Metal HAL driverlink
This document lists technical details regarding the Metal implemenation of IREE's Hardware Abstraction Layer, called a Metal HAL driver.
IREE provides a Hardware Abstraction Layer (HAL) as a common interface to different compute accelerators. IREE HAL's design draws inspiration from modern GPU architecture and APIs; so implementing a HAL driver using modern GPU APIs is generally straightforward. This applies to the Metal HAL driver.
Overall Design Choiceslink
Metal Versionslink
Currently the Metal HAL driver expects Metal 3 capabilities. Metal 3 was released late 2022 and are supported since macOS Ventura and iOS 16. It covers recent Apple silicon GPUs including A13+ and M1+ chips and others.
In the future, we expect to increase the support to cover Metal 2 capabilities. Metal 2 introduces useful features like argument buffer and others that are necessary for performance and make IREE HAL implementation simpler. Metal 2 was released late 2017 and are supported since macOS High Sierra and iOS 11. It is already dominant (macOS, iOS).
Programming Languages and Librarieslink
The Metal framework only exposes Objective-C or Swift programming language APIs.
Metal HAL driver needs to inherit from common HAL abstraction definitions, which
are in C. To minimize dependency and binary size and increase performance, we
use Metal's Objective-C API for implementing the Metal HAL driver.
Header (.h
) and implementation (.m
) files are put adjacent to each other.
Object Lifetime Managementlink
Objective-C uses refcount for tracking object lifetime and managing memory. This
is traditionally done manually by sending retain
and release
messages to
Objective-C objects. Modern Objective-C allows developers to opt in to use
Automatic Reference Counting to let the compiler to automatically
deduce and insert retain
/release
where possible to simplify the burdern of
manual management.
We don't use ARC in the Metal HAL driver given that IREE has its own object
refcount and lifetime management mechanism. Metal HAL GPU objects are tracked
with that to be consistent with others. Each Metal HAL GPU object retain
s
the underlying Metal id<MTL*>
object on construction and release
s on
destruction.
GPU Objectslink
Metal is one of the main modern GPU APIs that provide more explicit control over the hardware. The mapping between IREE HAL classes and Metal protocols are relatively straightforward:
In the following subsections, we go over each pair to provide more details.
Driverlink
There is no native driver abstraction in Metal. IREE's Metal HAL driver still
provides a iree_hal_metal_driver_t
struct to implement the
common iree_hal_driver_t
struct. iree_hal_metal_driver_t
just
retain
s all available Metal devices in the system during its lifetime, to
guarantee that we have the same id<MTLDevice>
for device querying and
creation.
Devicelink
iree_hal_metal_device_t
implements iree_hal_device_t
to provide the interface to Metal GPU device by wrapping a id<MTLDevice>
. Upon
construction, iree_hal_metal_device_t
creates and retains one queue for both
dispatch and transfer during its lifetime. In the future we expect to spport
multiple queues for better concurrency.
Command buffer submissionlink
In IREE HAL, command buffers are directly created from the iree_hal_device_t
.
It's also directly submitted there via iree_hal_device_queue_execute()
.
Each execution takes a batch of command buffers, together with a list of waiting
iree_hal_semaphore_t
s and a list signaling iree_hal_semaphore_t
s.
There is no direct mapping of such structure in Metal; so we performs the submission
in three steps:
- Create a new
MTLCommandBuffer
toencodeWaitForEvent:value
for all waitingiree_hal_semaphore_t
s and commit this command buffer. - Commit all command buffers in the submmision batch.
- Create a new
MTLCommandBuffer
toencodeSignalEvent:value
for all signalingiree_hal_semaphore_t
s and commit this command buffer.
Such submission enables asynchronous execution of the workload on the GPU.
Queue-ordered allocationlink
Queue-ordered asynchronous allocations via iree_hal_device_queue_alloc
is not fully
supported yet; it just translates to blocking wait and allocation.
Collectiveslink
Collectives suppport is not yet implemented.
Profilinglink
The Metal HAL driver supports profiling via MTLCaptureManager
. We can either
capture to a trace file or XCode.
To perform profiling in the command line, attach --device_profiling_mode=queue
--device_profiling_file=/path/to/metal.gputrace
to IREE binaries.
Command bufferlink
Command buffers are where IREE HAL and Metal API have a major difference.
IREE HAL command buffers follow the flat Vulkan recording model, where all memory or dispatch commands are recorded into a command buffer directly. Unlike Vulkan, Metal adopts a multi-level command recording model--memory/dispatch commands are not directly recorded into a command buffer; rather, they must go through the additional level of blit/compute encoders. Implementing IREE's HAL using Metal would require switching encoders for interleaved memory and dispatch commands. Additionally, certain IREE HAL API features do not have direct mapping in Metal APIs, e.g., various forms of IREE HAL execution/memory barriers. Translating them would require looking at both previous and next commands to decide the proper mapping.
Due to these reasons, it's beneficial to have a complete view of the full command buffer and extra flexibility during recording, in order to fixup past commands, or inspect future commands.
Therefore, to implement IREE HAL command buffers using Metal, we perform two
steps using a linked list of command segments:
First we create segments to keep track of all IREE HAL commands and the
associated data. And then, when finalizing the command buffer, we iterate
through all the segments and record their contents into a proper
MTLCommandBuffer
. A linked list gives us the flexibility to organize
command sequence in low overhead; and a deferred recording gives us the
complete picture of the command buffer when really started recording.
The Metal HAL driver right now only support one-shot command buffers, by mapping
to MTLCommandBuffer
s.
Fill/copy/update bufferlink
Metal APIs for fill and copy buffers have alignment restrictions on the offset
and length. iree_hal_command_buffer_{fill|copy|update}_buffer()
is more
flexible regarding that. So for cases aren't directly supported by Metal APIs,
we use polyfill compute kernels to perform the memory
operation using GPU threads.
Semaphorelink
iree_hal_semaphore_t
allows host->device, device->host, host->host,
and device->device synchronization. It maps to Vulkan timeline semaphore. In
Metal world, the counterpart would be MTLSharedEvent
. Most
of the iree_hal_semaphore_t
APIs are simple to implement in
MetalSharedEvent
, with iree_hal_semaphore_wait()
as an
exception. A listener is registered on the MTLSharedEvent
with
notifyListener:atValue:block:
to singal a semaphore to wake the current
thread, which is put into sleep by waiting on the semaphore.
Allocatorlink
At the moment the Metal HAL driver just has a very simple
iree_hal_allocator_t
implementation. It just wraps a MTLDevice
and redirects all allocation requests to the MTLDevice
. No page/pool/slab or
whatever. This is meant to be used together with common allocator layers like the
caching allocator.
Bufferlink
IREE iree_hal_buffer_t
maps Metal MTLBuffer
. See
Object Lifetime Management for more details.
Executablelink
IREE iree_hal_executable_t
represents a GPU program archive with
a driver-defined format. It maps naturally to Metal MTLLibrary
.
An entry point in a MTLLibrary
is a MTLFunction
. We define
iree_hal_metal_executable_t
to wrap around a
MTLLibrary
, its MTLFunction
s, and also MTLComputePipelineState
objects
constructed from MTLFunction
s.
Executable cachelink
IREE iree_hal_executable_cache_t
is modeling a cache of
preprared GPU executables for a particular device. At the moment the Metal
HAL driver does not peforming any caching on GPU programs; it simply reads the
program from the FlatBuffer and hands it over to Metal driver.
Descriptor set / pipeline layoutlink
See Resource descriptors for more details.
Compute Pipelinelink
Shader/kernel compilationlink
Metal has Metal Shading Language (MSL) for authoring graphics shaders and compute kernels. MSL source code can be directly consumed by the Metal framework at run-time; it can also be compiled first into an opaque library using command-line tools at build-time.
IREE uses compilers to compile ML models expressed with high-level op semantics down to GPU native source format. This is also the case for the Metal HAL driver. Metal does not provide an open intermediate language; we reuse the SPIR-V code generation pipeline and then cross compile the generated SPIR-V into MSL source with SPIRV-Cross. This is actually a fair common practice for targeting multiple GPU APIs in graphics programming world. For example, the Vulkan implmenation in macOS/iOS, MoltenVK, is also doing the same for shaders/kernels. The path is quite robust, as demonstrated by various games on top of MoltenVK.
Therefore, in IREE, we have a MetalSPIRVTargetBackend
,
which pulls in the common SPIR-V passes to form the compilation pipeline.
The difference would be to provide a suitable SPIR-V target environment to drive
the compilation, which one can derive from the Metal GPU families to target.
The serialization step differs from
VulkanSPIRVTargetBackend
too: following the normal
SPIR-V serialization step, we additionally need to invoke SPRIV-Cross to
cross compile the generated SPIR-V into MSL, and then compile and/or serialize
the MSL source/library.
IREE uses FlatBuffer to encode the whole workload module,
including both GPU shader/kernel (called executable in IREE terminology) and
CPU scheduling logic. The GPU executables are embedded as part of the module's
FlatBuffer, which are mmap
ped when IREE runs.
For the Metal HAL driver, it means we need to embed the MSL kernels inside the module FlatBuffer. Right now we can either encode the MSL source strings and compile them at Metal run-time, or directly encoding the library instead.
Workgroup/threadgroup sizelink
When dispatching a compute kernel in Metal, we need to specify the number of thread groups in grid and the number of threads in thread group. Both are 3-D vectors. IREE HAL, which follows Vulkan, calls them workgroup count and workgroup size, respectively.
In Vulkan programming model, workgroup count and workgroup size are specified at
different places: the former is given when invoking
vkCmdDispatch()
, while the later is encoded in the
dispatched SPIR-V code. This split does not match the Metal model, where we
specify both in the API with dispatchThreads:threadsPerThreadgroup:
.
As said in shader/kernel compilation, MSL kernels
are cross compiled from SPIR-V code and then embeded in the module FlatBuffer.
The module FlatBuffer provides us a way to convey the threadgroup/workgroup size
information extracted from the SPIR-V code. We encode an additional 3-D vector
for each entry point and use it as the threadgroup size when later dispatching
the MTLFunction
corresponding to the entry point.
Command buffer dispatchlink
Metal HAL driver command buffer dispatch recording performs the following steps
with the current active MTLComputeCommandEncoder
:
- Bind the
MTLComputePipelineState
for the current entry function. - Encode the push constants using
setBytes:length:atIndex
. - For each bound descriptor set at set #
S
: - Create a
MTLArgumentEncoder
for encoding an associated argumentMTLBuffer
. - For each bound resource buffer at binding #
B
in this descriptor set, encode it to the argument buffer index #B
withsetBuffer::offset::atIndex:
and inform theMTLComputeCommandEncoder
that the dispatch will use this resource withuseResource:usage:
. - Set the argument
MTLBuffer
to buffer index #S
. - Dispatch with
dispatchThreadgroups:threadsPerThreadgroup:
.