CUDA utilisation optimisation during model inference

ycyinfo: <p>Hi everyone,</p>
<p>I am running an inference model to create speaker embeddings from a checkpoint model. The problem I encounter is that I have quite high GPU memory usage (around 6728MiB) but very low volatile GPU-Util (between 7%-12%) even using very high batch size. I do the preprocessing and padding using collate_fn in the torch dataset and when I load the data, the output are already batches of processed spectrograms and lengths information used for the checkpoint model.</p>
<p>I am using: NVIDIA-SMI 525.60.11    Driver Version: 525.60.11    CUDA Version: 12.0<br>
The following is an example how I wrap everything, could anyone give me some advice if you spot something might cause the low GPU usage?<br>
Much thanks!</p>
<pre><code class="lang-auto">class EmbeddingDataset(Dataset):
    def __init__(self, audio_paths, device):
        self.audio_paths = audio_paths  #lists of audio path
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    def __len__(self):
        return len(self.audio_paths)

    def __getitem__(self, idx):
        audio_file = self.audio_paths[idx]
        return audio_file

    def collate_fn(self, batch):
        specs = []
        spec_lens = []

        for audio_file in batch:
            spec = self.process_audio(audio_file).to(self.device)
             specs.append(spec)
            spec_lens.append(len(spec))
        # Pad spectrograms and calculate spec_lens tensor
        max_length = max([len(spec) for spec in specs])
        padded_specs = torch.zeros(len(specs), max_length, specs[0].shape[1], device=self.device)
        for i, spec in enumerate(specs):
            padded_specs[i, :len(spec), :] = spec
        spec_lens = torch.tensor(spec_lens, dtype=torch.long, device=self.device)
        return padded_specs, spec_lens

    def process_audio(self, path_to_reference_audio):
        assert os.path.exists(path_to_reference_audio)
        wave, sr = soundfile.read(path_to_reference_audio)
        self.audio_preprocessor = AudioPreprocessor(input_sr=sr, output_sr=16000, cut_silence=False, device=self.device)
        spec = self.audio_preprocessor.audio_to_mel_spec_tensor(wave).transpose(0, 1).to(self.device)
        return spec

if __name__ == '__main__':
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    audio_dataset = EmbeddingDataset(audio_dir, device)
    audio_dataloader = DataLoader(audio_dataset, batch_size=batch_size, shuffle=False, collate_fn=audio_dataset.collate_fn)

    style_emb = StyleEmbedding()
    check_dict = torch.load("checkpoint_model.pt"), map_location=device)
    style_emb.load_state_dict(check_dict["style_emb_func"])
    style_emb.to(device)
    style_emb.eval()

    embedding_list = []
    
    for batch_specs, batch_spec_lens in audio_dataloader:
        batch_specs = batch_specs.to(device)
        batch_spec_lens = batch_spec_lens.to(device)
        embeddings = style_emb(batch_specs, batch_spec_lens)
        embedding_list.extend(embeddings.cpu().detach().numpy())
</code></pre>
<p>Much thanks!</p>

ptrblck: <p>Profile your workload with a visual profiler, such as Nsight Systems, or the native PyTorch profiler to narrow down where the bottleneck in your code is. Without isolating the actual bottleneck all your optimizations might be useless.</p>