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t5_xxl_NCC

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text2text-generation
text-generation-inference
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  1. .gitattributes +1 -0
  2. config.gin +151 -0
  3. config.json +30 -0
  4. model-info.txt +0 -0
  5. pytorch_model-00001-of-00007.bin +3 -0
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  12. pytorch_model.bin.index.json +567 -0
  13. special_tokens_map.json +1 -0
  14. spiece.model +3 -0
  15. tokenizer.json +3 -0
  16. tokenizer_config.json +1 -0
  17. train/events.out.tfevents.1651509675.t1v-n-2e172807-w-2.42614.0.v2 +3 -0
  18. train/events.out.tfevents.1651512925.t1v-n-2e172807-w-2.60209.0.v2 +3 -0
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  26. train/events.out.tfevents.1652106455.t1v-n-25d830de-w-3.12788.0.v2 +3 -0
  27. training_eval/ncc_english_span_corruption_stream/events.out.tfevents.1651509675.t1v-n-2e172807-w-2.42614.1.v2 +3 -0
  28. training_eval/ncc_english_span_corruption_stream/events.out.tfevents.1651512926.t1v-n-2e172807-w-2.60209.1.v2 +3 -0
  29. training_eval/ncc_english_span_corruption_stream/events.out.tfevents.1651516245.t1v-n-2e172807-w-2.77098.1.v2 +3 -0
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  36. training_eval/ncc_english_span_corruption_stream/events.out.tfevents.1652106455.t1v-n-25d830de-w-3.12788.1.v2 +3 -0
.gitattributes CHANGED
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
25
  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
config.gin ADDED
@@ -0,0 +1,151 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from __gin__ import dynamic_registration
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+ import __main__ as train_script
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+ import seqio
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+ import t5.data.mixtures
5
+ from t5x import adafactor
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+ from t5x.examples.t5 import network
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+ from t5x import gin_utils
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+ from t5x import models
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+ from t5x import partitioning
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+ from t5x import trainer
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+ from t5x import utils
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+ import tasks
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+
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+ # Macros:
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+ # ==============================================================================
16
+ BATCH_SIZE = 48
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+ DROPOUT_RATE = 0.0
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+ INITIAL_CHECKPOINT_PATH = \
19
+ 'gs://t5-data/pretrained_models/t5x/mt5_xxl/checkpoint_1000000'
20
+ LABEL_SMOOTHING = 0.0
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+ LOSS_NORMALIZING_FACTOR = None
22
+ MIXTURE_OR_TASK_MODULE = None
23
+ MIXTURE_OR_TASK_NAME = 'ncc_english_span_corruption_stream'
24
+ MODEL = @models.EncoderDecoderModel()
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+ MODEL_DIR = 'gs://nb-t5x-us-central2/norwegian_NCC_plus_English_t5x_xxl'
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+ OPTIMIZER = @adafactor.Adafactor()
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+ RANDOM_SEED = None
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+ SHUFFLE_TRAIN_EXAMPLES = True
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+ TASK_FEATURE_LENGTHS = {'inputs': 512, 'targets': 512}
30
+ TRAIN_STEPS = 1500000
31
+ USE_CACHED_TASKS = True
32
+ USE_HARDWARE_RNG = False
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+ 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()
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+ adafactor.Adafactor.step_offset = 0
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+
43
+ # Parameters for utils.CheckpointConfig:
44
+ # ==============================================================================
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+ utils.CheckpointConfig.restore = @utils.RestoreCheckpointConfig()
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+ utils.CheckpointConfig.save = @utils.SaveCheckpointConfig()
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+
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+ # Parameters for utils.create_learning_rate_scheduler:
49
+ # ==============================================================================
50
+ utils.create_learning_rate_scheduler.base_learning_rate = 0.5
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+ utils.create_learning_rate_scheduler.factors = 'constant * rsqrt_decay'
52
+ utils.create_learning_rate_scheduler.warmup_steps = 10000
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+
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+ # Parameters for train/utils.DatasetConfig:
55
+ # ==============================================================================
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+ train/utils.DatasetConfig.batch_size = %BATCH_SIZE
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+ train/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
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+ train/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
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+ train/utils.DatasetConfig.pack = True
60
+ train/utils.DatasetConfig.seed = None
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+ train/utils.DatasetConfig.shuffle = %SHUFFLE_TRAIN_EXAMPLES
62
+ train/utils.DatasetConfig.split = 'train'
63
+ train/utils.DatasetConfig.task_feature_lengths = %TASK_FEATURE_LENGTHS
64
+ 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
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+ train_eval/utils.DatasetConfig.mixture_or_task_name = %MIXTURE_OR_TASK_NAME
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+ train_eval/utils.DatasetConfig.module = %MIXTURE_OR_TASK_MODULE
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+ 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
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+
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+ # 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
87
+
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 = 4
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 = 4096
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 = 10240
122
+ network.T5Config.num_decoder_layers = 24
123
+ network.T5Config.num_encoder_layers = 24
124
+ network.T5Config.num_heads = 64
125
+ network.T5Config.vocab_size = 250112
126
+
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+ # 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:
150
+ # ==============================================================================
151
+ network.Transformer.config = @network.T5Config()
config.json ADDED
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+ {
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+ "_name_or_path": "/home/patrick/t5/mt5-xxl",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ ],
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+ "d_ff": 10240,
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+ "d_kv": 64,
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+ "d_model": 4096,
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+ "decoder_start_token_id": 0,
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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "gated-gelu",
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "num_decoder_layers": 24,
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+ "num_heads": 64,
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+ "num_layers": 24,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "relative_attention_max_distance": 128,
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+ "relative_attention_num_buckets": 32,
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+ "tie_word_embeddings": false,
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+ "tokenizer_class": "T5Tokenizer",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.19.2",
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+ "use_cache": true,
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+ "vocab_size": 250112
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+ }
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