← Back to Leaderboard

The AI CUDA Engineer 👷

55_Matmul_MaxPool_Sum_Scale • unrolled_matmul_pool_base_base

Level 2 • Task 55
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    kernel_size: int,
    scale_factor: float,
    weight: torch.Tensor,
    bias: torch.Tensor,
) -> torch.Tensor:
    """
    Performs matrix multiplication, max pooling, sum, and scaling.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_features)
        kernel_size (int): Size of max pooling kernel
        scale_factor (float): Factor to scale the output by
        weight (torch.Tensor): Weight matrix of shape (out_features, in_features)
        bias (torch.Tensor): Bias vector of shape (out_features)

    Returns:
        torch.Tensor: Output tensor of shape (batch_size,)
    """
    x = F.linear(x, weight, bias)
    x = F.max_pool1d(x.unsqueeze(1), kernel_size).squeeze(1)
    x = torch.sum(x, dim=1)
    x = x * scale_factor
    return x


class Model(nn.Module):
    """
    Model that performs matrix multiplication, max pooling, sum, and scaling.
    """

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

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


batch_size = 128
in_features = 10
out_features = 5
kernel_size = 2
scale_factor = 0.5


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


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

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs matrix multiplication, max pooling, sum, and scaling.
    """
    def __init__(self, in_features, out_features, kernel_size, scale_factor):
        super(Model, self).__init__()
        self.matmul = nn.Linear(in_features, out_features)
        self.max_pool = nn.MaxPool1d(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.max_pool(x.unsqueeze(1)).squeeze(1)
        x = torch.sum(x, dim=1)
        x = x * self.scale_factor
        return x

batch_size = 128
in_features = 10
out_features = 5
kernel_size = 2
scale_factor = 0.5

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

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

Kernel Information

Operation Name 55_Matmul_MaxPool_Sum_Scale
Level ID 2
Task ID 55
Kernel Name unrolled_matmul_pool_base_base
CUDA Speedup (Native) 3.728x
CUDA Speedup (Compile) 2.232x
CUDA Runtime 0.007 ms
PyTorch Runtime (Native) 0.026 ms
PyTorch Runtime (Compile) 0.016 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 2, Task 55 • 55_Matmul_MaxPool_Sum_Scale)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 optimized_shared_warp_base 0.01 3.73 2.23
🥇 smem_accel_base 0.01 3.73 2.23
🥇 indexing_optimized_kernel_base 0.01 3.73 2.23
🥇 warp_divergence_min_optimized_base 0.01 3.73 2.23
🥇 divergence_free_warp_base_base 0.01 3.73 2.23
🥇 optimized_divergence_free_kernel_base 0.01 3.73 2.23
🥇 min_warp_div_kernel_base 0.01 3.73 2.23
🥇 optimized_reduction_shfl_base 0.01 3.73 2.23
🥇 blocksize_tuning_opt_base 0.01 3.73 2.23
🥇 optimized_warp_shared_kernel_base 0.01 3.73 2.23
🥇 fused_divergence_base 0.01 3.73 2.23
🥇 optimized_hybrid_matmul_pool_base 0.01 3.73 2.23
🥇 optimized_hybrid_kernel_base 0.01 3.73 2.23
🥇 evenly_distributed_kernel_base 0.01 3.73 2.23
🥇 warp_reduction_opt_kernel_base_base 0.01 3.73 2.23
🥇 unrolled_matmul_pool_base_base 0.01 3.73 2.23
🥇 hybrid_min_sync_kernel_base 0.01 3.73 2.23
🥇 uniform_no_div_kernel_base 0.01 3.73 2.23
🥇 shared_warp_optimized_base 0.01 3.73 2.23
🥇 optimized_divergence_free_kernel_base 0.01 3.73 2.23 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

__global__ void unrolled_matmul_pool_kernel(
    const float* __restrict__ x,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int B,           // batch_size
    int inF,         // number of input features
    int outF,        // number of output features
    int kernel_size,
    float scale_factor
) {
    const int tid = threadIdx.x;
    const int b = blockIdx.x;
    if (b >= B) return;

    extern __shared__ float smem[];
    float* linear_out = smem;                    // size: outF
    float* warp_results = &smem[outF];          // size: blockDim.x/32

    // Linear transformation with manual unrolling for the inner loop
    #pragma unroll 4
    for (int j = tid; j < outF; j += blockDim.x) {
        float sum = bias[j];
        const float* w_row = weight + j * inF;
        const float* x_row = x + b * inF;
        
        // Manual unroll of the inner product loop in chunks of 4
        int i = 0;
        for (; i + 3 < inF; i += 4) {
            sum = __fmaf_rn(x_row[i], w_row[i], sum);
            sum = __fmaf_rn(x_row[i+1], w_row[i+1], sum);
            sum = __fmaf_rn(x_row[i+2], w_row[i+2], sum);
            sum = __fmaf_rn(x_row[i+3], w_row[i+3], sum);
        }
        // Handle remaining elements
        for (; i < inF; i++) {
            sum = __fmaf_rn(x_row[i], w_row[i], sum);
        }
        linear_out[j] = sum;
    }
    __syncthreads();

    // Max pooling with manual unrolling for common kernel sizes
    int pooled_len = (outF >= kernel_size) ? ((outF - kernel_size) / kernel_size + 1) : 0;
    float thread_sum = 0.0f;

    #pragma unroll 4
    for (int seg = tid; seg < pooled_len; seg += blockDim.x) {
        const int start_idx = seg * kernel_size;
        float max_val = linear_out[start_idx];

        // Manually unrolled max computation based on common kernel sizes
        if (kernel_size == 2) {
            max_val = fmaxf(max_val, linear_out[start_idx + 1]);
        }
        else if (kernel_size == 4) {
            max_val = fmaxf(max_val, linear_out[start_idx + 1]);
            max_val = fmaxf(max_val, linear_out[start_idx + 2]);
            max_val = fmaxf(max_val, linear_out[start_idx + 3]);
        }
        else if (kernel_size == 8) {
            max_val = fmaxf(max_val, linear_out[start_idx + 1]);
            max_val = fmaxf(max_val, linear_out[start_idx + 2]);
            max_val = fmaxf(max_val, linear_out[start_idx + 3]);
            max_val = fmaxf(max_val, linear_out[start_idx + 4]);
            max_val = fmaxf(max_val, linear_out[start_idx + 5]);
            max_val = fmaxf(max_val, linear_out[start_idx + 6]);
            max_val = fmaxf(max_val, linear_out[start_idx + 7]);
        }
        else {
            #pragma unroll 4
            for (int k = 1; k < kernel_size; k++) {
                max_val = fmaxf(max_val, linear_out[start_idx + k]);
            }
        }
        thread_sum += max_val;
    }

    // Warp-level reduction using shuffle operations
    const unsigned int lane_id = tid % 32;
    const unsigned int warp_id = tid / 32;
    
    // Reduce within warp using explicitly unrolled shuffle operations
    #pragma unroll
    for (int offset = 16; offset > 0; offset >>= 1) {
        thread_sum += __shfl_down_sync(0xffffffff, thread_sum, offset);
    }

    // First thread in each warp writes to shared memory
    if (lane_id == 0) {
        warp_results[warp_id] = thread_sum;
    }
    __syncthreads();

    // Final reduction across warps by first warp
    if (tid < 32) {
        float warp_sum = (tid < (blockDim.x / 32)) ? warp_results[tid] : 0.0f;
        
        // Explicit unroll of final warp reduction
        #pragma unroll
        for (int offset = 16; offset > 0; offset >>= 1) {
            warp_sum += __shfl_down_sync(0xffffffff, warp_sum, offset);
        }

        if (tid == 0) {
            output[b] = warp_sum * scale_factor;
        }
    }
}

at::Tensor forward(
    at::Tensor x,
    int64_t kernel_size,
    double scale_factor,
    at::Tensor weight,
    at::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");
    TORCH_CHECK(x.dim() == 2, "x must have shape (batch_size, in_features)");
    TORCH_CHECK(weight.dim() == 2, "weight must have shape (out_features, in_features)");
    TORCH_CHECK(bias.dim() == 1, "bias must have shape (out_features)");

    const auto B = x.size(0);
    const auto inF = x.size(1);
    const auto outF = weight.size(0);

    auto out = torch::empty({B}, x.options());

    const int threads = 256;
    const size_t smem_size = (outF + (threads/32)) * sizeof(float);

    unrolled_matmul_pool_kernel<<<B, threads, smem_size>>>(
        x.data_ptr<float>(),
        weight.data_ptr<float>(),
        bias.data_ptr<float>(),
        out.data_ptr<float>(),
        B,
        inF,
        outF,
        static_cast<int>(kernel_size),
        static_cast<float>(scale_factor)
    );

    return out;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Unrolled CUDA implementation");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.196 inst/cycle 0.000 5
Executed Ipc Elapsed 0.100 inst/cycle 0.000 5
Issue Slots Busy 5.058 % 0.040 5
Issued Ipc Active 0.204 inst/cycle 0.000 5
SM Busy 5.058 % 0.040 5
Memory Throughput 9051618271.726 byte/second 7739350117604715.000 5
Mem Busy 6.716 % 0.002 5
Max Bandwidth 3.812 % 0.001 5
L1/TEX Hit Rate 82.260 % 0.000 5
L2 Hit Rate 118.234 % 0.069 5
Mem Pipes Busy 2.052 % 0.000 5
Warp Cycles Per Issued Instruction 35.948 cycle 1.285 5
Warp Cycles Per Executed Instruction 36.960 cycle 1.357 5
Avg. Active Threads Per Warp 23.790 0.000 5
Avg. Not Predicated Off Threads Per Warp 21.170 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 11.450 % 0.001 5
Achieved Active Warps Per SM 7.328 warp 0.001 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 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 23.8 threads being active per cycle. This is further reduced to 21.2 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 (11.5%) 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 361772.24 μs
Device Time 5.85 μs
Self CPU Time 49.57 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 361722.66 μs
Device Time 5.85 μs
Self CPU Time 111.44 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 361478.88 μs
Device Time 0.00 μs
Self CPU Time 105.27 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 361184.66 μs
Device Time 0.00 μs
Self CPU Time 361184.66 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
cudaLaunchKernel
CPU Time 496841.80 μs
Device Time 21256.00 μs
Self CPU Time 496841.80 μs
Self Device Time 21256.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
unrolled_matmul_pool_kernel(float const*, float const*, float const*, float*, int, int, int, int, float)
CPU Time 0.00 μs
Device Time 34966.26 μs
Self CPU Time 0.00 μs
Self Device Time 34966.26 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 21571.69 μs
Device Time 42276.71 μs
Self CPU Time 21571.69 μs
Self Device Time 42276.71 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 65374.25 μs
Device Time 632072.06 μs
Self CPU Time 12616.93 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 52762.26 μs
Device Time 632072.06 μs
Self CPU Time 17474.40 μs
Self Device Time 632072.06 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 632072.06 μs
Self CPU Time 0.00 μs
Self Device Time 632072.06 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 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_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:6:5 bugprone-easily-swappable-parameters
6 | const float* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
8 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:6:31: note: the first parameter in the range is 'x'
6 | const float* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:8:31: note: the last parameter in the range is 'bias'
8 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:10:5: warning: 3 adjacent parameters of 'unrolled_matmul_pool_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
10 | int B, // batch_size
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | int inF, // number of input features
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
12 | int outF, // number of output features
| ~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:10:9: note: the first parameter in the range is 'B'
10 | int B, // batch_size
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:12:9: note: the last parameter in the range is 'outF'
12 | int outF, // number of output features
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:13:5: warning: 2 adjacent parameters of 'unrolled_matmul_pool_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
13 | int kernel_size,
| ^~~~~~~~~~~~~~~~
14 | float scale_factor
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:13:9: note: the first parameter in the range is 'kernel_size'
13 | int kernel_size,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:14:11: note: the last parameter in the range is 'scale_factor'
14 | float scale_factor
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:14:5: note: 'int' and 'float' may be implicitly converted
14 | float scale_factor
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:16:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
16 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:17:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
17 | const int b = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:26:38: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | for (int j = tid; j < outF; j += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:28:30: 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]
28 | const float* w_row = weight + j * inF;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:28:39: note: make conversion explicit to silence this warning
4 | const float* w_row = weight + j * inF;
| ^~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:28:39: note: perform multiplication in a wider type
28 | const float* w_row = weight + j * inF;
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:29:30: 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]
29 | const float* x_row = x + b * inF;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:29:34: note: make conversion explicit to silence this warning
29 | const float* x_row = x + b * inF;
| ^~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:29:34: note: perform multiplication in a wider type
29 | const float* x_row = x + b * inF;
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:52:50: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
52 | for (int seg = tid; seg < pooled_len; seg += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:116:16: warning: the parameter 'x' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
116 | at::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:119:16: warning: the parameter 'weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
119 | at::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:120:16: warning: the parameter 'bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
120 | at::Tensor bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:143:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
143 | B,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:144:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
144 | inF,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b10_s1_unrolled_matmul_pool_base/base/base.cu:145:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
145 | outF,
| ^