https://github.com/sdpython/onnx-diagnostic
Investigate onnx models
https://github.com/sdpython/onnx-diagnostic
Last synced: 7 months ago
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Investigate onnx models
- Host: GitHub
- URL: https://github.com/sdpython/onnx-diagnostic
- Owner: sdpython
- License: mit
- Created: 2025-03-21T08:59:40.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-04-12T10:32:52.000Z (7 months ago)
- Last Synced: 2025-04-13T01:45:40.462Z (7 months ago)
- Language: Python
- Size: 407 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGELOGS.rst
- License: LICENSE.txt
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README
.. image:: https://github.com/sdpython/onnx-diagnostic/raw/main/_doc/_static/logo.png
:width: 120
onnx-diagnostic: investigate onnx models
========================================
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Helps investigating onnx models, exporting modes into onnx.
See `documentation of onnx-diagnostic `_.
Getting started
+++++++++++++++
::
git clone https://github.com/sdpython/onnx-diagnostic.git
cd onnx-diagnostic
pip install -e .
or
::
pip install onnx-diagnostic
Enlightening Examples
+++++++++++++++++++++
**Torch Export**
* `Use DYNAMIC or AUTO when exporting if dynamic shapes has constraints
`_
* `Find and fix an export issue due to dynamic shapes
`_
* `Export with DynamicCache and dynamic shapes
`_
* `Steel method forward to guess the dynamic shapes (with Tiny-LLM)
`_
* `Export Tiny-LLM with patches
`_
**Investigate ONNX models**
* `Find where a model is failing by running submodels
`_
* `Intermediate results with (ONNX) ReferenceEvaluator
`_
* `Intermediate results with onnxruntime
`_
Snapshot of usefuls tools
+++++++++++++++++++++++++
**string_type**
.. code-block:: python
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])
**onnx_dtype_name**
.. code-block:: python
import onnx
from onnx_diagnostic.helpers.onnx_helper import onnx_dtype_name
itype = onnx.TensorProto.BFLOAT16
print(onnx_dtype_name(itype))
print(onnx_dtype_name(7))
::
>>> BFLOAT16
>>> INT64
**max_diff**
Returns the maximum discrancies across nested containers containing tensors.