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Fix missing output_attentions in PT/Flax equivalence test (#16271)

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* fix - set output_attentions to True

* Update tests/test_modeling_flax_common.py

* update for has_attentions

* overwrite check_outputs in FlaxBigBirdModelTest

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
This commit is contained in:
Yih-Dar 2022-03-29 17:51:48 +02:00 committed by GitHub
parent 45abb37ac9
commit aebca696af
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GPG key ID: 4AEE18F83AFDEB23
2 changed files with 15 additions and 3 deletions
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9
tests/big_bird/test_modeling_flax_big_bird.py
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@ -190,3 +190,12 @@ class FlaxBigBirdModelTest(FlaxModelTesterMixin, unittest.TestCase):
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
# overwrite from common in order to skip the check on `attentions`
def check_outputs(self, fx_outputs, pt_outputs, model_class, names):
# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
# an effort was done to return `attention_probs` (yet to be verified).
if type(names) == str and names.startswith("attentions"):
return
else:
super().check_outputs(fx_outputs, pt_outputs, model_class, names)

9
tests/test_modeling_flax_common.py
View file

@ -120,6 +120,7 @@ class FlaxModelTesterMixin:
test_mismatched_shapes = True
is_encoder_decoder = False
test_head_masking = False
has_attentions = True
def _prepare_for_class(self, inputs_dict, model_class):
inputs_dict = copy.deepcopy(inputs_dict)
@ -168,6 +169,7 @@ class FlaxModelTesterMixin:
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
# (Copied from tests.test_modeling_common.ModelTesterMixin.check_outputs)
def check_outputs(self, fx_outputs, pt_outputs, model_class, names):
"""
Args:
@ -204,8 +206,7 @@ class FlaxModelTesterMixin:
pt_outputs[pt_nans] = 0
fx_outputs[pt_nans] = 0
max_diff = np.amax(np.abs(fx_outputs - pt_outputs))
self.assertLessEqual(max_diff, 1e-5)
self.assert_almost_equals(fx_outputs, pt_outputs, 1e-5)
else:
raise ValueError(
f"`fx_outputs` should be a `tuple` or an instance of `jnp.ndarray`. Got {type(fx_outputs)} instead."
@ -222,6 +223,7 @@ class FlaxModelTesterMixin:
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
# prepare inputs
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
@ -274,7 +276,7 @@ class FlaxModelTesterMixin:
# Output all for aggressive testing
config.output_hidden_states = True
# Pure convolutional models have no attention
config.output_attentions = self.has_attentions
# prepare inputs
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
@ -314,6 +316,7 @@ class FlaxModelTesterMixin:
# send pytorch model to the correct device
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)