You write custom CUDA kernels to replace the pytorch operators in the given architecture to get speedups.   
  
You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining matmul+relu), or algorithmic changes (such as online softmax). You are only limited by your imagination.  
  
Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:   
  
```python
# rope_torch.py
import torch
import torch.nn as nn
import math

BATCH_SIZE = 16
SEQ_LEN = 256
HEAD_DIM = 128 
TOTAL_DIM = SEQ_LEN * HEAD_DIM

ROPE_THETA = 10000.0

def precompute_freqs(dim: int, theta: float = ROPE_THETA):
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
    t = torch.arange(SEQ_LEN)
    freqs = torch.outer(t, freqs).float()
    
    cos = torch.cos(freqs).to(torch.float32)
    sin = torch.sin(freqs).to(torch.float32)
    return cos.cuda(), sin.cuda()


def rotate_half(x: torch.Tensor) -> torch.Tensor:
    x1, x2 = x.chunk(2, dim=-1)
    return torch.cat((-x2, x1), dim=-1)


class Model(nn.Module):

    def __init__(self, cos_cached, sin_cached):
        super().__init__()
        self.register_buffer('cos', cos_cached.unsqueeze(0))
        self.register_buffer('sin', sin_cached.unsqueeze(0))
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        B_times_L, D = x.shape
        B = BATCH_SIZE
        L = SEQ_LEN

        x_reshaped = x.view(B, L, D) 

        x_pass, x_rot = x_reshaped.chunk(2, dim=-1)
        
        x_rot_half = rotate_half(x_rot)

        x_rot_comp = x_rot * self.cos
        x_rot_half_comp = x_rot_half * self.sin
        
        x_rot_result = x_rot_comp + x_rot_half_comp
        
        output = torch.cat((x_pass, x_rot_result), dim=-1)
        return output.view(B_times_L, D)


def get_inputs():
    x = torch.randn(BATCH_SIZE * SEQ_LEN, HEAD_DIM, dtype=torch.float32)
    return [x]

def get_init_inputs():
    cos, sin = precompute_freqs(HEAD_DIM, ROPE_THETA)
    return [cos, sin]