Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fix bert and vit patch #1022

Merged
merged 2 commits into from
Nov 25, 2024
Merged

fix bert and vit patch #1022

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
991c75d
fix bert and vit patch
jiqing-feng Nov 25, 2024
1702e0f
fix vit and bert save
jiqing-feng Nov 25, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 0 additions & 1 deletion optimum/exporters/ipex/modeling_utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -806,7 +806,6 @@ def __init__(self, module, config):
super().__init__()
_setattr_from_module(self, module)
self.linear_gelu = LinearGelu(module.dense)
del self.__dict__["_modules"]["dense"]

def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.linear_gelu(hidden_states)
Expand Down
7 changes: 6 additions & 1 deletion optimum/intel/ipex/modeling_base.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,7 @@
from ...exporters.ipex.cache_utils import IPEXPagedCache
from ...exporters.ipex.model_config import ipex_onnx_config
from ...exporters.ipex.model_patcher import (
_IPEX_EXPORTED_GENERATION_TASKS,
_IPEX_MINIMUM_VERSION_FOR_PATCHING,
_patch_model,
)
Expand All @@ -73,7 +74,7 @@
def _is_patched_with_ipex(model, task, use_cache: bool = True):
if is_ipex_version("<", _IPEX_MINIMUM_VERSION_FOR_PATCHING):
return False
if not use_cache:
if not use_cache and task in _IPEX_EXPORTED_GENERATION_TASKS:
return False
return model.config.model_type in _IPEX_SUPPORT_MODEL_TYPES

Expand Down Expand Up @@ -299,6 +300,10 @@ def model_dtype(self):
)
return self._dtype

@property
def add_patch(self) -> bool:
return self._add_patch

def to(self, device: Union[torch.device, str]):
self._device = device if isinstance(device, torch.device) else torch.device(device)
self.model.to(self._device)
Expand Down
8 changes: 8 additions & 0 deletions tests/ipex/test_modeling.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -74,12 +74,15 @@ class IPEXModelTest(unittest.TestCase):
"squeezebert",
"xlm",
)
IPEX_PATCHED_SUPPORTED_ARCHITECTURES = ("bert",)

@parameterized.expand(SUPPORTED_ARCHITECTURES)
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ipex_model = self.IPEX_MODEL_CLASS.from_pretrained(model_id, export=True)
if model_arch in self.IPEX_PATCHED_SUPPORTED_ARCHITECTURES:
self.assertTrue(ipex_model.add_patch)
device = ipex_model.device
self.assertIsInstance(ipex_model.config, PretrainedConfig)
transformers_model = self.IPEX_MODEL_CLASS.auto_model_class.from_pretrained(model_id).to(device)
Expand Down Expand Up @@ -317,6 +320,8 @@ def test_ipex_patching_beam_search(self, test_name, model_arch, use_cache):
if IS_XPU:
dtype = torch.float16
model = IPEXModelForCausalLM.from_pretrained(model_id, export=True, use_cache=use_cache, torch_dtype=dtype)
if use_cache:
self.assertTrue(model.add_patch)
device = model.device
transformers_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=dtype).to(device)
self.assertEqual(model.use_cache, use_cache)
Expand Down Expand Up @@ -433,12 +438,15 @@ class IPEXModelForImageClassificationIntegrationTest(unittest.TestCase):
"resnet",
"vit",
)
IPEX_PATCHED_SUPPORTED_ARCHITECTURES = ("vit",)

@parameterized.expand(SUPPORTED_ARCHITECTURES)
def test_compare_to_transformers(self, model_arch):
model_id = MODEL_NAMES[model_arch]
set_seed(SEED)
ipex_model = self.IPEX_MODEL_CLASS.from_pretrained(model_id, export=True)
if model_arch in self.IPEX_PATCHED_SUPPORTED_ARCHITECTURES:
self.assertTrue(ipex_model.add_patch)
device = ipex_model.device
self.assertIsInstance(ipex_model.config, PretrainedConfig)
transformers_model = self.IPEX_MODEL_CLASS.auto_model_class.from_pretrained(model_id).to(device)
Expand Down
Loading