bug fixed

perf.py

@@ -5,12 +5,32 @@ import faiss
 import numpy as np
 import random
 import string
+import psutil
+import os
+# import pynvml
+
+# 初始化 GPU 监控
+# pynvml.nvmlInit()
+# gpu_handle = pynvml.nvmlDeviceGetHandleByIndex(0)  # 默认用第一个GPU
+
+def get_memory_info():
+    process = psutil.Process(os.getpid())
+    mem_info = process.memory_info()
+    return mem_info.rss / 1024 / 1024  # 单位 MB
+
+def get_gpu_memory_info():
+    # mem = pynvml.nvmlDeviceGetMemoryInfo(gpu_handle)
+    # used = mem.used / 1024 / 1024  # MB
+    # total = mem.total / 1024 / 1024
+    # return used, total
+    return 0, 0
+
 
 # 1. 初始化模型
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
 # 2. 模拟一个更大的错误数据库
-NUM_ERRORS = 1000
+NUM_ERRORS = 100
 error_db_large = []
 
 def generate_random_traceback(num_lines=10):
@@ -61,20 +81,32 @@ for entry in error_db_large:
         "original_error": entry["error"],
         "solution": entry["solution"]
     })
+
 cleaning_time = time.time() - start_time
 print(f"清洗 {NUM_ERRORS} 条错误信息耗时: {cleaning_time:.4f} 秒")
+print(f"内存使用: {get_memory_info():.2f} MB")
+gpu_used, gpu_total = get_gpu_memory_info()
+print(f"GPU 显存使用: {gpu_used:.2f} / {gpu_total:.2f} MB")
 
 # 5. 向量化清洗后的错误信息并构建索引
 start_time = time.time()
 embeddings_large = np.array([model.encode(e['cleaned_error']) for e in cleaned_error_db_large])
 embedding_time = time.time() - start_time
 print(f"向量化 {NUM_ERRORS} 条错误信息耗时: {embedding_time:.4f} 秒")
+print(f"内存使用: {get_memory_info():.2f} MB")
+gpu_used, gpu_total = get_gpu_memory_info()
+print(f"GPU 显存使用: {gpu_used:.2f} / {gpu_total:.2f} MB")
+
 
 start_time = time.time()
 index_large = faiss.IndexFlatL2(embeddings_large.shape[1])
 index_large.add(embeddings_large)
 index_building_time = time.time() - start_time
 print(f"构建包含 {NUM_ERRORS} 条向量的 FAISS 索引耗时: {index_building_time:.4f} 秒")
+print(f"内存使用: {get_memory_info():.2f} MB")
+gpu_used, gpu_total = get_gpu_memory_info()
+print(f"GPU 显存使用: {gpu_used:.2f} / {gpu_total:.2f} MB")
+
 
 # 6. 用户报错（测试查询）
 user_error_performance = generate_random_traceback(num_lines=12)
@@ -82,23 +114,38 @@ cleaned_user_error_performance = clean_traceback(user_error_performance)
 query_vec_performance = model.encode(cleaned_user_error_performance)
 query_vec_performance = np.array([query_vec_performance])
 
-# 7. 在索引中搜索最相似的错误
-start_time = time.time()
-D_performance, I_performance = index_large.search(query_vec_performance, k=1)
-search_time = time.time() - start_time
-print(f"在包含 {NUM_ERRORS} 条向量的 FAISS 索引中搜索耗时: {search_time:.6f} 秒")
 
-# 8. 输出结果
+NUM_QUERIES = 100  # 查询次数
-matched_index_performance = I_performance[0][0]
+k = 10  # 每次查询返回前 k 个最相似的错误
-distance_performance = D_performance[0][0]
-cosine_similarity_performance = 1 - (distance_performance**2) / 2
-matched_entry_performance = cleaned_error_db_large[matched_index_performance]
 
-print("\n--- 性能测试结果 ---")
+query_times = []
-print("\n--- 清洗后的用户错误信息 ---")
+similarities = []
-print(cleaned_user_error_performance)
+
-print("\n--- 最相似的已知错误信息 ---")
+for i in range(NUM_QUERIES):
-print(matched_entry_performance['cleaned_error'])
+    user_error = generate_random_traceback(num_lines=random.randint(8, 14))
-print("\n--- 建议的解决方案 ---")
+    cleaned_user_error = clean_traceback(user_error)
-print(matched_entry_performance['solution'])
+    query_vec = model.encode(cleaned_user_error)
-print(f"\n--- 匹配度 (Cosine Similarity): {cosine_similarity_performance:.4f}")
+    query_vec = np.array([query_vec])
+
+    start_query_time = time.time()
+    D, I = index_large.search(query_vec, k=k)
+    elapsed = time.time() - start_query_time
+    query_times.append(elapsed)
+
+    matched_index = I[0][0]
+    distance = D[0][0]
+    similarity = 1 - (distance ** 2) / 2
+    similarities.append(similarity)
+
+# 输出统计结果
+print(f"\n--- 多次查询性能测试 ---")
+print(f"总查询次数: {NUM_QUERIES}")
+print(f"平均查询耗时: {np.mean(query_times) * 1000:.4f} ms")
+print(f"最小查询耗时: {np.min(query_times) * 1000:.4f} ms")
+print(f"最大查询耗时: {np.max(query_times) * 1000:.4f} ms")
+print(f"查询耗时标准差: {np.std(query_times) * 1000:.4f} ms")
+print(f"平均匹配相似度: {np.mean(similarities):.4f}")
+print(f"相似度标准差: {np.std(similarities):.4f}")
+print(f"内存使用: {get_memory_info():.2f} MB")
+gpu_used, gpu_total = get_gpu_memory_info()
+print(f"GPU 显存使用: {gpu_used:.2f} / {gpu_total:.2f} MB")

vector_tests.py

@@ -90,7 +90,7 @@ for entry, agg_vec in zip(error_db, aggregate_vectors):
 
     layered_error_db.append({
         "layers": layers,
-        "vectors": embeddings,  # 保留原始向量（可选）
+        "vectors": embeddings,  # 保留原始向量
         "index": index,         # 添加 FAISS 索引
         "solution": entry["solution"],
         "agg_vector": agg_vec   # 添加聚合向量
@@ -105,9 +105,9 @@ logging.info(f"创建数据库耗时：{end_db_time - start_db_time:.2f} 秒")
 # 完全不同
 # user_error = """"Traceback (most recent call last):\n  File \"D:\\Develop\\Projects\\bash-hook\\matches\\perf_2.py\", line 94, in <module>\n    D, I = index.search(query_vec, k=1)\n  File \"C:\\Users\\qcqcqc\\.conda\\envs\\sentence_transformers\\lib\\site-packages\\faiss\\class_wrappers.py\", line 329, in replacement_search\n    assert d == self.d\nAssertionError\n"""
 # 底层不同
-user_error = """Traceback (most recent call last):\n  File \"image_error.py\", line 55, in <module>\n    loss = criterion(outputs, labels)\n  File \"module.py\", line 1553, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n  File \"module.py\", line 1570, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"loss.py\", line 1200, in forward\n    return F.mse_loss(input, target, reduction=self.reduction)\n  File \"functional.py\", line 2301, in mse_loss\n    return mean_squared_error_loss(input, target, ...)\nValueError: The size of input tensor must match the size of target tensor.\n"""
+# user_error = """Traceback (most recent call last):\n  File \"image_error.py\", line 55, in <module>\n    loss = criterion(outputs, labels)\n  File \"module.py\", line 1553, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n  File \"module.py\", line 1570, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"loss.py\", line 1200, in forward\n    return F.mse_loss(input, target, reduction=self.reduction)\n  File \"functional.py\", line 2301, in mse_loss\n    return mean_squared_error_loss(input, target, ...)\nValueError: The size of input tensor must match the size of target tensor.\n"""
 # 顶层不同
-# user_error = """Traceback (most recent call last):\n  File \"train_model.py\", line 72, in <module>\n    loss = loss_function(predictions, ground_truth)\n  File \"core.py\", line 442, in _call_function\n    return self._execute(*args, **kwargs)\n  File \"core.py\", line 450, in _execute\n    return forward_execution(*args, **kwargs)\n  File \"loss_functions.py\", line 205, in forward\n    return F.cross_entropy(input, target, weight=self.weight,\n  File \"functional.py\", line 3104, in cross_entropy\n    return cross_entropy_loss(input, target, ...)\nIndexError: Target 15 is out of bounds.\n"""
+user_error = """Traceback (most recent call last):\n  File \"train_model.py\", line 72, in <module>\n    loss = loss_function(predictions, ground_truth)\n  File \"core.py\", line 442, in _call_function\n    return self._execute(*args, **kwargs)\n  File \"core.py\", line 450, in _execute\n    return forward_execution(*args, **kwargs)\n  File \"loss_functions.py\", line 205, in forward\n    return F.cross_entropy(input, target, weight=self.weight,\n  File \"functional.py\", line 3104, in cross_entropy\n    return cross_entropy_loss(input, target, ...)\nIndexError: Target 15 is out of bounds.\n"""
 # 无错误信息
 # user_error = "success."
 
@@ -134,11 +134,12 @@ def compute_layered_similarity_avg(user_vecs, db_index, layer_weights=None):
 
 
 def compute_layered_similarity_sco(user_vecs, db_index, layer_weights=None):
-    score = 0.0
     weighted_score = 0.0
 
     if layer_weights is None:
-        layer_weights = np.linspace(0.1, 1, len(user_vecs))  # 默认从浅层到深层递增权重
+        # 使用对数函数生成非线性权重
+        layer_weights = np.logspace(0, 1, len(user_vecs))  # 从浅层到深层递增权重
+        layer_weights /= np.sum(layer_weights)  # 归一化权重，使其和为1
 
     for i, u_vec in enumerate(user_vecs):
         u_vec = u_vec.astype('float32').reshape(1, -1)
@@ -147,7 +148,7 @@ def compute_layered_similarity_sco(user_vecs, db_index, layer_weights=None):
 
         weighted_score += max_sim * layer_weights[i]
 
-    return weighted_score / np.sum(layer_weights) if len(user_vecs) > 0 else 0.0
+    return weighted_score if len(user_vecs) > 0 else 0.0
 
 
 # 计时：查询