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[Tests] Add attentions_option to ModelTesterMixin #15909

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Merged
NielsRogge merged 3 commits into from
Mar 10, 2022
Merged

[Tests] Add attentions_option to ModelTesterMixin #15909

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e9641a6
Add attentions_option to common tester
Mar 3, 2022
904d957
Fix tests, apply suggestion
Mar 8, 2022
603684d
Apply suggestion from code review
Mar 10, 2022
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81 changes: 1 addition & 80 deletions tests/convnext/test_modeling_convnext.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,6 @@

import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import ConvNextConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
Expand Down Expand Up @@ -142,6 +141,7 @@ class ConvNextModelTest(ModelTesterMixin, unittest.TestCase):
test_torchscript = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False

def setUp(self):
self.model_tester = ConvNextModelTester(self)
Expand Down Expand Up @@ -183,10 +183,6 @@ def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)

@unittest.skip(reason="Model doesn't have attention layers")
def test_attention_outputs(self):
pass

def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
Expand Down Expand Up @@ -219,81 +215,6 @@ def check_hidden_states_output(inputs_dict, config, model_class):

check_hidden_states_output(inputs_dict, config, model_class)

def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True

# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)

inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]

hidden_states = outputs.hidden_states[0]
hidden_states.retain_grad()

output.flatten()[0].backward(retain_graph=True)

self.assertIsNotNone(hidden_states.grad)

def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t

def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()

def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif isinstance(tuple_object, Dict):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values(), dict_object.values()
):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=f"Tuple and dict output are not equal. Difference: {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`: {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.",
)

recursive_check(tuple_output, dict_output)

for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()

tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)

tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)

tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
Expand Down
108 changes: 13 additions & 95 deletions tests/poolformer/test_modeling_poolformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,6 @@

import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
Expand Down Expand Up @@ -130,6 +129,7 @@ class PoolFormerModelTest(ModelTesterMixin, unittest.TestCase):
test_pruning = False
test_resize_embeddings = False
test_torchscript = False
has_attentions = False

def setUp(self):
self.model_tester = PoolFormerModelTester(self)
Expand All @@ -150,100 +150,6 @@ def test_inputs_embeds(self):
def test_model_common_attributes(self):
pass

def test_retain_grad_hidden_states_attentions(self):
# Since poolformer doesn't use Attention
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True

# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)

inputs = self._prepare_for_class(inputs_dict, model_class)

outputs = model(**inputs)

output = outputs[0]

hidden_states = outputs.hidden_states[0]

hidden_states.retain_grad()

output.flatten()[0].backward(retain_graph=True)

self.assertIsNotNone(hidden_states.grad)

def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t

def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()

def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif isinstance(tuple_object, Dict):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values(), dict_object.values()
):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=f"Tuple and dict output are not equal. Difference: {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`: {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.",
)

recursive_check(tuple_output, dict_output)

for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()

tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)

tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)

tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()

for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]

expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)

@unittest.skip("PoolFormer does not have attention")
def test_attention_outputs(self):
pass

def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
Expand Down Expand Up @@ -297,6 +203,18 @@ def test_training(self):
loss = model(**inputs).loss
loss.backward()

def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()

for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]

expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)

@slow
def test_model_from_pretrained(self):
for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
Expand Down
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