aidge-benchmark 0.9.1.post2

Benchmarking tool for Eclipse Aidge - validate model correctness, precision, and performance across multiple libraries.

🚧 Aidge Benchmark

[!WARN] This repository is still under active construction.

This module benchmarks ML models across Eclipse Aidge modules (CPU, CUDA, exportCPP), as well as external libraries (Torch, ONNXRuntime). It measures:

• Correctness (output similarity)
• Precision
• Inference time (performance)

Useful links

• Eclipse Aidge main repository
• Installation guide
• Usage
  • CLI
  • Python API

Quick start

System Requirements

• python >= 3.10
• numpy >= 1.22.0
• aidge_core >= 0.7.0

Any benchmarked library might require additional dependencies, notably onnx for external libraries.

Plotting results requires matplolib package.

pip install aidge-benchmark
python -c "import aidge_core; import aidge_benchmark; create_benchmark_from_dict({})"

🛠 Build from Source

Prerequisite (in addition to previous one):

• Please review the global installation instructions before proceeding.
• If using a virtual environment, use the same one for all Aidge modules.

1. Python installation using setup scripts

Environment	Python Development
Windows	.\setup.ps1 -Modules backend_cpu -Tests
Unix	./setup.sh -m backend_cpu --tests

[!TIP] Use Get-Help setup.ps1 (Windows) or ./setup.sh -h (Unix) for full documentation.

2. Python Installation using pip

Run these commands from the aidge_benchmark/ directory:

# Standard install
pip install . -v

# Install with testing dependencies
pip install .[test] -v && pytest

Editable Install (Experimental)

Use this for real-time development without re-installing.

pip install --no-build-isolation -ve . --config-settings=editable.rebuild=true -Cbuild-dir=build

Usage

Command-line interface (CLI)

This module comes with a callable script: aidge_benchmark

aidge_benchmark --config-file conv2d.json -c -t -m aidge_export_cpp aidge_backend_cpu aidge_backend_cuda torch onnxruntime --save-directory benchmark_results

aidge_benchmark --help

More details on the tutorials

Python API

Here is an example script to generate a result and time comparison of several libraries with a reference library for the convolution operation and save results as an svg image.

import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path

import aidge_benchmark as bench
from aidge_onnx import convert_aidge_to_onnx
import aidge_backend_cpu  # ensures backend is registered
import aidge_export_cpp  # ensures backend is registered

# List of backends we want to test against the reference implementation
TESTED_LIBS: list[str] = ["torch", "aidge_backend_cpu", "aidge_export_cpp"]
REFERENCE_LIB: str = "onnxruntime"
# Benchmark test cases description file
BENCHMARK_FILE = Path("conv2d.json")
N_ITER = 10


# Helper function
def format_time_stats(times: list[float]) -> str:
    """Return mean ± std formatted in ms (scientific notation)."""
    mean, std = np.mean(times), np.std(times)
    return f"{mean*1e3:.2e} ± {std*1e3:.2e} ms"


def run_single_benchmark(cfg):
    """
    Run a benchmark for a single configuration.
    Returns a dictionary mapping backend name -> list of timings (seconds).
    """
    # Create random input tensors according to the spec
    inputs = cfg.generate_inputs()

    # Build ONNX and Aidge model description from 'cfg' described with ONNX format
    model_aidge = cfg.as_model(input_arrays=inputs)
    model = convert_aidge_to_onnx(
            model_aidge, f"test-model_{cfg.operation}_{label}", opset=21, ir_version=10
        )
    # update input names for ONNX
    for idx, named_tensor in enumerate(inputs):
        if idx < len(model.graph.input):
            for node in model.graph.node:
                for i, inp in enumerate(node.input):
                    if inp == model.graph.input[idx].name:
                        node.input[i] = named_tensor.name
            model.graph.input[idx].name = named_tensor.name


    # /!\ specific to ONNX format
    # Determine how many inputs are actual data (excluding initializers)
    nb_data_inputs = cfg.format.metadata["initializer_rank"]

    onnx_param = {"model": model, "inputs": inputs[:nb_data_inputs]}
    aidge_param = {"model": model_aidge, "inputs": inputs[:nb_data_inputs]}

    backend_params = {
        **{lib: onnx_param for lib in ["torch", "onnxruntime"]},
        **{lib: aidge_param for lib in ["aidge_backend_cpu", "aidge_export_cpp"]},
    }

    # Reference run
    ref_out = bench.compute_output(REFERENCE_LIB, **backend_params[REFERENCE_LIB])
    ref_times = bench.measure_inference_time(
        REFERENCE_LIB, **backend_params[REFERENCE_LIB], nb_iterations=N_ITER
    )
    print(f"\t{REFERENCE_LIB:<20}✔️\t{format_time_stats(ref_times)}")
    results = {REFERENCE_LIB: ref_times}

    # Test each backend and compare results
    for lib in TESTED_LIBS:
        out = bench.compute_output(lib, **backend_params[lib])
        is_equal = bench.utils.compare_tensors(ref_out, out, verbose=False)
        times = bench.measure_inference_time(
            lib, **backend_params[lib], nb_iterations=N_ITER
        )
        print(f"\t{lib:<20}{'✔️' if is_equal else '❌'}\t{format_time_stats(times)}")
        results[lib] = times
    return results

if __name__ == "__main__":
    scheme, default_cfg = bench.create_benchmark_from_json(str(BENCHMARK_FILE))
    timing_results = {}
    for  label, cfg in scheme:
        print(f"\n- {label}")

        # Merge test config with default settings
        full_cfg = default_cfg.override_with(cfg)
        timing_results[label] = run_single_benchmark(full_cfg)

    fig, _ = bench.visualize.ratio_plot(timing_results, REFERENCE_LIB)
    fig.savefig(f"{str(BENCHMARK_FILE)}_performances.svg")
    print(f"\nSaved: '{str(BENCHMARK_FILE)}_performances.svg'")

License

Aidge is licensed under Eclipse Public License 2.0, as found in the LICENSE.