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8_Conv3d_Divide_Max_GlobalAvgPool_BiasAdd_Sum • fused_stride_loops_base

Level 2 • Task 8
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    divisor: float,
    pool_size: tuple,
    sum_dim: int,
    conv_weight: torch.Tensor,
    conv_bias: torch.Tensor,
    bias: torch.Tensor,
) -> torch.Tensor:
    """
    Applies 3D convolution, division, max pooling, global average pooling, bias addition and sum.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width)
        divisor (float): Constant to divide by
        pool_size (tuple): Size for max pooling (depth, height, width)
        sum_dim (int): Dimension to sum over
        conv_weight (torch.Tensor): 3D convolution weights
        conv_bias (torch.Tensor): 3D convolution bias
        bias (torch.Tensor): Bias tensor for addition

    Returns:
        torch.Tensor: Output tensor after applying all operations
    """
    x = F.conv3d(x, conv_weight, bias=conv_bias)
    x = x / divisor
    x = F.max_pool3d(x, pool_size)
    x = F.adaptive_avg_pool3d(x, (1, 1, 1))
    x = x + bias
    x = torch.sum(x, dim=sum_dim)
    return x


class Model(nn.Module):
    """
    Model that performs a 3D convolution, divides by a constant, applies max pooling,
    global average pooling, adds a bias term, and sums along a specific dimension.
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        divisor,
        pool_size,
        bias_shape,
        sum_dim,
    ):
        super(Model, self).__init__()
        conv_shape = (out_channels, in_channels, *kernel_size)
        conv = nn.Conv3d(in_channels, out_channels, kernel_size)
        self.bias = nn.Parameter(torch.randn(bias_shape) * 0.02)

        self.conv_weight = conv.weight
        self.conv_bias = conv.bias
        self.bias = self.bias

    def forward(self, x, fn=module_fn):
        return fn(
            x, divisor, pool_size, sum_dim, self.conv_weight, self.conv_bias, self.bias
        )


batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = (3, 3, 3)
divisor = 2.0
pool_size = (2, 2, 2)
bias_shape = (out_channels, 1, 1, 1)
sum_dim = 1


def get_inputs():
    return [torch.randn(batch_size, in_channels, depth, height, width)]


def get_init_inputs():
    return [
        in_channels,
        out_channels,
        kernel_size,
        divisor,
        pool_size,
        bias_shape,
        sum_dim,
    ]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a 3D convolution, divides by a constant, applies max pooling,
    global average pooling, adds a bias term, and sums along a specific dimension.
    """
    def __init__(self, in_channels, out_channels, kernel_size, divisor, pool_size, bias_shape, sum_dim):
        super(Model, self).__init__()
        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size)
        self.divisor = divisor
        self.max_pool = nn.MaxPool3d(pool_size)
        self.global_avg_pool = nn.AdaptiveAvgPool3d((1, 1, 1))
        self.bias = nn.Parameter(torch.randn(bias_shape) * 0.02)
        self.sum_dim = sum_dim

    def forward(self, x):
        x = self.conv(x)
        x = x / self.divisor
        x = self.max_pool(x)
        x = self.global_avg_pool(x)
        x = x + self.bias
        x = torch.sum(x, dim=self.sum_dim)
        return x

batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = (3, 3, 3)
divisor = 2.0
pool_size = (2, 2, 2)
bias_shape = (out_channels, 1, 1, 1)
sum_dim = 1

def get_inputs():
    return [torch.randn(batch_size, in_channels, depth, height, width)]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, divisor, pool_size, bias_shape, sum_dim]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 8_Conv3d_Divide_Max_GlobalAvgPool_BiasAdd_Sum
Level ID 2
Task ID 8
Kernel Name fused_stride_loops_base
CUDA Speedup (Native) 1.207x
CUDA Speedup (Compile) 0.912x
CUDA Runtime 0.748 ms
PyTorch Runtime (Native) 0.903 ms
PyTorch Runtime (Compile) 0.682 ms
Correct True
Max Diff (vs. Reference) 0.139000
Model azure-gpt-4o-2024-08-06
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 2, Task 8 • 8_Conv3d_Divide_Max_GlobalAvgPool_BiasAdd_Sum)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 fused_stride_loops_base 0.75 1.21 0.91
🥇 fused_pooling_warp_uniform_base 0.75 1.21 0.91
🥇 fused_divide_maxpool_avg_base 0.75 1.21 0.91
🥇 fused_divide_maxpool_avg_edit_1 0.75 1.21 0.91
5 fused_stride_loops_edit_1 0.75 1.21 0.91
5 fused_pooling_warp_uniform_edit_1 0.75 1.21 0.91
5 fused_stride_loops_base 0.75 1.21 0.91
8 block_size_tuned_base_base 0.75 1.20 0.91
8 fused_stride_loops_edit_1 0.75 1.20 0.91
10 fused_pooling_shared_memory_base 0.75 1.20 0.91
10 optimized_stride_boundary_base_base 0.75 1.20 0.91
10 fused_pooling_min_sync_base 0.75 1.20 0.91
13 fused_pooling_min_sync_opt_base 0.75 1.20 0.91
14 fused_pooling_opt_sync_edit_1 0.76 1.19 0.90
15 fused_pooling_uniform_edit_1 0.76 1.19 0.90
15 fused_pooling_uniform_base 0.76 1.19 0.90
15 fused_pooling_opt_sync_base 0.76 1.19 0.90
18 fused_pooling_stride_boundaries_base 0.76 1.19 0.90
18 fused_pooling_unroll_edit_1 0.76 1.19 0.90
20 fused_pooling_unroll_base 0.76 1.19 0.90 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <vector>
#include <cfloat>

// Tunable parameters for the fused kernel and reduction
#define BLOCK_SIZE_FUSED 256        // for fused division, max pooling, and avg pooling kernel
#define BLOCK_SIZE_REDUCTION 256    // for the reduction kernel (optimized with warp-level unrolling)

// Fused kernel: performs division, 3D max pooling over non-overlapping windows,
// and then adaptive average pooling (summing over all pooled windows) with bias addition.
// Input:
//   in         : Pointer to conv3d output (shape: N x C x D x H x W)
//   out        : Pointer to output tensor (shape: N x C) containing the average pooled results + bias
//   N, C, D, H, W: dimensions of conv3d output
//   poolD, poolH, poolW: dimensions of the pooling window
//   OD, OH, OW : number of pooling windows in each spatial dimension
//   divisor    : Division factor to be applied (using multiplication by reciprocal)
//   bias       : Bias pointer (assumed shape: C) to be added per channel
__global__ void fused_stride_divide_maxpool_avg_kernel(const float* __restrict__ in,
                                                  float* __restrict__ out,
                                                  int N, int C,
                                                  int D, int H, int W,
                                                  int poolD, int poolH, int poolW,
                                                  int OD, int OH, int OW,
                                                  float divisor,
                                                  const float* __restrict__ bias) {
    // Each block is responsible for one (n, c) pair
    int n = blockIdx.x;
    int c = blockIdx.y;

    // Total number of pooling windows for this (n, c)
    int total_windows = OD * OH * OW;

    float partialSum = 0.0f;
    // Each thread processes a subset of pooling windows in a grid-stride loop
    for (int idx = threadIdx.x; idx < total_windows; idx += blockDim.x) {
        // Decode linear index into pooling window coordinates (od, oh, ow)
        int ow = idx % OW;
        int tmp = idx / OW;
        int oh = tmp % OH;
        int od = tmp / OH;  // since tmp = od * OH + oh

        // Determine starting indices in D, H, W for the pooling window
        int d_start = od * poolD;
        int h_start = oh * poolH;
        int w_start = ow * poolW;

        float max_val = -FLT_MAX;
        // Iterate over the pooling window
        for (int d = d_start; d < d_start + poolD; ++d) {
            for (int h = h_start; h < h_start + poolH; ++h) {
                for (int w = w_start; w < w_start + poolW; ++w) {
                    // Compute linear index in conv output tensor: shape (N, C, D, H, W)
                    int index = (((n * C + c) * D + d) * H + h) * W + w;
                    float val = in[index] * (1.0f / divisor);
                    if (val > max_val) {
                        max_val = val;
                    }
                }
            }
        }
        partialSum += max_val;
    }

    // Use shared memory to reduce partial sums from threads within the block
    __shared__ float sdata[BLOCK_SIZE_FUSED];
    int tid = threadIdx.x;
    sdata[tid] = partialSum;
    __syncthreads();

    // Standard reduction in shared memory
    for (int s = blockDim.x / 2; s > 0; s >>= 1) {
        if (tid < s) {
            sdata[tid] += sdata[tid + s];
        }
        __syncthreads();
    }

    if (tid == 0) {
        // Compute adaptive average pooling (divide by total number of pooling windows)
        float avg = sdata[0] / static_cast<float>(total_windows);
        // Add bias for channel c
        out[n * C + c] = avg + bias[c];
    }
}

// Optimized reduction kernel (from snippet 2) to sum the (N, C) tensor along a chosen dimension
// For sum_dim == 1, reduction is over channels (output shape: N)
// For sum_dim == 0, reduction is over batch (output shape: C)
__global__ void reduction_sum_kernel(const float* __restrict__ in,
                                      float* __restrict__ out,
                                      int N, int C, int sum_dim) {
    extern __shared__ float sdata[];
    int tid = threadIdx.x;
    if (sum_dim == 1) {
        // Each block processes one sample (n)
        int n = blockIdx.x;
        float sum = 0.0f;
        for (int c = tid; c < C; c += blockDim.x) {
            sum += in[n * C + c];
        }
        sdata[tid] = sum;
        __syncthreads();
        for (int s = blockDim.x / 2; s > 32; s >>= 1) {
            if (tid < s) sdata[tid] += sdata[tid + s];
            __syncthreads();
        }
        if (tid < 32) {
            volatile float* smem = sdata;
            smem[tid] += smem[tid + 32];
            smem[tid] += smem[tid + 16];
            smem[tid] += smem[tid + 8];
            smem[tid] += smem[tid + 4];
            smem[tid] += smem[tid + 2];
            smem[tid] += smem[tid + 1];
        }
        if (tid == 0) {
            out[n] = sdata[0];
        }
    } else if (sum_dim == 0) {
        // Each block processes one channel (c)
        int c = blockIdx.x;
        float sum = 0.0f;
        for (int n = tid; n < N; n += blockDim.x) {
            sum += in[n * C + c];
        }
        sdata[tid] = sum;
        __syncthreads();
        for (int s = blockDim.x / 2; s > 32; s >>= 1) {
            if (tid < s) sdata[tid] += sdata[tid+s];
            __syncthreads();
        }
        if (tid < 32) {
            volatile float* smem = sdata;
            smem[tid] += smem[tid + 32];
            smem[tid] += smem[tid + 16];
            smem[tid] += smem[tid + 8];
            smem[tid] += smem[tid + 4];
            smem[tid] += smem[tid + 2];
            smem[tid] += smem[tid + 1];
        }
        if (tid == 0) {
            out[c] = sdata[0];
        }
    }
}

// The forward_cuda function performs:
// 1) 3D convolution (using at::conv3d for correctness),
// 2) a fused kernel that computes division, 3D max pooling across windows, adaptive average pooling, and bias addition,
// 3) a reduction kernel to sum the (N, C) tensor along the specified dimension (sum_dim == 0 or 1).

torch::Tensor forward_cuda(torch::Tensor x,
                             double divisor,
                             std::vector<int64_t> pool_size,
                             int64_t sum_dim,
                             torch::Tensor conv_weight,
                             torch::Tensor conv_bias,
                             torch::Tensor bias) {
    TORCH_CHECK(x.is_cuda(), "x must be a CUDA tensor.");
    TORCH_CHECK(conv_weight.is_cuda(), "conv_weight must be a CUDA tensor.");
    TORCH_CHECK(conv_bias.is_cuda(), "conv_bias must be a CUDA tensor.");
    TORCH_CHECK(bias.is_cuda(), "bias must be a CUDA tensor.");

    // 1) 3D convolution using PyTorch's conv3d
    auto conv_out = at::conv3d(x, conv_weight, conv_bias);
    // conv_out shape: (N, C, D, H, W)
    int N = conv_out.size(0);
    int C = conv_out.size(1);
    int D = conv_out.size(2);
    int H = conv_out.size(3);
    int W = conv_out.size(4);

    // Pooling window sizes
    int poolD = pool_size[0];
    int poolH = pool_size[1];
    int poolW = pool_size[2];

    // Compute output dimensions for the pooling stage (assumes perfect divisibility)
    int OD = D / poolD;
    int OH = H / poolH;
    int OW = W / poolW;

    auto options = conv_out.options();
    // Output of fused kernel: adaptive average pooling result per (n, c)
    auto avg_out = at::empty({N, C}, options);

    // Launch fused kernel with a 2D grid: one block for each (n, c) pair
    dim3 grid(N, C);
    fused_stride_divide_maxpool_avg_kernel<<<grid, BLOCK_SIZE_FUSED>>>(
        conv_out.data_ptr<float>(),
        avg_out.data_ptr<float>(),
        N, C, D, H, W,
        poolD, poolH, poolW,
        OD, OH, OW,
        static_cast<float>(divisor),
        bias.data_ptr<float>()
    );

    // 3) Reduction: sum over the (N, C) result along an input-specified dimension.
    torch::Tensor final_out;
    if (sum_dim == 1) {
        // Sum over channels; final output shape: (N)
        final_out = at::empty({N}, options);
        reduction_sum_kernel<<<N, BLOCK_SIZE_REDUCTION, BLOCK_SIZE_REDUCTION * sizeof(float)>>>(
            avg_out.data_ptr<float>(),
            final_out.data_ptr<float>(),
            N, C, sum_dim
        );
    } else if (sum_dim == 0) {
        // Sum over batch; final output shape: (C)
        final_out = at::empty({C}, options);
        reduction_sum_kernel<<<C, BLOCK_SIZE_REDUCTION, BLOCK_SIZE_REDUCTION * sizeof(float)>>>(
            avg_out.data_ptr<float>(),
            final_out.data_ptr<float>(),
            N, C, sum_dim
        );
    } else {
        TORCH_CHECK(false, "sum_dim must be 0 or 1");
    }

    return final_out;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward_cuda, "Fused conv3d, divide, max pool, adaptive avg pool, bias add, and reduction kernel with stride loops");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.216 inst/cycle 0.000 5
Executed Ipc Elapsed 0.078 inst/cycle 0.000 5
Issue Slots Busy 5.502 % 0.118 5
Issued Ipc Active 0.220 inst/cycle 0.000 5
SM Busy 5.502 % 0.118 5
Memory Throughput 3652206076.304 byte/second 7569171485054954.000 5
Mem Busy 8.972 % 0.054 5
Max Bandwidth 4.622 % 0.011 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 101.162 % 0.373 5
Mem Pipes Busy 2.884 % 0.004 5
Warp Cycles Per Issued Instruction 31.994 cycle 7.027 5
Warp Cycles Per Executed Instruction 32.570 cycle 7.287 5
Avg. Active Threads Per Warp 30.970 0.000 5
Avg. Not Predicated Off Threads Per Warp 26.030 0.000 5
Max Active Clusters 0.000 cluster 0.000 5
Max Cluster Size 8.000 block 0.000 5
Overall GPU Occupancy 0.000 % 0.000 5
Cluster Occupancy 0.000 % 0.000 5
Block Limit SM 32.000 block 0.000 5
Block Limit Registers 16.000 block 0.000 5
Block Limit Shared Mem 16.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 10.846 % 0.011 5
Achieved Active Warps Per SM 6.940 warp 0.004 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 (10.8%) 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::conv3d
CPU Time 4349201.82 μs
Device Time 4433908.20 μs
Self CPU Time 11866.83 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::convolution
CPU Time 4337334.99 μs
Device Time 4433908.20 μs
Self CPU Time 15906.96 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::_convolution
CPU Time 4321428.02 μs
Device Time 4433908.20 μs
Self CPU Time 34688.54 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::cudnn_convolution
CPU Time 3723209.07 μs
Device Time 3848502.63 μs
Self CPU Time 153390.63 μs
Self Device Time 3848502.63 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaLaunchKernelExC
CPU Time 3543567.48 μs
Device Time 0.00 μs
Self CPU Time 3543567.48 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
sm80_xmma_fprop_implicit_gemm_indexed_f32f32_f32f32_f32_nchwkcrs_nchw_tilesize32x32x8_stage3_warpsize1x2x1_g1_ffma_aligna4_alignc4_execute_kernel__5x_cudnn
CPU Time 0.00 μs
Device Time 3848501.35 μs
Self CPU Time 0.00 μs
Self Device Time 3848501.35 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 
45311 warnings generated when compiling for host.
Suppressed 45327 warnings (45280 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/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:23:51 bugprone-easily-swappable-parameters
23 | int N, int C,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:23:55: note: the first parameter in the range is 'N'
23 | int N, int C,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:23:62: note: the last parameter in the range is 'C'
23 | int N, int C,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:24:65: warning: 2 adjacent parameters of 'fused_stride_divide_maxpool_avg_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
24 | int D, int H, int W,
| ^~~~~~
25 | int poolD, int poolH, int poolW,
| ~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:24:69: note: the first parameter in the range is 'W'
24 | int D, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:25:55: note: the last parameter in the range is 'poolD'
25 | int poolD, int poolH, int poolW,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:25:73: warning: 2 adjacent parameters of 'fused_stride_divide_maxpool_avg_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
25 | int poolD, int poolH, int poolW,
| ^~~~~~~~~~
26 | int OD, int OH, int OW,
| ~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:25:77: note: the first parameter in the range is 'poolW'
25 | int poolD, int poolH, int poolW,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:26:55: note: the last parameter in the range is 'OD'
26 | int OD, int OH, int OW,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:26:67: warning: 2 adjacent parameters of 'fused_stride_divide_maxpool_avg_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
26 | int OD, int OH, int OW,
| ^~~~~~~
27 | float divisor,
| ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:26:71: note: the first parameter in the range is 'OW'
26 | int OD, int OH, int OW,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:27:57: note: the last parameter in the range is 'divisor'
27 | float divisor,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:27:51: note: 'int' and 'float' may be implicitly converted
27 | float divisor,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:30:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | int n = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:31:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | int c = blockIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:38:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
38 | for (int idx = threadIdx.x; idx < total_windows; idx += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:38:61: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
38 | for (int idx = threadIdx.x; idx < total_windows; idx += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:69:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
69 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:74:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
74 | for (int s = blockDim.x / 2; s > 0; s >>= 1) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:94:39: warning: 3 adjacent parameters of 'reduction_sum_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
94 | int N, int C, int sum_dim) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:94:43: note: the first parameter in the range is 'N'
94 | int N, int C, int sum_dim) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:94:57: note: the last parameter in the range is 'sum_dim'
94 | int N, int C, int sum_dim) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:96:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
96 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:99:17: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | int n = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:101:39: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
101 | for (int c = tid; c < C; c += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:106:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
106 | for (int s = blockDim.x / 2; s > 32; s >>= 1) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:124:17: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | int c = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:126:39: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | for (int n = tid; n < N; n += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:131:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
131 | for (int s = blockDim.x / 2; s > 32; s >>= 1) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:155:42: 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]
155 | torch::Tensor forward_cuda(torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:159:44: warning: the parameter 'conv_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
159 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:161:44: warning: the parameter 'bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
161 | torch::Tensor bias) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:170:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
170 | int N = conv_out.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:171:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
171 | int C = conv_out.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:172:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
172 | int D = conv_out.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:173:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
173 | int H = conv_out.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:174:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
174 | int W = conv_out.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:177:17: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
177 | int poolD = pool_size[0];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:178:17: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
178 | int poolH = pool_size[1];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:179:17: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
179 | int poolW = pool_size[2];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:210:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
210 | N, C, sum_dim
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_8/b5_s3_fused_stride_loops/base/base.cu:218:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
218 | N, C, sum_dim
| ^