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61_ConvTranspose3d_ReLU_GroupNorm • fused_rg_coalesced_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_coalesced_base
CUDA Speedup (Native) 1.193x
CUDA Speedup (Compile) 1.018x
CUDA Runtime 0.207 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

// This kernel fuses ReLU and Group Normalization on the output of conv_transpose3d.
// It ensures memory coalescing by aligning global memory accesses using vectorized loads/stores (float4) and
// by loading the per-channel gamma and beta values into shared memory. Threads in a warp access consecutive
// memory locations, which minimizes memory transaction overhead.

// Kernel parameters:
//   data: pointer to tensor of shape [N, C, D, H, W]
//   gamma, beta: GroupNorm parameters (each of length C)
//   N, C, D, H, W: tensor dimensions
//   G: number of groups; channels are divided into G contiguous groups
//   eps: epsilon for numerical stability in variance computation

__global__ void fused_relu_groupnorm_coalesced_kernel(
    float* __restrict__ data,
    const float* __restrict__ gamma,
    const float* __restrict__ beta,
    int N, int C, int D, int H, int W,
    int G, float eps) {

    // 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 spatial_size = D * H * W;
    int group_elems = channels_per_group * spatial_size;

    // Compute the starting offset for this group's data in the tensor
    int group_offset = n * (C * spatial_size) + g * channels_per_group * spatial_size;
    float* group_data = data + group_offset;

    // Allocate dynamic shared memory:
    // First part: two arrays for per-channel gamma and beta (size: channels_per_group each).
    // Second part: two arrays (of fixed size 32) for warp-level reduction of sum and sumsq.
    extern __shared__ float s_mem[];
    float* sh_gamma = s_mem;                           // size: channels_per_group
    float* sh_beta  = sh_gamma + channels_per_group;     // size: channels_per_group
    // Next 64 floats reserved for warp reduction (32 for sum, 32 for sumsq)
    float* s_sum    = sh_beta + channels_per_group;      // size: up to 32 floats
    float* s_sumsq  = s_sum + 32;                         // size: up to 32 floats

    // Load GroupNorm parameters for this group into shared memory for faster access
    if (threadIdx.x < channels_per_group) {
        sh_gamma[threadIdx.x] = gamma[g * channels_per_group + threadIdx.x];
        sh_beta[threadIdx.x]  = beta[g * channels_per_group + threadIdx.x];
    }
    __syncthreads();

    // First pass: Apply ReLU and compute the sum and sumofsquares over all elements in the group.
    // We use vectorized loads/stores (float4) to ensure that threads in a warp access consecutive memory locations.
    int total_vectors = group_elems / 4; // number of float4 elements
    int remainder = group_elems % 4;       // remaining elements that are not part of a full float4

    float local_sum = 0.0f;
    float local_sumsq = 0.0f;

    // Reinterpret the group's data as a float4 pointer for coalesced access
    float4* group_data4 = reinterpret_cast<float4*>(group_data);

    // Process the vectorized portion
    for (int i = threadIdx.x; i < total_vectors; i += blockDim.x) {
        float4 val4 = group_data4[i];
        // Apply ReLU element-wise
        float x0 = fmaxf(val4.x, 0.0f);
        float x1 = fmaxf(val4.y, 0.0f);
        float x2 = fmaxf(val4.z, 0.0f);
        float x3 = fmaxf(val4.w, 0.0f);
        
        // Store the activated values back
        val4.x = x0; val4.y = x1; val4.z = x2; val4.w = x3;
        group_data4[i] = val4;

        // Accumulate the sums and sums of squares
        local_sum   += (x0 + x1 + x2 + x3);
        local_sumsq += (x0*x0 + x1*x1 + x2*x2 + x3*x3);
    }

    // Process the remainder elements with scalar accesses
    int offset = total_vectors * 4;
    for (int i = threadIdx.x; i < remainder; i += blockDim.x) {
        int idx = offset + i;
        float val = group_data[idx];
        float relu_val = fmaxf(val, 0.0f);
        group_data[idx] = relu_val;
        local_sum += relu_val;
        local_sumsq += relu_val * relu_val;
    }

    // Warp-level reduction using shuffle intrinsics
    unsigned int mask = 0xffffffff;
    int lane = threadIdx.x & (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);
    }

    // Each warp’s lane 0 writes its result into shared memory
    int warp_id = threadIdx.x / WARP_SIZE;
    if (lane == 0) {
        s_sum[warp_id] = local_sum;
        s_sumsq[warp_id] = local_sumsq;
    }
    __syncthreads();

    // Thread 0 of the block performs the final reduction and computes group mean and inverse std.
    float group_mean, inv_std;
    if (threadIdx.x == 0) {
        int num_warps = (blockDim.x + WARP_SIZE - 1) / WARP_SIZE;
        float sum_total = 0.0f;
        float sumsq_total = 0.0f;
        for (int i = 0; i < num_warps; i++) {
            sum_total  += s_sum[i];
            sumsq_total += s_sumsq[i];
        }
        group_mean = sum_total / group_elems;
        float variance = sumsq_total / group_elems - group_mean * group_mean;
        inv_std = rsqrtf(variance + eps);
        // Store for broadcasting
        s_sum[0] = group_mean;
        s_sumsq[0] = inv_std;
    }
    __syncthreads();
    group_mean = s_sum[0];
    inv_std = s_sumsq[0];

    // Second pass: Normalize the data using the computed group statistics and the per-channel gamma and beta
    // Process the vectorized portion
    for (int i = threadIdx.x; i < total_vectors; i += blockDim.x) {
        float4 val4 = group_data4[i];
        int base_index = i * 4;  // linear index of the first element in this float4
        // Process each component of the float4
        #pragma unroll
        for (int j = 0; j < 4; j++) {
            int idx = base_index + j;            // overall index within the group
            int channel_idx = idx / spatial_size; // determine which channel this element belongs to
            float gamma_val = sh_gamma[channel_idx];
            float beta_val = sh_beta[channel_idx];
            float x = ((&val4.x)[j] - group_mean) * inv_std;
            (&val4.x)[j] = x * gamma_val + beta_val;
        }
        group_data4[i] = val4;
    }

    // Process any remaining elements
    for (int i = threadIdx.x; i < remainder; i += blockDim.x) {
        int idx = total_vectors * 4 + i;
        int channel_idx = idx / spatial_size;
        float x = (group_data[idx] - group_mean) * inv_std;
        group_data[idx] = x * sh_gamma[channel_idx] + sh_beta[channel_idx];
    }
}

// The forward function performs 3D transposed convolution using ATen and then launches the custom fused kernel
// to apply ReLU activation and Group Normalization with aligned, coalesced global memory accesses.

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) {

    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;
    int channels_per_group = C / G;
    
    // Compute shared memory size:
    //  - sh_gamma and sh_beta: 2 * channels_per_group floats
    //  - Warp reduction arrays: 64 floats (32 for sum and 32 for sumsq)
    size_t shared_mem_size = (channels_per_group * 2 + 64) * sizeof(float);
    
    dim3 grid(N, G);
    dim3 block(BLOCK_SIZE);
    
    fused_relu_groupnorm_coalesced_kernel<<<grid, block, shared_mem_size>>>(
         y.data_ptr<float>(),
         group_norm_weight.data_ptr<float>(),
         group_norm_bias.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 coalesced global memory accesses (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.880 inst/cycle 0.000 5
Executed Ipc Elapsed 0.770 inst/cycle 0.000 5
Issue Slots Busy 22.040 % 0.004 5
Issued Ipc Active 0.880 inst/cycle 0.000 5
SM Busy 22.558 % 0.004 5
Memory Throughput 991720271350.794 byte/second 51510348512542760960.000 5
Mem Busy 23.422 % 0.017 5
Max Bandwidth 32.252 % 0.027 5
L1/TEX Hit Rate 74.950 % 0.000 5
L2 Hit Rate 67.376 % 0.001 5
Mem Pipes Busy 7.158 % 0.002 5
Warp Cycles Per Issued Instruction 9.066 cycle 0.002 5
Warp Cycles Per Executed Instruction 9.072 cycle 0.002 5
Avg. Active Threads Per Warp 31.960 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.020 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 23.000 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 12.446 % 0.000 5
Achieved Active Warps Per SM 7.964 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (22.6%) 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 (12.4%) 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 467533.39 μs
Device Time 863.13 μs
Self CPU Time 53.73 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 467479.66 μs
Device Time 863.13 μs
Self CPU Time 102.99 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 466099.27 μs
Device Time 0.00 μs
Self CPU Time 117.52 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 466505.75 μs
Device Time 0.00 μs
Self CPU Time 466505.75 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 430218.43 μs
Device Time 1057192.21 μs
Self CPU Time 9977.79 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::convolution
CPU Time 420240.64 μs
Device Time 1057192.21 μs
Self CPU Time 15499.61 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 404741.03 μs
Device Time 1057192.21 μs
Self CPU Time 17098.97 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 387642.06 μs
Device Time 1057192.21 μs
Self CPU Time 196362.11 μs
Self Device Time 1057192.21 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 822377.48 μs
Self CPU Time 0.00 μs
Self Device Time 822377.48 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 1592730.67 μs
Device Time 110655.64 μs
Self CPU Time 1592730.67 μs
Self Device Time 110655.64 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 
45306 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_s1_fused_rg_coalesced/base/base.cu:23:5 bugprone-easily-swappable-parameters
23 | const float* __restrict__ gamma,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
24 | const float* __restrict__ beta,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:23:31: note: the first parameter in the range is 'gamma'
23 | const float* __restrict__ gamma,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:24:31: note: the last parameter in the range is 'beta'
24 | const float* __restrict__ beta,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:25:5: warning: 3 adjacent parameters of 'fused_relu_groupnorm_coalesced_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
25 | 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_s1_fused_rg_coalesced/base/base.cu:25:9: note: the first parameter in the range is 'N'
25 | 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_s1_fused_rg_coalesced/base/base.cu:25:23: note: the last parameter in the range is 'D'
25 | 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_s1_fused_rg_coalesced/base/base.cu:25:33: warning: 3 adjacent parameters of 'fused_relu_groupnorm_coalesced_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
25 | int N, int C, int D, int H, int W,
| ^~~~~~
26 | int G, float eps) {
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:25:37: note: the first parameter in the range is 'W'
25 | 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_s1_fused_rg_coalesced/base/base.cu:26:18: note: the last parameter in the range is 'eps'
26 | int G, float eps) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:26:12: note: 'int' and 'float' may be implicitly converted
26 | int G, float eps) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:29:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
29 | int n = blockIdx.x; // sample index
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:30:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | int g = blockIdx.y; // group index
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:68:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
68 | for (int i = threadIdx.x; i < total_vectors; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:68:55: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
68 | for (int i = threadIdx.x; i < total_vectors; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:87:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
87 | for (int i = threadIdx.x; i < remainder; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:87:51: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
87 | for (int i = threadIdx.x; i < remainder; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:98:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | int lane = threadIdx.x & (WARP_SIZE - 1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:105:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
105 | int warp_id = threadIdx.x / WARP_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:115:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
115 | int num_warps = (blockDim.x + WARP_SIZE - 1) / WARP_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:122:34: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
122 | group_mean = sum_total / group_elems;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:123:40: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
123 | float variance = sumsq_total / group_elems - group_mean * group_mean;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:135:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
135 | for (int i = threadIdx.x; i < total_vectors; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:135:55: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
135 | for (int i = threadIdx.x; i < total_vectors; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:152:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
152 | for (int i = threadIdx.x; i < remainder; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:152:51: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
152 | for (int i = threadIdx.x; i < remainder; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:164:19: 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]
164 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:165:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
165 | torch::Tensor conv_transpose,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
166 | torch::Tensor group_norm_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:165:19: note: the first parameter in the range is 'conv_transpose'
165 | torch::Tensor conv_transpose,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:166:19: note: the last parameter in the range is 'group_norm_weight'
166 | torch::Tensor group_norm_weight,
| ^~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:165:19: 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]
165 | torch::Tensor conv_transpose,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:166:19: 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]
166 | torch::Tensor group_norm_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:167:19: 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]
167 | torch::Tensor group_norm_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:168:5: warning: 2 adjacent parameters of 'forward' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
168 | int64_t groups,
| ^~~~~~~~~~~~~~~
169 | double eps) {
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:168:13: note: the first parameter in the range is 'groups'
168 | int64_t groups,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:169:12: note: the last parameter in the range is 'eps'
169 | double eps) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:168:5: note:
168 | int64_t groups,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:169:5: note: 'int64_t' and 'double' may be implicitly converted: 'int64_t' (as 'long') -> 'double', 'double' -> 'int64_t' (as 'long')
169 | double eps) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:182:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
182 | int N = y.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:183:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
183 | int C = y.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:184:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
184 | int D = y.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:185:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
185 | int H = y.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:186:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
186 | int W = y.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b6_s1_fused_rg_coalesced/base/base.cu:187:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
187 | int G = groups;
| ^