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Fix: loading DBRX back from saved path (#35728)

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* fix dtype as dict for some models + add test

* add comment in tests
This commit is contained in:
Raushan Turganbay 2025-01-28 11:38:45 +01:00 committed by GitHub
parent 3613f568cd
commit b764c20b09
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4 changed files with 13 additions and 4 deletions
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4
src/transformers/modeling_utils.py
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@ -4037,7 +4037,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
sub_config = getattr(config, sub_config_key) sub_config = getattr(config, sub_config_key)
sub_config.torch_dtype = torch_dtype sub_config.torch_dtype = torch_dtype
elif isinstance(torch_dtype, torch.dtype): elif isinstance(torch_dtype, torch.dtype):
pass for sub_config_key in config.sub_configs.keys():
sub_config = getattr(config, sub_config_key)
sub_config.torch_dtype = torch_dtype
elif isinstance(torch_dtype, dict): elif isinstance(torch_dtype, dict):
for key, curr_dtype in torch_dtype.items(): for key, curr_dtype in torch_dtype.items():
if hasattr(config, key): if hasattr(config, key):

4
src/transformers/models/dbrx/configuration_dbrx.py
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@ -57,7 +57,7 @@ class DbrxAttentionConfig(PretrainedConfig):
self.kv_n_heads = kv_n_heads self.kv_n_heads = kv_n_heads
self.rope_theta = rope_theta self.rope_theta = rope_theta
for k in ["model_type", "attn_implementation", "transformers_version", "_commit_hash"]: for k in ["model_type", "attn_implementation", "transformers_version", "_commit_hash", "torch_dtype"]:
if k in kwargs: if k in kwargs:
kwargs.pop(k) kwargs.pop(k)
if len(kwargs) != 0: if len(kwargs) != 0:
@ -109,7 +109,7 @@ class DbrxFFNConfig(PretrainedConfig):
self.moe_loss_weight = moe_loss_weight self.moe_loss_weight = moe_loss_weight
self.moe_normalize_expert_weights = moe_normalize_expert_weights self.moe_normalize_expert_weights = moe_normalize_expert_weights
for k in ["model_type", "attn_implementation", "transformers_version", "_commit_hash"]: for k in ["model_type", "attn_implementation", "transformers_version", "_commit_hash", "torch_dtype"]:
if k in kwargs: if k in kwargs:
kwargs.pop(k) kwargs.pop(k)
if len(kwargs) != 0: if len(kwargs) != 0:

6
tests/test_modeling_common.py
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@ -331,6 +331,12 @@ class ModelTesterMixin:
with torch.no_grad(): with torch.no_grad():
second = model(**self._prepare_for_class(inputs_dict, model_class))[0] second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
# Save and load second time because `from_pretrained` adds a bunch of new config fields
# so we need to make sure those fields can be loaded back after saving
# Simply init as `model(config)` doesn't add those fields
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
if isinstance(first, tuple) and isinstance(second, tuple): if isinstance(first, tuple) and isinstance(second, tuple):
for tensor1, tensor2 in zip(first, second): for tensor1, tensor2 in zip(first, second):
check_save_load(tensor1, tensor2) check_save_load(tensor1, tensor2)

3
tests/utils/test_modeling_utils.py
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@ -466,13 +466,14 @@ class ModelUtilsTest(TestCasePlus):
def test_model_from_config_torch_dtype_composite(self): def test_model_from_config_torch_dtype_composite(self):
""" """
Test that from_pretrained works with torch_dtype being as a dict per each sub-config in composite config 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.
""" """
# should be able to set torch_dtype as a simple string and the model loads it correctly # 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") model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, torch_dtype="float32")
self.assertEqual(model.language_model.dtype, torch.float32) self.assertEqual(model.language_model.dtype, torch.float32)
self.assertEqual(model.vision_tower.dtype, torch.float32) self.assertEqual(model.vision_tower.dtype, torch.float32)
model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, torch_dtype="float16") model = LlavaForConditionalGeneration.from_pretrained(TINY_LLAVA, torch_dtype=torch.float16)
self.assertEqual(model.language_model.dtype, torch.float16) self.assertEqual(model.language_model.dtype, torch.float16)
self.assertEqual(model.vision_tower.dtype, torch.float16) self.assertEqual(model.vision_tower.dtype, torch.float16)