onnx-diagnostic: investigate onnx models¶

onnx-diagnostic helps investigating onnx models, exporting models into onnx. It implements tools used to understand issues.

Source are sdpython/onnx-diagnostic.

Contents

git clone https://github.com/sdpython/onnx-diagnostic.git
cd onnx-diagnostic
pip install -e .

pip install onnx-diagnostic

Enlightening Examples¶

Torch Export

Investigate ONNX models

Some Usefuls Tools

import torch
from onnx_diagnostic.helpers import string_type

inputs = (
    torch.rand((3, 4), dtype=torch.float16),
    [
        torch.rand((5, 6), dtype=torch.float16),
        torch.rand((5, 6, 7), dtype=torch.float16),
    ]
)

# with shapes
print(string_type(inputs, with_shape=True))

>>> (T10s3x4,#2[T10s5x6,T10s5x6x7])

import onnx
from onnx_diagnostic.helpers import onnx_dtype_name

itype = onnx.TensorProto.BFLOAT16
print(onnx_dtype_name(itype))
print(onnx_dtype_name(7))

>>> BFLOAT16
>>> INT64

onnx_diagnostic.helpers.max_diff(), …

The documentation was updated on:

    2025-03-27 16:00:46.825026

With the following versions:

    numpy: 2.2.4
    ml_dtypes: 0.5.1
    sklearn: 1.6.1
    onnx: 1.18.0
    onnxruntime: 1.21.0+cu126
    onnxscript: 0.3.0.dev20250301
    torch: 2.8.0.dev20250324+cu126
    transformers: 4.51.0.dev0
    timm: 1.0.14
    has_onnxruntime_training: True

Size of the package:

                              lines   chars
    ext dir                                
    .py                        3558  102940
        export                  355   10320
        reference              1333   38318
        torch_export_patches   1115   39993
        torch_models            612   19341

onnx-diagnostic: investigate onnx models¶

Getting started¶

Enlightening Examples¶

Older versions¶