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61_ConvTranspose3d_ReLU_GroupNorm • fused_rg_const_base_base

Level 2 • Task 61
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    conv_transpose: torch.Tensor,
    group_norm_weight: torch.Tensor,
    group_norm_bias: torch.Tensor,
    groups: int,
    eps: float,
) -> torch.Tensor:
    """
    Applies a transposed 3D convolution, ReLU, and group normalization.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, D, H, W)
        conv_transpose (torch.Tensor): Transposed convolution weight tensor
        group_norm_weight (torch.Tensor): Weight tensor for group normalization
        group_norm_bias (torch.Tensor): Bias tensor for group normalization
        groups (int): Number of groups for group normalization
        eps (float): Epsilon for group normalization
    Returns:
        torch.Tensor: Output tensor of shape (batch_size, out_channels, D, H, W)
    """
    x = F.conv_transpose3d(x, conv_transpose, bias=None)
    x = F.relu(x)
    x = F.group_norm(x, groups, group_norm_weight, group_norm_bias, eps)
    return x


class Model(nn.Module):
    """
    Model that performs a transposed 3D convolution, applies ReLU, and then applies group normalization.
    """

    def __init__(self, in_channels, out_channels, kernel_size, groups, bias):
        super(Model, self).__init__()
        conv = nn.ConvTranspose3d(in_channels, out_channels, kernel_size)
        self.conv_transpose_parameter = conv.weight

        # set torch seed to 0
        torch.manual_seed(0)
        gn = nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps)
        self.group_norm_weight = nn.Parameter(
            gn.weight + torch.randn_like(gn.weight) * 0.02
        )
        self.group_norm_bias = nn.Parameter(gn.bias + torch.randn_like(gn.bias) * 0.02)

    def forward(self, x, fn=module_fn):
        return fn(
            x,
            self.conv_transpose_parameter,
            self.group_norm_weight,
            self.group_norm_bias,
            groups,
            eps,
        )


batch_size = 16
in_channels = 64
out_channels = 128
D, H, W = 8, 16, 16
kernel_size = 3
groups = 8
bias = False
eps = 1e-5


def get_inputs():
    return [torch.randn(batch_size, in_channels, D, H, W)]


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, groups, bias]

import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Model that performs a transposed 3D convolution, applies ReLU, and then applies group normalization.
    """

    def __init__(
        self, in_channels, out_channels, kernel_size, groups, bias=False, eps=1e-5
    ):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose3d(
            in_channels, out_channels, kernel_size, bias=bias
        )
        self.relu = nn.ReLU()
        # set torch seed to 0
        torch.manual_seed(0)
        self.group_norm = nn.GroupNorm(
            num_groups=groups, num_channels=out_channels, eps=eps
        )
        self.group_norm.weight = nn.Parameter(
            self.group_norm.weight + torch.randn_like(self.group_norm.weight) * 0.02
        )
        self.group_norm.bias = nn.Parameter(
            self.group_norm.bias + torch.randn_like(self.group_norm.bias) * 0.02
        )

    def forward(self, x):
        """
        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, D, H, W).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, D, H, W).
        """
        x = self.conv_transpose(x)
        x = self.relu(x)
        x = self.group_norm(x)
        return x


batch_size = 16
in_channels = 64
out_channels = 128
D, H, W = 8, 16, 16
kernel_size = 3
groups = 8
bias = False


def get_inputs():
    return [torch.randn(batch_size, in_channels, D, H, W)]


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, groups, bias]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 61_ConvTranspose3d_ReLU_GroupNorm
Level ID 2
Task ID 61
Kernel Name fused_rg_const_base_base
CUDA Speedup (Native) 1.160x
CUDA Speedup (Compile) 0.989x
CUDA Runtime 0.213 ms
PyTorch Runtime (Native) 0.247 ms
PyTorch Runtime (Compile) 0.211 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 2, Task 61 • 61_ConvTranspose3d_ReLU_GroupNorm)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 fused_rg_atomic_opt_base_base 0.18 1.34 1.14
🥈 fused_rg_warp_opt_base_base 0.18 1.34 1.14
🥉 optimized_fused_relu_groupnorm_base 0.19 1.31 1.11
4 fused_rg_atomic_min_base_base 0.19 1.29 1.10
5 fused_rg_dynamic_bs_opt_base 0.20 1.27 1.08
6 fused_rg_aligned_base_base 0.20 1.22 1.04
7 fused_rg_even_work_base 0.21 1.20 1.02
8 fused_rg_coalesced_base 0.21 1.19 1.02
9 fused_rg_const_base_base 0.21 1.19 1.01
10 fused_rg_const_base_base 0.21 1.16 0.99
10 fused_rg_no_divergence_base 0.21 1.16 0.99
12 fused_rg_modular_base 0.21 1.15 0.98
13 61_ConvTranspose3d_ReLU_GroupNorm_modular_base 0.23 1.09 0.93
14 61_ConvTranspose3d_ReLU_GroupNorm_modular_edit_1 0.23 1.09 0.93
15 61_ConvTranspose3d_ReLU_GroupNorm_grid_stride_base 0.23 1.08 0.92
15 61_ConvTranspose3d_ReLU_GroupNorm_aligned_memory_edit_1 0.23 1.08 0.92
15 61_ConvTranspose3d_ReLU_GroupNorm_modular_base 0.23 1.08 0.92
15 61_ConvTranspose3d_ReLU_GroupNorm_aligned_memory_base 0.23 1.08 0.92
19 61_convtranspose3d_relu_groupnorm_unroll_edit_1 0.23 1.07 0.92
19 61_ConvTranspose3d_ReLU_GroupNorm_grid_stride_edit_1 0.23 1.07 0.92 
#include <pybind11/pybind11.h>
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

#define BLOCK_SIZE 256
#define WARP_SIZE 32
#define MAX_CHANNELS 4096  // Maximum number of channels stored in constant memory

// Declare constant memory arrays for GroupNorm parameters (gamma and beta)
__constant__ float c_gamma[MAX_CHANNELS];
__constant__ float c_beta[MAX_CHANNELS];

// Kernel: Fused ConvTranspose3D, ReLU, and GroupNorm with constant memory for gamma and beta
// Data layout: [N, C, D, H, W]
__global__ void fused_relu_groupnorm_const_kernel(
    float* __restrict__ data,   // input/output tensor
    int N, int C, int D, int H, int W,
    int G, float eps)           // number of groups and epsilon
{
    // Each block processes one (sample, group) pair
    int n = blockIdx.x;        // sample index
    int g = blockIdx.y;        // group index
    int channels_per_group = C / G;
    int c_start = g * channels_per_group;

    int spatial_size = D * H * W;
    int group_elems = channels_per_group * spatial_size;
    int tid = threadIdx.x;

    // Use vectorized loads for data processing (float4 for 128-bit loads)
    int aligned_elements = (group_elems / 4) * 4;

    float4 local_sum4 = make_float4(0.f, 0.f, 0.f, 0.f);
    float local_sum = 0.f;
    float local_sumsq = 0.f;

    // Process aligned elements in chunks of 4
    for (int i = tid * 4; i < aligned_elements; i += blockDim.x * 4) {
        int base_idx = n * (C * spatial_size) + (c_start * spatial_size) + i;
        float4 data4;
        // Load 4 elements
        data4.x = data[base_idx];
        data4.y = data[base_idx + 1];
        data4.z = data[base_idx + 2];
        data4.w = data[base_idx + 3];

        // Apply branchless ReLU
        data4.x = fmaxf(data4.x, 0.f);
        data4.y = fmaxf(data4.y, 0.f);
        data4.z = fmaxf(data4.z, 0.f);
        data4.w = fmaxf(data4.w, 0.f);

        // Write back activated values
        data[base_idx]     = data4.x;
        data[base_idx + 1] = data4.y;
        data[base_idx + 2] = data4.z;
        data[base_idx + 3] = data4.w;

        // Accumulate local sums for mean and variance computation
        local_sum4.x += data4.x;
        local_sum4.y += data4.y;
        local_sum4.z += data4.z;
        local_sum4.w += data4.w;

        local_sumsq += data4.x * data4.x + data4.y * data4.y +
                      data4.z * data4.z + data4.w * data4.w;
    }

    // Process remaining elements
    for (int i = aligned_elements + tid; i < group_elems; i += blockDim.x) {
        int base_idx = n * (C * spatial_size) + (c_start * spatial_size) + i;
        float val = data[base_idx];
        val = fmaxf(val, 0.f);
        data[base_idx] = val;
        local_sum += val;
        local_sumsq += val * val;
    }

    // Combine vectorized accumulations
    local_sum += local_sum4.x + local_sum4.y + local_sum4.z + local_sum4.w;

    // Warp-level reduction using shuffle intrinsics
    unsigned int mask = 0xffffffff;
    int lane = tid & (WARP_SIZE - 1);
    for (int offset = WARP_SIZE / 2; offset > 0; offset /= 2) {
        local_sum  += __shfl_down_sync(mask, local_sum, offset);
        local_sumsq += __shfl_down_sync(mask, local_sumsq, offset);
    }

    // Use shared memory to aggregate warp-level results
    __shared__ float s_sum[WARP_SIZE];
    __shared__ float s_sumsq[WARP_SIZE];
    int warp_id = tid / WARP_SIZE;
    if (lane == 0) {
        s_sum[warp_id] = local_sum;
        s_sumsq[warp_id] = local_sumsq;
    }
    __syncthreads();

    float group_sum = 0.f;
    float group_sumsq = 0.f;
    if (tid == 0) {
        int num_warps = (BLOCK_SIZE + WARP_SIZE - 1) / WARP_SIZE;
        for (int i = 0; i < num_warps; i++) {
            group_sum += s_sum[i];
            group_sumsq += s_sumsq[i];
        }
        float group_mean = group_sum / group_elems;
        float var = group_sumsq / group_elems - group_mean * group_mean;
        float inv_std = rsqrtf(var + eps);
        s_sum[0] = group_mean;
        s_sumsq[0] = inv_std;
    }
    __syncthreads();

    float group_mean = s_sum[0];
    float inv_std = s_sumsq[0];

    // Second pass: Normalize the data using computed statistics and constant memory parameters
    for (int i = tid; i < group_elems; i += blockDim.x) {
        int channel_offset = i / spatial_size;
        int c = c_start + channel_offset;
        int base_idx = n * (C * spatial_size) + (c_start * spatial_size) + i;
        float val = data[base_idx];
        val = (val - group_mean) * inv_std;
        // Retrieve gamma and beta from constant memory
        float gamma_val = c_gamma[c];
        float beta_val  = c_beta[c];
        val = val * gamma_val + beta_val;
        data[base_idx] = val;
    }
}

// Forward function: applies ConvTranspose3d then launches the fused CUDA kernel
// Copies GroupNorm parameters to constant memory before kernel launch

torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor conv_transpose,
    torch::Tensor group_norm_weight,
    torch::Tensor group_norm_bias,
    int64_t groups,
    double eps) {

    // Perform 3D transposed convolution using ATen
    auto y = at::conv_transpose3d(
        x,
        conv_transpose,
        /*bias=*/c10::nullopt,
        /*stride=*/{1, 1, 1},
        /*padding=*/{0, 0, 0},
        /*output_padding=*/{0, 0, 0},
        /*groups=*/1,
        /*dilation=*/{1, 1, 1}
    );

    int N = y.size(0);
    int C = y.size(1);
    int D = y.size(2);
    int H = y.size(3);
    int W = y.size(4);
    int G = groups;

    // Copy GroupNorm parameters to constant memory (ensure C does not exceed MAX_CHANNELS)
    cudaMemcpyToSymbol(c_gamma, group_norm_weight.data_ptr<float>(), C * sizeof(float));
    cudaMemcpyToSymbol(c_beta, group_norm_bias.data_ptr<float>(), C * sizeof(float));

    dim3 grid(N, G);
    dim3 block(BLOCK_SIZE);

    fused_relu_groupnorm_const_kernel<<<grid, block>>>(
         y.data_ptr<float>(),
         N, C, D, H, W,
         G, static_cast<float>(eps)
    );

    cudaDeviceSynchronize();
    return y;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Fused ConvTranspose3D + ReLU + GroupNorm with constant memory (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.788 inst/cycle 0.000 5
Executed Ipc Elapsed 0.684 inst/cycle 0.000 5
Issue Slots Busy 19.644 % 0.002 5
Issued Ipc Active 0.788 inst/cycle 0.000 5
SM Busy 21.648 % 0.003 5
Memory Throughput 729817056269.496 byte/second 12062040277117696000.000 5
Mem Busy 21.340 % 0.005 5
Max Bandwidth 26.588 % 0.006 5
L1/TEX Hit Rate 89.740 % 0.000 5
L2 Hit Rate 67.416 % 0.001 5
Mem Pipes Busy 7.974 % 0.001 5
Warp Cycles Per Issued Instruction 10.150 cycle 0.001 5
Warp Cycles Per Executed Instruction 10.166 cycle 0.001 5
Avg. Active Threads Per Warp 31.930 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.360 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 25.000 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 12.470 % 0.000 5
Achieved Active Warps Per SM 7.980 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (21.7%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
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 (12.5%) 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 827292.17 μs
Device Time 820.98 μs
Self CPU Time 60.95 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 827231.22 μs
Device Time 820.98 μs
Self CPU Time 120.13 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 825886.15 μs
Device Time 0.00 μs
Self CPU Time 133.57 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 821612.57 μs
Device Time 0.00 μs
Self CPU Time 821612.57 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::conv_transpose3d
CPU Time 435446.21 μs
Device Time 1040062.47 μs
Self CPU Time 10279.20 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::convolution
CPU Time 425167.02 μs
Device Time 1040062.47 μs
Self CPU Time 17303.17 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 407863.84 μs
Device Time 1040062.47 μs
Self CPU Time 16875.64 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_transpose
CPU Time 390988.20 μs
Device Time 1040062.47 μs
Self CPU Time 192811.60 μs
Self Device Time 1040062.47 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
sm90_xmma_dgrad_implicit_gemm_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize256x64x32_warpgroupsize1x1x1_g1_execute_segment_k_off_kernel__5x_cudnn
CPU Time 0.00 μs
Device Time 811966.52 μs
Self CPU Time 0.00 μs
Self Device Time 811966.52 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceSynchronize
CPU Time 1592616.15 μs
Device Time 54241.37 μs
Self CPU Time 1592616.15 μs
Self Device Time 54241.37 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 
45298 warnings generated when compiling for host.
Suppressed 45323 warnings (45276 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:18:5 bugprone-easily-swappable-parameters
18 | int N, int C, int D, int H, int W,
| ^~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:18:9: note: the first parameter in the range is 'N'
18 | int N, int C, int D, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:18:23: note: the last parameter in the range is 'D'
18 | int N, int C, int D, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:18:33: warning: 3 adjacent parameters of 'fused_relu_groupnorm_const_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
18 | int N, int C, int D, int H, int W,
| ^~~~~~
19 | int G, float eps) // number of groups and epsilon
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:18:37: note: the first parameter in the range is 'W'
18 | int N, int C, int D, int H, int W,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:19:18: note: the last parameter in the range is 'eps'
19 | int G, float eps) // number of groups and epsilon
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:19:12: note: 'int' and 'float' may be implicitly converted
19 | int G, float eps) // number of groups and epsilon
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:22:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
22 | int n = blockIdx.x; // sample index
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:23:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
23 | int g = blockIdx.y; // group index
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/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/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:39:54: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
39 | for (int i = tid * 4; i < aligned_elements; i += blockDim.x * 4) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:71:64: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
71 | for (int i = aligned_elements + tid; i < group_elems; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:109:40: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
109 | float group_mean = group_sum / group_elems;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:110:35: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
110 | float var = group_sumsq / group_elems - group_mean * group_mean;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:121:45: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
121 | for (int i = tid; i < group_elems; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:139:19: 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]
139 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:140:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
140 | torch::Tensor conv_transpose,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
141 | torch::Tensor group_norm_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:140:19: note: the first parameter in the range is 'conv_transpose'
140 | torch::Tensor conv_transpose,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:141:19: note: the last parameter in the range is 'group_norm_weight'
141 | torch::Tensor group_norm_weight,
| ^~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:140:19: warning: the parameter 'conv_transpose' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
140 | torch::Tensor conv_transpose,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:141:19: warning: the parameter 'group_norm_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
141 | torch::Tensor group_norm_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:142:19: warning: the parameter 'group_norm_bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
142 | torch::Tensor group_norm_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:143:5: warning: 2 adjacent parameters of 'forward' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
143 | int64_t groups,
| ^~~~~~~~~~~~~~~
144 | double eps) {
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:143:13: note: the first parameter in the range is 'groups'
143 | int64_t groups,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:144:12: note: the last parameter in the range is 'eps'
144 | double eps) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:143:5: note:
143 | int64_t groups,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:144:5: note: 'int64_t' and 'double' may be implicitly converted: 'int64_t' (as 'long') -> 'double', 'double' -> 'int64_t' (as 'long')
144 | double eps) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:158:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
158 | int N = y.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:159:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
159 | int C = y.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:160:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
160 | int D = y.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:161:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
161 | int H = y.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:162:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
162 | int W = y.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s0_fused_rg_const_base/base/base.cu:163:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
163 | int G = groups;
| ^