Implement Best Fit Packing Algorithm in Grain Pipeline for Reduced Padding

src/MaxText/configs/base.yml

@@ -604,10 +604,10 @@ grain_train_files: ''
 grain_eval_files: ''
 grain_train_mixture_config_path: '' # Path to a JSON file specifying the mixture weights for Grain training data.
 grain_file_type: 'arrayrecord' # arrayrecord or parquet
-grain_packing_type: 'first_fit' # 'first_fit' or 'concat_then_split'. See details of the corresponding module in https://google-grain.readthedocs.io/en/latest/grain.experimental.html
+grain_packing_type: 'first_fit' # 'first_fit', 'best_fit' or 'concat_then_split'. See details of the corresponding module in https://google-grain.readthedocs.io/en/latest/grain.experimental.html
 grain_worker_count: 1 # Set to -1 to enable auto-tuning: automatically determines optimal worker count. See https://google-grain.readthedocs.io/en/latest/_autosummary/grain.experimental.pick_performance_config.html
 grain_per_worker_buffer_size: 1
-# num_threads and prefetch_buffer_size are per-worker per-dataset. 
+# num_threads and prefetch_buffer_size are per-worker per-dataset.
 # When using array_records, they are used in ReadOptions (https://google-grain.readthedocs.io/en/latest/tutorials/data_loader_tutorial.html#per-worker-readoptions)
 # The default value matches that in the Grain package. If mixing multiple data sources, consider lowering these values to reduce memory usage.
 # When using parquet, grain_num_threads is the number of files to read and interleave in parallel

src/MaxText/configs/types.py

@@ -874,9 +874,9 @@ class DatasetGeneral(BaseModel):
       True,
       description="Whether to pack multiple short examples into a single sequence.",
   )
-  grain_packing_type: Literal["first_fit", "concat_then_split"] = Field(
+  grain_packing_type: Literal["first_fit", "best_fit", "concat_then_split"] = Field(
       "first_fit",
-      description="Packing type when using Grain pipeline. 'first_fit' or 'concat_then_split'.",
+      description="Packing type when using Grain pipeline. 'first_fit', 'best_fit' or 'concat_then_split'.",
   )
   max_segments_per_seq: int = Field(
       32,

src/MaxText/input_pipeline/_grain_data_processing.py

@@ -23,7 +23,7 @@
 
 import jax
 
-from grain.experimental import pick_performance_config
+from grain.experimental import BestFitPackIterDataset, pick_performance_config
 import grain.python as grain
 
 from MaxText.utils import gcs_utils
@@ -246,6 +246,8 @@ def pretrain_preprocessing_pipeline(
       dataset = grain.experimental.FirstFitPackIterDataset(
           dataset, length_struct=length_struct, num_packing_bins=batch_size
       )
+    elif config.grain_packing_type == "best_fit":
+      dataset = BestFitPackIterDataset(dataset, length_struct=length_struct, num_packing_bins=batch_size)
     elif config.grain_packing_type == "concat_then_split":
       if config.add_bos and hasattr(tokenizer_model, "bos_id"):
         dataset = grain.experimental.ConcatThenSplitIterDataset(

tests/grain_data_processing_test.py

@@ -227,6 +227,40 @@ def test_batch_determinism(self):
     super().test_batch_determinism()
 
 
+class GrainArrayRecordBestFitPackingTest(GrainArrayRecordProcessingTest):
+  """Test grain data processing with best_fit packing strategy."""
+
+  def setUp(self):
+    super().setUp()
+    temp_dir = tempfile.gettempdir()
+    self.config = pyconfig.initialize(
+        [sys.argv[0], os.path.join(MAXTEXT_PKG_DIR, "configs", "base.yml")],
+        per_device_batch_size=1,
+        run_name="test",
+        mesh_axes=["data"],
+        logical_axis_rules=[["batch", "data"]],
+        data_sharding=["data"],
+        base_output_directory="gs://max-experiments/",
+        dataset_type="grain",
+        grain_train_files=os.path.join(
+            temp_dir, "gcsfuse", "array-record", "c4", "en", "3.0.1", "c4-train.array_record*"
+        ),
+        grain_packing_type="best_fit",  # Use best_fit packing
+        tokenizer_path=os.path.join(MAXTEXT_ASSETS_ROOT, "tokenizer"),
+        enable_checkpointing=False,
+    )
+    self.mesh_shape_1d = (len(jax.devices()),)
+    self.mesh = Mesh(mesh_utils.create_device_mesh(self.mesh_shape_1d), self.config.mesh_axes)
+    self.process_indices = input_pipeline_interface.get_process_loading_real_data(
+        self.config.data_sharding,
+        self.config.global_batch_size_to_load,
+        self.config.global_batch_size_to_train_on,
+        self.config.max_target_length,
+        self.mesh,
+    )
+    self.train_iter = _grain_data_processing.make_grain_train_iterator(self.config, self.mesh, self.process_indices)
+
+
 class GrainParquetProcessingTest(unittest.TestCase):
 
   @classmethod