correct
87_Conv2d_Subtract_Subtract_Mish.py
started
2026-05-04T03:13:02Z
timingkernelbench.timing
kernel mean / median / std
3.46 / — / 0.06 μs
ref mean / median / std
8.61 / — / 0.09 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)
2.50x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
683.81 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 convolution, subtracts two values, applies Mish activation.
"""
def __init__(self, in_channels, out_channels, kernel_size, subtract_value_1, subtract_value_2):
super(Model, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
self.subtract_value_1 = subtract_value_1
self.subtract_value_2 = subtract_value_2
def forward(self, x):
x = self.conv(x)
x = x - self.subtract_value_1
x = x - self.subtract_value_2
x = torch.nn.functional.mish(x)
return x
batch_size = 128
in_channels = 8
out_channels = 64
height, width = 256, 256
kernel_size = 3
subtract_value_1 = 0.5
subtract_value_2 = 0.2
def get_inputs():
return [torch.rand(batch_size, in_channels, height, width)]
def get_init_inputs():
return [in_channels, out_channels, kernel_size, subtract_value_1, subtract_value_2]turn 010.4s turn · 10.4s 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) · 70.1s turn · 70.1s LLM · 8,238 chars (click to expand)
turn 170.1s turn · 70.1s LLM · 0.0s tools
reasoning
**Analyzing PyTorch Conv Settings**
I'm digging into whether PyTorch Conv uses TF32 by default, especially with inputs at fp32. The Conv2d layer likely utilizes TF32 unless it's disabled, and I need to consider tolerances around 1e-4. There might be reference errors in TF32 that I have to figure out. Evaluating against PyTorch’s settings, I need to understand if my fp32 FMA matches up with cuDNN TF32 outputs. I’m considering using cuBLAS or torch.nn.functional.conv2d for optimization, but it seems complicated.
**Evaluating Performance Metrics**
I'm analyzing performance metrics: for ops at 76 GFLOPs, the H100 using TF32 hits 989 TFLOPS, translating to about 0.08 ms, while with FP32 FMA, it's around 67 TFLOPS at 1.1 ms. A direct custom operation might take 1-3 ms. Using PyTorch with TF32 is faster, but I'm concerned about correctness and tolerance measures. I wonder if leveraging Tensor Cores with TF32 can match cuDNN and how that compares against FP32. I need to inspect correctness and build a direct kernel to profile performance effectively.
**Loading Cuda and CPP Sources**
I need to use load_inline to bring in both cuda_sources and cpp_sources along with the function declaration. This seems like a good method to make sure everything is included efficiently. I’ll ensure the function definitions are properly set up for seamless integration. The goal here is to ensure smooth execution. It's a simple task, but I want to pay attention to detail to avoid any potential hiccups!
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 = r\"\"\"\n#include <torch/extension.h>\n\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\n\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math_constants.h>\n\n#define IC 8\n#define KT 3\n#define OC_TILE 4\n#define TILE_W 32\n#define TILE_H 16\n\n__device__ __forceinline__ float mish_func(float x) {\n float sp;\n // stable softplus, though values are generally small\n if (x > 20.0f) sp = x;\n else if (x < -20.0f) sp = expf(x);\n else sp = log1pf(expf(x));\n return x * tanhf(sp);\n}\n\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ b,\n float* __restrict__ out,\n int N, int H, int W, int OC,\n float sub) {\n const int OH = H - 2;\n const int OW = W - 2;\n const int bx = blockIdx.x;\n const int by = blockIdx.y;\n const int oc_base = (blockIdx.z % ((OC + OC_TILE - 1) / OC_TILE)) * OC_TILE;\n const int n = blockIdx.z / ((OC + OC_TILE - 1) / OC_TILE);\n const int tx = threadIdx.x; // 0..31\n const int ty = threadIdx.y; // 0..7\n\n __shared__ float sm[IC][TILE_H + 2][TILE_W + 2];\n\n // cooperative load: IC*(18*34)=4896 floats\n const int total = IC * (TILE_H + 2) * (TILE_W + 2);\n const int tid = ty * blockDim.x + tx;\n for (int idx = tid; idx < total; idx += blockDim.x * blockDim.y) {\n int lx = idx % (TILE_W + 2);\n int t = idx / (TILE_W + 2);\n int ly = t % (TILE_H + 2);\n int c = t / (TILE_H + 2);\n int gx = bx * TILE_W + lx;\n int gy = by * TILE_H + ly;\n float v = 0.0f;\n if (gx < W && gy < H) {\n v = x[((n * IC + c) * H + gy) * W + gx];\n }\n sm[c][ly][lx] = v;\n }\n __syncthreads();\n\n const int ox = bx * TILE_W + tx;\n const int oy0 = by * TILE_H + ty;\n const int oy1 = oy0 + 8;\n\n float acc00 = 0.f, acc01 = 0.f, acc02 = 0.f, acc03 = 0.f;\n float acc10 = 0.f, acc11 = 0.f, acc12 = 0.f, acc13 = 0.f;\n\n#pragma unroll\n for (int c = 0; c < IC; ++c) {\n#pragma unroll\n for (int ky = 0; ky < KT; ++ky) {\n#pragma unroll\n for (int kx = 0; kx < KT; ++kx) {\n float xv0 = sm[c][ty + ky][tx + kx];\n float xv1 = sm[c][ty + 8 + ky][tx + kx];\n int wbase = ((oc_base * IC + c) * KT + ky) * KT + kx;\n if (oc_base + 0 < OC) { float ww = __ldg(w + wbase + 0 * IC * KT * KT); acc00 += xv0 * ww; acc10 += xv1 * ww; }\n if (oc_base + 1 < OC) { float ww = __ldg(w + wbase + 1 * IC * KT * KT); acc01 += xv0 * ww; acc11 += xv1 * ww; }\n if (oc_base + 2 < OC) { float ww = __ldg(w + wbase + 2 * IC * KT * KT); acc02 += xv0 * ww; acc12 += xv1 * ww; }\n if (oc_base + 3 < OC) { float ww = __ldg(w + wbase + 3 * IC * KT * KT); acc03 += xv0 * ww; acc13 += xv1 * ww; }\n }\n }\n }\n\n if (ox < OW) {\n const long plane = (long)OH * OW;\n long out_base_n = (long)n * OC * plane;\n if (oy0 < OH) {\n long pos = out_base_n + (long)(oc_base + 0) * plane + (long)oy0 * OW + ox;\n if (oc_base + 0 < OC) out[pos] = mish_func(acc00 + __ldg(b + oc_base + 0) - sub);\n if (oc_base + 1 < OC) out[pos + plane] = mish_func(acc01 + __ldg(b + oc_base + 1) - sub);\n if (oc_base + 2 < OC) out[pos + 2 * plane] = mish_func(acc02 + __ldg(b + oc_base + 2) - sub);\n if (oc_base + 3 < OC) out[pos + 3 * plane] = mish_func(acc03 + __ldg(b + oc_base + 3) - sub);\n }\n if (oy1 < OH) {\n long pos = out_base_n + (long)(oc_base + 0) * plane + (long)oy1 * OW + ox;\n if (oc_base + 0 < OC) out[pos] = mish_func(acc10 + __ldg(b + oc_base + 0) - sub);\n if (oc_base + 1 < OC) out[pos + plane] = mish_func(acc11 + __ldg(b + oc_base + 1) - sub);\n if (oc_base + 2 < OC) out[pos + 2 * plane] = mish_func(acc12 + __ldg(b + oc_base + 2) - sub);\n if (oc_base + 3 < OC) out[pos + 3 * plane] = mish_func(acc13 + __ldg(b + oc_base + 3) - sub);\n }\n }\n}\n\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub) {\n x = x.contiguous();\n w = w.contiguous();\n b = b.contiguous();\n const int N = x.size(0);\n const int H = x.size(2);\n const int W = x.size(3);\n const int OC = w.size(0);\n const int OH = H - 2;\n const int OW = W - 2;\n auto out = torch::empty({N, OC, OH, OW}, x.options());\n dim3 block(TILE_W, 8);\n dim3 grid((OW + TILE_W - 1) / TILE_W, (OH + TILE_H - 1) / TILE_H, N * ((OC + OC_TILE - 1) / OC_TILE));\n conv3x3_mish_kernel<<<grid, block>>>(x.data_ptr<float>(), w.data_ptr<float>(), b.data_ptr<float>(), out.data_ptr<float>(), N, H, W, OC, sub);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"conv_mish_ext_v1\", cpp_sources=[cpp_src], cuda_sources=[cuda_src], functions=[\"conv_mish_forward\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], extra_cflags=[\"-O3\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, subtract_value_1, subtract_value_2):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.sub = float(subtract_value_1 + subtract_value_2)\n def forward(self, x):\n return _ext.conv_mish_forward(x, self.conv.weight, self.conv.bias, self.sub)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 2 · 1 tool call(s) · 11.3s turn · 11.3s LLM · 6,492 chars (click to expand)
turn 211.3s turn · 11.3s 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 = r\"\"\"\n#include <torch/extension.h>\n\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\n\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math_constants.h>\n\n#define IC 8\n#define KT 3\n#define OC_TILE 4\n#define TILE_W 32\n#define TILE_H 16\n\n__device__ __forceinline__ float mish_func(float x) {\n float sp;\n if (x > 20.0f) sp = x;\n else if (x < -20.0f) sp = expf(x);\n else sp = log1pf(expf(x));\n return x * tanhf(sp);\n}\n\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ b,\n float* __restrict__ out,\n int N, int H, int W, int OC,\n float sub) {\n const int OH = H - 2;\n const int OW = W - 2;\n const int bx = blockIdx.x;\n const int by = blockIdx.y;\n const int groups = (OC + OC_TILE - 1) / OC_TILE;\n const int oc_base = (blockIdx.z % groups) * OC_TILE;\n const int n = blockIdx.z / groups;\n const int tx = threadIdx.x;\n const int ty = threadIdx.y;\n __shared__ float sm[IC][TILE_H + 2][TILE_W + 2];\n const int total = IC * (TILE_H + 2) * (TILE_W + 2);\n const int tid = ty * blockDim.x + tx;\n for (int idx = tid; idx < total; idx += blockDim.x * blockDim.y) {\n int lx = idx % (TILE_W + 2);\n int t = idx / (TILE_W + 2);\n int ly = t % (TILE_H + 2);\n int c = t / (TILE_H + 2);\n int gx = bx * TILE_W + lx;\n int gy = by * TILE_H + ly;\n float v = 0.0f;\n if (gx < W && gy < H) v = x[((n * IC + c) * H + gy) * W + gx];\n sm[c][ly][lx] = v;\n }\n __syncthreads();\n const int ox = bx * TILE_W + tx;\n const int oy0 = by * TILE_H + ty;\n const int oy1 = oy0 + 8;\n float acc00 = 0.f, acc01 = 0.f, acc02 = 0.f, acc03 = 0.f;\n float acc10 = 0.f, acc11 = 0.f, acc12 = 0.f, acc13 = 0.f;\n#pragma unroll\n for (int c = 0; c < IC; ++c) {\n#pragma unroll\n for (int ky = 0; ky < KT; ++ky) {\n#pragma unroll\n for (int kx = 0; kx < KT; ++kx) {\n float xv0 = sm[c][ty + ky][tx + kx];\n float xv1 = sm[c][ty + 8 + ky][tx + kx];\n int wbase = ((oc_base * IC + c) * KT + ky) * KT + kx;\n if (oc_base + 0 < OC) { float ww = __ldg(w + wbase + 0 * IC * KT * KT); acc00 += xv0 * ww; acc10 += xv1 * ww; }\n if (oc_base + 1 < OC) { float ww = __ldg(w + wbase + 1 * IC * KT * KT); acc01 += xv0 * ww; acc11 += xv1 * ww; }\n if (oc_base + 2 < OC) { float ww = __ldg(w + wbase + 2 * IC * KT * KT); acc02 += xv0 * ww; acc12 += xv1 * ww; }\n if (oc_base + 3 < OC) { float ww = __ldg(w + wbase + 3 * IC * KT * KT); acc03 += xv0 * ww; acc13 += xv1 * ww; }\n }\n }\n }\n if (ox < OW) {\n const long plane = (long)OH * OW;\n long out_base_n = (long)n * OC * plane;\n if (oy0 < OH) {\n long pos = out_base_n + (long)(oc_base + 0) * plane + (long)oy0 * OW + ox;\n if (oc_base + 0 < OC) out[pos] = mish_func(acc00 + __ldg(b + oc_base + 0) - sub);\n if (oc_base + 1 < OC) out[pos + plane] = mish_func(acc01 + __ldg(b + oc_base + 1) - sub);\n if (oc_base + 2 < OC) out[pos + 2 * plane] = mish_func(acc02 + __ldg(b + oc_base + 2) - sub);\n if (oc_base + 3 < OC) out[pos + 3 * plane] = mish_func(acc03 + __ldg(b + oc_base + 3) - sub);\n }\n if (oy1 < OH) {\n long pos = out_base_n + (long)(oc_base + 0) * plane + (long)oy1 * OW + ox;\n if (oc_base + 0 < OC) out[pos] = mish_func(acc10 + __ldg(b + oc_base + 0) - sub);\n if (oc_base + 1 < OC) out[pos + plane] = mish_func(acc11 + __ldg(b + oc_base + 1) - sub);\n if (oc_base + 2 < OC) out[pos + 2 * plane] = mish_func(acc12 + __ldg(b + oc_base + 2) - sub);\n if (oc_base + 3 < OC) out[pos + 3 * plane] = mish_func(acc13 + __ldg(b + oc_base + 3) - sub);\n }\n }\n}\n\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub) {\n x = x.contiguous(); w = w.contiguous(); b = b.contiguous();\n const int N = x.size(0), H = x.size(2), W = x.size(3), OC = w.size(0);\n const int OH = H - 2, OW = W - 2;\n auto out = torch::empty({N, OC, OH, OW}, x.options());\n dim3 block(TILE_W, 8);\n dim3 grid((OW + TILE_W - 1) / TILE_W, (OH + TILE_H - 1) / TILE_H, N * ((OC + OC_TILE - 1) / OC_TILE));\n conv3x3_mish_kernel<<<grid, block>>>(x.data_ptr<float>(), w.data_ptr<float>(), b.data_ptr<float>(), out.data_ptr<float>(), N, H, W, OC, sub);\n return out;\n}\n\"\"\"\n_ext = load_inline(name=\"conv_mish_ext_v1c\", cpp_sources=[cpp_src], cuda_sources=[cuda_src], functions=[\"conv_mish_forward\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], extra_cflags=[\"-O3\"], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, subtract_value_1, subtract_value_2):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.sub = float(subtract_value_1 + subtract_value_2)\n def forward(self, x):\n return _ext.conv_mish_forward(x, self.conv.weight, self.conv.bias, self.sub)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=6.14e-06 mean_abs_err=6.75e-07 max_rel_err=1.00e+00
turn 39.7s turn · 9.7s 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 = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define IC 8\n#define KT 3\n#define OC_TILE 4\n#define TILE_W 32\n#define TILE_H 16\n__device__ __forceinline__ float mish_func(float x) { float sp; if (x > 20.0f) sp = x; else if (x < -20.0f) sp = expf(x); else sp = log1pf(expf(x)); return x * tanhf(sp); }\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int N,int H,int W,int OC,float sub) {\n const int OH=H-2, OW=W-2; const int groups=(OC+OC_TILE-1)/OC_TILE; const int oc_base=(blockIdx.z%groups)*OC_TILE; const int n=blockIdx.z/groups; const int tx=threadIdx.x, ty=threadIdx.y; __shared__ float sm[IC][TILE_H+2][TILE_W+2]; const int total=IC*(TILE_H+2)*(TILE_W+2); const int tid=ty*blockDim.x+tx;\n for(int idx=tid; idx<total; idx+=blockDim.x*blockDim.y){ int lx=idx%(TILE_W+2); int t=idx/(TILE_W+2); int ly=t%(TILE_H+2); int c=t/(TILE_H+2); int gx=blockIdx.x*TILE_W+lx; int gy=blockIdx.y*TILE_H+ly; float v=0.f; if(gx<W && gy<H) v=x[((n*IC+c)*H+gy)*W+gx]; sm[c][ly][lx]=v; } __syncthreads();\n const int ox=blockIdx.x*TILE_W+tx; const int oy0=blockIdx.y*TILE_H+ty; const int oy1=oy0+8; float acc00=0.f,acc01=0.f,acc02=0.f,acc03=0.f,acc10=0.f,acc11=0.f,acc12=0.f,acc13=0.f;\n#pragma unroll\n for(int c=0;c<IC;++c){\n#pragma unroll\n for(int ky=0;ky<KT;++ky){\n#pragma unroll\n for(int kx=0;kx<KT;++kx){ float xv0=sm[c][ty+ky][tx+kx]; float xv1=sm[c][ty+8+ky][tx+kx]; int wbase=((oc_base*IC+c)*KT+ky)*KT+kx; if(oc_base+0<OC){float ww=__ldg(w+wbase+0*IC*KT*KT); acc00+=xv0*ww; acc10+=xv1*ww;} if(oc_base+1<OC){float ww=__ldg(w+wbase+1*IC*KT*KT); acc01+=xv0*ww; acc11+=xv1*ww;} if(oc_base+2<OC){float ww=__ldg(w+wbase+2*IC*KT*KT); acc02+=xv0*ww; acc12+=xv1*ww;} if(oc_base+3<OC){float ww=__ldg(w+wbase+3*IC*KT*KT); acc03+=xv0*ww; acc13+=xv1*ww;} }\n }}\n if(ox<OW){ const long plane=(long)OH*OW; long out_base_n=(long)n*OC*plane; if(oy0<OH){ long pos=out_base_n+(long)(oc_base+0)*plane+(long)oy0*OW+ox; if(oc_base+0<OC) out[pos]=mish_func(acc00+__ldg(b+oc_base+0)-sub); if(oc_base+1<OC) out[pos+plane]=mish_func(acc01+__ldg(b+oc_base+1)-sub); if(oc_base+2<OC) out[pos+2*plane]=mish_func(acc02+__ldg(b+oc_base+2)-sub); if(oc_base+3<OC) out[pos+3*plane]=mish_func(acc03+__ldg(b+oc_base+3)-sub); } if(oy1<OH){ long pos=out_base_n+(long)(oc_base+0)*plane+(long)oy1*OW+ox; if(oc_base+0<OC) out[pos]=mish_func(acc10+__ldg(b+oc_base+0)-sub); if(oc_base+1<OC) out[pos+plane]=mish_func(acc11+__ldg(b+oc_base+1)-sub); if(oc_base+2<OC) out[pos+2*plane]=mish_func(acc12+__ldg(b+oc_base+2)-sub); if(oc_base+3<OC) out[pos+3*plane]=mish_func(acc13+__ldg(b+oc_base+3)-sub); } }\n}\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub){ x=x.contiguous(); w=w.contiguous(); b=b.contiguous(); int N=x.size(0),H=x.size(2),W=x.size(3),OC=w.size(0); int OH=H-2,OW=W-2; auto out=torch::empty({N,OC,OH,OW},x.options()); dim3 block(TILE_W,8); dim3 grid((OW+TILE_W-1)/TILE_W,(OH+TILE_H-1)/TILE_H,N*((OC+OC_TILE-1)/OC_TILE)); conv3x3_mish_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),out.data_ptr<float>(),N,H,W,OC,sub); return out; }\n\"\"\"\n_ext=load_inline(name=\"conv_mish_ext_v1p\",cpp_sources=[cpp_src],cuda_sources=[cuda_src],functions=[\"conv_mish_forward\"],extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"],extra_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,subtract_value_1,subtract_value_2):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.sub=float(subtract_value_1+subtract_value_2)\n def forward(self,x): return _ext.conv_mish_forward(x,self.conv.weight,self.conv.bias,self.sub)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 8672.0μs (100%) calls=1
--- Timing ---
GPU time: 7975.36 μs
--- Memory Bandwidth ---
DRAM BW: 296.1/3350 GB/s (8.8%)
read=33.7 write=262.4 GB/s
Hit rates: L2=95.3%
--- Compute Throughput ---
Dominant pipe: FP32 util=40.9%
Pipe util: FMA=40.9%, Tensor=1.4%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 99.3%
Eligible warps/cycle: 5.89
Launch: regs/thread=32, smem/blk=20608B, block=256, grid=262144
--- Memory Access Quality ---
Global ld coalescing: 1.26 sectors/req (1.3x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 240.9% ####################
short_scoreboard 201.9% ####################
--- Optimization Hints ---
-> 241% stalls: global memory latency (waiting for DRAM/L2). Try shared-memory tiling, prefetching, or reducing working set.
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
turn 428.9s turn · 28.9s LLM · 0.0s tools
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\ncuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define IC 8\n#define KT 3\n#define OC_TILE 8\n#define TILE_W 32\n#define TILE_H 16\n__device__ __forceinline__ float mish_func(float x){\n if (x > 20.0f) return x;\n float e = expf(x);\n if (x < -20.0f) return x * e;\n float t = e * (e + 2.0f);\n return x * (t / (t + 2.0f));\n}\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int H,int W,float sub){\n const int OH=H-2, OW=W-2; const int oc_base=(blockIdx.z & 7)*OC_TILE; const int n=blockIdx.z >> 3; const int tx=threadIdx.x, ty=threadIdx.y; __shared__ float sm[IC][TILE_H+2][TILE_W+2]; const int tid=ty*blockDim.x+tx; const int total=IC*(TILE_H+2)*(TILE_W+2);\n for(int idx=tid; idx<total; idx+=256){ int lx=idx%(TILE_W+2); int q=idx/(TILE_W+2); int ly=q%(TILE_H+2); int c=q/(TILE_H+2); int gx=blockIdx.x*TILE_W+lx; int gy=blockIdx.y*TILE_H+ly; float v=0.f; if(gx<W && gy<H) v=x[((n*IC+c)*H+gy)*W+gx]; sm[c][ly][lx]=v; }\n __syncthreads();\n const int ox=blockIdx.x*TILE_W+tx; const int oy0=blockIdx.y*TILE_H+ty; const int oy1=oy0+8;\n float a00=0.f,a01=0.f,a02=0.f,a03=0.f,a04=0.f,a05=0.f,a06=0.f,a07=0.f;\n float a10=0.f,a11=0.f,a12=0.f,a13=0.f,a14=0.f,a15=0.f,a16=0.f,a17=0.f;\n#pragma unroll\n for(int c=0;c<IC;++c){\n#pragma unroll\n for(int ky=0;ky<3;++ky){\n#pragma unroll\n for(int kx=0;kx<3;++kx){\n float xv0=sm[c][ty+ky][tx+kx]; float xv1=sm[c][ty+8+ky][tx+kx]; int wb=((oc_base*IC+c)*3+ky)*3+kx;\n float ww;\n ww=__ldg(w+wb+0*IC*9); a00+=xv0*ww; a10+=xv1*ww;\n ww=__ldg(w+wb+1*IC*9); a01+=xv0*ww; a11+=xv1*ww;\n ww=__ldg(w+wb+2*IC*9); a02+=xv0*ww; a12+=xv1*ww;\n ww=__ldg(w+wb+3*IC*9); a03+=xv0*ww; a13+=xv1*ww;\n ww=__ldg(w+wb+4*IC*9); a04+=xv0*ww; a14+=xv1*ww;\n ww=__ldg(w+wb+5*IC*9); a05+=xv0*ww; a15+=xv1*ww;\n ww=__ldg(w+wb+6*IC*9); a06+=xv0*ww; a16+=xv1*ww;\n ww=__ldg(w+wb+7*IC*9); a07+=xv0*ww; a17+=xv1*ww;\n }\n }\n }\n if(ox<OW){ const long plane=(long)OH*OW; long nb=(long)n*64*plane; long ocp=(long)oc_base*plane; if(oy0<OH){ long pos=nb+ocp+(long)oy0*OW+ox; out[pos+0*plane]=mish_func(a00+__ldg(b+oc_base+0)-sub); out[pos+1*plane]=mish_func(a01+__ldg(b+oc_base+1)-sub); out[pos+2*plane]=mish_func(a02+__ldg(b+oc_base+2)-sub); out[pos+3*plane]=mish_func(a03+__ldg(b+oc_base+3)-sub); out[pos+4*plane]=mish_func(a04+__ldg(b+oc_base+4)-sub); out[pos+5*plane]=mish_func(a05+__ldg(b+oc_base+5)-sub); out[pos+6*plane]=mish_func(a06+__ldg(b+oc_base+6)-sub); out[pos+7*plane]=mish_func(a07+__ldg(b+oc_base+7)-sub); } if(oy1<OH){ long pos=nb+ocp+(long)oy1*OW+ox; out[pos+0*plane]=mish_func(a10+__ldg(b+oc_base+0)-sub); out[pos+1*plane]=mish_func(a11+__ldg(b+oc_base+1)-sub); out[pos+2*plane]=mish_func(a12+__ldg(b+oc_base+2)-sub); out[pos+3*plane]=mish_func(a13+__ldg(b+oc_base+3)-sub); out[pos+4*plane]=mish_func(a14+__ldg(b+oc_base+4)-sub); out[pos+5*plane]=mish_func(a15+__ldg(b+oc_base+5)-sub); out[pos+6*plane]=mish_func(a16+__ldg(b+oc_base+6)-sub); out[pos+7*plane]=mish_func(a17+__ldg(b+oc_base+7)-sub); } }\n}\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub){ x=x.contiguous(); w=w.contiguous(); b=b.contiguous(); int N=x.size(0),H=x.size(2),W=x.size(3); int OH=H-2,OW=W-2; auto out=torch::empty({N,64,OH,OW},x.options()); dim3 block(32,8); dim3 grid((OW+31)/32,(OH+15)/16,N*8); conv3x3_mish_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),out.data_ptr<float>(),H,W,sub); return out; }\n\"\"\"\n_ext=load_inline(name=\"conv_mish_ext_v2\",cpp_sources=[cpp_src],cuda_sources=[cuda_src],functions=[\"conv_mish_forward\"],extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"],extra_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,subtract_value_1,subtract_value_2):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.sub=float(subtract_value_1+subtract_value_2)\n def forward(self,x): return _ext.conv_mish_forward(x,self.conv.weight,self.conv.bias,self.sub)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 510.8s turn · 10.8s 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\ncpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\ncuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define IC 8\n#define OC_TILE 8\n#define TILE_W 32\n#define TILE_H 16\n__device__ __forceinline__ float mish_func(float x){ if(x>20.0f)return x; float e=expf(x); if(x<-20.0f)return x*e; float t=e*(e+2.0f); return x*(t/(t+2.0f)); }\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int H,int W,float sub){\n const int OH=H-2, OW=W-2; const int oc_base=(blockIdx.z & 7)*OC_TILE; const int n=blockIdx.z >> 3; const int tx=threadIdx.x,ty=threadIdx.y; __shared__ float sm[IC][TILE_H+2][TILE_W+2]; int tid=ty*blockDim.x+tx; const int total=IC*(TILE_H+2)*(TILE_W+2);\n for(int idx=tid;idx<total;idx+=256){int lx=idx%(TILE_W+2);int q=idx/(TILE_W+2);int ly=q%(TILE_H+2);int c=q/(TILE_H+2);int gx=blockIdx.x*TILE_W+lx;int gy=blockIdx.y*TILE_H+ly;float v=0.f;if(gx<W&&gy<H)v=x[((n*IC+c)*H+gy)*W+gx];sm[c][ly][lx]=v;} __syncthreads();\n int ox=blockIdx.x*TILE_W+tx; int oy0=blockIdx.y*TILE_H+ty; int oy1=oy0+8;\n float a00=0.f,a01=0.f,a02=0.f,a03=0.f,a04=0.f,a05=0.f,a06=0.f,a07=0.f; float a10=0.f,a11=0.f,a12=0.f,a13=0.f,a14=0.f,a15=0.f,a16=0.f,a17=0.f;\n#pragma unroll\n for(int c=0;c<IC;++c){\n#pragma unroll\n for(int ky=0;ky<3;++ky){\n#pragma unroll\n for(int kx=0;kx<3;++kx){float xv0=sm[c][ty+ky][tx+kx];float xv1=sm[c][ty+8+ky][tx+kx];int wb=((oc_base*IC+c)*3+ky)*3+kx;float ww;ww=__ldg(w+wb+0*IC*9);a00+=xv0*ww;a10+=xv1*ww;ww=__ldg(w+wb+1*IC*9);a01+=xv0*ww;a11+=xv1*ww;ww=__ldg(w+wb+2*IC*9);a02+=xv0*ww;a12+=xv1*ww;ww=__ldg(w+wb+3*IC*9);a03+=xv0*ww;a13+=xv1*ww;ww=__ldg(w+wb+4*IC*9);a04+=xv0*ww;a14+=xv1*ww;ww=__ldg(w+wb+5*IC*9);a05+=xv0*ww;a15+=xv1*ww;ww=__ldg(w+wb+6*IC*9);a06+=xv0*ww;a16+=xv1*ww;ww=__ldg(w+wb+7*IC*9);a07+=xv0*ww;a17+=xv1*ww;}\n }}\n if(ox<OW){const long plane=(long)OH*OW;long nb=(long)n*64*plane;long ocp=(long)oc_base*plane;if(oy0<OH){long pos=nb+ocp+(long)oy0*OW+ox;out[pos+0*plane]=mish_func(a00+__ldg(b+oc_base+0)-sub);out[pos+1*plane]=mish_func(a01+__ldg(b+oc_base+1)-sub);out[pos+2*plane]=mish_func(a02+__ldg(b+oc_base+2)-sub);out[pos+3*plane]=mish_func(a03+__ldg(b+oc_base+3)-sub);out[pos+4*plane]=mish_func(a04+__ldg(b+oc_base+4)-sub);out[pos+5*plane]=mish_func(a05+__ldg(b+oc_base+5)-sub);out[pos+6*plane]=mish_func(a06+__ldg(b+oc_base+6)-sub);out[pos+7*plane]=mish_func(a07+__ldg(b+oc_base+7)-sub);}if(oy1<OH){long pos=nb+ocp+(long)oy1*OW+ox;out[pos+0*plane]=mish_func(a10+__ldg(b+oc_base+0)-sub);out[pos+1*plane]=mish_func(a11+__ldg(b+oc_base+1)-sub);out[pos+2*plane]=mish_func(a12+__ldg(b+oc_base+2)-sub);out[pos+3*plane]=mish_func(a13+__ldg(b+oc_base+3)-sub);out[pos+4*plane]=mish_func(a14+__ldg(b+oc_base+4)-sub);out[pos+5*plane]=mish_func(a15+__ldg(b+oc_base+5)-sub);out[pos+6*plane]=mish_func(a16+__ldg(b+oc_base+6)-sub);out[pos+7*plane]=mish_func(a17+__ldg(b+oc_base+7)-sub);}}\n}\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub){x=x.contiguous();w=w.contiguous();b=b.contiguous();int N=x.size(0),H=x.size(2),W=x.size(3);int OH=H-2,OW=W-2;auto out=torch::empty({N,64,OH,OW},x.options());dim3 block(32,8);dim3 grid((OW+31)/32,(OH+15)/16,N*8);conv3x3_mish_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),out.data_ptr<float>(),H,W,sub);return out;}\n\"\"\"\n_ext=load_inline(name=\"conv_mish_ext_v2c\",cpp_sources=[cpp_src],cuda_sources=[cuda_src],functions=[\"conv_mish_forward\"],extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"],extra_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,subtract_value_1,subtract_value_2):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.sub=float(subtract_value_1+subtract_value_2)\n def forward(self,x): return _ext.conv_mish_forward(x,self.conv.weight,self.conv.bias,self.sub)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=1.49e-07 mean_abs_err=1.77e-08 max_rel_err=1.00e+00
turn 6 · 1 tool call(s) · 11.0s turn · 11.0s LLM · 6,483 chars (click to expand)
turn 611.0s turn · 11.0s 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\ncpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\ncuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define IC 8\n#define OC_TILE 8\n#define TILE_W 32\n#define TILE_H 16\n__device__ __forceinline__ float mish_func(float x){ if(x>20.0f)return x; float e=expf(x); if(x<-20.0f)return x*e; float t=e*(e+2.0f); return x*(t/(t+2.0f)); }\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int H,int W,float sub){ const int OH=H-2,OW=W-2; const int oc_base=(blockIdx.z&7)*OC_TILE; const int n=blockIdx.z>>3; const int tx=threadIdx.x,ty=threadIdx.y; __shared__ float sm[IC][TILE_H+2][TILE_W+2]; int tid=ty*blockDim.x+tx; const int total=IC*(TILE_H+2)*(TILE_W+2); for(int idx=tid;idx<total;idx+=256){int lx=idx%(TILE_W+2);int q=idx/(TILE_W+2);int ly=q%(TILE_H+2);int c=q/(TILE_H+2);int gx=blockIdx.x*TILE_W+lx;int gy=blockIdx.y*TILE_H+ly;float v=0.f;if(gx<W&&gy<H)v=x[((n*IC+c)*H+gy)*W+gx];sm[c][ly][lx]=v;} __syncthreads(); int ox=blockIdx.x*TILE_W+tx; int oy0=blockIdx.y*TILE_H+ty; int oy1=oy0+8; float a00=0.f,a01=0.f,a02=0.f,a03=0.f,a04=0.f,a05=0.f,a06=0.f,a07=0.f; float a10=0.f,a11=0.f,a12=0.f,a13=0.f,a14=0.f,a15=0.f,a16=0.f,a17=0.f;\n#pragma unroll\n for(int c=0;c<IC;++c){\n#pragma unroll\n for(int ky=0;ky<3;++ky){\n#pragma unroll\n for(int kx=0;kx<3;++kx){float xv0=sm[c][ty+ky][tx+kx];float xv1=sm[c][ty+8+ky][tx+kx];int wb=((oc_base*IC+c)*3+ky)*3+kx;float ww;ww=__ldg(w+wb+0*IC*9);a00+=xv0*ww;a10+=xv1*ww;ww=__ldg(w+wb+1*IC*9);a01+=xv0*ww;a11+=xv1*ww;ww=__ldg(w+wb+2*IC*9);a02+=xv0*ww;a12+=xv1*ww;ww=__ldg(w+wb+3*IC*9);a03+=xv0*ww;a13+=xv1*ww;ww=__ldg(w+wb+4*IC*9);a04+=xv0*ww;a14+=xv1*ww;ww=__ldg(w+wb+5*IC*9);a05+=xv0*ww;a15+=xv1*ww;ww=__ldg(w+wb+6*IC*9);a06+=xv0*ww;a16+=xv1*ww;ww=__ldg(w+wb+7*IC*9);a07+=xv0*ww;a17+=xv1*ww;}}} if(ox<OW){const long plane=(long)OH*OW;long nb=(long)n*64*plane;long ocp=(long)oc_base*plane;if(oy0<OH){long pos=nb+ocp+(long)oy0*OW+ox;out[pos+0*plane]=mish_func(a00+__ldg(b+oc_base+0)-sub);out[pos+1*plane]=mish_func(a01+__ldg(b+oc_base+1)-sub);out[pos+2*plane]=mish_func(a02+__ldg(b+oc_base+2)-sub);out[pos+3*plane]=mish_func(a03+__ldg(b+oc_base+3)-sub);out[pos+4*plane]=mish_func(a04+__ldg(b+oc_base+4)-sub);out[pos+5*plane]=mish_func(a05+__ldg(b+oc_base+5)-sub);out[pos+6*plane]=mish_func(a06+__ldg(b+oc_base+6)-sub);out[pos+7*plane]=mish_func(a07+__ldg(b+oc_base+7)-sub);}if(oy1<OH){long pos=nb+ocp+(long)oy1*OW+ox;out[pos+0*plane]=mish_func(a10+__ldg(b+oc_base+0)-sub);out[pos+1*plane]=mish_func(a11+__ldg(b+oc_base+1)-sub);out[pos+2*plane]=mish_func(a12+__ldg(b+oc_base+2)-sub);out[pos+3*plane]=mish_func(a13+__ldg(b+oc_base+3)-sub);out[pos+4*plane]=mish_func(a14+__ldg(b+oc_base+4)-sub);out[pos+5*plane]=mish_func(a15+__ldg(b+oc_base+5)-sub);out[pos+6*plane]=mish_func(a16+__ldg(b+oc_base+6)-sub);out[pos+7*plane]=mish_func(a17+__ldg(b+oc_base+7)-sub);}} }\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub){x=x.contiguous();w=w.contiguous();b=b.contiguous();int N=x.size(0),H=x.size(2),W=x.size(3);int OH=H-2,OW=W-2;auto out=torch::empty({N,64,OH,OW},x.options());dim3 block(32,8);dim3 grid((OW+31)/32,(OH+15)/16,N*8);conv3x3_mish_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),out.data_ptr<float>(),H,W,sub);return out;}\n\"\"\"\n_ext=load_inline(name=\"conv_mish_ext_v2p\",cpp_sources=[cpp_src],cuda_sources=[cuda_src],functions=[\"conv_mish_forward\"],extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"],extra_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,subtract_value_1,subtract_value_2): super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.sub=float(subtract_value_1+subtract_value_2)\n def forward(self,x): return _ext.conv_mish_forward(x,self.conv.weight,self.conv.bias,self.sub)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 11871.0μs (100%) calls=1
--- Timing ---
GPU time: 3802.75 μs [↓4172.6μs ok]
--- Memory Bandwidth ---
DRAM BW: 620.8/3350 GB/s (18.5%) [↑9.7% ok]
read=70.6 write=550.2 GB/s
Hit rates: L2=93.5%
--- Compute Throughput ---
Dominant pipe: FP32 util=42.0% [↑1.2% ok]
Pipe util: FMA=42.0%, Tensor=0.9%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 74.2% [↓25.1% REGRESSED]
Eligible warps/cycle: 5.39
Launch: regs/thread=40, smem/blk=20608B, block=256, grid=131072
--- Memory Access Quality ---
Global ld coalescing: 1.13 sectors/req (1.1x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 189.2% ####################
mio_throttle 164.2% ####################
short_scoreboard 66.1% #############
--- Optimization Hints ---
-> 189% stalls: global memory latency (waiting for DRAM/L2). Try shared-memory tiling, prefetching, or reducing working set.
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
--- Delta vs Previous Iteration ---
GPU time: 7975.36→3802.75μs (↓4172.61) ok
DRAM BW%: 8.84→18.53% (↑9.69) ok
Compute%: 40.85→42.05% (↑1.19) ok
Occupancy: 99.29→74.17% (↓25.12) REGRESSED
Eligible warps/cyc: 5.89→5.39 (↓0.51) REGRESSED
Ld sectors/req: 1.26→1.13 (↓0.13) ok
L2 hit%: 95.27→93.49% (↓1.78) REGRESSED
turn 7 · 1 tool call(s) · 11.5s turn · 11.5s LLM · 8,893 chars (click to expand)
turn 711.5s turn · 11.5s 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\ncpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\ncuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define IC 8\n#define OC_TILE 16\n#define TILE_W 32\n#define TILE_H 16\n__device__ __forceinline__ float mish_func(float x){ if(x>20.0f)return x; float e=expf(x); if(x<-20.0f)return x*e; float t=e*(e+2.0f); return x*(t/(t+2.0f)); }\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int H,int W,float sub){\n const int OH=H-2,OW=W-2; const int oc_base=(blockIdx.z&3)*OC_TILE; const int n=blockIdx.z>>2; const int tx=threadIdx.x,ty=threadIdx.y; __shared__ float sm[IC][TILE_H+2][TILE_W+2]; int tid=ty*32+tx; const int total=IC*(TILE_H+2)*(TILE_W+2); for(int idx=tid;idx<total;idx+=256){int lx=idx%(TILE_W+2);int q=idx/(TILE_W+2);int ly=q%(TILE_H+2);int c=q/(TILE_H+2);int gx=blockIdx.x*TILE_W+lx;int gy=blockIdx.y*TILE_H+ly;float v=0.f;if(gx<W&&gy<H)v=x[((n*IC+c)*H+gy)*W+gx];sm[c][ly][lx]=v;} __syncthreads();\n int ox=blockIdx.x*TILE_W+tx; int oy0=blockIdx.y*TILE_H+ty; int oy1=oy0+8; float a0[OC_TILE]; float a1[OC_TILE];\n#pragma unroll\n for(int i=0;i<OC_TILE;i++){a0[i]=0.f; a1[i]=0.f;}\n#pragma unroll\n for(int c=0;c<IC;c++){\n#pragma unroll\n for(int ky=0;ky<3;ky++){\n#pragma unroll\n for(int kx=0;kx<3;kx++){float xv0=sm[c][ty+ky][tx+kx]; float xv1=sm[c][ty+8+ky][tx+kx]; int wb=((oc_base*IC+c)*3+ky)*3+kx;\n#pragma unroll\n for(int i=0;i<OC_TILE;i++){float ww=__ldg(w+wb+i*IC*9); a0[i]+=xv0*ww; a1[i]+=xv1*ww;}\n }}}\n if(ox<OW){const long plane=(long)OH*OW; long base=(long)n*64*plane+(long)oc_base*plane; if(oy0<OH){long pos=base+(long)oy0*OW+ox;\n#pragma unroll\n for(int i=0;i<OC_TILE;i++) out[pos+(long)i*plane]=mish_func(a0[i]+__ldg(b+oc_base+i)-sub);\n } if(oy1<OH){long pos=base+(long)oy1*OW+ox;\n#pragma unroll\n for(int i=0;i<OC_TILE;i++) out[pos+(long)i*plane]=mish_func(a1[i]+__ldg(b+oc_base+i)-sub);\n }}\n}\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub){x=x.contiguous();w=w.contiguous();b=b.contiguous();int N=x.size(0),H=x.size(2),W=x.size(3);int OH=H-2,OW=W-2;auto out=torch::empty({N,64,OH,OW},x.options());dim3 block(32,8);dim3 grid((OW+31)/32,(OH+15)/16,N*4);conv3x3_mish_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),H,W,sub);return out;}\n\"\"\"\n_ext=load_inline(name=\"conv_mish_ext_v3\",cpp_sources=[cpp_src],cuda_sources=[cuda_src],functions=[\"conv_mish_forward\"],extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"],extra_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,subtract_value_1,subtract_value_2): super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.sub=float(subtract_value_1+subtract_value_2)\n def forward(self,x): return _ext.conv_mish_forward(x,self.conv.weight,self.conv.bias,self.sub)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'conv_mish_ext_v3': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=conv_mish_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 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_87_cache/k_7189171e00d6/conv_mish_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=conv_mish_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 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_87_cache/k_7189171e00d6/conv_mish_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=conv_mish_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 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_87_cache/k_7189171e00d6/conv_mish_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_87_cache/k_7189171e00d6/conv_mish_ext_v3/cuda.cu(35): error: argument of type "int" is incompatible with parameter of type "float *"
torch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub){x=x.contiguous();w=w.contiguous();b=b.contiguous();int N=x.size(0),H=x.size(2),W=x.size(3);int OH=H-2,OW=W-2;auto out=torch::empty({N,64,OH,OW},x.options());dim3 block(32,8);dim3 grid((OW+31)/32,(OH+15)/16,N*4);conv3x3_mish_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),H,W,sub);return out;}
^
/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_87_cache/k_7189171e00d6/conv_mish_ext_v3/cuda.cu(35): error: too few arguments in function call
torch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub){x=x.contiguous();w=w.contiguous();b=b.contiguous();int N=x.size(0),H=x.size(2),W=x.size(3);int OH=H-2,OW=W-2;auto out=torch::empty({N,64,OH,OW},x.options());dim3 block(32,8);dim3 grid((OW+31)/32,(OH+15)/16,N*4);conv3x3_mish_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),H,W,sub);return out;}
^
2 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_87_cache/k_7189171e00d6/conv_mish_ext_v3/cuda.cu".
ninja: build stopped: subcommand failed.
turn 810.7s turn · 10.7s 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\ncpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\ncuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define IC 8\n#define OC_TILE 16\n#define TILE_W 32\n#define TILE_H 16\n__device__ __forceinline__ float mish_func(float x){ if(x>20.0f)return x; float e=expf(x); if(x<-20.0f)return x*e; float t=e*(e+2.0f); return x*(t/(t+2.0f)); }\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int H,int W,float sub){\n const int OH=H-2,OW=W-2; const int oc_base=(blockIdx.z&3)*OC_TILE; const int n=blockIdx.z>>2; const int tx=threadIdx.x,ty=threadIdx.y; __shared__ float sm[IC][TILE_H+2][TILE_W+2]; int tid=ty*32+tx; const int total=IC*(TILE_H+2)*(TILE_W+2); for(int idx=tid;idx<total;idx+=256){int lx=idx%(TILE_W+2);int q=idx/(TILE_W+2);int ly=q%(TILE_H+2);int c=q/(TILE_H+2);int gx=blockIdx.x*TILE_W+lx;int gy=blockIdx.y*TILE_H+ly;float v=0.f;if(gx<W&&gy<H)v=x[((n*IC+c)*H+gy)*W+gx];sm[c][ly][lx]=v;} __syncthreads();\n int ox=blockIdx.x*TILE_W+tx; int oy0=blockIdx.y*TILE_H+ty; int oy1=oy0+8; float a0[OC_TILE]; float a1[OC_TILE];\n#pragma unroll\n for(int i=0;i<OC_TILE;i++){a0[i]=0.f; a1[i]=0.f;}\n#pragma unroll\n for(int c=0;c<IC;c++){\n#pragma unroll\n for(int ky=0;ky<3;ky++){\n#pragma unroll\n for(int kx=0;kx<3;kx++){float xv0=sm[c][ty+ky][tx+kx]; float xv1=sm[c][ty+8+ky][tx+kx]; int wb=((oc_base*IC+c)*3+ky)*3+kx;\n#pragma unroll\n for(int i=0;i<OC_TILE;i++){float ww=__ldg(w+wb+i*IC*9); a0[i]+=xv0*ww; a1[i]+=xv1*ww;}\n }}}\n if(ox<OW){const long plane=(long)OH*OW; long base=(long)n*64*plane+(long)oc_base*plane; if(oy0<OH){long pos=base+(long)oy0*OW+ox;\n#pragma unroll\n for(int i=0;i<OC_TILE;i++) out[pos+(long)i*plane]=mish_func(a0[i]+__ldg(b+oc_base+i)-sub);\n } if(oy1<OH){long pos=base+(long)oy1*OW+ox;\n#pragma unroll\n for(int i=0;i<OC_TILE;i++) out[pos+(long)i*plane]=mish_func(a1[i]+__ldg(b+oc_base+i)-sub);\n }}\n}\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub){x=x.contiguous();w=w.contiguous();b=b.contiguous();int N=x.size(0),H=x.size(2),W=x.size(3);int OH=H-2,OW=W-2;auto out=torch::empty({N,64,OH,OW},x.options());dim3 block(32,8);dim3 grid((OW+31)/32,(OH+15)/16,N*4);conv3x3_mish_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),out.data_ptr<float>(),H,W,sub);return out;}\n\"\"\"\n_ext=load_inline(name=\"conv_mish_ext_v3b\",cpp_sources=[cpp_src],cuda_sources=[cuda_src],functions=[\"conv_mish_forward\"],extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"],extra_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,subtract_value_1,subtract_value_2): super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.sub=float(subtract_value_1+subtract_value_2)\n def forward(self,x): return _ext.conv_mish_forward(x,self.conv.weight,self.conv.bias,self.sub)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 9 · 2 tool call(s) · 14.9s turn · 14.9s LLM · 8,529 chars (click to expand)
turn 914.9s turn · 14.9s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\ncuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define IC 8\n#define OC_TILE 16\n#define TILE_W 32\n#define TILE_H 16\n__device__ __forceinline__ float mish_func(float x){ if(x>20.0f)return x; float e=expf(x); if(x<-20.0f)return x*e; float t=e*(e+2.0f); return x*(t/(t+2.0f)); }\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int H,int W,float sub){const int OH=H-2,OW=W-2;const int oc_base=(blockIdx.z&3)*OC_TILE;const int n=blockIdx.z>>2;const int tx=threadIdx.x,ty=threadIdx.y;__shared__ float sm[IC][TILE_H+2][TILE_W+2];int tid=ty*32+tx;const int total=IC*(TILE_H+2)*(TILE_W+2);for(int idx=tid;idx<total;idx+=256){int lx=idx%(TILE_W+2);int q=idx/(TILE_W+2);int ly=q%(TILE_H+2);int c=q/(TILE_H+2);int gx=blockIdx.x*TILE_W+lx;int gy=blockIdx.y*TILE_H+ly;float v=0.f;if(gx<W&&gy<H)v=x[((n*IC+c)*H+gy)*W+gx];sm[c][ly][lx]=v;}__syncthreads();int ox=blockIdx.x*TILE_W+tx;int oy0=blockIdx.y*TILE_H+ty;int oy1=oy0+8;float a0[OC_TILE];float a1[OC_TILE];\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++){a0[i]=0.f;a1[i]=0.f;}\n#pragma unroll\nfor(int c=0;c<IC;c++){\n#pragma unroll\nfor(int ky=0;ky<3;ky++){\n#pragma unroll\nfor(int kx=0;kx<3;kx++){float xv0=sm[c][ty+ky][tx+kx];float xv1=sm[c][ty+8+ky][tx+kx];int wb=((oc_base*IC+c)*3+ky)*3+kx;\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++){float ww=__ldg(w+wb+i*IC*9);a0[i]+=xv0*ww;a1[i]+=xv1*ww;}}}}\nif(ox<OW){const long plane=(long)OH*OW;long base=(long)n*64*plane+(long)oc_base*plane;if(oy0<OH){long pos=base+(long)oy0*OW+ox;\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++)out[pos+(long)i*plane]=mish_func(a0[i]+__ldg(b+oc_base+i)-sub);}if(oy1<OH){long pos=base+(long)oy1*OW+ox;\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++)out[pos+(long)i*plane]=mish_func(a1[i]+__ldg(b+oc_base+i)-sub);}}}\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub){x=x.contiguous();w=w.contiguous();b=b.contiguous();int N=x.size(0),H=x.size(2),W=x.size(3);int OH=H-2,OW=W-2;auto out=torch::empty({N,64,OH,OW},x.options());dim3 block(32,8);dim3 grid((OW+31)/32,(OH+15)/16,N*4);conv3x3_mish_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),out.data_ptr<float>(),H,W,sub);return out;}\n\"\"\"\n_ext=load_inline(name=\"conv_mish_ext_v3cp\",cpp_sources=[cpp_src],cuda_sources=[cuda_src],functions=[\"conv_mish_forward\"],extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"],extra_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,subtract_value_1,subtract_value_2): super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.sub=float(subtract_value_1+subtract_value_2)\n def forward(self,x): return _ext.conv_mish_forward(x,self.conv.weight,self.conv.bias,self.sub)\n"
}
call → disassemble_kernel
calldisassemble_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\ncuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define IC 8\n#define OC_TILE 16\n#define TILE_W 32\n#define TILE_H 16\n__device__ __forceinline__ float mish_func(float x){ if(x>20.0f)return x; float e=expf(x); if(x<-20.0f)return x*e; float t=e*(e+2.0f); return x*(t/(t+2.0f)); }\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int H,int W,float sub){const int OH=H-2,OW=W-2;const int oc_base=(blockIdx.z&3)*OC_TILE;const int n=blockIdx.z>>2;const int tx=threadIdx.x,ty=threadIdx.y;__shared__ float sm[IC][TILE_H+2][TILE_W+2];int tid=ty*32+tx;const int total=IC*(TILE_H+2)*(TILE_W+2);for(int idx=tid;idx<total;idx+=256){int lx=idx%(TILE_W+2);int q=idx/(TILE_W+2);int ly=q%(TILE_H+2);int c=q/(TILE_H+2);int gx=blockIdx.x*TILE_W+lx;int gy=blockIdx.y*TILE_H+ly;float v=0.f;if(gx<W&&gy<H)v=x[((n*IC+c)*H+gy)*W+gx];sm[c][ly][lx]=v;}__syncthreads();int ox=blockIdx.x*TILE_W+tx;int oy0=blockIdx.y*TILE_H+ty;int oy1=oy0+8;float a0[OC_TILE];float a1[OC_TILE];\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++){a0[i]=0.f;a1[i]=0.f;}\n#pragma unroll\nfor(int c=0;c<IC;c++){\n#pragma unroll\nfor(int ky=0;ky<3;ky++){\n#pragma unroll\nfor(int kx=0;kx<3;kx++){float xv0=sm[c][ty+ky][tx+kx];float xv1=sm[c][ty+8+ky][tx+kx];int wb=((oc_base*IC+c)*3+ky)*3+kx;\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++){float ww=__ldg(w+wb+i*IC*9);a0[i]+=xv0*ww;a1[i]+=xv1*ww;}}}}\nif(ox<OW){const long plane=(long)OH*OW;long base=(long)n*64*plane+(long)oc_base*plane;if(oy0<OH){long pos=base+(long)oy0*OW+ox;\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++)out[pos+(long)i*plane]=mish_func(a0[i]+__ldg(b+oc_base+i)-sub);}if(oy1<OH){long pos=base+(long)oy1*OW+ox;\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++)out[pos+(long)i*plane]=mish_func(a1[i]+__ldg(b+oc_base+i)-sub);}}}\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub){x=x.contiguous();w=w.contiguous();b=b.contiguous();int N=x.size(0),H=x.size(2),W=x.size(3);int OH=H-2,OW=W-2;auto out=torch::empty({N,64,OH,OW},x.options());dim3 block(32,8);dim3 grid((OW+31)/32,(OH+15)/16,N*4);conv3x3_mish_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),out.data_ptr<float>(),H,W,sub);return out;}\n\"\"\"\n_ext=load_inline(name=\"conv_mish_ext_v3dp\",cpp_sources=[cpp_src],cuda_sources=[cuda_src],functions=[\"conv_mish_forward\"],extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"],extra_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,subtract_value_1,subtract_value_2): super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.sub=float(subtract_value_1+subtract_value_2)\n def forward(self,x): return _ext.conv_mish_forward(x,self.conv.weight,self.conv.bias,self.sub)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=1.49e-07 mean_abs_err=1.77e-08 max_rel_err=1.00e+00
result ← disassemble_kernel (ok)
okdisassemble_kernel
disassemble_kernel PASSED: disassembly analysis complete.
=== Kernel Disassembly Summary ===
--- Instruction Mix ---
compute_fp32 2720 ( 60.3%)
memory_global 1221 ( 27.1%)
compute_int 244 ( 5.4%)
memory_shared 149 ( 3.3%)
control_flow 113 ( 2.5%)
move 27 ( 0.6%)
misc 13 ( 0.3%)
memory_const 10 ( 0.2%)
other 9 ( 0.2%)
compute_fp16 6 ( 0.1%)
TOTAL 4512
--- Warnings ---
cubin extraction: [Errno 18] Invalid cross-device link: 'conv_mish_ext_v3dp.1.sm_90.cubin' -> '/var/tmp/kb_nvdisasm_d1ocp7uk/conv_mish_ext_v3dp.1.sm_90.cubin'
turn 109.2s turn · 9.2s 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\ncpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\ncuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define IC 8\n#define OC_TILE 16\n#define TILE_W 32\n#define TILE_H 16\n__device__ __forceinline__ float mish_func(float x){ if(x>20.0f)return x; float e=expf(x); if(x<-20.0f)return x*e; float t=e*(e+2.0f); return x*(t/(t+2.0f)); }\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int H,int W,float sub){const int OH=H-2,OW=W-2;const int oc_base=(blockIdx.z&3)*OC_TILE;const int n=blockIdx.z>>2;const int tx=threadIdx.x,ty=threadIdx.y;__shared__ float sm[IC][TILE_H+2][TILE_W+2];int tid=ty*32+tx;const int total=IC*(TILE_H+2)*(TILE_W+2);for(int idx=tid;idx<total;idx+=256){int lx=idx%(TILE_W+2);int q=idx/(TILE_W+2);int ly=q%(TILE_H+2);int c=q/(TILE_H+2);int gx=blockIdx.x*TILE_W+lx;int gy=blockIdx.y*TILE_H+ly;float v=0.f;if(gx<W&&gy<H)v=x[((n*IC+c)*H+gy)*W+gx];sm[c][ly][lx]=v;}__syncthreads();int ox=blockIdx.x*TILE_W+tx;int oy0=blockIdx.y*TILE_H+ty;int oy1=oy0+8;float a0[OC_TILE];float a1[OC_TILE];\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++){a0[i]=0.f;a1[i]=0.f;}\n#pragma unroll\nfor(int c=0;c<IC;c++){\n#pragma unroll\nfor(int ky=0;ky<3;ky++){\n#pragma unroll\nfor(int kx=0;kx<3;kx++){float xv0=sm[c][ty+ky][tx+kx];float xv1=sm[c][ty+8+ky][tx+kx];int wb=((oc_base*IC+c)*3+ky)*3+kx;\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++){float ww=__ldg(w+wb+i*IC*9);a0[i]+=xv0*ww;a1[i]+=xv1*ww;}}}}\nif(ox<OW){const long plane=(long)OH*OW;long base=(long)n*64*plane+(long)oc_base*plane;if(oy0<OH){long pos=base+(long)oy0*OW+ox;\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++)out[pos+(long)i*plane]=mish_func(a0[i]+__ldg(b+oc_base+i)-sub);}if(oy1<OH){long pos=base+(long)oy1*OW+ox;\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++)out[pos+(long)i*plane]=mish_func(a1[i]+__ldg(b+oc_base+i)-sub);}}}\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub){x=x.contiguous();w=w.contiguous();b=b.contiguous();int N=x.size(0),H=x.size(2),W=x.size(3);int OH=H-2,OW=W-2;auto out=torch::empty({N,64,OH,OW},x.options());dim3 block(32,8);dim3 grid((OW+31)/32,(OH+15)/16,N*4);conv3x3_mish_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),out.data_ptr<float>(),H,W,sub);return out;}\n\"\"\"\n_ext=load_inline(name=\"conv_mish_ext_v3p\",cpp_sources=[cpp_src],cuda_sources=[cuda_src],functions=[\"conv_mish_forward\"],extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"],extra_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,subtract_value_1,subtract_value_2): super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.sub=float(subtract_value_1+subtract_value_2)\n def forward(self,x): return _ext.conv_mish_forward(x,self.conv.weight,self.conv.bias,self.sub)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 9242.0μs (100%) calls=1
--- Timing ---
GPU time: 3457.02 μs [↓345.7μs ok]
--- Memory Bandwidth ---
DRAM BW: 683.3/3350 GB/s (20.4%) [↑1.9% ok]
read=78.0 write=605.4 GB/s
Hit rates: L2=92.1%
--- Compute Throughput ---
Dominant pipe: FP32 util=41.7% [↓0.4% REGRESSED]
Pipe util: FMA=41.7%, Tensor=0.7%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 49.5% [↓24.7% REGRESSED]
Eligible warps/cycle: 3.22
Launch: regs/thread=56, smem/blk=20608B, block=256, grid=65536
--- Memory Access Quality ---
Global ld coalescing: 1.07 sectors/req (1.1x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 229.5% ####################
short_scoreboard 48.8% #########
mio_throttle 37.0% #######
--- Optimization Hints ---
-> 230% stalls: global memory latency (waiting for DRAM/L2). Try shared-memory tiling, prefetching, or reducing working set.
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
--- Delta vs Previous Iteration ---
GPU time: 3802.75→3457.02μs (↓345.73) ok
DRAM BW%: 18.53→20.40% (↑1.87) ok
Compute%: 42.05→41.68% (↓0.36) REGRESSED
Occupancy: 74.17→49.46% (↓24.72) REGRESSED
Eligible warps/cyc: 5.39→3.22 (↓2.17) REGRESSED
Ld sectors/req: 1.13→1.07 (↓0.07) ok
L2 hit%: 93.49→92.14% (↓1.35) REGRESSED
turn 11 · 1 tool call(s) · 9.5s turn · 9.5s LLM · 6,159 chars (click to expand)
turn 119.5s turn · 9.5s LLM · 0.0s tools
reasoning
**Considering optimization strategies**
I’m thinking about whether I should try OC32 since it has 64 access registers. That might feel low, though fewer blocks and loads could be beneficial. Maybe activation of 32 per thread will dominate the registers, possibly around 100. I should consider profiling this to see how it performs. Also, I wonder if arrays compile correctly; perhaps using N*2 and targeting specific blocks might help optimize performance further.
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\ncuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define IC 8\n#define OC_TILE 32\n#define TILE_W 32\n#define TILE_H 16\n__device__ __forceinline__ float mish_func(float x){ if(x>20.0f)return x; float e=expf(x); if(x<-20.0f)return x*e; float t=e*(e+2.0f); return x*(t/(t+2.0f)); }\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int H,int W,float sub){const int OH=H-2,OW=W-2;const int oc_base=(blockIdx.z&1)*OC_TILE;const int n=blockIdx.z>>1;const int tx=threadIdx.x,ty=threadIdx.y;__shared__ float sm[IC][TILE_H+2][TILE_W+2];int tid=ty*32+tx;const int total=IC*(TILE_H+2)*(TILE_W+2);for(int idx=tid;idx<total;idx+=256){int lx=idx%(TILE_W+2);int q=idx/(TILE_W+2);int ly=q%(TILE_H+2);int c=q/(TILE_H+2);int gx=blockIdx.x*TILE_W+lx;int gy=blockIdx.y*TILE_H+ly;float v=0.f;if(gx<W&&gy<H)v=x[((n*IC+c)*H+gy)*W+gx];sm[c][ly][lx]=v;}__syncthreads();int ox=blockIdx.x*TILE_W+tx;int oy0=blockIdx.y*TILE_H+ty;int oy1=oy0+8;float a0[OC_TILE];float a1[OC_TILE];\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++){a0[i]=0.f;a1[i]=0.f;}\n#pragma unroll\nfor(int c=0;c<IC;c++){\n#pragma unroll\nfor(int ky=0;ky<3;ky++){\n#pragma unroll\nfor(int kx=0;kx<3;kx++){float xv0=sm[c][ty+ky][tx+kx];float xv1=sm[c][ty+8+ky][tx+kx];int wb=((oc_base*IC+c)*3+ky)*3+kx;\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++){float ww=__ldg(w+wb+i*IC*9);a0[i]+=xv0*ww;a1[i]+=xv1*ww;}}}}\nif(ox<OW){const long plane=(long)OH*OW;long base=(long)n*64*plane+(long)oc_base*plane;if(oy0<OH){long pos=base+(long)oy0*OW+ox;\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++)out[pos+(long)i*plane]=mish_func(a0[i]+__ldg(b+oc_base+i)-sub);}if(oy1<OH){long pos=base+(long)oy1*OW+ox;\n#pragma unroll\nfor(int i=0;i<OC_TILE;i++)out[pos+(long)i*plane]=mish_func(a1[i]+__ldg(b+oc_base+i)-sub);}}}\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub){x=x.contiguous();w=w.contiguous();b=b.contiguous();int N=x.size(0),H=x.size(2),W=x.size(3);int OH=H-2,OW=W-2;auto out=torch::empty({N,64,OH,OW},x.options());dim3 block(32,8);dim3 grid((OW+31)/32,(OH+15)/16,N*2);conv3x3_mish_kernel<<<grid,block>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),out.data_ptr<float>(),H,W,sub);return out;}\n\"\"\"\n_ext=load_inline(name=\"conv_mish_ext_v4p\",cpp_sources=[cpp_src],cuda_sources=[cuda_src],functions=[\"conv_mish_forward\"],extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"],extra_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,subtract_value_1,subtract_value_2): super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.sub=float(subtract_value_1+subtract_value_2)\n def forward(self,x): return _ext.conv_mish_forward(x,self.conv.weight,self.conv.bias,self.sub)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 8864.0μs (100%) calls=1
--- Timing ---
GPU time: 3629.02 μs [↑172.0μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 650.9/3350 GB/s (19.4%) [↓1.0% REGRESSED]
read=74.3 write=576.6 GB/s
Hit rates: L2=91.5%
--- Compute Throughput ---
Dominant pipe: FP32 util=37.6% [↓4.0% REGRESSED]
Pipe util: FMA=37.6%, Tensor=0.2%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 24.7% [↓24.7% REGRESSED]
Eligible warps/cycle: 1.45
Launch: regs/thread=96, smem/blk=20608B, block=256, grid=32768
--- Memory Access Quality ---
Global ld coalescing: 1.03 sectors/req (perfect)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 117.8% ####################
short_scoreboard 33.9% ######
mio_throttle 8.2% #
--- Optimization Hints ---
-> 118% stalls: global memory latency (waiting for DRAM/L2). Try shared-memory tiling, prefetching, or reducing working set.
-> Low occupancy (25%) limited by registers (96/thread). Consider __launch_bounds__ or reducing register pressure.
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
--- Delta vs Previous Iteration ---
GPU time: 3457.02→3629.02μs (↑172.00) REGRESSED
DRAM BW%: 20.40→19.43% (↓0.97) REGRESSED
Compute%: 41.68→37.65% (↓4.03) REGRESSED
Occupancy: 49.46→24.75% (↓24.71) REGRESSED
Eligible warps/cyc: 3.22→1.45 (↓1.77) REGRESSED
Ld sectors/req: 1.07→1.03 (↓0.03) ok
L2 hit%: 92.14→91.49% (↓0.65) REGRESSED
turn 1214.7s turn · 14.7s LLM · 0.0s tools
reasoning
**Checking code submission**
I’m thinking about checking the final version 3 of the code. I want to make sure there are no issues, particularly with line formatting. It might be necessary to use the exact code to avoid problems during submission. Would a static check help catch anything I might have missed? I'm curious about the effectiveness of that approach, and I'm eager to submit once I feel confident everything is correct!
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 = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\n\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define IC 8\n#define OC_TILE 16\n#define TILE_W 32\n#define TILE_H 16\n\n__device__ __forceinline__ float mish_func(float x) {\n if (x > 20.0f) return x;\n float e = expf(x);\n if (x < -20.0f) return x * e;\n float t = e * (e + 2.0f);\n return x * (t / (t + 2.0f));\n}\n\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ b,\n float* __restrict__ out,\n int H, int W, float sub) {\n const int OH = H - 2, OW = W - 2;\n const int oc_base = (blockIdx.z & 3) * OC_TILE;\n const int n = blockIdx.z >> 2;\n const int tx = threadIdx.x, ty = threadIdx.y;\n\n __shared__ float sm[IC][TILE_H + 2][TILE_W + 2];\n const int tid = ty * 32 + tx;\n const int total = IC * (TILE_H + 2) * (TILE_W + 2);\n\n for (int idx = tid; idx < total; idx += 256) {\n int lx = idx % (TILE_W + 2);\n int q = idx / (TILE_W + 2);\n int ly = q % (TILE_H + 2);\n int c = q / (TILE_H + 2);\n int gx = blockIdx.x * TILE_W + lx;\n int gy = blockIdx.y * TILE_H + ly;\n float v = 0.0f;\n if (gx < W && gy < H) v = x[((n * IC + c) * H + gy) * W + gx];\n sm[c][ly][lx] = v;\n }\n __syncthreads();\n\n int ox = blockIdx.x * TILE_W + tx;\n int oy0 = blockIdx.y * TILE_H + ty;\n int oy1 = oy0 + 8;\n\n float a0[OC_TILE];\n float a1[OC_TILE];\n#pragma unroll\n for (int i = 0; i < OC_TILE; ++i) { a0[i] = 0.0f; a1[i] = 0.0f; }\n\n#pragma unroll\n for (int c = 0; c < IC; ++c) {\n#pragma unroll\n for (int ky = 0; ky < 3; ++ky) {\n#pragma unroll\n for (int kx = 0; kx < 3; ++kx) {\n float xv0 = sm[c][ty + ky][tx + kx];\n float xv1 = sm[c][ty + 8 + ky][tx + kx];\n int wb = ((oc_base * IC + c) * 3 + ky) * 3 + kx;\n#pragma unroll\n for (int i = 0; i < OC_TILE; ++i) {\n float ww = __ldg(w + wb + i * IC * 9);\n a0[i] += xv0 * ww;\n a1[i] += xv1 * ww;\n }\n }\n }\n }\n\n if (ox < OW) {\n const long plane = (long)OH * OW;\n long base = (long)n * 64 * plane + (long)oc_base * plane;\n if (oy0 < OH) {\n long pos = base + (long)oy0 * OW + ox;\n#pragma unroll\n for (int i = 0; i < OC_TILE; ++i)\n out[pos + (long)i * plane] = mish_func(a0[i] + __ldg(b + oc_base + i) - sub);\n }\n if (oy1 < OH) {\n long pos = base + (long)oy1 * OW + ox;\n#pragma unroll\n for (int i = 0; i < OC_TILE; ++i)\n out[pos + (long)i * plane] = mish_func(a1[i] + __ldg(b + oc_base + i) - sub);\n }\n }\n}\n\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub) {\n int N = x.size(0), H = x.size(2), W = x.size(3);\n int OH = H - 2, OW = W - 2;\n auto out = torch::empty({N, 64, OH, OW}, x.options());\n dim3 block(32, 8);\n dim3 grid((OW + 31) / 32, (OH + 15) / 16, N * 4);\n conv3x3_mish_kernel<<<grid, block>>>(x.data_ptr<float>(), w.data_ptr<float>(), b.data_ptr<float>(), out.data_ptr<float>(), H, W, sub);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"conv_mish_ext_final\", cpp_sources=[cpp_src], cuda_sources=[cuda_src], functions=[\"conv_mish_forward\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], extra_cflags=[\"-O3\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, subtract_value_1, subtract_value_2):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.sub = float(subtract_value_1 + subtract_value_2)\n\n def forward(self, x):\n return _ext.conv_mish_forward(x, self.conv.weight, self.conv.bias, self.sub)\n"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 1310.6s turn · 10.6s 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 = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);\n\"\"\"\n\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define IC 8\n#define OC_TILE 16\n#define TILE_W 32\n#define TILE_H 16\n\n__device__ __forceinline__ float mish_func(float x) {\n if (x > 20.0f) return x;\n float e = expf(x);\n if (x < -20.0f) return x * e;\n float t = e * (e + 2.0f);\n return x * (t / (t + 2.0f));\n}\n\n__global__ void conv3x3_mish_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ b,\n float* __restrict__ out,\n int H, int W, float sub) {\n const int OH = H - 2, OW = W - 2;\n const int oc_base = (blockIdx.z & 3) * OC_TILE;\n const int n = blockIdx.z >> 2;\n const int tx = threadIdx.x, ty = threadIdx.y;\n\n __shared__ float sm[IC][TILE_H + 2][TILE_W + 2];\n const int tid = ty * 32 + tx;\n const int total = IC * (TILE_H + 2) * (TILE_W + 2);\n\n for (int idx = tid; idx < total; idx += 256) {\n int lx = idx % (TILE_W + 2);\n int q = idx / (TILE_W + 2);\n int ly = q % (TILE_H + 2);\n int c = q / (TILE_H + 2);\n int gx = blockIdx.x * TILE_W + lx;\n int gy = blockIdx.y * TILE_H + ly;\n float v = 0.0f;\n if (gx < W && gy < H) v = x[((n * IC + c) * H + gy) * W + gx];\n sm[c][ly][lx] = v;\n }\n __syncthreads();\n\n int ox = blockIdx.x * TILE_W + tx;\n int oy0 = blockIdx.y * TILE_H + ty;\n int oy1 = oy0 + 8;\n\n float a0[OC_TILE];\n float a1[OC_TILE];\n#pragma unroll\n for (int i = 0; i < OC_TILE; ++i) { a0[i] = 0.0f; a1[i] = 0.0f; }\n\n#pragma unroll\n for (int c = 0; c < IC; ++c) {\n#pragma unroll\n for (int ky = 0; ky < 3; ++ky) {\n#pragma unroll\n for (int kx = 0; kx < 3; ++kx) {\n float xv0 = sm[c][ty + ky][tx + kx];\n float xv1 = sm[c][ty + 8 + ky][tx + kx];\n int wb = ((oc_base * IC + c) * 3 + ky) * 3 + kx;\n#pragma unroll\n for (int i = 0; i < OC_TILE; ++i) {\n float ww = __ldg(w + wb + i * IC * 9);\n a0[i] += xv0 * ww;\n a1[i] += xv1 * ww;\n }\n }\n }\n }\n\n if (ox < OW) {\n const long plane = (long)OH * OW;\n long base = (long)n * 64 * plane + (long)oc_base * plane;\n if (oy0 < OH) {\n long pos = base + (long)oy0 * OW + ox;\n#pragma unroll\n for (int i = 0; i < OC_TILE; ++i)\n out[pos + (long)i * plane] = mish_func(a0[i] + __ldg(b + oc_base + i) - sub);\n }\n if (oy1 < OH) {\n long pos = base + (long)oy1 * OW + ox;\n#pragma unroll\n for (int i = 0; i < OC_TILE; ++i)\n out[pos + (long)i * plane] = mish_func(a1[i] + __ldg(b + oc_base + i) - sub);\n }\n }\n}\n\ntorch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub) {\n int N = x.size(0), H = x.size(2), W = x.size(3);\n int OH = H - 2, OW = W - 2;\n auto out = torch::empty({N, 64, OH, OW}, x.options());\n dim3 block(32, 8);\n dim3 grid((OW + 31) / 32, (OH + 15) / 16, N * 4);\n conv3x3_mish_kernel<<<grid, block>>>(x.data_ptr<float>(), w.data_ptr<float>(), b.data_ptr<float>(), out.data_ptr<float>(), H, W, sub);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"conv_mish_ext_final\", cpp_sources=[cpp_src], cuda_sources=[cuda_src], functions=[\"conv_mish_forward\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], extra_cflags=[\"-O3\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, subtract_value_1, subtract_value_2):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.sub = float(subtract_value_1 + subtract_value_2)\n\n def forward(self, x):\n return _ext.conv_mish_forward(x, self.conv.weight, self.conv.bias, self.sub)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 3.46 μs
Runtime stats: mean=3.46μs median=?μs std=0.06μs
Numerical precision: max_abs_err=1.49e-07 mean_abs_err=1.77e-08 max_rel_err=1.00e+00
Memory: 2305.16 MB peak (ref: 4321.29 MB, ratio: 0.5334x)
Kernel launches: 1 (ref: 10, fusion ratio: 10.0)
Energy: 482.82 mJ/run (ref: 1207.94 mJ, ratio: 2.5018x)
SOL score: 0.4168
final submitted kernel (122 lines, 4,211 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
cpp_src = r"""
#include <torch/extension.h>
torch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub);
"""
cuda_src = r"""
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#define IC 8
#define OC_TILE 16
#define TILE_W 32
#define TILE_H 16
__device__ __forceinline__ float mish_func(float x) {
if (x > 20.0f) return x;
float e = expf(x);
if (x < -20.0f) return x * e;
float t = e * (e + 2.0f);
return x * (t / (t + 2.0f));
}
__global__ void conv3x3_mish_kernel(const float* __restrict__ x,
const float* __restrict__ w,
const float* __restrict__ b,
float* __restrict__ out,
int H, int W, float sub) {
const int OH = H - 2, OW = W - 2;
const int oc_base = (blockIdx.z & 3) * OC_TILE;
const int n = blockIdx.z >> 2;
const int tx = threadIdx.x, ty = threadIdx.y;
__shared__ float sm[IC][TILE_H + 2][TILE_W + 2];
const int tid = ty * 32 + tx;
const int total = IC * (TILE_H + 2) * (TILE_W + 2);
for (int idx = tid; idx < total; idx += 256) {
int lx = idx % (TILE_W + 2);
int q = idx / (TILE_W + 2);
int ly = q % (TILE_H + 2);
int c = q / (TILE_H + 2);
int gx = blockIdx.x * TILE_W + lx;
int gy = blockIdx.y * TILE_H + ly;
float v = 0.0f;
if (gx < W && gy < H) v = x[((n * IC + c) * H + gy) * W + gx];
sm[c][ly][lx] = v;
}
__syncthreads();
int ox = blockIdx.x * TILE_W + tx;
int oy0 = blockIdx.y * TILE_H + ty;
int oy1 = oy0 + 8;
float a0[OC_TILE];
float a1[OC_TILE];
#pragma unroll
for (int i = 0; i < OC_TILE; ++i) { a0[i] = 0.0f; a1[i] = 0.0f; }
#pragma unroll
for (int c = 0; c < IC; ++c) {
#pragma unroll
for (int ky = 0; ky < 3; ++ky) {
#pragma unroll
for (int kx = 0; kx < 3; ++kx) {
float xv0 = sm[c][ty + ky][tx + kx];
float xv1 = sm[c][ty + 8 + ky][tx + kx];
int wb = ((oc_base * IC + c) * 3 + ky) * 3 + kx;
#pragma unroll
for (int i = 0; i < OC_TILE; ++i) {
float ww = __ldg(w + wb + i * IC * 9);
a0[i] += xv0 * ww;
a1[i] += xv1 * ww;
}
}
}
}
if (ox < OW) {
const long plane = (long)OH * OW;
long base = (long)n * 64 * plane + (long)oc_base * plane;
if (oy0 < OH) {
long pos = base + (long)oy0 * OW + ox;
#pragma unroll
for (int i = 0; i < OC_TILE; ++i)
out[pos + (long)i * plane] = mish_func(a0[i] + __ldg(b + oc_base + i) - sub);
}
if (oy1 < OH) {
long pos = base + (long)oy1 * OW + ox;
#pragma unroll
for (int i = 0; i < OC_TILE; ++i)
out[pos + (long)i * plane] = mish_func(a1[i] + __ldg(b + oc_base + i) - sub);
}
}
}
torch::Tensor conv_mish_forward(torch::Tensor x, torch::Tensor w, torch::Tensor b, float sub) {
int N = x.size(0), H = x.size(2), W = x.size(3);
int OH = H - 2, OW = W - 2;
auto out = torch::empty({N, 64, OH, OW}, x.options());
dim3 block(32, 8);
dim3 grid((OW + 31) / 32, (OH + 15) / 16, N * 4);
conv3x3_mish_kernel<<<grid, block>>>(x.data_ptr<float>(), w.data_ptr<float>(), b.data_ptr<float>(), out.data_ptr<float>(), H, W, sub);
return out;
}
"""
_ext = load_inline(name="conv_mish_ext_final", cpp_sources=[cpp_src], cuda_sources=[cuda_src], functions=["conv_mish_forward"], extra_cuda_cflags=["-O3", "--use_fast_math"], extra_cflags=["-O3"], verbose=False)
class ModelNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, subtract_value_1, subtract_value_2):
super(ModelNew, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
self.sub = float(subtract_value_1 + subtract_value_2)
def forward(self, x):
return _ext.conv_mish_forward(x, self.conv.weight, self.conv.bias, self.sub)