small

finetune_small_byt5_sentencefix.gin

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+from __gin__ import dynamic_registration
+import tasks
+
+import __main__ as train_script
+from t5.data import mixtures
+from t5x import models
+from t5x import partitioning
+from t5x import utils
+
+include "t5x/examples/t5/byt5/small.gin"
+include "t5x/configs/runs/finetune.gin"
+
+MIXTURE_OR_TASK_NAME = "sentencefix"
+TASK_FEATURE_LENGTHS = {"inputs": 256, "targets": 256}
+TRAIN_STEPS = 1_100_000  # 1000000 pre-trained steps + 100000 fine-tuning steps.
+USE_CACHED_TASKS = False
+DROPOUT_RATE = 0.0
+RANDOM_SEED = 0
+
+# `LOSS_NORMALIZING_FACTOR`: When fine-tuning a model that was pre-trained
+# using Mesh Tensorflow (e.g. the public T5 / mT5 / ByT5 models), this should be
+# set to `pretraining batch_size` * `target_token_length`. For T5 and T5.1.1:
+# `2048 * 114`. For mT5: `1024 * 229`. For ByT5: `1024 * 189`.
+LOSS_NORMALIZING_FACTOR =193536
+INITIAL_CHECKPOINT_PATH = "gs://t5-data/pretrained_models/byt5/small/model.ckpt-1000000"
+
+train_script.train:
+  eval_period = 500
+  partitioner = @partitioning.ModelBasedPjitPartitioner()
+
+# `num_decodes` is equivalent to a beam size in a beam search decoding.
+models.EncoderDecoderModel.predict_batch_with_aux.num_decodes = 4
+
+partitioning.ModelBasedPjitPartitioner.num_partitions = 2
+
+
+#from t5.models import mesh_transformer
+#import t5.models
+#mesh_transformer.learning_rate_schedules.constant_learning_rate.learning_rate = 0.0005
+#run.learning_rate_schedule = @learning_rate_schedules.constant_learning_rate
+

train_small.sh

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+PROJECT_DIR=${HOME}"/models/multi-sentencefix-byt5"
+T5X_DIR="../../t5x"  # directory where the t5x is cloned.
+TFDS_DATA_DIR="gs://nb-t5x/corpus_multi_sentencefix_byt5"
+MODEL_DIR="gs://nb-t5x/small_model_multi_sentencefix_byt5"
+export PYTHONPATH=${PROJECT_DIR}
+
+python3 ${T5X_DIR}/t5x/train.py \
+  --gin_search_paths=${PROJECT_DIR} \
+  --gin_file="finetune_small_byt5_sentencefix.gin" \
+  --gin.MODEL_DIR="'${MODEL_DIR}'" \
+  --tfds_data_dir=${TFDS_DATA_DIR}
+