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89_cumsum • hybrid_cumsum_base

Level 1 • Task 89
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(x: torch.Tensor, dim: int) -> torch.Tensor:
    """
    Performs a cumulative sum operation.

    Args:
        x (torch.Tensor): Input tensor.
        dim (int): The dimension along which to perform the cumulative sum.

    Returns:
        torch.Tensor: Output tensor.
    """
    return torch.cumsum(x, dim=dim)


class Model(nn.Module):
    """
    A simple model that performs a cumulative sum (prefix sum) operation along a specified dimension.
    """

    def __init__(self, dim):
        """
        Initialize the Scan model.

        Args:
            dim (int): The dimension along which to perform the cumulative sum.
        """
        super(Model, self).__init__()
        self.dim = dim

    def forward(self, x, fn=module_fn):
        """
        Forward pass for the Scan model, computing the cumulative sum along the specified dimension.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, *input_shape)
            fn (callable): Function to compute the output, defaults to module_fn
        """
        return fn(x, self.dim)


# Define input dimensions and parameters
batch_size = 128
input_shape = (4000,)  # Example shape (arbitrary)
dim = 1


def get_inputs():
    """
    Generates random inputs for testing the Scan model.

    Returns:
        list: A list containing a single randomly generated tensor with shape
              (batch_size, *input_shape).
    """
    return [torch.randn(batch_size, *input_shape)]


def get_init_inputs():
    """
    Returns the initialization parameters for the Scan model.

    Returns:
        list: A list containing the `dim` parameter for model initialization.
    """
    return [dim]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A simple model that performs a cumulative sum (prefix sum) operation along a specified dimension.

    Parameters:
        dim (int): The dimension along which to perform the scan operation.
    """

    def __init__(self, dim):
        """
        Initialize the Scan model.

        Args:
            dim (int): The dimension along which to perform the cumulative sum.
        """
        super(Model, self).__init__()
        self.dim = dim

    def forward(self, x):
        """
        Forward pass for the Scan model, computing the cumulative sum along the specified dimension.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, *input_shape), where `*input_shape` 
                              can vary depending on the use case.

        Returns:
            torch.Tensor: Tensor of the same shape as `x` after applying cumulative sum along `dim`.
        """
        return torch.cumsum(x, dim=self.dim)

# Define input dimensions and parameters
batch_size = 128
input_shape = (4000,)  # Example shape (arbitrary)
dim = 1

def get_inputs():
    """
    Generates random inputs for testing the Scan model.

    Returns:
        list: A list containing a single randomly generated tensor with shape 
              (batch_size, *input_shape).
    """
    return [torch.randn(batch_size, *input_shape)]

def get_init_inputs():
    """
    Returns the initialization parameters for the Scan model.

    Returns:
        list: A list containing the `dim` parameter for model initialization.
    """
    return [dim]
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Kernel Information

Operation Name 89_cumsum
Level ID 1
Task ID 89
Kernel Name hybrid_cumsum_base
CUDA Speedup (Native) 2.067x
CUDA Speedup (Compile) 1.999x
CUDA Runtime 0.015 ms
PyTorch Runtime (Native) 0.031 ms
PyTorch Runtime (Compile) 0.030 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 89 • 89_cumsum)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 hybrid_aligned_cumsum_edit_1 0.01 2.21 2.14
🥇 tile_scan_cumsum_base 0.01 2.21 2.14
🥇 aligned_cumsum_ldg_edit_1 0.01 2.21 2.14
🥇 hybrid_aligned_cumsum_base 0.01 2.21 2.14
🥇 aligned_cumsum_ldg_base 0.01 2.21 2.14
🥇 tile_scan_cumsum_edit_1 0.01 2.21 2.14
🥇 shared_memory_cumsum_base 0.01 2.21 2.14
8 cumsum_even_dist_edit_1 0.01 2.07 2.00
8 hybrid_cumsum_edit_1 0.01 2.07 2.00
8 parallel_cumsum_base 0.01 2.07 2.00
8 cumsum_even_dist_base 0.01 2.07 2.00
8 parallel_cumsum_unroll_base 0.01 2.07 2.00
8 hybrid_cumsum_base 0.01 2.07 2.00
8 modular_cumsum_base 0.01 2.07 2.00
15 parallel_cumsum_stride_base 0.02 1.48 1.43
16 parallel_cumsum_stride_edit_1 0.02 1.29 1.25
17 cumsum_warp_atomic_base_base 0.04 0.82 0.79
18 cumsum_optimized_sync_base 0.04 0.79 0.77
18 cumsum_optimized_sync_base 0.04 0.79 0.77
20 hybrid_adaptive_cumsum_base 0.04 0.77 0.75 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.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)

// Threshold below which a simple sequential cumulative sum is more efficient
const int SEQ_THRESHOLD = 32;

// Hybrid kernel: each block processes one "line" of data corresponding to a fixed
// outer and inner index. For small stride (cumsum dimension length), we use a sequential
// accumulation to avoid the overhead of synchronization, and for large strides we use an
// even distribution of work with an intra-block scan over the partial sums.

__global__ void cumsum_hybrid_kernel(const float* input, float* output, int stride, int inner_size) {
    // Each block processes one line (combination of outer and inner indices).
    // Total number of lines = outer_size * inner_size.
    int line_index = blockIdx.x;
    int outer_idx = line_index / inner_size;
    int inner_idx = line_index % inner_size;

    // Pointers to the beginning of the line in memory. Elements along the cumulative
    // dimension are separated by inner_size.
    const float* in_line = input + outer_idx * stride * inner_size + inner_idx;
    float* out_line = output + outer_idx * stride * inner_size + inner_idx;

    int tid = threadIdx.x;
    int numThreads = blockDim.x;

    // If only one thread is launched, or the stride is small, execute a simple sequential cumsum.
    if (numThreads == 1 || stride <= SEQ_THRESHOLD) {
        if (tid == 0) {
            float sum = 0.0f;
            for (int i = 0; i < stride; ++i) {
                sum += in_line[i * inner_size];
                out_line[i * inner_size] = sum;
            }
        }
    } else {
        // Evenly partition the stride among the threads in this block.
        int chunk_size = (stride + numThreads - 1) / numThreads;  // ceiling division
        int start = tid * chunk_size;
        int end = start + chunk_size;
        if (end > stride) end = stride;

        // Phase 1: Each thread computes the sum of its assigned chunk.
        float thread_total = 0.0f;
        for (int i = start; i < end; ++i) {
            thread_total += in_line[i * inner_size];
        }

        // Use shared memory to perform an inclusive scan over thread_total values.
        extern __shared__ float sdata[];
        sdata[tid] = thread_total;
        __syncthreads();

        for (int offset = 1; offset < numThreads; offset *= 2) {
            float temp = 0.0f;
            if (tid >= offset) {
                temp = sdata[tid - offset];
            }
            __syncthreads();
            sdata[tid] += temp;
            __syncthreads();
        }

        // Convert the inclusive scan to an exclusive scan by subtracting the thread's own input
        float thread_offset = (tid == 0) ? 0.0f : sdata[tid - 1];

        // Phase 2: Each thread recomputes its chunk's local cumulative sum and adds the offset.
        float running = thread_offset;
        for (int i = start; i < end; ++i) {
            running += in_line[i * inner_size];
            out_line[i * inner_size] = running;
        }
    }
}

// The forward function sets up the tensor dimensions, selects the number of threads based on the
// stride (cumulative dimension length), and launches one block per "line" (each unique combination
// of outer and inner indices).

torch::Tensor forward(torch::Tensor x, int dim) {
    CHECK_INPUT(x);
    auto output = torch::empty_like(x);
    int ndim = x.dim();
    dim = (dim + ndim) % ndim;

    // Compute outer_size: product of dimensions before the cumsum dimension
    int outer_size = 1;
    for (int i = 0; i < dim; ++i) {
        outer_size *= x.size(i);
    }

    // Compute inner_size: product of dimensions after the cumsum dimension
    int inner_size = 1;
    for (int i = dim + 1; i < ndim; ++i) {
        inner_size *= x.size(i);
    }

    // The length of the cumulative sum dimension
    int stride = x.size(dim);

    // Total number of lines to process
    int total_lines = outer_size * inner_size;

    int threads;
    size_t smem_size = 0;

    // For small strides, a sequential approach is preferred
    if (stride <= SEQ_THRESHOLD) {
        threads = 1;
    } else {
        // For larger strides, use parallel processing with a maximum of 256 threads per block
        threads = (stride < 256) ? stride : 256;
        smem_size = threads * sizeof(float);
    }

    // Launch one block per line
    cumsum_hybrid_kernel<<<total_lines, threads, smem_size>>>(
        x.data_ptr<float>(), output.data_ptr<float>(), stride, inner_size
    );

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Hybrid CUDA cumulative sum kernel with dynamic mode selection");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.230 inst/cycle 0.000 5
Executed Ipc Elapsed 0.160 inst/cycle 0.000 5
Issue Slots Busy 5.712 % 0.001 5
Issued Ipc Active 0.230 inst/cycle 0.000 5
SM Busy 5.712 % 0.001 5
Memory Throughput 166332148729.988 byte/second 549317675358548608.000 5
Mem Busy 30.890 % 0.019 5
Max Bandwidth 48.470 % 0.056 5
L1/TEX Hit Rate 93.750 % 0.000 5
L2 Hit Rate 93.274 % 0.011 5
Mem Pipes Busy 3.002 % 0.000 5
Warp Cycles Per Issued Instruction 34.266 cycle 0.006 5
Warp Cycles Per Executed Instruction 34.350 cycle 0.005 5
Avg. Active Threads Per Warp 31.590 0.000 5
Avg. Not Predicated Off Threads Per Warp 29.960 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 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 12.308 % 0.000 5
Achieved Active Warps Per SM 7.878 warp 0.000 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 (12.3%) 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 502275.38 μs
Device Time 145.18 μs
Self CPU Time 45.59 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 502229.79 μs
Device Time 145.18 μs
Self CPU Time 105.16 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 520704.47 μs
Device Time 0.00 μs
Self CPU Time 19055.26 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 501036.72 μs
Device Time 0.00 μs
Self CPU Time 501036.72 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 522729.72 μs
Device Time 21200.06 μs
Self CPU Time 522729.72 μs
Self Device Time 21200.06 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cumsum_hybrid_kernel(float const*, float*, int, int)
CPU Time 0.00 μs
Device Time 85139.46 μs
Self CPU Time 0.00 μs
Self Device Time 85139.46 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 17300.19 μs
Device Time 40811.60 μs
Self CPU Time 17300.19 μs
Self Device Time 40811.60 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 71781.89 μs
Device Time 604204.55 μs
Self CPU Time 11861.20 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 59922.72 μs
Device Time 604204.55 μs
Self CPU Time 16240.40 μs
Self Device Time 604204.55 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 604204.55 μs
Self CPU Time 0.00 μs
Self Device Time 604204.55 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 
45292 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/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:5:35 bugprone-macro-parentheses
5 | #define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
| ^
| ()
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:6:41: warning: macro argument should be enclosed in parentheses [bugprone-macro-parentheses]
6 | #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
| ^
| ()
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:20:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
20 | int line_index = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:26:28: 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]
26 | const float* in_line = input + outer_idx * stride * inner_size + inner_idx;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:26:36: note: make conversion explicit to silence this warning
4 | const float* in_line = input + outer_idx * stride * inner_size + inner_idx;
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:26:36: note: perform multiplication in a wider type
26 | const float* in_line = input + outer_idx * stride * inner_size + inner_idx;
| ^~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:27:23: 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]
27 | float* out_line = output + outer_idx * stride * inner_size + inner_idx;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:27:32: note: make conversion explicit to silence this warning
27 | float* out_line = output + outer_idx * stride * inner_size + inner_idx;
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:27:32: note: perform multiplication in a wider type
27 | float* out_line = output + outer_idx * stride * inner_size + inner_idx;
| ^~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:29:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
29 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:30:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | int numThreads = blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:37:24: 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]
37 | sum += in_line[i * inner_size];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:37:32: note: make conversion explicit to silence this warning
37 | sum += in_line[i * inner_size];
| ^~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:37:32: note: perform multiplication in a wider type
37 | sum += in_line[i * inner_size];
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:38:17: 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]
38 | out_line[i * inner_size] = sum;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:38:26: note: make conversion explicit to silence this warning
38 | out_line[i * inner_size] = sum;
| ^~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:38:26: note: perform multiplication in a wider type
38 | out_line[i * inner_size] = sum;
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:51:29: 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]
51 | thread_total += in_line[i * inner_size];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:51:37: note: make conversion explicit to silence this warning
51 | thread_total += in_line[i * inner_size];
| ^~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:51:37: note: perform multiplication in a wider type
51 | thread_total += in_line[i * inner_size];
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:75:24: 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]
75 | running += in_line[i * inner_size];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:75:32: note: make conversion explicit to silence this warning
75 | running += in_line[i * inner_size];
| ^~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:75:32: note: perform multiplication in a wider type
75 | running += in_line[i * inner_size];
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:76:13: 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]
76 | out_line[i * inner_size] = running;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:76:22: note: make conversion explicit to silence this warning
76 | out_line[i * inner_size] = running;
| ^~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:76:22: note: perform multiplication in a wider type
76 | out_line[i * inner_size] = running;
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:85:37: warning: the parameter 'x' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
85 | torch::Tensor forward(torch::Tensor x, int dim) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:88:16: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
88 | int ndim = x.dim();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:94:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
94 | outer_size *= x.size(i);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:100:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
100 | inner_size *= x.size(i);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s2_hybrid_cumsum/base/base.cu:104:18: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
104 | int stride = x.size(dim);
| ^