Add execution-based numerical tests for scf.for checkpointing with mutable memory The existing scf_for*mutable_memory* tests only FileCheck the generated IR shape; they can't catch numerical bugs like the checkpointing forward-loop induction variable double-scaling fixed in the previous commit. Add mlir-runner-executed variants (checkpointing off, uniform, and binomial) that JIT-run the differentiated function and check the printed primal/gradient values match across all three checkpointing strategies. This requires two small additions: - %mlir-opt/%mlir-runner/%mlir_runner_utils/%mlir_c_runner_utils lit substitutions, gated behind a `mlir-runner` feature so the new tests are skipped (not failed) wherever these tools aren't built. - Lowering enzyme.load/enzyme.store (dynamic-size-annotated memref load/store) in --convert-enzyme-to-memref, which previously only handled init/push/pop/get/set; the uniform checkpointing path emits load/store for its per-iteration caches, so this is needed to fully lower to executable IR.
Enzyme is a plugin that performs automatic differentiation (AD) of statically analyzable LLVM and MLIR.
Enzyme can be used by calling __enzyme_autodiff on a function to be differentiated as shown below. Running the Enzyme transformation pass then replaces the call to __enzyme_autodiff with the gradient of its first argument.
double foo(double); double grad_foo(double x) { return __enzyme_autodiff(foo, x); }
Enzyme is highly-efficient and its ability to perform AD on optimized code allows Enzyme to meet or exceed the performance of state-of-the-art AD tools.
Detailed information on installing and using Enzyme can be found on our website: https://enzyme.mit.edu.
A short example of how to install Enzyme is below:
cd /path/to/Enzyme/enzyme mkdir build && cd build cmake -G Ninja .. -DLLVM_DIR=/path/to/llvm/lib/cmake/llvm -DLLVM_EXTERNAL_LIT=/path/to/lit/lit.py ninja
Or, install Enzyme using a package manager:
brew install enzyme
spack install enzyme
nix-shell -p enzyme
To get involved or if you have questions, please join our mailing list.
If using this code in an academic setting, please cite the following three papers (first for Enzyme as a whole, second for GPU+optimizations, and third for AD of all other parallel programs (OpenMP, MPI, Julia Tasks, etc.)):
@inproceedings{NEURIPS2020_9332c513,
author = {Moses, William and Churavy, Valentin},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
pages = {12472--12485},
publisher = {Curran Associates, Inc.},
title = {Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients},
url = {https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b682e9347822c2e457ac-Paper.pdf},
volume = {33},
year = {2020}
}
@inproceedings{10.1145/3458817.3476165,
author = {Moses, William S. and Churavy, Valentin and Paehler, Ludger and H\"{u}ckelheim, Jan and Narayanan, Sri Hari Krishna and Schanen, Michel and Doerfert, Johannes},
title = {Reverse-Mode Automatic Differentiation and Optimization of GPU Kernels via Enzyme},
year = {2021},
isbn = {9781450384421},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3458817.3476165},
doi = {10.1145/3458817.3476165},
booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
articleno = {61},
numpages = {16},
keywords = {CUDA, LLVM, ROCm, HPC, AD, GPU, automatic differentiation},
location = {St. Louis, Missouri},
series = {SC '21}
}
@inproceedings{10.5555/3571885.3571964,
author = {Moses, William S. and Narayanan, Sri Hari Krishna and Paehler, Ludger and Churavy, Valentin and Schanen, Michel and H\"{u}ckelheim, Jan and Doerfert, Johannes and Hovland, Paul},
title = {Scalable Automatic Differentiation of Multiple Parallel Paradigms through Compiler Augmentation},
year = {2022},
isbn = {9784665454445},
publisher = {IEEE Press},
booktitle = {Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis},
articleno = {60},
numpages = {18},
keywords = {automatic differentiation, tasks, OpenMP, compiler, Julia, parallel, Enzyme, C++, RAJA, hybrid parallelization, MPI, distributed, LLVM},
location = {Dallas, Texas},
series = {SC '22}
}
Julia bindings, Rust bindings, and Fortran bindings are available for Enzyme.