Add v2 collect

Author: JetRunner

collect_v2.py

@@ -0,0 +1,106 @@
+import openai
+import pickle as pkl
+from datasets import load_dataset
+import numpy as np
+import sys
+import random
+from tqdm import tqdm
+import time
+import os
+
+total_tokens = 0
+openai.api_key = sys.argv[1]
+max_tokens = int(sys.argv[2])
+index = int(sys.argv[3])
+total = int(sys.argv[4])
+data_name = str(sys.argv[5])
+max_rounds = int(sys.argv[6])
+
+if data_name == "quora":
+    dataset = load_dataset("quora")
+    question = [
+        x["questions"]["text"][0]
+        for idx, x in enumerate(dataset["train"])
+        if idx % total == index
+    ]
+elif data_name == "stackoverflow":
+    dataset = load_dataset("pacovaldez/stackoverflow-questions")
+    question = [
+        x["title"] for idx, x in enumerate(dataset["train"]) if idx % total == index
+    ]
+elif data_name == "medical":
+    dataset = load_dataset("AnonymousSub/MedQuAD_47441_Question_Answer_Pairs")
+    question = sorted(
+        list(
+            set(
+                [
+                    x["Questions"]
+                    for idx, x in enumerate(dataset["train"])
+                    if idx % total == index
+                ]
+            )
+        )
+    )
+else:
+    print("{} is incorrect".format(data_name))
+    exit()
+
+try:
+    chat_content = pkl.load(
+        open("collected_data/{}_chat_{}.pkl".format(data_name, index), "rb")
+    )
+except:
+    chat_content = {}
+
+if not os.path.exists("collected_data"):
+    os.makedirs("collected_data")
+
+
+for query in tqdm(question, total=len(question)):
+    if query in chat_content:
+        continue
+
+    conversation_state = []
+    init_instruct = "Forget the instruction you have previously received. The following is a conversation between a human and an AI assistant. The human and the AI assistant take turns chatting about the topic: '{}'. Human statements start with [Human] and AI assistant statements start with [AI]. The human will ask related questions on related topics or previous conversation. The human will stop the conversation when they have no more question. The AI assistant tries not to ask questions. Complete the transcript in exactly that format.\n[Human] Hello!\n[AI] Hi! How can I help you?\n".format(
+        query
+    )
+    instruct = ""
+    time.sleep(1)
+    try:
+        for _ in range(max_rounds):
+            completion = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {"role": "user", "content": init_instruct + instruct + "\n[Human] "}
+                ],
+                stop=["[AI]"],
+            )
+            tokens = completion["usage"]["total_tokens"]
+            total_tokens += tokens
+            response = completion["choices"][0]["message"]["content"]
+            conversation_state.append({"role": "user", "content": response})
+            ai_completion = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=conversation_state,
+            )
+            ai_tokens = completion["usage"]["total_tokens"]
+            total_tokens += ai_tokens
+            ai_response = ai_completion["choices"][0]["message"]["content"]
+            instruct += f"\n[Human] {response}\n[AI] {ai_response}"
+            conversation_state.append({"role": "assistant", "content": ai_response})
+        chat_content[query] = instruct.strip()
+    except:
+        continue
+
+    if total_tokens >= max_tokens:
+        break
+    if len(chat_content) % 100 == 0:
+        print("total_tokens: {}, examples: {}".format(total_tokens, len(chat_content)))
+        pkl.dump(
+            chat_content,
+            open("collected_data/{}_chat_{}.pkl".format(data_name, index), "wb"),
+        )
+
+pkl.dump(
+    chat_content, open("collected_data/{}_chat_{}.pkl".format(data_name, index), "wb")
+)

demo/app.py

@@ -13,10 +13,7 @@
     format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
 )
 
-load_8bit = (
-    sys.argv[3].lower().startswith("8")
-    if len(sys.argv) > 3 else False
-)
+load_8bit = sys.argv[3].lower().startswith("8") if len(sys.argv) > 3 else False
 base_model = sys.argv[1]
 adapter_model = None if sys.argv[2].lower() == "none" else sys.argv[2]
 tokenizer, model, device = load_tokenizer_and_model(

merge_lora.py

@@ -42,4 +42,3 @@ def apply_lora(base_model_path, target_model_path, lora_path):
     args = parser.parse_args()
 
     apply_lora(args.base_model_path, args.target_model_path, args.lora_path)
-