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78_conv_transposed_2D_asymmetric_input_asymmetric_kernel___padded__ • combined_warp_reduction_base

Level 1 • Task 78
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    weight: torch.Tensor,
    bias: torch.Tensor,
    stride: tuple,
    padding: tuple,
) -> torch.Tensor:
    """
    Performs a 2D transposed convolution operation with asymmetric input and kernel, with optional padding.

    Args:
        x (torch.Tensor): Input tensor
        stride (tuple): Stride of convolution
        padding (tuple): Padding to apply
        weight (torch.Tensor): Convolution weights
        bias (torch.Tensor): Bias tensor (optional)

    Returns:
        torch.Tensor: Output tensor
    """
    return F.conv_transpose2d(x, weight, bias=bias, stride=stride, padding=padding)


class Model(nn.Module):
    """
    Performs a 2D transposed convolution operation with asymmetric input and kernel, with optional padding.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (tuple): Size of the convolution kernel (height, width).
        stride (tuple): Stride of the convolution (height, width).
        padding (tuple): Padding applied to the input (height, width).
        bias (bool): If `True`, adds a learnable bias to the output.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: tuple,
        stride: tuple,
        padding: tuple,
        bias: bool,
    ):
        super(Model, self).__init__()
        self.conv_transpose2d = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            bias=bias,
        )

        # Copy the initialized parameters
        self.weight = nn.Parameter(self.conv_transpose2d.weight.clone())
        self.bias = nn.Parameter(self.conv_transpose2d.bias.clone()) if bias else None

        self.stride = stride
        self.padding = padding

    def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
        """
        Performs the 2D transposed convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).
            fn: Function to use for forward pass

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, height_out, width_out).
        """
        return fn(
            x,
            self.weight,
            self.bias,
            self.stride,
            self.padding,
        )


# Constants
batch_size = 16
in_channels = 32
out_channels = 64
kernel_size = (3, 5)
height = 128
width = 256
stride = (1, 1)
padding = (1, 2)
bias = False


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


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

import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Performs a 2D transposed convolution operation with asymmetric input and kernel, with optional padding.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (tuple): Size of the convolution kernel (height, width).
        stride (tuple, optional): Stride of the convolution (height, width). Defaults to (1, 1).
        padding (tuple, optional): Padding applied to the input (height, width). Defaults to (0, 0).
        bias (bool, optional): If `True`, adds a learnable bias to the output. Defaults to `False`.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: tuple,
        stride: tuple = (1, 1),
        padding: tuple = (0, 0),
        bias: bool = False,
    ):
        super(Model, self).__init__()
        self.conv_transpose2d = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            bias=bias,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the 2D transposed convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, height_out, width_out).
        """
        return self.conv_transpose2d(x)


# Constants
batch_size = 16
in_channels = 32
out_channels = 64
kernel_size = (3, 5)
height = 128
width = 256
stride = (1, 1)
padding = (1, 2)
bias = False


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


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding, bias]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 78_conv_transposed_2D_asymmetric_input_asymmetric_kernel___padded__
Level ID 1
Task ID 78
Kernel Name combined_warp_reduction_base
CUDA Speedup (Native) 0.993x
CUDA Speedup (Compile) 1.130x
CUDA Runtime 0.334 ms
PyTorch Runtime (Native) 0.331 ms
PyTorch Runtime (Compile) 0.377 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 1, Task 78 • 78_conv_transposed_2D_asymmetric_input_asymmetric_kernel___padded__)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 conv_trans_tuned_blocks_base_base 0.33 1.00 1.14
🥈 78_conv_transposed_2D_asymmetric_input_asymmetric_kernel___padded__ 0.33 1.00 1.14
🥈 conv_trans_shared_warp_reduction_base 0.33 1.00 1.14
🥈 conv_trans_tiled_workload_base_base 0.33 1.00 1.14
🥈 conv_trans_optimized_blocksize_base_base 0.33 1.00 1.14
6 conv_trans_unrolled_base 0.33 1.00 1.14
7 optimized_conv_transpose2d_base 0.33 1.00 1.13
7 conv_trans_warp_reduce_base_base 0.33 1.00 1.13
7 conv_trans_unroll_optimized_base 0.33 1.00 1.13
7 conv_trans_workload_balanced_base_base_base 0.33 1.00 1.13
7 conv_trans_const_mem_base_base 0.33 1.00 1.13
7 conv_trans_strided_loop_base_base 0.33 1.00 1.13
7 conv_trans_warp_optimized_base_base 0.33 1.00 1.13
7 conv_trans_stream_pipeline_base_base 0.33 1.00 1.13
7 conv_trans_coalesced_base 0.33 1.00 1.13
7 conv_trans_atomic_free_base_base 0.33 1.00 1.13
7 conv_trans_stride_loop_optim_base 0.33 1.00 1.13
18 conv_trans_coalesced_access_base_base 0.33 0.99 1.13
18 combined_warp_reduction_base 0.33 0.99 1.13
18 conv_trans_shared_minimal_sync_base_base 0.33 0.99 1.13 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <pybind11/pybind11.h>
#include <vector>

namespace py = pybind11;

// Use constant memory for kernel weights (max 64KB)
__constant__ float c_weight[16384];

// Combined kernel: each warp computes one output element using warp-level reduction
// This kernel fuses dynamic block configuration ideas with efficient warp shuffle reduction,
// eliminating extra shared memory usage while handling various output sizes.

__global__ void conv_transpose2d_forward_kernel_combined(
    const float* __restrict__ input,
    const float* __restrict__ bias,
    float* __restrict__ output,
    const int N,
    const int C_in,
    const int H_in,
    const int W_in,
    const int C_out,
    const int H_out,
    const int W_out,
    const int kH,
    const int kW,
    const int sH,
    const int sW,
    const int pH,
    const int pW
) {
    // Each warp computes one output element.
    int num_warps_per_block = blockDim.x / warpSize;  // warpSize is typically 32
    int warp_id_in_block = threadIdx.x / warpSize;
    int global_warp_id = blockIdx.x * num_warps_per_block + warp_id_in_block;
    int total_output = N * C_out * H_out * W_out;

    if (global_warp_id >= total_output) return;

    // Decode the output index from the global warp id
    int ow = global_warp_id % W_out;
    int oh = (global_warp_id / W_out) % H_out;
    int oc = (global_warp_id / (W_out * H_out)) % C_out;
    int n  = global_warp_id / (W_out * H_out * C_out);

    float local_sum = 0.0f;
    int total_work = C_in * kH * kW;

    int lane = threadIdx.x % warpSize;  // lane within the warp

    // Each lane sums over a portion of the reduction dimension
    for (int i = lane; i < total_work; i += warpSize) {
        int ic = i / (kH * kW);
        int rem = i % (kH * kW);
        int kh = rem / kW;
        int kw = rem % kW;

        int i_val = oh + pH - kh;
        int j_val = ow + pW - kw;
        
        if ((i_val % sH == 0) && (j_val % sW == 0)) {
            int i_in = i_val / sH;
            int j_in = j_val / sW;
            if (i_in >= 0 && i_in < H_in && j_in >= 0 && j_in < W_in) {
                int input_idx = ((n * C_in + ic) * H_in + i_in) * W_in + j_in;
                int weight_idx = ((ic * C_out + oc) * kH + kh) * kW + kw;
                local_sum += input[input_idx] * c_weight[weight_idx];
            }
        }
    }

    // Perform warp-level reduction using shuffle intrinsics
    unsigned int mask = 0xffffffff;
    for (int offset = warpSize / 2; offset > 0; offset /= 2) {
        local_sum += __shfl_down_sync(mask, local_sum, offset);
    }

    // The first thread in the warp writes the final result
    if (lane == 0) {
        if (bias != nullptr) {
            local_sum += bias[oc];
        }
        int output_idx = ((n * C_out + oc) * H_out + oh) * W_out + ow;
        output[output_idx] = local_sum;
    }
}

// Host function: sets up and launches the combined kernel

torch::Tensor conv_transpose2d_forward(
    torch::Tensor x,
    torch::Tensor weight,
    py::object bias_obj,
    std::vector<int64_t> stride,
    std::vector<int64_t> padding
) {
    int weight_size = weight.numel() * sizeof(float);
    const int max_const_size = 64 * 1024;
    if (weight_size > max_const_size) {
        // Fallback to cuDNN for large weights
        c10::optional<torch::Tensor> bias = c10::nullopt;
        if (!bias_obj.is_none()) {
            bias = bias_obj.cast<torch::Tensor>();
        }
        return at::conv_transpose2d(x, weight, bias, stride, padding);
    }

    // Copy weight to constant memory
    cudaMemcpyToSymbol(c_weight, weight.data_ptr<float>(), weight_size);

    torch::Tensor bias;
    const float* bias_ptr = nullptr;
    if (!bias_obj.is_none()) {
        bias = bias_obj.cast<torch::Tensor>();
        bias_ptr = bias.data_ptr<float>();
    }

    const int N = x.size(0);
    const int C_in = x.size(1);
    const int H_in = x.size(2);
    const int W_in = x.size(3);
    const int C_out = weight.size(1);
    const int kH = weight.size(2);
    const int kW = weight.size(3);
    const int sH = stride[0];
    const int sW = stride[1];
    const int pH = padding[0];
    const int pW = padding[1];

    const int H_out = (H_in - 1) * sH - 2 * pH + kH;
    const int W_out = (W_in - 1) * sW - 2 * pW + kW;

    auto output = torch::zeros({N, C_out, H_out, W_out}, x.options());

    // Launch configuration: assign one warp per output element
    const int BLOCK_SIZE = 256; // must be a multiple of warpSize (32)
    int warpsPerBlock = BLOCK_SIZE / 32;
    int total_output = N * C_out * H_out * W_out;
    int grid = (total_output + warpsPerBlock - 1) / warpsPerBlock;

    conv_transpose2d_forward_kernel_combined<<<grid, BLOCK_SIZE>>>(
         x.data_ptr<float>(),
         bias_ptr,
         output.data_ptr<float>(),
         N, C_in, H_in, W_in,
         C_out, H_out, W_out,
         kH, kW, sH, sW, pH, pW
    );

    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &conv_transpose2d_forward, "Combined Conv Transpose 2D forward kernel with warp-level reduction",
          py::arg("x"),
          py::arg("weight"),
          py::arg("bias") = py::none(),
          py::arg("stride"),
          py::arg("padding"));
}
Performance Metrics
Metric Value Unit Variance Samples
Analysis Rules
Rule Description
Operation / Metric Value Unit
aten::conv_transpose2d
CPU Time 2568550.44 μs
Device Time 2324583.97 μs
Self CPU Time 11108.09 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::convolution
CPU Time 2557442.35 μs
Device Time 2324583.97 μs
Self CPU Time 13106.58 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::_convolution
CPU Time 2544335.77 μs
Device Time 2324583.97 μs
Self CPU Time 16655.28 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::cudnn_convolution_transpose
CPU Time 2527680.49 μs
Device Time 2324583.97 μs
Self CPU Time 176594.35 μs
Self Device Time 2324583.97 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 1632796.92 μs
Device Time 0.00 μs
Self CPU Time 1632796.92 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
sm90_xmma_dgrad_implicit_gemm_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize128x64x32_warpgroupsize1x1x1_g1_execute_segment_k_off_kernel__5x_cudnn
CPU Time 0.00 μs
Device Time 1204306.47 μs
Self CPU Time 0.00 μs
Self Device Time 1204306.47 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 
45303 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/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:17:5 bugprone-easily-swappable-parameters
17 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
18 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:17:31: note: the first parameter in the range is 'input'
17 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:18:31: note: the last parameter in the range is 'bias'
18 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:20:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel_combined' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
20 | const int N,
| ^~~~~~~~~~~~
21 | const int C_in,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:20:15: note: the first parameter in the range is 'N'
20 | const int N,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:21:15: note: the last parameter in the range is 'C_in'
21 | const int C_in,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:23:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel_combined' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
23 | const int W_in,
| ^~~~~~~~~~~~~~~
24 | const int C_out,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:23:15: note: the first parameter in the range is 'W_in'
23 | const int W_in,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:24:15: note: the last parameter in the range is 'C_out'
24 | const int C_out,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:26:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel_combined' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
26 | const int W_out,
| ^~~~~~~~~~~~~~~~
27 | const int kH,
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:26:15: note: the first parameter in the range is 'W_out'
26 | const int W_out,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:27:15: note: the last parameter in the range is 'kH'
27 | const int kH,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:28:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel_combined' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
28 | const int kW,
| ^~~~~~~~~~~~~
29 | const int sH,
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:28:15: note: the first parameter in the range is 'kW'
28 | const int kW,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:29:15: note: the last parameter in the range is 'sH'
29 | const int sH,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:30:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel_combined' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
30 | const int sW,
| ^~~~~~~~~~~~~
31 | const int pH,
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:30:15: note: the first parameter in the range is 'sW'
30 | const int sW,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:31:15: note: the last parameter in the range is 'pH'
31 | const int pH,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:35:31: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
35 | int num_warps_per_block = blockDim.x / warpSize; // warpSize is typically 32
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:36:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
36 | int warp_id_in_block = threadIdx.x / warpSize;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:37:26: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
37 | int global_warp_id = blockIdx.x * num_warps_per_block + warp_id_in_block;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:51:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
51 | int lane = threadIdx.x % warpSize; // lane within the warp
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:93:19: 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 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:94:19: 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]
94 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:95:16: warning: the parameter 'bias_obj' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
95 | py::object bias_obj,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:99:23: warning: narrowing conversion from 'unsigned long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | int weight_size = weight.numel() * sizeof(float);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:120:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
120 | const int N = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:121:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
121 | const int C_in = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:122:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
122 | const int H_in = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:123:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
123 | const int W_in = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:124:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | const int C_out = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:125:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
125 | const int kH = weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:126:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | const int kW = weight.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:127:20: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
127 | const int sH = stride[0];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:128:20: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
128 | const int sW = stride[1];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:129:20: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
129 | const int pH = padding[0];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b8_s3_combined_warp_reduction/base/base.cu:130:20: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
130 | const int pW = padding[1];
| ^