Kernel Details - 52_argmin_coalesced_base

import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(x: torch.Tensor, dim: int) -> torch.Tensor:
    """
    Finds the index of the minimum value along the specified dimension.

    Args:
        x (torch.Tensor): Input tensor.
        dim (int): Dimension along which to find the minimum value.

    Returns:
        torch.Tensor: Tensor containing the indices of the minimum values along the specified dimension.
    """
    return torch.argmin(x, dim)


class Model(nn.Module):
    """
    Simple model that finds the index of the minimum value along a specified dimension.
    """

    def __init__(self, dim):
        """
        Initializes the model with the dimension to perform argmin on.

        Args:
            dim (int): Dimension along which to find the minimum value.
        """
        super(Model, self).__init__()
        self.dim = dim

    def forward(self, x, fn=module_fn):
        """
        Finds the index of the minimum value along the specified dimension.

        Args:
            x (torch.Tensor): Input tensor.
            fn (callable): Function to compute the output. Defaults to module_fn.

        Returns:
            torch.Tensor: Tensor containing the indices of the minimum values along the specified dimension.
        """
        return fn(x, self.dim)


batch_size = 16
dim1 = 256
dim2 = 256
dim = 1


def get_inputs():
    x = torch.randn(batch_size, dim1, dim2)
    return [x]


def get_init_inputs():
    return [dim]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that finds the index of the minimum value along a specified dimension.
    """
    def __init__(self, dim: int):
        """
        Initializes the model with the dimension to perform argmin on.

        Args:
            dim (int): Dimension along which to find the minimum value.
        """
        super(Model, self).__init__()
        self.dim = dim

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Finds the index of the minimum value along the specified dimension.

        Args:
            x (torch.Tensor): Input tensor.

        Returns:
            torch.Tensor: Tensor containing the indices of the minimum values along the specified dimension.
        """
        return torch.argmin(x, dim=self.dim)

batch_size = 16
dim1 = 256
dim2 = 256
dim = 1

def get_inputs():
    x = torch.randn(batch_size, dim1, dim2)
    return [x]

def get_init_inputs():
    return [dim]

Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name	52_Argmin_over_a_dimension
Level ID	1
Task ID	52
Kernel Name	52_argmin_coalesced_base_base
CUDA Speedup (Native)	1.099x
CUDA Speedup (Compile)	1.526x
CUDA Runtime	0.022 ms
PyTorch Runtime (Native)	0.024 ms
PyTorch Runtime (Compile)	0.034 ms
Correct	True
Max Diff (vs. Reference)	0.000000
Model	deepseek-reasoner
Temperature	1.00

View Experiment Progress Details

Related Kernels (Level 1, Task 52 • 52_Argmin_over_a_dimension)

Rank	Kernel Name	Runtime (ms)	Speedup Native	Speedup Compile
🥇	52_argmin_tuned_blocks_base_base	0.01	1.73	2.40
🥇	argmin_parallel_tuned_2_edit_1	0.01	1.73	2.40
🥉	52_argmin_optimized_memory_base	0.01	1.61	2.24
🥉	52_argmin_optimal_blks_base	0.01	1.61	2.24
🥉	52_argmin_shared_tiled_base_base	0.01	1.61	2.24
🥉	52_argmin_ldg_aligned_base	0.01	1.61	2.24
7	52_argmin_balanced_workload_base	0.02	1.51	2.10
7	52_argmin_optimized_indexing_base_base	0.02	1.51	2.10
7	52_argmin_stride_loops_base	0.02	1.51	2.10
7	argmin_combined_kernel_base	0.02	1.51	2.10
7	optimized_argmin_kernel_base	0.02	1.51	2.10
7	argmin_parallel_edit_1	0.02	1.51	2.10
7	52_argmin_coalesced_base	0.02	1.51	2.10
14	52_argmin_coalesced_base_base	0.02	1.10	1.53
14	argmin_coalesced_edit_1	0.02	1.10	1.53
14	coalesced_argmin_base	0.02	1.10	1.53
14	coalesced_argmin_edit_1	0.02	1.10	1.53
14	aligned_memory_argmin_base	0.02	1.10	1.53
14	modular_argmin_optimized_indexing_base	0.02	1.10	1.53
14	modular_argmin_refactored_base	0.02	1.10	1.53

#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <vector>
#include <stdexcept>

template <typename scalar_t>
__global__ void argmin_coalesced_kernel(
    const scalar_t* __restrict__ x,
    int64_t* __restrict__ output,
    int K,
    int64_t outer_size,
    int64_t inner_size) {
    
    const int tid = threadIdx.x;
    const int outer = blockIdx.x;
    const int inner_chunk = blockIdx.y;
    
    const int block_size = blockDim.x;
    const int64_t inner_start = inner_chunk * block_size;
    const int64_t inner = inner_start + tid;
    
    if (outer >= outer_size || inner >= inner_size) return;
    
    scalar_t min_val = std::numeric_limits<scalar_t>::max();
    int min_index = 0;
    
    const scalar_t* outer_ptr = x + outer * K * inner_size;
    
    for (int k = 0; k < K; ++k) {
        scalar_t val = __ldg(outer_ptr + k * inner_size + inner);
        if (val < min_val) {
            min_val = val;
            min_index = k;
        }
    }
    
    output[outer * inner_size + inner] = min_index;
}

at::Tensor argmin_cuda_forward(const at::Tensor &x, int64_t dim) {
    TORCH_CHECK(x.is_cuda(), "Input tensor must be a CUDA tensor");
    
    int dims = x.dim();
    if (dim < 0) dim += dims;
    TORCH_CHECK(dim >= 0 && dim < dims, "Reduction dim out of range");
    
    int64_t outer_size = 1;
    for (int i = 0; i < dim; i++) {
        outer_size *= x.size(i);
    }
    int K = static_cast<int>(x.size(dim));
    int64_t inner_size = 1;
    for (int i = dim + 1; i < dims; i++) {
        inner_size *= x.size(i);
    }
    
    std::vector<int64_t> out_sizes;
    for (int i = 0; i < dims; i++) {
        if (i == dim) continue;
        out_sizes.push_back(x.size(i));
    }
    auto output = at::empty(out_sizes, x.options().dtype(at::kLong));
    
    const int block_size = 256;
    const int inner_chunks = (inner_size + block_size - 1) / block_size;
    dim3 grid(outer_size, inner_chunks);
    
    AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, x.scalar_type(), "argmin_cuda_forward", ([&] {
        const scalar_t* x_data = x.data_ptr<scalar_t>();
        int64_t* output_data = output.data_ptr<int64_t>();
        argmin_coalesced_kernel<scalar_t><<<grid, block_size>>>(x_data, output_data, K, outer_size, inner_size);
    }));
    
    cudaError_t err = cudaGetLastError();
    if (err != cudaSuccess) {
        throw std::runtime_error(std::string("CUDA kernel failed: ") + cudaGetErrorString(err));
    }
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &argmin_cuda_forward, "Argmin forward (CUDA)");
}

Performance Metrics

Metric	Value	Unit	Variance	Samples
Executed Ipc Active	0.530	inst/cycle	0.000	5
Executed Ipc Elapsed	0.058	inst/cycle	0.000	5
Issue Slots Busy	13.256	%	0.004	5
Issued Ipc Active	0.530	inst/cycle	0.000	5
SM Busy	17.114	%	0.008	5
Memory Throughput	225043312423.686	byte/second	3958271103101329408.000	5
Mem Busy	3.892	%	0.001	5
Max Bandwidth	6.724	%	0.003	5
L1/TEX Hit Rate	0.000	%	0.000	5
L2 Hit Rate	19.016	%	0.000	5
Mem Pipes Busy	0.846	%	0.000	5
Warp Cycles Per Issued Instruction	14.852	cycle	0.003	5
Warp Cycles Per Executed Instruction	14.884	cycle	0.003	5
Avg. Active Threads Per Warp	32.000		0.000	5
Avg. Not Predicated Off Threads Per Warp	27.460		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	12.254	%	0.001	5
Achieved Active Warps Per SM	7.842	warp	0.000	5

Analysis Rules

Rule	Description
WRN HighPipeUtilization	All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler. Check the Launch Statistics and Scheduler Statistics sections for further details.
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 (12.2%) 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
aten::to
CPU Time	215707.86	μs
Device Time	295.46	μs
Self CPU Time	40.17	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::_to_copy
CPU Time	215667.69	μs
Device Time	295.46	μs
Self CPU Time	97.02	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::empty_strided
CPU Time	215044.29	μs
Device Time	0.00	μs
Self CPU Time	80.48	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
cudaDeviceGetStreamPriorityRange
CPU Time	214770.06	μs
Device Time	0.00	μs
Self CPU Time	214770.06	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
cudaLaunchKernel
CPU Time	598240.51	μs
Device Time	60227.87	μs
Self CPU Time	598240.51	μs
Self Device Time	60227.87	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
void argmin_coalesced_kernel<float>(float const, long, int, long, long)
CPU Time	0.00	μs
Device Time	152866.80	μs
Self CPU Time	0.00	μs
Self Device Time	152866.80	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	115389.01	μs
Device Time	616112.13	μs
Self CPU Time	12547.22	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	102843.39	μs
Device Time	616112.13	μs
Self CPU Time	15028.00	μs
Self Device Time	616112.13	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor<int>, at::detail::Array<char, 1> >(int, at::native::FillFunctor<int>, at::detail::Array<char, 1>)
CPU Time	0.00	μs
Device Time	616112.13	μs
Self CPU Time	0.00	μs
Self Device Time	616112.13	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B

Status: Failed

45259 warnings and 1 error generated when compiling for host.
Error while processing /home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu.
Suppressed 45287 warnings (45240 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.
Found compiler error(s).

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:11:5 bugprone-easily-swappable-parameters

11 | int K,

| ^~~~~~

12 | int64_t outer_size,

| ~~~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:11:9: note: the first parameter in the range is 'K'

11 | int K,

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:12:13: note: the last parameter in the range is 'outer_size'

12 | int64_t outer_size,

| ^~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:11:5: note:

11 | int K,

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:12:5: note: 'int' and 'int64_t' may be implicitly converted: 'int' -> 'int64_t' (as 'long'), 'int64_t' (as 'long') -> 'int'

12 | int64_t outer_size,

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:15:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

15 | const int tid = threadIdx.x;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:16:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

16 | const int outer = blockIdx.x;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:17:29: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

17 | const int inner_chunk = blockIdx.y;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:19:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

19 | const int block_size = blockDim.x;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:20:33: warning: performing an implicit widening conversion to type 'const int64_t' (aka 'const long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]

20 | const int64_t inner_start = inner_chunk * block_size;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:20:33: note: make conversion explicit to silence this warning

4 | const int64_t inner_start = inner_chunk * block_size;

| ^~~~~~~~~~~~~~~~~~~~~~~~

| static_cast<const int64_t>( )

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:20:33: note: perform multiplication in a wider type

20 | const int64_t inner_start = inner_chunk * block_size;

| ^~~~~~~~~~~

| static_cast<const int64_t>( )

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:28:37: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]

28 | const scalar_t* outer_ptr = x + outer * K * inner_size;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:28:37: note: make conversion explicit to silence this warning

28 | const scalar_t* outer_ptr = x + outer * K * inner_size;

| ^~~~~~~~~

| static_cast<int64_t>( )

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:28:37: note: perform multiplication in a wider type

28 | const scalar_t* outer_ptr = x + outer * K * inner_size;

| ^~~~~

| static_cast<int64_t>( )

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: error: use of overloaded operator '<' is ambiguous (with operand types 'c10::Half' and 'c10::Half') [clang-diagnostic-error]

32 | if (val < min_val) {

| ~~~ ^ ~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:72:9: note: in instantiation of function template specialization 'argmin_coalesced_kernel<c10::Half>' requested here

72 | argmin_coalesced_kernel<scalar_t><<<grid, block_size>>>(x_data, output_data, K, outer_size, inner_size);

| ^

/usr/local/cuda/include/cuda_fp16.hpp:310:33: note: candidate function

310 | __device__ __forceinline__ bool operator< (const __half &lh, const __half &rh) { return __hlt(lh, rh); }

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(float, float)

32 | if (val < min_val) {

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(float, double)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(float, long double)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(float, __float128)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(float, int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(float, long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(float, long long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(float, __int128)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(float, unsigned int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(double, float)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(double, double)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(double, __float128)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(double, int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(double, long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(double, long long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(double, __int128)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long double, float)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(__float128, float)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(int, float)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long, float)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long long, float)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(__int128, float)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(__float128, double)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(int, double)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long, double)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long long, double)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(__int128, double)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long double, int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long double, long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(__float128, int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(__float128, long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(int, long double)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(int, __float128)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(int, int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(int, long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(int, long long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(int, __int128)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(int, unsigned int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(int, unsigned long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long, long double)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long, __float128)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long, int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long, long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long, long long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long, __int128)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long, unsigned int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long long, int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(long long, long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(__int128, int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(__int128, long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(__int128, __int128)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(unsigned int, int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(unsigned int, long)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:32:17: note: built-in candidate operator<(unsigned long, int)

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:44:16: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

44 | int dims = x.dim();

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:54:18: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

54 | for (int i = dim + 1; i < dims; i++) {

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:66:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

66 | const int inner_chunks = (inner_size + block_size - 1) / block_size;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_52/b9_s3_52_argmin_coalesced_base/base/base.cu:69:5: 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]

69 | AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, x.scalar_type(), "argmin_cuda_forward", ([&] {

| ^

/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:542:19: note: expanded from macro 'AT_DISPATCH_ALL_TYPES_AND'

542 | TYPE, NAME, AT_DISPATCH_CASE_ALL_TYPES_AND(SCALARTYPE, __VA_ARGS__))

| ^

/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:537:3: note: expanded from macro 'AT_DISPATCH_CASE_ALL_TYPES_AND'

537 | AT_DISPATCH_CASE_ALL_TYPES(__VA_ARGS__) \

| ^

/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:478:3: note: expanded from macro 'AT_DISPATCH_CASE_ALL_TYPES'

478 | AT_DISPATCH_CASE_INTEGRAL_TYPES(__VA_ARGS__) \

| ^

note: (skipping 3 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__, \

| ^

The AI CUDA Engineer 👷

`52_Argmin_over_a_dimension` • `52_argmin_coalesced_base_base`

Kernel Information

Related Kernels (Level 1, Task 52 • 52_Argmin_over_a_dimension)

The AI CUDA Engineer 👷

52_Argmin_over_a_dimension • 52_argmin_coalesced_base_base

Kernel Information

Related Kernels (Level 1, Task 52 • 52_Argmin_over_a_dimension)

`52_Argmin_over_a_dimension` • `52_argmin_coalesced_base_base`