Spaces:
Running on CPU Upgrade
Running on CPU Upgrade
| # coding=utf-8 | |
| # Copyright 2018 Google T5 Authors and HuggingFace Inc. team. | |
| # | |
| # 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. | |
| import unittest | |
| from transformers import T5Config, is_tf_available | |
| from .test_configuration_common import ConfigTester | |
| from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor | |
| from .utils import CACHE_DIR, require_tf, slow | |
| if is_tf_available(): | |
| from transformers.modeling_tf_t5 import TFT5Model, TFT5WithLMHeadModel | |
| class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase): | |
| is_encoder_decoder = True | |
| all_model_classes = (TFT5Model, TFT5WithLMHeadModel) if is_tf_available() else () | |
| class TFT5ModelTester(object): | |
| def __init__( | |
| self, | |
| parent, | |
| batch_size=13, | |
| seq_length=7, | |
| is_training=True, | |
| use_input_mask=True, | |
| use_labels=True, | |
| vocab_size=99, | |
| n_positions=14, | |
| hidden_size=32, | |
| num_hidden_layers=5, | |
| num_attention_heads=4, | |
| d_ff=37, | |
| relative_attention_num_buckets=8, | |
| dropout_rate=0.1, | |
| initializer_factor=0.002, | |
| 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_labels = use_labels | |
| self.vocab_size = vocab_size | |
| self.n_positions = n_positions | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.d_ff = d_ff | |
| self.relative_attention_num_buckets = relative_attention_num_buckets | |
| self.dropout_rate = dropout_rate | |
| self.initializer_factor = initializer_factor | |
| 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_labels = None | |
| if self.use_labels: | |
| token_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) | |
| config = T5Config( | |
| vocab_size=self.vocab_size, | |
| n_positions=self.n_positions, | |
| d_model=self.hidden_size, | |
| d_ff=self.d_ff, | |
| d_kv=self.hidden_size // self.num_attention_heads, | |
| num_layers=self.num_hidden_layers, | |
| num_heads=self.num_attention_heads, | |
| relative_attention_num_buckets=self.relative_attention_num_buckets, | |
| dropout_rate=self.dropout_rate, | |
| initializer_factor=self.initializer_factor, | |
| ) | |
| return (config, input_ids, input_mask, token_labels) | |
| def create_and_check_t5_model(self, config, input_ids, input_mask, token_labels): | |
| model = TFT5Model(config=config) | |
| inputs = { | |
| "encoder_input_ids": input_ids, | |
| "decoder_input_ids": input_ids, | |
| "decoder_attention_mask": input_mask, | |
| } | |
| encoder_output, decoder_output = model(inputs) | |
| encoder_output, decoder_output = model( | |
| input_ids, decoder_attention_mask=input_mask, encoder_input_ids=input_ids | |
| ) | |
| result = { | |
| "encoder_output": encoder_output.numpy(), | |
| "decoder_output": decoder_output.numpy(), | |
| } | |
| self.parent.assertListEqual( | |
| list(result["encoder_output"].shape), [self.batch_size, self.seq_length, self.hidden_size] | |
| ) | |
| self.parent.assertListEqual( | |
| list(result["decoder_output"].shape), [self.batch_size, self.seq_length, self.hidden_size] | |
| ) | |
| def create_and_check_t5_with_lm_head(self, config, input_ids, input_mask, token_labels): | |
| model = TFT5WithLMHeadModel(config=config) | |
| inputs = { | |
| "encoder_input_ids": input_ids, | |
| "decoder_input_ids": input_ids, | |
| "decoder_attention_mask": input_mask, | |
| } | |
| prediction_scores, decoder_output = 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 prepare_config_and_inputs_for_common(self): | |
| config_and_inputs = self.prepare_config_and_inputs() | |
| (config, input_ids, input_mask, token_labels) = config_and_inputs | |
| inputs_dict = { | |
| "encoder_input_ids": input_ids, | |
| "decoder_input_ids": input_ids, | |
| "decoder_attention_mask": input_mask, | |
| } | |
| return config, inputs_dict | |
| def setUp(self): | |
| self.model_tester = TFT5ModelTest.TFT5ModelTester(self) | |
| self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37) | |
| def test_config(self): | |
| self.config_tester.run_common_tests() | |
| def test_t5_model(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.create_and_check_t5_model(*config_and_inputs) | |
| def test_with_lm_head(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.create_and_check_t5_with_lm_head(*config_and_inputs) | |
| def test_model_from_pretrained(self): | |
| for model_name in ["t5-small"]: | |
| model = TFT5Model.from_pretrained(model_name, cache_dir=CACHE_DIR) | |
| self.assertIsNotNone(model) | |