correct
21_Conv2d_Add_Scale_Sigmoid_GroupNorm.py
started
2026-05-04T06:39:34Z
timingkernelbench.timing
kernel mean / median / std
4.24 / — / 0.03 μs
ref mean / median / std
6.51 / — / 0.06 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
2.10x
energyNVML (pynvml)
ratio (ref / kernel)
2.00x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
7006.11 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, adds a bias term, scales, applies sigmoid, and performs group normalization.
"""
def __init__(self, in_channels, out_channels, kernel_size, num_groups, bias_shape, scale_shape):
super(Model, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
self.bias = nn.Parameter(torch.randn(bias_shape))
self.scale = nn.Parameter(torch.randn(scale_shape))
self.group_norm = nn.GroupNorm(num_groups, out_channels)
def forward(self, x):
x = self.conv(x)
x = x + self.bias
x = x * self.scale
x = torch.sigmoid(x)
x = self.group_norm(x)
return x
batch_size = 128
in_channels = 8
out_channels = 32
height = width = 256
kernel_size = 3
num_groups = 8
bias_shape = (out_channels, 1, 1)
scale_shape = (out_channels, 1, 1)
def get_inputs():
return [torch.rand(batch_size, in_channels, height, width)]
def get_init_inputs():
return [in_channels, out_channels, kernel_size, num_groups, bias_shape, scale_shape]turn 018.8s turn · 18.8s LLM · 0.0s tools
reasoning
**Optimizing Python Model**
I need to craft a Python extension, focusing on optimization. The operation involves inputting dimensions and specifying parameters for a Conv2D layer, which needs to handle per-channel bias and scale with a sigmoid function, plus GroupNorm. I’m also thinking about incorporating parameters for copying state dictionaries and ensuring model names match for benchmarks. Considering the large computational needs, I’ll explore using PyTorch's fast convolution capabilities for efficiency. Lastly, I need to see how to fuse operations effectively while handling normalization.
**Exploring CUDA Kernel Options**
I’m working on a task where a custom CUDA kernel replaces the forward function. I wonder if I should use `torch.nn.functional.conv2d` and then apply a custom fused operation along with GroupNorm. This could be faster compared to using PyTorch because of reduced overhead. The reference I looked at uses cuDNN for convolution and elementwise operations, which seems efficient. I'll need to benchmark the naive convolution's performance against cuDNN since input channels are small but output is large. Finally, I need to ensure state_dict compatibility.
**Considering Input Reuse**
I'm thinking about the input reuse and wondering if it’s okay that it's not cached. PyTorch's cuDNN is highly optimized, which gives a performance edge. However, I realize that fusing the sigmoid sums could help reduce memory usage. It's important for me to compare these approaches and see which one offers better performance overall. Overall, I need to balance optimization with memory efficiency in my implementation.
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) · 26.0s turn · 26.0s LLM · 6,997 chars (click to expand)
turn 126.0s turn · 26.0s LLM · 0.0s tools
reasoning
**Explaining GroupNorm in PyTorch**
I'm thinking about how PyTorch's GroupNorm works. It seems to use the mean of input x and bias the variance correction to 0. The formula appears to be y = (x - mean) / sqrt(var + eps) * weight + bias. So, I want to ensure the user understands that this normalization approach might help with batch size variations. It's intriguing how these details can impact model performance!
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\n_cpp_src = r'''\n#include <torch/extension.h>\n\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups);\n\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups) {\n return fused_post_gn_cuda(x, bias, scale, gn_weight, gn_bias, num_groups);\n}\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#define BLOCK 256\n\n__inline__ __device__ float warp_reduce_sum(float v) {\n #pragma unroll\n for (int offset = 16; offset > 0; offset >>= 1)\n v += __shfl_down_sync(0xffffffff, v, offset);\n return v;\n}\n\n__inline__ __device__ float block_reduce_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_reduce_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_reduce_sum(v);\n return v;\n}\n\n__global__ __launch_bounds__(BLOCK)\nvoid post_reduce_kernel(const float* __restrict__ x,\n const float* __restrict__ bias,\n const float* __restrict__ scale,\n float* __restrict__ tmp,\n float* __restrict__ stats,\n int N, int C, int HW, int G) {\n int ng = blockIdx.x;\n int n = ng / G;\n int g = ng - n * G;\n int channels_per_group = C / G;\n int group_elems = channels_per_group * HW;\n int c0 = g * channels_per_group;\n float sum = 0.0f;\n float sumsq = 0.0f;\n for (int e = threadIdx.x; e < group_elems; e += blockDim.x) {\n int ci = e / HW;\n int hw = e - ci * HW;\n int c = c0 + ci;\n int idx = (n * C + c) * HW + hw;\n float z = (x[idx] + bias[c]) * scale[c];\n float y = 1.0f / (1.0f + expf(-z));\n tmp[idx] = y;\n sum += y;\n sumsq += y * y;\n }\n sum = block_reduce_sum(sum);\n sumsq = block_reduce_sum(sumsq);\n if (threadIdx.x == 0) {\n float mean = sum / (float)group_elems;\n float var = sumsq / (float)group_elems - mean * mean;\n if (var < 0.0f) var = 0.0f;\n stats[2 * ng] = mean;\n stats[2 * ng + 1] = rsqrtf(var + 1.0e-5f);\n }\n}\n\n__global__ __launch_bounds__(256)\nvoid norm_kernel(const float* __restrict__ tmp,\n const float* __restrict__ stats,\n const float* __restrict__ gn_weight,\n const float* __restrict__ gn_bias,\n float* __restrict__ out,\n int total, int C, int HW, int G) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int stride = blockDim.x * gridDim.x;\n int channels_per_group = C / G;\n for (; idx < total; idx += stride) {\n int t = idx / HW;\n int c = t % C;\n int n = t / C;\n int g = c / channels_per_group;\n int ng = n * G + g;\n float y = (tmp[idx] - stats[2 * ng]) * stats[2 * ng + 1];\n out[idx] = y * gn_weight[c] + gn_bias[c];\n }\n}\n\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups) {\n TORCH_CHECK(x.is_cuda(), \"x must be CUDA\");\n TORCH_CHECK(x.scalar_type() == at::kFloat, \"float only\");\n x = x.contiguous();\n bias = bias.contiguous();\n scale = scale.contiguous();\n gn_weight = gn_weight.contiguous();\n gn_bias = gn_bias.contiguous();\n int N = (int)x.size(0);\n int C = (int)x.size(1);\n int H = (int)x.size(2);\n int W = (int)x.size(3);\n int HW = H * W;\n int G = (int)num_groups;\n auto tmp = torch::empty_like(x);\n auto out = torch::empty_like(x);\n auto stats = torch::empty({N * G * 2}, x.options());\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_reduce_kernel<<<N * G, BLOCK, 0, stream>>>(x.data_ptr<float>(), bias.data_ptr<float>(), scale.data_ptr<float>(), tmp.data_ptr<float>(), stats.data_ptr<float>(), N, C, HW, G);\n int total = N * C * HW;\n int blocks = (total + 255) / 256;\n if (blocks > 65535) blocks = 65535;\n norm_kernel<<<blocks, 256, 0, stream>>>(tmp.data_ptr<float>(), stats.data_ptr<float>(), gn_weight.data_ptr<float>(), gn_bias.data_ptr<float>(), out.data_ptr<float>(), total, C, HW, G);\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_post_gn_ext_v1', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['fused_post_gn'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, num_groups, bias_shape, scale_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.bias = nn.Parameter(torch.randn(bias_shape))\n self.scale = nn.Parameter(torch.randn(scale_shape))\n self.group_norm = nn.GroupNorm(num_groups, out_channels)\n self.num_groups = num_groups\n\n def forward(self, x):\n y = F.conv2d(x, self.conv.weight, self.conv.bias)\n return _ext.fused_post_gn(y, self.bias, self.scale, self.group_norm.weight, self.group_norm.bias, self.num_groups)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 2 · 1 tool call(s) · 15.5s turn · 15.5s LLM · 6,581 chars (click to expand)
turn 215.5s turn · 15.5s 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\n_cpp_src = r'''\n#include <torch/extension.h>\n\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups);\n\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups) {\n return fused_post_gn_cuda(x, bias, scale, gn_weight, gn_bias, num_groups);\n}\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#define BLOCK 256\n\n__inline__ __device__ float warp_reduce_sum(float v) {\n #pragma unroll\n for (int offset = 16; offset > 0; offset >>= 1)\n v += __shfl_down_sync(0xffffffff, v, offset);\n return v;\n}\n\n__inline__ __device__ float block_reduce_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_reduce_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_reduce_sum(v);\n return v;\n}\n\n__global__ __launch_bounds__(BLOCK)\nvoid post_reduce_kernel(const float* __restrict__ x,\n const float* __restrict__ bias,\n const float* __restrict__ scale,\n float* __restrict__ tmp,\n float* __restrict__ stats,\n int N, int C, int HW, int G) {\n int ng = blockIdx.x;\n int n = ng / G;\n int g = ng - n * G;\n int channels_per_group = C / G;\n int group_elems = channels_per_group * HW;\n int c0 = g * channels_per_group;\n float sum = 0.0f;\n float sumsq = 0.0f;\n for (int e = threadIdx.x; e < group_elems; e += blockDim.x) {\n int ci = e / HW;\n int hw = e - ci * HW;\n int c = c0 + ci;\n int idx = (n * C + c) * HW + hw;\n float z = (x[idx] + bias[c]) * scale[c];\n float y = 1.0f / (1.0f + expf(-z));\n tmp[idx] = y;\n sum += y;\n sumsq += y * y;\n }\n sum = block_reduce_sum(sum);\n sumsq = block_reduce_sum(sumsq);\n if (threadIdx.x == 0) {\n float mean = sum / (float)group_elems;\n float var = sumsq / (float)group_elems - mean * mean;\n if (var < 0.0f) var = 0.0f;\n stats[2 * ng] = mean;\n stats[2 * ng + 1] = rsqrtf(var + 1.0e-5f);\n }\n}\n\n__global__ __launch_bounds__(256)\nvoid norm_kernel(const float* __restrict__ tmp,\n const float* __restrict__ stats,\n const float* __restrict__ gn_weight,\n const float* __restrict__ gn_bias,\n float* __restrict__ out,\n int total, int C, int HW, int G) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int stride = blockDim.x * gridDim.x;\n int channels_per_group = C / G;\n for (; idx < total; idx += stride) {\n int t = idx / HW;\n int c = t % C;\n int n = t / C;\n int g = c / channels_per_group;\n int ng = n * G + g;\n float y = (tmp[idx] - stats[2 * ng]) * stats[2 * ng + 1];\n out[idx] = y * gn_weight[c] + gn_bias[c];\n }\n}\n\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups) {\n TORCH_CHECK(x.is_cuda(), \"x must be CUDA\");\n TORCH_CHECK(x.scalar_type() == at::kFloat, \"float only\");\n x = x.contiguous();\n bias = bias.contiguous();\n scale = scale.contiguous();\n gn_weight = gn_weight.contiguous();\n gn_bias = gn_bias.contiguous();\n int N = (int)x.size(0);\n int C = (int)x.size(1);\n int H = (int)x.size(2);\n int W = (int)x.size(3);\n int HW = H * W;\n int G = (int)num_groups;\n auto tmp = torch::empty_like(x);\n auto out = torch::empty_like(x);\n auto stats = torch::empty({N * G * 2}, x.options());\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_reduce_kernel<<<N * G, BLOCK, 0, stream>>>(x.data_ptr<float>(), bias.data_ptr<float>(), scale.data_ptr<float>(), tmp.data_ptr<float>(), stats.data_ptr<float>(), N, C, HW, G);\n int total = N * C * HW;\n int blocks = (total + 255) / 256;\n if (blocks > 65535) blocks = 65535;\n norm_kernel<<<blocks, 256, 0, stream>>>(tmp.data_ptr<float>(), stats.data_ptr<float>(), gn_weight.data_ptr<float>(), gn_bias.data_ptr<float>(), out.data_ptr<float>(), total, C, HW, G);\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_post_gn_ext_v1', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['fused_post_gn'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, num_groups, bias_shape, scale_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.bias = nn.Parameter(torch.randn(bias_shape))\n self.scale = nn.Parameter(torch.randn(scale_shape))\n self.group_norm = nn.GroupNorm(num_groups, out_channels)\n self.num_groups = num_groups\n\n def forward(self, x):\n y = F.conv2d(x, self.conv.weight, self.conv.bias)\n return _ext.fused_post_gn(y, self.bias, self.scale, self.group_norm.weight, self.group_norm.bias, self.num_groups)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=4.36e-05 mean_abs_err=1.18e-06 max_rel_err=3.66e+02
turn 3 · 1 tool call(s) · 10.4s turn · 10.4s LLM · 6,186 chars (click to expand)
turn 310.4s turn · 10.4s LLM · 0.0s tools
reasoning
**Considering performance submission**
I'm thinking about the need for performance measurements. Should I submit something now? I wonder if profiling offers a reliable indication of timing, or if I need to rely on a different approach. It seems like utilizing profiling could be beneficial in this situation, but I'm unsure about how effective it will be in providing accurate information. Let me figure this out to ensure I'm on the right track!
call → profile_kernel
callprofile_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\n_cpp_src = r'''\n#include <torch/extension.h>\n\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups);\n\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups) {\n return fused_post_gn_cuda(x, bias, scale, gn_weight, gn_bias, num_groups);\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#define BLOCK 256\n__inline__ __device__ float warp_reduce_sum(float v) {#pragma unroll\n for (int offset = 16; offset > 0; offset >>= 1) v += __shfl_down_sync(0xffffffff, v, offset); return v;}\n__inline__ __device__ float block_reduce_sum(float v) {__shared__ float shared[32]; int lane=threadIdx.x&31; int wid=threadIdx.x>>5; v=warp_reduce_sum(v); if(lane==0) shared[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?shared[lane]:0.0f; if(wid==0) v=warp_reduce_sum(v); return v;}\n__global__ __launch_bounds__(BLOCK) void post_reduce_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,float* __restrict__ tmp,float* __restrict__ stats,int N,int C,int HW,int G){int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int group_elems=cpg*HW; int c0=g*cpg; float sum=0.0f,sumsq=0.0f; for(int e=threadIdx.x;e<group_elems;e+=blockDim.x){int ci=e/HW; int hw=e-ci*HW; int c=c0+ci; int idx=(n*C+c)*HW+hw; float z=(x[idx]+bias[c])*scale[c]; float y=1.0f/(1.0f+expf(-z)); tmp[idx]=y; sum+=y; sumsq+=y*y;} sum=block_reduce_sum(sum); sumsq=block_reduce_sum(sumsq); if(threadIdx.x==0){float mean=sum/(float)group_elems; float var=sumsq/(float)group_elems-mean*mean; if(var<0.0f)var=0.0f; stats[2*ng]=mean; stats[2*ng+1]=rsqrtf(var+1.0e-5f);}}\n__global__ __launch_bounds__(256) void norm_kernel(const float* __restrict__ tmp,const float* __restrict__ stats,const float* __restrict__ gn_weight,const float* __restrict__ gn_bias,float* __restrict__ out,int total,int C,int HW,int G){int idx=blockIdx.x*blockDim.x+threadIdx.x; int stride=blockDim.x*gridDim.x; int cpg=C/G; for(;idx<total;idx+=stride){int t=idx/HW; int c=t%C; int n=t/C; int g=c/cpg; int ng=n*G+g; float y=(tmp[idx]-stats[2*ng])*stats[2*ng+1]; out[idx]=y*gn_weight[c]+gn_bias[c];}}\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups){x=x.contiguous(); bias=bias.contiguous(); scale=scale.contiguous(); gn_weight=gn_weight.contiguous(); gn_bias=gn_bias.contiguous(); int N=x.size(0),C=x.size(1),HW=x.size(2)*x.size(3),G=(int)num_groups; auto tmp=torch::empty_like(x); auto out=torch::empty_like(x); auto stats=torch::empty({N*G*2},x.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); post_reduce_kernel<<<N*G,BLOCK,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),tmp.data_ptr<float>(),stats.data_ptr<float>(),N,C,HW,G); int total=N*C*HW; int blocks=(total+255)/256; if(blocks>65535)blocks=65535; norm_kernel<<<blocks,256,0,stream>>>(tmp.data_ptr<float>(),stats.data_ptr<float>(),gn_weight.data_ptr<float>(),gn_bias.data_ptr<float>(),out.data_ptr<float>(),total,C,HW,G); return out;}\n'''\n_ext = load_inline(name='fused_post_gn_ext_v1p', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['fused_post_gn'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, num_groups, bias_shape, scale_shape):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.bias=nn.Parameter(torch.randn(bias_shape)); self.scale=nn.Parameter(torch.randn(scale_shape)); self.group_norm=nn.GroupNorm(num_groups,out_channels); self.num_groups=num_groups\n def forward(self,x):\n y=F.conv2d(x,self.conv.weight,self.conv.bias); return _ext.fused_post_gn(y,self.bias,self.scale,self.group_norm.weight,self.group_norm.bias,self.num_groups)\n"
}
result ← profile_kernel (fail)
failprofile_kernel
profile_kernel FAILED: worker exited with code 1.
runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_21_cache/k_62c4f8706a00/fused_post_gn_ext_v1p/cuda.cu(11): error: expected an expression
float warp_reduce_sum(float v) {#pragma unroll
^
2 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_21_cache/k_62c4f8706a00/fused_post_gn_ext_v1p/cuda.cu".
ninja: build stopped: subcommand failed.
turn 4 · 1 tool call(s) · 11.3s turn · 11.3s LLM · 8,484 chars (click to expand)
turn 411.3s turn · 11.3s LLM · 0.0s tools
reasoning
**Reviewing profile code**
I'm looking at the profile code, and it seems like there's a compressed broken pragma. I should probably go back to the earlier correct code, but I wonder if I really need to do that. Maybe I can just use the profile with the exact specifications instead. I'm trying to weigh my options here, keeping in mind what’s best for achieving the desired result. It's a bit tricky, but I'll figure it out!
call → profile_kernel
callprofile_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\n_cpp_src = r'''\n#include <torch/extension.h>\n\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups);\n\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups) {\n return fused_post_gn_cuda(x, bias, scale, gn_weight, gn_bias, num_groups);\n}\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#define BLOCK 256\n\n__inline__ __device__ float warp_reduce_sum(float v) {\n #pragma unroll\n for (int offset = 16; offset > 0; offset >>= 1)\n v += __shfl_down_sync(0xffffffff, v, offset);\n return v;\n}\n\n__inline__ __device__ float block_reduce_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_reduce_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_reduce_sum(v);\n return v;\n}\n\n__global__ __launch_bounds__(BLOCK)\nvoid post_reduce_kernel(const float* __restrict__ x,\n const float* __restrict__ bias,\n const float* __restrict__ scale,\n float* __restrict__ tmp,\n float* __restrict__ stats,\n int N, int C, int HW, int G) {\n int ng = blockIdx.x;\n int n = ng / G;\n int g = ng - n * G;\n int channels_per_group = C / G;\n int group_elems = channels_per_group * HW;\n int c0 = g * channels_per_group;\n float sum = 0.0f;\n float sumsq = 0.0f;\n for (int e = threadIdx.x; e < group_elems; e += blockDim.x) {\n int ci = e / HW;\n int hw = e - ci * HW;\n int c = c0 + ci;\n int idx = (n * C + c) * HW + hw;\n float z = (x[idx] + bias[c]) * scale[c];\n float y = 1.0f / (1.0f + expf(-z));\n tmp[idx] = y;\n sum += y;\n sumsq += y * y;\n }\n sum = block_reduce_sum(sum);\n sumsq = block_reduce_sum(sumsq);\n if (threadIdx.x == 0) {\n float mean = sum / (float)group_elems;\n float var = sumsq / (float)group_elems - mean * mean;\n if (var < 0.0f) var = 0.0f;\n stats[2 * ng] = mean;\n stats[2 * ng + 1] = rsqrtf(var + 1.0e-5f);\n }\n}\n\n__global__ __launch_bounds__(256)\nvoid norm_kernel(const float* __restrict__ tmp,\n const float* __restrict__ stats,\n const float* __restrict__ gn_weight,\n const float* __restrict__ gn_bias,\n float* __restrict__ out,\n int total, int C, int HW, int G) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int stride = blockDim.x * gridDim.x;\n int channels_per_group = C / G;\n for (; idx < total; idx += stride) {\n int t = idx / HW;\n int c = t % C;\n int n = t / C;\n int g = c / channels_per_group;\n int ng = n * G + g;\n float y = (tmp[idx] - stats[2 * ng]) * stats[2 * ng + 1];\n out[idx] = y * gn_weight[c] + gn_bias[c];\n }\n}\n\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups) {\n x = x.contiguous();\n bias = bias.contiguous();\n scale = scale.contiguous();\n gn_weight = gn_weight.contiguous();\n gn_bias = gn_bias.contiguous();\n int N = (int)x.size(0);\n int C = (int)x.size(1);\n int H = (int)x.size(2);\n int W = (int)x.size(3);\n int HW = H * W;\n int G = (int)num_groups;\n auto tmp = torch::empty_like(x);\n auto out = torch::empty_like(x);\n auto stats = torch::empty({N * G * 2}, x.options());\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n post_reduce_kernel<<<N * G, BLOCK, 0, stream>>>(x.data_ptr<float>(), bias.data_ptr<float>(), scale.data_ptr<float>(), tmp.data_ptr<float>(), stats.data_ptr<float>(), N, C, HW, G);\n int total = N * C * HW;\n int blocks = (total + 255) / 256;\n if (blocks > 65535) blocks = 65535;\n norm_kernel<<<blocks, 256, 0, stream>>>(tmp.data_ptr<float>(), stats.data_ptr<float>(), gn_weight.data_ptr<float>(), gn_bias.data_ptr<float>(), out.data_ptr<float>(), total, C, HW, G);\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_post_gn_ext_v1', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['fused_post_gn'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3', '--use_fast_math'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, num_groups, bias_shape, scale_shape):\n super(ModelNew, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)\n self.bias = nn.Parameter(torch.randn(bias_shape))\n self.scale = nn.Parameter(torch.randn(scale_shape))\n self.group_norm = nn.GroupNorm(num_groups, out_channels)\n self.num_groups = num_groups\n\n def forward(self, x):\n y = F.conv2d(x, self.conv.weight, self.conv.bias)\n return _ext.fused_post_gn(y, self.bias, self.scale, self.group_norm.weight, self.group_norm.bias, self.num_groups)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv2d 99498.0μs (24%) calls=1
#2: aten::convolution 99461.0μs (24%) calls=1
#3: aten::_convolution 99433.0μs (24%) calls=1
#4: aten::cudnn_convolution 89411.0μs (22%) calls=1
#5: aten::add_ 9350.0μs (2%) calls=1
... +7 more kernels
--- Timing ---
GPU time: 4607.30 μs
--- Memory Bandwidth ---
DRAM BW: 7360.9/3350 GB/s (100.0%)
read=3482.1 write=3878.8 GB/s
Hit rates: L2=238.0%
--- Compute Throughput ---
Dominant pipe: FP32 util=114.1%
Pipe util: FMA=114.1%, Tensor=4.7%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 289.8%
Eligible warps/cycle: 6.01
Launch: regs/thread=248, smem/blk=21120B, block=768, grid=1163331
--- Memory Access Quality ---
Global ld coalescing: 10.79 sectors/req (10.8x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 7669.9% ####################
short_scoreboard 158.8% ####################
mio_throttle 9.6% #
--- Optimization Hints ---
-> 7670% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=10.8 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=10.8x (ideal=1.0) -> 11x wasted DRAM BW. Make per-warp accesses contiguous.
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
turn 516.4s turn · 16.4s LLM · 0.0s tools
reasoning
**Profiling performance**
I’m considering the 1GB temp and the double sigmoid. The first kernel seems to be already dominating with maybe 264M exp, which feels heavy. I wonder if the double will slow things down. I think I should profile for correctness and perhaps submit a comparison? It sounds like using a profile could help clarify things. I really want to make sure everything checks out before making any conclusions!
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\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups);\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups) { return fused_post_gn_cuda(x,bias,scale,gn_weight,gn_bias,num_groups); }\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#define BLOCK 256\n__inline__ __device__ float warp_reduce_sum(float v){\n#pragma unroll\n for(int off=16;off>0;off>>=1) v += __shfl_down_sync(0xffffffff,v,off); return v; }\n__inline__ __device__ float block_reduce_sum(float v){ __shared__ float sh[32]; int lane=threadIdx.x&31, wid=threadIdx.x>>5; v=warp_reduce_sum(v); if(lane==0) sh[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?sh[lane]:0.f; if(wid==0) v=warp_reduce_sum(v); return v; }\n__global__ __launch_bounds__(BLOCK) void reduce_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,float* __restrict__ stats,int C,int HW,int G){ int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int ge=cpg*HW; int c0=g*cpg; float sum=0.f,sumsq=0.f; for(int e=threadIdx.x;e<ge;e+=blockDim.x){ int ci=e/HW; int hw=e-ci*HW; int c=c0+ci; int idx=(n*C+c)*HW+hw; float z=(x[idx]+bias[c])*scale[c]; float y=1.f/(1.f+expf(-z)); sum+=y; sumsq+=y*y;} sum=block_reduce_sum(sum); sumsq=block_reduce_sum(sumsq); if(threadIdx.x==0){float mean=sum/(float)ge; float var=sumsq/(float)ge-mean*mean; if(var<0) var=0; stats[2*ng]=mean; stats[2*ng+1]=rsqrtf(var+1e-5f);} }\n__global__ __launch_bounds__(256) void norm_recompute_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,const float* __restrict__ stats,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int total,int C,int HW,int G){ int idx=blockIdx.x*blockDim.x+threadIdx.x; int stride=blockDim.x*gridDim.x; int cpg=C/G; for(;idx<total;idx+=stride){ int t=idx/HW; int c=t%C; int n=t/C; int ng=n*G+c/cpg; float z=(x[idx]+bias[c])*scale[c]; float y=1.f/(1.f+expf(-z)); float v=(y-stats[2*ng])*stats[2*ng+1]; out[idx]=v*w[c]+b[c]; } }\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups){ x=x.contiguous(); bias=bias.contiguous(); scale=scale.contiguous(); gn_weight=gn_weight.contiguous(); gn_bias=gn_bias.contiguous(); int N=x.size(0), C=x.size(1), HW=x.size(2)*x.size(3), G=(int)num_groups; auto out=torch::empty_like(x); auto stats=torch::empty({N*G*2},x.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); reduce_kernel<<<N*G,BLOCK,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),C,HW,G); int total=N*C*HW; int blocks=(total+255)/256; if(blocks>65535) blocks=65535; norm_recompute_kernel<<<blocks,256,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),gn_weight.data_ptr<float>(),gn_bias.data_ptr<float>(),out.data_ptr<float>(),total,C,HW,G); return out; }\n'''\n_ext=load_inline(name='fused_post_gn_ext_v2', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['fused_post_gn'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3','--use_fast_math'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,num_groups,bias_shape,scale_shape):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.bias=nn.Parameter(torch.randn(bias_shape)); self.scale=nn.Parameter(torch.randn(scale_shape)); self.group_norm=nn.GroupNorm(num_groups,out_channels); self.num_groups=num_groups\n def forward(self,x):\n y=F.conv2d(x,self.conv.weight,self.conv.bias); return _ext.fused_post_gn(y,self.bias,self.scale,self.group_norm.weight,self.group_norm.bias,self.num_groups)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 69.1s turn · 9.1s 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\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups);\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups) { return fused_post_gn_cuda(x,bias,scale,gn_weight,gn_bias,num_groups); }\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#define BLOCK 256\n__inline__ __device__ float warp_reduce_sum(float v){\n#pragma unroll\n for(int off=16;off>0;off>>=1) v += __shfl_down_sync(0xffffffff,v,off); return v; }\n__inline__ __device__ float block_reduce_sum(float v){ __shared__ float sh[32]; int lane=threadIdx.x&31, wid=threadIdx.x>>5; v=warp_reduce_sum(v); if(lane==0) sh[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?sh[lane]:0.f; if(wid==0) v=warp_reduce_sum(v); return v; }\n__global__ __launch_bounds__(BLOCK) void reduce_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,float* __restrict__ stats,int C,int HW,int G){ int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int ge=cpg*HW; int c0=g*cpg; float sum=0.f,sumsq=0.f; for(int e=threadIdx.x;e<ge;e+=blockDim.x){ int ci=e/HW; int hw=e-ci*HW; int c=c0+ci; int idx=(n*C+c)*HW+hw; float z=(x[idx]+bias[c])*scale[c]; float y=1.f/(1.f+expf(-z)); sum+=y; sumsq+=y*y;} sum=block_reduce_sum(sum); sumsq=block_reduce_sum(sumsq); if(threadIdx.x==0){float mean=sum/(float)ge; float var=sumsq/(float)ge-mean*mean; if(var<0) var=0; stats[2*ng]=mean; stats[2*ng+1]=rsqrtf(var+1e-5f);} }\n__global__ __launch_bounds__(256) void norm_recompute_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,const float* __restrict__ stats,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int total,int C,int HW,int G){ int idx=blockIdx.x*blockDim.x+threadIdx.x; int stride=blockDim.x*gridDim.x; int cpg=C/G; for(;idx<total;idx+=stride){ int t=idx/HW; int c=t%C; int n=t/C; int ng=n*G+c/cpg; float z=(x[idx]+bias[c])*scale[c]; float y=1.f/(1.f+expf(-z)); float v=(y-stats[2*ng])*stats[2*ng+1]; out[idx]=v*w[c]+b[c]; } }\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups){ x=x.contiguous(); bias=bias.contiguous(); scale=scale.contiguous(); gn_weight=gn_weight.contiguous(); gn_bias=gn_bias.contiguous(); int N=x.size(0), C=x.size(1), HW=x.size(2)*x.size(3), G=(int)num_groups; auto out=torch::empty_like(x); auto stats=torch::empty({N*G*2},x.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); reduce_kernel<<<N*G,BLOCK,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),C,HW,G); int total=N*C*HW; int blocks=(total+255)/256; if(blocks>65535) blocks=65535; norm_recompute_kernel<<<blocks,256,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),gn_weight.data_ptr<float>(),gn_bias.data_ptr<float>(),out.data_ptr<float>(),total,C,HW,G); return out; }\n'''\n_ext=load_inline(name='fused_post_gn_ext_v2', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['fused_post_gn'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3','--use_fast_math'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,num_groups,bias_shape,scale_shape):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.bias=nn.Parameter(torch.randn(bias_shape)); self.scale=nn.Parameter(torch.randn(scale_shape)); self.group_norm=nn.GroupNorm(num_groups,out_channels); self.num_groups=num_groups\n def forward(self,x):\n y=F.conv2d(x,self.conv.weight,self.conv.bias); return _ext.fused_post_gn(y,self.bias,self.scale,self.group_norm.weight,self.group_norm.bias,self.num_groups)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=4.36e-05 mean_abs_err=1.18e-06 max_rel_err=3.66e+02
turn 7 · 1 tool call(s) · 13.0s turn · 13.0s LLM · 7,003 chars (click to expand)
turn 713.0s turn · 13.0s LLM · 0.0s tools
call → profile_kernel
callprofile_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\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups);\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups) { return fused_post_gn_cuda(x,bias,scale,gn_weight,gn_bias,num_groups); }\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#define BLOCK 256\n__inline__ __device__ float warp_reduce_sum(float v){\n#pragma unroll\n for(int off=16;off>0;off>>=1) v += __shfl_down_sync(0xffffffff,v,off); return v; }\n__inline__ __device__ float block_reduce_sum(float v){ __shared__ float sh[32]; int lane=threadIdx.x&31, wid=threadIdx.x>>5; v=warp_reduce_sum(v); if(lane==0) sh[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?sh[lane]:0.f; if(wid==0) v=warp_reduce_sum(v); return v; }\n__global__ __launch_bounds__(BLOCK) void reduce_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,float* __restrict__ stats,int C,int HW,int G){ int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int ge=cpg*HW; int c0=g*cpg; float sum=0.f,sumsq=0.f; for(int e=threadIdx.x;e<ge;e+=blockDim.x){ int ci=e/HW; int hw=e-ci*HW; int c=c0+ci; int idx=(n*C+c)*HW+hw; float z=(x[idx]+bias[c])*scale[c]; float y=1.f/(1.f+expf(-z)); sum+=y; sumsq+=y*y;} sum=block_reduce_sum(sum); sumsq=block_reduce_sum(sumsq); if(threadIdx.x==0){float mean=sum/(float)ge; float var=sumsq/(float)ge-mean*mean; if(var<0) var=0; stats[2*ng]=mean; stats[2*ng+1]=rsqrtf(var+1e-5f);} }\n__global__ __launch_bounds__(256) void norm_recompute_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,const float* __restrict__ stats,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int total,int C,int HW,int G){ int idx=blockIdx.x*blockDim.x+threadIdx.x; int stride=blockDim.x*gridDim.x; int cpg=C/G; for(;idx<total;idx+=stride){ int t=idx/HW; int c=t%C; int n=t/C; int ng=n*G+c/cpg; float z=(x[idx]+bias[c])*scale[c]; float y=1.f/(1.f+expf(-z)); float v=(y-stats[2*ng])*stats[2*ng+1]; out[idx]=v*w[c]+b[c]; } }\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups){ x=x.contiguous(); bias=bias.contiguous(); scale=scale.contiguous(); gn_weight=gn_weight.contiguous(); gn_bias=gn_bias.contiguous(); int N=x.size(0), C=x.size(1), HW=x.size(2)*x.size(3), G=(int)num_groups; auto out=torch::empty_like(x); auto stats=torch::empty({N*G*2},x.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); reduce_kernel<<<N*G,BLOCK,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),C,HW,G); int total=N*C*HW; int blocks=(total+255)/256; if(blocks>65535) blocks=65535; norm_recompute_kernel<<<blocks,256,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),gn_weight.data_ptr<float>(),gn_bias.data_ptr<float>(),out.data_ptr<float>(),total,C,HW,G); return out; }\n'''\n_ext=load_inline(name='fused_post_gn_ext_v2p', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['fused_post_gn'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3','--use_fast_math'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,num_groups,bias_shape,scale_shape):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.bias=nn.Parameter(torch.randn(bias_shape)); self.scale=nn.Parameter(torch.randn(scale_shape)); self.group_norm=nn.GroupNorm(num_groups,out_channels); self.num_groups=num_groups\n def forward(self,x):\n y=F.conv2d(x,self.conv.weight,self.conv.bias); return _ext.fused_post_gn(y,self.bias,self.scale,self.group_norm.weight,self.group_norm.bias,self.num_groups)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv2d 100436.0μs (25%) calls=1
#2: aten::convolution 100393.0μs (25%) calls=1
#3: aten::_convolution 100364.0μs (25%) calls=1
#4: aten::cudnn_convolution 90119.0μs (22%) calls=1
#5: aten::add_ 9469.0μs (2%) calls=1
... +5 more kernels
--- Timing ---
GPU time: 4224.67 μs [↓382.6μs ok]
--- Memory Bandwidth ---
DRAM BW: 7006.6/3350 GB/s (100.0%)
read=4252.0 write=2754.5 GB/s
Hit rates: L2=188.2%
--- Compute Throughput ---
Dominant pipe: FP32 util=128.5% [↑14.5% ok]
Pipe util: FMA=128.5%, Tensor=4.5%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 292.8% [↑3.0% ok]
Eligible warps/cycle: 9.44
Launch: regs/thread=245, smem/blk=21120B, block=768, grid=1163331
--- Memory Access Quality ---
Global ld coalescing: 10.62 sectors/req (10.6x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 4192.8% ####################
short_scoreboard 187.4% ####################
mio_throttle 45.8% #########
--- Optimization Hints ---
-> 4193% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=10.6 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=10.6x (ideal=1.0) -> 11x 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: 4607.30→4224.67μs (↓382.62) ok
Compute%: 114.06→128.52% (↑14.46) ok
Occupancy: 289.81→292.81% (↑2.99) ok
Eligible warps/cyc: 6.01→9.44 (↑3.43) ok
Ld sectors/req: 10.79→10.62 (↓0.17) ok
L2 hit%: 238.01→188.18% (↓49.83) REGRESSED
turn 812.9s turn · 12.9s LLM · 0.0s tools
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 fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups);\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups){return fused_post_gn_cuda(x,bias,scale,gn_weight,gn_bias,num_groups);}\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#define RBLOCK 512\n__inline__ __device__ float warp_reduce_sum(float v){\n#pragma unroll\n for(int off=16;off>0;off>>=1) v+=__shfl_down_sync(0xffffffff,v,off); return v;}\n__inline__ __device__ float block_reduce_sum(float v){__shared__ float sh[32]; int lane=threadIdx.x&31,wid=threadIdx.x>>5; v=warp_reduce_sum(v); if(lane==0) sh[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?sh[lane]:0.f; if(wid==0) v=warp_reduce_sum(v); return v;}\n__global__ __launch_bounds__(RBLOCK,2) void reduce_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,float* __restrict__ stats,int C,int HW,int G){int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int ge=cpg*HW; int c0=g*cpg; float sum=0.f,sumsq=0.f; for(int e=threadIdx.x;e<ge;e+=blockDim.x){int ci=e/HW; int hw=e-ci*HW; int c=c0+ci; int idx=(n*C+c)*HW+hw; float y=1.f/(1.f+expf(-((x[idx]+bias[c])*scale[c]))); sum+=y; sumsq+=y*y;} sum=block_reduce_sum(sum); sumsq=block_reduce_sum(sumsq); if(threadIdx.x==0){float mean=sum/(float)ge; float var=sumsq/(float)ge-mean*mean; stats[2*ng]=mean; stats[2*ng+1]=rsqrtf(fmaxf(var,0.f)+1e-5f);}}\n__global__ __launch_bounds__(256) void norm_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,const float* __restrict__ stats,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int total,int C,int HW,int G){int idx=blockIdx.x*blockDim.x+threadIdx.x; int stride=blockDim.x*gridDim.x; int cpg=C/G; for(;idx<total;idx+=stride){int t=idx/HW; int c=t%C; int n=t/C; int ng=n*G+c/cpg; float y=1.f/(1.f+expf(-((x[idx]+bias[c])*scale[c]))); float v=(y-stats[2*ng])*stats[2*ng+1]; out[idx]=v*w[c]+b[c];}}\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x,torch::Tensor bias,torch::Tensor scale,torch::Tensor gn_weight,torch::Tensor gn_bias,int64_t num_groups){x=x.contiguous(); bias=bias.contiguous(); scale=scale.contiguous(); gn_weight=gn_weight.contiguous(); gn_bias=gn_bias.contiguous(); int N=x.size(0),C=x.size(1),HW=x.size(2)*x.size(3),G=(int)num_groups; auto out=torch::empty_like(x); auto stats=torch::empty({N*G*2},x.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); reduce_kernel<<<N*G,RBLOCK,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),C,HW,G); int total=N*C*HW; int blocks=(total+255)/256; if(blocks>65535) blocks=65535; norm_kernel<<<blocks,256,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),gn_weight.data_ptr<float>(),gn_bias.data_ptr<float>(),out.data_ptr<float>(),total,C,HW,G); return out;}\n'''\n_ext=load_inline(name='fused_post_gn_ext_v3',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_post_gn'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3','--use_fast_math'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,num_groups,bias_shape,scale_shape):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.bias=nn.Parameter(torch.randn(bias_shape)); self.scale=nn.Parameter(torch.randn(scale_shape)); self.group_norm=nn.GroupNorm(num_groups,out_channels); self.num_groups=num_groups\n def forward(self,x):\n y=F.conv2d(x,self.conv.weight,self.conv.bias); return _ext.fused_post_gn(y,self.bias,self.scale,self.group_norm.weight,self.group_norm.bias,self.num_groups)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 914.5s turn · 14.5s 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 fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups);\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups){return fused_post_gn_cuda(x,bias,scale,gn_weight,gn_bias,num_groups);}\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#define RBLOCK 512\n__inline__ __device__ float warp_reduce_sum(float v){\n#pragma unroll\n for(int off=16;off>0;off>>=1) v+=__shfl_down_sync(0xffffffff,v,off); return v;}\n__inline__ __device__ float block_reduce_sum(float v){__shared__ float sh[32]; int lane=threadIdx.x&31,wid=threadIdx.x>>5; v=warp_reduce_sum(v); if(lane==0) sh[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?sh[lane]:0.f; if(wid==0) v=warp_reduce_sum(v); return v;}\n__global__ __launch_bounds__(RBLOCK,2) void reduce_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,float* __restrict__ stats,int C,int HW,int G){int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int ge=cpg*HW; int c0=g*cpg; float sum=0.f,sumsq=0.f; for(int e=threadIdx.x;e<ge;e+=blockDim.x){int ci=e/HW; int hw=e-ci*HW; int c=c0+ci; int idx=(n*C+c)*HW+hw; float y=1.f/(1.f+expf(-((x[idx]+bias[c])*scale[c]))); sum+=y; sumsq+=y*y;} sum=block_reduce_sum(sum); sumsq=block_reduce_sum(sumsq); if(threadIdx.x==0){float mean=sum/(float)ge; float var=sumsq/(float)ge-mean*mean; stats[2*ng]=mean; stats[2*ng+1]=rsqrtf(fmaxf(var,0.f)+1e-5f);}}\n__global__ __launch_bounds__(256) void norm_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,const float* __restrict__ stats,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int total,int C,int HW,int G){int idx=blockIdx.x*blockDim.x+threadIdx.x; int stride=blockDim.x*gridDim.x; int cpg=C/G; for(;idx<total;idx+=stride){int t=idx/HW; int c=t%C; int n=t/C; int ng=n*G+c/cpg; float y=1.f/(1.f+expf(-((x[idx]+bias[c])*scale[c]))); float v=(y-stats[2*ng])*stats[2*ng+1]; out[idx]=v*w[c]+b[c];}}\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x,torch::Tensor bias,torch::Tensor scale,torch::Tensor gn_weight,torch::Tensor gn_bias,int64_t num_groups){x=x.contiguous(); bias=bias.contiguous(); scale=scale.contiguous(); gn_weight=gn_weight.contiguous(); gn_bias=gn_bias.contiguous(); int N=x.size(0),C=x.size(1),HW=x.size(2)*x.size(3),G=(int)num_groups; auto out=torch::empty_like(x); auto stats=torch::empty({N*G*2},x.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); reduce_kernel<<<N*G,RBLOCK,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),C,HW,G); int total=N*C*HW; int blocks=(total+255)/256; if(blocks>65535) blocks=65535; norm_kernel<<<blocks,256,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),gn_weight.data_ptr<float>(),gn_bias.data_ptr<float>(),out.data_ptr<float>(),total,C,HW,G); return out;}\n'''\n_ext=load_inline(name='fused_post_gn_ext_v3',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_post_gn'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3','--use_fast_math'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,num_groups,bias_shape,scale_shape):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.bias=nn.Parameter(torch.randn(bias_shape)); self.scale=nn.Parameter(torch.randn(scale_shape)); self.group_norm=nn.GroupNorm(num_groups,out_channels); self.num_groups=num_groups\n def forward(self,x):\n y=F.conv2d(x,self.conv.weight,self.conv.bias); return _ext.fused_post_gn(y,self.bias,self.scale,self.group_norm.weight,self.group_norm.bias,self.num_groups)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=5.05e-05 mean_abs_err=1.09e-06 max_rel_err=3.66e+02
turn 10 · 1 tool call(s) · 10.0s turn · 10.0s LLM · 6,881 chars (click to expand)
turn 1010.0s turn · 10.0s LLM · 0.0s tools
call → profile_kernel
callprofile_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 fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups);\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups){return fused_post_gn_cuda(x,bias,scale,gn_weight,gn_bias,num_groups);}\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#define RBLOCK 512\n__inline__ __device__ float warp_reduce_sum(float v){\n#pragma unroll\n for(int off=16;off>0;off>>=1) v+=__shfl_down_sync(0xffffffff,v,off); return v;}\n__inline__ __device__ float block_reduce_sum(float v){__shared__ float sh[32]; int lane=threadIdx.x&31,wid=threadIdx.x>>5; v=warp_reduce_sum(v); if(lane==0) sh[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?sh[lane]:0.f; if(wid==0) v=warp_reduce_sum(v); return v;}\n__global__ __launch_bounds__(RBLOCK,2) void reduce_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,float* __restrict__ stats,int C,int HW,int G){int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int ge=cpg*HW; int c0=g*cpg; float sum=0.f,sumsq=0.f; for(int e=threadIdx.x;e<ge;e+=blockDim.x){int ci=e/HW; int hw=e-ci*HW; int c=c0+ci; int idx=(n*C+c)*HW+hw; float y=1.f/(1.f+expf(-((x[idx]+bias[c])*scale[c]))); sum+=y; sumsq+=y*y;} sum=block_reduce_sum(sum); sumsq=block_reduce_sum(sumsq); if(threadIdx.x==0){float mean=sum/(float)ge; float var=sumsq/(float)ge-mean*mean; stats[2*ng]=mean; stats[2*ng+1]=rsqrtf(fmaxf(var,0.f)+1e-5f);}}\n__global__ __launch_bounds__(256) void norm_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,const float* __restrict__ stats,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int total,int C,int HW,int G){int idx=blockIdx.x*blockDim.x+threadIdx.x; int stride=blockDim.x*gridDim.x; int cpg=C/G; for(;idx<total;idx+=stride){int t=idx/HW; int c=t%C; int n=t/C; int ng=n*G+c/cpg; float y=1.f/(1.f+expf(-((x[idx]+bias[c])*scale[c]))); float v=(y-stats[2*ng])*stats[2*ng+1]; out[idx]=v*w[c]+b[c];}}\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x,torch::Tensor bias,torch::Tensor scale,torch::Tensor gn_weight,torch::Tensor gn_bias,int64_t num_groups){x=x.contiguous(); bias=bias.contiguous(); scale=scale.contiguous(); gn_weight=gn_weight.contiguous(); gn_bias=gn_bias.contiguous(); int N=x.size(0),C=x.size(1),HW=x.size(2)*x.size(3),G=(int)num_groups; auto out=torch::empty_like(x); auto stats=torch::empty({N*G*2},x.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); reduce_kernel<<<N*G,RBLOCK,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),C,HW,G); int total=N*C*HW; int blocks=(total+255)/256; if(blocks>65535) blocks=65535; norm_kernel<<<blocks,256,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),gn_weight.data_ptr<float>(),gn_bias.data_ptr<float>(),out.data_ptr<float>(),total,C,HW,G); return out;}\n'''\n_ext=load_inline(name='fused_post_gn_ext_v3p',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_post_gn'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3','--use_fast_math'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,num_groups,bias_shape,scale_shape):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.bias=nn.Parameter(torch.randn(bias_shape)); self.scale=nn.Parameter(torch.randn(scale_shape)); self.group_norm=nn.GroupNorm(num_groups,out_channels); self.num_groups=num_groups\n def forward(self,x):\n y=F.conv2d(x,self.conv.weight,self.conv.bias); return _ext.fused_post_gn(y,self.bias,self.scale,self.group_norm.weight,self.group_norm.bias,self.num_groups)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv2d 144983.0μs (22%) calls=1
#2: aten::convolution 144936.0μs (22%) calls=1
#3: aten::_convolution 144903.0μs (22%) calls=1
#4: aten::cudnn_convolution 122451.0μs (19%) calls=1
#5: aten::empty_like 36500.0μs (6%) calls=1
... +5 more kernels
--- Timing ---
GPU time: 4224.64 μs [↓0.0μs ok]
--- Memory Bandwidth ---
DRAM BW: 7014.4/3350 GB/s (100.0%)
read=4263.5 write=2750.8 GB/s
Hit rates: L2=188.3%
--- Compute Throughput ---
Dominant pipe: FP32 util=127.2% [↓1.3% REGRESSED]
Pipe util: FMA=127.2%, Tensor=4.5%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 294.0% [↑1.2% ok]
Eligible warps/cycle: 9.43
Launch: regs/thread=247, smem/blk=21120B, block=1024, grid=1163331
--- Memory Access Quality ---
Global ld coalescing: 10.62 sectors/req (10.6x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 4238.9% ####################
short_scoreboard 190.8% ####################
mio_throttle 46.5% #########
--- Optimization Hints ---
-> 4239% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=10.6 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=10.6x (ideal=1.0) -> 11x 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: 4224.67→4224.64μs (↓0.03) ok
Compute%: 128.52→127.22% (↓1.30) REGRESSED
Occupancy: 292.81→294.00% (↑1.20) ok
Eligible warps/cyc: 9.44→9.43 (↓0.01) REGRESSED
L2 hit%: 188.18→188.29% (↑0.11) ok
turn 1119.6s turn · 19.6s LLM · 0.0s tools
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 fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gw, torch::Tensor gb, int64_t G);\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gw, torch::Tensor gb, int64_t G){return fused_post_gn_cuda(x,bias,scale,gw,gb,G);}\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#define BLOCK 256\n__inline__ __device__ float warp_sum(float v){\n#pragma unroll\n for(int o=16;o>0;o>>=1) v+=__shfl_down_sync(0xffffffff,v,o); return v;}\n__inline__ __device__ float block_sum(float v){__shared__ float sh[32]; int lane=threadIdx.x&31,wid=threadIdx.x>>5; v=warp_sum(v); if(lane==0) sh[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?sh[lane]:0.f; if(wid==0) v=warp_sum(v); return v;}\n__global__ __launch_bounds__(BLOCK) void reduce_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,float* __restrict__ stats,int C,int HW,int G){int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int c0=g*cpg; float sum=0.f,sumsq=0.f; int base_n=n*C*HW; for(int ci=0;ci<cpg;++ci){int c=c0+ci; float bc=bias[c], sc=scale[c]; int base=base_n+c*HW; for(int hw=threadIdx.x; hw<HW; hw+=blockDim.x){float y=1.f/(1.f+expf(-((x[base+hw]+bc)*sc))); sum+=y; sumsq+=y*y;}} sum=block_sum(sum); sumsq=block_sum(sumsq); if(threadIdx.x==0){float inv=1.f/(float)(cpg*HW); float mean=sum*inv; float var=sumsq*inv-mean*mean; stats[2*ng]=mean; stats[2*ng+1]=rsqrtf(fmaxf(var,0.f)+1e-5f);}}\n__global__ __launch_bounds__(BLOCK) void norm_row_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,const float* __restrict__ stats,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int C,int HW,int G){int row=blockIdx.x; int c=row % C; int n=row / C; int cpg=C/G; int ng=n*G+c/cpg; float mean=stats[2*ng], rstd=stats[2*ng+1]; float bc=bias[c], sc=scale[c], wc=w[c], bb=b[c]; int base=row*HW; for(int hw=threadIdx.x; hw<HW; hw+=blockDim.x){float y=1.f/(1.f+expf(-((x[base+hw]+bc)*sc))); float v=(y-mean)*rstd; out[base+hw]=v*wc+bb;}}\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x,torch::Tensor bias,torch::Tensor scale,torch::Tensor gw,torch::Tensor gb,int64_t num_groups){x=x.contiguous(); bias=bias.contiguous(); scale=scale.contiguous(); gw=gw.contiguous(); gb=gb.contiguous(); int N=x.size(0), C=x.size(1), HW=x.size(2)*x.size(3), G=(int)num_groups; auto out=torch::empty_like(x); auto stats=torch::empty({N*G*2},x.options()); cudaStream_t s=at::cuda::getCurrentCUDAStream(); reduce_kernel<<<N*G,BLOCK,0,s>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),C,HW,G); norm_row_kernel<<<N*C,BLOCK,0,s>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),gw.data_ptr<float>(),gb.data_ptr<float>(),out.data_ptr<float>(),C,HW,G); return out;}\n'''\n_ext=load_inline(name='fused_post_gn_ext_v4',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_post_gn'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3','--use_fast_math'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,num_groups,bias_shape,scale_shape):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.bias=nn.Parameter(torch.randn(bias_shape)); self.scale=nn.Parameter(torch.randn(scale_shape)); self.group_norm=nn.GroupNorm(num_groups,out_channels); self.num_groups=num_groups\n def forward(self,x):\n y=F.conv2d(x,self.conv.weight,self.conv.bias); return _ext.fused_post_gn(y,self.bias,self.scale,self.group_norm.weight,self.group_norm.bias,self.num_groups)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 1213.2s turn · 13.2s 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 fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gw, torch::Tensor gb, int64_t G);\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gw, torch::Tensor gb, int64_t G){return fused_post_gn_cuda(x,bias,scale,gw,gb,G);}\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#define BLOCK 256\n__inline__ __device__ float warp_sum(float v){\n#pragma unroll\n for(int o=16;o>0;o>>=1) v+=__shfl_down_sync(0xffffffff,v,o); return v;}\n__inline__ __device__ float block_sum(float v){__shared__ float sh[32]; int lane=threadIdx.x&31,wid=threadIdx.x>>5; v=warp_sum(v); if(lane==0) sh[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?sh[lane]:0.f; if(wid==0) v=warp_sum(v); return v;}\n__global__ __launch_bounds__(BLOCK) void reduce_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,float* __restrict__ stats,int C,int HW,int G){int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int c0=g*cpg; float sum=0.f,sumsq=0.f; int base_n=n*C*HW; for(int ci=0;ci<cpg;++ci){int c=c0+ci; float bc=bias[c], sc=scale[c]; int base=base_n+c*HW; for(int hw=threadIdx.x; hw<HW; hw+=blockDim.x){float y=1.f/(1.f+expf(-((x[base+hw]+bc)*sc))); sum+=y; sumsq+=y*y;}} sum=block_sum(sum); sumsq=block_sum(sumsq); if(threadIdx.x==0){float inv=1.f/(float)(cpg*HW); float mean=sum*inv; float var=sumsq*inv-mean*mean; stats[2*ng]=mean; stats[2*ng+1]=rsqrtf(fmaxf(var,0.f)+1e-5f);}}\n__global__ __launch_bounds__(BLOCK) void norm_row_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,const float* __restrict__ stats,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int C,int HW,int G){int row=blockIdx.x; int c=row % C; int n=row / C; int cpg=C/G; int ng=n*G+c/cpg; float mean=stats[2*ng], rstd=stats[2*ng+1]; float bc=bias[c], sc=scale[c], wc=w[c], bb=b[c]; int base=row*HW; for(int hw=threadIdx.x; hw<HW; hw+=blockDim.x){float y=1.f/(1.f+expf(-((x[base+hw]+bc)*sc))); float v=(y-mean)*rstd; out[base+hw]=v*wc+bb;}}\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x,torch::Tensor bias,torch::Tensor scale,torch::Tensor gw,torch::Tensor gb,int64_t num_groups){x=x.contiguous(); bias=bias.contiguous(); scale=scale.contiguous(); gw=gw.contiguous(); gb=gb.contiguous(); int N=x.size(0), C=x.size(1), HW=x.size(2)*x.size(3), G=(int)num_groups; auto out=torch::empty_like(x); auto stats=torch::empty({N*G*2},x.options()); cudaStream_t s=at::cuda::getCurrentCUDAStream(); reduce_kernel<<<N*G,BLOCK,0,s>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),C,HW,G); norm_row_kernel<<<N*C,BLOCK,0,s>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),gw.data_ptr<float>(),gb.data_ptr<float>(),out.data_ptr<float>(),C,HW,G); return out;}\n'''\n_ext=load_inline(name='fused_post_gn_ext_v4',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_post_gn'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3','--use_fast_math'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,num_groups,bias_shape,scale_shape):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.bias=nn.Parameter(torch.randn(bias_shape)); self.scale=nn.Parameter(torch.randn(scale_shape)); self.group_norm=nn.GroupNorm(num_groups,out_channels); self.num_groups=num_groups\n def forward(self,x):\n y=F.conv2d(x,self.conv.weight,self.conv.bias); return _ext.fused_post_gn(y,self.bias,self.scale,self.group_norm.weight,self.group_norm.bias,self.num_groups)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=4.65e-05 mean_abs_err=1.20e-06 max_rel_err=3.66e+02
turn 13 · 1 tool call(s) · 10.6s turn · 10.6s LLM · 6,663 chars (click to expand)
turn 1310.6s turn · 10.6s LLM · 0.0s tools
call → profile_kernel
callprofile_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 fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gw, torch::Tensor gb, int64_t G);\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gw, torch::Tensor gb, int64_t G){return fused_post_gn_cuda(x,bias,scale,gw,gb,G);}\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#define BLOCK 256\n__inline__ __device__ float warp_sum(float v){\n#pragma unroll\n for(int o=16;o>0;o>>=1) v+=__shfl_down_sync(0xffffffff,v,o); return v;}\n__inline__ __device__ float block_sum(float v){__shared__ float sh[32]; int lane=threadIdx.x&31,wid=threadIdx.x>>5; v=warp_sum(v); if(lane==0) sh[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?sh[lane]:0.f; if(wid==0) v=warp_sum(v); return v;}\n__global__ __launch_bounds__(BLOCK) void reduce_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,float* __restrict__ stats,int C,int HW,int G){int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int c0=g*cpg; float sum=0.f,sumsq=0.f; int base_n=n*C*HW; for(int ci=0;ci<cpg;++ci){int c=c0+ci; float bc=bias[c], sc=scale[c]; int base=base_n+c*HW; for(int hw=threadIdx.x; hw<HW; hw+=blockDim.x){float y=1.f/(1.f+expf(-((x[base+hw]+bc)*sc))); sum+=y; sumsq+=y*y;}} sum=block_sum(sum); sumsq=block_sum(sumsq); if(threadIdx.x==0){float inv=1.f/(float)(cpg*HW); float mean=sum*inv; float var=sumsq*inv-mean*mean; stats[2*ng]=mean; stats[2*ng+1]=rsqrtf(fmaxf(var,0.f)+1e-5f);}}\n__global__ __launch_bounds__(BLOCK) void norm_row_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,const float* __restrict__ stats,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int C,int HW,int G){int row=blockIdx.x; int c=row % C; int n=row / C; int cpg=C/G; int ng=n*G+c/cpg; float mean=stats[2*ng], rstd=stats[2*ng+1]; float bc=bias[c], sc=scale[c], wc=w[c], bb=b[c]; int base=row*HW; for(int hw=threadIdx.x; hw<HW; hw+=blockDim.x){float y=1.f/(1.f+expf(-((x[base+hw]+bc)*sc))); float v=(y-mean)*rstd; out[base+hw]=v*wc+bb;}}\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x,torch::Tensor bias,torch::Tensor scale,torch::Tensor gw,torch::Tensor gb,int64_t num_groups){x=x.contiguous(); bias=bias.contiguous(); scale=scale.contiguous(); gw=gw.contiguous(); gb=gb.contiguous(); int N=x.size(0), C=x.size(1), HW=x.size(2)*x.size(3), G=(int)num_groups; auto out=torch::empty_like(x); auto stats=torch::empty({N*G*2},x.options()); cudaStream_t s=at::cuda::getCurrentCUDAStream(); reduce_kernel<<<N*G,BLOCK,0,s>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),C,HW,G); norm_row_kernel<<<N*C,BLOCK,0,s>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),gw.data_ptr<float>(),gb.data_ptr<float>(),out.data_ptr<float>(),C,HW,G); return out;}\n'''\n_ext=load_inline(name='fused_post_gn_ext_v4p',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_post_gn'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3','--use_fast_math'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,num_groups,bias_shape,scale_shape):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.bias=nn.Parameter(torch.randn(bias_shape)); self.scale=nn.Parameter(torch.randn(scale_shape)); self.group_norm=nn.GroupNorm(num_groups,out_channels); self.num_groups=num_groups\n def forward(self,x):\n y=F.conv2d(x,self.conv.weight,self.conv.bias); return _ext.fused_post_gn(y,self.bias,self.scale,self.group_norm.weight,self.group_norm.bias,self.num_groups)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv2d 113833.0μs (25%) calls=1
#2: aten::convolution 113792.0μs (25%) calls=1
#3: aten::_convolution 113757.0μs (25%) calls=1
#4: aten::cudnn_convolution 101963.0μs (22%) calls=1
#5: aten::add_ 11081.0μs (2%) calls=1
... +5 more kernels
--- Timing ---
GPU time: 4424.26 μs [↑199.6μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 7172.0/3350 GB/s (100.0%)
read=4523.2 write=2648.9 GB/s
Hit rates: L2=198.3%
--- Compute Throughput ---
Dominant pipe: FP32 util=84.0% [↓43.2% REGRESSED]
Pipe util: FMA=84.0%, Tensor=3.7%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 289.7% [↓4.3% REGRESSED]
Eligible warps/cycle: 3.47
Launch: regs/thread=230, smem/blk=21120B, block=768, grid=1101892
--- Memory Access Quality ---
Global ld coalescing: 16.08 sectors/req (16.1x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 20258.2% ####################
short_scoreboard 468.7% ####################
mio_throttle 16.1% ###
--- Optimization Hints ---
-> 20258% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=16.1 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=16.1x (ideal=1.0) -> 16x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 4224.64→4424.26μs (↑199.62) REGRESSED
Compute%: 127.22→84.04% (↓43.18) REGRESSED
Occupancy: 294.00→289.75% (↓4.26) REGRESSED
Eligible warps/cyc: 9.43→3.47 (↓5.96) REGRESSED
Ld sectors/req: 10.62→16.08 (↑5.46) REGRESSED
L2 hit%: 188.29→198.30% (↑10.02) ok
turn 148.2s turn · 8.2s LLM · 0.0s tools · FINAL
call → submit_kernel
callsubmit_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\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups);\ntorch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups) { return fused_post_gn_cuda(x,bias,scale,gn_weight,gn_bias,num_groups); }\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#define BLOCK 256\n__inline__ __device__ float warp_reduce_sum(float v){\n#pragma unroll\n for(int off=16;off>0;off>>=1) v += __shfl_down_sync(0xffffffff,v,off); return v; }\n__inline__ __device__ float block_reduce_sum(float v){ __shared__ float sh[32]; int lane=threadIdx.x&31; int wid=threadIdx.x>>5; v=warp_reduce_sum(v); if(lane==0) sh[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?sh[lane]:0.0f; if(wid==0) v=warp_reduce_sum(v); return v; }\n__global__ __launch_bounds__(BLOCK) void reduce_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,float* __restrict__ stats,int C,int HW,int G){ int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int group_elems=cpg*HW; int c0=g*cpg; float sum=0.f,sumsq=0.f; for(int e=threadIdx.x;e<group_elems;e+=blockDim.x){ int ci=e/HW; int hw=e-ci*HW; int c=c0+ci; int idx=(n*C+c)*HW+hw; float z=(x[idx]+bias[c])*scale[c]; float y=1.f/(1.f+expf(-z)); sum+=y; sumsq+=y*y;} sum=block_reduce_sum(sum); sumsq=block_reduce_sum(sumsq); if(threadIdx.x==0){float mean=sum/(float)group_elems; float var=sumsq/(float)group_elems-mean*mean; if(var<0) var=0; stats[2*ng]=mean; stats[2*ng+1]=rsqrtf(var+1e-5f);} }\n__global__ __launch_bounds__(256) void norm_recompute_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,const float* __restrict__ stats,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int total,int C,int HW,int G){ int idx=blockIdx.x*blockDim.x+threadIdx.x; int stride=blockDim.x*gridDim.x; int cpg=C/G; for(;idx<total;idx+=stride){ int t=idx/HW; int c=t%C; int n=t/C; int ng=n*G+c/cpg; float z=(x[idx]+bias[c])*scale[c]; float y=1.f/(1.f+expf(-z)); float v=(y-stats[2*ng])*stats[2*ng+1]; out[idx]=v*w[c]+b[c]; } }\ntorch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups){ x=x.contiguous(); bias=bias.contiguous(); scale=scale.contiguous(); gn_weight=gn_weight.contiguous(); gn_bias=gn_bias.contiguous(); int N=x.size(0), C=x.size(1), HW=x.size(2)*x.size(3), G=(int)num_groups; auto out=torch::empty_like(x); auto stats=torch::empty({N*G*2},x.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); reduce_kernel<<<N*G,BLOCK,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),C,HW,G); int total=N*C*HW; int blocks=(total+255)/256; if(blocks>65535) blocks=65535; norm_recompute_kernel<<<blocks,256,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),gn_weight.data_ptr<float>(),gn_bias.data_ptr<float>(),out.data_ptr<float>(),total,C,HW,G); return out; }\n'''\n_ext=load_inline(name='fused_post_gn_ext_final', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['fused_post_gn'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3','--use_fast_math'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,num_groups,bias_shape,scale_shape):\n super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.bias=nn.Parameter(torch.randn(bias_shape)); self.scale=nn.Parameter(torch.randn(scale_shape)); self.group_norm=nn.GroupNorm(num_groups,out_channels); self.num_groups=num_groups\n def forward(self,x):\n y=F.conv2d(x,self.conv.weight,self.conv.bias); return _ext.fused_post_gn(y,self.bias,self.scale,self.group_norm.weight,self.group_norm.bias,self.num_groups)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 4.24 μs
Runtime stats: mean=4.24μs median=?μs std=0.03μs
Numerical precision: max_abs_err=4.36e-05 mean_abs_err=1.18e-06 max_rel_err=3.66e+02
Memory: 2305.16 MB peak (ref: 2305.19 MB, ratio: 1.0x)
Kernel launches: 10 (ref: 21, fusion ratio: 2.1)
Energy: 483.84 mJ/run (ref: 967.62 mJ, ratio: 1.9999x)
SOL score: 1.0
final submitted kernel (32 lines, 4,249 chars) — full source, untruncated
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline
_cpp_src = r'''
#include <torch/extension.h>
torch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups);
torch::Tensor fused_post_gn(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups) { return fused_post_gn_cuda(x,bias,scale,gn_weight,gn_bias,num_groups); }
'''
_cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>
#define BLOCK 256
__inline__ __device__ float warp_reduce_sum(float v){
#pragma unroll
for(int off=16;off>0;off>>=1) v += __shfl_down_sync(0xffffffff,v,off); return v; }
__inline__ __device__ float block_reduce_sum(float v){ __shared__ float sh[32]; int lane=threadIdx.x&31; int wid=threadIdx.x>>5; v=warp_reduce_sum(v); if(lane==0) sh[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?sh[lane]:0.0f; if(wid==0) v=warp_reduce_sum(v); return v; }
__global__ __launch_bounds__(BLOCK) void reduce_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,float* __restrict__ stats,int C,int HW,int G){ int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int group_elems=cpg*HW; int c0=g*cpg; float sum=0.f,sumsq=0.f; for(int e=threadIdx.x;e<group_elems;e+=blockDim.x){ int ci=e/HW; int hw=e-ci*HW; int c=c0+ci; int idx=(n*C+c)*HW+hw; float z=(x[idx]+bias[c])*scale[c]; float y=1.f/(1.f+expf(-z)); sum+=y; sumsq+=y*y;} sum=block_reduce_sum(sum); sumsq=block_reduce_sum(sumsq); if(threadIdx.x==0){float mean=sum/(float)group_elems; float var=sumsq/(float)group_elems-mean*mean; if(var<0) var=0; stats[2*ng]=mean; stats[2*ng+1]=rsqrtf(var+1e-5f);} }
__global__ __launch_bounds__(256) void norm_recompute_kernel(const float* __restrict__ x,const float* __restrict__ bias,const float* __restrict__ scale,const float* __restrict__ stats,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ out,int total,int C,int HW,int G){ int idx=blockIdx.x*blockDim.x+threadIdx.x; int stride=blockDim.x*gridDim.x; int cpg=C/G; for(;idx<total;idx+=stride){ int t=idx/HW; int c=t%C; int n=t/C; int ng=n*G+c/cpg; float z=(x[idx]+bias[c])*scale[c]; float y=1.f/(1.f+expf(-z)); float v=(y-stats[2*ng])*stats[2*ng+1]; out[idx]=v*w[c]+b[c]; } }
torch::Tensor fused_post_gn_cuda(torch::Tensor x, torch::Tensor bias, torch::Tensor scale, torch::Tensor gn_weight, torch::Tensor gn_bias, int64_t num_groups){ x=x.contiguous(); bias=bias.contiguous(); scale=scale.contiguous(); gn_weight=gn_weight.contiguous(); gn_bias=gn_bias.contiguous(); int N=x.size(0), C=x.size(1), HW=x.size(2)*x.size(3), G=(int)num_groups; auto out=torch::empty_like(x); auto stats=torch::empty({N*G*2},x.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); reduce_kernel<<<N*G,BLOCK,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),C,HW,G); int total=N*C*HW; int blocks=(total+255)/256; if(blocks>65535) blocks=65535; norm_recompute_kernel<<<blocks,256,0,stream>>>(x.data_ptr<float>(),bias.data_ptr<float>(),scale.data_ptr<float>(),stats.data_ptr<float>(),gn_weight.data_ptr<float>(),gn_bias.data_ptr<float>(),out.data_ptr<float>(),total,C,HW,G); return out; }
'''
_ext=load_inline(name='fused_post_gn_ext_final', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['fused_post_gn'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3','--use_fast_math'], verbose=False)
class ModelNew(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size,num_groups,bias_shape,scale_shape):
super(ModelNew,self).__init__(); self.conv=nn.Conv2d(in_channels,out_channels,kernel_size); self.bias=nn.Parameter(torch.randn(bias_shape)); self.scale=nn.Parameter(torch.randn(scale_shape)); self.group_norm=nn.GroupNorm(num_groups,out_channels); self.num_groups=num_groups
def forward(self,x):
y=F.conv2d(x,self.conv.weight,self.conv.bias); return _ext.fused_post_gn(y,self.bias,self.scale,self.group_norm.weight,self.group_norm.bias,self.num_groups)