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25_Conv2d_Min_Tanh_Tanh • conv_min_tanh_optimized_base

Level 2 • Task 25
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    conv_weight: torch.Tensor,
    conv_bias: torch.Tensor,
) -> torch.Tensor:
    """
    Applies convolution, minimum operation along channels, and double tanh activation.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width)
        conv_weight (torch.Tensor): Convolution weight tensor of shape
            (out_channels, in_channels, kernel_size, kernel_size)
        conv_bias (torch.Tensor): Convolution bias tensor of shape (out_channels)

    Returns:
        torch.Tensor: Output tensor after applying convolution, min operation and double tanh,
            with shape (batch_size, 1, height', width') where:
            height' = height - kernel_size + 1
            width' = width - kernel_size + 1
    """
    x = F.conv2d(x, conv_weight, bias=conv_bias)
    x = torch.min(x, dim=1, keepdim=True)[0]
    x = torch.tanh(x)
    x = torch.tanh(x)
    return x


class Model(nn.Module):
    """
    Model that performs a convolution, applies minimum operation, Tanh, and another Tanh.
    """

    def __init__(self, in_channels, out_channels, kernel_size):
        super(Model, self).__init__()
        conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.conv_weight = conv.weight
        self.conv_bias = nn.Parameter(
            conv.bias + torch.ones_like(conv.bias) * 0.02
        )  # make sure its nonzero

    def forward(self, x, fn=module_fn):
        return fn(x, self.conv_weight, self.conv_bias)


batch_size = 128
in_channels = 3
out_channels = 16
height, width = 32, 32
kernel_size = 3


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


def get_init_inputs():
    return [in_channels, out_channels, kernel_size]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a convolution, applies minimum operation, Tanh, and another Tanh.
    """
    def __init__(self, in_channels, out_channels, kernel_size):
        super(Model, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.conv.bias = nn.Parameter(self.conv.bias + torch.ones_like(self.conv.bias) * 0.02)

    def forward(self, x):
        x = self.conv(x)
        x = torch.min(x, dim=1, keepdim=True)[0] # Apply minimum operation along the channel dimension
        x = torch.tanh(x)
        x = torch.tanh(x)
        return x

batch_size = 128
in_channels = 3
out_channels = 16
height, width = 32, 32
kernel_size = 3

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size]
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Kernel Information

Operation Name 25_Conv2d_Min_Tanh_Tanh
Level ID 2
Task ID 25
Kernel Name conv_min_tanh_optimized_base
CUDA Speedup (Native) 1.742x
CUDA Speedup (Compile) 1.831x
CUDA Runtime 0.030 ms
PyTorch Runtime (Native) 0.052 ms
PyTorch Runtime (Compile) 0.055 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 25 • 25_Conv2d_Min_Tanh_Tanh)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 conv_min_tanh_optimized_base 0.03 1.74 1.83
🥇 uniform_flow_conv_min_tanh_edit_1 0.03 1.74 1.83
🥉 modular_conv_min_tanh_optimized_edit_1 0.03 1.69 1.77
🥉 modular_conv_min_tanh_optimized_base 0.03 1.69 1.77
5 conv_min_tanh_3d_grid_512_threads_base 0.04 1.45 1.53
6 shared_memory_conv_min_tanh_base 0.04 1.41 1.48
6 unrolled_shared_memory_conv_min_tanh_base 0.04 1.41 1.48
6 optimized_shared_memory_conv_min_tanh_base 0.04 1.41 1.48
6 register_cache_min_tanh_base 0.04 1.41 1.48
6 optimized_block_size_conv_min_tanh_edit_1 0.04 1.41 1.48
6 warp_divergence_optimized_conv_min_tanh_base 0.04 1.41 1.48
6 optimized_block_size_conv_min_tanh_base 0.04 1.41 1.48
6 conv_min_tanh_3d_grid_optimized_base 0.04 1.41 1.48
6 conv_min_tanh_3d_optimized_block_size_edit_1_base 0.04 1.41 1.48
6 conv_min_tanh_3d_optimized_block_size_edit_1_edit_1 0.04 1.41 1.48
6 warp_divergence_optimized_conv_base 0.04 1.41 1.48
6 grid_stride_conv_min_tanh_ctrlflow_base 0.04 1.41 1.48
6 modular_conv_min_tanh_opt_base 0.04 1.41 1.48
6 optimized_shared_memory_convolution_base 0.04 1.41 1.48
20 conv_min_tanh_optimized_1d_mapping_base 0.04 1.38 1.45 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cmath>
#include <cstdio>

// Optimized CUDA kernel for convolution, min-reduction over channels and double tanh activation.
// This kernel combines the best features from two implementations:
// - Uses __ldg for read-only memory accesses
// - Manually unrolls the inner loops for the common kernel size of 3
// - Applies #pragma unroll for generic kernel sizes to aid compiler optimizations
// - Uses a grid-stride loop to efficiently cover all output pixels

__global__ void conv_min_tanh_forward_kernel_optimized(
    const float* __restrict__ x,      // Input: [B, C_in, H, W]
    const float* __restrict__ weight, // Weight: [C_out, C_in, K, K]
    const float* __restrict__ bias,   // Bias: [C_out]
    float* __restrict__ output,       // Output: [B, 1, H_out, W_out]
    const int batch,
    const int in_channels,
    const int in_height,
    const int in_width,
    const int out_channels,
    const int kernel_size,
    const int out_height,
    const int out_width) {

    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    int num_pixels = batch * out_height * out_width;
    
    // Grid-stride loop to cover all output pixels
    while (tid < num_pixels) {
        // Map tid to (b, out_y, out_x)
        int b = tid / (out_height * out_width);
        int rem = tid % (out_height * out_width);
        int out_y = rem / out_width;
        int out_x = rem % out_width;

        float min_val = 1e20f;

        // Iterate over all output channels
        for (int oc = 0; oc < out_channels; oc++) {
            // Load bias from read-only cache
            float conv_sum = __ldg(&bias[oc]);

            // Process all input channels
            for (int ic = 0; ic < in_channels; ic++) {
                int base_input = b * (in_channels * in_height * in_width) + ic * (in_height * in_width);
                int base_weight = oc * (in_channels * kernel_size * kernel_size) + ic * (kernel_size * kernel_size);

                if (kernel_size == 3) {
                    // Manually unrolled convolution for kernel size 3 to reduce loop overhead
                    int in_y = out_y;
                    int in_x = out_x;
                    conv_sum += __ldg(&x[base_input + (in_y + 0) * in_width + (in_x + 0)]) * __ldg(&weight[base_weight + 0]);
                    conv_sum += __ldg(&x[base_input + (in_y + 0) * in_width + (in_x + 1)]) * __ldg(&weight[base_weight + 1]);
                    conv_sum += __ldg(&x[base_input + (in_y + 0) * in_width + (in_x + 2)]) * __ldg(&weight[base_weight + 2]);
                    conv_sum += __ldg(&x[base_input + (in_y + 1) * in_width + (in_x + 0)]) * __ldg(&weight[base_weight + 3]);
                    conv_sum += __ldg(&x[base_input + (in_y + 1) * in_width + (in_x + 1)]) * __ldg(&weight[base_weight + 4]);
                    conv_sum += __ldg(&x[base_input + (in_y + 1) * in_width + (in_x + 2)]) * __ldg(&weight[base_weight + 5]);
                    conv_sum += __ldg(&x[base_input + (in_y + 2) * in_width + (in_x + 0)]) * __ldg(&weight[base_weight + 6]);
                    conv_sum += __ldg(&x[base_input + (in_y + 2) * in_width + (in_x + 1)]) * __ldg(&weight[base_weight + 7]);
                    conv_sum += __ldg(&x[base_input + (in_y + 2) * in_width + (in_x + 2)]) * __ldg(&weight[base_weight + 8]);
                } else {
                    // Use unrolling hints for generic kernel sizes
                    #pragma unroll
                    for (int ky = 0; ky < kernel_size; ky++) {
                        #pragma unroll
                        for (int kx = 0; kx < kernel_size; kx++) {
                            int in_y = out_y + ky;
                            int in_x = out_x + kx;
                            int x_index = base_input + in_y * in_width + in_x;
                            int w_index = base_weight + ky * kernel_size + kx;
                            conv_sum += __ldg(&x[x_index]) * __ldg(&weight[w_index]);
                        }
                    }
                }
            }
            // Update minimum using fminf to help avoid branch divergence
            min_val = fminf(min_val, conv_sum);
        }

        // Apply double tanh activation
        float activated = tanhf(tanhf(min_val));
        output[tid] = activated;

        tid += blockDim.x * gridDim.x;
    }
}

// Launcher function
void conv_min_tanh_forward_cuda(
    at::Tensor x,
    at::Tensor conv_weight,
    at::Tensor conv_bias,
    at::Tensor output) {

    const int batch = x.size(0);
    const int in_channels = x.size(1);
    const int in_height = x.size(2);
    const int in_width = x.size(3);

    const int out_channels = conv_weight.size(0);
    const int kernel_size = conv_weight.size(2);  // Assuming square kernel
    const int out_height = in_height - kernel_size + 1;
    const int out_width = in_width - kernel_size + 1;

    int num_pixels = batch * out_height * out_width;
    const int threads = 256;
    const int blocks = (num_pixels + threads - 1) / threads;

    conv_min_tanh_forward_kernel_optimized<<<blocks, threads>>>(
        x.data_ptr<float>(),
        conv_weight.data_ptr<float>(),
        conv_bias.data_ptr<float>(),
        output.data_ptr<float>(),
        batch,
        in_channels,
        in_height,
        in_width,
        out_channels,
        kernel_size,
        out_height,
        out_width
    );

    cudaError_t err = cudaGetLastError();
    if (err != cudaSuccess) {
        printf("Error in conv_min_tanh_forward_kernel_optimized: %s\n", cudaGetErrorString(err));
    }
}

// C++ interface (exposed to Python via pybind11)
at::Tensor forward(
    at::Tensor x,
    at::Tensor conv_weight,
    at::Tensor conv_bias) {

    TORCH_CHECK(x.is_cuda(), "x must be a CUDA tensor");
    TORCH_CHECK(conv_weight.is_cuda(), "conv_weight must be a CUDA tensor");
    TORCH_CHECK(conv_bias.is_cuda(), "conv_bias must be a CUDA tensor");

    const int in_height = x.size(2);
    const int in_width = x.size(3);
    const int kernel_size = conv_weight.size(2);
    const int out_height = in_height - kernel_size + 1;
    const int out_width = in_width - kernel_size + 1;
    const int batch = x.size(0);

    // Allocate output tensor with shape [batch, 1, out_height, out_width]
    auto output = at::empty({batch, 1, out_height, out_width}, x.options());
    conv_min_tanh_forward_cuda(x, conv_weight, conv_bias, output);
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Optimized convolution with min reduction and double tanh activation (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.038 inst/cycle 0.000 5
Executed Ipc Elapsed 1.674 inst/cycle 0.000 5
Issue Slots Busy 51.110 % 0.032 5
Issued Ipc Active 2.044 inst/cycle 0.000 5
SM Busy 51.110 % 0.032 5
Memory Throughput 47013816614.596 byte/second 68910284347919080.000 5
Mem Busy 67.450 % 0.134 5
Max Bandwidth 45.288 % 0.062 5
L1/TEX Hit Rate 99.130 % 0.000 5
L2 Hit Rate 55.564 % 0.043 5
Mem Pipes Busy 45.268 % 0.062 5
Warp Cycles Per Issued Instruction 12.706 cycle 0.001 5
Warp Cycles Per Executed Instruction 12.746 cycle 0.001 5
Avg. Active Threads Per Warp 31.840 0.000 5
Avg. Not Predicated Off Threads Per Warp 31.370 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 32.000 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 40.490 % 0.000 5
Achieved Active Warps Per SM 25.910 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization FMA is the highest-utilized pipeline (21.6%) based on active cycles, taking into account the rates of its different instructions. It executes 32-bit floating point (FADD, FMUL, FMAD, ...) and integer (IMUL, IMAD) operations. It is well-utilized, but should not be a bottleneck.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
WRN Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (40.5%) 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 269643.11 μs
Device Time 98.33 μs
Self CPU Time 58.65 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 269584.47 μs
Device Time 98.33 μs
Self CPU Time 121.08 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 269123.72 μs
Device Time 0.00 μs
Self CPU Time 114.86 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 266282.76 μs
Device Time 0.00 μs
Self CPU Time 266282.76 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
aten::fill_
CPU Time 150966.52 μs
Device Time 579706.89 μs
Self CPU Time 15680.07 μs
Self Device Time 579706.89 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 601363.69 μs
Device Time 15014.77 μs
Self CPU Time 601363.69 μs
Self Device Time 15014.77 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
conv_min_tanh_forward_kernel_optimized(float const*, float const*, float const*, float*, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 198662.46 μs
Self CPU Time 0.00 μs
Self Device Time 198662.46 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 16030.27 μs
Device Time 30032.32 μs
Self CPU Time 16030.27 μs
Self Device Time 30032.32 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 163301.08 μs
Device Time 579706.89 μs
Self CPU Time 12348.00 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 579706.89 μs
Self CPU Time 0.00 μs
Self Device Time 579706.89 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 
45298 warnings generated when compiling for host.
Suppressed 45323 warnings (45276 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/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:16:5 bugprone-easily-swappable-parameters
16 | const float* __restrict__ weight, // Weight: [C_out, C_in, K, K]
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
17 | const float* __restrict__ bias, // Bias: [C_out]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:16:31: note: the first parameter in the range is 'weight'
16 | const float* __restrict__ weight, // Weight: [C_out, C_in, K, K]
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:17:31: note: the last parameter in the range is 'bias'
17 | const float* __restrict__ bias, // Bias: [C_out]
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:19:5: warning: 2 adjacent parameters of 'conv_min_tanh_forward_kernel_optimized' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | const int batch,
| ^~~~~~~~~~~~~~~~
20 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:19:15: note: the first parameter in the range is 'batch'
19 | const int batch,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:20:15: note: the last parameter in the range is 'in_channels'
20 | const int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:22:5: warning: 4 adjacent parameters of 'conv_min_tanh_forward_kernel_optimized' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
22 | const int in_width,
| ^~~~~~~~~~~~~~~~~~~
23 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~~
24 | const int kernel_size,
| ~~~~~~~~~~~~~~~~~~~~~~
25 | const int out_height,
| ~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:22:15: note: the first parameter in the range is 'in_width'
22 | const int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:25:15: note: the last parameter in the range is 'out_height'
25 | const int out_height,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:28:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | int tid = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:87:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
87 | tid += blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:93:16: 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]
93 | at::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:94:16: warning: the parameter 'conv_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
94 | at::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:95:16: warning: the parameter 'conv_bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
95 | at::Tensor conv_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:96:16: warning: the parameter 'output' 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 output) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:98:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | const int batch = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:99:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | const int in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:100:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
100 | const int in_height = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:101:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
101 | const int in_width = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:103:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
103 | const int out_channels = conv_weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:104:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
104 | const int kernel_size = conv_weight.size(2); // Assuming square kernel
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:135:16: 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]
135 | at::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:136:16: warning: the parameter 'conv_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
136 | at::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:137:16: warning: the parameter 'conv_bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
137 | at::Tensor conv_bias) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:143:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
143 | const int in_height = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:144:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
144 | const int in_width = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:145:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
145 | const int kernel_size = conv_weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_25/b4_s1_conv_min_tanh_optimized/base/base.cu:148:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
148 | const int batch = x.size(0);
| ^