Add FLOPs computation into run_experiment.

PiperOrigin-RevId: 385674527

official/core/train_lib.py

@@ -138,6 +138,11 @@ def run_experiment(
     logging.info('Number of trainable params in model: %f Millions.',
                  num_params / 10.**6)
 
+  flops = train_utils.try_count_flops(trainer.model)
+  if flops is not None:
+    logging.info('FLOPs (multi-adds) in model: %f Billions.',
+                 flops / 10.**9 / 2)
+
   if run_post_eval:
     with distribution_strategy.scope():
       return trainer.model, trainer.evaluate(

official/core/train_utils.py

@@ -17,7 +17,7 @@ import copy
 import json
 import os
 import pprint
-from typing import Any, Callable, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional, Union
 
 from absl import logging
 import dataclasses
@@ -25,6 +25,9 @@ import gin
 import orbit
 import tensorflow as tf
 
+# pylint: disable=g-direct-tensorflow-import
+from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph
+# pylint: enable=g-direct-tensorflow-import
 from official.core import base_task
 from official.core import base_trainer
 from official.core import config_definitions
@@ -393,3 +396,48 @@ def try_count_params(model: tf.keras.Model):
                    'train step already reached before this run.')
       return None
   return None
+
+
+def try_count_flops(model: Union[tf.Module, tf.keras.Model],
+                    inputs_kwargs: Optional[Dict[str, Any]] = None):
+  """Counts and returns model FLOPs.
+
+  Args:
+    model: A model instance.
+    inputs_kwargs: An optional dictionary of argument pairs specifying inputs'
+      shape specifications to getting corresponding concrete function.
+
+  Returns:
+    The model's FLOPs.
+  """
+  if hasattr(model, 'inputs'):
+    try:
+      # Get input shape and set batch size to 1.
+      if model.inputs:
+        inputs = [
+            tf.TensorSpec([1] + input.shape[1:], input.dtype)
+            for input in model.inputs
+        ]
+        concrete_func = tf.function(model).get_concrete_function(inputs)
+      # If model.inputs is invalid, try to use the input to get concrete
+      # function for model.call (subclass model).
+      else:
+        concrete_func = tf.function(model.call).get_concrete_function(
+            **inputs_kwargs)
+      frozen_func, _ = convert_variables_to_constants_v2_as_graph(concrete_func)
+
+      # Calculate FLOPs.
+      run_meta = tf.compat.v1.RunMetadata()
+      opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation()
+      opts['output'] = 'none'
+      flops = tf.compat.v1.profiler.profile(
+          graph=frozen_func.graph, run_meta=run_meta, options=opts)
+      return flops.total_float_ops
+    except Exception as e:  # pylint: disable=broad-except
+      logging.info(
+          'Failed to count model FLOPs with error %s, because the build() '
+          'methods in keras layers were not called. This is probably because '
+          'the model was not feed any input, e.g., the max train step already '
+          'reached before this run.', e)
+      return None
+  return None