Machine Learning - Guided Optimization (MLGO)¶
Introduction¶
MLGO refers to integrating ML techniques (primarily) to replace heuristics within LLVM with machine learned models.
Currently the following heuristics feature such integration:
Inlining for size
Register allocation (LLVM greedy eviction heuristic) for performance
This document is an outline of the tooling and APIs facilitating MLGO.
Note that tools for orchestrating ML training are not part of LLVM, as they are dependency-heavy - both on the ML infrastructure choice, as well as choices of distrubuted computing. For the training scenario, LLVM only contains facilities enabling it, such as corpus extraction, training data extraction, and evaluation of models during training.
Corpus Tooling¶
Interacting with ML models¶
We interact with ML models in 2 primary scenarios: one is to train such a model. The other, inference, is to use a model during compilation, to make optimization decisions.
For a specific optimization problem - i.e. inlining, or regalloc eviction - we first separate correctness - preserving decisions from optimization decisions. For example, not inlining functions marked “no inline” is an example of the former. Same is not evicting an unevictable live range. An exmple of the latter is deciding to inline a function that will bloat the caller size, just because we have reason to believe that later, the effect will be some constant propagation that will actually reduce the size (or dynamic instruction count).
ML models can be understood as functions. Their inputs are tensors - buffers of scalars. The output (in our case, singular) is a scalar. For example, for inlining, the inputs are properties of the caller, callee, and the callsite being analyzed for inlining. The output is a boolean.
Inputs and outputs are named, have a scalar type (e.g. int32_t) and a shape (e.g. 3x4). These are the elements that we use to bind to a ML model.
In both training and inference, we want to expose to ML (training algorithms or trained model, respectively) the features we want to make optimization decisions on. In that regard, the interface from the compiler side to the ML side is the same: pass features, and get a decision. It’s essentially a function call, where the parameters and result are bound by name and are described by name, scalar type, and shape tuples.
The main types in LLVM are:
- MLModelRunner
- an abstraction for the decision making mechanism
- TensorSpec
which describes a tensor.
TensorSpec¶
See llvm/Analysis/TensorSpec.h
. This is a simple data bag, identifying a
tensor by name (a string), scalar type, and shape (a vector of ints). The scalar
type can only be int (8, 16, 32, or 64), signed or unsigned; float; or double.
MLModelRunner¶
See llvm/Analysis/MLModelRunner.h
. The abstraction has a pure virtual,
evaluateUntyped
, but the contract with implementers is a bit more involved:
Implementers¶
At construction, the implementer is expected to receive a list of TensorSpec
for input features and the TensorSpec
of the output (e.g.
std::vector<TensorSpec>
). The list type is not contractual, but it must be
a 0-based indexing array-like container. Given a TensorSpec
at index “I” in
the input list, that has a name “N”, shape “D1 x D2x … Dn”, and scalar type
“T”, the implementer must:
set up a contiguous buffer sized
sizeof(T) * D1 * D2 * ... * Dn
. This buffer’s lifetime must be the same as the lifetime of the implementer object.call
MLModelRunner::setUpBufferForTensor
passing I, theTensorSpec
, and the buffer above.
Internally, the expectation is that the implementer uses the name (and maybe
shape) of a TensorSpec
for binding (e.g. lookup in an underlying ML model).
MLModelRunner::setUpBufferForTensor
stores each buffer at the corresponding
index (i.e. its position in the list used at construction). The expectation is
that the user will use that position when calling MLModelRunner::getTensor
to retrieve the underlying buffer (more on that in a bit).
The implementation of evaluateUntyped
is expected to use the value in the
buffers described above, carry out whatever computation (e.g. evaluate a ML
model) and then place the outcome in an output buffer which will be returned to
the caller. Importantly, evaluateUntyped
must not reset the input buffers.
This is because during training we may want to log the features and decisions,
and since the data is already buffered, there’s no reason to force backing it
up elsewhere.
Users¶
The users must pass the input TensorSpec
list at the construction of a
specific MLModelRunner
object. After that, users can be agnostic of the
specific implementation, and would typically follow the following workflow:
call
getTensor
orgetTensorUntyped
, for each input tensor, identified by its index (i.e. the index of the correspondingTensorSpec
in the list used at construction).populate the tensor buffer of each input tensor with values. Users can take advantage of the stability of the tensor buffers like set only once those that don’t change, or cache the buffer address
call
evaluate
and use its result.
Versioning¶
We support a model “knowing” less inputs than the compiler. This is supported by
MLModelRunner::setUpBufferForTensor
. If a TensorSpec
requested by the
compiler is not supported by the underlying model, the MLModelRunner
implementer must still call setUpBufferForTensor
with a nullptr
value
for the buffer. In turn, MLModelRunner
will allocate an appropriately - sized
buffer and track its lifetime. The user can safely populate that buffer. Since
the rest of the inputs are still provided, this allows an evolution model where
we first add features to the compiler and continue using older models without
regressing. Then, the new compiler can be used to train new models. Deprecating
features in the compiler involves, then, training first a model without those
features.
MLModelRunner
implementations¶
We currently feature 3 implementations:
ModelUnderTrainingRunner
. This requires the compiler be built with TFLite support. It allows loading a TFLite model dynamically and is primarily intended for training scenarios, but it can be used relatively easily in production build environments, as it does not change how the compiler operates (why this remark is necessary will become clear in a few paragraphs)ReleaseModeModelRunner
. This is intended for inference scenarios. This uses the rules defined inllvm/cmake/modules/TensorFlowCompile.cmake
to convert, at the time the compiler is built, TensorFlow Saved Models into a header (.h) and native object (.o). The latter is a CPU-based implementation of the neural network, together with its weights (essentially, loops performing matrix multiplications)
NOTE: we are actively working on replacing this with an EmitC implementation requiring no out of tree build-time dependencies.
InteractiveModelRunner
. This is intended for training scenarios where the training algorithm drives compilation. This model runner has no special dependencies, and relies on I/O pipes to communicate with a separate process, presumably a python training algorithm. We do not envision using this in a production environment.
Note that training leaves it to the training infrastructure to handle distributed computing. The assumed architecture has python processes communicating remotely between themselves, but managing local communication with clang.