Time a TensorFlow function under a variety of strategies and hardware.

tfp.debugging.benchmarking.benchmark_tf_function(
    user_fn,
    iters=1,
    config=default_benchmark_config(),
    extra_columns=None,
    use_autograph=False,
    print_intermediates=False,
    cpu_device='cpu:0',
    gpu_device='gpu:0'
)

Defined in python/debugging/benchmarking/benchmark_tf_function.py.

Runs the zero-argument callable user_fn iters times under the strategies (any of Eager, tfe.function + graph, and XLA) and hardware (CPU, GPU).

Example:

data_dicts = []
for inner_iters in [10, 100]:
  for size in [100, 1000]:
    def f():
      total = tf.constant(0.0)
      for _ in np.arange(inner_iters):
        m = tf.random_uniform((size, size))
        total += tf.reduce_sum(tf.matmul(m, m))
        return total

    data_dicts += benchmark_tf_function.benchmark_tf_function(
        f,
        iters=5,
        extra_columns={'inner_iters': inner_iters,
                       'size': size})

Args:

• user_fn: A zero-argument, callable function of TensorFlow code.
• iters: The number of times to run the function for each runtime and hardware combination.
• config: A BenchmarkTfFunctionConfig, specifying which strategies and hardware to use. Valid strategies are RUNTIME_EAGER, RUNTIME_FUNCTION, and RUNTIME_XLA. Valid hardware choices are HARDWARE_CPU, HARDWARE_GPU.
• extra_columns: A dictionary of extra information to add to each dictionary in data_dicts.
• use_autograph: Boolean, controlling whether autograph is used for the graph and XLA strategies.
• print_intermediates: Boolean. If true, print out each row before adding it to the data_dicts.
• cpu_device: String, the TensorFlow device to use for CPU.
• gpu_device: String, the TensorFlow device to use for GPU.

Returns:

• data_dicts: A list of dictionaries containing the results of benchmarking Time for the first run is stored under the first_iter_time key, and time for all runs is stored under the total_time key.