correct
43_Conv3d_Max_LogSumExp_ReLU.py
started
2026-05-04T07:40:17Z
timingkernelbench.timing
kernel mean / median / std
6.51 / — / 0.02 μs
ref mean / median / std
6.39 / — / 0.03 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
4.00x
energyNVML (pynvml)
ratio (ref / kernel)
0.77x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
6342.32 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):
"""
Model that performs a 3D convolution, max pooling, log sum exp, and ReLU activation.
"""
def __init__(self, in_channels, out_channels, kernel_size, stride, padding):
super(Model, self).__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)
self.max_pool = nn.MaxPool3d(kernel_size=2, stride=2)
def forward(self, x):
"""
Args:
x: Input tensor of shape (batch_size, in_channels, depth, height, width)
Returns:
Output tensor of shape (batch_size, out_channels, depth', height', width')
"""
x = self.conv(x)
x = self.max_pool(x)
x = torch.logsumexp(x, dim=1, keepdim=True)
x = torch.relu(x)
return x
batch_size = 4
in_channels = 32
out_channels = 64
depth, height, width = 32, 128, 128
kernel_size = 3
stride = 1
padding = 1
def get_inputs():
return [torch.rand(batch_size, in_channels, depth, height, width)]
def get_init_inputs():
return [in_channels, out_channels, kernel_size, stride, padding]turn 010.2s turn · 10.2s LLM · 0.0s tools
reasoning
**Considering custom C++ in float FMAs**
I'm thinking about how custom C++ can utilize float FMAs and how that may differ when using cuDNN, with convolution outputs potentially being around the 1e-4 level. There’s a possibility that the order in which these operations are performed could influence the results. It seems I need to analyze the specifics further to understand how these factors might affect computations and outputs. Let’s explore this in more depth!
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 127.8s turn · 27.8s 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 fused_conv_pool_lse_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias);\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 fused_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ bias,\n float* __restrict__ out,\n int B, int C, int D, int H, int Wd,\n int O, int Dp, int Hp, int Wp) {\n int idx = blockIdx.x;\n int tid = threadIdx.x; // 0..63, one per output channel\n int px = idx % Wp;\n int t = idx / Wp;\n int py = t % Hp;\n t /= Hp;\n int pz = t % Dp;\n int b = t / Dp;\n\n float pooled = -INFINITY;\n if (tid < O) {\n // max over 2x2x2 pooled convolution outputs\n #pragma unroll\n for (int dzp = 0; dzp < 2; ++dzp) {\n int oz = (pz << 1) + dzp;\n #pragma unroll\n for (int dyp = 0; dyp < 2; ++dyp) {\n int oy = (py << 1) + dyp;\n #pragma unroll\n for (int dxp = 0; dxp < 2; ++dxp) {\n int ox = (px << 1) + dxp;\n float acc = bias[tid];\n // conv3d k=3 stride=1 pad=1\n for (int ic = 0; ic < C; ++ic) {\n #pragma unroll\n for (int kz = 0; kz < 3; ++kz) {\n int iz = oz + kz - 1;\n if ((unsigned)iz >= (unsigned)D) continue;\n #pragma unroll\n for (int ky = 0; ky < 3; ++ky) {\n int iy = oy + ky - 1;\n if ((unsigned)iy >= (unsigned)H) continue;\n #pragma unroll\n for (int kx = 0; kx < 3; ++kx) {\n int ix = ox + kx - 1;\n if ((unsigned)ix >= (unsigned)Wd) continue;\n size_t xoff = (((size_t)b*C + ic)*D + iz)*H*Wd + (size_t)iy*Wd + ix;\n size_t woff = (((size_t)tid*C + ic)*3 + kz)*3*3 + ky*3 + kx;\n acc = fmaf(x[xoff], w[woff], acc);\n }\n }\n }\n }\n pooled = fmaxf(pooled, acc);\n }\n }\n }\n }\n __shared__ float sm[64];\n sm[tid] = pooled;\n __syncthreads();\n // reduce max over channels\n for (int s = 32; s > 0; s >>= 1) {\n if (tid < s) sm[tid] = fmaxf(sm[tid], sm[tid + s]);\n __syncthreads();\n }\n float m = sm[0];\n float e = 0.0f;\n if (tid < O) e = expf(pooled - m);\n sm[tid] = e;\n __syncthreads();\n for (int s = 32; s > 0; s >>= 1) {\n if (tid < s) sm[tid] += sm[tid + s];\n __syncthreads();\n }\n if (tid == 0) {\n float val = logf(sm[0]) + m;\n if (val < 0.0f) val = 0.0f;\n out[idx] = val;\n }\n}\n\ntorch::Tensor fused_conv_pool_lse_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias) {\n TORCH_CHECK(x.is_cuda(), \"x must be cuda\");\n TORCH_CHECK(x.scalar_type() == at::kFloat, \"float only\");\n auto xc = x.contiguous();\n auto wc = w.contiguous();\n auto bc = bias.contiguous();\n int B = xc.size(0), C = xc.size(1), D = xc.size(2), H = xc.size(3), Wd = xc.size(4);\n int O = wc.size(0);\n int Dp = D / 2, Hp = H / 2, Wp = Wd / 2;\n auto out = torch::empty({B, 1, Dp, Hp, Wp}, xc.options());\n int blocks = B * Dp * Hp * Wp;\n fused_kernel<<<blocks, 64>>>(xc.data_ptr<float>(), wc.data_ptr<float>(), bc.data_ptr<float>(), out.data_ptr<float>(), B, C, D, H, Wd, O, Dp, Hp, Wp);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"fused_conv3d_pool_lse_ext_v1\", cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=[\"fused_conv_pool_lse_forward\"], extra_cuda_cflags=[\"-O3\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)\n def forward(self, x):\n return _ext.fused_conv_pool_lse_forward(x, self.conv.weight, self.conv.bias)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 28.4s turn · 8.4s 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 fused_conv_pool_lse_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias);\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 fused_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ bias,\n float* __restrict__ out,\n int B, int C, int D, int H, int Wd,\n int O, int Dp, int Hp, int Wp) {\n int idx = blockIdx.x;\n int tid = threadIdx.x; // 0..63, one per output channel\n int px = idx % Wp;\n int t = idx / Wp;\n int py = t % Hp;\n t /= Hp;\n int pz = t % Dp;\n int b = t / Dp;\n\n float pooled = -INFINITY;\n if (tid < O) {\n // max over 2x2x2 pooled convolution outputs\n #pragma unroll\n for (int dzp = 0; dzp < 2; ++dzp) {\n int oz = (pz << 1) + dzp;\n #pragma unroll\n for (int dyp = 0; dyp < 2; ++dyp) {\n int oy = (py << 1) + dyp;\n #pragma unroll\n for (int dxp = 0; dxp < 2; ++dxp) {\n int ox = (px << 1) + dxp;\n float acc = bias[tid];\n // conv3d k=3 stride=1 pad=1\n for (int ic = 0; ic < C; ++ic) {\n #pragma unroll\n for (int kz = 0; kz < 3; ++kz) {\n int iz = oz + kz - 1;\n if ((unsigned)iz >= (unsigned)D) continue;\n #pragma unroll\n for (int ky = 0; ky < 3; ++ky) {\n int iy = oy + ky - 1;\n if ((unsigned)iy >= (unsigned)H) continue;\n #pragma unroll\n for (int kx = 0; kx < 3; ++kx) {\n int ix = ox + kx - 1;\n if ((unsigned)ix >= (unsigned)Wd) continue;\n size_t xoff = (((size_t)b*C + ic)*D + iz)*H*Wd + (size_t)iy*Wd + ix;\n size_t woff = (((size_t)tid*C + ic)*3 + kz)*3*3 + ky*3 + kx;\n acc = fmaf(x[xoff], w[woff], acc);\n }\n }\n }\n }\n pooled = fmaxf(pooled, acc);\n }\n }\n }\n }\n __shared__ float sm[64];\n sm[tid] = pooled;\n __syncthreads();\n // reduce max over channels\n for (int s = 32; s > 0; s >>= 1) {\n if (tid < s) sm[tid] = fmaxf(sm[tid], sm[tid + s]);\n __syncthreads();\n }\n float m = sm[0];\n float e = 0.0f;\n if (tid < O) e = expf(pooled - m);\n sm[tid] = e;\n __syncthreads();\n for (int s = 32; s > 0; s >>= 1) {\n if (tid < s) sm[tid] += sm[tid + s];\n __syncthreads();\n }\n if (tid == 0) {\n float val = logf(sm[0]) + m;\n if (val < 0.0f) val = 0.0f;\n out[idx] = val;\n }\n}\n\ntorch::Tensor fused_conv_pool_lse_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias) {\n TORCH_CHECK(x.is_cuda(), \"x must be cuda\");\n TORCH_CHECK(x.scalar_type() == at::kFloat, \"float only\");\n auto xc = x.contiguous();\n auto wc = w.contiguous();\n auto bc = bias.contiguous();\n int B = xc.size(0), C = xc.size(1), D = xc.size(2), H = xc.size(3), Wd = xc.size(4);\n int O = wc.size(0);\n int Dp = D / 2, Hp = H / 2, Wp = Wd / 2;\n auto out = torch::empty({B, 1, Dp, Hp, Wp}, xc.options());\n int blocks = B * Dp * Hp * Wp;\n fused_kernel<<<blocks, 64>>>(xc.data_ptr<float>(), wc.data_ptr<float>(), bc.data_ptr<float>(), out.data_ptr<float>(), B, C, D, H, Wd, O, Dp, Hp, Wp);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"fused_conv3d_pool_lse_ext_v1\", cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=[\"fused_conv_pool_lse_forward\"], extra_cuda_cflags=[\"-O3\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)\n def forward(self, x):\n return _ext.fused_conv_pool_lse_forward(x, self.conv.weight, self.conv.bias)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=4.77e-07 mean_abs_err=7.81e-08 max_rel_err=1.08e-07
turn 3 · 1 tool call(s) · 8.6s turn · 8.6s LLM · 6,536 chars (click to expand)
turn 38.6s turn · 8.6s 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 fused_conv_pool_lse_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias);\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 fused_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ bias,\n float* __restrict__ out,\n int B, int C, int D, int H, int Wd,\n int O, int Dp, int Hp, int Wp) {\n int idx = blockIdx.x;\n int tid = threadIdx.x;\n int px = idx % Wp;\n int t = idx / Wp;\n int py = t % Hp;\n t /= Hp;\n int pz = t % Dp;\n int b = t / Dp;\n\n float pooled = -INFINITY;\n if (tid < O) {\n #pragma unroll\n for (int dzp = 0; dzp < 2; ++dzp) {\n int oz = (pz << 1) + dzp;\n #pragma unroll\n for (int dyp = 0; dyp < 2; ++dyp) {\n int oy = (py << 1) + dyp;\n #pragma unroll\n for (int dxp = 0; dxp < 2; ++dxp) {\n int ox = (px << 1) + dxp;\n float acc = bias[tid];\n for (int ic = 0; ic < C; ++ic) {\n #pragma unroll\n for (int kz = 0; kz < 3; ++kz) {\n int iz = oz + kz - 1;\n if ((unsigned)iz >= (unsigned)D) continue;\n #pragma unroll\n for (int ky = 0; ky < 3; ++ky) {\n int iy = oy + ky - 1;\n if ((unsigned)iy >= (unsigned)H) continue;\n #pragma unroll\n for (int kx = 0; kx < 3; ++kx) {\n int ix = ox + kx - 1;\n if ((unsigned)ix >= (unsigned)Wd) continue;\n size_t xoff = (((size_t)b*C + ic)*D + iz)*H*Wd + (size_t)iy*Wd + ix;\n size_t woff = (((size_t)tid*C + ic)*3 + kz)*3*3 + ky*3 + kx;\n acc = fmaf(x[xoff], w[woff], acc);\n }\n }\n }\n }\n pooled = fmaxf(pooled, acc);\n }\n }\n }\n }\n __shared__ float sm[64];\n sm[tid] = pooled;\n __syncthreads();\n for (int s = 32; s > 0; s >>= 1) {\n if (tid < s) sm[tid] = fmaxf(sm[tid], sm[tid + s]);\n __syncthreads();\n }\n float m = sm[0];\n float e = 0.0f;\n if (tid < O) e = expf(pooled - m);\n sm[tid] = e;\n __syncthreads();\n for (int s = 32; s > 0; s >>= 1) {\n if (tid < s) sm[tid] += sm[tid + s];\n __syncthreads();\n }\n if (tid == 0) {\n float val = logf(sm[0]) + m;\n if (val < 0.0f) val = 0.0f;\n out[idx] = val;\n }\n}\n\ntorch::Tensor fused_conv_pool_lse_forward(torch::Tensor x, torch::Tensor w, torch::Tensor bias) {\n auto xc = x.contiguous(); auto wc = w.contiguous(); auto bc = bias.contiguous();\n int B = xc.size(0), C = xc.size(1), D = xc.size(2), H = xc.size(3), Wd = xc.size(4);\n int O = wc.size(0); int Dp = D/2, Hp=H/2, Wp=Wd/2;\n auto out = torch::empty({B,1,Dp,Hp,Wp}, xc.options());\n fused_kernel<<<B*Dp*Hp*Wp,64>>>(xc.data_ptr<float>(), wc.data_ptr<float>(), bc.data_ptr<float>(), out.data_ptr<float>(), B,C,D,H,Wd,O,Dp,Hp,Wp);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"fused_conv3d_pool_lse_ext_v1p\", cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=[\"fused_conv_pool_lse_forward\"], extra_cuda_cflags=[\"-O3\"], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding):\n super(ModelNew,self).__init__(); self.conv=nn.Conv3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding)\n def forward(self,x): return _ext.fused_conv_pool_lse_forward(x,self.conv.weight,self.conv.bias)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 38.0μs (100%) calls=1
--- Timing ---
GPU time: 443225.22 μs
--- Memory Bandwidth ---
DRAM BW: 0.7/3350 GB/s (0.0%)
read=0.6 write=0.0 GB/s
Hit rates: L2=99.2%
--- Compute Throughput ---
Pipe util: FMA=2.7%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 62.0%
Eligible warps/cycle: 0.13
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=48, smem/blk=1280B, block=64, grid=262144
--- Memory Access Quality ---
Global ld coalescing: 16.50 sectors/req (16.5x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 7201.8% ####################
mio_throttle 104.0% ####################
short_scoreboard 39.5% #######
--- Optimization Hints ---
-> 7202% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=16.5 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=16.5x (ideal=1.0) -> 16x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching).
turn 425.1s turn · 25.1s LLM · 0.0s tools
reasoning
**Evaluating convolutional performance**
I’m thinking about making things faster since convolution dominates but maybe post-processing will be significant. I need a benchmark for each output block or thread. Should I use `torch.nn.functional.conv3d` in `ModelNew.forward`? Importing `F` seems necessary. Using an extension for `pool_lse(conv)` might work. I'm curious if calling `F.conv3d` would be a fallback or acceptable, especially if I seek custom modifications. I’ll need to test correctness and speed for that custom extension.
**Analyzing data throughput**
I’m calculating throughput with 262k blocks and 64 threads, resulting in 134 million floats, which translates to about 536MB. I'm wondering if coalescing is managed correctly. Does each thread read one channel across nearby positions continuously across threads? I want to ensure that the memory accesses are efficient. It seems like I need to double-check how the channel reads are structured for optimal performance. Let’s make sure everything is aligned properly for better efficiency!
**Reviewing implementation choices**
I’m considering the coalesced and transposed stages and wondering if a simpler one-kernel approach would work better. It seems like a straightforward profiling could be beneficial. I think implementing a fused convolution layer, like F.conv3d plus fused_pool_lse, might simplify things, allowing one block per output. I suspect the profiling could show that the timing includes both convolution and the kernel, so I’m going to take a closer look at that.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor pool_lse_forward(torch::Tensor y);\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__ void pool_lse_kernel(const float* __restrict__ y, float* __restrict__ out, int B,int C,int D,int H,int W,int Dp,int Hp,int Wp){\n int idx=blockIdx.x; int tid=threadIdx.x;\n int px=idx%Wp; int t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; int b=t/Dp;\n int oz=pz*2, oy=py*2, ox=px*2;\n float v=-INFINITY;\n if(tid<C){\n size_t base=(((size_t)b*C+tid)*D+oz)*H*W+(size_t)oy*W+ox;\n float a0=y[base]; float a1=y[base+1]; float a2=y[base+W]; float a3=y[base+W+1];\n size_t base2=base+(size_t)H*W;\n float a4=y[base2]; float a5=y[base2+1]; float a6=y[base2+W]; float a7=y[base2+W+1];\n v=fmaxf(fmaxf(fmaxf(a0,a1),fmaxf(a2,a3)), fmaxf(fmaxf(a4,a5),fmaxf(a6,a7)));\n }\n __shared__ float sm[64]; sm[tid]=v; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]=fmaxf(sm[tid],sm[tid+s]); __syncthreads(); }\n float m=sm[0]; float e=0.f; if(tid<C) e=expf(v-m); sm[tid]=e; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]+=sm[tid+s]; __syncthreads(); }\n if(tid==0){ float val=logf(sm[0])+m; out[idx]= val>0.f?val:0.f; }\n}\ntorch::Tensor pool_lse_forward(torch::Tensor y){\n auto yc=y.contiguous(); int B=yc.size(0), C=yc.size(1), D=yc.size(2), H=yc.size(3), W=yc.size(4); int Dp=D/2, Hp=H/2, Wp=W/2;\n auto out=torch::empty({B,1,Dp,Hp,Wp}, yc.options());\n pool_lse_kernel<<<B*Dp*Hp*Wp,64>>>(yc.data_ptr<float>(),out.data_ptr<float>(),B,C,D,H,W,Dp,Hp,Wp);\n return out;\n}\n\"\"\"\n_ext=load_inline(name=\"pool_lse_ext_v1\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"pool_lse_forward\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding):\n super().__init__(); self.conv=nn.Conv3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding)\n def forward(self,x):\n y=F.conv3d(x,self.conv.weight,self.conv.bias,stride=1,padding=1)\n return _ext.pool_lse_forward(y)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 55.3s turn · 5.3s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor pool_lse_forward(torch::Tensor y);\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__ void pool_lse_kernel(const float* __restrict__ y, float* __restrict__ out, int B,int C,int D,int H,int W,int Dp,int Hp,int Wp){\n int idx=blockIdx.x; int tid=threadIdx.x;\n int px=idx%Wp; int t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; int b=t/Dp;\n int oz=pz*2, oy=py*2, ox=px*2;\n float v=-INFINITY;\n if(tid<C){\n size_t base=(((size_t)b*C+tid)*D+oz)*H*W+(size_t)oy*W+ox;\n float a0=y[base]; float a1=y[base+1]; float a2=y[base+W]; float a3=y[base+W+1];\n size_t base2=base+(size_t)H*W;\n float a4=y[base2]; float a5=y[base2+1]; float a6=y[base2+W]; float a7=y[base2+W+1];\n v=fmaxf(fmaxf(fmaxf(a0,a1),fmaxf(a2,a3)), fmaxf(fmaxf(a4,a5),fmaxf(a6,a7)));\n }\n __shared__ float sm[64]; sm[tid]=v; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]=fmaxf(sm[tid],sm[tid+s]); __syncthreads(); }\n float m=sm[0]; float e=0.f; if(tid<C) e=expf(v-m); sm[tid]=e; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]+=sm[tid+s]; __syncthreads(); }\n if(tid==0){ float val=logf(sm[0])+m; out[idx]= val>0.f?val:0.f; }\n}\ntorch::Tensor pool_lse_forward(torch::Tensor y){\n auto yc=y.contiguous(); int B=yc.size(0), C=yc.size(1), D=yc.size(2), H=yc.size(3), W=yc.size(4); int Dp=D/2, Hp=H/2, Wp=W/2;\n auto out=torch::empty({B,1,Dp,Hp,Wp}, yc.options());\n pool_lse_kernel<<<B*Dp*Hp*Wp,64>>>(yc.data_ptr<float>(),out.data_ptr<float>(),B,C,D,H,W,Dp,Hp,Wp);\n return out;\n}\n\"\"\"\n_ext=load_inline(name=\"pool_lse_ext_v1\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"pool_lse_forward\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding):\n super().__init__(); self.conv=nn.Conv3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding)\n def forward(self,x):\n y=F.conv3d(x,self.conv.weight,self.conv.bias,stride=1,padding=1)\n return _ext.pool_lse_forward(y)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (2 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.275778', '0.312114', '0.266972']
avg_difference: ['0.000001', '0.000001', '0.000003']
turn 614.8s turn · 14.8s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor pool_lse_forward(torch::Tensor y);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n__global__ void pool_lse_kernel(const float* __restrict__ y, float* __restrict__ out, int B,int C,int D,int H,int W,int Dp,int Hp,int Wp){\n int idx=blockIdx.x; int tid=threadIdx.x;\n int px=idx%Wp; int t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; int b=t/Dp;\n int oz=pz*2, oy=py*2, ox=px*2;\n float v=-INFINITY;\n if(tid<C){\n size_t base=(((size_t)b*C+tid)*D+oz)*H*W+(size_t)oy*W+ox;\n float a0=y[base]; float a1=y[base+1]; float a2=y[base+W]; float a3=y[base+W+1];\n size_t base2=base+(size_t)H*W;\n float a4=y[base2]; float a5=y[base2+1]; float a6=y[base2+W]; float a7=y[base2+W+1];\n v=fmaxf(fmaxf(fmaxf(a0,a1),fmaxf(a2,a3)), fmaxf(fmaxf(a4,a5),fmaxf(a6,a7)));\n }\n __shared__ float sm[64]; sm[tid]=v; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]=fmaxf(sm[tid],sm[tid+s]); __syncthreads(); }\n float m=sm[0]; float e=0.f; if(tid<C) e=expf(v-m); sm[tid]=e; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]+=sm[tid+s]; __syncthreads(); }\n if(tid==0){ float val=logf(sm[0])+m; out[idx]= val>0.f?val:0.f; }\n}\ntorch::Tensor pool_lse_forward(torch::Tensor y){\n auto yc=y.contiguous(); int B=yc.size(0), C=yc.size(1), D=yc.size(2), H=yc.size(3), W=yc.size(4); int Dp=D/2, Hp=H/2, Wp=W/2;\n auto out=torch::empty({B,1,Dp,Hp,Wp}, yc.options());\n cudaStream_t stream=at::cuda::getCurrentCUDAStream();\n pool_lse_kernel<<<B*Dp*Hp*Wp,64,0,stream>>>(yc.data_ptr<float>(),out.data_ptr<float>(),B,C,D,H,W,Dp,Hp,Wp);\n return out;\n}\n\"\"\"\n_ext=load_inline(name=\"pool_lse_ext_v2\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"pool_lse_forward\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding):\n super().__init__(); self.conv=nn.Conv3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding)\n def forward(self,x):\n y=F.conv3d(x,self.conv.weight,self.conv.bias,stride=self.conv.stride,padding=self.conv.padding)\n return _ext.pool_lse_forward(y)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (2 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.498487', '0.276892', '0.453501']
avg_difference: ['0.000003', '0.000001', '0.000002']
turn 7 · 1 tool call(s) · 15.8s turn · 15.8s LLM · 7,103 chars (click to expand)
turn 715.8s turn · 15.8s LLM · 0.0s tools
reasoning
**Evaluating convolution methods**
I’m realizing that our reference convolution and the fused convolution runs are separate. It seems that the full fused convolution computed is deterministic in FP32 and matches with the reference. But wait, the full fused didn’t call cuDNN, which could explain the matching since the reference convolution doesn't use TF32 and has the same accumulation. Overall, it all seems to work out well!
**Considering convolution layers**
I'm thinking about how this relates to nn.Conv3d. It seems like they are pretty similar, but maybe there's a difference that I’m missing. Perhaps harness does copy the state_dict, but the self.conv module doesn’t have max pooling. I wonder if it would be better to use y = self.conv(x) instead of using F.conv3d directly. Let’s explore that option and see if it simplifies things or improves performance.
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 pool_lse_forward(torch::Tensor y);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n__global__ void pool_lse_kernel(const float* __restrict__ y, float* __restrict__ out, int B,int C,int D,int H,int W,int Dp,int Hp,int Wp){\n int idx=blockIdx.x; int tid=threadIdx.x;\n int px=idx%Wp; int t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; int b=t/Dp;\n size_t spatial=(size_t)D*H*W;\n int oz=pz*2, oy=py*2, ox=px*2;\n float v=-INFINITY;\n if(tid<C){\n size_t base=((size_t)b*C+tid)*spatial + (size_t)oz*H*W + (size_t)oy*W + ox;\n float a0=y[base]; float a1=y[base+1]; float a2=y[base+W]; float a3=y[base+W+1];\n size_t base2=base+(size_t)H*W;\n float a4=y[base2]; float a5=y[base2+1]; float a6=y[base2+W]; float a7=y[base2+W+1];\n v=fmaxf(fmaxf(fmaxf(a0,a1),fmaxf(a2,a3)), fmaxf(fmaxf(a4,a5),fmaxf(a6,a7)));\n }\n __shared__ float sm[64]; sm[tid]=v; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]=fmaxf(sm[tid],sm[tid+s]); __syncthreads(); }\n float m=sm[0]; sm[tid]=(tid<C)?expf(v-m):0.f; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]+=sm[tid+s]; __syncthreads(); }\n if(tid==0){ float val=logf(sm[0])+m; out[idx]= val>0.f?val:0.f; }\n}\ntorch::Tensor pool_lse_forward(torch::Tensor y){\n auto yc=y.contiguous(); int B=yc.size(0), C=yc.size(1), D=yc.size(2), H=yc.size(3), W=yc.size(4); int Dp=D/2, Hp=H/2, Wp=W/2;\n auto out=torch::empty({B,1,Dp,Hp,Wp}, yc.options());\n at::cuda::CUDAGuard device_guard(yc.device());\n cudaStream_t stream=at::cuda::getCurrentCUDAStream(yc.get_device());\n pool_lse_kernel<<<B*Dp*Hp*Wp,64,0,stream>>>(yc.data_ptr<float>(),out.data_ptr<float>(),B,C,D,H,W,Dp,Hp,Wp);\n return out;\n}\n\"\"\"\n_ext=load_inline(name=\"pool_lse_ext_v3\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"pool_lse_forward\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding):\n super().__init__(); self.conv=nn.Conv3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding)\n def forward(self,x):\n y=self.conv(x)\n return _ext.pool_lse_forward(y)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: kernel did not compile.
Error building extension 'pool_lse_ext_v3': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=pool_lse_ext_v3 -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/original/gpt-5.5-priority/level_2_problem_43_cache/k_286ef44f122f/pool_lse_ext_v3/main.cpp -o main.o
[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=pool_lse_ext_v3 -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_2_problem_43_cache/k_286ef44f122f/pool_lse_ext_v3/cuda.cu -o cuda.cuda.o
FAILED: [code=2] 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=pool_lse_ext_v3 -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_2_problem_43_cache/k_286ef44f122f/pool_lse_ext_v3/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_43_cache/k_286ef44f122f/pool_lse_ext_v3/cuda.cu(32): error: namespace "at::cuda" has no member "CUDAGuard"
at::cuda::CUDAGuard device_guard(yc.device());
^
1 error detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_43_cache/k_286ef44f122f/pool_lse_ext_v3/cuda.cu".
ninja: build stopped: subcommand failed.
turn 811.4s turn · 11.4s LLM · 0.0s tools
reasoning
**Investigating CUDA issues**
I’m noting a mismatch that seems random but isn't related to race. It looks like I might need to launch with `at::cuda::getDefaultCUDAStream()` if the conv function uses the default. I remember that the v1 default had the same failure. But I’ll switch gears and try finalizing with custom commands, using `torch.cuda.synchronize()` to check for correctness and see if it resolves the race condition. Let's see how that goes!
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 pool_lse_forward(torch::Tensor y);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n__global__ void pool_lse_kernel(const float* __restrict__ y, float* __restrict__ out, int B,int C,int D,int H,int W,int Dp,int Hp,int Wp){\n int idx=blockIdx.x, tid=threadIdx.x; int px=idx%Wp; int t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; int b=t/Dp; int oz=pz*2,oy=py*2,ox=px*2; float v=-INFINITY; if(tid<C){size_t base=(((size_t)b*C+tid)*D+oz)*H*W+(size_t)oy*W+ox; float a0=y[base],a1=y[base+1],a2=y[base+W],a3=y[base+W+1]; size_t b2=base+(size_t)H*W; float a4=y[b2],a5=y[b2+1],a6=y[b2+W],a7=y[b2+W+1]; v=fmaxf(fmaxf(fmaxf(a0,a1),fmaxf(a2,a3)),fmaxf(fmaxf(a4,a5),fmaxf(a6,a7)));} __shared__ float sm[64]; sm[tid]=v; __syncthreads(); for(int s=32;s>0;s>>=1){if(tid<s) sm[tid]=fmaxf(sm[tid],sm[tid+s]); __syncthreads();} float m=sm[0]; sm[tid]=tid<C?expf(v-m):0; __syncthreads(); for(int s=32;s>0;s>>=1){if(tid<s) sm[tid]+=sm[tid+s]; __syncthreads();} if(tid==0){float val=logf(sm[0])+m; out[idx]=val>0?val:0;}}\ntorch::Tensor pool_lse_forward(torch::Tensor y){auto yc=y.contiguous(); int B=yc.size(0),C=yc.size(1),D=yc.size(2),H=yc.size(3),W=yc.size(4),Dp=D/2,Hp=H/2,Wp=W/2; auto out=torch::empty({B,1,Dp,Hp,Wp},yc.options()); auto stream=at::cuda::getCurrentCUDAStream(); pool_lse_kernel<<<B*Dp*Hp*Wp,64,0,stream>>>(yc.data_ptr<float>(),out.data_ptr<float>(),B,C,D,H,W,Dp,Hp,Wp); return out;}\n\"\"\"\n_ext=load_inline(name=\"pool_lse_ext_v4\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"pool_lse_forward\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding): super().__init__(); self.conv=nn.Conv3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding)\n def forward(self,x):\n y=self.conv(x); torch.cuda.synchronize(); return _ext.pool_lse_forward(y)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (2 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.319743', '0.418358', '0.389413']
avg_difference: ['0.000003', '0.000003', '0.000002']
turn 927.6s turn · 27.6s LLM · 0.0s tools
reasoning
**Thinking about optimization**
I’m considering how to handle weight addresses for constant oc values and c loops. Hmm, should I separate the boundaries from the interior kernel to avoid branches? It feels like focusing more on the interior would be simpler. I want to utilize the current stream effectively to ensure everything is correct. It’s important to get this right for optimal performance in whatever task I’m tackling!
call → compile_kernel
callcompile_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 fused_two_stage(torch::Tensor x, torch::Tensor w, torch::Tensor bias);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void poolconv_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ p,\n int B,int C,int D,int H,int Wd,int O,int Dp,int Hp,int Wp){\n size_t idx=blockIdx.x*blockDim.x+threadIdx.x; size_t total=(size_t)B*O*Dp*Hp*Wp; if(idx>=total) return;\n int px=idx%Wp; size_t t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; t/=Dp; int oc=t%O; int b=t/O;\n float best=-INFINITY;\n #pragma unroll\n for(int dzp=0;dzp<2;++dzp){int oz=(pz<<1)+dzp;\n #pragma unroll\n for(int dyp=0;dyp<2;++dyp){int oy=(py<<1)+dyp;\n #pragma unroll\n for(int dxp=0;dxp<2;++dxp){int ox=(px<<1)+dxp; float acc=bias[oc];\n for(int ic=0;ic<C;++ic){\n #pragma unroll\n for(int kz=0;kz<3;++kz){int iz=oz+kz-1; if((unsigned)iz>=(unsigned)D) continue;\n #pragma unroll\n for(int ky=0;ky<3;++ky){int iy=oy+ky-1; if((unsigned)iy>=(unsigned)H) continue;\n #pragma unroll\n for(int kx=0;kx<3;++kx){int ix=ox+kx-1; if((unsigned)ix>=(unsigned)Wd) continue;\n size_t xo=(((size_t)b*C+ic)*D+iz)*H*Wd+(size_t)iy*Wd+ix;\n size_t wo=(((size_t)oc*C+ic)*3+kz)*9+ky*3+kx;\n acc=fmaf(x[xo],w[wo],acc);\n }\n }\n }\n }\n best=fmaxf(best,acc);\n }\n }\n }\n p[idx]=best;\n}\n\n__global__ void lse_kernel(const float* __restrict__ p,float* __restrict__ out,int B,int O,int Dp,int Hp,int Wp){\n int idx=blockIdx.x; int tid=threadIdx.x; int px=idx%Wp; int t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; int b=t/Dp;\n float v=-INFINITY; if(tid<O){ size_t po=(((size_t)b*O+tid)*Dp+pz)*Hp*Wp+(size_t)py*Wp+px; v=p[po]; }\n __shared__ float sm[64]; sm[tid]=v; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]=fmaxf(sm[tid],sm[tid+s]); __syncthreads(); }\n float m=sm[0]; sm[tid]=(tid<O)?expf(v-m):0.f; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]+=sm[tid+s]; __syncthreads(); }\n if(tid==0){ float val=logf(sm[0])+m; out[idx]=val>0.f?val:0.f; }\n}\n\ntorch::Tensor fused_two_stage(torch::Tensor x, torch::Tensor w, torch::Tensor bias){\n auto xc=x.contiguous(); auto wc=w.contiguous(); auto bc=bias.contiguous();\n int B=xc.size(0),C=xc.size(1),D=xc.size(2),H=xc.size(3),Wd=xc.size(4),O=wc.size(0); int Dp=D/2,Hp=H/2,Wp=Wd/2;\n auto pool=torch::empty({B,O,Dp,Hp,Wp},xc.options()); auto out=torch::empty({B,1,Dp,Hp,Wp},xc.options());\n cudaStream_t stream=at::cuda::getCurrentCUDAStream(); size_t total=(size_t)B*O*Dp*Hp*Wp; int block=128; int grid=(total+block-1)/block;\n poolconv_kernel<<<grid,block,0,stream>>>(xc.data_ptr<float>(),wc.data_ptr<float>(),bc.data_ptr<float>(),pool.data_ptr<float>(),B,C,D,H,Wd,O,Dp,Hp,Wp);\n lse_kernel<<<B*Dp*Hp*Wp,64,0,stream>>>(pool.data_ptr<float>(),out.data_ptr<float>(),B,O,Dp,Hp,Wp);\n return out;\n}\n\"\"\"\n_ext=load_inline(name=\"fused_two_stage_v1\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"fused_two_stage\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding): super().__init__(); self.conv=nn.Conv3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding)\n def forward(self,x): return _ext.fused_two_stage(x,self.conv.weight,self.conv.bias)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 109.2s turn · 9.2s 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_cpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor fused_two_stage(torch::Tensor x, torch::Tensor w, torch::Tensor bias);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void poolconv_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ p,\n int B,int C,int D,int H,int Wd,int O,int Dp,int Hp,int Wp){\n size_t idx=blockIdx.x*blockDim.x+threadIdx.x; size_t total=(size_t)B*O*Dp*Hp*Wp; if(idx>=total) return;\n int px=idx%Wp; size_t t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; t/=Dp; int oc=t%O; int b=t/O;\n float best=-INFINITY;\n #pragma unroll\n for(int dzp=0;dzp<2;++dzp){int oz=(pz<<1)+dzp;\n #pragma unroll\n for(int dyp=0;dyp<2;++dyp){int oy=(py<<1)+dyp;\n #pragma unroll\n for(int dxp=0;dxp<2;++dxp){int ox=(px<<1)+dxp; float acc=bias[oc];\n for(int ic=0;ic<C;++ic){\n #pragma unroll\n for(int kz=0;kz<3;++kz){int iz=oz+kz-1; if((unsigned)iz>=(unsigned)D) continue;\n #pragma unroll\n for(int ky=0;ky<3;++ky){int iy=oy+ky-1; if((unsigned)iy>=(unsigned)H) continue;\n #pragma unroll\n for(int kx=0;kx<3;++kx){int ix=ox+kx-1; if((unsigned)ix>=(unsigned)Wd) continue;\n size_t xo=(((size_t)b*C+ic)*D+iz)*H*Wd+(size_t)iy*Wd+ix;\n size_t wo=(((size_t)oc*C+ic)*3+kz)*9+ky*3+kx;\n acc=fmaf(x[xo],w[wo],acc);\n }\n }\n }\n }\n best=fmaxf(best,acc);\n }\n }\n }\n p[idx]=best;\n}\n\n__global__ void lse_kernel(const float* __restrict__ p,float* __restrict__ out,int B,int O,int Dp,int Hp,int Wp){\n int idx=blockIdx.x; int tid=threadIdx.x; int px=idx%Wp; int t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; int b=t/Dp;\n float v=-INFINITY; if(tid<O){ size_t po=(((size_t)b*O+tid)*Dp+pz)*Hp*Wp+(size_t)py*Wp+px; v=p[po]; }\n __shared__ float sm[64]; sm[tid]=v; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]=fmaxf(sm[tid],sm[tid+s]); __syncthreads(); }\n float m=sm[0]; sm[tid]=(tid<O)?expf(v-m):0.f; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]+=sm[tid+s]; __syncthreads(); }\n if(tid==0){ float val=logf(sm[0])+m; out[idx]=val>0.f?val:0.f; }\n}\n\ntorch::Tensor fused_two_stage(torch::Tensor x, torch::Tensor w, torch::Tensor bias){\n auto xc=x.contiguous(); auto wc=w.contiguous(); auto bc=bias.contiguous();\n int B=xc.size(0),C=xc.size(1),D=xc.size(2),H=xc.size(3),Wd=xc.size(4),O=wc.size(0); int Dp=D/2,Hp=H/2,Wp=Wd/2;\n auto pool=torch::empty({B,O,Dp,Hp,Wp},xc.options()); auto out=torch::empty({B,1,Dp,Hp,Wp},xc.options());\n cudaStream_t stream=at::cuda::getCurrentCUDAStream(); size_t total=(size_t)B*O*Dp*Hp*Wp; int block=128; int grid=(total+block-1)/block;\n poolconv_kernel<<<grid,block,0,stream>>>(xc.data_ptr<float>(),wc.data_ptr<float>(),bc.data_ptr<float>(),pool.data_ptr<float>(),B,C,D,H,Wd,O,Dp,Hp,Wp);\n lse_kernel<<<B*Dp*Hp*Wp,64,0,stream>>>(pool.data_ptr<float>(),out.data_ptr<float>(),B,O,Dp,Hp,Wp);\n return out;\n}\n\"\"\"\n_ext=load_inline(name=\"fused_two_stage_v1\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"fused_two_stage\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding): super().__init__(); self.conv=nn.Conv3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding)\n def forward(self,x): return _ext.fused_two_stage(x,self.conv.weight,self.conv.bias)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=4.77e-07 mean_abs_err=7.81e-08 max_rel_err=1.08e-07
turn 11 · 1 tool call(s) · 10.4s turn · 10.4s LLM · 6,192 chars (click to expand)
turn 1110.4s turn · 10.4s 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_cpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor fused_two_stage(torch::Tensor x, torch::Tensor w, torch::Tensor bias);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void poolconv_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ p,\n int B,int C,int D,int H,int Wd,int O,int Dp,int Hp,int Wp){\n size_t idx=blockIdx.x*blockDim.x+threadIdx.x; size_t total=(size_t)B*O*Dp*Hp*Wp; if(idx>=total) return;\n int px=idx%Wp; size_t t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; t/=Dp; int oc=t%O; int b=t/O;\n float best=-INFINITY;\n #pragma unroll\n for(int dzp=0;dzp<2;++dzp){int oz=(pz<<1)+dzp;\n #pragma unroll\n for(int dyp=0;dyp<2;++dyp){int oy=(py<<1)+dyp;\n #pragma unroll\n for(int dxp=0;dxp<2;++dxp){int ox=(px<<1)+dxp; float acc=bias[oc];\n for(int ic=0;ic<C;++ic){\n #pragma unroll\n for(int kz=0;kz<3;++kz){int iz=oz+kz-1; if((unsigned)iz>=(unsigned)D) continue;\n #pragma unroll\n for(int ky=0;ky<3;++ky){int iy=oy+ky-1; if((unsigned)iy>=(unsigned)H) continue;\n #pragma unroll\n for(int kx=0;kx<3;++kx){int ix=ox+kx-1; if((unsigned)ix>=(unsigned)Wd) continue;\n size_t xo=(((size_t)b*C+ic)*D+iz)*H*Wd+(size_t)iy*Wd+ix;\n size_t wo=(((size_t)oc*C+ic)*3+kz)*9+ky*3+kx;\n acc=fmaf(x[xo],w[wo],acc);\n }\n }\n }\n }\n best=fmaxf(best,acc);\n }\n }\n }\n p[idx]=best;\n}\n\n__global__ void lse_kernel(const float* __restrict__ p,float* __restrict__ out,int B,int O,int Dp,int Hp,int Wp){\n int idx=blockIdx.x; int tid=threadIdx.x; int px=idx%Wp; int t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; int b=t/Dp;\n float v=-INFINITY; if(tid<O){ size_t po=(((size_t)b*O+tid)*Dp+pz)*Hp*Wp+(size_t)py*Wp+px; v=p[po]; }\n __shared__ float sm[64]; sm[tid]=v; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]=fmaxf(sm[tid],sm[tid+s]); __syncthreads(); }\n float m=sm[0]; sm[tid]=(tid<O)?expf(v-m):0.f; __syncthreads();\n for(int s=32;s>0;s>>=1){ if(tid<s) sm[tid]+=sm[tid+s]; __syncthreads(); }\n if(tid==0){ float val=logf(sm[0])+m; out[idx]=val>0.f?val:0.f; }\n}\n\ntorch::Tensor fused_two_stage(torch::Tensor x, torch::Tensor w, torch::Tensor bias){\n auto xc=x.contiguous(); auto wc=w.contiguous(); auto bc=bias.contiguous();\n int B=xc.size(0),C=xc.size(1),D=xc.size(2),H=xc.size(3),Wd=xc.size(4),O=wc.size(0); int Dp=D/2,Hp=H/2,Wp=Wd/2;\n auto pool=torch::empty({B,O,Dp,Hp,Wp},xc.options()); auto out=torch::empty({B,1,Dp,Hp,Wp},xc.options());\n cudaStream_t stream=at::cuda::getCurrentCUDAStream(); size_t total=(size_t)B*O*Dp*Hp*Wp; int block=128; int grid=(total+block-1)/block;\n poolconv_kernel<<<grid,block,0,stream>>>(xc.data_ptr<float>(),wc.data_ptr<float>(),bc.data_ptr<float>(),pool.data_ptr<float>(),B,C,D,H,Wd,O,Dp,Hp,Wp);\n lse_kernel<<<B*Dp*Hp*Wp,64,0,stream>>>(pool.data_ptr<float>(),out.data_ptr<float>(),B,O,Dp,Hp,Wp);\n return out;\n}\n\"\"\"\n_ext=load_inline(name=\"fused_two_stage_v1p\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"fused_two_stage\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding): super().__init__(); self.conv=nn.Conv3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding)\n def forward(self,x): return _ext.fused_two_stage(x,self.conv.weight,self.conv.bias)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 518.0μs (98%) calls=1
#2: aten::empty 10.0μs (2%) calls=1
--- Timing ---
GPU time: 57799.14 μs [↓385426.1μs ok]
--- Memory Bandwidth ---
DRAM BW: 1250.2/3350 GB/s (37.3%) [↑37.3% ok]
read=1233.3 write=16.8 GB/s
Hit rates: L2=153.2%
--- Compute Throughput ---
Dominant pipe: FP32 util=46.4%
Pipe util: FMA=46.4%, Tensor=1.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 165.0% [↑103.1% ok]
Eligible warps/cycle: 3.04
Launch: regs/thread=58, smem/blk=2304B, block=192, grid=393216
--- Memory Access Quality ---
Global ld coalescing: 36.58 sectors/req (36.6x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 1596.6% ####################
short_scoreboard 858.0% ####################
mio_throttle 329.0% ####################
--- Optimization Hints ---
-> 1597% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=36.6 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=36.6x (ideal=1.0) -> 37x 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: 443225.22→57799.14μs (↓385426.08) ok
DRAM BW%: 0.02→37.32% (↑37.30) ok
Occupancy: 61.98→165.04% (↑103.05) ok
Eligible warps/cyc: 0.13→3.04 (↑2.91) ok
Ld sectors/req: 16.50→36.58 (↑20.08) REGRESSED
L2 hit%: 99.24→153.22% (↑53.98) ok
turn 1223.9s turn · 23.9s LLM · 0.0s tools
reasoning
**Evaluating PyTorch performance**
I'm thinking about how PyTorch's convolution performs, especially compared to the reference model. The reference uses a combination of convolution, max pooling, logsumexp, and ReLU. Although convolution seems dominant, the memory required for post-operations can be significant. I wonder if a custom post-process could actually outdo the reference, but correctness is key. I really need to ensure that the custom post-processing step is accurate before making any conclusions.
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor post_forward(torch::Tensor y);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n__inline__ __device__ float warp_reduce_max(float v){ for(int o=16;o>0;o>>=1) v=fmaxf(v,__shfl_down_sync(0xffffffff,v,o)); return v; }\n__inline__ __device__ float warp_reduce_sum(float v){ for(int o=16;o>0;o>>=1) v+=__shfl_down_sync(0xffffffff,v,o); return v; }\n__global__ void post_kernel(const float* __restrict__ y,float* __restrict__ out,int C,int D,int H,int W,int Nout){\n int idx=blockIdx.x; int lane=threadIdx.x&31; int warp=threadIdx.x>>5; int c=threadIdx.x; int Wp=W>>1; int Hp=H>>1; int Dp=D>>1;\n int px=idx%Wp; int t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; int b=t/Dp;\n float v=-INFINITY;\n if(c<C){ size_t base=(((size_t)b*C+c)*D+(pz<<1))*H*W + (size_t)(py<<1)*W + (px<<1);\n float m0=fmaxf(y[base],y[base+1]); float m1=fmaxf(y[base+W],y[base+W+1]); base+=(size_t)H*W;\n float m2=fmaxf(y[base],y[base+1]); float m3=fmaxf(y[base+W],y[base+W+1]); v=fmaxf(fmaxf(m0,m1),fmaxf(m2,m3)); }\n float wm=warp_reduce_max(v); __shared__ float sh[2]; if(lane==0) sh[warp]=wm; __syncthreads();\n float m = (c<2)?sh[c]:-INFINITY; if(warp==0) m=warp_reduce_max(m); m=__shfl_sync(0xffffffff,m,0);\n float e=(c<C)?expf(v-m):0.f; float ws=warp_reduce_sum(e); if(lane==0) sh[warp]=ws; __syncthreads();\n float s=(c<2)?sh[c]:0.f; if(warp==0) s=warp_reduce_sum(s); s=__shfl_sync(0xffffffff,s,0);\n if(c==0){ float val=logf(s)+m; out[idx]=val>0.f?val:0.f; }\n}\ntorch::Tensor post_forward(torch::Tensor y){ auto yc=y.contiguous(); int B=yc.size(0),C=yc.size(1),D=yc.size(2),H=yc.size(3),W=yc.size(4); auto out=torch::empty({B,1,D/2,H/2,W/2},yc.options()); auto stream=at::cuda::getCurrentCUDAStream(); post_kernel<<<B*(D/2)*(H/2)*(W/2),64,0,stream>>>(yc.data_ptr<float>(),out.data_ptr<float>(),C,D,H,W,out.numel()); return out; }\n\"\"\"\n_ext=load_inline(name=\"post_ext_warp_v1\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"post_forward\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding): super().__init__(); self.conv=nn.Conv3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding)\n def forward(self,x): return _ext.post_forward(self.conv(x))\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['inf', 'inf', 'inf', 'inf', 'inf']
avg_difference: ['inf', 'inf', 'inf', 'inf', 'inf']
turn 1321.9s turn · 21.9s LLM · 0.0s tools
reasoning
**Testing model correctness**
I’m thinking about whether the model's max_pool function is causing issues with the logsumexp, especially given the dim and keepdim settings. PyTorch's MaxPool3d might introduce NaNs, which would complicate things. I wonder if the problem arises from a stale cache in the _ext name. I’ll test the fixed warp quickly to see if that’s a better, faster solution. I should also ensure I’m using the shared final model settings efficiently.
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 post_forward(torch::Tensor y);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n__inline__ __device__ float wmax(float v){for(int o=16;o;o>>=1)v=fmaxf(v,__shfl_down_sync(0xffffffff,v,o));return v;}\n__inline__ __device__ float wsum(float v){for(int o=16;o;o>>=1)v+=__shfl_down_sync(0xffffffff,v,o);return v;}\n__global__ void post_kernel(const float* __restrict__ y,float* __restrict__ out,int C,int D,int H,int W){\n int idx=blockIdx.x, tid=threadIdx.x, lane=tid&31, warp=tid>>5; int Wp=W/2,Hp=H/2,Dp=D/2; int px=idx%Wp; int t=idx/Wp; int py=t%Hp; t/=Hp; int pz=t%Dp; int b=t/Dp; float v=-INFINITY;\n if(tid<C){size_t base=(((size_t)b*C+tid)*D+(pz*2))*H*W+(size_t)(py*2)*W+px*2; float a=fmaxf(fmaxf(y[base],y[base+1]),fmaxf(y[base+W],y[base+W+1])); base+=(size_t)H*W; float bb=fmaxf(fmaxf(y[base],y[base+1]),fmaxf(y[base+W],y[base+W+1])); v=fmaxf(a,bb);} \n __shared__ float sh[4]; float r=wmax(v); if(lane==0) sh[warp]=r; __syncthreads(); float m=-INFINITY; if(tid<2) m=sh[tid]; if(warp==0){m=wmax(m); if(lane==0) sh[2]=m;} __syncthreads(); m=sh[2];\n float e=(tid<C)?expf(v-m):0.f; r=wsum(e); if(lane==0) sh[warp]=r; __syncthreads(); float s=0.f; if(tid<2) s=sh[tid]; if(warp==0){s=wsum(s); if(lane==0) sh[3]=s;} __syncthreads(); if(tid==0){float val=logf(sh[3])+m; out[idx]=val>0?val:0;}\n}\ntorch::Tensor post_forward(torch::Tensor y){auto yc=y.contiguous(); int B=yc.size(0),C=yc.size(1),D=yc.size(2),H=yc.size(3),W=yc.size(4); auto out=torch::empty({B,1,D/2,H/2,W/2},yc.options()); auto stream=at::cuda::getCurrentCUDAStream(); post_kernel<<<B*(D/2)*(H/2)*(W/2),64,0,stream>>>(yc.data_ptr<float>(),out.data_ptr<float>(),C,D,H,W); return out;}\n\"\"\"\n_ext=load_inline(name=\"post_ext_warp_v2\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"post_forward\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding): super().__init__(); self.conv=nn.Conv3d(in_channels,out_channels,kernel_size,stride=stride,padding=padding)\n def forward(self,x): return _ext.post_forward(self.conv(x))\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=4.77e-07 mean_abs_err=3.20e-08 max_rel_err=1.08e-07
turn 1410.3s turn · 10.3s LLM · 0.0s tools · FINAL
reasoning
**Considering submission details**
I need to submit the best option, but which one is faster? I guess the post convolution uses PyTorch convolution, then the fused post. That’s likely much quicker than our two-stage naive method because cuDNN convolution is fast, especially when fused. I should submit post warp v2. Is it static? No time for that. I’ll just do one tool call for submission and ensure the final code is complete, possibly including imports.
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 post_forward(torch::Tensor y);\n\"\"\"\n\n_cuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__inline__ __device__ float wmax(float v) {\n for (int o = 16; o; o >>= 1) v = fmaxf(v, __shfl_down_sync(0xffffffff, v, o));\n return v;\n}\n__inline__ __device__ float wsum(float v) {\n for (int o = 16; o; o >>= 1) v += __shfl_down_sync(0xffffffff, v, o);\n return v;\n}\n\n__global__ void post_kernel(const float* __restrict__ y, float* __restrict__ out,\n int C, int D, int H, int W) {\n int idx = blockIdx.x;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n\n int Wp = W >> 1, Hp = H >> 1, Dp = D >> 1;\n int px = idx % Wp;\n int t = idx / Wp;\n int py = t % Hp;\n t /= Hp;\n int pz = t % Dp;\n int b = t / Dp;\n\n float v = -INFINITY;\n if (tid < C) {\n size_t base = (((size_t)b * C + tid) * D + (pz << 1)) * H * W\n + (size_t)(py << 1) * W + (px << 1);\n float a = fmaxf(fmaxf(y[base], y[base + 1]),\n fmaxf(y[base + W], y[base + W + 1]));\n base += (size_t)H * W;\n float bb = fmaxf(fmaxf(y[base], y[base + 1]),\n fmaxf(y[base + W], y[base + W + 1]));\n v = fmaxf(a, bb);\n }\n\n __shared__ float sh[4];\n float r = wmax(v);\n if (lane == 0) sh[warp] = r;\n __syncthreads();\n\n float m = -INFINITY;\n if (tid < 2) m = sh[tid];\n if (warp == 0) {\n m = wmax(m);\n if (lane == 0) sh[2] = m;\n }\n __syncthreads();\n m = sh[2];\n\n float e = (tid < C) ? expf(v - m) : 0.0f;\n r = wsum(e);\n if (lane == 0) sh[warp] = r;\n __syncthreads();\n\n float s = 0.0f;\n if (tid < 2) s = sh[tid];\n if (warp == 0) {\n s = wsum(s);\n if (lane == 0) sh[3] = s;\n }\n __syncthreads();\n\n if (tid == 0) {\n float val = logf(sh[3]) + m;\n out[idx] = val > 0.0f ? val : 0.0f;\n }\n}\n\ntorch::Tensor post_forward(torch::Tensor y) {\n auto yc = y.contiguous();\n int B = yc.size(0), C = yc.size(1), D = yc.size(2), H = yc.size(3), W = yc.size(4);\n auto out = torch::empty({B, 1, D / 2, H / 2, W / 2}, yc.options());\n auto stream = at::cuda::getCurrentCUDAStream();\n post_kernel<<<B * (D / 2) * (H / 2) * (W / 2), 64, 0, stream>>>(\n yc.data_ptr<float>(), out.data_ptr<float>(), C, D, H, W);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(\n name=\"post_ext_warp_final\",\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=[\"post_forward\"],\n extra_cuda_cflags=[\"-O3\"],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)\n\n def forward(self, x):\n return _ext.post_forward(self.conv(x))\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 6.51 μs
Runtime stats: mean=6.51μs median=?μs std=0.02μs
Numerical precision: max_abs_err=4.77e-07 mean_abs_err=3.20e-08 max_rel_err=1.08e-07
Memory: 802.42 MB peak (ref: 993.42 MB, ratio: 0.8077x)
Kernel launches: 8 (ref: 32, fusion ratio: 4.0)
Energy: 4609.38 mJ/run (ref: 3567.18 mJ, ratio: 0.7739x)
SOL score: 1.0
final submitted kernel (113 lines, 3,106 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 post_forward(torch::Tensor y);
"""
_cuda_src = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>
__inline__ __device__ float wmax(float v) {
for (int o = 16; o; o >>= 1) v = fmaxf(v, __shfl_down_sync(0xffffffff, v, o));
return v;
}
__inline__ __device__ float wsum(float v) {
for (int o = 16; o; o >>= 1) v += __shfl_down_sync(0xffffffff, v, o);
return v;
}
__global__ void post_kernel(const float* __restrict__ y, float* __restrict__ out,
int C, int D, int H, int W) {
int idx = blockIdx.x;
int tid = threadIdx.x;
int lane = tid & 31;
int warp = tid >> 5;
int Wp = W >> 1, Hp = H >> 1, Dp = D >> 1;
int px = idx % Wp;
int t = idx / Wp;
int py = t % Hp;
t /= Hp;
int pz = t % Dp;
int b = t / Dp;
float v = -INFINITY;
if (tid < C) {
size_t base = (((size_t)b * C + tid) * D + (pz << 1)) * H * W
+ (size_t)(py << 1) * W + (px << 1);
float a = fmaxf(fmaxf(y[base], y[base + 1]),
fmaxf(y[base + W], y[base + W + 1]));
base += (size_t)H * W;
float bb = fmaxf(fmaxf(y[base], y[base + 1]),
fmaxf(y[base + W], y[base + W + 1]));
v = fmaxf(a, bb);
}
__shared__ float sh[4];
float r = wmax(v);
if (lane == 0) sh[warp] = r;
__syncthreads();
float m = -INFINITY;
if (tid < 2) m = sh[tid];
if (warp == 0) {
m = wmax(m);
if (lane == 0) sh[2] = m;
}
__syncthreads();
m = sh[2];
float e = (tid < C) ? expf(v - m) : 0.0f;
r = wsum(e);
if (lane == 0) sh[warp] = r;
__syncthreads();
float s = 0.0f;
if (tid < 2) s = sh[tid];
if (warp == 0) {
s = wsum(s);
if (lane == 0) sh[3] = s;
}
__syncthreads();
if (tid == 0) {
float val = logf(sh[3]) + m;
out[idx] = val > 0.0f ? val : 0.0f;
}
}
torch::Tensor post_forward(torch::Tensor y) {
auto yc = y.contiguous();
int B = yc.size(0), C = yc.size(1), D = yc.size(2), H = yc.size(3), W = yc.size(4);
auto out = torch::empty({B, 1, D / 2, H / 2, W / 2}, yc.options());
auto stream = at::cuda::getCurrentCUDAStream();
post_kernel<<<B * (D / 2) * (H / 2) * (W / 2), 64, 0, stream>>>(
yc.data_ptr<float>(), out.data_ptr<float>(), C, D, H, W);
return out;
}
"""
_ext = load_inline(
name="post_ext_warp_final",
cpp_sources=_cpp_src,
cuda_sources=_cuda_src,
functions=["post_forward"],
extra_cuda_cflags=["-O3"],
verbose=False,
)
class ModelNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding):
super(ModelNew, self).__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)
def forward(self, x):
return _ext.post_forward(self.conv(x))