mirror of https://github.com/tensorflow/models.git
121 lines
3.1 KiB
Python
121 lines
3.1 KiB
Python
# Copyright 2025 The TensorFlow Authors. 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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"""Tests for configs."""
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import tensorflow as tf, tf_keras
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from official.projects.nhnet import configs
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BERT2BERT_CONFIG = {
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"vocab_size": 30522,
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"hidden_size": 768,
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"num_hidden_layers": 12,
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"num_attention_heads": 12,
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"intermediate_size": 3072,
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"hidden_act": "gelu",
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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": 2,
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"initializer_range": 0.02,
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# model params
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"decoder_intermediate_size": 3072,
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"num_decoder_attn_heads": 12,
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"num_decoder_layers": 12,
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# training params
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"label_smoothing": 0.1,
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"learning_rate": 0.05,
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"learning_rate_warmup_steps": 20000,
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"optimizer": "Adam",
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"adam_beta1": 0.9,
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"adam_beta2": 0.997,
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"adam_epsilon": 1e-09,
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# predict params
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"beam_size": 5,
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"alpha": 0.6,
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"initializer_gain": 1.0,
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"use_cache": True,
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# input params
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"input_sharding": False,
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"input_data_not_padded": False,
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"pad_token_id": 0,
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"end_token_id": 102,
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"start_token_id": 101,
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}
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NHNET_CONFIG = {
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"vocab_size": 30522,
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"hidden_size": 768,
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"num_hidden_layers": 12,
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"num_attention_heads": 12,
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"intermediate_size": 3072,
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"hidden_act": "gelu",
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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": 2,
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"initializer_range": 0.02,
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# model params
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"decoder_intermediate_size": 3072,
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"num_decoder_attn_heads": 12,
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"num_decoder_layers": 12,
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"multi_channel_cross_attention": True,
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# training params
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"label_smoothing": 0.1,
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"learning_rate": 0.05,
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"learning_rate_warmup_steps": 20000,
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"optimizer": "Adam",
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"adam_beta1": 0.9,
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"adam_beta2": 0.997,
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"adam_epsilon": 1e-09,
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# predict params
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"beam_size": 5,
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"alpha": 0.6,
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"initializer_gain": 1.0,
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"use_cache": True,
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# input params
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"passage_list": ["b", "c", "d", "e", "f"],
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"input_sharding": False,
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"input_data_not_padded": False,
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"pad_token_id": 0,
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"end_token_id": 102,
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"start_token_id": 101,
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"init_from_bert2bert": True,
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}
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class ConfigsTest(tf.test.TestCase):
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def test_configs(self):
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cfg = configs.BERT2BERTConfig()
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cfg.validate()
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self.assertEqual(cfg.as_dict(), BERT2BERT_CONFIG)
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def test_nhnet_config(self):
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cfg = configs.NHNetConfig()
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cfg.validate()
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self.assertEqual(cfg.as_dict(), NHNET_CONFIG)
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if __name__ == "__main__":
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tf.test.main()
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