Fix PaliGemma intructions and big_vision deps.

big_vision/configs/proj/paligemma/README.md

@@ -255,16 +255,15 @@ export KAGGLE_USERNAME=
 export KAGGLE_KEY=
 
 # See https://www.kaggle.com/models/google/paligemma-2 for a full list of models.
-export MODEL_NAME=paligemma-2
-export CKPT_FILE=paligemma2-3b-pt-224.npz.b16
+export MODEL_NAME=paligemma2-3b-pt-224
 
 mkdir ckpts/
 cd ckpts/
 
 # Store as a "vanity name" from models/proj/paligemma/paligemma.py
 curl -L -u $KAGGLE_USERNAME:$KAGGLE_KEY\
   -o pt_3b_224.bf16.npz \
-  https://www.kaggle.com/api/v1/models/google/paligemma-2/jax/$MODEL_NAME/1/download/$CKPT_FILE
+  https://www.kaggle.com/api/v1/models/google/paligemma-2/jax/$MODEL_NAME/1/download/$MODEL_NAME.b16.npz
 ```
 
 As an example, we provide the `forkme.py` config that is based on the easily-adjustable jsonl data source:

big_vision/datasets/sequence_packing.py

@@ -21,7 +21,6 @@
 from typing import Dict, Optional, List, Union
 
 from flax import traverse_util
-import grain.tensorflow as tf_grain
 import tensorflow as tf
 
 AUTOTUNE = tf.data.experimental.AUTOTUNE
@@ -75,39 +74,4 @@ def pack_dataset(
   Returns:
     A `tf.data.Dataset`.
   """
-  def _maybe_join(k):
-    if isinstance(k, int):
-      k = (k,)
-    return FLATTEN_SEPARATOR.join(k)
-
-  if isinstance(key2length, int):
-    key2length = {_maybe_join(k): key2length for k in keys}
-  else:
-    key2length = dict(key2length)  # Make new dict, we'll edit in-place.
-
-  def _add_fake_index(x):
-    x = dict(x)
-    x[tf_grain.INDEX] = -1
-    return x
-
-  def _flatten_dict(x):
-    return traverse_util.flatten_dict(x, sep=FLATTEN_SEPARATOR)
-
-  def _unflatten_dict(x):
-    return traverse_util.unflatten_dict(x, sep=FLATTEN_SEPARATOR)
-
-  def _remove_index(x):
-    x = dict(x)
-    x.pop(tf_grain.INDEX)
-    return x
-
-  pack_op = tf_grain.TfBatchAndPack(
-      batch_size=batch_size or 1,
-      sequence_lengths=_flatten_dict(key2length))
-
-  dataset = dataset.map(_add_fake_index, num_parallel_calls=AUTOTUNE)
-  dataset = dataset.map(_flatten_dict, num_parallel_calls=AUTOTUNE)
-  dataset = pack_op.apply_to_dataset(dataset)
-  dataset = dataset.map(_unflatten_dict, num_parallel_calls=AUTOTUNE)
-  dataset = dataset.map(_remove_index, num_parallel_calls=AUTOTUNE)
-  return dataset.unbatch()
+  raise ValueError("Not implemented in OSS yet.")