193 lines
7.0 KiB
Python
193 lines
7.0 KiB
Python
# Copyright 2020 The HuggingFace 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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import unittest
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import numpy as np
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from transformers import BigBirdConfig, is_flax_available
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from transformers.testing_utils import require_flax, slow
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from .test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
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if is_flax_available():
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import jax
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from transformers.models.big_bird.modeling_flax_big_bird import (
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FlaxBigBirdForMaskedLM,
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FlaxBigBirdForMultipleChoice,
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FlaxBigBirdForPreTraining,
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FlaxBigBirdForQuestionAnswering,
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FlaxBigBirdForSequenceClassification,
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FlaxBigBirdForTokenClassification,
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FlaxBigBirdModel,
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)
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class FlaxBigBirdModelTester(unittest.TestCase):
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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=56,
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is_training=True,
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use_attention_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu_new",
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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=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_choices=4,
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attention_type="block_sparse",
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use_bias=True,
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rescale_embeddings=False,
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block_size=4,
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num_random_blocks=3,
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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_attention_mask = use_attention_mask
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self.use_token_type_ids = use_token_type_ids
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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_act = hidden_act
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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.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_choices = num_choices
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self.rescale_embeddings = rescale_embeddings
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self.attention_type = attention_type
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self.use_bias = use_bias
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self.block_size = block_size
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self.num_random_blocks = num_random_blocks
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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attention_mask = None
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if self.use_attention_mask:
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attention_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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config = BigBirdConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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is_decoder=False,
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initializer_range=self.initializer_range,
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attention_type=self.attention_type,
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block_size=self.block_size,
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num_random_blocks=self.num_random_blocks,
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use_bias=self.use_bias,
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rescale_embeddings=self.rescale_embeddings,
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)
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return config, input_ids, token_type_ids, attention_mask
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, token_type_ids, attention_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
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return config, inputs_dict
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@require_flax
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class FlaxBigBirdModelTest(FlaxModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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FlaxBigBirdModel,
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FlaxBigBirdForPreTraining,
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FlaxBigBirdForMaskedLM,
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FlaxBigBirdForMultipleChoice,
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FlaxBigBirdForQuestionAnswering,
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FlaxBigBirdForSequenceClassification,
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FlaxBigBirdForTokenClassification,
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)
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if is_flax_available()
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else ()
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)
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test_attn_probs = False
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test_mismatched_shapes = False
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def setUp(self):
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self.model_tester = FlaxBigBirdModelTester(self)
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@slow
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def test_model_from_pretrained(self):
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for model_class_name in self.all_model_classes:
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model = model_class_name.from_pretrained("google/bigbird-roberta-base", from_pt=True)
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outputs = model(np.ones((1, 1)))
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self.assertIsNotNone(outputs)
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def test_attention_outputs(self):
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if self.test_attn_probs:
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super().test_attention_outputs()
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@slow
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# copied from `test_modeling_flax_common` because it takes much longer than other models
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def test_jit_compilation(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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with self.subTest(model_class.__name__):
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prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
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model = model_class(config)
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@jax.jit
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def model_jitted(input_ids, attention_mask=None, **kwargs):
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return model(input_ids=input_ids, attention_mask=attention_mask, **kwargs)
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with self.subTest("JIT Enabled"):
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jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
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with self.subTest("JIT Disabled"):
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with jax.disable_jit():
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outputs = model_jitted(**prepared_inputs_dict).to_tuple()
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self.assertEqual(len(outputs), len(jitted_outputs))
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for jitted_output, output in zip(jitted_outputs, outputs):
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self.assertEqual(jitted_output.shape, output.shape)
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