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40_LayerNorm • layernorm_modular_base_base

Level 1 • Task 40
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, eps: float = 1e-5
) -> torch.Tensor:
    """
    Functional implementation of LayerNorm.

    Args:
        x (torch.Tensor): Input tensor of shape (*, normalized_shape).
        weight (torch.Tensor): Weight tensor of shape (normalized_shape).
        bias (torch.Tensor): Bias tensor of shape (normalized_shape).
        eps (float): Epsilon parameter for numerical stability.

    Returns:
        torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
    """
    # Get the normalized shape from the weight tensor
    normalized_shape = tuple(x.shape[-len(weight.shape) :])
    return F.layer_norm(
        x, normalized_shape=normalized_shape, weight=weight, bias=bias, eps=eps
    )


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

    def __init__(self, normalized_shape: tuple):
        """
        Initializes the LayerNorm layer parameters.

        Args:
            normalized_shape (tuple): Shape of the input tensor to be normalized.
        """
        super(Model, self).__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))

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

        Args:
            x (torch.Tensor): Input tensor of shape (*, normalized_shape).
            fn: Function to apply (defaults to module_fn)

        Returns:
            torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
        """
        return fn(x, self.weight, self.bias)


batch_size = 16
features = 64
dim1 = 256
dim2 = 256


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


def get_init_inputs():
    return [(features, dim1, dim2)]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs Layer Normalization.
    """
    def __init__(self, normalized_shape: tuple):
        """
        Initializes the LayerNorm layer.

        Args:
            normalized_shape (tuple): Shape of the input tensor to be normalized.
        """
        super(Model, self).__init__()
        self.ln = nn.LayerNorm(normalized_shape=normalized_shape)

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

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

        Returns:
            torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
        """
        return self.ln(x)

batch_size = 16
features = 64
dim1 = 256
dim2 = 256

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

def get_init_inputs():
    return [(features, dim1, dim2)]
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Kernel Information

Operation Name 40_LayerNorm
Level ID 1
Task ID 40
Kernel Name layernorm_modular_base_base
CUDA Speedup (Native) 7.913x
CUDA Speedup (Compile) 0.645x
CUDA Runtime 1.024 ms
PyTorch Runtime (Native) 8.103 ms
PyTorch Runtime (Compile) 0.661 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 1, Task 40 • 40_LayerNorm)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 optimized_layernorm_streamed_base 0.94 8.60 0.70
🥈 layernorm_unrolled_base_base 1.01 7.99 0.65
🥉 layernorm_forward_optimized_base 1.02 7.94 0.65
4 layernorm_modular_base_base 1.02 7.91 0.65
5 layernorm_aligned_base_base 1.03 7.90 0.64
6 layernorm_forward_optimized_base 1.03 7.89 0.64
7 optimized_layernorm_2d_base 1.12 7.23 0.59
8 layernorm_vector8_aligned_base_base 1.24 6.53 0.53
9 optimized_layernorm_unrolled_base 1.63 4.96 0.40
10 optimized_layernorm_unrolled_edit_1 1.64 4.94 0.40
11 layernorm_ldg_optimized_base_edit_1 2.22 3.65 0.30
12 40_LayerNorm_stride_loops_edit_1 2.24 3.62 0.30
13 layernorm_hybrid_optimized_base 2.24 3.62 0.29
14 layernorm_ldg_optimized_base_base 2.24 3.61 0.29
15 layernorm_coalesced_base 3.26 2.49 0.20
16 layernorm_2d_indexing_base 3.26 2.48 0.20
17 optimized_layernorm_base 3.27 2.48 0.20
18 warp_shfl_layernorm_base 3.28 2.47 0.20
19 layernorm_forward_opt_base 3.28 2.47 0.20
20 layernorm_uniform_control_flow_base 3.28 2.47 0.20 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <vector>
#include <ATen/AccumulateType.h>

template <typename scalar_t, typename accscalar_t>
__device__ void compute_statistics(
    const scalar_t* __restrict__ in_ptr,
    const int tid,
    const int normalized_size,
    const int blockDim_x,
    accscalar_t& local_sum,
    accscalar_t& local_sum_sq) {
    
    const int vector_size = 4;
    const int aligned_size = normalized_size / vector_size * vector_size;
    
    local_sum = 0;
    local_sum_sq = 0;
    
    for (int i = tid * vector_size; i < aligned_size; i += blockDim_x * vector_size) {
        float4 in_vec = *reinterpret_cast<const float4*>(&in_ptr[i]);
        accscalar_t vals[4] = {
            static_cast<accscalar_t>(in_vec.x),
            static_cast<accscalar_t>(in_vec.y),
            static_cast<accscalar_t>(in_vec.z),
            static_cast<accscalar_t>(in_vec.w)
        };
        
        #pragma unroll
        for (int j = 0; j < 4; j++) {
            local_sum += vals[j];
            local_sum_sq += vals[j] * vals[j];
        }
    }

    for (int i = aligned_size + tid; i < normalized_size; i += blockDim_x) {
        accscalar_t val = static_cast<accscalar_t>(__ldg(&in_ptr[i]));
        local_sum += val;
        local_sum_sq += val * val;
    }
}

template <typename accscalar_t>
__device__ void reduce_statistics(
    accscalar_t* s_sum,
    accscalar_t* s_sum_sq,
    const int tid,
    const int blockDim_x) {
    
    for (int stride = blockDim_x / 2; stride > 0; stride >>= 1) {
        if (tid < stride) {
            s_sum[tid] += s_sum[tid + stride];
            s_sum_sq[tid] += s_sum_sq[tid + stride];
        }
        __syncthreads();
    }
}

template <typename scalar_t, typename accscalar_t>
__device__ void compute_normalized_output(
    const scalar_t* __restrict__ in_ptr,
    const scalar_t* __restrict__ weight,
    const scalar_t* __restrict__ bias,
    scalar_t* __restrict__ out_ptr,
    const accscalar_t mean,
    const accscalar_t inv_std,
    const int tid,
    const int normalized_size,
    const int blockDim_x) {
    
    const int vector_size = 4;
    const int aligned_size = normalized_size / vector_size * vector_size;
    
    for (int i = tid * vector_size; i < aligned_size; i += blockDim_x * vector_size) {
        float4 in_vec = *reinterpret_cast<const float4*>(&in_ptr[i]);
        float4 w_vec = *reinterpret_cast<const float4*>(&weight[i]);
        float4 b_vec = *reinterpret_cast<const float4*>(&bias[i]);
        
        float4 out_vec;
        accscalar_t vals[4] = {in_vec.x, in_vec.y, in_vec.z, in_vec.w};
        accscalar_t w_vals[4] = {w_vec.x, w_vec.y, w_vec.z, w_vec.w};
        accscalar_t b_vals[4] = {b_vec.x, b_vec.y, b_vec.z, b_vec.w};
        
        #pragma unroll
        for (int j = 0; j < 4; j++) {
            accscalar_t norm_val = (static_cast<accscalar_t>(vals[j]) - mean) * inv_std;
            reinterpret_cast<scalar_t*>(&out_vec)[j] = 
                static_cast<scalar_t>(norm_val * static_cast<accscalar_t>(w_vals[j]) + 
                                    static_cast<accscalar_t>(b_vals[j]));
        }
        
        *reinterpret_cast<float4*>(&out_ptr[i]) = out_vec;
    }

    for (int i = aligned_size + tid; i < normalized_size; i += blockDim_x) {
        scalar_t in_val = __ldg(&in_ptr[i]);
        scalar_t w_val = __ldg(&weight[i]);
        scalar_t b_val = __ldg(&bias[i]);
        accscalar_t norm_val = (static_cast<accscalar_t>(in_val) - mean) * inv_std;
        out_ptr[i] = static_cast<scalar_t>(norm_val * static_cast<accscalar_t>(w_val) + 
                                          static_cast<accscalar_t>(b_val));
    }
}

template <typename scalar_t>
__global__ void layernorm_forward_kernel_modular(
    const scalar_t* __restrict__ input,
    const scalar_t* __restrict__ weight,
    const scalar_t* __restrict__ bias,
    const float eps,
    scalar_t* __restrict__ output,
    const int normalized_size) {

    const int instance_idx = blockIdx.x;
    const int tid = threadIdx.x;
    
    const scalar_t* __restrict__ in_ptr = input + instance_idx * normalized_size;
    scalar_t* __restrict__ out_ptr = output + instance_idx * normalized_size;

    using accscalar_t = at::acc_type<scalar_t, true>;
    
    extern __shared__ char smem[];
    accscalar_t* s_sum = reinterpret_cast<accscalar_t*>(smem);
    accscalar_t* s_sum_sq = s_sum + blockDim.x;

    accscalar_t local_sum, local_sum_sq;
    compute_statistics<scalar_t, accscalar_t>(in_ptr, tid, normalized_size, blockDim.x, 
                                            local_sum, local_sum_sq);

    s_sum[tid] = local_sum;
    s_sum_sq[tid] = local_sum_sq;
    __syncthreads();

    reduce_statistics<accscalar_t>(s_sum, s_sum_sq, tid, blockDim.x);

    __shared__ accscalar_t mean;
    __shared__ accscalar_t inv_std;
    if (tid == 0) {
        mean = s_sum[0] / static_cast<accscalar_t>(normalized_size);
        accscalar_t var = s_sum_sq[0] / static_cast<accscalar_t>(normalized_size) - mean * mean;
        inv_std = static_cast<accscalar_t>(1) / sqrt(var + static_cast<accscalar_t>(eps));
    }
    __syncthreads();

    compute_normalized_output<scalar_t, accscalar_t>(in_ptr, weight, bias, out_ptr, 
                                                    mean, inv_std, tid, normalized_size, blockDim.x);
}

torch::Tensor layernorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double eps = 1e-5) {
    auto output = torch::empty_like(x);
    int normalized_size = weight.numel();
    int outer_size = x.numel() / normalized_size;

    int threads = std::min(((normalized_size + 31) / 32) * 32, 1024);
    int blocks = outer_size;

    AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "layernorm_forward_cuda", ([&] {
        using accscalar_t = at::acc_type<scalar_t, true>;
        int shared_size = threads * 2 * sizeof(accscalar_t);
        layernorm_forward_kernel_modular<scalar_t><<<blocks, threads, shared_size>>>(
            x.data_ptr<scalar_t>(),
            weight.data_ptr<scalar_t>(),
            bias.data_ptr<scalar_t>(),
            static_cast<float>(eps),
            output.data_ptr<scalar_t>(),
            normalized_size);
    }));

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &layernorm_forward, "LayerNorm forward (CUDA) modular",
          py::arg("x"), py::arg("weight"), py::arg("bias"), py::arg("eps") = 1e-5);
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.860 inst/cycle 0.000 5
Executed Ipc Elapsed 0.100 inst/cycle 0.000 5
Issue Slots Busy 21.594 % 0.000 5
Issued Ipc Active 0.860 inst/cycle 0.000 5
SM Busy 21.594 % 0.000 5
Memory Throughput 761555814353.104 byte/second 225509626108681312.000 5
Mem Busy 14.790 % 0.001 5
Max Bandwidth 22.718 % 0.000 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 47.956 % 0.123 5
Mem Pipes Busy 1.160 % 0.000 5
Warp Cycles Per Issued Instruction 36.984 cycle 0.000 5
Warp Cycles Per Executed Instruction 36.986 cycle 0.000 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 31.990 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 2.000 block 0.000 5
Block Limit Shared Mem 3.000 block 0.000 5
Block Limit Warps 2.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 49.910 % 0.000 5
Achieved Active Warps Per SM 31.942 warp 0.000 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 (49.9%) 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 414125.37 μs
Device Time 30941.87 μs
Self CPU Time 62.99 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 414062.39 μs
Device Time 30941.87 μs
Self CPU Time 149.12 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 3999423.13 μs
Device Time 323368.65 μs
Self CPU Time 46332.02 μs
Self Device Time 323368.65 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 3989036.46 μs
Device Time 323368.65 μs
Self CPU Time 8658.25 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 4186930.71 μs
Device Time 10872.58 μs
Self CPU Time 4186930.71 μs
Self Device Time 10872.58 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void layernorm_forward_kernel_modular<float>(float const*, float const*, float const*, float, float*, int)
CPU Time 0.00 μs
Device Time 4262298.07 μs
Self CPU Time 0.00 μs
Self Device Time 4262298.07 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 323368.65 μs
Self CPU Time 0.00 μs
Self Device Time 323368.65 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Completed 
45295 warnings generated when compiling for host.
Suppressed 45326 warnings (45279 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/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:10:5 bugprone-easily-swappable-parameters
10 | const int tid,
| ^~~~~~~~~~~~~~
11 | const int normalized_size,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~
12 | const int blockDim_x,
| ~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:10:15: note: the first parameter in the range is 'tid'
10 | const int tid,
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:12:15: note: the last parameter in the range is 'blockDim_x'
12 | const int blockDim_x,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:13:5: warning: 2 adjacent parameters of 'compute_statistics' of similar type ('accscalar_t &') are easily swapped by mistake [bugprone-easily-swappable-parameters]
13 | accscalar_t& local_sum,
| ^~~~~~~~~~~~~~~~~~~~~~~
14 | accscalar_t& local_sum_sq) {
| ~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:13:18: note: the first parameter in the range is 'local_sum'
13 | accscalar_t& local_sum,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:14:18: note: the last parameter in the range is 'local_sum_sq'
14 | accscalar_t& local_sum_sq) {
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:47:5: warning: 2 adjacent parameters of 'reduce_statistics' of similar type ('accscalar_t *') are easily swapped by mistake [bugprone-easily-swappable-parameters]
47 | accscalar_t* s_sum,
| ^~~~~~~~~~~~~~~~~~~
48 | accscalar_t* s_sum_sq,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:47:18: note: the first parameter in the range is 's_sum'
47 | accscalar_t* s_sum,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:48:18: note: the last parameter in the range is 's_sum_sq'
48 | accscalar_t* s_sum_sq,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:49:5: warning: 2 adjacent parameters of 'reduce_statistics' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
49 | const int tid,
| ^~~~~~~~~~~~~~
50 | const int blockDim_x) {
| ~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:49:15: note: the first parameter in the range is 'tid'
49 | const int tid,
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:50:15: note: the last parameter in the range is 'blockDim_x'
50 | const int blockDim_x) {
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:63:5: warning: 3 adjacent parameters of 'compute_normalized_output' of similar type ('const scalar_t *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
63 | const scalar_t* __restrict__ in_ptr,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
64 | const scalar_t* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
65 | const scalar_t* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:63:34: note: the first parameter in the range is 'in_ptr'
63 | const scalar_t* __restrict__ in_ptr,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:65:34: note: the last parameter in the range is 'bias'
65 | const scalar_t* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:69:5: warning: 3 adjacent parameters of 'compute_normalized_output' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
69 | const int tid,
| ^~~~~~~~~~~~~~
70 | const int normalized_size,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~
71 | const int blockDim_x) {
| ~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:69:15: note: the first parameter in the range is 'tid'
69 | const int tid,
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:71:15: note: the last parameter in the range is 'blockDim_x'
71 | const int blockDim_x) {
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:109:5: warning: 2 adjacent parameters of 'layernorm_forward_kernel_modular' of similar type ('const scalar_t *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
109 | const scalar_t* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
110 | const scalar_t* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:109:34: note: the first parameter in the range is 'input'
109 | const scalar_t* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:110:34: note: the last parameter in the range is 'weight'
110 | const scalar_t* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:116:30: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
116 | const int instance_idx = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:117:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:153:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
153 | int normalized_size = weight.numel();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:154:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
154 | int outer_size = x.numel() / normalized_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:159:5: 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]
159 | AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "layernorm_forward_cuda", ([&] {
| ^
/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/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:161:27: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
161 | int shared_size = threads * 2 * sizeof(accscalar_t);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:161:27: note: make conversion explicit to silence this warning
161 | int shared_size = threads * 2 * sizeof(accscalar_t);
| ^
| static_cast<unsigned long>(
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:66: 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:233:44: 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:74:56: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:70:12: note: expanded from macro 'AT_PRIVATE_CASE_TYPE_USING_HINT'
70 | return __VA_ARGS__(); \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:221:7: note: expanded from macro 'AT_DISPATCH_SWITCH'
221 | __VA_ARGS__ \
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s2_layernorm_modular_base/base/base.cu:161:27: note: perform multiplication in a wider type
161 | int shared_size = threads * 2 * sizeof(accscalar_t);
| ^
| static_cast<long>(
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:66: 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:233:44: 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:74:56: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:70:12: note: expanded from macro 'AT_PRIVATE_CASE_TYPE_USING_HINT'
70 | return __VA_ARGS__(); \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:221:7: note: expanded from macro 'AT_DISPATCH_SWITCH'
221 | __VA_ARGS__ \
| ^~~~~~~~~~~