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Coding Implementation to End-to-End Transformer Model Optimization with Hugging Face Optimum, ONNX Runtime, and Quantization

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Coding Implementation to End-to-End Transformer Model Optimization with Hugging Face Optimum, ONNX Runtime, and Quantization

In this tutorial, we walk through how to use Hugging Face Optimum to optimize Transformer models, enhancing their speed while maintaining accuracy. We will set up DistilBERT on the SST-2 dataset, and compare different execution engines, including plain PyTorch and torch.compile, ONNX Runtime, and quantized ONNX. This step-by-step guide provides hands-on experience with model export, optimization, quantization, and benchmarking, all within a Google Colab environment.

Target Audience Analysis

The target audience for this content primarily includes:

  • Data scientists and machine learning engineers looking to optimize Transformer models for deployment.
  • Business managers and decision-makers in AI and tech companies interested in improving model performance and efficiency.

Common pain points include:

  • Challenges in improving model inference speed without sacrificing accuracy.
  • Complexity in implementing optimization techniques and understanding the various execution engines.

Goals of the audience typically focus on:

  • Reducing latency in AI applications.
  • Maximizing resource utilization in cloud or edge environments.

Interests may include:

  • Latest advancements in AI model optimization.
  • Practical implementations of optimization techniques.

Communication preferences lean towards clear, technical explanations with practical examples and code snippets.

Setting Up the Environment

We begin by installing the required libraries and setting up our environment for Hugging Face Optimum with ONNX Runtime:

!pip -q install "transformers>=4.49" "optimum[onnxruntime]>=1.20.0" "datasets>=2.20" "evaluate>=0.4" accelerate

Next, we configure paths, batch size, and iteration settings, confirming whether we are running on CPU or GPU.

Loading the Dataset

We load an SST-2 validation slice and prepare tokenization, an accuracy metric, and batching:

ds = load_dataset("glue", "sst2", split="validation[:20%]")
texts, labels = ds["sentence"], ds["label"]
metric = evaluate.load("accuracy")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

Defining Helper Functions

We define run_eval to compute accuracy from any predictor and bench to warm up and time end-to-end inference:

def make_batches(texts, max_len=MAXLEN, batch=BATCH):
   for i in range(0, len(texts), batch):
       yield tokenizer(texts[i:i+batch], padding=True, truncation=True, max_length=max_len, return_tensors="pt")

Benchmarking Different Execution Engines

We load the baseline PyTorch classifier and benchmark it on SST-2:

torch_model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID).to(DEVICE).eval()

Next, we attempt torch.compile for just-in-time graph optimizations:

compiled_model = torch_model
try:
   compiled_model = torch.compile(torch_model, mode="reduce-overhead", fullgraph=False)
except Exception as e:
   print("torch.compile unavailable or failed -> skipping:", repr(e))

Using ONNX Runtime

We export the model to ONNX and run it with ONNX Runtime:

provider = "CUDAExecutionProvider" if DEVICE == "cuda" else "CPUExecutionProvider"
ort_model = ORTModelForSequenceClassification.from_pretrained(MODEL_ID, export=True, provider=provider, cache_dir=ORT_DIR)

Applying Quantization

We apply dynamic quantization with Optimum’s ORTQuantizer:

quantizer = ORTQuantizer.from_pretrained(ORT_DIR)
qconfig = QuantizationConfig(approach="dynamic", per_channel=False, reduce_range=True)
quantizer.quantize(model_input=ORT_DIR, quantization_config=qconfig, save_dir=Q_DIR)

Comparative Results

We compile results from the benchmarks:

rows = [["PyTorch eager", pt_ms, pt_sd, pt_acc],
       ["ONNX Runtime",  ort_ms, ort_sd, ort_acc],
       ["ORT Quantized", oq_ms, oq_sd, oq_acc]]

We present a summary table to compare latency and accuracy across engines.

Conclusion

This workflow demonstrates how Optimum helps bridge the gap between standard PyTorch models and production-ready, optimized deployments. We achieve speedups with ONNX Runtime and quantization while retaining accuracy, and explore how torch.compile provides gains directly within PyTorch. This practical approach balances performance and efficiency for Transformer models.

For further exploration, feel free to check out our GitHub Page for tutorials, codes, and notebooks. Follow us on Twitter, and join our 100k+ ML SubReddit. Subscribe to our Newsletter for updates.

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