adding perfzero test configs to keras_imagenet_benchmarks.py

official/resnet/keras/keras_imagenet_benchmark.py

@@ -78,7 +78,24 @@ class Resnet50KerasAccuracy(keras_benchmark.KerasBenchmark):
     FLAGS.datasets_num_private_threads = 14
     FLAGS.use_tensor_lr = True
     self._run_and_report_benchmark()
-
+    
+  def benchmark_8_gpu_amp(self):
+    """Test Keras model with eager, dist_strat and 8 GPUs with automatic mixed precision."""
+    self._setup()
+    FLAGS.num_gpus = 8
+    FLAGS.data_dir = self.data_dir
+    FLAGS.batch_size = 128 * 8
+    FLAGS.train_epochs = 90
+    FLAGS.epochs_between_evals = 10
+    FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_amp')
+    FLAGS.dtype = 'fp32'
+    FLAGS.enable_eager = True
+    FLAGS.automatic_mixed_precision = True
+    # Add some thread tunings to improve performance.
+    FLAGS.datasets_num_private_threads = 14
+    FLAGS.use_tensor_lr = True
+    self._run_and_report_benchmark()
+    
   def benchmark_8_gpu_fp16(self):
     """Test Keras model with eager, dist_strat, 8 GPUs, and fp16."""
     self._setup()
@@ -371,6 +388,17 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
     FLAGS.batch_size = 128
     self._run_and_report_benchmark()
 
+  def benchmark_1_gpu_amp(self):
+    """Test Keras model with 1 GPU with automatic mixed precision."""
+    self._setup()
+
+    FLAGS.num_gpus = 1
+    FLAGS.enable_eager = True
+    FLAGS.automatic_mixed_precision = True
+    FLAGS.distribution_strategy = 'default'
+    FLAGS.model_dir = self._get_model_dir('benchmark_1_gpu_amp')
+    FLAGS.batch_size = 256
+    self._run_and_report_benchmark()
 
   def benchmark_xla_1_gpu(self):
     """Test Keras model with XLA and 1 GPU."""
@@ -384,6 +412,19 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
     FLAGS.batch_size = 128
     self._run_and_report_benchmark()
 
+  def benchmark_xla_1_gpu_amp(self):
+    """Test Keras model with XLA and 1 GPU with automatic mixed precision."""
+    self._setup()
+
+    FLAGS.num_gpus = 1
+    FLAGS.enable_eager = True
+    FLAGS.automatic_mixed_precision = True
+    FLAGS.enable_xla = True
+    FLAGS.distribution_strategy = 'default'
+    FLAGS.model_dir = self._get_model_dir('benchmark_xla_1_gpu_amp')
+    FLAGS.batch_size = 256
+    self._run_and_report_benchmark()
+
   def benchmark_1_gpu_fp16(self):
     """Test Keras model with 1 GPU and fp16."""
     self._setup()
@@ -555,6 +596,18 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
     FLAGS.batch_size = 128 * 8  # 8 GPUs
     self._run_and_report_benchmark()
 
+  def benchmark_8_gpu_amp(self):
+    """Test Keras model with 8 GPUs with automatic mixed precision."""
+    self._setup()
+
+    FLAGS.num_gpus = 8
+    FLAGS.enable_eager = True
+    FLAGS.automatic_mixed_precision = True
+    FLAGS.distribution_strategy = 'default'
+    FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_amp')
+    FLAGS.batch_size = 256 * 8  # 8 GPUs
+    self._run_and_report_benchmark()
+    
   def benchmark_8_gpu_tweaked(self):
     """Test Keras model with manual config tuning and 8 GPUs."""
     self._setup()
@@ -592,6 +645,19 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
     FLAGS.batch_size = 128 * 8  # 8 GPUs
     self._run_and_report_benchmark()
 
+  def benchmark_xla_8_gpu_amp(self):
+    """Test Keras model with XLA and 8 GPUs with automatic mixed precision."""
+    self._setup()
+
+    FLAGS.num_gpus = 8
+    FLAGS.enable_eager = True
+    FLAGS.automatic_mixed_precision = True
+    FLAGS.enable_xla = True
+    FLAGS.distribution_strategy = 'default'
+    FLAGS.model_dir = self._get_model_dir('benchmark_xla_8_gpu_amp')
+    FLAGS.batch_size = 256 * 8  # 8 GPUs
+    self._run_and_report_benchmark()
+    
   def benchmark_xla_8_gpu_tweaked(self):
     """Test Keras model with manual config tuning, 8 GPUs, and XLA."""
     self._setup()