correct
17_CSRMaxAggregation.py
started
2026-05-11T11:05:13Z
timingkernelbench.timing
kernel mean / median / std
0.02 / — / 5.65e-04 μs
ref mean / median / std
37.00 / — / 1.55 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
1226.92x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
warp stalls (top 5)nsight
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Per-destination max aggregation over neighbor features in CSR format.
This pattern appears in max-pooling GNNs and neighborhood feature
extraction where reductions are not sums but feature-wise maxima.
"""
def __init__(self):
super().__init__()
def forward(
self,
row_ptr: torch.Tensor,
col_idx: torch.Tensor,
node_feat: torch.Tensor,
) -> torch.Tensor:
num_nodes = row_ptr.numel() - 1
feat_dim = node_feat.shape[1]
out = torch.full((num_nodes, feat_dim), float("-inf"), dtype=node_feat.dtype, device=node_feat.device)
for dst in range(num_nodes):
start = int(row_ptr[dst].item())
end = int(row_ptr[dst + 1].item())
if end > start:
out[dst] = node_feat[col_idx[start:end].long()].amax(dim=0)
return out
num_nodes = 640
avg_degree = 16
feat_dim = 80
def get_inputs():
return list(popcorn_pri.make_csr_graph(num_nodes, avg_degree, feat_dim))
def get_init_inputs():
return []
turn 0 · 1 tool call(s) · 255.5s turn · 12.5s LLM · tokens in=1,631 · out=964 · total=2,595 · 6,036 chars (click to expand)
turn 0255.5s turn · 12.5s LLM · 243.0s tools · tokens in=1,631 · out=964 · total=2,595
reasoning
Let me analyze this task. I need to implement a CUDA kernel that performs per-destination max aggregation over neighbor features in CSR format.
The reference implementation:
1. Takes `row_ptr` (CSR row pointers), `col_idx` (column indices), and `node_feat` (node features)
2. For each destination node, it looks up all its neighbors via the CSR format
3. Computes the feature-wise maximum over all neighbor features
Key observations:
- `num_nodes = row_ptr.numel() - 1`
- `feat_dim = node_feat.shape[1]`
- For each destination `dst`, neighbors are `col_idx[start:end]` where `start = row_ptr[dst]` and `end = row_ptr[dst+1]`
- Output is the max over neighbor features for each dimension
This is essentially a CSR-based segmented max reduction.
Let me think about the CUDA implementation:
Option 1: One thread per destination node
- Each thread iterates over neighbors and computes max
- Simple but may not have enough parallelism if num_nodes is small
- Memory access pattern could be irregular
Option 2: One warp per destination node
- Similar but with warp-level cooperation for the max reduction
- Could be beneficial for larger feature dimensions
Option 3: Block-level parallelism
- Use shared memory for reduction
- Good for larger feature dimensions
Given feat_dim = 80 and num_nodes = 640, and avg_degree = 16:
- Each destination has ~16 neighbors on average
- Each neighbor contributes 80 feature values
- Total work per destination: ~16 * 80 = 1280 operations
Option 1 seems reasonable - each thread handles one destination node and computes max across all neighbors. With 640 nodes, we get 640 threads which is decent parallelism.
Let me think about memory access:
- `row_ptr` access is coalesced (int32)
- `col_idx` access is random/irregular based on graph structure
- `node_feat` access becomes highly irregular since we're scattered across different nodes based on the CSR structure
For feature dimension handling, each thread processes all 80 feature values per neighbor. To improve memory throughput, I could have threads load features in a coalesced pattern when accessing the same feature dimension across different neighbors. The most practical starting point is assigning one thread per destination, then optimizing from there based on performance.
However, with only 640 nodes, I'd have just 640 threads across ~7 SMs—roughly 91 threads per SM, or about 3 warps. This limits parallelism. I could instead parallelize across both nodes and feature dimensions: 640 × 80 = 51,200 threads, which is much better for GPU utilization. Assigning one thread per (destination, feature) pair would have each thread iterate over neighbors and compute max for its specific feature dimension. But this approach creates an irregular access pattern when fetching neighbor features, since threads within a warp would be accessing different neighbors simultaneously, causing scattered memory reads. Better approach: one thread per destination node, load feature vectors sequentially, compute max in registers.
Let me try a straightforward implementation first with one thread per destination node. I'll write a kernel that takes the number of nodes, feature dimension, row pointers, column indices, and node features as inputs, then have each thread iterate through its neighbors and maintain a running maximum for each feature dimension.
The kernel loads the start and end indices from row_ptr, initializes the output to negative infinity, then for each neighbor it reads the feature vector and updates the max. After processing all neighbors, it writes the result. I need to be careful with data types - row_ptr and col_idx are int32, so I should cast them properly when indexing.
Since the reference uses `long()` on col_idx, the indices need to be int64 for indexing operations. I should verify that row_ptr elements are cast correctly to avoid type mismatches when accessing tensor elements. The `.long()` call converts col_idx to int64, which matches what I'm doing in the CUDA kernel with `int64_t` casting. I need to verify the pointer arithmetic is correct - `col_idx_ptr[start]` accesses the right element after casting. The implementation should handle the CSR format properly with row pointers defining the ranges for each destination node's neighbors.
assistantLooking at this task, I need to implement a CUDA kernel for per-destination max aggregation over neighbor features in CSR format. This is essentially a segmented max reduction where each destination node takes the feature-wise max across its neighbors.
Let me first check the GPU specs to understand my optimization targets:
call → get_gpu_specs
callget_gpu_specs
{}
result ← get_gpu_specs (ok)
okget_gpu_specs
GPU specs for NVIDIA H100 80GB HBM3:
total memory (runtime): 79.1 GB
spec entry: H100
GPU Architecture: Hopper
GPU Memory: 80GB
Memory Bandwidth: 3.35 TB/s
FP64 TFLOPS: 34
FP64 Tensor Core TFLOPS: 67
FP32 TFLOPS: 67
TF32 Tensor Core TFLOPS: 989 with sparsity
BFLOAT16 Tensore Core TFLOPS: 1979 with sparsity
FP16 Tensor Core TFLOPS: 1979 with sparsity
FP8 Tensor Core TFLOPS: 3958 with sparsity
INT8 Tensor Core TOPS: 3958 with sparsity
Register File Size: 64K 32-bit registers per SM
Maximum number of registers per thread: 255
Maximum number of thread blocks per SM: 32
Shared memory capacity per SM: 228 KB
Maximum shared memory per thread block: 227 KB
turn 1 · 1 tool call(s) · 377.7s turn · 10.9s LLM · tokens in=1,937 · out=950 · total=2,887 · 10,023 chars (click to expand)
turn 1377.7s turn · 10.9s LLM · 366.8s tools · tokens in=1,937 · out=950 · total=2,887
reasoning
Now let me design my CUDA kernel. The task is:
- Input: CSR sparse graph (row_ptr, col_idx) and node features (num_nodes, feat_dim)
- Output: For each destination node, take the max across all its neighbors' features
Key observations:
- num_nodes = 640, avg_degree = 16, feat_dim = 80
- Total edges ≈ 640 * 16 = 10,240
- Each node processes its neighbors independently
For optimization on H100:
- Should use shared memory for intermediate max values
- Consider warp-level reductions for efficiency
- Handle variable degree (clamped degree varies from 1 to ~19)
Let me write a first version that processes one node per thread block, using shared memory for the reduction.
assistantNow let me design and implement the CUDA kernel. This is a segmented max reduction problem where each destination node computes the feature-wise maximum across its neighbors.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\n// Kernel for max aggregation over neighbor features in CSR format\n__global__ void csr_max_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n float* __restrict__ out,\n const int num_nodes,\n const int feat_dim\n) {\n const int dst = blockIdx.x; // One block per destination node\n const int tid = threadIdx.x;\n \n // Get neighbor range for this destination\n const int start = row_ptr[dst];\n const int end = row_ptr[dst + 1];\n const int num_neighbors = end - start;\n \n // Initialize output to -inf for this thread's feature slice\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n out[dst * feat_dim + f] = -CUDART_INF_F;\n }\n \n __syncthreads();\n \n // If no neighbors, output remains -inf\n if (num_neighbors == 0) return;\n \n // Each thread processes a subset of neighbors and features\n // Grid-stride loop over neighbors\n for (int neighbor_idx = tid; neighbor_idx < num_neighbors; neighbor_idx += blockDim.x) {\n const int src = col_idx[start + neighbor_idx];\n const float* src_feat = node_feat + src * feat_dim;\n float* dst_out = out + dst * feat_dim;\n \n // Update max for all features\n for (int f = 0; f < feat_dim; f++) {\n float val = src_feat[f];\n if (val > dst_out[f]) {\n // Atomic max operation\n atomicMax(reinterpret_cast<int*>(&dst_out[f]), __float_as_int(val));\n }\n }\n }\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor csr_max_aggregate_cuda(\n torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat\n) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n \n // Create output tensor initialized to -inf\n auto out = torch::full({num_nodes, feat_dim}, -std::numeric_limits<float>::infinity(),\n torch::dtype(torch::kFloat32).device(node_feat.device()));\n \n // Launch kernel\n const int threads_per_block = 256;\n csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(\n row_ptr.data_ptr<int>(),\n col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n \n return out;\n}\n\"\"\"\n\n# Load the extension\ncsr_max_module = load_inline(\n name='csr_max_module',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['csr_max_aggregate_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n \n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n ) -> torch.Tensor:\n return csr_max_module.csr_max_aggregate_cuda(row_ptr, col_idx, node_feat)"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'csr_max_module': [1/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=csr_max_module -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 -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_a80c14b343a6/csr_max_module/cuda.cu -o cuda.cuda.o
FAILED: [code=2] cuda.cuda.o
/usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=csr_max_module -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 -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_a80c14b343a6/csr_max_module/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_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_a80c14b343a6/csr_max_module/cuda.cu(28): error: identifier "CUDART_INF_F" is undefined
out[dst * feat_dim + f] = -CUDART_INF_F;
^
1 error detected in the compilation of "/scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_a80c14b343a6/csr_max_module/cuda.cu".
[2/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=csr_max_module -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_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_a80c14b343a6/csr_max_module/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=csr_max_module -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_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_a80c14b343a6/csr_max_module/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_a80c14b343a6/csr_max_module/main.cpp: In function 'at::Tensor csr_max_aggregate_cuda(at::Tensor, at::Tensor, at::Tensor)':
/scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_a80c14b343a6/csr_max_module/main.cpp:17:5: error: 'csr_max_aggregate_kernel' was not declared in this scope; did you mean 'csr_max_aggregate_cuda'?
17 | csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(
| ^~~~~~~~~~~~~~~~~~~~~~~~
| csr_max_aggregate_cuda
/scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_a80c14b343a6/csr_max_module/main.cpp:17:31: error: expected primary-expression before '<' token
17 | csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(
| ^
/scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_a80c14b343a6/csr_max_module/main.cpp:17:62: error: expected primary-expression before '>' token
17 | csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(
| ^
ninja: build stopped: subcommand failed.
turn 2 · 1 tool call(s) · 208.3s turn · 8.1s LLM · tokens in=4,275 · out=836 · total=5,111 · 7,444 chars (click to expand)
turn 2208.3s turn · 8.1s LLM · 200.2s tools · tokens in=4,275 · out=836 · total=5,111
reasoning
I have two issues:
1. The CUDART_INF_F constant isn't defined - I need to use a different approach
2. The kernel function needs to be declared in the C++ source
Let me fix these issues:
1. Use `-INFINITY` or define my own constant
2. Put both the kernel and the host function in the CUDA source
assistantI need to fix compilation errors. Let me use proper CUDA infinity constants and ensure the kernel is properly declared:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Define the CUDA kernel and host wrapper together\ncuda_source = \"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n\n__global__ void csr_max_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n float* __restrict__ out,\n const int num_nodes,\n const int feat_dim\n) {\n const int dst = blockIdx.x; // One block per destination node\n const int tid = threadIdx.x;\n \n // Get neighbor range for this destination\n const int start = row_ptr[dst];\n const int end = row_ptr[dst + 1];\n const int num_neighbors = end - start;\n \n // Initialize output to -inf for this thread's feature slice\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n out[dst * feat_dim + f] = -INFINITY;\n }\n \n __syncthreads();\n \n // If no neighbors, output remains -inf\n if (num_neighbors == 0) return;\n \n // Each thread processes a subset of neighbors and features\n // Grid-stride loop over neighbors\n for (int neighbor_idx = tid; neighbor_idx < num_neighbors; neighbor_idx += blockDim.x) {\n const int src = col_idx[start + neighbor_idx];\n const float* src_feat = node_feat + src * feat_dim;\n float* dst_out = out + dst * feat_dim;\n \n // Update max for all features\n for (int f = 0; f < feat_dim; f++) {\n float val = src_feat[f];\n if (val > dst_out[f]) {\n // Atomic max operation\n atomicMax(reinterpret_cast<int*>(&dst_out[f]), __float_as_int(val));\n }\n }\n }\n}\n\ntorch::Tensor csr_max_aggregate_cuda(\n torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat\n) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n \n // Create output tensor initialized to -inf\n auto out = torch::full({num_nodes, feat_dim}, -std::numeric_limits<float>::infinity(),\n torch::dtype(torch::kFloat32).device(node_feat.device()));\n \n // Launch kernel\n const int threads_per_block = 256;\n csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(\n row_ptr.data_ptr<int>(),\n col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n \n return out;\n}\n\"\"\"\n\n# Load the extension\ncsr_max_module = load_inline(\n name='csr_max_module_v2',\n cpp_sources=\"\",\n cuda_sources=cuda_source,\n functions=['csr_max_aggregate_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n \n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n ) -> torch.Tensor:\n return csr_max_module.csr_max_aggregate_cuda(row_ptr, col_idx, node_feat)"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'csr_max_module_v2': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=csr_max_module_v2 -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_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_25165120ed17/csr_max_module_v2/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=csr_max_module_v2 -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_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_25165120ed17/csr_max_module_v2/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_25165120ed17/csr_max_module_v2/main.cpp: In function 'void pybind11_init_csr_max_module_v2(pybind11::module_&)':
/scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_25165120ed17/csr_max_module_v2/main.cpp:4:61: error: 'csr_max_aggregate_cuda' was not declared in this scope
4 | m.def("csr_max_aggregate_cuda", torch::wrap_pybind_function(csr_max_aggregate_cuda), "csr_max_aggregate_cuda");
| ^~~~~~~~~~~~~~~~~~~~~~
[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=csr_max_module_v2 -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 -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_1_problem_17_cache/k_25165120ed17/csr_max_module_v2/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
ninja: build stopped: subcommand failed.
turn 3203.1s turn · 9.9s LLM · 193.2s tools · tokens in=5,965 · out=970 · total=6,935
reasoning
I see - the CUDA host function declaration isn't being picked up properly. Let me use the standard pattern with separate cpp_sources and cuda_sources where we declare the host function signature in cpp_sources:
assistantI need to properly separate C++ and CUDA sources with proper declarations:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# CUDA kernel code\ncuda_source = \"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n\n__global__ void csr_max_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n float* __restrict__ out,\n const int num_nodes,\n const int feat_dim\n) {\n const int dst = blockIdx.x; // One block per destination node\n const int tid = threadIdx.x;\n \n // Get neighbor range for this destination\n const int start = row_ptr[dst];\n const int end = row_ptr[dst + 1];\n const int num_neighbors = end - start;\n \n // Initialize output to -inf for this thread's feature slice\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n out[dst * feat_dim + f] = -INFINITY;\n }\n \n __syncthreads();\n \n // If no neighbors, output remains -inf\n if (num_neighbors == 0) return;\n \n // Each thread processes a subset of neighbors and features\n // Grid-stride loop over neighbors\n for (int neighbor_idx = tid; neighbor_idx < num_neighbors; neighbor_idx += blockDim.x) {\n const int src = col_idx[start + neighbor_idx];\n const float* src_feat = node_feat + src * feat_dim;\n float* dst_out = out + dst * feat_dim;\n \n // Update max for all features\n for (int f = 0; f < feat_dim; f++) {\n float val = src_feat[f];\n if (val > dst_out[f]) {\n // Atomic max operation\n atomicMax(reinterpret_cast<int*>(&dst_out[f]), __float_as_int(val));\n }\n }\n }\n}\n\"\"\"\n\n# C++ host code\ncpp_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <limits>\n\n// Declaration of the CUDA kernel\nvoid csr_max_aggregate_kernel_launcher(\n const int* row_ptr,\n const int* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n);\n\ntorch::Tensor csr_max_aggregate_cuda(\n torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat\n) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n \n // Create output tensor initialized to -inf\n auto out = torch::full({num_nodes, feat_dim}, -std::numeric_limits<float>::infinity(),\n torch::dtype(torch::kFloat32).device(node_feat.device()));\n \n // Launch kernel\n csr_max_aggregate_kernel_launcher(\n row_ptr.data_ptr<int>(),\n col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n \n return out;\n}\n\"\"\"\n\n# Need to add the launcher function in cuda code\ncuda_source_full = cuda_source + \"\"\"\nvoid csr_max_aggregate_kernel_launcher(\n const int* row_ptr,\n const int* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n) {\n const int threads_per_block = 256;\n csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(\n row_ptr, col_idx, node_feat, out, num_nodes, feat_dim\n );\n}\n\"\"\"\n\n# Load the extension\ncsr_max_module = load_inline(\n name='csr_max_module_v3',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source_full,\n functions=['csr_max_aggregate_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n \n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n ) -> torch.Tensor:\n return csr_max_module.csr_max_aggregate_cuda(row_ptr, col_idx, node_feat)"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 4106.3s turn · 8.5s LLM · 97.8s tools · tokens in=6,909 · out=950 · total=7,859
reasoning
Great, it compiled! Now let me run the correctness check to make sure it produces the right output.
assistantThe kernel compiled successfully. Now let me check correctness:
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# CUDA kernel code\ncuda_source = \"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n\n__global__ void csr_max_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n float* __restrict__ out,\n const int num_nodes,\n const int feat_dim\n) {\n const int dst = blockIdx.x; // One block per destination node\n const int tid = threadIdx.x;\n \n // Get neighbor range for this destination\n const int start = row_ptr[dst];\n const int end = row_ptr[dst + 1];\n const int num_neighbors = end - start;\n \n // Initialize output to -inf for this thread's feature slice\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n out[dst * feat_dim + f] = -INFINITY;\n }\n \n __syncthreads();\n \n // If no neighbors, output remains -inf\n if (num_neighbors == 0) return;\n \n // Each thread processes a subset of neighbors and features\n // Grid-stride loop over neighbors\n for (int neighbor_idx = tid; neighbor_idx < num_neighbors; neighbor_idx += blockDim.x) {\n const int src = col_idx[start + neighbor_idx];\n const float* src_feat = node_feat + src * feat_dim;\n float* dst_out = out + dst * feat_dim;\n \n // Update max for all features\n for (int f = 0; f < feat_dim; f++) {\n float val = src_feat[f];\n if (val > dst_out[f]) {\n // Atomic max operation\n atomicMax(reinterpret_cast<int*>(&dst_out[f]), __float_as_int(val));\n }\n }\n }\n}\n\"\"\"\n\n# C++ host code\ncpp_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <limits>\n\n// Declaration of the CUDA kernel\nvoid csr_max_aggregate_kernel_launcher(\n const int* row_ptr,\n const int* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n);\n\ntorch::Tensor csr_max_aggregate_cuda(\n torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat\n) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n \n // Create output tensor initialized to -inf\n auto out = torch::full({num_nodes, feat_dim}, -std::numeric_limits<float>::infinity(),\n torch::dtype(torch::kFloat32).device(node_feat.device()));\n \n // Launch kernel\n csr_max_aggregate_kernel_launcher(\n row_ptr.data_ptr<int>(),\n col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n \n return out;\n}\n\"\"\"\n\n# Need to add the launcher function in cuda code\ncuda_source_full = cuda_source + \"\"\"\nvoid csr_max_aggregate_kernel_launcher(\n const int* row_ptr,\n const int* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n) {\n const int threads_per_block = 256;\n csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(\n row_ptr, col_idx, node_feat, out, num_nodes, feat_dim\n );\n}\n\"\"\"\n\n# Load the extension\ncsr_max_module = load_inline(\n name='csr_max_module_v3',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source_full,\n functions=['csr_max_aggregate_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n \n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n ) -> torch.Tensor:\n return csr_max_module.csr_max_aggregate_cuda(row_ptr, col_idx, node_feat)"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: ? trials did not all match.
runtime_error: expected scalar type Int but found Float
turn 5 · 1 tool call(s) · 143.2s turn · 16.2s LLM · tokens in=7,863 · out=1,597 · total=9,460 · 7,871 chars (click to expand)
turn 5143.2s turn · 16.2s LLM · 127.0s tools · tokens in=7,863 · out=1,597 · total=9,460
reasoning
The issue is that the row_ptr and col_idx are expected to be int32, but there's a mismatch. The kernel is using int* but the inputs from PyTorch might be Long (int64). Let me check and fix this.
The reference implementation uses:
- row_ptr.numel() - 1 → so row_ptr is tensor
- col_idx[start:end].long() → it explicitly converts to long before indexing
So the issue might be that my kernel expects int32 but the inputs are int64. Let me make the kernel work with both types by casting, or specify that inputs need to be int32.
Actually, looking at get_inputs():
- row_ptr = torch.zeros(num_nodes + 1, dtype=torch.int32)
- col_idx = torch.randint(0, num_nodes, (num_edges,), dtype=torch.int32)
So the inputs are int32! The issue might be in how I'm accessing the tensor data. Let me check if the problem is with the integer types in CUDA kernel.
Wait, the error message says "expected scalar type Int but found Float". This sounds like a mismatch in how the kernel is being called. Let me check if the problem is with the data type of some parameter.
Actually, looking more carefully at the issue - I think the problem might be that the index data needs to be int64 (Long) for indexing. Let me look at the reference code again:
```python
out[dst] = node_feat[col_idx[start:end].long()].amax(dim=0)
```
So col_idx is being converted to long before indexing. This suggests the indexing requires int64.
But the get_inputs function creates int32 tensors. So when the reference does col_idx[start:end].long(), it converts to int64 for indexing.
My kernel should work with int32, but perhaps I need to handle the tensor indexing properly. Let me cast the inputs to int32 inside the host code explicitly.
Actually, the error might be happening during some PyTorch operation. Let me check my kernel's data types more carefully.
Wait, I think the issue might be that in my kernel I'm using `int` which is 32-bit, but the indexing into node_feat might be problematic if col_idx contains values larger than int32 can handle. But that shouldn't be the case for this problem.
Actually, looking at the error message more carefully: "expected scalar type Int but found Float". This is a PyTorch error, not a CUDA error. It might be happening in the reference implementation comparison.
Let me think about this differently. The issue might be that when I use `row_ptr.data_ptr<int>()`, PyTorch expects `int32_t` but there might be a type mismatch.
Let me try using `int32_t` explicitly and ensure proper type matching.
assistantThe issue is with tensor type matching. Let me fix the kernel to properly handle int32 tensors and ensure correct data types:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# CUDA kernel code\ncuda_source = \"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <cstdint>\n\n__global__ void csr_max_aggregate_kernel(\n const int32_t* __restrict__ row_ptr,\n const int32_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n float* __restrict__ out,\n const int num_nodes,\n const int feat_dim\n) {\n const int dst = blockIdx.x; // One block per destination node\n const int tid = threadIdx.x;\n \n // Get neighbor range for this destination\n const int start = row_ptr[dst];\n const int end = row_ptr[dst + 1];\n const int num_neighbors = end - start;\n \n // Initialize output to -inf for this thread's feature slice\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n out[dst * feat_dim + f] = -INFINITY;\n }\n \n __syncthreads();\n \n // If no neighbors, output remains -inf\n if (num_neighbors == 0) return;\n \n // Each thread processes a subset of neighbors and features\n // Grid-stride loop over neighbors\n for (int neighbor_idx = tid; neighbor_idx < num_neighbors; neighbor_idx += blockDim.x) {\n const int src = col_idx[start + neighbor_idx];\n const float* src_feat = node_feat + src * feat_dim;\n float* dst_out = out + dst * feat_dim;\n \n // Update max for all features\n for (int f = 0; f < feat_dim; f++) {\n float val = src_feat[f];\n if (val > dst_out[f]) {\n // Atomic max operation\n atomicMax(reinterpret_cast<int*>(&dst_out[f]), __float_as_int(val));\n }\n }\n }\n}\n\"\"\"\n\n# C++ host code\ncpp_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <limits>\n#include <cstdint>\n\n// Declaration of the CUDA kernel launcher\nvoid csr_max_aggregate_kernel_launcher(\n const int32_t* row_ptr,\n const int32_t* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n);\n\ntorch::Tensor csr_max_aggregate_cuda(\n torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat\n) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n \n // Ensure contiguous int32 tensors\n row_ptr = row_ptr.contiguous().to(torch::kInt32);\n col_idx = col_idx.contiguous().to(torch::kInt32);\n node_feat = node_feat.contiguous().to(torch::kFloat32);\n \n // Create output tensor initialized to -inf\n auto out = torch::full({num_nodes, feat_dim}, -std::numeric_limits<float>::infinity(),\n torch::dtype(torch::kFloat32).device(node_feat.device()));\n \n // Launch kernel\n csr_max_aggregate_kernel_launcher(\n row_ptr.data_ptr<int32_t>(),\n col_idx.data_ptr<int32_t>(),\n node_feat.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n \n return out;\n}\n\"\"\"\n\n# Add the launcher function in cuda code\ncuda_source_full = cuda_source + \"\"\"\nvoid csr_max_aggregate_kernel_launcher(\n const int32_t* row_ptr,\n const int32_t* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n) {\n const int threads_per_block = 256;\n csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(\n row_ptr, col_idx, node_feat, out, num_nodes, feat_dim\n );\n}\n\"\"\"\n\n# Load the extension\ncsr_max_module = load_inline(\n name='csr_max_module_v4',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source_full,\n functions=['csr_max_aggregate_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n \n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n ) -> torch.Tensor:\n return csr_max_module.csr_max_aggregate_cuda(row_ptr, col_idx, node_feat)"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 6108.7s turn · 9.3s LLM · 99.4s tools · tokens in=8,898 · out=1,012 · total=9,910
reasoning
Good, let me now test correctness.
assistantNow let me check correctness:
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# CUDA kernel code\ncuda_source = \"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <cstdint>\n\n__global__ void csr_max_aggregate_kernel(\n const int32_t* __restrict__ row_ptr,\n const int32_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n float* __restrict__ out,\n const int num_nodes,\n const int feat_dim\n) {\n const int dst = blockIdx.x; // One block per destination node\n const int tid = threadIdx.x;\n \n // Get neighbor range for this destination\n const int start = row_ptr[dst];\n const int end = row_ptr[dst + 1];\n const int num_neighbors = end - start;\n \n // Initialize output to -inf for this thread's feature slice\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n out[dst * feat_dim + f] = -INFINITY;\n }\n \n __syncthreads();\n \n // If no neighbors, output remains -inf\n if (num_neighbors == 0) return;\n \n // Each thread processes a subset of neighbors and features\n // Grid-stride loop over neighbors\n for (int neighbor_idx = tid; neighbor_idx < num_neighbors; neighbor_idx += blockDim.x) {\n const int src = col_idx[start + neighbor_idx];\n const float* src_feat = node_feat + src * feat_dim;\n float* dst_out = out + dst * feat_dim;\n \n // Update max for all features\n for (int f = 0; f < feat_dim; f++) {\n float val = src_feat[f];\n if (val > dst_out[f]) {\n // Atomic max operation\n atomicMax(reinterpret_cast<int*>(&dst_out[f]), __float_as_int(val));\n }\n }\n }\n}\n\"\"\"\n\n# C++ host code\ncpp_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <limits>\n#include <cstdint>\n\n// Declaration of the CUDA kernel launcher\nvoid csr_max_aggregate_kernel_launcher(\n const int32_t* row_ptr,\n const int32_t* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n);\n\ntorch::Tensor csr_max_aggregate_cuda(\n torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat\n) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n \n // Ensure contiguous int32 tensors\n row_ptr = row_ptr.contiguous().to(torch::kInt32);\n col_idx = col_idx.contiguous().to(torch::kInt32);\n node_feat = node_feat.contiguous().to(torch::kFloat32);\n \n // Create output tensor initialized to -inf\n auto out = torch::full({num_nodes, feat_dim}, -std::numeric_limits<float>::infinity(),\n torch::dtype(torch::kFloat32).device(node_feat.device()));\n \n // Launch kernel\n csr_max_aggregate_kernel_launcher(\n row_ptr.data_ptr<int32_t>(),\n col_idx.data_ptr<int32_t>(),\n node_feat.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n \n return out;\n}\n\"\"\"\n\n# Add the launcher function in cuda code\ncuda_source_full = cuda_source + \"\"\"\nvoid csr_max_aggregate_kernel_launcher(\n const int32_t* row_ptr,\n const int32_t* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n) {\n const int threads_per_block = 256;\n csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(\n row_ptr, col_idx, node_feat, out, num_nodes, feat_dim\n );\n}\n\"\"\"\n\n# Load the extension\ncsr_max_module = load_inline(\n name='csr_max_module_v4',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source_full,\n functions=['csr_max_aggregate_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n \n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n ) -> torch.Tensor:\n return csr_max_module.csr_max_aggregate_cuda(row_ptr, col_idx, node_feat)"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (1 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['inf', 'inf', 'inf', 'inf']
avg_difference: ['inf', 'inf', 'inf', 'inf']
turn 7132.8s turn · 10.7s LLM · 122.0s tools · tokens in=9,959 · out=1,125 · total=11,084
reasoning
There's an issue with the atomic max operation. The atomicMax on floats via integer reinterpretation doesn't handle NaN and infinity correctly in all cases. Let me rewrite this kernel to use a more reliable approach without atomic operations - I can use shared memory for thread-local maxima and then combine them.
Actually, the issue is that atomicMax on integer bits doesn't work correctly for negative floats (including -infinity) because of how floating-point representation works. For IEEE 754 floats, the bit pattern for negative infinity is 0xFF800000, which when interpreted as an int is a very large negative number. This causes issues with atomicMax.
I should instead use a proper approach:
1. Each thread processes its assigned neighbors and accumulates the max in local memory
2. Use shared memory to combine thread results
3. Only one thread writes to global memory
Or I could use a simpler approach without atomics by having each thread handle one feature dimension.
assistantThe atomic max on float bits doesn't work correctly for negative infinity. Let me redesign the kernel using shared memory for thread-local maxima aggregation:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# CUDA kernel code - improved version without atomics\ncuda_source = \"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <cstdint>\n\n__global__ void csr_max_aggregate_kernel(\n const int32_t* __restrict__ row_ptr,\n const int32_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n float* __restrict__ out,\n const int num_nodes,\n const int feat_dim\n) {\n const int dst = blockIdx.x; // One block per destination node\n const int tid = threadIdx.x;\n const int num_threads = blockDim.x;\n \n // Get neighbor range for this destination\n const int start = row_ptr[dst];\n const int end = row_ptr[dst + 1];\n const int num_neighbors = end - start;\n \n // Each thread handles a subset of feature dimensions\n for (int f = tid; f < feat_dim; f += num_threads) {\n float max_val = -INFINITY;\n \n // Process all neighbors for this feature\n for (int n = 0; n < num_neighbors; n++) {\n const int src = col_idx[start + n];\n float val = node_feat[src * feat_dim + f];\n if (val > max_val) {\n max_val = val;\n }\n }\n \n out[dst * feat_dim + f] = max_val;\n }\n}\n\"\"\"\n\n# C++ host code\ncpp_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <limits>\n#include <cstdint>\n\n// Declaration of the CUDA kernel launcher\nvoid csr_max_aggregate_kernel_launcher(\n const int32_t* row_ptr,\n const int32_t* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n);\n\ntorch::Tensor csr_max_aggregate_cuda(\n torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat\n) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n \n // Ensure contiguous int32 tensors\n row_ptr = row_ptr.contiguous().to(torch::kInt32);\n col_idx = col_idx.contiguous().to(torch::kInt32);\n node_feat = node_feat.contiguous().to(torch::kFloat32);\n \n // Create output tensor initialized to -inf\n auto out = torch::full({num_nodes, feat_dim}, -std::numeric_limits<float>::infinity(),\n torch::dtype(torch::kFloat32).device(node_feat.device()));\n \n // Launch kernel\n csr_max_aggregate_kernel_launcher(\n row_ptr.data_ptr<int32_t>(),\n col_idx.data_ptr<int32_t>(),\n node_feat.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n \n return out;\n}\n\"\"\"\n\n# Add the launcher function in cuda code\ncuda_source_full = cuda_source + \"\"\"\nvoid csr_max_aggregate_kernel_launcher(\n const int32_t* row_ptr,\n const int32_t* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n) {\n const int threads_per_block = 256;\n csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(\n row_ptr, col_idx, node_feat, out, num_nodes, feat_dim\n );\n}\n\"\"\"\n\n# Load the extension\ncsr_max_module = load_inline(\n name='csr_max_module_v5',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source_full,\n functions=['csr_max_aggregate_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n \n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n ) -> torch.Tensor:\n return csr_max_module.csr_max_aggregate_cuda(row_ptr, col_idx, node_feat)"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 8158.1s turn · 9.2s LLM · 148.9s tools · tokens in=10,902 · out=933 · total=11,835
reasoning
Now let me test correctness for this simpler version where each thread handles multiple feature dimensions and iterates over all neighbors for each feature.
assistantLet me test correctness:
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# CUDA kernel code - improved version without atomics\ncuda_source = \"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <cstdint>\n\n__global__ void csr_max_aggregate_kernel(\n const int32_t* __restrict__ row_ptr,\n const int32_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n float* __restrict__ out,\n const int num_nodes,\n const int feat_dim\n) {\n const int dst = blockIdx.x; // One block per destination node\n const int tid = threadIdx.x;\n const int num_threads = blockDim.x;\n \n // Get neighbor range for this destination\n const int start = row_ptr[dst];\n const int end = row_ptr[dst + 1];\n const int num_neighbors = end - start;\n \n // Each thread handles a subset of feature dimensions\n for (int f = tid; f < feat_dim; f += num_threads) {\n float max_val = -INFINITY;\n \n // Process all neighbors for this feature\n for (int n = 0; n < num_neighbors; n++) {\n const int src = col_idx[start + n];\n float val = node_feat[src * feat_dim + f];\n if (val > max_val) {\n max_val = val;\n }\n }\n \n out[dst * feat_dim + f] = max_val;\n }\n}\n\"\"\"\n\n# C++ host code\ncpp_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <limits>\n#include <cstdint>\n\n// Declaration of the CUDA kernel launcher\nvoid csr_max_aggregate_kernel_launcher(\n const int32_t* row_ptr,\n const int32_t* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n);\n\ntorch::Tensor csr_max_aggregate_cuda(\n torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat\n) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n \n // Ensure contiguous int32 tensors\n row_ptr = row_ptr.contiguous().to(torch::kInt32);\n col_idx = col_idx.contiguous().to(torch::kInt32);\n node_feat = node_feat.contiguous().to(torch::kFloat32);\n \n // Create output tensor initialized to -inf\n auto out = torch::full({num_nodes, feat_dim}, -std::numeric_limits<float>::infinity(),\n torch::dtype(torch::kFloat32).device(node_feat.device()));\n \n // Launch kernel\n csr_max_aggregate_kernel_launcher(\n row_ptr.data_ptr<int32_t>(),\n col_idx.data_ptr<int32_t>(),\n node_feat.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n \n return out;\n}\n\"\"\"\n\n# Add the launcher function in cuda code\ncuda_source_full = cuda_source + \"\"\"\nvoid csr_max_aggregate_kernel_launcher(\n const int32_t* row_ptr,\n const int32_t* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n) {\n const int threads_per_block = 256;\n csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(\n row_ptr, col_idx, node_feat, out, num_nodes, feat_dim\n );\n}\n\"\"\"\n\n# Load the extension\ncsr_max_module = load_inline(\n name='csr_max_module_v5',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source_full,\n functions=['csr_max_aggregate_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n \n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n ) -> torch.Tensor:\n return csr_max_module.csr_max_aggregate_cuda(row_ptr, col_idx, node_feat)"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 99.6s turn · 9.6s LLM · 0.0s tools · tokens in=11,898 · out=993 · total=12,891
reasoning
The correctness test passed with perfect numerical accuracy (max_abs_err=0.00e+00). The kernel is working correctly. Now I should check if there are any issues with the code before submitting, and then submit it.
Let me run a static check to make sure there are no reward-hacking patterns, and then submit.
assistantThe kernel is correct with perfect numerical accuracy. Let me run a static check to ensure there are no issues, then submit:
call → static_check
callstatic_check
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# CUDA kernel code - improved version without atomics\ncuda_source = \"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <cstdint>\n\n__global__ void csr_max_aggregate_kernel(\n const int32_t* __restrict__ row_ptr,\n const int32_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n float* __restrict__ out,\n const int num_nodes,\n const int feat_dim\n) {\n const int dst = blockIdx.x; // One block per destination node\n const int tid = threadIdx.x;\n const int num_threads = blockDim.x;\n \n // Get neighbor range for this destination\n const int start = row_ptr[dst];\n const int end = row_ptr[dst + 1];\n const int num_neighbors = end - start;\n \n // Each thread handles a subset of feature dimensions\n for (int f = tid; f < feat_dim; f += num_threads) {\n float max_val = -INFINITY;\n \n // Process all neighbors for this feature\n for (int n = 0; n < num_neighbors; n++) {\n const int src = col_idx[start + n];\n float val = node_feat[src * feat_dim + f];\n if (val > max_val) {\n max_val = val;\n }\n }\n \n out[dst * feat_dim + f] = max_val;\n }\n}\n\"\"\"\n\n# C++ host code\ncpp_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <limits>\n#include <cstdint>\n\n// Declaration of the CUDA kernel launcher\nvoid csr_max_aggregate_kernel_launcher(\n const int32_t* row_ptr,\n const int32_t* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n);\n\ntorch::Tensor csr_max_aggregate_cuda(\n torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat\n) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n \n // Ensure contiguous int32 tensors\n row_ptr = row_ptr.contiguous().to(torch::kInt32);\n col_idx = col_idx.contiguous().to(torch::kInt32);\n node_feat = node_feat.contiguous().to(torch::kFloat32);\n \n // Create output tensor initialized to -inf\n auto out = torch::full({num_nodes, feat_dim}, -std::numeric_limits<float>::infinity(),\n torch::dtype(torch::kFloat32).device(node_feat.device()));\n \n // Launch kernel\n csr_max_aggregate_kernel_launcher(\n row_ptr.data_ptr<int32_t>(),\n col_idx.data_ptr<int32_t>(),\n node_feat.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n \n return out;\n}\n\"\"\"\n\n# Add the launcher function in cuda code\ncuda_source_full = cuda_source + \"\"\"\nvoid csr_max_aggregate_kernel_launcher(\n const int32_t* row_ptr,\n const int32_t* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n) {\n const int threads_per_block = 256;\n csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(\n row_ptr, col_idx, node_feat, out, num_nodes, feat_dim\n );\n}\n\"\"\"\n\n# Load the extension\ncsr_max_module = load_inline(\n name='csr_max_module_v5',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source_full,\n functions=['csr_max_aggregate_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n \n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n ) -> torch.Tensor:\n return csr_max_module.csr_max_aggregate_cuda(row_ptr, col_idx, node_feat)"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 10325.6s turn · 8.8s LLM · 316.9s tools · tokens in=12,870 · out=948 · total=13,818 · FINAL
reasoning
The kernel passes all checks:
1. Compiles successfully
2. Correctness verified with perfect numerical precision (max_abs_err=0.00e+00)
3. Static check passed with no violations
I should submit now.
call → submit_kernel
callsubmit_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# CUDA kernel code - improved version without atomics\ncuda_source = \"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n#include <cstdint>\n\n__global__ void csr_max_aggregate_kernel(\n const int32_t* __restrict__ row_ptr,\n const int32_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n float* __restrict__ out,\n const int num_nodes,\n const int feat_dim\n) {\n const int dst = blockIdx.x; // One block per destination node\n const int tid = threadIdx.x;\n const int num_threads = blockDim.x;\n \n // Get neighbor range for this destination\n const int start = row_ptr[dst];\n const int end = row_ptr[dst + 1];\n const int num_neighbors = end - start;\n \n // Each thread handles a subset of feature dimensions\n for (int f = tid; f < feat_dim; f += num_threads) {\n float max_val = -INFINITY;\n \n // Process all neighbors for this feature\n for (int n = 0; n < num_neighbors; n++) {\n const int src = col_idx[start + n];\n float val = node_feat[src * feat_dim + f];\n if (val > max_val) {\n max_val = val;\n }\n }\n \n out[dst * feat_dim + f] = max_val;\n }\n}\n\"\"\"\n\n# C++ host code\ncpp_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <limits>\n#include <cstdint>\n\n// Declaration of the CUDA kernel launcher\nvoid csr_max_aggregate_kernel_launcher(\n const int32_t* row_ptr,\n const int32_t* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n);\n\ntorch::Tensor csr_max_aggregate_cuda(\n torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat\n) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n \n // Ensure contiguous int32 tensors\n row_ptr = row_ptr.contiguous().to(torch::kInt32);\n col_idx = col_idx.contiguous().to(torch::kInt32);\n node_feat = node_feat.contiguous().to(torch::kFloat32);\n \n // Create output tensor initialized to -inf\n auto out = torch::full({num_nodes, feat_dim}, -std::numeric_limits<float>::infinity(),\n torch::dtype(torch::kFloat32).device(node_feat.device()));\n \n // Launch kernel\n csr_max_aggregate_kernel_launcher(\n row_ptr.data_ptr<int32_t>(),\n col_idx.data_ptr<int32_t>(),\n node_feat.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n \n return out;\n}\n\"\"\"\n\n# Add the launcher function in cuda code\ncuda_source_full = cuda_source + \"\"\"\nvoid csr_max_aggregate_kernel_launcher(\n const int32_t* row_ptr,\n const int32_t* col_idx,\n const float* node_feat,\n float* out,\n const int num_nodes,\n const int feat_dim\n) {\n const int threads_per_block = 256;\n csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(\n row_ptr, col_idx, node_feat, out, num_nodes, feat_dim\n );\n}\n\"\"\"\n\n# Load the extension\ncsr_max_module = load_inline(\n name='csr_max_module_v5',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source_full,\n functions=['csr_max_aggregate_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n \n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n ) -> torch.Tensor:\n return csr_max_module.csr_max_aggregate_cuda(row_ptr, col_idx, node_feat)"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 0.02 μs
Runtime stats: mean=0.02μs median=?μs std=0.00μs
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
Memory: 33.47 MB peak (ref: 33.44 MB, ratio: 1.0011x)
Kernel launches: 12 (ref: 14723, fusion ratio: 1226.9167)
SOL score: 0.0702
Flagged for excessive speedup — this submission may have been rejected by automated review.
final submitted kernel (133 lines, 3,610 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
# CUDA kernel code - improved version without atomics
cuda_source = """
#include <cuda.h>
#include <cuda_runtime.h>
#include <cmath>
#include <cstdint>
__global__ void csr_max_aggregate_kernel(
const int32_t* __restrict__ row_ptr,
const int32_t* __restrict__ col_idx,
const float* __restrict__ node_feat,
float* __restrict__ out,
const int num_nodes,
const int feat_dim
) {
const int dst = blockIdx.x; // One block per destination node
const int tid = threadIdx.x;
const int num_threads = blockDim.x;
// Get neighbor range for this destination
const int start = row_ptr[dst];
const int end = row_ptr[dst + 1];
const int num_neighbors = end - start;
// Each thread handles a subset of feature dimensions
for (int f = tid; f < feat_dim; f += num_threads) {
float max_val = -INFINITY;
// Process all neighbors for this feature
for (int n = 0; n < num_neighbors; n++) {
const int src = col_idx[start + n];
float val = node_feat[src * feat_dim + f];
if (val > max_val) {
max_val = val;
}
}
out[dst * feat_dim + f] = max_val;
}
}
"""
# C++ host code
cpp_source = """
#include <torch/extension.h>
#include <cuda_runtime.h>
#include <limits>
#include <cstdint>
// Declaration of the CUDA kernel launcher
void csr_max_aggregate_kernel_launcher(
const int32_t* row_ptr,
const int32_t* col_idx,
const float* node_feat,
float* out,
const int num_nodes,
const int feat_dim
);
torch::Tensor csr_max_aggregate_cuda(
torch::Tensor row_ptr,
torch::Tensor col_idx,
torch::Tensor node_feat
) {
const int num_nodes = row_ptr.numel() - 1;
const int feat_dim = node_feat.size(1);
// Ensure contiguous int32 tensors
row_ptr = row_ptr.contiguous().to(torch::kInt32);
col_idx = col_idx.contiguous().to(torch::kInt32);
node_feat = node_feat.contiguous().to(torch::kFloat32);
// Create output tensor initialized to -inf
auto out = torch::full({num_nodes, feat_dim}, -std::numeric_limits<float>::infinity(),
torch::dtype(torch::kFloat32).device(node_feat.device()));
// Launch kernel
csr_max_aggregate_kernel_launcher(
row_ptr.data_ptr<int32_t>(),
col_idx.data_ptr<int32_t>(),
node_feat.data_ptr<float>(),
out.data_ptr<float>(),
num_nodes,
feat_dim
);
return out;
}
"""
# Add the launcher function in cuda code
cuda_source_full = cuda_source + """
void csr_max_aggregate_kernel_launcher(
const int32_t* row_ptr,
const int32_t* col_idx,
const float* node_feat,
float* out,
const int num_nodes,
const int feat_dim
) {
const int threads_per_block = 256;
csr_max_aggregate_kernel<<<num_nodes, threads_per_block>>>(
row_ptr, col_idx, node_feat, out, num_nodes, feat_dim
);
}
"""
# Load the extension
csr_max_module = load_inline(
name='csr_max_module_v5',
cpp_sources=cpp_source,
cuda_sources=cuda_source_full,
functions=['csr_max_aggregate_cuda'],
extra_cuda_cflags=['-O3'],
verbose=False
)
class ModelNew(nn.Module):
def __init__(self):
super().__init__()
def forward(
self,
row_ptr: torch.Tensor,
col_idx: torch.Tensor,
node_feat: torch.Tensor,
) -> torch.Tensor:
return csr_max_module.csr_max_aggregate_cuda(row_ptr, col_idx, node_feat)