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55_Matmul_MaxPool_Sum_Scale • evenly_distributed_kernel_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 evenly_distributed_kernel_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 o3-mini-2025-01-31
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>

// This kernel evenly distributes workloads across threads for the matrix multiplication (linear layer)
// and the pooling stage. For each sample b, all threads in the block cooperatively load the input vector
// into shared memory, compute a portion of the output features, and then perform max pooling in an evenly
// distributed manner using only the necessary number of threads for pooling. This avoids bottlenecks when
// the number of pooling segments is less than the block size.

__global__ void evenly_distributed_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
) {
    // Each block processes one sample in the batch
    int b = blockIdx.x;
    if (b >= B) return;

    // Shared memory layout:
    // [0, inF)         -> shared copy of input vector x[b]
    // [inF, inF+outF)  -> local_out array (linear transformation result)
    // [inF+outF, ...)  -> temporary storage for pooling reduction (size = blockDim.x)
    extern __shared__ float smem[];
    float* shared_x = smem;                    // size: inF
    float* local_out = shared_x + inF;         // size: outF
    float* pool_shared = local_out + outF;     // size: blockDim.x (for pooling reduction)

    // 1. Cooperative load of input vector x[b] into shared memory
    for (int i = threadIdx.x; i < inF; i += blockDim.x) {
        shared_x[i] = x[b * inF + i];
    }
    __syncthreads();

    // 2. Compute the linear transformation for each output feature j:
    //    local_out[j] = bias[j] + dot(x[b], weight[j])
    for (int j = threadIdx.x; j < outF; j += blockDim.x) {
        float sum_val = bias[j];
        // Use shared_x for input vector
        for (int i = 0; i < inF; i++) {
            sum_val = __fmaf_rn(shared_x[i], weight[j * inF + i], sum_val);
        }
        local_out[j] = sum_val;
    }
    __syncthreads();

    // 3. Max pooling over segments of size 'kernel_size'
    //    Each pooling segment covers kernel_size consecutive output features
    int pooled_len = (outF >= kernel_size) ? (1 + (outF - kernel_size) / kernel_size) : 0;

    // Distribute pooling work evenly:
    // Use only as many threads as needed if pooled_len is smaller than blockDim.x
    int pool_threads = (pooled_len < blockDim.x) ? pooled_len : blockDim.x;
    float pool_local = 0.0f;

    if (pooled_len > 0 && threadIdx.x < pool_threads) {
        // Each active thread processes multiple segments in a grid-stride loop
        for (int seg = threadIdx.x; seg < pooled_len; seg += pool_threads) {
            int start = seg * kernel_size;
            float seg_max = local_out[start];
            for (int k = 1; k < kernel_size; k++) {
                seg_max = fmaxf(seg_max, local_out[start + k]);
            }
            pool_local += seg_max;
        }
    }

    // 4. Reduction of pooling results among the active pool threads
    if (threadIdx.x < pool_threads) {
        pool_shared[threadIdx.x] = pool_local;
    }
    __syncthreads();

    // Simple tree reduction in shared memory
    for (int stride = pool_threads / 2; stride > 0; stride /= 2) {
        if (threadIdx.x < stride && threadIdx.x < pool_threads) {
            pool_shared[threadIdx.x] += pool_shared[threadIdx.x + stride];
        }
        __syncthreads();
    }

    // 5. Write the final result for sample b
    if (threadIdx.x == 0) {
        output[b] = pool_shared[0] * scale_factor;
    }
}

// Forward function callable from Python
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)");

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

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

    // Set block size and calculate shared memory usage:
    const int threads = 256;
    size_t sharedMemSize = (inF + outF + threads) * sizeof(float);

    // Launch one block per sample in the batch
    evenly_distributed_kernel<<<B, threads, sharedMemSize>>>(
        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, "Evenly distributed CUDA forward kernel");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.238 inst/cycle 0.000 5
Executed Ipc Elapsed 0.112 inst/cycle 0.000 5
Issue Slots Busy 6.088 % 0.015 5
Issued Ipc Active 0.246 inst/cycle 0.000 5
SM Busy 6.088 % 0.015 5
Memory Throughput 3330856326.796 byte/second 1878500296464977.250 5
Mem Busy 6.990 % 0.002 5
Max Bandwidth 3.686 % 0.000 5
L1/TEX Hit Rate 79.630 % 0.000 5
L2 Hit Rate 104.020 % 0.290 5
Mem Pipes Busy 2.258 % 0.000 5
Warp Cycles Per Issued Instruction 32.306 cycle 0.798 5
Warp Cycles Per Executed Instruction 32.948 cycle 0.829 5
Avg. Active Threads Per Warp 27.320 0.000 5
Avg. Not Predicated Off Threads Per Warp 22.780 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 30.000 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 12.152 % 0.000 5
Achieved Active Warps Per SM 7.774 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 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 27.3 threads being active per cycle. This is further reduced to 22.8 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 (12.2%) 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 416539.53 μs
Device Time 5.73 μs
Self CPU Time 71.63 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::_to_copy
CPU Time 416467.90 μs
Device Time 5.73 μs
Self CPU Time 123.35 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 416193.47 μs
Device Time 0.00 μs
Self CPU Time 119.39 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 411576.30 μs
Device Time 0.00 μs
Self CPU Time 411576.30 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 482063.36 μs
Device Time 21340.20 μs
Self CPU Time 482063.36 μs
Self Device Time 21340.20 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
evenly_distributed_kernel(float const*, float const*, float const*, float*, int, int, int, int, float)
CPU Time 0.00 μs
Device Time 32749.84 μs
Self CPU Time 0.00 μs
Self Device Time 32749.84 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 17647.85 μs
Device Time 42435.16 μs
Self CPU Time 17647.85 μs
Self Device Time 42435.16 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 64223.66 μs
Device Time 633856.37 μs
Self CPU Time 13100.62 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 51126.10 μs
Device Time 633856.37 μs
Self CPU Time 17579.23 μs
Self Device Time 633856.37 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 633856.37 μs
Self CPU Time 0.00 μs
Self Device Time 633856.37 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Completed 
45295 warnings generated when compiling for host.
Suppressed 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/b6_s1_evenly_distributed_kernel/base/base.cu:12:5 bugprone-easily-swappable-parameters
12 | const float* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
13 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
14 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:12:31: note: the first parameter in the range is 'x'
12 | const float* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:14:31: note: the last parameter in the range is 'bias'
14 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:16:5: warning: 3 adjacent parameters of 'evenly_distributed_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
16 | int B, // batch size
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
17 | int inF, // number of input features
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
18 | int outF, // number of output features
| ~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:16:9: note: the first parameter in the range is 'B'
16 | int B, // batch size
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:18:9: note: the last parameter in the range is 'outF'
18 | int outF, // number of output features
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:19:5: warning: 2 adjacent parameters of 'evenly_distributed_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | int kernel_size,
| ^~~~~~~~~~~~~~~~
20 | float scale_factor
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:19:9: note: the first parameter in the range is 'kernel_size'
19 | int kernel_size,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:20:11: note: the last parameter in the range is 'scale_factor'
20 | float scale_factor
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:20:5: note: 'int' and 'float' may be implicitly converted
20 | float scale_factor
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:23:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
23 | int b = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:36:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
36 | for (int i = threadIdx.x; i < inF; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:36:45: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
36 | for (int i = threadIdx.x; i < inF; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:43:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
43 | for (int j = threadIdx.x; j < outF; j += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:43:46: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
43 | for (int j = threadIdx.x; j < outF; j += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:59:52: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
59 | int pool_threads = (pooled_len < blockDim.x) ? pooled_len : blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:59:65: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
59 | int pool_threads = (pooled_len < blockDim.x) ? pooled_len : blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:64:24: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
64 | for (int seg = threadIdx.x; seg < pooled_len; seg += pool_threads) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:96:16: 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]
96 | at::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:99:16: 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]
99 | at::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:100:16: 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]
100 | at::Tensor bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:109:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
109 | int B = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:110:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
110 | int inF = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_55/b6_s1_evenly_distributed_kernel/base/base.cu:111:16: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | int outF = weight.size(0);
| ^