import torch
import torch.nn as nn
import torch.nn.functional as F
def module_fn(
x: torch.Tensor,
multiplier: float,
negative_slope: float,
weight: torch.Tensor,
bias: torch.Tensor,
) -> torch.Tensor:
"""
Applies linear transformation, multiplies by scalar, and applies LeakyReLU.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, in_features)
multiplier (float): Scalar multiplier
negative_slope (float): Negative slope for LeakyReLU
weight (torch.Tensor): Weight matrix of shape (out_features, in_features)
bias (torch.Tensor): Bias vector of shape (out_features)
Returns:
torch.Tensor: Output tensor of shape (batch_size, out_features)
"""
x = F.linear(x, weight, bias)
x = x * multiplier
x = F.leaky_relu(x, negative_slope=negative_slope)
return x
class Model(nn.Module):
"""
Simple model that performs a Gemm, multiplies the result, and applies LeakyReLU.
"""
def __init__(self, in_features, out_features, multiplier, negative_slope):
super(Model, self).__init__()
gemm = nn.Linear(in_features, out_features)
self.weight = gemm.weight
self.bias = gemm.bias
def forward(self, x, fn=module_fn):
return fn(x, multiplier, negative_slope, self.weight, self.bias)
batch_size = 128
in_features = 1024
out_features = 512
multiplier = 2.0
negative_slope = 0.1
def get_inputs():
return [torch.randn(batch_size, in_features)]
def get_init_inputs():
return [in_features, out_features, multiplier, negative_slope]
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Simple model that performs a Gemm, multiplies the result, and applies LeakyReLU.
"""
def __init__(self, in_features, out_features, multiplier, negative_slope):
super(Model, self).__init__()
self.gemm = nn.Linear(in_features, out_features)
self.multiplier = multiplier
self.leaky_relu = nn.LeakyReLU(negative_slope)
def forward(self, x):
x = self.gemm(x)
x = x * self.multiplier
x = self.leaky_relu(x)
return x
batch_size = 128
in_features = 1024
out_features = 512
multiplier = 2.0
negative_slope = 0.1
def get_inputs():
return [torch.randn(batch_size, in_features)]
def get_init_inputs():
return [in_features, out_features, multiplier, negative_slope]#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#define BLOCK_SIZE 16
// Efficient CUDA kernel that performs GEMM using shared memory tiling with transposed weight load
// and fuses bias addition, scaling, and LeakyReLU activation.
__global__ void gemm_tiled_shared_kernel(
const float* __restrict__ x,
const float* __restrict__ weight,
const float* __restrict__ bias,
float* __restrict__ output,
const int batch_size,
const int in_features,
const int out_features,
const float multiplier,
const float negative_slope
) {
// Calculate global row and column indices
int row = blockIdx.x * BLOCK_SIZE + threadIdx.x;
int col = blockIdx.y * BLOCK_SIZE + threadIdx.y;
float sum = 0.0f;
// Shared memory tiles for x and transposed weight
__shared__ float s_x[BLOCK_SIZE][BLOCK_SIZE];
__shared__ float s_w[BLOCK_SIZE][BLOCK_SIZE];
// Number of tiles needed along the in_features dimension
const int numTiles = (in_features + BLOCK_SIZE - 1) / BLOCK_SIZE;
for (int t = 0; t < numTiles; t++) {
int tiled_index = t * BLOCK_SIZE;
// Load tile from x into shared memory
if (row < batch_size && (tiled_index + threadIdx.y) < in_features)
s_x[threadIdx.x][threadIdx.y] = x[row * in_features + tiled_index + threadIdx.y];
else
s_x[threadIdx.x][threadIdx.y] = 0.0f;
// Load tile from weight and transpose it for better coalescing
// weight is stored in row-major with each row corresponding to an output feature
if (col < out_features && (tiled_index + threadIdx.x) < in_features)
s_w[threadIdx.y][threadIdx.x] = weight[col * in_features + tiled_index + threadIdx.x];
else
s_w[threadIdx.y][threadIdx.x] = 0.0f;
__syncthreads();
// Compute partial product for this tile
#pragma unroll
for (int k = 0; k < BLOCK_SIZE; k++) {
sum += s_x[threadIdx.x][k] * s_w[threadIdx.y][k];
}
__syncthreads();
}
// Write the result back to global memory with bias addition, scaling and LeakyReLU
if (row < batch_size && col < out_features) {
sum = (sum + bias[col]) * multiplier;
output[row * out_features + col] = (sum > 0.0f) ? sum : sum * negative_slope;
}
}
// Host function to launch the kernel
torch::Tensor gemm_tiled_shared_forward(
torch::Tensor x,
float multiplier,
float negative_slope,
torch::Tensor weight,
torch::Tensor bias
) {
TORCH_CHECK(x.is_cuda(), "x must be a CUDA tensor");
TORCH_CHECK(weight.is_cuda(), "weight must be a CUDA tensor");
TORCH_CHECK(bias.is_cuda(), "bias must be a CUDA tensor");
const int batch_size = x.size(0);
const int in_features = x.size(1);
const int out_features = weight.size(0);
TORCH_CHECK(weight.size(1) == in_features, "Weight in_features must match x in_features");
TORCH_CHECK(bias.size(0) == out_features, "Bias size must match weight out_features");
auto output = torch::zeros({batch_size, out_features}, x.options());
dim3 block(BLOCK_SIZE, BLOCK_SIZE);
dim3 grid((batch_size + BLOCK_SIZE - 1) / BLOCK_SIZE,
(out_features + BLOCK_SIZE - 1) / BLOCK_SIZE);
gemm_tiled_shared_kernel<<<grid, block>>>(
x.data_ptr<float>(),
weight.data_ptr<float>(),
bias.data_ptr<float>(),
output.data_ptr<float>(),
batch_size,
in_features,
out_features,
multiplier,
negative_slope
);
return output;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("forward", &gemm_tiled_shared_forward, "Efficient GEMM with shared memory tiling, bias, scaling and LeakyReLU activation");
}
| Metric | Value | Unit | Variance | Samples |
|---|---|---|---|---|
| Executed Ipc Active | 0.414 | inst/cycle | 0.000 | 5 |
| Executed Ipc Elapsed | 0.380 | inst/cycle | 0.000 | 5 |
| Issue Slots Busy | 10.376 | % | 0.001 | 5 |
| Issued Ipc Active | 0.416 | inst/cycle | 0.000 | 5 |
| SM Busy | 10.376 | % | 0.001 | 5 |
| Memory Throughput | 33592403040.278 | byte/second | 13054593029037804.000 | 5 |
| Mem Busy | 79.820 | % | 0.071 | 5 |
| Max Bandwidth | 23.660 | % | 0.007 | 5 |
| L1/TEX Hit Rate | 61.508 | % | 0.000 | 5 |
| L2 Hit Rate | 86.864 | % | 3.760 | 5 |
| Mem Pipes Busy | 9.642 | % | 0.001 | 5 |
| Warp Cycles Per Issued Instruction | 37.036 | cycle | 0.010 | 5 |
| Warp Cycles Per Executed Instruction | 37.108 | cycle | 0.010 | 5 |
| Avg. Active Threads Per Warp | 32.000 | 0.000 | 5 | |
| Avg. Not Predicated Off Threads Per Warp | 31.960 | 0.000 | 5 | |
| Max Active Clusters | 0.000 | cluster | 0.000 | 5 |
| Max Cluster Size | 8.000 | block | 0.000 | 5 |
| Overall GPU Occupancy | 0.000 | % | 0.000 | 5 |
| Cluster Occupancy | 0.000 | % | 0.000 | 5 |
| Block Limit SM | 32.000 | block | 0.000 | 5 |
| Block Limit Registers | 8.000 | block | 0.000 | 5 |
| Block Limit Shared Mem | 21.000 | block | 0.000 | 5 |
| Block Limit Warps | 8.000 | block | 0.000 | 5 |
| Theoretical Active Warps per SM | 64.000 | warp | 0.000 | 5 |
| Theoretical Occupancy | 100.000 | % | 0.000 | 5 |
| Achieved Occupancy | 24.196 | % | 0.001 | 5 |
| Achieved Active Warps Per SM | 15.486 | warp | 0.000 | 5 |
| Rule | Description |
|---|---|
| WRN HighPipeUtilization | All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler. Check the Launch Statistics and Scheduler Statistics sections for further details. |
| INF CPIStall | Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason. |
| WRN Occupancy | This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (24.2%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. See the CUDA Best Practices Guide (https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy) for more details on optimizing occupancy. |
| Operation / Metric | Value | Unit |
|---|---|---|
| aten::to | ||
| CPU Time | 19952077.73 | μs |
| Device Time | 156.42 | μs |
| Self CPU Time | 80.55 | 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 | 19951997.17 | μs |
| Device Time | 156.42 | μs |
| Self CPU Time | 160.48 | 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 | 19951331.43 | μs |
| Device Time | 0.00 | μs |
| Self CPU Time | 160.30 | 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 | 19949343.94 | μs |
| Device Time | 0.00 | μs |
| Self CPU Time | 19949343.94 | 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::zero_ | ||
| CPU Time | 7173047.69 | μs |
| Device Time | 4722504.95 | μs |
| Self CPU Time | 196056.35 | 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 | 6976993.12 | μs |
| Device Time | 4722504.95 | μs |
| Self CPU Time | 243667.53 | μs |
| Self Device Time | 4722427.67 | 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 | 7003245.69 | μs |
| Device Time | 205754.91 | μs |
| Self CPU Time | 7003245.69 | μs |
| Self Device Time | 205754.91 | μs |
| CPU Memory Usage | 0 | B |
| Device Memory Usage | 0 | B |
| Self CPU Memory Usage | 0 | B |
| Self Device Memory Usage | 0 | B |
| gemm_tiled_shared_kernel(float const*, float const*, float const*, float*, int, int, int, float, float) | ||
| CPU Time | 0.00 | μs |
| Device Time | 3848874.33 | μs |
| Self CPU Time | 0.00 | μs |
| Self Device Time | 3848874.33 | 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 | 4638400.17 | μs |
| Self CPU Time | 0.00 | μs |
| Self Device Time | 4638400.17 | μs |
| CPU Memory Usage | 0 | B |
| Device Memory Usage | 0 | B |
| Self CPU Memory Usage | 0 | B |
| Self Device Memory Usage | 0 | B |
45288 warnings generated when compiling for host. Suppressed 45325 warnings (45278 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.