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HIP HAL driverlink

This document lists technical details regarding the HIP implemenation of IREE's Hardware Abstraction Layer, called a HIP 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 HIP is mostly straightforward; though there are places we need emulation given no direct mapping concepts or mechanisms. HIP HAL driver draws inspiration from the CUDA HAL driver and the code structure is based off of that implementation.

Overall design choiceslink

HIP driver vs runtime APIlink

IREE HAL's design draws inspiration from modern GPU APIs--it provides explicit control of low-level GPU objects. The compiler is expected to plan the object lifetime and schedule workload and synchronization in an optimized way; IREE HAL implementation and the underlying GPU driver stack is expected to be a thin layer without much smarts and magic.

Unlike CUDA, HIP doesn't provide two separate API's with the same functionality in the name of driver and runtime. Instead it extends the HIP API with Modules and Ctx control API's that the CUDA driver API's exclusively offer.At runtime the HIP HAL driver will load the library dynamically.

GPU Objectslink


There is no direct HIP construct that map to the IREE HAL iree_hal_driver_t abstraction. We use it to hold the dynamic symbols loaded for all devices, and device enumeration and creation.


iree_hal_hip_device_t implements iree_hal_device_t to provide the interface to HIP GPU device by wrapping a hipDevice_t. For each device, right now we create two hipStream_ts--one for issuing commands for memory allocation and kernel lauches as instructed by the program; the other for issue host callback functions after dispatched command buffers completes. See synchronization section regarding the details.

Async allocationlink

The HIP HAL drivers supports async allocation (iree_hal_device_queue_alloca() and iree_hal_device_queue_dealloca()) via HIP stream ordered memory allocation.

The async_allocations in the iree_hal_hip_device_params_t struct allows to enable this feature.

Command bufferlink

iree_hal_command_buffer_t is a recording of commands to issue to the GPU; when the command buffer is submitted to the device it's then actually executed on the GPU asynchronously.

Two implementations of iree_hal_command_buffer_t exist in the HIP HAL driver--one backed by hipGraph_t and the other backed by hipStream_t.

hipGraph_t conceptually matches iree_hal_command_buffer_t better given it's a recording of commands to issue to the GPU. Also using the hipGraph_t API allows to easily encode fine grain dependencies between dispatch without having to create multiple streams. Therefore, the hipGraph_t-backed implementation is a more natural one. Though note that hipGraph_t API is meant to be used for recording once and replaying multiple times and there may be a performance penalty to using hipGraph_t API for one-shot command buffer.

The hipStream_t-backed implementation just issues commands directly to a hipStream_t when recording. Commands issued to hipStream_t can be immediately sent to the GPU for execution; there is no recording and replaying separation. In order to match the recording semantics of iree_hal_command_buffer_t, to use the hipStream_t-backed command buffer, we need to first record the command buffer into an in-memory iree_hal_deferred_command_buffer_t, and then when applying the command buffer, we create a new hipStream_t-backed implementation.

The command_buffer_mode in the iree_hal_hips_device_params_t struct allows to select which implementation to use.


The allocator will forward allocation requests to hipHostMalloc() for host local memory, hipMalloc() for device local and host invisible memory, and hipMallocManaged() for device local and host visible memory.


HIP buffers are represented either as a host pointer or a device pointer of type hipDeviceptr_t.


iree_hal_executable_t maps naturally to hipModule_t.

The compiler generates a FlatBuffer containing a HSACO image as well as a list of entry point functions and their associated metadata (names, workgroup size, dynamic shared memory size, etc.). At runtime, the HIP HAL driver loads the HSACO image and creates hipFunction_ts out of it for various entry points.



iree_hal_event_t right now is not used in the compiler so it's not yet implemented in the HIP HAL driver.


The IREE HAL uses semaphores to synchronize work between host CPU threads and device GPU streams. It's a unified primitive that covers all directions--host to host, host to device, device to host, and device to device, and allows flexible signal and wait ordering--signal before wait, or wait before signal. There is no limit on the number of waits of the same value too.

The core state of a HAL semaphore consists of a monotonically increasing 64-bit integer value, which forms a timeline--signaling the semaphore to a larger value advances the timeline and unblocks work waiting on some earlier values. The semantics closely mirrors Vulkan timeline semaphore.

In HIP, there is no direct equivalent primitives providing all the capabilities needed by the HAL semaphore abstraction. Therefore, to implement the support, we need to leverage multiple native CPU or HIP primitives under the hood.

hipEvent_t capabilitieslink

The main synchronization mechanism is HIP event--hipEvent_t. As a functionality and integration baseline, we use hipEvent_t to implement the IREE HAL semaphore abstraction.

hipEvent_t natively supports the following capabilities:

  • State: binary; either unsignaled or signaled. There can exist multiple waits (e.g., via hipEventSynchronize() or hipGraphAddEventWaitNode()) for the same hipEvent_t signal (e.g., via hipEventRecord() or hipGraphAddEventRecordNode()).
  • Ordering: must be signal before wait. Waiting before signal would mean waiting an empty set of work, or previously recorded work.
  • Direction: device to device, device to host.

We need to fill the remaining capability gaps. Before going into details, the overall approach would be to:

  • State: we need a 64-bit integer value timeline. Given the binary state of a hipEvent_t, each hipEvent_t would just be a "timepoint" on the timeline.
  • Ordering: we need to defer releasing the workload to the GPU until the semaphore waits are reached on the host, or we can have some device hipEvent_t to wait on.
  • Direction: host to host and host to device is missing; we can support that with host synchronization mechanisms.

Signal to wait analysislink

Concretely, for a given HAL semaphore, looking at the four directions:

CPU signallink

A CPU thread signals the semaphore timeline to a new value.

If there are CPU waits, it is purely on the CPU side. We just need to use common CPU notification mechanisms. In IREE we have iree_event_t wrapping various low-level OS primitives for it. So we can just use that to represent a wait timepoint. We need to keep track of all CPU wait timepoints in the timeline. After a new signaled value, go through the timeline and notify all those waiting on earlier values.

If there are GPU waits, given that there are no way we can signal a hipEvent_t on CPU, one way to handle this is to cache and defer the submission batches by ourselves until CPU signals past the desired value. To support this, we would need to implement a deferred/pending actions queue.

GPU signallink

GPU signals can only be through a hipEvent_t object, which has a binary state. We need to advance the timeline too. One way is to use hipLaunchHostFunc() to advance from the CPU side with iree_hal_semaphore_list_signal(). This additionally would mean we can reuse the logic form CPU signaling to unblock CPU waits.

After advancing the timeline from the CPU side with hipLaunchHostFunc(), we can release more workload from the deferred/pending actions queue to the GPU. Though, per the documentation of hipLaunchHostFunc(), "the host function must not make any HIP API calls." So we cannot do that directly inside hipLaunchHostFunc(); we need to notify another separate thread to call HIP APIs to push more work to the GPU. So the deferred/pending action queue should have an associcated thread.

For GPU waits, we can also leverage the same logic--using CPU signaling to unblock deferred GPU queue actions. Though this is performant, given that the CPU is involved for GPU internal synchronization. We want to use hipEvent_t instead:

  • We keep track of all GPU signals in the timeline. Once we see a GPU wait request, try to scan the timeline to find a GPU signal that advances the timeline past the desired value, and use that for waiting instead. (This actually applies to CPU waits too, and it's an optimization over pure CPU side iree_event_t polling.)
  • We may not see GPU signal before seeing GPU wait requests, then we can also keep track of all GPU waits in the timeline. Later once see either a CPU signal or GPU signal advancing past the waited value, we can handle them accordingly--submitting immediately or associating the hipEvent_t. This would also guarantee the requirement of hipEvent_t--recording should happen before waiting.
  • We can use the same hipEvent_t to unblock multiple GPU waits. That's allowed, though it would mean we need to be careful regarding hipEvent_t lifetime management. Here we can use reference counting to see how many timepoints are using it and automatically return to a pool once done.

Another problem is that per the hipLaunchHostFunc() doc, "the function will be called after currently enqueued work and will block work added after it." We don't want the blocking behavior involving host. So we can use a dedicated hipStream_t for launching the host function, waiting on the hipEvent_t from the original stream too. We can also handle resource deallocation together there.

Data structureslink

To summarize, we need the following data structures to implement HAL semaphore:

  • iree_event_t: CPU notification mechanism wrapping low-level OS primitives. Used by host wait timepoints.
  • iree_event_pool_t: a pool for CPU iree_event_t objects to recycle.
  • iree_hal_hip_event_t: GPU notification mechanism wrapping a hipEvent_t and a reference count. Used by device signal and wait timepoints. Associates with a iree_hal_hip_event_pool_t pool--returns to the pool directly on once reference count goes to 0.
  • iree_hal_hip_event_pool_t: a pool for GPU iree_hal_hip_event_t objects to recycle.
  • iree_hal_hip_timepoint_t: an object that wraps a CPU iree_event_t or GPU iree_hal_hip_event_t to represent wait/signal of a timepoint on a timeline.
  • iree_hal_hip_timepoint_pool_t: a pool for iree_hal_hip_timepoint_t objects to recycle. This pool builds upon the CPU and GPU event pool--it acquires CPU/GPU event objects there.
  • iree_hal_hip_timeline_semaphore_t: contains a list of CPU wait and GPU wait/signal timepoints.
  • iree_hal_hip_queue_action_t: a pending queue action (kernel launch or stream-ordered allocation).
  • iree_hal_hip_pending_queue_actions_t: a data structure to manage pending queue actions. It provides APIs to enqueue actions, and advance the queue on demand--queue actions are released to the GPU when all their wait semaphores are signaled past the desired value, or we can have a hipEvent_t object already recorded to some hipStream_t to wait on.