Update train script with new interleave function (#171)

* new name for interleave dataset

* jat-small to jat

* change output dir

README.md

@@ -82,8 +82,8 @@ Here are some examples of how you might use JAT in both evaluation and fine-tuni
 - **Training JAT**: Train your own JAT model from scratch (run on 8xA100)
     ```shell
     accelerate launch scripts/train_jat_tokenized.py \
-    --output_dir checkpoints/jat_small_v100 \
-    --model_name_or_path jat-project/jat-small \
+    --output_dir checkpoints/jat \
+    --model_name_or_path jat-project/jat \
     --tasks all \
     --trust_remote_code \
     --per_device_train_batch_size 20 \

jat/processing_jat.py

@@ -182,6 +182,7 @@ class JatProcessor(ProcessorMixin):
         tokenizer ([`AutoTokenizer`]):
             The tokenizer is a required input.
     """
+
     attributes = ["image_processor", "tokenizer"]
     image_processor_class = "AutoImageProcessor"
     tokenizer_class = "AutoTokenizer"

scripts/train_jat.py

@@ -15,7 +15,7 @@
 
 from jat.eval.rl.core import TASK_NAME_TO_ENV_ID
 from jat.modeling_jat import JatModel
-from jat.utils import mix_iterable_datasets
+from jat.utils_interleave_datasets import interleave_datasets
 
 
 # Sometimes, the server is down; increasing the number of
@@ -185,9 +185,15 @@ def add_loss_weight(example, loss_weight):
         eval_dataset[key] = eval_dataset[key].take(data_args.eval_num_samples)
 
     weights = [SAMPLE_WEIGHTS.get(t, 1.0) for t in train_dataset.keys()]
-    train_dataset = mix_iterable_datasets(
-        list(train_dataset.values()), batch_size=training_args.per_device_train_batch_size, weights=weights
+
+    train_dataset = interleave_datasets(
+        list(train_dataset.values()),
+        probabilities=[w / sum(weights) for w in weights],
+        seed=training_args.seed,
+        stopping_strategy="all_exhausted",
+        n_contiguous=training_args.per_device_train_batch_size,
     )
+
     # Due to the train dataset's structure, where every 'n' consecutive samples share the same modalities, we can't
     # load all samples at once. Different sets of 'n' samples have different modalities. Therefore, we must load and
     # process each set of 'n' samples separately.