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import torch
from common.diff_engine import DiffCase
import activation
class RMS(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(hidden, dtype=dtype),
"dim": hidden,
"eps": eps,
"dtype": dtype,
}
def make_naive(self, I):
m = torch.nn.RMSNorm(I["dim"], I["eps"], dtype=I["dtype"])
m.weight = torch.nn.Parameter(I["weight"].detach().clone())
return m
def make_cuda(self, I):
m = activation.layers.RMSNorm(I["dim"], I["eps"], dtype=I["dtype"])
m.weight = torch.nn.Parameter(I["weight"].detach().clone())
return m
def forward(self, obj, I):
return obj(I["x"])
def grad_inputs(self, I):
return [I["x"]]
CASE = RMS()
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