correct
11_DegreeNormalizedAggregation.py
started
2026-05-11T20:11:32Z
timingkernelbench.timing
kernel mean / median / std
0.54 / — / 0.00 μs
ref mean / median / std
77.00 / — / 1.27 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
1772.62x
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):
"""
Degree-normalized neighbor aggregation.
Computes a GCN-style sparse aggregation where each incoming message is
scaled by 1 / sqrt(deg(dst) * deg(src)). This captures a common spectral
graph normalization pattern over CSR adjacency.
"""
def __init__(self):
super().__init__()
def forward(
self,
row_ptr: torch.Tensor,
col_idx: torch.Tensor,
node_feat: torch.Tensor,
degrees: torch.Tensor,
) -> torch.Tensor:
num_nodes = row_ptr.numel() - 1
feat_dim = node_feat.shape[1]
out = torch.zeros(num_nodes, feat_dim, 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:
continue
src = col_idx[start:end].long()
norm = 1.0 / torch.sqrt(degrees[dst] * degrees[src]).unsqueeze(-1)
out[dst] = (node_feat[src] * norm).sum(dim=0)
return out
num_nodes = 640
avg_degree = 16
feat_dim = 80
def get_inputs():
return list(popcorn_pri.make_csr_degree_normalized_graph(num_nodes, avg_degree, feat_dim))
def get_init_inputs():
return []
turn 09.0s turn · 2.6s LLM · 6.4s tools · tokens in=2,298 · out=161 · total=2,459
reasoning
Let me analyze this task. I need to implement a GCN-style sparse aggregation where each incoming message is scaled by `1 / sqrt(deg(dst) * deg(src))`.
The reference implementation:
1. Iterates over each destination node `dst`
2. For each `dst`, gets the source nodes from `col_idx[row_ptr[dst]:row_ptr[dst+1]]`
3. Computes normalization factor: `1.0 / sqrt(degrees[dst] * degrees[src])`
4. Aggregates: `out[dst] = sum(node_feat[src] * norm)`
This is essentially a sparse matrix-vector multiplication with degree normalization. The adjacency matrix is in CSR format.
Let me first get the GPU specs to understand the hardware better.
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 129.5s turn · 8.5s LLM · 21.0s tools · tokens in=2,546 · out=819 · total=3,365
reasoning
Now let me write an initial CUDA kernel for this GCN-style sparse aggregation. The key operations are:
1. For each destination node, iterate over its neighbors
2. For each neighbor, compute norm = 1/sqrt(deg[dst] * deg[src])
3. Accumulate node_feat[src] * norm
I'll use a standard approach: one thread per destination node, iterating over all its neighbors. Let me write the kernel:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <cuda_runtime.h>\n#include <cmath>\n\n__global__ void gcn_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes,\n int feat_dim\n) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n \n int start = row_ptr[dst];\n int end = row_ptr[dst + 1];\n \n if (end <= start) return;\n \n float dst_deg = degrees[dst];\n float dst_deg_inv = 1.0f / sqrtf(dst_deg);\n \n // Initialize output accumulator\n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] = 0.0f;\n }\n \n for (int i = start; i < end; i++) {\n int src = col_idx[i];\n float src_deg = degrees[src];\n float norm = dst_deg_inv / sqrtf(src_deg); // 1/sqrt(dst_deg * src_deg)\n \n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n }\n}\n\"\"\"\n\ncpp_source = \"\"\"\nvoid gcn_aggregate_cuda(\n const int* row_ptr,\n const int* col_idx,\n const float* node_feat,\n const float* degrees,\n float* out,\n int num_nodes,\n int feat_dim\n) {\n int threads = 256;\n int blocks = (num_nodes + threads - 1) / threads;\n gcn_aggregate_kernel<<<blocks, threads>>>(\n row_ptr, col_idx, node_feat, degrees, out, num_nodes, feat_dim\n );\n}\n\"\"\"\n\nmodule = load_inline(\n name='gcn_aggregate',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['gcn_aggregate_cuda'],\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.module = module\n\n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n degrees: torch.Tensor,\n ) -> torch.Tensor:\n num_nodes = row_ptr.numel() - 1\n feat_dim = node_feat.shape[1]\n \n out = torch.zeros(num_nodes, feat_dim, dtype=node_feat.dtype, device=node_feat.device)\n \n # Ensure tensors are contiguous and on the right device\n row_ptr_c = row_ptr.contiguous()\n col_idx_c = col_idx.contiguous()\n node_feat_c = node_feat.contiguous()\n degrees_c = degrees.contiguous()\n \n self.module.gcn_aggregate_cuda(\n row_ptr_c.data_ptr(),\n col_idx_c.data_ptr(),\n node_feat_c.data_ptr(),\n degrees_c.data_ptr(),\n out.data_ptr(),\n num_nodes,\n feat_dim\n )\n \n return out"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'gcn_aggregate'
turn 2148.6s turn · 6.7s LLM · 141.9s tools · tokens in=3,295 · out=756 · total=4,051
reasoning
The compilation failed. Let me try a different approach - I need to include proper CUDA headers and maybe use a simpler structure.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = r\"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <cmath>\n\n__global__ void gcn_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes,\n int feat_dim\n) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n \n int start = row_ptr[dst];\n int end = row_ptr[dst + 1];\n \n if (end <= start) return;\n \n float dst_deg = degrees[dst];\n float dst_deg_inv = 1.0f / sqrtf(dst_deg);\n \n // Initialize output accumulator\n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] = 0.0f;\n }\n \n for (int i = start; i < end; i++) {\n int src = col_idx[i];\n float src_deg = degrees[src];\n float norm = dst_deg_inv / sqrtf(src_deg); // 1/sqrt(dst_deg * src_deg)\n \n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n }\n}\n\nvoid gcn_aggregate_cuda(\n const int* row_ptr,\n const int* col_idx,\n const float* node_feat,\n const float* degrees,\n float* out,\n int num_nodes,\n int feat_dim\n) {\n int threads = 256;\n int blocks = (num_nodes + threads - 1) / threads;\n gcn_aggregate_kernel<<<blocks, threads>>>(\n row_ptr, col_idx, node_feat, degrees, out, num_nodes, feat_dim\n );\n}\n\"\"\"\n\nmodule = load_inline(\n name='gcn_aggregate',\n cpp_sources=cuda_source,\n cuda_sources=cuda_source,\n functions=['gcn_aggregate_cuda'],\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.module = module\n\n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n degrees: torch.Tensor,\n ) -> torch.Tensor:\n num_nodes = row_ptr.numel() - 1\n feat_dim = node_feat.shape[1]\n \n out = torch.zeros(num_nodes, feat_dim, dtype=node_feat.dtype, device=node_feat.device)\n \n # Ensure tensors are contiguous and on the right device\n row_ptr_c = row_ptr.contiguous()\n col_idx_c = col_idx.contiguous()\n node_feat_c = node_feat.contiguous()\n degrees_c = degrees.contiguous()\n \n self.module.gcn_aggregate_cuda(\n row_ptr_c.data_ptr(),\n col_idx_c.data_ptr(),\n node_feat_c.data_ptr(),\n degrees_c.data_ptr(),\n out.data_ptr(),\n num_nodes,\n feat_dim\n )\n \n return out"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'gcn_aggregate_v1'
turn 3161.0s turn · 7.1s LLM · 153.9s tools · tokens in=4,049 · out=752 · total=4,801
reasoning
Let me try a more structured approach with torch bindings properly included.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void gcn_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes,\n int feat_dim\n) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n \n int start = row_ptr[dst];\n int end = row_ptr[dst + 1];\n \n if (end <= start) return;\n \n float dst_deg = degrees[dst];\n float dst_deg_inv = 1.0f / sqrtf(dst_deg);\n \n // Initialize output accumulator\n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] = 0.0f;\n }\n \n for (int i = start; i < end; i++) {\n int src = col_idx[i];\n float src_deg = degrees[src];\n float norm = dst_deg_inv / sqrtf(src_deg);\n \n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n }\n}\n\nvoid launch_gcn_aggregate(\n at::Tensor row_ptr,\n at::Tensor col_idx,\n at::Tensor node_feat,\n at::Tensor degrees,\n at::Tensor out\n) {\n int num_nodes = row_ptr.size(0) - 1;\n int feat_dim = node_feat.size(1);\n \n int threads = 256;\n int blocks = (num_nodes + threads - 1) / threads;\n \n gcn_aggregate_kernel<<<blocks, threads>>>(\n row_ptr.data_ptr<int>(),\n col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(),\n degrees.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n}\n\"\"\"\n\ncpp_source = r\"\"\"\nvoid launch_gcn_aggregate(\n at::Tensor row_ptr,\n at::Tensor col_idx,\n at::Tensor node_feat,\n at::Tensor degrees,\n at::Tensor out\n);\n\"\"\"\n\nmodule = load_inline(\n name='gcn_aggregate_v2',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['launch_gcn_aggregate'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.module = module\n\n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n degrees: torch.Tensor,\n ) -> torch.Tensor:\n num_nodes = row_ptr.numel() - 1\n feat_dim = node_feat.shape[1]\n \n out = torch.zeros(num_nodes, feat_dim, dtype=node_feat.dtype, device=node_feat.device)\n \n self.module.launch_gcn_aggregate(\n row_ptr.contiguous(),\n col_idx.contiguous(),\n node_feat.contiguous(),\n degrees.contiguous(),\n out\n )\n \n return out"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 47.3s turn · 7.2s LLM · 0.1s tools · tokens in=4,801 · out=759 · total=5,560
reasoning
The kernel compiled. Now let me run correctness to see if the output is numerically equivalent.
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void gcn_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes,\n int feat_dim\n) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n \n int start = row_ptr[dst];\n int end = row_ptr[dst + 1];\n \n if (end <= start) return;\n \n float dst_deg = degrees[dst];\n float dst_deg_inv = 1.0f / sqrtf(dst_deg);\n \n // Initialize output accumulator\n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] = 0.0f;\n }\n \n for (int i = start; i < end; i++) {\n int src = col_idx[i];\n float src_deg = degrees[src];\n float norm = dst_deg_inv / sqrtf(src_deg);\n \n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n }\n}\n\nvoid launch_gcn_aggregate(\n at::Tensor row_ptr,\n at::Tensor col_idx,\n at::Tensor node_feat,\n at::Tensor degrees,\n at::Tensor out\n) {\n int num_nodes = row_ptr.size(0) - 1;\n int feat_dim = node_feat.size(1);\n \n int threads = 256;\n int blocks = (num_nodes + threads - 1) / threads;\n \n gcn_aggregate_kernel<<<blocks, threads>>>(\n row_ptr.data_ptr<int>(),\n col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(),\n degrees.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n}\n\"\"\"\n\ncpp_source = r\"\"\"\nvoid launch_gcn_aggregate(\n at::Tensor row_ptr,\n at::Tensor col_idx,\n at::Tensor node_feat,\n at::Tensor degrees,\n at::Tensor out\n);\n\"\"\"\n\nmodule = load_inline(\n name='gcn_aggregate_v2',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['launch_gcn_aggregate'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.module = module\n\n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n degrees: torch.Tensor,\n ) -> torch.Tensor:\n num_nodes = row_ptr.numel() - 1\n feat_dim = node_feat.shape[1]\n \n out = torch.zeros(num_nodes, feat_dim, dtype=node_feat.dtype, device=node_feat.device)\n \n self.module.launch_gcn_aggregate(\n row_ptr.contiguous(),\n col_idx.contiguous(),\n node_feat.contiguous(),\n degrees.contiguous(),\n out\n )\n \n return out"
}
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) · 92.8s turn · 17.9s LLM · tokens in=5,566 · out=1,943 · total=7,509 · 8,732 chars (click to expand)
turn 592.8s turn · 17.9s LLM · 74.9s tools · tokens in=5,566 · out=1,943 · total=7,509
reasoning
The error says "expected scalar type Int but found Float". The issue is that degrees is a float tensor but I'm trying to access it as int in one of the operations. Wait no, I have degrees.data_ptr<float>(), so that should be fine.
Actually, looking at the reference code, I see that row_ptr is int32, and col_idx is also int32. Let me check if there's an issue with the tensor types.
In the reference:
- row_ptr: int32
- col_idx: int32
- node_feat: float32
- degrees: float32
Let me trace through... The error is coming from the PyTorch side. Maybe there's an issue with how I'm calling the function. Let me check the data_ptr type.
Actually, I notice that I'm using `row_ptr.data_ptr<int>()`, but `row_ptr` is an int32 tensor, so I should use `int32_t` or `int` which should be the same on most systems.
Wait, the error might be from the PyTorch tensor operations. Let me look more carefully...
Actually I think the issue might be that `degrees` in the reference is a float32 tensor but in the _make_graph function:
```python
degrees = torch.bincount(col_idx.long(), minlength=num_nodes).to(torch.float32) + 1.0
```
So degrees should be float32. But then why the "expected scalar type Int but found Float" error?
Oh wait, I think I see the issue. When calling the CUDA kernel with `row_ptr.data_ptr<int>()`, the CUDA kernel expects `const int*` which should be fine. But maybe the issue is that `row_ptr` in PyTorch is `torch.int32` and `int` in C++ is typically 32-bit, but maybe there's a mismatch.
Actually, let me look at this again. The error might be from the PyTorch call itself. When I do `row_ptr.contiguous()`, that returns a tensor. Maybe PyTorch is expecting certain tensor types.
Actually, wait. Let me look at the error more carefully: "expected scalar type Int but found Float". This suggests there's a type mismatch where an Int tensor was expected but a Float tensor was provided.
Looking at the reference code's `_make_graph`:
- row_ptr: int32
- col_idx: int32
- node_feat: float32
- degrees: float32
But wait, in the reference forward method, degrees[src] is used, and `src = col_idx[start:end].long()`. So col_idx needs to be convertible to long (int64).
Actually, I think the issue might be that `degrees` in PyTorch is a float tensor, but somewhere I'm treating it as an Int. Let me look at my kernel call:
```cpp
degrees.data_ptr<float>()
```
This should be fine for a float tensor.
Hmm, but the error is happening at runtime, not compile time. Let me think about what could cause this...
Actually, I just realized - the issue might be that `degrees` is a float tensor but I'm accessing it as `degrees[src]` where `src` is from `col_idx`. But in my CUDA kernel, I'm doing `int src = col_idx[i]` which should be fine since col_idx is int32.
Wait, let me re-read the error: "expected scalar type Int but found Float". This is a PyTorch error, not a CUDA error. It's probably happening during the `.contiguous()` call or somewhere in the tensor operations.
Actually, I think the issue might be that one of the tensors is being cast incorrectly. Let me look at the function signature again:
```python
def forward(
self,
row_ptr: torch.Tensor,
col_idx: torch.Tensor,
node_feat: torch.Tensor,
degrees: torch.Tensor,
) -> torch.Tensor:
```
So `degrees` is a float tensor, and I'm using it correctly. But wait, in the reference code:
```python
norm = 1.0 / torch.sqrt(degrees[dst] * degrees[src]).unsqueeze(-1)
```
This uses `degrees[dst]` where dst is an int index. That should be fine.
Actually, looking more carefully at my kernel, I wonder if the issue is with how `row_ptr.data_ptr<int>()` works. On some systems, `int` might be 32-bit, but PyTorch's `torch.int32` is explicitly 32-bit. Let me try using `int32_t` instead.
Actually, let me just print out the tensor types to understand what's happening. But since I can't do that in this context, let me try to be more explicit about the types.
Actually, I realize the issue now. Looking at the reference implementation more carefully:
```python
src = col_idx[start:end].long()
norm = 1.0 / torch.sqrt(degrees[dst] * degrees[src]).unsqueeze(-1)
out[dst] = (node_feat[src] * norm).sum(dim=0)
```
Here `src` is used to index into `degrees`, which is a float tensor. The `.long()` converts col_idx to int64, which is then used for indexing. This is all valid PyTorch operations.
But in my kernel, I'm using `int src = col_idx[i]` which gets the int32 value from col_idx, and then using it to index degrees as `degrees[src]` which is `float src_deg = degrees[src]`. This should also be valid CUDA.
Let me try to use explicit int32_t types to make sure there's no mismatch.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cstdint>\n\n__global__ void gcn_aggregate_kernel(\n const int32_t* __restrict__ row_ptr,\n const int32_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes,\n int feat_dim\n) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n \n int32_t start = row_ptr[dst];\n int32_t end = row_ptr[dst + 1];\n \n if (end <= start) return;\n \n float dst_deg = degrees[dst];\n float dst_deg_inv = 1.0f / sqrtf(dst_deg);\n \n // Initialize output accumulator\n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] = 0.0f;\n }\n \n for (int32_t i = start; i < end; i++) {\n int32_t src = col_idx[i];\n float src_deg = degrees[src];\n float norm = dst_deg_inv / sqrtf(src_deg);\n \n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n }\n}\n\nvoid launch_gcn_aggregate(\n at::Tensor row_ptr,\n at::Tensor col_idx,\n at::Tensor node_feat,\n at::Tensor degrees,\n at::Tensor out\n) {\n int num_nodes = row_ptr.size(0) - 1;\n int feat_dim = node_feat.size(1);\n \n int threads = 256;\n int blocks = (num_nodes + threads - 1) / threads;\n \n gcn_aggregate_kernel<<<blocks, threads>>>(\n row_ptr.data_ptr<int32_t>(),\n col_idx.data_ptr<int32_t>(),\n node_feat.data_ptr<float>(),\n degrees.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n}\n\"\"\"\n\ncpp_source = r\"\"\"\nvoid launch_gcn_aggregate(\n at::Tensor row_ptr,\n at::Tensor col_idx,\n at::Tensor node_feat,\n at::Tensor degrees,\n at::Tensor out\n);\n\"\"\"\n\nmodule = load_inline(\n name='gcn_aggregate_v3',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['launch_gcn_aggregate'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.module = module\n\n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n degrees: torch.Tensor,\n ) -> torch.Tensor:\n num_nodes = row_ptr.numel() - 1\n feat_dim = node_feat.shape[1]\n \n out = torch.zeros(num_nodes, feat_dim, dtype=node_feat.dtype, device=node_feat.device)\n \n self.module.launch_gcn_aggregate(\n row_ptr.contiguous(),\n col_idx.contiguous(),\n node_feat.contiguous(),\n degrees.contiguous(),\n out\n )\n \n return out"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 67.3s turn · 7.2s LLM · 0.1s tools · tokens in=6,338 · out=766 · total=7,104
reasoning
Let me test correctness again.
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cstdint>\n\n__global__ void gcn_aggregate_kernel(\n const int32_t* __restrict__ row_ptr,\n const int32_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes,\n int feat_dim\n) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n \n int32_t start = row_ptr[dst];\n int32_t end = row_ptr[dst + 1];\n \n if (end <= start) return;\n \n float dst_deg = degrees[dst];\n float dst_deg_inv = 1.0f / sqrtf(dst_deg);\n \n // Initialize output accumulator\n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] = 0.0f;\n }\n \n for (int32_t i = start; i < end; i++) {\n int32_t src = col_idx[i];\n float src_deg = degrees[src];\n float norm = dst_deg_inv / sqrtf(src_deg);\n \n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n }\n}\n\nvoid launch_gcn_aggregate(\n at::Tensor row_ptr,\n at::Tensor col_idx,\n at::Tensor node_feat,\n at::Tensor degrees,\n at::Tensor out\n) {\n int num_nodes = row_ptr.size(0) - 1;\n int feat_dim = node_feat.size(1);\n \n int threads = 256;\n int blocks = (num_nodes + threads - 1) / threads;\n \n gcn_aggregate_kernel<<<blocks, threads>>>(\n row_ptr.data_ptr<int32_t>(),\n col_idx.data_ptr<int32_t>(),\n node_feat.data_ptr<float>(),\n degrees.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n}\n\"\"\"\n\ncpp_source = r\"\"\"\nvoid launch_gcn_aggregate(\n at::Tensor row_ptr,\n at::Tensor col_idx,\n at::Tensor node_feat,\n at::Tensor degrees,\n at::Tensor out\n);\n\"\"\"\n\nmodule = load_inline(\n name='gcn_aggregate_v3',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['launch_gcn_aggregate'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.module = module\n\n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n degrees: torch.Tensor,\n ) -> torch.Tensor:\n num_nodes = row_ptr.numel() - 1\n feat_dim = node_feat.shape[1]\n \n out = torch.zeros(num_nodes, feat_dim, dtype=node_feat.dtype, device=node_feat.device)\n \n self.module.launch_gcn_aggregate(\n row_ptr.contiguous(),\n col_idx.contiguous(),\n node_feat.contiguous(),\n degrees.contiguous(),\n out\n )\n \n return out"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: ? trials did not all match.
runtime_error: expected scalar type Int but found Float
turn 7 · 1 tool call(s) · 86.3s turn · 11.7s LLM · tokens in=7,123 · out=1,284 · total=8,407 · 6,253 chars (click to expand)
turn 786.3s turn · 11.7s LLM · 74.6s tools · tokens in=7,123 · out=1,284 · total=8,407
reasoning
Hmm, still failing with the same error. The issue might be in how the arguments are passed to the function. Let me check the reference code again...
In the reference:
- `degrees` is computed as `torch.bincount(col_idx.long(), minlength=num_nodes).to(torch.float32) + 1.0`
- So `degrees` should be float32
The error message says "expected scalar type Int but found Float". This suggests that somewhere in the code, a tensor is expected to be Int but is Float.
Wait, let me check the data types more carefully. In the `_make_graph()` function:
- `row_ptr` is `torch.int32`
- `col_idx` is `torch.int32`
- `node_feat` is `torch.float32`
- `degrees` is `torch.float32` (from `.to(torch.float32)`)
But the error says "expected scalar type Int but found Float" - this suggests we're passing a Float tensor where an Int is expected.
Oh wait, I think I see the issue now. Looking at the reference code more carefully:
```python
src = col_idx[start:end].long() # .long() converts to int64
```
So `src` in the reference is int64 (long), not int32. But col_idx itself is int32. So when indexing with src, it's a tensor of int64 values.
But actually, that shouldn't matter for our CUDA kernel since we're just using the integer values.
Let me look more carefully at the function signature. Maybe the issue is that we need to check types at runtime. Actually, looking at the error again - it says the error is happening when calling the function, not inside the kernel.
Let me try using TORCH_CHECK to validate types, or better yet, just use templates to handle different data types. Or maybe I need to explicitly cast the tensors to the right types.
Actually, let me try a different approach - use the generic at::Tensor accessor and let PyTorch handle the type conversions.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void gcn_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes,\n int feat_dim\n) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n \n int start = row_ptr[dst];\n int end = row_ptr[dst + 1];\n \n if (end <= start) return;\n \n float dst_deg = degrees[dst];\n float dst_deg_inv = 1.0f / sqrtf(dst_deg);\n \n // Initialize output accumulator\n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] = 0.0f;\n }\n \n for (int i = start; i < end; i++) {\n int src = col_idx[i];\n float src_deg = degrees[src];\n float norm = dst_deg_inv / sqrtf(src_deg);\n \n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n }\n}\n\"\"\"\n\ncpp_source = r\"\"\"\n#include <torch/extension.h>\n\nvoid launch_gcn_aggregate(\n at::Tensor row_ptr,\n at::Tensor col_idx,\n at::Tensor node_feat,\n at::Tensor degrees,\n at::Tensor out\n) {\n // Ensure types are correct\n TORCH_CHECK(row_ptr.scalar_type() == at::kInt, \"row_ptr must be int32\");\n TORCH_CHECK(col_idx.scalar_type() == at::kInt, \"col_idx must be int32\");\n TORCH_CHECK(node_feat.scalar_type() == at::kFloat, \"node_feat must be float32\");\n TORCH_CHECK(degrees.scalar_type() == at::kFloat, \"degrees must be float32\");\n TORCH_CHECK(out.scalar_type() == at::kFloat, \"out must be float32\");\n \n int num_nodes = row_ptr.size(0) - 1;\n int feat_dim = node_feat.size(1);\n \n int threads = 256;\n int blocks = (num_nodes + threads - 1) / threads;\n \n gcn_aggregate_kernel<<<blocks, threads>>>(\n row_ptr.data_ptr<int>(),\n col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(),\n degrees.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n}\n\"\"\"\n\nmodule = load_inline(\n name='gcn_aggregate_v4',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['launch_gcn_aggregate'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.module = module\n\n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n degrees: torch.Tensor,\n ) -> torch.Tensor:\n num_nodes = row_ptr.numel() - 1\n feat_dim = node_feat.shape[1]\n \n # Ensure correct types\n row_ptr = row_ptr.to(torch.int32)\n col_idx = col_idx.to(torch.int32)\n degrees = degrees.to(torch.float32)\n \n out = torch.zeros(num_nodes, feat_dim, dtype=node_feat.dtype, device=node_feat.device)\n \n self.module.launch_gcn_aggregate(\n row_ptr.contiguous(),\n col_idx.contiguous(),\n node_feat.contiguous(),\n degrees.contiguous(),\n out\n )\n \n return out"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'gcn_aggregate_v4'
turn 829.3s turn · 7.5s LLM · 21.8s tools · tokens in=8,009 · out=855 · total=8,864
reasoning
Let me try a different approach - putting the kernel function definition in a separate .cu file source block and using extern.
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 CUDA kernel\ncuda_source = r\"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void gcn_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes,\n int feat_dim\n) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n \n int start = row_ptr[dst];\n int end = row_ptr[dst + 1];\n \n if (end <= start) return;\n \n float dst_deg = degrees[dst];\n float dst_deg_inv = 1.0f / sqrtf(dst_deg);\n \n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] = 0.0f;\n }\n \n for (int i = start; i < end; i++) {\n int src = col_idx[i];\n float src_deg = degrees[src];\n float norm = dst_deg_inv / sqrtf(src_deg);\n \n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n }\n}\n\"\"\"\n\n# Define C++ wrapper that calls CUDA kernel\ncpp_source = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n// Declare the CUDA kernel\n__global__ void gcn_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes,\n int feat_dim\n);\n\nvoid launch_gcn_aggregate(\n at::Tensor row_ptr,\n at::Tensor col_idx,\n at::Tensor node_feat,\n at::Tensor degrees,\n at::Tensor out\n) {\n int num_nodes = row_ptr.size(0) - 1;\n int feat_dim = node_feat.size(1);\n \n int threads = 256;\n int blocks = (num_nodes + threads - 1) / threads;\n \n gcn_aggregate_kernel<<<blocks, threads>>>(\n row_ptr.data_ptr<int>(),\n col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(),\n degrees.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n}\n\"\"\"\n\nmodule = load_inline(\n name='gcn_aggregate_v5',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['launch_gcn_aggregate'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.module = module\n\n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n degrees: torch.Tensor,\n ) -> torch.Tensor:\n num_nodes = row_ptr.numel() - 1\n feat_dim = node_feat.shape[1]\n \n # Convert to correct types\n row_ptr = row_ptr.to(torch.int32)\n col_idx = col_idx.to(torch.int32)\n degrees = degrees.to(torch.float32)\n \n out = torch.zeros(num_nodes, feat_dim, dtype=node_feat.dtype, device=node_feat.device)\n \n self.module.launch_gcn_aggregate(\n row_ptr.contiguous(),\n col_idx.contiguous(),\n node_feat.contiguous(),\n degrees.contiguous(),\n out\n )\n \n return out"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'gcn_aggregate_v5'
turn 98.4s turn · 7.4s LLM · 1.0s tools · tokens in=8,863 · out=752 · total=9,615
reasoning
The compilation keeps failing. Let me try a simpler approach - using the full source in one file and using __global__ declaration properly in cpp_sources.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfull_source = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void gcn_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes,\n int feat_dim\n) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n \n int start = row_ptr[dst];\n int end = row_ptr[dst + 1];\n \n if (end <= start) return;\n \n float dst_deg = degrees[dst];\n float dst_deg_inv = 1.0f / sqrtf(dst_deg);\n \n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] = 0.0f;\n }\n \n for (int i = start; i < end; i++) {\n int src = col_idx[i];\n float src_deg = degrees[src];\n float norm = dst_deg_inv / sqrtf(src_deg);\n \n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n }\n}\n\nvoid launch_gcn_aggregate(\n at::Tensor row_ptr,\n at::Tensor col_idx,\n at::Tensor node_feat,\n at::Tensor degrees,\n at::Tensor out\n) {\n int num_nodes = row_ptr.size(0) - 1;\n int feat_dim = node_feat.size(1);\n \n int threads = 256;\n int blocks = (num_nodes + threads - 1) / threads;\n \n gcn_aggregate_kernel<<<blocks, threads>>>(\n row_ptr.data_ptr<int>(),\n col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(),\n degrees.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n}\n\"\"\"\n\nmodule = load_inline(\n name='gcn_aggregate_v6',\n cpp_sources=full_source,\n cuda_sources='',\n functions=['launch_gcn_aggregate'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.module = module\n\n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n degrees: torch.Tensor,\n ) -> torch.Tensor:\n num_nodes = row_ptr.numel() - 1\n feat_dim = node_feat.shape[1]\n \n # Convert to correct types\n row_ptr = row_ptr.to(torch.int32)\n col_idx = col_idx.to(torch.int32)\n degrees = degrees.to(torch.float32)\n \n out = torch.zeros(num_nodes, feat_dim, dtype=node_feat.dtype, device=node_feat.device)\n \n self.module.launch_gcn_aggregate(\n row_ptr.contiguous(),\n col_idx.contiguous(),\n node_feat.contiguous(),\n degrees.contiguous(),\n out\n )\n \n return out"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'gcn_aggregate_v6'
turn 1082.1s turn · 7.5s LLM · 74.6s tools · tokens in=9,608 · out=769 · total=10,377
reasoning
Let me try a different approach. Perhaps using torch.ops approach or checking how other similar kernels are structured. Let me use the ATen accessor approach.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = r\"\"\"\n#include <torch/extension.h>\n\n__global__ void gcn_aggregate_kernel(\n const int* __restrict__ row_ptr,\n const int* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n const int num_nodes,\n const int feat_dim\n) {\n const int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n \n const int start = row_ptr[dst];\n const int end = row_ptr[dst + 1];\n \n if (end <= start) return;\n \n const float dst_deg = degrees[dst];\n const float dst_deg_inv = 1.0f / sqrtf(dst_deg);\n \n // Initialize output\n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] = 0.0f;\n }\n \n for (int i = start; i < end; i++) {\n const int src = col_idx[i];\n const float src_deg = degrees[src];\n const float norm = dst_deg_inv / sqrtf(src_deg);\n \n const float* feat_ptr = node_feat + src * feat_dim;\n float* out_ptr = out + dst * feat_dim;\n \n for (int f = 0; f < feat_dim; f++) {\n out_ptr[f] += feat_ptr[f] * norm;\n }\n }\n}\n\ntorch::Tensor gcn_aggregate_cuda(\n torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat,\n torch::Tensor degrees\n) {\n const int num_nodes = row_ptr.size(0) - 1;\n const int feat_dim = node_feat.size(1);\n \n auto out = torch::zeros({num_nodes, feat_dim}, node_feat.options());\n \n const int threads = 256;\n const int blocks = (num_nodes + threads - 1) / threads;\n \n AT_DISPATCH_FLOATING_TYPES(node_feat.scalar_type(), \"gcn_aggregate\", [&] {\n gcn_aggregate_kernel<<<blocks, threads>>>(\n row_ptr.data_ptr<int>(),\n col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(),\n degrees.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n });\n \n return out;\n}\n\"\"\"\n\nmodule = load_inline(\n name='gcn_aggregate_v7',\n cpp_sources='',\n cuda_sources=cuda_source,\n functions=['gcn_aggregate_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.module = module\n\n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n degrees: torch.Tensor,\n ) -> torch.Tensor:\n # Convert to correct types\n row_ptr = row_ptr.to(torch.int32)\n col_idx = col_idx.to(torch.int32)\n degrees = degrees.to(torch.float32)\n \n return self.module.gcn_aggregate_cuda(\n row_ptr.contiguous(),\n col_idx.contiguous(),\n node_feat.contiguous(),\n degrees.contiguous()\n )"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'gcn_aggregate_v7'
turn 1182.5s turn · 6.4s LLM · 76.1s tools · tokens in=10,370 · out=699 · total=11,069
reasoning
The verbose=True doesn't give us the actual error. Let me try a simpler approach - maybe I need to use the full source in the cpp_sources with CUDA syntax.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nsource = r\"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n\n__global__ void gcn_aggregate_kernel(\n const int* row_ptr,\n const int* col_idx,\n const float* node_feat,\n const float* degrees,\n float* out,\n int num_nodes,\n int feat_dim\n) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n \n int start = row_ptr[dst];\n int end = row_ptr[dst + 1];\n \n if (end <= start) return;\n \n float dst_deg = degrees[dst];\n float dst_deg_inv = 1.0f / sqrtf(dst_deg);\n \n // Initialize output\n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] = 0.0f;\n }\n \n for (int i = start; i < end; i++) {\n int src = col_idx[i];\n float src_deg = degrees[src];\n float norm = dst_deg_inv / sqrtf(src_deg);\n \n for (int f = 0; f < feat_dim; f++) {\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n }\n}\n\ntorch::Tensor gcn_aggregate_cuda(\n torch::Tensor row_ptr,\n torch::Tensor col_idx,\n torch::Tensor node_feat,\n torch::Tensor degrees\n) {\n int num_nodes = row_ptr.size(0) - 1;\n int feat_dim = node_feat.size(1);\n \n auto out = torch::zeros({num_nodes, feat_dim}, node_feat.options());\n \n int threads = 256;\n int blocks = (num_nodes + threads - 1) / threads;\n \n gcn_aggregate_kernel<<<blocks, threads>>>(\n row_ptr.data_ptr<int>(),\n col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(),\n degrees.data_ptr<float>(),\n out.data_ptr<float>(),\n num_nodes,\n feat_dim\n );\n \n return out;\n}\n\"\"\"\n\nmodule = load_inline(\n name='gcn_agg_v8',\n cpp_sources='',\n cuda_sources=source,\n functions=['gcn_aggregate_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.module = module\n\n def forward(\n self,\n row_ptr: torch.Tensor,\n col_idx: torch.Tensor,\n node_feat: torch.Tensor,\n degrees: torch.Tensor,\n ) -> torch.Tensor:\n row_ptr = row_ptr.to(torch.int32)\n col_idx = col_idx.to(torch.int32)\n degrees = degrees.to(torch.float32)\n \n return self.module.gcn_aggregate_cuda(\n row_ptr.contiguous(),\n col_idx.contiguous(),\n node_feat.contiguous(),\n degrees.contiguous()\n )"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'gcn_agg_v8'
turn 1282.9s turn · 5.8s LLM · 77.0s tools · tokens in=11,089 · out=566 · total=11,655
reasoning
The compilation is failing repeatedly. Let me try a simpler approach - looking at how PyTorch extensions are typically written. The issue might be with how I'm structuring the load_inline call.
Let me try a very minimal version that should definitely work.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nmodule = load_inline(\n name='gcn_ext',\n cpp_sources=[],\n cuda_sources=['''\n#include <torch/extension.h>\n\n__global__ void gcn_kernel(const int* row_ptr, const int* col_idx, \n const float* node_feat, const float* degrees,\n float* out, int num_nodes, int feat_dim) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n int start = row_ptr[dst], end = row_ptr[dst + 1];\n if (end <= start) return;\n float d_deg = degrees[dst];\n float inv = 1.0f / sqrtf(d_deg);\n for (int f = 0; f < feat_dim; f++) out[dst * feat_dim + f] = 0.0f;\n for (int i = start; i < end; i++) {\n int src = col_idx[i];\n float norm = inv / sqrtf(degrees[src]);\n for (int f = 0; f < feat_dim; f++)\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n}\n\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx,\n torch::Tensor node_feat, torch::Tensor degrees) {\n int N = row_ptr.size(0) - 1, D = node_feat.size(1);\n auto out = torch::zeros({N, D}, node_feat.options());\n gcn_kernel<<<(N+255)/256, 256>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(), degrees.data_ptr<float>(),\n out.data_ptr<float>(), N, D);\n return out;\n}\n'''],\n functions=['gcn_forward'],\n extra_cuda_cflags=['-O3']\n)\n\nclass ModelNew(nn.Module):\n def __init__(self): \n super().__init__()\n \n def forward(self, row_ptr, col_idx, node_feat, degrees):\n return module.gcn_forward(\n row_ptr.to(torch.int32).contiguous(),\n col_idx.to(torch.int32).contiguous(),\n node_feat.contiguous(),\n degrees.to(torch.float32).contiguous()\n )"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'gcn_ext': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=gcn_ext -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_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_11_cache/k_df7ecbf4a3d9/gcn_ext/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=gcn_ext -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_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_11_cache/k_df7ecbf4a3d9/gcn_ext/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_11_cache/k_df7ecbf4a3d9/gcn_ext/main.cpp: In function 'void pybind11_init_gcn_ext(pybind11::module_&)':
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_11_cache/k_df7ecbf4a3d9/gcn_ext/main.cpp:3:50: error: 'gcn_forward' was not declared in this scope
3 | m.def("gcn_forward", torch::wrap_pybind_function(gcn_forward), "gcn_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=gcn_ext -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=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_1_problem_11_cache/k_df7ecbf4a3d9/gcn_ext/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 13152.4s turn · 11.1s LLM · 141.3s tools · tokens in=12,516 · out=587 · total=13,103
reasoning
The issue is that the function is defined in CUDA but not declared for C++. I need to provide both cpp_sources and cuda_sources, with the function declaration in cpp_sources and definition in cuda_sources.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nmodule = load_inline(\n name='gcn_ext',\n cpp_sources=['''torch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx,\n torch::Tensor node_feat, torch::Tensor degrees);'''],\n cuda_sources=['''\n#include <torch/extension.h>\n\n__global__ void gcn_kernel(const int* row_ptr, const int* col_idx, \n const float* node_feat, const float* degrees,\n float* out, int num_nodes, int feat_dim) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n int start = row_ptr[dst], end = row_ptr[dst + 1];\n if (end <= start) return;\n float d_deg = degrees[dst];\n float inv = 1.0f / sqrtf(d_deg);\n for (int f = 0; f < feat_dim; f++) out[dst * feat_dim + f] = 0.0f;\n for (int i = start; i < end; i++) {\n int src = col_idx[i];\n float norm = inv / sqrtf(degrees[src]);\n for (int f = 0; f < feat_dim; f++)\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n}\n\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx,\n torch::Tensor node_feat, torch::Tensor degrees) {\n int N = row_ptr.size(0) - 1, D = node_feat.size(1);\n auto out = torch::zeros({N, D}, node_feat.options());\n gcn_kernel<<<(N+255)/256, 256>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(), degrees.data_ptr<float>(),\n out.data_ptr<float>(), N, D);\n return out;\n}\n'''],\n functions=['gcn_forward'],\n extra_cuda_cflags=['-O3']\n)\n\nclass ModelNew(nn.Module):\n def __init__(self): \n super().__init__()\n \n def forward(self, row_ptr, col_idx, node_feat, degrees):\n return module.gcn_forward(\n row_ptr.to(torch.int32).contiguous(),\n col_idx.to(torch.int32).contiguous(),\n node_feat.contiguous(),\n degrees.to(torch.float32).contiguous()\n )"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 14106.5s turn · 5.7s LLM · 100.8s tools · tokens in=13,165 · out=598 · total=13,763 · FINAL
reasoning
The kernel compiled successfully. Now I need to submit it. But I should first check if it's correct by running the correctness check. However, the policy says this is my last turn and I must submit. Let me submit the kernel that compiled successfully.
call → submit_kernel
callsubmit_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nmodule = load_inline(\n name='gcn_ext',\n cpp_sources=['''torch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx,\n torch::Tensor node_feat, torch::Tensor degrees);'''],\n cuda_sources=['''\n#include <torch/extension.h>\n\n__global__ void gcn_kernel(const int* row_ptr, const int* col_idx, \n const float* node_feat, const float* degrees,\n float* out, int num_nodes, int feat_dim) {\n int dst = blockIdx.x * blockDim.x + threadIdx.x;\n if (dst >= num_nodes) return;\n int start = row_ptr[dst], end = row_ptr[dst + 1];\n if (end <= start) return;\n float d_deg = degrees[dst];\n float inv = 1.0f / sqrtf(d_deg);\n for (int f = 0; f < feat_dim; f++) out[dst * feat_dim + f] = 0.0f;\n for (int i = start; i < end; i++) {\n int src = col_idx[i];\n float norm = inv / sqrtf(degrees[src]);\n for (int f = 0; f < feat_dim; f++)\n out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;\n }\n}\n\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx,\n torch::Tensor node_feat, torch::Tensor degrees) {\n int N = row_ptr.size(0) - 1, D = node_feat.size(1);\n auto out = torch::zeros({N, D}, node_feat.options());\n gcn_kernel<<<(N+255)/256, 256>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<int>(),\n node_feat.data_ptr<float>(), degrees.data_ptr<float>(),\n out.data_ptr<float>(), N, D);\n return out;\n}\n'''],\n functions=['gcn_forward'],\n extra_cuda_cflags=['-O3']\n)\n\nclass ModelNew(nn.Module):\n def __init__(self): \n super().__init__()\n \n def forward(self, row_ptr, col_idx, node_feat, degrees):\n return module.gcn_forward(\n row_ptr.to(torch.int32).contiguous(),\n col_idx.to(torch.int32).contiguous(),\n node_feat.contiguous(),\n degrees.to(torch.float32).contiguous()\n )"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (3 / 3) correctness trials all passed.
Kernel runtime: 0.54 μs
Runtime stats: mean=0.54μs median=?μs std=0.00μs
Numerical precision: max_abs_err=2.38e-07 mean_abs_err=1.90e-08 max_rel_err=1.90e-02
Memory: 33.48 MB peak (ref: 33.45 MB, ratio: 1.0008x)
Kernel launches: 13 (ref: 23044, fusion ratio: 1772.6154)
SOL score: 0.0074
Flagged for excessive speedup — this submission may have been rejected by automated review.
final submitted kernel (54 lines, 2,094 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
module = load_inline(
name='gcn_ext',
cpp_sources=['''torch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx,
torch::Tensor node_feat, torch::Tensor degrees);'''],
cuda_sources=['''
#include <torch/extension.h>
__global__ void gcn_kernel(const int* row_ptr, const int* col_idx,
const float* node_feat, const float* degrees,
float* out, int num_nodes, int feat_dim) {
int dst = blockIdx.x * blockDim.x + threadIdx.x;
if (dst >= num_nodes) return;
int start = row_ptr[dst], end = row_ptr[dst + 1];
if (end <= start) return;
float d_deg = degrees[dst];
float inv = 1.0f / sqrtf(d_deg);
for (int f = 0; f < feat_dim; f++) out[dst * feat_dim + f] = 0.0f;
for (int i = start; i < end; i++) {
int src = col_idx[i];
float norm = inv / sqrtf(degrees[src]);
for (int f = 0; f < feat_dim; f++)
out[dst * feat_dim + f] += node_feat[src * feat_dim + f] * norm;
}
}
torch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx,
torch::Tensor node_feat, torch::Tensor degrees) {
int N = row_ptr.size(0) - 1, D = node_feat.size(1);
auto out = torch::zeros({N, D}, node_feat.options());
gcn_kernel<<<(N+255)/256, 256>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<int>(),
node_feat.data_ptr<float>(), degrees.data_ptr<float>(),
out.data_ptr<float>(), N, D);
return out;
}
'''],
functions=['gcn_forward'],
extra_cuda_cflags=['-O3']
)
class ModelNew(nn.Module):
def __init__(self):
super().__init__()
def forward(self, row_ptr, col_idx, node_feat, degrees):
return module.gcn_forward(
row_ptr.to(torch.int32).contiguous(),
col_idx.to(torch.int32).contiguous(),
node_feat.contiguous(),
degrees.to(torch.float32).contiguous()
)