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88_Gemm_GroupNorm_Swish_Multiply_Swish • optimized_kernel_base

Level 2 • Task 88
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    gemm_weight: torch.Tensor,
    gemm_bias: torch.Tensor,
    group_norm_weight: torch.Tensor,
    group_norm_bias: torch.Tensor,
    multiply_weight: torch.Tensor,
    num_groups: int,
) -> torch.Tensor:
    """
    Performs GEMM, GroupNorm, Swish, Multiply, and Swish operations.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_features)
        gemm_weight (torch.Tensor): Weight matrix for linear layer of shape (out_features, in_features)
        gemm_bias (torch.Tensor): Bias vector for linear layer of shape (out_features)
        group_norm_weight (torch.Tensor): Weight parameter for group norm of shape (out_features)
        group_norm_bias (torch.Tensor): Bias parameter for group norm of shape (out_features)
        multiply_weight (torch.Tensor): Weight tensor for multiplication of shape (out_features)
        num_groups (int): Number of groups for group normalization

    Returns:
        torch.Tensor: Output tensor of shape (batch_size, out_features)
    """
    x = F.linear(x, gemm_weight, gemm_bias)
    x = F.group_norm(x, num_groups, group_norm_weight, group_norm_bias)
    x = x * torch.sigmoid(x)
    x = x * multiply_weight
    x = x * torch.sigmoid(x)
    return x


class Model(nn.Module):
    """
    Model that performs a GEMM, GroupNorm, Swish, Multiply, and Swish operations.
    """

    def __init__(self, in_features, out_features, num_groups, multiply_weight_shape):
        super(Model, self).__init__()
        gemm = nn.Linear(in_features, out_features)
        self.gemm_weight = gemm.weight
        self.gemm_bias = gemm.bias
        group_norm = nn.GroupNorm(num_groups, out_features)
        self.group_norm_weight = group_norm.weight
        self.group_norm_bias = group_norm.bias
        self.multiply_weight = nn.Parameter(torch.randn(multiply_weight_shape) * 0.02)
        self.num_groups = num_groups

    def forward(self, x, fn=module_fn):
        return fn(
            x,
            self.gemm_weight,
            self.gemm_bias,
            self.group_norm_weight,
            self.group_norm_bias,
            self.multiply_weight,
            self.num_groups,
        )


batch_size = 128
in_features = 512
out_features = 1024
num_groups = 16
multiply_weight_shape = (out_features,)


def get_inputs():
    return [torch.randn(batch_size, in_features)]


def get_init_inputs():
    return [in_features, out_features, num_groups, multiply_weight_shape]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a GEMM, GroupNorm, Swish, Multiply, and Swish operations.
    """
    def __init__(self, in_features, out_features, num_groups, multiply_weight_shape):
        super(Model, self).__init__()
        self.gemm = nn.Linear(in_features, out_features)
        self.group_norm = nn.GroupNorm(num_groups, out_features)
        self.multiply_weight = nn.Parameter(torch.randn(multiply_weight_shape) * 0.02) 

    def forward(self, x):
        # (batch_size, in_features) -> (batch_size, out_features)
        x = self.gemm(x)
        # (batch_size, out_features) -> (batch_size, out_features)
        x = self.group_norm(x)
        # (batch_size, out_features) -> (batch_size, out_features)
        x = x * torch.sigmoid(x)
        # (batch_size, out_features) -> (batch_size, out_features)
        x = x * self.multiply_weight
        # (batch_size, out_features) -> (batch_size, out_features)
        x = x * torch.sigmoid(x)
        return x

batch_size = 128
in_features = 512
out_features = 1024
num_groups = 16
multiply_weight_shape = (out_features,)

def get_inputs():
    return [torch.randn(batch_size, in_features)]

def get_init_inputs():
    return [in_features, out_features, num_groups, multiply_weight_shape]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 88_Gemm_GroupNorm_Swish_Multiply_Swish
Level ID 2
Task ID 88
Kernel Name optimized_kernel_base
CUDA Speedup (Native) 1.675x
CUDA Speedup (Compile) 1.693x
CUDA Runtime 0.028 ms
PyTorch Runtime (Native) 0.047 ms
PyTorch Runtime (Compile) 0.047 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 88 • 88_Gemm_GroupNorm_Swish_Multiply_Swish)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 fused_multistream_base 0.02 1.95 1.98
🥇 pipelined_88_gemm_groupnorm_swish_edit_1 0.02 1.95 1.98
🥇 minimal_sync_88_gemm_groupnorm_swish_base 0.02 1.95 1.98
4 optimized_module_fn_kernel_edit_1 0.03 1.88 1.90
5 unrolled_groupnorm_kernel_base 0.03 1.74 1.76
5 88_gemm_groupnorm_swish_multiply_swish_atomic_optimized_base 0.03 1.74 1.76
5 88_gemm_groupnorm_swish_multiply_swish_shared_warp_reduction_base 0.03 1.74 1.76
5 optimized_fused_groupnorm_swish_base 0.03 1.74 1.76
5 88_Gemm_GroupNorm_Swish_Multiply_Swish_unroll_base_base 0.03 1.74 1.76
5 88_gemm_groupnorm_swish_multiply_swish_2d_base 0.03 1.74 1.76
5 88_gemm_groupnorm_swish_multiply_swish_uniform_control_flow_base 0.03 1.74 1.76
5 88_gemm_groupnorm_swish_multiply_swish_warp_base 0.03 1.74 1.76
5 88_gemm_groupnorm_swish_multiply_swish_reduction_opt_base 0.03 1.74 1.76
5 88_gemm_groupnorm_swish_multiply_swish_uniform_control_base 0.03 1.74 1.76
5 88_gemm_groupnorm_swish_multiply_swish_ldg_align_base 0.03 1.74 1.76
16 88_Gemm_GroupNorm_Swish_Multiply_Swish 0.03 1.68 1.69
16 optimized_kernel_base 0.03 1.68 1.69
16 88_gemm_groupnorm_swish_multiply_swish_atomic_minimal_base 0.03 1.68 1.69
16 grid2d_warpreduce_base 0.03 1.68 1.69
16 88_gemm_groupnorm_swish_minimal_sync_base 0.03 1.68 1.69 
/*
Optimized CUDA Kernel: Combines efficient warp-level reductions and 2D grid mapping
for group normalization with two swish activations. The kernel uses a grid of
blocks where blockIdx.x corresponds to the sample index and blockIdx.y corresponds
to the group index, reducing within each group using warp-level primitives.
*/

#include <torch/extension.h>
#include <ATen/ATen.h>
#include <vector>
#include <algorithm>

#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) \
  CHECK_CUDA(x);       \
  CHECK_CONTIGUOUS(x)

// Warp-level reduction using __shfl_down_sync
template <typename scalar_t>
__inline__ __device__
scalar_t warpReduceSum(scalar_t val) {
    for (int offset = warpSize/2; offset > 0; offset /= 2)
        val += __shfl_down_sync(0xffffffff, val, offset);
    return val;
}


// Optimized kernel: each block processes one group of one sample.
// Grid: blockIdx.x = sample index, blockIdx.y = group index.
// Each thread handles multiple channels in its group; reduction is done with warp-level primitives.

template<typename scalar_t>
__global__ void module_fn_kernel_optimized(
    const scalar_t* __restrict__ x,         // Input after GEMM, shape (N, C)
    scalar_t* __restrict__ output,            // Output, shape (N, C)
    const scalar_t* __restrict__ group_norm_weight,  // gamma, shape (C)
    const scalar_t* __restrict__ group_norm_bias,    // beta, shape (C)
    const scalar_t* __restrict__ multiply_weight,    // weight, shape (C)
    const int C,                            // Total channels
    const int channels_per_group            // Channels per group
) {
    // Determine sample and group indices
    int n = blockIdx.x;  // sample index
    int g = blockIdx.y;  // group index
    int base = g * channels_per_group;

    scalar_t partial_sum = 0;
    scalar_t partial_sumsq = 0;

    // Each thread processes part of the channels for its group
    for (int c = threadIdx.x; c < channels_per_group; c += blockDim.x) {
        int idx = n * C + base + c;
        scalar_t val = x[idx];
        partial_sum += val;
        partial_sumsq += val * val;
    }

    // Perform warp-level reduction within each warp
    partial_sum = warpReduceSum<scalar_t>(partial_sum);
    partial_sumsq = warpReduceSum<scalar_t>(partial_sumsq);

    int lane = threadIdx.x % warpSize;
    int warpId = threadIdx.x / warpSize;

    // Use shared memory to combine results from different warps (max 32 warps assumed)
    __shared__ scalar_t warp_sum[32];
    __shared__ scalar_t warp_sumsq[32];

    if (lane == 0) {
        warp_sum[warpId] = partial_sum;
        warp_sumsq[warpId] = partial_sumsq;
    }
    __syncthreads();

    scalar_t total_sum = 0;
    scalar_t total_sumsq = 0;
    int numWarps = (blockDim.x + warpSize - 1) / warpSize;

    // First few threads load the per-warp results
    if (threadIdx.x < numWarps) {
        total_sum = warp_sum[threadIdx.x];
        total_sumsq = warp_sumsq[threadIdx.x];
    } 
    // Reduce the warp results using the first warp
    if (threadIdx.x < warpSize) {
        total_sum = warpReduceSum<scalar_t>(total_sum);
        total_sumsq = warpReduceSum<scalar_t>(total_sumsq);
    }

    // Compute mean and inverse standard deviation
    __shared__ scalar_t s_mean;
    __shared__ scalar_t s_inv_std;
    if (threadIdx.x == 0) {
        s_mean = total_sum / channels_per_group;
        scalar_t var = total_sumsq / channels_per_group - s_mean * s_mean;
        s_inv_std = rsqrtf(var + static_cast<scalar_t>(1e-5));
    }
    __syncthreads();

    // Final pass: compute normalized activation and apply two swish activations
    for (int c = threadIdx.x; c < channels_per_group; c += blockDim.x) {
        int idx = n * C + base + c;
        scalar_t val = x[idx];
        scalar_t normalized = (val - s_mean) * s_inv_std;

        // Apply GroupNorm: scale and shift
        scalar_t gamma = group_norm_weight[base + c];
        scalar_t beta  = group_norm_bias[base + c];
        scalar_t y = normalized * gamma + beta;

        // First Swish activation
        scalar_t sigmoid_y = static_cast<scalar_t>(1.0) / (static_cast<scalar_t>(1.0) + expf(-y));
        y = y * sigmoid_y;

        // Multiply by weight and apply second Swish
        scalar_t w = multiply_weight[base + c];
        y = y * w;
        sigmoid_y = static_cast<scalar_t>(1.0) / (static_cast<scalar_t>(1.0) + expf(-y));
        output[idx] = y * sigmoid_y;
    }
}

// Host function: performs GEMM then launches the optimized kernel for GroupNorm+Swish activations

torch::Tensor module_fn_cuda_forward(
    torch::Tensor x,
    torch::Tensor gemm_weight,
    torch::Tensor gemm_bias,
    torch::Tensor group_norm_weight,
    torch::Tensor group_norm_bias,
    torch::Tensor multiply_weight,
    int64_t num_groups
) {
    // Perform GEMM: x_linear = x.matmul(gemm_weight.t()) + gemm_bias
    auto x_linear = torch::addmm(gemm_bias, x, gemm_weight.t());
    auto N = x_linear.size(0);
    auto C = x_linear.size(1);
    
    torch::Tensor output = torch::empty_like(x_linear);
    int channels_per_group = C / num_groups;

    // Grid: one block per (sample, group)
    dim3 blocks(N, num_groups);
    // Determine thread block size (capped at 1024 threads)
    int threads = std::min(channels_per_group, 1024);

    AT_DISPATCH_FLOATING_TYPES(x_linear.scalar_type(), "module_fn_cuda_forward", ([&] {
        module_fn_kernel_optimized<scalar_t><<<blocks, threads>>>(
            x_linear.data_ptr<scalar_t>(),
            output.data_ptr<scalar_t>(),
            group_norm_weight.data_ptr<scalar_t>(),
            group_norm_bias.data_ptr<scalar_t>(),
            multiply_weight.data_ptr<scalar_t>(),
            C,
            channels_per_group
        );
    }));

    return output;
}

// C++ interface

torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor gemm_weight,
    torch::Tensor gemm_bias,
    torch::Tensor group_norm_weight,
    torch::Tensor group_norm_bias,
    torch::Tensor multiply_weight,
    int64_t num_groups
) {
    CHECK_INPUT(x);
    CHECK_INPUT(gemm_weight);
    CHECK_INPUT(gemm_bias);
    CHECK_INPUT(group_norm_weight);
    CHECK_INPUT(group_norm_bias);
    CHECK_INPUT(multiply_weight);

    return module_fn_cuda_forward(
        x,
        gemm_weight,
        gemm_bias,
        group_norm_weight,
        group_norm_bias,
        multiply_weight,
        num_groups
    );
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Optimized module forward");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.610 inst/cycle 0.001 5
Executed Ipc Elapsed 0.882 inst/cycle 0.000 5
Issue Slots Busy 40.896 % 0.406 5
Issued Ipc Active 1.638 inst/cycle 0.001 5
SM Busy 40.896 % 0.406 5
Memory Throughput 93476334677.220 byte/second 972133353889158528.000 5
Mem Busy 10.550 % 0.014 5
Max Bandwidth 9.536 % 0.010 5
L1/TEX Hit Rate 18.520 % 0.000 5
L2 Hit Rate 78.940 % 0.316 5
Mem Pipes Busy 13.020 % 0.019 5
Warp Cycles Per Issued Instruction 15.630 cycle 0.004 5
Warp Cycles Per Executed Instruction 15.874 cycle 0.004 5
Avg. Active Threads Per Warp 29.020 0.000 5
Avg. Not Predicated Off Threads Per Warp 27.460 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 32.000 block 0.000 5
Block Limit Shared Mem 46.000 block 0.000 5
Block Limit Warps 32.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 40.262 % 0.598 5
Achieved Active Warps Per SM 25.768 warp 0.245 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (25.1%) 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.
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 (40.1%) 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 521243.92 μs
Device Time 186.01 μs
Self CPU Time 57.53 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 521186.39 μs
Device Time 186.01 μs
Self CPU Time 108.41 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 534577.05 μs
Device Time 0.00 μs
Self CPU Time 14070.48 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 520118.12 μs
Device Time 0.00 μs
Self CPU Time 520118.12 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::fill_
CPU Time 50695.68 μs
Device Time 413527.31 μs
Self CPU Time 11898.37 μs
Self Device Time 413527.31 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::zero_
CPU Time 59229.01 μs
Device Time 413527.31 μs
Self CPU Time 8556.40 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::addmm
CPU Time 352088.45 μs
Device Time 104775.07 μs
Self CPU Time 129771.60 μs
Self Device Time 104775.07 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
sm80_xmma_gemm_f32f32_f32f32_f32_tn_n_tilesize32x32x8_stage3_warpsize1x2x1_ffma_aligna4_alignc4_execute_kernel__51_cublas
CPU Time 0.00 μs
Device Time 104775.07 μs
Self CPU Time 0.00 μs
Self Device Time 104775.07 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor<int>, at::detail::Array<char*, 1> >(int, at::native::FillFunctor<int>, at::detail::Array<char*, 1>)
CPU Time 0.00 μs
Device Time 413527.31 μs
Self CPU Time 0.00 μs
Self Device Time 413527.31 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 
45309 warnings generated when compiling for host.
Suppressed 45329 warnings (45282 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/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:13:35 bugprone-macro-parentheses
13 | #define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
| ^
| ()
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:14:41: warning: macro argument should be enclosed in parentheses [bugprone-macro-parentheses]
14 | #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
| ^
| ()
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:37:5: warning: 3 adjacent parameters of 'module_fn_kernel_optimized' of similar type ('const scalar_t *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
37 | const scalar_t* __restrict__ group_norm_weight, // gamma, shape (C)
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
38 | const scalar_t* __restrict__ group_norm_bias, // beta, shape (C)
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
39 | const scalar_t* __restrict__ multiply_weight, // weight, shape (C)
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:37:34: note: the first parameter in the range is 'group_norm_weight'
37 | const scalar_t* __restrict__ group_norm_weight, // gamma, shape (C)
| ^~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:39:34: note: the last parameter in the range is 'multiply_weight'
39 | const scalar_t* __restrict__ multiply_weight, // weight, shape (C)
| ^~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:40:5: warning: 2 adjacent parameters of 'module_fn_kernel_optimized' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
40 | const int C, // Total channels
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
41 | const int channels_per_group // Channels per group
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:40:15: note: the first parameter in the range is 'C'
40 | const int C, // Total channels
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:41:15: note: the last parameter in the range is 'channels_per_group'
41 | const int channels_per_group // Channels per group
| ^~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:44:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
44 | int n = blockIdx.x; // sample index
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:45:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
45 | int g = blockIdx.y; // group index
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:52:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
52 | for (int c = threadIdx.x; c < channels_per_group; c += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:52:60: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
52 | for (int c = threadIdx.x; c < channels_per_group; c += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:63:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
63 | int lane = threadIdx.x % warpSize;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:64:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
64 | int warpId = threadIdx.x / warpSize;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:78:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
78 | int numWarps = (blockDim.x + warpSize - 1) / warpSize;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:102:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
102 | for (int c = threadIdx.x; c < channels_per_group; c += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:102:60: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
102 | for (int c = threadIdx.x; c < channels_per_group; c += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:127:19: 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]
127 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:128:19: warning: the parameter 'gemm_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
128 | torch::Tensor gemm_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:129:5: warning: 2 adjacent parameters of 'module_fn_cuda_forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
129 | torch::Tensor gemm_bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~
130 | torch::Tensor group_norm_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:129:19: note: the first parameter in the range is 'gemm_bias'
129 | torch::Tensor gemm_bias,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:130:19: note: the last parameter in the range is 'group_norm_weight'
130 | torch::Tensor group_norm_weight,
| ^~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:129:19: warning: the parameter 'gemm_bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
129 | torch::Tensor gemm_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:141:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
141 | int channels_per_group = C / num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:148:5: warning: inside a lambda, '__func__' expands to the name of the function call operator; consider capturing the name of the enclosing function explicitly [bugprone-lambda-function-name]
148 | AT_DISPATCH_FLOATING_TYPES(x_linear.scalar_type(), "module_fn_cuda_forward", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:34: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:3: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:3: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^
note: (skipping 1 expansions in backtrace; use -fmacro-backtrace-limit=0 to see all)
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:58:7: note: expanded from macro 'AT_PRIVATE_CHECK_SELECTIVE_BUILD'
58 | AT_ERROR( \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:711:32: note: expanded from macro 'AT_ERROR'
711 | C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK(false, ::c10::str(__VA_ARGS__))); \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:536:9: note: expanded from macro 'TORCH_CHECK'
536 | __func__, \
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:166:19: 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]
166 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:167:19: warning: the parameter 'gemm_weight' 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 gemm_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:168:19: warning: the parameter 'gemm_bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
168 | torch::Tensor gemm_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:169:19: 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]
169 | torch::Tensor group_norm_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:170:19: 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]
170 | torch::Tensor group_norm_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_88/b4_s1_optimized_kernel/base/base.cu:171:19: warning: the parameter 'multiply_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
171 | torch::Tensor multiply_weight,
| ^
| const &