[tests] device_map tests for all models.

tests/models/test_modeling_common.py

@@ -1736,6 +1736,44 @@ def test_auto_model(self, expected_max_diff=5e-5):
             f"AutoModel forward pass diff: {max_diff} exceeds threshold {expected_max_diff}",
         )
 
+    @parameterized.expand(
+        [
+            (-1, "You can't pass device_map as a negative int"),
+            ("foo", "When passing device_map as a string, the value needs to be a device name"),
+        ]
+    )
+    def test_wrong_device_map_raises_error(self, device_map, msg_substring):
+        init_dict, _ = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+        with tempfile.TemporaryDirectory() as tmpdir:
+            model.save_pretrained(tmpdir)
+            with self.assertRaises(ValueError) as err_ctx:
+                _ = self.model_class.from_pretrained(tmpdir, device_map=device_map)
+
+        assert msg_substring in str(err_ctx.exception)
+
+    @parameterized.expand([0, "cuda", torch.device("cuda"), torch.device("cuda:0")])
+    @require_torch_gpu
+    def test_passing_non_dict_device_map_works(self, device_map):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict).eval()
+        with tempfile.TemporaryDirectory() as tmpdir:
+            model.save_pretrained(tmpdir)
+            loaded_model = self.model_class.from_pretrained(tmpdir, device_map=device_map)
+            _ = loaded_model(**inputs_dict)
+
+    @parameterized.expand([("", "cuda"), ("", torch.device("cuda"))])
+    @require_torch_gpu
+    def test_passing_dict_device_map_works(self, name, device_map):
+        # There are other valid dict-based `device_map` values too. It's best to refer to
+        # the docs for those: https://huggingface.co/docs/accelerate/en/concept_guides/big_model_inference#the-devicemap.
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict).eval()
+        with tempfile.TemporaryDirectory() as tmpdir:
+            model.save_pretrained(tmpdir)
+            loaded_model = self.model_class.from_pretrained(tmpdir, device_map=device_map)
+            _ = loaded_model(**inputs_dict)
+
 
 @is_staging_test
 class ModelPushToHubTester(unittest.TestCase):

tests/models/unets/test_models_unet_2d_condition.py

@@ -46,7 +46,6 @@
     require_peft_backend,
     require_torch_accelerator,
     require_torch_accelerator_with_fp16,
-    require_torch_gpu,
     skip_mps,
     slow,
     torch_all_close,
@@ -1084,42 +1083,6 @@ def test_load_sharded_checkpoint_device_map_from_hub_local_subfolder(self):
         assert loaded_model
         assert new_output.sample.shape == (4, 4, 16, 16)
 
-    @parameterized.expand(
-        [
-            (-1, "You can't pass device_map as a negative int"),
-            ("foo", "When passing device_map as a string, the value needs to be a device name"),
-        ]
-    )
-    def test_wrong_device_map_raises_error(self, device_map, msg_substring):
-        with self.assertRaises(ValueError) as err_ctx:
-            _ = self.model_class.from_pretrained(
-                "hf-internal-testing/unet2d-sharded-dummy-subfolder", subfolder="unet", device_map=device_map
-            )
-
-        assert msg_substring in str(err_ctx.exception)
-
-    @parameterized.expand([0, "cuda", torch.device("cuda"), torch.device("cuda:0")])
-    @require_torch_gpu
-    def test_passing_non_dict_device_map_works(self, device_map):
-        _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-        loaded_model = self.model_class.from_pretrained(
-            "hf-internal-testing/unet2d-sharded-dummy-subfolder", subfolder="unet", device_map=device_map
-        )
-        output = loaded_model(**inputs_dict)
-        assert output.sample.shape == (4, 4, 16, 16)
-
-    @parameterized.expand([("", "cuda"), ("", torch.device("cuda"))])
-    @require_torch_gpu
-    def test_passing_dict_device_map_works(self, name, device_map):
-        # There are other valid dict-based `device_map` values too. It's best to refer to
-        # the docs for those: https://huggingface.co/docs/accelerate/en/concept_guides/big_model_inference#the-devicemap.
-        _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-        loaded_model = self.model_class.from_pretrained(
-            "hf-internal-testing/unet2d-sharded-dummy-subfolder", subfolder="unet", device_map={name: device_map}
-        )
-        output = loaded_model(**inputs_dict)
-        assert output.sample.shape == (4, 4, 16, 16)
-
     @require_peft_backend
     def test_load_attn_procs_raise_warning(self):
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()