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

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import json
import math
import os
import re
from typing import Dict, List, Tuple, Optional, Union

import numpy as np
import torch
from torch.utils.dlpack import from_dlpack, to_dlpack
import triton_python_backend_utils as pb_utils
from transformers import AutoTokenizer

from sparktts.utils.token_parser import TASK_TOKEN_MAP

def process_prompt(
    text: str,
    prompt_text: Optional[str] = None,
    global_token_ids: torch.Tensor = None,
    semantic_token_ids: torch.Tensor = None,
) -> Tuple[str, torch.Tensor]:
    """
    Process input for voice cloning.

    Args:
        text: The text input to be converted to speech.
        prompt_text: Transcript of the prompt audio.
        global_token_ids: Global token IDs extracted from reference audio.
        semantic_token_ids: Semantic token IDs extracted from reference audio.

    Returns:
        Tuple containing the formatted input prompt and global token IDs.
    """
    # Convert global tokens to string format
    global_tokens = "".join(
        [f"<|bicodec_global_{i}|>" for i in global_token_ids.squeeze()]
    )

    
    # Prepare the input tokens for the model
    if prompt_text is not None:
        # Include semantic tokens when prompt text is provided
        semantic_tokens = "".join(
            [f"<|bicodec_semantic_{i}|>" for i in semantic_token_ids.squeeze()]
        )

        inputs = [
            TASK_TOKEN_MAP["tts"],
            "<|start_content|>",
            prompt_text,
            text,
            "<|end_content|>",
            "<|start_global_token|>",
            global_tokens,
            "<|end_global_token|>",
            "<|start_semantic_token|>",
            semantic_tokens,
        ]
    else:
        # Without prompt text, exclude semantic tokens
        inputs = [
            TASK_TOKEN_MAP["tts"],
            "<|start_content|>",
            text,
            "<|end_content|>",
            "<|start_global_token|>",
            global_tokens,
            "<|end_global_token|>",
        ]

    # Join all input components into a single string
    inputs = "".join(inputs)
    return inputs, global_token_ids


class TritonPythonModel:
    """Triton Python model for Spark TTS.
    
    This model orchestrates the end-to-end TTS pipeline by coordinating
    between audio tokenizer, LLM, and vocoder components.
    """
    
    def initialize(self, args):
        """Initialize the model.
        
        Args:
            args: Dictionary containing model configuration
        """
        self.logger = pb_utils.Logger
        # Parse model parameters
        self.model_config = json.loads(args['model_config'])
        parameters = self.model_config['parameters']
        model_params = {k: v["string_value"] for k, v in parameters.items()}
        self.logger.log_info(f"model_params:{model_params}")
        # streaming TTS parameters
        assert (
            float(model_params["audio_chunk_duration"]) >= 0.5
        ), f"audio_chunk_duration at least 0.5 seconds"
        self.audio_chunk_duration = float(model_params["audio_chunk_duration"])
        self.max_audio_chunk_duration = float(model_params["max_audio_chunk_duration"])
        assert (
            float(model_params["audio_chunk_size_scale_factor"]) >= 1.0
        ), "audio_chunk_size_scale_factor should be greater than 1, change it according to your actual rtf"
        self.audio_chunk_size_scale_factor = float(model_params["audio_chunk_size_scale_factor"])  # scale speed
        self.audio_chunk_overlap_duration = float(model_params["audio_chunk_overlap_duration"])
        self.audio_tokenizer_frame_rate = int(model_params["audio_tokenizer_frame_rate"])

        # Initialize tokenizer
        llm_tokenizer_dir = model_params["llm_tokenizer_dir"]
        self.tokenizer = AutoTokenizer.from_pretrained(llm_tokenizer_dir)
        self.device = torch.device("cuda")
        self.decoupled = pb_utils.using_decoupled_model_transaction_policy(self.model_config)

    def forward_llm(self, input_ids):
        """
        Prepares the response from the language model based on the provided
        inputs. Creates a `pb_utils.InferenceRequest` object with passed
        `llm_request_inputs` to send to a decoupled TensorRTLLM model.
        For each response from the language model:
            - Checks for errors and raise an exception if any are found.
            - Extracts the "output_ids" tensor from the response.
            - Determines the finish reason based on the presence of the
              end-of-sequence token or reaching the maximum length.
            - Appends the generated token IDs to `output_ids`.
            - If the finish reason is determined, decodes the output IDs to text
              and prepares the final response.

        The final response includes the generated text, finish reason,
        completion tokens, prompt tokens, and total tokens.

        Parameters
        ----------
        - llm_request_inputs (dict): A dictionary containing the inputs for the language model.

        Returns
        -------
        - pb_utils.InferenceResponse: The response object containing the generated text and additional metadata.
        """
        # convert input_ids to numpy, with shape [1, sequence_length]
        input_ids = input_ids.cpu().numpy()
        max_tokens = 512
        input_dict = {
            "request_output_len": np.array([[max_tokens]], dtype=np.int32),
            "end_id": np.array([[self.tokenizer.eos_token_id]], dtype=np.int32),
            "pad_id": np.array([[self.tokenizer.pad_token_id]], dtype=np.int32),
            "streaming": np.array([[self.decoupled]], dtype=np.bool_),
            "runtime_top_p": np.array([[0.95]], dtype=np.float32),
            "runtime_top_k": np.array([[50]], dtype=np.int32),
            "temperature": np.array([[0.8]], dtype=np.float32),
            "input_ids": input_ids,
            "input_lengths": np.array([[input_ids.shape[1]]], dtype=np.int32),
        }
        
        # Convert inputs to Triton tensors
        input_tensor_list = [
            pb_utils.Tensor(k, v) for k, v in input_dict.items()
        ]
        
        # Create and execute inference request
        llm_request = pb_utils.InferenceRequest(
            model_name="tensorrt_llm",
            requested_output_names=["output_ids", "sequence_length"],
            inputs=input_tensor_list,
        )
        
        llm_responses = llm_request.exec(decoupled=self.decoupled)
        if self.decoupled:
            for llm_response in llm_responses:
                if llm_response.has_error():
                    raise pb_utils.TritonModelException(llm_response.error().message())
                
                # Extract and process output
                output_ids = pb_utils.get_output_tensor_by_name(
                    llm_response, "output_ids").as_numpy()
                seq_lens = pb_utils.get_output_tensor_by_name(
                    llm_response, "sequence_length").as_numpy()
                
                # Get actual output IDs up to the sequence length
                actual_output_ids = output_ids[0][0][:seq_lens[0][0]]
                
                yield actual_output_ids
        else:
            llm_response = llm_responses
            if llm_response.has_error():
                raise pb_utils.TritonModelException(llm_response.error().message())
            
            # Extract and process output
            output_ids = pb_utils.get_output_tensor_by_name(
                llm_response, "output_ids").as_numpy()
            seq_lens = pb_utils.get_output_tensor_by_name(
                llm_response, "sequence_length").as_numpy()
            
            # Get actual output IDs up to the sequence length
            actual_output_ids = output_ids[0][0][:seq_lens[0][0]]
            
            yield actual_output_ids    
                
    def forward_audio_tokenizer(self, wav, wav_len):
        """Forward pass through the audio tokenizer component.
        
        Args:
            wav: Input waveform tensor
            wav_len: Waveform length tensor
            
        Returns:
            Tuple of global and semantic tokens
        """
        inference_request = pb_utils.InferenceRequest(
            model_name='audio_tokenizer',
            requested_output_names=['global_tokens', 'semantic_tokens'],
            inputs=[wav, wav_len]
        )
        
        inference_response = inference_request.exec()
        if inference_response.has_error():
            raise pb_utils.TritonModelException(inference_response.error().message())
        
        # Extract and convert output tensors
        global_tokens = pb_utils.get_output_tensor_by_name(inference_response, 'global_tokens')
        global_tokens = torch.utils.dlpack.from_dlpack(global_tokens.to_dlpack()).cpu()
        
        semantic_tokens = pb_utils.get_output_tensor_by_name(inference_response, 'semantic_tokens')
        semantic_tokens = torch.utils.dlpack.from_dlpack(semantic_tokens.to_dlpack()).cpu()
        
        return global_tokens, semantic_tokens

    def forward_vocoder(self, global_token_ids: torch.Tensor, pred_semantic_ids: torch.Tensor) -> torch.Tensor:
        """Forward pass through the vocoder component.
        
        Args:
            global_token_ids: Global token IDs tensor
            pred_semantic_ids: Predicted semantic token IDs tensor
            
        Returns:
            Generated waveform tensor
        """
        # Convert tensors to Triton format
        global_token_ids_tensor = pb_utils.Tensor.from_dlpack("global_tokens", to_dlpack(global_token_ids))
        pred_semantic_ids_tensor = pb_utils.Tensor.from_dlpack("semantic_tokens", to_dlpack(pred_semantic_ids))
        
        # Create and execute inference request
        inference_request = pb_utils.InferenceRequest(
            model_name='vocoder',
            requested_output_names=['waveform'],
            inputs=[global_token_ids_tensor, pred_semantic_ids_tensor]
        )
        
        inference_response = inference_request.exec()
        if inference_response.has_error():
            raise pb_utils.TritonModelException(inference_response.error().message())
        
        # Extract and convert output waveform
        waveform = pb_utils.get_output_tensor_by_name(inference_response, 'waveform')
        waveform = torch.utils.dlpack.from_dlpack(waveform.to_dlpack()).cpu()
        
        return waveform
    
    def token2wav(self, generated_token_ids, global_token_ids):
        # Decode and extract semantic token IDs from generated text
        predicted_text = self.tokenizer.batch_decode(
            [generated_token_ids],
            skip_special_tokens=True,
        )[0]
        pred_semantic_ids = (
            torch.tensor(
                [int(token) for token in re.findall(r"bicodec_semantic_(\d+)", predicted_text)]
            )
            .unsqueeze(0)
            .to(torch.int32)
        )

        # Generate audio with vocoder
        audio = self.forward_vocoder(
            global_token_ids.to(self.device),
            pred_semantic_ids.to(self.device),
        )

        return audio

    def execute(self, requests):
        """Execute inference on the batched requests.
        
        Args:
            requests: List of inference requests
            
        Returns:
            List of inference responses containing generated audio
        """
        responses = []
        
        for request in requests:
            # Extract input tensors
            wav = pb_utils.get_input_tensor_by_name(request, "reference_wav")
            wav_len = pb_utils.get_input_tensor_by_name(request, "reference_wav_len")
            
            # Process reference audio through audio tokenizer
            global_tokens, semantic_tokens = self.forward_audio_tokenizer(wav, wav_len)
            
            # Extract text inputs
            reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy()
            reference_text = reference_text[0][0].decode('utf-8')
            
            target_text = pb_utils.get_input_tensor_by_name(request, "target_text").as_numpy()
            target_text = target_text[0][0].decode('utf-8')
            
            # Prepare prompt for LLM
            prompt, global_token_ids = process_prompt(
                text=target_text,
                prompt_text=reference_text,
                global_token_ids=global_tokens,
                semantic_token_ids=semantic_tokens,
            )
            
            
            # Tokenize prompt for LLM
            model_inputs = self.tokenizer([prompt], return_tensors="pt").to(self.device)
            input_ids = model_inputs.input_ids.to(torch.int32)
            
            # Generate semantic tokens with LLM
            generated_ids_iter = self.forward_llm(input_ids)

            if self.decoupled:
                response_sender = request.get_response_sender()
                request_id = request.request_id()
                semantic_token_ids_arr = []
                max_chunk_size = math.ceil(self.max_audio_chunk_duration * self.audio_tokenizer_frame_rate)
                chunk_size = math.ceil(self.audio_chunk_duration * self.audio_tokenizer_frame_rate)
                overlap_chunk_size = math.ceil(self.audio_chunk_overlap_duration * self.audio_tokenizer_frame_rate)
                self.logger.log_info(
                    f"[{request_id}] init chunk_size: {chunk_size} max_chunk_size: {max_chunk_size}"
                )
                for generated_ids in generated_ids_iter:
                    if generated_ids is None or len(generated_ids) == 0:
                        break

                    semantic_token_ids_arr.append(generated_ids)
                    if len(semantic_token_ids_arr) >= chunk_size:
                        chunk = semantic_token_ids_arr[:chunk_size]
                        generated_semantic_token_ids = np.hstack(chunk)
                        # Process each chunk
                        sub_tts_speech = self.token2wav(generated_semantic_token_ids, global_token_ids)
                        # Prepare response to send
                        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)

                        semantic_token_ids_arr = semantic_token_ids_arr[chunk_size - overlap_chunk_size:]
                        # increase chunk size for better speech quality
                        chunk_size = min(max_chunk_size, int(chunk_size * self.audio_chunk_size_scale_factor))
                        self.logger.log_info(f"[{request_id}] increase chunk_size: {chunk_size}")

                if len(semantic_token_ids_arr) > 0:  # end to finalize
                    generated_semantic_token_ids = np.hstack(semantic_token_ids_arr)
                    # Process each chunk
                    sub_tts_speech = self.token2wav(generated_semantic_token_ids, global_token_ids)
                    # Prepare response to send
                    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)
                    self.logger.log_info(f"[{request_id}] last chunk len: {len(semantic_token_ids_arr)}")
            else:
                generated_ids = next(generated_ids_iter)
                if generated_ids is None or len(generated_ids) == 0:
                    raise pb_utils.TritonModelException("Generated IDs is None or empty")

                audio = self.token2wav(generated_ids, global_token_ids)
                
                # Prepare response
                audio_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(audio))
                inference_response = pb_utils.InferenceResponse(output_tensors=[audio_tensor])
                responses.append(inference_response)
            
            if self.decoupled:
                response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL)
                self.logger.log_info(f"send tritonserver_response_complete_final to end")
        
        if not self.decoupled:
            return responses