TDT Inference: High CPU & CUDA Graph Decoder Slowdown

Dec 9, 2025 by Alex Johnson 54 views

Navigating NeMo's TDT Inference: CPU Usage and CUDA Graph Decoder Quirks

Understanding TDT inference performance can sometimes feel like a deep dive into the intricate workings of your hardware and software. When you're working with models like nvidia/parakeet-tdt-0.6b-v3, optimizing inference speed is crucial for real-time applications. This article aims to shed light on some peculiar behaviors observed during TDT inference, specifically concerning high CPU utilization and the performance impact of the use_cuda_graph_decoder setting.

We've encountered situations where the CPU usage spikes dramatically during TDT inference, especially as we push the model for higher throughput. Imagine processing audio samples at an impressive 100ms per sample, achieving an inverse real-time factor (RTF) of nearly 600. In such scenarios, CPU utilization can soar beyond 1000%. This might sound alarming, and it's natural to question if this is an intended behavior or a potential bug. While the exact reasons can be complex, involving data preprocessing, batch management, and communication between CPU and GPU, such high CPU load can become a bottleneck. It's important to remember that even with powerful GPUs, the CPU plays a vital role in orchestrating the entire inference pipeline. From loading data, managing batches, to post-processing results, a significant portion of the computational load can rest on the CPU's shoulders. If the CPU can't keep up with feeding data to the GPU or processing its outputs efficiently, you'll see these extreme utilization numbers. This is often exacerbated in highly parallelized inference setups where the CPU is working overtime to prepare work for the GPU.

The Impact of `use_cuda_graph_decoder` on TDT Inference

One of the more perplexing observations arises when toggling the use_cuda_graph_decoder flag within the TDT decoding configuration. Specifically, enabling use_cuda_graph_decoder=True alongside timestamps=True can lead to a significant performance degradation, extending inference time from a swift 330ms per sample to a sluggish 1100ms. This is quite the jump! However, the situation changes when timestamps are disabled. In that context, use_cuda_graph_decoder=True actually improves performance. This contrast suggests that the interaction between CUDA graphs and timestamp generation is not as straightforward as one might initially assume. CUDA graphs are designed to capture a sequence of CUDA kernel calls and replay them with minimal overhead. This can be highly beneficial for repetitive tasks in inference. When timestamps are not required, the decoder can potentially run as a more cohesive, optimized graph. However, when timestamps are needed, the process of calculating and retrieving these time-based annotations might disrupt the smooth execution of the CUDA graph, or perhaps require additional synchronization and data transfers that negate the graph's benefits. This could involve additional kernel launches or data movements that break the sequential, optimized flow that CUDA graphs excel at. Understanding this nuanced behavior is key to unlocking the best performance for your specific TDT inference tasks. It highlights the importance of empirical testing and profiling different configuration options to find the optimal setup for your unique needs.

Reproducing the Behavior: A Code Snippet

To help illustrate the observed issues, let's look at the Python code snippet used for TDT inference:

import numpy as np
import torch
import logging
import nemo.collections.asr as nemo_asr
from nemo.core.neural_types import Logits
from nemo.core.classes import Model
from nemo.core.optim import Optimizer
from nemo.core.config import Configurable
from nemo.collections.asr.parts.utils.decoder_utils import RNNTDecodingConfig

class TDTNemoWrapper:
    def __init__(
        self, 
        model_size_or_path: str,
        device: str = "auto",
    ):
        self.model_name = model_size_or_path
        if ".nemo" in self.model_name:
            self.model = nemo_asr.models.ASRModel.restore_from(self.model_name)
        else:
            self.model = nemo_asr.models.ASRModel.from_pretrained(model_name=self.model_name)
        self.model.to(device)
        self.model.eval()
        # This is where the decoding strategy is configured.
        tdt_decoding = RNNTDecodingConfig(strategy="greedy_batch", model_type="tdt", fused_batch_size=-1) 
        tdt_decoding.greedy.loop_labels = True
        tdt_decoding.greedy.use_cuda_graph_decoder = True # Flag for CUDA graph optimization
        self.model.change_decoding_strategy(tdt_decoding)

        logging.info(f"Decoding config: {self.model.cfg.decoding}")

    def transcribe(
        self, 
        audio: list[np.ndarray],
        add_word_timestamps: bool = False,
        max_batch_size: int = 1,
    ) -> list[TranscriptSegment]: # Assuming TranscriptSegment is a defined type
        # For the purpose of reproducing the described issue, we might force this to False
        # based on the observation that timestamps=True causes slowdown with CUDA graphs.
        # However, the original description implies testing both scenarios.
        # Let's assume the user intends to test 'add_word_timestamps=True' for the bug report.
        # If the goal is just to show the slow path, add_word_timestamps=True is relevant.
        # If the goal is to show the faster path (disabled timestamps), then it would be False.
        # Given the problem description, we are focused on the scenario where timestamps=True
        # causes slowdown with use_cuda_graph_decoder=True.
        
        # The original code had `add_word_timestamps = False` which contradicts the description
        # of the slowdown occurring with `timestamps=True`. Assuming the description is the
        # intended test case:
        # add_word_timestamps = False # Original line in the provided code
        # To test the described bug, we need to ensure timestamps are enabled if that's the trigger:
        actual_timestamp_setting = add_word_timestamps 

        batch_size = min(len(audio), max_batch_size)
        with torch.inference_mode():
            # `torch.cuda.amp.autocast` is used for mixed precision.
            with torch.amp.autocast("cuda" if torch.cuda.is_available() else "cpu", enabled=True):
                # The `transcribe` method is called here. The `timestamps` argument is crucial.
                out = self.model.transcribe(audio, timestamps=actual_timestamp_setting, batch_size=batch_size, verbose=False)

        return out

# Example Usage (requires actual audio data and model path):
# model_path = "nvidia/parakeet-tdt-0.6b-v3"
# # Assume audio_data is a list of numpy arrays representing audio segments
# # audio_data = [...] 
# 
# # Instantiate the wrapper with CUDA graph decoder enabled by default
# wrapper = TDTNemoWrapper(model_path, device="cuda")
# 
# # --- Scenario 1: use_cuda_graph_decoder=True, timestamps=True (Expected Slowdown)
# print("Testing with use_cuda_graph_decoder=True and timestamps=True...")
# # Ensure the wrapper's config reflects this or change it dynamically if needed for testing
# # For this example, we assume it's set during __init__
# # If testing different flags, the model might need to be re-initialized or the strategy changed.
# # For reproduction, we'd call: wrapper.transcribe(audio_data, add_word_timestamps=True)
# 
# # --- Scenario 2: use_cuda_graph_decoder=False, timestamps=False (Expected Faster)
# # To test this, one would typically re-initialize the wrapper with `use_cuda_graph_decoder=False`
# # or modify the `change_decoding_strategy` call in __init__.
# # e.g., tdt_decoding.greedy.use_cuda_graph_decoder = False
# # and potentially set `add_word_timestamps=False` in the transcribe call.
# # For this example, assuming the wrapper is re-initialized or configured for this:
# # print("Testing with use_cuda_graph_decoder=False and timestamps=False...")
# # wrapper_fast = TDTNemoWrapper(model_path, device="cuda")
# # wrapper_fast.model.cfg.decoding.greedy.use_cuda_graph_decoder = False # Assuming direct access or a method to change
# # out_fast = wrapper_fast.transcribe(audio_data, add_word_timestamps=False)

Expected Behavior and Environment Details

Our expectation was straightforward: higher throughput should ideally be supported by efficient hardware utilization, and use_cuda_graph_decoder=True should consistently offer performance benefits, or at least not introduce a significant bottleneck when timestamps are enabled. The observed high CPU usage and the performance penalty with use_cuda_graph_decoder=True when timestamps=True deviate from these expectations.

Environment Overview:

Environment Location: Docker on a local A100 instance.
NVIDIA NeMo Toolkit: 2.5.0

Environment Details:

OS Version: Ubuntu 24.04.3 LTS
PyTorch Version: 2.5.1
Python Version: 3.10.16

Understanding these performance characteristics is vital for anyone deploying NeMo models for demanding applications. While the CPU's role is often underestimated, optimizing its interaction with the GPU and carefully configuring inference parameters like CUDA graph usage can lead to significant improvements. It’s a reminder that achieving peak inference performance is an iterative process of testing, profiling, and fine-tuning.

For further insights into optimizing your AI workloads on NVIDIA hardware, exploring resources from NVIDIA Developer can be highly beneficial. You can find valuable documentation and best practices at NVIDIA Developer.

Navigating NeMo's TDT Inference: CPU Usage and CUDA Graph Decoder Quirks

The Impact of use_cuda_graph_decoder on TDT Inference

Expected Behavior and Environment Details

The Impact of `use_cuda_graph_decoder` on TDT Inference