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89_cumsum • hybrid_aligned_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_aligned_cumsum_base
CUDA Speedup (Native) 2.214x
CUDA Speedup (Compile) 2.142x
CUDA Runtime 0.014 ms
PyTorch Runtime (Native) 0.031 ms
PyTorch Runtime (Compile) 0.030 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 0.50
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>
#include <algorithm>

#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)

const int BLOCK_SIZE = 256;
const int STRIDE_THRESHOLD = 512;  // Threshold to choose between kernels

__global__ void small_stride_kernel(const float* __restrict__ input, 
                                  float* __restrict__ output, 
                                  int outer_size, 
                                  int inner_size, 
                                  int stride) {
    int outer_idx = blockIdx.x;
    int inner_idx = blockIdx.y * blockDim.x + threadIdx.x;

    if (outer_idx < outer_size && inner_idx < inner_size) {
        float sum = 0.0f;
        const float* in_ptr = input + outer_idx * stride * inner_size + inner_idx;
        float* out_ptr = output + outer_idx * stride * inner_size + inner_idx;
        
        #pragma unroll 4
        for (int i = 0; i < stride; ++i) {
            sum += __ldg(in_ptr + i * inner_size);
            out_ptr[i * inner_size] = sum;
        }
    }
}

__global__ void large_stride_kernel(const float* __restrict__ input, 
                                  float* __restrict__ output, 
                                  int stride, 
                                  int inner_size) {
    int line_index = blockIdx.x;
    int outer_idx = line_index / inner_size;
    int inner_idx = line_index % 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 chunk_size = (stride + blockDim.x - 1) / blockDim.x;
    int start = tid * chunk_size;
    int end = min(start + chunk_size, stride);

    extern __shared__ float sdata[];
    
    float thread_sum = 0.0f;
    #pragma unroll 2
    for (int i = start; i < end; i++) {
        thread_sum += __ldg(&in_line[i * inner_size]);
    }
    
    sdata[tid] = thread_sum;
    __syncthreads();
    
    for (int offset = 1; offset < blockDim.x; offset *= 2) {
        float temp = (tid >= offset) ? sdata[tid - offset] : 0.0f;
        __syncthreads();
        sdata[tid] += temp;
        __syncthreads();
    }
    
    float add_offset = (tid == 0) ? 0.0f : sdata[tid - 1];
    
    float local_running = 0.0f;
    for (int i = start; i < end; i++) {
        local_running += __ldg(&in_line[i * inner_size]);
        out_line[i * inner_size] = local_running + add_offset;
    }
}

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;

    int outer_size = 1;
    for (int i = 0; i < dim; ++i) outer_size *= x.size(i);
    
    int inner_size = 1;
    for (int i = dim + 1; i < ndim; ++i) inner_size *= x.size(i);
    
    int stride = x.size(dim);
    
    if (stride <= STRIDE_THRESHOLD) {
        dim3 grid(outer_size, (inner_size + BLOCK_SIZE - 1) / BLOCK_SIZE);
        small_stride_kernel<<<grid, BLOCK_SIZE>>>(
            x.data_ptr<float>(), output.data_ptr<float>(), 
            outer_size, inner_size, stride);
    } else {
        int total_lines = outer_size * inner_size;
        large_stride_kernel<<<total_lines, BLOCK_SIZE, BLOCK_SIZE * sizeof(float)>>>(
            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 implementation");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.288 inst/cycle 0.000 5
Executed Ipc Elapsed 0.200 inst/cycle 0.000 5
Issue Slots Busy 7.212 % 0.000 5
Issued Ipc Active 0.290 inst/cycle 0.000 5
SM Busy 7.212 % 0.000 5
Memory Throughput 170611380094.336 byte/second 4398574043078472704.000 5
Mem Busy 31.916 % 0.133 5
Max Bandwidth 43.938 % 0.295 5
L1/TEX Hit Rate 88.780 % 0.005 5
L2 Hit Rate 90.842 % 0.311 5
Mem Pipes Busy 3.092 % 0.001 5
Warp Cycles Per Issued Instruction 26.608 cycle 0.025 5
Warp Cycles Per Executed Instruction 26.876 cycle 0.026 5
Avg. Active Threads Per Warp 31.520 0.000 5
Avg. Not Predicated Off Threads Per Warp 29.790 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 11.958 % 0.000 5
Achieved Active Warps Per SM 7.654 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 (12.0%) 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 458753.86 μs
Device Time 144.57 μs
Self CPU Time 34.96 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 458718.90 μs
Device Time 144.57 μs
Self CPU Time 95.81 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 479113.25 μs
Device Time 0.00 μs
Self CPU Time 20953.07 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 457959.28 μs
Device Time 0.00 μs
Self CPU Time 457959.28 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 532518.04 μs
Device Time 22144.98 μs
Self CPU Time 532518.04 μs
Self Device Time 22144.98 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
large_stride_kernel(float const*, float*, int, int)
CPU Time 0.00 μs
Device Time 89543.46 μs
Self CPU Time 0.00 μs
Self Device Time 89543.46 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 23784.07 μs
Device Time 42639.14 μs
Self CPU Time 23784.07 μs
Self Device Time 42639.14 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 70554.41 μs
Device Time 632111.32 μs
Self CPU Time 15662.54 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::fill_
CPU Time 54893.33 μs
Device Time 632111.32 μs
Self CPU Time 16905.62 μs
Self Device Time 632111.32 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 632111.32 μs
Self CPU Time 0.00 μs
Self Device Time 632111.32 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 
45296 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_s3_hybrid_aligned_cumsum/base/base.cu:6:35 bugprone-macro-parentheses
6 | #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_s3_hybrid_aligned_cumsum/base/base.cu:7:41: warning: macro argument should be enclosed in parentheses [bugprone-macro-parentheses]
7 | #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_s3_hybrid_aligned_cumsum/base/base.cu:18:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
18 | int outer_idx = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:19:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
19 | int inner_idx = blockIdx.y * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:23:31: 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]
23 | const float* in_ptr = 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_s3_hybrid_aligned_cumsum/base/base.cu:23:39: note: make conversion explicit to silence this warning
5 | const float* in_ptr = 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_s3_hybrid_aligned_cumsum/base/base.cu:23:39: note: perform multiplication in a wider type
23 | const float* in_ptr = 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_s3_hybrid_aligned_cumsum/base/base.cu:24:26: 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]
24 | float* out_ptr = 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_s3_hybrid_aligned_cumsum/base/base.cu:24:35: note: make conversion explicit to silence this warning
24 | float* out_ptr = 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_s3_hybrid_aligned_cumsum/base/base.cu:24:35: note: perform multiplication in a wider type
24 | float* out_ptr = 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_s3_hybrid_aligned_cumsum/base/base.cu:28:26: 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]
28 | sum += __ldg(in_ptr + i * inner_size);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:28:35: note: make conversion explicit to silence this warning
28 | sum += __ldg(in_ptr + 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_s3_hybrid_aligned_cumsum/base/base.cu:28:35: note: perform multiplication in a wider type
28 | sum += __ldg(in_ptr + 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_s3_hybrid_aligned_cumsum/base/base.cu:29:13: 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]
29 | out_ptr[i * inner_size] = sum;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:29:21: note: make conversion explicit to silence this warning
29 | out_ptr[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_s3_hybrid_aligned_cumsum/base/base.cu:29:21: note: perform multiplication in a wider type
29 | out_ptr[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_s3_hybrid_aligned_cumsum/base/base.cu:38:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
38 | int line_index = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:42:28: 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]
42 | 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_s3_hybrid_aligned_cumsum/base/base.cu:42:36: note: make conversion explicit to silence this warning
42 | 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_s3_hybrid_aligned_cumsum/base/base.cu:42:36: note: perform multiplication in a wider type
42 | 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_s3_hybrid_aligned_cumsum/base/base.cu:43:23: 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]
43 | 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_s3_hybrid_aligned_cumsum/base/base.cu:43:32: note: make conversion explicit to silence this warning
43 | 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_s3_hybrid_aligned_cumsum/base/base.cu:43:32: note: perform multiplication in a wider type
43 | 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_s3_hybrid_aligned_cumsum/base/base.cu:45:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
45 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:46:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
46 | int chunk_size = (stride + blockDim.x - 1) / blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:55:30: 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]
55 | thread_sum += __ldg(&in_line[i * inner_size]);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:55:38: note: make conversion explicit to silence this warning
55 | thread_sum += __ldg(&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_s3_hybrid_aligned_cumsum/base/base.cu:55:38: note: perform multiplication in a wider type
55 | thread_sum += __ldg(&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_s3_hybrid_aligned_cumsum/base/base.cu:72:33: 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]
72 | local_running += __ldg(&in_line[i * inner_size]);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:72:41: note: make conversion explicit to silence this warning
72 | local_running += __ldg(&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_s3_hybrid_aligned_cumsum/base/base.cu:72:41: note: perform multiplication in a wider type
72 | local_running += __ldg(&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_s3_hybrid_aligned_cumsum/base/base.cu:73:9: 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]
73 | out_line[i * inner_size] = local_running + add_offset;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:73:18: note: make conversion explicit to silence this warning
73 | out_line[i * inner_size] = local_running + add_offset;
| ^~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:73:18: note: perform multiplication in a wider type
73 | out_line[i * inner_size] = local_running + add_offset;
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:77:37: 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]
77 | 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_s3_hybrid_aligned_cumsum/base/base.cu:80:16: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | int ndim = x.dim();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:84:49: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
84 | for (int i = 0; i < dim; ++i) outer_size *= x.size(i);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:87:56: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
87 | for (int i = dim + 1; i < ndim; ++i) inner_size *= x.size(i);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_89/b4_s3_hybrid_aligned_cumsum/base/base.cu:89:18: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
89 | int stride = x.size(dim);
| ^