correct
30_Gemm_GroupNorm_Hardtanh.py
started
2026-05-04T08:55:45Z
timingkernelbench.timing
kernel mean / median / std
2.77 / — / 0.27 μs
ref mean / median / std
3.08 / — / 0.86 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)
2.16x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
2121.24 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 a GEMM, applies Group Normalization, and then HardTanh.
"""
def __init__(self, in_features, out_features, num_groups, hardtanh_min, hardtanh_max):
super(Model, self).__init__()
self.gemm = nn.Linear(in_features, out_features)
self.group_norm = nn.GroupNorm(num_groups, out_features)
self.hardtanh = nn.Hardtanh(min_val=hardtanh_min, max_val=hardtanh_max)
def forward(self, x):
"""
Args:
x (torch.Tensor): Input tensor of shape (batch_size, in_features).
Returns:
torch.Tensor: Output tensor of shape (batch_size, out_features).
"""
x = self.gemm(x)
x = self.group_norm(x)
x = self.hardtanh(x)
return x
batch_size = 1024
in_features = 8192
out_features = 8192
num_groups = 16
hardtanh_min = -2.0
hardtanh_max = 2.0
def get_inputs():
return [torch.rand(batch_size, in_features)]
def get_init_inputs():
return [in_features, out_features, num_groups, hardtanh_min, hardtanh_max]turn 09.9s turn · 9.9s 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 122.7s turn · 22.7s LLM · 0.0s tools
reasoning
**Evaluating processing efficiency**
I'm thinking about this approach. It looks efficient: one block with eight warps means 256 threads, and that processes eight groups. Using __shfl_down_sync seems like a smart move since no synchronization is needed. I see this as a better strategy overall. I need to calculate total groups, which is N times the number of groups—16384 total groups. It feels satisfying to see this kind of efficiency in action!
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_forward(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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\nstatic __inline__ __device__ float warp_reduce_sum(float val) {\n unsigned mask = 0xffffffffu;\n val += __shfl_down_sync(mask, val, 16);\n val += __shfl_down_sync(mask, val, 8);\n val += __shfl_down_sync(mask, val, 4);\n val += __shfl_down_sync(mask, val, 2);\n val += __shfl_down_sync(mask, val, 1);\n return val;\n}\n\n__global__ void gn_ht_warp_kernel(const float* __restrict__ x,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ out,\n int N, int C, int G, int group_size,\n float ht_min, float ht_max) {\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5; // 0..7 for 256 threads\n int group_idx = blockIdx.x * 8 + warp;\n int total_groups = N * G;\n if (group_idx >= total_groups) return;\n int n = group_idx / G;\n int g = group_idx - n * G;\n int base = n * C + g * group_size;\n\n float sum = 0.0f;\n float ssq = 0.0f;\n for (int i = lane; i < group_size; i += 32) {\n float v = x[base + i];\n sum += v;\n ssq = fmaf(v, v, ssq);\n }\n sum = warp_reduce_sum(sum);\n ssq = warp_reduce_sum(ssq);\n float mean = __shfl_sync(0xffffffffu, sum, 0) / (float)group_size;\n float var = __shfl_sync(0xffffffffu, ssq, 0) / (float)group_size - mean * mean;\n var = fmaxf(var, 0.0f);\n float inv = rsqrtf(var + 1.0e-5f);\n\n int cbase = g * group_size;\n for (int i = lane; i < group_size; i += 32) {\n float v = x[base + i];\n float y = (v - mean) * inv;\n y = fmaf(y, gamma[cbase + i], beta[cbase + i]);\n y = fminf(fmaxf(y, ht_min), ht_max);\n out[base + i] = y;\n }\n}\n\ntorch::Tensor gn_hardtanh_forward(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max) {\n TORCH_CHECK(x.is_cuda(), \"x must be CUDA\");\n TORCH_CHECK(x.scalar_type() == at::kFloat, \"x must be float32\");\n auto out = torch::empty_like(x);\n int N = (int)x.size(0);\n int C = (int)x.size(1);\n int G = (int)num_groups;\n int group_size = C / G;\n int total_groups = N * G;\n dim3 block(256);\n dim3 grid((total_groups + 7) / 8);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n gn_ht_warp_kernel<<<grid, block, 0, stream>>>(x.data_ptr<float>(), gamma.data_ptr<float>(), beta.data_ptr<float>(), out.data_ptr<float>(), N, C, G, group_size, (float)ht_min, (float)ht_max);\n return out;\n}\n'''\n\n_ext = load_inline(name='gn_ht_ext_v1', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['gn_hardtanh_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, num_groups, hardtanh_min, hardtanh_max):\n super().__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.group_norm = nn.GroupNorm(num_groups, out_features)\n self.num_groups = num_groups\n self.hardtanh_min = float(hardtanh_min)\n self.hardtanh_max = float(hardtanh_max)\n def forward(self, x):\n y = torch.nn.functional.linear(x, self.gemm.weight, self.gemm.bias)\n return _ext.gn_hardtanh_forward(y, self.group_norm.weight, self.group_norm.bias, self.num_groups, self.hardtanh_min, self.hardtanh_max)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 28.0s 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\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_forward(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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\nstatic __inline__ __device__ float warp_reduce_sum(float val) {\n unsigned mask = 0xffffffffu;\n val += __shfl_down_sync(mask, val, 16);\n val += __shfl_down_sync(mask, val, 8);\n val += __shfl_down_sync(mask, val, 4);\n val += __shfl_down_sync(mask, val, 2);\n val += __shfl_down_sync(mask, val, 1);\n return val;\n}\n\n__global__ void gn_ht_warp_kernel(const float* __restrict__ x,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ out,\n int N, int C, int G, int group_size,\n float ht_min, float ht_max) {\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5; // 0..7 for 256 threads\n int group_idx = blockIdx.x * 8 + warp;\n int total_groups = N * G;\n if (group_idx >= total_groups) return;\n int n = group_idx / G;\n int g = group_idx - n * G;\n int base = n * C + g * group_size;\n\n float sum = 0.0f;\n float ssq = 0.0f;\n for (int i = lane; i < group_size; i += 32) {\n float v = x[base + i];\n sum += v;\n ssq = fmaf(v, v, ssq);\n }\n sum = warp_reduce_sum(sum);\n ssq = warp_reduce_sum(ssq);\n float mean = __shfl_sync(0xffffffffu, sum, 0) / (float)group_size;\n float var = __shfl_sync(0xffffffffu, ssq, 0) / (float)group_size - mean * mean;\n var = fmaxf(var, 0.0f);\n float inv = rsqrtf(var + 1.0e-5f);\n\n int cbase = g * group_size;\n for (int i = lane; i < group_size; i += 32) {\n float v = x[base + i];\n float y = (v - mean) * inv;\n y = fmaf(y, gamma[cbase + i], beta[cbase + i]);\n y = fminf(fmaxf(y, ht_min), ht_max);\n out[base + i] = y;\n }\n}\n\ntorch::Tensor gn_hardtanh_forward(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max) {\n TORCH_CHECK(x.is_cuda(), \"x must be CUDA\");\n TORCH_CHECK(x.scalar_type() == at::kFloat, \"x must be float32\");\n auto out = torch::empty_like(x);\n int N = (int)x.size(0);\n int C = (int)x.size(1);\n int G = (int)num_groups;\n int group_size = C / G;\n int total_groups = N * G;\n dim3 block(256);\n dim3 grid((total_groups + 7) / 8);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n gn_ht_warp_kernel<<<grid, block, 0, stream>>>(x.data_ptr<float>(), gamma.data_ptr<float>(), beta.data_ptr<float>(), out.data_ptr<float>(), N, C, G, group_size, (float)ht_min, (float)ht_max);\n return out;\n}\n'''\n\n_ext = load_inline(name='gn_ht_ext_v1', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['gn_hardtanh_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, num_groups, hardtanh_min, hardtanh_max):\n super().__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.group_norm = nn.GroupNorm(num_groups, out_features)\n self.num_groups = num_groups\n self.hardtanh_min = float(hardtanh_min)\n self.hardtanh_max = float(hardtanh_max)\n def forward(self, x):\n y = torch.nn.functional.linear(x, self.gemm.weight, self.gemm.bias)\n return _ext.gn_hardtanh_forward(y, self.group_norm.weight, self.group_norm.bias, self.num_groups, self.hardtanh_min, self.hardtanh_max)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=5.96e-07 mean_abs_err=1.90e-08 max_rel_err=5.00e-01
turn 3 · 1 tool call(s) · 13.0s turn · 13.0s LLM · 6,793 chars (click to expand)
turn 313.0s turn · 13.0s LLM · 0.0s tools
reasoning
**Considering performance improvement**
I'm thinking about how to enhance performance. Submitting the current setup might be an option, but I wonder if there's room for improvement, particularly with GEMM. Profiling the system seems like a solid next step to identify any bottlenecks or areas that can be refined. This way, I can gather more concrete data before making final decisions. Analyzing the results will help determine the most effective approach!
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_forward(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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\nstatic __inline__ __device__ float warp_reduce_sum(float val) {\n unsigned mask = 0xffffffffu;\n val += __shfl_down_sync(mask, val, 16);\n val += __shfl_down_sync(mask, val, 8);\n val += __shfl_down_sync(mask, val, 4);\n val += __shfl_down_sync(mask, val, 2);\n val += __shfl_down_sync(mask, val, 1);\n return val;\n}\n\n__global__ void gn_ht_warp_kernel(const float* __restrict__ x,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ out,\n int N, int C, int G, int group_size,\n float ht_min, float ht_max) {\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5; // 0..7 for 256 threads\n int group_idx = blockIdx.x * 8 + warp;\n int total_groups = N * G;\n if (group_idx >= total_groups) return;\n int n = group_idx / G;\n int g = group_idx - n * G;\n int base = n * C + g * group_size;\n\n float sum = 0.0f;\n float ssq = 0.0f;\n for (int i = lane; i < group_size; i += 32) {\n float v = x[base + i];\n sum += v;\n ssq = fmaf(v, v, ssq);\n }\n sum = warp_reduce_sum(sum);\n ssq = warp_reduce_sum(ssq);\n float mean = __shfl_sync(0xffffffffu, sum, 0) / (float)group_size;\n float var = __shfl_sync(0xffffffffu, ssq, 0) / (float)group_size - mean * mean;\n var = fmaxf(var, 0.0f);\n float inv = rsqrtf(var + 1.0e-5f);\n\n int cbase = g * group_size;\n for (int i = lane; i < group_size; i += 32) {\n float v = x[base + i];\n float y = (v - mean) * inv;\n y = fmaf(y, gamma[cbase + i], beta[cbase + i]);\n y = fminf(fmaxf(y, ht_min), ht_max);\n out[base + i] = y;\n }\n}\n\ntorch::Tensor gn_hardtanh_forward(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max) {\n TORCH_CHECK(x.is_cuda(), \"x must be CUDA\");\n TORCH_CHECK(x.scalar_type() == at::kFloat, \"x must be float32\");\n auto out = torch::empty_like(x);\n int N = (int)x.size(0);\n int C = (int)x.size(1);\n int G = (int)num_groups;\n int group_size = C / G;\n int total_groups = N * G;\n dim3 block(256);\n dim3 grid((total_groups + 7) / 8);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n gn_ht_warp_kernel<<<grid, block, 0, stream>>>(x.data_ptr<float>(), gamma.data_ptr<float>(), beta.data_ptr<float>(), out.data_ptr<float>(), N, C, G, group_size, (float)ht_min, (float)ht_max);\n return out;\n}\n'''\n\n_ext = load_inline(name='gn_ht_ext_v1', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['gn_hardtanh_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, num_groups, hardtanh_min, hardtanh_max):\n super().__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.group_norm = nn.GroupNorm(num_groups, out_features)\n self.num_groups = num_groups\n self.hardtanh_min = float(hardtanh_min)\n self.hardtanh_max = float(hardtanh_max)\n def forward(self, x):\n y = torch.nn.functional.linear(x, self.gemm.weight, self.gemm.bias)\n return _ext.gn_hardtanh_forward(y, self.group_norm.weight, self.group_norm.bias, self.num_groups, self.hardtanh_min, self.hardtanh_max)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::linear 69154.0μs (50%) calls=1
#2: aten::addmm 69028.0μs (50%) calls=1
#3: aten::t 75.0μs (0%) calls=1
#4: aten::transpose 42.0μs (0%) calls=1
#5: aten::as_strided 20.0μs (0%) calls=1
... +2 more kernels
--- Timing ---
GPU time: 2694.37 μs
--- Memory Bandwidth ---
DRAM BW: 2219.3/3350 GB/s (66.2%)
read=1605.2 write=614.2 GB/s
Hit rates: L2=150.7%
--- Compute Throughput ---
Dominant pipe: FP32 util=93.4%
Pipe util: FMA=93.4%, Tensor=0.3%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 94.8%
Eligible warps/cycle: 2.89
Launch: regs/thread=286, smem/blk=35840B, block=384, grid=2560
--- Memory Access Quality ---
Global ld coalescing: 8.00 sectors/req (8.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 2070.5% ####################
mio_throttle 266.4% ####################
short_scoreboard 171.6% ####################
--- Optimization Hints ---
-> 2071% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=8.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=8.0x (ideal=1.0) -> 8x 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 414.5s turn · 14.5s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_bias_forward(torch::Tensor x, torch::Tensor bias, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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\nstatic __inline__ __device__ float warp_reduce_sum(float val) {\n unsigned mask = 0xffffffffu;\n val += __shfl_down_sync(mask, val, 16);\n val += __shfl_down_sync(mask, val, 8);\n val += __shfl_down_sync(mask, val, 4);\n val += __shfl_down_sync(mask, val, 2);\n val += __shfl_down_sync(mask, val, 1);\n return val;\n}\n\n__global__ void gn_ht_bias_warp_kernel(const float* __restrict__ x,\n const float* __restrict__ bias,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ out,\n int N, int C, int G, int group_size,\n float ht_min, float ht_max) {\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n int group_idx = blockIdx.x * 8 + warp;\n int total_groups = N * G;\n if (group_idx >= total_groups) return;\n int n = group_idx / G;\n int g = group_idx - n * G;\n int base = n * C + g * group_size;\n int cbase = g * group_size;\n float sum = 0.0f;\n float ssq = 0.0f;\n for (int i = lane; i < group_size; i += 32) {\n float v = x[base + i] + bias[cbase + i];\n sum += v;\n ssq = fmaf(v, v, ssq);\n }\n sum = warp_reduce_sum(sum);\n ssq = warp_reduce_sum(ssq);\n float mean = __shfl_sync(0xffffffffu, sum, 0) / (float)group_size;\n float var = __shfl_sync(0xffffffffu, ssq, 0) / (float)group_size - mean * mean;\n var = fmaxf(var, 0.0f);\n float inv = rsqrtf(var + 1.0e-5f);\n for (int i = lane; i < group_size; i += 32) {\n float v = x[base + i] + bias[cbase + i];\n float y = (v - mean) * inv;\n y = fmaf(y, gamma[cbase + i], beta[cbase + i]);\n y = fminf(fmaxf(y, ht_min), ht_max);\n out[base + i] = y;\n }\n}\n\ntorch::Tensor gn_hardtanh_bias_forward(torch::Tensor x, torch::Tensor bias, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max) {\n auto out = torch::empty_like(x);\n int N = (int)x.size(0), C = (int)x.size(1), G=(int)num_groups;\n int group_size = C/G, total_groups=N*G;\n gn_ht_bias_warp_kernel<<<(total_groups+7)/8, 256, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), bias.data_ptr<float>(), gamma.data_ptr<float>(), beta.data_ptr<float>(), out.data_ptr<float>(), N,C,G,group_size,(float)ht_min,(float)ht_max);\n return out;\n}\n'''\n_ext = load_inline(name='gn_ht_ext_v2', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['gn_hardtanh_bias_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, num_groups, hardtanh_min, hardtanh_max):\n super().__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.group_norm = nn.GroupNorm(num_groups, out_features)\n self.num_groups = num_groups\n self.hardtanh_min = float(hardtanh_min)\n self.hardtanh_max = float(hardtanh_max)\n def forward(self, x):\n y = torch.matmul(x, self.gemm.weight.t())\n return _ext.gn_hardtanh_bias_forward(y, self.gemm.bias, self.group_norm.weight, self.group_norm.bias, self.num_groups, self.hardtanh_min, self.hardtanh_max)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=5.96e-07 mean_abs_err=1.90e-08 max_rel_err=5.00e-01
turn 5 · 1 tool call(s) · 8.1s turn · 8.1s LLM · 6,519 chars (click to expand)
turn 58.1s turn · 8.1s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_bias_forward(torch::Tensor x, torch::Tensor bias, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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\nstatic __inline__ __device__ float warp_reduce_sum(float val) {\n unsigned mask = 0xffffffffu;\n val += __shfl_down_sync(mask, val, 16);\n val += __shfl_down_sync(mask, val, 8);\n val += __shfl_down_sync(mask, val, 4);\n val += __shfl_down_sync(mask, val, 2);\n val += __shfl_down_sync(mask, val, 1);\n return val;\n}\n\n__global__ void gn_ht_bias_warp_kernel(const float* __restrict__ x,\n const float* __restrict__ bias,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ out,\n int N, int C, int G, int group_size,\n float ht_min, float ht_max) {\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n int group_idx = blockIdx.x * 8 + warp;\n int total_groups = N * G;\n if (group_idx >= total_groups) return;\n int n = group_idx / G;\n int g = group_idx - n * G;\n int base = n * C + g * group_size;\n int cbase = g * group_size;\n float sum = 0.0f;\n float ssq = 0.0f;\n for (int i = lane; i < group_size; i += 32) {\n float v = x[base + i] + bias[cbase + i];\n sum += v;\n ssq = fmaf(v, v, ssq);\n }\n sum = warp_reduce_sum(sum);\n ssq = warp_reduce_sum(ssq);\n float mean = __shfl_sync(0xffffffffu, sum, 0) / (float)group_size;\n float var = __shfl_sync(0xffffffffu, ssq, 0) / (float)group_size - mean * mean;\n var = fmaxf(var, 0.0f);\n float inv = rsqrtf(var + 1.0e-5f);\n for (int i = lane; i < group_size; i += 32) {\n float v = x[base + i] + bias[cbase + i];\n float y = (v - mean) * inv;\n y = fmaf(y, gamma[cbase + i], beta[cbase + i]);\n y = fminf(fmaxf(y, ht_min), ht_max);\n out[base + i] = y;\n }\n}\n\ntorch::Tensor gn_hardtanh_bias_forward(torch::Tensor x, torch::Tensor bias, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max) {\n auto out = torch::empty_like(x);\n int N = (int)x.size(0), C = (int)x.size(1), G=(int)num_groups;\n int group_size = C/G, total_groups=N*G;\n gn_ht_bias_warp_kernel<<<(total_groups+7)/8, 256, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), bias.data_ptr<float>(), gamma.data_ptr<float>(), beta.data_ptr<float>(), out.data_ptr<float>(), N,C,G,group_size,(float)ht_min,(float)ht_max);\n return out;\n}\n'''\n_ext = load_inline(name='gn_ht_ext_v2', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['gn_hardtanh_bias_forward'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, num_groups, hardtanh_min, hardtanh_max):\n super().__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.group_norm = nn.GroupNorm(num_groups, out_features)\n self.num_groups = num_groups\n self.hardtanh_min = float(hardtanh_min)\n self.hardtanh_max = float(hardtanh_max)\n def forward(self, x):\n y = torch.matmul(x, self.gemm.weight.t())\n return _ext.gn_hardtanh_bias_forward(y, self.gemm.bias, self.group_norm.weight, self.group_norm.bias, self.num_groups, self.hardtanh_min, self.hardtanh_max)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty_like 319656.0μs (35%) calls=1
#2: aten::empty_strided 319641.0μs (35%) calls=1
#3: aten::matmul 132852.0μs (15%) calls=1
#4: aten::mm 132832.0μs (15%) calls=1
#5: aten::t 96.0μs (0%) calls=1
... +2 more kernels
--- Timing ---
GPU time: 2750.34 μs [↑56.0μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 2088.9/3350 GB/s (62.4%) [↓3.9% REGRESSED]
read=1479.5 write=609.4 GB/s
Hit rates: L2=148.5%
--- Compute Throughput ---
Dominant pipe: FP32 util=92.8% [↓0.6% REGRESSED]
Pipe util: FMA=92.8%, Tensor=0.3%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 72.8% [↓22.0% REGRESSED]
Eligible warps/cycle: 2.70
Launch: regs/thread=295, smem/blk=35328B, block=384, grid=2560
--- Memory Access Quality ---
Global ld coalescing: 8.00 sectors/req (8.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 1785.5% ####################
short_scoreboard 79.1% ###############
mio_throttle 58.5% ###########
--- Optimization Hints ---
-> 1786% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=8.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=8.0x (ideal=1.0) -> 8x 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: 2694.37→2750.34μs (↑55.97) REGRESSED
DRAM BW%: 66.25→62.35% (↓3.89) REGRESSED
Compute%: 93.41→92.76% (↓0.64) REGRESSED
Occupancy: 94.77→72.76% (↓22.01) REGRESSED
Eligible warps/cyc: 2.89→2.70 (↓0.20) REGRESSED
L2 hit%: 150.74→148.48% (↓2.26) REGRESSED
turn 620.5s turn · 20.5s LLM · 0.0s tools
reasoning
**Evaluating model precision**
I'm considering the fp32 tolerance of 1e-4 and wondering if using TF32 might introduce errors greater than that. There's a chance that PyTorch's evaluation function allows TF32, but I'm not entirely sure. The error margin could be significant when working with large matrices. I'm thinking about using torch.set_float32_matmul_precision('high') to possibly allow TF32, but I’m also considering cuBLAS for efficiency to minimize overhead. My focus is on reducing Python overhead and ensuring efficiency in the implementation.
**Considering PyTorch efficiency**
I'm contemplating whether to use the PyTorch profiler, which shows the GPU's activity. It might be possible to incorporate at::addmm within an extension to reduce Python operations, but then I wonder if this doesn’t allow for customization. It feels like I need to balance between efficiency and maintaining the flexibility I want in my implementation. I’m thinking about the trade-offs involved and how they might impact performance versus ease of use.
**Evaluating performance optimizations**
I'm considering how to optimize the loop and shuffle operations within a single warp. Since everything's happening in place, it avoids the overhead of allocating separate memory for outputs. Instead of using a second allocation that's possibly expensive, the function `gn_hardtanh_inplace` can return `x` directly. Since it doesn't impact correctness, I think this is a valuable adjustment. I also wonder if I can incorporate bias in linear operations while maintaining these enhancements. Let's test that!
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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>\nstatic __inline__ __device__ float warp_reduce_sum(float val){\n unsigned mask=0xffffffffu;\n val += __shfl_down_sync(mask,val,16);\n val += __shfl_down_sync(mask,val,8);\n val += __shfl_down_sync(mask,val,4);\n val += __shfl_down_sync(mask,val,2);\n val += __shfl_down_sync(mask,val,1);\n return val;\n}\n__global__ void gn_ht_inplace_kernel(float* __restrict__ x, const float* __restrict__ gamma, const float* __restrict__ beta,\n int N,int C,int G,int group_size,float ht_min,float ht_max){\n int tid=threadIdx.x; int lane=tid&31; int warp=tid>>5; int group_idx=blockIdx.x*8+warp; int total=N*G; if(group_idx>=total) return;\n int n=group_idx/G; int g=group_idx-n*G; int base=n*C+g*group_size; int cbase=g*group_size;\n float sum=0.f, ssq=0.f;\n #pragma unroll\n for(int i=lane;i<512;i+=32){ // optimized for known group_size=512\n if(i<group_size){ float v=x[base+i]; sum+=v; ssq=fmaf(v,v,ssq); }\n }\n sum=warp_reduce_sum(sum); ssq=warp_reduce_sum(ssq);\n float mean=__shfl_sync(0xffffffffu,sum,0)/(float)group_size;\n float var=__shfl_sync(0xffffffffu,ssq,0)/(float)group_size - mean*mean;\n float inv=rsqrtf(fmaxf(var,0.f)+1.0e-5f);\n #pragma unroll\n for(int i=lane;i<512;i+=32){\n if(i<group_size){ float v=x[base+i]; float y=(v-mean)*inv; y=fmaf(y,gamma[cbase+i],beta[cbase+i]); y=fminf(fmaxf(y,ht_min),ht_max); x[base+i]=y; }\n }\n}\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max){\n int N=(int)x.size(0), C=(int)x.size(1), G=(int)num_groups; int group_size=C/G; int total=N*G;\n gn_ht_inplace_kernel<<<(total+7)/8,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),gamma.data_ptr<float>(),beta.data_ptr<float>(),N,C,G,group_size,(float)ht_min,(float)ht_max);\n return x;\n}\n'''\n_ext=load_inline(name='gn_ht_ext_v3', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['gn_hardtanh_inplace'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features,num_groups,hardtanh_min,hardtanh_max):\n super().__init__(); self.gemm=nn.Linear(in_features,out_features); self.group_norm=nn.GroupNorm(num_groups,out_features); self.num_groups=num_groups; self.hardtanh_min=float(hardtanh_min); self.hardtanh_max=float(hardtanh_max)\n def forward(self,x):\n y=torch.nn.functional.linear(x,self.gemm.weight,self.gemm.bias)\n return _ext.gn_hardtanh_inplace(y,self.group_norm.weight,self.group_norm.bias,self.num_groups,self.hardtanh_min,self.hardtanh_max)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=5.96e-07 mean_abs_err=1.90e-08 max_rel_err=5.00e-01
turn 76.4s turn · 6.4s 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\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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>\nstatic __inline__ __device__ float warp_reduce_sum(float val){\n unsigned mask=0xffffffffu;\n val += __shfl_down_sync(mask,val,16);\n val += __shfl_down_sync(mask,val,8);\n val += __shfl_down_sync(mask,val,4);\n val += __shfl_down_sync(mask,val,2);\n val += __shfl_down_sync(mask,val,1);\n return val;\n}\n__global__ void gn_ht_inplace_kernel(float* __restrict__ x, const float* __restrict__ gamma, const float* __restrict__ beta,\n int N,int C,int G,int group_size,float ht_min,float ht_max){\n int tid=threadIdx.x; int lane=tid&31; int warp=tid>>5; int group_idx=blockIdx.x*8+warp; int total=N*G; if(group_idx>=total) return;\n int n=group_idx/G; int g=group_idx-n*G; int base=n*C+g*group_size; int cbase=g*group_size;\n float sum=0.f, ssq=0.f;\n #pragma unroll\n for(int i=lane;i<512;i+=32){\n if(i<group_size){ float v=x[base+i]; sum+=v; ssq=fmaf(v,v,ssq); }\n }\n sum=warp_reduce_sum(sum); ssq=warp_reduce_sum(ssq);\n float mean=__shfl_sync(0xffffffffu,sum,0)/(float)group_size;\n float var=__shfl_sync(0xffffffffu,ssq,0)/(float)group_size - mean*mean;\n float inv=rsqrtf(fmaxf(var,0.f)+1.0e-5f);\n #pragma unroll\n for(int i=lane;i<512;i+=32){\n if(i<group_size){ float v=x[base+i]; float y=(v-mean)*inv; y=fmaf(y,gamma[cbase+i],beta[cbase+i]); y=fminf(fmaxf(y,ht_min),ht_max); x[base+i]=y; }\n }\n}\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max){\n int N=(int)x.size(0), C=(int)x.size(1), G=(int)num_groups; int group_size=C/G; int total=N*G;\n gn_ht_inplace_kernel<<<(total+7)/8,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),gamma.data_ptr<float>(),beta.data_ptr<float>(),N,C,G,group_size,(float)ht_min,(float)ht_max);\n return x;\n}\n'''\n_ext=load_inline(name='gn_ht_ext_v3', cpp_sources=[_cpp_src], cuda_sources=[_cuda_src], functions=['gn_hardtanh_inplace'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features,num_groups,hardtanh_min,hardtanh_max):\n super().__init__(); self.gemm=nn.Linear(in_features,out_features); self.group_norm=nn.GroupNorm(num_groups,out_features); self.num_groups=num_groups; self.hardtanh_min=float(hardtanh_min); self.hardtanh_max=float(hardtanh_max)\n def forward(self,x):\n y=torch.nn.functional.linear(x,self.gemm.weight,self.gemm.bias)\n return _ext.gn_hardtanh_inplace(y,self.group_norm.weight,self.group_norm.bias,self.num_groups,self.hardtanh_min,self.hardtanh_max)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::linear 78127.0μs (50%) calls=1
#2: aten::addmm 77899.0μs (50%) calls=1
#3: aten::t 139.0μs (0%) calls=1
#4: aten::transpose 89.0μs (0%) calls=1
#5: aten::as_strided 19.0μs (0%) calls=1
--- Timing ---
GPU time: 2692.00 μs [↓58.3μs ok]
--- Memory Bandwidth ---
DRAM BW: 1896.3/3350 GB/s (56.6%) [↓5.7% REGRESSED]
read=1439.5 write=456.8 GB/s
Hit rates: L2=149.5%
--- Compute Throughput ---
Dominant pipe: FP32 util=89.3% [↓3.5% REGRESSED]
Pipe util: FMA=89.3%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 65.7% [↓7.1% REGRESSED]
Eligible warps/cycle: 2.10
Launch: regs/thread=301, smem/blk=35840B, block=384, grid=2560
--- Memory Access Quality ---
Global ld coalescing: 8.00 sectors/req (8.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 2250.9% ####################
short_scoreboard 120.7% ####################
mio_throttle 48.1% #########
--- Optimization Hints ---
-> 2251% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=8.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=8.0x (ideal=1.0) -> 8x 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: 2750.34→2692.00μs (↓58.34) ok
DRAM BW%: 62.35→56.61% (↓5.75) REGRESSED
Compute%: 92.76→89.28% (↓3.48) REGRESSED
Occupancy: 72.76→65.69% (↓7.08) REGRESSED
Eligible warps/cyc: 2.70→2.10 (↓0.60) REGRESSED
L2 hit%: 148.48→149.54% (↑1.06) ok
turn 86.8s turn · 6.8s LLM · 0.0s tools
reasoning
**Evaluating code optimization**
I'm considering that version 3 might be the slightly better option. I wonder if unrolling with "if" increases the registers being used. It seems like the original generic approach with in-place modifications could be more effective. I’m thinking about testing it without the unrolling fixed to see how that impacts performance. There are some interesting factors here that I need to analyze further to arrive at a solid conclusion.
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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>\nstatic __inline__ __device__ float warp_reduce_sum(float val){unsigned mask=0xffffffffu; val+=__shfl_down_sync(mask,val,16); val+=__shfl_down_sync(mask,val,8); val+=__shfl_down_sync(mask,val,4); val+=__shfl_down_sync(mask,val,2); val+=__shfl_down_sync(mask,val,1); return val;}\n__global__ void gn_ht_inplace_kernel(float* __restrict__ x,const float* __restrict__ gamma,const float* __restrict__ beta,int N,int C,int G,int group_size,float ht_min,float ht_max){\n int tid=threadIdx.x,lane=tid&31,warp=tid>>5; int group_idx=blockIdx.x*8+warp,total=N*G; if(group_idx>=total) return; int n=group_idx/G,g=group_idx-n*G; int base=n*C+g*group_size,cbase=g*group_size; float sum=0.f,ssq=0.f;\n for(int i=lane;i<group_size;i+=32){float v=x[base+i]; sum+=v; ssq=fmaf(v,v,ssq);} sum=warp_reduce_sum(sum); ssq=warp_reduce_sum(ssq); float mean=__shfl_sync(0xffffffffu,sum,0)/(float)group_size; float var=__shfl_sync(0xffffffffu,ssq,0)/(float)group_size-mean*mean; float inv=rsqrtf(fmaxf(var,0.f)+1.0e-5f);\n for(int i=lane;i<group_size;i+=32){float v=x[base+i]; float y=(v-mean)*inv; y=fmaf(y,gamma[cbase+i],beta[cbase+i]); y=fminf(fmaxf(y,ht_min),ht_max); x[base+i]=y;}\n}\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x,torch::Tensor gamma,torch::Tensor beta,int64_t num_groups,double ht_min,double ht_max){int N=(int)x.size(0),C=(int)x.size(1),G=(int)num_groups; int gs=C/G,total=N*G; gn_ht_inplace_kernel<<<(total+7)/8,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),gamma.data_ptr<float>(),beta.data_ptr<float>(),N,C,G,gs,(float)ht_min,(float)ht_max); return x;}\n'''\n_ext=load_inline(name='gn_ht_ext_v4',cpp_sources=[_cpp_src],cuda_sources=[_cuda_src],functions=['gn_hardtanh_inplace'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features,num_groups,hardtanh_min,hardtanh_max):\n super().__init__(); self.gemm=nn.Linear(in_features,out_features); self.group_norm=nn.GroupNorm(num_groups,out_features); self.num_groups=num_groups; self.hardtanh_min=float(hardtanh_min); self.hardtanh_max=float(hardtanh_max)\n def forward(self,x):\n y=torch.nn.functional.linear(x,self.gemm.weight,self.gemm.bias)\n return _ext.gn_hardtanh_inplace(y,self.group_norm.weight,self.group_norm.bias,self.num_groups,self.hardtanh_min,self.hardtanh_max)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::linear 115449.0μs (50%) calls=1
#2: aten::addmm 115310.0μs (50%) calls=1
#3: aten::t 88.0μs (0%) calls=1
#4: aten::transpose 50.0μs (0%) calls=1
#5: aten::as_strided 14.0μs (0%) calls=1
--- Timing ---
GPU time: 2689.15 μs [↓2.8μs ok]
--- Memory Bandwidth ---
DRAM BW: 2068.1/3350 GB/s (61.7%) [↑5.1% ok]
read=1583.2 write=484.9 GB/s
Hit rates: L2=146.4%
--- Compute Throughput ---
Dominant pipe: FP32 util=92.5% [↑3.2% ok]
Pipe util: FMA=92.5%, Tensor=0.3%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 100.0% [↑34.4% ok]
Eligible warps/cycle: 2.89
Launch: regs/thread=286, smem/blk=35840B, block=384, grid=2560
--- Memory Access Quality ---
Global ld coalescing: 8.00 sectors/req (8.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 2190.7% ####################
mio_throttle 278.4% ####################
short_scoreboard 206.8% ####################
--- Optimization Hints ---
-> 2191% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=8.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=8.0x (ideal=1.0) -> 8x 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: 2692.00→2689.15μs (↓2.85) ok
DRAM BW%: 56.61→61.73% (↑5.13) ok
Compute%: 89.28→92.48% (↑3.20) ok
Occupancy: 65.69→100.05% (↑34.36) ok
Eligible warps/cyc: 2.10→2.89 (↑0.79) ok
L2 hit%: 149.54→146.38% (↓3.15) REGRESSED
turn 911.6s turn · 11.6s LLM · 0.0s tools
reasoning
**Testing performance issues**
I'm considering how gamma beta contiguous might help improve performance by removing the second x read. It seems like this could enhance GN, but I'm a bit worried about potential register pressure. I think it might only be relevant for custom scenarios and not necessarily GEMM. I need to run some tests to see how this plays out and if there are any unexpected consequences. Let's see what the results yield!
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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>\nstatic __inline__ __device__ float warp_reduce_sum(float val){unsigned mask=0xffffffffu; val+=__shfl_down_sync(mask,val,16); val+=__shfl_down_sync(mask,val,8); val+=__shfl_down_sync(mask,val,4); val+=__shfl_down_sync(mask,val,2); val+=__shfl_down_sync(mask,val,1); return val;}\n__global__ void gn_ht_cache_kernel(float* __restrict__ x,const float* __restrict__ gamma,const float* __restrict__ beta,int N,int C,int G,int group_size,float ht_min,float ht_max){\n int tid=threadIdx.x,lane=tid&31,warp=tid>>5; int group_idx=blockIdx.x*8+warp,total=N*G; if(group_idx>=total) return; int n=group_idx/G,g=group_idx-n*G; int base=n*C+g*group_size,cbase=g*group_size; float vals[16]; int offs[16]; float sum=0.f,ssq=0.f; int cnt=0;\n for(int i=lane;i<group_size;i+=32){float v=x[base+i]; vals[cnt]=v; offs[cnt]=i; cnt++; sum+=v; ssq=fmaf(v,v,ssq);} sum=warp_reduce_sum(sum); ssq=warp_reduce_sum(ssq); float mean=__shfl_sync(0xffffffffu,sum,0)/(float)group_size; float var=__shfl_sync(0xffffffffu,ssq,0)/(float)group_size-mean*mean; float inv=rsqrtf(fmaxf(var,0.f)+1.0e-5f);\n for(int j=0;j<cnt;j++){int i=offs[j]; float y=(vals[j]-mean)*inv; y=fmaf(y,gamma[cbase+i],beta[cbase+i]); y=fminf(fmaxf(y,ht_min),ht_max); x[base+i]=y;}\n}\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x,torch::Tensor gamma,torch::Tensor beta,int64_t num_groups,double ht_min,double ht_max){int N=(int)x.size(0),C=(int)x.size(1),G=(int)num_groups; int gs=C/G,total=N*G; gn_ht_cache_kernel<<<(total+7)/8,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),gamma.data_ptr<float>(),beta.data_ptr<float>(),N,C,G,gs,(float)ht_min,(float)ht_max); return x;}\n'''\n_ext=load_inline(name='gn_ht_ext_v5',cpp_sources=[_cpp_src],cuda_sources=[_cuda_src],functions=['gn_hardtanh_inplace'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features,num_groups,hardtanh_min,hardtanh_max):\n super().__init__(); self.gemm=nn.Linear(in_features,out_features); self.group_norm=nn.GroupNorm(num_groups,out_features); self.num_groups=num_groups; self.hardtanh_min=float(hardtanh_min); self.hardtanh_max=float(hardtanh_max)\n def forward(self,x):\n y=torch.nn.functional.linear(x,self.gemm.weight,self.gemm.bias)\n return _ext.gn_hardtanh_inplace(y,self.group_norm.weight,self.group_norm.bias,self.num_groups,self.hardtanh_min,self.hardtanh_max)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=5.96e-07 mean_abs_err=1.90e-08 max_rel_err=5.00e-01
turn 106.6s turn · 6.6s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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>\nstatic __inline__ __device__ float warp_reduce_sum(float val){unsigned mask=0xffffffffu; val+=__shfl_down_sync(mask,val,16); val+=__shfl_down_sync(mask,val,8); val+=__shfl_down_sync(mask,val,4); val+=__shfl_down_sync(mask,val,2); val+=__shfl_down_sync(mask,val,1); return val;}\n__global__ void gn_ht_cache_kernel(float* __restrict__ x,const float* __restrict__ gamma,const float* __restrict__ beta,int N,int C,int G,int group_size,float ht_min,float ht_max){\n int tid=threadIdx.x,lane=tid&31,warp=tid>>5; int group_idx=blockIdx.x*8+warp,total=N*G; if(group_idx>=total) return; int n=group_idx/G,g=group_idx-n*G; int base=n*C+g*group_size,cbase=g*group_size; float vals[16]; int offs[16]; float sum=0.f,ssq=0.f; int cnt=0;\n for(int i=lane;i<group_size;i+=32){float v=x[base+i]; vals[cnt]=v; offs[cnt]=i; cnt++; sum+=v; ssq=fmaf(v,v,ssq);} sum=warp_reduce_sum(sum); ssq=warp_reduce_sum(ssq); float mean=__shfl_sync(0xffffffffu,sum,0)/(float)group_size; float var=__shfl_sync(0xffffffffu,ssq,0)/(float)group_size-mean*mean; float inv=rsqrtf(fmaxf(var,0.f)+1.0e-5f);\n for(int j=0;j<cnt;j++){int i=offs[j]; float y=(vals[j]-mean)*inv; y=fmaf(y,gamma[cbase+i],beta[cbase+i]); y=fminf(fmaxf(y,ht_min),ht_max); x[base+i]=y;}\n}\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x,torch::Tensor gamma,torch::Tensor beta,int64_t num_groups,double ht_min,double ht_max){int N=(int)x.size(0),C=(int)x.size(1),G=(int)num_groups; int gs=C/G,total=N*G; gn_ht_cache_kernel<<<(total+7)/8,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),gamma.data_ptr<float>(),beta.data_ptr<float>(),N,C,G,gs,(float)ht_min,(float)ht_max); return x;}\n'''\n_ext=load_inline(name='gn_ht_ext_v5',cpp_sources=[_cpp_src],cuda_sources=[_cuda_src],functions=['gn_hardtanh_inplace'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features,num_groups,hardtanh_min,hardtanh_max):\n super().__init__(); self.gemm=nn.Linear(in_features,out_features); self.group_norm=nn.GroupNorm(num_groups,out_features); self.num_groups=num_groups; self.hardtanh_min=float(hardtanh_min); self.hardtanh_max=float(hardtanh_max)\n def forward(self,x):\n y=torch.nn.functional.linear(x,self.gemm.weight,self.gemm.bias)\n return _ext.gn_hardtanh_inplace(y,self.group_norm.weight,self.group_norm.bias,self.num_groups,self.hardtanh_min,self.hardtanh_max)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::linear 72251.0μs (50%) calls=1
#2: aten::addmm 72122.0μs (50%) calls=1
#3: aten::t 73.0μs (0%) calls=1
#4: aten::transpose 37.0μs (0%) calls=1
#5: aten::as_strided 11.0μs (0%) calls=1
--- Timing ---
GPU time: 2728.61 μs [↑39.5μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 2485.7/3350 GB/s (74.2%) [↑12.5% ok]
read=1076.9 write=1408.8 GB/s
Hit rates: L2=160.1%
--- Compute Throughput ---
Dominant pipe: FP32 util=87.4% [↓5.0% REGRESSED]
Pipe util: FMA=87.4%, Tensor=0.1%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 103.1% [↑3.1% ok]
Eligible warps/cycle: 1.89
Launch: regs/thread=286, smem/blk=35840B, block=384, grid=2560
--- Memory Access Quality ---
Global ld coalescing: 8.00 sectors/req (8.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 7272.7% ####################
mio_throttle 483.0% ####################
short_scoreboard 403.1% ####################
--- Optimization Hints ---
-> 7273% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=8.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=8.0x (ideal=1.0) -> 8x 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: 2689.15→2728.61μs (↑39.46) REGRESSED
DRAM BW%: 61.73→74.20% (↑12.47) ok
Compute%: 92.48→87.44% (↓5.04) REGRESSED
Occupancy: 100.05→103.11% (↑3.06) ok
Eligible warps/cyc: 2.89→1.89 (↓1.00) REGRESSED
L2 hit%: 146.38→160.05% (↑13.67) ok
turn 118.7s turn · 8.7s LLM · 0.0s tools
reasoning
**Considering function optimization**
I’m thinking about optimizing my process. Using `torch.addmm` instead of `F.linear` might reduce function overhead. It could offer the same performance on the GPU while lowering the CPU workload, which is interesting! In the forward pass, I can set it up like this: `y = torch.addmm(self.gemm.bias, x, self.gemm.weight.t())`. Then I can continue with the `F.linear` calls as before. I’ll profile it to check if there’s any difference. Should I test this out?
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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>\nstatic __inline__ __device__ float warp_reduce_sum(float val){unsigned mask=0xffffffffu; val+=__shfl_down_sync(mask,val,16); val+=__shfl_down_sync(mask,val,8); val+=__shfl_down_sync(mask,val,4); val+=__shfl_down_sync(mask,val,2); val+=__shfl_down_sync(mask,val,1); return val;}\n__global__ void gn_ht_inplace_kernel(float* __restrict__ x,const float* __restrict__ gamma,const float* __restrict__ beta,int N,int C,int G,int group_size,float ht_min,float ht_max){\n int tid=threadIdx.x,lane=tid&31,warp=tid>>5; int group_idx=blockIdx.x*8+warp,total=N*G; if(group_idx>=total) return; int n=group_idx/G,g=group_idx-n*G; int base=n*C+g*group_size,cbase=g*group_size; float sum=0.f,ssq=0.f;\n for(int i=lane;i<group_size;i+=32){float v=x[base+i]; sum+=v; ssq=fmaf(v,v,ssq);} sum=warp_reduce_sum(sum); ssq=warp_reduce_sum(ssq); float mean=__shfl_sync(0xffffffffu,sum,0)/(float)group_size; float var=__shfl_sync(0xffffffffu,ssq,0)/(float)group_size-mean*mean; float inv=rsqrtf(fmaxf(var,0.f)+1.0e-5f);\n for(int i=lane;i<group_size;i+=32){float v=x[base+i]; float y=(v-mean)*inv; y=fmaf(y,gamma[cbase+i],beta[cbase+i]); y=fminf(fmaxf(y,ht_min),ht_max); x[base+i]=y;}\n}\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x,torch::Tensor gamma,torch::Tensor beta,int64_t num_groups,double ht_min,double ht_max){int N=(int)x.size(0),C=(int)x.size(1),G=(int)num_groups; int gs=C/G,total=N*G; gn_ht_inplace_kernel<<<(total+7)/8,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),gamma.data_ptr<float>(),beta.data_ptr<float>(),N,C,G,gs,(float)ht_min,(float)ht_max); return x;}\n'''\n_ext=load_inline(name='gn_ht_ext_v6',cpp_sources=[_cpp_src],cuda_sources=[_cuda_src],functions=['gn_hardtanh_inplace'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features,num_groups,hardtanh_min,hardtanh_max):\n super().__init__(); self.gemm=nn.Linear(in_features,out_features); self.group_norm=nn.GroupNorm(num_groups,out_features); self.num_groups=num_groups; self.hardtanh_min=float(hardtanh_min); self.hardtanh_max=float(hardtanh_max)\n def forward(self,x):\n y=torch.addmm(self.gemm.bias, x, self.gemm.weight.t())\n return _ext.gn_hardtanh_inplace(y,self.group_norm.weight,self.group_norm.bias,self.num_groups,self.hardtanh_min,self.hardtanh_max)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::addmm 69328.0μs (100%) calls=1
#2: aten::t 113.0μs (0%) calls=1
#3: aten::transpose 56.0μs (0%) calls=1
#4: aten::as_strided 15.0μs (0%) calls=1
--- Timing ---
GPU time: 2689.66 μs [↓38.9μs ok]
--- Memory Bandwidth ---
DRAM BW: 2082.2/3350 GB/s (62.2%) [↓12.0% REGRESSED]
read=1590.4 write=491.8 GB/s
Hit rates: L2=146.9%
--- Compute Throughput ---
Dominant pipe: FP32 util=92.2% [↑4.7% ok]
Pipe util: FMA=92.2%, Tensor=0.3%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 100.1% [↓3.0% REGRESSED]
Eligible warps/cycle: 2.94
Launch: regs/thread=286, smem/blk=35840B, block=384, grid=2560
--- Memory Access Quality ---
Global ld coalescing: 8.00 sectors/req (8.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 2177.9% ####################
mio_throttle 276.1% ####################
short_scoreboard 193.4% ####################
--- Optimization Hints ---
-> 2178% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=8.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=8.0x (ideal=1.0) -> 8x 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: 2728.61→2689.66μs (↓38.94) ok
DRAM BW%: 74.20→62.15% (↓12.05) REGRESSED
Compute%: 87.44→92.18% (↑4.74) ok
Occupancy: 103.11→100.11% (↓3.00) REGRESSED
Eligible warps/cyc: 1.89→2.94 (↑1.06) ok
L2 hit%: 160.05→146.89% (↓13.16) REGRESSED
turn 1212.9s turn · 12.9s LLM · 0.0s tools
call → static_check
callstatic_check
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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>\nstatic __inline__ __device__ float warp_reduce_sum(float val){unsigned mask=0xffffffffu; val+=__shfl_down_sync(mask,val,16); val+=__shfl_down_sync(mask,val,8); val+=__shfl_down_sync(mask,val,4); val+=__shfl_down_sync(mask,val,2); val+=__shfl_down_sync(mask,val,1); return val;}\n__global__ void gn_ht_inplace_kernel(float* __restrict__ x,const float* __restrict__ gamma,const float* __restrict__ beta,int N,int C,int G,int group_size,float ht_min,float ht_max){\n int tid=threadIdx.x,lane=tid&31,warp=tid>>5; int group_idx=blockIdx.x*8+warp,total=N*G; if(group_idx>=total) return; int n=group_idx/G,g=group_idx-n*G; int base=n*C+g*group_size,cbase=g*group_size; float sum=0.f,ssq=0.f;\n for(int i=lane;i<group_size;i+=32){float v=x[base+i]; sum+=v; ssq=fmaf(v,v,ssq);} sum=warp_reduce_sum(sum); ssq=warp_reduce_sum(ssq); float mean=__shfl_sync(0xffffffffu,sum,0)/(float)group_size; float var=__shfl_sync(0xffffffffu,ssq,0)/(float)group_size-mean*mean; float inv=rsqrtf(fmaxf(var,0.f)+1.0e-5f);\n for(int i=lane;i<group_size;i+=32){float v=x[base+i]; float y=(v-mean)*inv; y=fmaf(y,gamma[cbase+i],beta[cbase+i]); y=fminf(fmaxf(y,ht_min),ht_max); x[base+i]=y;}\n}\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x,torch::Tensor gamma,torch::Tensor beta,int64_t num_groups,double ht_min,double ht_max){int N=(int)x.size(0),C=(int)x.size(1),G=(int)num_groups; int gs=C/G,total=N*G; gn_ht_inplace_kernel<<<(total+7)/8,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),gamma.data_ptr<float>(),beta.data_ptr<float>(),N,C,G,gs,(float)ht_min,(float)ht_max); return x;}\n'''\n_ext=load_inline(name='gn_ht_ext_final',cpp_sources=[_cpp_src],cuda_sources=[_cuda_src],functions=['gn_hardtanh_inplace'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features,num_groups,hardtanh_min,hardtanh_max):\n super().__init__(); self.gemm=nn.Linear(in_features,out_features); self.group_norm=nn.GroupNorm(num_groups,out_features); self.num_groups=num_groups; self.hardtanh_min=float(hardtanh_min); self.hardtanh_max=float(hardtanh_max)\n def forward(self,x):\n y=torch.addmm(self.gemm.bias, x, self.gemm.weight.t())\n return _ext.gn_hardtanh_inplace(y,self.group_norm.weight,self.group_norm.bias,self.num_groups,self.hardtanh_min,self.hardtanh_max)\n"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 136.5s turn · 6.5s LLM · 0.0s tools · FINAL
call → submit_kernel
callsubmit_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);\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>\nstatic __inline__ __device__ float warp_reduce_sum(float val){unsigned mask=0xffffffffu; val+=__shfl_down_sync(mask,val,16); val+=__shfl_down_sync(mask,val,8); val+=__shfl_down_sync(mask,val,4); val+=__shfl_down_sync(mask,val,2); val+=__shfl_down_sync(mask,val,1); return val;}\n__global__ void gn_ht_inplace_kernel(float* __restrict__ x,const float* __restrict__ gamma,const float* __restrict__ beta,int N,int C,int G,int group_size,float ht_min,float ht_max){\n int tid=threadIdx.x,lane=tid&31,warp=tid>>5; int group_idx=blockIdx.x*8+warp,total=N*G; if(group_idx>=total) return; int n=group_idx/G,g=group_idx-n*G; int base=n*C+g*group_size,cbase=g*group_size; float sum=0.f,ssq=0.f;\n for(int i=lane;i<group_size;i+=32){float v=x[base+i]; sum+=v; ssq=fmaf(v,v,ssq);} sum=warp_reduce_sum(sum); ssq=warp_reduce_sum(ssq); float mean=__shfl_sync(0xffffffffu,sum,0)/(float)group_size; float var=__shfl_sync(0xffffffffu,ssq,0)/(float)group_size-mean*mean; float inv=rsqrtf(fmaxf(var,0.f)+1.0e-5f);\n for(int i=lane;i<group_size;i+=32){float v=x[base+i]; float y=(v-mean)*inv; y=fmaf(y,gamma[cbase+i],beta[cbase+i]); y=fminf(fmaxf(y,ht_min),ht_max); x[base+i]=y;}\n}\ntorch::Tensor gn_hardtanh_inplace(torch::Tensor x,torch::Tensor gamma,torch::Tensor beta,int64_t num_groups,double ht_min,double ht_max){int N=(int)x.size(0),C=(int)x.size(1),G=(int)num_groups; int gs=C/G,total=N*G; gn_ht_inplace_kernel<<<(total+7)/8,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),gamma.data_ptr<float>(),beta.data_ptr<float>(),N,C,G,gs,(float)ht_min,(float)ht_max); return x;}\n'''\n_ext=load_inline(name='gn_ht_ext_final',cpp_sources=[_cpp_src],cuda_sources=[_cuda_src],functions=['gn_hardtanh_inplace'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features,num_groups,hardtanh_min,hardtanh_max):\n super().__init__(); self.gemm=nn.Linear(in_features,out_features); self.group_norm=nn.GroupNorm(num_groups,out_features); self.num_groups=num_groups; self.hardtanh_min=float(hardtanh_min); self.hardtanh_max=float(hardtanh_max)\n def forward(self,x):\n y=torch.addmm(self.gemm.bias, x, self.gemm.weight.t())\n return _ext.gn_hardtanh_inplace(y,self.group_norm.weight,self.group_norm.bias,self.num_groups,self.hardtanh_min,self.hardtanh_max)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 2.77 μs
Runtime stats: mean=2.77μs median=?μs std=0.27μs
Numerical precision: max_abs_err=5.96e-07 mean_abs_err=1.90e-08 max_rel_err=5.00e-01
Memory: 609.19 MB peak (ref: 641.31 MB, ratio: 0.9499x)
Kernel launches: 4 (ref: 22, fusion ratio: 5.5)
Energy: 1228.62 mJ/run (ref: 2649.86 mJ, ratio: 2.1568x)
SOL score: 0.9247
final submitted kernel (28 lines, 2,704 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 gn_hardtanh_inplace(torch::Tensor x, torch::Tensor gamma, torch::Tensor beta, int64_t num_groups, double ht_min, double ht_max);
'''
_cuda_src=r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
static __inline__ __device__ float warp_reduce_sum(float val){unsigned mask=0xffffffffu; val+=__shfl_down_sync(mask,val,16); val+=__shfl_down_sync(mask,val,8); val+=__shfl_down_sync(mask,val,4); val+=__shfl_down_sync(mask,val,2); val+=__shfl_down_sync(mask,val,1); return val;}
__global__ void gn_ht_inplace_kernel(float* __restrict__ x,const float* __restrict__ gamma,const float* __restrict__ beta,int N,int C,int G,int group_size,float ht_min,float ht_max){
int tid=threadIdx.x,lane=tid&31,warp=tid>>5; int group_idx=blockIdx.x*8+warp,total=N*G; if(group_idx>=total) return; int n=group_idx/G,g=group_idx-n*G; int base=n*C+g*group_size,cbase=g*group_size; float sum=0.f,ssq=0.f;
for(int i=lane;i<group_size;i+=32){float v=x[base+i]; sum+=v; ssq=fmaf(v,v,ssq);} sum=warp_reduce_sum(sum); ssq=warp_reduce_sum(ssq); float mean=__shfl_sync(0xffffffffu,sum,0)/(float)group_size; float var=__shfl_sync(0xffffffffu,ssq,0)/(float)group_size-mean*mean; float inv=rsqrtf(fmaxf(var,0.f)+1.0e-5f);
for(int i=lane;i<group_size;i+=32){float v=x[base+i]; float y=(v-mean)*inv; y=fmaf(y,gamma[cbase+i],beta[cbase+i]); y=fminf(fmaxf(y,ht_min),ht_max); x[base+i]=y;}
}
torch::Tensor gn_hardtanh_inplace(torch::Tensor x,torch::Tensor gamma,torch::Tensor beta,int64_t num_groups,double ht_min,double ht_max){int N=(int)x.size(0),C=(int)x.size(1),G=(int)num_groups; int gs=C/G,total=N*G; gn_ht_inplace_kernel<<<(total+7)/8,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),gamma.data_ptr<float>(),beta.data_ptr<float>(),N,C,G,gs,(float)ht_min,(float)ht_max); return x;}
'''
_ext=load_inline(name='gn_ht_ext_final',cpp_sources=[_cpp_src],cuda_sources=[_cuda_src],functions=['gn_hardtanh_inplace'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)
class ModelNew(nn.Module):
def __init__(self,in_features,out_features,num_groups,hardtanh_min,hardtanh_max):
super().__init__(); self.gemm=nn.Linear(in_features,out_features); self.group_norm=nn.GroupNorm(num_groups,out_features); self.num_groups=num_groups; self.hardtanh_min=float(hardtanh_min); self.hardtanh_max=float(hardtanh_max)
def forward(self,x):
y=torch.addmm(self.gemm.bias, x, self.gemm.weight.t())
return _ext.gn_hardtanh_inplace(y,self.group_norm.weight,self.group_norm.bias,self.num_groups,self.hardtanh_min,self.hardtanh_max)