Set version to 0.2.0 (#41)

Fixes #37.

docs/kernel-requirements.md

@@ -26,8 +26,13 @@ recommended build variants are:
 - `torch26-cxx98-cu124-x86_64-linux`
 - `torch26-cxx98-cu126-x86_64-linux`
 
-This list will be updated as new PyTorch versions are released. Each
-variant directory should contain a single directory with the same name
+This list will be updated as new PyTorch versions are released. Kernels
+that are in pure Python (e.g. Triton kernels) only need to provide a
+single build variant:
+
+- `torch-universal`
+
+Each variant directory should contain a single directory with the same name
 as the repository (replacing `-` by `_`). For instance, kernels in the
 `kernels-community/activation` repository have a directories like
 `build/<variant>/activation`. This directory

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "kernels"
-version = "0.1.7"
+version = "0.2.0"
 description = "Download cuda kernels"
 authors = [
   { name = "OlivierDehaene", email = "olivier@huggingface.co" },
@@ -14,7 +14,7 @@ dependencies = [
   "huggingface-hub>=0.26.3",
   "packaging>=24.2",
   "tomli>=2.0.1; python_version<'3.11'",
-  "torch>=2.4",
+  "torch>=2.5",
 ]
 
 [build-system]
@@ -38,24 +38,24 @@ kernels = "kernels.cli:main"
 
 [tool.ruff]
 exclude = [
-    ".eggs",
-    ".git",
-    ".git-rewrite",
-    ".hg",
-    ".mypy_cache",
-    ".nox",
-    ".pants.d",
-    ".pytype",
-    ".ruff_cache",
-    ".svn",
-    ".tox",
-    ".venv",
-    ".venv*",
-    "__pypackages__",
-    "_build",
-    "build",
-    "dist",
-    "venv",
+  ".eggs",
+  ".git",
+  ".git-rewrite",
+  ".hg",
+  ".mypy_cache",
+  ".nox",
+  ".pants.d",
+  ".pytype",
+  ".ruff_cache",
+  ".svn",
+  ".tox",
+  ".venv",
+  ".venv*",
+  "__pypackages__",
+  "_build",
+  "build",
+  "dist",
+  "venv",
 ]
 line-length = 119
 # Ignored rules:

src/kernels/utils.py

@@ -37,9 +37,9 @@ def build_variant() -> str:
     return f"torch{torch_version.major}{torch_version.minor}-{cxxabi}-cu{cuda_version.major}{cuda_version.minor}-{cpu}-{os}"
 
 
-def noarch_build_variant() -> str:
+def universal_build_variant() -> str:
     # Once we support other frameworks, detection goes here.
-    return "torch-noarch"
+    return "torch-universal"
 
 
 def import_from_path(module_name: str, file_path: Path) -> ModuleType:
@@ -73,11 +73,11 @@ def install_kernel(
     """
     package_name = package_name_from_repo_id(repo_id)
     variant = build_variant()
-    noarch_variant = noarch_build_variant()
+    universal_variant = universal_build_variant()
     repo_path = Path(
         snapshot_download(
             repo_id,
-            allow_patterns=[f"build/{variant}/*", f"build/{noarch_variant}/*"],
+            allow_patterns=[f"build/{variant}/*", f"build/{universal_variant}/*"],
             cache_dir=CACHE_DIR,
             revision=revision,
             local_files_only=local_files_only,
@@ -85,12 +85,12 @@ def install_kernel(
     )
 
     variant_path = repo_path / "build" / variant
-    noarch_variant_path = repo_path / "build" / noarch_variant
+    universal_variant_path = repo_path / "build" / universal_variant
 
-    if not variant_path.exists() and noarch_variant_path.exists():
-        # Fall back to noarch variant.
-        variant = noarch_variant
-        variant_path = noarch_variant_path
+    if not variant_path.exists() and universal_variant_path.exists():
+        # Fall back to universal variant.
+        variant = universal_variant
+        variant_path = universal_variant_path
 
     if variant_locks is not None:
         variant_lock = variant_locks.get(variant)
@@ -156,23 +156,23 @@ def load_kernel(repo_id: str) -> ModuleType:
     package_name = package_name_from_repo_id(repo_id)
 
     variant = build_variant()
-    noarch_variant = noarch_build_variant()
+    universal_variant = universal_build_variant()
 
     repo_path = Path(
         snapshot_download(
             repo_id,
-            allow_patterns=[f"build/{variant}/*", f"build/{noarch_variant}/*"],
+            allow_patterns=[f"build/{variant}/*", f"build/{universal_variant}/*"],
             cache_dir=CACHE_DIR,
             local_files_only=True,
         )
     )
 
     variant_path = repo_path / "build" / variant
-    noarch_variant_path = repo_path / "build" / noarch_variant
-    if not variant_path.exists() and noarch_variant_path.exists():
-        # Fall back to noarch variant.
-        variant = noarch_variant
-        variant_path = noarch_variant_path
+    universal_variant_path = repo_path / "build" / universal_variant
+    if not variant_path.exists() and universal_variant_path.exists():
+        # Fall back to universal variant.
+        variant = universal_variant
+        variant_path = universal_variant_path
 
     module_init_path = variant_path / package_name / "__init__.py"
     if not os.path.exists(module_init_path):

tests/hash_validation/kernels.lock

@@ -55,9 +55,9 @@
   },
   {
     "repo_id": "kernels-community/triton-scaled-mm",
-    "sha": "9baccbeb763fe5f1b8fbdb9c1e5699548c46632c",
+    "sha": "af10d8c1affe8efce93d228c3e6e64ff673d493f",
     "variants": {
-      "torch-noarch": {
+      "torch-universal": {
         "hash": "sha256-b843c5f30b52b6c1c56fca28cb0cf453be71d6ce7d308f383dce71a8050f7b52",
         "hash_type": "git_lfs_concat"
       }

tests/hash_validation/pyproject.toml

@@ -1,3 +1,3 @@
 [tool.kernels.dependencies]
 "kernels-community/activation" = ">=0.0.2"
-"kernels-community/triton-scaled-mm" = ">=0.0.1"
+"kernels-community/triton-scaled-mm" = ">=0.0.2"

tests/test_basic.py

@@ -10,7 +10,7 @@ def kernel():
 
 
 @pytest.fixture
-def noarch_kernel():
+def universal_kernel():
     return get_kernel("kernels-community/triton-scaled-mm")
 
 
@@ -36,14 +36,14 @@ def test_gelu_fast(kernel, device):
     assert torch.allclose(y, expected)
 
 
-def test_noarch_kernel(noarch_kernel):
+def test_universal_kernel(universal_kernel):
     torch.manual_seed(0)
     A = torch.randint(-10, 10, (64, 128), dtype=torch.int8, device="cuda")
     B = torch.randint(-10, 10, (128, 96), dtype=torch.int8, device="cuda")
     scale_a = torch.tensor(0.4, dtype=torch.float16, device="cuda")
     scale_b = torch.tensor(0.6, dtype=torch.float16, device="cuda")
 
-    out = noarch_kernel.triton_scaled_mm(A, B, scale_a, scale_b, torch.float16)
+    out = universal_kernel.triton_scaled_mm(A, B, scale_a, scale_b, torch.float16)
     out_check = (A * scale_a) @ (B * scale_b)
     out_check = out_check.to(torch.float16)