SUMIN/HuggingFace_transformer
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Fix: dtype cannot be str (#36262)

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* 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
2 changed files with 18 additions and 4 deletions
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14
tests/utils/test_modeling_utils.py
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@@ -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):