L1 · problem 11
gpt-5.5-priority · popcorn · l12_default_gpt
← all variants← popcorn← gpt-5.5-prioritygenerated 2026-05-28 03:33:16 UTC
correct 11_DegreeNormalizedAggregation.py
model
gpt-5.5-priority
turns / max
10 / 10
tool calls
10
tokens (total)
0
wall clock
—
truncated
no
backend
cuda fp32
variant
popcorn
runtime
0.01 μs
ref runtime
78.00 μs
speedup
9629.63x
started
2026-05-02T02:14:38Z
timingkernelbench.timing
kernel runtime
0.01 μs
ref runtime
78.00 μs
speedup
9629.63x
kernel mean / median / std
0.01 / — / 1.50e-04 μs
ref mean / median / std
78.00 / — / 39.80 μs
numerical precisioneval_kernel_against_ref
max abs error
3.58e-07
mean abs error
1.92e-08
max rel error
0.14
mean rel error
1.31e-06
gpu memoryextended_metrics.measure_memory
kernel peak
33.44 MB
ref peak
33.45 MB
kernel / ref
1.00x
kernel launches / fusiontorch.profiler
kernel launches
1
ref launches
23044
fusion ratio (ref / kernel)
23044.00x
total cuda time
23 μs
SOL (speed-of-light)nsight_hardware_counters
SOL score (0–1)
0.015
dram util %
1.5%
compute util %
0.0%
bottleneck
unknown
dominant pipe
None
arithmetic intensity
—
ridge point
20.00
roofline / occupancynsight_hardware_counters
dram bandwidth
49.56 GB/s
dram util %
1.5%
fp32 tflops
—
fp32 util %
—
fp16 tflops
—
occupancy %
25.2%
bottleneck
unknown
dominant pipe %
—
arithmetic intensity
—
ridge point
20.00
L1 hit %
—
L2 hit %
91.0%
ld sectors / req
3.16
st sectors / req
—
regs / thread
32
smem / block
9216 B
block size
128
peak bw
3350.00 GB/s
peak fp32 tflops
67.00
peak fp16 tflops
1979.00
warp stalls (top 5)nsight
long_scoreboard
1333.0%
short_scoreboard
161.7%
mio_throttle
17.6%
flagssubmit_kernel guard
excessive speedup
flagged
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 025.5s turn · 25.5s LLM · 0.0s tools
reasoning
**Evaluating resource allocation** I'm thinking about how I might allocate for (end-start), but I'm unsure if the wrapper would know the maximum. It could be dynamic per kernel launch, but maybe not per block. I wonder if I should just avoid shared memory altogether, or if using a fixed size of 1024 would be okay. I need to handle cases where deg exceeds 1024 as a fallback. It might not be necessary, but I’m considering correctness if things get weird, especially since the inputs are fixed.
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 147.1s turn · 47.1s LLM · 0.0s tools
reasoning
**Optimizing array allocation** I'm considering the safest approach for allocating static arrays in shared memory—maybe 256 arrays. If the degree exceeds 256, there could be an overflow, so I might handle that case differently. The graph is limited to 21, using a `constexpr MAX_DEG` of 1024, which works fine for me. I believe there's potential for further optimization here, maybe using fewer threads to improve efficiency. I'm also thinking about block overhead and how to balance features and norms. **Testing numerical matching** I'm exploring the idea of using PyTorch for numerical matching, particularly for calculating norms. I think I could achieve this using a formula like `norm = 1.0/torch.sqrt(degrees[dst]*degrees[src])`. It seems I'll need to multiply the node features by this norm and sum over the source dimensions, likely benefiting from vectorization on the GPU. The order of summation looks crucial, and I'll need to ensure precision within 1e-4. It's interesting that our approximation could be accurate within 1e-7. I need to test it carefully!
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n\n#define MAX_DEG_SHARED 1024\n\n__global__ void gcn_kernel(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, int feat_dim) {\n int dst = blockIdx.x;\n int tid = threadIdx.x;\n if (dst >= num_nodes) return;\n\n int start = row_ptr[dst];\n int end = row_ptr[dst + 1];\n int deg = end - start;\n\n if (deg <= 0) {\n for (int f = tid; f < feat_dim; f += blockDim.x) out[dst * feat_dim + f] = 0.0f;\n return;\n }\n\n __shared__ int sh_cols[MAX_DEG_SHARED];\n __shared__ float sh_norm[MAX_DEG_SHARED];\n\n if (deg <= MAX_DEG_SHARED) {\n float dd = degrees[dst];\n for (int i = tid; i < deg; i += blockDim.x) {\n int src = col_idx[start + i];\n sh_cols[i] = src;\n sh_norm[i] = rsqrtf(dd * degrees[src]);\n }\n __syncthreads();\n\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n #pragma unroll 32\n for (int i = 0; i < deg; ++i) {\n sum += node_feat[sh_cols[i] * feat_dim + f] * sh_norm[i];\n }\n out[dst * feat_dim + f] = sum;\n }\n } else {\n float dd = degrees[dst];\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n for (int e = start; e < end; ++e) {\n int src = col_idx[e];\n float norm = rsqrtf(dd * degrees[src]);\n sum += node_feat[src * feat_dim + f] * norm;\n }\n out[dst * feat_dim + f] = sum;\n }\n }\n}\n\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n auto out = torch::empty({num_nodes, feat_dim}, node_feat.options());\n if (num_nodes == 0 || feat_dim == 0) return out;\n c10::cuda::CUDAGuard device_guard(node_feat.device());\n const int threads = 128;\n dim3 blocks(num_nodes);\n gcn_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n row_ptr.data_ptr<int>(), col_idx.data_ptr<int>(), node_feat.data_ptr<float>(),\n degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n return out;\n}\n'''\n\n_gcn_ext = load_inline(\n name='gcn_norm_agg_ext_v1',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['gcn_forward'],\n with_cuda=True,\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, row_ptr: torch.Tensor, col_idx: torch.Tensor, node_feat: torch.Tensor, degrees: torch.Tensor) -> torch.Tensor:\n return _gcn_ext.gcn_forward(row_ptr, col_idx, node_feat, degrees)\n" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 26.6s turn · 6.6s LLM · 0.0s tools
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n\n#define MAX_DEG_SHARED 1024\n\n__global__ void gcn_kernel(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, int feat_dim) {\n int dst = blockIdx.x;\n int tid = threadIdx.x;\n if (dst >= num_nodes) return;\n\n int start = row_ptr[dst];\n int end = row_ptr[dst + 1];\n int deg = end - start;\n\n if (deg <= 0) {\n for (int f = tid; f < feat_dim; f += blockDim.x) out[dst * feat_dim + f] = 0.0f;\n return;\n }\n\n __shared__ int sh_cols[MAX_DEG_SHARED];\n __shared__ float sh_norm[MAX_DEG_SHARED];\n\n if (deg <= MAX_DEG_SHARED) {\n float dd = degrees[dst];\n for (int i = tid; i < deg; i += blockDim.x) {\n int src = col_idx[start + i];\n sh_cols[i] = src;\n sh_norm[i] = rsqrtf(dd * degrees[src]);\n }\n __syncthreads();\n\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n #pragma unroll 32\n for (int i = 0; i < deg; ++i) {\n sum += node_feat[sh_cols[i] * feat_dim + f] * sh_norm[i];\n }\n out[dst * feat_dim + f] = sum;\n }\n } else {\n float dd = degrees[dst];\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n for (int e = start; e < end; ++e) {\n int src = col_idx[e];\n float norm = rsqrtf(dd * degrees[src]);\n sum += node_feat[src * feat_dim + f] * norm;\n }\n out[dst * feat_dim + f] = sum;\n }\n }\n}\n\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n auto out = torch::empty({num_nodes, feat_dim}, node_feat.options());\n if (num_nodes == 0 || feat_dim == 0) return out;\n c10::cuda::CUDAGuard device_guard(node_feat.device());\n const int threads = 128;\n dim3 blocks(num_nodes);\n gcn_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n row_ptr.data_ptr<int>(), col_idx.data_ptr<int>(), node_feat.data_ptr<float>(),\n degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n return out;\n}\n'''\n\n_gcn_ext = load_inline(\n name='gcn_norm_agg_ext_v1',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['gcn_forward'],\n with_cuda=True,\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, row_ptr: torch.Tensor, col_idx: torch.Tensor, node_feat: torch.Tensor, degrees: torch.Tensor) -> torch.Tensor:\n return _gcn_ext.gcn_forward(row_ptr, col_idx, node_feat, degrees)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: ? trials did not all match. runtime_error: expected scalar type Int but found Float
turn 322.5s turn · 22.5s LLM · 0.0s tools
reasoning
**Exploring kernel implementation** I'm considering whether to implement two kernels. It might be simpler to wrap and check the scalar type, launching either an int32 or a float for the row pointer. I need to think about compile conditions as well. But then, should the column index be a float? I wonder if adding templates for row pointers and column pointers might help resolve runtime issues. It feels a bit unusual, but I think it's worth exploring!
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n__global__ void dummy(float* out){out[0]=0;}\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees) {\n TORCH_CHECK(row_ptr.scalar_type()==at::kInt, \"row dtype bad \", row_ptr.scalar_type());\n TORCH_CHECK(col_idx.scalar_type()==at::kInt, \"col dtype bad \", col_idx.scalar_type());\n TORCH_CHECK(node_feat.scalar_type()==at::kFloat, \"feat dtype bad \", node_feat.scalar_type());\n TORCH_CHECK(degrees.scalar_type()==at::kFloat, \"degrees dtype bad \", degrees.scalar_type());\n auto out = torch::zeros({row_ptr.numel()-1, node_feat.size(1)}, node_feat.options());\n return out;\n}\n'''\n_ext = load_inline(name='debug_gcn_ext_dtype', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['gcn_forward'], with_cuda=True, extra_cuda_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__()\n def forward(self, row_ptr, col_idx, node_feat, degrees): return _ext.gcn_forward(row_ptr, col_idx, node_feat, degrees)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: ? trials did not all match. runtime_error: row dtype bad Float
turn 4 · 1 tool call(s) · 14.6s turn · 14.6s LLM · 6,111 chars (click to expand)
turn 414.6s turn · 14.6s LLM · 0.0s tools
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cstdint>\n\n#define MAX_DEG_SHARED 1024\n\ntemplate <typename row_t, typename col_t>\n__global__ void gcn_kernel_t(const row_t* __restrict__ row_ptr,\n const col_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes, int feat_dim) {\n int dst = blockIdx.x;\n int tid = threadIdx.x;\n if (dst >= num_nodes) return;\n\n int start = (int)row_ptr[dst];\n int end = (int)row_ptr[dst + 1];\n int deg = end - start;\n\n if (deg <= 0) {\n for (int f = tid; f < feat_dim; f += blockDim.x) out[dst * feat_dim + f] = 0.0f;\n return;\n }\n\n __shared__ int sh_cols[MAX_DEG_SHARED];\n __shared__ float sh_norm[MAX_DEG_SHARED];\n\n if (deg <= MAX_DEG_SHARED) {\n float dd = degrees[dst];\n for (int i = tid; i < deg; i += blockDim.x) {\n int src = (int)col_idx[start + i];\n sh_cols[i] = src;\n sh_norm[i] = rsqrtf(dd * degrees[src]);\n }\n __syncthreads();\n\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n #pragma unroll 32\n for (int i = 0; i < deg; ++i) {\n sum += node_feat[sh_cols[i] * feat_dim + f] * sh_norm[i];\n }\n out[dst * feat_dim + f] = sum;\n }\n } else {\n float dd = degrees[dst];\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n for (int e = start; e < end; ++e) {\n int src = (int)col_idx[e];\n float norm = rsqrtf(dd * degrees[src]);\n sum += node_feat[src * feat_dim + f] * norm;\n }\n out[dst * feat_dim + f] = sum;\n }\n }\n}\n\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n auto out = torch::empty({num_nodes, feat_dim}, node_feat.options());\n if (num_nodes == 0 || feat_dim == 0) return out;\n c10::cuda::CUDAGuard device_guard(node_feat.device());\n const int threads = 128;\n dim3 blocks(num_nodes);\n auto stream = at::cuda::getCurrentCUDAStream();\n\n if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kFloat) {\n gcn_kernel_t<float, float><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<float>(), col_idx.data_ptr<float>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kInt) {\n gcn_kernel_t<int, int><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<int>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kLong && col_idx.scalar_type() == at::kLong) {\n gcn_kernel_t<int64_t, int64_t><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int64_t>(), col_idx.data_ptr<int64_t>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kInt) {\n gcn_kernel_t<float, int><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<float>(), col_idx.data_ptr<int>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kFloat) {\n gcn_kernel_t<int, float><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<float>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else {\n TORCH_CHECK(false, \"unsupported row_ptr/col_idx dtypes\");\n }\n return out;\n}\n'''\n\n_gcn_ext = load_inline(\n name='gcn_norm_agg_ext_v2',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['gcn_forward'],\n with_cuda=True,\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, row_ptr: torch.Tensor, col_idx: torch.Tensor, node_feat: torch.Tensor, degrees: torch.Tensor) -> torch.Tensor:\n return _gcn_ext.gcn_forward(row_ptr, col_idx, node_feat, degrees)\n" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 5 · 1 tool call(s) · 8.9s turn · 8.9s LLM · 6,211 chars (click to expand)
turn 58.9s turn · 8.9s LLM · 0.0s tools
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cstdint>\n\n#define MAX_DEG_SHARED 1024\n\ntemplate <typename row_t, typename col_t>\n__global__ void gcn_kernel_t(const row_t* __restrict__ row_ptr,\n const col_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes, int feat_dim) {\n int dst = blockIdx.x;\n int tid = threadIdx.x;\n if (dst >= num_nodes) return;\n\n int start = (int)row_ptr[dst];\n int end = (int)row_ptr[dst + 1];\n int deg = end - start;\n\n if (deg <= 0) {\n for (int f = tid; f < feat_dim; f += blockDim.x) out[dst * feat_dim + f] = 0.0f;\n return;\n }\n\n __shared__ int sh_cols[MAX_DEG_SHARED];\n __shared__ float sh_norm[MAX_DEG_SHARED];\n\n if (deg <= MAX_DEG_SHARED) {\n float dd = degrees[dst];\n for (int i = tid; i < deg; i += blockDim.x) {\n int src = (int)col_idx[start + i];\n sh_cols[i] = src;\n sh_norm[i] = rsqrtf(dd * degrees[src]);\n }\n __syncthreads();\n\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n #pragma unroll 32\n for (int i = 0; i < deg; ++i) {\n sum += node_feat[sh_cols[i] * feat_dim + f] * sh_norm[i];\n }\n out[dst * feat_dim + f] = sum;\n }\n } else {\n float dd = degrees[dst];\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n for (int e = start; e < end; ++e) {\n int src = (int)col_idx[e];\n float norm = rsqrtf(dd * degrees[src]);\n sum += node_feat[src * feat_dim + f] * norm;\n }\n out[dst * feat_dim + f] = sum;\n }\n }\n}\n\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n auto out = torch::empty({num_nodes, feat_dim}, node_feat.options());\n if (num_nodes == 0 || feat_dim == 0) return out;\n c10::cuda::CUDAGuard device_guard(node_feat.device());\n const int threads = 128;\n dim3 blocks(num_nodes);\n auto stream = at::cuda::getCurrentCUDAStream();\n\n if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kFloat) {\n gcn_kernel_t<float, float><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<float>(), col_idx.data_ptr<float>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kInt) {\n gcn_kernel_t<int, int><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<int>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kLong && col_idx.scalar_type() == at::kLong) {\n gcn_kernel_t<int64_t, int64_t><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int64_t>(), col_idx.data_ptr<int64_t>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kInt) {\n gcn_kernel_t<float, int><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<float>(), col_idx.data_ptr<int>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kFloat) {\n gcn_kernel_t<int, float><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<float>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else {\n TORCH_CHECK(false, \"unsupported row_ptr/col_idx dtypes\");\n }\n return out;\n}\n'''\n\n_gcn_ext = load_inline(\n name='gcn_norm_agg_ext_v2',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['gcn_forward'],\n with_cuda=True,\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, row_ptr: torch.Tensor, col_idx: torch.Tensor, node_feat: torch.Tensor, degrees: torch.Tensor) -> torch.Tensor:\n return _gcn_ext.gcn_forward(row_ptr, col_idx, node_feat, degrees)\n" }
result ← run_correctness (ok)
okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=3.58e-07 mean_abs_err=1.92e-08 max_rel_err=1.40e-01
turn 6 · 1 tool call(s) · 25.8s turn · 25.8s LLM · 8,146 chars (click to expand)
turn 625.8s turn · 25.8s LLM · 0.0s tools
reasoning
**Considering optimization strategies** I'm thinking about optimization and whether I should submit what I have. It seems like there might be ways to improve performance—maybe by looking at timing and correctness instead of just running the tool. I'm referencing a PyTorch loop, which is slow, and examining my current kernel configuration. I wonder if I can reduce the number of threads and if the occupancy is a concern with my shared memory limits. There could be alternatives, like specializing for specific dimensions. **Exploring thread optimization** I’m considering a configuration of 640 blocks with 32 threads each to reduce thread overhead while using shared weights. The idea is to make each thread compute multiple feature columns for a total of 20,480 threads instead of 81,920. I need to think about memory coalescing to maximize efficiency. If I precompute norms in shared memory, I might speed things up even if it reduces parallelism. With the H100 having 120 SMs, maybe one block with multiple warps could help lower the block count. **Assessing occupancy and latency** I’m looking at occupancy and how having one warp per block limits the maximum blocks per SM to 32, which gives about 32 warps per SM. With 640 blocks across 120 SMs, I’m only getting around 5.3 warps per SM, which seems low, but is it enough? The total work is around 819k, and I wonder if latency is an issue due to accessing random node features. I’m thinking that having more warps might help hide latency a bit better. **Evaluating dtype support** I'm thinking about supporting the row/column data type as float, which seems feasible with minimal overhead on the host. I believe the potential node features can also be float. I need to perform a static check before submitting, but I’m unsure if timing is necessary before the final decision. Maybe the static nature could work for this? I should clarify these details before moving forward.
call → static_check
callstatic_check { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cstdint>\n\n#define MAX_DEG_SHARED 1024\n\ntemplate <typename row_t, typename col_t>\n__global__ void gcn_kernel_t(const row_t* __restrict__ row_ptr,\n const col_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes, int feat_dim) {\n int dst = blockIdx.x;\n int tid = threadIdx.x;\n if (dst >= num_nodes) return;\n\n int start = (int)row_ptr[dst];\n int end = (int)row_ptr[dst + 1];\n int deg = end - start;\n\n if (deg <= 0) {\n for (int f = tid; f < feat_dim; f += blockDim.x) out[dst * feat_dim + f] = 0.0f;\n return;\n }\n\n __shared__ int sh_cols[MAX_DEG_SHARED];\n __shared__ float sh_norm[MAX_DEG_SHARED];\n\n if (deg <= MAX_DEG_SHARED) {\n float dd = degrees[dst];\n for (int i = tid; i < deg; i += blockDim.x) {\n int src = (int)col_idx[start + i];\n sh_cols[i] = src;\n sh_norm[i] = rsqrtf(dd * degrees[src]);\n }\n __syncthreads();\n\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n #pragma unroll 32\n for (int i = 0; i < deg; ++i) {\n sum += node_feat[sh_cols[i] * feat_dim + f] * sh_norm[i];\n }\n out[dst * feat_dim + f] = sum;\n }\n } else {\n float dd = degrees[dst];\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n for (int e = start; e < end; ++e) {\n int src = (int)col_idx[e];\n float norm = rsqrtf(dd * degrees[src]);\n sum += node_feat[src * feat_dim + f] * norm;\n }\n out[dst * feat_dim + f] = sum;\n }\n }\n}\n\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n auto out = torch::empty({num_nodes, feat_dim}, node_feat.options());\n if (num_nodes == 0 || feat_dim == 0) return out;\n c10::cuda::CUDAGuard device_guard(node_feat.device());\n const int threads = 128;\n dim3 blocks(num_nodes);\n auto stream = at::cuda::getCurrentCUDAStream();\n\n if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kFloat) {\n gcn_kernel_t<float, float><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<float>(), col_idx.data_ptr<float>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kInt) {\n gcn_kernel_t<int, int><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<int>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kLong && col_idx.scalar_type() == at::kLong) {\n gcn_kernel_t<int64_t, int64_t><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int64_t>(), col_idx.data_ptr<int64_t>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kInt) {\n gcn_kernel_t<float, int><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<float>(), col_idx.data_ptr<int>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kFloat) {\n gcn_kernel_t<int, float><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<float>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else {\n TORCH_CHECK(false, \"unsupported row_ptr/col_idx dtypes\");\n }\n return out;\n}\n'''\n\n_gcn_ext = load_inline(\n name='gcn_norm_agg_ext_v2',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['gcn_forward'],\n with_cuda=True,\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, row_ptr: torch.Tensor, col_idx: torch.Tensor, node_feat: torch.Tensor, degrees: torch.Tensor) -> torch.Tensor:\n return _gcn_ext.gcn_forward(row_ptr, col_idx, node_feat, degrees)\n" }
result ← static_check (ok)
okstatic_check static_check PASSED: no violations or warnings detected.
turn 7 · 1 tool call(s) · 20.9s turn · 20.9s LLM · 9,743 chars (click to expand)
turn 720.9s turn · 20.9s LLM · 0.0s tools
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cstdint>\n\n#define MAX_DEG_SHARED 1024\n\ntemplate <typename row_t, typename col_t>\n__global__ __launch_bounds__(128) void gcn_kernel80_t(const row_t* __restrict__ row_ptr,\n const col_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 dst = blockIdx.x;\n int tid = threadIdx.x;\n int start = (int)row_ptr[dst];\n int end = (int)row_ptr[dst + 1];\n int deg = end - start;\n\n __shared__ int sh_cols[MAX_DEG_SHARED];\n __shared__ float sh_norm[MAX_DEG_SHARED];\n\n if (deg > 0 && deg <= MAX_DEG_SHARED) {\n float dd = degrees[dst];\n for (int i = tid; i < deg; i += 128) {\n int src = (int)col_idx[start + i];\n sh_cols[i] = src;\n sh_norm[i] = rsqrtf(dd * degrees[src]);\n }\n __syncthreads();\n if (tid < 80) {\n float sum = 0.0f;\n #pragma unroll 32\n for (int i = 0; i < deg; ++i) {\n sum += node_feat[sh_cols[i] * 80 + tid] * sh_norm[i];\n }\n out[dst * 80 + tid] = sum;\n }\n } else if (tid < 80) {\n float sum = 0.0f;\n if (deg > 0) {\n float dd = degrees[dst];\n for (int e = start; e < end; ++e) {\n int src = (int)col_idx[e];\n sum += node_feat[src * 80 + tid] * rsqrtf(dd * degrees[src]);\n }\n }\n out[dst * 80 + tid] = sum;\n }\n}\n\ntemplate <typename row_t, typename col_t>\n__global__ void gcn_kernel_generic_t(const row_t* __restrict__ row_ptr,\n const col_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes, int feat_dim) {\n int dst = blockIdx.x;\n int tid = threadIdx.x;\n int start = (int)row_ptr[dst];\n int end = (int)row_ptr[dst + 1];\n int deg = end - start;\n if (deg <= 0) {\n for (int f = tid; f < feat_dim; f += blockDim.x) out[dst * feat_dim + f] = 0.0f;\n return;\n }\n __shared__ int sh_cols[MAX_DEG_SHARED];\n __shared__ float sh_norm[MAX_DEG_SHARED];\n if (deg <= MAX_DEG_SHARED) {\n float dd = degrees[dst];\n for (int i = tid; i < deg; i += blockDim.x) {\n int src = (int)col_idx[start + i];\n sh_cols[i] = src;\n sh_norm[i] = rsqrtf(dd * degrees[src]);\n }\n __syncthreads();\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n for (int i = 0; i < deg; ++i) sum += node_feat[sh_cols[i] * feat_dim + f] * sh_norm[i];\n out[dst * feat_dim + f] = sum;\n }\n } else {\n float dd = degrees[dst];\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n for (int e = start; e < end; ++e) {\n int src = (int)col_idx[e];\n sum += node_feat[src * feat_dim + f] * rsqrtf(dd * degrees[src]);\n }\n out[dst * feat_dim + f] = sum;\n }\n }\n}\n\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n auto out = torch::empty({num_nodes, feat_dim}, node_feat.options());\n if (num_nodes == 0 || feat_dim == 0) return out;\n c10::cuda::CUDAGuard device_guard(node_feat.device());\n const int threads = 128;\n dim3 blocks(num_nodes);\n auto stream = at::cuda::getCurrentCUDAStream();\n\n if (feat_dim == 80) {\n if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kFloat) {\n gcn_kernel80_t<float, float><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<float>(), col_idx.data_ptr<float>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes);\n } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kInt) {\n gcn_kernel80_t<int, int><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<int>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes);\n } else if (row_ptr.scalar_type() == at::kLong && col_idx.scalar_type() == at::kLong) {\n gcn_kernel80_t<int64_t, int64_t><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int64_t>(), col_idx.data_ptr<int64_t>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes);\n } else if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kInt) {\n gcn_kernel80_t<float, int><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<float>(), col_idx.data_ptr<int>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes);\n } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kFloat) {\n gcn_kernel80_t<int, float><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<float>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes);\n } else {\n TORCH_CHECK(false, \"unsupported row_ptr/col_idx dtypes\");\n }\n } else {\n if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kFloat) {\n gcn_kernel_generic_t<float, float><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<float>(), col_idx.data_ptr<float>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kInt) {\n gcn_kernel_generic_t<int, int><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<int>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kLong && col_idx.scalar_type() == at::kLong) {\n gcn_kernel_generic_t<int64_t, int64_t><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int64_t>(), col_idx.data_ptr<int64_t>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kInt) {\n gcn_kernel_generic_t<float, int><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<float>(), col_idx.data_ptr<int>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kFloat) {\n gcn_kernel_generic_t<int, float><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<int>(), col_idx.data_ptr<float>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim);\n } else {\n TORCH_CHECK(false, \"unsupported row_ptr/col_idx dtypes\");\n }\n }\n return out;\n}\n'''\n\n_gcn_ext = load_inline(\n name='gcn_norm_agg_ext_v3',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['gcn_forward'],\n with_cuda=True,\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, row_ptr: torch.Tensor, col_idx: torch.Tensor, node_feat: torch.Tensor, degrees: torch.Tensor) -> torch.Tensor:\n return _gcn_ext.gcn_forward(row_ptr, col_idx, node_feat, degrees)\n" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 8 · 1 tool call(s) · 10.1s turn · 10.1s LLM · 6,227 chars (click to expand)
turn 810.1s turn · 10.1s LLM · 0.0s tools
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cstdint>\n#define MAX_DEG_SHARED 1024\ntemplate <typename row_t, typename col_t>\n__global__ __launch_bounds__(128) void gcn_kernel80_t(const row_t* __restrict__ row_ptr,const col_t* __restrict__ col_idx,const float* __restrict__ node_feat,const float* __restrict__ degrees,float* __restrict__ out,int num_nodes) {\n int dst = blockIdx.x; int tid = threadIdx.x;\n int start = (int)row_ptr[dst]; int end = (int)row_ptr[dst + 1]; int deg = end - start;\n __shared__ int sh_cols[MAX_DEG_SHARED]; __shared__ float sh_norm[MAX_DEG_SHARED];\n if (deg > 0 && deg <= MAX_DEG_SHARED) {\n float dd = degrees[dst];\n for (int i = tid; i < deg; i += 128) { int src = (int)col_idx[start + i]; sh_cols[i] = src; sh_norm[i] = rsqrtf(dd * degrees[src]); }\n __syncthreads();\n if (tid < 80) { float sum = 0.0f; for (int i = 0; i < deg; ++i) sum += node_feat[sh_cols[i] * 80 + tid] * sh_norm[i]; out[dst * 80 + tid] = sum; }\n } else if (tid < 80) {\n float sum = 0.0f; if (deg > 0) { float dd = degrees[dst]; for (int e = start; e < end; ++e) { int src = (int)col_idx[e]; sum += node_feat[src * 80 + tid] * rsqrtf(dd * degrees[src]); }} out[dst * 80 + tid] = sum;\n }\n}\ntemplate <typename row_t, typename col_t>\n__global__ void gcn_kernel_generic_t(const row_t* __restrict__ row_ptr,const col_t* __restrict__ col_idx,const float* __restrict__ node_feat,const float* __restrict__ degrees,float* __restrict__ out,int num_nodes, int feat_dim) {\n int dst = blockIdx.x; int tid = threadIdx.x; int start = (int)row_ptr[dst]; int end = (int)row_ptr[dst + 1]; int deg = end - start;\n if (deg <= 0) { for (int f = tid; f < feat_dim; f += blockDim.x) out[dst * feat_dim + f] = 0.0f; return; }\n __shared__ int sh_cols[MAX_DEG_SHARED]; __shared__ float sh_norm[MAX_DEG_SHARED];\n if (deg <= MAX_DEG_SHARED) { float dd = degrees[dst]; for (int i = tid; i < deg; i += blockDim.x) { int src = (int)col_idx[start + i]; sh_cols[i] = src; sh_norm[i] = rsqrtf(dd * degrees[src]); } __syncthreads(); for (int f = tid; f < feat_dim; f += blockDim.x) { float sum = 0.0f; for (int i = 0; i < deg; ++i) sum += node_feat[sh_cols[i] * feat_dim + f] * sh_norm[i]; out[dst * feat_dim + f] = sum; }}\n else { float dd = degrees[dst]; for (int f = tid; f < feat_dim; f += blockDim.x) { float sum = 0.0f; for (int e = start; e < end; ++e) { int src = (int)col_idx[e]; sum += node_feat[src * feat_dim + f] * rsqrtf(dd * degrees[src]); } out[dst * feat_dim + f] = sum; }}\n}\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees) {\n const int num_nodes = row_ptr.numel() - 1; const int feat_dim = node_feat.size(1); auto out = torch::empty({num_nodes, feat_dim}, node_feat.options()); if (num_nodes == 0 || feat_dim == 0) return out; c10::cuda::CUDAGuard device_guard(node_feat.device()); const int threads = 128; dim3 blocks(num_nodes); auto stream = at::cuda::getCurrentCUDAStream();\n#define DISPATCH80(RT,CT) gcn_kernel80_t<RT,CT><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<RT>(), col_idx.data_ptr<CT>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes)\n#define DISPATCHG(RT,CT) gcn_kernel_generic_t<RT,CT><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<RT>(), col_idx.data_ptr<CT>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim)\n bool f80 = feat_dim == 80;\n if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kFloat) { if (f80) { DISPATCH80(float,float); } else { DISPATCHG(float,float); } }\n else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kInt) { if (f80) { DISPATCH80(int,int); } else { DISPATCHG(int,int); } }\n else if (row_ptr.scalar_type() == at::kLong && col_idx.scalar_type() == at::kLong) { if (f80) { DISPATCH80(int64_t,int64_t); } else { DISPATCHG(int64_t,int64_t); } }\n else if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kInt) { if (f80) { DISPATCH80(float,int); } else { DISPATCHG(float,int); } }\n else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kFloat) { if (f80) { DISPATCH80(int,float); } else { DISPATCHG(int,float); } }\n else { TORCH_CHECK(false, \"unsupported row_ptr/col_idx dtypes\"); }\n#undef DISPATCH80\n#undef DISPATCHG\n return out;\n}\n'''\n_gcn_ext = load_inline(name='gcn_norm_agg_ext_v3b', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['gcn_forward'], with_cuda=True, extra_cuda_cflags=['-O3','--use_fast_math'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__()\n def forward(self, row_ptr, col_idx, node_feat, degrees): return _gcn_ext.gcn_forward(row_ptr, col_idx, node_feat, degrees)\n" }
result ← run_correctness (ok)
okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=3.58e-07 mean_abs_err=1.92e-08 max_rel_err=1.40e-01
turn 9 · FINAL · 1 tool call(s) · 10.8s turn · 10.8s LLM · 7,672 chars (click to expand)
turn 910.8s turn · 10.8s LLM · 0.0s tools · FINAL
call → submit_kernel
callsubmit_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <c10/cuda/CUDAGuard.h>\n#include <cstdint>\n\n#define MAX_DEG_SHARED 1024\n\ntemplate <typename row_t, typename col_t>\n__global__ __launch_bounds__(128) void gcn_kernel80_t(const row_t* __restrict__ row_ptr,\n const col_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 dst = blockIdx.x;\n int tid = threadIdx.x;\n int start = (int)row_ptr[dst];\n int end = (int)row_ptr[dst + 1];\n int deg = end - start;\n\n __shared__ int sh_cols[MAX_DEG_SHARED];\n __shared__ float sh_norm[MAX_DEG_SHARED];\n\n if (deg > 0 && deg <= MAX_DEG_SHARED) {\n float dd = degrees[dst];\n for (int i = tid; i < deg; i += 128) {\n int src = (int)col_idx[start + i];\n sh_cols[i] = src;\n sh_norm[i] = rsqrtf(dd * degrees[src]);\n }\n __syncthreads();\n if (tid < 80) {\n float sum = 0.0f;\n for (int i = 0; i < deg; ++i) {\n sum += node_feat[sh_cols[i] * 80 + tid] * sh_norm[i];\n }\n out[dst * 80 + tid] = sum;\n }\n } else if (tid < 80) {\n float sum = 0.0f;\n if (deg > 0) {\n float dd = degrees[dst];\n for (int e = start; e < end; ++e) {\n int src = (int)col_idx[e];\n sum += node_feat[src * 80 + tid] * rsqrtf(dd * degrees[src]);\n }\n }\n out[dst * 80 + tid] = sum;\n }\n}\n\ntemplate <typename row_t, typename col_t>\n__global__ void gcn_kernel_generic_t(const row_t* __restrict__ row_ptr,\n const col_t* __restrict__ col_idx,\n const float* __restrict__ node_feat,\n const float* __restrict__ degrees,\n float* __restrict__ out,\n int num_nodes, int feat_dim) {\n int dst = blockIdx.x;\n int tid = threadIdx.x;\n int start = (int)row_ptr[dst];\n int end = (int)row_ptr[dst + 1];\n int deg = end - start;\n if (deg <= 0) {\n for (int f = tid; f < feat_dim; f += blockDim.x) out[dst * feat_dim + f] = 0.0f;\n return;\n }\n __shared__ int sh_cols[MAX_DEG_SHARED];\n __shared__ float sh_norm[MAX_DEG_SHARED];\n if (deg <= MAX_DEG_SHARED) {\n float dd = degrees[dst];\n for (int i = tid; i < deg; i += blockDim.x) {\n int src = (int)col_idx[start + i];\n sh_cols[i] = src;\n sh_norm[i] = rsqrtf(dd * degrees[src]);\n }\n __syncthreads();\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n for (int i = 0; i < deg; ++i) {\n sum += node_feat[sh_cols[i] * feat_dim + f] * sh_norm[i];\n }\n out[dst * feat_dim + f] = sum;\n }\n } else {\n float dd = degrees[dst];\n for (int f = tid; f < feat_dim; f += blockDim.x) {\n float sum = 0.0f;\n for (int e = start; e < end; ++e) {\n int src = (int)col_idx[e];\n sum += node_feat[src * feat_dim + f] * rsqrtf(dd * degrees[src]);\n }\n out[dst * feat_dim + f] = sum;\n }\n }\n}\n\ntorch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees) {\n const int num_nodes = row_ptr.numel() - 1;\n const int feat_dim = node_feat.size(1);\n auto out = torch::empty({num_nodes, feat_dim}, node_feat.options());\n if (num_nodes == 0 || feat_dim == 0) return out;\n c10::cuda::CUDAGuard device_guard(node_feat.device());\n const int threads = 128;\n dim3 blocks(num_nodes);\n auto stream = at::cuda::getCurrentCUDAStream();\n\n#define DISPATCH80(RT,CT) gcn_kernel80_t<RT,CT><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<RT>(), col_idx.data_ptr<CT>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes)\n#define DISPATCHG(RT,CT) gcn_kernel_generic_t<RT,CT><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<RT>(), col_idx.data_ptr<CT>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim)\n bool f80 = (feat_dim == 80);\n if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kFloat) {\n if (f80) { DISPATCH80(float,float); } else { DISPATCHG(float,float); }\n } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kInt) {\n if (f80) { DISPATCH80(int,int); } else { DISPATCHG(int,int); }\n } else if (row_ptr.scalar_type() == at::kLong && col_idx.scalar_type() == at::kLong) {\n if (f80) { DISPATCH80(int64_t,int64_t); } else { DISPATCHG(int64_t,int64_t); }\n } else if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kInt) {\n if (f80) { DISPATCH80(float,int); } else { DISPATCHG(float,int); }\n } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kFloat) {\n if (f80) { DISPATCH80(int,float); } else { DISPATCHG(int,float); }\n } else {\n TORCH_CHECK(false, \"unsupported row_ptr/col_idx dtypes\");\n }\n#undef DISPATCH80\n#undef DISPATCHG\n return out;\n}\n'''\n\n_gcn_ext = load_inline(\n name='gcn_norm_agg_ext_final',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['gcn_forward'],\n with_cuda=True,\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, row_ptr: torch.Tensor, col_idx: torch.Tensor, node_feat: torch.Tensor, degrees: torch.Tensor) -> torch.Tensor:\n return _gcn_ext.gcn_forward(row_ptr, col_idx, node_feat, degrees)\n" }
result ← submit_kernel (ok)
oksubmit_kernel submit_kernel PASSED: (5 / 5) correctness trials all passed. Kernel runtime: 0.01 μs Runtime stats: mean=0.01μs median=?μs std=0.00μs Numerical precision: max_abs_err=3.58e-07 mean_abs_err=1.92e-08 max_rel_err=1.40e-01 Memory: 33.44 MB peak (ref: 33.45 MB, ratio: 0.9996x) Kernel launches: 1 (ref: 23044, fusion ratio: 23044.0) SOL score: 0.0148 Flagged for excessive speedup — this submission may have been rejected by automated review.
final submitted kernel (156 lines, 6,211 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

_cpp_src = r'''
#include <torch/extension.h>
torch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees);
'''

_cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <cstdint>

#define MAX_DEG_SHARED 1024

template <typename row_t, typename col_t>
__global__ __launch_bounds__(128) void gcn_kernel80_t(const row_t* __restrict__ row_ptr,
                             const col_t* __restrict__ col_idx,
                             const float* __restrict__ node_feat,
                             const float* __restrict__ degrees,
                             float* __restrict__ out,
                             int num_nodes) {
    int dst = blockIdx.x;
    int tid = threadIdx.x;
    int start = (int)row_ptr[dst];
    int end = (int)row_ptr[dst + 1];
    int deg = end - start;

    __shared__ int sh_cols[MAX_DEG_SHARED];
    __shared__ float sh_norm[MAX_DEG_SHARED];

    if (deg > 0 && deg <= MAX_DEG_SHARED) {
        float dd = degrees[dst];
        for (int i = tid; i < deg; i += 128) {
            int src = (int)col_idx[start + i];
            sh_cols[i] = src;
            sh_norm[i] = rsqrtf(dd * degrees[src]);
        }
        __syncthreads();
        if (tid < 80) {
            float sum = 0.0f;
            for (int i = 0; i < deg; ++i) {
                sum += node_feat[sh_cols[i] * 80 + tid] * sh_norm[i];
            }
            out[dst * 80 + tid] = sum;
        }
    } else if (tid < 80) {
        float sum = 0.0f;
        if (deg > 0) {
            float dd = degrees[dst];
            for (int e = start; e < end; ++e) {
                int src = (int)col_idx[e];
                sum += node_feat[src * 80 + tid] * rsqrtf(dd * degrees[src]);
            }
        }
        out[dst * 80 + tid] = sum;
    }
}

template <typename row_t, typename col_t>
__global__ void gcn_kernel_generic_t(const row_t* __restrict__ row_ptr,
                             const col_t* __restrict__ col_idx,
                             const float* __restrict__ node_feat,
                             const float* __restrict__ degrees,
                             float* __restrict__ out,
                             int num_nodes, int feat_dim) {
    int dst = blockIdx.x;
    int tid = threadIdx.x;
    int start = (int)row_ptr[dst];
    int end = (int)row_ptr[dst + 1];
    int deg = end - start;
    if (deg <= 0) {
        for (int f = tid; f < feat_dim; f += blockDim.x) out[dst * feat_dim + f] = 0.0f;
        return;
    }
    __shared__ int sh_cols[MAX_DEG_SHARED];
    __shared__ float sh_norm[MAX_DEG_SHARED];
    if (deg <= MAX_DEG_SHARED) {
        float dd = degrees[dst];
        for (int i = tid; i < deg; i += blockDim.x) {
            int src = (int)col_idx[start + i];
            sh_cols[i] = src;
            sh_norm[i] = rsqrtf(dd * degrees[src]);
        }
        __syncthreads();
        for (int f = tid; f < feat_dim; f += blockDim.x) {
            float sum = 0.0f;
            for (int i = 0; i < deg; ++i) {
                sum += node_feat[sh_cols[i] * feat_dim + f] * sh_norm[i];
            }
            out[dst * feat_dim + f] = sum;
        }
    } else {
        float dd = degrees[dst];
        for (int f = tid; f < feat_dim; f += blockDim.x) {
            float sum = 0.0f;
            for (int e = start; e < end; ++e) {
                int src = (int)col_idx[e];
                sum += node_feat[src * feat_dim + f] * rsqrtf(dd * degrees[src]);
            }
            out[dst * feat_dim + f] = sum;
        }
    }
}

torch::Tensor gcn_forward(torch::Tensor row_ptr, torch::Tensor col_idx, torch::Tensor node_feat, torch::Tensor degrees) {
    const int num_nodes = row_ptr.numel() - 1;
    const int feat_dim = node_feat.size(1);
    auto out = torch::empty({num_nodes, feat_dim}, node_feat.options());
    if (num_nodes == 0 || feat_dim == 0) return out;
    c10::cuda::CUDAGuard device_guard(node_feat.device());
    const int threads = 128;
    dim3 blocks(num_nodes);
    auto stream = at::cuda::getCurrentCUDAStream();

#define DISPATCH80(RT,CT) gcn_kernel80_t<RT,CT><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<RT>(), col_idx.data_ptr<CT>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes)
#define DISPATCHG(RT,CT) gcn_kernel_generic_t<RT,CT><<<blocks, threads, 0, stream>>>(row_ptr.data_ptr<RT>(), col_idx.data_ptr<CT>(), node_feat.data_ptr<float>(), degrees.data_ptr<float>(), out.data_ptr<float>(), num_nodes, feat_dim)
    bool f80 = (feat_dim == 80);
    if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kFloat) {
        if (f80) { DISPATCH80(float,float); } else { DISPATCHG(float,float); }
    } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kInt) {
        if (f80) { DISPATCH80(int,int); } else { DISPATCHG(int,int); }
    } else if (row_ptr.scalar_type() == at::kLong && col_idx.scalar_type() == at::kLong) {
        if (f80) { DISPATCH80(int64_t,int64_t); } else { DISPATCHG(int64_t,int64_t); }
    } else if (row_ptr.scalar_type() == at::kFloat && col_idx.scalar_type() == at::kInt) {
        if (f80) { DISPATCH80(float,int); } else { DISPATCHG(float,int); }
    } else if (row_ptr.scalar_type() == at::kInt && col_idx.scalar_type() == at::kFloat) {
        if (f80) { DISPATCH80(int,float); } else { DISPATCHG(int,float); }
    } else {
        TORCH_CHECK(false, "unsupported row_ptr/col_idx dtypes");
    }
#undef DISPATCH80
#undef DISPATCHG
    return out;
}
'''

_gcn_ext = load_inline(
    name='gcn_norm_agg_ext_final',
    cpp_sources=_cpp_src,
    cuda_sources=_cuda_src,
    functions=['gcn_forward'],
    with_cuda=True,
    extra_cuda_cflags=['-O3', '--use_fast_math'],
    extra_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, degrees: torch.Tensor) -> torch.Tensor:
        return _gcn_ext.gcn_forward(row_ptr, col_idx, node_feat, degrees)