chore(poly-norm): remove unnecessary file

tests/test.py

@@ -1,38 +0,0 @@
-import activation
-import torch
-
-
-def norm(x, eps: float) -> torch.Tensor:
-    return x / torch.sqrt(x.pow(2).mean(-1, keepdim=True) + eps)
-
-
-def poly_norm(
-    x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, eps: float
-) -> torch.Tensor:
-    x = x.float()
-    return (
-        weight[0] * norm(x**3, eps)
-        + weight[1] * norm(x**2, eps)
-        + weight[2] * norm(x, eps)
-        + bias
-    ).to(weight.dtype)
-
-
-dtype = torch.bfloat16
-torch.set_default_device("cuda:0")
-a = torch.randn(3, 3, dtype=dtype, requires_grad=True)
-w = torch.randn(3, dtype=dtype, requires_grad=True)
-b = torch.randn(1, dtype=dtype, requires_grad=True)
-
-a.retain_grad()
-w.retain_grad()
-b.retain_grad()
-
-out = activation.poly_norm(a, w, b, 1e-6)
-# out = poly_norm(a, w, b, 1e-6)
-
-out.backward(torch.ones_like(out))
-
-print(a.grad)
-print(w.grad)
-print(b.grad)