mirror of
https://github.com/FunAudioLLM/CosyVoice.git
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235 lines
10 KiB
Python
235 lines
10 KiB
Python
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import time
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import queue
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import asyncio
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import threading
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from typing import List, Generator, AsyncGenerator
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import torch
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from cosyvoice.utils.file_utils import logging
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from cosyvoice.llm.llm import Qwen2LM
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# 启用vllm V1版本
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import os
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os.environ["VLLM_USE_V1"] = '1'
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from vllm import ModelRegistry
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from vllm import LLMEngine, AsyncLLMEngine, CompletionOutput
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from vllm.engine.arg_utils import EngineArgs, AsyncEngineArgs
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from vllm.sampling_params import SamplingParams
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from cosyvoice.llm.vllm_use_cosyvoice2_model import CosyVoice2Model as CosyVoice2LLM
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ModelRegistry.register_model("CosyVoice2Model", CosyVoice2LLM)
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# EngineArgs
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ENGINE_ARGS = {
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"block_size": 16,
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"swap_space": 0,
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# "enforce_eager": True,
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"gpu_memory_utilization": 0.4,
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"max_num_batched_tokens": 1024,
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"max_model_len": 1024,
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"max_num_seqs": 256,
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"disable_log_requests": True,
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"disable_log_stats": True,
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"dtype": "float16"
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}
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from vllm.sampling_params import RequestOutputKind
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# SamplingParams
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SAMPLING_PARAMS = {
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"temperature": 1, # 不能低于0.8, 否则会生成非常多的空音频，或者无法正常生成语音Token
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"top_p": 1, # 不能低于0.8, 否则会生成非常多的空音频，或者无法正常生成语音Token
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"top_k": 25,
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# "min_tokens": 80, # 不支持设置最小的tokens数量设置，开启后vllm直接崩溃，无法启动
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# "presence_penalty": 1.0, # 不支持设置
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# "frequency_penalty": 0.0, # 不支持设置
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"max_tokens": 1024,
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"detokenize": False, # 目前 vllm 0.7.3 v1版本中设置无效，待后续版本更新后减少计算
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"ignore_eos": False,
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"output_kind": RequestOutputKind.DELTA # 设置为DELTA，如调整该参数，请同时调整llm_inference的处理代码
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}
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def tensor_to_list(tensor: torch.tensor):
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return tensor.view(-1).cpu().numpy().tolist()
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class VllmQwen2LM(Qwen2LM):
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def __init__(
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self,
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model_dir,
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mix_ratio: List[int] = [5, 15],
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):
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self.fp16 = False
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self.half = lambda: None
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self.mix_ratio = mix_ratio
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# ---------------------------------------------
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# vllm engine 的参数配置
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engine_args = AsyncEngineArgs(
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model=model_dir,
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**ENGINE_ARGS,
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)
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self.llm_engine: AsyncLLMEngine = AsyncLLMEngine.from_engine_args(engine_args)
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self.speech_token_size = 6564 # 6561 + 3
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self.llm_token_size = 151936 # llm vocab_size
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self.sos_eos_token_id = self.speech_token_size + self.llm_token_size + 1
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self.task_token_id = self.sos_eos_token_id + 1
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self.zero_token_id = self.task_token_id + 1
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# 不能直接在同步函数正确的使用 异步的生成器函数，即使使用协程也会对vllm造成崩溃
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# 使用 queue 的方式，后台线程运行推理任务
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self.task_queue = queue.Queue()
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self.loop = asyncio.new_event_loop()
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self.loop_thread = threading.Thread(target=self._run_event_loop, daemon=True)
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self.loop_thread.start()
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# 运行后台协程，用于处理任务队列中的任务
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# TODO: 目前只能单任务运行，多任务运行需要对 inference_processor 进行修改
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asyncio.run_coroutine_threadsafe(self.inference_processor(self.task_queue), self.loop)
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def _run_event_loop(self):
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asyncio.set_event_loop(self.loop)
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self.loop.run_forever()
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async def inference_processor(self, task_queue):
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while True:
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try:
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print(f"inference_processor")
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out_queue, prompt_token_ids, request_id, stop_token_ids, max_tokens = task_queue.get()
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sampling_params = SamplingParams(**SAMPLING_PARAMS)
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sampling_params.stop_token_ids = stop_token_ids or [6561]
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if max_tokens:
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sampling_params.max_tokens = max_tokens
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async for output in self.llm_engine.generate(
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{
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"prompt_token_ids": prompt_token_ids,
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},
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sampling_params=sampling_params,
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request_id=request_id or f"{time.time()}",
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):
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out_queue.put((output.outputs[0], output.finished))
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except Exception as e:
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logging.error(f"Error in inference_processor: {e}")
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def llm_inference(self, prompt_token_ids: List[int], request_id: str=None, stop_token_ids=None, max_tokens=None):
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# 使用 同步转异步 会导致vllm崩溃，目前选择 queue 的方式，后台线程运行推理任务
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# 提交推理任务到队列中
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out_queue = queue.Queue()
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self.task_queue.put((out_queue, prompt_token_ids, request_id, stop_token_ids, max_tokens))
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# 将 out_queue 的结果返回
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finished = False
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while not finished:
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(output, finished) = out_queue.get_nowait() if not out_queue.empty() else out_queue.get()
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yield output
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def inference(
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self,
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text: torch.Tensor,
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text_len: torch.Tensor,
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prompt_text: torch.Tensor,
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prompt_text_len: torch.Tensor,
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prompt_speech_token: torch.Tensor,
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prompt_speech_token_len: torch.Tensor,
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embedding: torch.Tensor,
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sampling: int = 25,
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max_token_text_ratio: float = 20,
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min_token_text_ratio: float = 2,
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) -> Generator[torch.Tensor|int, None, None]:
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prompt_text = tensor_to_list(prompt_text + torch.tensor(6564))
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prompt_speech_token = tensor_to_list(prompt_speech_token)
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text = tensor_to_list(text + torch.tensor(6564))
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prompt_token_ids = [self.sos_eos_token_id] + prompt_text + text + \
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[self.task_token_id] + prompt_speech_token
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max_tokens = len(text) * 20
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for output in self.llm_inference(
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prompt_token_ids,
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stop_token_ids=[6561],
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max_tokens=max_tokens,
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):
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if output.token_ids[-1] == 6561:
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need_add_tokens = output.token_ids[:-1]
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else:
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need_add_tokens = output.token_ids
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# 单个token 循环处理比较耗时，建议是在model中进行批量（extend）处理，减少循环
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# yield need_add_tokens
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for token in need_add_tokens:
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yield token
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def inference_bistream(
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self,
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text: Generator,
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prompt_text: torch.Tensor,
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prompt_text_len: torch.Tensor,
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prompt_speech_token: torch.Tensor,
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prompt_speech_token_len: torch.Tensor,
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embedding: torch.Tensor,
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sampling: int = 25,
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max_token_text_ratio: float = 20,
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min_token_text_ratio: float = 2,
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) -> Generator[torch.Tensor, None, None]:
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prompt_text = tensor_to_list(prompt_text + torch.tensor(6564))
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prompt_speech_token = tensor_to_list(prompt_speech_token)
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last_tokens = []
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prompt_token_ids = [self.sos_eos_token_id]
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text_tokens_cache = prompt_text
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for this_text in text:
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this_text = tensor_to_list(this_text + torch.tensor(6564))
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# text need tokens
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assert isinstance(this_text, list), "text need token ids List[int]."
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text_tokens_cache += this_text
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while len(prompt_speech_token) != 0:
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if len(text_tokens_cache) >= self.mix_ratio[0]:
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text_input_token = text_tokens_cache[:self.mix_ratio[0]]
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speech_input_token = prompt_speech_token[:self.mix_ratio[1]]
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prompt_token_ids += text_input_token + speech_input_token
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# reset the last cache
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text_tokens_cache = text_tokens_cache[self.mix_ratio[0]:]
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prompt_speech_token = prompt_speech_token[self.mix_ratio[1]:]
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else:
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logging.info('not enough text token to decode, wait for more')
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break
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if len(prompt_speech_token) == 0:
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if (len(last_tokens) > 0 and last_tokens[-1] == 6563) or len(prompt_token_ids) == 1:
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logging.info('get fill token, need to append more text token')
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if len(text_tokens_cache) >= self.mix_ratio[0]:
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text_tokens_temp = text_tokens_cache[:self.mix_ratio[0]]
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prompt_token_ids += text_tokens_temp
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logging.info('append {} text token'.format(len(text_tokens_temp)))
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text_tokens_cache = text_tokens_cache[self.mix_ratio[0]:]
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else:
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logging.info('not enough text token to decode, wait for more')
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continue
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for output in self.llm_inference(prompt_token_ids, stop_token_ids=[6563]):
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last_tokens = output.token_ids
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if last_tokens[-1] == 6563:
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need_add_tokens = last_tokens[:-1]
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else:
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need_add_tokens = last_tokens
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# 单个token 循环处理比较耗时，建议是在model中进行批量（extend）处理，减少循环
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# yield need_add_tokens
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for token in need_add_tokens:
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yield token
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prompt_token_ids.extend(need_add_tokens)
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prompt_token_ids += text_tokens_cache + [self.task_token_id]
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logging.info('no more text token, decode until met eos')
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for output in self.llm_inference(prompt_token_ids, stop_token_ids=[6561]):
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if output.token_ids[-1] == 6561:
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need_add_tokens = output.token_ids[:-1]
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else:
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need_add_tokens = output.token_ids
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# 单个token 循环处理比较耗时，建议是在model中进行批量（extend）处理，减少循环
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# yield need_add_tokens
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for token in need_add_tokens:
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yield token
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