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import torch
from common.diff_engine import DiffCase
import activation
class FusedMulPolyNorm(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 forward(self, x, mul):
output = activation.poly_norm(x, self.weight, self.bias, self.eps)
return output * mul
class MulPoly(DiffCase):
def build_inputs(self, bs, sl, hidden, dtype, eps):
return {
"x": torch.randn(bs, sl, hidden, dtype=dtype, requires_grad=True),
"mul": 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 = FusedMulPolyNorm(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.FusedMulPolyNorm(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"], I["mul"])
def grad_inputs(self, I):
return [I["x"], I["mul"]]
CASE = MulPoly()
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