tinygrad/docs/runtime/hcq.md

HCQ Compatible Runtime

Overview

The main aspect of HCQ-compatible runtimes is how they interact with devices. In HCQ, all interactions with devices occur in a hardware-friendly manner using command queues. This approach allows commands to be issued directly to devices, bypassing runtime overhead such as HIP, or CUDA. Additionally, by using the HCQ API, these runtimes can benefit from various optimizations and features, including HCQGraph and built-in profiling capabilities.

Command Queues

To interact with devices, there are 2 types of queues: HWComputeQueue and HWCopyQueue. Commands which are defined in a base HWCommandQueue class should be supported by both queues. These methods are timestamp and synchronization methods like signal and wait.

For example, the following Python code enqueues a wait, execute, and signal command on the HCQ-compatible device:

HWComputeQueue().wait(signal_to_wait, value_to_wait) \
                .exec(program, kernargs_ptr, global_dims, local_dims) \
                .signal(signal_to_fire, value_to_fire) \
                .submit(your_device)

Each runtime should implement the required functions that are defined in the HWCommandQueue, HWComputeQueue, and HWCopyQueue classes.

::: tinygrad.device.HWCommandQueue options: members: [ "signal", "wait", "timestamp", "update_signal", "update_wait", "submit", ] show_source: false

::: tinygrad.device.HWComputeQueue options: members: [ "memory_barrier", "exec", "update_exec", ] show_source: false

::: tinygrad.device.HWCopyQueue options: members: [ "copy", "update_copy", ] show_source: false

Implementing custom commands

To implement custom commands in the queue, use the @hcq_command decorator for your command implementations.

::: tinygrad.device.hcq_command options: members: [ "copy", "update_copy", ] show_source: false

HCQ Compatible Device

The HCQCompatCompiled class defines the API for HCQ-compatible devices. This class serves as an abstract base class that device-specific implementations should inherit from and implement.

::: tinygrad.device.HCQCompatCompiled options: members: [ "_alloc_signal", "_free_signal", "_read_signal", "_read_timestamp", "_set_signal", "_wait_signal", "_gpu2cpu_time", ] show_source: false

Signals

Signals are device-dependent structures used for synchronization and timing in HCQ-compatible devices. They should be designed to record both a value and a timestamp within the same signal. The following Python code demonstrates the usage of signals:

signal = your_device._alloc_signal()

HWComputeQueue().timestamp(signal) \
                .signal(signal, value_to_fire) \
                .submit(your_device)

your_device._wait_signal(signal, value_to_fire)
timestamp = your_device._read_timestamp()

Synchronization signals

Each HCQ-compatible device must allocate two signals for global synchronization purposes. These signals are passed to the HCQCompatCompiled base class during initialization: an active timeline signal self.timeline_signal and a shadow timeline signal self._shadow_timeline_signal which helps to handle signal value overflow issues. You can find more about synchronization in the synchronization section

HCQ Compatible Allocator

The HCQCompatAllocator base class simplifies allocator logic by leveraging command queues abstractions. This class efficiently handles copy and transfer operations, leaving only the alloc and free functions to be implemented by individual backends.

::: tinygrad.device.HCQCompatAllocator options: members: [ "_alloc", "_free", ] show_source: false

HCQ Allocator Result Protocol

Backends must adhere to the HCQCompatAllocRes protocol when returning allocation results.

::: tinygrad.device.HCQCompatAllocRes options: members: true show_source: false

Synchronization

HCQ-compatible devices use a global timeline signal for synchronizing all operations. This mechanism ensures proper ordering and completion of tasks across the device. By convention, self.timeline_value points to the next value to signal. So, to wait for all previous operations on the device to complete, wait for self.timeline_value - 1 value. The following Python code demonstrates the typical usage of signals to synchronize execution to other operations on the device:

HWComputeQueue().wait(your_device.timeline_signal, your_device.timeline_value - 1) \
                .exec(...)
                .signal(your_device.timeline_signal, your_device.timeline_value) \
                .submit(your_device)
your_device.timeline_value += 1

# Optionally wait for execution
your_device._wait_signal(your_device.timeline_signal, your_device.timeline_value - 1)

HCQGraph

HCQGraph is a core feature that implements GraphRunner for HCQ-compatible devices. HCQGraph builds a static HWComputeQueue and HWCopyQueue for all operations per device. To optimize enqueue time, only the necessary parts of the queues are updated for each run using the update APIs of the queues, avoiding a complete rebuild. Optionally, queues can implement a bind API, which allows further optimization by eliminating the need to copy the queues into the device ring.