Inject enable_runtime_flags into benchmarks and add train_steps to shakespeare_main.

This will help general debugging by enabling custom execution with
--benchmark_method_flags.

E.g --benchmark_method_flags=train_steps=7 will run the benchmark for only 7
steps without modifying benchmark code.

PiperOrigin-RevId: 282799341

official/staging/shakespeare/shakespeare_benchmark.py

@@ -26,6 +26,7 @@ import tensorflow as tf  # pylint: disable=g-bad-import-order
 from official.staging.shakespeare import shakespeare_main
 from official.utils.flags import core as flags_core
 from official.utils.misc import keras_utils
+from official.utils.testing import benchmark_wrappers
 from official.utils.testing.perfzero_benchmark import PerfZeroBenchmark
 
 SHAKESPEARE_TRAIN_DATA = 'shakespeare/shakespeare.txt'
@@ -42,6 +43,7 @@ class ShakespeareBenchmarkBase(PerfZeroBenchmark):
         default_flags=default_flags,
         flag_methods=[shakespeare_main.define_flags])
 
+  @benchmark_wrappers.enable_runtime_flags
   def _run_and_report_benchmark(self,
                                 top_1_train_min=0.91,
                                 top_1_train_max=0.94,
@@ -75,6 +77,7 @@ class ShakespeareBenchmarkBase(PerfZeroBenchmark):
     for callback in stats['callbacks']:
       if isinstance(callback, keras_utils.TimeHistory):
         epoch_timings = callback.epoch_runtime_log
+        if len(epoch_timings) > 1:
           average_time = sum(epoch_timings[1:]) / len(epoch_timings[1:])
           metrics.append({'name': 'avg_epoch_time',
                           'value': average_time})

official/staging/shakespeare/shakespeare_main.py

@@ -75,6 +75,8 @@ def define_flags():
   flags.DEFINE_integer(
       name='predict_length', default=1000,
       help='Length of the predicted text including the context.')
+  flags.DEFINE_integer(name='train_steps', default=None,
+                       help='Overrides train_steps per epoch if not None.')
   flags.DEFINE_integer(
       name='log_steps', default=100,
       help='For every log_steps, we log the timing information such as '
@@ -174,6 +176,9 @@ def train_model(flags_obj, dataset, vocab_size, strategy, checkpoint_dir=None):
   Returns:
     The training history and callbacks.
   """
+  if flags_obj.train_steps:
+    train_steps = flags_obj.train_steps
+  else:
     train_steps = BATCHES_PER_EPOCH // flags_obj.batch_size
   strategy_scope = distribution_utils.get_strategy_scope(strategy)