初步合并vllm支持，异步推理的通道处理还存在bug

cosyvoice/cli/cosyvoice.py

@@ -19,7 +19,7 @@ from hyperpyyaml import load_hyperpyyaml
 from modelscope import snapshot_download
 import torch
 from cosyvoice.cli.frontend import CosyVoiceFrontEnd
-from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model
+from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model, VllmCosyVoice2Model
 from cosyvoice.utils.file_utils import logging
 from cosyvoice.utils.class_utils import get_model_type
 
@@ -63,6 +63,9 @@ class CosyVoice:
         spks = list(self.frontend.spk2info.keys())
         return spks
 
+    def add_spk_info(self, spk_id, spk_info):
+        self.frontend.add_spk_info(spk_id, spk_info)
+
     def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True):
         for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
             model_input = self.frontend.frontend_sft(i, spk_id)
@@ -88,6 +91,22 @@ class CosyVoice:
                 yield model_output
                 start_time = time.time()
 
+    def inference_zero_shot_by_spk_id(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True):
+        """使用预定义的说话人执行 zero_shot 推理"""
+        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+            model_input = self.frontend.frontend_zero_shot_by_spk_id(i, spk_id)
+            start_time = time.time()
+            last_time = start_time
+            chunk_index = 0
+            logging.info('synthesis text {}'.format(i))
+            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                logging.info('yield speech index:{}, len {:.2f}, rtf {:.3f},  cost {:.3f}s,  all cost time {:.3f}s'.format(
+                    chunk_index, speech_len,  (time.time()-last_time)/speech_len, time.time()-last_time, time.time()-start_time))
+                yield model_output
+                last_time = time.time()
+                chunk_index += 1
+
     def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
         for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
             model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k, self.sample_rate)
@@ -126,7 +145,7 @@ class CosyVoice:
 
 class CosyVoice2(CosyVoice):
 
-    def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False):
+    def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, use_vllm=False):
         self.instruct = True if '-Instruct' in model_dir else False
         self.model_dir = model_dir
         self.fp16 = fp16
@@ -145,7 +164,14 @@ class CosyVoice2(CosyVoice):
         if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
             load_jit, load_trt, fp16 = False, False, False
             logging.warning('no cuda device, set load_jit/load_trt/fp16 to False')
-        self.model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'], fp16)
+        if use_vllm:
+            try:
+                self.model = VllmCosyVoice2Model(model_dir, configs['flow'], configs['hift'], fp16)
+            except Exception as e:
+                logging.warning(f'use vllm inference failed. \n{e}')
+                raise e
+        else:
+            self.model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'], fp16)
         self.model.load('{}/llm.pt'.format(model_dir),
                         '{}/flow.pt'.format(model_dir),
                         '{}/hift.pt'.format(model_dir))
@@ -171,3 +197,14 @@ class CosyVoice2(CosyVoice):
                 logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
                 yield model_output
                 start_time = time.time()
+
+    def inference_instruct2_by_spk_id(self, tts_text, instruct_text, spk_id, stream=False, speed=1.0, text_frontend=True):
+        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+            model_input = self.frontend.frontend_instruct2_by_spk_id(i, instruct_text, spk_id)
+            start_time = time.time()
+            logging.info('synthesis text {}'.format(i))
+            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+                yield model_output
+                start_time = time.time()

cosyvoice/cli/frontend.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from functools import partial
-from typing import Generator
+from typing import Generator, Optional
 import json
 import onnxruntime
 import torch
@@ -24,6 +24,8 @@ import torchaudio
 import os
 import re
 import inflect
+from pydantic import BaseModel, ConfigDict
+
 try:
     import ttsfrd
     use_ttsfrd = True
@@ -36,6 +38,18 @@ from cosyvoice.utils.file_utils import logging
 from cosyvoice.utils.frontend_utils import contains_chinese, replace_blank, replace_corner_mark, remove_bracket, spell_out_number, split_paragraph, is_only_punctuation
 
 
+class SpeakerInfo(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    name: Optional[str] = None
+    spk_id: str
+    prompt_text: str
+    prompt_text_token: torch.Tensor
+    speech_feat: torch.Tensor
+    speech_token: torch.Tensor
+    embedding: torch.Tensor
+
+
 class CosyVoiceFrontEnd:
 
     def __init__(self,
@@ -55,8 +69,9 @@ class CosyVoiceFrontEnd:
         self.speech_tokenizer_session = onnxruntime.InferenceSession(speech_tokenizer_model, sess_options=option,
                                                                      providers=["CUDAExecutionProvider" if torch.cuda.is_available() else
                                                                                 "CPUExecutionProvider"])
+        self.spk2info_path = spk2info
         if os.path.exists(spk2info):
-            self.spk2info = torch.load(spk2info, map_location=self.device)
+            self.spk2info = torch.load(spk2info, map_location=self.device, weights_only=False)
         else:
             self.spk2info = {}
         self.allowed_special = allowed_special
@@ -68,7 +83,8 @@ class CosyVoiceFrontEnd:
                 'failed to initialize ttsfrd resource'
             self.frd.set_lang_type('pinyinvg')
         else:
-            self.zh_tn_model = ZhNormalizer(remove_erhua=False, full_to_half=False, overwrite_cache=True)
+            # self.zh_tn_model = ZhNormalizer(remove_erhua=False, full_to_half=False, overwrite_cache=True)
+            self.zh_tn_model = ZhNormalizer(remove_erhua=False, full_to_half=False, overwrite_cache=False)
             self.en_tn_model = EnNormalizer()
             self.inflect_parser = inflect.engine()
 
@@ -86,8 +102,9 @@ class CosyVoiceFrontEnd:
     def _extract_text_token_generator(self, text_generator):
         for text in text_generator:
             text_token, _ = self._extract_text_token(text)
-            for i in range(text_token.shape[1]):
-                yield text_token[:, i: i + 1]
+            # for i in range(text_token.shape[1]):
+            #     yield text_token[:, i: i + 1]
+            yield text_token
 
     def _extract_speech_token(self, speech):
         assert speech.shape[1] / 16000 <= 30, 'do not support extract speech token for audio longer than 30s'
@@ -138,11 +155,15 @@ class CosyVoiceFrontEnd:
                 text = text.replace(" - ", "，")
                 text = remove_bracket(text)
                 text = re.sub(r'[，,、]+$', '。', text)
+                if not split:
+                    return text
                 texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "zh", token_max_n=80,
                                              token_min_n=60, merge_len=20, comma_split=False))
             else:
                 text = self.en_tn_model.normalize(text)
                 text = spell_out_number(text, self.inflect_parser)
+                if not split:
+                    return text
                 texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "en", token_max_n=80,
                                              token_min_n=60, merge_len=20, comma_split=False))
         texts = [i for i in texts if not is_only_punctuation(i)]
@@ -151,6 +172,7 @@ class CosyVoiceFrontEnd:
     def frontend_sft(self, tts_text, spk_id):
         tts_text_token, tts_text_token_len = self._extract_text_token(tts_text)
         embedding = self.spk2info[spk_id]['embedding']
+        assert embedding is not None
         model_input = {'text': tts_text_token, 'text_len': tts_text_token_len, 'llm_embedding': embedding, 'flow_embedding': embedding}
         return model_input
 
@@ -209,3 +231,60 @@ class CosyVoiceFrontEnd:
                        'prompt_speech_feat': prompt_speech_feat, 'prompt_speech_feat_len': prompt_speech_feat_len,
                        'flow_embedding': embedding}
         return model_input
+
+    def generate_spk_info(self, spk_id: str, prompt_text: str, prompt_speech_16k: torch.Tensor, resample_rate:int=24000, name: str=None):
+        assert isinstance(spk_id, str)
+        assert spk_id not in self.spk2info, "spk_id already exists"
+        prompt_text_token, _ = self._extract_text_token(prompt_text)
+        prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=resample_rate)(prompt_speech_16k)
+        speech_feat, _ = self._extract_speech_feat(prompt_speech_resample)
+        speech_token, speech_token_len = self._extract_speech_token(prompt_speech_16k)
+        if resample_rate == 24000:
+            # cosyvoice2, force speech_feat % speech_token = 2
+            token_len = min(int(speech_feat.shape[1] / 2), speech_token.shape[1])
+            speech_feat = speech_feat[:, :2 * token_len]
+            speech_token = speech_token[:, :token_len]
+        embedding = self._extract_spk_embedding(prompt_speech_16k)
+        spk_info = SpeakerInfo(
+            name=name,
+            spk_id=spk_id,
+            prompt_text=prompt_text,
+            prompt_text_token=prompt_text_token,
+            speech_feat=speech_feat,
+            speech_token=speech_token,
+            embedding=embedding,
+        )
+        self.add_spk_info(spk_id, spk_info)
+
+    def add_spk_info(self, spk_id: str, spk_info: dict|SpeakerInfo):
+        if isinstance(spk_info, BaseModel):
+            spk_info = spk_info.model_dump()
+        self.spk2info[spk_id] = spk_info
+        if self.spk2info_path:
+            torch.save(self.spk2info, self.spk2info_path)
+
+    def frontend_instruct2_by_spk_id(self, tts_text, instruct_text, spk_id):
+        assert spk_id in self.spk2info
+        tts_text_token, _ = self._extract_text_token(tts_text)
+        prompt_text_token, _ = self._extract_text_token(instruct_text + '<|endofprompt|>')
+        model_input = {'text': tts_text_token,
+                       'prompt_text': prompt_text_token,
+                       'flow_prompt_speech_token': self.spk2info[spk_id]['speech_token'],
+                       'prompt_speech_feat': self.spk2info[spk_id]['speech_feat'],
+                       'llm_embedding': self.spk2info[spk_id]['embedding'],
+                       'flow_embedding': self.spk2info[spk_id]['embedding'],
+        }
+        return model_input
+
+    def frontend_zero_shot_by_spk_id(self, tts_text, spk_id):
+        assert spk_id in self.spk2info
+        tts_text_token, _ = self._extract_text_token(tts_text)
+        model_input = {'text': tts_text_token,
+                       'prompt_text': self.spk2info[spk_id]['prompt_text_token'],
+                       'llm_prompt_speech_token': self.spk2info[spk_id]['speech_token'],
+                       'flow_prompt_speech_token': self.spk2info[spk_id]['speech_token'],
+                       'prompt_speech_feat': self.spk2info[spk_id]['speech_feat'],
+                       'llm_embedding': self.spk2info[spk_id]['embedding'],
+                       'flow_embedding': self.spk2info[spk_id]['embedding']
+        }
+        return model_input

cosyvoice/cli/model.py

@@ -409,3 +409,26 @@ class CosyVoice2Model(CosyVoiceModel):
             self.tts_speech_token_dict.pop(this_uuid)
             self.llm_end_dict.pop(this_uuid)
         torch.cuda.empty_cache()
+
+
+class VllmCosyVoice2Model(CosyVoice2Model):
+    def __init__(self,
+                 model_dir: str,
+                 flow: torch.nn.Module,
+                 hift: torch.nn.Module,
+                 fp16: bool):
+        try:
+            from cosyvoice.llm.llm_vllm import VllmQwen2LM
+        except Exception as e:
+            raise e
+        llm = VllmQwen2LM(model_dir)
+        super().__init__(llm,flow,hift,fp16)
+
+    def load(self, llm_model, flow_model, hift_model):
+        self.flow.load_state_dict(torch.load(flow_model, weights_only=True, map_location=self.device), strict=True)
+        self.flow.to(self.device).eval()
+        # in case hift_model is a hifigan model
+        hift_state_dict = {k.replace('generator.', ''): v for k, v in
+                           torch.load(hift_model, weights_only=True, map_location=self.device).items()}
+        self.hift.load_state_dict(hift_state_dict, strict=True)
+        self.hift.to(self.device).eval()

cosyvoice/llm/llm_vllm.py

@@ -0,0 +1,248 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import asyncio
+import contextlib
+import time
+from typing import List, Generator, AsyncGenerator
+import torch
+from cosyvoice.utils.file_utils import logging
+from cosyvoice.llm.llm import Qwen2LM
+
+# 启用vllm V1版本
+import os
+os.environ["VLLM_USE_V1"] = '1'
+from vllm import ModelRegistry
+from vllm import LLMEngine, AsyncLLMEngine, CompletionOutput
+from vllm.engine.arg_utils import EngineArgs, AsyncEngineArgs
+from vllm.sampling_params import SamplingParams
+
+from cosyvoice.llm.vllm_use_cosyvoice2_model import CosyVoice2Model as CosyVoice2LLM
+ModelRegistry.register_model("CosyVoice2Model", CosyVoice2LLM)
+
+# EngineArgs
+ENGINE_ARGS = {
+    "block_size": 16,
+    "swap_space": 0,
+    # "enforce_eager": True,
+    "gpu_memory_utilization": 0.4,
+    "max_num_batched_tokens": 1024,
+    "max_model_len": 1024,
+    "max_num_seqs": 256,
+    "disable_log_requests": True,
+    "disable_log_stats": True,
+}
+
+from vllm.sampling_params import RequestOutputKind
+# SamplingParams
+SAMPLING_PARAMS = {
+    "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
+    "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
+    "top_k": 25,
+    # "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
+    # "presence_penalty": 1.0,    # 不支持设置
+    # "frequency_penalty": 0.0,   # 不支持设置
+    "max_tokens": 1024,
+    "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
+    "ignore_eos": False,
+    "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
+}
+
+def tensor_to_list(tensor: torch.tensor):
+    return tensor.view(-1).cpu().numpy().tolist()
+
+class VllmQwen2LM(Qwen2LM):
+    def __init__(
+            self,
+            model_dir,
+            mix_ratio: List[int] = [5, 15],
+    ):
+        self.fp16 = False
+        self.half = lambda: None
+        self.mix_ratio = mix_ratio
+        # ---------------------------------------------
+        # vllm engine 的参数配置
+        engine_args = AsyncEngineArgs(
+            model=model_dir,
+            **ENGINE_ARGS,
+        )
+        self.llm_engine: AsyncLLMEngine = AsyncLLMEngine.from_engine_args(engine_args)
+
+        self.speech_token_size = 6564       # 6561 + 3
+        self.llm_token_size = 151936        # llm  vocab_size
+        self.sos_eos_token_id = self.speech_token_size + self.llm_token_size + 1
+        self.task_token_id = self.sos_eos_token_id + 1
+        self.zero_token_id = self.task_token_id + 1
+
+    async def async_llm_inference(self, prompt_token_ids: List[int], request_id: str=None, stop_token_ids=None, max_tokens=None)\
+            -> AsyncGenerator[CompletionOutput, None]:
+        assert isinstance(prompt_token_ids, list) , "prompt_token_ids should be List[int]"
+        invalid = next((i for i, x in enumerate(prompt_token_ids) if not isinstance(x, int)), None)
+        assert invalid is None, f"Error in prompt_token_ids, Non-int element at index {invalid}: {prompt_token_ids[invalid]}"
+        # logging.debug('prompt_token_ids:', prompt_token_ids)
+        # TODO: 增加上下文控制，取消请求时
+        sampling_params = SamplingParams(**SAMPLING_PARAMS)
+        sampling_params.stop_token_ids = stop_token_ids or [6561]
+        if max_tokens:
+            sampling_params.max_tokens = max_tokens
+        async for output in self.llm_engine.generate(
+                {
+                    "prompt_token_ids": prompt_token_ids,
+                },
+                sampling_params=sampling_params,
+                request_id=request_id or f"{time.time()}",
+        ):
+            yield output.outputs[0]
+
+
+    def llm_inference(self, prompt_token_ids: List[int], request_id: str=None, stop_token_ids=None, max_tokens=None)\
+            -> Generator[CompletionOutput, None, None]:
+        assert isinstance(prompt_token_ids, list) , "prompt_token_ids should be List[int]"
+        invalid = next((i for i, x in enumerate(prompt_token_ids) if not isinstance(x, int)), None)
+        assert invalid is None, f"Error in prompt_token_ids, Non-int element at index {invalid}: {prompt_token_ids[invalid]}"
+        # logging.debug('prompt_token_ids:', prompt_token_ids)
+        # TODO: 增加上下文控制，取消请求时
+        sampling_params = SamplingParams(**SAMPLING_PARAMS)
+        sampling_params.stop_token_ids = stop_token_ids or [6561]
+        if max_tokens:
+            sampling_params.max_tokens = max_tokens
+
+        # 创建独立事件循环
+        loop = asyncio.new_event_loop()
+        try:
+            asyncio.set_event_loop(loop)
+            # 初始化异步生成器
+            async_gen = self.llm_engine.generate(
+                    {
+                        "prompt_token_ids": prompt_token_ids,
+                    },
+                    sampling_params=sampling_params,
+                    request_id=request_id or f"{time.time()}",
+            )
+            while True:
+                try:
+                    # 同步获取异步结果
+                    output = loop.run_until_complete(async_gen.__anext__())
+                    yield output.outputs[0]
+                except StopAsyncIteration:
+                    break
+        except GeneratorExit:
+            if async_gen is not None:
+                loop.run_until_complete(async_gen.aclose())
+            raise
+        finally:
+            # 资源清理
+            print("资源清理...")
+            if async_gen is not None:
+                loop.run_until_complete(async_gen.aclose())
+                loop.close()
+            print("资源清理成功")
+
+    def inference(
+            self,
+            text: torch.Tensor,
+            text_len: torch.Tensor,
+            prompt_text: torch.Tensor,
+            prompt_text_len: torch.Tensor,
+            prompt_speech_token: torch.Tensor,
+            prompt_speech_token_len: torch.Tensor,
+            embedding: torch.Tensor,
+            sampling: int = 25,
+            max_token_text_ratio: float = 20,
+            min_token_text_ratio: float = 2,
+    ) -> Generator[torch.Tensor|int, None, None]:
+        prompt_text = tensor_to_list(prompt_text + torch.tensor(6564))
+        prompt_speech_token = tensor_to_list(prompt_speech_token)
+
+        text = tensor_to_list(text + torch.tensor(6564))
+        prompt_token_ids = [self.sos_eos_token_id] + prompt_text + text + \
+                           [self.task_token_id] + prompt_speech_token
+        max_tokens = len(text) * 20
+        for output in self.llm_inference(
+                prompt_token_ids,
+                stop_token_ids=[6561],
+                max_tokens=max_tokens,
+        ):
+            if output.token_ids[-1] == 6561:
+                need_add_tokens = output.token_ids[:-1]
+            else:
+                need_add_tokens = output.token_ids
+            # 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
+            # yield need_add_tokens
+            for token in need_add_tokens:
+                yield token
+
+    def inference_bistream(
+            self,
+            text: Generator,
+            prompt_text: torch.Tensor,
+            prompt_text_len: torch.Tensor,
+            prompt_speech_token: torch.Tensor,
+            prompt_speech_token_len: torch.Tensor,
+            embedding: torch.Tensor,
+            sampling: int = 25,
+            max_token_text_ratio: float = 20,
+            min_token_text_ratio: float = 2,
+    ) -> Generator[torch.Tensor, None, None]:
+        last_tokens = []
+        prompt_token_ids = [self.sos_eos_token_id]
+        text_tokens_cache = prompt_text
+        for this_text in text:
+            this_text = tensor_to_list(this_text + torch.tensor(6564))
+            # text need tokens
+            assert isinstance(this_text, list), "text need token ids List[int]."
+            text_tokens_cache += this_text
+            while len(llm_prompt_speech_token) != 0:
+                if len(text_tokens_cache) >= self.mix_ratio[0]:
+                    text_input_token = text_tokens_cache[:self.mix_ratio[0]]
+                    speech_input_token = llm_prompt_speech_token[:self.mix_ratio[1]]
+                    prompt_token_ids += text_input_token + speech_input_token
+                    # reset the last cache
+                    text_tokens_cache = text_tokens_cache[self.mix_ratio[0]:]
+                    llm_prompt_speech_token = llm_prompt_speech_token[self.mix_ratio[1]:]
+                else:
+                    logging.info('not enough text token to decode, wait for more')
+                    break
+            if len(llm_prompt_speech_token) == 0:
+                if (len(last_tokens) > 0 and last_tokens[-1] == 6563) or len(prompt_token_ids) == 1:
+                    logging.info('get fill token, need to append more text token')
+                    if len(text_tokens_cache) >= self.mix_ratio[0]:
+                        text_tokens_temp = text_tokens_cache[:self.mix_ratio[0]]
+                        prompt_token_ids += text_tokens_temp
+                        logging.info('append {} text token'.format(len(text_tokens_temp)))
+                        text_tokens_cache = text_tokens_cache[self.mix_ratio[0]:]
+                    else:
+                        logging.info('not enough text token to decode, wait for more')
+                        continue
+                for output in self.llm_inference(prompt_token_ids, stop_token_ids=[6563]):
+                    last_tokens = output.token_ids
+                    if last_tokens[-1] == 6563:
+                        need_add_tokens = last_tokens[:-1]
+                    else:
+                        need_add_tokens = last_tokens
+                    # 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
+                    # yield need_add_tokens
+                    for token in need_add_tokens:
+                        yield token
+                    prompt_token_ids.extend(need_add_tokens)
+        prompt_token_ids += text_tokens_cache + [self.task_token_id]
+        logging.info('no more text token, decode until met eos')
+        for output in self.llm_inference(prompt_token_ids, stop_token_ids=[6561]):
+            if output.token_ids[-1] == 6561:
+                need_add_tokens = output.token_ids[:-1]
+            else:
+                need_add_tokens = output.token_ids
+            # 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
+            # yield need_add_tokens
+            for token in need_add_tokens:
+                yield token

cosyvoice/llm/vllm_use_cosyvoice2_model.py

@@ -0,0 +1,263 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2/modeling_qwen2.py
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Qwen2 model compatible with HuggingFace weights."""
+from typing import Iterable, List, Optional, Set, Tuple, Union, Iterator, overload, TypedDict, Mapping, Any
+from typing_extensions import TypeVar
+
+import torch
+from torch import nn
+
+from vllm.attention import AttentionMetadata
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from vllm.model_executor.models.interfaces import T
+from vllm.model_executor.models.qwen2 import Qwen2Model
+
+from vllm.model_executor.models.utils import AutoWeightsLoader, maybe_prefix, merge_multimodal_embeddings
+
+logger = init_logger(__name__)
+
+IGNORE_ID = -1
+
+
+class CosyVoice2Model(nn.Module):
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.lora_config = lora_config
+        self.quant_config = quant_config
+
+        self.llm_input_size = 896
+        self.llm_output_size = 896
+
+        self.speech_token_size = 6561+3
+        self.llm_token_size = config.vocab_size
+
+        # 2. build speech token language model related modules
+        self.sos_eos = 0
+        self.task_id = 1
+        self.fill_token = 2
+
+
+        self.allow_patterns_overrides = ["llm.*"]
+        self.llm_embedding = torch.nn.Embedding(2, self.llm_input_size)
+        self.model = Qwen2Model(vllm_config=vllm_config,
+                              prefix=maybe_prefix(prefix, "model"))
+
+        # self.llm_decoder = nn.Linear(self.llm_output_size, self.speech_token_size)
+        self.llm_decoder = ParallelLMHead(self.speech_token_size,
+                                      self.llm_output_size,
+                                      bias=True,
+                                      quant_config=quant_config,
+                                      prefix=maybe_prefix(
+                                          prefix, "llm_decoder"))
+        self.logits_processor = LogitsProcessor(self.speech_token_size)
+
+        # length_normalized_loss: bool = True,
+        # lsm_weight: float = 0.0,
+        # self.criterion_ce = LabelSmoothingLoss(
+        #     size=self.speech_token_size,
+        #     padding_idx=IGNORE_ID,
+        #     smoothing=lsm_weight,
+        #     normalize_length=length_normalized_loss,
+        # )
+
+        # 3. [Optional] build speech token related modules
+        self.speech_embedding = torch.nn.Embedding(self.speech_token_size, self.llm_input_size)
+
+        # 4. sampling method
+        ## use vllm sampling method
+        self.sampler = get_sampler()
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+        self.mix_ratio: List[int] = [5, 15]
+
+        # 定义特殊token常量
+        self.llm_token_id_delta = torch.tensor(self.speech_token_size, dtype=torch.int32)
+        self.sos_eos_token_id = torch.tensor((self.llm_token_id_delta + self.llm_token_size + 1), dtype=torch.int32)  # 163840 + 6564 = 170404
+        self.task_token_id = self.sos_eos_token_id + torch.tensor(1, dtype=torch.int32)  # 170405
+        self.zero_token_id = self.task_token_id + torch.tensor(1, dtype=torch.int32)
+
+        self.zero_embed_buffer = torch.zeros(
+            (vllm_config.scheduler_config.max_num_seqs, self.llm_input_size),
+            dtype=self.llm_embedding.weight.dtype,
+            device=self.llm_embedding.weight.device
+        )
+        self.inputs_embed_buffer = torch.zeros(
+            (vllm_config.scheduler_config.max_num_batched_tokens, self.llm_input_size),
+            dtype=self.llm_embedding.weight.dtype,
+            device=self.llm_embedding.weight.device,
+        )
+
+    def get_sos_eos_emb(self):
+        return self.llm_embedding.weight[self.sos_eos].reshape(1, 1, -1)
+
+    def get_task_id_emb(self):
+        return self.llm_embedding.weight[self.task_id].reshape(1, 1, -1)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[T] = None,
+        attn_metadata: Optional["AttentionMetadata"] = None,
+    ) -> torch.Tensor:
+        """
+        Returns the input embeddings merged from the text embeddings from
+        input_ids and the multimodal embeddings generated from multimodal
+        kwargs.
+        """
+        # 创建掩码，标记哪些 token_id 属于音频 Token
+        mask = input_ids < self.speech_token_size
+
+        # 获取 input_ids 的原始形状
+        input_shape = input_ids.shape
+        # 展平 input_ids 和掩码以便统一处理
+        flat_input_ids = input_ids.view(-1)
+        flat_mask = mask.view(-1)
+
+        inputs_embeds = self.inputs_embed_buffer[:flat_input_ids.shape[0]]
+        inputs_embeds.zero_()
+
+        # Process speech tokens
+        if flat_mask.any():
+            speech_token_ids = flat_input_ids[flat_mask]
+            inputs_embeds[flat_mask] = self.speech_embedding(speech_token_ids)
+
+        # 处理大于 delta 的 token_id
+        if (~flat_mask).any():
+            llm_token_ids = flat_input_ids[~flat_mask]
+            llm_embeds = torch.zeros_like(inputs_embeds[~flat_mask])
+
+            sos_eos_mask = llm_token_ids == self.sos_eos_token_id
+            task_mask = llm_token_ids == self.task_token_id
+            zero_mask = llm_token_ids == self.zero_token_id
+            normal_mask = ~(sos_eos_mask | task_mask | zero_mask)
+
+            # 分层处理逻辑
+            # 第一优先级：SOS/EOS标记
+            if sos_eos_mask.any():
+                llm_embeds[sos_eos_mask] = self.llm_embedding.weight[self.sos_eos].unsqueeze(0)
+
+            # 第二优先级：任务标记
+            if task_mask.any():
+                llm_embeds[task_mask] = self.llm_embedding.weight[self.task_id].unsqueeze(0)
+
+            # 第二优先级：空音频标记
+            if zero_mask.any():
+                llm_embeds[zero_mask] = self.zero_embed_buffer[:len(llm_embeds[zero_mask])]
+
+            # 常规LLM token
+            if normal_mask.any():
+                original_ids = llm_token_ids[normal_mask] - self.llm_token_id_delta
+                # print('original_ids: ',original_ids)
+                llm_embeds[normal_mask] = self.model.get_input_embeddings(original_ids)
+
+            inputs_embeds[~flat_mask] = llm_embeds
+
+        inputs_embeds = inputs_embeds.view(*input_shape, self.llm_input_size)
+
+        # 合并多模态嵌入（如果有）
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                self.config.audio_token_index
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings(
+                input_ids,
+                attn_metadata=attn_metadata,
+            )
+        return self.model(input_ids, positions, kv_caches,
+                        attn_metadata, intermediate_tensors,
+                        inputs_embeds)
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.llm_decoder, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    @staticmethod
+    def convert_weights(weights: Iterable[Tuple[str, torch.Tensor]]) -> Iterable[Tuple[str, torch.Tensor]]:
+        for name, param in weights:
+            # 处理Qwen2Model核心参数
+            if name.startswith("llm."):
+                if name.startswith("llm.model.model."):
+                    name = name.replace("llm.model.model.", "model.")
+                else:
+                    continue
+            # print('weights name: ', name)
+            yield name, param
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        weights = self.convert_weights(weights)
+        loader = AutoWeightsLoader(self)
+        loader.load_weights(weights)