Fix RT-DETR cache for generate_anchors (#31671)

* Fix cache and type conversion

* Add test

* Fixup

* nit

* [run slow] rt_detr

* Fix test

* Fixup

* [run slow] rt_detr

* Update src/transformers/models/rt_detr/modeling_rt_detr.py

tests/models/rt_detr/test_modeling_rt_detr.py

@@ -16,6 +16,7 @@
 
 import inspect
 import math
+import tempfile
 import unittest
 
 from parameterized import parameterized
@@ -630,6 +631,48 @@ class RTDetrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
             with torch.no_grad():
                 _ = model(**self._prepare_for_class(inputs_dict, model_class))
 
+    @parameterized.expand(["float32", "float16", "bfloat16"])
+    @require_torch_gpu
+    @slow
+    def test_inference_equivalence_for_static_and_dynamic_anchors(self, torch_dtype_str):
+        torch_dtype = {
+            "float32": torch.float32,
+            "float16": torch.float16,
+            "bfloat16": torch.bfloat16,
+        }[torch_dtype_str]
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        h, w = inputs_dict["pixel_values"].shape[-2:]
+
+        # convert inputs to the desired dtype
+        for key, tensor in inputs_dict.items():
+            if tensor.dtype == torch.float32:
+                inputs_dict[key] = tensor.to(torch_dtype)
+
+        for model_class in self.all_model_classes:
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model_class(config).save_pretrained(tmpdirname)
+                model_static = model_class.from_pretrained(
+                    tmpdirname, anchor_image_size=[h, w], device_map=torch_device, torch_dtype=torch_dtype
+                ).eval()
+                model_dynamic = model_class.from_pretrained(
+                    tmpdirname, anchor_image_size=None, device_map=torch_device, torch_dtype=torch_dtype
+                ).eval()
+
+            self.assertIsNotNone(model_static.config.anchor_image_size)
+            self.assertIsNone(model_dynamic.config.anchor_image_size)
+
+            with torch.no_grad():
+                outputs_static = model_static(**self._prepare_for_class(inputs_dict, model_class))
+                outputs_dynamic = model_dynamic(**self._prepare_for_class(inputs_dict, model_class))
+
+            self.assertTrue(
+                torch.allclose(
+                    outputs_static.last_hidden_state, outputs_dynamic.last_hidden_state, rtol=1e-4, atol=1e-4
+                ),
+                f"Max diff: {(outputs_static.last_hidden_state - outputs_dynamic.last_hidden_state).abs().max()}",
+            )
+
 
 TOLERANCE = 1e-4