252 lines
12 KiB
Python
252 lines
12 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import unittest
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from transformers import AutoTokenizer, PegasusConfig, is_tf_available
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from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
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from transformers.utils import cached_property
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_tf_available():
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import tensorflow as tf
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from transformers import TFAutoModelForSeq2SeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
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@require_tf
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class TFPegasusModelTester:
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config_cls = PegasusConfig
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config_updates = {}
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hidden_act = "gelu"
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_labels=False,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=40,
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eos_token_id=2,
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pad_token_id=1,
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bos_token_id=0,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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def prepare_config_and_inputs_for_common(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size)
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eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1)
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input_ids = tf.concat([input_ids, eos_tensor], axis=1)
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decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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config = self.config_cls(
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vocab_size=self.vocab_size,
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d_model=self.hidden_size,
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encoder_layers=self.num_hidden_layers,
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decoder_layers=self.num_hidden_layers,
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encoder_attention_heads=self.num_attention_heads,
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decoder_attention_heads=self.num_attention_heads,
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encoder_ffn_dim=self.intermediate_size,
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decoder_ffn_dim=self.intermediate_size,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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eos_token_ids=[2],
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bos_token_id=self.bos_token_id,
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pad_token_id=self.pad_token_id,
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decoder_start_token_id=self.pad_token_id,
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**self.config_updates,
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)
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inputs_dict = prepare_pegasus_inputs_dict(config, input_ids, decoder_input_ids)
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return config, inputs_dict
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def check_decoder_model_past_large_inputs(self, config, inputs_dict):
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model = TFPegasusModel(config=config).get_decoder()
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input_ids = inputs_dict["input_ids"]
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input_ids = input_ids[:1, :]
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attention_mask = inputs_dict["attention_mask"][:1, :]
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head_mask = inputs_dict["head_mask"]
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self.batch_size = 1
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# first forward pass
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outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
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output, past_key_values = outputs.to_tuple()
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8)
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# append to next input_ids and
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next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
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next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1)
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output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0]
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output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0]
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self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
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# select random slice
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random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
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output_from_past_slice = output_from_past[:, :, random_slice_idx]
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# test that outputs are equal for slice
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tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
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def prepare_pegasus_inputs_dict(
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config,
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input_ids,
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decoder_input_ids,
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attention_mask=None,
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decoder_attention_mask=None,
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head_mask=None,
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decoder_head_mask=None,
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cross_attn_head_mask=None,
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):
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if attention_mask is None:
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attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8)
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if decoder_attention_mask is None:
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decoder_attention_mask = tf.concat(
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[
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tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8),
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tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8),
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],
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axis=-1,
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)
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if head_mask is None:
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head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads))
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if decoder_head_mask is None:
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decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
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if cross_attn_head_mask is None:
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cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
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return {
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"input_ids": input_ids,
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"decoder_input_ids": decoder_input_ids,
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"attention_mask": attention_mask,
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"decoder_attention_mask": decoder_attention_mask,
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"head_mask": head_mask,
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"decoder_head_mask": decoder_head_mask,
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"cross_attn_head_mask": cross_attn_head_mask,
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}
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@require_tf
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class TFPegasusModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
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all_generative_model_classes = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
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pipeline_model_mapping = (
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{
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"conversational": TFPegasusForConditionalGeneration,
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"feature-extraction": TFPegasusModel,
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"summarization": TFPegasusForConditionalGeneration,
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"text2text-generation": TFPegasusForConditionalGeneration,
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"translation": TFPegasusForConditionalGeneration,
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}
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if is_tf_available()
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else {}
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)
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is_encoder_decoder = True
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test_pruning = False
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test_onnx = False
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def setUp(self):
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self.model_tester = TFPegasusModelTester(self)
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self.config_tester = ConfigTester(self, config_class=PegasusConfig)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_decoder_model_past_large_inputs(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
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self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)
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@require_sentencepiece
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@require_tokenizers
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@require_tf
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class TFPegasusIntegrationTests(unittest.TestCase):
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src_text = [
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""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
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""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning 'Oh I think you're nominated'", said Dappy."And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around."At the end of the day we're grateful to be where we are in our careers."If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" """,
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]
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expected_text = [
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"California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
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" reduce the risk of wildfires.",
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'N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.',
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] # differs slightly from pytorch, likely due to numerical differences in linear layers
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model_name = "google/pegasus-xsum"
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@cached_property
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def tokenizer(self):
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return AutoTokenizer.from_pretrained(self.model_name)
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@cached_property
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def model(self):
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model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name)
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return model
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def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
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generated_words = self.translate_src_text(**tokenizer_kwargs)
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assert self.expected_text == generated_words
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def translate_src_text(self, **tokenizer_kwargs):
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model_inputs = self.tokenizer(self.src_text, **tokenizer_kwargs, padding=True, return_tensors="tf")
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generated_ids = self.model.generate(
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model_inputs.input_ids,
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attention_mask=model_inputs.attention_mask,
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num_beams=2,
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use_cache=True,
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)
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generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True)
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return generated_words
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@slow
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def test_batch_generation(self):
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self._assert_generated_batch_equal_expected()
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