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TensorFlow-ZenDNN Plug-in For AMD CPUs

**The latest ZenDNN Plugin for TensorFlow* (zentf) 5.0 is here!**

This powerful upgrade continues to redefine deep learning performance on AMD EPYC™ CPUs, combining relentless optimization, innovative features, and industry-leading support for modern workloads.

zentf 5.0 includes enhancements for bfloat16 performance, primarily by leveraging microkernels and operators from the ZenDNN 5.0 library. These operators are designed to better leverage the EPYC microarchitecture and cache hierarchy.

The zentf 5.0 plugin works seamlessly with TensorFlow versions 2.17 and 2.16, offering a high-performance experience for deep learning on AMD EPYC™ platforms.

Support

We welcome feedback, suggestions, and bug reports. Should you have any of the these, please kindly file an issue on the ZenDNN Plugin for TensorFlow Github page: https://github.com/amd/ZenDNN-tensorflow-plugin/issues

License

AMD copyrighted code in ZenDNN is subject to the Apache-2.0, MIT, or BSD-3-Clause licenses; consult the source code file headers for the applicable license. Third party copyrighted code in ZenDNN is subject to the licenses set forth in the source code file headers of such code.

Overview

The following is a high-level block diagram for the zenTF package which utilizes ZenDNN as the core inference library:

TensorFlow-ZenDNN Plug-in

This file shows how to implement, build, install and run a TensorFlow-ZenDNN plug-in for AMD CPUs.

Supported OS

Prerequisites

Tools/Frameworks Version
Bazel >=5.3.0 and <=6.5.0
Git >=1.8
Python >=3.9 and <=3.12
TensorFlow >=2.16

Installation Guide

Prerequisite

1. Install wheel file using pip:

$ pip install zentf==5.0.0

2. Install zenTF using release package.

Build and install from source.

1. Clone the repository

$ git clone https://github.com/amd/ZenDNN-tensorflow-plugin.git
$ cd ZenDNN-tensorflow-plugin/

Note: Repository is defaults to master branch, to build the version 5.0 checkout the branch r5.0.

$ git checkout r5.0

2. Configuring & Building the TensorFlow-ZenDNN Plug-in using script.

Note: Configure & Build Tensorflow-ZenDNN Plug-in manually by following the steps [3-6].

The setup script will configure & build and install Tensorflow-ZenDNN Plug-in. It will also set the necessary environment variables of ZenDNN execution. However, these variables should be verified empirically.

ZenDNN-tensorflow-plugin$ source scripts/zentf_setup.sh

3. Configure the build options:

ZenDNN-tensorflow-plugin$ ./configure
You have bazel 6.5.0 installed.
Please specify the location of python. [Default is /home/user/anaconda3/envs/zentf-env/bin/python]:

Found possible Python library paths:
  /home/user/anaconda3/envs/zentf-env/lib/python3.10/site-packages
Please input the desired Python library path to use.  Default is [/home/user/anaconda3/envs/zentf-env/lib/python3.10/site-packages]

Do you wish to build TensorFlow plug-in with MPI support? [y/N]:
No MPI support will be enabled for TensorFlow plug-in.

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]:

Configuration finished

4. Build the TensorFlow-ZenDNN Plug-in:

ZenDNN-tensorflow-plugin$ bazel clean --expunge
ZenDNN-tensorflow-plugin$ bazel build  -c opt //tensorflow_plugin/tools/pip_package:build_pip_package --verbose_failures --spawn_strategy=standalone

5. Generate python wheel file:

ZenDNN-tensorflow-plugin$ bazel-bin/tensorflow_plugin/tools/pip_package/build_pip_package .
  Note: It will generate and save python wheel file for TensorFlow-ZenDNN Plug-in into the current directory (i.e., ZenDNN-tensorflow-plugin/).

6. Install wheel file using pip:

ZenDNN-tensorflow-plugin$ pip install zentf-5.0.0-cp310-cp310-linux_x86_64.whl

The build and installation from source is done!

Enable TensorFlow-ZenDNN Plug-in:

$ export TF_ENABLE_ZENDNN_OPTS=1
$ export TF_ENABLE_ONEDNN_OPTS=0

Note: To disable ZenDNN optimizations in your inference execution, you can set the corresponding ZenDNN environment variable export TF_ENABLE_ZENDNN_OPTS=0

Execute sample kernel:

ZenDNN-tensorflow-plugin$ python tests/softmax.py
2024-03-27 22:51:57.292569: I tensorflow/core/util/port.cc:140] ZenDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ZENDNN_OPTS=0`.
2024-03-27 22:51:57.292832: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2024-03-27 22:51:57.295704: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2024-03-27 22:51:57.339363: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-27 22:51:57.969156: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Tensor("random_normal:0", shape=(10,), dtype=float32)
2024-03-27 22:51:58.407520: I tensorflow/core/common_runtime/direct_session.cc:380] Device mapping: no known devices.
2024-03-27 22:51:58.408159: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
random_normal/RandomStandardNormal: (RandomStandardNormal): /job:localhost/replica:0/task:0/device:CPU:0
2024-03-27 22:51:58.409983: I tensorflow/core/common_runtime/placer.cc:125] random_normal/RandomStandardNormal: (RandomStandardNormal): /job:localhost/replica:0/task:0/device:CPU:0
random_normal/mul: (Mul): /job:localhost/replica:0/task:0/device:CPU:0
2024-03-27 22:51:58.409998: I tensorflow/core/common_runtime/placer.cc:125] random_normal/mul: (Mul): /job:localhost/replica:0/task:0/device:CPU:0
random_normal: (AddV2): /job:localhost/replica:0/task:0/device:CPU:0
2024-03-27 22:51:58.410008: I tensorflow/core/common_runtime/placer.cc:125] random_normal: (AddV2): /job:localhost/replica:0/task:0/device:CPU:0
Softmax: (Softmax): /job:localhost/replica:0/task:0/device:CPU:0
2024-03-27 22:51:58.410018: I tensorflow/core/common_runtime/placer.cc:125] Softmax: (Softmax): /job:localhost/replica:0/task:0/device:CPU:0
random_normal/shape: (Const): /job:localhost/replica:0/task:0/device:CPU:0
2024-03-27 22:51:58.410025: I tensorflow/core/common_runtime/placer.cc:125] random_normal/shape: (Const): /job:localhost/replica:0/task:0/device:CPU:0
random_normal/mean: (Const): /job:localhost/replica:0/task:0/device:CPU:0
2024-03-27 22:51:58.410033: I tensorflow/core/common_runtime/placer.cc:125] random_normal/mean: (Const): /job:localhost/replica:0/task:0/device:CPU:0
random_normal/stddev: (Const): /job:localhost/replica:0/task:0/device:CPU:0
2024-03-27 22:51:58.410041: I tensorflow/core/common_runtime/placer.cc:125] random_normal/stddev: (Const): /job:localhost/replica:0/task:0/device:CPU:0
2024-03-27 22:51:58.429409: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type CPU is enabled.
[0.05660784 0.09040404 0.03201076 0.11204024 0.2344563  0.162052
 0.09466095 0.11205972 0.0752109  0.03049729]

Resources

Performance tuning and Benchmarking