update titles

Author: xadupre

README.rst

@@ -52,7 +52,7 @@ Enlightening Examples
   <https://sdpython.github.io/doc/onnx-diagnostic/dev/auto_examples/plot_export_with_dynamic_shapes_auto.html>`_
 * `Export with DynamicCache and dynamic shapes
   <https://sdpython.github.io/doc/onnx-diagnostic/dev/auto_examples/plot_export_with_dynamic_cache.html>`_
-* `Steel method forward to guess the dynamic shapes
+* `Steel method forward to guess the dynamic shapes (with Tiny-LLM)
   <https://sdpython.github.io/doc/onnx-diagnostic/dev/auto_examples/plot_export_tiny_llm.html>`_
 
 **Investigate ONNX models**

_doc/examples/plot_export_tiny_llm.py

@@ -1,8 +1,8 @@
 """
 .. _l-plot-tiny-llm-export:
 
-Steel method forward to guess the dynamic shapes
-================================================
+Steel method forward to guess the dynamic shapes (with Tiny-LLM)
+================================================================
 
 Inputs are always dynamic with LLMs that is why dynamic shapes
 needs to be specified when a LLM is exported with:func:`torch.export.export`.

_doc/examples/plot_export_with_dynamic_cache.py

@@ -27,6 +27,12 @@
 from onnx_diagnostic.helpers import string_type
 from onnx_diagnostic.export import ModelInputs
 
+# %%
+# We need addition import in case ``transformers<4.50``.
+# Exporting DynamicCache is not supported before that.
+from onnx_diagnostic.ext_test_case import has_transformers
+from onnx_diagnostic.torch_export_patches import bypass_export_some_errors
+
 
 class Model(torch.nn.Module):
     def forward(self, x, y):
@@ -201,6 +207,20 @@ def forward(self, cache, z):
 
 # %%
 # And finally the export.
-
-ep = torch.export.export(model, inputs[0], dynamic_shapes=ds[0], strict=False)
+# The export is simple if ``transformers>=4.50``, otherwise,
+# transformers needs to be patched.
+# :func:`onnx_diagnostic.torch_export_patches.bypass_export_some_errors`
+# registers functions to serialize ``DynamicCache`` and another class
+# called ``patched_DynamicCache``. This one is modified to make
+# the shape inference implemented in :epkg:`torch` happy.
+
+if has_transformers("4.50"):
+    ep = torch.export.export(model, inputs[0], dynamic_shapes=ds[0], strict=False)
+else:
+    with bypass_export_some_errors(
+        patch_transformers=True, replace_dynamic_cache=True
+    ) as modificator:
+        ep = torch.export.export(
+            model, modificator(inputs[0]), dynamic_shapes=ds[0], strict=False
+        )
 print(ep)