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Add head_mask/decoder_head_mask for TF BART models (#9639)

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* Add head_mask/decoder_head_mask for TF BART models

* Add head_mask and decoder_head_mask input arguments for TF BART-based
models as a TF counterpart to the PR #9569

* Add test_headmasking functionality to tests/test_modeling_tf_common.py

* TODO: Add a test to verify that we can get a gradient back for
importance score computation

* Remove redundant #TODO note

Remove redundant #TODO note from tests/test_modeling_tf_common.py

* Fix assertions

* Make style

* Fix ...Model input args and adjust one new test

* Add back head_mask and decoder_head_mask to BART-based ...Model
after the last commit

* Remove head_mask ande decoder_head_mask from input_dict
in TF test_train_pipeline_custom_model as these two have different
shape than other input args (Necessary for passing this test)

* Revert adding global_rng in test_modeling_tf_common.py
This commit is contained in:
Daniel Stancl
2021-01-26 09:50:00 +01:00
committed by GitHub
parent cb73ab5a38
commit 1867d9a8d7
32 changed files with 849 additions and 36 deletions
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12
tests/test_modeling_tf_blenderbot.py
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@@ -107,10 +107,11 @@ class TFBlenderbotModelTester:
input_ids = input_ids[:1, :]
attention_mask = inputs_dict["attention_mask"][:1, :]
head_mask = inputs_dict["head_mask"]
self.batch_size = 1
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
past_key_values = past_key_values[1]
@@ -143,6 +144,8 @@ def prepare_blenderbot_inputs_dict(
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
):
if attention_mask is None:
attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8)
@@ -154,11 +157,17 @@ def prepare_blenderbot_inputs_dict(
],
axis=-1,
)
if head_mask is None:
head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@@ -168,6 +177,7 @@ class TFBlenderbotModelTest(TFModelTesterMixin, unittest.TestCase):
all_generative_model_classes = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
is_encoder_decoder = True
test_pruning = False
test_head_masking = True
def setUp(self):
self.model_tester = TFBlenderbotModelTester(self)