Kernel Details - combined_grid_unroll

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	combined_grid_unroll_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	o3-mini-2025-01-31
Temperature	1.00

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>

// Combined kernel: uses grid-stride loop over samples and manual unrolling for feature reduction
// This kernel processes each sample (a specific offset in the batch) by accumulating the sum of squares
// with 32-wide unrolling and then normalizes using the computed RMS value.

template <typename scalar_t>
__global__ void rms_norm_kernel_combined(
    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
) {
    // Each thread processes one sample across features; total_samples = batch_size * numel_per_batch
    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 with manual unrolling
        scalar_t sumsq = 0;
        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;
        }

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

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

        // Normalize input values with the computed RMS (using similar unrolling)
        #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;
        }
        
        // Process any trailing features
        for (int feat = n_unrolled; feat < num_features; feat++) {
            int j = batch_offset + feat * numel_per_batch + offset;
            output[j] = input[j] / rms;
        }
    }
}

// Host function that launches the kernel

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_combined<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, "Combined 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	%	0.001	5
Issued Ipc Active	0.400	inst/cycle	0.000	5
SM Busy	14.120	%	0.002	5
Memory Throughput	2954192925999.685	byte/second	15330745663015645184.000	5
Mem Busy	48.538	%	0.005	5
Max Bandwidth	88.140	%	0.013	5
L1/TEX Hit Rate	1.048	%	0.001	5
L2 Hit Rate	34.392	%	0.002	5
Mem Pipes Busy	11.498	%	0.000	5
Warp Cycles Per Issued Instruction	112.574	cycle	0.087	5
Warp Cycles Per Executed Instruction	112.740	cycle	0.088	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	69.904	%	0.004	5
Achieved Active Warps Per SM	44.740	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	300018.16	μs
Device Time	0.00	μs
Self CPU Time	79.36	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	299907.32	μs
Device Time	0.00	μs
Self CPU Time	299907.32	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	1906525.85	μs
Device Time	16746.30	μs
Self CPU Time	1906525.85	μs
Self Device Time	16746.30	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_combined<float>(float const, float, int, int, int, float)
CPU Time	0.00	μs
Device Time	1649266.52	μs
Self CPU Time	0.00	μs
Self Device Time	1649266.52	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	17497.82	μs
Device Time	32227.63	μs
Self CPU Time	17497.82	μs
Self Device Time	32227.63	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	1543082.83	μs
Device Time	480157.99	μs
Self CPU Time	12086.71	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	1530997.87	μs
Device Time	480157.99	μs
Self CPU Time	14367.17	μs
Self Device Time	480157.99	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	480157.99	μs
Self CPU Time	0.00	μs
Self Device Time	480157.99	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

45286 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/b4_s2_combined_grid_unroll/base/base.cu:13:5 bugprone-easily-swappable-parameters

13 | const int batch_size,

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

14 | const int num_features,

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

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b4_s2_combined_grid_unroll/base/base.cu:13:15: note: the first parameter in the range is 'batch_size'

13 | const int batch_size,

| ^~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b4_s2_combined_grid_unroll/base/base.cu:14:15: note: the last parameter in the range is 'num_features'

14 | const int num_features,

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

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b4_s2_combined_grid_unroll/base/base.cu:15:5: warning: 2 adjacent parameters of 'rms_norm_kernel_combined' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]

15 | const int numel_per_batch,

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

16 | const float eps

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

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b4_s2_combined_grid_unroll/base/base.cu:15:15: note: the first parameter in the range is 'numel_per_batch'

15 | const int numel_per_batch,

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

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b4_s2_combined_grid_unroll/base/base.cu:16:17: note: the last parameter in the range is 'eps'

16 | const float eps

| ^~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b4_s2_combined_grid_unroll/base/base.cu:16:5: 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')

16 | const float eps

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b4_s2_combined_grid_unroll/base/base.cu:20:24: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

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

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b4_s2_combined_grid_unroll/base/base.cu:22:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

22 | 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/b4_s2_combined_grid_unroll/base/base.cu:169:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

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

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b4_s2_combined_grid_unroll/base/base.cu:170:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

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

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b4_s2_combined_grid_unroll/base/base.cu:173:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

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

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_1/task_36/b4_s2_combined_grid_unroll/base/base.cu:182:5: 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]

182 | 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_` • `combined_grid_unroll_base`

Kernel Information

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

The AI CUDA Engineer 👷

36_RMSNorm_ • combined_grid_unroll_base

Kernel Information

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

`36_RMSNorm_` • `combined_grid_unroll_base`