fix AutoModel.from_pretrained(..., torch_dtype=...) (#13209)

* fix AutoModel.from_pretrained(..., torch_dtype=...)

* fix to_diff_dict

* add better test

* torch is not always available when a model has self.torch_dtype

tests/test_modeling_common.py

@@ -16,6 +16,7 @@
 import copy
 import gc
 import inspect
+import json
 import os.path
 import random
 import tempfile
@@ -1663,9 +1664,11 @@ class ModelUtilsTest(TestCasePlus):
     @require_torch
     def test_model_from_pretrained_torch_dtype(self):
         # test that the model can be instantiated with dtype of either
-        # 1. config.torch_dtype setting in the saved model (priority)
-        # 2. via autodiscovery by looking at model weights
+        # 1. explicit from_pretrained's torch_dtype argument
+        # 2. via autodiscovery by looking at model weights (torch_dtype="auto")
         # so if a model.half() was saved, we want it to be instantiated as such.
+        #
+        # test an explicit model class, but also AutoModel separately as the latter goes through a different code path
         model_path = self.get_auto_remove_tmp_dir()
 
         # baseline - we know TINY_T5 is fp32 model
@@ -1688,13 +1691,26 @@ class ModelUtilsTest(TestCasePlus):
         model = model.half()
         model.save_pretrained(model_path)
         model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
-        self.assertEqual(model.config.torch_dtype, "float16")  # tests `config.torch_dtype` saving
+        self.assertEqual(model.config.torch_dtype, torch.float16)
         self.assertEqual(model.dtype, torch.float16)
 
+        # tests `config.torch_dtype` saving
+        with open(f"{model_path}/config.json") as f:
+            config_dict = json.load(f)
+        self.assertEqual(config_dict["torch_dtype"], "float16")
+
         # test fp16 save_pretrained, loaded with the explicit fp16
         model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
         self.assertEqual(model.dtype, torch.float16)
 
+        # test AutoModel separately as it goes through a different path
+        # test auto-detection
+        model = AutoModel.from_pretrained(TINY_T5, torch_dtype="auto")
+        self.assertEqual(model.dtype, torch.float32)
+        # test forcing an explicit dtype
+        model = AutoModel.from_pretrained(TINY_T5, torch_dtype=torch.float16)
+        self.assertEqual(model.dtype, torch.float16)
+
 
 @require_torch
 @is_staging_test