STT-tensorflow/tensorflow/lite/tools/benchmark

TFLite Model Benchmark Tool

Description

A simple C++ binary to benchmark a TFLite model and its individual operators, both on desktop machines and on Android. The binary takes a TFLite model, generates random inputs and then repeatedly runs the model for specified number of runs. Aggregate latency statistics are reported after running the benchmark.

The instructions below are for running the binary on Desktop and Android, for iOS please use the iOS benchmark app.

An experimental Android APK wrapper for the benchmark model utility offers more faithful execution behavior on Android (via a foreground Activity). It is located here.

Parameters

The binary takes the following required parameters:

• graph: string
  The path to the TFLite model file.

and the following optional parameters:

• num_threads: int (default=1)
  The number of threads to use for running TFLite interpreter.
• warmup_runs: int (default=1)
  The number of warmup runs to do before starting the benchmark.
• num_runs: int (default=50)
  The number of runs. Increase this to reduce variance.
• run_delay: float (default=-1.0)
  The delay in seconds between subsequent benchmark runs. Non-positive values mean use no delay.
• use_legacy_nnapi: bool (default=false)
  Whether to use the legacy Android NNAPI TFLite path, which requires the graph to be fully compatible with NNAPI. This is available on recent Android devices. Note that some Android P devices will fail to use NNAPI for models in /data/local/tmp/ and this benchmark tool will not correctly use NNAPI.
• enable_op_profiling: bool (default=false)
  Whether to enable per-operator profiling measurement.
• enable_platform_tracing: bool (default=false)
  Whether to enable platform-wide tracing. Needs to be combined with 'enable_op_profiling'. Note, the platform-wide tracing might not work if the tool runs as a commandline native binary. For example, on Android, the ATrace-based tracing only works when the tool is launched as an APK.
• profiling_output_csv_file: str (default="")
  File path to export profile data to as CSV. The results are printed to stdout if option is not set. Requires enable_op_profiling to be true and the path to include the name of the output CSV; otherwise results are printed to stdout.

TFLite delegate parameters

The tool supports all runtime/delegate parameters introduced by the delegate registrar. The following simply lists the names of these parameters and additional notes where applicable. For details about each parameter, please refer to this page.

Common parameters

• max_delegated_partitions: int (default=0)
  Note when use_legacy_nnapi is selected, this parameter won't work.
• min_nodes_per_partition:int (default=0)

GPU delegate

• use_gpu: bool (default=false)
• gpu_precision_loss_allowed: bool (default=true)
• gpu_experimental_enable_quant: bool (default=true)
• gpu_backend: string (default="")
• gpu_wait_type: str (default="")

NNAPI delegate

• use_nnapi: bool (default=false)
  Note some Android P devices will fail to use NNAPI for models in /data/local/tmp/ and this benchmark tool will not correctly use NNAPI.
• nnapi_accelerator_name: str (default="")
• disable_nnapi_cpu: bool (default=false)
• nnapi_allow_fp16: bool (default=false)

Hexagon delegate

• use_hexagon: bool (default=false)
• hexagon_profiling: bool (default=false)
  Note enabling this option will not produce profiling results outputs unless enable_op_profiling is also turned on. When both parameters are set to true, the profile of ops on hexagon DSP will be added to the profile table. Note that, the reported data on hexagon is in cycles, not in ms like on cpu.

XNNPACK delegate

• use_xnnpack: bool (default=false)

CoreML delegate

• use_coreml: bool (default=false)
• coreml_version: int (default=0)

External delegate

• external_delegate_path: string (default="")
• external_delegate_options: string (default="")

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 \
  --config=android_arm64 \
  tensorflow/lite/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/lite/tools/benchmark/benchmark_model /data/local/tmp

(3) Make the binary executable.

adb shell chmod +x /data/local/tmp/benchmark_model

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

adb push mobilenet_quant_v1_224.tflite /data/local/tmp

(5) Optionally, install Hexagon libraries on device.

That step is only needed when using the Hexagon delegate.

bazel build --config=android_arm64 \
  tensorflow/lite/delegates/hexagon/hexagon_nn:libhexagon_interface.so
adb push bazel-bin/tensorflow/lite/delegates/hexagon/hexagon_nn/libhexagon_interface.so /data/local/tmp
adb push libhexagon_nn_skel*.so /data/local/tmp

(6) Run the benchmark. For example:

adb shell /data/local/tmp/benchmark_model \
  --graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \
  --num_threads=4

On desktop:

(1) build the binary

bazel build -c opt tensorflow/lite/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/lite/tools/benchmark/benchmark_model \
  --graph=mobilenet_quant_v1_224.tflite \
  --num_threads=4

The MobileNet graph used as an example here may be downloaded from here.

Reducing variance between runs on Android.

Most modern Android phones use ARM big.LITTLE architecture where some cores are more power hungry but faster than other cores. When running benchmarks on these phones there can be significant variance between different runs of the benchmark. One way to reduce variance between runs is to set the CPU affinity before running the benchmark. On Android this can be done using the taskset command. E.g. for running the benchmark on big cores on Pixel 2 with a single thread one can use the following command:

adb shell taskset f0 /data/local/tmp/benchmark_model \
  --graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \
  --num_threads=1

where f0 is the affinity mask for big cores on Pixel 2. Note: The affinity mask varies with the device.

Profiling model operators

The benchmark model binary also allows you to profile operators and give execution times of each operator. To do this, pass the flag --enable_op_profiling=true to benchmark_model during invocation, e.g.,

adb shell taskset f0 /data/local/tmp/benchmark_model \
  --graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \
  --enable_op_profiling=true

When enabled, the benchmark_model binary will produce detailed statistics for each operation similar to those shown below:

============================== Run Order ==============================
	             [node type]	  [start]	  [first]	 [avg ms]	     [%]	  [cdf%]	  [mem KB]	[times called]	[Name]
	                 CONV_2D	    0.000	    4.269	    4.269	  0.107%	  0.107%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_0/Relu6]
	       DEPTHWISE_CONV_2D	    4.270	    2.150	    2.150	  0.054%	  0.161%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6]
	                 CONV_2D	    6.421	    6.107	    6.107	  0.153%	  0.314%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6]
	       DEPTHWISE_CONV_2D	   12.528	    1.366	    1.366	  0.034%	  0.348%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6]
	                 CONV_2D	   13.895	    4.195	    4.195	  0.105%	  0.454%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6]
	       DEPTHWISE_CONV_2D	   18.091	    1.260	    1.260	  0.032%	  0.485%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6]
	                 CONV_2D	   19.352	    6.652	    6.652	  0.167%	  0.652%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6]
	       DEPTHWISE_CONV_2D	   26.005	    0.698	    0.698	  0.018%	  0.670%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6]
	                 CONV_2D	   26.703	    3.344	    3.344	  0.084%	  0.754%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6]
	       DEPTHWISE_CONV_2D	   30.047	    0.646	    0.646	  0.016%	  0.770%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6]
	                 CONV_2D	   30.694	    5.800	    5.800	  0.145%	  0.915%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6]
	       DEPTHWISE_CONV_2D	   36.495	    0.331	    0.331	  0.008%	  0.924%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6]
	                 CONV_2D	   36.826	    2.838	    2.838	  0.071%	  0.995%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6]
	       DEPTHWISE_CONV_2D	   39.665	    0.439	    0.439	  0.011%	  1.006%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6]
	                 CONV_2D	   40.105	    5.293	    5.293	  0.133%	  1.139%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6]
	       DEPTHWISE_CONV_2D	   45.399	    0.352	    0.352	  0.009%	  1.147%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6]
	                 CONV_2D	   45.752	    5.322	    5.322	  0.133%	  1.281%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6]
	       DEPTHWISE_CONV_2D	   51.075	    0.357	    0.357	  0.009%	  1.290%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6]
	                 CONV_2D	   51.432	    5.693	    5.693	  0.143%	  1.433%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6]
	       DEPTHWISE_CONV_2D	   57.126	    0.366	    0.366	  0.009%	  1.442%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6]
	                 CONV_2D	   57.493	    5.472	    5.472	  0.137%	  1.579%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6]
	       DEPTHWISE_CONV_2D	   62.966	    0.364	    0.364	  0.009%	  1.588%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6]
	                 CONV_2D	   63.330	    5.404	    5.404	  0.136%	  1.724%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6]
	       DEPTHWISE_CONV_2D	   68.735	    0.155	    0.155	  0.004%	  1.728%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6]
	                 CONV_2D	   68.891	    2.970	    2.970	  0.074%	  1.802%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6]
	       DEPTHWISE_CONV_2D	   71.862	    0.206	    0.206	  0.005%	  1.807%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6]
	                 CONV_2D	   72.069	    5.888	    5.888	  0.148%	  1.955%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6]
	         AVERAGE_POOL_2D	   77.958	    0.036	    0.036	  0.001%	  1.956%	     0.000	        0	[MobilenetV1/Logits/AvgPool_1a/AvgPool]
	                 CONV_2D	   77.994	    1.445	    1.445	  0.036%	  1.992%	     0.000	        0	[MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd]
	                 RESHAPE	   79.440	    0.002	    0.002	  0.000%	  1.992%	     0.000	        0	[MobilenetV1/Predictions/Reshape]
	                 SOFTMAX	   79.443	    0.029	    0.029	  0.001%	  1.993%	     0.000	        0	[MobilenetV1/Predictions/Softmax]

============================== Top by Computation Time ==============================
	             [node type]	  [start]	  [first]	 [avg ms]	     [%]	  [cdf%]	  [mem KB]	[times called]	[Name]
	                 CONV_2D	   19.352	    6.652	    6.652	  0.167%	  0.167%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6]
	                 CONV_2D	    6.421	    6.107	    6.107	  0.153%	  0.320%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6]
	                 CONV_2D	   72.069	    5.888	    5.888	  0.148%	  0.468%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6]
	                 CONV_2D	   30.694	    5.800	    5.800	  0.145%	  0.613%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6]
	                 CONV_2D	   51.432	    5.693	    5.693	  0.143%	  0.756%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6]
	                 CONV_2D	   57.493	    5.472	    5.472	  0.137%	  0.893%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6]
	                 CONV_2D	   63.330	    5.404	    5.404	  0.136%	  1.029%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6]
	                 CONV_2D	   45.752	    5.322	    5.322	  0.133%	  1.162%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6]
	                 CONV_2D	   40.105	    5.293	    5.293	  0.133%	  1.295%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6]
	                 CONV_2D	    0.000	    4.269	    4.269	  0.107%	  1.402%	     0.000	        0	[MobilenetV1/MobilenetV1/Conv2d_0/Relu6]

Number of nodes executed: 31
============================== Summary by node type ==============================
	             [Node type]	  [count]	  [avg ms]	    [avg %]	    [cdf %]	  [mem KB]	[times called]
	                 CONV_2D	       15	     1.406	    89.270%	    89.270%	     0.000	        0
	       DEPTHWISE_CONV_2D	       13	     0.169	    10.730%	   100.000%	     0.000	        0
	                 SOFTMAX	        1	     0.000	     0.000%	   100.000%	     0.000	        0
	                 RESHAPE	        1	     0.000	     0.000%	   100.000%	     0.000	        0
	         AVERAGE_POOL_2D	        1	     0.000	     0.000%	   100.000%	     0.000	        0

Timings (microseconds): count=50 first=79449 curr=81350 min=77385 max=88213 avg=79732 std=1929
Memory (bytes): count=0
31 nodes observed


Average inference timings in us: Warmup: 83235, Init: 38467, Inference: 79760.9

Benchmark multiple performance options in a single run

A convenient and simple C++ binary is also provided to benchmark multiple performance options in a single run. This binary is built based on the aforementioned benchmark tool that could only benchmark a single performance option at a time. They share the same build/install/run process, but the BUILD target name of this binary is benchmark_model_performance_options and it takes some additional parameters as detailed below.

Additional Parameters

• perf_options_list: string (default='all')
  A comma-separated list of TFLite performance options to benchmark.
• option_benchmark_run_delay: float (default=-1.0)
  The delay between two consecutive runs of benchmarking performance options in seconds.
• random_shuffle_benchmark_runs: bool (default=true)
  Whether to perform all benchmark runs, each of which has different performance options, in a random order.