Update generate.py

inference/generate.py

@@ -12,19 +12,13 @@ from model import Transformer, ModelArgs
 
 
 def sample(logits, temperature: float = 1.0):
-    """
-    Samples a token from the logits using temperature scaling.
-
-    Args:
-        logits (torch.Tensor): The logits tensor for token predictions.
-        temperature (float, optional): Temperature for scaling logits. Defaults to 1.0.
-
-    Returns:
-        torch.Tensor: The sampled token.
-    """
+    try:
         logits = logits / max(temperature, 1e-5)
         probs = torch.softmax(logits, dim=-1)
         return probs.div_(torch.empty_like(probs).exponential_(1)).argmax(dim=-1)
+    except Exception as e:
+        print(f"Erro ao amostrar token: {e}")
+        raise
 
 
 @torch.inference_mode()
@@ -35,47 +29,40 @@ def generate(
     eos_id: int,
     temperature: float = 1.0
 ) -> List[List[int]]:
-    """
-    Generates new tokens based on the given prompt tokens using the specified model.
-
-    Args:
-        model (Transformer): The transformer model used for token generation.
-        prompt_tokens (List[List[int]]): A list of lists containing the prompt tokens for each sequence.
-        max_new_tokens (int): The maximum number of new tokens to generate.
-        eos_id (int): The end-of-sequence token ID.
-        temperature (float, optional): The temperature value for sampling. Defaults to 1.0.
-
-    Returns:
-        List[List[int]]: A list of lists containing the generated tokens for each sequence.
-    """
+    try:
         prompt_lens = [len(t) for t in prompt_tokens]
-    assert max(prompt_lens) <= model.max_seq_len
+        assert max(prompt_lens) <= model.max_seq_len, f"Prompt é maior do que o comprimento máximo do modelo ({model.max_seq_len})"
         total_len = min(model.max_seq_len, max_new_tokens + max(prompt_lens))
         tokens = torch.full((len(prompt_tokens), total_len), -1, dtype=torch.long, device="cuda")
+        
         for i, t in enumerate(prompt_tokens):
             tokens[i, :len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
+            
         prev_pos = 0
         finished = torch.tensor([False] * len(prompt_tokens), device="cuda")
         prompt_mask = tokens != -1
+        
         for cur_pos in range(min(prompt_lens), total_len):
             logits = model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
-        if temperature > 0:
-            next_token = sample(logits, temperature)
-        else:
-            next_token = logits.argmax(dim=-1)
+            next_token = sample(logits, temperature) if temperature > 0 else logits.argmax(dim=-1)
             next_token = torch.where(prompt_mask[:, cur_pos], tokens[:, cur_pos], next_token)
             tokens[:, cur_pos] = next_token
             finished |= torch.logical_and(~prompt_mask[:, cur_pos], next_token == eos_id)
             prev_pos = cur_pos
             if finished.all():
                 break
+        
         completion_tokens = []
         for i, toks in enumerate(tokens.tolist()):
-        toks = toks[prompt_lens[i]:prompt_lens[i]+max_new_tokens]
+            toks = toks[prompt_lens[i]:prompt_lens[i] + max_new_tokens]
             if eos_id in toks:
                 toks = toks[:toks.index(eos_id)]
             completion_tokens.append(toks)
+        
         return completion_tokens
+    except Exception as e:
+        print(f"Erro ao gerar texto: {e}")
+        raise
 
 
 def main(
@@ -86,41 +73,46 @@ def main(
     max_new_tokens: int = 100,
     temperature: float = 1.0,
 ) -> None:
-    """
-    Main function to load the model and perform interactive or batch text generation.
+    try:
+        if not os.path.exists(ckpt_path):
+            raise FileNotFoundError(f"O caminho para o checkpoint '{ckpt_path}' não existe.")
+        
+        if not os.path.exists(config):
+            raise FileNotFoundError(f"O arquivo de configuração '{config}' não foi encontrado.")
         
-    Args:
-        ckpt_path (str): Path to the model checkpoint directory.
-        config (str): Path to the model configuration file.
-        input_file (str, optional): Path to a file containing input prompts. Defaults to "".
-        interactive (bool, optional): Whether to run in interactive mode. Defaults to True.
-        max_new_tokens (int, optional): Maximum number of new tokens to generate. Defaults to 100.
-        temperature (float, optional): Temperature for sampling. Defaults to 1.0.
-    """
         world_size = int(os.getenv("WORLD_SIZE", "1"))
         rank = int(os.getenv("RANK", "0"))
         local_rank = int(os.getenv("LOCAL_RANK", "0"))
+        
         if world_size > 1:
             dist.init_process_group("nccl")
+        
         global print
         if rank != 0:
             print = lambda *_, **__: None
+        
         torch.cuda.set_device(local_rank)
         torch.set_default_dtype(torch.bfloat16)
         torch.set_num_threads(8)
         torch.manual_seed(965)
+        
         with open(config) as f:
             args = ModelArgs(**json.load(f))
         print(args)
+        
         with torch.device("cuda"):
             model = Transformer(args)
+        
         tokenizer = AutoTokenizer.from_pretrained(ckpt_path)
+        
         tokenizer.decode(generate(model, [tokenizer.encode("DeepSeek")], 2, -1, 1.)[0])
+        
         load_model(model, os.path.join(ckpt_path, f"model{rank}-mp{world_size}.safetensors"))
         
         if interactive:
             messages = []
             while True:
+                try:
                     if world_size == 1:
                         prompt = input(">>> ")
                     elif rank == 0:
@@ -131,48 +123,51 @@ def main(
                         objects = [None]
                         dist.broadcast_object_list(objects, 0)
                         prompt = objects[0]
+                    
                     if prompt == "/exit":
                         break
                     elif prompt == "/clear":
                         messages.clear()
                         continue
+                    
                     messages.append({"role": "user", "content": prompt})
                     prompt_tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
                     completion_tokens = generate(model, [prompt_tokens], max_new_tokens, tokenizer.eos_token_id, temperature)
                     completion = tokenizer.decode(completion_tokens[0], skip_special_tokens=True)
                     print(completion)
                     messages.append({"role": "assistant", "content": completion})
+                
+                except Exception as e:
+                    print(f"Erro durante a interação: {e}")
+                    continue
+        
         else:
             with open(input_file) as f:
                 prompts = [line.strip() for line in f.readlines()]
+            
             assert len(prompts) <= args.max_batch_size
             prompt_tokens = [tokenizer.apply_chat_template([{"role": "user", "content": prompt}], add_generation_prompt=True) for prompt in prompts]
             completion_tokens = generate(model, prompt_tokens, max_new_tokens, tokenizer.eos_token_id, temperature)
             completions = tokenizer.batch_decode(completion_tokens, skip_special_tokens=True)
+            
             for prompt, completion in zip(prompts, completions):
                 print("Prompt:", prompt)
                 print("Completion:", completion)
                 print()
     
+    except FileNotFoundError as e:
+        print(f"Erro de arquivo: {e}")
+    
+    except Exception as e:
+        print(f"Erro na execução principal: {e}")
+    
+    finally:
         if world_size > 1:
             dist.destroy_process_group()
 
 
 if __name__ == "__main__":
-    """
-    Command-line interface for distributed text generation.
-
-    Arguments:
-        --ckpt-path (str): Path to the model checkpoint directory.
-        --config (str): Path to the model configuration file.
-        --input-file (str, optional): File containing prompts for batch processing.
-        --interactive (bool, optional): Enable interactive mode for generating text.
-        --max-new-tokens (int, optional): Maximum number of new tokens to generate. Defaults to 200.
-        --temperature (float, optional): Temperature for sampling. Defaults to 0.2.
-
-    Raises:
-        AssertionError: If neither input-file nor interactive mode is specified.
-    """
+    try:
         parser = ArgumentParser()
         parser.add_argument("--ckpt-path", type=str, required=True)
         parser.add_argument("--config", type=str, required=True)
@@ -183,3 +178,5 @@ if __name__ == "__main__":
         args = parser.parse_args()
         assert args.input_file or args.interactive
         main(args.ckpt_path, args.config, args.input_file, args.interactive, args.max_new_tokens, args.temperature)
+    except Exception as e:
+        print(f"Erro na execução do script: {e}")