app.py

import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import Transformer

# helper Module that adds positional encoding to the token embedding to introduce a notion of word order.
class PositionalEncoding(nn.Module):
    def __init__(self,
                 emb_size: int,
                 dropout: float,
                 maxlen: int = 5000):
        super(PositionalEncoding, self).__init__()
        den = torch.exp(- torch.arange(0, emb_size, 2)* torch.log(10000) / emb_size)
        pos = torch.arange(0, maxlen).reshape(maxlen, 1)
        pos_embedding = torch.zeros((maxlen, emb_size))
        pos_embedding[:, 0::2] = torch.sin(pos * den)
        pos_embedding[:, 1::2] = torch.cos(pos * den)
        pos_embedding = pos_embedding.unsqueeze(-2)

        self.dropout = nn.Dropout(dropout)
        self.register_buffer('pos_embedding', pos_embedding)

    def forward(self, token_embedding: Tensor):
        return self.dropout(token_embedding + self.pos_embedding[:token_embedding.size(0), :])

# helper Module to convert tensor of input indices into corresponding tensor of token embeddings
class TokenEmbedding(nn.Module):
    def __init__(self, vocab_size: int, emb_size):
        super(TokenEmbedding, self).__init__()
        self.embedding = nn.Embedding(vocab_size, emb_size)
        self.emb_size = emb_size

    def forward(self, tokens: Tensor):
        return self.embedding(tokens.long()) * math.sqrt(self.emb_size)

class Seq2SeqTransformer(nn.Module):
    def __init__(self,
                 num_encoder_layers: int,
                 num_decoder_layers: int,
                 emb_size: int,
                 nhead: int,
                 src_vocab_size: int,
                 tgt_vocab_size: int,
                 dim_feedforward: int = 512,
                 dropout: float = 0.1):
        super(Seq2SeqTransformer, self).__init__()
        self.transformer = Transformer(d_model=emb_size,
                                       nhead=nhead,
                                       num_encoder_layers=num_encoder_layers,
                                       num_decoder_layers=num_decoder_layers,
                                       dim_feedforward=dim_feedforward,
                                       dropout=dropout,
                                       batch_first=True)
        self.generator = nn.Linear(emb_size, tgt_vocab_size)
        self.src_tok_emb = TokenEmbedding(src_vocab_size, emb_size)
        self.tgt_tok_emb = TokenEmbedding(tgt_vocab_size, emb_size)
        self.positional_encoding = PositionalEncoding(
            emb_size, dropout=dropout)

    def forward(self,
                src: Tensor,
                trg: Tensor,
                src_mask: Tensor,
                tgt_mask: Tensor,
                src_padding_mask: Tensor,
                tgt_padding_mask: Tensor,
                memory_key_padding_mask: Tensor):
        src_emb = self.positional_encoding(self.src_tok_emb(src))
        tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg))
        outs = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask, None,
                                src_padding_mask, tgt_padding_mask, memory_key_padding_mask)
        return self.generator(outs)

    def encode(self, src: Tensor, src_mask: Tensor):
        return self.transformer.encoder(self.positional_encoding(
                            self.src_tok_emb(src)), src_mask)

    def decode(self, tgt: Tensor, memory: Tensor, tgt_mask: Tensor):
        return self.transformer.decoder(self.positional_encoding(
                          self.tgt_tok_emb(tgt)), memory,
                          tgt_mask)

import yaml 
from transformers import AutoTokenizer
from transformers import PreTrainedTokenizerFast
from tokenizers.processors import TemplateProcessing


def addPreprocessing(tokenizer):
      tokenizer._tokenizer.post_processor = TemplateProcessing(
          single=tokenizer.bos_token + " $A " + tokenizer.eos_token,
          special_tokens=[(tokenizer.eos_token, tokenizer.eos_token_id), (tokenizer.bos_token, tokenizer.bos_token_id)])

def load_model(model_checkpoint_dir='model.pt',config_dir='config.yaml'):
    
    with open(config_dir, 'r') as yaml_file:
        loaded_model_params = yaml.safe_load(yaml_file)
        
    # Create a new instance of the model with the loaded configuration
    model = Seq2SeqTransformer(
        loaded_model_params["num_encoder_layers"],
        loaded_model_params["num_decoder_layers"],
        loaded_model_params["emb_size"],
        loaded_model_params["nhead"],
        loaded_model_params["source_vocab_size"],
        loaded_model_params["target_vocab_size"],
        loaded_model_params["ffn_hid_dim"]
    )
    
    checkpoint = torch.load(model_checkpoint_dir) if torch.cuda.is_available() else torch.load(model_checkpoint_dir,map_location=torch.device('cpu'))
    model.load_state_dict(checkpoint)
    
    return model


def greedy_decode(model, src, src_mask, max_len, start_symbol):
    # Move inputs to the device
    src = src.to(device)
    src_mask = src_mask.to(device)

    # Encode the source sequence
    memory = model.encode(src, src_mask)

    # Initialize the target sequence with the start symbol
    ys = torch.tensor([[start_symbol]]).type(torch.long).to(device)

    for i in range(max_len - 1):
        memory = memory.to(device)
        # Create a target mask for autoregressive decoding
        tgt_mask = torch.tril(torch.full((ys.size(1), ys.size(1)), float('-inf'), device=device), diagonal=-1).transpose(0, 1).to(device)
        # Decode the target sequence
        out = model.decode(ys, memory, tgt_mask)
        # Generate the probability distribution over the vocabulary
        prob = model.generator(out[:, -1])

        # Select the next word with the highest probability
        _, next_word = torch.max(prob, dim=1)
        next_word = next_word.item()

        # Append the next word to the target sequence
        ys = torch.cat([ys,
                        torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=1)

        # Check if the generated word is the end-of-sequence token
        if next_word == target_tokenizer.eos_token_id:
            break

    return ys


def beam_search_decode(model, src, src_mask, max_len, start_symbol, beam_size ,length_penalty):
    # Move inputs to the device
    src = src.to(device)
    src_mask = src_mask.to(device)

    # Encode the source sequence
    memory = model.encode(src, src_mask) # b * seqlen_src * hdim

    # Initialize the beams (sequences, score)
    beams = [(torch.tensor([[start_symbol]]).type(torch.long).to(device), 0)] 

    for i in range(max_len - 1):
        new_beams = []
        complete_beams = []
        cbl = []

        for ys, score in beams:

            # Create a target mask for autoregressive decoding
            tgt_mask = torch.tril(torch.full((ys.size(1), ys.size(1)), float('-inf'), device=device), diagonal=-1).transpose(0, 1).to(device)
            # Decode the target sequence
            out = model.decode(ys, memory, tgt_mask) # b * seqlen_tgt * hdim
            #print(f'shape out {out.shape}')
            # Generate the probability distribution over the vocabulary
            prob = model.generator(out[:, -1]) # b * tgt_vocab_size
            #print(f'shape prob {prob.shape}')

            # Get the top beam_size candidates for the next word
            _, top_indices = torch.topk(prob, beam_size, dim=1) # b * beam_size
            
            for j,next_word in enumerate(top_indices[0]):

                next_word = next_word.item()
                
                # Append the next word to the target sequence
                new_ys = torch.cat([ys, torch.full((1, 1), fill_value=next_word, dtype=src.dtype).to(device)], dim=1)
                
                length_factor = (5 + j / 6) ** length_penalty
                new_score = (score + prob[0][next_word].item()) / length_factor
                
                if next_word == target_tokenizer.eos_token_id:
                    complete_beams.append((new_ys, new_score))
                else:
                    new_beams.append((new_ys, new_score))
            
        
        # Sort the beams by score and select the top beam_size beams
        new_beams.sort(key=lambda x: x[1], reverse=True)
        try:
            beams = new_beams[:beam_size]
        except:
            beams = new_beams

    beams = new_beams + complete_beams
    beams.sort(key=lambda x: x[1], reverse=True)
    
    best_beam = beams[0][0]
    return best_beam

def translate(model: torch.nn.Module, src_sentence: str, strategy:str = 'greedy' , lenght_extend :int = 5, beam_size: int = 5,  length_penalty:float = 0.6):
    assert strategy in ['greedy','beam search'], 'the strategy for decoding has to be either greedy or beam search'
    # Tokenize the source sentence
    src = source_tokenizer(src_sentence, **token_config)['input_ids']
    num_tokens = src.shape[1]
    # Create a source mask
    src_mask = (torch.zeros(num_tokens, num_tokens)).type(torch.bool)
    if strategy == 'greedy':
       tgt_tokens = greedy_decode(model, src, src_mask, max_len=num_tokens + lenght_extend, start_symbol=target_tokenizer.bos_token_id).flatten()
    # Generate the target tokens using beam search decoding
    else:
        tgt_tokens = beam_search_decode(model, src, src_mask, max_len=num_tokens + lenght_extend, start_symbol=target_tokenizer.bos_token_id, beam_size=beam_size,length_penalty=length_penalty).flatten()
    # Decode the target tokens and clean up the result
    return target_tokenizer.decode(tgt_tokens, clean_up_tokenization_spaces=True, skip_special_tokens=True)

special_tokens = {'unk_token':"[UNK]",
                  'cls_token':"[CLS]",
                  'eos_token': '[EOS]',
                  'sep_token':"[SEP]",
                  'pad_token':"[PAD]",
                  'mask_token':"[MASK]",
                  'bos_token':"[BOS]"}

source_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", **special_tokens)
target_tokenizer = PreTrainedTokenizerFast.from_pretrained('Sifal/E2KT')

addPreprocessing(source_tokenizer)
addPreprocessing(target_tokenizer)

token_config = {
                "add_special_tokens": True,
                "return_tensors": True,
             }

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

model = load_model()
model.to(device)
model.eval()

import gradio as gr

iface = gr.Interface(
    fn=translate,
    inputs=[
        gr.inputs.Textbox("Enter a sentence to translate"),
        gr.inputs.Radio(['greedy', 'beam search'], label="Decoding Strategy"),
        gr.inputs.Number(label="Length Extend (for greedy)", default=5),
        gr.inputs.Number(label="Beam Size (for beam search)", default=5),
        gr.inputs.Number(label="Length Penalty (for beam search)", default=0.6)
    ],
    outputs=gr.outputs.Textbox("Translation"),
    title="Translation Interface",
    description="Translate text using a pre-trained model.",
)

# Launch the Gradio interface
iface.launch()