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/mt5/base.gin" | |
include "t5x/configs/runs/finetune.gin" | |
MIXTURE_OR_TASK_NAME = "continue_pretrain" | |
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 | |
# Pere: Only necessary if we load a t5 model. We can start with an t5x model here | |
# `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 = 234496 | |
INITIAL_CHECKPOINT_PATH = "gs://t5-data/pretrained_models/t5x/mt5_base/checkpoint_1000000" | |
#train_script.train: | |
# eval_period = 500 | |
# partitioner = @partitioning.ModelBasedPjitPartitioner() | |
# partitioning.ModelBasedPjitPartitioner.num_partitions = 2 | |
# `num_decodes` is equivalent to a beam size in a beam search decoding. | |
models.EncoderDecoderModel.predict_batch_with_aux.num_decodes = 4 | |
#mesh_transformer.learning_rate_schedules.constant_learning_rate.learning_rate = 0.0005 | |
#run.learning_rate_schedule = @learning_rate_schedules.constant_learning_rate | |