[`core`] fix silent bug `keep_in_fp32` modules (#26589)

* fix silent bug `keep_in_fp32` modules

* final fix

* added a common test.

* Trigger CI

* revert

src/transformers/modeling_utils.py

@@ -693,7 +693,9 @@ def _load_state_dict_into_meta_model(
         if dtype is not None and torch.is_floating_point(param):
             if (
                 keep_in_fp32_modules is not None
-                and any(module_to_keep_in_fp32 in param_name for module_to_keep_in_fp32 in keep_in_fp32_modules)
+                and any(
+                    module_to_keep_in_fp32 in param_name.split(".") for module_to_keep_in_fp32 in keep_in_fp32_modules
+                )
                 and dtype == torch.float16
             ):
                 param = param.to(torch.float32)
@@ -3534,7 +3536,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 if (
                     keep_in_fp32_modules is not None
                     and dtype == torch.float16
-                    and any(module_to_keep_in_fp32 in key for module_to_keep_in_fp32 in keep_in_fp32_modules)
+                    and any(
+                        module_to_keep_in_fp32 in key.split(".") for module_to_keep_in_fp32 in keep_in_fp32_modules
+                    )
                 ):
                     target_dtype = torch.float32
 
@@ -3561,7 +3565,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         # Set some modules to fp32 if any
         if keep_in_fp32_modules is not None:
             for name, param in model.named_parameters():
-                if any(module_to_keep_in_fp32 in name for module_to_keep_in_fp32 in keep_in_fp32_modules):
+                if any(module_to_keep_in_fp32 in name.split(".") for module_to_keep_in_fp32 in keep_in_fp32_modules):
                     # param = param.to(torch.float32) does not work here as only in the local scope.
                     param.data = param.data.to(torch.float32)
 

tests/models/instructblip/test_modeling_instructblip.py

@@ -533,7 +533,7 @@ class InstructBlipModelIntegrationTest(unittest.TestCase):
     def test_inference_vicuna_7b(self):
         processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b")
         model = InstructBlipForConditionalGeneration.from_pretrained(
-            "Salesforce/instructblip-vicuna-7b", load_in_8bit=True
+            "Salesforce/instructblip-vicuna-7b", load_in_8bit=True, low_cpu_mem_usage=True
         )
 
         url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
@@ -569,6 +569,7 @@ class InstructBlipModelIntegrationTest(unittest.TestCase):
         model = InstructBlipForConditionalGeneration.from_pretrained(
             "Salesforce/instructblip-flan-t5-xl",
             torch_dtype=torch.bfloat16,
+            low_cpu_mem_usage=True,
         ).to(torch_device)
 
         url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"

tests/test_modeling_common.py

@@ -275,6 +275,24 @@ class ModelTesterMixin:
             for p1, p2 in zip(model.parameters(), new_model.parameters()):
                 self.assertTrue(torch.equal(p1, p2))
 
+    def test_keep_in_fp32_modules(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            if model_class._keep_in_fp32_modules is None:
+                return
+
+            model = model_class(config)
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+
+                model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16)
+
+                for name, param in model.named_parameters():
+                    if any(n in model_class._keep_in_fp32_modules for n in name.split(".")):
+                        self.assertTrue(param.dtype == torch.float32)
+                    else:
+                        self.assertTrue(param.dtype == torch.float16, name)
+
     def test_save_load_keys_to_ignore_on_save(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()