feat: add flashinfer as 3rdparty and use rmsnorm as example (#3033)

sgl-kernel/tests/test_rmsnorm.py

@@ -0,0 +1,31 @@
+import pytest
+import torch
+from sgl_kernel import rmsnorm
+
+
+def llama_rms_norm(x, w, eps=1e-6):
+    orig_dtype = x.dtype
+    x = x.float()
+    variance = x.pow(2).mean(dim=-1, keepdim=True)
+    x = x * torch.rsqrt(variance + eps)
+    x = x * w.float()
+    x = x.to(orig_dtype)
+    return x
+
+
+@pytest.mark.parametrize("batch_size", [1, 19, 99, 989])
+@pytest.mark.parametrize("hidden_size", [111, 500, 1024, 3072, 3584, 4096, 8192, 16384])
+@pytest.mark.parametrize("dtype", [torch.float16])
+@pytest.mark.parametrize("specify_out", [True, False])
+def test_norm(batch_size, hidden_size, dtype, specify_out):
+    x = torch.randn(batch_size, hidden_size).to(0).to(dtype)
+    w = torch.randn(hidden_size).to(0).to(dtype)
+
+    y_ref = llama_rms_norm(x, w)
+    if specify_out:
+        y = torch.empty_like(x)
+        rmsnorm(x, w, out=y)
+    else:
+        y = rmsnorm(x, w)
+
+    torch.testing.assert_close(y_ref, y, rtol=1e-3, atol=1e-3)