speedtest

norwegian_byt5_speedtest_1part_base.gin

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+include 't5x/examples/t5/byt5/base.gin'
+include 'pretrain_cont.gin'
+#include 't5x/configs/runs/pretrain.gin'
+#iinclude 't5x/configs/runs/finetune.gin'
+
+
+# Register necessary SeqIO Tasks/Mixtures.
+import t5.data.mixtures
+import tasks
+
+MIXTURE_OR_TASK_NAME = "byt5_ncc_english_span_corruption_stream"
+TASK_FEATURE_LENGTHS = {"inputs": 512, "targets": 512}
+TRAIN_STEPS = 1_500_000
+DROPOUT_RATE = 0.0 # Changed from the default since T5-1.1 recomments this.
+INITIAL_CHECKPOINT_PATH = "gs://t5-data/pretrained_models/byt5/base/model.ckpt-1000000"
+PjitPartitioner.num_partitions = 1
+
+# `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`.
+
+# The instructions above is from T5X. We here have to convert the Mesh Tensorflow byt5-model, so this needs to be set
+LOSS_NORMALIZING_FACTOR = 193536
+
+
+
+
+

train_byt5_speedtest_1part_base.sh

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+PROJECT_DIR=${HOME}"/models/pk-nb-t5x"
+T5X_DIR="../../t5x"  # directory where the t5x is cloned.
+MODEL_DIR="gs://t5x-training/pretrained_models/speedtest_1part_byt5x_base"
+export PYTHONPATH=${PROJECT_DIR}
+
+python3 ${T5X_DIR}/t5x/train.py \
+  --gin_search_paths=${PROJECT_DIR} \
+  --gin_file="norwegian_byt5_speedtest_1part_base.gin" \
+  --gin.MODEL_DIR="'${MODEL_DIR}'" \