Visualizing Graphs of nested tf.function

Consider the below nested functions with tf operations and the generated Op Graphs in the following cases:

  • If the inner function is decorated with tf.function, it is displayed in the Graph only as generic StatefulPartitionedCall.

  • If the inner function is not decorated, its operations are expanded and added to the Graph, however without any denoting for being part of a nested process.

Is there a way to display the nested function with its name and then be able to expand its operations when needed? Next I will try to add the functions to a Keras model in order to have a conceptual graph. However, I wonder what are the general possibilities of displaying nested functions with and without decorators and for different types of graphs. Some examples would be really helpful!

logdir = 'logs/func/' +"%Y%m%d-%H%M%S")
writer = tf.summary.create_file_writer(logdir)

nT = tf.constant(400)
n = tf.constant(100000)
dt = tf.constant(1/365)
drift = tf.constant(0.08)
sigma = tf.constant(0.1)
K = tf.constant(1.0)

def brownian_motion(dt, nT, n):
  dWt = tf.random.normal(mean=0, stddev=tf.math.sqrt(dt), shape=[nT, n])
  return dWt

def option_mc(n, nT, dt, drift, sigma, K):
  dWt = brownian_motion(dt, nT, n)
  dYt = drift*dt + sigma*dWt
  C = tf.cumsum(dYt, axis=0)
  S = tf.exp(C)
  A = tf.reduce_mean(S, axis=0)
  P = tf.reduce_mean(tf.maximum(A - K, 0))
  return P

tf.summary.trace_on(graph=True, profiler=True)

result = option_mc(n, nT, dt, drift, sigma, K)

with writer.as_default():