wehub-resource-sync
69 lines
3.8 KiB
Markdown
69 lines
3.8 KiB
Markdown
# XLA Terminology
|
|
|
|
There are several terms that are used in the context of XLA, MLIR, LLVM, and
|
|
other related technologies. Below is a partial list of these terms and their
|
|
definitions.
|
|
|
|
- **OpenXLA**
|
|
- OpenXLA is an open ecosystem of performant, portable, and extensible
|
|
machine learning (ML) infrastructure components that simplify ML
|
|
development by defragmenting the tools between frontend frameworks and
|
|
hardware backends. It includes the XLA compiler, StableHLO, VHLO,
|
|
[PJRT](https://openxla.org/xla/pjrt) and other components.
|
|
- **XLA**
|
|
- XLA (Accelerated Linear Algebra) is an open source compiler for machine
|
|
learning. The XLA compiler takes models from popular frameworks such as
|
|
PyTorch, TensorFlow, and JAX, and optimizes the models for
|
|
high-performance execution across different hardware platforms including
|
|
GPUs, CPUs, and ML accelerators. The XLA compiler outputs some code to
|
|
LLVM, some to "standard" MLIR, and some to
|
|
[Triton MLIR](https://triton-lang.org/main/dialects/dialects.html) that
|
|
is processed by (MLIR-based) OpenAI Triton compiler.
|
|
- **PJRT**
|
|
- [PJRT](https://github.com/openxla/xla/blob/main/xla/pjrt/c/pjrt_c_api.h)
|
|
is a uniform Device API that simplifies the growing complexity of ML
|
|
workload execution across hardware and frameworks. It provides a
|
|
hardware and framework independent interface for compilers and runtimes.
|
|
- **StableHLO**
|
|
- StableHLO is the public interface to OpenXLA, it is a standardized MLIR
|
|
dialect that may be used by different frameworks and compilers in the
|
|
OpenXLA ecosystem. XLA supports StableHLO, and immediately converts it
|
|
to HLO on the input. There are some
|
|
[StableHLO to StableHLO](https://openxla.org/stablehlo/generated/stablehlo_passes)
|
|
passes implemented using the MLIR framework. It is also possible to
|
|
convert StableHLO to other compilers' IR without using HLO, for example
|
|
in cases where an existing IR is more appropriate.
|
|
- **CHLO**
|
|
- CHLO is a collection of higher level operations which are optionally
|
|
decomposable to StableHLO.
|
|
- **VHLO**
|
|
- The [VHLO Dialect](https://openxla.org/stablehlo/vhlo) is a MLIR dialect
|
|
that is a compatibility layer on top of StableHLO. It provides a
|
|
snapshot of the StableHLO dialect at a given point in time by versioning
|
|
individual program elements, and is used for serialization and
|
|
stability.
|
|
- **MHLO**
|
|
- MHLO is a standalone MLIR-based representation of XLA's HLO IR. The
|
|
dialect is being evaluated for deprecation, and new users of the dialect
|
|
should prefer to use StableHLO instead.
|
|
- **HLO**
|
|
- HLO (High Level Optimizer) is an internal graph representation (IR) for
|
|
the XLA compiler (and also supported input). It is **not** based on
|
|
MLIR, and has its own textual syntax and binary (protobuf based)
|
|
representation.
|
|
- **MLIR**
|
|
- [MLIR](https://mlir.llvm.org) is a hybrid IR infrastructure that allows
|
|
users to define "dialects" of operations at varying degrees of
|
|
abstraction, and gradually lower between these opsets, performing
|
|
transformations at each level of granularity. StableHLO and CHLO are two
|
|
examples of MLIR dialects.
|
|
- **LLVM**
|
|
- [LLVM](https://llvm.org/) is a compiler backend, and a language that it
|
|
takes as an input. Many compilers generate LLVM code as a first step,
|
|
and then LLVM generates machine code from it. This allows developers to
|
|
reuse code that is similar in different compilers, and also makes
|
|
supporting different target platforms easier. XLA:GPU and CPU backends
|
|
have
|
|
[LLVM IR emitters](https://github.com/openxla/xla/tree/main/xla/service/llvm_ir)
|
|
for targeting specific hardware.
|