fix to_any for BaseModelOutput

CHANGELOGS.rst

@@ -1,6 +1,12 @@
 Change Logs
 ===========
 
+0.7.3
++++++
+
+* :pr:`173`: fixes function to_any for BaseModelOutput
+
+
 0.7.2
 +++++
 

_doc/index.rst

@@ -213,9 +213,7 @@ The function replaces dynamic dimensions defined as strings by
 Older versions
 ==============
 
-* `0.7.2 <../v0.7.2/index.html>`_
-* `0.7.1 <../v0.7.1/index.html>`_
-* `0.7.0 <../v0.7.0/index.html>`_
+* `0.7.3 <../v0.7.3/index.html>`_
 * `0.6.3 <../v0.6.3/index.html>`_
 * `0.5.0 <../v0.5.0/index.html>`_
 * `0.4.4 <../v0.4.4/index.html>`_

_doc/patches.rst

@@ -104,7 +104,8 @@ and triggered by ``with torch_export_patches(patch_transformers=True)``.
 This function does one class, 
 :func:`onnx_diagnostic.torch_export_patches.onnx_export_serialization.register_cache_serialization`
 does all known classes.
-It can be undone with :func:`onnx_diagnostic.torch_export_patches.onnx_export_serialization.unregister_class_serialization`
+It can be undone with
+:func:`onnx_diagnostic.torch_export_patches.onnx_export_serialization.unregister_class_serialization`
 or :func:`onnx_diagnostic.torch_export_patches.onnx_export_serialization.unregister_cache_serialization`.
 Here is the list of supported caches:
 

_unittests/ut_tasks/test_tasks.py

@@ -6,6 +6,7 @@
     has_transformers,
     requires_transformers,
 )
+from onnx_diagnostic.helpers.torch_helper import to_any
 from onnx_diagnostic.torch_models.hghub.model_inputs import get_untrained_model_with_inputs
 from onnx_diagnostic.torch_export_patches import torch_export_patches
 from onnx_diagnostic.torch_export_patches.patch_inputs import use_dyn_not_str
@@ -42,12 +43,13 @@ def test_text_generation(self):
             )
 
     @hide_stdout()
-    def test_automatic_speech_recognition(self):
+    def test_automatic_speech_recognition_float32(self):
         mid = "openai/whisper-tiny"
         data = get_untrained_model_with_inputs(mid, verbose=1, add_second_input=True)
         self.assertEqual(data["task"], "automatic-speech-recognition")
         self.assertIn((data["size"], data["n_weights"]), [(132115968, 33028992)])
         model, inputs, ds = data["model"], data["inputs"], data["dynamic_shapes"]
+        model(**data["inputs"])
         model(**data["inputs2"])
         Dim = torch.export.Dim
         self.maxDiff = None
@@ -113,6 +115,83 @@ def test_automatic_speech_recognition(self):
                 model, (), kwargs=inputs, dynamic_shapes=use_dyn_not_str(ds), strict=False
             )
 
+    @hide_stdout()
+    def test_automatic_speech_recognition_float16(self):
+        mid = "openai/whisper-tiny"
+        data = get_untrained_model_with_inputs(mid, verbose=1, add_second_input=True)
+        self.assertEqual(data["task"], "automatic-speech-recognition")
+        self.assertIn((data["size"], data["n_weights"]), [(132115968, 33028992)])
+        self.assertIn("encoder_outputs:BaseModelOutput", self.string_type(data["inputs"]))
+        data["inputs"] = to_any(data["inputs"], torch.float16)
+        self.assertIn("encoder_outputs:BaseModelOutput", self.string_type(data["inputs"]))
+        data["inputs2"] = to_any(data["inputs2"], torch.float16)
+        model, inputs, ds = data["model"], data["inputs"], data["dynamic_shapes"]
+        model = to_any(model, torch.float16)
+        model(**data["inputs2"])
+        Dim = torch.export.Dim
+        self.maxDiff = None
+        self.assertIn("{0:Dim(batch),1:DYN(seq_length)}", self.string_type(ds))
+        self.assertEqualAny(
+            {
+                "decoder_input_ids": {
+                    0: Dim("batch", min=1, max=1024),
+                    1: "seq_length",
+                },
+                "cache_position": {0: "seq_length"},
+                "encoder_outputs": [{0: Dim("batch", min=1, max=1024)}],
+                "past_key_values": [
+                    [
+                        [
+                            {0: Dim("batch", min=1, max=1024)},
+                            {0: Dim("batch", min=1, max=1024)},
+                        ],
+                        [
+                            {0: Dim("batch", min=1, max=1024)},
+                            {0: Dim("batch", min=1, max=1024)},
+                        ],
+                    ],
+                    [
+                        [
+                            {0: Dim("batch", min=1, max=1024)},
+                            {0: Dim("batch", min=1, max=1024)},
+                        ],
+                        [
+                            {0: Dim("batch", min=1, max=1024)},
+                            {0: Dim("batch", min=1, max=1024)},
+                        ],
+                    ],
+                ],
+            },
+            ds,
+        )
+        self.assertEqual(
+            "#1[T10r3]",
+            self.string_type(torch.utils._pytree.tree_flatten(inputs["encoder_outputs"])[0]),
+        )
+        with torch_export_patches(patch_transformers=True, verbose=10):
+            model(**inputs)
+            flat = torch.utils._pytree.tree_flatten(inputs["past_key_values"])[0]
+            self.assertIsInstance(flat, list)
+            self.assertIsInstance(flat[0], torch.Tensor)
+            self.assertEqual(
+                "#8[T10r4,T10r4,T10r4,T10r4,T10r4,T10r4,T10r4,T10r4]",
+                self.string_type(flat),
+            )
+            torch.export.export(
+                model, (), kwargs=inputs, dynamic_shapes=use_dyn_not_str(ds), strict=False
+            )
+        with torch_export_patches(patch_transformers=True, verbose=10):
+            flat = torch.utils._pytree.tree_flatten(inputs["past_key_values"])[0]
+            self.assertIsInstance(flat, list)
+            self.assertIsInstance(flat[0], torch.Tensor)
+            self.assertEqual(
+                "#8[T10r4,T10r4,T10r4,T10r4,T10r4,T10r4,T10r4,T10r4]",
+                self.string_type(flat),
+            )
+            torch.export.export(
+                model, (), kwargs=inputs, dynamic_shapes=use_dyn_not_str(ds), strict=False
+            )
+
     @hide_stdout()
     def test_fill_mask(self):
         mid = "google-bert/bert-base-multilingual-cased"

onnx_diagnostic/__init__.py

@@ -3,5 +3,5 @@
 Functions, classes to dig into a model when this one is right, slow, wrong...
 """
 
-__version__ = "0.7.2"
+__version__ = "0.7.3"
 __author__ = "Xavier Dupré"

onnx_diagnostic/helpers/torch_helper.py

@@ -717,7 +717,7 @@ def to_any(value: Any, to_value: Union[torch.dtype, torch.device, str]) -> Any:
         return tuple(to_any(t, to_value) for t in value)
     if isinstance(value, set):
         return {to_any(t, to_value) for t in value}
-    if isinstance(value, dict):
+    if type(value) is dict:
         return {k: to_any(t, to_value) for k, t in value.items()}
     if value.__class__.__name__ == "DynamicCache":
         return make_dynamic_cache(

onnx_diagnostic/torch_export_patches/patches/patch_transformers.py

@@ -214,8 +214,8 @@ def update(
             if len(self.key_cache) <= layer_idx:
                 # There may be skipped layers, fill them with empty lists
                 for _ in range(len(self.key_cache), layer_idx):
-                    self.key_cache.append(torch.tensor([]))
-                    self.value_cache.append(torch.tensor([]))
+                    self.key_cache.append(torch.tensor([], dtype=key_states.dtype))
+                    self.value_cache.append(torch.tensor([], dtype=key_states.dtype))
                 self.key_cache.append(key_states)
                 self.value_cache.append(value_states)
             elif not self.key_cache[
@@ -231,7 +231,6 @@ def update(
                 self.value_cache[layer_idx] = torch.cat(
                     [self.value_cache[layer_idx], value_states], dim=-2
                 )
-
         return self.key_cache[layer_idx], self.value_cache[layer_idx]
 
     def crop(self, max_length: int):