IREE Benchmarks

This directory contains configuration definition for IREE's continuous benchmarks suite. Benchmark results are posted to https://perf.iree.dev.

The https://buildkite.com/iree/iree-benchmark Buildkite pipeline runs on each commit to the main branch and posts those results to the dashboard. The pipeline also runs on pull requests with the buildkite:benchmark label, posting results compared against their base commit as comments.

Types of benchmarks

├── TensorFlow
│     * models authored in TensorFlow and imported with `iree-import-tf`
└── TFLite
      * models converted to TensorFlow Lite and imported with `iree-import-tflite`

Adding new benchmarks

Machine learning model latency

1. Pick the model you want to benchmark and find its source, which could be a Python script, TensorFlow SavedModel from https://tfhub.dev/, TensorFlow Lite FlatBuffer, or some other format with a supported path into IREE. The model can optionally include trained weights if those are important for benchmarking.

2. Import the model into an MLIR file that IREE can compile using the core iree-translate tool. For TensorFlow models use iree-import-tf, for TensorFlow Lite models use iree-import-tflite, etc. Take notes for where the model came from and how it was imported in case the MLIR file needs to be regenerated in the future.

   We may further automate this over time, such as by importing from Python sources as part of the benchmarks pipeline directly (see iree-org#6942). For now, here are some references:

   • https://gist.github.com/antiagainst/35b0989bd0188dd9df4630bb0cf778f2
   • https://colab.research.google.com/gist/ScottTodd/10838c0ccc87fa6d1b1c72e0fabea064/iree-keyword_spotting_streaming-benchmarks.ipynb

3. Package the imported .mlir model file(s) for storage (see iree_mlir_benchmark_suite.cmake and download_file.py), then upload them to the iree-model-artifacts Google Cloud Storage bucket with the help of a team member. Files currently hosted in that bucket can be viewed at https://storage.googleapis.com/iree-model-artifacts/index.html.

4. Edit the appropriate CMakeLists.txt file under this directory to include your desired benchmark configuration with the iree_mlir_benchmark_suite function. You can test your change by running the https://buildkite.com/iree/iree-benchmark pipeline on a GitHub pull request with the buildkite:benchmark label.

5. Once your changes are merged to the main branch, results will start to appear on the benchmarks dashboard at https://perf.iree.dev.

Other project metrics

TODO(#6161): Collect metrics for miscellaneous IREE system states

Developer notes

These are ad-hoc notes added for developers to help triage errors.

Repro of TFLite model errors

These steps help reproduce the failures in TFLite models.

1. Install iree-import-tflite.

   $ python -m pip install iree-tools-tflite -f https://github.com/google/iree/releases
   

2. Confirm the binary iree-import-tflite is in your path by running

   $ iree-import-tflite --help
   

3. Download the TFLite flatbuffer for the failing benchmarks. The location can be found from this CMakeLists.txt file

4. The input TOSA model can be generated by running

   $ iree-import-tflite <tflite file> -o <tosa output file>
   

5. The exact flags used to compile and run the benchmarks can be found in this CMakeLists.txt file