benchmark

TensorFlow Model Benchmark Tool

Description

A simple C++ binary to benchmark a compute graph and its individual operators, both on desktop machines and on Android.

To build/install/run

On Android:

(0) Refer to https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android to edit the WORKSPACE to configure the android NDK/SDK.

(1) build for your specific platform, e.g.:

bazel build -c opt \
  --crosstool_top=//external:android/crosstool \
  --cpu=armeabi-v7a \
  --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
  --config monolithic \
  tensorflow/tools/benchmark:benchmark_model

(2) Connect your phone. Push the binary to your phone with adb push (make the directory if required):

adb push bazel-bin/tensorflow/tools/benchmark/benchmark_model /data/local/tmp

(3) Push the compute graph that you need to test. For example:

adb push tensorflow_inception_graph.pb /data/local/tmp

(4) Run the benchmark. For example:

adb shell /data/local/tmp/benchmark_model \
  --graph=/data/local/tmp/tensorflow_inception_graph.pb \
  --input_layer="input:0" \
  --input_layer_shape="1,224,224,3" \
  --input_layer_type="float" \
  --output_layer="output:0"

On desktop:

(1) build the binary

bazel build -c opt tensorflow/tools/benchmark:benchmark_model

(2) Run on your compute graph, similar to the Android case but without the need of adb shell. For example:

bazel-bin/tensorflow/tools/benchmark/benchmark_model \
  --graph=tensorflow_inception_graph.pb \
  --input_layer="input:0" \
  --input_layer_shape="1,224,224,3" \
  --input_layer_type="float" \
  --output_layer="output:0"

The Inception graph used as an example here may be downloaded from https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip

Model downloader

To download TF .pb graphs of several popular models, run:

bash download_models.sh

Comparing performance with vanilla TF

We provide example scripts comparing TF-oneDNN performance with vanilla TF's that users can modify for their own benchmarks. The scripts assume that models are already downloaded by download_models.sh. To run end-to-end model performance comparison between TF-oneDNN and vanilla TF, call

bash download_models.sh  # Skip this step if models are already downloaded.
bash run_onednn_benchmarks.sh

The output is a summary table in a CSV file: results.csv. Example output:

Showing runtimes in microseconds. `?` means not available.
               Model,  Batch,        Vanilla,         oneDNN,    Speedup
          bert-large,      1,              x,              y,        x/y
          bert-large,     16,            ...,            ...,        ...
          bert-large,     64,            ...,            ...,        ...          
           inception,      1,            ...,            ...,        ...
           inception,     16,            ...,            ...,        ...
           inception,     64,            ...,            ...,        ...           
                                        ⋮
        ssd-resnet34,      1,              ?,            ...,          ?
        ssd-resnet34,     16,              ?,            ...,          ?
        ssd-resnet34,     64,              ?,            ...,          ?        

Vanilla TF can't run ssd-resnet34 on CPU because it doesn't support NCHW format.