correct
35_GroupNorm_.py
started
2026-05-04T09:24:11Z
timingkernelbench.timing
kernel mean / median / std
7.98 / — / 0.17 μs
ref mean / median / std
10.40 / — / 2.19 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
5.50x
energyNVML (pynvml)
ratio (ref / kernel)
1.25x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
2934.11 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):
"""
Simple model that performs Group Normalization.
"""
def __init__(self, num_features: int, num_groups: int):
"""
Initializes the GroupNorm layer.
Args:
num_features (int): Number of features in the input tensor.
num_groups (int): Number of groups to divide the channels into.
"""
super(Model, self).__init__()
self.gn = nn.GroupNorm(num_groups=num_groups, num_channels=num_features)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies Group Normalization to the input tensor.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, num_features, *).
Returns:
torch.Tensor: Output tensor with Group Normalization applied, same shape as input.
"""
return self.gn(x)
batch_size = 112 # scaled up
features = 64
num_groups = 8
dim1 = 512
dim2 = 512
def get_inputs():
x = torch.rand(batch_size, features, dim1, dim2)
return [x]
def get_init_inputs():
return [features, num_groups] # num_featuresturn 010.0s turn · 10.0s 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 121.7s turn · 21.7s LLM · 0.0s tools
reasoning
**Evaluating CUDA configurations**
I’m thinking about how the cpg variable could unroll if I loop small. For fixed C=64 and G=8, there might be an issue with block reduction and double precision. I need to ensure that my correctness test is solid. I'm considering creating a Python module using load_inline. I also wonder if setting extra_cuda_cflags to optimize would be helpful, but it may negatively affect rsqrt tolerance. I think I should avoid using fast math and rely on standard sqrt for accuracy. Let’s compile and check it all out!
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 groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\n\"\"\"\n\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <vector>\n\n__inline__ __device__ float warp_reduce_sum(float v) {\n for (int offset = 16; offset > 0; offset >>= 1) {\n v += __shfl_down_sync(0xffffffff, v, offset);\n }\n return v;\n}\n\n__inline__ __device__ void block_reduce2(float &sum, float &ssq) {\n __shared__ float smem_sum[32];\n __shared__ float smem_ssq[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n sum = warp_reduce_sum(sum);\n ssq = warp_reduce_sum(ssq);\n if (lane == 0) {\n smem_sum[wid] = sum;\n smem_ssq[wid] = ssq;\n }\n __syncthreads();\n int nw = (blockDim.x + 31) >> 5;\n sum = (threadIdx.x < nw) ? smem_sum[lane] : 0.0f;\n ssq = (threadIdx.x < nw) ? smem_ssq[lane] : 0.0f;\n if (wid == 0) {\n sum = warp_reduce_sum(sum);\n ssq = warp_reduce_sum(ssq);\n }\n __syncthreads();\n if (threadIdx.x == 0) {\n smem_sum[0] = sum;\n smem_ssq[0] = ssq;\n }\n __syncthreads();\n sum = smem_sum[0];\n ssq = smem_ssq[0];\n}\n\n__global__ void groupnorm_kernel(const float* __restrict__ x,\n const float* __restrict__ weight,\n const float* __restrict__ bias,\n float* __restrict__ y,\n int N, int C, int G, int S) {\n int ng = blockIdx.x;\n int n = ng / G;\n int g = ng - n * G;\n int cpg = C / G;\n int c0 = g * cpg;\n int base_n = n * C * S;\n float sum = 0.0f;\n float ssq = 0.0f;\n\n for (int cc = 0; cc < cpg; ++cc) {\n const float* ptr = x + base_n + (c0 + cc) * S;\n for (int s = threadIdx.x; s < S; s += blockDim.x) {\n float v = ptr[s];\n sum += v;\n ssq += v * v;\n }\n }\n block_reduce2(sum, ssq);\n float m = (float)(cpg * S);\n float mean = sum / m;\n float var = ssq / m - mean * mean;\n if (var < 0.0f) var = 0.0f;\n float inv = 1.0f / sqrtf(var + 1.0e-5f);\n\n for (int cc = 0; cc < cpg; ++cc) {\n int c = c0 + cc;\n const float* inptr = x + base_n + c * S;\n float* outptr = y + base_n + c * S;\n float w = weight[c];\n float b = bias[c];\n for (int s = threadIdx.x; s < S; s += blockDim.x) {\n outptr[s] = (inptr[s] - mean) * inv * w + b;\n }\n }\n}\n\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups) {\n auto y = torch::empty_like(x);\n int N = (int)x.size(0);\n int C = (int)x.size(1);\n int64_t S64 = 1;\n for (int i = 2; i < x.dim(); ++i) S64 *= x.size(i);\n int S = (int)S64;\n int G = (int)num_groups;\n dim3 grid(N * G);\n dim3 block(256);\n groupnorm_kernel<<<grid, block>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>(), N, C, G, S);\n return y;\n}\n\"\"\"\n\n_ext = load_inline(name=\"gn_ext_v1\", cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=[\"groupnorm_forward\"], verbose=False,\n extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\"])\n\nclass ModelNew(nn.Module):\n def __init__(self, num_features: int, num_groups: int):\n super(ModelNew, self).__init__()\n self.gn = nn.GroupNorm(num_groups=num_groups, num_channels=num_features)\n self.num_groups = num_groups\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(), self.gn.weight, self.gn.bias, self.num_groups)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 28.8s turn · 8.8s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r\"\"\"\n#include <torch/extension.h>\n\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\n\"\"\"\n\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <vector>\n\n__inline__ __device__ float warp_reduce_sum(float v) {\n for (int offset = 16; offset > 0; offset >>= 1) {\n v += __shfl_down_sync(0xffffffff, v, offset);\n }\n return v;\n}\n\n__inline__ __device__ void block_reduce2(float &sum, float &ssq) {\n __shared__ float smem_sum[32];\n __shared__ float smem_ssq[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n sum = warp_reduce_sum(sum);\n ssq = warp_reduce_sum(ssq);\n if (lane == 0) {\n smem_sum[wid] = sum;\n smem_ssq[wid] = ssq;\n }\n __syncthreads();\n int nw = (blockDim.x + 31) >> 5;\n sum = (threadIdx.x < nw) ? smem_sum[lane] : 0.0f;\n ssq = (threadIdx.x < nw) ? smem_ssq[lane] : 0.0f;\n if (wid == 0) {\n sum = warp_reduce_sum(sum);\n ssq = warp_reduce_sum(ssq);\n }\n __syncthreads();\n if (threadIdx.x == 0) {\n smem_sum[0] = sum;\n smem_ssq[0] = ssq;\n }\n __syncthreads();\n sum = smem_sum[0];\n ssq = smem_ssq[0];\n}\n\n__global__ void groupnorm_kernel(const float* __restrict__ x,\n const float* __restrict__ weight,\n const float* __restrict__ bias,\n float* __restrict__ y,\n int N, int C, int G, int S) {\n int ng = blockIdx.x;\n int n = ng / G;\n int g = ng - n * G;\n int cpg = C / G;\n int c0 = g * cpg;\n int base_n = n * C * S;\n float sum = 0.0f;\n float ssq = 0.0f;\n\n for (int cc = 0; cc < cpg; ++cc) {\n const float* ptr = x + base_n + (c0 + cc) * S;\n for (int s = threadIdx.x; s < S; s += blockDim.x) {\n float v = ptr[s];\n sum += v;\n ssq += v * v;\n }\n }\n block_reduce2(sum, ssq);\n float m = (float)(cpg * S);\n float mean = sum / m;\n float var = ssq / m - mean * mean;\n if (var < 0.0f) var = 0.0f;\n float inv = 1.0f / sqrtf(var + 1.0e-5f);\n\n for (int cc = 0; cc < cpg; ++cc) {\n int c = c0 + cc;\n const float* inptr = x + base_n + c * S;\n float* outptr = y + base_n + c * S;\n float w = weight[c];\n float b = bias[c];\n for (int s = threadIdx.x; s < S; s += blockDim.x) {\n outptr[s] = (inptr[s] - mean) * inv * w + b;\n }\n }\n}\n\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups) {\n auto y = torch::empty_like(x);\n int N = (int)x.size(0);\n int C = (int)x.size(1);\n int64_t S64 = 1;\n for (int i = 2; i < x.dim(); ++i) S64 *= x.size(i);\n int S = (int)S64;\n int G = (int)num_groups;\n dim3 grid(N * G);\n dim3 block(256);\n groupnorm_kernel<<<grid, block>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>(), N, C, G, S);\n return y;\n}\n\"\"\"\n\n_ext = load_inline(name=\"gn_ext_v1\", cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=[\"groupnorm_forward\"], verbose=False,\n extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\"])\n\nclass ModelNew(nn.Module):\n def __init__(self, num_features: int, num_groups: int):\n super(ModelNew, self).__init__()\n self.gn = nn.GroupNorm(num_groups=num_groups, num_channels=num_features)\n self.num_groups = num_groups\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(), self.gn.weight, self.gn.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=3.10e-06 mean_abs_err=4.72e-07 max_rel_err=6.05e+03
turn 39.4s turn · 9.4s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r\"\"\"\n#include <torch/extension.h>\n\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\n\"\"\"\n\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <vector>\n\n__inline__ __device__ float warp_reduce_sum(float v) {\n for (int offset = 16; offset > 0; offset >>= 1) {\n v += __shfl_down_sync(0xffffffff, v, offset);\n }\n return v;\n}\n\n__inline__ __device__ void block_reduce2(float &sum, float &ssq) {\n __shared__ float smem_sum[32];\n __shared__ float smem_ssq[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n sum = warp_reduce_sum(sum);\n ssq = warp_reduce_sum(ssq);\n if (lane == 0) {\n smem_sum[wid] = sum;\n smem_ssq[wid] = ssq;\n }\n __syncthreads();\n int nw = (blockDim.x + 31) >> 5;\n sum = (threadIdx.x < nw) ? smem_sum[lane] : 0.0f;\n ssq = (threadIdx.x < nw) ? smem_ssq[lane] : 0.0f;\n if (wid == 0) {\n sum = warp_reduce_sum(sum);\n ssq = warp_reduce_sum(ssq);\n }\n __syncthreads();\n if (threadIdx.x == 0) {\n smem_sum[0] = sum;\n smem_ssq[0] = ssq;\n }\n __syncthreads();\n sum = smem_sum[0];\n ssq = smem_ssq[0];\n}\n\n__global__ void groupnorm_kernel(const float* __restrict__ x,\n const float* __restrict__ weight,\n const float* __restrict__ bias,\n float* __restrict__ y,\n int N, int C, int G, int S) {\n int ng = blockIdx.x;\n int n = ng / G;\n int g = ng - n * G;\n int cpg = C / G;\n int c0 = g * cpg;\n int base_n = n * C * S;\n float sum = 0.0f;\n float ssq = 0.0f;\n\n for (int cc = 0; cc < cpg; ++cc) {\n const float* ptr = x + base_n + (c0 + cc) * S;\n for (int s = threadIdx.x; s < S; s += blockDim.x) {\n float v = ptr[s];\n sum += v;\n ssq += v * v;\n }\n }\n block_reduce2(sum, ssq);\n float m = (float)(cpg * S);\n float mean = sum / m;\n float var = ssq / m - mean * mean;\n if (var < 0.0f) var = 0.0f;\n float inv = 1.0f / sqrtf(var + 1.0e-5f);\n\n for (int cc = 0; cc < cpg; ++cc) {\n int c = c0 + cc;\n const float* inptr = x + base_n + c * S;\n float* outptr = y + base_n + c * S;\n float w = weight[c];\n float b = bias[c];\n for (int s = threadIdx.x; s < S; s += blockDim.x) {\n outptr[s] = (inptr[s] - mean) * inv * w + b;\n }\n }\n}\n\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups) {\n auto y = torch::empty_like(x);\n int N = (int)x.size(0);\n int C = (int)x.size(1);\n int64_t S64 = 1;\n for (int i = 2; i < x.dim(); ++i) S64 *= x.size(i);\n int S = (int)S64;\n int G = (int)num_groups;\n dim3 grid(N * G);\n dim3 block(256);\n groupnorm_kernel<<<grid, block>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>(), N, C, G, S);\n return y;\n}\n\"\"\"\n\n_ext = load_inline(name=\"gn_ext_v1\", cpp_sources=cpp_src, cuda_sources=cuda_src,\n functions=[\"groupnorm_forward\"], verbose=False,\n extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\"])\n\nclass ModelNew(nn.Module):\n def __init__(self, num_features: int, num_groups: int):\n super(ModelNew, self).__init__()\n self.gn = nn.GroupNorm(num_groups=num_groups, num_channels=num_features)\n self.num_groups = num_groups\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(), self.gn.weight, self.gn.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::empty_like 44278.0μs (50%) calls=1
#2: aten::empty_strided 44244.0μs (50%) calls=1
--- Timing ---
GPU time: 10231.62 μs
--- Memory Bandwidth ---
DRAM BW: 2202.4/3350 GB/s (65.7%)
read=1469.2 write=733.1 GB/s
Hit rates: L2=33.6%
--- Compute Throughput ---
Pipe util: FMA=3.9%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 84.1%
Eligible warps/cycle: 0.16
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=22, smem/blk=1280B, block=256, grid=896
--- Memory Access Quality ---
Global ld coalescing: 4.00 sectors/req (4.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 8946.5% ####################
--- Optimization Hints ---
-> 8947% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=4.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=4.0x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
turn 413.5s turn · 13.5s LLM · 0.0s tools
reasoning
**Considering memory performance**
I'm thinking that using larger blocks might improve memory latency, so I should consider testing sizes like 512 and 1024. I wonder if splitting into multiple blocks could help. Currently, there are 7.5 waves with an occupancy of 84. If I use partial reductions, I might have to manage multiple kernels, which could enhance parallelism. For the normalization pass, using multiple blocks looks promising. With a single CTA per group, I have 896 blocks but need to account for their length. I think I’ll try the 512 block size.
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 groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\n\"\"\"\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__inline__ __device__ float warp_reduce_sum(float v){for(int o=16;o>0;o>>=1)v+=__shfl_down_sync(0xffffffff,v,o);return v;}\n__inline__ __device__ void block_reduce2(float &sum,float &ssq){\n __shared__ float sm[32], ss[32]; int lane=threadIdx.x&31,wid=threadIdx.x>>5;\n sum=warp_reduce_sum(sum); ssq=warp_reduce_sum(ssq); if(lane==0){sm[wid]=sum;ss[wid]=ssq;} __syncthreads();\n int nw=(blockDim.x+31)>>5; sum=(threadIdx.x<nw)?sm[lane]:0.0f; ssq=(threadIdx.x<nw)?ss[lane]:0.0f; if(wid==0){sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);} __syncthreads(); if(threadIdx.x==0){sm[0]=sum;ss[0]=ssq;} __syncthreads(); sum=sm[0]; ssq=ss[0];}\n__global__ void groupnorm_kernel(const float* __restrict__ x,const float* __restrict__ weight,const float* __restrict__ bias,float* __restrict__ y,int N,int C,int G,int S){\n int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int c0=g*cpg; long long base_n=(long long)n*C*S; float sum=0,ssq=0;\n for(int cc=0;cc<cpg;++cc){const float* ptr=x+base_n+(c0+cc)*S; for(int s=threadIdx.x;s<S;s+=blockDim.x){float v=ptr[s]; sum+=v; ssq+=v*v;}}\n block_reduce2(sum,ssq); float m=(float)(cpg*S); float mean=sum/m; float var=ssq/m-mean*mean; if(var<0)var=0; float inv=1.0f/sqrtf(var+1e-5f);\n for(int cc=0;cc<cpg;++cc){int c=c0+cc; const float* in=x+base_n+c*S; float* out=y+base_n+c*S; float w=weight[c],b=bias[c]; for(int s=threadIdx.x;s<S;s+=blockDim.x){out[s]=(in[s]-mean)*inv*w+b;}}\n}\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups){auto y=torch::empty_like(x);int N=x.size(0),C=x.size(1);long long S64=1;for(int i=2;i<x.dim();++i)S64*=x.size(i);int S=(int)S64,G=(int)num_groups;groupnorm_kernel<<<N*G,512>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),N,C,G,S);return y;}\n\"\"\"\n_ext=load_inline(name=\"gn_ext_v2\",cpp_sources=cpp_src,cuda_sources=cuda_src,functions=[\"groupnorm_forward\"],verbose=False,extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"])\nclass ModelNew(nn.Module):\n def __init__(self,num_features:int,num_groups:int):\n super(ModelNew,self).__init__(); self.gn=nn.GroupNorm(num_groups=num_groups,num_channels=num_features); self.num_groups=num_groups\n def forward(self,x:torch.Tensor)->torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(),self.gn.weight,self.gn.bias,self.num_groups)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 57.6s turn · 7.6s 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 groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\n\"\"\"\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__inline__ __device__ float warp_reduce_sum(float v){for(int o=16;o>0;o>>=1)v+=__shfl_down_sync(0xffffffff,v,o);return v;}\n__inline__ __device__ void block_reduce2(float &sum,float &ssq){\n __shared__ float sm[32], ss[32]; int lane=threadIdx.x&31,wid=threadIdx.x>>5;\n sum=warp_reduce_sum(sum); ssq=warp_reduce_sum(ssq); if(lane==0){sm[wid]=sum;ss[wid]=ssq;} __syncthreads();\n int nw=(blockDim.x+31)>>5; sum=(threadIdx.x<nw)?sm[lane]:0.0f; ssq=(threadIdx.x<nw)?ss[lane]:0.0f; if(wid==0){sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);} __syncthreads(); if(threadIdx.x==0){sm[0]=sum;ss[0]=ssq;} __syncthreads(); sum=sm[0]; ssq=ss[0];}\n__global__ void groupnorm_kernel(const float* __restrict__ x,const float* __restrict__ weight,const float* __restrict__ bias,float* __restrict__ y,int N,int C,int G,int S){\n int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int c0=g*cpg; long long base_n=(long long)n*C*S; float sum=0,ssq=0;\n for(int cc=0;cc<cpg;++cc){const float* ptr=x+base_n+(c0+cc)*S; for(int s=threadIdx.x;s<S;s+=blockDim.x){float v=ptr[s]; sum+=v; ssq+=v*v;}}\n block_reduce2(sum,ssq); float m=(float)(cpg*S); float mean=sum/m; float var=ssq/m-mean*mean; if(var<0)var=0; float inv=1.0f/sqrtf(var+1e-5f);\n for(int cc=0;cc<cpg;++cc){int c=c0+cc; const float* in=x+base_n+c*S; float* out=y+base_n+c*S; float w=weight[c],b=bias[c]; for(int s=threadIdx.x;s<S;s+=blockDim.x){out[s]=(in[s]-mean)*inv*w+b;}}\n}\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups){auto y=torch::empty_like(x);int N=x.size(0),C=x.size(1);long long S64=1;for(int i=2;i<x.dim();++i)S64*=x.size(i);int S=(int)S64,G=(int)num_groups;groupnorm_kernel<<<N*G,512>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),N,C,G,S);return y;}\n\"\"\"\n_ext=load_inline(name=\"gn_ext_v2\",cpp_sources=cpp_src,cuda_sources=cuda_src,functions=[\"groupnorm_forward\"],verbose=False,extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"])\nclass ModelNew(nn.Module):\n def __init__(self,num_features:int,num_groups:int):\n super(ModelNew,self).__init__(); self.gn=nn.GroupNorm(num_groups=num_groups,num_channels=num_features); self.num_groups=num_groups\n def forward(self,x:torch.Tensor)->torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(),self.gn.weight,self.gn.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=2.38e-06 mean_abs_err=2.17e-07 max_rel_err=3.02e+03
turn 611.6s turn · 11.6s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\n\"\"\"\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__inline__ __device__ float warp_reduce_sum(float v){for(int o=16;o>0;o>>=1)v+=__shfl_down_sync(0xffffffff,v,o);return v;}\n__inline__ __device__ void block_reduce2(float &sum,float &ssq){\n __shared__ float sm[32], ss[32]; int lane=threadIdx.x&31,wid=threadIdx.x>>5;\n sum=warp_reduce_sum(sum); ssq=warp_reduce_sum(ssq); if(lane==0){sm[wid]=sum;ss[wid]=ssq;} __syncthreads();\n int nw=(blockDim.x+31)>>5; sum=(threadIdx.x<nw)?sm[lane]:0.0f; ssq=(threadIdx.x<nw)?ss[lane]:0.0f; if(wid==0){sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);} __syncthreads(); if(threadIdx.x==0){sm[0]=sum;ss[0]=ssq;} __syncthreads(); sum=sm[0]; ssq=ss[0];}\n__global__ void groupnorm_kernel(const float* __restrict__ x,const float* __restrict__ weight,const float* __restrict__ bias,float* __restrict__ y,int N,int C,int G,int S){\n int ng=blockIdx.x; int n=ng/G; int g=ng-n*G; int cpg=C/G; int c0=g*cpg; long long base_n=(long long)n*C*S; float sum=0,ssq=0;\n for(int cc=0;cc<cpg;++cc){const float* ptr=x+base_n+(c0+cc)*S; for(int s=threadIdx.x;s<S;s+=blockDim.x){float v=ptr[s]; sum+=v; ssq+=v*v;}}\n block_reduce2(sum,ssq); float m=(float)(cpg*S); float mean=sum/m; float var=ssq/m-mean*mean; if(var<0)var=0; float inv=1.0f/sqrtf(var+1e-5f);\n for(int cc=0;cc<cpg;++cc){int c=c0+cc; const float* in=x+base_n+c*S; float* out=y+base_n+c*S; float w=weight[c],b=bias[c]; for(int s=threadIdx.x;s<S;s+=blockDim.x){out[s]=(in[s]-mean)*inv*w+b;}}\n}\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups){auto y=torch::empty_like(x);int N=x.size(0),C=x.size(1);long long S64=1;for(int i=2;i<x.dim();++i)S64*=x.size(i);int S=(int)S64,G=(int)num_groups;groupnorm_kernel<<<N*G,512>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),N,C,G,S);return y;}\n\"\"\"\n_ext=load_inline(name=\"gn_ext_v2\",cpp_sources=cpp_src,cuda_sources=cuda_src,functions=[\"groupnorm_forward\"],verbose=False,extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"])\nclass ModelNew(nn.Module):\n def __init__(self,num_features:int,num_groups:int):\n super(ModelNew,self).__init__(); self.gn=nn.GroupNorm(num_groups=num_groups,num_channels=num_features); self.num_groups=num_groups\n def forward(self,x:torch.Tensor)->torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(),self.gn.weight,self.gn.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::empty_like 44547.0μs (50%) calls=1
#2: aten::empty_strided 44510.0μs (50%) calls=1
--- Timing ---
GPU time: 10155.49 μs [↓76.1μs ok]
--- Memory Bandwidth ---
DRAM BW: 2218.9/3350 GB/s (66.2%) [↑0.5% ok]
read=1480.2 write=738.6 GB/s
Hit rates: L2=33.6%
--- Compute Throughput ---
Pipe util: FMA=4.1%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 90.5% [↑6.4% ok]
Eligible warps/cycle: 0.17
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=23, smem/blk=1280B, block=512, grid=896
--- Memory Access Quality ---
Global ld coalescing: 3.99 sectors/req (4.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 9065.1% ####################
--- Optimization Hints ---
-> 9065% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=4.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=4.0x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 10231.62→10155.49μs (↓76.13) ok
DRAM BW%: 65.74→66.23% (↑0.49) ok
Occupancy: 84.09→90.53% (↑6.44) ok
Eligible warps/cyc: 0.16→0.17 (↑0.01) ok
L2 hit%: 33.59→33.61% (↑0.02) ok
turn 710.1s turn · 10.1s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\n\"\"\"\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__inline__ __device__ float warp_reduce_sum(float v){for(int o=16;o>0;o>>=1)v+=__shfl_down_sync(0xffffffff,v,o);return v;}\n__inline__ __device__ void block_reduce2(float &sum,float &ssq){__shared__ float sm[32],ss[32];int lane=threadIdx.x&31,wid=threadIdx.x>>5;sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);if(lane==0){sm[wid]=sum;ss[wid]=ssq;}__syncthreads();int nw=(blockDim.x+31)>>5;sum=(threadIdx.x<nw)?sm[lane]:0.0f;ssq=(threadIdx.x<nw)?ss[lane]:0.0f;if(wid==0){sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);}__syncthreads();if(threadIdx.x==0){sm[0]=sum;ss[0]=ssq;}__syncthreads();sum=sm[0];ssq=ss[0];}\n__global__ void groupnorm_kernel(const float* __restrict__ x,const float* __restrict__ weight,const float* __restrict__ bias,float* __restrict__ y,int N,int C,int G,int S){int ng=blockIdx.x;int n=ng/G;int g=ng-n*G;int cpg=C/G;int c0=g*cpg;long long base_n=(long long)n*C*S;float sum=0,ssq=0;for(int cc=0;cc<cpg;++cc){const float* ptr=x+base_n+(c0+cc)*S;for(int s=threadIdx.x;s<S;s+=blockDim.x){float v=ptr[s];sum+=v;ssq+=v*v;}}block_reduce2(sum,ssq);float m=(float)(cpg*S);float mean=sum/m;float var=ssq/m-mean*mean;if(var<0)var=0;float inv=1.0f/sqrtf(var+1e-5f);for(int cc=0;cc<cpg;++cc){int c=c0+cc;const float* in=x+base_n+c*S;float* out=y+base_n+c*S;float w=weight[c],b=bias[c];for(int s=threadIdx.x;s<S;s+=blockDim.x){out[s]=(in[s]-mean)*inv*w+b;}}}\ntorch::Tensor groupnorm_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t num_groups){auto y=torch::empty_like(x);int N=x.size(0),C=x.size(1);long long S64=1;for(int i=2;i<x.dim();++i)S64*=x.size(i);int S=(int)S64,G=(int)num_groups;groupnorm_kernel<<<N*G,1024>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),N,C,G,S);return y;}\n\"\"\"\n_ext=load_inline(name=\"gn_ext_v3\",cpp_sources=cpp_src,cuda_sources=cuda_src,functions=[\"groupnorm_forward\"],verbose=False,extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"])\nclass ModelNew(nn.Module):\n def __init__(self,num_features:int,num_groups:int):\n super(ModelNew,self).__init__();self.gn=nn.GroupNorm(num_groups=num_groups,num_channels=num_features);self.num_groups=num_groups\n def forward(self,x:torch.Tensor)->torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(),self.gn.weight,self.gn.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::empty_like 53136.0μs (50%) calls=1
#2: aten::empty_strided 53094.0μs (50%) calls=1
--- Timing ---
GPU time: 10274.88 μs [↑119.4μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 2193.1/3350 GB/s (65.5%) [↓0.8% REGRESSED]
read=1463.0 write=730.0 GB/s
Hit rates: L2=33.6%
--- Compute Throughput ---
Pipe util: FMA=4.2%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 95.7% [↑5.2% ok]
Eligible warps/cycle: 0.18
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=23, smem/blk=1280B, block=1024, grid=896
--- Memory Access Quality ---
Global ld coalescing: 3.99 sectors/req (4.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 9338.6% ####################
short_scoreboard 0.8%
mio_throttle 0.7%
--- Optimization Hints ---
-> 9339% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=4.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=4.0x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 10155.49→10274.88μs (↑119.39) REGRESSED
DRAM BW%: 66.23→65.46% (↓0.77) REGRESSED
Occupancy: 90.53→95.68% (↑5.15) ok
L2 hit%: 33.61→33.57% (↓0.04) REGRESSED
turn 822.5s turn · 22.5s LLM · 0.0s tools
reasoning
**Evaluating performance metrics**
I’m looking at some performance metrics: 1024 results in 1.8M CTAs, which seems bad. For tile 4096, it’s 458k CTAs — could it be that the overhead isn't too high? I'm considering that single kernel loops might be alright, but I wonder if using partial reductions with chunks would enhance the reduction pass, while keeping normalization in mind. It's an interesting balance to think through!
**Considering vector coding**
I'm thinking about improving correctness by grouping four components together. I'll focus on coding a specialized vector. In the reduction process, each thread will accumulate these four components. For normalization, I should ensure to set v.x properly. I need to use a float4 store for organization and consider the channel pointer. Additionally, I want to profile the code for performance. I'm excited to see how this will improve efficiency!
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 groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\n\"\"\"\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__inline__ __device__ float warp_reduce_sum(float v){for(int o=16;o>0;o>>=1)v+=__shfl_down_sync(0xffffffff,v,o);return v;}\n__inline__ __device__ void block_reduce2(float &sum,float &ssq){__shared__ float sm[32],ss[32];int lane=threadIdx.x&31,wid=threadIdx.x>>5;sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);if(lane==0){sm[wid]=sum;ss[wid]=ssq;}__syncthreads();int nw=(blockDim.x+31)>>5;sum=(threadIdx.x<nw)?sm[lane]:0.0f;ssq=(threadIdx.x<nw)?ss[lane]:0.0f;if(wid==0){sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);}__syncthreads();if(threadIdx.x==0){sm[0]=sum;ss[0]=ssq;}__syncthreads();sum=sm[0];ssq=ss[0];}\n__global__ void groupnorm_kernel4(const float* __restrict__ x,const float* __restrict__ weight,const float* __restrict__ bias,float* __restrict__ y,int N,int C,int G,int S){\n int ng=blockIdx.x;int n=ng/G;int g=ng-n*G;int cpg=C/G;int c0=g*cpg;long long base_n=(long long)n*C*S;int S4=S>>2;float sum=0,ssq=0;\n for(int cc=0;cc<cpg;++cc){const float4* ptr=(const float4*)(x+base_n+(c0+cc)*S);for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=ptr[j];sum+=v.x+v.y+v.z+v.w;ssq+=v.x*v.x+v.y*v.y+v.z*v.z+v.w*v.w;}}\n block_reduce2(sum,ssq);float m=(float)(cpg*S);float mean=sum/m;float var=ssq/m-mean*mean;if(var<0)var=0;float inv=1.0f/sqrtf(var+1e-5f);\n for(int cc=0;cc<cpg;++cc){int c=c0+cc;const float4* in=(const float4*)(x+base_n+c*S);float4* out=(float4*)(y+base_n+c*S);float w=weight[c],b=bias[c];float scale=inv*w;float shift=b-mean*scale;for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=in[j];v.x=v.x*scale+shift;v.y=v.y*scale+shift;v.z=v.z*scale+shift;v.w=v.w*scale+shift;out[j]=v;}}\n}\ntorch::Tensor groupnorm_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t num_groups){auto y=torch::empty_like(x);int N=x.size(0),C=x.size(1);long long S64=1;for(int i=2;i<x.dim();++i)S64*=x.size(i);int S=(int)S64,G=(int)num_groups;groupnorm_kernel4<<<N*G,512>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),N,C,G,S);return y;}\n\"\"\"\n_ext=load_inline(name=\"gn_ext_v4\",cpp_sources=cpp_src,cuda_sources=cuda_src,functions=[\"groupnorm_forward\"],verbose=False,extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"])\nclass ModelNew(nn.Module):\n def __init__(self,num_features:int,num_groups:int):\n super(ModelNew,self).__init__();self.gn=nn.GroupNorm(num_groups=num_groups,num_channels=num_features);self.num_groups=num_groups\n def forward(self,x:torch.Tensor)->torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(),self.gn.weight,self.gn.bias,self.num_groups)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 98.0s turn · 8.0s 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 groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\n\"\"\"\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__inline__ __device__ float warp_reduce_sum(float v){for(int o=16;o>0;o>>=1)v+=__shfl_down_sync(0xffffffff,v,o);return v;}\n__inline__ __device__ void block_reduce2(float &sum,float &ssq){__shared__ float sm[32],ss[32];int lane=threadIdx.x&31,wid=threadIdx.x>>5;sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);if(lane==0){sm[wid]=sum;ss[wid]=ssq;}__syncthreads();int nw=(blockDim.x+31)>>5;sum=(threadIdx.x<nw)?sm[lane]:0.0f;ssq=(threadIdx.x<nw)?ss[lane]:0.0f;if(wid==0){sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);}__syncthreads();if(threadIdx.x==0){sm[0]=sum;ss[0]=ssq;}__syncthreads();sum=sm[0];ssq=ss[0];}\n__global__ void groupnorm_kernel4(const float* __restrict__ x,const float* __restrict__ weight,const float* __restrict__ bias,float* __restrict__ y,int N,int C,int G,int S){\n int ng=blockIdx.x;int n=ng/G;int g=ng-n*G;int cpg=C/G;int c0=g*cpg;long long base_n=(long long)n*C*S;int S4=S>>2;float sum=0,ssq=0;\n for(int cc=0;cc<cpg;++cc){const float4* ptr=(const float4*)(x+base_n+(c0+cc)*S);for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=ptr[j];sum+=v.x+v.y+v.z+v.w;ssq+=v.x*v.x+v.y*v.y+v.z*v.z+v.w*v.w;}}\n block_reduce2(sum,ssq);float m=(float)(cpg*S);float mean=sum/m;float var=ssq/m-mean*mean;if(var<0)var=0;float inv=1.0f/sqrtf(var+1e-5f);\n for(int cc=0;cc<cpg;++cc){int c=c0+cc;const float4* in=(const float4*)(x+base_n+c*S);float4* out=(float4*)(y+base_n+c*S);float w=weight[c],b=bias[c];float scale=inv*w;float shift=b-mean*scale;for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=in[j];v.x=v.x*scale+shift;v.y=v.y*scale+shift;v.z=v.z*scale+shift;v.w=v.w*scale+shift;out[j]=v;}}\n}\ntorch::Tensor groupnorm_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t num_groups){auto y=torch::empty_like(x);int N=x.size(0),C=x.size(1);long long S64=1;for(int i=2;i<x.dim();++i)S64*=x.size(i);int S=(int)S64,G=(int)num_groups;groupnorm_kernel4<<<N*G,512>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),N,C,G,S);return y;}\n\"\"\"\n_ext=load_inline(name=\"gn_ext_v4\",cpp_sources=cpp_src,cuda_sources=cuda_src,functions=[\"groupnorm_forward\"],verbose=False,extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"])\nclass ModelNew(nn.Module):\n def __init__(self,num_features:int,num_groups:int):\n super(ModelNew,self).__init__();self.gn=nn.GroupNorm(num_groups=num_groups,num_channels=num_features);self.num_groups=num_groups\n def forward(self,x:torch.Tensor)->torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(),self.gn.weight,self.gn.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=1.79e-06 mean_abs_err=1.72e-07 max_rel_err=5.23e+03
turn 108.2s turn · 8.2s 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 groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\n\"\"\"\ncuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__inline__ __device__ float warp_reduce_sum(float v){for(int o=16;o>0;o>>=1)v+=__shfl_down_sync(0xffffffff,v,o);return v;}\n__inline__ __device__ void block_reduce2(float &sum,float &ssq){__shared__ float sm[32],ss[32];int lane=threadIdx.x&31,wid=threadIdx.x>>5;sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);if(lane==0){sm[wid]=sum;ss[wid]=ssq;}__syncthreads();int nw=(blockDim.x+31)>>5;sum=(threadIdx.x<nw)?sm[lane]:0.0f;ssq=(threadIdx.x<nw)?ss[lane]:0.0f;if(wid==0){sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);}__syncthreads();if(threadIdx.x==0){sm[0]=sum;ss[0]=ssq;}__syncthreads();sum=sm[0];ssq=ss[0];}\n__global__ void groupnorm_kernel4(const float* __restrict__ x,const float* __restrict__ weight,const float* __restrict__ bias,float* __restrict__ y,int N,int C,int G,int S){\n int ng=blockIdx.x;int n=ng/G;int g=ng-n*G;int cpg=C/G;int c0=g*cpg;long long base_n=(long long)n*C*S;int S4=S>>2;float sum=0,ssq=0;\n for(int cc=0;cc<cpg;++cc){const float4* ptr=(const float4*)(x+base_n+(c0+cc)*S);for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=ptr[j];sum+=v.x+v.y+v.z+v.w;ssq+=v.x*v.x+v.y*v.y+v.z*v.z+v.w*v.w;}}\n block_reduce2(sum,ssq);float m=(float)(cpg*S);float mean=sum/m;float var=ssq/m-mean*mean;if(var<0)var=0;float inv=1.0f/sqrtf(var+1e-5f);\n for(int cc=0;cc<cpg;++cc){int c=c0+cc;const float4* in=(const float4*)(x+base_n+c*S);float4* out=(float4*)(y+base_n+c*S);float w=weight[c],b=bias[c];float scale=inv*w;float shift=b-mean*scale;for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=in[j];v.x=v.x*scale+shift;v.y=v.y*scale+shift;v.z=v.z*scale+shift;v.w=v.w*scale+shift;out[j]=v;}}\n}\ntorch::Tensor groupnorm_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t num_groups){auto y=torch::empty_like(x);int N=x.size(0),C=x.size(1);long long S64=1;for(int i=2;i<x.dim();++i)S64*=x.size(i);int S=(int)S64,G=(int)num_groups;groupnorm_kernel4<<<N*G,512>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),N,C,G,S);return y;}\n\"\"\"\n_ext=load_inline(name=\"gn_ext_v4\",cpp_sources=cpp_src,cuda_sources=cuda_src,functions=[\"groupnorm_forward\"],verbose=False,extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"])\nclass ModelNew(nn.Module):\n def __init__(self,num_features:int,num_groups:int):\n super(ModelNew,self).__init__();self.gn=nn.GroupNorm(num_groups=num_groups,num_channels=num_features);self.num_groups=num_groups\n def forward(self,x:torch.Tensor)->torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(),self.gn.weight,self.gn.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::empty_like 47115.0μs (50%) calls=1
#2: aten::empty_strided 47075.0μs (50%) calls=1
--- Timing ---
GPU time: 7725.98 μs [↓2548.9μs ok]
--- Memory Bandwidth ---
DRAM BW: 2916.5/3350 GB/s (87.1%) [↑21.6% ok]
read=1945.7 write=970.8 GB/s
Hit rates: L2=34.3%
--- Compute Throughput ---
Pipe util: FMA=4.1%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 95.4% [↓0.3% REGRESSED]
Eligible warps/cycle: 0.05
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=26, smem/blk=1280B, block=512, grid=896
--- Memory Access Quality ---
Global ld coalescing: 15.88 sectors/req (15.9x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 28587.7% ####################
short_scoreboard 1.8%
mio_throttle 1.6%
--- Optimization Hints ---
-> 28588% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=15.9 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=15.9x (ideal=1.0) -> 16x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 10274.88→7725.98μs (↓2548.90) ok
DRAM BW%: 65.46→87.06% (↑21.59) ok
Occupancy: 95.68→95.40% (↓0.29) REGRESSED
Eligible warps/cyc: 0.18→0.05 (↓0.12) REGRESSED
Ld sectors/req: 3.99→15.88 (↑11.90) REGRESSED
L2 hit%: 33.57→34.26% (↑0.69) ok
turn 118.9s turn · 8.9s LLM · 0.0s tools
reasoning
**Considering vectorized block size**
I’m thinking that I might need a vectorized block size of either 256 or 1024. Hmm, 256 sounds like a reasonable starting point. I wonder if that size will be efficient enough for my purposes. It could help keep things lightweight while still being effective. I’ll give 256 a try and see how it works out! Let’s explore this option together and assess its performance.
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\"\"\"#include <torch/extension.h>\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\"\"\"\ncuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__inline__ __device__ float warp_reduce_sum(float v){for(int o=16;o>0;o>>=1)v+=__shfl_down_sync(0xffffffff,v,o);return v;}\n__inline__ __device__ void block_reduce2(float &sum,float &ssq){__shared__ float sm[32],ss[32];int lane=threadIdx.x&31,wid=threadIdx.x>>5;sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);if(lane==0){sm[wid]=sum;ss[wid]=ssq;}__syncthreads();int nw=(blockDim.x+31)>>5;sum=(threadIdx.x<nw)?sm[lane]:0.0f;ssq=(threadIdx.x<nw)?ss[lane]:0.0f;if(wid==0){sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);}__syncthreads();if(threadIdx.x==0){sm[0]=sum;ss[0]=ssq;}__syncthreads();sum=sm[0];ssq=ss[0];}\n__global__ void k(const float* __restrict__ x,const float* __restrict__ weight,const float* __restrict__ bias,float* __restrict__ y,int N,int C,int G,int S){int ng=blockIdx.x;int n=ng/G;int g=ng-n*G;int cpg=C/G;int c0=g*cpg;long long base_n=(long long)n*C*S;int S4=S>>2;float sum=0,ssq=0;for(int cc=0;cc<cpg;++cc){const float4* ptr=(const float4*)(x+base_n+(c0+cc)*S);for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=ptr[j];sum+=v.x+v.y+v.z+v.w;ssq+=v.x*v.x+v.y*v.y+v.z*v.z+v.w*v.w;}}block_reduce2(sum,ssq);float m=(float)(cpg*S);float mean=sum/m;float var=ssq/m-mean*mean;if(var<0)var=0;float inv=1.0f/sqrtf(var+1e-5f);for(int cc=0;cc<cpg;++cc){int c=c0+cc;const float4* in=(const float4*)(x+base_n+c*S);float4* out=(float4*)(y+base_n+c*S);float scale=inv*weight[c];float shift=bias[c]-mean*scale;for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=in[j];v.x=v.x*scale+shift;v.y=v.y*scale+shift;v.z=v.z*scale+shift;v.w=v.w*scale+shift;out[j]=v;}}}\ntorch::Tensor groupnorm_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t num_groups){auto y=torch::empty_like(x);int N=x.size(0),C=x.size(1);long long S64=1;for(int i=2;i<x.dim();++i)S64*=x.size(i);int S=(int)S64,G=(int)num_groups;k<<<N*G,256>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),N,C,G,S);return y;}\n\"\"\"\n_ext=load_inline(name=\"gn_ext_v5\",cpp_sources=cpp_src,cuda_sources=cuda_src,functions=[\"groupnorm_forward\"],verbose=False,extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"])\nclass ModelNew(nn.Module):\n def __init__(self,num_features:int,num_groups:int):\n super(ModelNew,self).__init__();self.gn=nn.GroupNorm(num_groups=num_groups,num_channels=num_features);self.num_groups=num_groups\n def forward(self,x:torch.Tensor)->torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(),self.gn.weight,self.gn.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::empty_like 46359.0μs (50%) calls=1
#2: aten::empty_strided 46320.0μs (50%) calls=1
--- Timing ---
GPU time: 7686.02 μs [↓40.0μs ok]
--- Memory Bandwidth ---
DRAM BW: 2931.7/3350 GB/s (87.5%) [↑0.5% ok]
read=1955.8 write=975.8 GB/s
Hit rates: L2=34.1%
--- Compute Throughput ---
Pipe util: FMA=3.9%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 84.4% [↓11.0% REGRESSED]
Eligible warps/cycle: 0.05
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=26, smem/blk=1280B, block=256, grid=896
--- Memory Access Quality ---
Global ld coalescing: 15.94 sectors/req (15.9x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 27448.4% ####################
mio_throttle 0.9%
short_scoreboard 0.9%
--- Optimization Hints ---
-> 27448% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=15.9 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=15.9x (ideal=1.0) -> 16x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 7725.98→7686.02μs (↓39.97) ok
DRAM BW%: 87.06→87.51% (↑0.45) ok
Occupancy: 95.40→84.42% (↓10.98) REGRESSED
Ld sectors/req: 15.88→15.94 (↑0.06) REGRESSED
L2 hit%: 34.26→34.15% (↓0.11) REGRESSED
turn 1217.8s turn · 17.8s LLM · 0.0s tools
call → static_check
callstatic_check
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src=r\"\"\"#include <torch/extension.h>\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\"\"\"\ncuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__inline__ __device__ float warp_reduce_sum(float v){for(int o=16;o>0;o>>=1)v+=__shfl_down_sync(0xffffffff,v,o);return v;}\n__inline__ __device__ void block_reduce2(float &sum,float &ssq){__shared__ float sm[32],ss[32];int lane=threadIdx.x&31,wid=threadIdx.x>>5;sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);if(lane==0){sm[wid]=sum;ss[wid]=ssq;}__syncthreads();int nw=(blockDim.x+31)>>5;sum=(threadIdx.x<nw)?sm[lane]:0.0f;ssq=(threadIdx.x<nw)?ss[lane]:0.0f;if(wid==0){sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);}__syncthreads();if(threadIdx.x==0){sm[0]=sum;ss[0]=ssq;}__syncthreads();sum=sm[0];ssq=ss[0];}\n__global__ void k(const float* __restrict__ x,const float* __restrict__ weight,const float* __restrict__ bias,float* __restrict__ y,int N,int C,int G,int S){int ng=blockIdx.x;int n=ng/G;int g=ng-n*G;int cpg=C/G;int c0=g*cpg;long long base_n=(long long)n*C*S;int S4=S>>2;float sum=0,ssq=0;for(int cc=0;cc<cpg;++cc){const float4* ptr=(const float4*)(x+base_n+(c0+cc)*S);for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=ptr[j];sum+=v.x+v.y+v.z+v.w;ssq+=v.x*v.x+v.y*v.y+v.z*v.z+v.w*v.w;}}block_reduce2(sum,ssq);float m=(float)(cpg*S);float mean=sum/m;float var=ssq/m-mean*mean;if(var<0)var=0;float inv=1.0f/sqrtf(var+1e-5f);for(int cc=0;cc<cpg;++cc){int c=c0+cc;const float4* in=(const float4*)(x+base_n+c*S);float4* out=(float4*)(y+base_n+c*S);float scale=inv*weight[c];float shift=bias[c]-mean*scale;for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=in[j];v.x=v.x*scale+shift;v.y=v.y*scale+shift;v.z=v.z*scale+shift;v.w=v.w*scale+shift;out[j]=v;}}}\ntorch::Tensor groupnorm_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t num_groups){auto y=torch::empty_like(x);int N=x.size(0),C=x.size(1);long long S64=1;for(int i=2;i<x.dim();++i)S64*=x.size(i);int S=(int)S64,G=(int)num_groups;k<<<N*G,256>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),N,C,G,S);return y;}\n\"\"\"\n_ext=load_inline(name=\"gn_ext_final\",cpp_sources=cpp_src,cuda_sources=cuda_src,functions=[\"groupnorm_forward\"],verbose=False,extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"])\nclass ModelNew(nn.Module):\n def __init__(self,num_features:int,num_groups:int):\n super(ModelNew,self).__init__();self.gn=nn.GroupNorm(num_groups=num_groups,num_channels=num_features);self.num_groups=num_groups\n def forward(self,x:torch.Tensor)->torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(),self.gn.weight,self.gn.bias,self.num_groups)\n"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 136.8s turn · 6.8s 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\ncpp_src=r\"\"\"#include <torch/extension.h>\ntorch::Tensor groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);\"\"\"\ncuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__inline__ __device__ float warp_reduce_sum(float v){for(int o=16;o>0;o>>=1)v+=__shfl_down_sync(0xffffffff,v,o);return v;}\n__inline__ __device__ void block_reduce2(float &sum,float &ssq){__shared__ float sm[32],ss[32];int lane=threadIdx.x&31,wid=threadIdx.x>>5;sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);if(lane==0){sm[wid]=sum;ss[wid]=ssq;}__syncthreads();int nw=(blockDim.x+31)>>5;sum=(threadIdx.x<nw)?sm[lane]:0.0f;ssq=(threadIdx.x<nw)?ss[lane]:0.0f;if(wid==0){sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);}__syncthreads();if(threadIdx.x==0){sm[0]=sum;ss[0]=ssq;}__syncthreads();sum=sm[0];ssq=ss[0];}\n__global__ void k(const float* __restrict__ x,const float* __restrict__ weight,const float* __restrict__ bias,float* __restrict__ y,int N,int C,int G,int S){int ng=blockIdx.x;int n=ng/G;int g=ng-n*G;int cpg=C/G;int c0=g*cpg;long long base_n=(long long)n*C*S;int S4=S>>2;float sum=0,ssq=0;for(int cc=0;cc<cpg;++cc){const float4* ptr=(const float4*)(x+base_n+(c0+cc)*S);for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=ptr[j];sum+=v.x+v.y+v.z+v.w;ssq+=v.x*v.x+v.y*v.y+v.z*v.z+v.w*v.w;}}block_reduce2(sum,ssq);float m=(float)(cpg*S);float mean=sum/m;float var=ssq/m-mean*mean;if(var<0)var=0;float inv=1.0f/sqrtf(var+1e-5f);for(int cc=0;cc<cpg;++cc){int c=c0+cc;const float4* in=(const float4*)(x+base_n+c*S);float4* out=(float4*)(y+base_n+c*S);float scale=inv*weight[c];float shift=bias[c]-mean*scale;for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=in[j];v.x=v.x*scale+shift;v.y=v.y*scale+shift;v.z=v.z*scale+shift;v.w=v.w*scale+shift;out[j]=v;}}}\ntorch::Tensor groupnorm_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t num_groups){auto y=torch::empty_like(x);int N=x.size(0),C=x.size(1);long long S64=1;for(int i=2;i<x.dim();++i)S64*=x.size(i);int S=(int)S64,G=(int)num_groups;k<<<N*G,256>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),N,C,G,S);return y;}\n\"\"\"\n_ext=load_inline(name=\"gn_ext_final\",cpp_sources=cpp_src,cuda_sources=cuda_src,functions=[\"groupnorm_forward\"],verbose=False,extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"])\nclass ModelNew(nn.Module):\n def __init__(self,num_features:int,num_groups:int):\n super(ModelNew,self).__init__();self.gn=nn.GroupNorm(num_groups=num_groups,num_channels=num_features);self.num_groups=num_groups\n def forward(self,x:torch.Tensor)->torch.Tensor:\n return _ext.groupnorm_forward(x.contiguous(),self.gn.weight,self.gn.bias,self.num_groups)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 7.98 μs
Runtime stats: mean=7.98μs median=?μs std=0.17μs
Numerical precision: max_abs_err=1.67e-06 mean_abs_err=1.99e-07 max_rel_err=1.04e+03
Memory: 14369.0 MB peak (ref: 14369.06 MB, ratio: 1.0x)
Kernel launches: 2 (ref: 11, fusion ratio: 5.5)
Energy: 967.46 mJ/run (ref: 1209.02 mJ, ratio: 1.2497x)
SOL score: 0.8759
final submitted kernel (21 lines, 2,800 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 groupnorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, int64_t num_groups);"""
cuda_src=r"""
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
__inline__ __device__ float warp_reduce_sum(float v){for(int o=16;o>0;o>>=1)v+=__shfl_down_sync(0xffffffff,v,o);return v;}
__inline__ __device__ void block_reduce2(float &sum,float &ssq){__shared__ float sm[32],ss[32];int lane=threadIdx.x&31,wid=threadIdx.x>>5;sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);if(lane==0){sm[wid]=sum;ss[wid]=ssq;}__syncthreads();int nw=(blockDim.x+31)>>5;sum=(threadIdx.x<nw)?sm[lane]:0.0f;ssq=(threadIdx.x<nw)?ss[lane]:0.0f;if(wid==0){sum=warp_reduce_sum(sum);ssq=warp_reduce_sum(ssq);}__syncthreads();if(threadIdx.x==0){sm[0]=sum;ss[0]=ssq;}__syncthreads();sum=sm[0];ssq=ss[0];}
__global__ void k(const float* __restrict__ x,const float* __restrict__ weight,const float* __restrict__ bias,float* __restrict__ y,int N,int C,int G,int S){int ng=blockIdx.x;int n=ng/G;int g=ng-n*G;int cpg=C/G;int c0=g*cpg;long long base_n=(long long)n*C*S;int S4=S>>2;float sum=0,ssq=0;for(int cc=0;cc<cpg;++cc){const float4* ptr=(const float4*)(x+base_n+(c0+cc)*S);for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=ptr[j];sum+=v.x+v.y+v.z+v.w;ssq+=v.x*v.x+v.y*v.y+v.z*v.z+v.w*v.w;}}block_reduce2(sum,ssq);float m=(float)(cpg*S);float mean=sum/m;float var=ssq/m-mean*mean;if(var<0)var=0;float inv=1.0f/sqrtf(var+1e-5f);for(int cc=0;cc<cpg;++cc){int c=c0+cc;const float4* in=(const float4*)(x+base_n+c*S);float4* out=(float4*)(y+base_n+c*S);float scale=inv*weight[c];float shift=bias[c]-mean*scale;for(int j=threadIdx.x;j<S4;j+=blockDim.x){float4 v=in[j];v.x=v.x*scale+shift;v.y=v.y*scale+shift;v.z=v.z*scale+shift;v.w=v.w*scale+shift;out[j]=v;}}}
torch::Tensor groupnorm_forward(torch::Tensor x,torch::Tensor weight,torch::Tensor bias,int64_t num_groups){auto y=torch::empty_like(x);int N=x.size(0),C=x.size(1);long long S64=1;for(int i=2;i<x.dim();++i)S64*=x.size(i);int S=(int)S64,G=(int)num_groups;k<<<N*G,256>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>(),N,C,G,S);return y;}
"""
_ext=load_inline(name="gn_ext_final",cpp_sources=cpp_src,cuda_sources=cuda_src,functions=["groupnorm_forward"],verbose=False,extra_cflags=["-O3"],extra_cuda_cflags=["-O3"])
class ModelNew(nn.Module):
def __init__(self,num_features:int,num_groups:int):
super(ModelNew,self).__init__();self.gn=nn.GroupNorm(num_groups=num_groups,num_channels=num_features);self.num_groups=num_groups
def forward(self,x:torch.Tensor)->torch.Tensor:
return _ext.groupnorm_forward(x.contiguous(),self.gn.weight,self.gn.bias,self.num_groups)