A complete set of requirements—covering UX, CLI, and code—that builds on the previous pipeline for training and optimizing ONNX models with test‑time compute methods using DSPy. This document specifies user stories, command‐line interface arguments, and sample code snippets to guide implementation.

The goal is to build a unified tool (or pipeline application) that:

• Trains a model using DSPy (with integrated or hybrid PyTorch training).
• Exports the optimized model to ONNX format.
• Applies test‑time compute techniques (e.g. test‑time augmentation and, optionally, test‑time training or iterative inference) during inference.
• Optimizes the ONNX model through quantization and graph fusions.
• Allows users to choose hardware execution providers (CPU, CUDA, TensorRT, etc.) for deployment.
• Exposes all functionality via a friendly CLI and, optionally, a minimal UX (e.g. progress bars and clear status messages).

• DSPy Module Definition:
  • Users write or use an existing DSPy module that wraps a neural network (e.g., a PyTorch model).
  • The system must support training via DSPy’s optimizer, using a provided training dataset.
• Model Optimization:
  • The training phase must output an optimized model (weights tuned via DSPy).
  • Users can then export the optimized model to ONNX with dynamic axes enabled (e.g. for batch size).
• Validation:
  • After export, a validation step must compare a dummy inference (with a test input) from the original and ONNX models.

• Test-Time Augmentation (TTA):
  • Allow users to specify a set of augmentation functions to be applied to test inputs.
  • The pipeline should run inference on each augmented input and then aggregate (e.g. average or majority vote) the outputs.
• Test-Time Training (TTT) (Optional/Advanced):
  • Provide an option for on-device adaptation (such as adapting batch normalization statistics) on test batches.
  • This step must be configurable (e.g., number of fine-tuning iterations, learning rate) and run only when enabled.
• Iterative Inference:
  • Support an iterative inference mode where the output is refined over multiple forward passes.
  • This may be implemented either as an external loop in Python or by using ONNX Loop operators.

• Quantization:
  • Support dynamic quantization (and optionally static quantization) to reduce model size and increase inference speed.
• Graph Optimizations:
  • Automatically enable full graph optimization (ORT_ENABLE_ALL) when creating the ONNX Runtime session.
• Execution Provider Selection:
  • Let users specify the preferred execution provider(s) via CLI (e.g., “CUDAExecutionProvider”, “TensorrtExecutionProvider”, “CPUExecutionProvider”, etc.).
  • Use fallback order if the chosen provider cannot support a node.

• Interactive CLI:
  • The CLI must display clear progress messages at each stage (training, conversion, optimization, inference).
  • Use progress bars (for example, with TQDM) during training and inference loops.
  • On error, display descriptive messages and hints (e.g., “Check that your model’s ops are ONNX compliant”).
• Configuration Summary:
  • When the tool starts, print a summary of the settings (model type, dataset path, test-time compute settings, chosen EP, etc.) so the user can verify the configuration.
• Help Documentation:
  • Provide a comprehensive --help option that lists all CLI arguments with descriptions.
• Logging:
  • Enable configurable log levels (DEBUG, INFO, WARNING, ERROR) for deeper insight into each pipeline stage.
• Feedback:
  • After completion, print a summary report that includes training metrics, export success, quantization details, and inference performance (latency and throughput).

The tool will be executed via a command-line interface. A sample usage might be:

python pipeline.py \
  --model_type "text_classifier" \
  --dataset_path "/path/to/train_data.csv" \
  --output_model "classifier.onnx" \
  --tta true \
  --tta_transforms "identity,flip" \
  --ttt false \
  --ttt_iterations 5 \
  --quantize dynamic \
  --ep "CUDAExecutionProvider" \
  --batch_size 32 \
  --learning_rate 2e-5 \
  --epochs 5 \
  --log_level INFO

CLI Arguments Explanation:

• --model_type: Type of model to train (e.g., "text_classifier", "image_classifier").
• --dataset_path: Path to the training dataset file (CSV, JSON, etc.).
• --output_model: Filename for the exported ONNX model.
• --tta: Enable test‑time augmentation (true/false).
• --tta_transforms: Comma-separated list of TTA transforms (e.g., “identity,flip,rotate”).
• --ttt: Enable test‑time training (true/false).
• --ttt_iterations: Number of test‑time training iterations (if enabled).
• --quantize: Type of quantization to apply (“none”, “dynamic”, or “static”).
• --ep: Preferred execution provider (e.g., "CUDAExecutionProvider", "TensorrtExecutionProvider", "CPUExecutionProvider").
• --batch_size: Batch size for training and inference.
• --learning_rate: Learning rate for training and (if applicable) test‑time training.
• --epochs: Number of training epochs.
• --log_level: Logging verbosity level (DEBUG, INFO, etc.).

The project should have a modular structure with these components:

1. Training Module (train.py):

   • Uses DSPy to define and train the model.
   • Loads dataset from --dataset_path.
   • Accepts parameters like --batch_size, --epochs, and --learning_rate.
   • Outputs a trained model instance.

2. Export Module (export.py):

   • Loads the trained model from the training module.
   • Uses the framework’s exporter (e.g., torch.onnx.export) to convert the model to ONNX.
   • Supports dynamic axes configuration.
   • Validates the exported model (using onnx.checker).

3. Inference Module (inference.py):

   • Loads the ONNX model using ONNX Runtime with configurable execution providers.
   • Implements test‑time compute methods:
     • A TTA routine that applies the transformations specified in --tta_transforms.
     • Optionally, a TTT routine if --ttt is enabled.
     • Iterative inference loop if configured.
   • Measures and logs inference time per batch.
   • Provides a CLI option to run inference on a test sample.

4. CLI Entry Point (pipeline.py):

   • Parses CLI arguments (using Python’s argparse or Click).
   • Orchestrates the pipeline: calls the training module, then export, then optionally runs inference with test‑time compute.
   • Displays a configuration summary and final performance metrics.

5. Configuration Module (config.py):

   • Contains default parameters.
   • Validates CLI input (e.g., checks that file paths exist, numerical values are in valid ranges).

6. Utilities (utils.py):

   • Helper functions for logging, progress bars (using TQDM), error handling, and ONNX graph optimization/quantization.
   • Functions to parse TTA transformation lists and create augmentation functions.

• Python Version: ≥3.8
• DSPy: Latest stable release (ensure compatibility with your training framework)
• PyTorch (if using PyTorch backend): ≥1.8
• ONNX: ≥1.10
• onnxruntime: For inference; use onnxruntime-gpu if using CUDA (≥1.10 recommended)
• onnxruntime-tools and onnxruntime-quantization: For quantization and model optimization.
• numpy, pandas: For numerical data handling.
• argparse or Click: For CLI parsing.
• TQDM: For progress display.
• Pillow: If working with images (for TTA example).

Below is a minimal outline of how the files might look.

import argparse
import logging
from train import run_training
from export import export_to_onnx
from inference import run_inference_pipeline
from config import validate_config, print_config_summary
def parse_args():
    parser = argparse.ArgumentParser(description="Train and optimize ONNX model with test-time compute using DSPy.")
    parser.add_argument("--model_type", type=str, required=True)
    parser.add_argument("--dataset_path", type=str, required=True)
    parser.add_argument("--output_model", type=str, required=True)
    parser.add_argument("--tta", type=str, default="false")
    parser.add_argument("--tta_transforms", type=str, default="identity")
    parser.add_argument("--ttt", type=str, default="false")
    parser.add_argument("--ttt_iterations", type=int, default=5)
    parser.add_argument("--quantize", type=str, default="none", choices=["none", "dynamic", "static"])
    parser.add_argument("--ep", type=str, default="CPUExecutionProvider")
    parser.add_argument("--batch_size", type=int, default=32)
    parser.add_argument("--learning_rate", type=float, default=2e-5)
    parser.add_argument("--epochs", type=int, default=5)
    parser.add_argument("--log_level", type=str, default="INFO", choices=["DEBUG", "INFO", "WARNING", "ERROR"])
    return parser.parse_args()
def main():
    args = parse_args()
    validate_config(args)
    logging.basicConfig(level=getattr(logging, args.log_level.upper()))
    print_config_summary(args)
    
    # 1. Train the model using DSPy
    trained_model = run_training(
        model_type=args.model_type,
        dataset_path=args.dataset_path,
        batch_size=args.batch_size,
        epochs=args.epochs,
        learning_rate=args.learning_rate
    )
    
    # 2. Export the trained model to ONNX
    onnx_path = export_to_onnx(trained_model, args.output_model)
    
    # 3. Optionally quantize the model
    if args.quantize != "none":
        from utils import quantize_model
        onnx_path = quantize_model(onnx_path, quantization_type=args.quantize)
    
    # 4. Run inference with test-time compute enhancements
    run_inference_pipeline(
        onnx_model_path=onnx_path,
        tta_enabled=(args.tta.lower() == "carview.php?tsp=true"),
        tta_transforms=args.tta_transforms.split(","),
        ttt_enabled=(args.ttt.lower() == "carview.php?tsp=true"),
        ttt_iterations=args.ttt_iterations,
        ep=args.ep,
        batch_size=args.batch_size
    )
    
if __name__ == "__main__":
    main()

import dspy
import logging
def run_training(model_type, dataset_path, batch_size, epochs, learning_rate):
    logging.info("Starting training for model type: %s", model_type)
    # Load dataset (this can be a CSV, JSON, etc.)
    train_data = load_dataset(dataset_path)  # implement load_dataset
    # Define DSPy module (model) based on model_type
    module = dspy.Tool()  # Replace with actual model definition
    # Compile/Optimize model using DSPy optimizer
    optimizer = dspy.Optimizer(metric="accuracy")
    optimized_module = optimizer.compile(module, trainset=train_data, batch_size=batch_size, epochs=epochs, lr=learning_rate)
    logging.info("Training completed.")
    return optimized_module.model  # assuming the underlying PyTorch model is accessible

import torch
import onnx
import logging
def export_to_onnx(model, output_model_path):
    logging.info("Exporting model to ONNX format...")
    model.eval()
    dummy_input = torch.randn(1, 768)  # adjust shape based on your model
    torch.onnx.export(model, dummy_input, output_model_path,
                      opset_version=13,
                      do_constant_folding=True,
                      input_names=["input"],
                      output_names=["output"],
                      dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}})
    onnx_model = onnx.load(output_model_path)
    onnx.checker.check_model(onnx_model)
    logging.info("Export successful: %s", output_model_path)
    return output_model_path

import onnxruntime as ort
import numpy as np
import logging
from utils import preprocess_input, apply_augmentations
def run_inference_pipeline(onnx_model_path, tta_enabled, tta_transforms, ttt_enabled, ttt_iterations, ep, batch_size):
    logging.info("Setting up ONNX Runtime session with provider: %s", ep)
    sess_options = ort.SessionOptions()
    sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
    session = ort.InferenceSession(onnx_model_path, sess_options=sess_options, providers=[ep])
    input_name = session.get_inputs()[0].name
    # Dummy test input – in practice, load your real test sample here
    test_input = preprocess_input("Sample input text")  # user-defined preprocessing
    # Apply TTA if enabled
    if tta_enabled:
        logging.info("Running test-time augmentation...")
        outputs = []
        augmented_inputs = apply_augmentations(test_input, tta_transforms)  # returns list of numpy arrays
        for aug_inp in augmented_inputs:
            output = session.run(None, {input_name: aug_inp})[0]
            outputs.append(output)
        final_output = np.mean(outputs, axis=0)
        logging.info("TTA completed. Final aggregated output computed.")
    else:
        final_output = session.run(None, {input_name: test_input})[0]
    # Optional: test-time training adaptation (advanced)
    if ttt_enabled:
        logging.info("Running test-time training for %d iterations...", ttt_iterations)
        # Here you would integrate ORT training API or a lightweight adaptation loop
        # For this example, we simulate by re-running inference
        for _ in range(ttt_iterations):
            final_output = session.run(None, {input_name: test_input})[0]
        logging.info("TTT adaptation completed.")
    logging.info("Inference pipeline complete. Final output: %s", final_output)
    return final_output

import numpy as np
from PIL import Image
import onnxruntime as ort
def preprocess_input(input_text):
    # Convert text to model input – e.g., embedding lookup, tokenization, etc.
    # For illustration, return a dummy numpy array
    return np.random.randn(1, 768).astype(np.float32)
def apply_augmentations(input_data, transforms):
    # If input_data is image data, apply image-based transforms.
    # For text, one might do paraphrasing. Here we assume numeric augmentation.
    aug_inputs = []
    for t in transforms:
        if t == "identity":
            aug_inputs.append(input_data.copy())
        elif t == "flip":
            aug_inputs.append(np.flip(input_data, axis=1))  # dummy example
        # Add more transforms as needed.
    return aug_inputs
def quantize_model(onnx_path, quantization_type):
    from onnxruntime.quantization import quantize_dynamic, QuantType
    quantized_model_path = onnx_path.replace(".onnx", f"_{quantization_type}.onnx")
    if quantization_type == "dynamic":
        quantize_dynamic(onnx_path, quantized_model_path, weight_type=QuantType.QInt8)
    # For static quantization, include calibration routines.
    return quantized_model_path
def print_config_summary(args):
    print("Configuration Summary:")
    for arg in vars(args):
        print(f"  {arg}: {getattr(args, arg)}")
def validate_config(args):
    # Add checks (e.g., dataset file exists, valid numbers)
    pass

This complete requirements document defines:

• UX Requirements: A clear CLI with help, progress feedback, logging, and summary reports.
• CLI Requirements: Arguments to control model type, training parameters, TTA/TTT options, quantization, and hardware provider.
• Code Requirements: A modular Python project structure with training (using DSPy), export to ONNX, inference with test‑time compute enhancements, and utility functions for quantization and input preprocessing.

By following these requirements, you can implement a robust pipeline that trains, converts, optimizes, and deploys ONNX models while leveraging test‑time compute methods to enhance inference quality and robustness.