fix bug

runtime/triton_trtllm/client_grpc.py

@@ -424,7 +424,7 @@ def run_sync_streaming_inference(
     audios = []
     while True:
         try:
-            result = user_data._completed_requests.get(timeout=20)
+            result = user_data._completed_requests.get(timeout=200)
             if isinstance(result, InferenceServerException):
                 print(f"Received InferenceServerException: {result}")
                 return None, None, None, None

runtime/triton_trtllm/docker-compose.dit.yml

@@ -17,4 +17,4 @@ services:
               device_ids: ['0']
               capabilities: [gpu]
     command: >
-      /bin/bash -c "pip install modelscope && cd /workspace && git clone https://github.com/yuekaizhang/Step-Audio2.git -b trt && git clone https://github.com/yuekaizhang/CosyVoice.git -b streaming && cd CosyVoice && git submodule update --init --recursive && cd runtime/triton_trtllm && bash run.sh 0 3"
+      /bin/bash -c "pip install modelscope && cd /workspace && git clone https://github.com/yuekaizhang/Step-Audio2.git -b trt && git clone https://github.com/yuekaizhang/CosyVoice.git -b streaming && cd CosyVoice && git submodule update --init --recursive && cd runtime/triton_trtllm && bash run_stepaudio2_dit_token2wav.sh 0 3"

runtime/triton_trtllm/model_repo/cosyvoice2_dit/1/model.py

@@ -103,6 +103,7 @@ class TritonPythonModel:
 
         self.http_client = httpx.AsyncClient()
         self.api_base = "http://localhost:8000/v1/chat/completions"
+        self.speaker_cache = {}
 
     def _convert_speech_tokens_to_str(self, speech_tokens: Union[torch.Tensor, List]) -> str:
         """Converts a tensor or list of speech token IDs to a string representation."""
@@ -240,10 +241,12 @@ class TritonPythonModel:
         """Forward pass through the vocoder component.
 
         Args:
-            prompt_speech_tokens: Prompt speech tokens tensor
+            index: Index of the request
-            prompt_speech_feat: Prompt speech feat tensor
-            prompt_spk_embedding: Prompt spk embedding tensor
             target_speech_tokens: Target speech tokens tensor
+            request_id: Request ID
+            reference_wav: Reference waveform tensor
+            reference_wav_len: Reference waveform length tensor
+            finalize: Whether to finalize the request
 
         Returns:
             Generated waveform tensor
@@ -292,26 +295,17 @@ class TritonPythonModel:
 
     async def _process_request(self, request):
         request_id = request.request_id()
-        # Extract input tensors
-        wav = pb_utils.get_input_tensor_by_name(request, "reference_wav")
-
-        # Process reference audio through audio tokenizer
-
-        wav_len = pb_utils.get_input_tensor_by_name(request, "reference_wav_len")
-        prompt_speech_tokens = self.forward_audio_tokenizer(wav, wav_len)
-        prompt_speech_tokens = prompt_speech_tokens.unsqueeze(0)
-
-        wav_tensor = wav.as_numpy()
-        wav_tensor = torch.from_numpy(wav_tensor)[:, :wav_len.as_numpy()[0][0]]
-        prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=24000)(wav_tensor)
-        speech_feat = self._extract_speech_feat(prompt_speech_resample)
-        token_len = min(int(speech_feat.shape[1] / 2), prompt_speech_tokens.shape[-1])
-        prompt_speech_feat = speech_feat[:, :2 * token_len].contiguous().half()
-        prompt_speech_tokens = prompt_speech_tokens[:, :token_len].contiguous()
 
         reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy()
         reference_text = reference_text[0][0].decode('utf-8')
 
+        wav = pb_utils.get_input_tensor_by_name(request, "reference_wav")
+        wav_len = pb_utils.get_input_tensor_by_name(request, "reference_wav_len")
+
+        if reference_text not in self.speaker_cache:
+            self.speaker_cache[reference_text] = self.forward_audio_tokenizer(wav, wav_len).unsqueeze(0)
+        prompt_speech_tokens = self.speaker_cache[reference_text]
+
         target_text = pb_utils.get_input_tensor_by_name(request, "target_text").as_numpy()
         target_text = target_text[0][0].decode('utf-8')
 

runtime/triton_trtllm/model_repo/token2wav_dit/1/token2wav_dit.py

@@ -57,10 +57,7 @@ def convert_onnx_to_trt(trt_model, trt_kwargs, onnx_model, dtype):
     # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 32)  # 4GB
     if dtype == torch.float16:
         config.set_flag(trt.BuilderFlag.FP16)
-    elif dtype == torch.bfloat16:
+
-        config.set_flag(trt.BuilderFlag.BF16)
-    elif dtype == torch.float32:
-        config.set_flag(trt.BuilderFlag.FP32)
     profile = builder.create_optimization_profile()
     # load onnx model
     with open(onnx_model, "rb") as f:
@@ -199,7 +196,7 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
     def load_spk_trt(self, spk_model, spk_onnx_model, trt_concurrent=1, fp16=True):
         if not os.path.exists(spk_model) or os.path.getsize(spk_model) == 0:
             trt_kwargs = self.get_spk_trt_kwargs()
-            convert_onnx_to_trt(spk_model, trt_kwargs, spk_onnx_model, fp16)
+            convert_onnx_to_trt(spk_model, trt_kwargs, spk_onnx_model, torch.float32)
         import tensorrt as trt
         with open(spk_model, 'rb') as f:
             spk_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read())

runtime/triton_trtllm/run_stepaudio2_dit_token2wav.sh

@@ -42,7 +42,7 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
 
     echo "Step-Audio2-mini"
     huggingface-cli download --local-dir $step_audio_model_dir stepfun-ai/Step-Audio-2-mini
-    cd $stepaudio2_path/token2wav
+    cd $step_audio_model_dir/token2wav
     wget https://huggingface.co/yuekai/cosyvoice2_dit_flow_matching_onnx/resolve/main/flow.decoder.estimator.fp32.dynamic_batch.onnx -O flow.decoder.estimator.fp32.dynamic_batch.onnx
     wget https://huggingface.co/yuekai/cosyvoice2_dit_flow_matching_onnx/resolve/main/flow.decoder.estimator.chunk.fp32.dynamic_batch.simplify.onnx -O flow.decoder.estimator.chunk.fp32.dynamic_batch.simplify.onnx
     cd -
@@ -100,8 +100,8 @@ fi
 
 if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
    echo "Starting Token2wav Triton server and Cosyvoice2 llm using trtllm-serve"
-   tritonserver --model-repository $model_repo --http-port 18000 &
    mpirun -np 1 --allow-run-as-root --oversubscribe trtllm-serve serve --tokenizer $huggingface_model_local_dir $trt_engines_dir --max_batch_size 16  --kv_cache_free_gpu_memory_fraction 0.4 &
+   tritonserver --model-repository $model_repo --http-port 18000 &
    wait
     # Test using curl
     # curl http://localhost:8000/v1/chat/completions \
@@ -168,7 +168,7 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
    # Note: Using pre-computed cosyvoice2 tokens
    python3 streaming_inference.py --enable-trt --strategy equal # equal, exponential
    # Offline Token2wav inference
-   # python3 token2wav_dit.py --enable-trt
+   python3 token2wav_dit.py --enable-trt
 fi