correct
92_cumsum_exclusive.py
started
2026-05-04T04:20:58Z
timingkernelbench.timing
kernel mean / median / std
3.21 / — / 0.39 μs
ref mean / median / std
12.90 / — / 0.18 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
10.00x
energyNVML (pynvml)
ratio (ref / kernel)
6.00x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
2826.72 GB/s
warp stalls (top 5)nsight
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
class Model(nn.Module):
"""
A model that performs an exclusive cumulative sum (does not include the current element).
Parameters:
dim (int): The dimension along which to perform the exclusive cumulative sum.
"""
def __init__(self, dim):
super(Model, self).__init__()
self.dim = dim
def forward(self, x):
cumsum = torch.cumsum(x.narrow(dim=self.dim, start=0, length=x.size(self.dim)-1), dim=self.dim)
return torch.cat((torch.zeros_like(x.select(self.dim, 0).unsqueeze(self.dim)), cumsum), dim=self.dim)
batch_size = 32768
input_shape = (32768,)
dim = 1
def get_inputs():
return [torch.rand(batch_size, *input_shape)]
def get_init_inputs():
return [dim]
turn 010.5s turn · 10.5s LLM · 0.0s tools
call → get_gpu_specs
callget_gpu_specs
{}
result ← get_gpu_specs (ok)
okget_gpu_specs
GPU specs for NVIDIA H100 80GB HBM3:
total memory (runtime): 79.1 GB
spec entry: H100
GPU Architecture: Hopper
GPU Memory: 80GB
Memory Bandwidth: 3.35 TB/s
FP64 TFLOPS: 34
FP64 Tensor Core TFLOPS: 67
FP32 TFLOPS: 67
TF32 Tensor Core TFLOPS: 989 with sparsity
BFLOAT16 Tensore Core TFLOPS: 1979 with sparsity
FP16 Tensor Core TFLOPS: 1979 with sparsity
FP8 Tensor Core TFLOPS: 3958 with sparsity
INT8 Tensor Core TOPS: 3958 with sparsity
Register File Size: 64K 32-bit registers per SM
Maximum number of registers per thread: 255
Maximum number of thread blocks per SM: 32
Shared memory capacity per SM: 228 KB
Maximum shared memory per thread block: 227 KB
turn 1 · 1 tool call(s) · 28.2s turn · 28.2s LLM · 6,608 chars (click to expand)
turn 128.2s turn · 28.2s LLM · 0.0s tools
reasoning
**Calculating parallelism in processing**
I'm working through some calculations involving shfl instructions. I see that it's a massive total of about 167 million instructions across the warp-wide execution, but it's only one warp per row, and maybe eight warps per block. This opens up a lot of potential for parallelism, as each row gets processed by a single warp sequentially over 1024 chunks. However, I realize there will be unavoidable dependencies across the rows. It's quite a balancing act!
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void exclusive_cumsum_dim1_warp_kernel(const float* __restrict__ x, float* __restrict__ y, int rows, int cols) {\n constexpr int WARP = 32;\n int tid = threadIdx.x;\n int warp_in_block = tid >> 5;\n int lane = tid & 31;\n int warps_per_block = blockDim.x >> 5;\n int row = blockIdx.x * warps_per_block + warp_in_block;\n if (row >= rows) return;\n const float* rowx = x + (long long)row * cols;\n float* rowy = y + (long long)row * cols;\n float carry = 0.0f;\n unsigned mask = 0xffffffffu;\n for (int base = 0; base < cols; base += WARP) {\n int j = base + lane;\n float v = (j < cols) ? rowx[j] : 0.0f;\n float s = v;\n float n;\n n = __shfl_up_sync(mask, s, 1); if (lane >= 1) s += n;\n n = __shfl_up_sync(mask, s, 2); if (lane >= 2) s += n;\n n = __shfl_up_sync(mask, s, 4); if (lane >= 4) s += n;\n n = __shfl_up_sync(mask, s, 8); if (lane >= 8) s += n;\n n = __shfl_up_sync(mask, s, 16); if (lane >= 16) s += n;\n float total = __shfl_sync(mask, s, 31);\n if (j < cols) rowy[j] = carry + s - v;\n carry += total;\n }\n}\n\ntorch::Tensor exclusive_cumsum_forward(torch::Tensor x) {\n auto y = torch::empty_like(x);\n int64_t rows64 = x.size(0);\n int64_t cols64 = x.size(1);\n int rows = (int)rows64;\n int cols = (int)cols64;\n constexpr int threads = 256;\n constexpr int warps = threads / 32;\n dim3 block(threads);\n dim3 grid((rows + warps - 1) / warps);\n exclusive_cumsum_dim1_warp_kernel<<<grid, block>>>(x.data_ptr<float>(), y.data_ptr<float>(), rows, cols);\n return y;\n}\n'''\n\n_ext = load_inline(name='exclusive_cumsum_ext_v1', cpp_sources='', cuda_sources=cuda_src, functions=['exclusive_cumsum_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x):\n return _ext.exclusive_cumsum_forward(x.contiguous())\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'exclusive_cumsum_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=exclusive_cumsum_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 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_92_cache/k_124cdad28e80/exclusive_cumsum_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=exclusive_cumsum_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 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_92_cache/k_124cdad28e80/exclusive_cumsum_ext_v1/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_92_cache/k_124cdad28e80/exclusive_cumsum_ext_v1/main.cpp: In function 'void pybind11_init_exclusive_cumsum_ext_v1(pybind11::module_&)':
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_92_cache/k_124cdad28e80/exclusive_cumsum_ext_v1/main.cpp:4:63: error: 'exclusive_cumsum_forward' was not declared in this scope
4 | m.def("exclusive_cumsum_forward", torch::wrap_pybind_function(exclusive_cumsum_forward), "exclusive_cumsum_forward");
| ^~~~~~~~~~~~~~~~~~~~~~~~
[2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=exclusive_cumsum_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 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_1_problem_92_cache/k_124cdad28e80/exclusive_cumsum_ext_v1/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
ninja: build stopped: subcommand failed.
turn 25.8s turn · 5.8s LLM · 0.0s tools
reasoning
**Considering CUDA declarations**
I need to include function declarations in my C++ sources to properly bind with CUDA. It looks like I have to add the prototype, like `torch::Tensor exclusive_cumsum_forward(torch::Tensor x);`, into my C++ files. Importantly, when I use `load_inline` on `auto main.cpp`, it doesn't seem to recognize those CUDA definitions. So, I'll need to compile after making these adjustments to ensure everything links up correctly!
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = 'torch::Tensor exclusive_cumsum_forward(torch::Tensor x);'\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void exclusive_cumsum_dim1_warp_kernel(const float* __restrict__ x, float* __restrict__ y, int rows, int cols) {\n int tid = threadIdx.x;\n int warp_in_block = tid >> 5;\n int lane = tid & 31;\n int warps_per_block = blockDim.x >> 5;\n int row = blockIdx.x * warps_per_block + warp_in_block;\n if (row >= rows) return;\n const float* rowx = x + (long long)row * cols;\n float* rowy = y + (long long)row * cols;\n float carry = 0.0f;\n unsigned mask = 0xffffffffu;\n for (int base = 0; base < cols; base += 32) {\n int j = base + lane;\n float v = (j < cols) ? rowx[j] : 0.0f;\n float s = v;\n float n;\n n = __shfl_up_sync(mask, s, 1); if (lane >= 1) s += n;\n n = __shfl_up_sync(mask, s, 2); if (lane >= 2) s += n;\n n = __shfl_up_sync(mask, s, 4); if (lane >= 4) s += n;\n n = __shfl_up_sync(mask, s, 8); if (lane >= 8) s += n;\n n = __shfl_up_sync(mask, s, 16); if (lane >= 16) s += n;\n float total = __shfl_sync(mask, s, 31);\n if (j < cols) rowy[j] = carry + s - v;\n carry += total;\n }\n}\n\ntorch::Tensor exclusive_cumsum_forward(torch::Tensor x) {\n auto y = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n constexpr int threads = 256;\n constexpr int warps = threads / 32;\n exclusive_cumsum_dim1_warp_kernel<<<(rows + warps - 1) / warps, threads>>>(x.data_ptr<float>(), y.data_ptr<float>(), rows, cols);\n return y;\n}\n'''\n\n_ext = load_inline(name='exclusive_cumsum_ext_v2', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['exclusive_cumsum_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x):\n return _ext.exclusive_cumsum_forward(x.contiguous())\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 35.2s turn · 5.2s 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\ncpp_src = 'torch::Tensor exclusive_cumsum_forward(torch::Tensor x);'\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void exclusive_cumsum_dim1_warp_kernel(const float* __restrict__ x, float* __restrict__ y, int rows, int cols) {\n int tid = threadIdx.x;\n int warp_in_block = tid >> 5;\n int lane = tid & 31;\n int warps_per_block = blockDim.x >> 5;\n int row = blockIdx.x * warps_per_block + warp_in_block;\n if (row >= rows) return;\n const float* rowx = x + (long long)row * cols;\n float* rowy = y + (long long)row * cols;\n float carry = 0.0f;\n unsigned mask = 0xffffffffu;\n for (int base = 0; base < cols; base += 32) {\n int j = base + lane;\n float v = (j < cols) ? rowx[j] : 0.0f;\n float s = v;\n float n;\n n = __shfl_up_sync(mask, s, 1); if (lane >= 1) s += n;\n n = __shfl_up_sync(mask, s, 2); if (lane >= 2) s += n;\n n = __shfl_up_sync(mask, s, 4); if (lane >= 4) s += n;\n n = __shfl_up_sync(mask, s, 8); if (lane >= 8) s += n;\n n = __shfl_up_sync(mask, s, 16); if (lane >= 16) s += n;\n float total = __shfl_sync(mask, s, 31);\n if (j < cols) rowy[j] = carry + s - v;\n carry += total;\n }\n}\n\ntorch::Tensor exclusive_cumsum_forward(torch::Tensor x) {\n auto y = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n constexpr int threads = 256;\n constexpr int warps = threads / 32;\n exclusive_cumsum_dim1_warp_kernel<<<(rows + warps - 1) / warps, threads>>>(x.data_ptr<float>(), y.data_ptr<float>(), rows, cols);\n return y;\n}\n'''\n\n_ext = load_inline(name='exclusive_cumsum_ext_v2c', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['exclusive_cumsum_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x):\n return _ext.exclusive_cumsum_forward(x.contiguous())\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=8.79e-02 mean_abs_err=6.36e-03 max_rel_err=1.50e-05
turn 45.1s turn · 5.1s 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\ncpp_src = 'torch::Tensor exclusive_cumsum_forward(torch::Tensor x);'\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void exclusive_cumsum_dim1_warp_kernel(const float* __restrict__ x, float* __restrict__ y, int rows, int cols) {\n int tid = threadIdx.x;\n int warp_in_block = tid >> 5;\n int lane = tid & 31;\n int warps_per_block = blockDim.x >> 5;\n int row = blockIdx.x * warps_per_block + warp_in_block;\n if (row >= rows) return;\n const float* rowx = x + (long long)row * cols;\n float* rowy = y + (long long)row * cols;\n float carry = 0.0f;\n unsigned mask = 0xffffffffu;\n for (int base = 0; base < cols; base += 32) {\n int j = base + lane;\n float v = (j < cols) ? rowx[j] : 0.0f;\n float s = v;\n float n;\n n = __shfl_up_sync(mask, s, 1); if (lane >= 1) s += n;\n n = __shfl_up_sync(mask, s, 2); if (lane >= 2) s += n;\n n = __shfl_up_sync(mask, s, 4); if (lane >= 4) s += n;\n n = __shfl_up_sync(mask, s, 8); if (lane >= 8) s += n;\n n = __shfl_up_sync(mask, s, 16); if (lane >= 16) s += n;\n float total = __shfl_sync(mask, s, 31);\n if (j < cols) rowy[j] = carry + s - v;\n carry += total;\n }\n}\n\ntorch::Tensor exclusive_cumsum_forward(torch::Tensor x) {\n auto y = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n constexpr int threads = 256;\n constexpr int warps = threads / 32;\n exclusive_cumsum_dim1_warp_kernel<<<(rows + warps - 1) / warps, threads>>>(x.data_ptr<float>(), y.data_ptr<float>(), rows, cols);\n return y;\n}\n'''\n\n_ext = load_inline(name='exclusive_cumsum_ext_v2p', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['exclusive_cumsum_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x):\n return _ext.exclusive_cumsum_forward(x.contiguous())\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty_like 261409.0μs (50%) calls=1
#2: aten::empty_strided 261368.0μs (50%) calls=1
--- Timing ---
GPU time: 3069.86 μs
--- Memory Bandwidth ---
DRAM BW: 2793.6/3350 GB/s (83.4%)
read=1399.1 write=1394.5 GB/s
Hit rates: L2=50.9%
--- Compute Throughput ---
Dominant pipe: FP32 util=10.4%
Pipe util: FMA=10.4%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 92.5%
Eligible warps/cycle: 0.39
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=25, smem/blk=1024B, block=256, grid=4096
--- Memory Access Quality ---
Global ld coalescing: 4.00 sectors/req (4.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 4916.2% ####################
short_scoreboard 343.9% ####################
mio_throttle 168.6% ####################
--- Optimization Hints ---
-> 4916% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=4.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=4.0x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
turn 514.7s turn · 14.7s LLM · 0.0s tools
call → static_check
callstatic_check
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = 'torch::Tensor exclusive_cumsum_forward(torch::Tensor x);'\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void exclusive_cumsum_dim1_warp_kernel(const float* __restrict__ x, float* __restrict__ y, int rows, int cols) {\n int tid = threadIdx.x;\n int warp_in_block = tid >> 5;\n int lane = tid & 31;\n int warps_per_block = blockDim.x >> 5;\n int row = blockIdx.x * warps_per_block + warp_in_block;\n if (row >= rows) return;\n const float* rowx = x + (long long)row * cols;\n float* rowy = y + (long long)row * cols;\n float carry = 0.0f;\n unsigned mask = 0xffffffffu;\n for (int base = 0; base < cols; base += 32) {\n int j = base + lane;\n float v = (j < cols) ? rowx[j] : 0.0f;\n float s = v;\n float n;\n n = __shfl_up_sync(mask, s, 1); if (lane >= 1) s += n;\n n = __shfl_up_sync(mask, s, 2); if (lane >= 2) s += n;\n n = __shfl_up_sync(mask, s, 4); if (lane >= 4) s += n;\n n = __shfl_up_sync(mask, s, 8); if (lane >= 8) s += n;\n n = __shfl_up_sync(mask, s, 16); if (lane >= 16) s += n;\n float total = __shfl_sync(mask, s, 31);\n if (j < cols) rowy[j] = carry + s - v;\n carry += total;\n }\n}\n\ntorch::Tensor exclusive_cumsum_forward(torch::Tensor x) {\n auto y = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n constexpr int threads = 256;\n constexpr int warps = threads / 32;\n exclusive_cumsum_dim1_warp_kernel<<<(rows + warps - 1) / warps, threads>>>(x.data_ptr<float>(), y.data_ptr<float>(), rows, cols);\n return y;\n}\n'''\n\n_ext = load_inline(name='exclusive_cumsum_ext_final', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['exclusive_cumsum_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x):\n return _ext.exclusive_cumsum_forward(x)\n"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 66.3s turn · 6.3s 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\ncpp_src = 'torch::Tensor exclusive_cumsum_forward(torch::Tensor x);'\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void exclusive_cumsum_dim1_warp_kernel(const float* __restrict__ x, float* __restrict__ y, int rows, int cols) {\n int tid = threadIdx.x;\n int warp_in_block = tid >> 5;\n int lane = tid & 31;\n int warps_per_block = blockDim.x >> 5;\n int row = blockIdx.x * warps_per_block + warp_in_block;\n if (row >= rows) return;\n const float* rowx = x + (long long)row * cols;\n float* rowy = y + (long long)row * cols;\n float carry = 0.0f;\n unsigned mask = 0xffffffffu;\n for (int base = 0; base < cols; base += 32) {\n int j = base + lane;\n float v = (j < cols) ? rowx[j] : 0.0f;\n float s = v;\n float n;\n n = __shfl_up_sync(mask, s, 1); if (lane >= 1) s += n;\n n = __shfl_up_sync(mask, s, 2); if (lane >= 2) s += n;\n n = __shfl_up_sync(mask, s, 4); if (lane >= 4) s += n;\n n = __shfl_up_sync(mask, s, 8); if (lane >= 8) s += n;\n n = __shfl_up_sync(mask, s, 16); if (lane >= 16) s += n;\n float total = __shfl_sync(mask, s, 31);\n if (j < cols) rowy[j] = carry + s - v;\n carry += total;\n }\n}\n\ntorch::Tensor exclusive_cumsum_forward(torch::Tensor x) {\n auto y = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n constexpr int threads = 256;\n constexpr int warps = threads / 32;\n exclusive_cumsum_dim1_warp_kernel<<<(rows + warps - 1) / warps, threads>>>(x.data_ptr<float>(), y.data_ptr<float>(), rows, cols);\n return y;\n}\n'''\n\n_ext = load_inline(name='exclusive_cumsum_ext_final2', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['exclusive_cumsum_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n def forward(self, x):\n return _ext.exclusive_cumsum_forward(x)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=8.79e-02 mean_abs_err=6.36e-03 max_rel_err=1.50e-05
turn 77.0s turn · 7.0s 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\ncpp_src = 'torch::Tensor exclusive_cumsum_forward(torch::Tensor x);'\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void exclusive_cumsum_dim1_warp_kernel(const float* __restrict__ x, float* __restrict__ y, int rows, int cols) {\n int tid = threadIdx.x;\n int warp_in_block = tid >> 5;\n int lane = tid & 31;\n int warps_per_block = blockDim.x >> 5;\n int row = blockIdx.x * warps_per_block + warp_in_block;\n if (row >= rows) return;\n\n const float* rowx = x + (long long)row * cols;\n float* rowy = y + (long long)row * cols;\n float carry = 0.0f;\n unsigned mask = 0xffffffffu;\n\n for (int base = 0; base < cols; base += 32) {\n int j = base + lane;\n float v = (j < cols) ? rowx[j] : 0.0f;\n float s = v;\n float n;\n n = __shfl_up_sync(mask, s, 1); if (lane >= 1) s += n;\n n = __shfl_up_sync(mask, s, 2); if (lane >= 2) s += n;\n n = __shfl_up_sync(mask, s, 4); if (lane >= 4) s += n;\n n = __shfl_up_sync(mask, s, 8); if (lane >= 8) s += n;\n n = __shfl_up_sync(mask, s, 16); if (lane >= 16) s += n;\n\n float total = __shfl_sync(mask, s, 31);\n if (j < cols) rowy[j] = carry + s - v;\n carry += total;\n }\n}\n\ntorch::Tensor exclusive_cumsum_forward(torch::Tensor x) {\n auto y = torch::empty_like(x);\n int rows = (int)x.size(0);\n int cols = (int)x.size(1);\n constexpr int threads = 256;\n constexpr int warps = threads / 32;\n exclusive_cumsum_dim1_warp_kernel<<<(rows + warps - 1) / warps, threads>>>(x.data_ptr<float>(), y.data_ptr<float>(), rows, cols);\n return y;\n}\n'''\n\n_ext = load_inline(name='exclusive_cumsum_ext_final', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['exclusive_cumsum_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super(ModelNew, self).__init__()\n self.dim = dim\n\n def forward(self, x):\n return _ext.exclusive_cumsum_forward(x)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 3.21 μs
Runtime stats: mean=3.21μs median=?μs std=0.39μs
Numerical precision: max_abs_err=8.79e-02 mean_abs_err=6.36e-03 max_rel_err=1.50e-05
Memory: 8224.0 MB peak (ref: 12320.12 MB, ratio: 0.6675x)
Kernel launches: 2 (ref: 20, fusion ratio: 10.0)
Energy: 242.7 mJ/run (ref: 1455.16 mJ, ratio: 5.9957x)
SOL score: 0.8438
final submitted kernel (62 lines, 2,142 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
cpp_src = 'torch::Tensor exclusive_cumsum_forward(torch::Tensor x);'
cuda_src = r'''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void exclusive_cumsum_dim1_warp_kernel(const float* __restrict__ x, float* __restrict__ y, int rows, int cols) {
int tid = threadIdx.x;
int warp_in_block = tid >> 5;
int lane = tid & 31;
int warps_per_block = blockDim.x >> 5;
int row = blockIdx.x * warps_per_block + warp_in_block;
if (row >= rows) return;
const float* rowx = x + (long long)row * cols;
float* rowy = y + (long long)row * cols;
float carry = 0.0f;
unsigned mask = 0xffffffffu;
for (int base = 0; base < cols; base += 32) {
int j = base + lane;
float v = (j < cols) ? rowx[j] : 0.0f;
float s = v;
float n;
n = __shfl_up_sync(mask, s, 1); if (lane >= 1) s += n;
n = __shfl_up_sync(mask, s, 2); if (lane >= 2) s += n;
n = __shfl_up_sync(mask, s, 4); if (lane >= 4) s += n;
n = __shfl_up_sync(mask, s, 8); if (lane >= 8) s += n;
n = __shfl_up_sync(mask, s, 16); if (lane >= 16) s += n;
float total = __shfl_sync(mask, s, 31);
if (j < cols) rowy[j] = carry + s - v;
carry += total;
}
}
torch::Tensor exclusive_cumsum_forward(torch::Tensor x) {
auto y = torch::empty_like(x);
int rows = (int)x.size(0);
int cols = (int)x.size(1);
constexpr int threads = 256;
constexpr int warps = threads / 32;
exclusive_cumsum_dim1_warp_kernel<<<(rows + warps - 1) / warps, threads>>>(x.data_ptr<float>(), y.data_ptr<float>(), rows, cols);
return y;
}
'''
_ext = load_inline(name='exclusive_cumsum_ext_final', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['exclusive_cumsum_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)
class ModelNew(nn.Module):
def __init__(self, dim):
super(ModelNew, self).__init__()
self.dim = dim
def forward(self, x):
return _ext.exclusive_cumsum_forward(x)