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import torch
from common.diff_engine import DiffCase
import activation
class PolyNorm(torch.nn.Module):
def __init__(self, eps=1e-6, dtype: torch.dtype = torch.float32):
super().__init__()
self.weight = torch.nn.Parameter(torch.ones(3, dtype=dtype) / 3)
self.bias = torch.nn.Parameter(torch.zeros(1, dtype=dtype))
self.eps = eps
def _norm(self, x):
return x / torch.sqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
orig_dtype = x.dtype
x_float = x.to(torch.float32)
output = (self.weight[0] * self._norm(x_float**3) +
self.weight[1] * self._norm(x_float**2) +
self.weight[2] * self._norm(x_float) + self.bias)
return output.to(orig_dtype)
class Poly(DiffCase):
def build_inputs(self, bs, sl, hidden, dtype, eps):
return {
"x": torch.randn(bs, sl, hidden, dtype=dtype, requires_grad=True),
"weight": torch.ones(3, dtype=dtype),
"bias": torch.ones(1, dtype=dtype),
"dim": hidden,
"eps": eps,
"dtype": dtype,
}
def make_naive(self, I):
m = PolyNorm(I["eps"], dtype=I["dtype"])
m.weight = torch.nn.Parameter(I["weight"].detach().clone())
m.bias = torch.nn.Parameter(I["bias"].detach().clone())
return m
def make_cuda(self, I):
m = activation.layers.PolyNorm(I["eps"], dtype=I["dtype"])
m.weight = torch.nn.Parameter(I["weight"].detach().clone())
m.bias = torch.nn.Parameter(I["bias"].detach().clone())
return m
def forward(self, obj, I):
return obj(I["x"])
def grad_inputs(self, I):
return [I["x"]]
CASE = Poly()
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