onnx-diagnostic 0.7.14

Tools to help converting pytorch models into ONNX.

onnx-diagnostic: investigate onnx models

The main feature is about patches: it helps exporting pytorch models into ONNX, mostly designed for LLMs using dynamic caches. Patches can be enabled as follows:

from onnx_diagnostic.torch_export_patches import torch_export_patches

with torch_export_patches(patch_transformers=True) as f:
    ep = torch.export.export(model, args, kwargs=kwargs, dynamic_shapes=dynamic_shapes)
    # ...

Dynamic shapes are difficult to guess for caches, one function returns a structure defining all dimensions as dynamic. You need then to remove those which are not dynamic in your model.

from onnx_diagnostic.export.shape_helper import all_dynamic_shape_from_inputs

dynamic_shapes = all_dynamic_shape_from_inputs(cache)

It also implements tools to investigate, validate exported models (ExportedProgramm, ONNXProgram, …). See documentation of onnx-diagnostic and torch_export_patches.

Getting started

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

or

pip install onnx-diagnostic

Enlightening Examples

Where to start to export a model

• Export microsoft/phi-2

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 guessed 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

torch_export_patches

from onnx_diagnostic.torch_export_patches import torch_export_patches

with torch_export_patches(patch_transformers=True) as f:
    ep = torch.export.export(model, args, kwargs=kwargs, dynamic_shapes=dynamic_shapes)
    # ...

all_dynamic_shape_from_inputs

from onnx_diagnostic.export.shape_helper import all_dynamic_shape_from_inputs

dynamic_shapes = all_dynamic_shape_from_inputs(cache)

torch_export_rewrite

from onnx_diagnostic.torch_export_patches import torch_export_rewrite

with torch_export_rewrite(rewrite=[Model.forward]) as f:
    ep = torch.export.export(model, args, kwargs=kwargs, dynamic_shapes=dynamic_shapes)
    # ...

string_type

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

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

import torch
from onnx_diagnostic.helpers import max_diff

print(
    max_diff(
        (torch.Tensor([1, 2]), (torch.Tensor([1, 2]),)),
        (torch.Tensor([1, 2]), (torch.Tensor([1, 2]),)),
    )
)

>>> {"abs": 0.0, "rel": 0.0, "sum": 0.0, "n": 4.0, "dnan": 0.0}s

guess_dynamic_shapes

inputs = [
    (torch.randn((5, 6)), torch.randn((1, 6))),
    (torch.randn((7, 8)), torch.randn((1, 8))),
]
ds = ModelInputs(model, inputs).guess_dynamic_shapes(auto="dim")
print(ds)

>>> (({0: 'dim_0I0', 1: 'dim_0I1'}, {1: 'dim_1I1'}), {})