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Update evaluate_efficiency_salmonn.py #3

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Update evaluate_efficiency_salmonn.py #3

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31 changes: 26 additions & 5 deletions evaluate_efficiency_salmonn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,9 +106,25 @@ def get_gpu_memory_usage():
return gpu_memory


def model_inference(cfg, samples, test_prompt, salmonn):
def audio_preprocess(cfg, wav_processor, audio, sr=16000):
spectrogram = wav_processor(audio, sampling_rate=sr, return_tensors="pt")["input_features"]
audio = audio.unsqueeze(0)
sample_batch = {
"spectrogram": spectrogram,
"raw_wav": audio,
"text": ["test"],
"task": ["asr"],
"Q": [""],
"id": [0],
}
sample_batch = prepare_sample(sample_batch, cuda_enabled=torch.cuda.is_available(), device=cfg.config.run.device)
return sample_batch


def model_inference(cfg, audio, test_prompt, salmonn, wav_processor):
# TTFT
start_time = time.time()
samples = audio_preprocess(cfg, wav_processor, samples)
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samples = audio_preprocess(cfg, wav_processor, audio)

llm = salmonn.llama_model

batch_size = samples["spectrogram"].shape[0]
Expand Down Expand Up @@ -181,9 +197,13 @@ def main(args):
# Load dataset
with open("audiolm-trainer/prompts/test_prompt.json", "r") as f:
test_prompt = json.load(f)
dataloader = MockDataset.make_mock_dataloader(cfg, sr=16000, audio_length=10)
sample_batch = next(iter(dataloader))
sample_batch = prepare_sample(sample_batch, cuda_enabled=torch.cuda.is_available())
# dataloader = MockDataset.make_mock_dataloader(cfg, sr=16000, audio_length=10)
# sample_batch = next(iter(dataloader))
# sample_batch = prepare_sample(sample_batch, cuda_enabled=torch.cuda.is_available())
sr=16000
audio_length=10
wav_processor = WhisperFeatureExtractor.from_pretrained(cfg.config.model['whisper_path'])
random_sample = torch.randn(sr * audio_length)

# Measure memory and latency
memory_usages = []
Expand All @@ -196,9 +216,10 @@ def main(args):
with torch.no_grad():
inference_time, ttft, tpot = model_inference(
cfg,
sample_batch,
random_sample,
test_prompt,
salmonn_preprocessor,
wav_processor
)
torch.cuda.synchronize()
after_memory_allocated = torch.cuda.max_memory_allocated()
Expand Down