internal

PiperOrigin-RevId: 873907256

Author: init2winit Team

init2winit/dataset_lib/criteo_terabyte_dataset.py

@@ -37,14 +37,16 @@
 
 # Change to the path to raw dataset files.
 RAW_CRITEO1TB_FILE_PATH = ''
-CRITEO1TB_DEFAULT_HPARAMS = config_dict.ConfigDict(dict(
-    input_shape=(13 + 26,),
-    train_size=4_195_197_692,
-    # We assume the tie breaking example went to the validation set, because
-    # the test set in the mlperf version has 89_137_318 examples.
-    valid_size=89_137_319,
-    test_size=89_137_318,
-))
+CRITEO1TB_DEFAULT_HPARAMS = config_dict.ConfigDict(
+    dict(
+        input_shape=(13 + 26,),
+        train_size=4_195_197_692,
+        # We assume the tie breaking example went to the validation set, because
+        # the test set in the mlperf version has 89_137_318 examples.
+        valid_size=89_137_319,
+        test_size=89_137_318,
+    )
+)
 CRITEO1TB_METADATA = {
     'apply_one_hot_in_loss': True,
 }
@@ -142,9 +144,9 @@ def criteo_tsv_reader(
     ds = ds.repeat()
   ds = ds.interleave(
       tf.data.TextLineDataset,
-      cycle_length=128,
+      cycle_length=64,
       block_length=batch_size // 8,
-      num_parallel_calls=128,
+      num_parallel_calls=64,
       deterministic=False)
   if is_training:
     ds = ds.shuffle(buffer_size=524_288 * 100, seed=data_shuffle_seed)
@@ -218,14 +220,25 @@ def get_criteo1tb(shuffle_rng,
   num_batches_to_prefetch = (hps.num_tf_data_prefetches
                              if hps.num_tf_data_prefetches > 0 else tf.data.AUTOTUNE)
 
+  num_device_prefetches = hps.get('num_device_prefetches', 0)
+
   train_dataset = criteo_tsv_reader(
       split='train',
       shuffle_rng=shuffle_rng,
       file_path=train_file_path,
       num_dense_features=hps.num_dense_features,
       batch_size=per_host_batch_size,
       num_batches_to_prefetch=num_batches_to_prefetch)
-  train_iterator_fn = lambda: tfds.as_numpy(train_dataset)
+  data_utils.log_rss('train dataset created')
+  if num_device_prefetches > 0:
+    train_iterator_fn = lambda: data_utils.prefetch_iterator(
+        tfds.as_numpy(train_dataset), num_device_prefetches
+    )
+    data_utils.log_rss(
+        f'using prefetching with {num_device_prefetches} in the train dataset'
+    )
+  else:
+    train_iterator_fn = lambda: tfds.as_numpy(train_dataset)
   eval_train_dataset = criteo_tsv_reader(
       split='eval_train',
       shuffle_rng=None,
@@ -238,6 +251,7 @@ def get_criteo1tb(shuffle_rng,
       per_host_eval_batch_size=per_host_eval_batch_size,
       tf_dataset=eval_train_dataset,
       split_size=hps.train_size)
+  data_utils.log_rss('eval_train dataset created')
   validation_dataset = criteo_tsv_reader(
       split='validation',
       shuffle_rng=None,
@@ -250,6 +264,7 @@ def get_criteo1tb(shuffle_rng,
       per_host_eval_batch_size=per_host_eval_batch_size,
       tf_dataset=validation_dataset,
       split_size=hps.valid_size)
+  data_utils.log_rss('validation dataset created')
   test_dataset = criteo_tsv_reader(
       split='test',
       shuffle_rng=None,
@@ -262,6 +277,18 @@ def get_criteo1tb(shuffle_rng,
       per_host_eval_batch_size=per_host_eval_batch_size,
       tf_dataset=test_dataset,
       split_size=hps.test_size)
+  data_utils.log_rss('test dataset created')
+
+  # Cache all the eval_train/validation/test iterators to avoid re-processing the
+  # same data files.
+  eval_train_iterator_fn = data_utils.CachedEvalIterator(
+      eval_train_iterator_fn, 'eval_train'
+  )
+  validation_iterator_fn = data_utils.CachedEvalIterator(
+      validation_iterator_fn, 'validation'
+  )
+  test_iterator_fn = data_utils.CachedEvalIterator(test_iterator_fn, 'test')
+
   return data_utils.Dataset(
       train_iterator_fn,
       eval_train_iterator_fn,

init2winit/dataset_lib/data_utils.py

@@ -16,7 +16,12 @@
 """Common code used by different models."""
 
 import collections
+import queue
+import resource
+import threading
+from typing import Iterator
 
+from absl import logging
 import flax.linen as nn
 import jax
 from jax.nn import one_hot
@@ -33,6 +38,97 @@
 ])
 
 
+def log_rss(msg: str):
+  """Logs the current memory usage and prints the given message."""
+  rss_mb = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024
+  logging.info('%s — RSS: %.1f MB', msg, rss_mb)
+
+
+def prefetch_iterator(source_iter: Iterator[jax.typing.ArrayLike],
+                      num_prefetch: int) -> Iterator[jax.typing.ArrayLike]:
+  """Wraps the given iterator with prefetching.
+
+  Args:
+    source_iter: The iterator to wrap.
+    num_prefetch: The number of items to prefetch.
+
+  Yields:
+    Prefetched items from `source_iter`.
+  """
+  buf = queue.Queue(maxsize=num_prefetch)
+  sentinel = object()  # Used to signal end of iterator
+
+  def producer():
+    try:
+      for item in source_iter:
+        buf.put(item)
+    except Exception as e:  # pylint: disable=broad-except
+      buf.put(e)
+    buf.put(sentinel)
+
+  t = threading.Thread(target=producer, daemon=True)
+  t.start()
+
+  while True:
+    item = buf.get()
+    if item is sentinel:
+      return
+    if isinstance(item, Exception):
+      raise item
+    yield item
+
+
+class CachedEvalIterator:
+  """Lazily caches eval batches, which are typically small enough to fit into host memory."""
+
+  def __init__(self, iterator_factory, split_name='eval'):
+    self._factory = iterator_factory
+    self._split_name = split_name
+    self._cache = []
+    self._iterator = None
+    self._fully_cached = False
+
+  def __call__(self, num_batches=None):
+    yielded = 0
+
+    limit = (
+        len(self._cache)
+        if num_batches is None
+        else min(len(self._cache), num_batches)
+    )
+    for i in range(limit):
+      yield self._cache[i]
+      yielded += 1
+
+    if num_batches is not None and yielded >= num_batches:
+      return
+
+    if self._fully_cached:
+      return
+
+    if self._iterator is None:
+      logging.info('Building %s cache lazily...', self._split_name)
+      self._iterator = iter(self._factory(None))
+
+    for batch in self._iterator:
+      self._cache.append(batch)
+      yield batch
+      yielded += 1
+      if num_batches is not None and yielded >= num_batches:
+        return
+
+    self._fully_cached = True
+    self._factory = None
+    self._iterator = None
+    rss_mb = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024
+    logging.info(
+        '%s cache complete: %d batches — RSS: %.1f MB',
+        self._split_name,
+        len(self._cache),
+        rss_mb,
+    )
+
+
 def iterator_as_numpy(iterator):
   for x in iterator:
     yield jax.tree.map(lambda y: y._numpy(), x)  # pylint: disable=protected-access