Let me understand the task: 1. We have a graph edge operation where for each edge (src_idx[i], dst_idx[i]), we compute a dot product between the source node embedding and destination node embedding. 2. The reference implementation does: - `src = src_feat[src_idx.long()]` - gather source features based on indices - `dst = dst_feat[dst_idx.long()]` - gather destination features based on indices - `(src * dst).sum(dim=-1)` - element-wise multiply and sum along feature dimension 3. Parameters: - num_src_nodes = 1024 - num_dst_nodes = 768 - num_edges = 16384 - feat_dim = 128 So for each edge, we're computing a dot product between two feature vectors. Let me write a CUDA kernel for this. The key operations are: 1. Gather operations (indexing) 2. Element-wise multiplication 3. Sum reduction along feature dimension I'll write a kernel where each thread handles one edge (computing the dot product for that edge). ```python import torch from torch.utils.cpp_extension import load_inline cuda_source = """ #include <torch/extension.h> #include <cuda.h> #include <cuda_runtime.h> __global__ void edge_matmul_kernel( const int* __restrict__ src_idx, const int* __restrict__ dst_idx, const float* __restrict__ src_feat, const float* __restrict__ dst_feat, float* __restrict__ output, int num_edges, int feat_dim, int num_src_nodes, int num_dst_nodes ) { int edge_idx = blockIdx.x * blockDim.x + threadIdx.x; if (edge_idx >= num_edges) return; int s_idx = src_idx[edge_idx]; int d_idx = dst_idx[edge_idx]; const float* src_ptr = src_feat + s_idx * feat_dim; const float* dst_ptr = dst_feat + d_idx * feat_dim; float sum = 0.0f; for (int f = 0; f < feat_dim; f++) { sum += src_ptr[f] * dst_ptr[f]; } output[edge_idx] = sum; } torch::Tensor edge_matmul_cuda( torch::Tensor src_idx, torch::Tensor dst_idx, torch::Tensor src_feat, torch::Tensor dst_feat ) { int num_edges = src_idx.size(0); int feat_dim = src_feat.size(1); int num_src_nodes = src_feat.size(0); int num_dst_nodes = dst_feat.size(0); auto output = torch::empty({num_edges}, src_feat.options()); const int block_size = 256; const int num_blocks = (num_edges + block_size - 1) / block_size; edge_matmul_kernel<<<num_blocks, block_size>>>( src_idx.data_ptr<int>(), dst_idx.data_ptr<int>(), src_feat.data_ptr<float>(), dst_feat.data_ptr<float>(), output.data_ptr<float>(), num_edges, feat_dim, num_src_nodes, num_dst_nodes ); return output; } """ cpp_source = "torch::Tensor edge_matmul_cuda(torch::Tensor src_idx, torch::Tensor dst_idx, torch::Tensor src_feat, torch::Tensor dst_feat);" edge_matmul_extension = load_inline( name="edge_matmul", cpp_sources=cpp_source, cuda_sources=cuda_source, functions=["edge_matmul_cuda"], verbose=True ) class ModelNew(nn.Module): def __init__(self): super().__init__() self.edge_matmul = edge_matmul_extension def forward(self, src_idx, dst_idx, src_feat, dst_feat): return self.edge_matmul.edge_matmul_cuda(src_idx, dst_idx, src_feat, dst_feat) ``` I'm developing a CUDA kernel for edge-based matrix multiplication. The kernel handles feature dot products for graph edges, using parallel computation across CUDA blocks and threads. It efficiently computes source and destination feature interactions by indexing into feature tensors and performing element-wise multiplication followed by summation. The implementation includes careful memory management and indexing, ensuring each thread processes a unique edge with its corresponding source and destination features. Block and grid dimensions are strategically calculated to cover all edges while maintaining computational efficiency. The kernel design focuses on minimizing memory access overhead by using direct pointer arithmetic and leveraging CUDA's parallel processing capabilities for graph-based feature computations. I'll optimize the kernel design by carefully considering memory access patterns and leveraging CUDA's parallel processing capabilities. The goal is to create an efficient implementation that maximizes memory bandwidth and computational throughput for graph embedding dot product calculations. The kernel will focus on efficient memory access strategies, utilizing vectorized loads and shared memory techniques to enhance performance across multiple edges and feature dimensions.

The error is still happening. Looking more closely, the reference does: ```python src = src_feat[src_idx.long()] ``` So the indices need to be converted to long (int64). The input src_idx is int32 but we need to convert it to int64 for the gather operation. But wait - my custom kernel is not using the gather, I'm using indexing directly. Let me check - the problem might be that PyTorch is somehow trying to use the old extension. Actually wait - the issue might be that torch.compile is being invoked and caching the old extension. Let me try renaming the extension and also ensure we properly handle the conversion. Actually, looking more carefully at the error: "expected scalar type Int but found Float" - this might be a PyTorch type check. Let me look at how torch.utils.cpp_extension.load_inline works. Actually I think the issue is the indices. The reference model uses `src_idx.long()` which converts int32 to int64 before indexing. My kernel receives int32 indices and uses them directly. But wait, my kernel compiles and should work with int32. The error might be from somewhere else. Let me re-read the error: "expected scalar type Int but found Float" Hmm, this is likely coming from tensor operations. Let me try to check if the issue is with how we're passing data to the kernel. Actually wait - the reference uses `src_idx.long()` but `get_inputs()` creates the indices as `torch.int32`. Then `src_idx.long()` converts them to `torch.int64`. So the indexing operation expects int64. But in my kernel, I'm directly using the int32 indices. This should still work mathematically. The error might be coming from somewhere else. Let me think about this more carefully. The error says "expected scalar type Int but found Float". This suggests there's a type mismatch somewhere in tensor operations. Oh wait! I see it now - when we call `.data_ptr<int32_t>()`, if the tensor is actually int64 (because of some implicit conversion), then we'd have a mismatch. Let me explicitly handle the conversion in the Python wrapper to ensure we get the right types.

Now the kernel is correct. Let me profile it to understand how to optimize it further.

Let me profile this version.

The kernel is correct and working. Let me profile it to see if there are improvements, then submit.