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Commit from model create scripts

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.gitattributes CHANGED
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config.gin ADDED
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1
+ from __gin__ import dynamic_registration
2
+ import __main__ as train_script
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+ import seqio
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+ import t5.data.mixtures
5
+ from t5x import adafactor
6
+ from t5x.examples.t5 import network
7
+ from t5x import gin_utils
8
+ from t5x import models
9
+ from t5x import partitioning
10
+ from t5x import trainer
11
+ from t5x import utils
12
+ import tasks
13
+
14
+ # Macros:
15
+ # ==============================================================================
16
+ BATCH_SIZE = 128
17
+ DROPOUT_RATE = 0.0
18
+ INITIAL_CHECKPOINT_PATH = \
19
+ 'gs://t5-data/pretrained_models/t5x/mt5_xl/checkpoint_1000000'
20
+ LABEL_SMOOTHING = 0.0
21
+ LOSS_NORMALIZING_FACTOR = None
22
+ MIXTURE_OR_TASK_MODULE = None
23
+ MIXTURE_OR_TASK_NAME = 'ncc_english_span_corruption_stream'
24
+ MODEL = @models.EncoderDecoderModel()
25
+ MODEL_DIR = 'gs://nb-t5x-us-central2/norwegian_NCC_plus_English_t5x_xl'
26
+ OPTIMIZER = @adafactor.Adafactor()
27
+ RANDOM_SEED = None
28
+ SHUFFLE_TRAIN_EXAMPLES = True
29
+ TASK_FEATURE_LENGTHS = {'inputs': 512, 'targets': 512}
30
+ TRAIN_STEPS = 1500000
31
+ USE_CACHED_TASKS = True
32
+ USE_HARDWARE_RNG = False
33
+ VOCABULARY = @seqio.SentencePieceVocabulary()
34
+ Z_LOSS = 0.0001
35
+
36
+ # Parameters for adafactor.Adafactor:
37
+ # ==============================================================================
38
+ adafactor.Adafactor.decay_rate = 0.8
39
+ adafactor.Adafactor.logical_factor_rules = \
40
+ @adafactor.standard_logical_factor_rules()
41
+ adafactor.Adafactor.step_offset = 0
42
+
43
+ # Parameters for utils.CheckpointConfig:
44
+ # ==============================================================================
45
+ utils.CheckpointConfig.restore = @utils.RestoreCheckpointConfig()
46
+ utils.CheckpointConfig.save = @utils.SaveCheckpointConfig()
47
+
48
+ # Parameters for utils.create_learning_rate_scheduler:
49
+ # ==============================================================================
50
+ utils.create_learning_rate_scheduler.base_learning_rate = 0.5
51
+ utils.create_learning_rate_scheduler.factors = 'constant * rsqrt_decay'
52
+ utils.create_learning_rate_scheduler.warmup_steps = 10000
53
+
54
+ # Parameters for train/utils.DatasetConfig:
55
+ # ==============================================================================
56
+ train/utils.DatasetConfig.batch_size = %BATCH_SIZE
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+ train/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
58
+ train/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
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+ train/utils.DatasetConfig.pack = True
60
+ train/utils.DatasetConfig.seed = None
61
+ train/utils.DatasetConfig.shuffle = %SHUFFLE_TRAIN_EXAMPLES
62
+ train/utils.DatasetConfig.split = 'train'
63
+ train/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS
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+ train/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS
65
+
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+ # Parameters for train_eval/utils.DatasetConfig:
67
+ # ==============================================================================
68
+ train_eval/utils.DatasetConfig.batch_size = %BATCH_SIZE
69
+ train_eval/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
70
+ train_eval/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
71
+ train_eval/utils.DatasetConfig.pack = True
72
+ train_eval/utils.DatasetConfig.seed = 42
73
+ train_eval/utils.DatasetConfig.shuffle = False
74
+ train_eval/utils.DatasetConfig.split = 'validation'
75
+ train_eval/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS
76
+ train_eval/utils.DatasetConfig.use_cached = %USE_CACHED_TASKS
77
+
78
+ # Parameters for models.EncoderDecoderModel:
79
+ # ==============================================================================
80
+ models.EncoderDecoderModel.input_vocabulary = %VOCABULARY
81
+ models.EncoderDecoderModel.label_smoothing = %LABEL_SMOOTHING
82
+ models.EncoderDecoderModel.loss_normalizing_factor = %LOSS_NORMALIZING_FACTOR
83
+ models.EncoderDecoderModel.module = @network.Transformer()
84
+ models.EncoderDecoderModel.optimizer_def = %OPTIMIZER
85
+ models.EncoderDecoderModel.output_vocabulary = %VOCABULARY
86
+ models.EncoderDecoderModel.z_loss = %Z_LOSS
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+
88
+ # Parameters for partitioning.PjitPartitioner:
89
+ # ==============================================================================
90
+ partitioning.PjitPartitioner.logical_axis_rules = \
91
+ @partitioning.standard_logical_axis_rules()
92
+ partitioning.PjitPartitioner.model_parallel_submesh = None
93
+ partitioning.PjitPartitioner.num_partitions = 2
94
+
95
+ # Parameters for utils.RestoreCheckpointConfig:
96
+ # ==============================================================================
97
+ utils.RestoreCheckpointConfig.dtype = 'float32'
98
+ utils.RestoreCheckpointConfig.mode = 'specific'
99
+ utils.RestoreCheckpointConfig.path = %INITIAL_CHECKPOINT_PATH
100
+
101
+ # Parameters for utils.SaveCheckpointConfig:
102
+ # ==============================================================================
103
+ utils.SaveCheckpointConfig.dtype = 'float32'
104
+ utils.SaveCheckpointConfig.keep = 3
105
+ utils.SaveCheckpointConfig.period = 20000
106
+ utils.SaveCheckpointConfig.save_dataset = False
107
+
108
+ # Parameters for seqio.SentencePieceVocabulary:
109
+ # ==============================================================================
110
+ seqio.SentencePieceVocabulary.sentencepiece_model_file = \
111
+ 'gs://t5-data/vocabs/mc4.250000.100extra/sentencepiece.model'
112
+
113
+ # Parameters for network.T5Config:
114
+ # ==============================================================================
115
+ network.T5Config.dropout_rate = %DROPOUT_RATE
116
+ network.T5Config.dtype = 'bfloat16'
117
+ network.T5Config.emb_dim = 2048
118
+ network.T5Config.head_dim = 64
119
+ network.T5Config.logits_via_embedding = False
120
+ network.T5Config.mlp_activations = ('gelu', 'linear')
121
+ network.T5Config.mlp_dim = 5120
122
+ network.T5Config.num_decoder_layers = 24
123
+ network.T5Config.num_encoder_layers = 24
124
+ network.T5Config.num_heads = 32
125
+ network.T5Config.vocab_size = 250112
126
+
127
+ # Parameters for train_script.train:
128
+ # ==============================================================================
129
+ train_script.train.checkpoint_cfg = @utils.CheckpointConfig()
130
+ train_script.train.eval_period = 1000
131
+ train_script.train.eval_steps = 20
132
+ train_script.train.infer_eval_dataset_cfg = None
133
+ train_script.train.model = %MODEL
134
+ train_script.train.model_dir = %MODEL_DIR
135
+ train_script.train.partitioner = @partitioning.PjitPartitioner()
136
+ train_script.train.random_seed = %RANDOM_SEED
137
+ train_script.train.summarize_config_fn = @gin_utils.summarize_gin_config
138
+ train_script.train.total_steps = %TRAIN_STEPS
139
+ train_script.train.train_dataset_cfg = @train/utils.DatasetConfig()
140
+ train_script.train.train_eval_dataset_cfg = @train_eval/utils.DatasetConfig()
141
+ train_script.train.trainer_cls = @trainer.Trainer
142
+ train_script.train.use_hardware_rng = %USE_HARDWARE_RNG
143
+
144
+ # Parameters for trainer.Trainer:
145
+ # ==============================================================================
146
+ trainer.Trainer.learning_rate_fn = @utils.create_learning_rate_scheduler()
147
+ trainer.Trainer.num_microbatches = None
148
+
149
+ # Parameters for network.Transformer:
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+ # ==============================================================================
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+ network.Transformer.config = @network.T5Config()
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+ "vocab_size": 250112
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