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61_ConvTranspose3d_ReLU_GroupNorm • fused_rg_dynamic_bs_opt_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_dynamic_bs_opt_base
CUDA Speedup (Native) 1.267x
CUDA Speedup (Compile) 1.080x
CUDA Runtime 0.195 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 WARP_SIZE 32

// Warp-level sum reduction using shuffle
template <typename T>
__device__ __forceinline__ T warp_reduce_sum(T val) {
    #pragma unroll
    for (int offset = WARP_SIZE / 2; offset > 0; offset /= 2) {
         val += __shfl_down_sync(0xffffffff, val, offset);
    }
    return val;
}

// Templated function for warp reduction on two values
template <int BLOCK_SIZE>
__device__ __forceinline__ void warp_reduce(float &sum, float &sumsq) {
    sum = warp_reduce_sum(sum);
    sumsq = warp_reduce_sum(sumsq);
}

// Templated kernel with configurable BLOCK_SIZE
// This kernel applies ReLU and then computes group normalization in a fused manner
// using vectorized loads (float4) and hierarchical warp-level reductions.

template <int BLOCK_SIZE>
__global__ void fused_relu_groupnorm_dynamic_bs_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) {

    constexpr int BLOCK_SIZE_CONST = BLOCK_SIZE;
    const int NUM_WARPS = BLOCK_SIZE_CONST / WARP_SIZE;

    __shared__ float s_warp_sum[32];      // using 32 as safe upper bound for warps
    __shared__ float s_warp_sumsq[32];
    __shared__ float s_mean, s_inv_std;

    // Each block processes one (sample, group) pair
    const int n = blockIdx.x; // sample index
    const int g = blockIdx.y; // group index
    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 spatial_size = D * H * W;
    const int group_size = channels_per_group * spatial_size;
    const int group_offset = n * C * spatial_size + c_start * spatial_size;

    // Process elements in chunks of float4 for coalesced memory accesses
    const int vec_size = 4;
    int num_vectors = group_size / vec_size;
    int vectors_per_thread = (num_vectors + BLOCK_SIZE_CONST - 1) / BLOCK_SIZE_CONST;

    float thread_sum = 0.f;
    float thread_sumsq = 0.f;

    float4* data4 = reinterpret_cast<float4*>(data + group_offset);

    #pragma unroll
    for (int i = 0; i < vectors_per_thread; i++) {
         int idx = tid + i * BLOCK_SIZE_CONST;
         if (idx < num_vectors) {
              float4 val4 = data4[idx];
              
              // Apply ReLU activation to each component
              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_sum += (val4.x + val4.y + val4.z + val4.w);
              thread_sumsq += (val4.x * val4.x + val4.y * val4.y +
                               val4.z * val4.z + val4.w * val4.w);
         }
    }

    // Process remaining elements that do not form a complete float4
    int remainder_start = num_vectors * vec_size;
    for (int i = tid; i < group_size - remainder_start; i += BLOCK_SIZE_CONST) {
         int idx = group_offset + remainder_start + i;
         float val = data[idx];
         val = fmaxf(val, 0.f);
         data[idx] = val;
         thread_sum += val;
         thread_sumsq += val * val;
    }

    // Warp-level reduction
    warp_reduce<BLOCK_SIZE_CONST>(thread_sum, thread_sumsq);

    // Write per-warp results to shared memory
    if (lane == 0) {
         s_warp_sum[wid] = thread_sum;
         s_warp_sumsq[wid] = thread_sumsq;
    }
    __syncthreads();

    // Final reduction across warps (first warp only)
    if (wid == 0) {
         float block_sum = (lane < NUM_WARPS) ? s_warp_sum[lane] : 0.f;
         float block_sumsq = (lane < NUM_WARPS) ? s_warp_sumsq[lane] : 0.f;
         block_sum = warp_reduce_sum(block_sum);
         block_sumsq = warp_reduce_sum(block_sumsq);
         if (lane == 0) {
              float mean = block_sum / group_size;
              float variance = block_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;

    // Normalization phase: adjust values using computed mean and inv_std
    #pragma unroll
    for (int i = 0; i < vectors_per_thread; i++) {
         int idx = tid + i * BLOCK_SIZE_CONST;
         if (idx < num_vectors) {
             float4 val4 = data4[idx];
             int base_idx = idx * vec_size;
             #pragma unroll
             for (int j = 0; j < 4; j++) {
                  int channel_idx = (base_idx + j) / spatial_size; 
                  int c = c_start + channel_idx;
                  float* v = &((&val4.x)[j]);
                  *v = ((*v - mean) * inv_std);
                  *v = *v * __ldg(&gamma[c]) + __ldg(&beta[c]);
             }
             data4[idx] = val4;
         }
    }

    // Normalization for remaining elements
    for (int i = tid; i < group_size - remainder_start; i += BLOCK_SIZE_CONST) {
         int idx = group_offset + remainder_start + i;
         int channel_idx = (remainder_start + i) / spatial_size;
         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;
    }
}

// Forward function: chooses optimal block size based on group size and launches
// the appropriate templated kernel instantiation.

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 ConvTranspose3d operation
    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;

    const int spatial_size = D * H * W;
    const int channels_per_group = C / G;
    const int group_size = channels_per_group * spatial_size;

    // Select an optimal block size based on the number of elements per group
    int block_size;
    if (group_size < 64)
         block_size = 32;
    else if (group_size < 128)
         block_size = 64;
    else if (group_size < 256)
         block_size = 128;
    else if (group_size < 512)
         block_size = 256;
    else
         block_size = 512;

    dim3 grid(N, G);

    switch(block_size) {
       case 32:
         fused_relu_groupnorm_dynamic_bs_kernel<32><<<grid, 32>>>(
             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)
         );
         break;
       case 64:
         fused_relu_groupnorm_dynamic_bs_kernel<64><<<grid, 64>>>(
             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)
         );
         break;
       case 128:
         fused_relu_groupnorm_dynamic_bs_kernel<128><<<grid, 128>>>(
             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)
         );
         break;
       case 256:
         fused_relu_groupnorm_dynamic_bs_kernel<256><<<grid, 256>>>(
             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)
         );
         break;
       case 512:
         fused_relu_groupnorm_dynamic_bs_kernel<512><<<grid, 512>>>(
             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)
         );
         break;
       default:
         // Default to 256 if none matched
         fused_relu_groupnorm_dynamic_bs_kernel<256><<<grid, 256>>>(
             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)
         );
         break;
    }

    cudaDeviceSynchronize();
    return y;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("forward", &forward, "Dynamic BlockSize Optimized Fused ConvTranspose3D + ReLU + GroupNorm (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.340 inst/cycle 0.000 5
Executed Ipc Elapsed 1.160 inst/cycle 0.000 5
Issue Slots Busy 33.496 % 0.002 5
Issued Ipc Active 1.340 inst/cycle 0.000 5
SM Busy 33.496 % 0.002 5
Memory Throughput 1392589346020.152 byte/second 108608154345871622144.000 5
Mem Busy 33.092 % 0.036 5
Max Bandwidth 45.374 % 0.058 5
L1/TEX Hit Rate 77.710 % 0.000 5
L2 Hit Rate 67.446 % 0.000 5
Mem Pipes Busy 10.394 % 0.002 5
Warp Cycles Per Issued Instruction 11.738 cycle 0.002 5
Warp Cycles Per Executed Instruction 11.752 cycle 0.002 5
Avg. Active Threads Per Warp 31.890 0.000 5
Avg. Not Predicated Off Threads Per Warp 27.840 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 4.000 block 0.000 5
Block Limit Shared Mem 11.000 block 0.000 5
Block Limit Warps 4.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 24.490 % 0.000 5
Achieved Active Warps Per SM 15.674 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (29.8%) 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 (24.5%) 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::conv_transpose3d
CPU Time 432627.85 μs
Device Time 1138751.82 μs
Self CPU Time 10475.18 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 422152.66 μs
Device Time 1138751.82 μs
Self CPU Time 16392.83 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::_convolution
CPU Time 405759.84 μs
Device Time 1138751.82 μs
Self CPU Time 17593.19 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 388166.65 μs
Device Time 1138751.82 μs
Self CPU Time 190288.06 μs
Self Device Time 1138751.82 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 886255.12 μs
Self CPU Time 0.00 μs
Self Device Time 886255.12 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 1619248.22 μs
Device Time 1136.37 μs
Self CPU Time 1619248.22 μs
Self Device Time 1136.37 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 
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_s1_fused_rg_dynamic_bs_opt/base/base.cu:34:5 bugprone-easily-swappable-parameters
34 | 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_s1_fused_rg_dynamic_bs_opt/base/base.cu:34:9: note: the first parameter in the range is 'N'
34 | 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_s1_fused_rg_dynamic_bs_opt/base/base.cu:34:23: note: the last parameter in the range is 'D'
34 | 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_s1_fused_rg_dynamic_bs_opt/base/base.cu:34:33: warning: 3 adjacent parameters of 'fused_relu_groupnorm_dynamic_bs_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
34 | int N, int C, int D, int H, int W,
| ^~~~~~
35 | int G, float eps) {
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:34:37: note: the first parameter in the range is 'W'
34 | 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_s1_fused_rg_dynamic_bs_opt/base/base.cu:35:18: note: the last parameter in the range is 'eps'
35 | int G, float eps) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:35:12: note: 'int' and 'float' may be implicitly converted
35 | int G, float eps) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:45:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
45 | const int n = blockIdx.x; // sample index
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:46:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
46 | const int g = blockIdx.y; // group index
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:47:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
47 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:115:40: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
115 | float mean = block_sum / group_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:116:46: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
116 | float variance = block_sumsq / group_size - mean * mean;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:161:19: 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]
161 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:162:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
162 | torch::Tensor conv_transpose,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
163 | torch::Tensor group_norm_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:162:19: note: the first parameter in the range is 'conv_transpose'
162 | torch::Tensor conv_transpose,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:163:19: note: the last parameter in the range is 'group_norm_weight'
163 | torch::Tensor group_norm_weight,
| ^~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:162:19: 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]
162 | torch::Tensor conv_transpose,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:163:19: 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]
163 | torch::Tensor group_norm_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:164:19: 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]
164 | torch::Tensor group_norm_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:165:5: warning: 2 adjacent parameters of 'forward' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
165 | int64_t groups,
| ^~~~~~~~~~~~~~~
166 | double eps) {
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:165:13: note: the first parameter in the range is 'groups'
165 | int64_t groups,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:166:12: note: the last parameter in the range is 'eps'
166 | double eps) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:165:5: note:
165 | int64_t groups,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/base/base.cu:166:5: note: 'int64_t' and 'double' may be implicitly converted: 'int64_t' (as 'long') -> 'double', 'double' -> 'int64_t' (as 'long')
166 | double eps) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/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 N = y.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/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 C = y.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/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 D = y.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/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 H = y.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/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 W = y.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250204_optimize_b10_s4_e0_sweep/level_2/task_61/b10_s1_fused_rg_dynamic_bs_opt/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 G = groups;
| ^