mark stateless token2wav

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

@@ -103,39 +103,91 @@ class TritonPythonModel:
             List of inference responses containing generated waveforms
         """
         responses = []
-        # Process each request in batch
         for request in requests:
-            target_speech_tokens_tensor = pb_utils.get_input_tensor_by_name(request, "target_speech_tokens").as_numpy()
-            target_speech_tokens = torch.from_numpy(target_speech_tokens_tensor)#.to(self.device)
-            # shift the speech tokens according to the original vocab size
-            target_speech_tokens = target_speech_tokens - ORIGINAL_VOCAB_SIZE
+            request_id = request.request_id()
+
+            # Get inputs
+            target_speech_tokens_tensor = pb_utils.get_input_tensor_by_name(request, "target_speech_tokens")
+            target_speech_tokens = torch.utils.dlpack.from_dlpack(target_speech_tokens_tensor.to_dlpack())
             target_speech_tokens = target_speech_tokens.squeeze().tolist()
 
-            # We set token_offset as an optional input to support streaming/offline tts. It has to be None when offline tts.
-           
             finalize = pb_utils.get_input_tensor_by_name(request, "finalize").as_numpy().item()
-                
-            request_id = request.request_id()
-               
-
-            wav_array = pb_utils.get_input_tensor_by_name(
-                request, "reference_wav").as_numpy()
-            wav_len = pb_utils.get_input_tensor_by_name(
-                request, "reference_wav_len").as_numpy().item()
-
-            wav_array = torch.from_numpy(wav_array)
-            # Prepare inputs
-            wav = wav_array[:, :wav_len].squeeze(0)
-
+            wav_array = pb_utils.get_input_tensor_by_name(request, "reference_wav").as_numpy()
+            wav_len = pb_utils.get_input_tensor_by_name(request, "reference_wav_len").as_numpy().item()
+            wav = torch.from_numpy(wav_array)[:, :wav_len].squeeze(0)
             spk_id = get_spk_id_from_prompt_audio(wav)
-            # wav = wav.to(self.device)
 
-            audio_hat = self.token2wav_model.forward_streaming(target_speech_tokens, finalize, request_id=request_id, speaker_id=f"{spk_id}", prompt_audio=wav, prompt_audio_sample_rate=16000)
+            # Handle cache
+            conformer_cnn_cache = pb_utils.get_input_tensor_by_name(request, "conformer_cnn_cache")
+            if conformer_cnn_cache is not None:
+                self.token2wav_model.streaming_flow_cache[request_id]['conformer_cnn_cache'] = torch.utils.dlpack.from_dlpack(conformer_cnn_cache.to_dlpack())
+                
+                conformer_att_cache_np = pb_utils.get_input_tensor_by_name(request, "conformer_att_cache")
+                self.token2wav_model.streaming_flow_cache[request_id]['conformer_att_cache'] = torch.utils.dlpack.from_dlpack(conformer_att_cache_np.to_dlpack()).transpose(0,1)
+                
+                estimator_cnn_cache_np = pb_utils.get_input_tensor_by_name(request, "estimator_cnn_cache")
+                self.token2wav_model.streaming_flow_cache[request_id]['estimator_cnn_cache'] = torch.utils.dlpack.from_dlpack(estimator_cnn_cache_np.to_dlpack()).squeeze(0)
 
-            generated_wave = audio_hat.squeeze(0).cpu().numpy()
+                estimator_att_cache_np = pb_utils.get_input_tensor_by_name(request, "estimator_att_cache")
+                self.token2wav_model.streaming_flow_cache[request_id]['estimator_att_cache'] = torch.utils.dlpack.from_dlpack(estimator_att_cache_np.to_dlpack()).squeeze(0)
 
+                mel_np = pb_utils.get_input_tensor_by_name(request, "mel")
+                self.token2wav_model.streaming_flow_cache[request_id]['mel'] = torch.utils.dlpack.from_dlpack(mel_np.to_dlpack())
+                
+                source_np = pb_utils.get_input_tensor_by_name(request, "source")
+                self.token2wav_model.hift_cache_dict[request_id]['source'] = torch.utils.dlpack.from_dlpack(source_np.to_dlpack())
+                
+                speech_np = pb_utils.get_input_tensor_by_name(request, "speech")
+                self.token2wav_model.hift_cache_dict[request_id]['speech'] = torch.utils.dlpack.from_dlpack(speech_np.to_dlpack())
+
+            # Forward pass
+            audio_hat = self.token2wav_model.forward_streaming(
+                target_speech_tokens, 
+                finalize, 
+                request_id=request_id, 
+                speaker_id=f"{spk_id}", 
+                prompt_audio=wav, 
+                prompt_audio_sample_rate=16000
+            )
+            
+            # Prepare outputs
+            outputs = []
             wav_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(audio_hat))
-            inference_response = pb_utils.InferenceResponse(output_tensors=[wav_tensor])
-            responses.append(inference_response)
+            outputs.append(wav_tensor)
+            
+            if request_id in self.token2wav_model.streaming_flow_cache:
+                cache = self.token2wav_model.streaming_flow_cache[request_id]
+                hifigan_cache = self.token2wav_model.hift_cache_dict[request_id]
+                conformer_cnn_cache = cache['conformer_cnn_cache']
+                conformer_att_cache = cache['conformer_att_cache'].transpose(0,1)
+                estimator_cnn_cache = cache['estimator_cnn_cache'].unsqueeze(0)
+                estimator_att_cache = cache['estimator_att_cache'].unsqueeze(0)
+                mel = hifigan_cache['mel']
+                source = hifigan_cache['source']
+                speech = hifigan_cache['speech']
 
+                outputs.extend([
+                    pb_utils.Tensor.from_dlpack("conformer_cnn_cache", to_dlpack(conformer_cnn_cache)),
+                    pb_utils.Tensor.from_dlpack("conformer_att_cache", to_dlpack(conformer_att_cache)),
+                    pb_utils.Tensor.from_dlpack("estimator_cnn_cache", to_dlpack(estimator_cnn_cache)),
+                    pb_utils.Tensor.from_dlpack("estimator_att_cache", to_dlpack(estimator_att_cache)),
+                    pb_utils.Tensor.from_dlpack("mel", to_dlpack(mel)),
+                    pb_utils.Tensor.from_dlpack("source", to_dlpack(source)),
+                    pb_utils.Tensor.from_dlpack("speech", to_dlpack(speech)),
+                ])
+            else:
+                outputs.extend([pb_utils.Tensor("conformer_cnn_cache", np.array([], dtype=np.float16)),
+                pb_utils.Tensor("conformer_att_cache", np.array([], dtype=np.float16)),
+                pb_utils.Tensor("estimator_cnn_cache", np.array([], dtype=np.float16)),
+                pb_utils.Tensor("estimator_att_cache", np.array([], dtype=np.float16)),
+                pb_utils.Tensor("mel", np.array([], dtype=np.float32)),
+                pb_utils.Tensor("source", np.array([], dtype=np.float32)),
+                pb_utils.Tensor("speech", np.array([], dtype=np.float32)),
+                ])
+
+            inference_response = pb_utils.InferenceResponse(output_tensors=outputs)
+            responses.append(inference_response)
         return responses
+
+    def finalize(self):
+        self.logger.log_info("Finalizing Token2WavDiT model")

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

@@ -372,7 +372,6 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
         self, generated_speech_tokens: list[int], last_chunk: bool, request_id: str, speaker_id: str, prompt_audio: torch.Tensor = None, prompt_audio_sample_rate: int = 16000
     ):  
         if speaker_id not in self.speaker_cache:
-        # if 1:
             assert prompt_audio is not None, "prompt_audio is required for new speaker"
             assert prompt_audio_sample_rate == 16000
 
@@ -384,20 +383,10 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
 
             prompt_audio_dict = {'spk_emb_for_flow': spk_emb_for_flow, 'prompt_mels_for_flow': prompt_mels_for_flow}
 
-        # if speaker_id not in self.speaker_cache:
-        # if 1:
-            
             cache_dict = self.get_prompt_audio_cache_for_streaming_tts(prompt_speech_tokens_list, prompt_mels_for_flow, prompt_mels_lens_for_flow, spk_emb_for_flow)
             self.speaker_cache[speaker_id] = {'prompt_audio_dict': prompt_audio_dict, 'cache_dict': cache_dict}
             print(f"speaker_id {speaker_id} added to cache")
 
-            # get a clone of cache dict ['estimator_att_cache'] and later check if it would be change 
-        att_cache_clone = self.speaker_cache[speaker_id]['cache_dict']['estimator_att_cache'].clone()
-        cnn_cache_clone = self.speaker_cache[speaker_id]['cache_dict']['estimator_cnn_cache'].clone()
-        conformer_cnn_cache_clone = self.speaker_cache[speaker_id]['cache_dict']['conformer_cnn_cache'].clone()
-        conformer_att_cache_clone = self.speaker_cache[speaker_id]['cache_dict']['conformer_att_cache'].clone()
-    
-
         if request_id not in self.streaming_flow_cache:
             self.streaming_flow_cache[request_id] = {k: v.clone() for k, v in self.speaker_cache[speaker_id]['cache_dict'].items()}
             self.hift_cache_dict[request_id] = dict(
@@ -405,6 +394,12 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
             source = torch.zeros(1, 1, 0, device='cuda'),
             speech = torch.zeros(1, 0, device='cuda'),
             )
+        # else:
+        #     for k, v in self.streaming_flow_cache[request_id].items():
+        #         print(f"k: {k}, v: {v.shape}, dtype: {v.dtype}")
+        #     for k, v in self.hift_cache_dict[request_id].items():
+        #         print(f"k: {k}, v: {v.shape}, dtype: {v.dtype}")
+        #     breakpoint()
 
         current_request_cache = self.streaming_flow_cache[request_id]
 
@@ -420,33 +415,6 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
             n_timesteps=10,
         )
 
-        # get the original att_cache
-        original_att_cache = self.speaker_cache[speaker_id]['cache_dict']['estimator_att_cache']
-        original_cnn_cache = self.speaker_cache[speaker_id]['cache_dict']['estimator_cnn_cache']
-        original_conformer_cnn_cache = self.speaker_cache[speaker_id]['cache_dict']['conformer_cnn_cache']
-        original_conformer_att_cache = self.speaker_cache[speaker_id]['cache_dict']['conformer_att_cache']
-        if not torch.allclose(original_att_cache, att_cache_clone):
-            print("att_cache changed")
-            # print the last 10 elements of original_att_cache and att_cache_clone
-            print(original_att_cache[:, :, :, -10:])
-            print(att_cache_clone[:, :, :, -10:])
-            breakpoint()
-        if not torch.allclose(original_cnn_cache, cnn_cache_clone):
-            print("cnn_cache changed")
-            print(original_cnn_cache[..., -10:])
-            print(cnn_cache_clone[..., -10:])
-            breakpoint()
-        if not torch.allclose(original_conformer_cnn_cache, conformer_cnn_cache_clone):
-            print("conformer_cnn_cache changed")
-            print(original_conformer_cnn_cache[..., -10:])
-            print(conformer_cnn_cache_clone[..., -10:])
-            breakpoint()
-        if not torch.allclose(original_conformer_att_cache, conformer_att_cache_clone):
-            print("conformer_att_cache changed")
-            print(original_conformer_att_cache[..., -10:])
-            print(conformer_att_cache_clone[..., -10:])
-            breakpoint()
-
         self.streaming_flow_cache[request_id] = new_streaming_flow_cache
 
 
@@ -482,7 +450,7 @@ class CosyVoice2_Token2Wav(torch.nn.Module):
             assert request_id in self.streaming_flow_cache
             self.streaming_flow_cache.pop(request_id)
             self.hift_cache_dict.pop(request_id)
-        # breakpoint()
+
         return speech
 
 def collate_fn(batch):

runtime/triton_trtllm/model_repo/token2wav_dit/config.pbtxt

@@ -15,11 +15,14 @@
 name: "token2wav_dit"
 backend: "python"
 max_batch_size: ${triton_max_batch_size}
+
 dynamic_batching {
     max_queue_delay_microseconds: ${max_queue_delay_microseconds}
     priority_levels: 10
     default_priority_level: 10
 }
+
+parameters: { key: "FORCE_CPU_ONLY_INPUT_TENSORS" value: {string_value:"no"}}
 parameters [
   {
    key: "model_dir",
@@ -49,6 +52,48 @@ input [
     dims: [ 1 ]
     reshape: { shape: [ ] }
     optional: true
+  },
+  {
+    name: "conformer_cnn_cache"
+    data_type: TYPE_FP16
+    dims: [ 512, -1 ]
+    optional: true
+  },
+  {
+    name: "conformer_att_cache"
+    data_type: TYPE_FP16
+    dims: [ 10, 8, -1, 128 ]
+    optional: true
+  },
+  {
+    name: "estimator_cnn_cache"
+    data_type: TYPE_FP16
+    dims: [ 10, 16, -1, 1024, 2 ]
+    optional: true
+  },
+  {
+    name: "estimator_att_cache"
+    data_type: TYPE_FP16
+    dims: [ 10, 16, -1, 8, -1, 128 ]
+    optional: true
+  },
+  {
+    name: "mel"
+    data_type: TYPE_FP32
+    dims: [ 80, -1 ]
+    optional: true
+  },
+  {
+    name: "source"
+    data_type: TYPE_FP32
+    dims: [ 1, -1 ]
+    optional: true
+  },
+  {
+    name: "speech"
+    data_type: TYPE_FP32
+    dims: [ -1 ]
+    optional: true
   }
 ]
 output [
@@ -56,6 +101,41 @@ output [
     name: "waveform"
     data_type: TYPE_FP32
     dims: [ -1 ]
+  },
+  {
+    name: "conformer_cnn_cache"
+    data_type: TYPE_FP16
+    dims: [ 512, -1 ]
+  },
+  {
+    name: "conformer_att_cache"
+    data_type: TYPE_FP16
+    dims: [ 10, 8, -1, 128 ]
+  },
+  {
+    name: "estimator_cnn_cache"
+    data_type: TYPE_FP16
+    dims: [ 10, 16, -1, 1024, 2 ]
+  },
+  {
+    name: "estimator_att_cache"
+    data_type: TYPE_FP16
+    dims: [ 10, 16, -1, 8, -1, 128 ]
+  },
+  {
+    name: "mel"
+    data_type: TYPE_FP32
+    dims: [ 80, -1 ]
+  },
+  {
+    name: "source"
+    data_type: TYPE_FP32
+    dims: [ 1, -1 ]
+  },
+  {
+    name: "speech"
+    data_type: TYPE_FP32
+    dims: [ -1 ]
   }
 ]