feat: add a benchmark script (via web ui)

benchmark.py

@@ -0,0 +1,42 @@
+# /// script
+# dependencies = [
+#   "numpy",
+#   "torch",
+#   "kernels"
+# ]
+# ///
+import torch
+from kernels import get_kernel
+
+# Setup
+torch.manual_seed(42)
+activation = get_kernel("kernels-community/activation")
+device = torch.device("cuda")
+
+# Create test tensor for SwiGLU (SiLU and Mul)
+# Input has shape (num_tokens, 2 * hidden_dim)
+num_tokens, hidden_dim = 128, 512
+input_tensor = torch.randn(
+    num_tokens, 2 * hidden_dim, device=device, dtype=torch.float16
+)
+
+
+# Reference implementation: SwiGLU computes silu(x[:d]) * x[d:]
+def reference_silu_and_mul(x):
+    d = x.shape[-1] // 2
+    return torch.nn.functional.silu(x[..., :d]) * x[..., d:]
+
+
+out_ref = reference_silu_and_mul(input_tensor)
+
+# Custom kernel SwiGLU
+out_shape = input_tensor.shape[:-1] + (hidden_dim,)
+out_kernel = torch.empty(out_shape, dtype=input_tensor.dtype, device=device)
+out_kernel = activation.silu_and_mul(out_kernel, input_tensor)
+
+print(f"Reference output: {out_ref.shape}")
+print(f"Kernel output: {out_kernel.shape}")
+print(f"Outputs close: {torch.allclose(out_kernel, out_ref, atol=1e-3, rtol=1e-3)}")
+# Reference output: torch.Size([128, 512])
+# Kernel output: torch.Size([128, 512])
+# Outputs close: True