Model.py

import torch
import torch.nn as nn
import numpy as np
from transformers import  AutoTokenizer 
import pickle

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("DistillMDPI1/DistillMDPI1/saved_tokenizer")



# Step 1: Ensure the tokenizer has the [MULT] token
tokenizer.add_special_tokens({'additional_special_tokens': ['<MULT>']})
mult_token_id = tokenizer.convert_tokens_to_ids('<MULT>')
cls_token_id = tokenizer.cls_token_id
sep_token_id = tokenizer.sep_token_id
pad_token_id = tokenizer.pad_token_id

## Voice Part functions
maxlen = 255 # maximum of length
batch_size = 32
max_pred = 5  # max tokens of prediction
n_layers = 6 # number of Encoder of Encoder Layer
n_heads = 12 # number of heads in Multi-Head Attention
d_model = 768 # Embedding Size
d_ff = 768 * 4  # 4*d_model, FeedForward dimension
d_k = d_v = 64  # dimension of K(=Q), V
n_segments = 2
vocab_size = tokenizer.vocab_size +1

def get_attn_pad_mask(seq_q, seq_k):
    batch_size, len_q = seq_q.size()
    batch_size, len_k = seq_k.size()
    # eq(zero) is PAD token
    pad_attn_mask = seq_k.data.eq(1).unsqueeze(1)  # batch_size x 1 x len_k(=len_q), one is masking
    return pad_attn_mask.expand(batch_size, len_q, len_k) # batch_size x len_q x len_k

class Embedding(nn.Module):
    def __init__(self):
        super(Embedding, self).__init__()
        self.tok_embed = nn.Embedding(vocab_size, d_model)  # token embedding
        self.pos_embed = nn.Embedding(maxlen, d_model)  # position embedding
        self.seg_embed = nn.Embedding(n_segments, d_model)  # segment(token type) embedding
        self.norm = nn.LayerNorm(d_model)

    def forward(self, x, seg):
        seq_len = x.size(1)
        pos = torch.arange(seq_len, dtype=torch.long, device=x.device)
        pos = pos.unsqueeze(0).expand_as(x)  # (seq_len,) -> (batch_size, seq_len)
        embedding = self.tok_embed(x)
        embedding += self.pos_embed(pos)
        embedding += self.seg_embed(seg)
        return self.norm(embedding)

class ScaledDotProductAttention(nn.Module):
    def __init__(self):
        super(ScaledDotProductAttention, self).__init__()

    def forward(self, Q, K, V, attn_mask):
        scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k) # scores : [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]
        scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is one.
        attn = nn.Softmax(dim=-1)(scores)
        context = torch.matmul(attn, V)
        return scores , context, attn

class MultiHeadAttention(nn.Module):
    def __init__(self):
        super(MultiHeadAttention, self).__init__()
        self.W_Q = nn.Linear(d_model, d_k * n_heads)
        self.W_K = nn.Linear(d_model, d_k * n_heads)
        self.W_V = nn.Linear(d_model, d_v * n_heads)
        self.fc = nn.Linear(n_heads * d_v, d_model)
        self.norm = nn.LayerNorm(d_model)
    def forward(self, Q, K, V, attn_mask):
        # q: [batch_size x len_q x d_model], k: [batch_size x len_k x d_model], v: [batch_size x len_k x d_model]
        residual, batch_size = Q, Q.size(0)
        device = Q.device
        Q, K, V = Q.to(device), K.to(device), V.to(device)
        # (B, S, D) -proj-> (B, S, D) -split-> (B, S, H, W) -trans-> (B, H, S, W)
        q_s = self.W_Q(Q).view(batch_size, -1, n_heads, d_k).transpose(1,2)  # q_s: [batch_size x n_heads x len_q x d_k]
        k_s = self.W_K(K).view(batch_size, -1, n_heads, d_k).transpose(1,2)  # k_s: [batch_size x n_heads x len_k x d_k]
        v_s = self.W_V(V).view(batch_size, -1, n_heads, d_v).transpose(1,2)  # v_s: [batch_size x n_heads x len_k x d_v]

        attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) # attn_mask : [batch_size x n_heads x len_q x len_k]

        # context: [batch_size x n_heads x len_q x d_v], attn: [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]
        scores ,context, attn = ScaledDotProductAttention()(q_s, k_s, v_s, attn_mask)
        context = context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v) # context: [batch_size x len_q x n_heads * d_v]
        output = self.fc(context)
        return self.norm(output + residual), attn # output: [batch_size x len_q x d_model]

class PoswiseFeedForwardNet(nn.Module):
    def __init__(self):
        super(PoswiseFeedForwardNet, self).__init__()
        self.fc1 = nn.Linear(d_model, d_ff)
        self.fc2 = nn.Linear(d_ff, d_model)
        self.gelu = nn.GELU()
    def forward(self, x):
        # (batch_size, len_seq, d_model) -> (batch_size, len_seq, d_ff) -> (batch_size, len_seq, d_model)
        return self.fc2(self.gelu(self.fc1(x)))

class EncoderLayer(nn.Module):
    def __init__(self):
        super(EncoderLayer, self).__init__()
        self.enc_self_attn = MultiHeadAttention()
        self.pos_ffn = PoswiseFeedForwardNet()

    def forward(self, enc_inputs, enc_self_attn_mask):
        enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs, enc_self_attn_mask.to(enc_inputs.device)) # enc_inputs to same Q,K,V
        enc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size x len_q x d_model]
        return enc_outputs, attn

class BERT(nn.Module):
    def __init__(self):
        super(BERT, self).__init__()
        self.embedding = Embedding()
        self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])
        self.fc = nn.Linear(d_model, d_model)
        self.activ1 = nn.Tanh()
        self.linear = nn.Linear(d_model, d_model)
        self.activ2 = nn.GELU()
        self.norm = nn.LayerNorm(d_model)
        self.classifier = nn.Linear(d_model, 2)
        # decoder is shared with embedding layer
        embed_weight = self.embedding.tok_embed.weight
        n_vocab, n_dim = embed_weight.size()
        self.decoder = nn.Linear(n_dim, n_vocab, bias=False)
        self.decoder.weight = embed_weight
        self.decoder_bias = nn.Parameter(torch.zeros(n_vocab))
        self.mclassifier = nn.Linear(d_model, 17)

    def forward(self, input_ids, segment_ids, masked_pos):
        output = self.embedding(input_ids, segment_ids)
        enc_self_attn_mask = get_attn_pad_mask(input_ids, input_ids).to(output.device)
        for layer in self.layers:
            output, enc_self_attn = layer(output, enc_self_attn_mask)
        # output : [batch_size, len, d_model], attn : [batch_size, n_heads, d_mode, d_model]
        # it will be decided by first token(CLS)
        h_pooled = self.activ1(self.fc(output[:, 0])) # [batch_size, d_model]
        logits_clsf = self.classifier(h_pooled) # [batch_size, 2]

        masked_pos = masked_pos[:, :, None].expand(-1, -1, output.size(-1)) # [batch_size, max_pred, d_model]
        # get masked position from final output of transformer.
        h_masked = torch.gather(output, 1, masked_pos) # masking position [batch_size, max_pred, d_model]
        h_masked = self.norm(self.activ2(self.linear(h_masked)))
        logits_lm = self.decoder(h_masked) + self.decoder_bias # [batch_size, max_pred, n_vocab]

        h_mult_sent1 = self.activ1(self.fc(output[:, 1]))
        logits_mclsf1 = self.mclassifier(h_mult_sent1)

        mult2_token_id = mult_token_id  # Assuming mult_token_id is defined globally
        mult2_positions = (input_ids == mult2_token_id).nonzero(as_tuple=False)  # Find positions of [MULT2] tokens
        # Ensure there are exactly two [MULT] tokens in each input sequence
        assert mult2_positions.size(0) == 2 * input_ids.size(0)
        mult2_positions = mult2_positions[1::2][:, 1]
        # Gather the hidden states corresponding to the second [MULT] token
        h_mult_sent2 = output[torch.arange(output.size(0)), mult2_positions]

        logits_mclsf2 = self.mclassifier(h_mult_sent2)
        logits_mclsf2 = self.mclassifier(h_mult_sent2)
        return logits_lm, logits_clsf , logits_mclsf1 , logits_mclsf2