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98_Matmul_AvgPool_GELU_Scale_Max • constant_memory_fusion_base

Level 2 • Task 98
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    pool_kernel_size: int,
    scale_factor: float,
    weight: torch.Tensor,
    bias: torch.Tensor,
) -> torch.Tensor:
    """
    Implements Matmul_AvgPool_GELU_Scale_Max pattern using functional operations.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_features)
        pool_kernel_size (int): Kernel size for average pooling
        scale_factor (float): Scale factor to multiply features by
        weight (torch.Tensor): Weight matrix for linear layer
        bias (torch.Tensor): Bias vector for linear layer

    Returns:
        torch.Tensor: Output tensor of shape (batch_size,)
    """
    x = F.linear(x, weight, bias)
    x = F.avg_pool1d(x.unsqueeze(1), kernel_size=pool_kernel_size).squeeze(1)
    x = F.gelu(x)
    x = x * scale_factor
    x = torch.max(x, dim=1).values
    return x


class Model(nn.Module):
    """
    A model implementing the pattern "Matmul_AvgPool_GELU_Scale_Max".
    """

    def __init__(self, in_features, out_features, pool_kernel_size, scale_factor):
        super(Model, self).__init__()
        gemm = nn.Linear(in_features, out_features)
        self.weight = gemm.weight
        self.bias = gemm.bias

    def forward(self, x, pool_kernel_size, scale_factor, fn=module_fn):
        return fn(x, pool_kernel_size, scale_factor, self.weight, self.bias)


batch_size = 128
in_features = 512
out_features = 256
pool_kernel_size = 4
scale_factor = 2.0


def get_inputs():
    return [torch.randn(batch_size, in_features), pool_kernel_size, scale_factor]


def get_init_inputs():
    return [in_features, out_features, pool_kernel_size, scale_factor]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A model implementing the pattern "Matmul_AvgPool_GELU_Scale_Max".
    """
    def __init__(self, in_features, out_features, pool_kernel_size, scale_factor):
        super(Model, self).__init__()
        self.matmul = nn.Linear(in_features, out_features)
        self.avg_pool = nn.AvgPool1d(kernel_size=pool_kernel_size)
        self.scale_factor = scale_factor

    def forward(self, x):
        """
        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_features).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_features).
        """
        x = self.matmul(x)
        x = self.avg_pool(x.unsqueeze(1)).squeeze(1)
        x = torch.nn.functional.gelu(x)
        x = x * self.scale_factor
        x = torch.max(x, dim=1).values
        return x

batch_size = 128
in_features = 512
out_features = 256
pool_kernel_size = 4
scale_factor = 2.0

def get_inputs():
    return [torch.randn(batch_size, in_features)]

def get_init_inputs():
    return [in_features, out_features, pool_kernel_size, scale_factor]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 98_Matmul_AvgPool_GELU_Scale_Max
Level ID 2
Task ID 98
Kernel Name constant_memory_fusion_base
CUDA Speedup (Native) 0.975x
CUDA Speedup (Compile) 1.408x
CUDA Runtime 0.032 ms
PyTorch Runtime (Native) 0.031 ms
PyTorch Runtime (Compile) 0.045 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 98 • 98_Matmul_AvgPool_GELU_Scale_Max)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 unrolled_fused_pipeline_base_base 0.03 1.04 1.50
🥇 shared_memory_optimization_base 0.03 1.04 1.50
🥇 modular_fused_pipeline_base 0.03 1.04 1.50
🥇 strided_fused_pipeline_optimization_base 0.03 1.04 1.50
🥇 fused_pipeline_base 0.03 1.04 1.50
6 fused_pipeline_optimized_block_size_base 0.03 1.01 1.45
6 shared_memory_optimized_base_base 0.03 1.01 1.45
6 strided_fused_pipeline_optimization_base 0.03 1.01 1.45
6 memory_coalesced_fused_pipeline_base 0.03 1.01 1.45
6 even_workload_fused_kernel_base 0.03 1.01 1.45
6 warp_divergence_optimized_base 0.03 1.01 1.45
6 fused_pool_act_max_warp_base 0.03 1.01 1.45
13 constant_memory_fusion_base 0.03 0.97 1.41
14 fusedpoolactmax_base 0.03 0.95 1.37
14 constant_memory_optimization_base_base 0.03 0.95 1.37
16 fused_pipeline_shared_memory_base 0.03 0.92 1.33
17 fused_actmax_atomic_base 0.04 0.80 1.16
17 aligned_matmul_pool_act_max_edit_1 0.04 0.80 1.16
17 aligned_vectorized_ldg_optimized_base 0.04 0.80 1.16
17 fused_matmul_pool_act_max_base 0.04 0.80 1.16 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>
#include <float.h>

#ifndef TILE_SIZE
#define TILE_SIZE 16
#endif

// Constant memory for frequently accessed, read-only data
__constant__ float const_bias[1024]; // Assuming max N is 1024, adjust if needed

// Device function for GELU activation
__device__ inline float gelu_activation(float x) {
    return 0.5f * x * (1.0f + erff(x * 0.70710678f));
}

//--------------------------------------------------------------------------
// Modular device functions for tiled matrix multiplication with shared memory
//--------------------------------------------------------------------------

// Loads a tile of matrix A into shared memory
__device__ inline void load_A_tile(const float* __restrict__ A, float* A_tile, int row, int t, int M, int K) {
    int col = t * TILE_SIZE + threadIdx.x;
    if (row < M && col < K)
        A_tile[threadIdx.y * TILE_SIZE + threadIdx.x] = A[row * K + col];
    else
        A_tile[threadIdx.y * TILE_SIZE + threadIdx.x] = 0.0f;
}

// Loads a tile of matrix B into shared memory
// Note: B is stored row-major, but we access it as if it were transposed
__device__ inline void load_B_tile(const float* __restrict__ B, float* B_tile, int col, int t, int K, int N) {
    int rowB = t * TILE_SIZE + threadIdx.y;
    if (col < N && rowB < K)
        B_tile[threadIdx.y * TILE_SIZE + threadIdx.x] = B[col * K + rowB];
    else
        B_tile[threadIdx.y * TILE_SIZE + threadIdx.x] = 0.0f;
}

// Computes the product of the loaded A and B tiles
__device__ inline float compute_tile_product(const float* A_tile, const float* B_tile) {
    float sum = 0.0f;
    #pragma unroll
    for (int i = 0; i < TILE_SIZE; i++) {
        sum += A_tile[threadIdx.y * TILE_SIZE + i] * B_tile[i * TILE_SIZE + threadIdx.x];
    }
    return sum;
}

//--------------------------------------------------------------------------
// Modular Fused Matrix Multiplication with Bias Addition Kernel
// Computes: C = A * (B^T) + bias
// M: number of rows in A
// N: number of rows in B (output features)
// K: common dimension
//--------------------------------------------------------------------------
__global__ void ModularFusedMatMulBiasKernel(const float* __restrict__ A,
                                              const float* __restrict__ B,
                                              float* __restrict__ C,
                                              int M, int N, int K) {
    // Shared memory tiles for A and B
    __shared__ float A_tile[TILE_SIZE * TILE_SIZE];
    __shared__ float B_tile[TILE_SIZE * TILE_SIZE];

    int row = blockIdx.y * TILE_SIZE + threadIdx.y;
    int col = blockIdx.x * TILE_SIZE + threadIdx.x;
    float sum = 0.0f;
    
    int numTiles = (K + TILE_SIZE - 1) / TILE_SIZE;
    for (int t = 0; t < numTiles; t++) {
        load_A_tile(A, A_tile, row, t, M, K);
        load_B_tile(B, B_tile, col, t, K, N);
        __syncthreads();  // ensure the tiles are loaded
        sum += compute_tile_product(A_tile, B_tile);
        __syncthreads();  // prepare for next tile
    }
    if (row < M && col < N) {
        C[row * N + col] = sum + const_bias[col];
    }
}

//--------------------------------------------------------------------------
// Modular device functions for pooling, activation, scaling and reduction
//--------------------------------------------------------------------------

// Computes the average pooling over a pooling window, applies GELU and scales the result
__device__ inline float compute_pool_activation(const float* __restrict__ row_ptr, int N, int start, int pool_kernel_size, float scale_factor) {
    float sum = 0.0f;
    int count = 0;
    #pragma unroll
    for (int j = 0; j < pool_kernel_size; j++) {
        int col = start + j;
        if (col < N) {
            sum += row_ptr[col];
            count++;
        }
    }
    float avg = (count > 0) ? (sum / count) : 0.0f;
    return gelu_activation(avg) * scale_factor;
}

// Warp-level maximum reduction using shuffle operations
__device__ inline float warpReduceMax(float val) {
    for (int offset = warpSize / 2; offset > 0; offset /= 2) {
        val = fmaxf(val, __shfl_down_sync(0xffffffff, val, offset));
    }
    return val;
}

//--------------------------------------------------------------------------
// Modular Fused Pooling, Activation, Scaling and Max Reduction Kernel
// Processes one row per block
//--------------------------------------------------------------------------
__global__ void ModularFusedPoolActMaxKernel(const float* __restrict__ linear_output,
                                              float* __restrict__ output,
                                              int N,
                                              int pool_kernel_size,
                                              int output_length,
                                              float scale_factor) {
    int row = blockIdx.x; // one block per row
    int tid = threadIdx.x;
    float local_max = -FLT_MAX;
    const float* row_ptr = linear_output + row * N;

    // Each thread processes multiple pooling bins in a strided loop
    for (int bin = tid; bin < output_length; bin += blockDim.x) {
        int start = bin * pool_kernel_size;
        float activated = compute_pool_activation(row_ptr, N, start, pool_kernel_size, scale_factor);
        local_max = fmaxf(local_max, activated);
    }

    // Warp-level reduction
    local_max = warpReduceMax(local_max);

    // Use shared memory to store per-warp results
    __shared__ float shared_max[32]; // up to 32 warps per block
    int lane = threadIdx.x % warpSize;
    int warpId = threadIdx.x / warpSize;
    if (lane == 0) {
        shared_max[warpId] = local_max;
    }
    __syncthreads();

    // First warp reduces the warp results
    if (warpId == 0) {
        local_max = (tid < (blockDim.x + warpSize - 1) / warpSize) ? shared_max[lane] : -FLT_MAX;
        local_max = warpReduceMax(local_max);
        if (lane == 0) {
            output[row] = local_max;
        }
    }
}

//--------------------------------------------------------------------------
// Forward function: Chains the modular operations
// 1. Computes linear transformation using ModularFusedMatMulBiasKernel
// 2. Applies fused pooling, GELU activation, scaling, and max reduction
//--------------------------------------------------------------------------

torch::Tensor forward(
    torch::Tensor x,
    int pool_kernel_size,
    float scale_factor,
    torch::Tensor weight,
    torch::Tensor bias) {

    TORCH_CHECK(x.is_cuda(), "x must be a CUDA tensor");
    TORCH_CHECK(weight.is_cuda(), "weight must be a CUDA tensor");
    TORCH_CHECK(bias.is_cuda(), "bias must be a CUDA tensor");

    // Ensure tensors are contiguous
    x = x.contiguous();
    weight = weight.contiguous();
    bias = bias.contiguous();

    int M = x.size(0);   // Batch size
    int K = x.size(1);   // Input features
    int N = weight.size(0); // Output features

    auto options = torch::TensorOptions().dtype(x.dtype()).device(x.device());
    auto linear_output = torch::empty({M, N}, options);

    // Copy bias to constant memory
    cudaMemcpyToSymbol(const_bias, bias.data_ptr<float>(), N * sizeof(float));

    // Launch the modular fused matrix multiplication with bias addition kernel
    dim3 blockDimMat(TILE_SIZE, TILE_SIZE);
    dim3 gridDimMat((N + TILE_SIZE - 1) / TILE_SIZE, (M + TILE_SIZE - 1) / TILE_SIZE);
    ModularFusedMatMulBiasKernel<<<gridDimMat, blockDimMat>>>(
        x.data_ptr<float>(),
        weight.data_ptr<float>(),
        linear_output.data_ptr<float>(),
        M, N, K);

    // Compute the number of pooling bins
    int output_length = (N + pool_kernel_size - 1) / pool_kernel_size;
    auto output = torch::empty({M}, options);

    // Launch the modular fused pooling, activation, scaling and max reduction kernel
    int threads = 256;
    ModularFusedPoolActMaxKernel<<<M, threads>>>(
        linear_output.data_ptr<float>(),
        output.data_ptr<float>(),
        N,
        pool_kernel_size,
        output_length,
        scale_factor
    );

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Modular Fused CUDA forward (MatMul+Bias, Pool, GELU, Scale, Max Reduction)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.338 inst/cycle 0.000 5
Executed Ipc Elapsed 0.138 inst/cycle 0.000 5
Issue Slots Busy 8.842 % 0.079 5
Issued Ipc Active 0.354 inst/cycle 0.000 5
SM Busy 8.842 % 0.079 5
Memory Throughput 34770797028.654 byte/second 602944881672059648.000 5
Mem Busy 8.412 % 0.030 5
Max Bandwidth 4.408 % 0.021 5
L1/TEX Hit Rate 74.420 % 0.000 5
L2 Hit Rate 89.970 % 0.253 5
Mem Pipes Busy 1.526 % 0.001 5
Warp Cycles Per Issued Instruction 18.530 cycle 0.167 5
Warp Cycles Per Executed Instruction 19.364 cycle 0.181 5
Avg. Active Threads Per Warp 31.860 0.000 5
Avg. Not Predicated Off Threads Per Warp 27.660 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 10.634 % 0.004 5
Achieved Active Warps Per SM 6.804 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 (10.6%) 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 832890.90 μs
Device Time 52.90 μs
Self CPU Time 60.45 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 832830.46 μs
Device Time 52.90 μs
Self CPU Time 113.41 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 832442.56 μs
Device Time 0.00 μs
Self CPU Time 125.66 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 822182.76 μs
Device Time 0.00 μs
Self CPU Time 822182.76 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
cudaMemcpyToSymbol
CPU Time 483982.82 μs
Device Time 391.20 μs
Self CPU Time 483982.82 μs
Self Device Time 391.20 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
ModularFusedMatMulBiasKernel(float const*, float const*, float*, int, int, int)
CPU Time 0.00 μs
Device Time 192979.82 μs
Self CPU Time 0.00 μs
Self Device Time 192979.82 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaEventRecord
CPU Time 82438.25 μs
Device Time 67458.48 μs
Self CPU Time 82438.25 μs
Self Device Time 67458.48 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 76543.29 μs
Device Time 623511.20 μs
Self CPU Time 15004.63 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::fill_
CPU Time 61540.39 μs
Device Time 623511.20 μs
Self CPU Time 16620.59 μs
Self Device Time 623511.20 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 623588.86 μs
Self CPU Time 0.00 μs
Self Device Time 623588.86 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 
45297 warnings generated when compiling for host.
Suppressed 45325 warnings (45278 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_98/b9_s3_constant_memory_fusion/base/base.cu:24:80 bugprone-easily-swappable-parameters
24 | __device__ inline void load_A_tile(const float* __restrict__ A, float* A_tile, int row, int t, int M, int K) {
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:24:84: note: the first parameter in the range is 'row'
24 | __device__ inline void load_A_tile(const float* __restrict__ A, float* A_tile, int row, int t, int M, int K) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:24:93: note: the last parameter in the range is 't'
24 | __device__ inline void load_A_tile(const float* __restrict__ A, float* A_tile, int row, int t, int M, int K) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:25:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
25 | int col = t * TILE_SIZE + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:34:80: warning: 2 adjacent parameters of 'load_B_tile' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
34 | __device__ inline void load_B_tile(const float* __restrict__ B, float* B_tile, int col, int t, int K, int N) {
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:34:84: note: the first parameter in the range is 'col'
34 | __device__ inline void load_B_tile(const float* __restrict__ B, float* B_tile, int col, int t, int K, int N) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:34:93: note: the last parameter in the range is 't'
34 | __device__ inline void load_B_tile(const float* __restrict__ B, float* B_tile, int col, int t, int K, int N) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:35:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
35 | int rowB = t * TILE_SIZE + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:59:46: warning: 2 adjacent parameters of 'ModularFusedMatMulBiasKernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
59 | __global__ void ModularFusedMatMulBiasKernel(const float* __restrict__ A,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
60 | const float* __restrict__ B,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:59:72: note: the first parameter in the range is 'A'
59 | __global__ void ModularFusedMatMulBiasKernel(const float* __restrict__ A,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:60:73: note: the last parameter in the range is 'B'
60 | const float* __restrict__ B,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:67:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
67 | int row = blockIdx.y * TILE_SIZE + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:68:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
68 | int col = blockIdx.x * TILE_SIZE + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:89:84: warning: 4 adjacent parameters of 'compute_pool_activation' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
89 | __device__ inline float compute_pool_activation(const float* __restrict__ row_ptr, int N, int start, int pool_kernel_size, float scale_factor) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:89:88: note: the first parameter in the range is 'N'
89 | __device__ inline float compute_pool_activation(const float* __restrict__ row_ptr, int N, int start, int pool_kernel_size, float scale_factor) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:89:130: note: the last parameter in the range is 'scale_factor'
89 | __device__ inline float compute_pool_activation(const float* __restrict__ row_ptr, int N, int start, int pool_kernel_size, float scale_factor) {
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:89:124: note: 'int' and 'float' may be implicitly converted
89 | __device__ inline float compute_pool_activation(const float* __restrict__ row_ptr, int N, int start, int pool_kernel_size, float scale_factor) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:100:38: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
100 | float avg = (count > 0) ? (sum / count) : 0.0f;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:119:47: warning: 2 adjacent parameters of 'ModularFusedPoolActMaxKernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
119 | int pool_kernel_size,
| ^~~~~~~~~~~~~~~~~~~~~
120 | int output_length,
| ~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:119:51: note: the first parameter in the range is 'pool_kernel_size'
119 | int pool_kernel_size,
| ^~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:120:51: note: the last parameter in the range is 'output_length'
120 | int output_length,
| ^~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:122:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
122 | int row = blockIdx.x; // one block per row
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:123:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
123 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:125:28: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
125 | const float* row_ptr = linear_output + row * N;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:125:44: note: make conversion explicit to silence this warning
6 | const float* row_ptr = linear_output + row * N;
| ^~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:125:44: note: perform multiplication in a wider type
125 | const float* row_ptr = linear_output + row * N;
| ^~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:128:53: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
128 | for (int bin = tid; bin < output_length; bin += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:139:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
139 | int lane = threadIdx.x % warpSize;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:140:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
140 | int warpId = threadIdx.x / warpSize;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:178:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
178 | int M = x.size(0); // Batch size
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:179:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
179 | int K = x.size(1); // Input features
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_98/b9_s3_constant_memory_fusion/base/base.cu:180:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
180 | int N = weight.size(0); // Output features
| ^