using existing flag --fp16_implementation in...

using existing flag --fp16_implementation in official/utils/flags/_performance.py to enable automatic mixed precision

official/benchmark/keras_imagenet_benchmark.py

@@ -90,7 +90,7 @@ class Resnet50KerasAccuracy(keras_benchmark.KerasBenchmark):
     FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_amp')
     FLAGS.dtype = 'fp32'
     FLAGS.enable_eager = True
-    FLAGS.automatic_mixed_precision = True
+    FLAGS.fp16_implementation = 'graph_rewrite'
     # Add some thread tunings to improve performance.
     FLAGS.datasets_num_private_threads = 14
     FLAGS.use_tensor_lr = True
@@ -326,7 +326,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
 
     FLAGS.num_gpus = 1
     FLAGS.enable_eager = True
-    FLAGS.automatic_mixed_precision = True
+    FLAGS.fp16_implementation = 'graph_rewrite'
     FLAGS.distribution_strategy = 'default'
     FLAGS.model_dir = self._get_model_dir('benchmark_1_gpu_amp')
     FLAGS.batch_size = 256
@@ -350,7 +350,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
 
     FLAGS.num_gpus = 1
     FLAGS.enable_eager = True
-    FLAGS.automatic_mixed_precision = True
+    FLAGS.fp16_implementation = 'graph_rewrite'
     FLAGS.enable_xla = True
     FLAGS.distribution_strategy = 'default'
     FLAGS.model_dir = self._get_model_dir('benchmark_xla_1_gpu_amp')
@@ -505,7 +505,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
 
     FLAGS.num_gpus = 8
     FLAGS.enable_eager = True
-    FLAGS.automatic_mixed_precision = True
+    FLAGS.fp16_implementation = 'graph_rewrite'
     FLAGS.distribution_strategy = 'default'
     FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_amp')
     FLAGS.batch_size = 256 * 8  # 8 GPUs
@@ -542,7 +542,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
 
     FLAGS.num_gpus = 8
     FLAGS.enable_eager = True
-    FLAGS.automatic_mixed_precision = True
+    FLAGS.fp16_implementation = 'graph_rewrite'
     FLAGS.enable_xla = True
     FLAGS.distribution_strategy = 'default'
     FLAGS.model_dir = self._get_model_dir('benchmark_xla_8_gpu_amp')

official/vision/image_classification/common.py

@@ -253,6 +253,7 @@ def define_keras_flags(dynamic_loss_scale=True):
                                 datasets_num_private_threads=True,
                                 dynamic_loss_scale=dynamic_loss_scale,
                                 loss_scale=True,
+                                fp16_implementation=True,
                                 tf_data_experimental_slack=True,
                                 enable_xla=True,
                                 force_v2_in_keras_compile=True)
@@ -307,9 +308,6 @@ def define_keras_flags(dynamic_loss_scale=True):
   flags.DEFINE_boolean(
       name='enable_get_next_as_optional', default=False,
       help='Enable get_next_as_optional behavior in DistributedIterator.')
-  flags.DEFINE_boolean(
-      name='automatic_mixed_precision', default=False,
-      help='Enable automatic mixed precision training via a graph rewrite.')
 
 def get_synth_input_fn(height, width, num_channels, num_classes,
                        dtype=tf.float32, drop_remainder=True):

official/vision/image_classification/resnet_imagenet_main.py

@@ -96,6 +96,9 @@ def run(flags_obj):
 
   dtype = flags_core.get_tf_dtype(flags_obj)
   if dtype == 'float16':
+    # Mixed precision training via graph rewrite should not be used in conjunction
+    # with tf.keras.mixed_precision
+    if flags_obj["fp16_implementation"] != "graph_rewrite":
       policy = tf.keras.mixed_precision.experimental.Policy('infer_float32_vars')
       tf.keras.mixed_precision.experimental.set_policy(policy)
 
@@ -182,14 +185,12 @@ def run(flags_obj):
     if dtype == 'float16':
       # TODO(reedwm): Remove manually wrapping optimizer once mixed precision
       # can be enabled with a single line of code.
+      if flags_dict["fp16_implementation"] == "graph_rewrite":
+          optimizer = tf.compat.v1.train.experimental.enable_mixed_precision_graph_rewrite(optimizer)
+      else:
           optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
               optimizer, loss_scale=flags_core.get_loss_scale(flags_obj,
                                                               default_for_fp16=128))
-    if flags_obj.automatic_mixed_precision:
-        if dtype == 'float16':
-            raise RuntimeError("Automatic mixed precision should not be called in conjunction with "
-                               "other types of mixed precision training. Set --dtype=fp32 instead.")
-        optimizer = tf.compat.v1.train.experimental.enable_mixed_precision_graph_rewrite(optimizer)
 
     if flags_obj.use_trivial_model:
       model = trivial_model.trivial_model(