Bump minimum PyTorch to 2.3

.github/workflows/tests.yml

@@ -102,7 +102,7 @@ jobs:
       matrix:
         os: [ubuntu-22.04, ubuntu-22.04-arm, windows-2025, macos-15]
         # Test with the oldest supported torch version, the newest two stable/RC.
-        torch_version: ["2.2.2", "2.7.1", "2.8.0"]
+        torch_version: ["2.3.1", "2.7.1", "2.8.0"]
         include:
           - os: ubuntu-22.04
             arch: x86_64
@@ -118,7 +118,7 @@ jobs:
             arch: arm64
         exclude:
           - os: ubuntu-22.04-arm
-            torch_version: "2.2.2"
+            torch_version: "2.3.1"
 
     runs-on: ${{ matrix.runner || matrix.os }}
     env:
@@ -144,13 +144,14 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip install torch==${{ matrix.torch_version }} --index-url https://download.pytorch.org/whl/${{ (matrix.torch_version == '2.8.0' && 'test/cpu') || 'cpu' }}
+          pip install torch==${{ matrix.torch_version }} --index-url https://download.pytorch.org/whl/cpu
           pip install -e ".[test]"
           pip install pytest-cov
 
-      # We need to downgrade to numpy<2 for torch<2.3 compatibility.
+      # We need to downgrade to numpy<2 for torch<2.4.1 compatibility on Windows
+      # See: https://github.com/pytorch/pytorch/issues/131668
       - name: Downgrade NumPy
-        if: startsWith(matrix.torch_version, '2.2.')
+        if: startsWith(matrix.os, 'windows') && startsWith(matrix.torch_version, '2.3.')
         run: pip install "numpy<2"
 
       - name: Show installed packages
@@ -345,7 +346,7 @@ jobs:
         cuda_version: ["11.8.0", "12.6.3", "12.8.1", "12.9.1"]
         include:
           - cuda_version: "11.8.0"
-            torch_version: "2.2.2"
+            torch_version: "2.3.1"
             pypi_index: "https://download.pytorch.org/whl/cu118"
           - cuda_version: "12.6.3"
             torch_version: "2.6.0"
@@ -374,7 +375,7 @@ jobs:
             gpu: T4
             runner: CUDA-Windows-x64
             cuda_version: "11.8.0"
-            torch_version: "2.2.0"
+            torch_version: "2.3.1"
             pypi_index: "https://download.pytorch.org/whl/cu118"
           - os: windows-2025
             arch: x86_64
@@ -430,12 +431,6 @@ jobs:
           pip install --pre torch~=${{ matrix.torch_version }}.dev0 --index-url ${{ matrix.pypi_index }}
           pip install -e ".[test]"
           pip install pytest-cov
-
-        # We need to downgrade to numpy<2 for torch<2.3 compatibility.
-      - name: Downgrade NumPy
-        if: startsWith(matrix.torch_version, '2.2.')
-        run: pip install "numpy<2"
-
       - name: Show installed packages
         run: pip list
 

README.md

@@ -20,7 +20,7 @@ The library includes quantization primitives for 8-bit & 4-bit operations, throu
 bitsandbytes has the following minimum requirements for all platforms:
 
 * Python 3.9+
-* [PyTorch](https://pytorch.org/get-started/locally/) 2.2+
+* [PyTorch](https://pytorch.org/get-started/locally/) 2.3+
   * _Note: While we aim to provide wide backwards compatibility, we recommend using the latest version of PyTorch for the best experience._
 
 #### Accelerator support:

bitsandbytes/autograd/_functions.py

@@ -84,11 +84,7 @@ def get_inverse_transform_indices(
     return permuted_tile_indices
 
 
-# torch.compiler.is_compiling() is available only in torch >= 2.3
-if hasattr(torch.compiler, "is_compiling"):
-    _is_compiling = torch.compiler.is_compiling
-else:
-    _is_compiling = torch._dynamo.is_compiling
+_is_compiling = torch.compiler.is_compiling
 
 
 @deprecated(

bitsandbytes/triton/triton_utils.py

@@ -4,11 +4,8 @@
 @functools.lru_cache(None)
 def is_triton_available():
     try:
-        # torch>=2.2.0
         from torch.utils._triton import has_triton, has_triton_package
 
         return has_triton_package() and has_triton()
-    except ImportError:
-        from torch._inductor.utils import has_triton
-
-        return has_triton()
+    except Exception:
+        return False

pyproject.toml

@@ -42,7 +42,7 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Artificial Intelligence"
 ]
 dependencies = [
-    "torch>=2.2,<3",
+    "torch>=2.3,<3",
     "numpy>=1.17",
     "packaging>=20.9"
 ]

tests/test_functional.py

@@ -1413,9 +1413,6 @@ def test_gemv_4bit(self, device, dim, dtype, storage_type, quant_storage, double
         reason="this test is not supported on ROCm with gfx90a architecture yet",
     )
     def test_gemv_eye_4bit(self, device, storage_type, dtype):
-        if device == "cpu" and dtype == torch.bfloat16 and torch.__version__ < (2, 3):
-            pytest.skip("eye doe not support bfloat16 on CPU in torch < 2.3")
-
         if device == "hpu" and not is_supported_on_hpu(storage_type, dtype):
             pytest.skip("This configuration is not supported on HPU.")