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Merge pull request #1561 from yuekaizhang/streaming

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[Runtime] Support Streaming TTS for Triton + TensorRT-LLM runtime
This commit is contained in:
Xiang Lyu
2025-09-04 09:48:43 +08:00
committed by GitHub
parent b7ec6c4678 8a4309d89c
commit 86e7c2d731
11 changed files with 544 additions and 134 deletions
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88
runtime/triton_trtllm/README.md
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@@ -1,15 +1,17 @@
## Best Practices for Serving CosyVoice with NVIDIA Triton Inference Server ## Serving CosyVoice with NVIDIA Triton Inference Server
Thanks to the contribution from NVIDIA Yuekai Zhang. Contributed by Yuekai Zhang (NVIDIA).
### Quick Start ### Quick Start
Launch the service directly with Docker Compose: Launch the service directly with Docker Compose:
```sh ```sh
docker compose up docker compose up
``` ```
### Build the Docker Image ### Build the Docker Image
Build the image from scratch:
To build the image from scratch:
```sh ```sh
docker build . -f Dockerfile.server -t soar97/triton-cosyvoice:25.06 docker build . -f Dockerfile.server -t soar97/triton-cosyvoice:25.06
``` ```
@@ -21,71 +23,89 @@ docker run -it --name "cosyvoice-server" --gpus all --net host -v $your_mount_di
``` ```
### Understanding `run.sh` ### Understanding `run.sh`
The `run.sh` script orchestrates the entire workflow through numbered stages. The `run.sh` script orchestrates the entire workflow through numbered stages.
Run a subset of stages with: You can run a subset of stages with:
```sh ```sh
bash run.sh <start_stage> <stop_stage> [service_type] bash run.sh <start_stage> <stop_stage> [service_type]
``` ```
- `<start_stage>` – stage to start from (0-5). - `<start_stage>`: The stage to start from (0-5).
- `<stop_stage>` – stage to stop after (0-5). - `<stop_stage>`: The stage to stop after (0-5).
Stages: **Stages:**
- **Stage 0** – Download the cosyvoice-2 0.5B model from HuggingFace.
- **Stage 1** – Convert the HuggingFace checkpoint to TensorRT-LLM format and build TensorRT engines. - **Stage 0**: Downloads the `cosyvoice-2 0.5B` model from HuggingFace.
- **Stage 2** – Create the Triton model repository and configure the model files (adjusts depending on whether `Decoupled=True/False` will be used later). - **Stage 1**: Converts the HuggingFace checkpoint to the TensorRT-LLM format and builds the TensorRT engines.
- **Stage 3** – Launch the Triton Inference Server. - **Stage 2**: Creates the Triton model repository and configures the model files. The configuration is adjusted based on whether `Decoupled=True` (streaming) or `Decoupled=False` (offline) will be used.
- **Stage 4** – Run the single-utterance HTTP client. - **Stage 3**: Launches the Triton Inference Server.
- **Stage 5** – Run the gRPC benchmark client. - **Stage 4**: Runs the single-utterance HTTP client for testing.
- **Stage 5**: Runs the gRPC benchmark client.
### Export Models and Launch Server
### Export Models to TensorRT-LLM and Launch the Server
Inside the Docker container, prepare the models and start the Triton server by running stages 0-3: Inside the Docker container, prepare the models and start the Triton server by running stages 0-3:
```sh ```sh
# Runs stages 0, 1, 2, and 3 # This command runs stages 0, 1, 2, and 3
bash run.sh 0 3 bash run.sh 0 3
``` ```
*Note: Stage 2 prepares the model repository differently depending on whether you intend to run with `Decoupled=False` or `Decoupled=True`. Rerun stage 2 if you switch the service type.* > [!TIP]
> Both streaming and offline (non-streaming) TTS modes are supported. For streaming TTS, set `Decoupled=True`. For offline TTS, set `Decoupled=False`. You need to rerun stage 2 if you switch between modes.
### Single-Utterance HTTP Client ### Single-Utterance HTTP Client
Send a single HTTP inference request:
Sends a single HTTP inference request. This is intended for testing the offline TTS mode (`Decoupled=False`):
```sh ```sh
bash run.sh 4 4 bash run.sh 4 4
``` ```
### Benchmark with a Dataset ### Benchmark with a Dataset
Benchmark the running Triton server. Pass either `streaming` or `offline` as the third argument.
```sh
bash run.sh 5 5
# You can also customise parameters such as num_task and dataset split directly: To benchmark the running Triton server, pass `streaming` or `offline` as the third argument:
```sh
bash run.sh 5 5 # [streaming|offline]
# You can also customize parameters such as the number of tasks and the dataset split:
# python3 client_grpc.py --num-tasks 2 --huggingface-dataset yuekai/seed_tts_cosy2 --split-name test_zh --mode [streaming|offline] # python3 client_grpc.py --num-tasks 2 --huggingface-dataset yuekai/seed_tts_cosy2 --split-name test_zh --mode [streaming|offline]
``` ```
> [!TIP] > [!TIP]
> Only offline CosyVoice TTS is currently supported. Setting the client to `streaming` simply enables NVIDIA Triton’s decoupled mode so that responses are returned as soon as they are ready. > It is recommended to run the benchmark multiple times to get stable results after the initial server warm-up.
### Benchmark Results ### Benchmark Results
Decoding on a single L20 GPU with 26 prompt_audio/target_text [pairs](https://huggingface.co/datasets/yuekai/seed_tts) (≈221 s of audio): The following results were obtained by decoding on a single L20 GPU with 26 prompt audio/target text pairs from the [yuekai/seed_tts](https://huggingface.co/datasets/yuekai/seed_tts) dataset (approximately 170 seconds of audio):
**Streaming TTS (First Chunk Latency)**
| Mode | Concurrency | Avg Latency (ms) | P50 Latency (ms) | RTF |
|---|---|---|---|---|
| Streaming, Decoupled=True | 1 | 220.43 | 218.07 | 0.1237 |
| Streaming, Decoupled=True | 2 | 476.97 | 369.25 | 0.1022 |
| Streaming, Decoupled=True | 4 | 1107.34 | 1243.75| 0.0922 |
**Offline TTS (Full Sentence Latency)**
| Mode | Note | Concurrency | Avg Latency (ms) | P50 Latency (ms) | RTF | | Mode | Note | Concurrency | Avg Latency (ms) | P50 Latency (ms) | RTF |
|------|------|-------------|------------------|------------------|-----| |---|---|---|---|---|---|
| Decoupled=False | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 1 | 758.04 | 615.79 | 0.0891 | | Offline, Decoupled=False | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 1 | 758.04 | 615.79 | 0.0891 |
| Decoupled=False | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 2 | 1025.93 | 901.68 | 0.0657 | | Offline, Decoupled=False | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 2 | 1025.93 | 901.68 | 0.0657 |
| Decoupled=False | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 4 | 1914.13 | 1783.58 | 0.0610 | | Offline, Decoupled=False | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 4 | 1914.13 | 1783.58 | 0.0610 |
| Decoupled=True | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 1 | 659.87 | 655.63 | 0.0891 |
| Decoupled=True | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 2 | 1103.16 | 992.96 | 0.0693 |
| Decoupled=True | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 4 | 1790.91 | 1668.63 | 0.0604 |
### OpenAI-Compatible Server ### OpenAI-Compatible Server
To launch an OpenAI-compatible service, run:
To launch an OpenAI-compatible API service, run the following commands:
```sh ```sh
git clone https://github.com/yuekaizhang/Triton-OpenAI-Speech.git git clone https://github.com/yuekaizhang/Triton-OpenAI-Speech.git
cd Triton-OpenAI-Speech
pip install -r requirements.txt pip install -r requirements.txt
# After the Triton service is up, start the FastAPI bridge:
# After the Triton service is running, start the FastAPI bridge:
python3 tts_server.py --url http://localhost:8000 --ref_audios_dir ./ref_audios/ --port 10086 --default_sample_rate 24000 python3 tts_server.py --url http://localhost:8000 --ref_audios_dir ./ref_audios/ --port 10086 --default_sample_rate 24000
# Test with curl
# Test the service with curl:
bash test/test_cosyvoice.sh bash test/test_cosyvoice.sh
``` ```
> [!NOTE]
> Currently, only the offline TTS mode is compatible with the OpenAI-compatible server.
### Acknowledgements ### Acknowledgements
This section originates from the NVIDIA CISI project. We also provide other multimodal resources—see [mair-hub](https://github.com/nvidia-china-sae/mair-hub) for details.
This work originates from the NVIDIA CISI project. For more multimodal resources, please see [mair-hub](https://github.com/nvidia-china-sae/mair-hub).

62
runtime/triton_trtllm/client_grpc.py
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@@ -395,38 +395,45 @@ def run_sync_streaming_inference(
# Reconstruct audio using cross-fade (from client_grpc_streaming.py) # Reconstruct audio using cross-fade (from client_grpc_streaming.py)
actual_duration = 0 actual_duration = 0
if audios: if audios:
cross_fade_samples = int(chunk_overlap_duration * save_sample_rate) # Only spark_tts model uses cross-fade
fade_out = np.linspace(1, 0, cross_fade_samples) if model_name == "spark_tts":
fade_in = np.linspace(0, 1, cross_fade_samples) cross_fade_samples = int(chunk_overlap_duration * save_sample_rate)
reconstructed_audio = None fade_out = np.linspace(1, 0, cross_fade_samples)
fade_in = np.linspace(0, 1, cross_fade_samples)
reconstructed_audio = None
# Simplified reconstruction based on client_grpc_streaming.py # Simplified reconstruction based on client_grpc_streaming.py
if not audios: if not audios:
print("Warning: No audio chunks received.") print("Warning: No audio chunks received.")
reconstructed_audio = np.array([], dtype=np.float32) # Empty array reconstructed_audio = np.array([], dtype=np.float32) # Empty array
elif len(audios) == 1: elif len(audios) == 1:
reconstructed_audio = audios[0] reconstructed_audio = audios[0]
else:
reconstructed_audio = audios[0][:-cross_fade_samples] # Start with first chunk minus overlap
for i in range(1, len(audios)):
# Cross-fade section
cross_faded_overlap = (audios[i][:cross_fade_samples] * fade_in +
audios[i - 1][-cross_fade_samples:] * fade_out)
# Middle section of the current chunk
middle_part = audios[i][cross_fade_samples:-cross_fade_samples]
# Concatenate
reconstructed_audio = np.concatenate([reconstructed_audio, cross_faded_overlap, middle_part])
# Add the last part of the final chunk
reconstructed_audio = np.concatenate([reconstructed_audio, audios[-1][-cross_fade_samples:]])
if reconstructed_audio is not None and reconstructed_audio.size > 0:
actual_duration = len(reconstructed_audio) / save_sample_rate
# Save reconstructed audio
sf.write(audio_save_path, reconstructed_audio, save_sample_rate, "PCM_16")
else:
print("Warning: No audio chunks received or reconstructed.")
actual_duration = 0 # Set duration to 0 if no audio
else: else:
reconstructed_audio = audios[0][:-cross_fade_samples] # Start with first chunk minus overlap reconstructed_audio = np.concatenate(audios)
for i in range(1, len(audios)): print(f"reconstructed_audio: {reconstructed_audio.shape}")
# Cross-fade section
cross_faded_overlap = (audios[i][:cross_fade_samples] * fade_in +
audios[i - 1][-cross_fade_samples:] * fade_out)
# Middle section of the current chunk
middle_part = audios[i][cross_fade_samples:-cross_fade_samples]
# Concatenate
reconstructed_audio = np.concatenate([reconstructed_audio, cross_faded_overlap, middle_part])
# Add the last part of the final chunk
reconstructed_audio = np.concatenate([reconstructed_audio, audios[-1][-cross_fade_samples:]])
if reconstructed_audio is not None and reconstructed_audio.size > 0:
actual_duration = len(reconstructed_audio) / save_sample_rate actual_duration = len(reconstructed_audio) / save_sample_rate
# Save reconstructed audio # Save reconstructed audio
os.makedirs(os.path.dirname(audio_save_path), exist_ok=True)
sf.write(audio_save_path, reconstructed_audio, save_sample_rate, "PCM_16") sf.write(audio_save_path, reconstructed_audio, save_sample_rate, "PCM_16")
else:
print("Warning: No audio chunks received or reconstructed.")
actual_duration = 0 # Set duration to 0 if no audio
else: else:
print("Warning: No audio chunks received.") print("Warning: No audio chunks received.")
@@ -667,6 +674,7 @@ async def main():
manifest_item_list = split_data(manifest_item_list, num_tasks) manifest_item_list = split_data(manifest_item_list, num_tasks)
os.makedirs(args.log_dir, exist_ok=True) os.makedirs(args.log_dir, exist_ok=True)
tasks = [] tasks = []
start_time = time.time() start_time = time.time()
for i in range(num_tasks): for i in range(num_tasks):

2
runtime/triton_trtllm/model_repo/audio_tokenizer/1/model.py
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@@ -32,7 +32,7 @@ import triton_python_backend_utils as pb_utils
import os import os
import numpy as np import numpy as np
import s3tokenizer import s3tokenizer
torch.set_num_threads(1)
ORIGINAL_VOCAB_SIZE = 151663 ORIGINAL_VOCAB_SIZE = 151663

2
runtime/triton_trtllm/model_repo/audio_tokenizer/config.pbtxt
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@@ -20,7 +20,7 @@ dynamic_batching {
} }
parameters [ parameters [
{ {
key: "model_dir", key: "model_dir",
value: {string_value:"${model_dir}"} value: {string_value:"${model_dir}"}
} }
] ]

165
runtime/triton_trtllm/model_repo/cosyvoice2/1/model.py
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@@ -28,6 +28,8 @@ import json
import math import math
import os import os
import re import re
import threading
import time
from typing import Dict, List, Tuple, Optional, Union from typing import Dict, List, Tuple, Optional, Union
import numpy as np import numpy as np
@@ -35,13 +37,14 @@ import torch
from torch.utils.dlpack import from_dlpack, to_dlpack from torch.utils.dlpack import from_dlpack, to_dlpack
import triton_python_backend_utils as pb_utils import triton_python_backend_utils as pb_utils
from transformers import AutoTokenizer from transformers import AutoTokenizer
import torchaudio.compliance.kaldi as kaldi
import torchaudio import torchaudio
import onnxruntime
from matcha.utils.audio import mel_spectrogram from matcha.utils.audio import mel_spectrogram
torch.set_num_threads(1)
class TritonPythonModel: class TritonPythonModel:
"""Triton Python model for Spark TTS. """Triton Python model for Spark TTS.
@@ -62,6 +65,8 @@ class TritonPythonModel:
parameters = self.model_config['parameters'] parameters = self.model_config['parameters']
model_params = {k: v["string_value"] for k, v in parameters.items()} model_params = {k: v["string_value"] for k, v in parameters.items()}
self.logger.log_info(f"model_params:{model_params}") self.logger.log_info(f"model_params:{model_params}")
self.dynamic_chunk_strategy = model_params.get("dynamic_chunk_strategy", "exponential") # "exponential" or "time_based"
self.logger.log_info(f"Using dynamic chunk strategy: {self.dynamic_chunk_strategy}")
# Initialize tokenizer # Initialize tokenizer
llm_tokenizer_dir = model_params["llm_tokenizer_dir"] llm_tokenizer_dir = model_params["llm_tokenizer_dir"]
@@ -72,11 +77,9 @@ class TritonPythonModel:
self.device = torch.device("cuda") self.device = torch.device("cuda")
self.decoupled = pb_utils.using_decoupled_model_transaction_policy(self.model_config) self.decoupled = pb_utils.using_decoupled_model_transaction_policy(self.model_config)
campplus_model = f'{model_params["model_dir"]}/campplus.onnx' self.token_frame_rate = 25
option = onnxruntime.SessionOptions() self.flow_pre_lookahead_len = 3
option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL self.token_hop_len = 15
option.intra_op_num_threads = 1
self.campplus_session = onnxruntime.InferenceSession(campplus_model, sess_options=option, providers=["CPUExecutionProvider"])
def forward_llm(self, input_ids): def forward_llm(self, input_ids):
""" """
@@ -105,7 +108,7 @@ class TritonPythonModel:
""" """
# convert input_ids to numpy, with shape [1, sequence_length] # convert input_ids to numpy, with shape [1, sequence_length]
input_ids = input_ids.cpu().numpy() input_ids = input_ids.cpu().numpy()
max_tokens = 1024 max_tokens = 750
input_dict = { input_dict = {
"request_output_len": np.array([[max_tokens]], dtype=np.int32), "request_output_len": np.array([[max_tokens]], dtype=np.int32),
"end_id": np.array([[self.eos_token_id]], dtype=np.int32), "end_id": np.array([[self.eos_token_id]], dtype=np.int32),
@@ -114,6 +117,8 @@ class TritonPythonModel:
"runtime_top_p": np.array([[0.95]], dtype=np.float32), "runtime_top_p": np.array([[0.95]], dtype=np.float32),
"runtime_top_k": np.array([[50]], dtype=np.int32), "runtime_top_k": np.array([[50]], dtype=np.int32),
"temperature": np.array([[0.8]], dtype=np.float32), "temperature": np.array([[0.8]], dtype=np.float32),
"repetition_penalty": np.array([[1.1]], dtype=np.float32),
"random_seed": np.array([[42]], dtype=np.uint64),
"input_ids": input_ids, "input_ids": input_ids,
"input_lengths": np.array([[input_ids.shape[1]]], dtype=np.int32), "input_lengths": np.array([[input_ids.shape[1]]], dtype=np.int32),
} }
@@ -188,12 +193,40 @@ class TritonPythonModel:
return prompt_speech_tokens return prompt_speech_tokens
def forward_speaker_embedding(self, wav):
"""Forward pass through the speaker embedding component.
Args:
wav: Input waveform tensor
Returns:
Prompt speaker embedding tensor
"""
inference_request = pb_utils.InferenceRequest(
model_name='speaker_embedding',
requested_output_names=['prompt_spk_embedding'],
inputs=[pb_utils.Tensor.from_dlpack("reference_wav", to_dlpack(wav))]
)
inference_response = inference_request.exec()
if inference_response.has_error():
raise pb_utils.TritonModelException(inference_response.error().message())
# Extract and convert output tensors
prompt_spk_embedding = pb_utils.get_output_tensor_by_name(inference_response, 'prompt_spk_embedding')
prompt_spk_embedding = torch.utils.dlpack.from_dlpack(prompt_spk_embedding.to_dlpack())
return prompt_spk_embedding
def forward_token2wav( def forward_token2wav(
self, self,
prompt_speech_tokens: torch.Tensor, prompt_speech_tokens: torch.Tensor,
prompt_speech_feat: torch.Tensor, prompt_speech_feat: torch.Tensor,
prompt_spk_embedding: torch.Tensor, prompt_spk_embedding: torch.Tensor,
target_speech_tokens: torch.Tensor) -> torch.Tensor: target_speech_tokens: torch.Tensor,
request_id: str,
token_offset: int = None,
finalize: bool = None) -> torch.Tensor:
"""Forward pass through the vocoder component. """Forward pass through the vocoder component.
Args: Args:
@@ -210,11 +243,21 @@ class TritonPythonModel:
prompt_spk_embedding_tensor = pb_utils.Tensor.from_dlpack("prompt_spk_embedding", to_dlpack(prompt_spk_embedding)) prompt_spk_embedding_tensor = pb_utils.Tensor.from_dlpack("prompt_spk_embedding", to_dlpack(prompt_spk_embedding))
target_speech_tokens_tensor = pb_utils.Tensor.from_dlpack("target_speech_tokens", to_dlpack(target_speech_tokens)) target_speech_tokens_tensor = pb_utils.Tensor.from_dlpack("target_speech_tokens", to_dlpack(target_speech_tokens))
inputs_tensor = [prompt_speech_tokens_tensor, prompt_speech_feat_tensor, prompt_spk_embedding_tensor, target_speech_tokens_tensor]
if token_offset is not None:
assert finalize is not None
token_offset_tensor = pb_utils.Tensor("token_offset", np.array([[token_offset]], dtype=np.int32))
finalize_tensor = pb_utils.Tensor("finalize", np.array([[finalize]], dtype=np.bool_))
inputs_tensor.append(token_offset_tensor)
inputs_tensor.append(finalize_tensor)
# Create and execute inference request # Create and execute inference request
inference_request = pb_utils.InferenceRequest( inference_request = pb_utils.InferenceRequest(
model_name='token2wav', model_name='token2wav',
requested_output_names=['waveform'], requested_output_names=['waveform'],
inputs=[prompt_speech_tokens_tensor, prompt_speech_feat_tensor, prompt_spk_embedding_tensor, target_speech_tokens_tensor] inputs=inputs_tensor,
request_id=request_id,
) )
inference_response = inference_request.exec() inference_response = inference_request.exec()
@@ -235,17 +278,6 @@ class TritonPythonModel:
input_ids = torch.cat([input_ids, prompt_speech_tokens], dim=1) input_ids = torch.cat([input_ids, prompt_speech_tokens], dim=1)
return input_ids return input_ids
def _extract_spk_embedding(self, speech):
feat = kaldi.fbank(speech,
num_mel_bins=80,
dither=0,
sample_frequency=16000)
feat = feat - feat.mean(dim=0, keepdim=True)
embedding = self.campplus_session.run(None,
{self.campplus_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist()
embedding = torch.tensor([embedding]).to(self.device).half()
return embedding
def _extract_speech_feat(self, speech): def _extract_speech_feat(self, speech):
speech_feat = mel_spectrogram( speech_feat = mel_spectrogram(
speech, speech,
@@ -263,6 +295,14 @@ class TritonPythonModel:
speech_feat = speech_feat.unsqueeze(dim=0) speech_feat = speech_feat.unsqueeze(dim=0)
return speech_feat return speech_feat
def _llm_gen_thread(self, generated_ids_iter, semantic_token_ids_arr, llm_is_done_flag):
for generated_ids in generated_ids_iter:
generated_ids = generated_ids.tolist()
if len(generated_ids) == 0:
break
semantic_token_ids_arr.extend(generated_ids)
llm_is_done_flag[0] = True
def execute(self, requests): def execute(self, requests):
"""Execute inference on the batched requests. """Execute inference on the batched requests.
@@ -275,6 +315,7 @@ class TritonPythonModel:
responses = [] responses = []
for request in requests: for request in requests:
request_id = request.request_id()
# Extract input tensors # Extract input tensors
wav = pb_utils.get_input_tensor_by_name(request, "reference_wav") wav = pb_utils.get_input_tensor_by_name(request, "reference_wav")
wav_len = pb_utils.get_input_tensor_by_name(request, "reference_wav_len") wav_len = pb_utils.get_input_tensor_by_name(request, "reference_wav_len")
@@ -292,6 +333,8 @@ class TritonPythonModel:
prompt_speech_feat = speech_feat[:, :2 * token_len].contiguous().half() prompt_speech_feat = speech_feat[:, :2 * token_len].contiguous().half()
prompt_speech_tokens = prompt_speech_tokens[:, :token_len].contiguous() prompt_speech_tokens = prompt_speech_tokens[:, :token_len].contiguous()
flow_prompt_speech_token_len = prompt_speech_tokens.shape[-1]
reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy() reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy()
reference_text = reference_text[0][0].decode('utf-8') reference_text = reference_text[0][0].decode('utf-8')
@@ -307,25 +350,76 @@ class TritonPythonModel:
# Generate semantic tokens with LLM # Generate semantic tokens with LLM
generated_ids_iter = self.forward_llm(input_ids) generated_ids_iter = self.forward_llm(input_ids)
prompt_spk_embedding = self.forward_speaker_embedding(wav_tensor)
if self.decoupled: if self.decoupled:
response_sender = request.get_response_sender() response_sender = request.get_response_sender()
request_id = request.request_id()
generated_ids = []
for generated_id in generated_ids_iter:
# convert the numpy array into a int32 tensor
generated_id = generated_id.tolist()
if len(generated_id) > 0:
assert len(generated_id) == 1, "Generated ID is not a single integer"
generated_ids.append(generated_id[0])
generated_ids = torch.tensor(generated_ids).unsqueeze(0).to(torch.int32).to(self.device)
prompt_spk_embedding = self._extract_spk_embedding(wav_tensor)
audio = self.forward_token2wav(prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding, generated_ids)
# Prepare response semantic_token_ids_arr = []
audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(audio)) llm_is_done_flag = [False]
llm_thread = threading.Thread(
target=self._llm_gen_thread,
args=(generated_ids_iter, semantic_token_ids_arr, llm_is_done_flag)
)
llm_thread.start()
token_offset, chunk_index = 0, 0
start_time = time.time()
this_token_hop_len = self.token_hop_len
while True:
pending_num = len(semantic_token_ids_arr) - token_offset
if llm_is_done_flag[0]:
break
if pending_num >= this_token_hop_len + self.flow_pre_lookahead_len:
this_tts_speech_token = semantic_token_ids_arr[:token_offset + this_token_hop_len + self.flow_pre_lookahead_len]
this_tts_speech_token = torch.tensor(this_tts_speech_token).unsqueeze(dim=0).to(torch.int32).to(self.device)
sub_tts_speech = self.forward_token2wav(
prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding,
this_tts_speech_token, request_id, token_offset, False)
audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech))
inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor])
response_sender.send(inference_response)
token_offset += this_token_hop_len
self.logger.log_info(f"chunk_index: {chunk_index}, current_token_hop_len: {this_token_hop_len}")
if self.dynamic_chunk_strategy == "exponential":
this_token_hop_len = self.token_frame_rate * (2 ** chunk_index)
elif self.dynamic_chunk_strategy == "time_based":
# see https://github.com/qi-hua/async_cosyvoice/blob/main/model.py#L306
cost_time = time.time() - start_time
duration = token_offset / self.token_frame_rate
if chunk_index > 0 and cost_time > 0:
avg_chunk_processing_time = cost_time / (chunk_index + 1)
if avg_chunk_processing_time > 0:
multiples = (duration - cost_time) / avg_chunk_processing_time
self.logger.log_info(f"multiples: {multiples}")
next_pending_num = len(semantic_token_ids_arr) - token_offset
if multiples > 4:
this_token_hop_len = (next_pending_num // self.token_hop_len + 1) * self.token_hop_len
elif multiples > 2:
this_token_hop_len = (next_pending_num // self.token_hop_len) * self.token_hop_len
else:
this_token_hop_len = self.token_hop_len
this_token_hop_len = max(self.token_hop_len, this_token_hop_len)
chunk_index += 1
else:
time.sleep(0.02)
this_tts_speech_token = torch.tensor(semantic_token_ids_arr).unsqueeze(dim=0).to(torch.int32).to(self.device)
sub_tts_speech = self.forward_token2wav(prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding, this_tts_speech_token, request_id, token_offset, True)
audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(sub_tts_speech))
inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor]) inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor])
response_sender.send(inference_response) response_sender.send(inference_response)
llm_thread.join()
response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL) response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL)
self.logger.log_info("send tritonserver_response_complete_final to end") self.logger.log_info("send tritonserver_response_complete_final to end")
else: else:
@@ -334,8 +428,7 @@ class TritonPythonModel:
if generated_ids is None or len(generated_ids) == 0: if generated_ids is None or len(generated_ids) == 0:
raise pb_utils.TritonModelException("Generated IDs is None or empty") raise pb_utils.TritonModelException("Generated IDs is None or empty")
prompt_spk_embedding = self._extract_spk_embedding(wav_tensor) audio = self.forward_token2wav(prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding, generated_ids, request_id)
audio = self.forward_token2wav(prompt_speech_tokens, prompt_speech_feat, prompt_spk_embedding, generated_ids)
# Prepare response # Prepare response
audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(audio)) audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(audio))

4
runtime/triton_trtllm/model_repo/cosyvoice2/config.pbtxt
View File

@@ -23,11 +23,11 @@ model_transaction_policy {
} }
parameters [ parameters [
{ {
key: "llm_tokenizer_dir", key: "llm_tokenizer_dir",
value: {string_value:"${llm_tokenizer_dir}"} value: {string_value:"${llm_tokenizer_dir}"}
}, },
{ {
key: "model_dir", key: "model_dir",
value: {string_value:"${model_dir}"} value: {string_value:"${model_dir}"}
} }
] ]

153
runtime/triton_trtllm/model_repo/speaker_embedding/1/model.py Normal file
View File

@@ -0,0 +1,153 @@
# 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 torchaudio.compliance.kaldi as kaldi
from cosyvoice.utils.file_utils import convert_onnx_to_trt
from cosyvoice.utils.common import TrtContextWrapper
import onnxruntime
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_dir = model_params["model_dir"]
gpu = "l20"
enable_trt = True
if enable_trt:
self.load_spk_trt(f'{model_dir}/campplus.{gpu}.fp32.trt',
f'{model_dir}/campplus.onnx',
1,
False)
else:
campplus_model = f'{model_dir}/campplus.onnx'
option = onnxruntime.SessionOptions()
option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
option.intra_op_num_threads = 1
self.spk_model = onnxruntime.InferenceSession(campplus_model, sess_options=option, providers=["CPUExecutionProvider"])
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)
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())
assert spk_engine is not None, 'failed to load trt {}'.format(spk_model)
self.spk_model = TrtContextWrapper(spk_engine, trt_concurrent=trt_concurrent, device=self.device)
def get_spk_trt_kwargs(self):
min_shape = [(1, 4, 80)]
opt_shape = [(1, 500, 80)]
max_shape = [(1, 3000, 80)]
input_names = ["input"]
return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names}
def _extract_spk_embedding(self, speech):
feat = kaldi.fbank(speech,
num_mel_bins=80,
dither=0,
sample_frequency=16000)
spk_feat = feat - feat.mean(dim=0, keepdim=True)
if isinstance(self.spk_model, onnxruntime.InferenceSession):
embedding = self.spk_model.run(
None, {self.spk_model.get_inputs()[0].name: spk_feat.unsqueeze(dim=0).cpu().numpy()}
)[0].flatten().tolist()
embedding = torch.tensor([embedding]).to(self.device)
else:
[spk_model, stream], trt_engine = self.spk_model.acquire_estimator()
# NOTE need to synchronize when switching stream
with torch.cuda.device(self.device):
torch.cuda.current_stream().synchronize()
spk_feat = spk_feat.unsqueeze(dim=0).to(self.device)
batch_size = spk_feat.size(0)
with stream:
spk_model.set_input_shape('input', (batch_size, spk_feat.size(1), 80))
embedding = torch.empty((batch_size, 192), device=spk_feat.device)
data_ptrs = [spk_feat.contiguous().data_ptr(),
embedding.contiguous().data_ptr()]
for i, j in enumerate(data_ptrs):
spk_model.set_tensor_address(trt_engine.get_tensor_name(i), j)
# run trt engine
assert spk_model.execute_async_v3(torch.cuda.current_stream().cuda_stream) is True
torch.cuda.current_stream().synchronize()
self.spk_model.release_estimator(spk_model, stream)
return embedding.half()
def execute(self, requests):
"""Execute inference on the batched requests.
Args:
requests: List of inference requests
Returns:
List of inference responses containing tokenized outputs
"""
responses = []
# 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_array = torch.from_numpy(wav_array).to(self.device)
embedding = self._extract_spk_embedding(wav_array)
prompt_spk_embedding_tensor = pb_utils.Tensor.from_dlpack(
"prompt_spk_embedding", to_dlpack(embedding))
inference_response = pb_utils.InferenceResponse(
output_tensors=[prompt_spk_embedding_tensor])
responses.append(inference_response)
return responses

48
runtime/triton_trtllm/model_repo/speaker_embedding/config.pbtxt Normal file
View File

@@ -0,0 +1,48 @@
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# 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.
name: "speaker_embedding"
backend: "python"
max_batch_size: ${triton_max_batch_size}
dynamic_batching {
max_queue_delay_microseconds: ${max_queue_delay_microseconds}
}
parameters [
{
key: "model_dir",
value: {string_value:"${model_dir}"}
}
]
input [
{
name: "reference_wav"
data_type: TYPE_FP32
dims: [-1]
}
]
output [
{
name: "prompt_spk_embedding"
data_type: TYPE_FP16
dims: [-1]
}
]
instance_group [
{
count: 1
kind: KIND_CPU
}
]

119
runtime/triton_trtllm/model_repo/token2wav/1/model.py
View File

@@ -32,22 +32,27 @@ from typing import List, Dict
import torch import torch
from torch.utils.dlpack import to_dlpack from torch.utils.dlpack import to_dlpack
from torch.nn import functional as F
import triton_python_backend_utils as pb_utils import triton_python_backend_utils as pb_utils
from hyperpyyaml import load_hyperpyyaml from hyperpyyaml import load_hyperpyyaml
from cosyvoice.utils.common import fade_in_out
from cosyvoice.utils.file_utils import convert_onnx_to_trt, export_cosyvoice2_vllm from cosyvoice.utils.file_utils import convert_onnx_to_trt, export_cosyvoice2_vllm
from cosyvoice.utils.common import TrtContextWrapper from cosyvoice.utils.common import TrtContextWrapper
from collections import defaultdict
import numpy as np
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
ORIGINAL_VOCAB_SIZE = 151663 ORIGINAL_VOCAB_SIZE = 151663
torch.set_num_threads(1)
class CosyVoice2: class CosyVoice2:
def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, trt_concurrent=1): def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, trt_concurrent=1, device='cuda'):
self.model_dir = model_dir self.model_dir = model_dir
self.fp16 = fp16 self.fp16 = fp16
@@ -57,7 +62,7 @@ class CosyVoice2:
raise ValueError('{} not found!'.format(hyper_yaml_path)) raise ValueError('{} not found!'.format(hyper_yaml_path))
with open(hyper_yaml_path, 'r') as f: with open(hyper_yaml_path, 'r') as f:
configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')}) configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')})
self.model = CosyVoice2Model(configs['flow'], configs['hift'], fp16) self.model = CosyVoice2Model(configs['flow'], configs['hift'], fp16, device)
self.model.load('{}/flow.pt'.format(model_dir), '{}/hift.pt'.format(model_dir)) self.model.load('{}/flow.pt'.format(model_dir), '{}/hift.pt'.format(model_dir))
if load_jit: if load_jit:
self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32')) self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
@@ -73,14 +78,22 @@ class CosyVoice2Model:
def __init__(self, def __init__(self,
flow: torch.nn.Module, flow: torch.nn.Module,
hift: torch.nn.Module, hift: torch.nn.Module,
fp16: bool = False): fp16: bool = False,
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') device: str = 'cuda'):
self.device = device
self.flow = flow self.flow = flow
self.hift = hift self.hift = hift
self.fp16 = fp16 self.fp16 = fp16
if self.fp16 is True: if self.fp16 is True:
self.flow.half() self.flow.half()
# streaming tts config
self.token_hop_len = 25
self.mel_cache_len = 8
self.source_cache_len = int(self.mel_cache_len * 480)
self.speech_window = np.hamming(2 * self.source_cache_len)
self.hift_cache_dict = defaultdict(lambda: None)
def load_jit(self, flow_encoder_model): def load_jit(self, flow_encoder_model):
flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device) flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
self.flow.encoder = flow_encoder self.flow.encoder = flow_encoder
@@ -111,6 +124,42 @@ class CosyVoice2Model:
input_names = ["x", "mask", "mu", "cond"] input_names = ["x", "mask", "mu", "cond"]
return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names} return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names}
def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0):
with torch.cuda.amp.autocast(self.fp16):
tts_mel, _ = self.flow.inference(token=token.to(self.device),
token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
prompt_token=prompt_token.to(self.device),
prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
prompt_feat=prompt_feat.to(self.device),
prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
embedding=embedding.to(self.device),
streaming=stream,
finalize=finalize)
tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:]
# append hift cache
if self.hift_cache_dict[uuid] is not None:
hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
else:
hift_cache_source = torch.zeros(1, 1, 0)
# keep overlap mel and hift cache
if finalize is False:
tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
if self.hift_cache_dict[uuid] is not None:
tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
'source': tts_source[:, :, -self.source_cache_len:],
'speech': tts_speech[:, -self.source_cache_len:]}
tts_speech = tts_speech[:, :-self.source_cache_len]
else:
if speed != 1.0:
assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode'
tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear')
tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
if self.hift_cache_dict[uuid] is not None:
tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
return tts_speech
class TritonPythonModel: class TritonPythonModel:
"""Triton Python model for vocoder. """Triton Python model for vocoder.
@@ -131,11 +180,11 @@ class TritonPythonModel:
model_dir = model_params["model_dir"] model_dir = model_params["model_dir"]
# Initialize device and vocoder # Initialize device and vocoder
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logger.info(f"Initializing vocoder from {model_dir} on {self.device}") logger.info(f"Initializing vocoder from {model_dir} on {self.device}")
self.token2wav_model = CosyVoice2( self.token2wav_model = CosyVoice2(
model_dir, load_jit=True, load_trt=True, fp16=True model_dir, load_jit=False, load_trt=True, fp16=True, device=self.device
) )
logger.info("Token2Wav initialized successfully") logger.info("Token2Wav initialized successfully")
@@ -166,25 +215,47 @@ class TritonPythonModel:
prompt_speech_tokens = prompt_speech_tokens - ORIGINAL_VOCAB_SIZE prompt_speech_tokens = prompt_speech_tokens - ORIGINAL_VOCAB_SIZE
target_speech_tokens = target_speech_tokens - ORIGINAL_VOCAB_SIZE target_speech_tokens = target_speech_tokens - ORIGINAL_VOCAB_SIZE
tts_mel, _ = self.token2wav_model.model.flow.inference( # We set token_offset as an optional input to support streaming/offline tts. It has to be None when offline tts.
token=target_speech_tokens, token_offset = pb_utils.get_input_tensor_by_name(request, "token_offset")
token_len=torch.tensor([target_speech_tokens.shape[1]], dtype=torch.int32).to( if token_offset is not None:
self.device token_offset = token_offset.as_numpy().item()
), finalize = pb_utils.get_input_tensor_by_name(request, "finalize").as_numpy().item()
prompt_token=prompt_speech_tokens, if not finalize:
prompt_token_len=torch.tensor( stream = True
[prompt_speech_tokens.shape[1]], dtype=torch.int32 else:
).to(self.device), stream = False
prompt_feat=prompt_speech_feat, request_id = request.request_id()
prompt_feat_len=torch.tensor([prompt_speech_feat.shape[1]], dtype=torch.int32).to(self.device), audio_hat = self.token2wav_model.model.token2wav(token=target_speech_tokens,
embedding=prompt_spk_embedding, prompt_token=prompt_speech_tokens,
streaming=False, prompt_feat=prompt_speech_feat,
finalize=True, embedding=prompt_spk_embedding,
) token_offset=token_offset,
uuid=request_id,
stream=stream,
finalize=finalize)
if finalize:
self.token2wav_model.model.hift_cache_dict.pop(request_id)
audio_hat, _ = self.token2wav_model.model.hift.inference( else:
speech_feat=tts_mel, cache_source=torch.zeros(1, 1, 0) tts_mel, _ = self.token2wav_model.model.flow.inference(
) token=target_speech_tokens,
token_len=torch.tensor([target_speech_tokens.shape[1]], dtype=torch.int32).to(
self.device
),
prompt_token=prompt_speech_tokens,
prompt_token_len=torch.tensor(
[prompt_speech_tokens.shape[1]], dtype=torch.int32
).to(self.device),
prompt_feat=prompt_speech_feat,
prompt_feat_len=torch.tensor([prompt_speech_feat.shape[1]], dtype=torch.int32).to(self.device),
embedding=prompt_spk_embedding,
streaming=False,
finalize=True,
)
audio_hat, _ = self.token2wav_model.model.hift.inference(
speech_feat=tts_mel, cache_source=torch.zeros(1, 1, 0)
)
generated_wave = audio_hat.squeeze(0).cpu().numpy() generated_wave = audio_hat.squeeze(0).cpu().numpy()

16
runtime/triton_trtllm/model_repo/token2wav/config.pbtxt
View File

@@ -20,7 +20,7 @@ dynamic_batching {
} }
parameters [ parameters [
{ {
key: "model_dir", key: "model_dir",
value: {string_value:"${model_dir}"} value: {string_value:"${model_dir}"}
} }
] ]
@@ -45,6 +45,20 @@ input [
name: "prompt_spk_embedding" name: "prompt_spk_embedding"
data_type: TYPE_FP16 data_type: TYPE_FP16
dims: [-1] dims: [-1]
},
{
name: "token_offset"
data_type: TYPE_INT32
dims: [ 1 ]
reshape: { shape: [ ] }
optional: true
},
{
name: "finalize"
data_type: TYPE_BOOL
dims: [ 1 ]
reshape: { shape: [ ] }
optional: true
} }
] ]
output [ output [

19
runtime/triton_trtllm/run.sh
View File

@@ -60,6 +60,7 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
cp -r ./model_repo/audio_tokenizer $model_repo cp -r ./model_repo/audio_tokenizer $model_repo
cp -r ./model_repo/tensorrt_llm $model_repo cp -r ./model_repo/tensorrt_llm $model_repo
cp -r ./model_repo/token2wav $model_repo cp -r ./model_repo/token2wav $model_repo
cp -r ./model_repo/speaker_embedding $model_repo
ENGINE_PATH=$trt_engines_dir ENGINE_PATH=$trt_engines_dir
MAX_QUEUE_DELAY_MICROSECONDS=0 MAX_QUEUE_DELAY_MICROSECONDS=0
@@ -67,11 +68,12 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
LLM_TOKENIZER_DIR=$huggingface_model_local_dir LLM_TOKENIZER_DIR=$huggingface_model_local_dir
BLS_INSTANCE_NUM=4 BLS_INSTANCE_NUM=4
TRITON_MAX_BATCH_SIZE=16 TRITON_MAX_BATCH_SIZE=16
DECOUPLED_MODE=False DECOUPLED_MODE=True # True for streaming, False for offline
python3 scripts/fill_template.py -i ${model_repo}/token2wav/config.pbtxt model_dir:${MODEL_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS} python3 scripts/fill_template.py -i ${model_repo}/token2wav/config.pbtxt model_dir:${MODEL_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS}
python3 scripts/fill_template.py -i ${model_repo}/audio_tokenizer/config.pbtxt model_dir:${MODEL_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS} python3 scripts/fill_template.py -i ${model_repo}/audio_tokenizer/config.pbtxt model_dir:${MODEL_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS}
python3 scripts/fill_template.py -i ${model_repo}/${cosyvoice2_dir}/config.pbtxt model_dir:${MODEL_DIR},bls_instance_num:${BLS_INSTANCE_NUM},llm_tokenizer_dir:${LLM_TOKENIZER_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},decoupled_mode:${DECOUPLED_MODE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS} python3 scripts/fill_template.py -i ${model_repo}/${cosyvoice2_dir}/config.pbtxt model_dir:${MODEL_DIR},bls_instance_num:${BLS_INSTANCE_NUM},llm_tokenizer_dir:${LLM_TOKENIZER_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},decoupled_mode:${DECOUPLED_MODE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS}
python3 scripts/fill_template.py -i ${model_repo}/speaker_embedding/config.pbtxt model_dir:${MODEL_DIR},triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS}
python3 scripts/fill_template.py -i ${model_repo}/tensorrt_llm/config.pbtxt triton_backend:tensorrtllm,triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},decoupled_mode:${DECOUPLED_MODE},max_beam_width:1,engine_dir:${ENGINE_PATH},max_tokens_in_paged_kv_cache:2560,max_attention_window_size:2560,kv_cache_free_gpu_mem_fraction:0.5,exclude_input_in_output:True,enable_kv_cache_reuse:False,batching_strategy:inflight_fused_batching,max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS},encoder_input_features_data_type:TYPE_FP16,logits_datatype:TYPE_FP32 python3 scripts/fill_template.py -i ${model_repo}/tensorrt_llm/config.pbtxt triton_backend:tensorrtllm,triton_max_batch_size:${TRITON_MAX_BATCH_SIZE},decoupled_mode:${DECOUPLED_MODE},max_beam_width:1,engine_dir:${ENGINE_PATH},max_tokens_in_paged_kv_cache:2560,max_attention_window_size:2560,kv_cache_free_gpu_mem_fraction:0.5,exclude_input_in_output:True,enable_kv_cache_reuse:False,batching_strategy:inflight_fused_batching,max_queue_delay_microseconds:${MAX_QUEUE_DELAY_MICROSECONDS},encoder_input_features_data_type:TYPE_FP16,logits_datatype:TYPE_FP32
fi fi
@@ -82,7 +84,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
fi fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
echo "Single request test http" echo "Single request test http, only work for offline TTS mode"
python3 client_http.py \ python3 client_http.py \
--reference-audio ./assets/prompt_audio.wav \ --reference-audio ./assets/prompt_audio.wav \
--reference-text "吃燕窝就选燕之屋，本节目由26年专注高品质燕窝的燕之屋冠名播出。豆奶牛奶换着喝，营养更均衡，本节目由豆本豆豆奶特约播出。" \ --reference-text "吃燕窝就选燕之屋，本节目由26年专注高品质燕窝的燕之屋冠名播出。豆奶牛奶换着喝，营养更均衡，本节目由豆本豆豆奶特约播出。" \
@@ -92,15 +94,16 @@ fi
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
echo "Running benchmark client grpc" echo "Running benchmark client grpc"
num_task=4 num_task=1
# set mode=streaming, when decoupled=True
# set mode=offline, when decoupled=False mode=streaming
mode=offline BLS_INSTANCE_NUM=4
python3 client_grpc.py \ python3 client_grpc.py \
--server-addr localhost \ --server-addr localhost \
--model-name cosyvoice2 \ --model-name cosyvoice2 \
--num-tasks $num_task \ --num-tasks $num_task \
--mode $mode \ --mode $mode \
--huggingface-dataset yuekai/seed_tts_cosy2 \ --huggingface-dataset yuekai/seed_tts_cosy2 \
--log-dir ./log_concurrent_tasks_${num_task}_${mode}_bls_4_${trt_dtype} --log-dir ./log_concurrent_tasks_${num_task}_${mode}_bls_${BLS_INSTANCE_NUM}
fi fi