235 lines
8.6 KiB
Python
235 lines
8.6 KiB
Python
# coding=utf-8
|
|
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# 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.
|
|
|
|
from __future__ import annotations
|
|
|
|
import unittest
|
|
|
|
from transformers import AutoTokenizer, MBartConfig, is_tf_available
|
|
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
|
|
from transformers.utils import cached_property
|
|
|
|
from ...test_configuration_common import ConfigTester
|
|
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
|
|
from ...test_pipeline_mixin import PipelineTesterMixin
|
|
|
|
|
|
if is_tf_available():
|
|
import tensorflow as tf
|
|
|
|
from transformers import TFAutoModelForSeq2SeqLM, TFMBartForConditionalGeneration, TFMBartModel
|
|
|
|
|
|
@require_tf
|
|
class TFMBartModelTester:
|
|
config_cls = MBartConfig
|
|
config_updates = {}
|
|
hidden_act = "gelu"
|
|
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=13,
|
|
seq_length=7,
|
|
is_training=True,
|
|
use_labels=False,
|
|
vocab_size=99,
|
|
hidden_size=32,
|
|
num_hidden_layers=2,
|
|
num_attention_heads=4,
|
|
intermediate_size=37,
|
|
hidden_dropout_prob=0.1,
|
|
attention_probs_dropout_prob=0.1,
|
|
max_position_embeddings=20,
|
|
eos_token_id=2,
|
|
pad_token_id=1,
|
|
bos_token_id=0,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.seq_length = seq_length
|
|
self.is_training = is_training
|
|
self.use_labels = use_labels
|
|
self.vocab_size = vocab_size
|
|
self.hidden_size = hidden_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.intermediate_size = intermediate_size
|
|
self.hidden_dropout_prob = hidden_dropout_prob
|
|
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.eos_token_id = eos_token_id
|
|
self.pad_token_id = pad_token_id
|
|
self.bos_token_id = bos_token_id
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size)
|
|
eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1)
|
|
input_ids = tf.concat([input_ids, eos_tensor], axis=1)
|
|
|
|
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
|
|
config = self.config_cls(
|
|
vocab_size=self.vocab_size,
|
|
d_model=self.hidden_size,
|
|
encoder_layers=self.num_hidden_layers,
|
|
decoder_layers=self.num_hidden_layers,
|
|
encoder_attention_heads=self.num_attention_heads,
|
|
decoder_attention_heads=self.num_attention_heads,
|
|
encoder_ffn_dim=self.intermediate_size,
|
|
decoder_ffn_dim=self.intermediate_size,
|
|
dropout=self.hidden_dropout_prob,
|
|
attention_dropout=self.attention_probs_dropout_prob,
|
|
max_position_embeddings=self.max_position_embeddings,
|
|
eos_token_ids=[2],
|
|
bos_token_id=self.bos_token_id,
|
|
pad_token_id=self.pad_token_id,
|
|
decoder_start_token_id=self.pad_token_id,
|
|
**self.config_updates,
|
|
)
|
|
inputs_dict = prepare_mbart_inputs_dict(config, input_ids, decoder_input_ids)
|
|
return config, inputs_dict
|
|
|
|
def check_decoder_model_past_large_inputs(self, config, inputs_dict):
|
|
model = TFMBartModel(config=config).get_decoder()
|
|
input_ids = inputs_dict["input_ids"]
|
|
|
|
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, head_mask=head_mask, use_cache=True)
|
|
|
|
output, past_key_values = outputs.to_tuple()
|
|
past_key_values = past_key_values[1]
|
|
|
|
|
|
def prepare_mbart_inputs_dict(
|
|
config,
|
|
input_ids,
|
|
decoder_input_ids,
|
|
attention_mask=None,
|
|
decoder_attention_mask=None,
|
|
head_mask=None,
|
|
decoder_head_mask=None,
|
|
cross_attn_head_mask=None,
|
|
):
|
|
if attention_mask is None:
|
|
attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8)
|
|
if decoder_attention_mask is None:
|
|
decoder_attention_mask = tf.concat(
|
|
[
|
|
tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8),
|
|
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8),
|
|
],
|
|
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))
|
|
if cross_attn_head_mask is None:
|
|
cross_attn_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,
|
|
"cross_attn_head_mask": cross_attn_head_mask,
|
|
}
|
|
|
|
|
|
@require_tf
|
|
class TFMBartModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
|
|
all_generative_model_classes = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
|
|
pipeline_model_mapping = (
|
|
{
|
|
"conversational": TFMBartForConditionalGeneration,
|
|
"feature-extraction": TFMBartModel,
|
|
"summarization": TFMBartForConditionalGeneration,
|
|
"text2text-generation": TFMBartForConditionalGeneration,
|
|
"translation": TFMBartForConditionalGeneration,
|
|
}
|
|
if is_tf_available()
|
|
else {}
|
|
)
|
|
is_encoder_decoder = True
|
|
test_pruning = False
|
|
test_onnx = False
|
|
|
|
# TODO: Fix the failed tests
|
|
def is_pipeline_test_to_skip(
|
|
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
|
|
):
|
|
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
|
|
# Exception encountered when calling layer '...'
|
|
return True
|
|
|
|
return False
|
|
|
|
def setUp(self):
|
|
self.model_tester = TFMBartModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=MBartConfig)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_decoder_model_past_large_inputs(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
|
|
self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)
|
|
|
|
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
@require_tf
|
|
class TFMBartModelIntegrationTest(unittest.TestCase):
|
|
src_text = [
|
|
" UN Chief Says There Is No Military Solution in Syria",
|
|
]
|
|
expected_text = [
|
|
"Şeful ONU declară că nu există o soluţie militară în Siria",
|
|
]
|
|
model_name = "facebook/mbart-large-en-ro"
|
|
|
|
@cached_property
|
|
def tokenizer(self):
|
|
return AutoTokenizer.from_pretrained(self.model_name)
|
|
|
|
@cached_property
|
|
def model(self):
|
|
model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name)
|
|
return model
|
|
|
|
def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
|
|
generated_words = self.translate_src_text(**tokenizer_kwargs)
|
|
self.assertListEqual(self.expected_text, generated_words)
|
|
|
|
def translate_src_text(self, **tokenizer_kwargs):
|
|
model_inputs = self.tokenizer(self.src_text, **tokenizer_kwargs, return_tensors="tf")
|
|
generated_ids = self.model.generate(
|
|
model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2
|
|
)
|
|
generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
|
return generated_words
|
|
|
|
@slow
|
|
def test_batch_generation_en_ro(self):
|
|
self._assert_generated_batch_equal_expected()
|