Merge pull request #1 from mMrBun/main

Support conversation via API.

src/api.py

@@ -0,0 +1,115 @@
+# coding=utf-8
+# Chat with LLaMA in API mode.
+# Usage: python cli_demo.py --model_name_or_path path_to_model --checkpoint_dir path_to_checkpoint
+
+# Call:
+# curl --location 'http://127.0.0.1:8000' \
+# --header 'Content-Type: application/json' \
+# --data '{"prompt": "Hello there!","history": []}'
+
+# Response:
+# {
+# "response":"'I am a second year student at the University of British Columbia, in Vancouver.\\nMy major
+# is Computer Science and my minor (double degree) area was Mathematics/Statistics with an emphasis on Operations
+# Research & Management Sciences which means that when it comes to solving problems using computers or any kind data
+# analysis; whether its from businesses , governments etc., i can help you out :) .'",
+# "history":"[('Hello there!',
+# 'I am a second year student at the University of British Columbia, in Vancouver.\\nMy major is Computer Science and
+# my minor (double degree) area was Mathematics/Statistics with an emphasis on Operations Research & Management
+# Sciences which means that when it comes to solving problems using computers or any kind data analysis; whether its
+# from businesses , governments etc., i can help you out :) .')]",
+# "status":200,
+# "time":"2023-05-30 06:57:38" }
+
+import datetime
+import torch
+from utils import ModelArguments, auto_configure_device_map, load_pretrained
+from transformers import HfArgumentParser
+import json
+import uvicorn
+from fastapi import FastAPI, Request
+
+DEVICE = "cuda"
+
+
+def torch_gc():
+    if torch.cuda.is_available():
+        num_gpus = torch.cuda.device_count()
+        for device_id in range(num_gpus):
+            with torch.cuda.device(device_id):
+                torch.cuda.empty_cache()
+                torch.cuda.ipc_collect()
+
+
+app = FastAPI()
+
+
+@app.post("/")
+async def create_item(request: Request):
+    global model, tokenizer
+
+    # Parse the request JSON
+    json_post_raw = await request.json()
+    json_post = json.dumps(json_post_raw)
+    json_post_list = json.loads(json_post)
+    prompt = json_post_list.get('prompt')
+    history = json_post_list.get('history')
+
+    # Tokenize the input prompt
+    inputs = tokenizer([prompt], return_tensors="pt")
+    inputs = inputs.to(model.device)
+
+    # Generation arguments
+    gen_kwargs = {
+        "do_sample": True,
+        "top_p": 0.9,
+        "top_k": 40,
+        "temperature": 0.7,
+        "num_beams": 1,
+        "max_new_tokens": 256,
+        "repetition_penalty": 1.5
+    }
+
+    # Generate response
+    with torch.no_grad():
+        generation_output = model.generate(**inputs, **gen_kwargs)
+    outputs = generation_output.tolist()[0][len(inputs["input_ids"][0]):]
+    response = tokenizer.decode(outputs, skip_special_tokens=True)
+
+    # Update history
+    history = history + [(prompt, response)]
+
+    # Prepare response
+    now = datetime.datetime.now()
+    time = now.strftime("%Y-%m-%d %H:%M:%S")
+    answer = {
+        "response": repr(response),
+        "history": repr(history),
+        "status": 200,
+        "time": time
+    }
+
+    # Log and clean up
+    log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
+    print(log)
+    torch_gc()
+
+    return answer
+
+
+if __name__ == "__main__":
+    parser = HfArgumentParser(ModelArguments)
+    model_args, = parser.parse_args_into_dataclasses()
+    model, tokenizer = load_pretrained(model_args)
+
+    if torch.cuda.device_count() > 1:
+        from accelerate import dispatch_model
+
+        device_map = auto_configure_device_map(torch.cuda.device_count())
+        model = dispatch_model(model, device_map)
+    else:
+        model = model.cuda()
+
+    model.eval()
+
+    uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)