Support Grain with Tfrecord in train_tokenizer

src/maxtext/trainers/tokenizer/train_tokenizer.py

@@ -42,6 +42,7 @@
 import jax
 import grain.python as grain
 
+from maxtext.input_pipeline import input_pipeline_utils
 from maxtext.utils.globals import MAXTEXT_ASSETS_ROOT
 from maxtext.utils import gcs_utils
 
@@ -50,7 +51,7 @@
     "grain_train_files", None, "File pattern for training data (local or gs://)", required=True
 )
 _GRAIN_FILE_TYPE = flags.DEFINE_string(
-    "grain_file_type", "parquet", "Type of data files. Supported: 'parquet', 'arrayrecord'."
+    "grain_file_type", "parquet", "Type of data files. Supported: 'parquet', 'arrayrecord', 'tfrecord'."
 )
 _DATA_COLUMN = flags.DEFINE_string("data_column", "text", "Column name to extract text from (used for arrayrecord).")
 _VOCAB_SIZE = flags.DEFINE_integer("vocab_size", 32_768, "Vocab size")
@@ -82,27 +83,23 @@ def build_grain_iterator(data_file_pattern: str, data_file_type: str, data_keys:
     dataset = grain.MapDataset.source(data_files)
     dataset = dataset.map(grain.experimental.ParquetIterDataset)
     dataset = grain.experimental.InterleaveIterDataset(dataset, cycle_length=len(data_files))
+    dataset = dataset.map(input_pipeline_utils.KeepFeatures(feature_names=list(data_keys)))
     return iter(dataset)
   elif data_file_type == "arrayrecord":
-    from maxtext.input_pipeline.protos import example_pb2  # pylint: disable=import-outside-toplevel
-
     source = grain.ArrayRecordDataSource(data_files)
     dataset = grain.MapDataset.source(source)
-
-    def _parse_example(raw_bytes):
-      example = example_pb2.Example()
-      example.ParseFromString(raw_bytes)
-      features = example.features.feature
-      parsed = {}
-      for col in data_keys:
-        if col in features:
-          parsed[col] = features[col].bytes_list.value[0]
-      return parsed
-
-    dataset = dataset.map(_parse_example)
+    dataset = dataset.map(input_pipeline_utils.ParseFeatures(list(data_keys), tokenize=True))
+    dataset = dataset.map(input_pipeline_utils.NormalizeFeatures(list(data_keys), tokenize=True))
+    return iter(dataset)
+  elif data_file_type == "tfrecord":
+    dataset = grain.MapDataset.source(data_files)
+    dataset = dataset.map(input_pipeline_utils.make_tfrecord_iter_dataset)
+    dataset = grain.experimental.InterleaveIterDataset(dataset, cycle_length=len(data_files))
+    dataset = dataset.map(input_pipeline_utils.ParseFeatures(list(data_keys), tokenize=True))
+    dataset = dataset.map(input_pipeline_utils.NormalizeFeatures(list(data_keys), tokenize=True))
     return iter(dataset)
   else:
-    raise ValueError(f"Unsupported grain_file_type: {data_file_type!r}. Use 'parquet' or 'arrayrecord'.")
+    raise ValueError(f"Unsupported grain_file_type: {data_file_type!r}. Use 'parquet', 'arrayrecord', or 'tfrecord'.")
 
 
 def _dump_chars_to_textfile(dataset_iter: Iterator, maxchars: int = int(1e7), data_keys=("text",)) -> tuple[str, int]:

tests/integration/smoke/train_tokenizer_smoke_test.py

@@ -0,0 +1,66 @@
+# Copyright 2023–2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Smoke tests for train_tokenizer file format support."""
+
+import os
+import unittest
+import pytest
+
+from maxtext.input_pipeline import input_pipeline_utils
+from maxtext.trainers.tokenizer import train_tokenizer
+
+
+class TrainTokenizerFormatTest(unittest.TestCase):
+  """Smoke-tests that train_tokenizer runs end-to-end for each supported file format."""
+
+  def _run_format_test(self, file_pattern, file_type):
+    """Uses a tiny corpus; the resulting tokenizer is not stored — only verify
+    it can be loaded and used for encode/decode.
+    """
+    output_path = os.path.join("tests", f"test_tokenizer_{file_type}")
+    try:
+      dataset_iter = train_tokenizer.build_grain_iterator(file_pattern, file_type)
+      train_tokenizer.train_tokenizer(
+          dataset_iter,
+          vocab_path=output_path,
+          vocab_size=512,
+          max_corpus_chars=10_000,
+      )
+      tok = input_pipeline_utils.get_tokenizer(output_path, "sentencepiece", add_bos=False, add_eos=False)
+      text = "This is a test"
+      tokens = tok.encode(text)
+      self.assertGreater(len(tokens), 0)
+      self.assertEqual(tok.decode(tokens), text)
+    finally:
+      if os.path.exists(output_path):
+        os.remove(output_path)
+
+  @pytest.mark.cpu_only
+  def test_parquet(self):
+    self._run_format_test("gs://maxtext-dataset/hf/c4/c4-train-00000-of-01637.parquet", "parquet")
+
+  @pytest.mark.cpu_only
+  def test_arrayrecord(self):
+    self._run_format_test(
+        "gs://maxtext-dataset/array-record/c4/en/3.0.1/c4-train.array_record-00000-of-01024", "arrayrecord"
+    )
+
+  @pytest.mark.cpu_only
+  def test_tfrecord(self):
+    self._run_format_test("gs://maxtext-dataset/c4/en/3.0.1/c4-train.tfrecord-00000-of-01024", "tfrecord")
+
+
+if __name__ == "__main__":
+  unittest.main()