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Inference Speed Optimization

Fast inference is critical for deploying DNA language models in real-world applications, from large-scale genomic screening to interactive analysis. This guide covers key techniques to accelerate model inference.

1. Use Half-Precision (FP16/BF16)

The Problem

Running inference in full 32-bit precision (FP32) is often unnecessarily slow and memory-intensive.

How to Optimize

Just like in training, using 16-bit floating-point numbers can provide a significant speedup for inference, especially on GPUs with Tensor Cores.

  • FP16: Best for general-purpose speedup.
  • BF16: Best for stability on Ampere and newer GPUs.

You can load the model directly in half-precision.

inference:
  batch_size: 16
  device: auto
  num_workers: 4
  use_fp16: true

2. Batching Inference Requests

The Problem

Processing sequences one by one is highly inefficient. The overhead of launching the model for a single sequence dominates the actual computation time.

How to Optimize

Group multiple DNA sequences together and process them as a single batch. This allows the GPU to perform computations in parallel, dramatically increasing throughput.

import torch

dna_sequences = ["GATTACA" * 10, "ACGT" * 20, "TTTAAA" * 15]

# Tokenize all sequences together with padding
inputs = tokenizer(
    dna_sequences,
    return_tensors="pt",
    padding=True,  # Pad to the length of the longest sequence in the batch
    truncation=True,
).to(model.device)

with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state
print("Processed batch of size:", len(dna_sequences))
print("Output shape:", embeddings.shape)

3. Compile the Model with torch.compile

The Problem

Standard PyTorch execution involves Python overhead that can slow down model execution.

How to Optimize

torch.compile() is a feature in PyTorch 2.0+ that JIT (Just-In-Time) compiles your model into optimized kernel code. It can provide significant speedups (1.3x-2x) with a single line of code.

# Before your inference loop, compile the model
compiled_model = torch.compile(model)

# Use the compiled model for inference
with torch.no_grad():
    outputs = compiled_model(**inputs)

Note: The first run after torch.compile() will be slow as the compilation happens. Subsequent runs will be much faster.