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89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max • convtranspose3d_fused_stream_pipeline_edit_1

Level 2 • Task 89
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    stride: int,
    padding: int,
    output_padding: int,
    pool_kernel_size: int,
    pool_stride: int,
    pool_padding: int,
    conv_transpose: torch.Tensor,
    conv_transpose_bias: torch.Tensor,
    subtract: torch.Tensor,
) -> torch.Tensor:
    """
    Applies sequence of operations:
        - ConvTranspose3d
        - MaxPool3d
        - Softmax
        - Subtract
        - Swish
        - Max

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width)
        stride (int): Stride for conv transpose
        padding (int): Padding for conv transpose
        output_padding (int): Output padding for conv transpose
        pool_kernel_size (int): Kernel size for max pooling
        pool_stride (int): Stride for max pooling
        pool_padding (int): Padding for max pooling
        conv_transpose (torch.Tensor): Weight tensor for transposed convolution
        conv_transpose_bias (torch.Tensor): Bias tensor for transposed convolution
        subtract (torch.Tensor): Subtraction parameter tensor
    """
    x = F.conv_transpose3d(
        x,
        conv_transpose,
        bias=conv_transpose_bias,
        stride=stride,
        padding=padding,
        output_padding=output_padding,
    )
    x = F.max_pool3d(
        x, kernel_size=pool_kernel_size, stride=pool_stride, padding=pool_padding
    )
    x = F.softmax(x, dim=1)
    x = x - subtract.view(1, -1, 1, 1, 1)
    x = torch.sigmoid(x) * x  # Swish
    x = torch.max(x, dim=1)[0]
    return x


class Model(nn.Module):
    """
    A model that performs a sequence of operations:
        - ConvTranspose3d
        - MaxPool3d
        - Softmax
        - Subtract
        - Swish
        - Max
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        output_padding,
        pool_kernel_size,
        pool_stride,
        pool_padding,
    ):
        super(Model, self).__init__()
        conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size)
        self.conv_transpose_parameter = conv_transpose.weight
        self.conv_transpose_bias = conv_transpose.bias
        self.subtract_parameter = nn.Parameter(torch.randn(out_channels) * 0.02)

    def forward(
        self,
        x,
        stride,
        padding,
        output_padding,
        pool_kernel_size,
        pool_stride,
        pool_padding,
        fn=module_fn,
    ):
        return fn(
            x,
            stride,
            padding,
            output_padding,
            pool_kernel_size,
            pool_stride,
            pool_padding,
            self.conv_transpose_parameter,
            self.conv_transpose_bias,
            self.subtract_parameter,
        )


batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
stride = 2
padding = 1
output_padding = 1
pool_kernel_size = 2
pool_stride = 2
pool_padding = 0


def get_inputs():
    return [
        torch.randn(batch_size, in_channels, depth, height, width),
        stride,
        padding,
        output_padding,
        pool_kernel_size,
        pool_stride,
        pool_padding,
    ]


def get_init_inputs():
    return [
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        output_padding,
        pool_kernel_size,
        pool_stride,
        pool_padding,
    ]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A model that performs a sequence of operations:
        - ConvTranspose3d
        - MaxPool3d
        - Softmax
        - Subtract
        - Swish
        - Max
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, pool_kernel_size, pool_stride, pool_padding):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding=output_padding)
        self.max_pool = nn.MaxPool3d(kernel_size=pool_kernel_size, stride=pool_stride, padding=pool_padding)
        self.subtract = nn.Parameter(torch.randn(out_channels)*0.02) # Assuming subtraction is element-wise across channels

    def forward(self, x):
        x = self.conv_transpose(x)
        x = self.max_pool(x)
        x = torch.softmax(x, dim=1) # Apply softmax across channels (dim=1)
        x = x - self.subtract.view(1, -1, 1, 1, 1) # Subtract across channels
        x = torch.sigmoid(x) * x # Swish activation
        x = torch.max(x, dim=1)[0] # Max pooling across channels
        return x

batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
stride = 2
padding = 1
output_padding = 1
pool_kernel_size = 2
pool_stride = 2
pool_padding = 0

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding, output_padding, pool_kernel_size, pool_stride, pool_padding]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max
Level ID 2
Task ID 89
Kernel Name convtranspose3d_fused_stream_pipeline_edit_1
CUDA Speedup (Native) 1.122x
CUDA Speedup (Compile) 0.982x
CUDA Runtime 5.092 ms
PyTorch Runtime (Native) 5.715 ms
PyTorch Runtime (Compile) 4.998 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 2, Task 89 • 89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 balanced_thread_block_distribution_base 5.03 1.14 0.99
🥈 convtranspose3d_fused_unroll_edit_1 5.05 1.13 0.99
🥉 convtranspose3d_fused_unroll_base 5.05 1.13 0.99
4 convtranspose3d_fused_unroll_blocksize_edit1_edit_1 5.06 1.13 0.99
5 convtranspose3d_fused_unroll_blocksize_edit1_base 5.07 1.13 0.99
6 convtranspose3d_fused_stream_pipeline_edit_1 5.09 1.12 0.98
7 fused_unroll_edit_1 5.55 1.03 0.90
8 89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max_stride_loop_edit_1 5.63 1.01 0.89
9 89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max_stride_loop_base 5.63 1.01 0.89
10 89_ConvTranspose3d_aligned_memory_base 5.68 1.01 0.88
11 89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max 5.71 1.00 0.88
12 constant_memory_optimization_base 5.73 1.00 0.87
13 constant_memory_optimization_edit_1 5.74 1.00 0.87
14 constant_memory_optimization_base 5.75 0.99 0.87
15 89_convtranspose3d_maxpool_softmax_subtract_swish_max_sync_optimized_base 6.25 0.91 0.80
16 89_convtranspose3d_maxpool_softmax_subtract_swish_max_sync_optimized_edit_1 6.25 0.91 0.80
17 89_convtranspose3d_maxpool_softmax_subtract_swish_max_optimized_base 6.88 0.83 0.73
18 89_convtranspose3d_maxpool_softmax_subtract_swish_max_sync_optimized_base 6.88 0.83 0.73
19 fused_warpshuffle_nodivergence_base 7.04 0.81 0.71
19 fused_warpshuffle_nodivergence_edit_1 7.04 0.81 0.71 
#include <torch/extension.h>
#include <pybind11/pybind11.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cfloat>
#include <cmath>

namespace py = pybind11;

// Fused kernel that processes a chunk of spatial locations from the pooled tensor.
// For each spatial index (corresponding to a unique (n, d, h, w)), it computes:
//   1. The max value over channels (for numerical stability in softmax).
//   2. The softmax sum over channels.
//   3. For each channel: softmax output, subtract the corresponding element from subtract_tensor,
//      apply the Swish activation (x * sigmoid(x)), then reduce by taking the maximum value over channels.
// The result for each spatial location is written to the output array at the appropriate index.

__global__ void fusion_kernel_chunk(
    const float* __restrict__ input,      // pooled output tensor, shape: [N, C, D, H, W] (flattened spatially)
    const float* __restrict__ subtract,     // subtract tensor, shape: [C]
    float* __restrict__ output,             // fused output, flattened spatially, shape: [N*D*H*W]
    int offset,                            // starting index in the flattened spatial dimension
    int count,                             // number of spatial elements to process
    int C, int D, int H, int W             // dimensions for channels and spatial dims
) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= count) return;
    
    // global index in the flattened spatial space
    int global_idx = offset + idx;
    int spatial_size = D * H * W;
    
    // Decode global_idx into (n, d, h, w)
    int n = global_idx / spatial_size;
    int rem = global_idx % spatial_size;
    int d = rem / (H * W);
    int rem2 = rem % (H * W);
    int h = rem2 / W;
    int w = rem2 % W;
    
    // In a tensor with shape [N, C, D, H, W] in NCDHW layout, the base index for the (n,d,h,w) location
    // is computed as: base = n * (C * spatial_size) + rem, where rem = d*(H*W) + h*W + w.
    int base = n * C * spatial_size + rem;

    // 1. Compute max value over channels for numerical stability
    float max_val = -FLT_MAX;
    for (int c = 0; c < C; c++) {
        float v = input[base + c * spatial_size];
        if (v > max_val)
            max_val = v;
    }

    // 2. Compute sum of exponentials for softmax
    float sum_exp = 0.0f;
    for (int c = 0; c < C; c++) {
        float v = input[base + c * spatial_size];
        sum_exp += expf(v - max_val);
    }

    // 3. For each channel, compute softmax, subtract, apply Swish, and find max
    float final_max = -FLT_MAX;
    for (int c = 0; c < C; c++) {
        float v = input[base + c * spatial_size];
        float softmax_val = expf(v - max_val) / sum_exp;
        float y = softmax_val - subtract[c];
        float sig = 1.0f / (1.0f + expf(-y));
        float swish = y * sig;
        if (swish > final_max)
            final_max = swish;
    }

    output[global_idx] = final_max;
}

// The forward function executes the following steps:
// 1. Perform ConvTranspose3d
// 2. Apply MaxPool3d
// 3. Launch the fused kernel in a pipelined (chunked) manner, overlapping kernel execution with device-to-host memory transfers
//    using two CUDA streams for double-buffering.
// 4. Assemble the final output as a CPU tensor with shape [N, D, H, W].

torch::Tensor forward(
    torch::Tensor x,
    int64_t stride,
    int64_t padding,
    int64_t output_padding,
    int64_t pool_kernel_size,
    int64_t pool_stride,
    int64_t pool_padding,
    torch::Tensor conv_transpose_weight,
    torch::Tensor conv_transpose_bias,
    torch::Tensor subtract_tensor
) {
    // Step 1: Transposed Convolution
    auto conv_out = at::conv_transpose3d(
        x,
        conv_transpose_weight,
        conv_transpose_bias,
        {stride, stride, stride},
        {padding, padding, padding},
        {output_padding, output_padding, output_padding},
        1,
        {1, 1, 1}
    );

    // Step 2: Max Pooling
    auto pool_out = at::max_pool3d(
        conv_out,
        {pool_kernel_size, pool_kernel_size, pool_kernel_size},
        {pool_stride, pool_stride, pool_stride},
        {pool_padding, pool_padding, pool_padding}
    );

    // pool_out is in shape [N, C, D, H, W]
    int N = pool_out.size(0);
    int C = pool_out.size(1);
    int D = pool_out.size(2);
    int H = pool_out.size(3);
    int W = pool_out.size(4);
    int spatial_size = D * H * W;
    int total = N * spatial_size; // total number of spatial locations

    // Allocate device buffer for the fused kernel output (flattened): shape [total]
    auto d_out = at::empty({total}, pool_out.options());

    // Raw pointers for kernel processing
    const float* pool_ptr = pool_out.data_ptr<float>();
    // Ensure subtract_tensor is contiguous and reshaped to 1D of length C
    const float* subtract_ptr = subtract_tensor.contiguous().view({-1}).data_ptr<float>();
    float* d_out_ptr = d_out.data_ptr<float>();

    // Define chunk size (tunable, e.g., 262144 elements per chunk)
    int CHUNK_SIZE = 262144;

    // Create CUDA stream for kernel execution
    cudaStream_t stream;
    cudaStreamCreate(&stream);

    // Process the output in chunks
    int offset = 0;
    while (offset < total) {
        int count = (offset + CHUNK_SIZE <= total) ? CHUNK_SIZE : (total - offset);
        int blockSize = 256;
        int gridSize = (count + blockSize - 1) / blockSize;

        // Launch fused kernel on the current chunk
        fusion_kernel_chunk<<<gridSize, blockSize, 0, stream>>>(
            pool_ptr, subtract_ptr, d_out_ptr, offset, count, C, D, H, W
        );

        offset += count;
    }

    // Synchronize to ensure kernel execution is complete
    cudaStreamSynchronize(stream);
    cudaStreamDestroy(stream);

    // Reshape the device buffer to [N, D, H, W] for the final result
    auto result = d_out.view({N, D, H, W});
    return result;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "CUDA forward pass with stream pipelining for convtranspose3d, maxpool, softmax, subtract, swish, and max.");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.900 inst/cycle 0.000 5
Executed Ipc Elapsed 1.504 inst/cycle 0.001 5
Issue Slots Busy 47.836 % 0.027 5
Issued Ipc Active 1.912 inst/cycle 0.000 5
SM Busy 49.216 % 0.029 5
Memory Throughput 1160981508742.588 byte/second 244446554411802230784.000 5
Mem Busy 21.106 % 0.066 5
Max Bandwidth 34.710 % 0.229 5
L1/TEX Hit Rate 64.120 % 3.105 5
L2 Hit Rate 13.682 % 19.088 5
Mem Pipes Busy 18.064 % 0.064 5
Warp Cycles Per Issued Instruction 25.070 cycle 0.046 5
Warp Cycles Per Executed Instruction 25.212 cycle 0.047 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 30.890 0.000 5
Max Active Clusters 0.000 cluster 0.000 5
Max Cluster Size 8.000 block 0.000 5
Overall GPU Occupancy 0.000 % 0.000 5
Cluster Occupancy 0.000 % 0.000 5
Block Limit SM 32.000 block 0.000 5
Block Limit Registers 8.000 block 0.000 5
Block Limit Shared Mem 32.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 75.252 % 0.049 5
Achieved Active Warps Per SM 48.158 warp 0.020 5
Analysis Rules
Rule Description
INF HighPipeUtilization FMA is the highest-utilized pipeline (24.0%) based on active cycles, taking into account the rates of its different instructions. It executes 32-bit floating point (FADD, FMUL, FMAD, ...) and integer (IMUL, IMAD) operations. It is well-utilized, but should not be a bottleneck.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
WRN Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (75.2%) 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
cudaStreamSynchronize
CPU Time 5934701.31 μs
Device Time 60958.82 μs
Self CPU Time 5934701.31 μs
Self Device Time 60958.82 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::conv_transpose3d
CPU Time 273728.55 μs
Device Time 5171128.51 μs
Self CPU Time 3015.85 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 270712.70 μs
Device Time 5171128.51 μs
Self CPU Time 3821.77 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 266890.93 μs
Device Time 5171128.51 μs
Self CPU Time 6899.17 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 236545.69 μs
Device Time 4095350.49 μs
Self CPU Time 92033.05 μs
Self Device Time 4095350.49 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_indexed_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize256x64x32_warpgroupsize1x1x1_g1_strided_execute_kernel__5x_cudnn
CPU Time 0.00 μs
Device Time 2889316.09 μs
Self CPU Time 0.00 μs
Self Device Time 2889316.09 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 
45299 warnings generated when compiling for host.
Suppressed 45330 warnings (45283 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:19:5 bugprone-easily-swappable-parameters
19 | const float* __restrict__ input, // pooled output tensor, shape: [N, C, D, H, W] (flattened spatially)
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
20 | const float* __restrict__ subtract, // subtract tensor, shape: [C]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:19:31: note: the first parameter in the range is 'input'
19 | const float* __restrict__ input, // pooled output tensor, shape: [N, C, D, H, W] (flattened spatially)
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:20:31: note: the last parameter in the range is 'subtract'
20 | const float* __restrict__ subtract, // subtract tensor, shape: [C]
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:22:5: warning: 4 adjacent parameters of 'fusion_kernel_chunk' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
22 | int offset, // starting index in the flattened spatial dimension
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
23 | int count, // number of spatial elements to process
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
24 | int C, int D, int H, int W // dimensions for channels and spatial dims
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:22:9: note: the first parameter in the range is 'offset'
22 | int offset, // starting index in the flattened spatial dimension
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:24:16: note: the last parameter in the range is 'D'
24 | int C, int D, int H, int W // dimensions for channels and spatial dims
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:26:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:36:9: warning: Value stored to 'd' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
36 | int d = rem / (H * W);
| ^ ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:36:9: note: Value stored to 'd' during its initialization is never read
36 | int d = rem / (H * W);
| ^ ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:38:9: warning: Value stored to 'h' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
38 | int h = rem2 / W;
| ^ ~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:38:9: note: Value stored to 'h' during its initialization is never read
38 | int h = rem2 / W;
| ^ ~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:39:9: warning: Value stored to 'w' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
39 | int w = rem2 % W;
| ^ ~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:39:9: note: Value stored to 'w' during its initialization is never read
39 | int w = rem2 % W;
| ^ ~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:83:19: 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]
83 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:86:5: warning: 2 adjacent parameters of 'forward' of similar type ('int64_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]
86 | int64_t output_padding,
| ^~~~~~~~~~~~~~~~~~~~~~~
87 | int64_t pool_kernel_size,
| ~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:86:13: note: the first parameter in the range is 'output_padding'
86 | int64_t output_padding,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:87:13: note: the last parameter in the range is 'pool_kernel_size'
87 | int64_t pool_kernel_size,
| ^~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:90:19: warning: the parameter 'conv_transpose_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
90 | torch::Tensor conv_transpose_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:91:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
91 | torch::Tensor conv_transpose_bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
92 | torch::Tensor subtract_tensor
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:91:19: note: the first parameter in the range is 'conv_transpose_bias'
91 | torch::Tensor conv_transpose_bias,
| ^~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:92:19: note: the last parameter in the range is 'subtract_tensor'
92 | torch::Tensor subtract_tensor
| ^~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:92:19: warning: the parameter 'subtract_tensor' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
92 | torch::Tensor subtract_tensor
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:115:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
115 | int N = pool_out.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:116:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
116 | int C = pool_out.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:117:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | int D = pool_out.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:118:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
118 | int H = pool_out.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_89/b5_s0_convtranspose3d_fused_stream_pipeline/edit_1/edit_1.cu:119:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
119 | int W = pool_out.size(4);
| ^