update mteb eval

FlagEmbedding/abc/evaluation/searcher.py

@@ -138,6 +138,9 @@ def __call__(
             (not os.path.exists(os.path.join(corpus_embd_save_dir, "doc.npy")) or self.overwrite):
             os.makedirs(corpus_embd_save_dir, exist_ok=True)
             np.save(os.path.join(corpus_embd_save_dir, "doc.npy"), corpus_emb)
+
+        gc.collect()
+        torch.cuda.empty_cache()
 
         faiss_index = index(corpus_embeddings=corpus_emb)
         all_scores, all_indices = search(query_embeddings=queries_emb, faiss_index=faiss_index, k=self.search_top_k)

FlagEmbedding/abc/inference/AbsEmbedder.py

@@ -264,8 +264,7 @@ def encode(
         return embeddings
 
     def __del__(self):
-        if self.pool is not None:
-            self.stop_multi_process_pool(self.pool)
+        self.stop_self_pool()
 
     @abstractmethod
     def encode_single_device(

FlagEmbedding/abc/inference/AbsReranker.py

@@ -210,8 +210,7 @@ def compute_score(
         return scores
 
     def __del__(self):
-        if self.pool is not None:
-            self.stop_multi_process_pool(self.pool)
+        self.stop_self_pool()
 
     @abstractmethod
     def compute_score_single_gpu(

FlagEmbedding/evaluation/mteb/runner.py

@@ -65,14 +65,15 @@ def read_results(self, output_folder, tasks):
                         print('ERROR')
                         break
 
-                    temp_data = data['scores'][split][0]
-
-                    if metric == 'ap':
-                        tasks_results[t_type][task_name] = round(temp_data['cos_sim']['ap'] * 100, 2)
-                    elif metric == 'cosine_spearman':
-                        tasks_results[t_type][task_name] = round(temp_data['cos_sim']['spearman'] * 100, 2)
-                    else:
-                        tasks_results[t_type][task_name] = round(temp_data[metric] * 100, 2)
+                    temp_datas = data['scores'][split][0]
+                    temp_data = None
+                    for td in temp_datas:
+                        if td['hf_subset'] == 'default':
+                            temp_data = td
+                    if temp_data is None:
+                        temp_data = temp_datas[0]
+                    tasks_results[t_type][task_name] = round(temp_data['main_score'] * 100, 2)
+
         print(f"tasks_results: {tasks_results}")
         return tasks_results
 
@@ -119,16 +120,13 @@ def run(self):
             task_types=task_types
         )
         output_folder = self.eval_args.output_dir
-        new_tasks = []
-        for task in tasks:
-            if task.languages is not None:
-                if len(task.languages) == len([e for e in languages if e in task.languages]):
-                    new_tasks.append(task)
 
-        for task in new_tasks:
+        for task in tasks:
             task_name = task.metadata.name
             task_type = task.metadata.type
 
+            self.retriever.stop_pool()
+
             if self.eval_args.use_special_instructions:
                 try:
                     instruction = get_task_def_by_task_name_and_type(task_name, task_type)

FlagEmbedding/evaluation/mteb/searcher.py

@@ -1,3 +1,5 @@
+import numpy as np
+
 from typing import List, Dict, Optional
 from FlagEmbedding.abc.evaluation import EvalDenseRetriever, EvalReranker
 
@@ -18,11 +20,18 @@ def get_instruction(self):
     def set_normalize_embeddings(self, normalize_embeddings: bool = True):
         self.embedder.normalize_embeddings = normalize_embeddings
 
+    def stop_pool(self):
+        self.embedder.stop_self_pool()
+        try:
+            self.embedder.stop_self_query_pool()
+        except:
+            pass
+
     def encode_queries(self, queries: List[str], **kwargs):
         emb = self.embedder.encode_queries(queries)
         if isinstance(emb, dict):
             emb = emb["dense_vecs"]
-        return emb
+        return emb.astype(np.float32)
 
     def encode_corpus(self, corpus: List[Dict[str, str]], **kwargs):
         if isinstance(corpus[0], dict):
@@ -32,7 +41,7 @@ def encode_corpus(self, corpus: List[Dict[str, str]], **kwargs):
         emb = self.embedder.encode_corpus(input_texts)
         if isinstance(emb, dict):
             emb = emb["dense_vecs"]
-        return emb
+        return emb.astype(np.float32)
 
     def encode(self, corpus: List[Dict[str, str]], **kwargs):
         if isinstance(corpus[0], dict):
@@ -42,7 +51,7 @@ def encode(self, corpus: List[Dict[str, str]], **kwargs):
         emb = self.embedder.encode_queries(input_texts)
         if isinstance(emb, dict):
             emb = emb["dense_vecs"]
-        return emb
+        return emb.astype(np.float32)
 
 class MTEBEvalReranker(EvalReranker):
     def __init__(self, reranker, **kwargs):

FlagEmbedding/inference/embedder/decoder_only/icl.py

@@ -4,6 +4,7 @@
 import queue
 from multiprocessing import Queue
 
+import gc
 import torch
 import numpy as np
 from transformers import AutoModel, AutoTokenizer
@@ -121,10 +122,8 @@ def __init__(
         self.query_pool = None
 
     def __del__(self):
-        if self.pool is not None:
-            self.stop_multi_process_pool(self.pool)
-        if self.query_pool is not None:
-            self.stop_multi_process_pool(self.query_pool)
+        self.stop_self_pool()
+        self.stop_self_query_pool()
 
     def set_examples(self, examples_for_task: Optional[List[dict]] = None):
         """Set the prefix to the provided examples.
@@ -175,6 +174,14 @@ def get_detailed_example(instruction_format: str, instruction: str, query: str,
         """
         return instruction_format.format(instruction, query, response)
 
+    def stop_self_query_pool(self):
+        if self.query_pool is not None:
+            self.stop_multi_process_pool(self.query_pool)
+            self.query_pool = None
+        self.model.to('cpu')
+        gc.collect()
+        torch.cuda.empty_cache()
+
     def encode_queries(
         self,
         queries: Union[List[str], str],
@@ -209,9 +216,7 @@ def encode_queries(
                 **kwargs
             )
 
-        if self.pool is not None:
-            self.stop_multi_process_pool(self.pool)
-            self.pool = None
+        self.stop_self_pool()
         if self.query_pool is None:
             self.query_pool = self.start_multi_process_pool(ICLLLMEmbedder._encode_queries_multi_process_worker)
         embeddings = self.encode_multi_process(
@@ -244,9 +249,7 @@ def encode_corpus(
         Returns:
             Union[torch.Tensor, np.ndarray]: Return the embedding vectors in a numpy array or tensor.
         """
-        if self.query_pool is not None:
-            self.stop_multi_process_pool(self.query_pool)
-            self.query_pool = None
+        self.stop_self_query_pool()
         return super().encode_corpus(
             corpus,
             batch_size=batch_size,