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[AutoBackbone] Improve API (#20407)

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* Add hidden states and attentions to backbone outputs

* Update ResNet

* Fix more tests

* Debug test

* Fix test_determinism

* Fix test_save_load

* Remove file

* Disable fx tests

* Test

* Add fx support for backbones

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
This commit is contained in:
NielsRogge
2022-11-28 17:20:24 +01:00
committed by GitHub
parent 39a72125e7
commit 0bae286de9
9 changed files with 96 additions and 22 deletions
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58
tests/test_modeling_common.py
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@@ -93,6 +93,7 @@ if is_torch_available():
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
MODEL_FOR_AUDIO_XVECTOR_MAPPING,
MODEL_FOR_BACKBONE_MAPPING,
MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
@@ -255,28 +256,35 @@ class ModelTesterMixin:
def test_save_load(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_save_load(out1, out2):
# make sure we don't have nans
out_2 = out2.cpu().numpy()
out_2[np.isnan(out_2)] = 0
out_1 = out1.cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
model.to(torch_device)
with torch.no_grad():
after_outputs = model(**self._prepare_for_class(inputs_dict, model_class))
second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
# Make sure we don't have nans
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
if isinstance(first, tuple) and isinstance(second, tuple):
for tensor1, tensor2 in zip(first, second):
check_save_load(tensor1, tensor2)
else:
check_save_load(first, second)
def test_save_load_keys_to_ignore_on_save(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -453,6 +461,15 @@ class ModelTesterMixin:
def test_determinism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_determinism(first, second):
out_1 = first.cpu().numpy()
out_2 = second.cpu().numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
@@ -461,12 +478,11 @@ class ModelTesterMixin:
first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
out_1 = first.cpu().numpy()
out_2 = second.cpu().numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
if isinstance(first, tuple) and isinstance(second, tuple):
for tensor1, tensor2 in zip(first, second):
check_determinism(tensor1, tensor2)
else:
check_determinism(first, second)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
@@ -502,7 +518,10 @@ class ModelTesterMixin:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
if model_class in get_values(MODEL_MAPPING):
if model_class in [
*get_values(MODEL_MAPPING),
*get_values(MODEL_FOR_BACKBONE_MAPPING),
]:
continue
model = model_class(config)
@@ -521,7 +540,10 @@ class ModelTesterMixin:
config.use_cache = False
config.return_dict = True
if model_class in get_values(MODEL_MAPPING) or not model_class.supports_gradient_checkpointing:
if (
model_class in [*get_values(MODEL_MAPPING), *get_values(MODEL_FOR_BACKBONE_MAPPING)]
or not model_class.supports_gradient_checkpointing
):
continue
model = model_class(config)
model.to(torch_device)