← Back to Leaderboard

The AI CUDA Engineer 👷

39_L2Norm_ • l2_norm_combined_base

Level 1 • Task 39
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(x: torch.Tensor) -> torch.Tensor:
    """
    Applies L2 normalization to the input tensor.

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

    Returns:
        torch.Tensor: Output tensor with L2 normalization applied, same shape as input.
    """
    return F.normalize(x, p=2, dim=1)


class Model(nn.Module):
    """
    Simple model that performs L2 normalization.
    """

    def __init__(self):
        """
        Initializes the L2Norm layer.
        """
        super(Model, self).__init__()

    def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
        """
        Applies L2 normalization to the input tensor.

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

        Returns:
            torch.Tensor: Output tensor with L2 normalization applied, same shape as input.
        """
        return fn(x)


batch_size = 16
dim = 16384


def get_inputs():
    x = torch.randn(batch_size, dim)
    return [x]


def get_init_inputs():
    return []

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs L2 normalization.
    """
    def __init__(self):
        """
        Initializes the L2Norm layer.

        Args:
            dim (int): Dimension along which to normalize.
        """
        super(Model, self).__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies L2 normalization to the input tensor.

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

        Returns:
            torch.Tensor: Output tensor with L2 normalization applied, same shape as input.
        """
        return x / torch.norm(x, p=2, dim=1, keepdim=True)

batch_size = 16
dim = 16384

def get_inputs():
    x = torch.randn(batch_size, dim)
    return [x]

def get_init_inputs():
    return []
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 39_L2Norm_
Level ID 1
Task ID 39
Kernel Name l2_norm_combined_base
CUDA Speedup (Native) 1.548x
CUDA Speedup (Compile) 4.578x
CUDA Runtime 0.011 ms
PyTorch Runtime (Native) 0.017 ms
PyTorch Runtime (Compile) 0.050 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 39 • 39_L2Norm_)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 l2norm_strided_optimized_base_base 0.01 1.89 5.60
🥈 l2_norm_combined_base 0.01 1.55 4.58
🥉 l2_norm_unroll_optimized_base 0.01 1.42 4.20
🥉 39_l2norm_modular_edit_1 0.01 1.42 4.20
🥉 39_l2norm_blocksize_experiment_base 0.01 1.42 4.20
6 39_l2norm_atomic_opt_base 0.01 1.31 3.87
6 l2norm_block_tuned_base 0.01 1.31 3.87
6 l2_norm_block_size_tuning_base 0.01 1.31 3.87
6 l2_norm_atomic_minimized_base_base 0.01 1.31 3.87
6 l2norm_stride_optimized_base 0.01 1.31 3.87
6 39_l2norm_coalesced_base 0.01 1.31 3.87
6 39_l2norm_memory_coalescing_base 0.01 1.31 3.87
6 39_l2norm_modular_refactored_edit_1 0.01 1.31 3.87
6 39_l2norm_optimized_indexing_edit_1 0.01 1.31 3.87
6 39_l2norm_sync_optimized_edit_1 0.01 1.31 3.87
6 39_l2norm_blocksize_experiment_edit_1 0.01 1.31 3.87
6 39_l2norm_memory_coalescing_edit_1 0.01 1.31 3.87
6 39_l2norm_modular_refactored_base 0.01 1.31 3.87
6 l2norm_even_workload_base 0.01 1.31 3.87
6 39_l2norm_atomic_edit_1 0.01 1.31 3.87 
/*
Combined L2 normalization kernel

This implementation chooses a strategy based on the size of the normalization dimension (C).
For small C (<= 1024), a single kernel is launched per vector that computes the L2 norm (using vectorized loads/stores and warp-level reduction) and then normalizes the vector.
For large C (> 1024), a two-phase approach is used:
  1) A partial reduction kernel (with multiple blocks per vector) computes segments of the sum-of-squares using vectorized loads when possible, and
     the results are atomically accumulated into a global sum array.
  2) A normalization kernel uses the computed norm to scale each segment of the vector (again employing vectorized stores if applicable).

Fallback paths for non-contiguous accesses are provided.
*/

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


// Single-kernel approach for small C (<= threshold)
template <typename scalar_t>
__global__ void l2_norm_single_kernel(
    const scalar_t* __restrict__ input,
    scalar_t* __restrict__ output,
    const int C,
    const int outer_stride,
    const int stride_C) {

    // Each block processes one vector
    int vector_idx = blockIdx.x;
    if (vector_idx >= gridDim.x) return;
    int base = vector_idx * outer_stride;
    int tid = threadIdx.x;
    scalar_t sum = 0;

    if (stride_C == 1) {
        // Use vectorized loads if possible
        // For float: vector size 4, for double: vector size 2
        const int factor = (sizeof(scalar_t) == 4) ? 4 : 2;
        int aligned_end = (C / factor) * factor;
        if constexpr (sizeof(scalar_t) == 4) {
            const float4* inp = reinterpret_cast<const float4*>(input + base);
            int num_vec = aligned_end / 4;
            for (int i = tid; i < num_vec; i += blockDim.x) {
                float4 v = inp[i];
                sum += (scalar_t)(v.x * v.x + v.y * v.y + v.z * v.z + v.w * v.w);
            }
        } else {
            const double2* inp = reinterpret_cast<const double2*>(input + base);
            int num_vec = aligned_end / 2;
            for (int i = tid; i < num_vec; i += blockDim.x) {
                double2 v = inp[i];
                sum += (scalar_t)(v.x * v.x + v.y * v.y);
            }
        }
        // Process remaining elements
        for (int i = aligned_end + tid; i < C; i += blockDim.x) {
            scalar_t v = input[base + i];
            sum += v * v;
        }
    } else {
        // Non-contiguous fallback
        for (int i = tid; i < C; i += blockDim.x) {
            scalar_t v = input[base + i * stride_C];
            sum += v * v;
        }
    }

    // Intra-block reduction using warp shuffle
    for (int offset = warpSize / 2; offset > 0; offset /= 2) {
        sum += __shfl_down_sync(0xffffffff, sum, offset);
    }

    // Use shared memory to accumulate sums from each warp
    __shared__ scalar_t shared[32];
    int lane = threadIdx.x % warpSize;
    int warpId = threadIdx.x / warpSize;
    if (lane == 0) shared[warpId] = sum;
    __syncthreads();

    if (warpId == 0) {
        sum = (lane < (blockDim.x + warpSize - 1) / warpSize) ? shared[lane] : 0;
        for (int offset = warpSize / 2; offset > 0; offset /= 2) {
            sum += __shfl_down_sync(0xffffffff, sum, offset);
        }
    }

    // The first thread computes the normalization and writes back
    if (lane == 0) {
        scalar_t norm = sqrt(sum) + (scalar_t)1e-12;
        scalar_t inv_norm = (scalar_t)1.0 / norm;

        // Normalize vector elements using vectorized stores if possible
        if (stride_C == 1) {
            const int factor = (sizeof(scalar_t) == 4) ? 4 : 2;
            int aligned_end = (C / factor) * factor;
            if constexpr (sizeof(scalar_t) == 4) {
                float4* outp = reinterpret_cast<float4*>(output + base);
                const float4* inp = reinterpret_cast<const float4*>(input + base);
                int num_vec = aligned_end / 4;
                for (int i = 0; i < num_vec; i++) {
                    float4 v = inp[i];
                    v.x *= inv_norm; v.y *= inv_norm;
                    v.z *= inv_norm; v.w *= inv_norm;
                    outp[i] = v;
                }
            } else {
                double2* outp = reinterpret_cast<double2*>(output + base);
                const double2* inp = reinterpret_cast<const double2*>(input + base);
                int num_vec = aligned_end / 2;
                for (int i = 0; i < num_vec; i++) {
                    double2 v = inp[i];
                    v.x *= inv_norm; v.y *= inv_norm;
                    outp[i] = v;
                }
            }
            for (int i = aligned_end; i < C; i++) {
                output[base + i] = input[base + i] * inv_norm;
            }
        } else {
            for (int i = 0; i < C; i++) {
                output[base + i * stride_C] = input[base + i * stride_C] * inv_norm;
            }
        }
    }
}


// Two-phase approach kernels for large C (> threshold)

// Phase 1: Partial reduction kernel with vectorized loads
template <typename scalar_t>
__global__ void l2_norm_partial_kernel_combined(
    const scalar_t* __restrict__ input,
    scalar_t* __restrict__ global_sum,
    const int C,
    const int total_vectors,
    const int stride_C,
    const int outer_stride,
    const int blocks_per_vector) {

    // Each block processes a segment of a vector
    int vector_idx = blockIdx.x / blocks_per_vector;
    int seg_idx = blockIdx.x % blocks_per_vector;
    if (vector_idx >= total_vectors) return;

    // Determine segment boundaries
    int seg_length = (C + blocks_per_vector - 1) / blocks_per_vector;  // ceil division
    int start = seg_idx * seg_length;
    int end = start + seg_length;
    if (end > C) end = C;

    int base = vector_idx * outer_stride;
    int tid = threadIdx.x;
    scalar_t partial = 0;

    if (stride_C == 1) {
        // Vectorized load path
        const int factor = (sizeof(scalar_t) == 4) ? 4 : 2;
        int aligned_end = start + ((end - start) / factor) * factor;
        if constexpr (sizeof(scalar_t) == 4) {
            const float4* in_vec = reinterpret_cast<const float4*>(input + base + start);
            int num_vec = (aligned_end - start) / 4;
            for (int i = tid; i < num_vec; i += blockDim.x) {
                float4 v = in_vec[i];
                partial += (scalar_t)(v.x * v.x + v.y * v.y + v.z * v.z + v.w * v.w);
            }
        } else {
            const double2* in_vec = reinterpret_cast<const double2*>(input + base + start);
            int num_vec = (aligned_end - start) / 2;
            for (int i = tid; i < num_vec; i += blockDim.x) {
                double2 v = in_vec[i];
                partial += (scalar_t)(v.x * v.x + v.y * v.y);
            }
        }
        for (int i = aligned_end + tid; i < end; i += blockDim.x) {
            scalar_t v = input[base + i];
            partial += v * v;
        }
    } else {
        // Non-contiguous fallback
        for (int i = start + tid; i < end; i += blockDim.x) {
            scalar_t v = input[base + i * stride_C];
            partial += v * v;
        }
    }

    // Intra-block reduction using shared memory
    __shared__ scalar_t sdata[256];
    sdata[tid] = partial;
    __syncthreads();
    for (int s = blockDim.x / 2; s > 0; s >>= 1) {
        if (tid < s) {
            sdata[tid] += sdata[tid + s];
        }
        __syncthreads();
    }

    if (tid == 0) {
        atomicAdd(&global_sum[vector_idx], sdata[0]);
    }
}

// Phase 2: Normalization kernel with vectorized stores
template <typename scalar_t>
__global__ void l2_normalize_kernel_combined(
    const scalar_t* __restrict__ input,
    scalar_t* __restrict__ output,
    const scalar_t* __restrict__ global_sum,
    const int C,
    const int total_vectors,
    const int stride_C,
    const int outer_stride,
    const int blocks_per_vector) {

    int vector_idx = blockIdx.x / blocks_per_vector;
    int seg_idx = blockIdx.x % blocks_per_vector;
    if (vector_idx >= total_vectors) return;

    int seg_length = (C + blocks_per_vector - 1) / blocks_per_vector;
    int start = seg_idx * seg_length;
    int end = start + seg_length;
    if (end > C) end = C;

    int base = vector_idx * outer_stride;
    scalar_t norm = sqrt(global_sum[vector_idx]) + (scalar_t)1e-12;
    scalar_t inv_norm = (scalar_t)1.0 / norm;

    int tid = threadIdx.x;
    if (stride_C == 1) {
        const int factor = (sizeof(scalar_t) == 4) ? 4 : 2;
        int aligned_end = start + ((end - start) / factor) * factor;
        if constexpr (sizeof(scalar_t) == 4) {
            float4* out_vec = reinterpret_cast<float4*>(output + base + start);
            const float4* in_vec = reinterpret_cast<const float4*>(input + base + start);
            int num_vec = (aligned_end - start) / 4;
            for (int i = tid; i < num_vec; i += blockDim.x) {
                float4 v = in_vec[i];
                v.x *= inv_norm;
                v.y *= inv_norm;
                v.z *= inv_norm;
                v.w *= inv_norm;
                out_vec[i] = v;
            }
        } else {
            double2* out_vec = reinterpret_cast<double2*>(output + base + start);
            const double2* in_vec = reinterpret_cast<const double2*>(input + base + start);
            int num_vec = (aligned_end - start) / 2;
            for (int i = tid; i < num_vec; i += blockDim.x) {
                double2 v = in_vec[i];
                v.x *= inv_norm;
                v.y *= inv_norm;
                out_vec[i] = v;
            }
        }
        for (int i = aligned_end + tid; i < end; i += blockDim.x) {
            output[base + i] = input[base + i] * inv_norm;
        }
    } else {
        for (int i = start + tid; i < end; i += blockDim.x) {
            output[base + i * stride_C] = input[base + i * stride_C] * inv_norm;
        }
    }
}


// Host forward function that selects the optimal strategy based on C
torch::Tensor forward(torch::Tensor input) {
    TORCH_CHECK(input.is_cuda(), "Input must be a CUDA tensor");
    TORCH_CHECK(input.dim() >= 2, "Input must be at least 2D");

    const int C = input.size(1);
    const int total_vectors = input.numel() / C;
    const int stride_C = input.stride(1);
    const int outer_stride = input.stride(0);

    auto output = torch::empty_like(input);

    // Choose strategy based on vector length
    // Use single-kernel approach if C is small; else use two-phase reduction
    const int threshold = 1024;
    const int threads = 256;

    if (C <= threshold) {
        // Launch one block per vector
        dim3 blocks(total_vectors);
        AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "l2_norm_single", ([&] {
            l2_norm_single_kernel<scalar_t><<<blocks, threads>>>(
                input.data_ptr<scalar_t>(),
                output.data_ptr<scalar_t>(),
                C, outer_stride, stride_C);
        }));
    } else {
        // Two-phase approach for large C
        int seg_size = 1024;  // number of elements each block processes
        int blocks_per_vector = (C + seg_size - 1) / seg_size;
        int total_blocks = total_vectors * blocks_per_vector;

        auto global_sum = torch::zeros({total_vectors}, input.options());

        AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "l2_norm_partial_combined", ([&] {
            l2_norm_partial_kernel_combined<scalar_t><<<total_blocks, threads>>>(
                input.data_ptr<scalar_t>(),
                global_sum.data_ptr<scalar_t>(),
                C, total_vectors, stride_C, outer_stride, blocks_per_vector);
        }));

        // Launch normalization kernel with same grid configuration
        AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "l2_norm_normalize_combined", ([&] {
            l2_normalize_kernel_combined<scalar_t><<<total_blocks, threads>>>(
                input.data_ptr<scalar_t>(),
                output.data_ptr<scalar_t>(),
                global_sum.data_ptr<scalar_t>(),
                C, total_vectors, stride_C, outer_stride, blocks_per_vector);
        }));
    }

    return output;
}


PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "L2 normalization combining vectorized memory accesses and multi-block reduction");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.606 inst/cycle 0.000 5
Executed Ipc Elapsed 0.258 inst/cycle 0.000 5
Issue Slots Busy 15.462 % 0.014 5
Issued Ipc Active 0.616 inst/cycle 0.000 5
SM Busy 15.462 % 0.014 5
Memory Throughput 267539244947.184 byte/second 3729315240586809344.000 5
Mem Busy 12.866 % 0.016 5
Max Bandwidth 11.770 % 0.001 5
L1/TEX Hit Rate 2.650 % 0.000 5
L2 Hit Rate 66.864 % 0.179 5
Mem Pipes Busy 4.504 % 0.001 5
Warp Cycles Per Issued Instruction 23.868 cycle 0.975 5
Warp Cycles Per Executed Instruction 24.326 cycle 1.013 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.930 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 10.000 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 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 21.754 % 0.004 5
Achieved Active Warps Per SM 13.922 warp 0.002 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 is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (21.8%) 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::empty_strided
CPU Time 480610.06 μs
Device Time 0.00 μs
Self CPU Time 227869.63 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::zeros
CPU Time 5490746.05 μs
Device Time 218186.25 μs
Self CPU Time 123973.39 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::zero_
CPU Time 5811170.80 μs
Device Time 7571970.06 μs
Self CPU Time 275818.46 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 5535354.29 μs
Device Time 7571970.06 μs
Self CPU Time 401580.53 μs
Self Device Time 7571970.06 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 5758878.86 μs
Device Time 362719.50 μs
Self CPU Time 5758878.86 μs
Self Device Time 362719.50 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void l2_norm_partial_kernel_combined<float>(float const*, float*, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 405799.25 μs
Self CPU Time 0.00 μs
Self Device Time 405799.25 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 7354097.05 μs
Self CPU Time 0.00 μs
Self Device Time 7354097.05 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 
45313 warnings generated when compiling for host.
Suppressed 45321 warnings (45274 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/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:25:5 bugprone-easily-swappable-parameters
25 | const int C,
| ^~~~~~~~~~~~
26 | const int outer_stride,
| ~~~~~~~~~~~~~~~~~~~~~~~
27 | const int stride_C) {
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:25:15: note: the first parameter in the range is 'C'
25 | const int C,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:27:15: note: the last parameter in the range is 'stride_C'
27 | const int stride_C) {
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:30:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | int vector_idx = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:33:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
33 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:44:49: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
44 | for (int i = tid; i < num_vec; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:51:49: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
51 | for (int i = tid; i < num_vec; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:57:53: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
57 | for (int i = aligned_end + tid; i < C; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:63:39: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
63 | for (int i = tid; i < C; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:76:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
76 | int lane = threadIdx.x % warpSize;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:77:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
77 | int warpId = threadIdx.x / warpSize;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:136:5: warning: 4 adjacent parameters of 'l2_norm_partial_kernel_combined' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
136 | const int C,
| ^~~~~~~~~~~~
137 | const int total_vectors,
| ~~~~~~~~~~~~~~~~~~~~~~~~
138 | const int stride_C,
| ~~~~~~~~~~~~~~~~~~~
139 | const int outer_stride,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:136:15: note: the first parameter in the range is 'C'
136 | const int C,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:139:15: note: the last parameter in the range is 'outer_stride'
139 | const int outer_stride,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:143:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
143 | int vector_idx = blockIdx.x / blocks_per_vector;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:144:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
144 | int seg_idx = blockIdx.x % blocks_per_vector;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:154:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
154 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:164:49: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
164 | for (int i = tid; i < num_vec; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:171:49: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
171 | for (int i = tid; i < num_vec; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:176:55: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
176 | for (int i = aligned_end + tid; i < end; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:182:49: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
182 | for (int i = start + tid; i < end; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:192:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
192 | for (int s = blockDim.x / 2; s > 0; s >>= 1) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:210:5: warning: 4 adjacent parameters of 'l2_normalize_kernel_combined' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
210 | const int C,
| ^~~~~~~~~~~~
211 | const int total_vectors,
| ~~~~~~~~~~~~~~~~~~~~~~~~
212 | const int stride_C,
| ~~~~~~~~~~~~~~~~~~~
213 | const int outer_stride,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:210:15: note: the first parameter in the range is 'C'
210 | const int C,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:213:15: note: the last parameter in the range is 'outer_stride'
213 | const int outer_stride,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:216:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
216 | int vector_idx = blockIdx.x / blocks_per_vector;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:217:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
217 | int seg_idx = blockIdx.x % blocks_per_vector;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:229:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
229 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:237:49: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
237 | for (int i = tid; i < num_vec; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:249:49: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
249 | for (int i = tid; i < num_vec; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:256:55: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
256 | for (int i = aligned_end + tid; i < end; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:260:49: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
260 | for (int i = start + tid; i < end; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:272:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
272 | const int C = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:273:31: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
273 | const int total_vectors = input.numel() / C;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:274:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
274 | const int stride_C = input.stride(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:275:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
275 | const int outer_stride = input.stride(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:287:9: 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]
287 | AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "l2_norm_single", ([&] {
| ^
/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/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:301:9: 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]
301 | AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "l2_norm_partial_combined", ([&] {
| ^
/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/20250202_optimize_b10_s4_e0_sweep/level_1/task_39/b4_s3_l2_norm_combined/base/base.cu:309:9: 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]
309 | AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "l2_norm_normalize_combined", ([&] {
| ^
/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__, \
| ^