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48_Conv3d_Scaling_Tanh_Multiply_Sigmoid • streamlined_syncthreads_conv3d_base_edit_1

Level 2 • Task 48
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,
    scaling_factor: torch.Tensor,
    bias: torch.Tensor,
) -> torch.Tensor:
    """
    Applies 3D convolution, scaling, tanh, bias multiplication and sigmoid.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_channels, depth, height, width)
        conv_weight (torch.Tensor): 3D convolution weight tensor
        conv_bias (torch.Tensor): 3D convolution bias tensor
        scaling_factor (torch.Tensor): Scaling factor tensor of shape (out_channels, 1, 1, 1)
        bias (torch.Tensor): Bias tensor of shape (out_channels, 1, 1, 1)

    Returns:
        torch.Tensor: Output tensor after applying convolution, scaling, tanh, bias and sigmoid
    """
    x = F.conv3d(x, conv_weight, bias=conv_bias)
    x = x * scaling_factor
    x = torch.tanh(x)
    x = x * bias
    x = torch.sigmoid(x)
    return x


class Model(nn.Module):
    """
    Model that performs a 3D convolution, scales the output, applies tanh, multiplies by a scaling factor, and applies sigmoid.
    """

    def __init__(
        self, in_channels, out_channels, kernel_size, scaling_factor, bias_shape
    ):
        super(Model, self).__init__()
        conv = nn.Conv3d(in_channels, out_channels, kernel_size)
        self.conv_weight = nn.Parameter(conv.weight)
        self.conv_bias = nn.Parameter(
            conv.bias
            + torch.randn(
                conv.bias.shape, device=conv.bias.device, dtype=conv.bias.dtype
            )
            * 0.02
        )
        self.scaling_factor = nn.Parameter(torch.randn(bias_shape) * 0.02)
        self.bias = nn.Parameter(torch.randn(bias_shape) * 0.02)

    def forward(self, x, fn=module_fn):
        return fn(x, self.conv_weight, self.conv_bias, self.scaling_factor, self.bias)


batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
scaling_factor = 2
bias_shape = (out_channels, 1, 1, 1)


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


def get_init_inputs():
    return [in_channels, out_channels, kernel_size, scaling_factor, bias_shape]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a 3D convolution, scales the output, applies tanh, multiplies by a scaling factor, and applies sigmoid.
    """
    def __init__(self, in_channels, out_channels, kernel_size, scaling_factor, bias_shape):
        super(Model, self).__init__()
        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size)
        self.conv.bias = nn.Parameter(self.conv.bias + torch.randn(self.conv.bias.shape, device=self.conv.bias.device, dtype=self.conv.bias.dtype) * 0.02)
        self.scaling_factor = nn.Parameter(torch.randn(bias_shape) * 0.02)
        self.bias = nn.Parameter(torch.randn(bias_shape) * 0.02)

    def forward(self, x):
        x = self.conv(x)
        x = x * self.scaling_factor 
        x = torch.tanh(x)
        x = x * self.bias
        x = torch.sigmoid(x)
        return x

batch_size = 128
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 3
scaling_factor = 2
bias_shape = (out_channels, 1, 1, 1)

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, scaling_factor, bias_shape]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 48_Conv3d_Scaling_Tanh_Multiply_Sigmoid
Level ID 2
Task ID 48
Kernel Name streamlined_syncthreads_conv3d_base_edit_1
CUDA Speedup (Native) 1.289x
CUDA Speedup (Compile) 0.674x
CUDA Runtime 0.783 ms
PyTorch Runtime (Native) 1.009 ms
PyTorch Runtime (Compile) 0.527 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 48 • 48_Conv3d_Scaling_Tanh_Multiply_Sigmoid)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 optimized_hybrid_conv3d_base 0.78 1.29 0.68
🥈 streamlined_syncthreads_conv3d_base_edit_1 0.78 1.29 0.67
🥉 aligned_coalesced_conv3d_edit_1 0.78 1.29 0.67
🥉 aligned_memory_access_conv3d_base 0.78 1.29 0.67
🥉 streamlined_syncthreads_conv3d_base_base 0.78 1.29 0.67
6 unrolled_conv3d_opt_edit_1 0.79 1.28 0.67
7 unrolled_conv3d_opt_base 0.79 1.28 0.67
8 block_size_experimentation_edit_1 0.79 1.27 0.67
8 warp_broadcast_tile_edit_1 0.79 1.27 0.67
10 modular_device_functions_edit_1 0.79 1.27 0.67
11 block_size_experimentation_base 0.79 1.27 0.66
12 constant_mem_optimization_base 0.80 1.27 0.66
12 constant_memory_optimization_base 0.80 1.27 0.66
12 constant_mem_opt_base 0.80 1.27 0.66
12 constant_mem_optimization_edit_1 0.80 1.27 0.66
12 strided_loops_conv3d_edit_1 0.80 1.27 0.66
17 constant_memory_optimization_edit_1 0.80 1.27 0.66
18 modular_device_functions_base 0.80 1.26 0.66
18 optimized_shared_mem_edit_1 0.80 1.26 0.66
18 unroll_optimization_base 0.80 1.26 0.66 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <vector>
#include <algorithm>

// Streamlined syncthreads: Adding synchronization only when necessary

template <typename scalar_t>
__global__ void streamlined_syncthreads_conv3d_kernel(
    const scalar_t* __restrict__ output,
    const scalar_t* __restrict__ scaling_factor,
    const scalar_t* __restrict__ bias,
    scalar_t* __restrict__ result,
    const int batch_size,
    const int out_channels,
    const int depth,
    const int height,
    const int width) {

    const int total_elements = batch_size * out_channels * depth * height * width;
    const int whd = width * height * depth;
    
    extern __shared__ char smem[];
    scalar_t* s_scaling = reinterpret_cast<scalar_t*>(smem);
    scalar_t* s_bias = s_scaling + out_channels;

    for (int i = threadIdx.x; i < out_channels; i += blockDim.x) {
        s_scaling[i] = scaling_factor[i];
        s_bias[i] = bias[i];
    }
    __syncthreads(); // synchronize once after loading shared data

    if constexpr (std::is_same<scalar_t, float>::value) {
        const int vec_size = 4;
        const int vec_total = total_elements / vec_size;
        
        auto output_vec = reinterpret_cast<const float4*>(output);
        auto result_vec = reinterpret_cast<float4*>(result);
        
        const int tid = blockIdx.x * blockDim.x + threadIdx.x;
        const int stride = blockDim.x * gridDim.x;

        #pragma unroll
        for (int i = tid; i < vec_total; i += stride) {
            float4 in_vec = __ldg(&output_vec[i]);
            float4 out_vec;
            
            #pragma unroll
            for (int j = 0; j < 4; j++) {
                int idx = i * 4 + j;
                int c_idx = (idx / whd) % out_channels;
                float val = ((float*)&in_vec)[j];
                
                val *= s_scaling[c_idx];
                val = __tanf(val);
                val *= s_bias[c_idx];
                val = __frcp_rn(1.0f + __expf(-val));
                
                ((float*)&out_vec)[j] = val;
            }
            result_vec[i] = out_vec;
        }

        for (int idx = vec_total * 4 + tid; idx < total_elements; idx += stride) {
            int c_idx = (idx / whd) % out_channels;
            float val = output[idx];
            val *= s_scaling[c_idx];
            val = __tanf(val);
            val *= s_bias[c_idx];
            val = __frcp_rn(1.0f + __expf(-val));
            result[idx] = val;
        }
    } else {
        const int tid = blockIdx.x * blockDim.x + threadIdx.x;
        const int stride = blockDim.x * gridDim.x;
        
        for (int idx = tid; idx < total_elements; idx += stride) {
            int c_idx = (idx / whd) % out_channels;
            scalar_t val = output[idx];
            val *= s_scaling[c_idx];
            val = tanh(val);
            val *= s_bias[c_idx];
            val = scalar_t(1) / (scalar_t(1) + exp(-val));
            result[idx] = val;
        }
    }
}

torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor conv_weight,
    torch::Tensor conv_bias,
    torch::Tensor scaling_factor,
    torch::Tensor bias) {

    auto conv_out = torch::conv3d(x, conv_weight, conv_bias);
    
    const int batch_size = conv_out.size(0);
    const int out_channels = conv_out.size(1);
    const int depth = conv_out.size(2);
    const int height = conv_out.size(3);
    const int width = conv_out.size(4);
    
    auto result = torch::empty_like(conv_out);
    
    constexpr int WARP_SIZE = 32;
    constexpr int WARPS_PER_BLOCK = 8;  // 8 warps = 256 threads
    const int threads = WARP_SIZE * WARPS_PER_BLOCK;
    const int total_elements = batch_size * out_channels * depth * height * width;
    const int blocks = std::min(65535, (total_elements + threads - 1) / threads);
    
    AT_DISPATCH_FLOATING_TYPES(conv_out.scalar_type(), "streamlined_syncthreads_conv3d_kernel", ([&] {
        streamlined_syncthreads_conv3d_kernel<scalar_t><<<blocks, threads, 2 * out_channels * sizeof(scalar_t)>>>(
            conv_out.data_ptr<scalar_t>(),
            scaling_factor.data_ptr<scalar_t>(),
            bias.data_ptr<scalar_t>(),
            result.data_ptr<scalar_t>(),
            batch_size,
            out_channels,
            depth,
            height,
            width
        );
    }));

    return result;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Streamlined sync Conv3d forward");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 3.044 inst/cycle 0.000 5
Executed Ipc Elapsed 2.970 inst/cycle 0.000 5
Issue Slots Busy 76.200 % 0.027 5
Issued Ipc Active 3.046 inst/cycle 0.000 5
SM Busy 76.200 % 0.027 5
Memory Throughput 1785510426315.990 byte/second 3189280508692775936.000 5
Mem Busy 29.710 % 0.001 5
Max Bandwidth 53.274 % 0.003 5
L1/TEX Hit Rate 3.900 % 0.000 5
L2 Hit Rate 49.910 % 0.012 5
Mem Pipes Busy 38.142 % 0.001 5
Warp Cycles Per Issued Instruction 16.670 cycle 0.000 5
Warp Cycles Per Executed Instruction 16.678 cycle 0.000 5
Avg. Active Threads Per Warp 31.680 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.730 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 28.000 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 80.034 % 0.030 5
Achieved Active Warps Per SM 51.226 warp 0.013 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (51.0%) 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 (79.9%) 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::conv3d
CPU Time 610083.10 μs
Device Time 4280233.50 μs
Self CPU Time 13861.15 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::convolution
CPU Time 596221.95 μs
Device Time 4280233.50 μs
Self CPU Time 17906.09 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 578315.86 μs
Device Time 4280233.50 μs
Self CPU Time 35521.06 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
CPU Time 464412.41 μs
Device Time 3714540.46 μs
Self CPU Time 177310.15 μs
Self Device Time 3714540.46 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
sm80_xmma_fprop_implicit_gemm_indexed_f32f32_f32f32_f32_nchwkcrs_nchw_tilesize32x32x8_stage3_warpsize1x2x1_g1_ffma_aligna4_alignc4_execute_kernel__5x_cudnn
CPU Time 0.00 μs
Device Time 3714538.98 μs
Self CPU Time 0.00 μs
Self Device Time 3714538.98 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 4063838.41 μs
Device Time 71948.87 μs
Self CPU Time 4063838.41 μs
Self Device Time 71948.87 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 616290.40 μs
Device Time 484351.47 μs
Self CPU Time 16804.08 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 599488.30 μs
Device Time 484351.47 μs
Self CPU Time 25268.61 μs
Self Device Time 484351.47 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 
45300 warnings generated when compiling for host.
Suppressed 45327 warnings (45280 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/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:11:5 bugprone-easily-swappable-parameters
11 | const scalar_t* __restrict__ output,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
12 | const scalar_t* __restrict__ scaling_factor,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
13 | const scalar_t* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:11:34: note: the first parameter in the range is 'output'
11 | const scalar_t* __restrict__ output,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:13:34: note: the last parameter in the range is 'bias'
13 | const scalar_t* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:28:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | for (int i = threadIdx.x; i < out_channels; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:28:54: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | for (int i = threadIdx.x; i < out_channels; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:41:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
41 | const int tid = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:42:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
42 | const int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:75:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
75 | const int tid = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:76:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
76 | const int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:91:19: 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]
91 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:92:19: 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]
92 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:93:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
93 | torch::Tensor conv_bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~
94 | torch::Tensor scaling_factor,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:93:19: note: the first parameter in the range is 'conv_bias'
93 | torch::Tensor conv_bias,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:94:19: note: the last parameter in the range is 'scaling_factor'
94 | torch::Tensor scaling_factor,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:99:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | const int batch_size = conv_out.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:100:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
100 | const int out_channels = conv_out.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:101:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
101 | const int depth = conv_out.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:102:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
102 | const int height = conv_out.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:103:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
103 | const int width = conv_out.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:113:5: warning: inside a lambda, '__func__' expands to the name of the function call operator; consider capturing the name of the enclosing function explicitly [bugprone-lambda-function-name]
113 | AT_DISPATCH_FLOATING_TYPES(conv_out.scalar_type(), "streamlined_syncthreads_conv3d_kernel", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:34: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:3: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:3: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^
note: (skipping 1 expansions in backtrace; use -fmacro-backtrace-limit=0 to see all)
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:58:7: note: expanded from macro 'AT_PRIVATE_CHECK_SELECTIVE_BUILD'
58 | AT_ERROR( \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:711:32: note: expanded from macro 'AT_ERROR'
711 | C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK(false, ::c10::str(__VA_ARGS__))); \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:536:9: note: expanded from macro 'TORCH_CHECK'
536 | __func__, \
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:114:76: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
114 | streamlined_syncthreads_conv3d_kernel<scalar_t><<<blocks, threads, 2 * out_channels * sizeof(scalar_t)>>>(
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:114:76: note: make conversion explicit to silence this warning
114 | streamlined_syncthreads_conv3d_kernel<scalar_t><<<blocks, threads, 2 * out_channels * sizeof(scalar_t)>>>(
| ^
| static_cast<unsigned long>(
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:66: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:44: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:56: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:70:12: note: expanded from macro 'AT_PRIVATE_CASE_TYPE_USING_HINT'
70 | return __VA_ARGS__(); \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:221:7: note: expanded from macro 'AT_DISPATCH_SWITCH'
221 | __VA_ARGS__ \
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b5_s0_streamlined_syncthreads_conv3d_base/edit_1/edit_1.cu:114:76: note: perform multiplication in a wider type
114 | streamlined_syncthreads_conv3d_kernel<scalar_t><<<blocks, threads, 2 * out_channels * sizeof(scalar_t)>>>(
| ^
| static_cast<long>(
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:66: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:44: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:56: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:70:12: note: expanded from macro 'AT_PRIVATE_CASE_TYPE_USING_HINT'
70 | return __VA_ARGS__(); \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:221:7: note: expanded from macro 'AT_DISPATCH_SWITCH'
221 | __VA_ARGS__ \
| ^~~~~~~~~~~