utlis.py

import yaml 

def load_checkpoint(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, strategy:str, src_sentence: str, lenght_extend :int = 0, beam_size: int = 5, raw: bool = False, length_penalty:float = 0.6):
    assert strategy in ['greedy','beam search'], 'the strategy for decoding has to be either greedy or beam search'
    model.to(device)
    model.eval()
    # 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 + 5, 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)