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22 Commits

Author SHA1 Message Date
03a8662f7f locking docs: fix command name (kernel -> kernels) 2025-04-14 13:19:24 +02:00
cf530c283a Set version to 0.4.4 (#73) 2025-04-11 10:23:26 +02:00
437f910336 Add has_kernel function (#69)
* Add `has_kernel` function

This function checks whether a kernel build exists for the current
environment (Torch version and compute framework).

* Test kernel repo that only contains Torch 2.4
2025-04-11 10:12:37 +02:00
6f1a6067c8 feat: add logo and shields (#72) 2025-04-11 10:07:24 +02:00
1d14abcef0 Do not use kernels without backward when training (#68)
* Do not use kernels without backward when training

* Update repo for backwards marker test
2025-04-11 10:05:57 +02:00
6fd2112e22 Set version to 0.4.3 (#71) 2025-04-10 11:57:15 +02:00
70f56ff856 Support DISABLE_KERNEL_MAPPING env var for completely disabling kernel mappings (#70)
* Disable kernel mappings with `DISABLE_KERNEL_MAPPING=1`

* Rename HF_KERNELS_CACHE to KERNELS_CACHE

But still recognize the old variant for compatibility.

* Add documentation for environment variables
2025-04-10 11:37:54 +02:00
7178b0b86c Add Apache License version 2.0 (#66)
Fixes #64
2025-04-04 20:35:29 +02:00
0bbf90a564 Update ABI requirement to manylinux_2_28 (#65) 2025-04-04 19:38:15 +02:00
27d6ffcb80 Add more details about the ABI requirements (#63) 2025-03-31 14:29:30 +02:00
f7bd21438b Set version to 0.4.2 (#62) 2025-03-27 16:57:28 +01:00
6174febb4b Add warning when layer_name not present in _KERNEL_MAPPING (#61)
* add warning

* fix import order
2025-03-27 16:22:58 +01:00
ff55bc201b Add support for fetching ROCm kernels (#59) 2025-03-25 15:11:03 +01:00
3808108d62 doc: add versioning (#58) 2025-03-24 16:48:20 +01:00
c4a16ef462 Actually export use_kernel_mapping at the top-level (#57)
* Actually export `use_kernel_mapping` at the top-level

* Set version to 0.4.1
2025-03-24 12:44:00 +01:00
9762794dd2 Set version to 0.4.0 (#56) 2025-03-21 20:49:01 +01:00
b7d6867c52 use_kernel_mapping: add inherit_mapping option (#55)
`inherit_mapping` is the default and extends the existing mapping
with the given mapping. If `inherit_mapping` is `False`, existing
mappings are not inherited.
2025-03-21 17:28:45 +01:00
fbcd0f2ebd Set version to 0.3.3 (#54) 2025-03-20 16:09:11 +01:00
5af46eca94 Align dependency versions with transformers (#53) 2025-03-20 15:13:45 +01:00
747dd66876 Set version to 0.3.2 (#51) 2025-03-20 11:46:36 +01:00
920590a592 Also export replace_kernel_forward_from_hub (#52) 2025-03-20 11:46:18 +01:00
5208ac4be5 Make torch an extra/dev dependency (#50)
To support use of this package when Torch is optional.
2025-03-20 10:18:19 +01:00
12 changed files with 482 additions and 36 deletions

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@ -52,3 +52,8 @@ jobs:
- name: Run tests
run: uv run pytest tests
- name: Import check without torch
run: |
uv pip uninstall torch
python -c "import kernels"

201
LICENSE Normal file
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@ -0,0 +1,201 @@
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@ -1,5 +1,16 @@
# kernels
<div align="center">
<img src="https://github.com/user-attachments/assets/64a652f3-0cd3-4829-b3c1-df13f7933569" width="450" height="450" alt="kernel-builder logo">
<p align="center">
<a href="https://pypi.org/project/kernels"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/kernels"></a>
<a href="https://github.com/huggingface/kernels/tags"><img alt="GitHub tag" src="https://img.shields.io/github/v/tag/huggingface/kernels"></a>
<a href="https://github.com/huggingface/kernels/actions/workflows/docker-build-push.yaml"><img alt="Test kernels" src="https://img.shields.io/github/actions/workflow/status/huggingface/kernels/test.yml?label=test"></a>
</p>
</div>
<hr/>
The Kernel Hub allows Python libraries and applications to load compute
kernels directly from the [Hub](https://hf.co/). To support this kind
of dynamic loading, Hub kernels differ from traditional Python kernel
@ -47,6 +58,7 @@ the Hub.
- [Using layers](docs/layers.md)
- [Locking kernel versions](docs/locking.md)
- [Environment variables](docs/env.md)
- [Using kernels in a Docker container](docs/docker.md)
- [Kernel requirements](docs/kernel-requirements.md)
- [Writing kernels](https://github.com/huggingface/kernel-builder/blob/main/docs/writing-kernels.md) using [kernel-builder](https://github.com/huggingface/kernel-builder/)

10
docs/env.md Normal file
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@ -0,0 +1,10 @@
# Environment variables
## `KERNELS_CACHE`
The directory to use as the local kernel cache. If not set, the cache
of the `huggingface_hub` package is used.
## `DISABLE_KERNEL_MAPPING`
Disables kernel mappings for [`layers`](layers.md).

View File

@ -38,6 +38,12 @@ as the repository (replacing `-` by `_`). For instance, kernels in the
`build/<variant>/activation`. This directory
must be a Python package with an `__init__.py` file.
## Versioning
Kernels are versioned on the Hub using Git tags. Version tags must be of
the form `v<major>.<minor>.<patch>`. Versions are used by [locking](./locking.md)
to resolve the version constraints.
## Native Python module
Kernels will typically contain a native Python module with precompiled
@ -46,16 +52,31 @@ requirements:
- Use [ABI3/Limited API](https://docs.python.org/3/c-api/stable.html#stable-application-binary-interface)
for compatibility with Python 3.9 and later.
- Compatible with glibc 2.27 or later. This means that no symbols
from later versions must be used. To archive this, the module should
be built against this glibc version. **Warning:** libgcc must also be
built against glibc 2.27 to avoid leaking symbols.
- No dynamic linkage against libstdc++/libc++. Linkage for C++ symbols
must be static.
- No dynamic library dependencies outside Torch or CUDA libraries
installed as dependencies of Torch.
- Compatible with [`manylinux_2_28`](https://github.com/pypa/manylinux?tab=readme-ov-file#manylinux_2_28-almalinux-8-based).
This means that the extension **must not** use symbols versions higher than:
(These requirements will be updated as new PyTorch versions are released.)
- GLIBC 2.28
- GLIBCXX 3.4.24
- CXXABI 1.3.11
- GCC 7.0.0
These requirement can be checked with the ABI checker (see below).
- No dynamic library dependencies outside:
- Torch;
- CUDA/ROCm libraries installed as dependencies of Torch.
The manylinux_2_28 and Python ABI 3.9 version requirements can be checked with
[`kernel-abi-check`](https://crates.io/crates/kernel-abi-check):
```bash
$ cargo install kernel-abi-check
$ kernel-abi-check result/relu/_relu_e87e0ca_dirty.abi3.so
🐍 Checking for compatibility with manylinux_2_28 and Python ABI version 3.9
✅ No compatibility issues found
```
## Torch extension
@ -98,10 +119,17 @@ requirements:
- The `forward` method has a signature that is compatible with the
`forward` method that it is extending.
The only exception to the _no class variables rule_ is addition of a
`has_backward` class variable. This variable is used to indicate whether
the layer has a backward pass implemented (`True` when absent).
This is an example of a pure layer:
```python
class SiluAndMul(nn.Module):
# This layer does not implement backward.
has_backward: bool = False
def forward(self, x: torch.Tensor):
d = x.shape[-1] // 2
output_shape = x.shape[:-1] + (d,)

View File

@ -13,7 +13,7 @@ build-backend = "setuptools.build_meta"
"kernels-community/activation" = ">=0.0.1"
```
Then run `kernel lock .` in the project directory. This generates a `kernels.lock` file with
Then run `kernels lock .` in the project directory. This generates a `kernels.lock` file with
the locked revisions. The locked revision will be used when loading a kernel with
`get_locked_kernel`:
@ -28,7 +28,7 @@ to `kernels` after doing an (editable or regular) installation of your project.
## Pre-downloading locked kernels
Locked kernels can be pre-downloaded by running `kernel download .` in your
Locked kernels can be pre-downloaded by running `kernels download .` in your
project directory. This will download the kernels to your local Hugging Face
Hub cache.

View File

@ -1,6 +1,6 @@
[project]
name = "kernels"
version = "0.3.1"
version = "0.4.4"
description = "Download compute kernels"
authors = [
{ name = "OlivierDehaene", email = "olivier@huggingface.co" },
@ -8,13 +8,13 @@ authors = [
{ name = "David Holtz", email = "david@huggingface.co" },
{ name = "Nicolas Patry", email = "nicolas@huggingface.co" },
]
license = { text = "Apache-2.0" }
readme = "README.md"
requires-python = ">= 3.9"
dependencies = [
"huggingface-hub>=0.26.3",
"packaging>=24.2",
"tomli>=2.0.1; python_version<'3.11'",
"torch>=2.5",
"huggingface_hub>=0.26.0,<1.0",
"packaging>=20.0",
"tomli>=2.0; python_version<'3.11'",
]
[build-system]
@ -27,8 +27,12 @@ dev = [
"pytest >=8",
# Whatever version is compatible with pytest.
"pytest-benchmark",
"torch >=2.5",
]
[project.optional-dependencies]
torch = ["torch"]
[project.scripts]
kernels = "kernels.cli:main"

View File

@ -2,11 +2,14 @@ from kernels.layer import (
Device,
LayerRepository,
register_kernel_mapping,
replace_kernel_forward_from_hub,
use_kernel_forward_from_hub,
use_kernel_mapping,
)
from kernels.utils import (
get_kernel,
get_locked_kernel,
has_kernel,
install_kernel,
load_kernel,
)
@ -14,10 +17,13 @@ from kernels.utils import (
__all__ = [
"get_kernel",
"get_locked_kernel",
"has_kernel",
"load_kernel",
"install_kernel",
"use_kernel_forward_from_hub",
"use_kernel_mapping",
"register_kernel_mapping",
"replace_kernel_forward_from_hub",
"LayerRepository",
"Device",
]

View File

@ -1,14 +1,18 @@
import inspect
import os
import warnings
from contextvars import ContextVar
from copy import deepcopy
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Callable, Dict, Union
from typing import TYPE_CHECKING, Dict, Union
from .utils import get_kernel
if TYPE_CHECKING:
from torch import nn
_DISABLE_KERNEL_MAPPING: bool = bool(int(os.environ.get("DISABLE_KERNEL_MAPPING", "0")))
@dataclass(frozen=True)
class Device:
@ -54,11 +58,26 @@ _KERNEL_MAPPING: ContextVar[Dict[str, Dict[Device, LayerRepository]]] = ContextV
)
def use_kernel_mapping(mapping: Dict[str, Dict[Union[Device, str], LayerRepository]]):
def use_kernel_mapping(
mapping: Dict[str, Dict[Union[Device, str], LayerRepository]],
*,
inherit_mapping: bool = True,
):
"""
Context manager that sets a mapping for a duration of the context.
When `inherit_mapping` is set to `True` the current mapping will be
extended by `mapping` inside the context. If it is `False`, only
`mapping` is used inside the context.
"""
class ContextManager:
def __enter__(self):
# Mappings always stack on previous mappings.
self.token = _KERNEL_MAPPING.set(deepcopy(_KERNEL_MAPPING.get()))
if inherit_mapping:
self.token = _KERNEL_MAPPING.set(deepcopy(_KERNEL_MAPPING.get()))
else:
self.token = _KERNEL_MAPPING.set({})
register_kernel_mapping(mapping)
def __exit__(self, exc_type, exc_value, traceback):
@ -112,11 +131,21 @@ def replace_kernel_forward_from_hub(cls, layer_name: str, *, use_fallback: bool
fallback_forward = cls.forward
cached_forward: Dict[LayerRepository, Callable] = {}
cached_layer: Dict[LayerRepository, nn.Module] = {}
def forward(self, x, *args, **kwargs):
if _DISABLE_KERNEL_MAPPING:
return fallback_forward(self, x, *args, **kwargs)
needs_backward = self.training
kernel = _KERNEL_MAPPING.get().get(layer_name)
if kernel is None:
warnings.warn(
"\n"
f"No kernel mapping found for layer `{layer_name}`. "
f"Check if the layer name matches one of the kernels in the mapping or add the kernel "
f"you want to use to the mapping. Defaulting to original forward implementation."
)
if not use_fallback:
raise ValueError(f"No layer mapping for `{layer_name}`")
return fallback_forward(self, x, *args, **kwargs)
@ -134,9 +163,11 @@ def replace_kernel_forward_from_hub(cls, layer_name: str, *, use_fallback: bool
return fallback_forward(self, x, *args, **kwargs)
# Short-circuit if we already loaded the layer.
layer_forward = cached_forward.get(repo, None)
if layer_forward is not None:
return layer_forward(self, x, *args, **kwargs)
layer = cached_layer.get(repo, None)
if layer is not None:
if needs_backward and not getattr(layer, "has_backward", True):
return fallback_forward(self, x, *args, **kwargs)
return layer.forward(self, x, *args, **kwargs)
layer = _get_kernel_layer(
repo_id=repo.repo_id,
@ -152,10 +183,11 @@ def replace_kernel_forward_from_hub(cls, layer_name: str, *, use_fallback: bool
finally:
cls.forward = orig_forward
layer_forward = layer.forward
cached_forward[repo] = layer_forward
cached_layer[repo] = layer
return layer_forward(self, x, *args, **kwargs)
if needs_backward and not getattr(layer, "has_backward", True):
return fallback_forward(self, x, *args, **kwargs)
return layer.forward(self, x, *args, **kwargs)
cls.forward = forward
@ -212,7 +244,8 @@ def _validate_layer(*, check_cls, cls):
# ... or predefined member variables.
torch_module_members = {name for name, _ in inspect.getmembers(nn.Module)}
cls_members = {name for name, _ in inspect.getmembers(cls)}
if cls_members - torch_module_members != set():
difference = cls_members - torch_module_members
if difference != set() and difference != {"has_backward"}:
raise TypeError("Layer must not contain additional members.")
# Check whether the forward signatures are similar.

View File

@ -4,6 +4,7 @@ import importlib
import importlib.metadata
import inspect
import json
import logging
import os
import platform
import sys
@ -12,29 +13,45 @@ from pathlib import Path
from types import ModuleType
from typing import Dict, List, Optional, Tuple
from huggingface_hub import snapshot_download
from huggingface_hub import file_exists, snapshot_download
from packaging.version import parse
from kernels.lockfile import KernelLock, VariantLock
CACHE_DIR: Optional[str] = os.environ.get("HF_KERNELS_CACHE", None)
def _get_cache_dir() -> Optional[str]:
"""Returns the kernels cache directory."""
cache_dir = os.environ.get("HF_KERNELS_CACHE", None)
if cache_dir is not None:
logging.warning(
"HF_KERNELS_CACHE will be removed in the future, use KERNELS_CACHE instead"
)
return cache_dir
return os.environ.get("KERNELS_CACHE", None)
CACHE_DIR: Optional[str] = _get_cache_dir()
def build_variant() -> str:
import torch
if torch.version.cuda is None:
raise AssertionError(
"This kernel requires CUDA to be installed. Torch was not compiled with CUDA enabled."
)
if torch.version.cuda is not None:
cuda_version = parse(torch.version.cuda)
compute_framework = f"cu{cuda_version.major}{cuda_version.minor}"
elif torch.version.hip is not None:
rocm_version = parse(torch.version.hip.split("-")[0])
compute_framework = f"rocm{rocm_version.major}{rocm_version.minor}"
else:
raise AssertionError("Torch was not compiled with CUDA or ROCm enabled.")
torch_version = parse(torch.__version__)
cuda_version = parse(torch.version.cuda)
cxxabi = "cxx11" if torch.compiled_with_cxx11_abi() else "cxx98"
cpu = platform.machine()
os = platform.system().lower()
return f"torch{torch_version.major}{torch_version.minor}-{cxxabi}-cu{cuda_version.major}{cuda_version.minor}-{cpu}-{os}"
return f"torch{torch_version.major}{torch_version.minor}-{cxxabi}-{compute_framework}-{cpu}-{os}"
def universal_build_variant() -> str:
@ -144,6 +161,29 @@ def get_kernel(repo_id: str, revision: str = "main") -> ModuleType:
return import_from_path(package_name, package_path / package_name / "__init__.py")
def has_kernel(repo_id: str, revision: str = "main") -> bool:
"""
Check whether a kernel build exists for the current environment
(Torch version and compute framework).
"""
package_name = package_name_from_repo_id(repo_id)
variant = build_variant()
universal_variant = universal_build_variant()
if file_exists(
repo_id,
revision=revision,
filename=f"build/{universal_variant}/{package_name}/__init__.py",
):
return True
return file_exists(
repo_id,
revision=revision,
filename=f"build/{variant}/{package_name}/__init__.py",
)
def load_kernel(repo_id: str, *, lockfile: Optional[Path] = None) -> ModuleType:
"""
Get a pre-downloaded, locked kernel.

View File

@ -1,7 +1,7 @@
import pytest
import torch
from kernels import get_kernel
from kernels import get_kernel, has_kernel
@pytest.fixture
@ -36,6 +36,22 @@ def test_gelu_fast(kernel, device):
assert torch.allclose(y, expected)
@pytest.mark.parametrize(
"kernel_exists",
[
("kernels-community/activation", "main", True),
("kernels-community/triton-layer-norm", "main", True),
# Repo only contains Torch 2.4 kernels (and we don't
# support/test against this version).
("kernels-test/only-torch-2.4", "main", False),
("google-bert/bert-base-uncased", "87565a309", False),
],
)
def test_has_kernel(kernel_exists):
repo_id, revision, kernel = kernel_exists
assert has_kernel(repo_id, revision=revision) == kernel
def test_universal_kernel(universal_kernel):
torch.manual_seed(0)
A = torch.randint(-10, 10, (64, 128), dtype=torch.int8, device="cuda")

View File

@ -152,6 +152,25 @@ def test_mapping_contexts():
== "kernels-community/activation"
)
with use_kernel_mapping(extra_mapping2, inherit_mapping=False):
assert set(_KERNEL_MAPPING.get().keys()) == {
"SiluAndMul",
}
assert (
_KERNEL_MAPPING.get()["SiluAndMul"][Device(type="cuda")].repo_id
== "kernels-community/non-existing"
)
assert set(_KERNEL_MAPPING.get().keys()) == {
"SiluAndMul",
"SiluAndMulStringDevice",
"TestKernel",
}
assert (
_KERNEL_MAPPING.get()["SiluAndMul"][Device(type="cuda")].repo_id
== "kernels-community/activation"
)
assert set(_KERNEL_MAPPING.get().keys()) == {
"SiluAndMul",
"SiluAndMulStringDevice",
@ -184,3 +203,75 @@ def test_validate_kernel_layer():
with pytest.raises(TypeError, match="different kind of arguments"):
_validate_layer(cls=BadLayer4, check_cls=SiluAndMul)
def test_fallback_used_when_training():
@use_kernel_forward_from_hub("Linear")
class TorchLinear(nn.Linear):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Used to check that we called hub kernel.
self.n_calls = 0
def forward(self, input: torch.Tensor) -> torch.Tensor:
self.n_calls += 1
return super().forward(input)
linear = TorchLinear(32, 32).to("cuda")
with use_kernel_mapping(
{
"Linear": {
Device(type="cuda"): LayerRepository(
repo_id="kernels-test/backward-marker-test",
layer_name="LinearImplicitBackward",
)
}
}
):
linear.train()
X = torch.randn(10, 32, device="cuda")
linear(X)
assert linear.n_calls == 0
linear.eval()
linear(X)
assert linear.n_calls == 0
with use_kernel_mapping(
{
"Linear": {
Device(type="cuda"): LayerRepository(
repo_id="kernels-test/backward-marker-test",
layer_name="LinearBackward",
)
}
}
):
linear.train()
X = torch.randn(10, 32, device="cuda")
linear(X)
assert linear.n_calls == 0
linear.eval()
linear(X)
assert linear.n_calls == 0
with use_kernel_mapping(
{
"Linear": {
Device(type="cuda"): LayerRepository(
repo_id="kernels-test/backward-marker-test",
layer_name="LinearNoBackward",
)
}
}
):
linear.train()
X = torch.randn(10, 32, device="cuda")
linear(X)
assert linear.n_calls == 1
linear.eval()
linear(X)
assert linear.n_calls == 1