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3_ConvTranspose3d_Sum_LayerNorm_AvgPool_GELU • modular_fused_atomic_layernorm_base

Level 2 • Task 3
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    conv_transpose_weight: torch.Tensor,
    conv_transpose_bias: torch.Tensor,
    sum_weight: torch.Tensor,
    norm_weight: torch.Tensor,
    norm_bias: torch.Tensor,
    stride: tuple,
    padding: tuple,
    output_padding: tuple,
    pool_kernel_size: tuple,
    norm_shape: tuple,
) -> torch.Tensor:
    """
    Functional implementation of a sequence of operations:
    1. 3D transposed convolution
    2. Addition with a learnable weight
    3. Layer normalization
    4. 3D average pooling
    5. GELU activation

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width)
        conv_transpose_weight (torch.Tensor): Weight tensor for transposed convolution
        conv_transpose_bias (torch.Tensor): Bias tensor for transposed convolution
        sum_weight (torch.Tensor): Learnable weight for addition
        norm_weight (torch.Tensor): Weight tensor for layer normalization
        norm_bias (torch.Tensor): Bias tensor for layer normalization
        stride (tuple): Stride for transposed convolution, as (depth_stride, height_stride, width_stride)
        padding (tuple): Padding for transposed convolution, as (depth_pad, height_pad, width_pad)
        output_padding (tuple): Output padding for transposed convolution, as (depth_pad, height_pad, width_pad)
        pool_kernel_size (tuple): Kernel size for average pooling, as (depth_kernel, height_kernel, width_kernel)
        norm_shape (tuple): Shape for layer normalization

    Returns:
        torch.Tensor: Output tensor after applying all operations
    """
    x = F.conv_transpose3d(
        x,
        conv_transpose_weight,
        bias=conv_transpose_bias,
        stride=stride,
        padding=padding,
        output_padding=output_padding,
    )
    x = x + sum_weight
    x = F.layer_norm(x, norm_shape, norm_weight, norm_bias)
    x = F.avg_pool3d(x, kernel_size=pool_kernel_size)
    x = F.gelu(x)
    return x


class Model(nn.Module):
    """
    Model that performs a 3D transposed convolution, followed by a sum, layer normalization, average pooling, and GELU activation.
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        output_padding,
        sum_weight,
        norm_shape,
        pool_kernel_size,
    ):
        super(Model, self).__init__()
        conv = nn.ConvTranspose3d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            output_padding=output_padding,
        )
        self.conv_transpose_weight = nn.Parameter(conv.weight)
        self.conv_transpose_bias = nn.Parameter(conv.bias)
        self.sum_weight = nn.Parameter(torch.tensor(sum_weight))
        norm = nn.LayerNorm(norm_shape)
        self.norm_weight = nn.Parameter(norm.weight + torch.randn(norm_shape) * 0.02)
        self.norm_bias = nn.Parameter(norm.bias + torch.randn(norm_shape) * 0.02)

    def forward(
        self,
        x,
        stride,
        padding,
        output_padding,
        pool_kernel_size,
        norm_shape,
        fn=module_fn,
    ):
        return fn(
            x,
            self.conv_transpose_weight,
            self.conv_transpose_bias,
            self.sum_weight,
            self.norm_weight,
            self.norm_bias,
            stride,
            padding,
            output_padding,
            pool_kernel_size,
            norm_shape,
        )


batch_size = 128
in_channels = 32
out_channels = 64
depth, height, width = 16, 32, 32
kernel_size = (3, 3, 3)
stride = (2, 2, 2)
padding = (1, 1, 1)
output_padding = (1, 1, 1)
sum_weight = 1.0
norm_shape = (out_channels,)
pool_kernel_size = (2, 2, 2)


def get_inputs():
    return [
        torch.randn(batch_size, in_channels, depth, height, width),
        stride,
        padding,
        output_padding,
        pool_kernel_size,
        norm_shape,
    ]


def get_init_inputs():
    return [
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        output_padding,
        sum_weight,
        norm_shape,
        pool_kernel_size,
    ]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a 3D transposed convolution, followed by a sum, layer normalization, average pooling, and GELU activation.
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, sum_weight, norm_shape, pool_kernel_size):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding=output_padding)
        self.sum_weight = nn.Parameter(torch.tensor(sum_weight))
        self.norm = nn.LayerNorm(norm_shape)
        self.norm.weight = nn.Parameter(self.norm.weight + torch.randn(norm_shape)*0.02)
        self.norm.bias = nn.Parameter(self.norm.bias + torch.randn(norm_shape)*0.02)
        self.avg_pool = nn.AvgPool3d(kernel_size=pool_kernel_size)
        self.gelu = nn.GELU()

    def forward(self, x):
        x = self.conv_transpose(x)
        x = x + self.sum_weight
        x = self.norm(x)
        x = self.avg_pool(x)
        x = self.gelu(x)
        return x

batch_size = 128
in_channels = 32
out_channels = 64
depth, height, width = 16, 32, 32
kernel_size = (3, 3, 3)
stride = (2, 2, 2)
padding = (1, 1, 1)
output_padding = (1, 1, 1)
sum_weight = 1.0
norm_shape = (out_channels,)
pool_kernel_size = (2, 2, 2)

def get_inputs():
    return [torch.randn(batch_size, in_channels, depth, height, width)]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding, output_padding, sum_weight, norm_shape, pool_kernel_size]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 3_ConvTranspose3d_Sum_LayerNorm_AvgPool_GELU
Level ID 2
Task ID 3
Kernel Name modular_fused_atomic_layernorm_base
CUDA Speedup (Native) 1.340x
CUDA Speedup (Compile) 0.297x
CUDA Runtime 34.080 ms
PyTorch Runtime (Native) 45.660 ms
PyTorch Runtime (Compile) 10.125 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model azure-gpt-4o-2024-08-06
Temperature 0.50
View Experiment Progress Details

Related Kernels (Level 2, Task 3 • 3_ConvTranspose3d_Sum_LayerNorm_AvgPool_GELU)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 3_convtranspose3d_sum_layernorm_avgpool_gelu_opt_customnorm_base 25.20 1.81 0.40
🥈 3_convtranspose3d_sum_layernorm_avgpool_gelu_opt_customnorm_edit_1 25.26 1.81 0.40
🥉 warp_only_layernorm_base 25.98 1.76 0.39
4 streamed_layernorm_overlap_base 34.07 1.34 0.30
5 minimized_divergence_norm_base 34.08 1.34 0.30
6 modular_fused_atomic_layernorm_base 34.08 1.34 0.30
7 fused_atomic_layernorm_base 34.08 1.34 0.30
8 unrolled_layernorm_base 34.09 1.34 0.30
9 warp_uniform_layernorm_base_base 34.09 1.34 0.30
10 coalesced_memory_access_layernorm_base 34.18 1.34 0.30
11 optimized_fused_3d_norm_base 34.37 1.33 0.29
12 modular_optimized_norm_base_base 34.38 1.33 0.29
13 unrolled_fused_norm_base_base 34.82 1.31 0.29
14 warp_optimized_layernorm_base_base 35.01 1.30 0.29
15 shm_warp_reduce_layernorm_base 36.04 1.27 0.28
16 selective_atomic_norm_base_base 42.59 1.07 0.24
17 constant_memory_layernorm_base_base 44.55 1.02 0.23
18 3_convtranspose3d_sum_layernorm_avgpool_gelu_opt_sync_base 45.07 1.01 0.22
19 3_convtranspose3d_sum_layernorm_avgpool_gelu_opt_sync_edit_1 45.07 1.01 0.22
20 3_convtranspose3d_sum_layernorm_avgpool_gelu_unroll_edit_1 45.09 1.01 0.22 
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <cuda_runtime.h>
#include <vector>

#define BLOCK_SIZE 256
#define WARP_SIZE 32

// Modular device functions for better organization and reusability
__device__ __forceinline__ void warp_reduce(float& sum, float& sq_sum) {
    #pragma unroll
    for (int offset = WARP_SIZE/2; offset > 0; offset >>= 1) {
        sum += __shfl_down_sync(0xffffffff, sum, offset);
        sq_sum += __shfl_down_sync(0xffffffff, sq_sum, offset);
    }
}

__device__ __forceinline__ void atomic_reduce_to_shared(float warp_sum, float warp_sq_sum, float* s_mean, float* s_variance, int lane) {
    if (lane == 0) {
        atomicAdd(s_mean, warp_sum);
        atomicAdd(s_variance, warp_sq_sum);
    }
}

__device__ void compute_layernorm_and_store(const float* __restrict__ input, const float* __restrict__ gamma, const float* __restrict__ beta, float* __restrict__ output, int offset, int n2, float mean, float inv_std, int tid) {
    #pragma unroll 4
    for (int i = tid; i < n2; i += BLOCK_SIZE) {
        int idx = offset + i;
        float normalized = (input[idx] - mean) * inv_std;
        output[idx] = gamma[i] * normalized + beta[i];
    }
}

// Fused layer norm kernel using atomic operations only where necessary.
template <typename T>
__global__ void fused_layernorm_kernel_atomic(
    const T* __restrict__ input,
    const T* __restrict__ gamma,
    const T* __restrict__ beta,
    T* __restrict__ output,
    int n1,
    int n2
) {
    __shared__ float s_mean;
    __shared__ float s_variance;
    if (threadIdx.x == 0) {
        s_mean = 0.0f;
        s_variance = 0.0f;
    }
    __syncthreads();

    int tid = threadIdx.x;
    int bid = blockIdx.x;
    int offset = bid * n2;

    float local_sum = 0.0f;
    float local_sq_sum = 0.0f;

    for (int i = tid; i < n2; i += BLOCK_SIZE) {
        float val = __ldg(&input[offset + i]);
        local_sum += val;
        local_sq_sum += val * val;
    }

    int lane = tid & (WARP_SIZE - 1);
    warp_reduce(local_sum, local_sq_sum);
    atomic_reduce_to_shared(local_sum, local_sq_sum, &s_mean, &s_variance, lane);
    __syncthreads();

    float mean = s_mean / n2;
    float variance = s_variance / n2 - mean * mean;
    float inv_std = rsqrtf(variance + 1e-5f);

    compute_layernorm_and_store(input, gamma, beta, output, offset, n2, mean, inv_std, tid);
}

// The forward function fuses conv_transpose3d, addition, custom layer normalization (using our atomic kernel),
// avg_pool3d and GELU activation.
torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor conv_transpose_weight,
    torch::Tensor conv_transpose_bias,
    torch::Tensor sum_weight,
    torch::Tensor norm_weight,
    torch::Tensor norm_bias,
    std::vector<int64_t> stride,
    std::vector<int64_t> padding,
    std::vector<int64_t> output_padding,
    std::vector<int64_t> pool_kernel_size,
    std::vector<int64_t> norm_shape
) {
    x = x.contiguous();
    conv_transpose_weight = conv_transpose_weight.contiguous();
    sum_weight = sum_weight.contiguous();
    norm_weight = norm_weight.contiguous();
    norm_bias = norm_bias.contiguous();

    at::IntArrayRef strideRef(stride);
    at::IntArrayRef paddingRef(padding);
    at::IntArrayRef outputPaddingRef(output_padding);
    at::IntArrayRef poolKernelRef(pool_kernel_size);

    auto out = at::conv_transpose3d(
        x,
        conv_transpose_weight,
        conv_transpose_bias,
        strideRef,
        paddingRef,
        outputPaddingRef,
        1,
        1
    );

    out.add_(sum_weight);

    auto out_size = out.sizes();
    int64_t n1 = 1;
    for (int i = 0; i < out.dim() - norm_shape.size(); ++i) {
        n1 *= out_size[i];
    }
    int64_t n2 = 1;
    for (size_t i = 0; i < norm_shape.size(); ++i) {
        n2 *= norm_shape[i];
    }

    auto output = torch::empty_like(out);
    dim3 grid(n1);
    dim3 block(BLOCK_SIZE);

    fused_layernorm_kernel_atomic<float><<<grid, block>>>(
        out.data_ptr<float>(),
        norm_weight.data_ptr<float>(),
        norm_bias.data_ptr<float>(),
        output.data_ptr<float>(),
        n1,
        n2
    );
    cudaDeviceSynchronize();

    output = at::avg_pool3d(
        output,
        poolKernelRef,
        poolKernelRef,
        {0,0,0},
        false,
        true
    );

    output = at::gelu(output);

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Modular fused atomic layer norm forward (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.750 inst/cycle 0.000 5
Executed Ipc Elapsed 2.748 inst/cycle 0.000 5
Issue Slots Busy 68.696 % 0.000 5
Issued Ipc Active 2.750 inst/cycle 0.000 5
SM Busy 68.696 % 0.000 5
Memory Throughput 402598254515.170 byte/second 1860858416877654.750 5
Mem Busy 71.364 % 0.000 5
Max Bandwidth 61.310 % 0.000 5
L1/TEX Hit Rate 59.960 % 0.000 5
L2 Hit Rate 50.038 % 0.000 5
Mem Pipes Busy 61.310 % 0.000 5
Warp Cycles Per Issued Instruction 18.888 cycle 0.000 5
Warp Cycles Per Executed Instruction 18.888 cycle 0.000 5
Avg. Active Threads Per Warp 25.740 0.000 5
Avg. Not Predicated Off Threads Per Warp 22.740 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 8.000 block 0.000 5
Block Limit Shared Mem 28.000 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 81.964 % 0.000 5
Achieved Active Warps Per SM 52.460 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (37.3%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
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 ThreadDivergence Instructions are executed in warps, which are groups of 32 threads. Optimal instruction throughput is achieved if all 32 threads of a warp execute the same instruction. The chosen launch configuration, early thread completion, and divergent flow control can significantly lower the number of active threads in a warp per cycle. This kernel achieves an average of 25.7 threads being active per cycle. This is further reduced to 22.7 threads per warp due to predication. The compiler may use predication to avoid an actual branch. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. Try to avoid different execution paths within a warp when possible. In addition, ensure your kernel makes use of Independent Thread Scheduling, which allows a warp to reconverge after a data-dependent conditional block by explicitly calling __syncwarp().
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 (82.0%) 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 442423.84 μs
Device Time 27801.99 μs
Self CPU Time 60.91 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 442362.94 μs
Device Time 27801.99 μs
Self CPU Time 112.61 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::empty_strided
CPU Time 415005.46 μs
Device Time 0.00 μs
Self CPU Time 1047.26 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
cudaDeviceGetStreamPriorityRange
CPU Time 413222.98 μs
Device Time 0.00 μs
Self CPU Time 413222.98 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::conv_transpose3d
CPU Time 139896.28 μs
Device Time 2654600.02 μs
Self CPU Time 716.36 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::convolution
CPU Time 139179.92 μs
Device Time 2654600.02 μs
Self CPU Time 952.69 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::_convolution
CPU Time 138227.23 μs
Device Time 2654600.02 μs
Self CPU Time 1903.25 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
aten::add_
CPU Time 22062.95 μs
Device Time 2001485.80 μs
Self CPU Time 3563.22 μs
Self Device Time 2001485.80 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceSynchronize
CPU Time 9991201.05 μs
Device Time 39697.07 μs
Self CPU Time 9991201.05 μs
Self Device Time 39697.07 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void fused_layernorm_kernel_atomic<float>(float const*, float const*, float const*, float*, int, int)
CPU Time 0.00 μs
Device Time 5495764.45 μs
Self CPU Time 0.00 μs
Self Device Time 5495764.45 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 
45299 warnings generated when compiling for host.
Suppressed 45331 warnings (45284 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:25:45 bugprone-easily-swappable-parameters
25 | __device__ void compute_layernorm_and_store(const float* __restrict__ input, const float* __restrict__ gamma, const float* __restrict__ beta, float* __restrict__ output, int offset, int n2, float mean, float inv_std, int tid) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:25:71: note: the first parameter in the range is 'input'
25 | __device__ void compute_layernorm_and_store(const float* __restrict__ input, const float* __restrict__ gamma, const float* __restrict__ beta, float* __restrict__ output, int offset, int n2, float mean, float inv_std, int tid) {
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:25:104: note: the last parameter in the range is 'gamma'
25 | __device__ void compute_layernorm_and_store(const float* __restrict__ input, const float* __restrict__ gamma, const float* __restrict__ beta, float* __restrict__ output, int offset, int n2, float mean, float inv_std, int tid) {
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:25:171: warning: 3 adjacent parameters of 'compute_layernorm_and_store' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
25 | __device__ void compute_layernorm_and_store(const float* __restrict__ input, const float* __restrict__ gamma, const float* __restrict__ beta, float* __restrict__ output, int offset, int n2, float mean, float inv_std, int tid) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:25:175: note: the first parameter in the range is 'offset'
25 | __device__ void compute_layernorm_and_store(const float* __restrict__ input, const float* __restrict__ gamma, const float* __restrict__ beta, float* __restrict__ output, int offset, int n2, float mean, float inv_std, int tid) {
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:25:197: note: the last parameter in the range is 'mean'
25 | __device__ void compute_layernorm_and_store(const float* __restrict__ input, const float* __restrict__ gamma, const float* __restrict__ beta, float* __restrict__ output, int offset, int n2, float mean, float inv_std, int tid) {
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:25:191: note: 'int' and 'float' may be implicitly converted
25 | __device__ void compute_layernorm_and_store(const float* __restrict__ input, const float* __restrict__ gamma, const float* __restrict__ beta, float* __restrict__ output, int offset, int n2, float mean, float inv_std, int tid) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:25:203: warning: 2 adjacent parameters of 'compute_layernorm_and_store' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
25 | __device__ void compute_layernorm_and_store(const float* __restrict__ input, const float* __restrict__ gamma, const float* __restrict__ beta, float* __restrict__ output, int offset, int n2, float mean, float inv_std, int tid) {
| ^~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:25:209: note: the first parameter in the range is 'inv_std'
25 | __device__ void compute_layernorm_and_store(const float* __restrict__ input, const float* __restrict__ gamma, const float* __restrict__ beta, float* __restrict__ output, int offset, int n2, float mean, float inv_std, int tid) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:25:222: note: the last parameter in the range is 'tid'
25 | __device__ void compute_layernorm_and_store(const float* __restrict__ input, const float* __restrict__ gamma, const float* __restrict__ beta, float* __restrict__ output, int offset, int n2, float mean, float inv_std, int tid) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:25:218: note: 'float' and 'int' may be implicitly converted
25 | __device__ void compute_layernorm_and_store(const float* __restrict__ input, const float* __restrict__ gamma, const float* __restrict__ beta, float* __restrict__ output, int offset, int n2, float mean, float inv_std, int tid) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:52:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
52 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:53:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
53 | int bid = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:70:27: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
70 | float mean = s_mean / n2;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:71:35: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
71 | float variance = s_variance / n2 - mean * mean;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:82:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
82 | torch::Tensor conv_transpose_bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
83 | torch::Tensor sum_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:82:19: note: the first parameter in the range is 'conv_transpose_bias'
82 | torch::Tensor conv_transpose_bias,
| ^~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:83:19: note: the last parameter in the range is 'sum_weight'
83 | torch::Tensor sum_weight,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:86:5: warning: 5 adjacent parameters of 'forward' of similar type ('std::vector<int64_t>') are easily swapped by mistake [bugprone-easily-swappable-parameters]
86 | std::vector<int64_t> stride,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
87 | std::vector<int64_t> padding,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
88 | std::vector<int64_t> output_padding,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
89 | std::vector<int64_t> pool_kernel_size,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
90 | std::vector<int64_t> norm_shape
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:86:26: note: the first parameter in the range is 'stride'
86 | std::vector<int64_t> stride,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:90:26: note: the last parameter in the range is 'norm_shape'
90 | std::vector<int64_t> norm_shape
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:86:26: warning: the parameter 'stride' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
86 | std::vector<int64_t> stride,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:87:26: warning: the parameter 'padding' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
87 | std::vector<int64_t> padding,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:88:26: warning: the parameter 'output_padding' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
88 | std::vector<int64_t> output_padding,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:89:26: warning: the parameter 'pool_kernel_size' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
89 | std::vector<int64_t> pool_kernel_size,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:135:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
135 | n1,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_3/b6_s3_modular_fused_atomic_layernorm/base/base.cu:136:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
136 | n2
| ^