Model.py

'''
 @Date  : 12/11/2019
 @Author: Zhihan Zhang
 @mail  : zhangzhihan@pku.edu.cn
 @homepage: ytyz1307zzh.github.io
'''

import torch
import torch.nn as nn
import torch.nn.functional as F
import json
import math
import os
import time
import numpy as np
from typing import List, Dict, Tuple
from Constants import *
import itertools
from utils import *
from torchcrf import CRF
import argparse
import pdb


class KOALA(nn.Module):

    def __init__(self, opt: argparse.Namespace, is_test: bool):

        super(KOALA, self).__init__()
        self.opt = opt
        self.hidden_size = opt.hidden_size
        self.embed_size = MODEL_HIDDEN[opt.plm_model_name]

        self.TokenEncoder = nn.LSTM(input_size = self.embed_size, hidden_size = opt.hidden_size,
                                    num_layers = 1, batch_first = True, bidirectional = True)
        self.Dropout = nn.Dropout(p = opt.dropout)

        # fixed pretrained language model
        assert opt.plm_model_name in MODEL_HIDDEN.keys(), 'Wrong model name provided'
        plm_model_class, plm_tokenizer_class, plm_config_class = MODEL_CLASSES[opt.plm_model_class]

        self.plm_config = plm_config_class.from_pretrained(opt.plm_model_name)
        self.plm_tokenizer = plm_tokenizer_class.from_pretrained(opt.plm_model_name)

        # ConceptNet encoder
        if is_test:
            self.cpnet_encoder = plm_model_class(config=self.plm_config)  # use saved parameters
            print(f'[INFO] Loaded an empty {opt.plm_model_name} for ConceptNet encoder during testing')
        elif opt.cpnet_plm_path:
            self.cpnet_encoder = plm_model_class.from_pretrained(opt.cpnet_plm_path)
            print(f'[INFO] Loaded {opt.cpnet_plm_path} for ConceptNet encoder')
        else:
            self.cpnet_encoder = plm_model_class.from_pretrained(opt.plm_model_name)
            print(f'[INFO] Loaded {opt.plm_model_name} for ConceptNet encoder')
        for param in self.cpnet_encoder.parameters():
            param.requires_grad = False

        self.CpnetEncoder = FixedSentEncoder(opt)

        if is_test:
            self.embed_encoder = plm_model_class(config=self.plm_config)  # use saved parameters
            print(f'[INFO] Loaded an empty {opt.plm_model_name} for embedding language model during testing')
        elif opt.wiki_plm_path:
            assert not opt.no_wiki, "Specified -no_wiki option but used a pre-fine-tuned BERT"
            self.embed_encoder = plm_model_class.from_pretrained(opt.wiki_plm_path)
            print(f'[INFO] Loaded {opt.wiki_plm_path} for embedding language model')
        else:
            self.embed_encoder = plm_model_class.from_pretrained(opt.plm_model_name)
            print(f'[INFO] Loaded {opt.plm_model_name} for embedding language model')
        if not is_test and not opt.finetune:
            for param in self.embed_encoder.parameters():
                param.requires_grad = False


        # state tracking modules
        self.StateTracker = StateTracker(opt)
        self.CRFLayer = CRF(NUM_STATES, batch_first = True)

        # location prediction modules
        self.LocationPredictor = LocationPredictor(opt)
        self.CrossEntropy = nn.CrossEntropyLoss(ignore_index = PAD_LOC, reduction = 'mean')
        self.BinaryCrossEntropy = nn.BCELoss(reduction='mean')

        self.is_test = is_test
        self.use_cuda = not opt.no_cuda


    def forward(self, token_ids: torch.Tensor, entity_mask: torch.IntTensor,
                verb_mask: torch.IntTensor, loc_mask: torch.IntTensor, gold_loc_seq: torch.IntTensor,
                gold_state_seq: torch.IntTensor,num_cands: torch.IntTensor, sentence_mask: torch.IntTensor,
                cpnet_triples: List, state_rel_labels: torch.IntTensor, loc_rel_labels: torch.IntTensor):
        """
        Args:
            token_ids: size (batch * max_wiki, max_ctx_tokens)
            *_mask: size (batch, max_sents, max_tokens)
            loc_mask: size (batch, max_cands, max_sents + 1, max_tokens), +1 for location 0
            gold_loc_seq: size (batch, max_sents)
            gold_state_seq: size (batch, max_sents)
            state_rel_labels: size (batch, max_sents, max_cpnet)
            loc_rel_labels: size (batch, max_sents, max_cpnet)
            num_cands: size (batch,)
        """
        # print(f'when token_ids enters model call:{token_ids.size()}')
        assert entity_mask.size(-2) == verb_mask.size(-2) == loc_mask.size(-2) - 1\
               == gold_state_seq.size(-1) == gold_loc_seq.size(-1) - 1
        assert entity_mask.size(-1) == verb_mask.size(-1) == loc_mask.size(-1)
        # print(f'In calling the KOALA model, token_ids size: {token_ids.size()}, entity_mask size: {entity_mask.size()}, loc_mask size: {loc_mask.size()}')
        batch_size = entity_mask.size(0)
        max_tokens = entity_mask.size(-1)
        max_sents = gold_state_seq.size(-1)
        max_cands = loc_mask.size(-3)

        attention_mask = (token_ids != self.plm_tokenizer.pad_token_id).to(torch.int)
        plm_outputs = self.embed_encoder(token_ids, attention_mask=attention_mask)
        embeddings = plm_outputs[0]  # hidden states at the last layer, (batch, max_tokens, plm_hidden_size)

        token_rep, _ = self.TokenEncoder(embeddings)  # (batch, max_tokens, 2*hidden_size)
        token_rep = self.Dropout(token_rep)
        assert token_rep.size() == (batch_size, max_tokens, 2 * self.hidden_size)
        # print('for debugging purposes, Model, KOALA, forward, the cpnet_triples input to CpnetEncoder:')
        # print(cpnet_triples, len(cpnet_triples))
        cpnet_rep = self.CpnetEncoder(cpnet_triples, tokenizer=self.plm_tokenizer, encoder=self.cpnet_encoder)

        # state change prediction
        # size (batch, max_sents, NUM_STATES)
        tag_logits, state_attn_probs = self.StateTracker(encoder_out = token_rep, entity_mask = entity_mask,
                                                         verb_mask = verb_mask, sentence_mask = sentence_mask,
                                                         cpnet_triples = cpnet_triples, cpnet_rep = cpnet_rep)
        tag_mask = (gold_state_seq != PAD_STATE) # mask the padded part so they won't count in loss
        log_likelihood = self.CRFLayer(emissions = tag_logits, tags = gold_state_seq.long(), mask = tag_mask, reduction = 'token_mean')

        state_loss = -log_likelihood  # State classification loss is negative log likelihood
        pred_state_seq = self.CRFLayer.decode(emissions=tag_logits, mask=tag_mask)
        assert len(pred_state_seq) == batch_size
        correct_state_pred, total_state_pred = compute_state_accuracy(pred=pred_state_seq, gold=gold_state_seq.tolist(),
                                                        pad_value=PAD_STATE)

        # location prediction
        # size (batch, max_cands, max_sents + 1)
        empty_mask = torch.zeros((batch_size, 1, max_tokens), dtype=torch.int)
        if self.use_cuda:
            empty_mask = empty_mask.cuda()
        entity_mask = torch.cat([empty_mask, entity_mask], dim=1)
        loc_logits, loc_attn_probs = self.LocationPredictor(encoder_out = token_rep, entity_mask = entity_mask,
                                                            loc_mask = loc_mask, sentence_mask = sentence_mask,
                                                            cpnet_triples = cpnet_triples, cpnet_rep = cpnet_rep)
        loc_logits = loc_logits.transpose(-1, -2)  # size (batch, max_sents + 1, max_cands)
        masked_loc_logits = self.mask_loc_logits(loc_logits = loc_logits, num_cands = num_cands)  # (batch, max_sents + 1, max_cands)
        masked_gold_loc_seq = self.mask_undefined_loc(gold_loc_seq = gold_loc_seq, mask_value = PAD_LOC)  # (batch, max_sents + 1)
        loc_loss = self.CrossEntropy(input = masked_loc_logits.view(batch_size * (max_sents + 1), max_cands + 1),
                                     target = masked_gold_loc_seq.view(batch_size * (max_sents + 1)).long())
        correct_loc_pred, total_loc_pred = compute_loc_accuracy(logits = masked_loc_logits, gold = masked_gold_loc_seq,
                                                                pad_value = PAD_LOC)

        if loc_attn_probs is not None:
            loc_attn_probs = self.get_gold_attn_probs(loc_attn_probs, gold_loc_seq)
        attn_loss, total_attn_pred = self.get_attn_loss(state_attn_probs, loc_attn_probs, state_rel_labels, loc_rel_labels)

        if self.is_test:  # inference
            pred_loc_seq = get_pred_loc(loc_logits = masked_loc_logits, gold_loc_seq = gold_loc_seq)
            return pred_state_seq, pred_loc_seq, correct_state_pred, total_state_pred, correct_loc_pred, total_loc_pred

        return state_loss, loc_loss, attn_loss, correct_state_pred, total_state_pred, \
               correct_loc_pred, total_loc_pred, total_attn_pred


    def get_attn_loss(self, state_attn_probs, loc_attn_probs, state_rel_labels, loc_rel_labels):
        """
        Compute attention loss. state_attn_probs or loc_attn_probs can be None if ConceptNet
        is not applied to both state and location predictors.
        All inputs: (batch, max_sents, max_cpnet)
        """

        pos_attn_probs = None
        if state_attn_probs is not None:
            state_pos_attn_probs = state_attn_probs.masked_select(mask=state_rel_labels.to(torch.bool))  # 1-D tensor
        if loc_attn_probs is not None:
            loc_pos_attn_probs = loc_attn_probs.masked_select(mask=loc_rel_labels.to(torch.bool))  # 1-D tensor

        if state_attn_probs is not None and loc_attn_probs is not None:
            pos_attn_probs = torch.cat([state_pos_attn_probs, loc_pos_attn_probs], dim=0)
        elif state_attn_probs is not None:
            pos_attn_probs = state_pos_attn_probs
        elif loc_attn_probs is not None:
            pos_attn_probs = loc_pos_attn_probs

        attn_loss = None
        total_attn_pred = None
        if pos_attn_probs is not None:
            gold_labels = torch.ones_like(pos_attn_probs)
            attn_loss = self.BinaryCrossEntropy(input=pos_attn_probs, target=gold_labels)
            total_attn_pred = gold_labels.size(0)

        return attn_loss, total_attn_pred


    def get_gold_attn_probs(self, loc_attn_probs, gold_loc_seq):
        """
        Get the attention weights of ConceptNet triples with the gold location, among all location candidates.
        Pick arbitrary one if no gold location at this timestep.
        """
        batch_size = gold_loc_seq.size(0)
        max_sents = gold_loc_seq.size(1)
        max_cpnet = loc_attn_probs.size(-1)
        pick_loc_seq = gold_loc_seq.masked_fill(mask=(gold_loc_seq < 0), value=0)
        gold_attn_probs = []

        for i in range(batch_size):
            for j in range(max_sents):
                gold_attn_probs.append(loc_attn_probs[i][pick_loc_seq[i][j]][j])

        gold_attn_probs = torch.stack(gold_attn_probs, dim=0)
        return gold_attn_probs.view(batch_size, max_sents, max_cpnet)


    def mask_loc_logits(self, loc_logits, num_cands: torch.IntTensor):
        """
        Mask the padded candidates with an -inf score, so they will have a likelihood = 0 after softmax
        Args:
            loc_logits - output scores for each candidate in each sentence, size (batch, max_sents, max_cands)
            num_cands - total number of candidates in each instance of the given batch, size (batch,)
        """
        assert torch.max(num_cands) == loc_logits.size(-1)
        assert loc_logits.size(0) == num_cands.size(0)
        batch_size = loc_logits.size(0)
        max_cands = loc_logits.size(-1)

        # first, we create a mask tensor that masked all positions above the num_cands limit
        range_tensor = torch.arange(start = 1, end = max_cands + 1)
        if self.use_cuda:
            range_tensor = range_tensor.cuda()
        range_tensor = range_tensor.unsqueeze(dim = 0).expand(batch_size, max_cands)
        bool_range = torch.gt(range_tensor, num_cands.unsqueeze(dim = -1))  # find the off-limit positions
        assert bool_range.size() == (batch_size, max_cands)

        bool_range = bool_range.unsqueeze(dim = -2).expand_as(loc_logits)  # use this bool tensor to mask loc_logits
        masked_loc_logits = loc_logits.masked_fill(bool_range, value = float('-inf'))  # mask padded positions to -inf
        assert masked_loc_logits.size() == loc_logits.size()

        return masked_loc_logits


    def mask_undefined_loc(self, gold_loc_seq, mask_value: int):
        """
        Mask all undefined locations (NIL, UNK, PAD) in order not to count them in loss nor accuracy.
        Since these three special labels are all negetive, any position with a negative target label will be masked to mask_value.
        Args:
            gold_loc_seq - sequence of gold locations, size (batch, max_sents)
            mask_value - Should be the same label with ignore_index argument in cross-entropy.
        """
        negative_labels = torch.lt(gold_loc_seq, 0)
        masked_gold_loc_seq = gold_loc_seq.masked_fill(mask = negative_labels, value = mask_value)
        return masked_gold_loc_seq


    @staticmethod
    def expand_dim_3d(vec: torch.Tensor, loc_cands: int):
        """
        Expand a 3-dim vector in the batch dimension (dimension 0)
        """
        assert len(vec.size()) == 3
        batch_size = vec.size(0)
        seq_len = vec.size(1)
        rep_size = vec.size(2)
        vec = vec.unsqueeze(1).repeat(1, loc_cands, 1, 1)
        vec = vec.view(batch_size * loc_cands, seq_len, rep_size)
        return vec

    @staticmethod
    def expand_dim_2d(vec: torch.Tensor, loc_cands: int):
        """
        Expand a 2-dim vector in the batch dimension (dimension 0)
        """
        assert len(vec.size()) == 2
        batch_size = vec.size(0)
        seq_len = vec.size(1)
        vec = vec.unsqueeze(1).repeat(1, loc_cands, 1)
        vec = vec.view(batch_size * loc_cands, seq_len)
        return vec


class StateTracker(nn.Module):
    """
    State tracking decoder: sentence-level Bi-LSTM + linear + CRF
    """
    def __init__(self, opt: argparse.Namespace):

        super(StateTracker, self).__init__()
        self.hidden_size = opt.hidden_size
        self.Decoder = nn.LSTM(input_size = 4 * opt.hidden_size, hidden_size = opt.hidden_size,
                                    num_layers = 1, batch_first = True, bidirectional = True)
        self.Dropout = nn.Dropout(p = opt.dropout)
        self.Hidden2Tag = Linear(d_in = 2 * opt.hidden_size, d_out = NUM_STATES, dropout = 0)
        self.CpnetMemory = CpnetMemory(opt, query_size = 4 * opt.hidden_size, input_size = 4 * opt.hidden_size)
        self.cpnet_inject = opt.cpnet_inject


    def forward(self, encoder_out, entity_mask, verb_mask, sentence_mask, cpnet_triples, cpnet_rep):
        """
        Args:
            encoder_out: output of the encoder, size (batch, max_tokens, 2 * hidden_size)
            entity_mask: size (batch, max_sents, max_tokens)
            verb_mask: size (batch, max_sents, max_tokens)
            sentence_mask: size(batch, max_sents, max_tokens)
            cpnet_triples: List, (batch, num_cpnet)
        """
        batch_size = encoder_out.size(0)
        max_sents = entity_mask.size(-2)
        # print(f'batch_size in StateTracker forward call (line 307): {batch_size}')
        # print(f'encoder_out size (line 308): {encoder_out.size()}')

        # (batch, max_sents, 4 * hidden_size)
        decoder_in = self.get_masked_input(encoder_out, entity_mask, verb_mask, batch_size = batch_size)
        # (batch, max_sents, 4 * hidden_size)
        attn_probs = None
        if self.cpnet_inject in ['state', 'both']:
            decoder_in, attn_probs = self.CpnetMemory(encoder_out, decoder_in, entity_mask,
                                          sentence_mask, cpnet_triples, cpnet_rep)
        decoder_out, _ = self.Decoder(decoder_in)  # (batch, max_sents, 2 * hidden_size), forward & backward concatenated
        decoder_out = self.Dropout(decoder_out)
        tag_logits = self.Hidden2Tag(decoder_out)  # (batch, max_sents, num_tags)
        assert tag_logits.size() == (batch_size, max_sents, NUM_STATES)

        return tag_logits, attn_probs


    def get_masked_input(self, encoder_out, entity_mask, verb_mask, batch_size: int):
        """
        If the entity does not exist in this sentence (entity_mask is all-zero),
        then replace it with an all-zero vector;
        Otherwise, concat the average embeddings of entity and verb
        """
        assert entity_mask.size() == verb_mask.size()
        assert entity_mask.size(-1) == encoder_out.size(-2)

        max_sents = entity_mask.size(-2)
        entity_rep = self.get_masked_mean(source = encoder_out, mask = entity_mask, batch_size = batch_size)  # (batch, max_sents, 2 * hidden_size)
        verb_rep = self.get_masked_mean(source = encoder_out, mask = verb_mask, batch_size = batch_size)  # (batch, max_sents, 2 * hidden_size)
        concat_rep = torch.cat([entity_rep, verb_rep], dim = -1)  # (batch, max_sents, 4 * hidden_size)

        assert concat_rep.size() == (batch_size, max_sents, 4 * self.hidden_size)
        entity_existence = find_allzero_rows(vector = entity_mask).unsqueeze(dim = -1)  # (batch, max_sents, 1)
        masked_rep = concat_rep.masked_fill(mask = entity_existence, value = 0)
        assert masked_rep.size() == (batch_size, max_sents, 4 * self.hidden_size)

        return masked_rep


    def get_masked_mean(self, source, mask, batch_size: int):
        """
        Args:
            source - input tensors, size(batch, tokens, 2 * hidden_size)
            mask - binary masked vectors, size(batch, sents, tokens)
        Return:
            the average of unmasked input tensors, size (batch, sents, 2 * hidden_size)
        """
        max_sents = mask.size(-2)

        bool_mask = (mask.unsqueeze(dim = -1) == 0)  # turn binary masks to boolean values
        masked_source = source.unsqueeze(dim = 1).masked_fill(bool_mask, value = 0)
        masked_source = torch.sum(masked_source, dim = -2)  # sum the unmasked vectors
        assert masked_source.size() == (batch_size, max_sents, 2 * self.hidden_size)

        num_unmasked_tokens = torch.sum(mask, dim = -1, keepdim = True)  # compute the denominator of average op
        masked_mean = torch.div(input = masked_source, other = num_unmasked_tokens)  # average the unmasked vectors

        # division op may cause nan while encoutering 0, so replace nan with 0
        is_nan = torch.isnan(masked_mean)
        masked_mean = masked_mean.masked_fill(is_nan, value = 0)

        assert masked_mean.size() == (batch_size, max_sents, 2 * self.hidden_size)
        return masked_mean


class LocationPredictor(nn.Module):
    """
    Location prediction decoder: sentence-level Bi-LSTM + linear + softmax
    """
    def __init__(self, opt: argparse.Namespace):

        super(LocationPredictor, self).__init__()
        self.hidden_size = opt.hidden_size
        self.Decoder = nn.LSTM(input_size = 4 * opt.hidden_size, hidden_size = opt.hidden_size,
                                    num_layers = 1, batch_first = True, bidirectional = True)
        self.Dropout = nn.Dropout(p = opt.dropout)
        self.Hidden2Score = Linear(d_in = 2 * opt.hidden_size, d_out = 1, dropout = 0)
        self.CpnetMemory = CpnetMemory(opt, query_size=4 * opt.hidden_size, input_size=4 * opt.hidden_size)
        self.cpnet_inject = opt.cpnet_inject
        unk_vec = torch.empty(2 * opt.hidden_size)
        nn.init.uniform_(unk_vec, -math.sqrt(1 / opt.hidden_size), math.sqrt(1 / opt.hidden_size))
        self.unk_vec = nn.Parameter(unk_vec, requires_grad=True)  # learnable vector for '?' location


    def forward(self, encoder_out, entity_mask, loc_mask, sentence_mask, cpnet_triples, cpnet_rep):
        """
        Args:
            encoder_out: output of the encoder, size (batch, max_tokens, 2 * hidden_size)
            entity_mask: size (batch, max_sents, max_tokens)
            sentence_mask: size(batch, max_sents, max_tokens)
            cpnet_triples: List, (batch, num_cpnet)
            loc_mask: size (batch, max_cands, max_sents, max_tokens)
        """
        batch_size = encoder_out.size(0)
        max_cands = loc_mask.size(-3) + 1
        max_sents = loc_mask.size(-2)

        decoder_in = self.get_masked_input(encoder_out, entity_mask, loc_mask, batch_size = batch_size)
        decoder_in = decoder_in.view(batch_size * max_cands, max_sents, 4 * self.hidden_size)
        attn_probs = None
        if self.cpnet_inject in ['location', 'both']:
            decoder_in, attn_probs = self.CpnetMemory(encoder_out=KOALA.expand_dim_3d(encoder_out, max_cands),
                                          decoder_in=decoder_in,
                                          entity_mask=KOALA.expand_dim_3d(entity_mask, max_cands),
                                          sentence_mask=KOALA.expand_dim_3d(sentence_mask, max_cands),
                                          cpnet_triples=cpnet_triples,
                                          cpnet_rep=cpnet_rep,
                                          loc_mask = loc_mask)
        decoder_out, _ = self.Decoder(decoder_in)  # (batch, max_sents, 2 * hidden_size), forward & backward concatenated
        assert decoder_out.size() == (batch_size * max_cands, max_sents, 2 * self.hidden_size)

        decoder_out = decoder_out.view(batch_size, max_cands, max_sents, 2 * self.hidden_size)
        decoder_out = self.Dropout(decoder_out)
        loc_logits = self.Hidden2Score(decoder_out).squeeze(dim = -1)  # (batch, max_cands, max_sents)

        return loc_logits, attn_probs

    def get_masked_input(self, encoder_out, entity_mask, loc_mask, batch_size: int):
        """
        Concat the mention positions of the entity and each location candidate
        """
        assert entity_mask.size(-1) == loc_mask.size(-1) == encoder_out.size(-2)
        assert entity_mask.size(-2) == loc_mask.size(-2)

        max_cands = loc_mask.size(-3)
        max_sents = loc_mask.size(-2)

        # (batch, max_sents, 2 * hidden_size)
        entity_rep = self.get_masked_mean(source = encoder_out, mask = entity_mask, batch_size = batch_size)
        # (batch, max_cands, max_sents, 2 * hidden_size)
        loc_rep = self.get_masked_loc_mean(source = encoder_out, mask = loc_mask, batch_size = batch_size)
        unk_vec = self.unk_vec.expand(batch_size, max_sents, 2 * self.hidden_size)
        entity_existence = find_allzero_rows(vector = entity_mask).unsqueeze(dim = -1)  # (batch, max_sents, 1)
        unk_vec = unk_vec.masked_fill(mask=entity_existence, value=0).unsqueeze(dim=1)  # (batch, 1, max_sents, 2*hidden)
        assert unk_vec.size() == (batch_size, 1, max_sents, 2 * self.hidden_size)
        loc_rep = torch.cat([unk_vec, loc_rep], dim=1)  # add vector for unk
        entity_rep = entity_rep.unsqueeze(dim = 1).expand_as(loc_rep)
        assert entity_rep.size() == loc_rep.size() == (batch_size, max_cands + 1, max_sents, 2 * self.hidden_size)

        concat_rep = torch.cat([entity_rep, loc_rep], dim = -1)
        assert concat_rep.size() == (batch_size, max_cands + 1, max_sents, 4 * self.hidden_size)

        return concat_rep


    def get_masked_loc_mean(self, source, mask, batch_size: int):
        """
        Args:
            source - input tensors, size(batch, tokens, 2 * hidden_size)
            mask - binary masked vectors, size(batch, cands, sents, tokens)
        Return:
            the average of unmasked input tensors, size (batch, cands, sents, 2 * hidden_size)
        """
        max_sents = mask.size(-2)
        max_cands = mask.size(-3)

        bool_mask = (mask.unsqueeze(dim = -1) == 0)  # turn binary masks to boolean values
        source = source.unsqueeze(dim = 1).unsqueeze(dim = 1)  # expand source to (batch, 1, 1, tokens, 2*hidden)
        masked_source = source.masked_fill(bool_mask, value = 0)  # (batch, cands, sents, tokens, 2*hidden)
        masked_source = torch.sum(masked_source, dim = -2)  # (batch, cands, sents, 2*hidden)
        assert masked_source.size() == (batch_size, max_cands, max_sents, 2 * self.hidden_size)

        num_unmasked_tokens = torch.sum(mask, dim = -1, keepdim = True)  # (batch, cands, sents, 1)
        masked_mean = torch.div(input = masked_source, other = num_unmasked_tokens)  # (batch, cands, sents, 2*hidden)

        # division op may cause nan while encoutering 0, so replace nan with 0
        is_nan = torch.isnan(masked_mean)
        masked_mean = masked_mean.masked_fill(is_nan, value = 0)

        assert masked_mean.size() == (batch_size, max_cands, max_sents, 2 * self.hidden_size)
        return masked_mean


    def get_masked_mean(self, source, mask, batch_size: int):
        """
        Args:
            source - input tensors, size(batch, tokens, 2 * hidden_size)
            mask - binary masked vectors, size(batch, sents, tokens)
        Return:
            the average of unmasked input tensors, size (batch, sents, 2 * hidden_size)
        """
        max_sents = mask.size(-2)

        bool_mask = (mask.unsqueeze(dim = -1) == 0)  # turn binary masks to boolean values
        masked_source = source.unsqueeze(dim = 1).masked_fill(bool_mask, value = 0)  # for masked tokens, turn its value to 0
        masked_source = torch.sum(masked_source, dim = -2)  # sum the unmasked token representations
        assert masked_source.size() == (batch_size, max_sents, 2 * self.hidden_size)

        num_unmasked_tokens = torch.sum(mask, dim = -1, keepdim = True)  # compute the denominator of average op (number of unmasked tokens)
        masked_mean = torch.div(input = masked_source, other = num_unmasked_tokens)  # average the unmasked vectors

        # division op may cause nan while encoutering 0, so replace nan with 0
        is_nan = torch.isnan(masked_mean)
        masked_mean = masked_mean.masked_fill(is_nan, value = 0)

        assert masked_mean.size() == (batch_size, max_sents, 2 * self.hidden_size)
        return masked_mean


class CpnetMemory(nn.Module):

    def __init__(self, opt, query_size: int, input_size: int):
        super(CpnetMemory, self).__init__()
        self.use_cuda = not opt.no_cuda
        self.hidden_size = opt.hidden_size
        self.query_size = query_size
        self.value_size = 2 * opt.hidden_size
        self.input_size = input_size
        self.AttnUpdate = GatedAttnUpdate(query_size=self.query_size, value_size=self.value_size,
                                          input_size=self.input_size, dropout=opt.dropout)


    def forward(self, encoder_out, decoder_in, entity_mask, sentence_mask, cpnet_triples: List[List[str]],
                cpnet_rep, loc_mask = None):
        """
        Args:
            encoder_out: size (batch, max_tokens, 2 * hidden_size)
            decoder_in: (batch, max_sents, 4 * hidden_size) for state tracking,
                        (batch * max_cands, max_sents, 4 * hidden_size) for location prediciton
            entity_mask: size(batch, max_sents, max_tokens)
            sentence_mask: size(batch, max_sents, max_tokens)
            cpnet_triples: List, (batch, num_cpnet)
        """
        assert encoder_out.size(0) == decoder_in.size(0) == entity_mask.size(0) == \
                sentence_mask.size(0)
        batch_size = encoder_out.size(0)
        ori_batch_size = cpnet_rep.size(0)

        # use the embedding of the current sentence as the attention query
        # (batch, max_sents, 2 * hidden_size)
        # query = self.get_masked_mean(source=encoder_out, mask=sentence_mask, batch_size=batch_size)
        query = decoder_in
        # print('For debugging purposes, Model, CpnetMemory, forward, query = decoder_in, size:')
        # print(query.size())
        attn_mask = self.get_attn_mask(cpnet_triples)
        if self.use_cuda:
            attn_mask = attn_mask.cuda()

        if loc_mask is not None:
            assert cpnet_rep.size(0) != batch_size, batch_size % cpnet_rep.size(0) == 0
            max_cands = batch_size // cpnet_rep.size(0)
            cpnet_rep = KOALA.expand_dim_3d(cpnet_rep, loc_cands=max_cands)
            attn_mask = KOALA.expand_dim_2d(attn_mask, loc_cands=max_cands)
        # print('For debugging purposes, Model, CpnetMemory, forward, values=cpnet_rep size')
        # print(cpnet_rep.size())
        update_in, attn_probs = self.AttnUpdate(query=query, values=cpnet_rep, ori_input=decoder_in, attn_mask=attn_mask,
                                    ori_batch_size = ori_batch_size)

        # mask_vec = torch.sum(entity_mask, dim=-1, keepdim=True)
        # if loc_mask is not None:
        #     mask_vec += torch.sum(loc_mask, dim=-1, keepdim=True)
        # update_in = update_in.masked_fill(mask_vec==0, value=0)

        return update_in, attn_probs


    def get_masked_mean(self, source, mask, batch_size: int):
        """
        Args:
            source - input tensors, size(batch, tokens, 2 * hidden_size)
            mask - binary masked vectors, size(batch, sents, tokens)
        Return:
            the average of unmasked input tensors, size (batch, sents, 2 * hidden_size)
        """
        max_sents = mask.size(-2)

        bool_mask = (mask.unsqueeze(dim = -1) == 0)  # turn binary masks to boolean values
        masked_source = source.unsqueeze(dim = 1).masked_fill(bool_mask, value = 0)  # for masked tokens, turn its value to 0
        masked_source = torch.sum(masked_source, dim = -2)  # sum the unmasked token representations
        assert masked_source.size() == (batch_size, max_sents, 2 * self.hidden_size)

        num_unmasked_tokens = torch.sum(mask, dim = -1, keepdim = True)  # compute the denominator of average op (number of unmasked tokens)
        masked_mean = torch.div(input = masked_source, other = num_unmasked_tokens)  # average the unmasked vectors

        # division op may cause nan while encoutering 0, so replace nan with 0
        is_nan = torch.isnan(masked_mean)
        masked_mean = masked_mean.masked_fill(is_nan, value = 0)

        assert masked_mean.size() == (batch_size, max_sents, 2 * self.hidden_size)
        return masked_mean


    def get_attn_mask(self, cpnet_triples: List):
        attn_mask = []
        for instance in cpnet_triples:
            attn_mask.append(list(map(lambda x: x != '', instance)))
        return torch.tensor(attn_mask, dtype=torch.int)



class GatedAttnUpdate(nn.Module):
    """
    Attention + gate update
    """

    def __init__(self, query_size: int, value_size: int, input_size: int, dropout: float):
        super(GatedAttnUpdate, self).__init__()
        self.query_size = query_size
        self.value_size = value_size
        self.input_size = input_size

        attn_vec = torch.empty(query_size, value_size)
        nn.init.xavier_normal_(attn_vec)
        self.attn_vec = nn.Parameter(attn_vec, requires_grad=True)

        self.gate_fc = Linear(input_size + value_size, input_size, dropout=dropout)
        self.concat_fc = Linear(input_size + value_size, input_size, dropout=dropout)
        self.Dropout = nn.Dropout(p=dropout)

        self.attn_log = []

    def forward(self, query, values, ori_input, attn_mask, ori_batch_size: int):
        """
        :param query: (batch, max_sents, query_size)
        :param values: (batch, num_cpnet, value_size)
        :param attn_mask: (batch, num_cpnet), 0 for pad values
        :param ori_input: (batch, max_sents, input_size), input vector to be merged with context vector
        :return:
        """
        # if not (query.size(0) == values.size(0)) and (query.size(1) == ori_input.size(1)):
        #     print(f'query.size(0):{query.size(0)}, values.size(0): {values.size(0)}, query.size(1): {query.size(1)}, ori_input.size(1): {ori_input.size(1)}')
        #     print(f'query.size():{query.size()}, values.size(): {values.size()}, ori_input.size(): {ori_input.size()}')
        assert query.size(0) == values.size(0), query.size(1) == ori_input.size(1)
        assert len(query.size()) == len(values.size()) == len(ori_input.size()) == 3
        batch_size = query.size(0)
        num_cpnet = values.size(1)
        max_sents = query.size(1)
        assert query.size(-1) == self.query_size
        assert values.size(-1) == self.value_size
        assert ori_input.size(-1) == self.input_size

        # attention
        attn_vec = self.attn_vec.unsqueeze(0).expand(batch_size, -1, -1)  # (batch, query_size, value_size)
        # similarity score, (batch, max_sents, num_cpnet)
        S = torch.bmm(torch.bmm(query, attn_vec), values.transpose(1, 2))

        if attn_mask is not None:
            attn_mask = attn_mask.unsqueeze(1)
            S = S.masked_fill(attn_mask == 0, float('-inf'))
        probs = F.softmax(S, dim=-1)  # attention weights, (batch, max_sents, num_cpnet)
        if ori_batch_size != batch_size:
            self.attn_log.extend(probs.view(ori_batch_size, -1, max_sents, num_cpnet).tolist())
        else:
            self.attn_log.extend(probs.tolist())
        is_nan = torch.isnan(probs)
        probs = probs.masked_fill(is_nan, value=0)  # if no valid triple exist, the system will output nan
        C = torch.bmm(probs, values).squeeze(dim=-1)  # weighted sum, (batch, max_sents, value_size)
        assert C.size() == (batch_size, max_sents, self.value_size)

        # select attention weights for attention loss
        # for location prediction, only select one case since they are the same
        if ori_batch_size != batch_size:
            select_probs = probs.view(ori_batch_size, -1, max_sents, num_cpnet)
        else:
            select_probs = probs

        # gate
        concat_vec = torch.cat([ori_input, C], dim=-1)
        gate_vec = torch.sigmoid(self.gate_fc(concat_vec))
        cand_input = self.concat_fc(concat_vec)
        final_input = torch.mul(gate_vec, cand_input) + torch.mul(1 - gate_vec, ori_input)
        assert final_input.size() == (batch_size, max_sents, self.input_size)

        return final_input, select_probs


class FixedSentEncoder(nn.Module):
    """
    An encoder that acquires sentence embedding from a fixed pretrained language model
    """
    def __init__(self, opt):
        super(FixedSentEncoder, self).__init__()
        self.embed_size = MODEL_HIDDEN[opt.plm_model_name]
        self.hidden_size = opt.hidden_size
        self.lm_batch_size = opt.batch_size
        self.LSTM = nn.LSTM(input_size=self.embed_size, hidden_size=self.hidden_size,
                            num_layers=1, batch_first=True, bidirectional=True)

        self.use_cuda = not opt.no_cuda
        self.Dropout = nn.Dropout(p=opt.dropout)


    def forward(self, input: List[List[str]], tokenizer, encoder):
        """
        Args:
            input: size(batch, num_cpnet), each is a list of untokenized strings.
        """
        # print('for debugging purposes, Model, FixedSentEncoder, forward, the input it receieves, here from CpnetEncoder:')
        # print('input:',input)
        batch_size = len(input)
        # print('batch_size = len(input) = ', batch_size)
        num_cpnet = len(input[0])
        # print('num_cpnet:', num_cpnet)
        all_sents = itertools.chain.from_iterable(input)  # batch * num_cpnet
        # all_sents = [j for i in input for j in i if j]
        # print('len(all_sents):', len(all_sents))
        input_ids = list(map(lambda s: tokenizer.convert_tokens_to_ids(s.split()), all_sents))  # should already add special tokens
        # print('len(input_ids):', len(input_ids))
        # print('len(input_ids) // self.lm_batch_size', len(input_ids) // self.lm_batch_size)
        # print(f'len(input_ids):{len(input_ids)}')
        input_batches = [input_ids[batch_idx * self.lm_batch_size: (batch_idx + 1) * self.lm_batch_size]
                         for batch_idx in range(len(input_ids) // self.lm_batch_size + 1)]
        sent_embed = []
        # print('Number of input_batches:', len(input_batches))
        # input_batches_idx = 0
        for batch_input_ids in input_batches:
            # print('input_batches idx:', input_batches_idx)
            # print('for debugging, in Model, FixedSentEncoder, forward method:', batch_input_ids)
            # if input_batches_idx == 0:
            #     print(type(batch_input_ids))
            mini_batch_size = len(batch_input_ids)
            # print('mini_batch_size:', mini_batch_size)
            if not batch_input_ids:
                # input_batches_idx += 1
                continue
            # print('max_len pre-calc',max([len(batch_input_ids[i]) for i in range(mini_batch_size)]))
            batch_input_ids, attention_mask, max_len = \
                FixedSentEncoder.pad_to_longest(batch=batch_input_ids,
                                              pad_id=tokenizer.pad_token_id)
            # print('len(batch_input_ids):', len(batch_input_ids))
            # print('max_len post-calc',max_len)
            # if max_len == 0:
            #     print('Here we go!')
            #     # print('batch_input_ids:', batch_input_ids)
            #     print('attention_mask:', attention_mask)
            #     print('input:', input)
            #     input_batches_idx += 1
            #     continue
            # if input_batches_idx == 0:
            #     print(type(batch_input_ids))                                   
            if self.use_cuda:
                batch_input_ids = batch_input_ids.cuda()
                attention_mask = attention_mask.cuda()
            # print((mini_batch_size, max_len, self.embed_size))
            # print(batch_input_ids, batch_input_ids.device) 
            with torch.no_grad():                
                outputs = encoder(batch_input_ids, attention_mask=attention_mask)
                # print('FixedSentEncoder forward output size:', outputs[0].size())
                # print('FixedSentEncoder forward output type:', type(outputs))
            # print('outputs size:', outputs.size())
            last_hidden = outputs[0]  # (batch, seq_len, hidden_size)
            # print('last_hidden.size()',last_hidden.size(), (mini_batch_size, max_len, self.embed_size))
            assert last_hidden.size() == (mini_batch_size, max_len, self.embed_size)            
            # print('line 712')
            encoder_out, _ = self.LSTM(last_hidden)
            encoder_out = self.Dropout(encoder_out)
            special_token_mask = batch_input_ids > tokenizer.sep_token_id
            # print('line 716')
            # print(f'last_hidden.size(0):{last_hidden.size(0)}')
            for i in range(last_hidden.size(0)):
                embedding = encoder_out[i]  # (max_length, hidden_size)
                real_tokens = special_token_mask[i]
                token_embed = embedding[real_tokens]  # get rid of <CLS> and <SEP>
                mean_embed = torch.mean(token_embed, dim=0)
                is_nan = torch.isnan(mean_embed)
                mean_embed = mean_embed.masked_fill(is_nan, value=0)
                sent_embed.append(mean_embed)
                # print(last_hidden.size(0), '->', i)
            # input_batches_idx += 1
        # print(f'sent_embed len before stack:{len(sent_embed)}') 
        sent_embed = torch.stack(sent_embed, dim=0)
        # print(f'sent_embed size after stack:{sent_embed.size()}') 
        # if sent_embed.size() != (batch_size * num_cpnet, 2 * self.hidden_size):
        #     print(sent_embed.size(), (batch_size * num_cpnet, 2 * self.hidden_size))
        assert sent_embed.size() == (batch_size * num_cpnet, 2 * self.hidden_size)
        sent_embed = self.Dropout(sent_embed)
        # print(f'sent_embed size after dropout:{sent_embed.size()}') 
        sent_embed = sent_embed.view(batch_size, num_cpnet, 2 * self.hidden_size)        
        # print(f'sent_embed.size() from FixedSentEncoder, after view >> values.size:{sent_embed.size()}') 
        return sent_embed


    @staticmethod
    def pad_to_longest(batch: List, pad_id: int) -> Tuple[torch.LongTensor, torch.FloatTensor]:
        """
        Pad the sentences to the longest length in a batch
        """
        batch_size = len(batch)
        max_length = max([len(batch[i]) for i in range(batch_size)])
        # if max_length == 0:
        #     print('max_length is zero!! size of input is', batch_size)
        #     print(batch)
        pad_batch = [batch[i] + [pad_id for _ in range(max_length - len(batch[i]))] for i in range(batch_size)]
        pad_batch = torch.tensor(pad_batch, dtype=torch.long)
        # avoid computing attention on padding tokens
        attention_mask = torch.ones_like(pad_batch).masked_fill(mask=(pad_batch==pad_id), value=0)
        assert pad_batch.size() == attention_mask.size() == (batch_size, max_length)

        return pad_batch, attention_mask, max_length


class Linear(nn.Module):
    """
    Simple Linear layer with xavier init
    """
    def __init__(self, d_in: int, d_out: int, dropout: float, bias: bool = True):
        super(Linear, self).__init__()
        self.linear = nn.Linear(d_in, d_out, bias=bias)
        self.dropout = nn.Dropout(p = dropout)
        nn.init.xavier_normal_(self.linear.weight)

    def forward(self, x):
        return self.dropout(self.linear(x))


class Bilinear(nn.Module):
    """
    Simple Bilinear layer with xavier init and dropout
    """
    def __init__(self, in_1: int, in_2: int, d_out: int, dropout: float, bias: bool = True):
        super(Bilinear, self).__init__()
        self.bilinear = nn.Bilinear(in1_features=in_1, in2_features=in_2, out_features=d_out, bias=bias)
        self.dropout = nn.Dropout(p=dropout)
        nn.init.xavier_normal_(self.bilinear.weight)

    def forward(self, x1, x2):
        return self.dropout(self.bilinear(x1, x2))