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Tutorial: Optimize TensorFlow Models with BladeDISC

In this tutorial, we show how to optimize TensorFlow models with BladeDISC for both inference and training. Users only need to add two lines of code to optimize the model just-in-time for the examples in this tutorial. Please refer to "Install BladeDISC With Docker" for environment setup.

The content of this tutorial is as following.

BERT CUDA Inference

BERT models are usually feed with data of dynamic shapes in real production. The dynamic shape mainly comes from two aspects, one is varied batch-size, and the other is varied data shape of each sample (e.g., varied sequence lengths). The model we show in this tutorial has varied batch-size.

Prologue: Download Frozen Model

!mkdir -p model
!wget -P model http://pai-blade.oss-cn-zhangjiakou.aliyuncs.com/bladedisc_notebook_binaries/models/disc_bert_example/frozen.pb

All You Need Are the Two Lines!

All you need to do to optimize the inference is to add the following two lines of code.

import blade_disc_tf as disc
disc.enable()

Epilogue: Normal Process to Run Inference

After enabling BladeDISC with the two lines above, we can load and run the frozen model with normal process.

First, we load the frozen model and configure the session.

import os
import time
import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

graph_def = tf.GraphDef()
with open('./model/frozen.pb', 'rb') as f:
    graph_def.ParseFromString(f.read())
graph = tf.Graph()
with graph.as_default():
    tf.import_graph_def(graph_def, name='')
graph = load_frozen_graph()

session_config = tf.ConfigProto()
session_config.allow_soft_placement = True
session_config.gpu_options.allow_growth = True
# Enable auto-mixed-precision in this example.
session_config.graph_options.rewrite_options.auto_mixed_precision = 1
sess = tf.Session(graph = graph, config = session_config)

Finally, we prepare input data and run the session. In this example, we fake the input data with varied batch-size to ease the setup.

for batch in [2, 2, 4, 1, 1, 8, 8, 2, 16, 2]:
    feed_dict = {
        'input_ids_1:0' : np.ones((batch, 384), dtype=int),
        'segment_ids_1:0' : np.zeros((batch, 384), dtype=int),
        'input_mask_1:0' : np.ones((batch, 384), dtype=int),
    }
    outs = sess.run(fetch, feed_dict = feed_dict)

The above code shows the complete process to optimize BERT inference model with BladeDISC. Please refer to TensorFlow BERT Inference Example for more scripts to compare the performance of BladeDISC optimization with naive TensorFlow and XLA.

BERT X86 Inference

Similar to the CUDA example, The BERT model we show in this tutorial has varied batch-size.

Prologue: Download Frozen Model For X86 CPU

!mkdir -p model
!wget -P model http://pai-blade.oss-cn-zhangjiakou.aliyuncs.com/bladedisc_notebook_binaries/models/disc_bert_cpu_example/frozen.pb

Still, All You Need Are the Two Lines!

Similar to the CUDA example, all you need to optimize the inference is to add the following two lines of code with some settings that are specific to cpu.

import blade_disc_tf as disc
disc.enable(
  disc_cpu=True, # this enable cpu optimization
  num_intra_op_threads=16 # similar to TF_NUM_INTRAOP_THREADS
)

Epilogue: Normal Process to Run Inference on X86 CPU

After enabling BladeDISC with the two lines above, we can load and run the frozen model with normal process.

First, we load the frozen model and configure the session.

import os
import time
import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

graph_def = tf.GraphDef()
with open('./model/frozen.pb', 'rb') as f:
    graph_def.ParseFromString(f.read())
graph = tf.Graph()
with graph.as_default():
    tf.import_graph_def(graph_def, name='')
graph = load_frozen_graph()

session_config = tf.ConfigProto()
session_config.inter_op_parallelism_threads = 1
session_config.intra_op_parallelism_threads = 16
sess = tf.Session(graph = graph, config = session_config)

Finally, we prepare input data and run the session. In this example, we fake the input data with varied batch-size to ease the setup.

fetch = ["loss/Softmax:0"]
for batch in [2, 2, 4, 1, 1, 8, 8, 2, 16, 2]:
    feed_dict = {
        'input_ids_1:0' : np.ones((batch, 128), dtype=int),
        'segment_ids_1:0' : np.zeros((batch, 128), dtype=int),
        'input_mask_1:0' : np.ones((batch, 128), dtype=int),
    }
    outs = sess.run(fetch, feed_dict = feed_dict)

The above code shows the complete process to optimize BERT inference model with BladeDISC. Please refer to TensorFlow BERT Inference Example for more scripts to compare the performance of BladeDISC optimization with naive TensorFlow and XLA.

DeePMD Training

We take a deep learning based model for molecular dynamics (MD) to show how to optimize a training model with BladeDISC. Please refer to DeePMD-kit for more information about deep learning based MD.

Prologue: Install DeePMD-kit and Download Data

We need to install DeePMD-kit python interface to run the MD model.

# Install DeePMD-kit according to https://github.com/deepmodeling/deepmd-kit/blob/master/doc/install/install-from-source.md#install-the-python-interface
!git clone https://github.com/deepmodeling/deepmd-kit.git
!cd deepmd-kit; pip install .; cd ..

# Download data
!wget http://pai-blade.oss-cn-zhangjiakou.aliyuncs.com/bladedisc_notebook_binaries/data/disc_deepmd_example/data.tar.gz
!tar -xzvf data.tar.gz

Aagin, All You Need Are the Two Lines!

All you need to do to optimize the training is to add the following two lines of code.

import blade_disc_tf as disc
disc.enable()

Epilogue: Normal Process to Run MD Training with DeePMD-kit API

import sys
# The entry point of DeePMD-kit is `main` here.
from deepmd.entrypoints.main import main
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

# Tell DeePMD-kit that we will perform training with the specified configuration.
sys.argv.append('train')
sys.argv.append('data/input.json')

# Run MD training.
sys.exit(main())

The above code shows the complete process to optimize MD training model with BladeDISC. You can also refer to TensorFlow DeePMD Training Example for all scripts to optimize MD model.