add func export_codec_vllm

cosyvoice/cli/cosyvoice.py

@@ -126,7 +126,7 @@ 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
@@ -149,6 +149,8 @@ class CosyVoice2(CosyVoice):
         self.model.load('{}/llm.pt'.format(model_dir),
                         '{}/flow.pt'.format(model_dir),
                         '{}/hift.pt'.format(model_dir))
+        if use_vllm:
+            self.model.export_codec_vllm(''.join([model_dir, '/codec_vllm_model']))
         if load_jit:
             self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
         if load_trt:

cosyvoice/cli/model.py

@@ -17,6 +17,7 @@ import torch
 import numpy as np
 import threading
 import time
+from torch import nn
 from torch.nn import functional as F
 from contextlib import nullcontext
 import uuid
@@ -317,6 +318,38 @@ class CosyVoice2Model(CosyVoiceModel):
     def load_jit(self, flow_encoder_model):
         flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
         self.flow.encoder = flow_encoder
+    
+    def export_codec_vllm(self, model_path):
+        if os.path.exists(model_path):
+            return
+        pad_to = DEFAULT_VOCAB_PADDING_SIZE = 64
+        vocab_size = self.llm.speech_embedding.num_embeddings
+        feature_size = self.llm.speech_embedding.embedding_dim
+        pad_vocab_size = ((vocab_size + pad_to - 1) // pad_to) * pad_to
+
+        dtype = torch.bfloat16
+        new_lm_head = nn.Linear(in_features=feature_size, out_features=pad_vocab_size, bias=True)
+        with torch.no_grad():
+            new_lm_head.weight[:vocab_size] = self.llm.llm_decoder.weight
+            new_lm_head.bias[:vocab_size] = self.llm.llm_decoder.bias
+            new_lm_head.weight[vocab_size:] = 0
+            new_lm_head.bias[vocab_size:] = 0
+        self.llm.llm.model.lm_head = new_lm_head
+        new_codec_embed = nn.Linear(in_features=feature_size, out_features=pad_vocab_size)
+        with torch.no_grad():
+            new_codec_embed.weight[:vocab_size] = self.llm.speech_embedding.weight
+            new_codec_embed.weight[vocab_size:] = 0
+        self.llm.llm.model.set_input_embeddings(new_codec_embed)
+        self.llm.llm.model.to(self.device)
+        self.llm.llm.model.to(dtype)
+        tmp_vocab_size = self.llm.llm.model.config.vocab_size
+        tmp_tie_embedding = self.llm.llm.model.config.tie_word_embeddings
+        self.llm.llm.model.config.vocab_size = pad_vocab_size
+        self.llm.llm.model.config.tie_word_embeddings = False
+        self.llm.llm.model.config.use_bias = True
+        self.llm.llm.model.save_pretrained(model_path)
+        self.llm.llm.model.config.vocab_size = tmp_vocab_size
+        self.llm.llm.model.config.tie_word_embeddings = tmp_tie_embedding
 
     def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, token_offset, finalize=False, speed=1.0):
         tts_mel, _ = self.flow.inference(token=token.to(self.device),