added non xla fp16 test

official/recommendation/ncf_keras_benchmark.py

@@ -23,7 +23,6 @@ import time
 
 from absl import flags
 from absl.testing import flagsaver
-
 import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 from official.recommendation import ncf_common
@@ -262,6 +261,15 @@ class NCFKerasAccuracy(NCFKerasBenchmarkBase):
     FLAGS.train_epochs = 7
     self._run_and_report_benchmark_mlperf_like()
 
+  def benchmark_1_gpu_ctl_fp16_mlperf_like(self):
+    """1 GPU using CTL."""
+    self._setup()
+    FLAGS.keras_use_ctl = True
+    FLAGS.train_epochs = 7
+    FLAGS.dtype = 'fp16'
+    FLAGS.loss_scale = 8192
+    self._run_and_report_benchmark_mlperf_like()
+
   def benchmark_1_gpu_ctl_run_eagerly_mlperf_like(self):
     """1 GPU using CTL with eager and distribution strategy."""
     self._setup()

official/recommendation/ncf_keras_main.py

@@ -261,8 +261,11 @@ def run_ncf(_):
           beta_2=params["beta2"],
           epsilon=params["epsilon"])
       if FLAGS.dtype == "fp16":
-        optimizer = tf.compat.v1.train.experimental.enable_mixed_precision_graph_rewrite(
-            optimizer, loss_scale=flags_core.get_loss_scale(FLAGS, default_for_fp16="dynamic"))
+        optimizer = \
+          tf.compat.v1.train.experimental.enable_mixed_precision_graph_rewrite(
+              optimizer,
+              loss_scale=flags_core.get_loss_scale(FLAGS,
+                                                   default_for_fp16="dynamic"))
 
       if params["keras_use_ctl"]:
         train_loss, eval_results = run_ncf_custom_training(