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TFMarian, TFMbart, TFPegasus, TFBlenderbot (#7987)

Browse Source 
* Start plumbing

* Marian close

* Small stubs for all children

* Fixed bart

* marian working

* pegasus test is good, but failing

* Checkin tests

* More model files

* Subtle marian, pegasus integration test failures

* Works well

* rm print

* boom boom

* Still failing model2doc

* merge master

* Equivalence test failing, all others fixed

* cleanup

* Fix embed_scale

* Cleanup marian pipeline test

* Undo extra changes

* Smaller delta

* Cleanup model testers

* undo delta

* fix tests import structure

* cross test decorator

* Cleaner set_weights

* Respect authorized_unexpected_keys

* No warnings

* No warnings

* style

* Nest tf import

* black

* Apply suggestions from code review

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* functional dropout

* fixup

* Fixup

* style_doc

* embs

* shape list

* delete slow force_token_id_to_be_generated func

* fixup

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Sam Shleifer
2020-10-30 11:23:16 -04:00
committed by GitHub
parent 6279072f5f
commit 566b083eb1
20 changed files with 1063 additions and 106 deletions
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132
tests/test_modeling_tf_blenderbot.py Normal file
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# coding=utf-8
# Copyright 2020 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
from tests.test_configuration_common import ConfigTester
from tests.test_modeling_tf_bart import TFBartModelTester
from tests.test_modeling_tf_common import TFModelTesterMixin
from transformers import BlenderbotConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.file_utils import cached_property
from transformers.testing_utils import is_pt_tf_cross_test, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeq2SeqLM, TFBlenderbotForConditionalGeneration
class ModelTester(TFBartModelTester):
config_updates = dict(
normalize_before=True,
static_position_embeddings=True,
do_blenderbot_90_layernorm=True,
normalize_embeddings=True,
)
config_cls = BlenderbotConfig
@require_tf
class TestTFBlenderbotCommon(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
all_generative_model_classes = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
model_tester_cls = ModelTester
is_encoder_decoder = True
test_pruning = False
def setUp(self):
self.model_tester = self.model_tester_cls(self)
self.config_tester = ConfigTester(self, config_class=BlenderbotConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_inputs_embeds(self):
# inputs_embeds not supported
pass
def test_saved_model_with_hidden_states_output(self):
# Should be uncommented during patrick TF refactor
pass
def test_saved_model_with_attentions_output(self):
# Should be uncommented during patrick TF refactor
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])
@is_pt_tf_cross_test
@require_tokenizers
class TFBlenderbot90MIntegrationTests(unittest.TestCase):
src_text = [
"Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like i'm going to throw up.\nand why is that?"
]
model_name = "facebook/blenderbot-90M"
@cached_property
def tokenizer(self):
return BlenderbotSmallTokenizer.from_pretrained(self.model_name)
@cached_property
def model(self):
model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name, from_pt=True)
return model
@slow
def test_90_generation_from_long_input(self):
model_inputs = self.tokenizer(self.src_text, return_tensors="tf")
generated_ids = self.model.generate(
model_inputs.input_ids,
attention_mask=model_inputs.attention_mask,
num_beams=2,
use_cache=True,
)
generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True)[0]
assert generated_words in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
"i'm not sure. i just feel like i've been in a bad situation.",
)