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48_Mean_reduction_over_a_dimension • evenly_distributed_mean_base

Level 1 • Task 48
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(x: torch.Tensor, dim: int) -> torch.Tensor:
    """
    Reduces the input tensor along the specified dimension by taking the mean.

    Args:
        x (torch.Tensor): Input tensor of arbitrary shape.
        dim (int): The dimension to reduce over.

    Returns:
        torch.Tensor: Output tensor with reduced dimension. The shape of the output is the same as the input except for the reduced dimension which is removed.
    """
    return torch.mean(x, dim=dim)


class Model(nn.Module):
    """
    Simple model that performs mean reduction over a specific dimension.
    """

    def __init__(self, dim: int):
        """
        Initializes the model with the dimension to reduce over.

        Args:
            dim (int): The dimension to reduce over.
        """
        super(Model, self).__init__()
        self.dim = dim

    def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
        """
        Reduces the input tensor along the specified dimension by taking the mean.

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

        Returns:
            torch.Tensor: Output tensor with reduced dimension. The shape of the output is the same as the input except for the reduced dimension which is removed.
        """
        return fn(x, self.dim)


batch_size = 16
dim1 = 256
dim2 = 256


def get_inputs():
    x = torch.randn(batch_size, dim1, dim2)
    return [x]


def get_init_inputs():
    return [1]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs mean reduction over a specific dimension.
    """
    def __init__(self, dim: int):
        """
        Initializes the model with the dimension to reduce over.

        Args:
            dim (int): The dimension to reduce over.
        """
        super(Model, self).__init__()
        self.dim = dim

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Reduces the input tensor along the specified dimension by taking the mean.

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

        Returns:
            torch.Tensor: Output tensor with reduced dimension. The shape of the output is the same as the input except for the reduced dimension which is removed.
        """
        return torch.mean(x, dim=self.dim)

batch_size = 16
dim1 = 256
dim2 = 256

def get_inputs():
    x = torch.randn(batch_size, dim1, dim2)
    return [x]

def get_init_inputs():
    return [1]
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Kernel Information

Operation Name 48_Mean_reduction_over_a_dimension
Level ID 1
Task ID 48
Kernel Name evenly_distributed_mean_base
CUDA Speedup (Native) 1.760x
CUDA Speedup (Compile) 3.622x
CUDA Runtime 0.009 ms
PyTorch Runtime (Native) 0.016 ms
PyTorch Runtime (Compile) 0.033 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model o3-mini-2025-01-31
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 1, Task 48 • 48_Mean_reduction_over_a_dimension)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 evenly_distributed_mean_base 0.01 1.76 3.62
🥈 modular_warp_reduce_base 0.01 1.32 2.72
🥈 thread_block_optimization_base 0.01 1.32 2.72
🥈 warp_reduce_shfl_base_edit_1 0.01 1.32 2.72
🥈 warp_reduce_shfl_base_base 0.01 1.32 2.72
6 shared_mean_reduction_edit_1 0.01 1.22 2.51
6 shared_mean_reduction_base 0.01 1.22 2.51
8 mean_reduce_unroll_base 0.01 1.13 2.33
8 hybrid_reduce_warp_shared_edit_1 0.01 1.13 2.33
8 mean_reduce_balanced_threads_edit_1 0.01 1.13 2.33
8 hybrid_mean_reduce_base 0.01 1.13 2.33
8 hybrid_reduce_warp_shared_base 0.01 1.13 2.33
8 mean_reduce_warp_base 0.01 1.13 2.33
8 mean_reduce_unroll_optimized_edit_1 0.01 1.13 2.33
8 modularized_mean_reduce_base 0.01 1.13 2.33
8 mean_reduce_opt_base 0.01 1.13 2.33
8 mean_reduce_warp_edit_1 0.01 1.13 2.33
8 mean_reduce_unroll_optimized_base 0.01 1.13 2.33
8 mean_reduce_memory_optimized_base 0.01 1.13 2.33
8 hybrid_warp_block_mean_reduce_base 0.01 1.13 2.33 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <vector>

// Constants defining the tile size (number of output elements processed per block) and the number of threads for reduction per output element
#define TILE 8
#define REDUCE_THREADS 32

// Kernel that distributes the reduction work evenly across a 2D thread block.
// Each block processes TILE output elements. The x-dimension indexes which output element in the tile,
// and the y-dimension partitions the work for the reduction along the reduction dimension L.

template <typename scalar_t>
__global__ void even_workload_mean_reduce_kernel(
    const scalar_t* __restrict__ input,
    scalar_t* __restrict__ output,
    int L,       // length of reduction dimension
    int stride,  // stride (inner_size) to traverse the reduction dimension
    int N        // total number of output elements
) {
    // Allocate shared memory dynamically; size: TILE * REDUCE_THREADS elements
    extern __shared__ char smem[];
    scalar_t* sdata = reinterpret_cast<scalar_t*>(smem);

    // Indices in the 2D block
    int tile_idx = threadIdx.x;      // which output element in the tile (0 to TILE-1)
    int reduce_idx = threadIdx.y;      // thread's index for reduction work (0 to REDUCE_THREADS-1)

    // Compute global output index
    int global_output_idx = blockIdx.x * TILE + tile_idx;
    if (global_output_idx >= N) return;

    // Decode the global output index into (outer, inner) indices
    // Input shape: [outer_size, L, inner_size]
    // Here, stride = inner_size
    int outer_idx = global_output_idx / stride;
    int inner_idx = global_output_idx % stride;
    int base_offset = outer_idx * (L * stride) + inner_idx;

    // Each thread accumulates a partial sum over the reduction dimension using a grid-stride loop
    scalar_t sum = static_cast<scalar_t>(0);
    for (int i = reduce_idx; i < L; i += REDUCE_THREADS) {
         sum += __ldg(input + base_offset + i * stride);
    }

    // Store the partial sum into shared memory
    int shmem_idx = tile_idx * REDUCE_THREADS + reduce_idx;
    sdata[shmem_idx] = sum;
    __syncthreads();

    // Perform reduction along the y-dimension for each output element in the tile
    for (int s = REDUCE_THREADS / 2; s > 0; s >>= 1) {
        if (reduce_idx < s) {
            sdata[shmem_idx] += sdata[shmem_idx + s];
        }
        __syncthreads();
    }

    // The first thread in the y-dimension writes the final result (mean) to global memory
    if (reduce_idx == 0) {
         output[global_output_idx] = sdata[tile_idx * REDUCE_THREADS] / static_cast<scalar_t>(L);
    }
}

// Host function to setup and launch the kernel
torch::Tensor mean_reduce_cuda(torch::Tensor input, int64_t dim) {
    // Handle negative dimensions
    if (dim < 0) dim += input.dim();

    // Get input sizes and compute L (length along reduction dimension), outer_size, and inner_size
    std::vector<int64_t> sizes = input.sizes().vec();
    int64_t L = sizes[dim];

    int64_t outer_size = 1;
    for (int i = 0; i < dim; i++) {
         outer_size *= sizes[i];
    }
    int64_t inner_size = 1;
    for (size_t i = dim + 1; i < sizes.size(); i++) {
         inner_size *= sizes[i];
    }
    
    // Total number of output elements (after reducing the dimension)
    int64_t N = outer_size * inner_size;
    int stride = inner_size;  // stride to jump across the reduction dimension in input

    // Create a 1D output tensor; later we will reshape it
    auto output = torch::empty({N}, input.options());

    // Determine grid and block dimensions
    // Each block processes TILE output elements
    int grid_x = (N + TILE - 1) / TILE;
    dim3 grid(grid_x);
    dim3 block(TILE, REDUCE_THREADS);

    // Shared memory size in bytes: TILE * REDUCE_THREADS * sizeof(scalar_t)
    size_t shared_mem_size = TILE * REDUCE_THREADS * sizeof(float);  // placeholder, will be set correctly in dispatch below

    AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "mean_reduce_cuda", ([&] {
         shared_mem_size = TILE * REDUCE_THREADS * sizeof(scalar_t);
         even_workload_mean_reduce_kernel<scalar_t><<<grid, block, shared_mem_size>>>(
             input.data_ptr<scalar_t>(),
             output.data_ptr<scalar_t>(),
             static_cast<int>(L),
             stride,
             static_cast<int>(N)
         );
    }));

    // Reshape the output to remove the reduced dimension
    sizes.erase(sizes.begin() + dim);
    output = output.view(sizes);
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &mean_reduce_cuda, "Even Workload Mean Reduction (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.882 inst/cycle 0.000 5
Executed Ipc Elapsed 0.502 inst/cycle 0.000 5
Issue Slots Busy 22.374 % 0.171 5
Issued Ipc Active 0.894 inst/cycle 0.000 5
SM Busy 22.374 % 0.171 5
Memory Throughput 731541881602.746 byte/second 452095853896560214016.000 5
Mem Busy 28.418 % 0.612 5
Max Bandwidth 21.920 % 0.354 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 45.988 % 0.007 5
Mem Pipes Busy 8.956 % 0.061 5
Warp Cycles Per Issued Instruction 30.538 cycle 0.821 5
Warp Cycles Per Executed Instruction 30.952 cycle 0.839 5
Avg. Active Threads Per Warp 31.820 0.000 5
Avg. Not Predicated Off Threads Per Warp 26.350 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 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 42.588 % 0.070 5
Achieved Active Warps Per SM 27.254 warp 0.029 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 (42.6%) 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 529777.71 μs
Device Time 427.39 μs
Self CPU Time 40.79 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 529736.91 μs
Device Time 427.39 μs
Self CPU Time 101.24 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 528959.30 μs
Device Time 0.00 μs
Self CPU Time 102.87 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 525411.55 μs
Device Time 0.00 μs
Self CPU Time 525411.55 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
cudaLaunchKernel
CPU Time 495058.35 μs
Device Time 21597.36 μs
Self CPU Time 495058.35 μs
Self Device Time 21597.36 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void even_workload_mean_reduce_kernel<float>(float const*, float*, int, int, int)
CPU Time 0.00 μs
Device Time 48899.68 μs
Self CPU Time 0.00 μs
Self Device Time 48899.68 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 22995.91 μs
Device Time 41699.88 μs
Self CPU Time 22995.91 μs
Self Device Time 41699.88 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 63403.27 μs
Device Time 616975.45 μs
Self CPU Time 11896.77 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 51508.49 μs
Device Time 616975.45 μs
Self CPU Time 15238.12 μs
Self Device Time 616975.45 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 616975.45 μs
Self CPU Time 0.00 μs
Self Device Time 616975.45 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 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.
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:19:5 bugprone-easily-swappable-parameters
19 | int stride, // stride (inner_size) to traverse the reduction dimension
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
20 | int N // total number of output elements
| ~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:19:9: note: the first parameter in the range is 'stride'
19 | int stride, // stride (inner_size) to traverse the reduction dimension
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:20:9: note: the last parameter in the range is 'N'
20 | int N // total number of output elements
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:27:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | int tile_idx = threadIdx.x; // which output element in the tile (0 to TILE-1)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:28:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | int reduce_idx = threadIdx.y; // thread's index for reduction work (0 to REDUCE_THREADS-1)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:31:29: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | int global_output_idx = blockIdx.x * TILE + tile_idx;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:62:38: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
62 | output[global_output_idx] = sdata[tile_idx * REDUCE_THREADS] / static_cast<scalar_t>(L);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:62:44: note: make conversion explicit to silence this warning
4 | output[global_output_idx] = sdata[tile_idx * REDUCE_THREADS] / static_cast<scalar_t>(L);
| ^~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:62:44: note: perform multiplication in a wider type
62 | output[global_output_idx] = sdata[tile_idx * REDUCE_THREADS] / static_cast<scalar_t>(L);
| ^~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:86:18: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
86 | int stride = inner_size; // stride to jump across the reduction dimension in input
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:93:18: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
93 | int grid_x = (N + TILE - 1) / TILE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:98:30: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
98 | size_t shared_mem_size = TILE * REDUCE_THREADS * sizeof(float); // placeholder, will be set correctly in dispatch below
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:7:14: note: expanded from macro 'TILE'
7 | #define TILE 8
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:98:30: note: make conversion explicit to silence this warning
98 | size_t shared_mem_size = TILE * REDUCE_THREADS * sizeof(float); // placeholder, will be set correctly in dispatch below
| ^
| static_cast<unsigned long>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:7:14: note: expanded from macro 'TILE'
7 | #define TILE 8
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:98:30: note: perform multiplication in a wider type
98 | size_t shared_mem_size = TILE * REDUCE_THREADS * sizeof(float); // placeholder, will be set correctly in dispatch below
| ^
| static_cast<long>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:7:14: note: expanded from macro 'TILE'
7 | #define TILE 8
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:100:5: 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]
100 | AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "mean_reduce_cuda", ([&] {
| ^
/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/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:101:28: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
101 | shared_mem_size = TILE * REDUCE_THREADS * sizeof(scalar_t);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:7:14: note: expanded from macro 'TILE'
7 | #define TILE 8
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:101:28: note: make conversion explicit to silence this warning
101 | shared_mem_size = TILE * REDUCE_THREADS * sizeof(scalar_t);
| ^
| static_cast<unsigned long>(
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:7:14: note: expanded from macro 'TILE'
7 | #define TILE 8
| ^
/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/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:101:28: note: perform multiplication in a wider type
101 | shared_mem_size = TILE * REDUCE_THREADS * sizeof(scalar_t);
| ^
| static_cast<long>(
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_48/b10_s2_evenly_distributed_mean/base/base.cu:7:14: note: expanded from macro 'TILE'
7 | #define TILE 8
| ^
/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__ \
| ^~~~~~~~~~~