import torch
import torch.nn as nn
import torch.nn.functional as F
def module_fn(x: torch.Tensor, alpha: float) -> torch.Tensor:
"""
Applies ELU activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
alpha (float): The alpha parameter for the ELU function.
Returns:
torch.Tensor: Output tensor with ELU applied, same shape as input.
"""
return F.elu(x, alpha=alpha)
class Model(nn.Module):
"""
Simple model that performs an ELU activation.
"""
def __init__(self, alpha):
"""
Initializes the ELU model.
Args:
alpha (float): The alpha parameter for the ELU function.
"""
super(Model, self).__init__()
self.alpha = alpha
def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
"""
Applies ELU activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with ELU applied, same shape as input.
"""
return fn(x, self.alpha)
batch_size = 16
dim = 16384
alpha = 1.0
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [alpha]
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Simple model that performs an ELU activation.
"""
def __init__(self, alpha: float = 1.0):
"""
Initializes the ELU model.
Args:
alpha (float, optional): The alpha parameter for the ELU function. Defaults to 1.0.
"""
super(Model, self).__init__()
self.alpha = alpha
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies ELU activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with ELU applied, same shape as input.
"""
return F.elu(x, alpha=self.alpha)
batch_size = 16
dim = 16384
def get_inputs():
x = torch.randn(batch_size, dim)
return [x]
def get_init_inputs():
return [1.0] # Provide alpha value for initialization#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>
#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
__global__ void elu_kernel(const float* x, float* out, float alpha, int n) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < n) {
out[idx] = (x[idx] > 0) ? x[idx] : alpha * (expf(x[idx]) - 1);
}
}
torch::Tensor elu_cuda(torch::Tensor x, float alpha) {
CHECK_INPUT(x);
auto out = torch::empty_like(x);
int n = x.numel();
const int threads = 256;
const int blocks = (n + threads - 1) / threads;
elu_kernel<<<blocks, threads>>>(x.data_ptr<float>(), out.data_ptr<float>(), alpha, n);
return out;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("forward", &elu_cuda, "ELU activation (CUDA)");
}