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25_Conv2d_Min_Tanh_Tanh • warp_divergence_optimized_conv_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 warp_divergence_optimized_conv_base
CUDA Speedup (Native) 1.413x
CUDA Speedup (Compile) 1.485x
CUDA Runtime 0.037 ms
PyTorch Runtime (Native) 0.052 ms
PyTorch Runtime (Compile) 0.055 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model azure-gpt-4o-2024-08-06
Temperature 0.50
View Experiment Progress Details

Related Kernels (Level 2, Task 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 to minimize warp divergence
__global__ void optimized_conv_min_tanh_forward_kernel(
    const float* __restrict__ x,           // Input tensor: [B, C_in, H, W]
    const float* __restrict__ weight,      // Convolution weight: [C_out, C_in, K, K]
    const float* __restrict__ bias,        // Convolution bias: [C_out]
    float* __restrict__ output,            // Output tensor: [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) {

    extern __shared__ float shared_mem[];
    const int weight_elems = out_channels * in_channels * kernel_size * kernel_size;
    float* s_weight = shared_mem; 
    float* s_bias = shared_mem + weight_elems;

    int tid = threadIdx.x;
    int blockThreads = blockDim.x;
    for (int i = tid; i < weight_elems; i += blockThreads) {
        s_weight[i] = weight[i];
    }
    for (int i = tid; i < out_channels; i += blockThreads) {
        s_bias[i] = bias[i];
    }
    __syncthreads();

    int index = blockIdx.x * blockDim.x + tid;
    int total_threads = batch * out_height * out_width;
    if (index >= total_threads) return;

    int b = index / (out_height * out_width);
    int rem = index % (out_height * out_width);
    int out_y = rem / out_width;
    int out_x = rem % out_width;

    float min_val = 1e20f;

    for (int oc = 0; oc < out_channels; ++oc) {
        float conv_sum = s_bias[oc];
        for (int ic = 0; ic < in_channels; ++ic) {
            #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 = b * (in_channels * in_height * in_width) +
                                  ic * (in_height * in_width) +
                                  in_y * in_width + in_x;
                    int w_index = oc * (in_channels * kernel_size * kernel_size) +
                                  ic * (kernel_size * kernel_size) +
                                  ky * kernel_size + kx;
                    conv_sum += x[x_index] * s_weight[w_index];
                }
            }
        }
        min_val = fminf(min_val, conv_sum);
    }

    float activated = tanhf(tanhf(min_val));
    int out_index = b * (out_height * out_width) + out_y * out_width + out_x;
    output[out_index] = activated;
}

void optimized_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);
    const int out_height = in_height - kernel_size + 1;
    const int out_width = in_width - kernel_size + 1;

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

    const int weight_elems = out_channels * in_channels * kernel_size * kernel_size;
    const int shared_bytes = (weight_elems + out_channels) * sizeof(float);

    optimized_conv_min_tanh_forward_kernel<<<blocks, threads, shared_bytes>>>(
        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 optimized_conv_min_tanh_forward_kernel: %s\n", cudaGetErrorString(err));
    }
}

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 batch = x.size(0);
    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;

    auto output = at::empty({batch, 1, out_height, out_width}, x.options());
    optimized_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, min (over channels), and double tanh activation (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.714 inst/cycle 0.000 5
Executed Ipc Elapsed 2.262 inst/cycle 0.000 5
Issue Slots Busy 67.898 % 0.049 5
Issued Ipc Active 2.718 inst/cycle 0.000 5
SM Busy 67.898 % 0.049 5
Memory Throughput 38593876487.540 byte/second 13563329439803782.000 5
Mem Busy 55.394 % 0.029 5
Max Bandwidth 37.222 % 0.013 5
L1/TEX Hit Rate 98.960 % 0.000 5
L2 Hit Rate 56.682 % 0.320 5
Mem Pipes Busy 37.222 % 0.013 5
Warp Cycles Per Issued Instruction 9.086 cycle 0.002 5
Warp Cycles Per Executed Instruction 9.092 cycle 0.002 5
Avg. Active Threads Per Warp 31.880 0.000 5
Avg. Not Predicated Off Threads Per Warp 29.560 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 23.000 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 38.518 % 0.000 5
Achieved Active Warps Per SM 24.652 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (35.4%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
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 (38.5%) 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 603406.55 μs
Device Time 83.62 μs
Self CPU Time 42.58 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 603363.97 μs
Device Time 83.62 μs
Self CPU Time 93.93 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::empty_strided
CPU Time 602895.82 μs
Device Time 0.00 μs
Self CPU Time 115.20 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 602374.15 μs
Device Time 0.00 μs
Self CPU Time 602374.15 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 182529.71 μs
Device Time 533909.15 μs
Self CPU Time 13368.88 μs
Self Device Time 533909.15 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 602878.98 μs
Device Time 624.80 μs
Self CPU Time 602878.98 μs
Self Device Time 624.80 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
optimized_conv_min_tanh_forward_kernel(float const*, float const*, float const*, float*, int, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 228188.79 μs
Self CPU Time 0.00 μs
Self Device Time 228188.79 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 193494.65 μs
Device Time 533909.15 μs
Self CPU Time 10978.56 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 533986.81 μs
Self CPU Time 0.00 μs
Self Device Time 533986.81 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 
45301 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/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:9:5 bugprone-easily-swappable-parameters
9 | const float* __restrict__ x, // Input tensor: [B, C_in, H, W]
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
10 | const float* __restrict__ weight, // Convolution weight: [C_out, C_in, K, K]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const float* __restrict__ bias, // Convolution bias: [C_out]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:9:31: note: the first parameter in the range is 'x'
9 | const float* __restrict__ x, // Input tensor: [B, C_in, H, W]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:11:31: note: the last parameter in the range is 'bias'
11 | const float* __restrict__ bias, // Convolution bias: [C_out]
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:13:5: warning: 2 adjacent parameters of 'optimized_conv_min_tanh_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
13 | const int batch,
| ^~~~~~~~~~~~~~~~
14 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:13:15: note: the first parameter in the range is 'batch'
13 | const int batch,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:14:15: note: the last parameter in the range is 'in_channels'
14 | const int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:16:5: warning: 2 adjacent parameters of 'optimized_conv_min_tanh_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
16 | const int in_width,
| ^~~~~~~~~~~~~~~~~~~
17 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:16:15: note: the first parameter in the range is 'in_width'
16 | const int in_width,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:17:15: note: the last parameter in the range is 'out_channels'
17 | const int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:18:5: warning: 2 adjacent parameters of 'optimized_conv_min_tanh_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
18 | const int kernel_size,
| ^~~~~~~~~~~~~~~~~~~~~~
19 | const int out_height,
| ~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:18:15: note: the first parameter in the range is 'kernel_size'
18 | const int kernel_size,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:19:15: note: the last parameter in the range is 'out_height'
19 | const int out_height,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:27:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:28:24: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | int blockThreads = blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:37:17: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
37 | int index = blockIdx.x * blockDim.x + tid;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:76:16: 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]
76 | at::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:77:16: 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]
77 | at::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:78:16: 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]
78 | at::Tensor conv_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:79:16: 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]
79 | at::Tensor output) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:81:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
81 | const int batch = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:82:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
82 | const int in_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:83:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
83 | const int in_height = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:84:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
84 | const int in_width = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:86:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
86 | const int out_channels = conv_weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:87:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
87 | const int kernel_size = conv_weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:96:30: warning: narrowing conversion from 'unsigned long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
96 | const int shared_bytes = (weight_elems + out_channels) * sizeof(float);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:120:16: 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]
120 | at::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:121:16: 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]
121 | at::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:122:16: 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]
122 | at::Tensor conv_bias) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:128:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
128 | const int batch = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:129:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
129 | const int in_height = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:130:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
130 | const int in_width = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_25/b9_s0_warp_divergence_optimized_conv/base/base.cu:131:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
131 | const int kernel_size = conv_weight.size(2);
| ^