Helion

⚠️ Early Development Warning Helion is currently in an experimental stage. You should expect bugs, incomplete features, and APIs that may change in future versions. Feedback and bug reports are welcome and appreciated!

Helion is a Python-embedded domain-specific language (DSL) for authoring machine learning kernels, designed to compile down to Triton, a performant backend for programming GPUs and other devices. Helion aims to raise the level of abstraction compared to Triton, making it easier to write correct and efficient kernels while enabling more automation in the autotuning process.

The name Helion refers to the nucleus of a helium-3 atom, while Triton refers to hydrogen-3.

Helion can be viewed either as PyTorch with tiles or as a higher-level Triton. Compared to Triton, Helion reduces manual coding effort through autotuning. Helion spends more time (approx 10 min) autotuning as it evaluates hundreds of potential Triton implementations generated from a single Helion kernel. This larger search space also makes kernels more performance portable between different hardware. Helion automates and autotunes over:

1. Tensor Indexing:

   • Automatically calculates strides and indices.
   • Autotunes choices among various indexing methods (pointers, block pointers, TensorDescriptors).

2. Masking:

   • Most masking is implicit in Helion, and is optimized away when not needed.

3. Grid Sizes and PID Calculations:

   • Automatically determines grid sizes.
   • Autotunes multiple mappings from Program IDs (PIDs) to data tiles.

4. Implicit Search Space Definition:

   • Eliminates the need to manually define search configurations.
   • Automatically generates configuration flags and exploration spaces.

5. Kernel Arguments Management:

   • Automates the handling of kernel arguments, including tensor sizes and strides.
   • Lifts global variables and (nested) closures into kernel arguments, allowing better templating.

6. Looping Reductions:

   • Can automatically convert large reductions into looped implementations.

7. Automated Optimizations:

   • PID swizzling for improved L2 cache reuse.
   • Loop reordering.
   • [Coming soon] Persistent kernel strategies, warp specialization choices, and more.

Example

A minimal matrix multiplication kernel in Helion looks like this:

import torch, helion, helion.language as hl

@helion.kernel()
def matmul(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
    m, k = x.size()
    k, n = y.size()
    out = torch.empty([m, n], dtype=x.dtype, device=x.device)

    for tile_m, tile_n in hl.tile([m, n]):
        acc = hl.zeros([tile_m, tile_n], dtype=torch.float32)
        for tile_k in hl.tile(k):
            acc = torch.addmm(acc, x[tile_m, tile_k], y[tile_k, tile_n])
        out[tile_m, tile_n] = acc

    return out

The code outside the for loops is standard PyTorch code executed on the CPU. It is typically used for tasks like allocating output tensors and performing shape computations.

The code inside the for loops is compiled into a Triton kernel, resulting in a single GPU kernel. A single Helion kernel is always compiled to exactly one GPU kernel.

The hl.tile function subdivides the iteration space (in this case m by n) into tiles. These tiles are executed in parallel on the GPU. Tiling details, such as dimensionality (1D vs 2D), tile sizes, and loop ordering, are automatically determined by Helion's autotuner. Alternatively, these details can be explicitly specified using the config= argument in helion.kernel.

• The outer for loop is mapped onto the grid of the generated kernel. The grid size is determined automatically based on the chosen tile size.

• The inner for loop translates into a loop within the generated kernel, and its tile size is also determined automatically.

Within a Helion kernel, standard PyTorch operators (like torch.addmm) are automatically mapped to Triton operations using TorchInductor. Thus, familiarity with PyTorch means you already know most of Helion. Helion supports a wide range of operations including pointwise (add, sigmoid, etc.), reductions (sum, softmax, etc.), views, and matrix multiplication operations. Arbitrary function calls within a Helion kernel are supported, but must be traceable with make_fx.

Autotuning

The above example can be executed with:

out = matmul(torch.randn([2048, 2028], device="cuda"),
             torch.randn([2048, 2028], device="cuda"))

When a kernel runs for the first time, Helion initiates autotuning. A typical autotuning session produces output similar to:

[0s] Starting DifferentialEvolutionSearch with population=40, generations=20, crossover_rate=0.8
[20s] Initial population: failed=10 min=0.9677 mid=3.0013 max=22.1430 best=Config(block_sizes=[[64, 32], [32]], loop_orders=[[1, 0]], num_warps=2, num_stages=2, indexing='pointer', l2_grouping=1, use_yz_grid=False)
[52s] Generation 2: replaced=16 min=0.7731 mid=1.7203 max=3.1227 best=Config(block_sizes=[[32, 128], [16]], loop_orders=[[0, 1]], num_warps=4, num_stages=4, indexing='block_ptr', l2_grouping=16)
[85s] Generation 3: replaced=19 min=0.6256 mid=1.3916 max=2.7868 best=Config(block_sizes=[[64, 128], [16]], loop_orders=[[0, 1]], num_warps=4, num_stages=4, indexing='block_ptr', l2_grouping=16)
...
[593s] Generation 19: replaced=7 min=0.6072 mid=0.6626 max=0.7496 best=Config(block_sizes=[[64, 128], [16]], loop_orders=[[1, 0]], num_warps=4, num_stages=3, indexing='block_ptr', l2_grouping=32)
[593s] Autotuning complete in 593.1s after searching 1520 configs.
One can hardcode the best config and skip autotuning with:
    @helion.kernel(config=helion.Config(block_sizes=[[64, 128], [16]], loop_orders=[[1, 0]], num_warps=4, num_stages=3, indexing='block_ptr', l2_grouping=32))

Because autotuning can be time-consuming (around 10 minutes in the above example), you may want to manually specify the best configuration found from autotuning to avoid repeated tuning:

@helion.kernel(config=helion.Config(
    block_sizes=[[64, 128], [16]],
    loop_orders=[[1, 0]],
    num_warps=4,
    num_stages=3,
    indexing='block_ptr',
    l2_grouping=32
))
def matmul(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
    ...

This explicit configuration skips autotuning on subsequent runs.

You can also specify multiple configurations, prompting Helion to perform a more lightweight autotuning process:

@helion.kernel(configs=[
    helion.Config(...),
    helion.Config(...),
])
def matmul(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
    ...

In this case, Helion evaluates the provided configurations and selects the fastest one.

Additionally, Helion provides programmatic APIs to manage autotuning and configurations directly from your code.

Configurations

Helion configurations include the following options:

• block_sizes (list[int]): Controls tile sizes corresponding to each dimension passed hl.tile or call to hl.register_block_size in the kernel.

• loop_orders (list[list[int]]): Contains one entry per hl.tile call with two or more dimensions, allowing you to permute the iteration order of the tiles.

• flatten_loops (list[bool]): Contains one entry per hl.tile call with two or more dimensions, allowing you to flatten the iteration space into a single dimension.

• reduction_loops (list[int | None]): Contains one entry per reduction dimension (see examples/softmax.py). Using None triggers a persistent reduction, where the entire reduction is processed in a single tile. Specifying an integer block size converts the reduction into a loop, beneficial for larger reductions that exceed the registers available.

• l2_groupings (list[int]): Reorders the program IDs (PIDs) of the generated kernel for improved L2 cache behavior. A value of 1 disables this optimization, while higher values specify the grouping size.

• indexing ("pointer", "tensor_descriptor" or "block_ptr"): Specifies the type of indexing code to generate. The "tensor_descriptor" option uses Tensor Memory Accelerators (TMAs) but requires a Hopper or newer GPU and the latest development version of Triton.

• use_yz_grid (bool): Determines if the y and z dimensions of the launch grid are utilized, or if only the x dimension is used. This option is ignored if l2_groupings[0] > 1.

• num_warps (int): Sets the number of warps the kernel will use.

• num_stages (int): Defines the number of pipeline stages to be passed to Triton.

Changing these options results in often significantly different output Triton code, allowing the autotuner to explore a wide range of implementations from a single Helion kernel.

Settings for Development and Debugging

When developing kernels with Helion, you might prefer skipping autotuning for faster iteration. To do this, set the environment variable HELION_USE_DEFAULT_CONFIG=1 or use the decorator argument @helion.kernel(use_default_config=True). Warning: The default configuration is slow and not intended for production or performance testing.

To view the generated Triton code, set the environment variable HELION_PRINT_OUTPUT_CODE=1 or include print_output_code=True in the @helion.kernel decorator. This prints the Triton code to stderr, which is helpful for debugging and understanding Helion's compilation process. One can also use foo_kernel.bind(args).to_triton_code(config) to get the Triton code as a string.

To force autotuning, bypassing provided configurations, set HELION_FORCE_AUTOTUNE=1 or invoke foo_kernel.autotune(args, force=True).

Additional settings are available in settings.py. If both an environment variable and a kernel decorator argument are set, the kernel decorator argument takes precedence, and the environment variable will be ignored.

Enable logging by setting the environment variable HELION_LOGS=all for INFO-level logs, or HELION_LOGS=+all for DEBUG-level logs. Alternatively, you can specify logging for specific modules using a comma-separated list (e.g., HELION_LOGS=+helion.runtime.kernel).

Requirements

Helion currently targets Linux systems and requires a recent Python and PyTorch environment:

• Linux-based OS
• Python 3.10, 3.11, or 3.12
• PyTorch 2.7 or newer
• A development version of Triton, installed from source (Older versions may work, but will lack support for features like TMA on Hopper/Blackwell GPUs and may exhibit lower performance.)

Installation

We recommend using a conda environment to manage dependencies. First, install compatible versions of PyTorch and Triton.

Once your environment is set up, you can install Helion directly from GitHub:

pip install git+https://github.com/pytorch-labs/helion.git

Alternatively, you may install from source for development purposes:

git clone https://github.com/pytorch-labs/helion.git
cd helion
# To install in editable w/ required dev packages
pip install -e .'[dev]'

This installs Helion in "editable" mode so that changes to the source code take effect without needing to reinstall.

License

Helion is BSD-style licensed, as found in the LICENSE file.