BIDINLTK/dev/bert/BIDIBert.py

import torch
import torch.nn as nn
import torch.nn.functional as F

import numpy as np
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

class PositionalEncoding(nn.Module):

    def __init__(self,dim_hid):
        super(PositionalEncoding,self).__init__()
        base_array = np.array([np.power(10000,2*(hid_j//2)/dim_hid) for hid_j in range(dim_hid)])
        self.base_tensor = torch.from_numpy(base_array).to(torch.float32).to(device) #[1,D]

    def forward(self,x):
        # x(B,N,d)
        B,N,d = x.shape
        pos = torch.arange(N).unsqueeze(-1).to(torch.float32).to(device) #[N,1]
        pos = pos/self.base_tensor
        pos = pos.unsqueeze(0)
        pos[:,:,0::2] = torch.sin(pos[:,:,0::2])
        pos[:,:,1::2] = torch.cos(pos[:,:,1::2])
        return x+pos

class ScaledDotProductAttention(nn.Module):
    ''' Scaled Dot-Product Attention '''

    def __init__(self, temperature, attn_dropout=0.1):
        super().__init__()
        self.temperature = temperature
        self.dropout = nn.Dropout(attn_dropout)

    def forward(self, q, k, v, mask=None):

        # print(q.shape,k.shape)
        attn = torch.matmul(q / self.temperature, k.transpose(2, 3))

        if mask is not None:
            attn = attn.masked_fill(mask == 0, -1e9)

        attn = self.dropout(torch.softmax(attn, dim=-1))
        output = torch.matmul(attn, v)

        return output, attn


class MultiHeadAttention(nn.Module):
    ''' Multi-Head Attention module '''

    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
        super().__init__()

        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v

        self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
        self.fc = nn.Linear(n_head * d_v, d_model, bias=False)

        self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)

        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)


    def forward(self, q, k, v, mask=None):

        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
        sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)

        residual = q


        # Pass through the pre-attention projection: b x lq x (n*dv)
        # Separate different heads: b x lq x n x dv
        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)

        # Transpose for attention dot product: b x n x lq x dv
        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)

        if mask is not None:
            # mask = mask.unsqueeze(1)   # For head axis broadcasting.
            mask = mask.unsqueeze(1).unsqueeze(2)   # For head axis broadcasting.

        q, attn = self.attention(q, k, v, mask=mask)

        #q (sz_b,n_head,N=len_q,d_k)
        #k (sz_b,n_head,N=len_k,d_k)
        #v (sz_b,n_head,N=len_v,d_v)

        # Transpose to move the head dimension back: b x lq x n x dv
        # Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv)
        q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)

        #q (sz_b,len_q,n_head,N * d_k)
        q = self.dropout(self.fc(q))
        q += residual

        q = self.layer_norm(q)

        return q, attn


class PositionwiseFeedForward(nn.Module):
    ''' A two-feed-forward-layer module '''

    def __init__(self, d_in, d_hid, dropout=0.1):
        super().__init__()
        self.w_1 = nn.Linear(d_in, d_hid) # position-wise
        self.w_2 = nn.Linear(d_hid, d_in) # position-wise
        self.layer_norm = nn.LayerNorm(d_in, eps=1e-6)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):

        residual = x
        x = self.w_2(torch.relu(self.w_1(x)))
        x = self.dropout(x)
        x += residual
        x = self.layer_norm(x)

        return x


class EncoderLayer(nn.Module):
    ''' Compose with two layers '''

    def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1):
        super(EncoderLayer, self).__init__()
        self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)
        self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, dropout=dropout)

    def forward(self, enc_input, slf_attn_mask=None):
        enc_output, enc_slf_attn = self.slf_attn(
            enc_input, enc_input, enc_input, mask=slf_attn_mask)
        enc_output = self.pos_ffn(enc_output)
        return enc_output, enc_slf_attn


class Encoder(nn.Module):
    ''' A encoder model with self attention mechanism. '''

    def __init__(
            self, n_src_vocab, d_word_vec, n_layers, n_head, d_k, d_v,
            d_model, d_inner, pad_idx, dropout=0.1, n_position=200, scale_emb=False,embedding=None):

        super().__init__()

        if embedding is None:
            self.src_word_emb = nn.Embedding(n_src_vocab, d_word_vec, padding_idx=pad_idx)
        else:
            self.src_word_emb = embedding

        self.position_enc = PositionalEncoding(d_word_vec)
        self.dropout = nn.Dropout(p=dropout)
        self.layer_stack = nn.ModuleList([
            EncoderLayer(d_model, d_inner, n_head, d_k, d_v, dropout=dropout)
            for _ in range(n_layers)])
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
        self.scale_emb = scale_emb
        self.d_model = d_model

    def forward(self, src_seq, src_mask, return_attns=False):

        enc_slf_attn_list = []

        # -- Forward
        enc_output = self.src_word_emb(src_seq)
        if self.scale_emb:
            enc_output *= self.d_model ** 0.5
        enc_output = self.dropout(self.position_enc(enc_output))
        enc_output = self.layer_norm(enc_output)

        for enc_layer in self.layer_stack:
            enc_output, enc_slf_attn = enc_layer(enc_output, slf_attn_mask=src_mask)
            enc_slf_attn_list += [enc_slf_attn] if return_attns else []

        if return_attns:
            return enc_output, enc_slf_attn_list
        return enc_output,

class BIDIBert(nn.Module):

    def __init__(self,n_src_vocab,d_word_vec, n_layers, n_head, d_k, d_v,
                 d_model, d_inner, pad_idx):
        super(BIDIBert, self).__init__()

        self.encoder = Encoder(n_src_vocab,d_word_vec, n_layers, n_head, d_k, d_v,
                               d_model, d_inner, pad_idx)


    def forward(self,inputs,attention_mask):

        out = self.encoder(inputs,attention_mask)
        return out

from transformers import PreTrainedModel
from transformers.modeling_outputs import MaskedLMOutput

class BIDIBertForMaskedLM(nn.Module):

    def __init__(self, n_src_vocab,d_word_vec, n_layers, n_head, d_k, d_v,
                 d_model, d_inner, pad_idx):
        super(BIDIBertForMaskedLM,self).__init__()

        self.electra = BIDIBert(n_src_vocab,d_word_vec, n_layers, n_head, d_k, d_v,
                                d_model, d_inner, pad_idx)


        self.n_src_vocab = n_src_vocab
        self.LayerNorm = nn.LayerNorm(d_model)
        self.dense = nn.Linear(d_model, d_model)

        self.generator_lm_head = nn.Linear(d_model, n_src_vocab)
        # Initialize weights and apply final processing


    def forward(
            self,
            input_ids,
            attention_mask,
            labels= None,
            return_dict=None
    ):
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
        """

        generator_hidden_states, = self.electra(
            input_ids,
            attention_mask=attention_mask

        )

        hidden_states = self.dense(generator_hidden_states)
        hidden_states = F.gelu(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        generator_sequence_output = hidden_states

        prediction_scores = self.generator_lm_head(generator_sequence_output)

        loss = None
        # Masked language modeling softmax layer
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()  # -100 index = padding token
            loss = loss_fct(prediction_scores.view(-1, self.n_src_vocab), labels.view(-1))

        if not return_dict:
            output = (prediction_scores,) + (generator_hidden_states,)
            return ((loss,) + output) if loss is not None else output

        return MaskedLMOutput(
            loss=loss,
            logits=prediction_scores,
            hidden_states=generator_hidden_states,
            attentions=generator_hidden_states.attentions,
        )