← Back to Leaderboard

The AI CUDA Engineer 👷

61_ConvTranspose3d_ReLU_GroupNorm • fused_rg_atomic_min_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_atomic_min_base_base
CUDA Speedup (Native) 1.287x
CUDA Speedup (Compile) 1.097x
CUDA Runtime 0.192 ms
PyTorch Runtime (Native) 0.247 ms
PyTorch Runtime (Compile) 0.211 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 0.00
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 NUM_WARPS (BLOCK_SIZE / WARP_SIZE)

template<typename T>
__device__ __forceinline__ T warp_shfl_down_sum(T val) {
    #pragma unroll
    for (int offset = WARP_SIZE/2; offset > 0; offset /= 2) {
        val += __shfl_down_sync(0xffffffff, val, offset);
    }
    return val;
}

__device__ __forceinline__ void load_store_vec4(float4* dst, float4* src, int idx) {
    float4 tmp = src[idx];
    tmp.x = fmaxf(tmp.x, 0.f);
    tmp.y = fmaxf(tmp.y, 0.f);
    tmp.z = fmaxf(tmp.z, 0.f);
    tmp.w = fmaxf(tmp.w, 0.f);
    dst[idx] = tmp;
}

__global__ void fused_relu_groupnorm_minimal_atomic_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)
{
    const int tid = threadIdx.x;
    const int wid = tid / WARP_SIZE;
    const int lane = tid % WARP_SIZE;
    const int n = blockIdx.x;
    const int g = blockIdx.y;
    
    const int channels_per_group = C / G;
    const int spatial_size = D * H * W;
    const int group_size = channels_per_group * spatial_size;
    const int group_offset = n * C * spatial_size + g * channels_per_group * spatial_size;

    // Shared memory for partial sums and intermediate results
    __shared__ float s_partial_sum[NUM_WARPS];
    __shared__ float s_partial_sumsq[NUM_WARPS];
    __shared__ float s_mean, s_inv_std;

    // Local accumulators
    float local_sum = 0.f;
    float local_sumsq = 0.f;

    // Process data in vectors of 4 for better memory coalescing
    float4* data4 = reinterpret_cast<float4*>(data + group_offset);
    const int vec_elements = group_size / 4;
    const int vectors_per_thread = (vec_elements + BLOCK_SIZE - 1) / BLOCK_SIZE;

    // First pass: ReLU and reduction
    #pragma unroll 2
    for (int i = 0; i < vectors_per_thread; i++) {
        const int idx = tid + i * BLOCK_SIZE;
        if (idx < vec_elements) {
            float4 val4 = data4[idx];
            
            // Apply ReLU
            val4.x = fmaxf(val4.x, 0.f);
            val4.y = fmaxf(val4.y, 0.f);
            val4.z = fmaxf(val4.z, 0.f);
            val4.w = fmaxf(val4.w, 0.f);
            
            // Store back
            data4[idx] = val4;
            
            // Accumulate
            local_sum += val4.x + val4.y + val4.z + val4.w;
            local_sumsq += val4.x * val4.x + val4.y * val4.y + 
                          val4.z * val4.z + val4.w * val4.w;
        }
    }

    // Handle remaining elements
    const int rem_start = vec_elements * 4;
    for (int i = rem_start + tid; i < group_size; i += BLOCK_SIZE) {
        float val = data[group_offset + i];
        val = fmaxf(val, 0.f);
        data[group_offset + i] = val;
        local_sum += val;
        local_sumsq += val * val;
    }

    // Warp-level reduction without atomics
    local_sum = warp_shfl_down_sum(local_sum);
    local_sumsq = warp_shfl_down_sum(local_sumsq);

    // Only lane 0 of each warp writes to shared memory
    if (lane == 0) {
        s_partial_sum[wid] = local_sum;
        s_partial_sumsq[wid] = local_sumsq;
    }
    __syncthreads();

    // Final reduction by first warp only
    if (wid == 0) {
        local_sum = (lane < NUM_WARPS) ? s_partial_sum[lane] : 0.f;
        local_sumsq = (lane < NUM_WARPS) ? s_partial_sumsq[lane] : 0.f;
        
        local_sum = warp_shfl_down_sum(local_sum);
        local_sumsq = warp_shfl_down_sum(local_sumsq);

        if (lane == 0) {
            float mean = local_sum / group_size;
            float variance = local_sumsq / group_size - mean * mean;
            s_mean = mean;
            s_inv_std = rsqrtf(variance + eps);
        }
    }
    __syncthreads();

    const float mean = s_mean;
    const float inv_std = s_inv_std;

    // Normalize with vectorized loads/stores
    #pragma unroll 2
    for (int i = 0; i < vectors_per_thread; i++) {
        const int idx = tid + i * BLOCK_SIZE;
        if (idx < vec_elements) {
            float4 val4 = data4[idx];
            const int base_idx = idx * 4;
            
            #pragma unroll
            for (int j = 0; j < 4; j++) {
                const int channel_idx = (base_idx + j) / spatial_size;
                const int c = g * channels_per_group + channel_idx;
                float* val_ptr = &((&val4.x)[j]);
                *val_ptr = (*val_ptr - mean) * inv_std;
                *val_ptr = *val_ptr * __ldg(&gamma[c]) + __ldg(&beta[c]);
            }
            
            data4[idx] = val4;
        }
    }

    // Handle remaining elements
    for (int i = rem_start + tid; i < group_size; i += BLOCK_SIZE) {
        const int channel_idx = i / spatial_size;
        const int c = g * channels_per_group + channel_idx;
        float val = data[group_offset + i];
        val = (val - mean) * inv_std;
        val = val * __ldg(&gamma[c]) + __ldg(&beta[c]);
        data[group_offset + i] = val;
    }
}

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;

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

    fused_relu_groupnorm_minimal_atomic_kernel<<<grid, block>>>(
         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)
    );

    return y;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Fused ConvTranspose3D + ReLU + GroupNorm with minimal atomics (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.036 inst/cycle 0.000 5
Executed Ipc Elapsed 0.908 inst/cycle 0.000 5
Issue Slots Busy 25.908 % 0.016 5
Issued Ipc Active 1.036 inst/cycle 0.000 5
SM Busy 25.908 % 0.016 5
Memory Throughput 1084281700459.636 byte/second 22850068530431377408.000 5
Mem Busy 25.574 % 0.010 5
Max Bandwidth 35.098 % 0.020 5
L1/TEX Hit Rate 77.710 % 0.000 5
L2 Hit Rate 67.434 % 0.002 5
Mem Pipes Busy 9.010 % 0.001 5
Warp Cycles Per Issued Instruction 7.638 cycle 0.002 5
Warp Cycles Per Executed Instruction 7.644 cycle 0.002 5
Avg. Active Threads Per Warp 31.940 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.110 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 28.000 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 12.348 % 0.000 5
Achieved Active Warps Per SM 7.900 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (21.6%) 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 (12.3%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. See the CUDA Best Practices Guide (https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy) for more details on optimizing occupancy.
Operation / Metric Value Unit
aten::conv_transpose3d
CPU Time 1198514.87 μs
Device Time 1153919.37 μs
Self CPU Time 11131.90 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 1187382.97 μs
Device Time 1153919.37 μs
Self CPU Time 17047.74 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 1170335.23 μs
Device Time 1153919.37 μs
Self CPU Time 18531.16 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::cudnn_convolution_transpose
CPU Time 1151804.07 μs
Device Time 1153919.37 μs
Self CPU Time 196445.07 μs
Self Device Time 1153919.37 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 715567.84 μs
Device Time 61822.36 μs
Self CPU Time 715567.84 μs
Self Device Time 61822.36 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 898586.87 μs
Self CPU Time 0.00 μs
Self Device Time 898586.87 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 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/b9_s3_fused_rg_atomic_min_base/base/base.cu:19:49 bugprone-easily-swappable-parameters
19 | __device__ __forceinline__ void load_store_vec4(float4* dst, float4* src, int idx) {
| ^~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:19:57: note: the first parameter in the range is 'dst'
19 | __device__ __forceinline__ void load_store_vec4(float4* dst, float4* src, int idx) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:19:70: note: the last parameter in the range is 'src'
19 | __device__ __forceinline__ void load_store_vec4(float4* dst, float4* src, int idx) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:32:5: warning: 3 adjacent parameters of 'fused_relu_groupnorm_minimal_atomic_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
32 | 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/b9_s3_fused_rg_atomic_min_base/base/base.cu:32:9: note: the first parameter in the range is 'N'
32 | 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/b9_s3_fused_rg_atomic_min_base/base/base.cu:32:23: note: the last parameter in the range is 'D'
32 | 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/b9_s3_fused_rg_atomic_min_base/base/base.cu:32:33: warning: 3 adjacent parameters of 'fused_relu_groupnorm_minimal_atomic_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
32 | int N, int C, int D, int H, int W,
| ^~~~~~
33 | int G, float eps)
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:32:37: note: the first parameter in the range is 'W'
32 | 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/b9_s3_fused_rg_atomic_min_base/base/base.cu:33:18: note: the last parameter in the range is 'eps'
33 | int G, float eps)
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:33:12: note: 'int' and 'float' may be implicitly converted
33 | int G, float eps)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:35:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
35 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:38:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
38 | const int n = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:39:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
39 | const int g = blockIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:113:38: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
113 | float mean = local_sum / group_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:114:44: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
114 | float variance = local_sumsq / group_size - mean * mean;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:157:19: 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]
157 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:158:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
158 | torch::Tensor conv_transpose,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
159 | torch::Tensor group_norm_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:158:19: note: the first parameter in the range is 'conv_transpose'
158 | torch::Tensor conv_transpose,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:159:19: note: the last parameter in the range is 'group_norm_weight'
159 | torch::Tensor group_norm_weight,
| ^~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:158:19: 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]
158 | torch::Tensor conv_transpose,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:159:19: 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]
159 | torch::Tensor group_norm_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:160:19: 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]
160 | torch::Tensor group_norm_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:161:5: warning: 2 adjacent parameters of 'forward' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
161 | int64_t groups,
| ^~~~~~~~~~~~~~~
162 | double eps) {
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:161:13: note: the first parameter in the range is 'groups'
161 | int64_t groups,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:162:12: note: the last parameter in the range is 'eps'
162 | double eps) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:161:5: note:
161 | int64_t groups,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:162:5: note: 'int64_t' and 'double' may be implicitly converted: 'int64_t' (as 'long') -> 'double', 'double' -> 'int64_t' (as 'long')
162 | double eps) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:175:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
175 | int N = y.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:176:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
176 | int C = y.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:177:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
177 | int D = y.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:178:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
178 | int H = y.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:179:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
179 | int W = y.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b9_s3_fused_rg_atomic_min_base/base/base.cu:180:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
180 | int G = groups;
| ^