SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

New TF model inputs (#8602)

Browse Source 
* Apply on BERT and ALBERT

* Update TF Bart

* Add input processing to TF BART

* Add input processing for TF CTRL

* Add input processing to TF Distilbert

* Add input processing to TF DPR

* Add input processing to TF Electra

* Add input processing for TF Flaubert

* Add deprecated arguments

* Add input processing to TF XLM

* remove unused imports

* Add input processing to TF Funnel

* Add input processing to TF GPT2

* Add input processing to TF Longformer

* Add input processing to TF Lxmert

* Apply style

* Add input processing to TF Mobilebert

* Add input processing to TF GPT

* Add input processing to TF Roberta

* Add input processing to TF T5

* Add input processing to TF TransfoXL

* Apply style

* Rebase on master

* Bug fix

* Retry to bugfix

* Retry bug fix

* Fix wrong model name

* Try another fix

* Fix BART

* Fix input precessing

* Apply style

* Put the deprecated warnings in the input processing function

* Remove the unused imports

* Raise an error when len(kwargs)>0

* test ModelOutput instead of TFBaseModelOutput

* Bug fix

* Address Patrick's comments

* Address Patrick's comments

* Address Sylvain's comments

* Add the new inputs in new Longformer models

* Update the template with the new input processing

* Remove useless assert

* Apply style

* Trigger CI
This commit is contained in:
Julien Plu
2020-11-24 19:55:00 +01:00
committed by GitHub
parent 82d443a7fd
commit 29d4992453
26 changed files with 4487 additions and 3243 deletions
Download Patch File Download Diff File Expand all files Collapse all files

41
tests/test_modeling_tf_bart.py
View File

@@ -14,7 +14,6 @@
# limitations under the License.
import tempfile
import unittest
import numpy as np
@@ -102,15 +101,14 @@ def prepare_bart_inputs_dict(
@require_tf
class TestTFBart(TFModelTesterMixin, unittest.TestCase):
class TFBartModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (TFBartForConditionalGeneration, TFBartModel) if is_tf_available() else ()
all_generative_model_classes = (TFBartForConditionalGeneration,) if is_tf_available() else ()
is_encoder_decoder = True
test_pruning = False
model_tester_cls = TFBartModelTester
def setUp(self):
self.model_tester = self.model_tester_cls(self)
self.model_tester = TFBartModelTester(self)
self.config_tester = ConfigTester(self, config_class=BartConfig)
def test_config(self):
@@ -120,37 +118,6 @@ class TestTFBart(TFModelTesterMixin, unittest.TestCase):
# inputs_embeds not supported
pass
def test_compile_tf_model(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
model_class = self.all_generative_model_classes[0]
input_ids = {
"decoder_input_ids": tf.keras.Input(batch_shape=(2, 2000), name="decoder_input_ids", dtype="int32"),
"input_ids": tf.keras.Input(batch_shape=(2, 2000), name="input_ids", dtype="int32"),
}
# Prepare our model
model = model_class(config)
model(self._prepare_for_class(inputs_dict, model_class)) # Model must be called before saving.
# Let's load it from the disk to be sure we can use pretrained weights
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
outputs_dict = model(input_ids)
hidden_states = outputs_dict[0]
# Add a dense layer on top to test integration with other keras modules
outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states)
# Compile extended model
extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs])
extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
def test_saved_model_with_hidden_states_output(self):
# Should be uncommented during patrick TF refactor
pass
@@ -190,7 +157,7 @@ class TFBartHeadTests(unittest.TestCase):
config, input_ids, batch_size = self._get_config_and_data()
decoder_lm_labels = ids_tensor([batch_size, input_ids.shape[1]], self.vocab_size)
lm_model = TFBartForConditionalGeneration(config)
outputs = lm_model(inputs=input_ids, lm_labels=decoder_lm_labels, decoder_input_ids=input_ids, use_cache=False)
outputs = lm_model(input_ids=input_ids, labels=decoder_lm_labels, decoder_input_ids=input_ids, use_cache=False)
expected_shape = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs.logits.shape, expected_shape)
@@ -209,7 +176,7 @@ class TFBartHeadTests(unittest.TestCase):
lm_model = TFBartForConditionalGeneration(config)
context = tf.fill((7, 2), 4)
summary = tf.fill((7, 7), 6)
outputs = lm_model(inputs=context, decoder_input_ids=summary, use_cache=False)
outputs = lm_model(input_ids=context, decoder_input_ids=summary, use_cache=False)
expected_shape = (*summary.shape, config.vocab_size)
self.assertEqual(outputs.logits.shape, expected_shape)