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21_Sigmoid • syncthreads_minimal_sigmoid_base

Level 1 • Task 21
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(x: torch.Tensor) -> torch.Tensor:
    """
    Applies Sigmoid activation to the input tensor.

    Args:
        x (torch.Tensor): Input tensor of any shape.

    Returns:
        torch.Tensor: Output tensor with Sigmoid applied, same shape as input.
    """
    return torch.sigmoid(x)


class Model(nn.Module):
    """
    Simple model that performs a Sigmoid activation.
    """

    def __init__(self):
        super(Model, self).__init__()

    def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
        return fn(x)


batch_size = 16
dim = 16384


def get_inputs():
    x = torch.randn(batch_size, dim)
    return [x]


def get_init_inputs():
    return []  # No special initialization inputs needed

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a Sigmoid activation.
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies Sigmoid activation to the input tensor.

        Args:
            x (torch.Tensor): Input tensor of any shape.

        Returns:
            torch.Tensor: Output tensor with Sigmoid applied, same shape as input.
        """
        return torch.sigmoid(x)

batch_size = 16
dim = 16384

def get_inputs():
    x = torch.randn(batch_size, dim)
    return [x]

def get_init_inputs():
    return []  # No special initialization inputs needed
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 21_Sigmoid
Level ID 1
Task ID 21
Kernel Name syncthreads_minimal_sigmoid_base
CUDA Speedup (Native) 1.109x
CUDA Speedup (Compile) 4.825x
CUDA Runtime 0.006 ms
PyTorch Runtime (Native) 0.007 ms
PyTorch Runtime (Compile) 0.029 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model azure-gpt-4o-2024-08-06
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 1, Task 21 • 21_Sigmoid)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 sigmoid_shared_mem_optimized_base 0.01 1.11 4.82
🥇 21_sigmoid_modular_device_base 0.01 1.11 4.82
🥇 sigmoid_unroll_optimized_base_base 0.01 1.11 4.82
🥇 sigmoid_min_sync_base_base 0.01 1.11 4.82
🥇 optimized_sigmoid_cuda_base 0.01 1.11 4.82
🥇 optimized_sigmoid_limited_sync_base 0.01 1.11 4.82
🥇 sigmoid_ldg_vectorized_base 0.01 1.11 4.82
🥇 optimized_sigmoid_cuda_base 0.01 1.11 4.82
🥇 optimized_sigmoid_vectorized_combined_edit_1 0.01 1.11 4.82
🥇 21_Sigmoid_optimized_memory_base 0.01 1.11 4.82
🥇 sigmoid_minimal_sync_base_base 0.01 1.11 4.82
🥇 vectorized_ldg_aligned_edit_1 0.01 1.11 4.82
🥇 nondivergent_vectorized_sigmoid_base 0.01 1.11 4.82
🥇 vectorized_no_sync_base 0.01 1.11 4.82
🥇 vectorized_sigmoid_base 0.01 1.11 4.82
🥇 syncthreads_minimal_sigmoid_base 0.01 1.11 4.82
🥇 vectorized_ldg_aligned_base 0.01 1.11 4.82
🥇 optimized_sigmoid_vectorized_combined_base 0.01 1.11 4.82
🥇 optimized_sigmoid_blocksize_tuning_edit_1 0.01 1.11 4.82
🥇 optimized_sigmoid_blocksize_tuning_base 0.01 1.11 4.82 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

__global__ void sigmoid_kernel_syncthreads(const float* __restrict__ input,
                                            float* __restrict__ output,
                                            const int64_t size) {
    extern __shared__ float shared_data[];
    const int tid = threadIdx.x;
    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
    const int stride = blockDim.x * gridDim.x;

    // Load data to shared memory if within bounds
    if (idx < size) {
        shared_data[tid] = input[idx];
    }
    
    // Synchronize to ensure all data is loaded to shared memory
    __syncthreads();

    // Perform sigmoid only if within bound
    if (idx < size) {
        float val = shared_data[tid];
        shared_data[tid] = 1.0f / (1.0f + expf(-val));
    }

    // Synchronize to ensure all threads have completed computation
    __syncthreads();

    // Write results back to global memory
    if (idx < size) {
        output[idx] = shared_data[tid];
    }
}



torch::Tensor forward(torch::Tensor input) {
    auto output = torch::empty_like(input);
    const int64_t size = input.numel();

    const int threads = 256;
    const int blocks = (size + threads - 1) / threads;
    const int shared_mem_size = threads * sizeof(float);

    sigmoid_kernel_syncthreads<<<blocks, threads, shared_mem_size>>>( 
        input.data_ptr<float>(),
        output.data_ptr<float>(),
        size
    );

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Sigmoid forward (CUDA) with minimal __syncthreads usage");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.806 inst/cycle 0.010 5
Executed Ipc Elapsed 0.350 inst/cycle 0.000 5
Issue Slots Busy 22.370 % 7.535 5
Issued Ipc Active 0.892 inst/cycle 0.012 5
SM Busy 22.370 % 7.535 5
Memory Throughput 274728135593.220 byte/second 3445742717607329280.000 5
Mem Busy 13.022 % 0.010 5
Max Bandwidth 12.094 % 0.003 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 66.710 % 0.197 5
Mem Pipes Busy 14.160 % 0.005 5
Warp Cycles Per Issued Instruction 57.506 cycle 12.153 5
Warp Cycles Per Executed Instruction 63.786 cycle 14.959 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 31.060 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 16.000 block 0.000 5
Block Limit Shared Mem 16.000 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 81.944 % 13.058 5
Achieved Active Warps Per SM 52.444 warp 5.356 5
Analysis Rules
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.
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 (84.4%) 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.
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.
Operation / Metric Value Unit
aten::to
CPU Time 416204.03 μs
Device Time 40.03 μs
Self CPU Time 50.71 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 416153.32 μs
Device Time 40.03 μs
Self CPU Time 116.54 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 434398.89 μs
Device Time 0.00 μs
Self CPU Time 18744.92 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 415433.00 μs
Device Time 0.00 μs
Self CPU Time 415433.00 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 455546.24 μs
Device Time 20750.95 μs
Self CPU Time 455546.24 μs
Self Device Time 20750.95 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
sigmoid_kernel_syncthreads(float const*, float*, long)
CPU Time 0.00 μs
Device Time 24477.06 μs
Self CPU Time 0.00 μs
Self Device Time 24477.06 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 17331.96 μs
Device Time 40084.94 μs
Self CPU Time 17331.96 μs
Self Device Time 40084.94 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 62237.19 μs
Device Time 594117.07 μs
Self CPU Time 11623.66 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 50618.83 μs
Device Time 594117.07 μs
Self CPU Time 14991.77 μs
Self Device Time 594117.07 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 594194.74 μs
Self CPU Time 0.00 μs
Self Device Time 594194.74 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Completed 
45280 warnings generated when compiling for host.
Suppressed 45321 warnings (45274 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.
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s0_syncthreads_minimal_sigmoid/base/base.cu:9:21 bugprone-narrowing-conversions
9 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s0_syncthreads_minimal_sigmoid/base/base.cu:10:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
10 | const int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s0_syncthreads_minimal_sigmoid/base/base.cu:11:15: warning: Value stored to 'stride' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
11 | const int stride = blockDim.x * gridDim.x;
| ^~~~~~ ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s0_syncthreads_minimal_sigmoid/base/base.cu:11:15: note: Value stored to 'stride' during its initialization is never read
11 | const int stride = blockDim.x * gridDim.x;
| ^~~~~~ ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s0_syncthreads_minimal_sigmoid/base/base.cu:11:24: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
11 | const int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s0_syncthreads_minimal_sigmoid/base/base.cu:38:37: warning: the parameter 'input' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
38 | torch::Tensor forward(torch::Tensor input) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s0_syncthreads_minimal_sigmoid/base/base.cu:43:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
43 | const int blocks = (size + threads - 1) / threads;
| ^