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, the TensorSpec, 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 or getTensorUntyped, for each input tensor, identified by its index (i.e. the index of the corresponding TensorSpec 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 in llvm/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.