some new t4 stuff

finetune_large_mt5_sentencefix_v4.gin

@@ -0,0 +1,41 @@
+from __gin__ import dynamic_registration
+import tasks_v4
+
+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/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 + 20000 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 = 234496
+INITIAL_CHECKPOINT_PATH = "gs://t5-data/pretrained_models/t5x/mt5_large/checkpoint_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
+

finetune_mt5_sentencefix_v4.gin

@@ -1,5 +1,5 @@
 from __gin__ import dynamic_registration
-import tasksv4
+import tasks_v4
 
 import __main__ as train_script
 from t5.data import mixtures

finetune_small_mt5_sentencefix_v4.gin

@@ -0,0 +1,41 @@
+from __gin__ import dynamic_registration
+import tasks_v4
+
+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/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 + 20000 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 = 234496
+INITIAL_CHECKPOINT_PATH = "gs://t5-data/pretrained_models/t5x/mt5_small/checkpoint_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
+