add triton solution

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

@@ -0,0 +1,95 @@
+# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+import json
+import torch
+from torch.utils.dlpack import to_dlpack
+
+import triton_python_backend_utils as pb_utils
+
+import os
+import numpy as np
+import s3tokenizer
+
+
+class TritonPythonModel:
+    """Triton Python model for audio tokenization.
+    
+    This model takes reference audio input and extracts semantic tokens
+    using s3tokenizer.
+    """
+
+    def initialize(self, args):
+        """Initialize the model.
+        
+        Args:
+            args: Dictionary containing model configuration
+        """
+        # Parse model parameters
+        parameters = json.loads(args['model_config'])['parameters']
+        model_params = {k: v["string_value"] for k, v in parameters.items()}
+        
+        self.device = torch.device("cuda")
+        model_path = os.path.join(model_params["model_dir"], "speech_tokenizer_v2.onnx")
+        self.audio_tokenizer = s3tokenizer.load_model(model_path).to(self.device)
+
+    def execute(self, requests):
+        """Execute inference on the batched requests.
+        
+        Args:
+            requests: List of inference requests
+            
+        Returns:
+            List of inference responses containing tokenized outputs
+        """
+        mels = []
+
+        # Process each request in batch
+        for request in requests:
+            # Extract input tensors
+            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).to(self.device)
+            # Prepare inputs
+            wav = wav_array[:, :wav_len].squeeze(0)
+            mels.append(s3tokenizer.log_mel_spectrogram(wav))
+            
+        mels, mels_lens = s3tokenizer.padding(mels)
+        codes, codes_lens = self.audio_tokenizer.quantize(mels.to(self.device), mels_lens.to(self.device))
+        codes = codes.clone() + 151663
+        
+        responses = []
+        for i in range(len(requests)):
+            prompt_speech_tokens = codes[i, :codes_lens[i].item()]            
+            prompt_speech_tokens_tensor = pb_utils.Tensor.from_dlpack(
+                "prompt_speech_tokens", to_dlpack(prompt_speech_tokens))
+            inference_response = pb_utils.InferenceResponse(
+                output_tensors=[prompt_speech_tokens_tensor])
+            responses.append(inference_response)
+                             
+        return responses