TF/Keras inference 4 times faster with FP32 precision than with FP16

Robotics & Edge Computing Jetson & Embedded Systems Jetson Nano
1

Hi, I know that there is no datasheet (source), but I have found preliminary datasheet: link. From there and from this_site
I conclude that Jetson Nano has ~500 GFLOPS of FP16 precision and god-knows how many FP32 precision, but I thought that Nano is FP16 oriented.

But I accidentally compared fp32 and fp16 inference time of standard Keras MobileNet model – FP16 inference time is x4 slower, why so?

My code:

import tensorflow as tf
import numpy as np
import tensorflow.contrib.keras as K


config = tf.ConfigProto()
config.gpu_options.allow_growth = True
tf.Session(config=config)


# FP32
K.backend.clear_session()
K.backend.set_learning_phase(0)
K.backend.set_floatx('float32')
with K.backend.get_session():
    mobilenet = tf.keras.applications.mobilenet.MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000)

    tmp = (np.random.standard_normal([1, 224, 224, 3]) * 255).astype(np.uint8)
    img = tmp.astype(np.float32)
    outs = mobilenet(img)


%timeit -n1 -r1  outs = mobilenet(img)
>> 1.08 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

%timeit -n1 -r2  outs = mobilenet(img)
>> 1.08 s ± 8.53 ms per loop (mean ± std. dev. of 2 runs, 1 loop each)

%timeit -n1 -r10  outs = mobilenet(img)
>> 1.07 s ± 5.48 ms per loop (mean ± std. dev. of 10 runs, 1 loop each)

%timeit -n1 -r20  outs = mobilenet(img)
>> 1.09 s ± 22.3 ms per loop (mean ± std. dev. of 20 runs, 1 loop each)


# FP16
K.backend.clear_session()
K.backend.set_learning_phase(0)
K.backend.set_floatx('float16')
with K.backend.get_session():
    mobilenet = tf.keras.applications.mobilenet.MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000)

    tmp = (np.random.standard_normal([1, 224, 224, 3]) * 255).astype(np.uint8)
    img = tmp.astype(np.float16)
    outs = mobilenet(img)


%timeit -n1 -r1  outs = mobilenet(img)
4.7 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

%timeit -n1 -r2  outs = mobilenet(img)
4.68 s ± 31.4 ms per loop (mean ± std. dev. of 2 runs, 1 loop each)

%timeit -n1 -r10  outs = mobilenet(img)
4.66 s ± 16.5 ms per loop (mean ± std. dev. of 10 runs, 1 loop each)

I used tensorflow from https://devtalk.nvidia.com/default/topic/1048776/jetson-nano/official-tensorflow-for-jetson-nano-/

2

Hi,

This is related to TensorFlow’s implementation.
We will try to reproduce this and forward to our internal team to see what we can help.

Thanks.

3

Hi,

We can reproduce this and forward to our internal team.
Will update information here if got any feedback.

Thanks.

4

My guess would be that the time is spent in data conversion.

My understanding is that the A57 in the Nano doesn’t have fp16 extensions: Documentation – Arm Developer
Meanwhile, I think the Carmel cores in the Xavier, do have such extensions, and thus code tuned on the Xavier, might make a CPU/GPU trade-off that’s a bad match for the Nano.
I’d love to learn more about this area, though!

5

As far as I know and could see from my code above:

  1. I am running t on GPU not on CPU
  2. I do data conversion of image outside of %%timeit
  3. GPU in Nano does have FP16 extension and it is faster than FP32
# This one should run on GPU
K.backend.set_floatx('float16')

# This one out of speed measurement 
img = tmp.astype(np.float16)

# This part will run forward inference of NN (with already converted to fp16 weights )
# using already converted to fp16 image 10 times 
%timeit -n1 -r10  outs = mobilenet(img)

Please, explain where it should spent time to data conversion?

6

Hi,

This is related to the TensorFlow implementation.
We have passed it to our related team and will be prioritized internally.

More, it’s also recommended to use TF-TRT.
With TF-TRT, we observe around 2x speedup on Mobilenet for FP16.

Thanks.

7

Hi, any updates? It’s been a while.

8

Hi,

As mentioned in the comment#6, this issue is related to the TensorFlow implementation, rather than TRT.
We can see 2x speedup on the Mobilenet + FP16 with TF-TRT. Is TF-TRT an optional for you?

Please understand that it’s not easy for us to update the TensorFlow implementation since it is a third-party library.
This issue is prioritized internally and it’s also recommended to find TensorFlow team for direct support.

Thanks.