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Mar 24, 2025
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documentation #12

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28e4851
documentation
xadupre Mar 24, 2025
50ee14a
fix try
xadupre Mar 24, 2025
e7c07db
doc
xadupre Mar 24, 2025
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2 changes: 1 addition & 1 deletion _doc/api/export/index.rst
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Expand Up @@ -10,7 +10,7 @@ onnx_diagnostic.export
ModelInputs
+++++++++++

.. autoclass:: onnx_diagnostic.dyanmic_shapes.ModelInputs
.. autoclass:: onnx_diagnostic.export.ModelInputs
:members:

Other functions
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1 change: 1 addition & 0 deletions _doc/conf.py
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Expand Up @@ -121,6 +121,7 @@
("py:class", "transformers.cache_utils.MambaCache"),
("py:func", "torch.export._draft_export.draft_export"),
("py:func", "torch._export.tools.report_exportability"),
("py:meth", "transformers.GenerationMixin.generate"),
]

nitpick_ignore_regex = [
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4 changes: 0 additions & 4 deletions _doc/examples/plot_export_tiny_llm.py
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Expand Up @@ -113,10 +113,6 @@ def _forward_(*args, _f=None, **kwargs):

print("result type", string_type(expected_output, with_shape=True))

ep = torch.export.export(
untrained_model, (), kwargs=cloned_inputs, dynamic_shapes=dynamic_shapes
)

# %%
# It works.
#
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2 changes: 1 addition & 1 deletion _doc/examples/plot_export_with_dynamic_cache.py
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Expand Up @@ -5,7 +5,7 @@
Export with DynamicCache and dynamic shapes
===========================================

Every LLMs implemented in :epkg:`trasnformers` use cache.
Every LLMs implemented in :epkg:`transformers` use cache.
One of the most used is :class:`transformers.cache_utils.DynamicCache`.
The cache size is dynamic to cope with the growing context.
The example shows a tool which determines the dynamic shapes
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16 changes: 8 additions & 8 deletions onnx_diagnostic/export/dynamic_shapes.py
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Expand Up @@ -45,15 +45,15 @@ def forward(self, x, y):
ds = mi.guess_dynamic_shapes()
pprint.pprint(ds)

import pprint
import torch
from onnx_diagnostic.export import ModelInputs

**kwargs**

.. runpython::
:showcode:

import pprint
import torch
from onnx_diagnostic.export import ModelInputs

class Model(torch.nn.Module):
def forward(self, x, y):
return x + y
Expand All @@ -69,15 +69,15 @@ def forward(self, x, y):
ds = mi.guess_dynamic_shapes()
pprint.pprint(ds)

import pprint
import torch
from onnx_diagnostic.export import ModelInputs

**and and kwargs**

.. runpython::
:showcode:

import pprint
import torch
from onnx_diagnostic.export import ModelInputs

class Model(torch.nn.Module):
def forward(self, x, y):
return x + y
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