Kernel Details - modular_rms_norm

import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(x: torch.Tensor, eps: float) -> torch.Tensor:
    """
    Applies RMS Normalization to the input tensor.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, num_features, *)
        eps (float): Small value added to denominator for numerical stability

    Returns:
        torch.Tensor: Output tensor with RMS Normalization applied
    """
    rms = torch.sqrt(torch.mean(x**2, dim=1, keepdim=True) + eps)
    return x / rms


class Model(nn.Module):
    """
    Simple model that performs RMS Normalization.
    """

    def __init__(self, num_features: int, eps: float):
        """
        Initializes the RMSNorm layer.

        Args:
            num_features (int): Number of features in the input tensor
            eps (float): Small value added to denominator for numerical stability
        """
        super(Model, self).__init__()
        self.eps = eps

    def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
        """
        Forward pass that calls module_fn.

        Args:
            x (torch.Tensor): Input tensor
            fn: Function to call, defaults to module_fn

        Returns:
            torch.Tensor: Output of module_fn
        """
        return fn(x, self.eps)


batch_size = 16
features = 64
dim1 = 256
dim2 = 256
eps = 1e-5


def get_inputs():
    x = torch.randn(batch_size, features, dim1, dim2)
    return [x]


def get_init_inputs():
    return [features, eps]

import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Simple model that performs RMS Normalization.
    """

    def __init__(self, num_features: int, eps: float = 1e-5):
        """
        Initializes the RMSNorm layer.

        Args:
            num_features (int): Number of features in the input tensor.
            eps (float, optional): A small value added to the denominator to avoid division by zero. Defaults to 1e-5.
        """
        super(Model, self).__init__()
        self.num_features = num_features
        self.eps = eps

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies RMS Normalization to the input tensor.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, num_features, *).

        Returns:
            torch.Tensor: Output tensor with RMS Normalization applied, same shape as input.
        """
        # Calculate the RMS along the feature dimension
        rms = torch.sqrt(torch.mean(x**2, dim=1, keepdim=True) + self.eps)

        # Normalize the input by dividing by the RMS
        return x / rms


batch_size = 16
features = 64
dim1 = 256
dim2 = 256
eps = 1e-5


def get_inputs():
    x = torch.randn(batch_size, features, dim1, dim2)
    return [x]


def get_init_inputs():
    return [features, eps]

Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name	36_RMSNorm_
Level ID	1
Task ID	36
Kernel Name	modular_rms_norm_base
CUDA Speedup (Native)	1.951x
CUDA Speedup (Compile)	1.864x
CUDA Runtime	0.272 ms
PyTorch Runtime (Native)	0.531 ms
PyTorch Runtime (Compile)	0.507 ms
Correct	True
Max Diff (vs. Reference)	0.000000
Model	azure-gpt-4o-2024-08-06
Temperature	0.00

View Experiment Progress Details

Related Kernels (Level 1, Task 36 • 36_RMSNorm_)

Rank	Kernel Name	Runtime (ms)	Speedup Native	Speedup Compile
🥇	36_rmsnorm_even_workload_base	0.19	2.81	2.68
🥇	36_rmsnorm_optimized_indexing_base_base	0.19	2.81	2.68
🥉	36_rmsnorm_optimized_indexing_base_base	0.19	2.79	2.67
4	modular_rms_norm_edit_1	0.27	1.95	1.86
4	modular_rms_norm_base	0.27	1.95	1.86
4	combined_grid_unroll_base	0.27	1.95	1.86
7	variable_block_size_rms_norm_edit_1	0.27	1.94	1.85
7	36_rmsnorm_ldg_aligned_opt_base	0.27	1.94	1.85
7	36_rmsnorm_modular_funcs_base	0.27	1.94	1.85
7	36_RMSNorm_uniform_control_base_base	0.27	1.94	1.85
7	balanced_workload_rms_base	0.27	1.94	1.85
7	36_rmsnorm_unroll_opt_base	0.27	1.94	1.85
7	unrolled_rms_norm_edit_1	0.27	1.94	1.85
7	unrolled_rms_norm_base	0.27	1.94	1.85
7	variable_block_size_rms_norm_base	0.27	1.94	1.85
7	36_rmsnorm_modular_inlined_base	0.27	1.94	1.85
7	36_RMSNorm_optimized_block_size_base	0.27	1.94	1.85
18	36_RMSNorm_stride_loop_base	0.28	1.93	1.84
18	efficient_rms_norm_kernel_base	0.28	1.93	1.84
18	36_RMSNorm_no_divergence_base	0.28	1.93	1.84

#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

// Device function to compute sum of squares
template <typename scalar_t>
__device__ scalar_t compute_sum_of_squares(const scalar_t* input, int batch_offset, int offset, int num_features, int numel_per_batch) {
    scalar_t sumsq = 0.0f;
    const int unroll_factor = 32;
    int n_unrolled = (num_features / unroll_factor) * unroll_factor;

    #pragma unroll
    for (int feat = 0; feat < n_unrolled; feat += unroll_factor) {
        scalar_t v0  = input[batch_offset + (feat + 0)  * numel_per_batch + offset];
        scalar_t v1  = input[batch_offset + (feat + 1)  * numel_per_batch + offset];
        scalar_t v2  = input[batch_offset + (feat + 2)  * numel_per_batch + offset];
        scalar_t v3  = input[batch_offset + (feat + 3)  * numel_per_batch + offset];
        scalar_t v4  = input[batch_offset + (feat + 4)  * numel_per_batch + offset];
        scalar_t v5  = input[batch_offset + (feat + 5)  * numel_per_batch + offset];
        scalar_t v6  = input[batch_offset + (feat + 6)  * numel_per_batch + offset];
        scalar_t v7  = input[batch_offset + (feat + 7)  * numel_per_batch + offset];
        scalar_t v8  = input[batch_offset + (feat + 8)  * numel_per_batch + offset];
        scalar_t v9  = input[batch_offset + (feat + 9)  * numel_per_batch + offset];
        scalar_t v10 = input[batch_offset + (feat + 10) * numel_per_batch + offset];
        scalar_t v11 = input[batch_offset + (feat + 11) * numel_per_batch + offset];
        scalar_t v12 = input[batch_offset + (feat + 12) * numel_per_batch + offset];
        scalar_t v13 = input[batch_offset + (feat + 13) * numel_per_batch + offset];
        scalar_t v14 = input[batch_offset + (feat + 14) * numel_per_batch + offset];
        scalar_t v15 = input[batch_offset + (feat + 15) * numel_per_batch + offset];
        scalar_t v16 = input[batch_offset + (feat + 16) * numel_per_batch + offset];
        scalar_t v17 = input[batch_offset + (feat + 17) * numel_per_batch + offset];
        scalar_t v18 = input[batch_offset + (feat + 18) * numel_per_batch + offset];
        scalar_t v19 = input[batch_offset + (feat + 19) * numel_per_batch + offset];
        scalar_t v20 = input[batch_offset + (feat + 20) * numel_per_batch + offset];
        scalar_t v21 = input[batch_offset + (feat + 21) * numel_per_batch + offset];
        scalar_t v22 = input[batch_offset + (feat + 22) * numel_per_batch + offset];
        scalar_t v23 = input[batch_offset + (feat + 23) * numel_per_batch + offset];
        scalar_t v24 = input[batch_offset + (feat + 24) * numel_per_batch + offset];
        scalar_t v25 = input[batch_offset + (feat + 25) * numel_per_batch + offset];
        scalar_t v26 = input[batch_offset + (feat + 26) * numel_per_batch + offset];
        scalar_t v27 = input[batch_offset + (feat + 27) * numel_per_batch + offset];
        scalar_t v28 = input[batch_offset + (feat + 28) * numel_per_batch + offset];
        scalar_t v29 = input[batch_offset + (feat + 29) * numel_per_batch + offset];
        scalar_t v30 = input[batch_offset + (feat + 30) * numel_per_batch + offset];
        scalar_t v31 = input[batch_offset + (feat + 31) * numel_per_batch + offset];

        sumsq += v0*v0 + v1*v1 + v2*v2 + v3*v3 +
                 v4*v4 + v5*v5 + v6*v6 + v7*v7 +
                 v8*v8 + v9*v9 + v10*v10 + v11*v11 +
                 v12*v12 + v13*v13 + v14*v14 + v15*v15 +
                 v16*v16 + v17*v17 + v18*v18 + v19*v19 +
                 v20*v20 + v21*v21 + v22*v22 + v23*v23 +
                 v24*v24 + v25*v25 + v26*v26 + v27*v27 +
                 v28*v28 + v29*v29 + v30*v30 + v31*v31;
    }

    for (int feat = n_unrolled; feat < num_features; feat++) {
        scalar_t val = input[batch_offset + feat * numel_per_batch + offset];
        sumsq += val * val;
    }

    return sumsq;
}

// Device function to normalize input
template <typename scalar_t>
__device__ void normalize_input(const scalar_t* input, scalar_t* output, int batch_offset, int offset, int num_features, int numel_per_batch, scalar_t rms) {
    const int unroll_factor = 32;
    int n_unrolled = (num_features / unroll_factor) * unroll_factor;

    #pragma unroll
    for (int feat = 0; feat < n_unrolled; feat += unroll_factor) {
        int j0  = batch_offset + (feat + 0)  * numel_per_batch + offset;
        int j1  = batch_offset + (feat + 1)  * numel_per_batch + offset;
        int j2  = batch_offset + (feat + 2)  * numel_per_batch + offset;
        int j3  = batch_offset + (feat + 3)  * numel_per_batch + offset;
        int j4  = batch_offset + (feat + 4)  * numel_per_batch + offset;
        int j5  = batch_offset + (feat + 5)  * numel_per_batch + offset;
        int j6  = batch_offset + (feat + 6)  * numel_per_batch + offset;
        int j7  = batch_offset + (feat + 7)  * numel_per_batch + offset;
        int j8  = batch_offset + (feat + 8)  * numel_per_batch + offset;
        int j9  = batch_offset + (feat + 9)  * numel_per_batch + offset;
        int j10 = batch_offset + (feat + 10) * numel_per_batch + offset;
        int j11 = batch_offset + (feat + 11) * numel_per_batch + offset;
        int j12 = batch_offset + (feat + 12) * numel_per_batch + offset;
        int j13 = batch_offset + (feat + 13) * numel_per_batch + offset;
        int j14 = batch_offset + (feat + 14) * numel_per_batch + offset;
        int j15 = batch_offset + (feat + 15) * numel_per_batch + offset;
        int j16 = batch_offset + (feat + 16) * numel_per_batch + offset;
        int j17 = batch_offset + (feat + 17) * numel_per_batch + offset;
        int j18 = batch_offset + (feat + 18) * numel_per_batch + offset;
        int j19 = batch_offset + (feat + 19) * numel_per_batch + offset;
        int j20 = batch_offset + (feat + 20) * numel_per_batch + offset;
        int j21 = batch_offset + (feat + 21) * numel_per_batch + offset;
        int j22 = batch_offset + (feat + 22) * numel_per_batch + offset;
        int j23 = batch_offset + (feat + 23) * numel_per_batch + offset;
        int j24 = batch_offset + (feat + 24) * numel_per_batch + offset;
        int j25 = batch_offset + (feat + 25) * numel_per_batch + offset;
        int j26 = batch_offset + (feat + 26) * numel_per_batch + offset;
        int j27 = batch_offset + (feat + 27) * numel_per_batch + offset;
        int j28 = batch_offset + (feat + 28) * numel_per_batch + offset;
        int j29 = batch_offset + (feat + 29) * numel_per_batch + offset;
        int j30 = batch_offset + (feat + 30) * numel_per_batch + offset;
        int j31 = batch_offset + (feat + 31) * numel_per_batch + offset;

        output[j0]  = input[j0]  / rms;
        output[j1]  = input[j1]  / rms;
        output[j2]  = input[j2]  / rms;
        output[j3]  = input[j3]  / rms;
        output[j4]  = input[j4]  / rms;
        output[j5]  = input[j5]  / rms;
        output[j6]  = input[j6]  / rms;
        output[j7]  = input[j7]  / rms;
        output[j8]  = input[j8]  / rms;
        output[j9]  = input[j9]  / rms;
        output[j10] = input[j10] / rms;
        output[j11] = input[j11] / rms;
        output[j12] = input[j12] / rms;
        output[j13] = input[j13] / rms;
        output[j14] = input[j14] / rms;
        output[j15] = input[j15] / rms;
        output[j16] = input[j16] / rms;
        output[j17] = input[j17] / rms;
        output[j18] = input[j18] / rms;
        output[j19] = input[j19] / rms;
        output[j20] = input[j20] / rms;
        output[j21] = input[j21] / rms;
        output[j22] = input[j22] / rms;
        output[j23] = input[j23] / rms;
        output[j24] = input[j24] / rms;
        output[j25] = input[j25] / rms;
        output[j26] = input[j26] / rms;
        output[j27] = input[j27] / rms;
        output[j28] = input[j28] / rms;
        output[j29] = input[j29] / rms;
        output[j30] = input[j30] / rms;
        output[j31] = input[j31] / rms;
    }

    for (int feat = n_unrolled; feat < num_features; feat++) {
        int j = batch_offset + feat * numel_per_batch + offset;
        output[j] = input[j] / rms;
    }
}

// Kernel function
template <typename scalar_t>
__global__ void rms_norm_kernel(
    const scalar_t* __restrict__ input,
    scalar_t* __restrict__ output,
    const int batch_size,
    const int num_features,
    const int numel_per_batch,
    const float eps
) {
    const int total_samples = batch_size * numel_per_batch;
    const int stride = blockDim.x * gridDim.x;
    
    for (int idx = blockIdx.x * blockDim.x + threadIdx.x; idx < total_samples; idx += stride) {
        int batch_id = idx / numel_per_batch;
        int offset = idx % numel_per_batch;
        int batch_offset = batch_id * num_features * numel_per_batch;

        // Compute sum of squares
        scalar_t sumsq = compute_sum_of_squares(input, batch_offset, offset, num_features, numel_per_batch);

        // Compute RMS
        scalar_t rms = sqrt(sumsq / num_features + eps);

        // Normalize input
        normalize_input(input, output, batch_offset, offset, num_features, numel_per_batch, rms);
    }
}

// Host function
torch::Tensor rms_norm_cuda_forward(torch::Tensor input, float eps) {
    auto output = torch::empty_like(input);

    const int batch_size = input.size(0);
    const int num_features = input.size(1);
    int numel_per_batch = 1;
    for (int i = 2; i < input.dim(); i++) {
        numel_per_batch *= input.size(i);
    }

    const int total_samples = batch_size * numel_per_batch;
    const int threads_per_block = 256;
    const int max_blocks = 65535;
    int blocks = (total_samples + threads_per_block - 1) / threads_per_block;
    if (blocks > max_blocks) blocks = max_blocks;

    AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "rms_norm_cuda", ([&] {
        rms_norm_kernel<scalar_t><<<blocks, threads_per_block>>>(
            input.data_ptr<scalar_t>(),
            output.data_ptr<scalar_t>(),
            batch_size,
            num_features,
            numel_per_batch,
            eps
        );
    }));

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &rms_norm_cuda_forward, "Modular RMS normalization forward (CUDA)");
}

Performance Metrics

Metric	Value	Unit	Variance	Samples
Executed Ipc Active	0.400	inst/cycle	0.000	5
Executed Ipc Elapsed	0.390	inst/cycle	0.000	5
Issue Slots Busy	9.944	%	0.000	5
Issued Ipc Active	0.400	inst/cycle	0.000	5
SM Busy	14.122	%	0.001	5
Memory Throughput	2956646027367.360	byte/second	11189034823267543040.000	5
Mem Busy	48.576	%	0.003	5
Max Bandwidth	88.202	%	0.011	5
L1/TEX Hit Rate	0.966	%	0.001	5
L2 Hit Rate	34.216	%	0.001	5
Mem Pipes Busy	11.512	%	0.000	5
Warp Cycles Per Issued Instruction	112.534	cycle	0.045	5
Warp Cycles Per Executed Instruction	112.704	cycle	0.045	5
Avg. Active Threads Per Warp	32.000		0.000	5
Avg. Not Predicated Off Threads Per Warp	31.460		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	6.000	block	0.000	5
Block Limit Shared Mem	32.000	block	0.000	5
Block Limit Warps	8.000	block	0.000	5
Theoretical Active Warps per SM	48.000	warp	0.000	5
Theoretical Occupancy	75.000	%	0.000	5
Achieved Occupancy	69.998	%	0.002	5
Achieved Active Warps Per SM	44.798	warp	0.001	5

Analysis Rules

Rule	Description
WRN HighPipeUtilization	All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler. Check the Launch Statistics and Scheduler Statistics sections for further details.
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 (75.0%) is limited by the number of required registers. 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::randn
CPU Time	315478.23	μs
Device Time	0.00	μs
Self CPU Time	126.36	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::normal_
CPU Time	315313.97	μs
Device Time	0.00	μs
Self CPU Time	315313.97	μs
Self Device Time	0.00	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
aten::to
CPU Time	257493.02	μs
Device Time	38140.91	μs
Self CPU Time	73.23	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
cudaLaunchKernel
CPU Time	669760.29	μs
Device Time	6113.64	μs
Self CPU Time	669760.29	μs
Self Device Time	6113.64	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
void rms_norm_kernel<float>(float const, float, int, int, int, float)
CPU Time	0.00	μs
Device Time	622697.25	μs
Self CPU Time	0.00	μs
Self Device Time	622697.25	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	544484.65	μs
Device Time	181649.22	μs
Self CPU Time	3977.26	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	540509.55	μs
Device Time	181649.22	μs
Self CPU Time	5504.98	μs
Self Device Time	181649.22	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	181649.22	μs
Self CPU Time	0.00	μs
Self Device Time	181649.22	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

45288 warnings generated when compiling for host.
Suppressed 45322 warnings (45275 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/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:7:85 bugprone-easily-swappable-parameters

7 | __device__ scalar_t compute_sum_of_squares(const scalar_t* input, int batch_offset, int offset, int num_features, int numel_per_batch) {

| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:7:89: note: the first parameter in the range is 'offset'

7 | __device__ scalar_t compute_sum_of_squares(const scalar_t* input, int batch_offset, int offset, int num_features, int numel_per_batch) {

| ^~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:7:101: note: the last parameter in the range is 'num_features'

7 | __device__ scalar_t compute_sum_of_squares(const scalar_t* input, int batch_offset, int offset, int num_features, int numel_per_batch) {

| ^~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:67:92: warning: 2 adjacent parameters of 'normalize_input' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]

67 | __device__ void normalize_input(const scalar_t* input, scalar_t* output, int batch_offset, int offset, int num_features, int numel_per_batch, scalar_t rms) {

| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:67:96: note: the first parameter in the range is 'offset'

67 | __device__ void normalize_input(const scalar_t* input, scalar_t* output, int batch_offset, int offset, int num_features, int numel_per_batch, scalar_t rms) {

| ^~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:67:108: note: the last parameter in the range is 'num_features'

67 | __device__ void normalize_input(const scalar_t* input, scalar_t* output, int batch_offset, int offset, int num_features, int numel_per_batch, scalar_t rms) {

| ^~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:151:5: warning: 2 adjacent parameters of 'rms_norm_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]

151 | const int batch_size,

| ^~~~~~~~~~~~~~~~~~~~~

152 | const int num_features,

| ~~~~~~~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:151:15: note: the first parameter in the range is 'batch_size'

151 | const int batch_size,

| ^~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:152:15: note: the last parameter in the range is 'num_features'

152 | const int num_features,

| ^~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:153:5: warning: 2 adjacent parameters of 'rms_norm_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]

153 | const int numel_per_batch,

| ^~~~~~~~~~~~~~~~~~~~~~~~~~

154 | const float eps

| ~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:153:15: note: the first parameter in the range is 'numel_per_batch'

153 | const int numel_per_batch,

| ^~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:154:17: note: the last parameter in the range is 'eps'

154 | const float eps

| ^~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:154:5: note: 'const int' and 'const float' may be implicitly converted: 'const int' (as 'int') -> 'const float' (as 'float'), 'const float' (as 'float') -> 'const int' (as 'int')

154 | const float eps

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:157:24: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

157 | const int stride = blockDim.x * gridDim.x;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:159:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

159 | for (int idx = blockIdx.x * blockDim.x + threadIdx.x; idx < total_samples; idx += stride) {

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:179:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

179 | const int batch_size = input.size(0);

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:180:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

180 | const int num_features = input.size(1);

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:183:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

183 | numel_per_batch *= input.size(i);

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b5_s3_modular_rms_norm/base/base.cu:192:5: 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]

192 | AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "rms_norm_cuda", ([&] {

| ^

/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:246:19: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES_AND_HALF'

246 | TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES_AND_HALF(__VA_ARGS__))

| ^

/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:240:3: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES_AND_HALF'

240 | 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__, \

| ^

The AI CUDA Engineer 👷

`36_RMSNorm_` • `modular_rms_norm_base`

Kernel Information

Related Kernels (Level 1, Task 36 • 36_RMSNorm_)

The AI CUDA Engineer 👷

36_RMSNorm_ • modular_rms_norm_base

Kernel Information

Related Kernels (Level 1, Task 36 • 36_RMSNorm_)

`36_RMSNorm_` • `modular_rms_norm_base`