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22_Matmul_Scale_ResidualAdd_Clamp_LogSumExp_Mish • 22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized_base

Level 2 • Task 22
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    scale_factor: float,
    clamp_min: float,
    clamp_max: float,
    weight: torch.Tensor,
    bias: torch.Tensor,
) -> torch.Tensor:
    """
    Applies matrix multiplication, scaling, residual connection, clamping, LogSumExp and Mish activation.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, input_size)
        scale_factor (float): Factor to scale the output by
        clamp_min (float): Minimum value for clamping
        clamp_max (float): Maximum value for clamping
        weight (torch.Tensor): Weight matrix of shape (hidden_size, input_size)
        bias (torch.Tensor): Bias vector of shape (hidden_size)

    Returns:
        torch.Tensor: Output tensor of shape (batch_size, hidden_size)
    """
    x = F.linear(x, weight, bias)
    x = x * scale_factor
    x = x + x
    x = torch.clamp(x, clamp_min, clamp_max)
    x = torch.logsumexp(x, dim=1, keepdim=True)
    x = x * F.mish(x)
    return x


class Model(nn.Module):
    """
    Model that performs a matrix multiplication, scales the result, adds a residual connection, clamps the output,
    applies LogSumExp, and finally applies the Mish activation function.
    """

    def __init__(self, input_size, hidden_size, scale_factor, clamp_min, clamp_max):
        super(Model, self).__init__()
        matmul = nn.Linear(input_size, hidden_size)
        self.weight = matmul.weight
        self.bias = nn.Parameter(
            matmul.bias + torch.ones_like(matmul.bias) * 0.02
        )  # make sure its nonzero

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


batch_size = 128
input_size = 512
hidden_size = 1024
scale_factor = 2.0
clamp_min = -10.0
clamp_max = 10.0


def get_inputs():
    return [torch.randn(batch_size, input_size), scale_factor, clamp_min, clamp_max]


def get_init_inputs():
    return [input_size, hidden_size, scale_factor, clamp_min, clamp_max]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a matrix multiplication, scales the result, adds a residual connection, clamps the output,
    applies LogSumExp, and finally applies the Mish activation function.
    """
    def __init__(self, input_size, hidden_size, scale_factor, clamp_min, clamp_max):
        super(Model, self).__init__()
        self.matmul = nn.Linear(input_size, hidden_size)
        self.matmul.bias = nn.Parameter(self.matmul.bias + torch.ones_like(self.matmul.bias) * 0.02)
        self.scale_factor = scale_factor
        self.clamp_min = clamp_min
        self.clamp_max = clamp_max

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

        Returns:
            Output tensor of shape (batch_size, hidden_size).
        """
        x = self.matmul(x)
        x = x * self.scale_factor
        x = x + x
        x = torch.clamp(x, self.clamp_min, self.clamp_max)
        x = torch.logsumexp(x, dim=1, keepdim=True)
        x = x * torch.nn.functional.mish(x)  # Mish activation
        return x

batch_size = 128
input_size = 512
hidden_size = 1024
scale_factor = 2.0
clamp_min = -10.0
clamp_max = 10.0

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

def get_init_inputs():
    return [input_size, hidden_size, scale_factor, clamp_min, clamp_max]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 22_Matmul_Scale_ResidualAdd_Clamp_LogSumExp_Mish
Level ID 2
Task ID 22
Kernel Name 22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized_base
CUDA Speedup (Native) 2.190x
CUDA Speedup (Compile) 1.489x
CUDA Runtime 0.029 ms
PyTorch Runtime (Native) 0.064 ms
PyTorch Runtime (Compile) 0.043 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model azure-gpt-4o-2024-08-06
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 2, Task 22 • 22_Matmul_Scale_ResidualAdd_Clamp_LogSumExp_Mish)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized_edit_1 0.03 2.19 1.49
🥇 22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized_base 0.03 2.19 1.49
🥇 22_matmul_scale_residualadd_clamp_logsumexp_mish_even_workload_edit_1 0.03 2.19 1.49
🥇 22_matmul_scale_residualadd_clamp_logsumexp_mish_ldg_aligned_edit_1 0.03 2.19 1.49
🥇 22_matmul_scale_residualadd_clamp_logsumexp_mish_ldg_aligned_base 0.03 2.19 1.49
6 22_matmul_scale_residualadd_clamp_logsumexp_mish_warp_optimized_base 0.03 2.12 1.44
6 22_matmul_scale_residualadd_clamp_logsumexp_mish_shared_memory_warp_optimized_base 0.03 2.12 1.44
6 22_matmul_scale_residualadd_clamp_logsumexp_mish_shared_memory_warp_optimized_edit_1 0.03 2.12 1.44
6 22_matmul_scale_residualadd_clamp_logsumexp_mish_stride_loop_base 0.03 2.12 1.44
6 22_matmul_scale_residualadd_clamp_logsumexp_mish_even_workload_base 0.03 2.12 1.44
6 22_matmul_scale_residualadd_clamp_logsumexp_mish_2dindex_optimized_base 0.03 2.12 1.44
12 coalesced_fused_kernel_base_base 0.04 1.76 1.20
12 block_size_optimization_base 0.04 1.76 1.20
12 atomic_optimized_fused_kernel_base 0.04 1.76 1.20
15 reduced_sync_fused_kernel_base 0.04 1.67 1.14
16 22_matmul_scale_residualadd_clamp_logsumexp_mish_shared_memory_optimized_base 0.04 1.63 1.11
16 fused_kernel_base 0.04 1.63 1.11
16 22_matmul_scale_residualadd_clamp_logsumexp_mish_shared_memory_optimized_edit_1 0.04 1.63 1.11
19 aligned_ldg_fused_kernel_base 0.04 1.44 0.98
20 unroll_optimization_base_base 0.04 1.41 0.96 
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <vector>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cstdint>

// Define block size for kernels
constexpr int BLOCK_SIZE = 256;

// Scalar kernel using __ldg() for read-only loads
__global__ void clamp_and_scale_scalar(const float* __restrict__ in, float* __restrict__ out, int num_elements, float factor, float min_val, float max_val) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < num_elements) {
        // Use __ldg() to load from global memory in read-only cache
        float v = __ldg(&in[idx]);
        v = v * (2.0f * factor);
        v = fminf(fmaxf(v, min_val), max_val);
        out[idx] = v;
    }
}

// Vectorized kernel processing 4 floats at a time using float4
__global__ void clamp_and_scale_vectorized(const float4* __restrict__ in, float4* __restrict__ out, int num_elements4, float factor, float min_val, float max_val) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < num_elements4) {
        // Load a vector of 4 floats using __ldg()
        float4 v = __ldg(&in[idx]);
        float s = 2.0f * factor;
        v.x = fminf(fmaxf(v.x * s, min_val), max_val);
        v.y = fminf(fmaxf(v.y * s, min_val), max_val);
        v.z = fminf(fmaxf(v.z * s, min_val), max_val);
        v.w = fminf(fmaxf(v.w * s, min_val), max_val);
        out[idx] = v;
    }
}

// Kernel to perform LogSumExp across rows and apply Mish activation
__global__ void logsumexp_mish_kernel(const float* __restrict__ input, float* __restrict__ output, int rows, int cols) {
    extern __shared__ float sdata[];
    int row = blockIdx.x; // each block works on one row
    int tid = threadIdx.x;

    // Find maximum value in the row using __ldg() to load read-only values
    float max_val = -INFINITY;
    for (int i = tid; i < cols; i += blockDim.x) {
        float val = __ldg(&input[row * cols + i]);
        max_val = fmaxf(max_val, val);
    }
    sdata[tid] = max_val;
    __syncthreads();
    // Reduction over shared memory to obtain the row maximum
    for (int s = blockDim.x / 2; s > 0; s >>= 1) {
        if (tid < s) {
            sdata[tid] = fmaxf(sdata[tid], sdata[tid + s]);
        }
        __syncthreads();
    }
    float row_max = sdata[0];

    // Compute the sum of exp(value - max) for numerical stability
    float sum = 0.0f;
    for (int i = tid; i < cols; i += blockDim.x) {
        float v = __ldg(&input[row * cols + i]);
        sum += expf(v - row_max);
    }
    sdata[tid] = sum;
    __syncthreads();
    // Reduction to sum up all the values
    for (int s = blockDim.x / 2; s > 0; s >>= 1) {
        if (tid < s) {
            sdata[tid] += sdata[tid + s];
        }
        __syncthreads();
    }
    float lse = logf(sdata[0]) + row_max;
    
    // Apply Mish activation: mish(x) = x * tanh(softplus(x)) => final: x * (x * tanh(softplus(x)))
    float softplus = log1pf(expf(lse));
    float mish = lse * tanhf(softplus);
    output[row] = lse * mish;
}

// Forward function that implements the complete fused operation
torch::Tensor module_fn_forward(
    torch::Tensor x,
    float scale_factor,
    float clamp_min,
    float clamp_max,
    torch::Tensor weight,
    torch::Tensor bias
) {
    // Ensure inputs are contiguous for aligned memory accesses
    x = x.contiguous();
    weight = weight.contiguous();
    bias = bias.contiguous();

    // 1. Matrix multiplication and bias addition
    auto out = torch::mm(x, weight.transpose(0, 1));
    out.add_(bias);

    // 2. Fuse scaling, residual addition, and clamping using a custom kernel
    int num_elements = out.numel();
    // Check for 128-bit alignment and divisibility by 4 for vectorized operations
    bool use_vectorized = (num_elements % 4 == 0) && (((uintptr_t)out.data_ptr<float>()) % 16 == 0);

    if (use_vectorized) {
        int num_elements4 = num_elements / 4;
        int blocks = (num_elements4 + BLOCK_SIZE - 1) / BLOCK_SIZE;
        clamp_and_scale_vectorized<<<blocks, BLOCK_SIZE>>>(
            reinterpret_cast<const float4*>(out.data_ptr<float>()),
            reinterpret_cast<float4*>(out.data_ptr<float>()),
            num_elements4,
            scale_factor,
            clamp_min,
            clamp_max);
    } else {
        int blocks = (num_elements + BLOCK_SIZE - 1) / BLOCK_SIZE;
        clamp_and_scale_scalar<<<blocks, BLOCK_SIZE>>>(
            out.data_ptr<float>(),
            out.data_ptr<float>(),
            num_elements,
            scale_factor,
            clamp_min,
            clamp_max);
    }
    
    // 3. Apply LogSumExp and Mish activation along rows using a reduction kernel
    auto output = torch::empty({out.size(0), 1}, out.options());
    int shared_mem = BLOCK_SIZE * sizeof(float);
    logsumexp_mish_kernel<<<out.size(0), BLOCK_SIZE, shared_mem>>>(
        out.data_ptr<float>(),
        output.data_ptr<float>(),
        out.size(0),
        out.size(1));
    
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &module_fn_forward, "Forward pass for module_fn (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.436 inst/cycle 0.000 5
Executed Ipc Elapsed 0.250 inst/cycle 0.000 5
Issue Slots Busy 10.954 % 0.014 5
Issued Ipc Active 0.436 inst/cycle 0.000 5
SM Busy 10.954 % 0.014 5
Memory Throughput 83658025405.908 byte/second 1202626354981653248.000 5
Mem Busy 5.730 % 0.005 5
Max Bandwidth 4.810 % 0.004 5
L1/TEX Hit Rate 51.140 % 0.000 5
L2 Hit Rate 65.172 % 0.011 5
Mem Pipes Busy 4.810 % 0.004 5
Warp Cycles Per Issued Instruction 18.006 cycle 0.019 5
Warp Cycles Per Executed Instruction 18.220 cycle 0.019 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 24.020 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 16.000 block 0.000 5
Block Limit Shared Mem 16.000 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 12.460 % 0.000 5
Achieved Active Warps Per SM 7.974 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 (12.5%) 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 321231.76 μs
Device Time 144.06 μs
Self CPU Time 67.60 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 321164.16 μs
Device Time 144.06 μs
Self CPU Time 123.50 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 320604.62 μs
Device Time 0.00 μs
Self CPU Time 157.85 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 76654.98 μs
Device Time 903432.89 μs
Self CPU Time 28029.99 μs
Self Device Time 903432.89 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::mm
CPU Time 326830.15 μs
Device Time 147164.06 μs
Self CPU Time 183916.82 μs
Self Device Time 147164.06 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
sm80_xmma_gemm_f32f32_f32f32_f32_tn_n_tilesize64x64x8_stage3_warpsize1x4x1_ffma_aligna4_alignc4_execute_kernel__5x_cublas
CPU Time 0.00 μs
Device Time 93346.88 μs
Self CPU Time 0.00 μs
Self Device Time 93346.88 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 581999.50 μs
Device Time 33256.44 μs
Self CPU Time 581999.50 μs
Self Device Time 33256.44 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 99821.70 μs
Device Time 903432.89 μs
Self CPU Time 23184.49 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
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 903510.49 μs
Self CPU Time 0.00 μs
Self Device Time 903510.49 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 
45293 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/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:12:95 bugprone-easily-swappable-parameters
12 | __global__ void clamp_and_scale_scalar(const float* __restrict__ in, float* __restrict__ out, int num_elements, float factor, float min_val, float max_val) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:12:99: note: the first parameter in the range is 'num_elements'
12 | __global__ void clamp_and_scale_scalar(const float* __restrict__ in, float* __restrict__ out, int num_elements, float factor, float min_val, float max_val) {
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:12:133: note: the last parameter in the range is 'min_val'
12 | __global__ void clamp_and_scale_scalar(const float* __restrict__ in, float* __restrict__ out, int num_elements, float factor, float min_val, float max_val) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:12:113: note: 'int' and 'float' may be implicitly converted
12 | __global__ void clamp_and_scale_scalar(const float* __restrict__ in, float* __restrict__ out, int num_elements, float factor, float min_val, float max_val) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:13:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
13 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:24:101: warning: 3 adjacent parameters of 'clamp_and_scale_vectorized' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
24 | __global__ void clamp_and_scale_vectorized(const float4* __restrict__ in, float4* __restrict__ out, int num_elements4, float factor, float min_val, float max_val) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:24:105: note: the first parameter in the range is 'num_elements4'
24 | __global__ void clamp_and_scale_vectorized(const float4* __restrict__ in, float4* __restrict__ out, int num_elements4, float factor, float min_val, float max_val) {
| ^~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:24:140: note: the last parameter in the range is 'min_val'
24 | __global__ void clamp_and_scale_vectorized(const float4* __restrict__ in, float4* __restrict__ out, int num_elements4, float factor, float min_val, float max_val) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:24:120: note: 'int' and 'float' may be implicitly converted
24 | __global__ void clamp_and_scale_vectorized(const float4* __restrict__ in, float4* __restrict__ out, int num_elements4, float factor, float min_val, float max_val) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:25:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
25 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:39:100: warning: 2 adjacent parameters of 'logsumexp_mish_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
39 | __global__ void logsumexp_mish_kernel(const float* __restrict__ input, float* __restrict__ output, int rows, int cols) {
| ^~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:39:104: note: the first parameter in the range is 'rows'
39 | __global__ void logsumexp_mish_kernel(const float* __restrict__ input, float* __restrict__ output, int rows, int cols) {
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:39:114: note: the last parameter in the range is 'cols'
39 | __global__ void logsumexp_mish_kernel(const float* __restrict__ input, float* __restrict__ output, int rows, int cols) {
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:41:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
41 | int row = blockIdx.x; // each block works on one row
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:42:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
42 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:46:38: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
46 | for (int i = tid; i < cols; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:53:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
53 | for (int s = blockDim.x / 2; s > 0; s >>= 1) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:63:38: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
63 | for (int i = tid; i < cols; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:70:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
70 | for (int s = blockDim.x / 2; s > 0; s >>= 1) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:103:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
103 | int num_elements = out.numel();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:134:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
134 | out.size(0),
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_22/b4_s0_22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized/base/base.cu:135:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
135 | out.size(1));
| ^