430 lines
18 KiB
Python
430 lines
18 KiB
Python
# coding=utf-8
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# Copyright Iz Beltagy, Matthew E. Peters, Arman Cohan and 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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import unittest
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from transformers import LEDConfig, is_tf_available
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from transformers.testing_utils import require_tf, slow
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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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if is_tf_available():
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import tensorflow as tf
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from transformers import TFLEDForConditionalGeneration, TFLEDModel
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@require_tf
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class TFLEDModelTester:
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config_cls = LEDConfig
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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=5,
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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=20,
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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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attention_window=4,
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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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self.attention_window = attention_window
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# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
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# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
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# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
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# because its local attention only attends to `self.attention_window` and one before and one after
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self.key_length = self.attention_window + 2
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# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
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# the `test_attention_outputs` and `test_hidden_states_output` tests
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self.encoder_seq_length = (
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self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
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)
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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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attention_window=self.attention_window,
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**self.config_updates,
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)
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inputs_dict = prepare_led_inputs_dict(config, input_ids, decoder_input_ids)
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global_attention_mask = tf.concat(
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[tf.zeros_like(input_ids)[:, :-1], tf.ones_like(input_ids)[:, -1:]],
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axis=-1,
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)
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inputs_dict["global_attention_mask"] = global_attention_mask
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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 = TFLEDModel(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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self.batch_size = 1
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# first forward pass
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outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
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output, past_key_values = outputs.to_tuple()
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past_key_values = past_key_values[1]
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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_led_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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):
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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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return {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"decoder_input_ids": decoder_input_ids,
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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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}
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@require_tf
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class TFLEDModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
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all_generative_model_classes = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
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is_encoder_decoder = True
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test_pruning = False
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test_head_masking = False
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test_onnx = False
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def setUp(self):
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self.model_tester = TFLEDModelTester(self)
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self.config_tester = ConfigTester(self, config_class=LEDConfig)
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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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def test_model_common_attributes(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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model = model_class(config)
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assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
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if model_class in self.all_generative_model_classes:
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x = model.get_output_embeddings()
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assert isinstance(x, tf.keras.layers.Layer)
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name = model.get_bias()
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assert isinstance(name, dict)
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for k, v in name.items():
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assert isinstance(v, tf.Variable)
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else:
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x = model.get_output_embeddings()
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assert x is None
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name = model.get_bias()
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assert name is None
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def test_resize_token_embeddings(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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def _get_word_embedding_weight(model, embedding_layer):
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if hasattr(embedding_layer, "weight"):
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return embedding_layer.weight
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else:
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# Here we build the word embeddings weights if not exists.
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# And then we retry to get the attribute once built.
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model(model.dummy_inputs)
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if hasattr(embedding_layer, "weight"):
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return embedding_layer.weight
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else:
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return None
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for model_class in self.all_model_classes:
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for size in [config.vocab_size - 10, config.vocab_size + 10, None]:
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# build the embeddings
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model = model_class(config=config)
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old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
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old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
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old_final_logits_bias = model.get_bias()
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# reshape the embeddings
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model.resize_token_embeddings(size)
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new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
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new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
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new_final_logits_bias = model.get_bias()
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# check that the resized embeddings size matches the desired size.
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assert_size = size if size is not None else config.vocab_size
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self.assertEqual(new_input_embeddings.shape[0], assert_size)
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# check that weights remain the same after resizing
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models_equal = True
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for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()):
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if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
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models_equal = False
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self.assertTrue(models_equal)
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if old_output_embeddings is not None and new_output_embeddings is not None:
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self.assertEqual(new_output_embeddings.shape[0], assert_size)
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models_equal = True
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for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()):
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if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
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models_equal = False
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self.assertTrue(models_equal)
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if old_final_logits_bias is not None and new_final_logits_bias is not None:
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old_final_logits_bias = old_final_logits_bias["final_logits_bias"]
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new_final_logits_bias = new_final_logits_bias["final_logits_bias"]
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self.assertEqual(new_final_logits_bias.shape[0], 1)
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self.assertEqual(new_final_logits_bias.shape[1], assert_size)
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models_equal = True
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for old, new in zip(old_final_logits_bias.value(), new_final_logits_bias.value()):
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for p1, p2 in zip(old, new):
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if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
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models_equal = False
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self.assertTrue(models_equal)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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inputs_dict["global_attention_mask"] = tf.zeros_like(inputs_dict["attention_mask"])
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num_global_attn_indices = 2
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inputs_dict["global_attention_mask"] = tf.where(
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tf.range(self.model_tester.seq_length)[None, :] < num_global_attn_indices,
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1,
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inputs_dict["global_attention_mask"],
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)
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config.return_dict = True
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seq_length = self.model_tester.seq_length
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encoder_seq_length = self.model_tester.encoder_seq_length
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def check_decoder_attentions_output(outputs):
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decoder_attentions = outputs.decoder_attentions
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self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(decoder_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_length, seq_length],
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)
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def check_encoder_attentions_output(outputs):
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attentions = [t.numpy() for t in outputs.encoder_attentions]
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global_attentions = [t.numpy() for t in outputs.encoder_global_attentions]
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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self.assertEqual(len(global_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, seq_length],
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)
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self.assertListEqual(
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list(global_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices],
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)
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["use_cache"] = False
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config.output_hidden_states = False
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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out_len = len(outputs)
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self.assertEqual(config.output_hidden_states, False)
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check_encoder_attentions_output(outputs)
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if self.is_encoder_decoder:
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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self.assertEqual(config.output_hidden_states, False)
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check_decoder_attentions_output(outputs)
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# Check that output attentions can also be changed via the config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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self.assertEqual(config.output_hidden_states, False)
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check_encoder_attentions_output(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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config.output_hidden_states = True
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
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self.assertEqual(model.config.output_hidden_states, True)
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check_encoder_attentions_output(outputs)
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def test_xla_mode(self):
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# TODO JP: Make LED XLA compliant
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pass
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def test_saved_model_creation(self):
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# This test is too long (>30sec) and makes fail the CI
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pass
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def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
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"""If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
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if a is None and b is None:
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return True
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try:
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if tf.debugging.assert_near(a, b, atol=atol):
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return True
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raise
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except Exception:
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msg = "{} != {}".format(a, b)
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if prefix:
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msg = prefix + ": " + msg
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raise AssertionError(msg)
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def _long_tensor(tok_lst):
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return tf.constant(tok_lst, dtype=tf.int32)
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TOLERANCE = 1e-4
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@slow
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@require_tf
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class TFLEDModelIntegrationTest(unittest.TestCase):
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def test_inference_no_head(self):
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model = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384").led
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# change to intended input here
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input_ids = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
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decoder_input_ids = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
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inputs_dict = prepare_led_inputs_dict(model.config, input_ids, decoder_input_ids)
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output = model(**inputs_dict)[0]
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expected_shape = (1, 1024, 768)
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self.assertEqual(output.shape, expected_shape)
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# change to expected output here
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expected_slice = tf.convert_to_tensor(
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[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]],
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)
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tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE)
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def test_inference_with_head(self):
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model = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384")
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# change to intended input here
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input_ids = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
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decoder_input_ids = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
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inputs_dict = prepare_led_inputs_dict(model.config, input_ids, decoder_input_ids)
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output = model(**inputs_dict)[0]
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expected_shape = (1, 1024, model.config.vocab_size)
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self.assertEqual(output.shape, expected_shape)
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# change to expected output here
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expected_slice = tf.convert_to_tensor(
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[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]],
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)
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tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE)
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