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""" |
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A model worker executes the model. |
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""" |
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import argparse |
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import asyncio |
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import json |
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import time |
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import threading |
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import uuid |
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from fastapi import FastAPI, Request, BackgroundTasks |
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from fastapi.responses import StreamingResponse |
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import requests |
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import torch |
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import uvicorn |
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import whisper |
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import numpy as np |
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from functools import partial |
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from transformers import PreTrainedTokenizer |
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from omni_speech.constants import WORKER_HEART_BEAT_INTERVAL |
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from omni_speech.utils import (build_logger, server_error_msg, |
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pretty_print_semaphore) |
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from omni_speech.model.builder import load_pretrained_model |
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from omni_speech.constants import SPEECH_TOKEN_INDEX, DEFAULT_SPEECH_TOKEN |
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from omni_speech.datasets.preprocess import tokenizer_speech_token |
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from transformers import TextIteratorStreamer |
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from threading import Thread |
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GB = 1 << 30 |
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worker_id = str(uuid.uuid4())[:6] |
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logger = build_logger("model_worker", f"model_worker_{worker_id}.log") |
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global_counter = 0 |
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model_semaphore = None |
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def heart_beat_worker(controller): |
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while True: |
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time.sleep(WORKER_HEART_BEAT_INTERVAL) |
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controller.send_heart_beat() |
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def load_speech(audio, input_type, mel_size, speech_normalize): |
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speech = np.array(audio, dtype=np.float32) |
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if input_type == "raw": |
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speech = torch.from_numpy(speech) |
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if speech_normalize: |
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speech = torch.nn.functional.layer_norm(speech, speech.shape) |
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elif input_type == "mel": |
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speech = whisper.pad_or_trim(speech) |
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speech = whisper.log_mel_spectrogram(speech, n_mels=mel_size).permute(1, 0) |
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return speech |
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def build_unit_tokenizer(vocab_size): |
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import os |
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from transformers import BertTokenizer |
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with open("unit_vocab.txt", "w") as f: |
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for i in range(vocab_size + 1): |
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f.write(str(i) + "\n") |
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tokenizer = BertTokenizer(vocab_file="unit_vocab.txt") |
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os.remove("unit_vocab.txt") |
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return tokenizer |
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class ModelWorker: |
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def __init__(self, controller_addr, worker_addr, |
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worker_id, no_register, |
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model_path, model_base, model_name, |
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load_8bit, load_4bit, device, input_type, mel_size, s2s, is_lora, use_flash_attn=False): |
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self.controller_addr = controller_addr |
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self.worker_addr = worker_addr |
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self.worker_id = worker_id |
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self.device = device |
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self.model_name = model_name |
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self.input_type = input_type |
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self.mel_size = mel_size |
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self.tokenizer, self.model, self.context_len = load_pretrained_model( |
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model_path, model_base, is_lora=is_lora, s2s=s2s, load_8bit=load_8bit, load_4bit=load_4bit, device=self.device, use_flash_attn=use_flash_attn) |
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self.unit_tokenizer = build_unit_tokenizer(self.model.config.unit_vocab_size) |
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if not no_register: |
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self.register_to_controller() |
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self.heart_beat_thread = threading.Thread( |
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target=heart_beat_worker, args=(self,), daemon=True) |
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self.heart_beat_thread.start() |
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def register_to_controller(self): |
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logger.info("Register to controller") |
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url = self.controller_addr + "/register_worker" |
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data = { |
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"worker_name": self.worker_addr, |
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"check_heart_beat": True, |
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"worker_status": self.get_status() |
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} |
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r = requests.post(url, json=data) |
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assert r.status_code == 200 |
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def send_heart_beat(self): |
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logger.info(f"Send heart beat. Models: {[self.model_name]}. " |
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f"Semaphore: {pretty_print_semaphore(model_semaphore)}. " |
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f"global_counter: {global_counter}") |
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url = self.controller_addr + "/receive_heart_beat" |
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while True: |
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try: |
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ret = requests.post(url, json={ |
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"worker_name": self.worker_addr, |
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"queue_length": self.get_queue_length()}, timeout=5) |
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exist = ret.json()["exist"] |
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break |
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except requests.exceptions.RequestException as e: |
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logger.error(f"heart beat error: {e}") |
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time.sleep(5) |
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if not exist: |
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self.register_to_controller() |
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def get_queue_length(self): |
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if model_semaphore is None: |
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return 0 |
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else: |
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return args.limit_model_concurrency - model_semaphore._value + (len( |
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model_semaphore._waiters) if model_semaphore._waiters is not None else 0) |
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def get_status(self): |
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return { |
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"model_names": [self.model_name], |
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"speed": 1, |
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"queue_length": self.get_queue_length(), |
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} |
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@torch.inference_mode() |
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def generate_stream(self, params): |
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tokenizer, model = self.tokenizer, self.model |
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prompt = params["prompt"] |
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ori_prompt = prompt |
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audio = params.get("audio", None) |
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if audio is not None and len(audio) > 0: |
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speech = load_speech(audio, self.input_type, self.mel_size, self.model.config.speech_normalize) |
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speech_length = torch.LongTensor([speech.shape[0]]).unsqueeze(0).to(self.device) |
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speech_tensor = speech.unsqueeze(0).to(self.device, dtype=torch.float16) |
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speech_args = {"speech": speech_tensor, "speech_lengths": speech_length} |
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else: |
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speech = None |
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speech_args = {} |
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temperature = float(params.get("temperature", 1.0)) |
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top_p = float(params.get("top_p", 1.0)) |
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max_context_length = getattr(model.config, 'max_position_embeddings', 2048) |
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max_new_tokens = min(int(params.get("max_new_tokens", 256)), 1024) |
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stop_str = params.get("stop", None) |
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do_sample = True if temperature > 0.001 else False |
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input_ids = tokenizer_speech_token(prompt, tokenizer, return_tensors='pt').unsqueeze(0).to(self.device) |
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15) |
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streamer_unit = TextIteratorStreamer(self.unit_tokenizer, skip_prompt=False, skip_special_tokens=True, timeout=15) |
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if max_new_tokens < 1: |
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yield json.dumps({"text": ori_prompt + "Exceeds max token length. Please start a new conversation, thanks.", "error_code": 0}).encode() + b"\0" |
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return |
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thread = Thread(target=model.generate, kwargs=dict( |
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inputs=input_ids, |
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do_sample=do_sample, |
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temperature=temperature, |
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top_p=top_p, |
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max_new_tokens=max_new_tokens, |
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streamer=streamer, |
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streamer_unit=streamer_unit, |
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streaming_unit_gen=True, |
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use_cache=True, |
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**speech_args |
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)) |
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thread.start() |
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generated_text = ori_prompt |
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for new_text in streamer: |
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generated_text += new_text |
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generated_unit = " ".join(map(str, streamer_unit.token_cache)) |
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if generated_text.endswith(stop_str): |
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generated_text = generated_text[:-len(stop_str)] |
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yield json.dumps({"text": generated_text, "unit": generated_unit, "error_code": 0}).encode() + b"\0" |
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def generate_stream_gate(self, params): |
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try: |
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for x in self.generate_stream(params): |
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yield x |
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except ValueError as e: |
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print("Caught ValueError:", e) |
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ret = { |
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"text": server_error_msg, |
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"error_code": 1, |
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} |
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yield json.dumps(ret).encode() + b"\0" |
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except torch.cuda.CudaError as e: |
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print("Caught torch.cuda.CudaError:", e) |
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ret = { |
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"text": server_error_msg, |
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"error_code": 1, |
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} |
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yield json.dumps(ret).encode() + b"\0" |
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except Exception as e: |
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print("Caught Unknown Error", e) |
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ret = { |
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"text": server_error_msg, |
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"error_code": 1, |
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} |
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yield json.dumps(ret).encode() + b"\0" |
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app = FastAPI() |
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def release_model_semaphore(fn=None): |
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model_semaphore.release() |
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if fn is not None: |
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fn() |
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@app.post("/worker_generate_stream") |
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async def generate_stream(request: Request): |
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global model_semaphore, global_counter |
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global_counter += 1 |
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params = await request.json() |
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if model_semaphore is None: |
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model_semaphore = asyncio.Semaphore(args.limit_model_concurrency) |
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await model_semaphore.acquire() |
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worker.send_heart_beat() |
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generator = worker.generate_stream_gate(params) |
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background_tasks = BackgroundTasks() |
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background_tasks.add_task(partial(release_model_semaphore, fn=worker.send_heart_beat)) |
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return StreamingResponse(generator, background=background_tasks) |
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@app.post("/worker_get_status") |
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async def get_status(request: Request): |
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return worker.get_status() |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--host", type=str, default="localhost") |
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parser.add_argument("--port", type=int, default=21002) |
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parser.add_argument("--worker-address", type=str, |
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default="http://localhost:21002") |
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parser.add_argument("--controller-address", type=str, |
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default="http://localhost:21001") |
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parser.add_argument("--model-path", type=str, default="facebook/opt-350m") |
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parser.add_argument("--model-base", type=str, default=None) |
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parser.add_argument("--model-name", type=str) |
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parser.add_argument("--device", type=str, default="cuda") |
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parser.add_argument("--limit-model-concurrency", type=int, default=5) |
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parser.add_argument("--stream-interval", type=int, default=1) |
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parser.add_argument("--no-register", action="store_true") |
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parser.add_argument("--load-8bit", action="store_true") |
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parser.add_argument("--load-4bit", action="store_true") |
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parser.add_argument("--use-flash-attn", action="store_true") |
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parser.add_argument("--input-type", type=str, default="mel") |
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parser.add_argument("--mel-size", type=int, default=128) |
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parser.add_argument("--s2s", action="store_true", default=False) |
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parser.add_argument("--is-lora", action="store_true", default=False) |
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args = parser.parse_args() |
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logger.info(f"args: {args}") |
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worker = ModelWorker(args.controller_address, |
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args.worker_address, |
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worker_id, |
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args.no_register, |
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args.model_path, |
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args.model_base, |
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args.model_name, |
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args.load_8bit, |
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args.load_4bit, |
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args.device, |
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args.input_type, |
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args.mel_size, |
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args.s2s, |
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args.is_lora, |
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use_flash_attn=args.use_flash_attn) |
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uvicorn.run(app, host=args.host, port=args.port, log_level="info") |
