benchmark
Windows Linux Environment Preparation Benchmark Testing Intermediate Expert
Overview
After model conversion and before inference, you can use the Benchmark tool to perform benchmark testing on a MindSpore Lite model. It can not only perform quantitative analysis (performance) on the forward inference execution duration of a MindSpore Lite model, but also perform comparative error analysis (accuracy) based on the output of the specified model.
Linux Environment Usage
Environment Preparation
To use the Benchmark tool, you need to prepare the environment as follows:
Compilation: Install build dependencies and perform build. The code of the Benchmark tool is stored in the
mindspore/lite/tools/benchmarkdirectory of the MindSpore source code. For details about the build operations, see the Environment Requirements and Compilation Example in the build document.Run: Obtain the
Benchmarktool and configure environment variables. For details, see Output Description in the build document.Add the path of dynamic library required by the inference code to the environment variables LD_LIBRARY_PATH.
export LD_LIBRARY_PATH=${PACKAGE_ROOT_PATH}/runtime/lib:${LD_LIBRARY_PATH}
${PACKAGE_ROOT_PATH} is the compiled inference package path after decompressing.
Parameter Description
The command used for benchmark testing based on the compiled Benchmark tool is as follows:
./benchmark [--modelFile=<MODELFILE>] [--accuracyThreshold=<ACCURACYTHRESHOLD>]
[--benchmarkDataFile=<BENCHMARKDATAFILE>] [--benchmarkDataType=<BENCHMARKDATATYPE>]
[--cpuBindMode=<CPUBINDMODE>] [--device=<DEVICE>] [--help]
[--inDataFile=<INDATAFILE>] [--loopCount=<LOOPCOUNT>]
[--numThreads=<NUMTHREADS>] [--warmUpLoopCount=<WARMUPLOOPCOUNT>]
[--enableFp16=<ENABLEFP16>] [--timeProfiling=<TIMEPROFILING>]
[--inputShapes=<INPUTSHAPES>] [--perfProfiling=<PERFPROFILING>]
[--perfEvent=<PERFEVENT>]
The following describes the parameters in detail.
Parameter |
Attribute |
Function |
Parameter Type |
Default Value |
Value Range |
|---|---|---|---|---|---|
|
Mandatory |
Specifies the file path of the MindSpore Lite model for benchmark testing. |
String |
Null |
- |
|
Optional |
Specifies the accuracy threshold. |
Float |
0.5 |
- |
|
Optional |
Specifies the file path of the benchmark data. The benchmark data, as the comparison output of the tested model, is output from the forward inference of the tested model under other deep learning frameworks using the same input. |
String |
Null |
- |
|
Optional |
Specifies the calibration data type. |
String |
FLOAT |
FLOAT, INT32, INT8 or UINT8 |
|
Optional |
Specifies the type of the CPU core bound to the model inference program. |
Integer |
1 |
2: medium core |
|
Optional |
Specifies the type of the device on which the model inference program runs. |
String |
CPU |
CPU or GPU or NPU |
|
Optional |
Displays the help information about the |
- |
- |
- |
|
Optional |
Specifies the file path of the input data of the tested model. If this parameter is not set, a random value will be used. |
String |
Null |
- |
|
Optional |
Specifies the number of forward inference times of the tested model when the Benchmark tool is used for the benchmark testing. The value should be a positive integer. |
Integer |
10 |
- |
|
Optional |
Specifies the number of threads for running the model inference program. |
Integer |
2 |
- |
|
Optional |
Specifies the number of preheating inference times of the tested model before multiple rounds of the benchmark test are executed. |
Integer |
3 |
- |
|
Optional |
Specifies whether the float16 operator is preferred. |
Boolean |
false |
true, false |
|
Optional |
Specifies whether to use TimeProfiler to print every kernel’s cost time. |
Boolean |
false |
true, false |
|
Optional |
Specifies the shape of input data, the format should be NHWC. Use “,” to segregate each dimension of input shape, and for several input shapes, use “:” to segregate. |
String |
Null |
- |
|
Optional |
Specifies whether to use PerfProfiler to print every kernel’s CPU performance data (PMU readings), it is disabled when timeProfiling is true. Only aarch64 CPU is supported. |
Boolean |
false |
true, false |
|
Optional |
Specifies what CPU performance data to measure when PerfProfiling is true. When set as CYCLE, the number of CPU cycles and instructions will be printed; when set as CACHE, cache reference times and cache miss times will be printed; when set as STALL, CPU front-end stall cycles and back-end stall cycles will be printed. |
String |
CYCLE |
CYCLE/CACHE/STALL |
Example
When using the Benchmark tool to perform benchmark testing on different MindSpore Lite models, you can set different parameters to implement different test functions. The testing is classified into performance test and accuracy test.
Performance Test
The main test indicator of the performance test performed by the Benchmark tool is the duration of a single forward inference. In a performance test, you do not need to set benchmark data parameters such as benchmarkDataFile. But you can set the parameter timeProfiling as True or False to decide whether to print the running time of the model at the network layer on a certain device. The default value of timeProfiling is False. For example:
./benchmark --modelFile=/path/to/model.ms
This command uses a random input, and other parameters use default values. After this command is executed, the following statistics are displayed. The statistics include the minimum duration, maximum duration, and average duration of a single inference after the tested model runs for the specified number of inference rounds.
Model = model.ms, numThreads = 2, MinRunTime = 72.228996 ms, MaxRuntime = 73.094002 ms, AvgRunTime = 72.556000 ms
./benchmark --modelFile=/path/to/model.ms --timeProfiling=true
This command uses a random input, sets the parameter timeProfiling as true, and other parameters use default values. After this command is executed, the statistics on the running time of the model at the network layer will be displayed as follows. In this case, the statistics are displayed byopName and optype. opName indicates the operator name, optype indicates the operator type, and avg indicates the average running time of the operator per single run, percent indicates the ratio of the operator running time to the total operator running time, calledTimess indicates the number of times that the operator is run, and opTotalTime indicates the total time that the operator is run for a specified number of times. Finally, total time and kernel cost show the average time consumed by a single inference operation of the model and the sum of the average time consumed by all operators in the model inference, respectively.
-----------------------------------------------------------------------------------------
opName avg(ms) percent calledTimess opTotalTime
conv2d_1/convolution 2.264800 0.824012 10 22.648003
conv2d_2/convolution 0.223700 0.081390 10 2.237000
dense_1/BiasAdd 0.007500 0.002729 10 0.075000
dense_1/MatMul 0.126000 0.045843 10 1.260000
dense_1/Relu 0.006900 0.002510 10 0.069000
max_pooling2d_1/MaxPool 0.035100 0.012771 10 0.351000
max_pooling2d_2/MaxPool 0.014300 0.005203 10 0.143000
max_pooling2d_2/MaxPool_nchw2nhwc_reshape_1/Reshape_0 0.006500 0.002365 10 0.065000
max_pooling2d_2/MaxPool_nchw2nhwc_reshape_1/Shape_0 0.010900 0.003966 10 0.109000
output/BiasAdd 0.005300 0.001928 10 0.053000
output/MatMul 0.011400 0.004148 10 0.114000
output/Softmax 0.013300 0.004839 10 0.133000
reshape_1/Reshape 0.000900 0.000327 10 0.009000
reshape_1/Reshape/shape 0.009900 0.003602 10 0.099000
reshape_1/Shape 0.002300 0.000837 10 0.023000
reshape_1/strided_slice 0.009700 0.003529 10 0.097000
-----------------------------------------------------------------------------------------
opType avg(ms) percent calledTimess opTotalTime
Activation 0.006900 0.002510 10 0.069000
BiasAdd 0.012800 0.004657 20 0.128000
Conv2D 2.488500 0.905401 20 24.885004
MatMul 0.137400 0.049991 20 1.374000
Nchw2Nhwc 0.017400 0.006331 20 0.174000
Pooling 0.049400 0.017973 20 0.494000
Reshape 0.000900 0.000327 10 0.009000
Shape 0.002300 0.000837 10 0.023000
SoftMax 0.013300 0.004839 10 0.133000
Stack 0.009900 0.003602 10 0.099000
StridedSlice 0.009700 0.003529 10 0.097000
total time : 2.90800 ms, kernel cost : 2.74851 ms
-----------------------------------------------------------------------------------------
Accuracy Test
The accuracy test performed by the Benchmark tool aims to verify the accuracy of the MinSpore model output by setting benchmark data (the default input and benchmark data type are float32). In an accuracy test, in addition to the modelFile parameter, the benchmarkDataFile parameter must be set. For example:
./benchmark --modelFile=/path/to/model.ms --inDataFile=/path/to/input.bin --device=CPU --accuracyThreshold=3 --benchmarkDataFile=/path/to/output.out
This command specifies the input data and benchmark data of the tested model, specifies that the model inference program runs on the CPU, and sets the accuracy threshold to 3%. After this command is executed, the following statistics are displayed, including the single input data of the tested model, output result and average deviation rate of the output node, and average deviation rate of all nodes.
InData0: 139.947 182.373 153.705 138.945 108.032 164.703 111.585 227.402 245.734 97.7776 201.89 134.868 144.851 236.027 18.1142 22.218 5.15569 212.318 198.43 221.853
================ Comparing Output data ================
Data of node age_out : 5.94584e-08 6.3317e-08 1.94726e-07 1.91809e-07 8.39805e-08 7.66035e-08 1.69285e-07 1.46246e-07 6.03796e-07 1.77631e-07 1.54343e-07 2.04623e-07 8.89609e-07 3.63487e-06 4.86876e-06 1.23939e-05 3.09981e-05 3.37098e-05 0.000107102 0.000213932 0.000533579 0.00062465 0.00296401 0.00993984 0.038227 0.0695085 0.162854 0.123199 0.24272 0.135048 0.169159 0.0221256 0.013892 0.00502971 0.00134921 0.00135701 0.000383242 0.000163475 0.000136294 9.77864e-05 8.00793e-05 5.73874e-05 3.53858e-05 2.18535e-05 2.04467e-05 1.85286e-05 1.05075e-05 9.34751e-06 6.12732e-06 4.55476e-06
Mean bias of node age_out : 0%
Mean bias of all nodes: 0%
=======================================================
To set specified input shapes (such as 1,32,32,1), use the command as follows:
./benchmark --modelFile=/path/to/model.ms --inDataFile=/path/to/input.bin --inputShapes=1,32,32,1 --device=CPU --accuracyThreshold=3 --benchmarkDataFile=/path/to/output.out
CPU Performance Test
The main test indicator of the CPU performance test performed by the Benchmark tool is the readings of CPU Performance Monitor Unit(PMU) of a single forward inference, including the number of CPU cycles and instructions, cache reference times and cache miss times, front-end stall cycles and back-end stall cycles. In a performance test, you do not need to set benchmark data parameters such as benchmarkDataFile. But you can set the parameter perfProfiling as True or False to decide whether to print the CPU performance data of the model at the network layer on a certain device, and set perfEvent as CYCLE/CACHE/STALL to decide what CPU performance data to measure. The default value of perfProfiling is False, the default value of perfEvent is CYCLE. Due to the fluctuation of PMU readings in multi-thread tests, numThreads is suggested to be set as 1. For example:
./benchmark --modelFile=/path/to/model.ms --perfProfiling=true --numThreads=1
This command uses a random input, sets the parameter perfProfiling as true, and other parameters use default values. After this command is executed, the statistics on the running time of the model at the network layer will be displayed as follows. In this case, the statistics are displayed byopName and optype. opName indicates the operator name, optype indicates the operator type, and cycles(k) indicates the average CPU cycles of the operator per single run (in thousand, affected by CPU frequency), cycles(%) indicates the ratio of the operator CPU cycles to the total operator CPU cycles, ins(k) indicates the average CPU instructions of the operator per single run (in thousand), and ins(%) indicates the ratio of the operator CPU instructions to the total operator CPU instructions. Finally, Model/NumThreads/MinRuntime/MaxRunTime/AvgRunTime is presented for reference.
-----------------------------------------------------------------------------------------
opName cycles(k) cycles(%) ins(k) ins(%)
Add_Plus214_Output_0 1.53 0.006572 1.27 0.002148
Conv_Convolution110_Output_0 91.12 0.390141 217.58 0.369177
Conv_COnvolution28_Output_0 114.61 0.490704 306.28 0.519680
Matmul_Times212_Output_0 8.75 0.037460 15.55 0.026385
MaxPool_Pooling160_Output_0 3.24 0.013873 8.70 0.014767
MaxPool_Pooling66_Output_0 11.63 0.049780 35.17 0.059671
Reshape_Pooling160_Output_0_reshape0 0.91 0.003899 1.58 0.002677
nhwc2nchw_MaxPool_Pooling160_Output_0_post8_0 1.77 0.007571 3.25 0.005508
-----------------------------------------------------------------------------------------
opType cycles(k) cycles(%) ins(k) ins(%)
Add 1.53 0.006572 1.27 0.002148
Conv2D 205.73 0.880845 523.85 0.888856
MatMul 8.75 0.037460 15.55 0.026385
Nhwc2nchw 1.77 0.007571 3.25 0.005508
Pooling 14.87 0.063654 43.87 0.074437
Reshape 0.91 0.003839 1.58 0.002677
Model = model.ms, NumThreads = 1, MinRunTime = 0.104000 ms, MaxRunTime = 0.179000 ms, AvgRunTime = 0.116000 ms
-----------------------------------------------------------------------------------------
When perfEvent is set as CACHE, the columns will be cache ref(k)/cache ref(%)/miss(k)/miss(%), which indicate cache reference times / cache reference ratio / cache miss times / cache miss ratio(to all cache misses, not to cache references); when perfEvent is set as STALL, the columns will befrontend(k)/frontend(%)/backend(k)/backend(%), which indicate CPU front-end stall cycles / front-end stall cycles ratio / back-end stall cycles / back-end stall cycles ratio. For example:
./benchmark --modelFile=/path/to/model.ms --perfProfiling=true --numThreads=1 --perfEvent="CACHE"
./benchmark --modelFile=/path/to/model.ms --perfProfiling=true --numThreads=1 --perfEvent="STALL"
Windows Environment Usage
Environment Preparation
To use the Benchmark tool, you need to prepare the environment as follows:
Compilation: Install build dependencies and perform build. The code of the Benchmark tool is stored in the
mindspore/lite/tools/benchmarkdirectory of the MindSpore source code. For details about the build operations, see the Environment Requirements and Compilation Example in the build document.Add the path of dynamic library required by the benchmark to the environment variables PATH.
set PATH=%PACKAGE_ROOT_PATH%\inference\lib;%PATH%
Parameter Description
The command used for benchmark testing based on the compiled Benchmark tool is as follows. The parameters are the same as those used in the Linux environment, and will not be repeated here.
call benchmark.exe [--modelFile=<MODELFILE>] [--accuracyThreshold=<ACCURACYTHRESHOLD>]
[--benchmarkDataFile=<BENCHMARKDATAFILE>] [--benchmarkDataType=<BENCHMARKDATATYPE>]
[--cpuBindMode=<CPUBINDMODE>] [--device=<DEVICE>] [--help]
[--inDataFile=<INDATAFILE>] [--loopCount=<LOOPCOUNT>]
[--numThreads=<NUMTHREADS>] [--warmUpLoopCount=<WARMUPLOOPCOUNT>]
[--enableFp16=<ENABLEFP16>] [--timeProfiling=<TIMEPROFILING>]
[--inputShapes=<INPUTSHAPES>]
Example
When using the Benchmark tool to perform benchmark testing on different MindSpore Lite models, you can set different parameters to implement different test functions. The testing is classified into performance test and accuracy test. The output statistics are the same as those in the Linux environment, and will not be repeated here.
Performance Test
Use a random input and default values for other parameters.
call benchmark.exe --modelFile=/path/to/model.ms
set
timeProfiling=true, use a random input and default values for other parameters.
call benchmark.exe --modelFile=/path/to/model.ms --timeProfiling=true
Accuracy Test
The input data is set by the inDataFile parameter, and the calibration data is set by the benchmarkDataFile parameter.
Set the accuracy threshold to 3%.
call benchmark.exe --modelFile=/path/to/model.ms --inDataFile=/path/to/input.bin --benchmarkDataFile=/path/to/output.out --accuracyThreshold=3
Run on the CPU.
call benchmark.exe --modelFile=/path/to/model.ms --inDataFile=/path/to/input.bin --benchmarkDataFile=/path/to/output.out --device=CPU
Set specified input shapes.
call benchmark.exe --modelFile=/path/to/model.ms --inDataFile=/path/to/input.bin --benchmarkDataFile=/path/to/output.out --inputShapes=1,32,32,1