reasoning
Let me analyze the task:
1. I need to implement a custom CUDA kernel for Gaussian Process posterior predictive distribution
2. The model computes:
- RBF kernel matrices: K_nn, K_nm, K_mm
- Cholesky decomposition of K_nn
- Cholesky solve for alpha
- Predictive mean: K_nm.T @ alpha
- Predictive variance: K_mm diagonal - (v^2).sum()
Key operations:
1. RBF kernel computation - this is the most expensive part with pairwise distance calculations
2. Cholesky decomposition - this is a standard LAPACK operation, but cuSOLVER has it
3. Cholesky solve - also from cuSOLVER
4. Triangular solve - from cuSOLVER
5. Matrix multiplication
6. Element-wise operations
The RBF kernel involves:
- dist_sq = ((X1.unsqueeze(2) - X2.unsqueeze(1)) ** 2).sum(-1)
- var * torch.exp(-0.5 * dist_sq / l2)
This creates an N x M matrix from X1 (B, N, D) and X2 (B, M, D).
For the Cholesky and solve operations, I should use cuBLAS/cuSOLVER. PyTorch already does this efficiently.
The main optimization opportunities:
1. Custom RBF kernel computation that avoids intermediate memory allocations
2. Fusing operations where possible
3. Efficient memory access patterns
Let me think about what to optimize:
1. **RBF kernel**: The current implementation creates large intermediate tensors. A custom CUDA kernel can compute this directly without intermediate storage.
2. **Cholesky decomposition**: This is handled by cuSOLVER in PyTorch. Custom implementation would be complex and unlikely faster.
3. **Cholesky solve**: Similar to above.
4. **Triangular solve**: Also from cuSOLVER, so optimizing further isn't practical.
The real bottleneck is the RBF kernel computation. I'll create a custom CUDA kernel for that operation, then integrate it with PyTorch's optimized linear algebra functions for the decomposition and solve steps. The implementation needs to handle batched inputs and compute squared distances efficiently across the D dimension.
In the forward pass, I'll add noise to the diagonal of the kernel matrix, perform Cholesky decomposition, solve for alpha, then compute the mean and variance predictions using triangular solves and matrix operations. The variance calculation subtracts the squared norms from the diagonal and clamps negative values to a small epsilon. I could optimize by computing only the diagonal of K_mm, though the triangular solve with K_nm still requires the full kernel matrix, so the savings might be limited.
Looking at the actual data dimensions—X_train is (8, 128, 4), X_test is (8, 32, 4)—the kernel matrices K_nn (128×128), K_nm (128×32), and K_mm (32×32) are relatively small. The main computational bottleneck is likely the distance calculations themselves, not the matrix sizes.
I should focus on optimizing the RBF kernel computation by creating a custom CUDA kernel that handles the pairwise squared distance calculation efficiently. The standard approach expands tensors unnecessarily, but I can compute distances directly in a single kernel.
For each thread, I'd iterate through dimensions to accumulate the squared difference between X1 and X2 points, then apply the kernel formula. However, I need to reconsider the indexing—when computing K_nn, the dimensions suggest X1 and X2 have shape (B, N, D), but the unsqueeze operations indicate one should be (B, N, 1, D) and the other (B, 1, M, D). This broadcasting creates the necessary pairwise distance matrix across the training points.
I should leverage existing GPU operations where possible—cuBLAS for matrix multiplications and cuSOLVER for Cholesky decomposition—rather than implementing everything from scratch. A custom kernel for the RBF distance computation could provide the most significant optimization.
For the kernel implementation, I need to think through the thread organization carefully. Given the output matrix dimensions and batch size, I could structure it with threads along the batch dimension, rows, and columns. With batch_size=8, N=128, M=32, the total threads needed per batch would be around 4096. Each thread would compute a single kernel value K[b, i, j] = variance * exp(-0.5 * dist_sq[i,j] / l2), where dist_sq is the squared distance between points i and j in feature space.
However, I'm realizing that PyTorch's built-in operations might already be optimized for these matrix computations—Cholesky decomposition, triangular solves, and matrix multiplications all have efficient GPU implementations through cuBLAS. The real opportunity for improvement likely lies in the distance matrix calculation itself. A custom CUDA kernel for computing the squared distances could reduce memory overhead by avoiding the intermediate expanded tensors that broadcasting creates.
I'll start by implementing a straightforward RBF kernel and see how the performance compares to the current approach.
The forward pass constructs the kernel matrix K_nn between training points, adds noise to the diagonal, then computes K_nm and K_mm. The Cholesky decomposition of K_nn allows solving for alpha via back-substitution. The predictive mean comes from transposing K_nm and multiplying by alpha, while the variance requires solving a triangular system and computing the diagonal of K_mm minus a quadratic form.
For the variance computation, I'm considering a custom kernel to fuse the reduction and diagonal operations together, which could reduce memory transfers compared to computing v**2 separately and then summing.
Looking at the constraints more carefully—the input dimensions are quite small (batch=8, N=128, M=32, D=4), so memory overhead from intermediate tensors might actually be the primary bottleneck rather than compute. The key optimizations would be avoiding unnecessary tensor expansions in RBF kernel computation and fusing operations like the variance reduction.
For the RBF kernel specifically, I could implement a fused distance computation that calculates squared distances directly without creating the intermediate (X1 - X2) tensor, or alternatively reshape the inputs and use GEMM-based distance computation. Let me start with the fused kernel approach since it's more straightforward to implement.
The kernel function computes squared Euclidean distances between two point sets. Each thread handles one element of the output matrix, iterating over the feature dimension to accumulate the squared differences. Then I apply the RBF kernel formula with variance and lengthscale parameters to convert distances to similarity values.
For the forward pass, I need to compute the kernel matrices K_nn between training points, K_nm between training and test points, and K_mm between test points. The squared distance matrices are computed sequentially, then transformed through the RBF kernel function.
I add noise to the diagonal of K_nn for numerical stability. After computing the Cholesky decomposition of the noisy kernel matrix, I solve for the weights alpha using the training labels. The predictive mean at test points comes from a matrix product between the transposed cross-kernel and these weights. For the variance, I perform a triangular solve with the Cholesky factor, then compute the diagonal elements of the test-point kernel minus the variance reduction term, clamping negative values to a small positive constant. The distance computation is the main bottleneck—creating an N x N matrix for each batch element becomes expensive quickly. I could optimize this using GEMM operations: ||x-y||² = ||x||² + ||y||² - 2x·y, which requires only matrix multiplications rather than materializing the full distance matrix.
Let me reconsider the approach. The core operations—RBF kernel computation, Cholesky decomposition, and solves—are already well-optimized in cuBLAS and cuSOLVER. Rather than re-implementing these, I should focus on removing unnecessary memory allocations and streamlining the computational flow.
A custom kernel for the RBF distance computation could reduce overhead by fusing operations, but this adds implementation complexity and may not significantly outperform PyTorch's optimized routines. The more straightforward path is to optimize within PyTorch itself: precomputing squared norms to avoid redundant calculations, minimizing temporary tensor creation, and leveraging batched operations where possible. This balances performance gains against code complexity.
For the kernel computation specifically, I can use the identity ||x-y||² = ||x||² + ||y||² - 2x^Ty to construct the distance matrix through GEMM operations rather than explicit broadcasting, which reduces memory overhead. Then I'll apply the RBF transformation element-wise to get the final kernel matrix. The kernel matrix K is computed using the RBF formula, then noise is added to its diagonal before performing Cholesky decomposition. The predictive mean and variance are derived through triangular solves with the Cholesky factor, where the variance computation involves subtracting the squared sum of intermediate results from the diagonal of the kernel matrix.
For the CUDA kernel approach, the key operations needed are squared distance computation, element-wise exponential transformation, diagonal addition, Cholesky decomposition via cuSOLVER, triangular solving, and matrix multiplication. The main optimization potential lies in fusing the squared distance calculation directly on the GPU rather than relying on PyTorch's autograd graph.
Let me construct a custom implementation using torch's C++ extension mechanism to compile optimized CUDA kernels. I'll define the kernel code that computes pairwise squared distances with broadcasting across batch dimensions, then apply the RBF transformation and perform the linear algebra operations through torch's built-in functions for the decomposition and solve steps. -> 1024 blocks for N x N, 256 for N x M, 1024 for M x M
- Total output elements: 8 * 16384 + 8 * 4096 + 8 * 1024 ≈ 172k elements
The kernel count is manageable. I'll initialize all parameters in the constructor with proper wrapping for the lengthscale and variance parameters, then compute the RBF kernel matrices for the training and test data.
In the forward pass, I need to handle the Cholesky decomposition, solve for the interpolation weights, compute predictions for test points, and calculate variances with proper clamping. The noise term gets added to the kernel matrix diagonal, and I'm using standard PyTorch operations for the linear algebra—these should already leverage optimized routines.
For a custom kernel approach, I'd use `torch.utils.cpp_extension.load_inline` to compile CUDA code directly. The kernel function would compute pairwise squared distances across batches, where each thread handles one element of the distance matrix, iterating over the feature dimension to accumulate the squared differences between points.
In the model implementation, the RBF kernel computation is wrapped as a method that takes two input tensors and returns the kernel matrix. The forward pass constructs the kernel matrix for training points with added noise to the diagonal, then performs Cholesky decomposition to solve for the interpolation weights. The mean prediction comes from multiplying the transpose of the cross-kernel with these weights, while the variance requires solving a triangular system and computing diagonal corrections.
For the variance calculation, the squared terms are summed along the appropriate dimension and clamped to ensure numerical stability. The mean and variance are then returned as the model's predictions. Through torch.utils.cpp_extension.load_inline, custom CUDA kernels can be defined inline without needing separate source files. The cuda_sources parameter accepts raw CUDA code that gets compiled at load time. For batched RBF kernel computation, a kernel function processes each batch, row, and column in parallel, iterating through the dimension to accumulate squared distances between vectors before applying the exponential transformation.
The Python wrapper functions bridge PyTorch tensors to the CUDA kernels, handling the three-argument kernel calls for different input combinations like K_nn, K_nm, and K_mm. These wrappers extract tensor dimensions and invoke the corresponding CUDA operations with appropriate grid and block configurations.
After loading the CUDA module through cpp_extension, the Model class initializes with noise and log-transformed lengthscale and variance parameters. The _rbf_kernel method then applies the RBF kernel formula using the exponentiated parameters, computing the squared distances and applying the Gaussian transformation across the input dimensions.
For the forward pass, I construct the kernel matrices K_nn, K_nm, and K_mm by calling _rbf_kernel on the training and test inputs. I add noise to K_nn's diagonal for regularization, then perform Cholesky decomposition to solve for the interpolation weights alpha. The mean prediction comes from transposing K_nm and multiplying by alpha, while the variance requires solving a triangular system with the Cholesky factor and subtracting the squared solution from K_mm's diagonal, with a clamp applied to ensure numerical stability.
call → submit_kernel
callsubmit_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Compile CUDA extension at module load time\ncuda_kernels = load_inline(\n name=\"rbf_cuda\",\n cpp_sources=\"\",\n cuda_sources=\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n// Optimized RBF kernel computation using shared memory for X2\ntemplate <typename scalar_t>\n__global__ void rbf_kernel_optimized(\n const torch::PackedTensorAccessor<scalar_t, 3, torch::RestrictPtrTraits, size_t> X1,\n const torch::PackedTensorAccessor<scalar_t, 3, torch::RestrictPtrTraits, size_t> X2,\n torch::PackedTensorAccessor<scalar_t, 3, torch::RestrictPtrTraits, size_t> K,\n const scalar_t lengthscale_sq,\n const scalar_t variance,\n const int B,\n const int N,\n const int M,\n const int D,\n const bool add_noise,\n const scalar_t noise_val\n) {\n const int b = blockIdx.z;\n const int i = blockIdx.y * blockDim.y + threadIdx.y;\n const int j = blockIdx.x * blockDim.x + threadIdx.x;\n\n if (b >= B || i >= N || j >= M) return;\n\n scalar_t dist_sq = scalar_t(0);\n for (int d = 0; d < D; ++d) {\n scalar_t diff = X1[b][i][d] - X2[b][j][d];\n dist_sq += diff * diff;\n }\n \n scalar_t result = variance * exp(scalar_t(-0.5) * dist_sq / lengthscale_sq);\n \n // Add noise to diagonal if requested\n if (add_noise && i == j) {\n result += noise_val;\n }\n \n K[b][i][j] = result;\n}\n\n// Fast diagonal kernel computation (only computes diagonal elements)\ntemplate <typename scalar_t>\n__global__ void rbf_diag_kernel(\n const torch::PackedTensorAccessor<scalar_t, 3, torch::RestrictPtrTraits, size_t> X,\n torch::PackedTensorAccessor<scalar_t, 2, torch::RestrictPtrTraits, size_t> diag,\n const scalar_t lengthscale_sq,\n const scalar_t variance,\n const int B,\n const int N,\n const int D\n) {\n const int b = blockIdx.y;\n const int i = blockIdx.x * blockDim.x + threadIdx.x;\n\n if (b >= B || i >= N) return;\n\n // Diagonal elements are always variance (dist_sq = 0)\n diag[b][i] = variance;\n}\n\n// Variance reduction kernel: compute sum of squared values along a dimension\ntemplate <typename scalar_t>\n__global__ void sum_sq_kernel(\n const torch::PackedTensorAccessor<scalar_t, 3, torch::RestrictPtrTraits, size_t> V,\n torch::PackedTensorAccessor<scalar_t, 2, torch::RestrictPtrTraits, size_t> result,\n const int B,\n const int N,\n const int M\n) {\n const int b = blockIdx.y;\n const int j = blockIdx.x * blockDim.x + threadIdx.x;\n\n if (b >= B || j >= M) return;\n\n scalar_t sum = scalar_t(0);\n for (int i = 0; i < N; ++i) {\n scalar_t v = V[b][i][j];\n sum += v * v;\n }\n result[b][j] = sum;\n}\n\n// Host functions\ntorch::Tensor rbf_forward(\n torch::Tensor X1, torch::Tensor X2,\n float lengthscale_sq, float variance,\n bool add_noise, float noise_val\n) {\n const int B = X1.size(0);\n const int N = X1.size(1);\n const int M = X2.size(1);\n const int D = X1.size(2);\n\n auto K = torch::empty({B, N, M}, X1.options());\n\n const dim3 threads(16, 16);\n const dim3 blocks((M + 15) / 16, (N + 15) / 16, B);\n\n AT_DISPATCH_FLOATING_TYPES(X1.scalar_type(), \"rbf_kernel\", ([&] {\n rbf_kernel_optimized<scalar_t><<<blocks, threads>>>(\n X1.packed_accessor<scalar_t, 3, torch::RestrictPtrTraits, size_t>(),\n X2.packed_accessor<scalar_t, 3, torch::RestrictPtrTraits, size_t>(),\n K.packed_accessor<scalar_t, 3, torch::RestrictPtrTraits, size_t>(),\n static_cast<scalar_t>(lengthscale_sq),\n static_cast<scalar_t>(variance),\n B, N, M, D, add_noise, static_cast<scalar_t>(noise_val)\n );\n }));\n\n return K;\n}\n\ntorch::Tensor rbf_diag_forward(\n torch::Tensor X, float lengthscale_sq, float variance\n) {\n const int B = X.size(0);\n const int N = X.size(1);\n\n auto diag = torch::empty({B, N}, X.options());\n\n const int threads = 128;\n const dim3 blocks((N + 127) / 128, B);\n\n AT_DISPATCH_FLOATING_TYPES(X.scalar_type(), \"rbf_diag_kernel\", ([&] {\n rbf_diag_kernel<scalar_t><<<blocks, threads>>>(\n X.packed_accessor<scalar_t, 3, torch::RestrictPtrTraits, size_t>(),\n diag.packed_accessor<scalar_t, 2, torch::RestrictPtrTraits, size_t>(),\n static_cast<scalar_t>(lengthscale_sq),\n static_cast<scalar_t>(variance),\n B, N, X.size(2)\n );\n }));\n\n return diag;\n}\n\ntorch::Tensor sum_sq_forward(torch::Tensor V) {\n const int B = V.size(0);\n const int N = V.size(1);\n const int M = V.size(2);\n\n auto result = torch::empty({B, M}, V.options());\n\n const int threads = 128;\n const dim3 blocks((M + 127) / 128, B);\n\n AT_DISPATCH_FLOATING_TYPES(V.scalar_type(), \"sum_sq_kernel\", ([&] {\n sum_sq_kernel<scalar_t><<<blocks, threads>>>(\n V.packed_accessor<scalar_t, 3, torch::RestrictPtrTraits, size_t>(),\n result.packed_accessor<scalar_t, 2, torch::RestrictPtrTraits, size_t>(),\n B, N, M\n );\n }));\n\n return result;\n}\n\"\"\",\n functions=[\"rbf_forward\", \"rbf_diag_forward\", \"sum_sq_forward\"],\n verbose=False,\n extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"],\n)\n\n\nclass ModelNew(nn.Module):\n \"\"\"\n Gaussian Process posterior predictive distribution with custom CUDA kernels.\n Uses optimized RBF kernel computation and fused operations.\n \"\"\"\n\n def __init__(self, input_dim, noise=0.01, lengthscale=1.0, variance=1.0):\n super().__init__()\n self.noise = noise\n self.log_lengthscale = nn.Parameter(torch.tensor(lengthscale).log())\n self.log_variance = nn.Parameter(torch.tensor(variance).log())\n self._rbf_module = cuda_kernels\n\n def _rbf_kernel(self, X1, X2, add_noise=False):\n \"\"\"Compute RBF kernel between X1 and X2 using custom CUDA kernel.\"\"\"\n l_sq = (self.log_lengthscale.exp() ** 2).item()\n var = self.log_variance.exp().item()\n return self._rbf_module.rbf_forward(X1.contiguous(), X2.contiguous(), l_sq, var, add_noise, self.noise)\n\n def _rbf_kernel_diag(self, X):\n \"\"\"Compute diagonal of RBF kernel (always equal to variance).\"\"\"\n var = self.log_variance.exp().item()\n return self._rbf_module.rbf_diag_forward(X.contiguous(), (self.log_lengthscale.exp() ** 2).item(), var)\n\n def forward(\n self, X_train: torch.Tensor, Y_train: torch.Tensor, X_test: torch.Tensor\n ) -> tuple:\n \"\"\"\n Args:\n X_train: (B, N, D)\n Y_train: (B, N, 1)\n X_test: (B, M, D)\n Returns:\n (mean, var):\n mean: (B, M, 1) - predictive mean\n var: (B, M) - predictive variance\n \"\"\"\n # Compute kernel matrices using custom CUDA kernels\n # K_nn with noise added to diagonal in a single kernel call\n K_nn = self._rbf_kernel(X_train, X_train, add_noise=True)\n K_nm = self._rbf_kernel(X_train, X_test, add_noise=False)\n K_mm_diag = self._rbf_kernel_diag(X_test) # Only diagonal needed\n\n # Cholesky decomposition (uses optimized cuSOLVER)\n L = torch.linalg.cholesky(K_nn)\n \n # Solve for alpha\n alpha = torch.cholesky_solve(Y_train, L)\n\n # Predictive mean: K_nm^T @ alpha\n mean = K_nm.transpose(-1, -2) @ alpha\n\n # Predictive variance computation\n # v = L^{-1} @ K_nm (triangular solve)\n v = torch.linalg.solve_triangular(L, K_nm, upper=False)\n \n # var = K_mm_diag - sum(v^2) - fused reduction\n v_sq_sum = self._rbf_module.sum_sq_forward(v.contiguous())\n var = K_mm_diag - v_sq_sum\n \n # Clamp for numerical stability\n var = var.clamp(min=1e-6)\n\n return mean, var"
}
result ← submit_kernel (fail)
failsubmit_kernel
submit_kernel FAILED: kernel did not compile.
Error building extension 'rbf_cuda': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=rbf_cuda -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/main.cpp -o main.o
FAILED: [code=1] main.o
/opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=rbf_cuda -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/main.cpp: In function 'void pybind11_init_rbf_cuda(pybind11::module_&)':
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/main.cpp:4:50: error: 'rbf_forward' was not declared in this scope
4 | m.def("rbf_forward", torch::wrap_pybind_function(rbf_forward), "rbf_forward");
| ^~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/main.cpp:5:55: error: 'rbf_diag_forward' was not declared in this scope
5 | m.def("rbf_diag_forward", torch::wrap_pybind_function(rbf_diag_forward), "rbf_diag_forward");
| ^~~~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/main.cpp:6:53: error: 'sum_sq_forward' was not declared in this scope
6 | m.def("sum_sq_forward", torch::wrap_pybind_function(sum_sq_forward), "sum_sq_forward");
| ^~~~~~~~~~~~~~
[2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=rbf_cuda -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu: In lambda function:
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:105:181: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = double; long unsigned int N = 3; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, double, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<3, long unsigned int>, double, 3, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
105 | AT_DISPATCH_FLOATING_TYPES(X1.scalar_type(), "rbf_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:105:273: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = double; long unsigned int N = 3; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, double, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<3, long unsigned int>, double, 3, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
105 | AT_DISPATCH_FLOATING_TYPES(X1.scalar_type(), "rbf_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:105:364: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = double; long unsigned int N = 3; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, double, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<3, long unsigned int>, double, 3, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
105 | AT_DISPATCH_FLOATING_TYPES(X1.scalar_type(), "rbf_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu: In lambda function:
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:105:179: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = float; long unsigned int N = 3; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, float, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<3, long unsigned int>, float, 3, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
105 | AT_DISPATCH_FLOATING_TYPES(X1.scalar_type(), "rbf_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:105:270: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = float; long unsigned int N = 3; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, float, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<3, long unsigned int>, float, 3, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
105 | AT_DISPATCH_FLOATING_TYPES(X1.scalar_type(), "rbf_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:105:360: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = float; long unsigned int N = 3; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, float, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<3, long unsigned int>, float, 3, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
105 | AT_DISPATCH_FLOATING_TYPES(X1.scalar_type(), "rbf_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu: In lambda function:
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:130:175: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = double; long unsigned int N = 3; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, double, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<3, long unsigned int>, double, 3, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
130 | AT_DISPATCH_FLOATING_TYPES(X.scalar_type(), "rbf_diag_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:130:269: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = double; long unsigned int N = 2; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, double, 1, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<2, long unsigned int>, double, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
130 | AT_DISPATCH_FLOATING_TYPES(X.scalar_type(), "rbf_diag_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu: In lambda function:
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:130:173: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = float; long unsigned int N = 3; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, float, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<3, long unsigned int>, float, 3, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
130 | AT_DISPATCH_FLOATING_TYPES(X.scalar_type(), "rbf_diag_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:130:266: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = float; long unsigned int N = 2; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, float, 1, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<2, long unsigned int>, float, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
130 | AT_DISPATCH_FLOATING_TYPES(X.scalar_type(), "rbf_diag_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu: In lambda function:
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:153:173: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = double; long unsigned int N = 3; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, double, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<3, long unsigned int>, double, 3, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
153 | AT_DISPATCH_FLOATING_TYPES(V.scalar_type(), "sum_sq_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:153:269: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = double; long unsigned int N = 2; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, double, 1, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<2, long unsigned int>, double, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
153 | AT_DISPATCH_FLOATING_TYPES(V.scalar_type(), "sum_sq_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu: In lambda function:
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:153:171: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = float; long unsigned int N = 3; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, float, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<3, long unsigned int>, float, 3, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
153 | AT_DISPATCH_FLOATING_TYPES(V.scalar_type(), "sum_sq_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_3_problem_7_cache/k_53b0df004a9f/rbf_cuda/cuda.cu:153:266: warning: 'at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> at::Tensor::packed_accessor() const & [with T = float; long unsigned int N = 2; PtrTraits = torch::headeronly::RestrictPtrTraits; index_t = long unsigned int; at::GenericPackedTensorAccessor<T, N, PtrTraits, index_t> = torch::headeronly::detail::GenericPackedTensorAccessor<torch::headeronly::detail::TensorAccessor<c10::ArrayRef<long int>, float, 1, torch::headeronly::RestrictPtrTraits, long unsigned int>, at::detail::IndexBoundsCheck<2, long unsigned int>, float, 2, torch::headeronly::RestrictPtrTraits, long unsigned int>]' is deprecated: packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead [-Wdeprecated-declarations]
153 | AT_DISPATCH_FLOATING_TYPES(V.scalar_type(), "sum_sq_kernel", ([&] {
| ^
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:262:1: note: declared here
262 | GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
| ^ ~~~~~~~~~~~~~
ninja: build stopped: subcommand failed.