268 lines
12 KiB
Python
268 lines
12 KiB
Python
import ctypes
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import sys
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import os
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import subprocess
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import resource
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import threading
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import time
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import argparse
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import json
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from flask import Flask, request, jsonify, Response
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from rkllm import *
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app = Flask(__name__,static_url_path='',static_folder='static')
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@app.route('/')
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def root():
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return app.send_static_file('index.html')
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PROMPT_TEXT_PREFIX = "<|im_start|>system\nYou are a helpful assistant. You only give short answers.<|im_end|>\n<|im_start|>user\n"
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PROMPT_TEXT_POSTFIX = "<|im_end|>\n<|im_start|>assistant\n"
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MSG_START_TOKEN = "<|im_start|>" # there work for Qwen, miniCPM and deepseek, but not for chatglm3
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MSG_END_TOKEN = "<|im_end|>"
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def msg_to_prompt(user, msg):
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return f'{MSG_START_TOKEN}{user}\n{msg}{MSG_END_TOKEN}\n'
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def msgs_to_prompt(msgs: list[dict]):
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system = msgs[0] if msgs and msgs[0]['role'] == 'system' else ""
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if not system:
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msgs.insert(0, {'role':'system', 'content': "You are a helpful assistant. You only give short but complete answers."})
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return (''.join(msg_to_prompt(msg['role'], msg['content']) for msg in msgs) +
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f'{MSG_START_TOKEN}assistant\n')
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# Create a lock to control multi-user access to the server.
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lock = threading.Lock()
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# Create a global variable to indicate whether the server is currently in a blocked state.
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is_blocking = False
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# Define global variables to store the callback function output for displaying in the Gradio interface
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global_text = []
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global_state = -1
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split_byte_data = bytes(b"") # Used to store the segmented byte data
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global_abort = False
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# Define the callback function
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def callback_impl(result, userdata, state):
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global global_text, global_state, split_byte_data
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if state == LLMCallState.RKLLM_RUN_FINISH:
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global_state = state
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print("\n")
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sys.stdout.flush()
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elif state == LLMCallState.RKLLM_RUN_ERROR:
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global_state = state
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print("run error")
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sys.stdout.flush()
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elif state == LLMCallState.RKLLM_RUN_GET_LAST_HIDDEN_LAYER:
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'''
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If using the GET_LAST_HIDDEN_LAYER function, the callback interface will return the memory pointer: last_hidden_layer, the number of tokens: num_tokens, and the size of the hidden layer: embd_size.
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With these three parameters, you can retrieve the data from last_hidden_layer.
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Note: The data needs to be retrieved during the current callback; if not obtained in time, the pointer will be released by the next callback.
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'''
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if result.last_hidden_layer.embd_size != 0 and result.last_hidden_layer.num_tokens != 0:
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data_size = result.last_hidden_layer.embd_size * result.last_hidden_layer.num_tokens * ctypes.sizeof(ctypes.c_float)
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print(f"data_size: {data_size}")
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global_text.append(f"data_size: {data_size}\n")
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output_path = os.getcwd() + "/last_hidden_layer.bin"
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with open(output_path, "wb") as outFile:
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data = ctypes.cast(result.last_hidden_layer.hidden_states, ctypes.POINTER(ctypes.c_float))
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float_array_type = ctypes.c_float * (data_size // ctypes.sizeof(ctypes.c_float))
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float_array = float_array_type.from_address(ctypes.addressof(data.contents))
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outFile.write(bytearray(float_array))
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print(f"Data saved to {output_path} successfully!")
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global_text.append(f"Data saved to {output_path} successfully!")
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else:
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print("Invalid hidden layer data.")
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global_text.append("Invalid hidden layer data.")
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global_state = state
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time.sleep(0.05) # Delay for 0.05 seconds to wait for the output result
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sys.stdout.flush()
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else:
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# Save the output token text and the RKLLM running state
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global_state = state
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# Monitor if the current byte data is complete; if incomplete, record it for later parsing
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try:
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global_text.append((split_byte_data + result.contents.text).decode('utf-8'))
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print((split_byte_data + result.contents.text).decode('utf-8'), end='')
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split_byte_data = bytes(b"")
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except:
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split_byte_data += result.contents.text
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sys.stdout.flush()
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# Connect the callback function between the Python side and the C++ side
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callback = callback_type(callback_impl)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--rkllm_model_path', type=str, required=True, help='Absolute path of the converted RKLLM model on the Linux board;')
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parser.add_argument('--target_platform', type=str, required=True, help='Target platform: e.g., rk3588/rk3576;')
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parser.add_argument('--lora_model_path', type=str, help='Absolute path of the lora_model on the Linux board;')
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parser.add_argument('--prompt_cache_path', type=str, help='Absolute path of the prompt_cache file on the Linux board;')
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args = parser.parse_args()
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if not os.path.exists(args.rkllm_model_path):
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print("Error: Please provide the correct rkllm model path, and ensure it is the absolute path on the board.")
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sys.stdout.flush()
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exit()
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if not (args.target_platform in ["rk3588", "rk3576"]):
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print("Error: Please specify the correct target platform: rk3588/rk3576.")
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sys.stdout.flush()
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exit()
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if args.lora_model_path:
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if not os.path.exists(args.lora_model_path):
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print("Error: Please provide the correct lora_model path, and advise it is the absolute path on the board.")
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sys.stdout.flush()
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exit()
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if args.prompt_cache_path:
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if not os.path.exists(args.prompt_cache_path):
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print("Error: Please provide the correct prompt_cache_file path, and advise it is the absolute path on the board.")
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sys.stdout.flush()
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exit()
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# Fix frequency
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command = "[ \"$(cat /sys/class/devfreq/fdab0000.npu/governor)\" = userspace ] || sudo bash fix_freq_{}.sh".format(args.target_platform)
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subprocess.run(command, shell=True)
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# Set resource limit
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resource.setrlimit(resource.RLIMIT_NOFILE, (102400, 102400))
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# Initialize RKLLM model
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print("=========init....===========")
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sys.stdout.flush()
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model_path = args.rkllm_model_path
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rkllm_model = RKLLM(model_path, callback, args.lora_model_path, args.prompt_cache_path)
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print("RKLLM Model has been initialized successfully！")
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print("==============================")
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sys.stdout.flush()
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@app.route('/rkllm_abort', methods=['POST'])
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def abort():
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global global_abort
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global_abort = True
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code = rkllm_model.abort()
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return {"code":code}, 200
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# Create a function to receive data sent by the user using a request
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@app.route('/rkllm_chat', methods=['POST'])
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def receive_message():
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# Link global variables to retrieve the output information from the callback function
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global global_text, global_state
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global is_blocking
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# If the server is in a blocking state, return a specific response.
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if is_blocking or global_state==0:
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return jsonify({'status': 'error', 'message': 'RKLLM_Server is busy! Maybe you can try again later.'}), 503
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lock.acquire()
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try:
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# Set the server to a blocking state.
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is_blocking = True
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# Get JSON data from the POST request.
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data = request.json
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if data and 'messages' in data:
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# Reset global variables.
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global_text = []
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global_state = -1
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# Define the structure for the returned response.
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rkllm_responses = {
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"id": "rkllm_chat",
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"object": "rkllm_chat",
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"created": None,
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"choices": [],
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"usage": {
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"prompt_tokens": None,
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"completion_tokens": None,
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"total_tokens": None
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}
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}
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if not "stream" in data.keys() or data["stream"] == False:
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# Process the received data here.
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messages = data['messages']
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print("Received messages:", messages)
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input_prompt = msgs_to_prompt(messages)
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print("generated prompt:", input_prompt)
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rkllm_output = ""
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# Create a thread for model inference.
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model_thread = threading.Thread(target=rkllm_model.run, args=(input_prompt,))
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model_thread.start()
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# Wait for the model to finish running and periodically check the inference thread of the model.
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model_thread_finished = False
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global global_abort
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while not model_thread_finished:
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while len(global_text) > 0:
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rkllm_output += global_text.pop(0)
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time.sleep(0.01)
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if global_abort:
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global_abort = False
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model_thread.join(timeout=0.005)
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model_thread_finished = not model_thread.is_alive()
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rkllm_responses["choices"].append(
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{"index": len(messages),
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"message": {
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"role": "assistant",
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"content": rkllm_output,
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},
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"logprobs": None,
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"finish_reason": "stop"
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}
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)
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return jsonify(rkllm_responses), 200
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else:
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messages = data['messages']
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print("Received messages:", messages)
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input_prompt = msgs_to_prompt(messages)
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print("generated prompt:", input_prompt)
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rkllm_output = ""
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def generate():
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model_thread = threading.Thread(target=rkllm_model.run, args=(input_prompt,))
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model_thread.start()
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model_thread_finished = False
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while not model_thread_finished:
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while len(global_text) > 0:
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rkllm_output = global_text.pop(0)
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rkllm_responses["choices"].append(
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{"index": len(messages),
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"delta": {
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"role": "assistant",
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"content": rkllm_output,
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},
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"logprobs": None,
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"finish_reason": "stop" if global_state == 1 else None,
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}
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)
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yield f"{json.dumps(rkllm_responses)}\n\n"
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model_thread.join(timeout=0.005)
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model_thread_finished = not model_thread.is_alive()
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return Response(generate(), content_type='text/plain')
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else:
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return jsonify({'status': 'error', 'message': 'Invalid JSON data!'}), 400
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finally:
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lock.release()
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is_blocking = False
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# Start the Flask application.
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app.run(host='0.0.0.0', port=8080, threaded=True, use_reloader=False, debug=True, use_debugger=False) # maybe no debug?
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print("====================")
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print("RKLLM model inference completed, releasing RKLLM model resources...")
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rkllm_model.release()
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print("====================")
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