compile_fail
19_PairwiseDistanceMatrix.py
started
2026-05-04T09:05:02Z
compile error: Error building extension 'pairdist_ext_final': [1/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=pairdist_ext_final -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_HALF
timingkernelbench.timing
kernel mean / median / std
—
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Compute all-pairs Euclidean distance matrix from a 3-D point cloud.
A core primitive in molecular dynamics simulations, docking, and
distance-geometry-based structure prediction.
"""
def __init__(self):
super().__init__()
def forward(self, coords: torch.Tensor) -> torch.Tensor:
"""
Args:
coords: (B, N, 3) – 3-D coordinates of N atoms / residues
Returns:
dist: (B, N, N) – pairwise Euclidean distance matrix
"""
diff = coords.unsqueeze(2) - coords.unsqueeze(1) # (B, N, N, 3)
dist = torch.sqrt((diff ** 2).sum(dim=-1) + 1e-8)
return dist
num_atoms = 512
batch_size = 8
def get_inputs():
p = popcorn_pri
return [torch.randn(p.jitter_int(batch_size), p.jitter_int(num_atoms), 3)]
def get_init_inputs():
return []
turn 05.4s turn · 5.4s LLM · 0.0s tools
reasoning
**Optimizing CUDA for Pairwise Distances**
I'm working out a plan to craft a Python implementation with a CUDA extension. The focus is on optimizing pairwise distances with fixed parameters, specifically B=8 and N=512, but I want to keep it generic. My goal is to compute the square root of the difference of squares and output a matrix with around 2 million floats. I might explore using PyTorch for reference and look into vectorized computations, considering symmetry for efficiency. I should also prepare for compiling and running the code.
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 113.1s turn · 13.1s 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_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void pairdist_kernel(const float* __restrict__ coords, float* __restrict__ out, int B, int N) {\n long long total = (long long)B * N * N;\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long stride = (long long)blockDim.x * gridDim.x;\n for (; idx < total; idx += stride) {\n int j = idx % N;\n long long t = idx / N;\n int i = t % N;\n int b = t / N;\n const float* pi = coords + ((long long)b * N + i) * 3;\n const float* pj = coords + ((long long)b * N + j) * 3;\n float dx = pi[0] - pj[0];\n float dy = pi[1] - pj[1];\n float dz = pi[2] - pj[2];\n out[idx] = sqrtf(dx*dx + dy*dy + dz*dz + 1.0e-8f);\n }\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n int N = (int)coords.size(1);\n auto out = torch::empty({B, N, N}, coords.options());\n long long total = (long long)B * N * N;\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n pairdist_kernel<<<blocks, threads>>>(coords.data_ptr<float>(), out.data_ptr<float>(), B, N);\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_v1', cpp_sources='', cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'pairdist_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=pairdist_ext_v1 -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/l12_all_gpt/popcorn/gpt-5.5-priority/level_1_problem_19_cache/k_f99df600ec1f/pairdist_ext_v1/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=pairdist_ext_v1 -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/l12_all_gpt/popcorn/gpt-5.5-priority/level_1_problem_19_cache/k_f99df600ec1f/pairdist_ext_v1/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_1_problem_19_cache/k_f99df600ec1f/pairdist_ext_v1/main.cpp: In function 'void pybind11_init_pairdist_ext_v1(pybind11::module_&)':
/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_1_problem_19_cache/k_f99df600ec1f/pairdist_ext_v1/main.cpp:4:55: error: 'pairdist_forward' was not declared in this scope
4 | m.def("pairdist_forward", torch::wrap_pybind_function(pairdist_forward), "pairdist_forward");
| ^~~~~~~~~~~~~~~~
ninja: build stopped: subcommand failed.
turn 26.6s turn · 6.6s LLM · 0.0s tools
reasoning
**Clarifying function declarations**
I'm considering how to properly declare a function in `cpp_sources`. It seems like I need to load an inline auto module in `main.cpp`, which should have the necessary declaration in `cpp_sources`. I’ll need to use `cpp_sources` with the Torch include and ensure there's a forward declaration. It looks like the CUDA source has the actual definition, so that should work out in the end! I just need to confirm all parts fit together correctly.
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 pairdist_forward(torch::Tensor coords);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void pairdist_kernel(const float* __restrict__ coords, float* __restrict__ out, int B, int N) {\n long long total = (long long)B * N * N;\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long stride = (long long)blockDim.x * gridDim.x;\n for (; idx < total; idx += stride) {\n int j = idx % N;\n long long t = idx / N;\n int i = t % N;\n int b = t / N;\n const float* pi = coords + ((long long)b * N + i) * 3;\n const float* pj = coords + ((long long)b * N + j) * 3;\n float dx = pi[0] - pj[0];\n float dy = pi[1] - pj[1];\n float dz = pi[2] - pj[2];\n out[idx] = sqrtf(dx*dx + dy*dy + dz*dz + 1.0e-8f);\n }\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n int N = (int)coords.size(1);\n auto out = torch::empty({B, N, N}, coords.options());\n long long total = (long long)B * N * N;\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n pairdist_kernel<<<blocks, threads>>>(coords.data_ptr<float>(), out.data_ptr<float>(), B, N);\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_v2', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 34.5s turn · 4.5s 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 pairdist_forward(torch::Tensor coords);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void pairdist_kernel(const float* __restrict__ coords, float* __restrict__ out, int B, int N) {\n long long total = (long long)B * N * N;\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long stride = (long long)blockDim.x * gridDim.x;\n for (; idx < total; idx += stride) {\n int j = idx % N;\n long long t = idx / N;\n int i = t % N;\n int b = t / N;\n const float* pi = coords + ((long long)b * N + i) * 3;\n const float* pj = coords + ((long long)b * N + j) * 3;\n float dx = pi[0] - pj[0];\n float dy = pi[1] - pj[1];\n float dz = pi[2] - pj[2];\n out[idx] = sqrtf(dx*dx + dy*dy + dz*dz + 1.0e-8f);\n }\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n int N = (int)coords.size(1);\n auto out = torch::empty({B, N, N}, coords.options());\n long long total = (long long)B * N * N;\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n pairdist_kernel<<<blocks, threads>>>(coords.data_ptr<float>(), out.data_ptr<float>(), B, N);\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_v2c', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=9.54e-07 mean_abs_err=5.12e-08 max_rel_err=2.23e-07
turn 45.0s turn · 5.0s LLM · 0.0s tools
call → profile_kernel
callprofile_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 pairdist_forward(torch::Tensor coords);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void pairdist_kernel(const float* __restrict__ coords, float* __restrict__ out, int B, int N) {\n long long total = (long long)B * N * N;\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long stride = (long long)blockDim.x * gridDim.x;\n for (; idx < total; idx += stride) {\n int j = idx % N;\n long long t = idx / N;\n int i = t % N;\n int b = t / N;\n const float* pi = coords + ((long long)b * N + i) * 3;\n const float* pj = coords + ((long long)b * N + j) * 3;\n float dx = pi[0] - pj[0];\n float dy = pi[1] - pj[1];\n float dz = pi[2] - pj[2];\n out[idx] = sqrtf(dx*dx + dy*dy + dz*dz + 1.0e-8f);\n }\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n int N = (int)coords.size(1);\n auto out = torch::empty({B, N, N}, coords.options());\n long long total = (long long)B * N * N;\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n pairdist_kernel<<<blocks, threads>>>(coords.data_ptr<float>(), out.data_ptr<float>(), B, N);\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_v2p', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 2834.0μs (100%) calls=1
--- Timing ---
GPU time: 15.10 μs
--- Memory Bandwidth ---
DRAM BW: 4.2/3350 GB/s (0.1%)
read=4.0 write=0.2 GB/s
Hit rates: L2=97.0%
--- Compute Throughput ---
Dominant pipe: FP32 util=30.6%
Pipe util: FMA=30.6%, Tensor=1.9%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 85.7%
Eligible warps/cycle: 5.46
Launch: regs/thread=32, smem/blk=1024B, block=256, grid=8192
--- Memory Access Quality ---
Global ld coalescing: 6.50 sectors/req (6.5x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
short_scoreboard 92.4% ##################
long_scoreboard 84.1% ################
mio_throttle 28.5% #####
--- Optimization Hints ---
-> 92% stalls: shared memory / L1 / MIO dependency. Check smem layout for bank conflicts, double-buffer, or async copies.
-> Global load coalescing=6.5x (ideal=1.0) -> 6x wasted DRAM BW. Make per-warp accesses contiguous.
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
turn 515.8s turn · 15.8s 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 pairdist_forward(torch::Tensor coords);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ __launch_bounds__(256) void pairdist2d_kernel(const float* __restrict__ coords, float* __restrict__ out, int N) {\n int j = blockIdx.x * 32 + threadIdx.x;\n int i = blockIdx.y * 8 + threadIdx.y;\n int b = blockIdx.z;\n if (i < N && j < N) {\n long long base_b = (long long)b * N;\n const float* pi = coords + (base_b + i) * 3;\n const float* pj = coords + (base_b + j) * 3;\n float dx = pi[0] - pj[0];\n float dy = pi[1] - pj[1];\n float dz = pi[2] - pj[2];\n out[(base_b + i) * N + j] = sqrtf(fmaf(dx, dx, fmaf(dy, dy, dz*dz)) + 1.0e-8f);\n }\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n int N = (int)coords.size(1);\n auto out = torch::empty({B, N, N}, coords.options());\n dim3 block(32, 8, 1);\n dim3 grid((N + 31) / 32, (N + 7) / 8, B);\n pairdist2d_kernel<<<grid, block>>>(coords.data_ptr<float>(), out.data_ptr<float>(), N);\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_v3c', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=9.54e-07 mean_abs_err=5.03e-08 max_rel_err=2.13e-07
turn 65.5s turn · 5.5s LLM · 0.0s tools
call → profile_kernel
callprofile_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 pairdist_forward(torch::Tensor coords);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ __launch_bounds__(256) void pairdist2d_kernel(const float* __restrict__ coords, float* __restrict__ out, int N) {\n int j = blockIdx.x * 32 + threadIdx.x;\n int i = blockIdx.y * 8 + threadIdx.y;\n int b = blockIdx.z;\n if (i < N && j < N) {\n long long base_b = (long long)b * N;\n const float* pi = coords + (base_b + i) * 3;\n const float* pj = coords + (base_b + j) * 3;\n float dx = pi[0] - pj[0];\n float dy = pi[1] - pj[1];\n float dz = pi[2] - pj[2];\n out[(base_b + i) * N + j] = sqrtf(fmaf(dx, dx, fmaf(dy, dy, dz*dz)) + 1.0e-8f);\n }\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n int N = (int)coords.size(1);\n auto out = torch::empty({B, N, N}, coords.options());\n dim3 block(32, 8, 1);\n dim3 grid((N + 31) / 32, (N + 7) / 8, B);\n pairdist2d_kernel<<<grid, block>>>(coords.data_ptr<float>(), out.data_ptr<float>(), N);\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_v3p', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 2366.0μs (100%) calls=1
--- Timing ---
GPU time: 8.06 μs [↓7.0μs ok]
--- Memory Bandwidth ---
DRAM BW: 6.8/3350 GB/s (0.2%) [↑0.1% ok]
read=6.5 write=0.3 GB/s
Hit rates: L2=97.8%
--- Compute Throughput ---
Dominant pipe: FP32 util=22.8% [↓7.8% REGRESSED]
Pipe util: FMA=22.8%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 65.8% [↓19.9% REGRESSED]
Eligible warps/cycle: 1.38
Launch: regs/thread=18, smem/blk=1024B, block=256, grid=8192
--- Memory Access Quality ---
Global ld coalescing: 6.50 sectors/req (6.5x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 821.8% ####################
short_scoreboard 174.7% ####################
mio_throttle 90.0% #################
--- Optimization Hints ---
-> 822% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=6.5 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=6.5x (ideal=1.0) -> 6x wasted DRAM BW. Make per-warp accesses contiguous.
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
--- Delta vs Previous Iteration ---
GPU time: 15.10→8.06μs (↓7.04) ok
DRAM BW%: 0.13→0.20% (↑0.08) ok
Compute%: 30.58→22.78% (↓7.81) REGRESSED
Occupancy: 85.67→65.82% (↓19.85) REGRESSED
Eligible warps/cyc: 5.46→1.38 (↓4.08) REGRESSED
L2 hit%: 96.97→97.79% (↑0.82) ok
turn 716.4s turn · 16.4s 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 pairdist_forward(torch::Tensor coords);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ __launch_bounds__(256) void pairdist_sym_kernel(const float* __restrict__ coords, float* __restrict__ out, int N) {\n int tj = blockIdx.x;\n int ti = blockIdx.y;\n int b = blockIdx.z;\n if (tj < ti) return;\n int j = tj * 16 + threadIdx.x;\n int i = ti * 16 + threadIdx.y;\n if (i >= N || j >= N) return;\n long long base_b = (long long)b * N;\n if (tj == ti && j < i) return;\n const float* pi = coords + (base_b + i) * 3;\n const float* pj = coords + (base_b + j) * 3;\n float dx = pi[0] - pj[0];\n float dy = pi[1] - pj[1];\n float dz = pi[2] - pj[2];\n float d = sqrtf(fmaf(dx, dx, fmaf(dy, dy, dz*dz)) + 1.0e-8f);\n out[(base_b + i) * N + j] = d;\n if (j != i) out[(base_b + j) * N + i] = d;\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n int N = (int)coords.size(1);\n auto out = torch::empty({B, N, N}, coords.options());\n dim3 block(16, 16, 1);\n dim3 grid((N + 15) / 16, (N + 15) / 16, B);\n pairdist_sym_kernel<<<grid, block>>>(coords.data_ptr<float>(), out.data_ptr<float>(), N);\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_symc', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=9.54e-07 mean_abs_err=5.03e-08 max_rel_err=2.13e-07
turn 85.4s turn · 5.4s LLM · 0.0s tools
call → profile_kernel
callprofile_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 pairdist_forward(torch::Tensor coords);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ __launch_bounds__(256) void pairdist_sym_kernel(const float* __restrict__ coords, float* __restrict__ out, int N) {\n int tj = blockIdx.x;\n int ti = blockIdx.y;\n int b = blockIdx.z;\n if (tj < ti) return;\n int j = tj * 16 + threadIdx.x;\n int i = ti * 16 + threadIdx.y;\n if (i >= N || j >= N) return;\n long long base_b = (long long)b * N;\n if (tj == ti && j < i) return;\n const float* pi = coords + (base_b + i) * 3;\n const float* pj = coords + (base_b + j) * 3;\n float dx = pi[0] - pj[0];\n float dy = pi[1] - pj[1];\n float dz = pi[2] - pj[2];\n float d = sqrtf(fmaf(dx, dx, fmaf(dy, dy, dz*dz)) + 1.0e-8f);\n out[(base_b + i) * N + j] = d;\n if (j != i) out[(base_b + j) * N + i] = d;\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n int N = (int)coords.size(1);\n auto out = torch::empty({B, N, N}, coords.options());\n dim3 block(16, 16, 1);\n dim3 grid((N + 15) / 16, (N + 15) / 16, B);\n pairdist_sym_kernel<<<grid, block>>>(coords.data_ptr<float>(), out.data_ptr<float>(), N);\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_symp', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 2264.0μs (100%) calls=1
--- Timing ---
GPU time: 8.35 μs [↑0.3μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 6.3/3350 GB/s (0.2%) [↓0.0% REGRESSED]
read=6.3 write=0.0 GB/s
Hit rates: L2=96.9%
--- Compute Throughput ---
Dominant pipe: FP32 util=15.2% [↓7.6% REGRESSED]
Pipe util: FMA=15.2%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 67.6% [↑1.8% ok]
Eligible warps/cycle: 1.07
Launch: regs/thread=22, smem/blk=1024B, block=256, grid=8192
--- Memory Access Quality ---
Global ld coalescing: 3.60 sectors/req (3.6x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 968.2% ####################
short_scoreboard 291.2% ####################
mio_throttle 152.0% ####################
--- Optimization Hints ---
-> 968% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=3.6 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=3.6x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
--- Delta vs Previous Iteration ---
GPU time: 8.06→8.35μs (↑0.29) REGRESSED
DRAM BW%: 0.20→0.19% (↓0.01) REGRESSED
Compute%: 22.78→15.16% (↓7.61) REGRESSED
Occupancy: 65.82→67.61% (↑1.80) ok
Eligible warps/cyc: 1.38→1.07 (↓0.31) REGRESSED
Ld sectors/req: 6.50→3.60 (↓2.90) ok
L2 hit%: 97.79→96.95% (↓0.84) REGRESSED
turn 99.1s turn · 9.1s 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 pairdist_forward(torch::Tensor coords);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ __launch_bounds__(256) void pairdist2d_sh_kernel(const float* __restrict__ coords, float* __restrict__ out, int N) {\n __shared__ float si[8][3];\n __shared__ float sj[32][3];\n int tx = threadIdx.x;\n int ty = threadIdx.y;\n int tid = ty * 32 + tx;\n int i0 = blockIdx.y * 8;\n int j0 = blockIdx.x * 32;\n int b = blockIdx.z;\n long long base_b = (long long)b * N;\n if (tid < 24) {\n int r = tid / 3;\n int c = tid - r * 3;\n int ii = i0 + r;\n si[r][c] = (ii < N) ? coords[(base_b + ii) * 3 + c] : 0.0f;\n }\n if (tid < 96) {\n int r = tid / 3;\n int c = tid - r * 3;\n int jj = j0 + r;\n sj[r][c] = (jj < N) ? coords[(base_b + jj) * 3 + c] : 0.0f;\n }\n __syncthreads();\n int i = i0 + ty;\n int j = j0 + tx;\n if (i < N && j < N) {\n float dx = si[ty][0] - sj[tx][0];\n float dy = si[ty][1] - sj[tx][1];\n float dz = si[ty][2] - sj[tx][2];\n out[(base_b + i) * N + j] = sqrtf(fmaf(dx, dx, fmaf(dy, dy, dz*dz)) + 1.0e-8f);\n }\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n int N = (int)coords.size(1);\n auto out = torch::empty({B, N, N}, coords.options());\n dim3 block(32, 8, 1);\n dim3 grid((N + 31) / 32, (N + 7) / 8, B);\n pairdist2d_sh_kernel<<<grid, block>>>(coords.data_ptr<float>(), out.data_ptr<float>(), N);\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_v4c', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=9.54e-07 mean_abs_err=5.03e-08 max_rel_err=2.13e-07
turn 109.5s turn · 9.5s LLM · 0.0s tools
call → profile_kernel
callprofile_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 pairdist_forward(torch::Tensor coords);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ __launch_bounds__(256) void pairdist2d_sh_kernel(const float* __restrict__ coords, float* __restrict__ out, int N) {\n __shared__ float si[8][3];\n __shared__ float sj[32][3];\n int tx = threadIdx.x;\n int ty = threadIdx.y;\n int tid = ty * 32 + tx;\n int i0 = blockIdx.y * 8;\n int j0 = blockIdx.x * 32;\n int b = blockIdx.z;\n long long base_b = (long long)b * N;\n if (tid < 24) {\n int r = tid / 3;\n int c = tid - r * 3;\n int ii = i0 + r;\n si[r][c] = (ii < N) ? coords[(base_b + ii) * 3 + c] : 0.0f;\n }\n if (tid < 96) {\n int r = tid / 3;\n int c = tid - r * 3;\n int jj = j0 + r;\n sj[r][c] = (jj < N) ? coords[(base_b + jj) * 3 + c] : 0.0f;\n }\n __syncthreads();\n int i = i0 + ty;\n int j = j0 + tx;\n if (i < N && j < N) {\n float dx = si[ty][0] - sj[tx][0];\n float dy = si[ty][1] - sj[tx][1];\n float dz = si[ty][2] - sj[tx][2];\n out[(base_b + i) * N + j] = sqrtf(fmaf(dx, dx, fmaf(dy, dy, dz*dz)) + 1.0e-8f);\n }\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n int N = (int)coords.size(1);\n auto out = torch::empty({B, N, N}, coords.options());\n dim3 block(32, 8, 1);\n dim3 grid((N + 31) / 32, (N + 7) / 8, B);\n pairdist2d_sh_kernel<<<grid, block>>>(coords.data_ptr<float>(), out.data_ptr<float>(), N);\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_v4p', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 2159.0μs (100%) calls=1
--- Timing ---
GPU time: 10.14 μs [↑1.8μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 5.3/3350 GB/s (0.2%) [↓0.0% REGRESSED]
read=5.3 write=0.0 GB/s
Hit rates: L2=98.3%
--- Compute Throughput ---
Dominant pipe: FP32 util=19.0% [↑3.9% ok]
Pipe util: FMA=19.0%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 80.2% [↑12.6% ok]
Eligible warps/cycle: 1.62
Launch: regs/thread=20, smem/blk=1536B, block=256, grid=8192
--- Memory Access Quality ---
Global ld coalescing: 3.75 sectors/req (3.7x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 182.6% ####################
short_scoreboard 174.4% ####################
mio_throttle 165.4% ####################
--- Optimization Hints ---
-> 183% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=3.7 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=3.7x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
--- Delta vs Previous Iteration ---
GPU time: 8.35→10.14μs (↑1.79) REGRESSED
DRAM BW%: 0.19→0.16% (↓0.03) REGRESSED
Compute%: 15.16→19.02% (↑3.86) ok
Occupancy: 67.61→80.19% (↑12.58) ok
Eligible warps/cyc: 1.07→1.62 (↑0.54) ok
Ld sectors/req: 3.60→3.75 (↑0.15) REGRESSED
L2 hit%: 96.95→98.31% (↑1.36) ok
turn 117.5s turn · 7.5s LLM · 0.0s tools
call → disassemble_kernel
calldisassemble_kernel
{
"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 pairdist_forward(torch::Tensor coords);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n__global__ __launch_bounds__(256) void pairdist2d_kernel(const float* __restrict__ coords, float* __restrict__ out, int N) {\n int j = blockIdx.x * 32 + threadIdx.x;\n int i = blockIdx.y * 8 + threadIdx.y;\n int b = blockIdx.z;\n if (i < N && j < N) {\n long long base_b = (long long)b * N;\n const float* pi = coords + (base_b + i) * 3;\n const float* pj = coords + (base_b + j) * 3;\n float dx = pi[0] - pj[0];\n float dy = pi[1] - pj[1];\n float dz = pi[2] - pj[2];\n out[(base_b + i) * N + j] = sqrtf(fmaf(dx, dx, fmaf(dy, dy, dz*dz)) + 1.0e-8f);\n }\n}\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n int N = (int)coords.size(1);\n auto out = torch::empty({B, N, N}, coords.options());\n dim3 block(32, 8, 1);\n dim3 grid((N + 31) / 32, (N + 7) / 8, B);\n pairdist2d_kernel<<<grid, block>>>(coords.data_ptr<float>(), out.data_ptr<float>(), N);\n return out;\n}\n'''\n_ext = load_inline(name='pairdist_ext_dis', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['pairdist_forward'], extra_cuda_cflags=['-O3','--use_fast_math'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor: return _ext.pairdist_forward(coords)\n"
}
result ← disassemble_kernel (ok)
okdisassemble_kernel
disassemble_kernel PASSED: disassembly analysis complete.
=== Kernel Disassembly Summary ===
--- Instruction Mix ---
compute_int 20 ( 31.2%)
misc 15 ( 23.4%)
compute_fp32 8 ( 12.5%)
memory_global 7 ( 10.9%)
move 6 ( 9.4%)
other 3 ( 4.7%)
control_flow 3 ( 4.7%)
memory_const 2 ( 3.1%)
TOTAL 64
--- Warnings ---
cubin extraction: [Errno 18] Invalid cross-device link: 'conv_mish_ext_v3dp.1.sm_90.cubin' -> '/var/tmp/kb_nvdisasm_iouxyw29/conv_mish_ext_v3dp.1.sm_90.cubin'
turn 128.1s turn · 8.1s 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 pairdist_forward(torch::Tensor coords);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ __launch_bounds__(256) void pairdist512_kernel(const float* __restrict__ coords, float* __restrict__ out) {\n int j = (blockIdx.x << 5) + threadIdx.x;\n int i = (blockIdx.y << 3) + threadIdx.y;\n int b = blockIdx.z;\n long long base = ((long long)b << 9);\n const float* pi = coords + ((base + i) * 3);\n const float* pj = coords + ((base + j) * 3);\n float dx = pi[0] - pj[0];\n float dy = pi[1] - pj[1];\n float dz = pi[2] - pj[2];\n out[((base + i) << 9) + j] = sqrtf(fmaf(dx, dx, fmaf(dy, dy, dz*dz)) + 1.0e-8f);\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n auto out = torch::empty({B, 512, 512}, coords.options());\n dim3 block(32, 8, 1);\n dim3 grid(16, 64, B);\n pairdist512_kernel<<<grid, block>>>(coords.data_ptr<float>(), out.data_ptr<float>());\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_512c', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=9.54e-07 mean_abs_err=5.03e-08 max_rel_err=2.13e-07
turn 135.6s turn · 5.6s LLM · 0.0s tools
call → profile_kernel
callprofile_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 pairdist_forward(torch::Tensor coords);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ __launch_bounds__(256) void pairdist512_kernel(const float* __restrict__ coords, float* __restrict__ out) {\n int j = (blockIdx.x << 5) + threadIdx.x;\n int i = (blockIdx.y << 3) + threadIdx.y;\n int b = blockIdx.z;\n long long base = ((long long)b << 9);\n const float* pi = coords + ((base + i) * 3);\n const float* pj = coords + ((base + j) * 3);\n float dx = pi[0] - pj[0];\n float dy = pi[1] - pj[1];\n float dz = pi[2] - pj[2];\n out[((base + i) << 9) + j] = sqrtf(fmaf(dx, dx, fmaf(dy, dy, dz*dz)) + 1.0e-8f);\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n auto out = torch::empty({B, 512, 512}, coords.options());\n dim3 block(32, 8, 1);\n dim3 grid(16, 64, B);\n pairdist512_kernel<<<grid, block>>>(coords.data_ptr<float>(), out.data_ptr<float>());\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_512p', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 2335.0μs (100%) calls=1
--- Timing ---
GPU time: 8.10 μs [↓2.0μs ok]
--- Memory Bandwidth ---
DRAM BW: 6.5/3350 GB/s (0.2%) [↑0.0% ok]
read=6.5 write=0.0 GB/s
Hit rates: L2=97.9%
--- Compute Throughput ---
Dominant pipe: FP32 util=18.5% [↓0.5% REGRESSED]
Pipe util: FMA=18.5%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 64.7% [↓15.5% REGRESSED]
Eligible warps/cycle: 1.08
Launch: regs/thread=18, smem/blk=1024B, block=256, grid=8192
--- Memory Access Quality ---
Global ld coalescing: 6.50 sectors/req (6.5x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 1104.1% ####################
mio_throttle 217.6% ####################
short_scoreboard 102.5% ####################
--- Optimization Hints ---
-> 1104% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=6.5 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=6.5x (ideal=1.0) -> 6x wasted DRAM BW. Make per-warp accesses contiguous.
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
--- Delta vs Previous Iteration ---
GPU time: 10.14→8.10μs (↓2.05) ok
DRAM BW%: 0.16→0.19% (↑0.04) ok
Compute%: 19.02→18.53% (↓0.50) REGRESSED
Occupancy: 80.19→64.68% (↓15.51) REGRESSED
Eligible warps/cyc: 1.62→1.08 (↓0.54) REGRESSED
Ld sectors/req: 3.75→6.50 (↑2.75) REGRESSED
L2 hit%: 98.31→97.88% (↓0.43) REGRESSED
turn 147.9s turn · 7.9s LLM · 0.0s tools · FINAL
reasoning
**Considering submission options**
I need to submit, comparing the performance of version 8.06 generic against the hardcoded 8.10. The generic version might be more robust, but the hardcoded one is fixed at output shape 512, which seems fine for the constructor without arguments. I wonder if 8.06's slightly better performance in v3 generic matters here. Submitting the hardcoded version might reduce variance, but I still need clarity on whether static is necessary. I'll make sure to finalize and include complete code with a unique name!
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 pairdist_forward(torch::Tensor coords);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ __launch_bounds__(256) void pairdist2d_kernel(const float* __restrict__ coords, float* __restrict__ out, int N) {\n int j = blockIdx.x * 32 + threadIdx.x;\n int i = blockIdx.y * 8 + threadIdx.y;\n int b = blockIdx.z;\n if (i < N && j < N) {\n long long base_b = (long long)b * N;\n const float* pi = coords + (base_b + i) * 3;\n const float* pj = coords + (base_b + j) * 3;\n float dx = pi[0] - pj[0];\n float dy = pi[1] - pj[1];\n float dz = pi[2] - pj[2];\n out[(base_b + i) * N + j] = sqrtf(fmaf(dx, dx, fmaf(dy, dy, dz*dz)) + 1.0e-8f);\n }\n}\n\ntorch::Tensor pairdist_forward(torch::Tensor coords) {\n int B = (int)coords.size(0);\n int N = (int)coords.size(1);\n auto out = torch::empty({B, N, N}, coords.options());\n dim3 block(32, 8, 1);\n dim3 grid((N + 31) / 32, (N + 7) / 8, B);\n pairdist2d_kernel<<<grid, block>>>(coords.data_ptr<float>(), out.data_ptr<float>(), N);\n return out;\n}\n'''\n\n_ext = load_inline(name='pairdist_ext_final', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n def forward(self, coords: torch.Tensor) -> torch.Tensor:\n return _ext.pairdist_forward(coords)\n"
}
result ← submit_kernel (fail)
failsubmit_kernel
submit_kernel FAILED: kernel did not compile.
Error building extension 'pairdist_ext_final': [1/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=pairdist_ext_final -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_1_problem_19_cache/k_decb84b3ec5b/pairdist_ext_final/cuda.cu -o cuda.cuda.o
FAILED: [code=1] cuda.cuda.o
/usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=pairdist_ext_final -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_1_problem_19_cache/k_decb84b3ec5b/pairdist_ext_final/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
<built-in>: fatal error: when writing output to /var/tmp/tmpxft_003efb16_00000000-5_cuda.cpp4.ii: No space left on device
compilation terminated.
[2/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=pairdist_ext_final -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/l12_all_gpt/popcorn/gpt-5.5-priority/level_1_problem_19_cache/k_decb84b3ec5b/pairdist_ext_final/main.cpp -o main.o
ninja: build stopped: subcommand failed.
final submitted kernel (50 lines, 1,664 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 pairdist_forward(torch::Tensor coords);
'''
_cuda_src = r'''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>
__global__ __launch_bounds__(256) void pairdist2d_kernel(const float* __restrict__ coords, float* __restrict__ out, int N) {
int j = blockIdx.x * 32 + threadIdx.x;
int i = blockIdx.y * 8 + threadIdx.y;
int b = blockIdx.z;
if (i < N && j < N) {
long long base_b = (long long)b * N;
const float* pi = coords + (base_b + i) * 3;
const float* pj = coords + (base_b + j) * 3;
float dx = pi[0] - pj[0];
float dy = pi[1] - pj[1];
float dz = pi[2] - pj[2];
out[(base_b + i) * N + j] = sqrtf(fmaf(dx, dx, fmaf(dy, dy, dz*dz)) + 1.0e-8f);
}
}
torch::Tensor pairdist_forward(torch::Tensor coords) {
int B = (int)coords.size(0);
int N = (int)coords.size(1);
auto out = torch::empty({B, N, N}, coords.options());
dim3 block(32, 8, 1);
dim3 grid((N + 31) / 32, (N + 7) / 8, B);
pairdist2d_kernel<<<grid, block>>>(coords.data_ptr<float>(), out.data_ptr<float>(), N);
return out;
}
'''
_ext = load_inline(name='pairdist_ext_final', cpp_sources=_cpp_src, cuda_sources=_cuda_src,
functions=['pairdist_forward'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)
class ModelNew(nn.Module):
def __init__(self):
super().__init__()
def forward(self, coords: torch.Tensor) -> torch.Tensor:
return _ext.pairdist_forward(coords)