Principle:Neuml Txtai ONNX Export

Knowledge Sources	txtai txtai Documentation BERT
Domains	Deep_Learning, Training, NLP
Last Updated	2026-02-09 00:00 GMT

Overview

ONNX (Open Neural Network Exchange) export is the process of converting a trained PyTorch transformer model into the ONNX intermediate representation format. This enables optimized, cross-platform inference using runtimes such as ONNX Runtime, which can deliver significant speedups over native PyTorch inference through graph optimizations, operator fusion, and hardware-specific acceleration.

Description

After a model has been fine-tuned, deploying it efficiently often requires a format that decouples inference from the training framework. ONNX serves this purpose by representing the model as a directed acyclic graph of standardized operators. The export process involves:

1. Defining I/O schemas -- each task type produces different output tensors. The export must declare the correct input names (e.g., input_ids, attention_mask, token_type_ids) and output names (e.g., last_hidden_state, logits, start_logits/end_logits, embeddings) with their dynamic axes for variable batch sizes and sequence lengths.
2. Tracing the model -- PyTorch's torch.onnx.export() traces the model's forward pass using dummy inputs to capture the computation graph. Constant folding is applied to simplify the graph at export time.
3. Setting the opset version -- ONNX opsets define the available operators. Higher opsets support more operations but may have narrower runtime compatibility. The default opset 14 provides good coverage for transformer architectures.
4. Optional quantization -- after export, the ONNX model can be quantized using ONNX Runtime's dynamic quantization, which reduces model size and improves inference speed on CPU.

The task-to-output mapping defines what the exported model produces:

• default -- exports the full last_hidden_state tensor (all token representations).
• pooling -- exports a single embeddings vector per input (pooled representation).
• question-answering -- exports start_logits and end_logits for span extraction.
• text-classification -- exports classification logits.
• zero-shot-classification -- alias for text-classification (same output schema).

Usage

ONNX export is used after training is complete and the model needs to be deployed for inference. Typical scenarios include:

• Production deployment -- exporting a fine-tuned model for serving via ONNX Runtime in a production API.
• Edge deployment -- creating a compact, quantized model for inference on resource-constrained devices.
• Cross-platform compatibility -- enabling inference in environments where PyTorch is not available (e.g., C++, C#, Java, or JavaScript runtimes).
• Batch inference optimization -- leveraging ONNX Runtime's graph optimizations for faster batch processing.
• Model compression -- combining ONNX export with dynamic quantization to reduce model size by up to 4x.

Theoretical Basis

The ONNX format represents neural networks as computation graphs with well-defined operator semantics. The export process works through tracing: the model's forward pass is executed with concrete (dummy) inputs, and every operation is recorded as a node in the ONNX graph.

Pseudocode for the ONNX export pipeline:

FUNCTION export_to_onnx(model_path, task, output_path, quantize, opset):
    # Step 1: Define I/O schema based on task
    inputs = {"input_ids": {0: "batch", 1: "sequence"},
              "attention_mask": {0: "batch", 1: "sequence"},
              "token_type_ids": {0: "batch", 1: "sequence"}}

    outputs, model_loader = LOOKUP_TASK_CONFIG(task)
    # e.g. task="text-classification" -> outputs={"logits": {0: "batch"}}
    #                                   -> model_loader=AutoModelForSequenceClassification

    # Step 2: Load model and tokenizer
    IF model_path IS tuple:
        model, tokenizer = model_path
    ELSE:
        model = model_loader(model_path)
        tokenizer = AutoTokenizer(model_path)

    # Step 3: Generate dummy inputs
    dummy = tokenizer(["test inputs"], return_tensors="pt")

    # Step 4: Trace and export
    torch.onnx.export(
        model, dummy, output_path,
        opset_version=opset,
        do_constant_folding=True,
        input_names=inputs.keys(),
        output_names=outputs.keys(),
        dynamic_axes=MERGE(inputs, outputs)
    )

    # Step 5: Optional quantization
    IF quantize:
        quantize_dynamic(output_path, output_path)

    RETURN output_path OR output_bytes

Key theoretical considerations:

• Dynamic axes -- declaring batch and sequence dimensions as dynamic allows the exported model to accept inputs of any size at inference time, rather than being fixed to the dummy input shape.
• Constant folding -- operations that depend only on constant values (e.g., embedding lookups for fixed positional encodings) are precomputed at export time, reducing the runtime graph size.
• Dynamic quantization -- weights are converted from float32 to int8 after export. Unlike static quantization, dynamic quantization does not require calibration data, making it simpler to apply.
• Opset compatibility -- each ONNX opset defines a set of supported operators. Opset 14 supports all standard transformer operations including attention, layer normalization, and GELU activation.

Related Pages

Implemented By

• Implementation:Neuml_Txtai_HFOnnx_Call