Upload mteb_eval_openai.py

mteb_eval_openai.py

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+import os
+import sys
+import time
+import hashlib
+import numpy as np
+import requests
+
+import logging
+import functools
+import tiktoken
+from tqdm import tqdm
+from mteb import MTEB
+#from sentence_transformers import SentenceTransformer
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("main")
+
+all_task_list = ['Classification', 'Clustering', 'Reranking', 'Retrieval', 'STS', 'PairClassification']
+if len(sys.argv) > 1:
+    task_list = [t for t in sys.argv[1].split(',') if t in all_task_list]
+else:
+    task_list = all_task_list
+
+OPENAI_BASE_URL = os.environ.get('OPENAI_BASE_URL', '')
+OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY', '')
+EMB_CACHE_DIR = os.environ.get('EMB_CACHE_DIR', '.cache/embs')
+REQ_OPENAI_TIMEOUT = int(os.environ.get('REQ_OPENAI_TIMEOUT', 120))
+REQ_OPENAI_RETRY = int(os.environ.get('REQ_OPENAI_RETRY', 3))
+REQ_OPENAI_INTERVAL = int(os.environ.get('REQ_OPENAI_INTERVAL', 60))
+os.makedirs(EMB_CACHE_DIR, exist_ok=True)
+
+def log(*args):
+    print(*args, file=sys.stderr)
+
+def uuid_for_text(text):
+    return hashlib.md5(text.encode('utf8')).hexdigest()
+
+def count_openai_tokens(text, model="text-embedding-3-large"):
+    encoding = tiktoken.get_encoding("cl100k_base")
+    #encoding = tiktoken.encoding_for_model(model)
+    input_ids = encoding.encode(text)
+    return len(input_ids)
+
+def request_openai_emb(texts, model="text-embedding-3-large", 
+        base_url='https://api.openai.com', prefix_url='/v1/embeddings', 
+        timeout=4, retry=3, interval=2, caching=True):
+    if isinstance(texts, str):
+        texts = [texts]
+
+    data = []
+    if caching:
+        for text in texts:
+            emb_file = f"{EMB_CACHE_DIR}/{uuid_for_text(text)}"
+            if os.path.isfile(emb_file) and os.path.getsize(emb_file) > 0:
+                data.append(np.loadtxt(emb_file))
+        if len(texts) == len(data):
+            return data
+
+    url = f"{OPENAI_BASE_URL}{prefix_url}" if OPENAI_BASE_URL else f"{base_url}{prefix_url}"
+    headers = {
+        "Authorization": f"Bearer {OPENAI_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    payload = {"input": texts, "model": model}
+
+    data = []
+    while retry > 0 and len(data) == 0:
+        try:
+            r = requests.post(url, headers=headers, json=payload, 
+                timeout=timeout)
+            res = r.json()
+            for x in res["data"]:
+                data.append(np.array(x["embedding"]))
+        except Exception as e:
+            log(f"request openai, retry {retry}, error: {e}")
+        time.sleep(interval)
+        retry -= 1
+
+    if len(data) != len(texts):
+        log(f"request openai, failed, texts and embs DONT match!")
+        return []
+
+    if caching and len(data) > 0:
+        for text, emb in zip(texts, data):
+            emb_file = f"{EMB_CACHE_DIR}/{uuid_for_text(text)}"
+            np.savetxt(emb_file, emb)
+
+    return data
+
+
+class OpenaiEmbModel:
+
+    def __init__(self, model_name, model_dim, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.model_name = model_name
+        self.model_dim = model_dim
+
+    def encode(self, sentences, batch_size=32, **kwargs):
+        i = 0
+        max_tokens = kwargs.get("max_tokens", 8000)
+        batch_tokens = 0
+        batch = []
+        batch_list = []
+        while i < len(sentences):
+            num_tokens = count_openai_tokens(sentences[i], 
+                model=self.model_name)
+            if batch_tokens+num_tokens > max_tokens:
+                if batch:
+                    batch_list.append(batch)
+                    if num_tokens > max_tokens:
+                        batch = [sentences[i][:2048]]
+                        batch_tokens = count_openai_tokens(sentences[i][:2048],
+                            model=self.model_name)
+                    else:
+                        batch = [sentences[i]]
+                        batch_tokens = num_tokens
+                else:
+                    batch_list.append([sentences[i][:2048]])
+            else:
+                batch.append(sentences[i])
+                batch_tokens += num_tokens
+            i += 1
+        if batch:
+            batch_list.append(batch)
+
+        #batch_size = min(64, batch_size)
+        #
+        #for i in range(0, len(sentences), batch_size):
+        #    batch_texts = sentences[i:i+batch_size]
+        #    batch_list.append(batch_texts)
+
+        log(f"Total sentences={len(sentences)}, batches={len(batch_list)}")
+        embs = []
+        waiting = 0
+        for batch_idx, batch_texts in enumerate(tqdm(batch_list)):
+            batch_embs = request_openai_emb(batch_texts, model=self.model_name,
+                caching=kwargs.get("caching", True), 
+                timeout=kwargs.get("timeout", REQ_OPENAI_TIMEOUT), 
+                retry=kwargs.get("retry", REQ_OPENAI_RETRY), 
+                interval=kwargs.get("interval", REQ_OPENAI_INTERVAL))
+
+            if len(batch_texts) == len(batch_embs):
+                embs.extend(batch_embs)
+                waiting = waiting // 2
+                log(f"The batch-{batch_idx} encoding SUCCESS! waiting={waiting}s...")
+            else:
+                embs.extend([np.array([0.0 for j in range(self.model_dim)]) for i in range(len(batch_texts))])
+                waiting = 120 if waiting <= 0 else waiting+120
+                log(f"The batch-{batch_idx} encoding FAILED {len(batch_texts)}:{len(batch_embs)}! waiting={waiting}s...")
+
+            if waiting > 3600:
+                log(f"Frequently failed, should be waiting more then 3600s, break down!!!")
+                break
+            if waiting > 0:
+                time.sleep(waiting)
+
+        print(f'Total encoding sentences={len(sentences)}, embeddings={len(embs)}')
+        return embs
+
+
+model_name = "text-embedding-3-large"
+model_dim = 3072
+model = OpenaiEmbModel(model_name, model_dim)
+
+######
+# test
+#####
+#embs = model.encode(['全国', '北京'])
+#print(embs)
+#exit()
+
+# languages
+task_langs=["zh", "zh-CN"]
+
+evaluation = MTEB(task_types=task_list, task_langs=task_langs)
+evaluation.run(model, output_folder=f"results/zh/{model_name.split('/')[-1]}")