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48_Conv3d_Scaling_Tanh_Multiply_Sigmoid • optimized_hybrid_conv3d_base

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]
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Kernel Information

Operation Name 48_Conv3d_Scaling_Tanh_Multiply_Sigmoid
Level ID 2
Task ID 48
Kernel Name optimized_hybrid_conv3d_base
CUDA Speedup (Native) 1.292x
CUDA Speedup (Compile) 0.675x
CUDA Runtime 0.781 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 1.00
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>

template <typename scalar_t>
__global__ void optimized_hybrid_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();

    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);
    
    const int threads = 256;
    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(), "optimized_hybrid_conv3d_kernel", ([&] {
        optimized_hybrid_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, "Optimized hybrid Conv3d forward");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 3.044 inst/cycle 0.000 5
Executed Ipc Elapsed 2.978 inst/cycle 0.000 5
Issue Slots Busy 76.148 % 0.020 5
Issued Ipc Active 3.046 inst/cycle 0.000 5
SM Busy 76.148 % 0.020 5
Memory Throughput 1791917178575.512 byte/second 7258385802508094464.000 5
Mem Busy 29.736 % 0.003 5
Max Bandwidth 53.474 % 0.008 5
L1/TEX Hit Rate 3.900 % 0.000 5
L2 Hit Rate 49.860 % 0.010 5
Mem Pipes Busy 38.292 % 0.011 5
Warp Cycles Per Issued Instruction 16.644 cycle 0.000 5
Warp Cycles Per Executed Instruction 16.650 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 79.992 % 0.042 5
Achieved Active Warps Per SM 51.194 warp 0.017 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (51.1%) 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.
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 (80.1%) 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 607004.95 μs
Device Time 4346413.51 μs
Self CPU Time 11583.94 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 595421.01 μs
Device Time 4346413.51 μs
Self CPU Time 14795.52 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 580625.49 μs
Device Time 4346413.51 μs
Self CPU Time 30337.61 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 481190.23 μs
Device Time 3771982.86 μs
Self CPU Time 157566.29 μs
Self Device Time 3771982.86 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 3771981.17 μs
Self CPU Time 0.00 μs
Self Device Time 3771981.17 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 4161880.79 μs
Device Time 73399.42 μs
Self CPU Time 4161880.79 μs
Self Device Time 73399.42 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 626145.92 μs
Device Time 491475.71 μs
Self CPU Time 14310.93 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 611836.63 μs
Device Time 491475.71 μs
Self CPU Time 21857.38 μs
Self Device Time 491475.71 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/b4_s2_optimized_hybrid_conv3d/base/base.cu:9:5 bugprone-easily-swappable-parameters
9 | const scalar_t* __restrict__ output,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
10 | const scalar_t* __restrict__ scaling_factor,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const scalar_t* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.cu:9:34: note: the first parameter in the range is 'output'
9 | const scalar_t* __restrict__ output,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.cu:11:34: note: the last parameter in the range is 'bias'
11 | const scalar_t* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.cu:26:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | 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/b4_s2_optimized_hybrid_conv3d/base/base.cu:26:54: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | 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/b4_s2_optimized_hybrid_conv3d/base/base.cu:39:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
39 | 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/b4_s2_optimized_hybrid_conv3d/base/base.cu:40:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
40 | const int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.cu:73:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
73 | 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/b4_s2_optimized_hybrid_conv3d/base/base.cu:74:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
74 | const int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.cu:89:19: 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]
89 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.cu:90:19: 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]
90 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.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_bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~
92 | torch::Tensor scaling_factor,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.cu:91:19: note: the first parameter in the range is 'conv_bias'
91 | torch::Tensor conv_bias,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.cu:92:19: note: the last parameter in the range is 'scaling_factor'
92 | torch::Tensor scaling_factor,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.cu:97:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
97 | 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/b4_s2_optimized_hybrid_conv3d/base/base.cu:98:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | 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/b4_s2_optimized_hybrid_conv3d/base/base.cu:99:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | const int depth = conv_out.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.cu:100:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
100 | const int height = conv_out.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.cu:101:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
101 | const int width = conv_out.size(4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_2/task_48/b4_s2_optimized_hybrid_conv3d/base/base.cu:109:5: 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]
109 | AT_DISPATCH_FLOATING_TYPES(conv_out.scalar_type(), "optimized_hybrid_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/b4_s2_optimized_hybrid_conv3d/base/base.cu:110:69: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
110 | optimized_hybrid_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/b4_s2_optimized_hybrid_conv3d/base/base.cu:110:69: note: make conversion explicit to silence this warning
110 | optimized_hybrid_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/b4_s2_optimized_hybrid_conv3d/base/base.cu:110:69: note: perform multiplication in a wider type
110 | optimized_hybrid_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__ \
| ^~~~~~~~~~~