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

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import json
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
        """
        # Parse model parameters
        parameters = json.loads(args['model_config'])['parameters']
        model_params = {k: v["string_value"] for k, v in parameters.items()}
        
        # 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 = False

    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_response = llm_request.exec(decoupled=self.decoupled)
        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]]
        
        return 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 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 = self.forward_llm(input_ids)
            
            # Decode and extract semantic token IDs from generated text
            predicted_text = self.tokenizer.batch_decode([generated_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),
            )
            
            # 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)
                             
        return responses