ut

Author: xadupre

_scripts/test_backend_onnxruntime.py

@@ -26,12 +26,13 @@ def run(self, inputs, **kwargs):
         if isinstance(inputs, numpy.ndarray):
             inputs = [inputs]
         if isinstance(inputs, list):
-            if len(inputs) == len(self._session.input_names):
-                feeds = dict(zip(self._session.input_names, inputs))
+            if len(inputs) == len(self._session.get_inputs()):
+                feeds = dict(zip([i.name for i in self._session.get_inputs()], inputs))
             else:
+                input_names = [i.name for i in self._session.get_inputs()]
                 feeds = {}
                 pos_inputs = 0
-                for inp, tshape in zip(self._session.input_names, self._session.input_types):
+                for inp, tshape in zip(input_names, self._session.input_types):
                     shape = tuple(d.dim_value for d in tshape.tensor_type.shape.dim)
                     if shape == inputs[pos_inputs].shape:
                         feeds[inp] = inputs[pos_inputs]
@@ -54,20 +55,20 @@ def is_compatible(cls, model) -> bool:
     @classmethod
     def supports_device(cls, device: str) -> bool:
         d = Device(device)
-        if d == DeviceType.CPU:
+        if d.type == DeviceType.CPU:
             return True
-        if d == DeviceType.CUDA:
-            import torch
-
-            return torch.cuda.is_available()
+        # if d.type == DeviceType.CUDA:
+        #     import torch
+        #
+        #     return torch.cuda.is_available()
         return False
 
     @classmethod
     def create_inference_session(cls, model, device):
         d = Device(device)
-        if d == DeviceType.CUDA:
+        if d.type == DeviceType.CUDA:
             providers = ["CUDAExecutionProvider"]
-        elif d == DeviceType.CPU:
+        elif d.type == DeviceType.CPU:
             providers = ["CPUExecutionProvider"]
         else:
             raise ValueError(f"Unrecognized device {device!r} or {d!r}")

_unittests/ut_reference/test_backend_onnxruntime_evaluator.py

@@ -50,9 +50,9 @@ def is_compatible(cls, model) -> bool:
     @classmethod
     def supports_device(cls, device: str) -> bool:
         d = Device(device)
-        if d == DeviceType.CPU:
+        if d.type == DeviceType.CPU:
             return True
-        if d == DeviceType.CUDA:
+        if d.type == DeviceType.CUDA:
             import torch
 
             return torch.cuda.is_available()

_unittests/ut_torch_export_patches/test_patch_torch.py

@@ -154,7 +154,7 @@ def forward(self, batch_arange, head_arange, cache_position, kv_arange):
             self.assertEqualArray(causal_mask, ep.moule(*inputs))
 
     @requires_torch("2.8")
-    @requires_transformers("4.52")
+    @requires_transformers("4.53")
     def test_vmap_transformers_scenario_novmap(self):
         from onnx_diagnostic.torch_export_patches.patches.patch_transformers import (
             patched__vmap_for_bhqkv as _vmap_for_bhqkv2,