TensorFlow: Is there a way to measure FLOPS for a model?

0

Issue

The closest example I can get is found in this issue: https://github.com/tensorflow/tensorflow/issues/899

With this minimum reproducible code:

import tensorflow as tf
import tensorflow.python.framework.ops as ops 
g = tf.Graph()
with g.as_default():
  A = tf.Variable(tf.random_normal( [25,16] ))
  B = tf.Variable(tf.random_normal( [16,9] ))
  C = tf.matmul(A,B) # shape=[25,9]
for op in g.get_operations():
  flops = ops.get_stats_for_node_def(g, op.node_def, 'flops').value
  if flops is not None:
    print 'Flops should be ~',2*25*16*9
    print '25 x 25 x 9 would be',2*25*25*9 # ignores internal dim, repeats first
    print 'TF stats gives',flops

However, the FLOPS returned is always None. Is there a way to concretely measure FLOPS, especially with a PB file?

Solution

A little bit late but maybe it helps some visitors in future. For your example I successfully tested the following snippet:

g = tf.Graph()
run_meta = tf.RunMetadata()
with g.as_default():
    A = tf.Variable(tf.random_normal( [25,16] ))
    B = tf.Variable(tf.random_normal( [16,9] ))
    C = tf.matmul(A,B) # shape=[25,9]

    opts = tf.profiler.ProfileOptionBuilder.float_operation()    
    flops = tf.profiler.profile(g, run_meta=run_meta, cmd='op', options=opts)
    if flops is not None:
        print('Flops should be ~',2*25*16*9)
        print('25 x 25 x 9 would be',2*25*25*9) # ignores internal dim, repeats first
        print('TF stats gives',flops.total_float_ops)

It’s also possible to use the profiler in combination with Keras like the following snippet:

import tensorflow as tf
import keras.backend as K
from keras.applications.mobilenet import MobileNet

run_meta = tf.RunMetadata()
with tf.Session(graph=tf.Graph()) as sess:
    K.set_session(sess)
    net = MobileNet(alpha=.75, input_tensor=tf.placeholder('float32', shape=(1,32,32,3)))

    opts = tf.profiler.ProfileOptionBuilder.float_operation()    
    flops = tf.profiler.profile(sess.graph, run_meta=run_meta, cmd='op', options=opts)

    opts = tf.profiler.ProfileOptionBuilder.trainable_variables_parameter()    
    params = tf.profiler.profile(sess.graph, run_meta=run_meta, cmd='op', options=opts)

    print("{:,} --- {:,}".format(flops.total_float_ops, params.total_parameters))

I hope I could help!

Answered By – Tobias Scheck

This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0

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