copy

Author: xadupre

CHANGELOGS.rst

@@ -4,6 +4,9 @@ Change Logs
 0.3.0
 +++++
 
+* :pr:`24`: dummy inputs for ``text2text-generation``, add new function
+  ``convert_dynamic_axes_into_dynamic_shapes`` to convert dynamic axes
+  into dynamic shapes, add support for ``T5ForConditionalGeneration``
 * :pr:`23`: dummy inputs for ``image-classification``
 * :pr:`22`: api to create untrained model copying the architecture
   of the trained models and dummy inputs for them,

_doc/api/torch_export_patches/index.rst

@@ -6,6 +6,8 @@ onnx_diagnostic.torch_export_patches
     :caption: submodules
 
     patches/index
+    patch_inputs
+
 
 .. automodule:: onnx_diagnostic.torch_export_patches
     :members:

_doc/api/torch_export_patches/patch_inputs.rst

@@ -0,0 +1,7 @@
+
+onnx_diagnostic.torch_export_patches.patch_inputs
+=================================================
+
+.. automodule:: onnx_diagnostic.torch_export_patches.patch_inputs
+    :members:
+    :no-undoc-members:

onnx_diagnostic/torch_export_patches/__init__.py

@@ -2,3 +2,110 @@
     bypass_export_some_errors,
     register_additional_serialization_functions,
 )
+
+"""
+-- Missing dependencies --
+
+def is_torchdynamo_exporting() -> bool:
+    "Tells if torch is exporting a model."
+    import torch
+
+    if not hasattr(torch.compiler, "is_exporting"):
+        # torch.compiler.is_exporting requires torch>=2.7
+        return False
+
+    try:
+        return torch.compiler.is_exporting()
+    except Exception:
+        try:
+            import torch._dynamo as dynamo
+
+            return dynamo.is_exporting()  # type: ignore
+        except Exception:
+            return False
+
+
+def string_type(anything, **args):
+    # too long
+    # from onnx_diagnostic.helpers import string_type
+    return str(anything)
+
+
+if pv.Version(transformers.__version__) > pv.Version("4.49.99999"):
+
+    def make_dynamic_cache(
+        key_value_pairs: List[Tuple[torch.Tensor, torch.Tensor]],
+    ) -> transformers.cache_utils.DynamicCache:
+        '''
+        Creates an instance of :class:`transformers.cache_utils.DynamicCache`.
+        This version is valid for ``transformers >= 4.50``.
+
+        :param key_value_pairs: list of pairs of (key, values)
+        :return: :class:`transformers.cache_utils.DynamicCache`
+
+        Example:
+
+        ::
+
+            n_layers = 2
+            bsize, nheads, slen, dim = 2, 4, 3, 7
+
+            past_key_values = make_dynamic_cache(
+                [
+                    (
+                        torch.randn(bsize, nheads, slen, dim),
+                        torch.randn(bsize, nheads, slen, dim),
+                    )
+                    for i in range(n_layers)
+                ]
+            )
+            print(string_type(past_key_values, with_shape=True))
+        '''
+        return transformers.cache_utils.DynamicCache(key_value_pairs)
+
+else:
+
+    def make_dynamic_cache(
+        key_value_pairs: List[Tuple[torch.Tensor, torch.Tensor]],
+    ) -> transformers.cache_utils.DynamicCache:
+        '''
+        Creates an instance of :class:`transformers.cache_utils.DynamicCache`.
+        This version is valid for ``transformers < 4.50``.
+
+        :param key_value_pairs: list of pairs of (key, values)
+        :return: :class:`transformers.cache_utils.DynamicCache`
+
+        Example:
+
+        ::
+
+            n_layers = 2
+            bsize, nheads, slen, dim = 2, 4, 3, 7
+
+            past_key_values = make_dynamic_cache(
+                [
+                    (
+                        torch.randn(bsize, nheads, slen, dim),
+                        torch.randn(bsize, nheads, slen, dim),
+                    )
+                    for i in range(n_layers)
+                ]
+            )
+            print(string_type(past_key_values, with_shape=True))
+        '''
+        cache = transformers.cache_utils.DynamicCache(len(key_value_pairs))
+        for i, (key, value) in enumerate(key_value_pairs):
+            cache.update(key, value, i)
+        return cache
+
+
+def make_encoder_decoder_cache(
+    self_attention_cache: transformers.cache_utils.DynamicCache,
+    cross_attention_cache: transformers.cache_utils.DynamicCache,
+) -> transformers.cache_utils.EncoderDecoderCache:
+    "Creates an EncoderDecoderCache."
+    return transformers.cache_utils.EncoderDecoderCache(
+        self_attention_cache=self_attention_cache, cross_attention_cache=cross_attention_cache
+    )
+
+"""