Migrate PlaceHolderDataIterator from tf to np

src/maxtext/input_pipeline/grain_data_processing.py

@@ -238,7 +238,7 @@ def pretrain_preprocessing_pipeline(
     # global_batch_size_to_load has been expanded in pyconfig.py when expansion_factor_real_data > 1.
     # But when using Grain, we want to keep the batch_size consistent with that in the checkpoint.
     # We revert the batch_size expansion here, but load multiple batches per step in multihost_dataloading.py.
-    batch_size = batch_size // config.expansion_factor_real_data
+    batch_size = int(batch_size // config.expansion_factor_real_data)
 
   if config.packing:
     length_struct = {col: config.max_target_length for col in data_columns}

src/maxtext/input_pipeline/multihost_dataloading.py

@@ -125,7 +125,7 @@ def _get_next_batch_sharded(self) -> jax.Array:
             # expansion_loading_factor_for_grain times to get the
             # right batch_size for the host that is loading real data.
             local_data_list = [local_data]
-            for _ in range(1, self.expansion_loading_factor_for_grain):
+            for _ in range(1, int(self.expansion_loading_factor_for_grain)):
               next_batch = next(self.local_iterator)
               local_data_list.append(next_batch)
             local_data = jtu.tree_map(lambda *xs: np.concatenate(xs, axis=0), *local_data_list)

src/maxtext/input_pipeline/synthetic_data_processing.py

@@ -19,8 +19,6 @@
 
 import numpy as np
 
-import tensorflow as tf
-
 import jax
 import jax.numpy as jnp
 from jax.sharding import PartitionSpec as P
@@ -100,26 +98,19 @@ def reset(self):
   @staticmethod
   def get_place_holder_synthetic_data(config: pyconfig.HyperParameters):
     """fill negative value in synthetic data"""
-    output = {}
-    output["inputs"] = tf.data.Dataset.from_tensor_slices(
-        np.full((1, config.max_target_length), -1, dtype=jax.numpy.int32)
-    )
-    output["inputs_position"] = tf.data.Dataset.from_tensor_slices(
-        np.full((1, config.max_target_length), -1, dtype=jax.numpy.int32)
-    )
-    output["inputs_segmentation"] = tf.data.Dataset.from_tensor_slices(
-        np.full((1, config.max_target_length), -1, dtype=jax.numpy.int32)
-    )
-    output["targets"] = tf.data.Dataset.from_tensor_slices(
-        np.full((1, config.max_target_length), -1, dtype=jax.numpy.int32)
-    )
-    output["targets_position"] = tf.data.Dataset.from_tensor_slices(
-        np.full((1, config.max_target_length), -1, dtype=jax.numpy.int32)
-    )
-    output["targets_segmentation"] = tf.data.Dataset.from_tensor_slices(
-        np.full((1, config.max_target_length), -1, dtype=jax.numpy.int32)
-    )
-    dataset = tf.data.Dataset.zip((output))  # pytype: disable=wrong-arg-types
-    dataset = dataset.repeat()
-    dataset = dataset.batch(config.global_batch_size_to_load // jax.process_count())
-    return dataset
+    batch_size = config.global_batch_size_to_load // jax.process_count()
+    neg_ones = np.full((batch_size, config.max_target_length), -1, dtype=np.int32)
+    batch = {
+        "inputs": neg_ones,
+        "inputs_position": neg_ones,
+        "inputs_segmentation": neg_ones,
+        "targets": neg_ones,
+        "targets_position": neg_ones,
+        "targets_segmentation": neg_ones,
+    }
+
+    def infinite_iterator():
+      while True:
+        yield batch
+
+    return infinite_iterator()