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78_conv_transposed_2D_asymmetric_input_asymmetric_kernel___padded__ • conv_trans_tuned_blocks_base_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 conv_trans_tuned_blocks_base_base
CUDA Speedup (Native) 1.005x
CUDA Speedup (Compile) 1.144x
CUDA Runtime 0.330 ms
PyTorch Runtime (Native) 0.331 ms
PyTorch Runtime (Compile) 0.377 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 0.00
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;

// Constant memory for weights
__constant__ float c_weight[16384];

template<int BLOCK_X, int BLOCK_Y>
__global__ void conv_transpose2d_forward_kernel(
    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
) {
    const int tx = threadIdx.x;
    const int ty = threadIdx.y;
    const int bx = blockIdx.x;
    const int by = blockIdx.y;
    const int bz = blockIdx.z;

    const int ow = bx * BLOCK_X + tx;
    const int oh = by * BLOCK_Y + ty;
    const int oc = bz % C_out;
    const int n = bz / C_out;

    if (ow >= W_out || oh >= H_out) return;

    float sum = 0.0f;

    #pragma unroll
    for (int ic = 0; ic < C_in; ++ic) {
        #pragma unroll
        for (int kh = 0; kh < kH; ++kh) {
            #pragma unroll
            for (int kw = 0; kw < kW; ++kw) {
                const int i_val = oh + pH - kh;
                const int j_val = ow + pW - kw;

                if ((i_val % sH == 0) && (j_val % sW == 0)) {
                    const int i_in = i_val / sH;
                    const int j_in = j_val / sW;

                    if (i_in >= 0 && i_in < H_in && j_in >= 0 && j_in < W_in) {
                        const int input_idx = ((n * C_in + ic) * H_in + i_in) * W_in + j_in;
                        const int weight_idx = ((ic * C_out + oc) * kH + kh) * kW + kw;
                        sum += input[input_idx] * c_weight[weight_idx];
                    }
                }
            }
        }
    }

    if (bias != nullptr) {
        sum += bias[oc];
    }

    const int output_idx = ((n * C_out + oc) * H_out + oh) * W_out + ow;
    output[output_idx] = sum;
}

// Helper function to select optimal block dimensions
void get_optimal_block_config(
    int H_out, 
    int W_out, 
    dim3& block_dim, 
    dim3& grid_dim,
    int N,
    int C_out
) {
    // Try different block configurations based on output size
    if (H_out <= 8 && W_out <= 8) {
        block_dim = dim3(8, 8);
    } else if (H_out <= 16 && W_out <= 16) {
        block_dim = dim3(16, 8);
    } else if (H_out <= 32 && W_out <= 32) {
        block_dim = dim3(16, 16);
    } else {
        block_dim = dim3(32, 16);
    }

    // Calculate grid dimensions
    grid_dim = dim3(
        (W_out + block_dim.x - 1) / block_dim.x,
        (H_out + block_dim.y - 1) / block_dim.y,
        N * C_out
    );
}

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
) {
    const int weight_size = weight.numel() * sizeof(float);
    if (weight_size > 64 * 1024) {
        // 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);
    }

    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());

    dim3 block_dim, grid_dim;
    get_optimal_block_config(H_out, W_out, block_dim, grid_dim, N, C_out);

    // Launch kernel with dynamic block size selection
    if (block_dim.x == 8 && block_dim.y == 8) {
        conv_transpose2d_forward_kernel<8, 8><<<grid_dim, block_dim>>>(
            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
        );
    } else if (block_dim.x == 16 && block_dim.y == 8) {
        conv_transpose2d_forward_kernel<16, 8><<<grid_dim, block_dim>>>(
            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
        );
    } else if (block_dim.x == 16 && block_dim.y == 16) {
        conv_transpose2d_forward_kernel<16, 16><<<grid_dim, block_dim>>>(
            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
        );
    } else {
        conv_transpose2d_forward_kernel<32, 16><<<grid_dim, block_dim>>>(
            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, "Conv Transpose 2D forward with optimized block sizes",
          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 2643024.27 μs
Device Time 2170368.79 μs
Self CPU Time 10303.48 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 2632720.79 μs
Device Time 2170368.79 μs
Self CPU Time 12023.37 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 2620697.42 μs
Device Time 2170368.79 μs
Self CPU Time 15798.34 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
aten::cudnn_convolution_transpose
CPU Time 2604899.09 μs
Device Time 2170368.79 μs
Self CPU Time 416321.39 μs
Self Device Time 2170368.79 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 1506207.31 μs
Device Time 0.00 μs
Self CPU Time 1506207.31 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 1117293.82 μs
Self CPU Time 0.00 μs
Self Device Time 1117293.82 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 
45304 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/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:14:5 bugprone-easily-swappable-parameters
14 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
15 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:14:31: note: the first parameter in the range is 'input'
14 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:15:31: note: the last parameter in the range is 'bias'
15 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:17:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
17 | const int N,
| ^~~~~~~~~~~~
18 | const int C_in,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:17:15: note: the first parameter in the range is 'N'
17 | const int N,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:18:15: note: the last parameter in the range is 'C_in'
18 | const int C_in,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:20:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
20 | const int W_in,
| ^~~~~~~~~~~~~~~
21 | const int C_out,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:20:15: note: the first parameter in the range is 'W_in'
20 | const int W_in,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:21:15: note: the last parameter in the range is 'C_out'
21 | const int C_out,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:23:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
23 | const int W_out,
| ^~~~~~~~~~~~~~~~
24 | const int kH,
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:23:15: note: the first parameter in the range is 'W_out'
23 | const int W_out,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:24:15: note: the last parameter in the range is 'kH'
24 | const int kH,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:25:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
25 | const int kW,
| ^~~~~~~~~~~~~
26 | const int sH,
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:25:15: note: the first parameter in the range is 'kW'
25 | const int kW,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:26:15: note: the last parameter in the range is 'sH'
26 | const int sH,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:27:5: warning: 2 adjacent parameters of 'conv_transpose2d_forward_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
27 | const int sW,
| ^~~~~~~~~~~~~
28 | const int pH,
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:27:15: note: the first parameter in the range is 'sW'
27 | const int sW,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:28:15: note: the last parameter in the range is 'pH'
28 | const int pH,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:31:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | const int tx = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:32:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
32 | const int ty = threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:33:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
33 | const int bx = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:34:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
34 | const int by = blockIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:35:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
35 | const int bz = blockIdx.z;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:106:19: 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]
106 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:107:19: 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]
107 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:108:16: 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]
108 | py::object bias_obj,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:112:29: warning: narrowing conversion from 'unsigned long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | const int weight_size = weight.numel() * sizeof(float);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:131:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
131 | const int N = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:132:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
132 | const int C_in = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:133:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
133 | const int H_in = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:134:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
134 | const int W_in = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:135:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
135 | const int C_out = weight.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:136:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
136 | const int kH = weight.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:137:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
137 | const int kW = weight.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:138:20: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
138 | const int sH = stride[0];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:139:20: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
139 | const int sW = stride[1];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:140:20: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
140 | const int pH = padding[0];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_78/b6_s2_conv_trans_tuned_blocks_base/base/base.cu:141:20: warning: narrowing conversion from 'value_type' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
141 | const int pW = padding[1];
| ^