Merged commit includes the following changes: (#6998)

252697519 by dmchen<dmchen@google.com>:

        BERT SQuAD accuracy test

25266352 by hongjunchoi<hongjunchoi@google.com>:

        Internal change

252647871 by hongjunchoi<hongjunchoi@google.com>:

        Enable multi worker TPU training for BERT pretraining.

official/bert/benchmark/bert_squad_benchmark.py

@@ -30,13 +30,16 @@ import tensorflow as tf
 
 from official.bert import run_squad
 from official.bert.benchmark import benchmark_utils
+from official.bert.benchmark import squad_evaluate_v1_1
 from official.utils.misc import distribution_utils
 
 # pylint: disable=line-too-long
+PRETRAINED_CHECKPOINT_PATH = 'gs://cloud-tpu-checkpoints/bert/tf_20/uncased_L-24_H-1024_A-16/bert_model.ckpt'
 SQUAD_TRAIN_DATA_PATH = 'gs://tf-perfzero-data/bert/squad/squad_train.tf_record'
 SQUAD_PREDICT_FILE = 'gs://tf-perfzero-data/bert/squad/dev-v1.1.json'
 SQUAD_VOCAB_FILE = 'gs://tf-perfzero-data/bert/squad/vocab.txt'
 SQUAD_SMALL_INPUT_META_DATA_PATH = 'gs://tf-perfzero-data/bert/squad/squad_small_meta_data'
+SQUAD_FULL_INPUT_META_DATA_PATH = 'gs://tf-perfzero-data/bert/squad/squad_full_meta_data'
 MODEL_CONFIG_FILE_PATH = 'gs://cloud-tpu-checkpoints/bert/tf_20/uncased_L-24_H-1024_A-16/bert_config'
 # pylint: enable=line-too-long
 
@@ -46,9 +49,37 @@ FLAGS = flags.FLAGS
 class BertSquadBenchmarkBase(benchmark_utils.BertBenchmarkBase):
   """Base class to hold methods common to test classes in the module."""
 
+  def _run_and_report_benchmark(self, training_summary_path, min_accuracy,
+                                max_accuracy):
+    """Runs the benchmark and reports various metrics."""
+    start_time_sec = time.time()
+    self._run_bert_squad()
+    wall_time_sec = time.time() - start_time_sec
+
+    with tf.io.gfile.GFile(training_summary_path, 'rb') as reader:
+      summary = json.loads(reader.read().decode('utf-8'))
+      summary['eval_metrics'] = self.eval_metrics
+
+    super(BertSquadBenchmarkBase, self)._report_benchmark(
+        stats=summary,
+        wall_time_sec=wall_time_sec,
+        min_accuracy=min_accuracy,
+        max_accuracy=max_accuracy)
+
+  def _evaluate_squad(self, predictions_file):
+    """Evaluates a predictions file."""
+    with tf.io.gfile.GFile(SQUAD_PREDICT_FILE, 'r') as reader:
+      dataset_json = json.load(reader)
+      dataset = dataset_json['data']
+
+    with tf.io.gfile.GFile(predictions_file, 'r') as reader:
+      predictions = json.load(reader)
+
+    return squad_evaluate_v1_1.evaluate(dataset, predictions)
+
   @flagsaver.flagsaver
   def _run_bert_squad(self):
-    """Starts BERT SQuAD task."""
+    """Starts BERT SQuAD training and evaluation tasks."""
     with tf.io.gfile.GFile(FLAGS.input_meta_data_path, 'rb') as reader:
       input_meta_data = json.loads(reader.read().decode('utf-8'))
 
@@ -59,9 +90,14 @@ class BertSquadBenchmarkBase(benchmark_utils.BertBenchmarkBase):
         strategy=strategy,
         input_meta_data=input_meta_data,
         custom_callbacks=[self.timer_callback])
+    run_squad.predict_squad(strategy=strategy, input_meta_data=input_meta_data)
+    predictions_file = os.path.join(FLAGS.model_dir, 'predictions.json')
+    eval_metrics = self._evaluate_squad(predictions_file)
+    # Use F1 score as reported evaluation metric.
+    self.eval_metrics = eval_metrics['f1']
 
 
-class BertSquadBenchmark(BertSquadBenchmarkBase):
+class BertSquadBenchmarkReal(BertSquadBenchmarkBase):
   """Short benchmark performance tests for BERT SQuAD model.
 
   Tests BERT SQuAD performance in different GPU configurations.
@@ -70,10 +106,11 @@ class BertSquadBenchmark(BertSquadBenchmarkBase):
   """
 
   def __init__(self, output_dir=None, **kwargs):
-    super(BertSquadBenchmark, self).__init__(output_dir=output_dir)
+    super(BertSquadBenchmarkReal, self).__init__(output_dir=output_dir)
 
   def _setup(self):
-    super(BertSquadBenchmark, self)._setup()
+    """Sets up the benchmark and SQuAD flags."""
+    super(BertSquadBenchmarkReal, self)._setup()
     FLAGS.train_data_path = SQUAD_TRAIN_DATA_PATH
     FLAGS.predict_file = SQUAD_PREDICT_FILE
     FLAGS.vocab_file = SQUAD_VOCAB_FILE
@@ -81,27 +118,13 @@ class BertSquadBenchmark(BertSquadBenchmarkBase):
     FLAGS.bert_config_file = MODEL_CONFIG_FILE_PATH
     FLAGS.num_train_epochs = 1
 
-  def _run_and_report_benchmark(self,
-                                training_summary_path,
-                                min_accuracy=0,
-                                max_accuracy=1):
-    """Starts BERT SQuAD performance benchmark test."""
-
-    start_time_sec = time.time()
-    self._run_bert_squad()
-    wall_time_sec = time.time() - start_time_sec
-
-    with tf.io.gfile.GFile(training_summary_path, 'rb') as reader:
-      summary = json.loads(reader.read().decode('utf-8'))
-
-    super(BertSquadBenchmark, self)._report_benchmark(
-        stats=summary,
-        wall_time_sec=wall_time_sec,
-        min_accuracy=min_accuracy,
-        max_accuracy=max_accuracy)
+  def _run_and_report_benchmark(self, training_summary_path):
+    """Runs the benchmark and reports various metrics."""
+    super(BertSquadBenchmarkReal, self)._run_and_report_benchmark(
+        training_summary_path, min_accuracy=0, max_accuracy=1)
 
   def benchmark_1_gpu(self):
-    """Test BERT SQuAD model performance with 1 GPU."""
+    """Tests BERT SQuAD model performance with 1 GPU."""
 
     self._setup()
     self.num_gpus = 1
@@ -112,7 +135,7 @@ class BertSquadBenchmark(BertSquadBenchmarkBase):
     self._run_and_report_benchmark(summary_path)
 
   def benchmark_2_gpu(self):
-    """Test BERT SQuAD model performance with 2 GPUs."""
+    """Tests BERT SQuAD model performance with 2 GPUs."""
 
     self._setup()
     self.num_gpus = 2
@@ -123,7 +146,7 @@ class BertSquadBenchmark(BertSquadBenchmarkBase):
     self._run_and_report_benchmark(summary_path)
 
   def benchmark_4_gpu(self):
-    """Test BERT SQuAD model performance with 4 GPUs."""
+    """Tests BERT SQuAD model performance with 4 GPUs."""
 
     self._setup()
     self.num_gpus = 4
@@ -134,7 +157,45 @@ class BertSquadBenchmark(BertSquadBenchmarkBase):
     self._run_and_report_benchmark(summary_path)
 
   def benchmark_8_gpu(self):
-    """Test BERT SQuAD model performance with 8 GPUs."""
+    """Tests BERT SQuAD model performance with 8 GPUs."""
+
+    self._setup()
+    self.num_gpus = 8
+    FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_squad')
+    FLAGS.train_batch_size = 32
+
+    summary_path = os.path.join(FLAGS.model_dir, 'training_summary.txt')
+    self._run_and_report_benchmark(summary_path)
+
+
+class BertSquadAccuracy(BertSquadBenchmarkBase):
+  """Short accuracy test for BERT SQuAD model.
+
+  Tests BERT SQuAD accuracy. The naming convention of below test cases follow
+  `benchmark_(number of gpus)_gpu` format.
+  """
+
+  def __init__(self, output_dir=None, **kwargs):
+    super(BertSquadAccuracy, self).__init__(output_dir=output_dir)
+
+  def _setup(self):
+    """Sets up the benchmark and SQuAD flags."""
+    super(BertSquadAccuracy, self)._setup()
+    FLAGS.train_data_path = SQUAD_TRAIN_DATA_PATH
+    FLAGS.predict_file = SQUAD_PREDICT_FILE
+    FLAGS.vocab_file = SQUAD_VOCAB_FILE
+    FLAGS.input_meta_data_path = SQUAD_FULL_INPUT_META_DATA_PATH
+    FLAGS.bert_config_file = MODEL_CONFIG_FILE_PATH
+    FLAGS.init_checkpoint = PRETRAINED_CHECKPOINT_PATH
+    FLAGS.num_train_epochs = 2
+
+  def _run_and_report_benchmark(self, training_summary_path):
+    """Runs the benchmark and reports various metrics."""
+    super(BertSquadAccuracy, self)._run_and_report_benchmark(
+        training_summary_path, min_accuracy=0.902, max_accuracy=0.909)
+
+  def benchmark_8_gpu(self):
+    """Tests BERT SQuAD model accuracy with 8 GPUs."""
 
     self._setup()
     self.num_gpus = 8

official/bert/benchmark/squad_evaluate_v1_1.py

+# Copyright 2019 Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev and
+# Percy Liang. All Rights Reserved.
+#
+# 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
+#
+#     http://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.
+# ==============================================================================
+"""Evaluation of SQuAD predictions (version 1.1).
+
+The functions are copied from
+https://worksheets.codalab.org/rest/bundles/0xbcd57bee090b421c982906709c8c27e1/contents/blob/.
+
+The SQuAD dataset is described in this paper:
+SQuAD: 100,000+ Questions for Machine Comprehension of Text
+Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang
+https://nlp.stanford.edu/pubs/rajpurkar2016squad.pdf
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import re
+import string
+
+# pylint: disable=g-bad-import-order
+from absl import logging
+# pylint: enable=g-bad-import-order
+
+
+def _normalize_answer(s):
+  """Lowers text and remove punctuation, articles and extra whitespace."""
+
+  def remove_articles(text):
+    return re.sub(r"\b(a|an|the)\b", " ", text)
+
+  def white_space_fix(text):
+    return " ".join(text.split())
+
+  def remove_punc(text):
+    exclude = set(string.punctuation)
+    return "".join(ch for ch in text if ch not in exclude)
+
+  def lower(text):
+    return text.lower()
+
+  return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def _f1_score(prediction, ground_truth):
+  """Computes F1 score by comparing prediction to ground truth."""
+  prediction_tokens = _normalize_answer(prediction).split()
+  ground_truth_tokens = _normalize_answer(ground_truth).split()
+  prediction_counter = collections.Counter(prediction_tokens)
+  ground_truth_counter = collections.Counter(ground_truth_tokens)
+  common = prediction_counter & ground_truth_counter
+  num_same = sum(common.values())
+  if num_same == 0:
+    return 0
+  precision = 1.0 * num_same / len(prediction_tokens)
+  recall = 1.0 * num_same / len(ground_truth_tokens)
+  f1 = (2 * precision * recall) / (precision + recall)
+  return f1
+
+
+def _exact_match_score(prediction, ground_truth):
+  """Checks if predicted answer exactly matches ground truth answer."""
+  return _normalize_answer(prediction) == _normalize_answer(ground_truth)
+
+
+def _metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
+  """Computes the max over all metric scores."""
+  scores_for_ground_truths = []
+  for ground_truth in ground_truths:
+    score = metric_fn(prediction, ground_truth)
+    scores_for_ground_truths.append(score)
+  return max(scores_for_ground_truths)
+
+
+def evaluate(dataset, predictions):
+  """Evaluates predictions for a dataset."""
+  f1 = exact_match = total = 0
+  for article in dataset:
+    for paragraph in article["paragraphs"]:
+      for qa in paragraph["qas"]:
+        total += 1
+        if qa["id"] not in predictions:
+          message = "Unanswered question " + qa["id"] + " will receive score 0."
+          logging.error(message)
+          continue
+        ground_truths = [entry["text"] for entry in qa["answers"]]
+        prediction = predictions[qa["id"]]
+        exact_match += _metric_max_over_ground_truths(_exact_match_score,
+                                                      prediction, ground_truths)
+        f1 += _metric_max_over_ground_truths(_f1_score, prediction,
+                                             ground_truths)
+
+  exact_match = exact_match / total
+  f1 = f1 / total
+
+  return {"exact_match": exact_match, "f1": f1}

official/bert/model_training_utils.py

@@ -43,6 +43,21 @@ def _save_checkpoint(checkpoint, model_dir, checkpoint_prefix):
   return
 
 
+def _get_input_iterator(input_fn, strategy):
+  """Returns distributed dataset iterator."""
+
+  # When training with TPU pods, datasets needs to be cloned across
+  # workers. Since Dataset instance cannot be cloned in eager mode, we instead
+  # pass callable that returns a dataset.
+  input_data = input_fn()
+  if callable(input_data):
+    iterator = iter(
+        strategy.experimental_distribute_datasets_from_function(input_data))
+  else:
+    iterator = iter(strategy.experimental_distribute_dataset(input_data))
+  return iterator
+
+
 def run_customized_training_loop(
     # pylint: disable=invalid-name
     _sentinel=None,
@@ -125,8 +140,8 @@ def run_customized_training_loop(
   # To reduce unnecessary send/receive input pipeline operation, we place input
   # pipeline ops in worker task.
   with tf.device(get_primary_cpu_task(use_remote_tpu)):
-    train_iterator = iter(
-        strategy.experimental_distribute_dataset(train_input_fn()))
+    train_iterator = _get_input_iterator(train_input_fn, strategy)
+
     with strategy.scope():
       total_training_steps = steps_per_epoch * epochs
 
@@ -192,9 +207,8 @@ def run_customized_training_loop(
 
         strategy.experimental_run_v2(_test_step_fn, args=(next(iterator),))
 
-      def _run_evaluation(current_training_step, test_dataset):
+      def _run_evaluation(current_training_step, test_iterator):
         """Runs validation steps and aggregate metrics."""
-        test_iterator = iter(test_dataset)
         for _ in range(eval_steps):
           test_step(test_iterator)
 
@@ -260,9 +274,8 @@ def run_customized_training_loop(
 
           if eval_input_fn:
             logging.info('Running evaluation after step: %s.', current_step)
-            _run_evaluation(
-                current_step,
-                strategy.experimental_distribute_dataset(eval_input_fn()))
+            _run_evaluation(current_step,
+                            _get_input_iterator(eval_input_fn, strategy))
 
           # Re-initialize evaluation metric, except the last step.
           if metric and current_step < total_training_steps:
@@ -275,8 +288,7 @@ def run_customized_training_loop(
       if eval_input_fn:
         logging.info('Running final evaluation after training is complete.')
         eval_metric_result = _run_evaluation(
-            current_step,
-            strategy.experimental_distribute_dataset(eval_input_fn()))
+            current_step, _get_input_iterator(eval_input_fn, strategy))
 
       training_summary = {
           'total_training_steps': total_training_steps,

official/bert/run_classifier.py

@@ -204,7 +204,7 @@ def run_bert(strategy, input_meta_data):
   logging.info('Training using customized training loop TF 2.0 with distrubuted'
                'strategy.')
   use_remote_tpu = (FLAGS.strategy_type == 'tpu' and FLAGS.tpu)
-  run_customized_training(
+  return run_customized_training(
       strategy,
       bert_config,
       input_meta_data,

official/bert/run_pretraining.py

@@ -68,9 +68,28 @@ FLAGS = flags.FLAGS
 
 
 def get_pretrain_input_data(input_file_pattern, seq_length,
-                            max_predictions_per_seq, batch_size):
+                            max_predictions_per_seq, batch_size, strategy):
   """Returns input dataset from input file string."""
 
+  # When using TPU pods, we need to clone dataset across
+  # workers and need to pass in function that returns the dataset rather
+  # than passing dataset instance itself.
+  use_dataset_fn = isinstance(strategy, tf.distribute.experimental.TPUStrategy)
+  if use_dataset_fn:
+    if batch_size % strategy.num_replicas_in_sync != 0:
+      raise ValueError(
+          'Batch size must be divisible by number of replicas : {}'.format(
+              strategy.num_replicas_in_sync))
+
+    # As auto rebatching is not supported in
+    # `experimental_distribute_datasets_from_function()` API, which is
+    # required when cloning dataset to multiple workers in eager mode,
+    # we use per-replica batch size.
+    batch_size = int(batch_size / strategy.num_replicas_in_sync)
+
+  def _dataset_fn(ctx=None):
+    del ctx
+
     input_files = []
     for input_pattern in input_file_pattern.split(','):
       input_files.extend(tf.io.gfile.glob(input_pattern))
@@ -79,6 +98,8 @@ def get_pretrain_input_data(input_file_pattern, seq_length,
         input_files, seq_length, max_predictions_per_seq, batch_size)
     return train_dataset
 
+  return _dataset_fn if use_dataset_fn else _dataset_fn()
+
 
 def get_loss_fn(loss_scale=1.0):
   """Returns loss function for BERT pretraining."""
@@ -105,7 +126,7 @@ def run_customized_training(strategy,
 
   train_input_fn = functools.partial(get_pretrain_input_data, input_files,
                                      max_seq_length, max_predictions_per_seq,
-                                     train_batch_size)
+                                     train_batch_size, strategy)
 
   def _get_pretrain_model():
     pretrain_model, core_model = bert_models.pretrain_model(