From b18509c2085dce16e655dd86b7ea0129335da4e1 Mon Sep 17 00:00:00 2001 From: Lysandre Date: Fri, 8 Nov 2019 00:12:21 +0000 Subject: [PATCH] Tests for ALBERT in TF2 + fixes --- transformers/__init__.py | 1 + transformers/modeling_tf_albert.py | 18 +- transformers/tests/modeling_tf_albert_test.py | 229 ++++++++++++++++++ 3 files changed, 237 insertions(+), 11 deletions(-) create mode 100644 transformers/tests/modeling_tf_albert_test.py diff --git a/transformers/__init__.py b/transformers/__init__.py index a409ef772e4..baf430c17b8 100644 --- a/transformers/__init__.py +++ b/transformers/__init__.py @@ -169,6 +169,7 @@ if is_tf_available(): TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP) from .modeling_tf_albert import (TFAlbertPreTrainedModel, TFAlbertModel, TFAlbertForMaskedLM, + TFAlbertForSequenceClassification, TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) # TF 2.0 <=> PyTorch conversion utilities diff --git a/transformers/modeling_tf_albert.py b/transformers/modeling_tf_albert.py index 410d83ff780..a3f183b1923 100644 --- a/transformers/modeling_tf_albert.py +++ b/transformers/modeling_tf_albert.py @@ -126,16 +126,8 @@ class TFAlbertEmbeddings(tf.keras.layers.Layer): """ batch_size = tf.shape(inputs)[0] length = tf.shape(inputs)[1] - - print(inputs.shape) - x = tf.reshape(inputs, [-1, self.config.embedding_size]) - - print(x.shape, self.word_embeddings) - logits = tf.matmul(x, self.word_embeddings, transpose_b=True) - - print([batch_size, length, self.config.vocab_size]) return tf.reshape(logits, [batch_size, length, self.config.vocab_size]) @@ -460,20 +452,24 @@ class TFAlbertMLMHead(tf.keras.layers.Layer): # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. - self.input_embeddings = input_embeddings + self.decoder = input_embeddings def build(self, input_shape): self.bias = self.add_weight(shape=(self.vocab_size,), initializer='zeros', trainable=True, name='bias') + self.decoder_bias = self.add_weight(shape=(self.vocab_size,), + initializer='zeros', + trainable=True, + name='decoder/bias') super(TFAlbertMLMHead, self).build(input_shape) def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) hidden_states = self.LayerNorm(hidden_states) - hidden_states = self.input_embeddings(hidden_states, mode="linear") + hidden_states = self.decoder(hidden_states, mode="linear") + self.decoder_bias hidden_states = hidden_states + self.bias return hidden_states @@ -666,7 +662,7 @@ class TFAlbertModel(TFAlbertPreTrainedModel): [embedding_output, extended_attention_mask, head_mask], training=training) sequence_output = encoder_outputs[0] - pooled_output = self.pooler(sequence_output) + pooled_output = self.pooler(sequence_output[:, 0]) # add hidden_states and attentions if they are here outputs = (sequence_output, pooled_output,) + encoder_outputs[1:] diff --git a/transformers/tests/modeling_tf_albert_test.py b/transformers/tests/modeling_tf_albert_test.py new file mode 100644 index 00000000000..85fc62f34ff --- /dev/null +++ b/transformers/tests/modeling_tf_albert_test.py @@ -0,0 +1,229 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors. +# +# 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 absolute_import +from __future__ import division +from __future__ import print_function + +import unittest +import shutil +import pytest +import sys + +from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor) +from .configuration_common_test import ConfigTester + +from transformers import AlbertConfig, is_tf_available + +if is_tf_available(): + import tensorflow as tf + from transformers.modeling_tf_albert import (TFAlbertModel, TFAlbertForMaskedLM, + TFAlbertForSequenceClassification, + TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) +else: + pytestmark = pytest.mark.skip("Require TensorFlow") + + +class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester): + + all_model_classes = ( + TFAlbertModel, + TFAlbertForMaskedLM, + TFAlbertForSequenceClassification + ) if is_tf_available() else () + + class TFAlbertModelTester(object): + + def __init__(self, + parent, + batch_size=13, + seq_length=7, + is_training=True, + use_input_mask=True, + use_token_type_ids=True, + use_labels=True, + vocab_size=99, + hidden_size=32, + num_hidden_layers=5, + num_attention_heads=4, + intermediate_size=37, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=512, + type_vocab_size=16, + type_sequence_label_size=2, + initializer_range=0.02, + num_labels=3, + num_choices=4, + scope=None, + ): + self.parent = parent + self.batch_size = batch_size + self.seq_length = seq_length + self.is_training = is_training + self.use_input_mask = use_input_mask + self.use_token_type_ids = use_token_type_ids + 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_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.type_vocab_size = type_vocab_size + self.type_sequence_label_size = type_sequence_label_size + self.initializer_range = initializer_range + self.num_labels = num_labels + self.num_choices = num_choices + self.scope = scope + + def prepare_config_and_inputs(self): + input_ids = ids_tensor( + [self.batch_size, self.seq_length], self.vocab_size) + + input_mask = None + if self.use_input_mask: + input_mask = ids_tensor( + [self.batch_size, self.seq_length], vocab_size=2) + + token_type_ids = None + if self.use_token_type_ids: + token_type_ids = ids_tensor( + [self.batch_size, self.seq_length], self.type_vocab_size) + + sequence_labels = None + token_labels = None + choice_labels = None + if self.use_labels: + sequence_labels = ids_tensor( + [self.batch_size], self.type_sequence_label_size) + token_labels = ids_tensor( + [self.batch_size, self.seq_length], self.num_labels) + choice_labels = ids_tensor([self.batch_size], self.num_choices) + + config = AlbertConfig( + vocab_size_or_config_json_file=self.vocab_size, + hidden_size=self.hidden_size, + num_hidden_layers=self.num_hidden_layers, + num_attention_heads=self.num_attention_heads, + intermediate_size=self.intermediate_size, + hidden_act=self.hidden_act, + hidden_dropout_prob=self.hidden_dropout_prob, + attention_probs_dropout_prob=self.attention_probs_dropout_prob, + max_position_embeddings=self.max_position_embeddings, + type_vocab_size=self.type_vocab_size, + initializer_range=self.initializer_range) + + return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels + + def create_and_check_albert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): + model = TFAlbertModel(config=config) + # inputs = {'input_ids': input_ids, + # 'attention_mask': input_mask, + # 'token_type_ids': token_type_ids} + # sequence_output, pooled_output = model(**inputs) + inputs = {'input_ids': input_ids, + 'attention_mask': input_mask, + 'token_type_ids': token_type_ids} + sequence_output, pooled_output = model(inputs) + + inputs = [input_ids, input_mask] + sequence_output, pooled_output = model(inputs) + + sequence_output, pooled_output = model(input_ids) + + result = { + "sequence_output": sequence_output.numpy(), + "pooled_output": pooled_output.numpy(), + } + self.parent.assertListEqual( + list(result["sequence_output"].shape), + [self.batch_size, self.seq_length, self.hidden_size]) + self.parent.assertListEqual(list(result["pooled_output"].shape), [ + self.batch_size, self.hidden_size]) + + def create_and_check_albert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): + model = TFAlbertForMaskedLM(config=config) + inputs = {'input_ids': input_ids, + 'attention_mask': input_mask, + 'token_type_ids': token_type_ids} + prediction_scores, = model(inputs) + result = { + "prediction_scores": prediction_scores.numpy(), + } + self.parent.assertListEqual( + list(result["prediction_scores"].shape), + [self.batch_size, self.seq_length, self.vocab_size]) + + def create_and_check_albert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): + config.num_labels = self.num_labels + model = TFAlbertForSequenceClassification(config=config) + inputs = {'input_ids': input_ids, + 'attention_mask': input_mask, + 'token_type_ids': token_type_ids} + logits, = model(inputs) + result = { + "logits": logits.numpy(), + } + self.parent.assertListEqual( + list(result["logits"].shape), + [self.batch_size, self.num_labels]) + + def prepare_config_and_inputs_for_common(self): + config_and_inputs = self.prepare_config_and_inputs() + (config, input_ids, token_type_ids, input_mask, + sequence_labels, token_labels, choice_labels) = config_and_inputs + inputs_dict = {'input_ids': input_ids, + 'token_type_ids': token_type_ids, 'attention_mask': input_mask} + return config, inputs_dict + + def setUp(self): + self.model_tester = TFAlbertModelTest.TFAlbertModelTester(self) + self.config_tester = ConfigTester( + self, config_class=AlbertConfig, hidden_size=37) + + def test_config(self): + self.config_tester.run_common_tests() + + def test_albert_model(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_albert_model(*config_and_inputs) + + def test_for_masked_lm(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_albert_for_masked_lm( + *config_and_inputs) + + def test_for_sequence_classification(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_albert_for_sequence_classification( + *config_and_inputs) + + @pytest.mark.slow + def test_model_from_pretrained(self): + cache_dir = "/tmp/transformers_test/" + # for model_name in list(TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: + for model_name in ['albert-base-uncased']: + model = TFAlbertModel.from_pretrained( + model_name, cache_dir=cache_dir) + shutil.rmtree(cache_dir) + self.assertIsNotNone(model) + + +if __name__ == "__main__": + unittest.main()