riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-07-31 02:02:21 +06:00
Code Issues Actions 33 Packages Projects Releases Wiki Activity

Fix: dtype cannot be str (#36262)

Browse Source 
* fix

* this wan't supposed to be here, revert

* refine tests a bit more
This commit is contained in:
Raushan Turganbay 2025-03-21 13:27:47 +01:00 committed by GitHub
parent 3f9ff19b4e
commit 523f6e743c
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
2 changed files with 18 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
src/transformers/modeling_utils.py
View File

@ -1252,13 +1252,13 @@ def _get_torch_dtype(
for key, curr_dtype in torch_dtype.items():
if hasattr(config, key):
value = getattr(config, key)
curr_dtype = curr_dtype if not isinstance(curr_dtype, str) else getattr(torch, curr_dtype)
value.torch_dtype = curr_dtype
# main torch dtype for modules that aren't part of any sub-config
torch_dtype = torch_dtype.get("")
torch_dtype = torch_dtype if not isinstance(torch_dtype, str) else getattr(torch, torch_dtype)
config.torch_dtype = torch_dtype
if isinstance(torch_dtype, str) and hasattr(torch, torch_dtype):
torch_dtype = getattr(torch, torch_dtype)
elif torch_dtype is None:
if torch_dtype is None:
torch_dtype = torch.float32
else:
raise ValueError(
@ -1269,7 +1269,7 @@ def _get_torch_dtype(
dtype_orig = cls._set_default_torch_dtype(torch_dtype)
else:
# set fp32 as the default dtype for BC
default_dtype = str(torch.get_default_dtype()).split(".")[-1]
default_dtype = torch.get_default_dtype()
config.torch_dtype = default_dtype
for key in config.sub_configs.keys():
value = getattr(config, key)

14
tests/utils/test_modeling_utils.py
View File

@ -482,9 +482,11 @@ class ModelUtilsTest(TestCasePlus):
# test that from_pretrained works with torch_dtype being strings like "float32" for PyTorch backend
model = AutoModel.from_pretrained(TINY_T5, torch_dtype="float32")
self.assertEqual(model.dtype, torch.float32)
self.assertIsInstance(model.config.torch_dtype, torch.dtype)
model = AutoModel.from_pretrained(TINY_T5, torch_dtype="float16")
self.assertEqual(model.dtype, torch.float16)
self.assertIsInstance(model.config.torch_dtype, torch.dtype)
# torch.set_default_dtype() supports only float dtypes, so will fail with non-float type
with self.assertRaises(ValueError):
@ -495,14 +497,22 @@ class ModelUtilsTest(TestCasePlus):
Test that from_pretrained works with torch_dtype being as a dict per each sub-config in composite config
Tiny-Llava has saved auto dtype as `torch.float32` for all modules.
"""
# Load without dtype specified
model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA)
self.assertEqual(model.language_model.dtype, torch.float32)
self.assertEqual(model.vision_tower.dtype, torch.float32)
self.assertIsInstance(model.config.torch_dtype, torch.dtype)
# should be able to set torch_dtype as a simple string and the model loads it correctly
model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, torch_dtype="float32")
self.assertEqual(model.language_model.dtype, torch.float32)
self.assertEqual(model.vision_tower.dtype, torch.float32)
self.assertIsInstance(model.config.torch_dtype, torch.dtype)
model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, torch_dtype=torch.float16)
self.assertEqual(model.language_model.dtype, torch.float16)
self.assertEqual(model.vision_tower.dtype, torch.float16)
self.assertIsInstance(model.config.torch_dtype, torch.dtype)
# should be able to set torch_dtype as a dict for each sub-config
model = LlavaForConditionalGeneration.from_pretrained(
@ -511,6 +521,7 @@ class ModelUtilsTest(TestCasePlus):
self.assertEqual(model.language_model.dtype, torch.float32)
self.assertEqual(model.vision_tower.dtype, torch.float16)
self.assertEqual(model.multi_modal_projector.linear_1.weight.dtype, torch.bfloat16)
self.assertIsInstance(model.config.torch_dtype, torch.dtype)
# should be able to set the values as torch.dtype (not str)
model = LlavaForConditionalGeneration.from_pretrained(
@ -519,6 +530,7 @@ class ModelUtilsTest(TestCasePlus):
self.assertEqual(model.language_model.dtype, torch.float32)
self.assertEqual(model.vision_tower.dtype, torch.float16)
self.assertEqual(model.multi_modal_projector.linear_1.weight.dtype, torch.bfloat16)
self.assertIsInstance(model.config.torch_dtype, torch.dtype)
# should be able to set the values in configs directly and pass it to `from_pretrained`
config = copy.deepcopy(model.config)
@ -529,6 +541,7 @@ class ModelUtilsTest(TestCasePlus):
self.assertEqual(model.language_model.dtype, torch.float32)
self.assertEqual(model.vision_tower.dtype, torch.bfloat16)
self.assertEqual(model.multi_modal_projector.linear_1.weight.dtype, torch.float16)
self.assertIsInstance(model.config.torch_dtype, torch.dtype)
# but if the model has `_keep_in_fp32_modules` then those modules should be in fp32 no matter what
LlavaForConditionalGeneration._keep_in_fp32_modules = ["multi_modal_projector"]
@ -536,6 +549,7 @@ class ModelUtilsTest(TestCasePlus):
self.assertEqual(model.language_model.dtype, torch.float32)
self.assertEqual(model.vision_tower.dtype, torch.bfloat16)
self.assertEqual(model.multi_modal_projector.linear_1.weight.dtype, torch.float32)
self.assertIsInstance(model.config.torch_dtype, torch.dtype)
# torch.set_default_dtype() supports only float dtypes, so will fail with non-float type
with self.assertRaises(ValueError):