Create utlis.py

utlis.py

@@ -0,0 +1,134 @@
+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)