[PERFORMANCE OPTIMIZATION] add dot test script

README.md

@@ -21,7 +21,7 @@ The following common BLAS kernels have been implemented in multiple frameworks.
 | [swap](./docs/swap.md) | swap vectors | $x \leftrightarrow y$ | $0$ | $4n$ | [✅](./kernel_course/python_ops/swap.py) | [✅](./kernel_course/pytorch_ops/swap.py) | [✅](./kernel_course/triton_ops/swap.py) | ❌ | [✅](./tests/test_swap.py) |
 | [scal](./docs/scal.md) | scale vector | $y = \alpha y$ | $n$ | $2n$ | [✅](./kernel_course/python_ops/scal.py) | [✅](./kernel_course/pytorch_ops/scal.py) | [✅](./kernel_course/triton_ops/scal.py) | ❌ | [✅](./tests/test_scal.py) |
 | [axpby](./docs/axpby.md) | update vector| $y = \alpha x + \beta y$ | $3n$ | $3n$ | [✅](./kernel_course/python_ops/axpby.py) | [✅](./kernel_course/pytorch_ops/axpby.py) | [✅](./kernel_course/triton_ops/axpby.py) | ❌ | [✅](./tests/test_axpby.py) |
-| [dot](./docs/dot.md) | dot product | $z = x^\top y$ | $2n$ | $2n$ | [✅](./kernel_course/python_ops/dot.py) | [✅](./kernel_course/pytorch_ops/dot.py) | [✅](./kernel_course/triton_ops/dot.py) | ❌ | ❌ |
+| [dot](./docs/dot.md) | dot product | $z = x^\top y$ | $2n$ | $2n$ | [✅](./kernel_course/python_ops/dot.py) | [✅](./kernel_course/pytorch_ops/dot.py) | [✅](./kernel_course/triton_ops/dot.py) | ❌ | [✅](./tests/test_dot.py) |
 | gemv | general matrix-vector multiply | $y = \alpha A x + \beta y$ | $2mn$ | $mn + n + 2m$ | ❌ | ❌ | ❌ | ❌ | ❌ |
 | geru | general rank-1 update | $A = A + \alpha x y^\top$ | $2mn$ | $2mn + m + n$ | ❌ | ❌ | ❌ | ❌ | ❌ |
 | gemm | general matrix-matrix multiply | $C = \alpha A B + \beta C$ | $2mnk$ | $mk + nk + 2mn$ | ❌ | ❌ | ❌ | ❌ | ❌ |

tests/test_dot.py

@@ -0,0 +1,84 @@
+import pytest
+import torch
+
+from kernel_course import testing
+from kernel_course.python_ops import dot as python_dot
+
+try:
+    from kernel_course.pytorch_ops import dot as pytorch_dot
+
+    HAS_PYTORCH = True
+except Exception:
+    pytorch_dot = None
+    HAS_PYTORCH = False
+
+try:
+    from kernel_course.triton_ops import dot as triton_dot
+
+    HAS_TRITON = True
+except Exception:
+    triton_dot = None
+    HAS_TRITON = False
+
+try:
+    from kernel_course.cute_ops import dot as cute_dot
+
+    HAS_CUTE = True
+except Exception:
+    cute_dot = None
+    HAS_CUTE = False
+
+
+def factory(
+    numel: int,
+    device: torch.device,
+    dtype: torch.dtype = torch.float32,
+):
+    x = torch.linspace(0.0, 1.0, steps=numel, device=device, dtype=dtype)
+    y = torch.linspace(0.0, 1.0, steps=numel, device=device, dtype=dtype)
+    return (x, y), {}
+
+
+@pytest.mark.parametrize(
+    "device",
+    [
+        pytest.param(
+            torch.device("cuda"),
+            marks=pytest.mark.skipif(
+                not torch.cuda.is_available(), reason="requires CUDA"
+            ),
+        ),
+        pytest.param(
+            torch.device("mps"),
+            marks=pytest.mark.skipif(
+                not torch.backends.mps.is_available(), reason="requires MPS"
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "dtype",
+    [torch.float32, torch.float16, torch.bfloat16],
+)
+@pytest.mark.parametrize(
+    "numel",
+    [1 << 4, 1 << 8, 1 << 16],
+)
+def test_dot_benchmark(device: torch.device, dtype: torch.dtype, numel: int) -> None:
+    impls = testing.get_impls(
+        python_impl=python_dot.dot,
+        pytorch_impl=pytorch_dot.dot if HAS_PYTORCH else None,
+        triton_impl=triton_dot.dot if HAS_TRITON else None,
+        cute_impl=cute_dot.dot if HAS_CUTE else None,
+    )
+
+    # Benchmark each implementation
+    config = testing.BenchmarkConfig(warmup=3, repeat=1_000)
+    results = testing.run_benchmarks(
+        impls,
+        lambda: factory(numel, device, dtype),
+        flops=2 * numel,
+        config=config,
+    )
+
+    testing.show_benchmarks(results)