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67_Conv2d_GELU_GlobalAvgPool • optimized_reduction_conv_gelu_pool_base

Level 2 • Task 67
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, GELU activation, and global average pooling.

    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 of shape (batch_size, out_channels)
    """
    x = F.conv2d(x, conv_weight, bias=conv_bias)
    x = F.gelu(x)
    x = F.adaptive_avg_pool2d(x, 1)
    x = x.squeeze(-1).squeeze(-1)
    return x


class Model(nn.Module):
    """
    Simple model that performs a convolution, applies GELU, and then performs global average pooling.
    """

    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 = nn.Parameter(conv.weight)
        self.conv_bias = nn.Parameter(conv.bias)

    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):
    """
    Simple model that performs a convolution, applies GELU, and then performs global average pooling.
    """
    def __init__(self, in_channels, out_channels, kernel_size):
        super(Model, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)

    def forward(self, x):
        """
        Args:
            x: Input tensor of shape (batch_size, in_channels, height, width)
        Returns:
            Output tensor of shape (batch_size, out_channels)
        """
        x = self.conv(x)
        x = torch.nn.functional.gelu(x)
        x = torch.nn.functional.adaptive_avg_pool2d(x, 1)
        x = x.squeeze(-1).squeeze(-1)
        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]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 67_Conv2d_GELU_GlobalAvgPool
Level ID 2
Task ID 67
Kernel Name optimized_reduction_conv_gelu_pool_base
CUDA Speedup (Native) 1.371x
CUDA Speedup (Compile) 1.915x
CUDA Runtime 0.031 ms
PyTorch Runtime (Native) 0.042 ms
PyTorch Runtime (Compile) 0.059 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 0.50
View Experiment Progress Details

Related Kernels (Level 2, Task 67 • 67_Conv2d_GELU_GlobalAvgPool)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 optimized_reduction_shared_base_base 0.03 1.63 2.28
🥇 unrolled_fused_conv_gelu_pool_base 0.03 1.63 2.28
🥇 balanced_workload_distribution_base 0.03 1.63 2.28
4 coalesced_fused_conv_gelu_pool_base 0.03 1.52 2.12
5 balanced_workload_fused_conv_gelu_pool_base 0.03 1.47 2.05
5 fused_shared_base 0.03 1.47 2.05
7 optimized_reduction_conv_gelu_pool_base 0.03 1.37 1.91
7 blocksize_fused_conv_gelu_pool_base 0.03 1.37 1.91
7 minimal_sync_conv_gelu_pool_base_base 0.03 1.37 1.91
7 fused_conv_gelu_pool_base 0.03 1.37 1.91
7 modular_fused_conv_gelu_pool_base 0.03 1.37 1.91
7 modular_fused_conv_gelu_pool_base 0.03 1.37 1.91
7 sync_optimized_conv_gelu_pool_base 0.03 1.37 1.91
14 manual_unroll_fused_conv2d_gelu_pool_base 0.03 1.33 1.86
14 streamed_fused_conv_gelu_pool_base 0.03 1.33 1.86
14 unrolled_conv2d_gelu_pool_base 0.03 1.33 1.86
17 warp_optimized_conv2d_gelu_pool_base 0.03 1.29 1.80
18 warp_reduction_conv2d_gelu_pool_base 0.03 1.25 1.75
19 optimized_thread_mapping_conv_gelu_pool_base 0.04 1.06 1.48
19 conv_gelu_avgpool_unroll_edit_1 0.04 1.06 1.48 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cmath>

#define KERNEL_SIZE 3
#define BLOCK_SIZE 256
#define WARP_SIZE 32
#define NUM_WARPS (BLOCK_SIZE/WARP_SIZE)

__device__ __forceinline__ float gelu_activate(float x) {
    return 0.5f * x * (1.f + erff(x / 1.41421356f));
}

__device__ __forceinline__ float4 load_vector(const float* ptr) {
    return *reinterpret_cast<const float4*>(ptr);
}

__device__ __forceinline__ float reduce_warp(float val) {
    #pragma unroll
    for (int offset = WARP_SIZE/2; offset > 0; offset >>= 1) {
        val += __shfl_down_sync(0xffffffff, val, offset);
    }
    return val;
}

__device__ __forceinline__ float compute_conv_element(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const int in_channels,
    const int in_h,
    const int in_w,
    const int n,
    const int c_out,
    const int row,
    const int col
) {
    float sum = 0.0f;
    
    const int input_base = ((n * in_channels) * in_h + row) * in_w + col;
    const int weight_base = c_out * in_channels * KERNEL_SIZE * KERNEL_SIZE;
    
    #pragma unroll
    for (int c_in = 0; c_in < in_channels; ++c_in) {
        const float* in_ptr = input + input_base + c_in * in_h * in_w;
        const float* w_ptr = weight + weight_base + c_in * KERNEL_SIZE * KERNEL_SIZE;
        
        #pragma unroll
        for (int kh = 0; kh < KERNEL_SIZE; ++kh) {
            #pragma unroll
            for (int kw = 0; kw < KERNEL_SIZE; ++kw) {
                sum += in_ptr[kh * in_w + kw] * w_ptr[kh * KERNEL_SIZE + kw];
            }
        }
    }
    return sum;
}

__global__ void conv2d_gelu_pool_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    const int N,
    const int in_channels,
    const int in_h,
    const int in_w,
    const int out_channels,
    const int out_h,
    const int out_w
) {
    __shared__ float shared_data[BLOCK_SIZE];
    
    const int n = blockIdx.y;
    const int c_out = blockIdx.x;
    const int tid = threadIdx.x;
    const int lane_id = tid & (WARP_SIZE-1);
    const int warp_id = tid / WARP_SIZE;
    
    float thread_sum = 0.0f;
    const int total_pixels = out_h * out_w;
    const int pixels_per_thread = (total_pixels + BLOCK_SIZE - 1) / BLOCK_SIZE;
    
    #pragma unroll 4
    for (int i = 0; i < pixels_per_thread; ++i) {
        const int pixel_idx = tid + i * BLOCK_SIZE;
        if (pixel_idx < total_pixels) {
            const int row = pixel_idx / out_w;
            const int col = pixel_idx % out_w;
            
            float val = compute_conv_element(
                input, weight, in_channels, in_h, in_w,
                n, c_out, row, col
            );
            val += bias[c_out];
            val = gelu_activate(val);
            thread_sum += val;
        }
    }
    
    thread_sum = reduce_warp(thread_sum);
    
    if (lane_id == 0) {
        shared_data[warp_id] = thread_sum;
    }
    __syncthreads();
    
    if (warp_id == 0) {
        float warp_sum = (lane_id < NUM_WARPS) ? shared_data[lane_id] : 0.0f;
        warp_sum = reduce_warp(warp_sum);
        
        if (lane_id == 0) {
            output[n * out_channels + c_out] = warp_sum / float(total_pixels);
        }
    }
}

torch::Tensor forward(
    torch::Tensor input,
    torch::Tensor conv_weight,
    torch::Tensor conv_bias
) {
    TORCH_CHECK(input.is_cuda(), "input 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 N = input.size(0);
    const int in_channels = input.size(1);
    const int in_h = input.size(2);
    const int in_w = input.size(3);
    const int out_channels = conv_weight.size(0);
    const int out_h = in_h - 2;
    const int out_w = in_w - 2;
    
    auto options = torch::TensorOptions().dtype(input.dtype()).device(input.device());
    auto output = torch::empty({N, out_channels}, options);
    
    dim3 grid(out_channels, N);
    
    conv2d_gelu_pool_kernel<<<grid, BLOCK_SIZE>>>(
        input.data_ptr<float>(),
        conv_weight.data_ptr<float>(),
        conv_bias.data_ptr<float>(),
        output.data_ptr<float>(),
        N, in_channels, in_h, in_w,
        out_channels, out_h, out_w
    );
    
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Optimized reduction Conv2d + GELU + GlobalAvgPool");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.062 inst/cycle 0.000 5
Executed Ipc Elapsed 1.834 inst/cycle 0.000 5
Issue Slots Busy 51.578 % 0.023 5
Issued Ipc Active 2.062 inst/cycle 0.000 5
SM Busy 51.578 % 0.023 5
Memory Throughput 46813558105.282 byte/second 75339802561982816.000 5
Mem Busy 70.558 % 0.190 5
Max Bandwidth 52.870 % 0.118 5
L1/TEX Hit Rate 91.812 % 0.001 5
L2 Hit Rate 85.230 % 1.723 5
Mem Pipes Busy 48.284 % 0.090 5
Warp Cycles Per Issued Instruction 25.598 cycle 0.001 5
Warp Cycles Per Executed Instruction 25.624 cycle 0.002 5
Avg. Active Threads Per Warp 31.100 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.950 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 16.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 82.472 % 0.042 5
Achieved Active Warps Per SM 52.782 warp 0.017 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (30.2%) 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.
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 (82.3%) 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 695642.56 μs
Device Time 83.78 μs
Self CPU Time 56.66 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
aten::_to_copy
CPU Time 695585.91 μs
Device Time 83.78 μs
Self CPU Time 123.36 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 695080.24 μs
Device Time 0.00 μs
Self CPU Time 116.23 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 689304.34 μs
Device Time 0.00 μs
Self CPU Time 689304.34 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 590533.63 μs
Device Time 18662.10 μs
Self CPU Time 590533.63 μs
Self Device Time 18662.10 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
conv2d_gelu_pool_kernel(float const*, float const*, float const*, float*, int, int, int, int, int, int, int)
CPU Time 0.00 μs
Device Time 198230.81 μs
Self CPU Time 0.00 μs
Self Device Time 198230.81 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 19482.37 μs
Device Time 37228.77 μs
Self CPU Time 19482.37 μs
Self Device Time 37228.77 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 164509.49 μs
Device Time 556415.18 μs
Self CPU Time 12136.67 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 152374.77 μs
Device Time 556415.18 μs
Self CPU Time 14418.02 μs
Self Device Time 556415.18 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 556415.18 μs
Self CPU Time 0.00 μs
Self Device Time 556415.18 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 
45294 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_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:28:5 bugprone-easily-swappable-parameters
28 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
29 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:28:31: note: the first parameter in the range is 'input'
28 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:29:31: note: the last parameter in the range is 'weight'
29 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:33:5: warning: 2 adjacent parameters of 'compute_conv_element' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
33 | const int n,
| ^~~~~~~~~~~~
34 | const int c_out,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:33:15: note: the first parameter in the range is 'n'
33 | const int n,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:34:15: note: the last parameter in the range is 'c_out'
34 | const int c_out,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:45:31: 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]
45 | const float* in_ptr = input + input_base + c_in * in_h * in_w;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:45:52: note: make conversion explicit to silence this warning
5 | const float* in_ptr = input + input_base + c_in * in_h * in_w;
| ^~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:45:52: note: perform multiplication in a wider type
45 | const float* in_ptr = input + input_base + c_in * in_h * in_w;
| ^~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:46:30: 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]
46 | const float* w_ptr = weight + weight_base + c_in * KERNEL_SIZE * KERNEL_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:46:53: note: make conversion explicit to silence this warning
46 | const float* w_ptr = weight + weight_base + c_in * KERNEL_SIZE * KERNEL_SIZE;
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:46:53: note: perform multiplication in a wider type
46 | const float* w_ptr = weight + weight_base + c_in * KERNEL_SIZE * KERNEL_SIZE;
| ^~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:61:5: warning: 2 adjacent parameters of 'conv2d_gelu_pool_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
61 | const float* __restrict__ weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
62 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:61:31: note: the first parameter in the range is 'weight'
61 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:62:31: note: the last parameter in the range is 'bias'
62 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:64:5: warning: 2 adjacent parameters of 'conv2d_gelu_pool_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
64 | const int N,
| ^~~~~~~~~~~~
65 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:64:15: note: the first parameter in the range is 'N'
64 | const int N,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:65:15: note: the last parameter in the range is 'in_channels'
65 | const int in_channels,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:67:5: warning: 3 adjacent parameters of 'conv2d_gelu_pool_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
67 | const int in_w,
| ^~~~~~~~~~~~~~~
68 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~~
69 | const int out_h,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:67:15: note: the first parameter in the range is 'in_w'
67 | const int in_w,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:69:15: note: the last parameter in the range is 'out_h'
69 | const int out_h,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:74:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
74 | const int n = blockIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:75:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
75 | const int c_out = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:76:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
76 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:119:19: warning: the parameter 'input' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
119 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:120:19: 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]
120 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:121:19: 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]
121 | torch::Tensor conv_bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:127:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
127 | const int N = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:128:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
128 | const int in_channels = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:129:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
129 | const int in_h = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:130:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
130 | const int in_w = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_67/b7_s1_optimized_reduction_conv_gelu_pool/base/base.cu:131:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
131 | const int out_channels = conv_weight.size(0);
| ^