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)
// Vectorized kernel with shared memory for large chunks
__global__ void elu_kernel_vec4_shared(const float4* x, float4* out, float alpha, int n4) {
extern __shared__ float4 tile[];
int tid = threadIdx.x;
int globalIdx = blockIdx.x * blockDim.x + tid;
// Load input data using vectorized reads
if (globalIdx < n4) {
tile[tid] = x[globalIdx];
}
__syncthreads();
if (globalIdx < n4) {
float4 val = tile[tid];
float4 result;
result.x = (val.x > 0) ? val.x : alpha * (expf(val.x) - 1);
result.y = (val.y > 0) ? val.y : alpha * (expf(val.y) - 1);
result.z = (val.z > 0) ? val.z : alpha * (expf(val.z) - 1);
result.w = (val.w > 0) ? val.w : alpha * (expf(val.w) - 1);
out[globalIdx] = result;
}
}
// Regular kernel for remaining elements
__global__ void elu_kernel_remainder(const float* x, float* out, float alpha, int start, int n) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx + start < n) {
float val = x[idx + start];
out[idx + start] = (val > 0) ? val : alpha * (expf(val) - 1);
}
}
torch::Tensor elu_cuda_hybrid(torch::Tensor x, float alpha) {
CHECK_INPUT(x);
auto out = torch::empty_like(x);
int n = x.numel();
int n4 = n / 4; // Number of float4 elements
int remainder = n % 4; // Remaining elements
const int threads = 256;
const int blocks = (n4 + threads - 1) / threads;
// Process main part using vectorized loads and shared memory
if (n4 > 0) {
size_t sharedMemSize = threads * sizeof(float4);
elu_kernel_vec4_shared<<<blocks, threads, sharedMemSize>>>(
reinterpret_cast<const float4*>(x.data_ptr<float>()),
reinterpret_cast<float4*>(out.data_ptr<float>()),
alpha,
n4
);
}
// Process remaining elements
if (remainder > 0) {
const int remainder_blocks = (remainder + threads - 1) / threads;
elu_kernel_remainder<<<remainder_blocks, threads>>>(
x.data_ptr<float>(),
out.data_ptr<float>(),
alpha,
n4 * 4,
n
);
}
return out;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("forward", &elu_cuda_hybrid, "Hybrid ELU activation (CUDA)");
}| Metric | Value | Unit | Variance | Samples |
|---|
| Rule | Description |
|---|
| Operation / Metric | Value | Unit |
|---|---|---|
| aten::to | ||
| CPU Time | 465508.37 | μs |
| Device Time | 39.97 | μs |
| Self CPU Time | 33.03 | μs |
| Self Device Time | 0.00 | μs |
| CPU Memory Usage | 0 | B |
| Device Memory Usage | 0 | B |
| Self CPU Memory Usage | 0 | B |
| Self Device Memory Usage | 0 | B |
| aten::_to_copy | ||
| CPU Time | 465475.34 | μs |
| Device Time | 39.97 | μs |
| Self CPU Time | 87.53 | μs |
| Self Device Time | 0.00 | μs |
| CPU Memory Usage | 0 | B |
| Device Memory Usage | 0 | B |
| Self CPU Memory Usage | 0 | B |
| Self Device Memory Usage | 0 | B |
| aten::empty_strided | ||
| CPU Time | 477143.18 | μs |
| Device Time | 0.00 | μs |
| Self CPU Time | 12121.18 | μs |
| Self Device Time | 0.00 | μs |
| CPU Memory Usage | 0 | B |
| Device Memory Usage | 0 | B |
| Self CPU Memory Usage | 0 | B |
| Self Device Memory Usage | 0 | B |
| cudaDeviceGetStreamPriorityRange | ||
| CPU Time | 464828.38 | μs |
| Device Time | 0.00 | μs |
| Self CPU Time | 464828.38 | μs |
| Self Device Time | 0.00 | μs |
| CPU Memory Usage | 0 | B |
| Device Memory Usage | 0 | B |
| Self CPU Memory Usage | 0 | B |
| Self Device Memory Usage | 0 | B |
| cudaLaunchKernel | ||
| CPU Time | 333666.85 | μs |
| Device Time | 15032.45 | μs |
| Self CPU Time | 333666.85 | μs |
| Self Device Time | 15032.45 | μs |
| CPU Memory Usage | 0 | B |
| Device Memory Usage | 0 | B |
| Self CPU Memory Usage | 0 | B |
| Self Device Memory Usage | 0 | B |
| elu_kernel_vec4_shared(float4 const*, float4*, float, int) | ||
| CPU Time | 0.00 | μs |
| Device Time | 20934.33 | μs |
| Self CPU Time | 0.00 | μs |
| Self Device Time | 20934.33 | μs |
| CPU Memory Usage | 0 | B |
| Device Memory Usage | 0 | B |
| Self CPU Memory Usage | 0 | B |
| Self Device Memory Usage | 0 | B |
| cudaEventRecord | ||
| CPU Time | 15081.17 | μs |
| Device Time | 28951.38 | μs |
| Self CPU Time | 15081.17 | μs |
| Self Device Time | 28951.38 | μs |
| CPU Memory Usage | 0 | B |
| Device Memory Usage | 0 | B |
| Self CPU Memory Usage | 0 | B |
| Self Device Memory Usage | 0 | B |
| aten::zero_ | ||
| CPU Time | 49456.48 | μs |
| Device Time | 431566.27 | μs |
| Self CPU Time | 8344.39 | μs |
| Self Device Time | 0.00 | μs |
| CPU Memory Usage | 0 | B |
| Device Memory Usage | 0 | B |
| Self CPU Memory Usage | 0 | B |
| Self Device Memory Usage | 0 | B |
| aten::fill_ | ||
| CPU Time | 41114.12 | μs |
| Device Time | 431566.27 | μs |
| Self CPU Time | 12079.31 | μs |
| Self Device Time | 431566.27 | μs |
| CPU Memory Usage | 0 | B |
| Device Memory Usage | 0 | B |
| Self CPU Memory Usage | 0 | B |
| Self Device Memory Usage | 0 | B |
| void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor<int>, at::detail::Array<char*, 1> >(int, at::native::FillFunctor<int>, at::detail::Array<char*, 1>) | ||
| CPU Time | 0.00 | μs |
| Device Time | 431566.27 | μs |
| Self CPU Time | 0.00 | μs |
| Self Device Time | 431566.27 | μs |
| CPU Memory Usage | 0 | B |
| Device Memory Usage | 0 | B |
| Self CPU Memory Usage | 0 | B |
| Self Device Memory Usage | 0 | B |
45283 warnings generated when compiling for host. Suppressed 45322 warnings (45275 in non-user code, 47 NOLINT). Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.