# Benchmark IREE and TFLite

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](https://google.github.io/iree/developing-iree/tensorflow-integrations)
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.

## 1. Run IREE's E2E TensorFlow tests to generate the benchmarking artifacts

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/`.

```shell
# 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):

```shell
# 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
```

### Optional: Compile the Keras Applications Vision tests

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:

```shell
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`.

### Optional: Manually get the benchmarking artifacts for a specific test

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/`.

```shell
bazel run //integrations/tensorflow/e2e:matrix_ops_static_test_manual -- \
  --target_backends=iree_vmla,tflite
```

## 2. Benchmarking IREE on desktop

### 2.1 Optional: Build the `iree-benchmark-module`

This step is optional, but allows running the benchmarks without running `bazel`
at the same time.

```shell
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.

### 2.2 Benchmark the model on IREE

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:

```shell
/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:

```shell
./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:

```shell
./bazel-bin/iree/tools/iree-benchmark-module \
  --flagfile="/tmp/iree/modules/ResNet50/cifar10/iree_vmla/traces/predict/flagfile"
```

## 3. Benchmarking TFLite on desktop

### 3.1 Build TFLite's `benchmark_model` binary

```shell
# 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/
```

### 3.2 Benchmark the model on TFLite

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:

```shell
# 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
```

## 4. Benchmarking IREE on Android

### 4.1 Prepare the benchmarking tools

IREE only supports compiling to Android with CMake. Documentation on setting up
your environment to cross-compile to Android can be found
[here](https://google.github.io/iree/get-started/getting-started-android-cmake).

```shell
# 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
```

### 4.2 Push the IREE's compilation / benchmarking artifacts to the device

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:

```shell
# 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/
```

### 4.3 Benchmark the module

```shell
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.

## 5. Benchmarking TFLite on Android

### 5.1 Prepare the benchmarking tools

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](https://www.tensorflow.org/lite/guide/build_android#set_up_build_environment_using_docker)
or
[in your own environment](https://www.tensorflow.org/lite/guide/build_android#set_up_build_environment_without_docker). Assuming you can build
TensorFlow with Android, you can configure the TFLite `benchmark_model` binary
in the following ways:

```shell
# 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
```

```shell
# 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
```

```shell
# 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](https://www.tensorflow.org/lite/performance/measurement#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](https://www.tensorflow.org/lite/performance/measurement#run_benchmark).

### 5.2 Run the benchmark

```shell
# 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/
```

```shell
# 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 \
```

```shell
# 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 \
```

```shell
# 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 \
```

```shell
# 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:

```shell
# 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.

### Profile

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.