We use our end-to-end TensorFlow integration tests to test compilation and numerical accuracy, and to generate compilation and benchmarking artifacts. This allows us to validate that our benchmarks are behaving as we expect them to, and to run them using valid inputs for each model.
This guide assumes that you can run the tensorflow integration tests. See this doc for more information. That doc also covers writing new tests, which you‘ll need to do if you’d like to benchmark a new TensorFlow model.
The get_e2e_artifacts.py script compiles and tests all of our integrated TensorFlow models, and gathers their compilation and benchmarking artifacts in /tmp/iree/modules/.
# By default `get_e2e_artifacts.py` will run all of our test suites, including # those that take a long time to complete, so we specify # `--test_suites=e2e_tests` to only run the smaller tests. python3 ./scripts/get_e2e_artifacts.py --test_suites=e2e_tests
Each test/module has a folder with the following artifacts (filtered to only include those relevant for benchmarking):
# Example for a generic module `ModuleName`: /tmp/iree/modules/ModuleName ├── iree_vmla # Or any other IREE backend. │ ├── compiled.vmfb │ │ # A flatbuffer containing IREE's compiled code. │ └── traces │ # Directory with a trace for each unittest in vision_model_test.py. │ ├── traced_function_1 │ │ # Directory storing logs and serialization for a specific trace. │ │ └── flagfile │ │ # An Abseil flagfile containing arguments │ │ # iree-benchmark-module needs to benchmark this trace. │ ├── traced_function_2 │ └── ... └── tflite ├── module_method_1.tflite │ # A method on ModuleName compiled to bytes with TFLite, which can │ # be used by the TFLite's benchmark_model binary. ├── module_method_2.tflite ├── ... └── traces ├── traced_function_1 │ └── graph_path │ # In general, a trace's name does not have to match the name │ # of the method(s) on the tf.Module that it calls. This file │ # points to the correct module_method_*.tflite graph file │ # for TFLite's benchmark_model to use. ├── traced_function_2 └── ... # Example for MatrixOpsStaticModule: /tmp/iree/modules/MatrixOpsStaticModule ├── iree_llvmjit │ ├── compiled.vmfb │ └── traces │ ├── basic_matmul │ │ └── flagfile │ ├── matmul_broadcast_singleton_dimension │ │ └── flagfile │ ├── matmul_lhs_batch │ │ └── flagfile │ └── matmul_rhs_batch │ └── flagfile ├── iree_vmla │ ├── compiled.vmfb │ └── traces # ...same as iree_llvmjit/traces above. ├── iree_vulkan │ ├── compiled.vmfb │ └── traces # ...same as iree_llvmjit/traces above. └── tflite ├── basic_matmul.tflite ├── matmul_broadcast_singleton_dimension.tflite ├── matmul_lhs_batch.tflite ├── matmul_rhs_batch.tflite └── traces ├── basic_matmul │ └── graph_path ├── matmul_broadcast_singleton_dimension │ └── graph_path ├── matmul_lhs_batch │ └── graph_path └── matmul_rhs_batch └── graph_path
The vision tests take a while to run, so we exclude them from the CI and wildcard expansion. They can be run by invoking the following test suite:
python3 ./scripts/get_e2e_artifacts.py --test_suites=vision_external_tests
The previous command compiles MobileNet, MobileNetV2 and ResNet50 to run on cifar10 and imagenet weights on all backends. The artifacts generated by this test suite are slightly different than those above in that they are organized by /tmp/iree/modules/ModelName/Dataset/backends instead of just by /tmp/iree/modules/ModelName/backends.
You can manually get the benchmarking artifacts for a specific test by using bazel run on the _manual binary target we create for each test. This will automatically store the benchmarking artifacts in /tmp/iree/modules/.
bazel run //integrations/tensorflow/e2e:matrix_ops_static_test_manual -- \ --target_backends=iree_vmla,tflite
iree-benchmark-moduleThis step is optional, but allows running the benchmarks without running bazel at the same time.
bazel build -c opt //iree/tools:iree-benchmark-module
This creates bazel-bin/iree/tools/iree-benchmark-module. The rest of the guide will use this binary, but you could also use bazel run iree/tools:iree-benchmark-module in its place if your prefer.
The E2E tests generate a flagfile with all of the information that iree-benchmark-module needs to benchmark each trace. Namely it handles providing the following flags:
| Flag | Description |
|---|---|
| --module_file | Absolute path to the IREE compiled VM flatbuffer |
| --function_inputs | A comma delimited string of input tensors |
| --driver | The backend driver to use for the benchmark |
| --entry_function | The method on the TensorFlow module to benchmark |
You can find the flagfile to benchmark a specific TensorFlow module on a specific IREE backend and trace at the following path:
/tmp/iree/modules/ModuleName/backend/traces/trace_name/flagfile
For example, if we wanted to benchmark a static left-hand-side batched matmul using MatrixOpsStaticModule on VMLA we would run the following command:
./bazel-bin/iree/tools/iree-benchmark-module \ --flagfile="/tmp/iree/modules/MatrixOpsStaticModule/iree_vmla/traces/matmul_lhs_batch/flagfile"
If you ran the Keras Applications vision test suite, then you'll be able to benchmark ResNet50, MobileNet or MobileNetV2 with cifar10 or imagenet weights. For example:
./bazel-bin/iree/tools/iree-benchmark-module \ --flagfile="/tmp/iree/modules/ResNet50/cifar10/iree_vmla/traces/predict/flagfile"
benchmark_model binary# Enter the TensorFlow Bazel workspace. cd third_party/tensorflow/ # Build the benchmark_model binary. bazel build --copt=-mavx2 -c opt \ //tensorflow/lite/tools/benchmark:benchmark_model # By default, TFLite/x86 uses various matrix multiplication libraries. # It is possible to force it to only use Ruy for all matrix multiplications. # That is the default on ARM but not on x86. This will overwrite the # previous binary unless you move it. # # Note that Ruy takes care of -mavx2 and other AVX extensions internally, # so this passing this flag here isn't going to make a difference to # matrix multiplications. However, the rest of TFLite's kernels outside # of ruy will still benefit from -mavx2. bazel build --copt=-mavx2 -c opt \ --define=tflite_with_ruy=true \ //tensorflow/lite/tools/benchmark:benchmark_model # The binary can now be found in the following directory: ls bazel-bin/tensorflow/lite/tools/benchmark/
TFLite doesn't support flagfiles, so we need to manually pass the path to the graph file via cat. TFLite will generate fake inputs for the model.
Using MatrixOpsStaticModule's left-hand-side batched matmul again as an example we can run the benchmark as follows:
# Run within `third_party/tensorflow/`. ./bazel-bin/tensorflow/lite/tools/benchmark/benchmark_model \ --graph=$(cat "/tmp/iree/modules/MatrixOpsStaticModule/tflite/traces/matmul_lhs_batch/graph_path") \ --warmup_runs=1 \ --num_threads=1 \ --num_runs=100 \ --enable_op_profiling=true
IREE only supports compiling to Android with CMake. Documentation on setting up your environment to cross-compile to Android can be found here.
# After following the instructions above up to 'Build all targets', the # iree-benchmark-module binary should be in the following directory: ls build-android/iree/tools/ # Copy the benchmarking binary to phone. adb push build-android/iree/tools/iree-benchmark-module /data/local/tmp
In this example we'll only copy over the files we need to benchmark a single module on a single backend, but you can easily copy all of the modules over as well.
Using MatrixOpsStaticModule's left-hand-side batched matmul again as an example:
# Make a directory for the module/backend pair we want to benchmark. adb shell mkdir -p /data/local/tmp/MatrixOpsStaticModule/iree_vmla/ # Transfer the files. adb push /tmp/iree/modules/MatrixOpsStaticModule/iree_vmla/* \ /data/local/tmp/MatrixOpsStaticModule/iree_vmla/
adb shell /data/local/tmp/iree-benchmark-module \ --flagfile="/data/local/tmp/MatrixOpsStaticModule/iree_vmla/traces/matmul_lhs_batch/flagfile" \ --module_file="/data/local/tmp/MatrixOpsStaticModule/iree_vmla/compiled.vmfb"
Note: Because the flagfile uses absolute paths, the --module_file flag must be specified manually if the location of the compiled flatbuffer (compiled.vmfb) changes. The flagfile can still take care of specifying the input data, driver and entry function however.
There are three options for getting TFLite's benchmark_model binary for Android.
The first two are to build it directly, either in a docker container or in your own environment. Assuming you can build TensorFlow with Android, you can configure the TFLite benchmark_model binary in the following ways:
# Build the benchmark_model binary without any add-ons. # Note that unlike TFLite/x86, TFLite/ARM uses Ruy by default for all # matrix multiplications (No need to pass tflite_with_ruy), except for some # matrix*vector products. Below we show how to force using ruy also for that. bazel build -c opt \ --config=android_arm64 \ --cxxopt='--std=c++17' \ //tensorflow/lite/tools/benchmark:benchmark_model # Copy the benchmarking binary to phone and allow execution. adb push bazel-bin/tensorflow/lite/tools/benchmark/benchmark_model \ /data/local/tmp adb shell chmod +x /data/local/tmp/benchmark_model
# Build the benchmark_model binary using ruy even for matrix*vector # products. This is only worth trying in models that are heavy on matrix*vector # shapes, typically LSTMs and other RNNs. bazel build -c opt \ --config=android_arm64 \ --cxxopt='--std=c++17' \ --copt=-DTFLITE_WITH_RUY_GEMV \ //tensorflow/lite/tools/benchmark:benchmark_model # Rename the binary for comparison with the standard benchmark_model. mv bazel-bin/tensorflow/lite/tools/benchmark/benchmark_model \ bazel-bin/tensorflow/lite/tools/benchmark/benchmark_model_plus_ruy_gemv adb push bazel-bin/tensorflow/lite/tools/benchmark/benchmark_model_plus_ruy_gemv \ /data/local/tmp/ adb shell chmod +x /data/local/tmp/benchmark_model_plus_ruy_gemv
# Build the benchmark_model binary with flex. bazel build -c opt \ --config=android_arm64 \ --cxxopt='--std=c++17' \ //tensorflow/lite/tools/benchmark:benchmark_model_plus_flex # Copy the benchmarking binary to phone and allow execution. adb push bazel-bin/tensorflow/lite/tools/benchmark/benchmark_model_plus_flex \ /data/local/tmp adb shell chmod +x /data/local/tmp/benchmark_model_plus_flex
Alternatively, you can download and install the Android Benchmark App. If you choose to install the app then you'll have to modify the benchmarking commands below slightly, as shown in this example.
# Copy the data over to the phone. mkdir -p /data/local/tmp/MatrixOpsStaticModule/tflite adb push /tmp/iree/modules/MatrixOpsStaticModule/tflite/* \ /data/local/tmp/MatrixOpsStaticModule/tflite/
# Benchmark with TFLite. adb shell taskset f0 /data/local/tmp/benchmark_model \ --graph=/data/local/tmp/MatrixOpsStaticModule/tflite/matmul_lhs_batch.tflite \ --warmup_runs=1 \ --num_threads=1 \ --num_runs=10 \
# Benchmark with TFLite + RUY GEMV adb shell taskset f0 /data/local/tmp/benchmark_model_plus_ruy_gemv \ --graph=/data/local/tmp/MatrixOpsStaticModule/tflite/matmul_lhs_batch.tflite \ --warmup_runs=1 \ --num_threads=1 \ --num_runs=10 \
# Benchmark with TFLite + Flex. adb shell taskset f0 /data/local/tmp/benchmark_model_plus_flex \ --graph=/data/local/tmp/MatrixOpsStaticModule/tflite/matmul_lhs_batch.tflite \ --warmup_runs=1 \ --num_threads=1 \ --num_runs=10 \
# Benchmark with TFLite running on GPU. adb shell taskset f0 /data/local/tmp/benchmark_model \ --graph=/data/local/tmp/MatrixOpsStaticModule/tflite/matmul_lhs_batch.tflite \ --warmup_runs=1 \ --num_threads=1 \ --num_runs=10 \ --use_gpu=true
Running benchmark on GPU won't give op profiling. To detailed profiling information for GPU you can run the following script:
# Op profiling on GPU using OpenCL backend. sh tensorflow/lite/delegates/gpu/cl/testing/run_performance_profiling.sh \ -m /data/local/tmp/MatrixOpsStaticModule/tflite/matmul_lhs_batch.tflite
Note: You will have to manually specify the TFLite graph that you want to benchmark, as the graph_path file assumes that the graph has not moved. The name of the .tflite graph that you need to benchmark may be different from the name of the trace that you want to benchmark, but you can use cat on the graph_path file to verify the correct .tflite filename if you're unsure.
There are 2 profilers built into TFLite's benchmark_model program. Both of them impact latencies, so they should only be used to get a breakdown of the relative time spent in each operator type, they should not be enabled for the purpose of measuring a latency.
The first is enable_op_profiling. It‘s based on timestamps before and after each op. It’s a runtime commandline flag taken by benchmark_model. Example:
adb shell taskset f0 /data/local/tmp/benchmark_model \ --graph=/data/local/tmp/MatrixOpsStaticModule/tflite/matmul_lhs_batch.tflite \ --warmup_runs=1 \ --num_threads=1 \ --num_runs=10 \ --enable_op_profiling=true
The second is ruy_profiler. Despite its name, it‘s available regardless of whether ruy is used for the matrix multiplications. It’s a sampling profiler, which allows it to provide some more detailed informations, particularly on matrix multiplications. It's a build-time switch:
bazel build \ --define=ruy_profiler=true \ -c opt \ --config=android_arm64 \ //tensorflow/lite/tools/benchmark:benchmark_model
The binary thus built can be run like above, no commandline flag needed.