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21_Sigmoid • vectorized_ldg_aligned_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 vectorized_ldg_aligned_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 openrouter-deepseek-reasoner
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>

template <typename scalar_t>
__global__ void sigmoid_kernel(const scalar_t* __restrict__ input,
                              scalar_t* __restrict__ output,
                              int64_t size) {
    constexpr int vec_size = sizeof(float4)/sizeof(scalar_t);
    int gidx = blockIdx.x * blockDim.x + threadIdx.x;
    int stride = gridDim.x * blockDim.x;
    
    if constexpr (std::is_same<scalar_t, float>::value) {
        // Vectorized path for floats with 128-bit alignment
        float4* output_vec = reinterpret_cast<float4*>(output);
        for(int vidx = gidx; vidx*vec_size < size; vidx += stride) {
            const float4* data = reinterpret_cast<const float4*>(input) + vidx;
            float4 val = __ldg(data);  // Cache optimized load
            val.x = 1.0f/(1.0f + expf(-val.x));
            val.y = 1.0f/(1.0f + expf(-val.y));
            val.z = 1.0f/(1.0f + expf(-val.z));
            val.w = 1.0f/(1.0f + expf(-val.w));
            output_vec[vidx] = val;
        }
    } else {
        // Scalar path for other types
        for(int i = gidx; i < size; i += stride) {
            scalar_t val = __ldg(input + i);
            output[i] = 1.0/(1.0 + exp(-val));
        }
    }
}

torch::Tensor forward(torch::Tensor input) {
    auto output = torch::empty_like(input);
    int64_t size = input.numel();
    
    const int threads = 256;
    int blocks = std::min(65535, (int)((size + threads * 4 - 1)/(threads * 4)));

    AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "sigmoid_kernel", [&] {
        if (std::is_same<scalar_t, float>::value) 
            blocks = (size + (threads * 4) - 1) / (threads * 4);
        else 
            blocks = (size + threads - 1)/threads;
        
        sigmoid_kernel<scalar_t><<<blocks, threads>>>(input.data_ptr<scalar_t>(),
                                                 output.data_ptr<scalar_t>(),
                                                 size);
    });
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Vectorized Sigmoid using LDG");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.286 inst/cycle 0.000 5
Executed Ipc Elapsed 0.116 inst/cycle 0.000 5
Issue Slots Busy 7.938 % 0.085 5
Issued Ipc Active 0.318 inst/cycle 0.000 5
SM Busy 10.584 % 0.152 5
Memory Throughput 283027574992.428 byte/second 52703688428545564672.000 5
Mem Busy 13.432 % 0.125 5
Max Bandwidth 12.390 % 0.122 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 67.336 % 0.003 5
Mem Pipes Busy 3.326 % 0.008 5
Warp Cycles Per Issued Instruction 42.842 cycle 0.615 5
Warp Cycles Per Executed Instruction 47.824 cycle 0.768 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 30.510 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 32.000 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 21.622 % 0.030 5
Achieved Active Warps Per SM 13.838 warp 0.013 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.
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 (21.8%) 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 482101.90 μs
Device Time 40.32 μs
Self CPU Time 47.99 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 482053.90 μs
Device Time 40.32 μs
Self CPU Time 98.25 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 501803.96 μs
Device Time 0.00 μs
Self CPU Time 20233.29 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 481254.43 μs
Device Time 0.00 μs
Self CPU Time 481254.43 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 491620.47 μs
Device Time 22807.61 μs
Self CPU Time 491620.47 μs
Self Device Time 22807.61 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void sigmoid_kernel<float>(float const*, float*, long)
CPU Time 0.00 μs
Device Time 31729.12 μs
Self CPU Time 0.00 μs
Self Device Time 31729.12 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 20164.58 μs
Device Time 43859.34 μs
Self CPU Time 20164.58 μs
Self Device Time 43859.34 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 67576.37 μs
Device Time 650800.15 μs
Self CPU Time 14531.44 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 53046.52 μs
Device Time 650800.15 μs
Self CPU Time 17572.02 μs
Self Device Time 650800.15 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 650800.15 μs
Self CPU Time 0.00 μs
Self Device Time 650800.15 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 
45288 warnings generated when compiling for host.
Suppressed 45323 warnings (45276 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_s3_vectorized_ldg_aligned/base/base.cu:10:16 bugprone-narrowing-conversions
10 | int gidx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:11:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
11 | int stride = gridDim.x * blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:16:30: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
16 | for(int vidx = gidx; vidx*vec_size < size; vidx += stride) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:16:30: note: make conversion explicit to silence this warning
4 |
5 | template <typename scalar_t>
6 | __global__ void sigmoid_kernel(const scalar_t* __restrict__ input,
7 | scalar_t* __restrict__ output,
8 | int64_t size) {
9 | constexpr int vec_size = sizeof(float4)/sizeof(scalar_t);
10 | int gidx = blockIdx.x * blockDim.x + threadIdx.x;
11 | int stride = gridDim.x * blockDim.x;
12 |
13 | if constexpr (std::is_same<scalar_t, float>::value) {
14 | // Vectorized path for floats with 128-bit alignment
15 | float4* output_vec = reinterpret_cast<float4*>(output);
16 | for(int vidx = gidx; vidx*vec_size < size; vidx += stride) {
| ^~~~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:16:30: note: perform multiplication in a wider type
16 | for(int vidx = gidx; vidx*vec_size < size; vidx += stride) {
| ^~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:39:48: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
39 | int blocks = std::min(65535, (int)((size + threads * 4 - 1)/(threads * 4)));
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:39:48: note: make conversion explicit to silence this warning
39 | int blocks = std::min(65535, (int)((size + threads * 4 - 1)/(threads * 4)));
| ^~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:39:48: note: perform multiplication in a wider type
39 | int blocks = std::min(65535, (int)((size + threads * 4 - 1)/(threads * 4)));
| ^~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:39:66: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
39 | int blocks = std::min(65535, (int)((size + threads * 4 - 1)/(threads * 4)));
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:39:66: note: make conversion explicit to silence this warning
39 | int blocks = std::min(65535, (int)((size + threads * 4 - 1)/(threads * 4)));
| ^~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:39:66: note: perform multiplication in a wider type
39 | int blocks = std::min(65535, (int)((size + threads * 4 - 1)/(threads * 4)));
| ^~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:41:5: warning: inside a lambda, '__func__' expands to the name of the function call operator; consider capturing the name of the enclosing function explicitly [bugprone-lambda-function-name]
41 | AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "sigmoid_kernel", [&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:34: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:3: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:3: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^
note: (skipping 1 expansions in backtrace; use -fmacro-backtrace-limit=0 to see all)
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:58:7: note: expanded from macro 'AT_PRIVATE_CHECK_SELECTIVE_BUILD'
58 | AT_ERROR( \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:711:32: note: expanded from macro 'AT_ERROR'
711 | C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK(false, ::c10::str(__VA_ARGS__))); \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:536:9: note: expanded from macro 'TORCH_CHECK'
536 | __func__, \
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:43:31: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
43 | blocks = (size + (threads * 4) - 1) / (threads * 4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:43:31: note: make conversion explicit to silence this warning
43 | blocks = (size + (threads * 4) - 1) / (threads * 4);
| ^
| static_cast<int64_t>(
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:66: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:44: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:56: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:70:12: note: expanded from macro 'AT_PRIVATE_CASE_TYPE_USING_HINT'
70 | return __VA_ARGS__(); \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:221:7: note: expanded from macro 'AT_DISPATCH_SWITCH'
221 | __VA_ARGS__ \
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:43:31: note: perform multiplication in a wider type
43 | blocks = (size + (threads * 4) - 1) / (threads * 4);
| ^
| static_cast<int64_t>(
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:66: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:44: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:56: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:70:12: note: expanded from macro 'AT_PRIVATE_CASE_TYPE_USING_HINT'
70 | return __VA_ARGS__(); \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:221:7: note: expanded from macro 'AT_DISPATCH_SWITCH'
221 | __VA_ARGS__ \
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:43:52: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
43 | blocks = (size + (threads * 4) - 1) / (threads * 4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:43:52: note: make conversion explicit to silence this warning
43 | blocks = (size + (threads * 4) - 1) / (threads * 4);
| ^
| static_cast<int64_t>(
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:66: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:44: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:56: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:70:12: note: expanded from macro 'AT_PRIVATE_CASE_TYPE_USING_HINT'
70 | return __VA_ARGS__(); \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:221:7: note: expanded from macro 'AT_DISPATCH_SWITCH'
221 | __VA_ARGS__ \
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_1/task_21/b4_s3_vectorized_ldg_aligned/base/base.cu:43:52: note: perform multiplication in a wider type
43 | blocks = (size + (threads * 4) - 1) / (threads * 4);
| ^
| static_cast<int64_t>(
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:66: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:44: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:56: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:70:12: note: expanded from macro 'AT_PRIVATE_CASE_TYPE_USING_HINT'
70 | return __VA_ARGS__(); \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:221:7: note: expanded from macro 'AT_DISPATCH_SWITCH'
221 | __VA_ARGS__ \
| ^~~~~~~~~~~