Upload 11 files

added_tokens.json

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config.json

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+}

requirements.txt

@@ -0,0 +1,8 @@
+transformers
+numpy
+scipy
+datasets
+pyarrow
+tqdm
+torch
+bitsandbytes

scorer.py

@@ -0,0 +1,325 @@
+#!/usr/bin/env python3
+import sys
+import os
+
+# Passo 1: Parsear os argumentos de linha de comando antecipadamente para definir variáveis de ambiente
+# Antes de importar transformers, parseamos os argumentos que podem afetar a configuração do modelo
+def early_parse_args():
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Scorer de Qualidade para Datasets do Hugging Face (Early Parsing)")
+    parser.add_argument(
+        "--4bit",
+        action="store_true",
+        dest="fourbit",
+        help="Carrega o modelo em precisão de 4 bits utilizando BitsAndBytes."
+    )
+    # Parse apenas os argumentos necessários para BitsAndBytes
+    args, _ = parser.parse_known_args()
+    return args
+
+early_args = early_parse_args()
+
+# Definir a variável de ambiente antes de importar transformers, se --4bit for usado
+if early_args.fourbit:
+    os.environ["LLM_INT8_ENABLE_FP32_CPU_OFFLOAD"] = "true"
+
+# Passo 2: Importar as bibliotecas restantes após definir variáveis de ambiente
+import argparse
+import json
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import numpy as np
+from scipy.special import softmax
+from datasets import load_dataset
+import pyarrow.parquet as pq
+from tqdm import tqdm
+import torch
+
+def infer_quality(model, tokenizer, input_text, resp_text, device):
+    """
+    Calcula a pontuação de qualidade para uma resposta baseada na entrada e na resposta fornecida.
+    
+    Args:
+        model: O modelo de linguagem causal carregado.
+        tokenizer: O tokenizer correspondente ao modelo.
+        input_text (str): O texto da instrução/pergunta.
+        resp_text (str): O texto da resposta a ser avaliada.
+        device: O dispositivo (CPU ou GPU) para realizar os cálculos.
+    
+    Returns:
+        float: A pontuação de qualidade calculada.
+    """
+    # Template traduzido para português
+    quality_template = (
+        "Você é um assistente útil. Por favor, identifique a pontuação de qualidade da Resposta correspondente à Pergunta.\n"
+        "#Pergunta#:\n{instruction}\n"
+        "#Resposta#:\n{output}\n"
+        "##Qualidade: "
+    )
+    
+    # Formatar o input do usuário
+    user_input = quality_template.format(instruction=input_text, output=resp_text)
+    
+    # Tokenizar a entrada
+    input_ids = tokenizer.encode(user_input, return_tensors="pt").to(device)
+    max_length = 512  # Definir comprimento máximo
+    
+    # Gerar a resposta do modelo
+    with torch.no_grad():
+        outputs = model.generate(
+            input_ids,
+            max_length=max_length,
+            num_return_sequences=1,
+            return_dict_in_generate=True,
+            output_scores=True,
+            do_sample=False  # Desativar amostragem para obter logits determinísticos
+        )
+    
+    # Extrair os scores das saídas
+    scores = outputs.scores  # Tuple of tensors, one for each generated token
+    score_logits = []
+    
+    # Mapeamento de IDs de tokens para pontuações
+    id2score = {
+        29896: "1",  # Token ID para "1"
+        29906: "2",  # Token ID para "2"
+        29941: "3",  # Token ID para "3"
+        29946: "4",  # Token ID para "4"
+        29945: "5",  # Token ID para "5"
+        29953: "6"   # Token ID para "6"
+    }
+    
+    score_template = np.array([1, 2, 3, 4, 5, 6])
+    
+    # Verificar se há scores gerados
+    if not scores:
+        return 0.0  # Retorna 0 se não houver scores
+    
+    # Assumindo que queremos a última posição gerada
+    last_score = scores[-1]  # Último conjunto de logits, shape: (batch_size * num_beams, vocab_size)
+    
+    # Garantir que last_score tenha o formato esperado
+    if last_score.ndim != 2 or last_score.size(0) != 1:
+        print("Formato inesperado para 'last_score'.")
+        return 0.0
+    
+    last_score = last_score[0]  # Shape: (vocab_size,)
+    
+    # Extrair logits para os token_ids específicos
+    for token_id in id2score:
+        if token_id < last_score.size(0):
+            logit = last_score[token_id].item()
+            score_logits.append(logit)
+        else:
+            # Se o token_id estiver fora do alcance, atribuir um valor muito baixo
+            score_logits.append(-1e10)
+    
+    score_logits = np.array(score_logits)
+    
+    # Aplicar softmax para obter probabilidades
+    score_probs = softmax(score_logits)
+    
+    # Calcular a pontuação ponderada
+    quality_score = np.sum(score_probs * score_template)
+    
+    return quality_score
+
+def process_dataset(model, tokenizer, dataset_repo, input_field, output_field, output_dir, device):
+    """
+    Processa um dataset específico, calculando a pontuação de qualidade para cada exemplo e salvando o resultado.
+    
+    Args:
+        model: O modelo de linguagem causal carregado.
+        tokenizer: O tokenizer correspondente ao modelo.
+        dataset_repo (str): O repositório do dataset no Hugging Face.
+        input_field (str): O nome do campo de entrada no dataset.
+        output_field (str): O nome do campo de resposta no dataset.
+        output_dir (str): O diretório onde os datasets processados serão salvos.
+        device: O dispositivo (CPU ou GPU) para realizar os cálculos.
+    """
+    print(f"\nProcessando dataset: {dataset_repo}")
+    try:
+        dataset = load_dataset(dataset_repo)
+    except Exception as e:
+        print(f"Erro ao carregar o dataset {dataset_repo}: {e}")
+        return
+    
+    # Considerar apenas splits que contêm dados (por exemplo, 'train', 'validation', 'test')
+    for split in dataset.keys():
+        split_data = dataset[split]
+        if len(split_data) == 0:
+            continue
+        
+        print(f"  Processando split: {split} com {len(split_data)} exemplos")
+        
+        # Preparar listas para armazenar os scores
+        quality_scores = []
+        
+        # Iterar sobre os exemplos com tqdm para visualizar o progresso
+        for example in tqdm(split_data, desc=f"    Avaliando {split}"):
+            input_text = example.get(input_field, "")
+            output_text = example.get(output_field, "")
+            if not isinstance(input_text, str) or not isinstance(output_text, str):
+                quality_scores.append(0.0)
+                continue
+            score = infer_quality(model, tokenizer, input_text, output_text, device)
+            quality_scores.append(score)
+        
+        # Adicionar a nova coluna ao dataset
+        split_data = split_data.add_column("quality_score", quality_scores)
+        
+        # Definir o caminho de salvamento
+        dataset_name = dataset_repo.split('/')[-1]
+        split_output_dir = os.path.join(output_dir, dataset_name)
+        os.makedirs(split_output_dir, exist_ok=True)
+        output_path = os.path.join(split_output_dir, f"{split}.parquet")
+        
+        # Salvar o dataset como .parquet
+        try:
+            split_data.to_parquet(output_path)
+            print(f"    Salvado em {output_path}")
+        except Exception as e:
+            print(f"    Erro ao salvar {output_path}: {e}")
+
+def verify_token_ids(tokenizer, id2score):
+    """
+    Verifica se os token_ids mapeados correspondem aos tokens corretos.
+    
+    Args:
+        tokenizer: O tokenizer correspondente ao modelo.
+        id2score (dict): Mapeamento de token_id para pontuação.
+    """
+    print("Verificando mapeamento de token IDs:")
+    for token_id, label in id2score.items():
+        try:
+            token = tokenizer.convert_ids_to_tokens(token_id)
+            print(f"Token ID {token_id}: '{token}' -> {label}")
+        except Exception as e:
+            print(f"Erro ao converter token_id {token_id}: {e}")
+
+def main():
+    parser = argparse.ArgumentParser(description="Scorer de Qualidade para Datasets do Hugging Face")
+    parser.add_argument(
+        "--datasets",
+        type=str,
+        required=True,
+        help='Lista de datasets no formato JSON. Exemplo: \'[{{ "repo": "orion-research/gsmqnaoa-pt_BR", "input": "INSTRUCTION", "output": "RESPONSE" }}, {{ "repo": "orion-research/Aura-CoT-Multilang-v1", "input": "input", "output": "output" }}]\''
+    )
+    parser.add_argument(
+        "--output",
+        type=str,
+        required=True,
+        help="Diretório de saída onde os datasets processados serão salvos."
+    )
+    parser.add_argument(
+        "--cpu",
+        action="store_true",
+        help="Força o uso da CPU em vez da GPU."
+    )
+    parser.add_argument(
+        "--4bit",
+        action="store_true",
+        dest="fourbit",
+        help="Carrega o modelo em precisão de 4 bits utilizando BitsAndBytes."
+    )
+    
+    args = parser.parse_args()
+    
+    # Parsear o argumento datasets
+    try:
+        datasets_list = json.loads(args.datasets)
+        if not isinstance(datasets_list, list):
+            raise ValueError("O argumento --datasets deve ser uma lista de objetos JSON.")
+    except json.JSONDecodeError as e:
+        print(f"Erro ao parsear o argumento --datasets: {e}")
+        sys.exit(1)
+    except ValueError as ve:
+        print(ve)
+        sys.exit(1)
+    
+    # Verificar se o diretório de saída existe, senão criar
+    if not os.path.exists(args.output):
+        try:
+            os.makedirs(args.output)
+            print(f"Criado diretório de saída: {args.output}")
+        except Exception as e:
+            print(f"Erro ao criar o diretório de saída {args.output}: {e}")
+            sys.exit(1)
+    
+    # Determinar o dispositivo a ser usado
+    if args.cpu:
+        device = torch.device("cpu")
+        print("Forçando o uso da CPU.")
+    else:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"Usando dispositivo: {device}")
+    
+    # Carregar modelo e tokenizer
+    model_name = "/ors/models/LLM/Orion-Quality-Scorer"
+    print("Carregando tokenizer e modelo...")
+    try:
+        if args.fourbit:
+            # Verificar se bitsandbytes está instalado
+            try:
+                import bitsandbytes as bnb
+            except ImportError:
+                print("Erro: bitsandbytes não está instalado. Instale com 'pip install bitsandbytes'.")
+                sys.exit(1)
+            
+            from transformers import BitsAndBytesConfig
+            
+            # Configuração para 4-bit
+            bnb_config = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_use_double_quant=True,
+                bnb_4bit_quant_type="nf4",
+                bnb_4bit_compute_dtype=torch.float16
+            )
+            
+            # Definir o device_map para permitir offloading
+            device_map = "auto" if not args.cpu else {"": "cpu"}
+            
+            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            model = AutoModelForCausalLM.from_pretrained(
+                model_name,
+                quantization_config=bnb_config,
+                device_map=device_map,
+                trust_remote_code=True  # Se o modelo requer código remoto
+            )
+        else:
+            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            model = AutoModelForCausalLM.from_pretrained(model_name)
+            model.to(device)
+        
+        model.eval()
+        print("Modelo e tokenizer carregados com sucesso.")
+        
+        # Verificar mapeamento de token IDs
+        verify_token_ids(tokenizer, {
+            29896: "1",
+            29906: "2",
+            29941: "3",
+            29946: "4",
+            29945: "5",
+            29953: "6"
+        })
+    except Exception as e:
+        print(f"Erro ao carregar o modelo ou tokenizer: {e}")
+        sys.exit(1)
+    
+    # Processar cada dataset
+    for dataset_info in datasets_list:
+        repo = dataset_info.get("repo")
+        input_field = dataset_info.get("input")
+        output_field = dataset_info.get("output")
+        
+        if not repo or not input_field or not output_field:
+            print(f"Informações incompletas para o dataset: {dataset_info}. Pulando...")
+            continue
+        
+        process_dataset(model, tokenizer, repo, input_field, output_field, args.output, device)
+    
+    print("\nProcessamento concluído.")
+
+if __name__ == "__main__":
+    main()

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}