

            if cls_index is not None and is_impossible is not None:
                # Predict answerability from the representation of CLS and START
                cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
                loss_fct_cls = nn.BCEWithLogitsLoss()
                cls_loss = loss_fct_cls(cls_logits, is_impossible)

                # note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss
                total_loss += cls_loss * 0.5

            outputs = (total_loss,) + outputs

        else:
            # during inference, compute the end logits based on beam search
            bsz, slen, hsz = hidden_states.size()
            start_log_probs = F.softmax(start_logits, dim=-1) # shape (bsz, slen)

            start_top_log_probs, start_top_index = torch.topk(start_log_probs, self.start_n_top, dim=-1) # shape (bsz, start_n_top)
            start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)
            start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz)
            start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz)

            hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(start_states) # shape (bsz, slen, start_n_top, hsz)
            p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
            end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)
            end_log_probs = F.softmax(end_logits, dim=1) # shape (bsz, slen, start_n_top)

            end_top_log_probs, end_top_index = torch.topk(end_log_probs, self.end_n_top, dim=1) # shape (bsz, end_n_top, start_n_top)
            end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
            end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)

            start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)  # get the representation of START as weighted sum of hidden states
            cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)  # Shape (batch size,): one single `cls_logits` for each sample

            outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs

        # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
        # or (if labels are provided) (total_loss,)
        return outputs