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61_ConvTranspose3d_ReLU_GroupNorm • fused_rg_atomic_opt_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_opt_base_base
CUDA Speedup (Native) 1.343x
CUDA Speedup (Compile) 1.145x
CUDA Runtime 0.184 ms
PyTorch Runtime (Native) 0.247 ms
PyTorch Runtime (Compile) 0.211 ms
Correct True
Max Diff (vs. Reference) 0.005000
Model azure-gpt-4o-2024-08-06
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)

// Warp-level reduction function
__device__ __forceinline__ float warp_reduce_sum(float 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 warp_reduce(float& sum, float& sumsq) {
    sum = warp_reduce_sum(sum);
    sumsq = warp_reduce_sum(sumsq);
}

__global__ void fused_relu_groupnorm_atomic_opt_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)
{
    __shared__ float s_warp_sum[NUM_WARPS];
    __shared__ float s_warp_sumsq[NUM_WARPS];
    __shared__ float s_mean, s_inv_std;

    const int n = blockIdx.x;
    const int g = blockIdx.y;
    const int tid = threadIdx.x;
    const int wid = tid / WARP_SIZE;
    const int lane = tid % WARP_SIZE;
    
    const int channels_per_group = C / G;
    const int c_start = g * channels_per_group;
    const int group_size = channels_per_group * D * H * W;
    const int group_offset = n * C * D * H * W + c_start * D * H * W;

    // Each thread processes multiple elements using vectorized loads
    float4 thread_sum4 = make_float4(0.f, 0.f, 0.f, 0.f);
    float thread_sum = 0.f;
    float thread_sumsq = 0.f;

    // Process elements in chunks of float4
    const int vec_size = 4;
    const int num_vectors = group_size / vec_size;
    const int vectors_per_thread = (num_vectors + BLOCK_SIZE - 1) / BLOCK_SIZE;

    float4* data4 = reinterpret_cast<float4*>(data + group_offset);
    
    #pragma unroll 4
    for (int i = 0; i < vectors_per_thread; i++) {
        const int idx = tid + i * BLOCK_SIZE;
        if (idx < num_vectors) {
            float4 val4 = data4[idx];
            
            // Apply ReLU and accumulate
            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);
            
            data4[idx] = val4;
            
            thread_sum4.x += val4.x;
            thread_sum4.y += val4.y;
            thread_sum4.z += val4.z;
            thread_sum4.w += val4.w;
            
            thread_sumsq += val4.x * val4.x + val4.y * val4.y + 
                          val4.z * val4.z + val4.w * val4.w;
        }
    }

    // Handle remaining elements
    const int remaining_start = num_vectors * vec_size;
    #pragma unroll 4
    for (int i = tid; i < group_size - remaining_start; i += BLOCK_SIZE) {
        const int idx = group_offset + remaining_start + i;
        float val = data[idx];
        val = fmaxf(val, 0.f);
        data[idx] = val;
        thread_sum += val;
        thread_sumsq += val * val;
    }

    // Combine vector and scalar sums
    float warp_sum = thread_sum4.x + thread_sum4.y + thread_sum4.z + thread_sum4.w + thread_sum;
    float warp_sumsq = thread_sumsq;

    // First level: warp-level reduction
    warp_reduce(warp_sum, warp_sumsq);

    // Store warp results
    if (lane == 0) {
        atomicAdd(&s_warp_sum[wid], warp_sum);
        atomicAdd(&s_warp_sumsq[wid], warp_sumsq);
    }
    __syncthreads();

    // Second level: reduce across warps
    if (wid == 0) {
        warp_sum = (lane < NUM_WARPS) ? s_warp_sum[lane] : 0.f;
        warp_sumsq = (lane < NUM_WARPS) ? s_warp_sumsq[lane] : 0.f;
        
        warp_reduce(warp_sum, warp_sumsq);

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

    // Normalize using the computed statistics
    const float mean = s_mean;
    const float inv_std = s_inv_std;

    #pragma unroll 4
    for (int i = 0; i < vectors_per_thread; i++) {
        const int idx = tid + i * BLOCK_SIZE;
        if (idx < num_vectors) {
            float4 val4 = data4[idx];
            const int base_idx = idx * vec_size;
            
            #pragma unroll
            for (int j = 0; j < 4; j++) {
                const int channel_idx = (base_idx + j) / (D * H * W);
                const int c = c_start + 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
    #pragma unroll 4
    for (int i = tid; i < group_size - remaining_start; i += BLOCK_SIZE) {
        const int idx = group_offset + remaining_start + i;
        const int channel_idx = (remaining_start + i) / (D * H * W);
        const int c = c_start + channel_idx;
        
        float val = data[idx];
        val = (val - mean) * inv_std;
        val = val * __ldg(&gamma[c]) + __ldg(&beta[c]);
        data[idx] = 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_atomic_opt_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 optimized atomic operations (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.180 inst/cycle 0.000 5
Executed Ipc Elapsed 1.006 inst/cycle 0.000 5
Issue Slots Busy 29.466 % 0.001 5
Issued Ipc Active 1.180 inst/cycle 0.000 5
SM Busy 29.466 % 0.001 5
Memory Throughput 1281905580883.550 byte/second 45606237542697598976.000 5
Mem Busy 30.484 % 0.026 5
Max Bandwidth 41.772 % 0.053 5
L1/TEX Hit Rate 77.710 % 0.000 5
L2 Hit Rate 67.430 % 0.001 5
Mem Pipes Busy 9.304 % 0.002 5
Warp Cycles Per Issued Instruction 6.704 cycle 0.000 5
Warp Cycles Per Executed Instruction 6.710 cycle 0.000 5
Avg. Active Threads Per Warp 31.890 0.000 5
Avg. Not Predicated Off Threads Per Warp 27.740 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 12.342 % 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 (25.3%) 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 1188565.53 μs
Device Time 1186774.44 μs
Self CPU Time 12099.75 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::convolution
CPU Time 1176465.78 μs
Device Time 1186774.44 μs
Self CPU Time 18384.68 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 1158081.10 μs
Device Time 1186774.44 μs
Self CPU Time 19523.91 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 1138557.20 μs
Device Time 1186774.44 μs
Self CPU Time 200815.26 μs
Self Device Time 1186774.44 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 745685.80 μs
Device Time 63903.46 μs
Self CPU Time 745685.80 μs
Self Device Time 63903.46 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 923625.63 μs
Self CPU Time 0.00 μs
Self Device Time 923625.63 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 
45295 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/b10_s2_fused_rg_atomic_opt_base/base/base.cu:28:5 bugprone-easily-swappable-parameters
28 | 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/b10_s2_fused_rg_atomic_opt_base/base/base.cu:28:9: note: the first parameter in the range is 'N'
28 | 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/b10_s2_fused_rg_atomic_opt_base/base/base.cu:28:16: note: the last parameter in the range is 'C'
28 | 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/b10_s2_fused_rg_atomic_opt_base/base/base.cu:28:33: warning: 3 adjacent parameters of 'fused_relu_groupnorm_atomic_opt_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
28 | int N, int C, int D, int H, int W,
| ^~~~~~
29 | int G, float eps)
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:28:37: note: the first parameter in the range is 'W'
28 | 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/b10_s2_fused_rg_atomic_opt_base/base/base.cu:29:18: note: the last parameter in the range is 'eps'
29 | int G, float eps)
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:29:12: note: 'int' and 'float' may be implicitly converted
29 | int G, float eps)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:35:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
35 | const int n = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:36:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
36 | const int g = blockIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:37:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
37 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:116:37: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
116 | float mean = warp_sum / group_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:117:43: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
117 | float variance = warp_sumsq / group_size - mean * mean;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:163:19: 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]
163 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:164:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
164 | torch::Tensor conv_transpose,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
165 | torch::Tensor group_norm_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:164:19: note: the first parameter in the range is 'conv_transpose'
164 | torch::Tensor conv_transpose,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:165:19: note: the last parameter in the range is 'group_norm_weight'
165 | torch::Tensor group_norm_weight,
| ^~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:164:19: 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]
164 | torch::Tensor conv_transpose,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:165:19: 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]
165 | torch::Tensor group_norm_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:166:19: 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]
166 | torch::Tensor group_norm_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:167:5: warning: 2 adjacent parameters of 'forward' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
167 | int64_t groups,
| ^~~~~~~~~~~~~~~
168 | double eps) {
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:167:13: note: the first parameter in the range is 'groups'
167 | int64_t groups,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:168:12: note: the last parameter in the range is 'eps'
168 | double eps) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:167:5: note:
167 | int64_t groups,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:168:5: note: 'int64_t' and 'double' may be implicitly converted: 'int64_t' (as 'long') -> 'double', 'double' -> 'int64_t' (as 'long')
168 | double eps) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/base/base.cu:181:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
181 | int N = y.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/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 C = y.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/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 D = y.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/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 H = y.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/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 W = y.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s2_fused_rg_atomic_opt_base/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 G = groups;
| ^