correct
1_DilatedConvTower_GenomicBlock.py
started
2026-05-04T15:55:39Z
timingkernelbench.timing
kernel mean / median / std
25.80 / — / 1.37 μs
ref mean / median / std
26.00 / — / 1.53 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
1.10x
energyNVML (pynvml)
ratio (ref / kernel)
1.00x
SOL (speed-of-light)heuristic_from_speedup
roofline / occupancyheuristic
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Dilated convolution tower with exponentially increasing dilation rates and residual connections.
Each block applies LayerNorm -> GELU -> DilatedConv1d, then adds back the original input as a residual.
This architecture is a core building block of genomic foundation models (Enformer, Basenji, Caduceus)
for capturing multi-scale sequence dependencies in DNA/RNA.
"""
def __init__(self, channels, seq_len, kernel_size, max_dilation_power):
super(Model, self).__init__()
self.norms = nn.ModuleList()
self.convs = nn.ModuleList()
self.activation = nn.GELU()
for i in range(max_dilation_power + 1):
dilation = 2 ** i
self.norms.append(nn.LayerNorm([channels, seq_len]))
self.convs.append(nn.Conv1d(channels, channels, kernel_size, padding=dilation, dilation=dilation))
def forward(self, x):
residual = x
for norm, conv in zip(self.norms, self.convs):
x = norm(x)
x = self.activation(x)
x = conv(x)
x = x + residual
return x
batch_size = 32
channels = 256
seq_len = 4096
kernel_size = 3
max_dilation_power = 8
def get_inputs():
return [torch.randn(popcorn_pri.jitter_int(batch_size), channels, seq_len)]
def get_init_inputs():
return [channels, seq_len, kernel_size, max_dilation_power]turn 020.8s turn · 20.8s 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 164.6s turn · 64.6s LLM · 0.0s tools
reasoning
**Considering activation functions**
I'm thinking about how to combine statistics and apply them effectively. Also, the LayerNorm weights are structured as [C,L] per block and apply elementwise, which is important if x is contiguous. I wonder about the GELU function — is it exact? The default nn.GELU() uses the exact erf rather than an approximate version. I should make sure to utilize the exact form: 0.5*x*(1+erf(x/sqrt2)).
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\ntorch::Tensor ln_gelu_cuda(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\ntorch::Tensor ln_gelu(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n return ln_gelu_cuda(x, weight, bias);\n}\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <ATen/cuda/CUDAContext.h>\n\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" must be a CUDA tensor\")\n#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x \" must be contiguous\")\n\n__inline__ __device__ float warp_sum(float v) {\n for (int offset = 16; offset > 0; offset >>= 1) v += __shfl_down_sync(0xffffffff, v, offset);\n return v;\n}\n\n__inline__ __device__ float block_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_sum(v);\n return v;\n}\n\n__global__ void stats_kernel(const float* __restrict__ x, float* __restrict__ mean, float* __restrict__ rstd, int N) {\n int b = blockIdx.x;\n const float* xb = x + ((long long)b) * N;\n float sum = 0.0f;\n float ss = 0.0f;\n for (int i = threadIdx.x; i < N; i += blockDim.x) {\n float v = xb[i];\n sum += v;\n ss = fmaf(v, v, ss);\n }\n sum = block_sum(sum);\n ss = block_sum(ss);\n if (threadIdx.x == 0) {\n float m = sum / (float)N;\n float var = ss / (float)N - m * m;\n var = var > 0.0f ? var : 0.0f;\n mean[b] = m;\n rstd[b] = rsqrtf(var + 1.0e-5f);\n }\n}\n\n__global__ void apply_gelu_kernel(const float* __restrict__ x, const float* __restrict__ w, const float* __restrict__ beta,\n const float* __restrict__ mean, const float* __restrict__ rstd,\n float* __restrict__ y, long long total, int N) {\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n const float k = 0.70710678118654752440f;\n for (; idx < total; idx += (long long)blockDim.x * gridDim.x) {\n int b = (int)(idx / N);\n int j = (int)(idx - (long long)b * N);\n float z = (x[idx] - mean[b]) * rstd[b];\n z = z * w[j] + beta[j];\n y[idx] = 0.5f * z * (1.0f + erff(z * k));\n }\n}\n\ntorch::Tensor ln_gelu_cuda(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n CHECK_CUDA(x); CHECK_CUDA(weight); CHECK_CUDA(bias);\n CHECK_CONTIGUOUS(x); CHECK_CONTIGUOUS(weight); CHECK_CONTIGUOUS(bias);\n TORCH_CHECK(x.scalar_type() == at::kFloat, \"x must be float\");\n int B = x.size(0);\n int C = x.size(1);\n int L = x.size(2);\n int N = C * L;\n auto y = torch::empty_like(x);\n auto opts = x.options().dtype(torch::kFloat32);\n auto mean = torch::empty({B}, opts);\n auto rstd = torch::empty({B}, opts);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n stats_kernel<<<B, 256, 0, stream>>>(x.data_ptr<float>(), mean.data_ptr<float>(), rstd.data_ptr<float>(), N);\n long long total = (long long)B * N;\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n apply_gelu_kernel<<<blocks, threads, 0, stream>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), mean.data_ptr<float>(), rstd.data_ptr<float>(), y.data_ptr<float>(), total, N);\n return y;\n}\n'''\n\n_ext = load_inline(name='ln_gelu_ext_v1', cpp_sources=[cpp_source], cuda_sources=[cuda_source], functions=['ln_gelu'], verbose=False, extra_cuda_cflags=['-O3', '--use_fast_math'], extra_cflags=['-O3'])\n\nclass ModelNew(nn.Module):\n def __init__(self, channels, seq_len, kernel_size, max_dilation_power):\n super(ModelNew, self).__init__()\n self.norms = nn.ModuleList()\n self.convs = nn.ModuleList()\n for i in range(max_dilation_power + 1):\n dilation = 2 ** i\n self.norms.append(nn.LayerNorm([channels, seq_len]))\n self.convs.append(nn.Conv1d(channels, channels, kernel_size, padding=dilation, dilation=dilation))\n def forward(self, x):\n residual = x\n for norm, conv in zip(self.norms, self.convs):\n x = _ext.ln_gelu(x.contiguous(), norm.weight, norm.bias)\n x = conv(x)\n x = x + residual\n return x\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 29.6s turn · 9.6s 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\n\ncpp_source = r'''\n#include <torch/extension.h>\ntorch::Tensor ln_gelu_cuda(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\ntorch::Tensor ln_gelu(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n return ln_gelu_cuda(x, weight, bias);\n}\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <ATen/cuda/CUDAContext.h>\n\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" must be a CUDA tensor\")\n#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x \" must be contiguous\")\n\n__inline__ __device__ float warp_sum(float v) {\n for (int offset = 16; offset > 0; offset >>= 1) v += __shfl_down_sync(0xffffffff, v, offset);\n return v;\n}\n\n__inline__ __device__ float block_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_sum(v);\n return v;\n}\n\n__global__ void stats_kernel(const float* __restrict__ x, float* __restrict__ mean, float* __restrict__ rstd, int N) {\n int b = blockIdx.x;\n const float* xb = x + ((long long)b) * N;\n float sum = 0.0f;\n float ss = 0.0f;\n for (int i = threadIdx.x; i < N; i += blockDim.x) {\n float v = xb[i];\n sum += v;\n ss = fmaf(v, v, ss);\n }\n sum = block_sum(sum);\n ss = block_sum(ss);\n if (threadIdx.x == 0) {\n float m = sum / (float)N;\n float var = ss / (float)N - m * m;\n var = var > 0.0f ? var : 0.0f;\n mean[b] = m;\n rstd[b] = rsqrtf(var + 1.0e-5f);\n }\n}\n\n__global__ void apply_gelu_kernel(const float* __restrict__ x, const float* __restrict__ w, const float* __restrict__ beta,\n const float* __restrict__ mean, const float* __restrict__ rstd,\n float* __restrict__ y, long long total, int N) {\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n const float k = 0.70710678118654752440f;\n for (; idx < total; idx += (long long)blockDim.x * gridDim.x) {\n int b = (int)(idx / N);\n int j = (int)(idx - (long long)b * N);\n float z = (x[idx] - mean[b]) * rstd[b];\n z = z * w[j] + beta[j];\n y[idx] = 0.5f * z * (1.0f + erff(z * k));\n }\n}\n\ntorch::Tensor ln_gelu_cuda(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n CHECK_CUDA(x); CHECK_CUDA(weight); CHECK_CUDA(bias);\n CHECK_CONTIGUOUS(x); CHECK_CONTIGUOUS(weight); CHECK_CONTIGUOUS(bias);\n TORCH_CHECK(x.scalar_type() == at::kFloat, \"x must be float\");\n int B = x.size(0);\n int C = x.size(1);\n int L = x.size(2);\n int N = C * L;\n auto y = torch::empty_like(x);\n auto opts = x.options().dtype(torch::kFloat32);\n auto mean = torch::empty({B}, opts);\n auto rstd = torch::empty({B}, opts);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n stats_kernel<<<B, 256, 0, stream>>>(x.data_ptr<float>(), mean.data_ptr<float>(), rstd.data_ptr<float>(), N);\n long long total = (long long)B * N;\n int threads = 256;\n int blocks = (int)((total + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n apply_gelu_kernel<<<blocks, threads, 0, stream>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), mean.data_ptr<float>(), rstd.data_ptr<float>(), y.data_ptr<float>(), total, N);\n return y;\n}\n'''\n\n_ext = load_inline(name='ln_gelu_ext_v1', cpp_sources=[cpp_source], cuda_sources=[cuda_source], functions=['ln_gelu'], verbose=False, extra_cuda_cflags=['-O3', '--use_fast_math'], extra_cflags=['-O3'])\n\nclass ModelNew(nn.Module):\n def __init__(self, channels, seq_len, kernel_size, max_dilation_power):\n super(ModelNew, self).__init__()\n self.norms = nn.ModuleList()\n self.convs = nn.ModuleList()\n for i in range(max_dilation_power + 1):\n dilation = 2 ** i\n self.norms.append(nn.LayerNorm([channels, seq_len]))\n self.convs.append(nn.Conv1d(channels, channels, kernel_size, padding=dilation, dilation=dilation))\n def forward(self, x):\n residual = x\n for norm, conv in zip(self.norms, self.convs):\n x = _ext.ln_gelu(x.contiguous(), norm.weight, norm.bias)\n x = conv(x)\n x = x + residual\n return x\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.000415', '0.000420', '0.000437', '0.000406', '0.000386']
avg_difference: ['0.000046', '0.000046', '0.000046', '0.000046', '0.000046']
turn 312.2s turn · 12.2s LLM · 0.0s tools
reasoning
**Refining calculations for layer normalization**
I need to ensure accuracy in my calculations involving layer normalization. There could be discrepancies due to using fast math approximations. So, I should remove the `--use_fast_math` and utilize the exact `erf` function. I’m considering if I should switch to PyTorch’s native `layer_norm`.
To boost performance, it might help to create a custom CUDA kernel for the GELU function. I'll also explore using Welford for better precision with tolerances across blocks. Reducing fast math could improve it all.
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_source = r'''\n#include <torch/extension.h>\ntorch::Tensor ln_gelu_cuda(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\ntorch::Tensor ln_gelu(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) { return ln_gelu_cuda(x, weight, bias); }\n'''\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <ATen/cuda/CUDAContext.h>\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" must be CUDA\")\n#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x \" must be contiguous\")\n__inline__ __device__ float warp_sum(float v){ for(int o=16;o>0;o>>=1) v += __shfl_down_sync(0xffffffff,v,o); return v; }\n__inline__ __device__ float block_sum(float v){ __shared__ float s[32]; int lane=threadIdx.x&31,wid=threadIdx.x>>5; v=warp_sum(v); if(lane==0) s[wid]=v; __syncthreads(); v=(threadIdx.x<(blockDim.x>>5))?s[lane]:0.0f; if(wid==0) v=warp_sum(v); return v; }\n__global__ void stats_kernel(const float* __restrict__ x,float* __restrict__ mean,float* __restrict__ rstd,int N){ int b=blockIdx.x; const float* xb=x+(long long)b*N; float sum=0,ss=0; for(int i=threadIdx.x;i<N;i+=blockDim.x){ float v=xb[i]; sum += v; ss += v*v;} sum=block_sum(sum); ss=block_sum(ss); if(threadIdx.x==0){ float m=sum/(float)N; float var=ss/(float)N-m*m; if(var<0) var=0; mean[b]=m; rstd[b]=rsqrtf(var+1e-5f);} }\n__global__ void apply_gelu_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ beta,const float* __restrict__ mean,const float* __restrict__ rstd,float* __restrict__ y,long long total,int N){ long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x; const float k=0.70710678118654752440f; for(;idx<total;idx+=(long long)blockDim.x*gridDim.x){ int b=(int)(idx/N); int j=(int)(idx-(long long)b*N); float z=(x[idx]-mean[b])*rstd[b]; z=z*w[j]+beta[j]; y[idx]=0.5f*z*(1.0f+erff(z*k)); } }\ntorch::Tensor ln_gelu_cuda(torch::Tensor x,torch::Tensor weight,torch::Tensor bias){ CHECK_CUDA(x);CHECK_CUDA(weight);CHECK_CUDA(bias);CHECK_CONTIGUOUS(x);CHECK_CONTIGUOUS(weight);CHECK_CONTIGUOUS(bias); int B=x.size(0),N=x.size(1)*x.size(2); auto y=torch::empty_like(x); auto mean=torch::empty({B},x.options()); auto rstd=torch::empty({B},x.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); stats_kernel<<<B,256,0,stream>>>(x.data_ptr<float>(),mean.data_ptr<float>(),rstd.data_ptr<float>(),N); long long total=(long long)B*N; int threads=256; int blocks=(int)((total+threads-1)/threads); if(blocks>65535) blocks=65535; apply_gelu_kernel<<<blocks,threads,0,stream>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),mean.data_ptr<float>(),rstd.data_ptr<float>(),y.data_ptr<float>(),total,N); return y; }\n'''\n_ext = load_inline(name='ln_gelu_ext_v2', cpp_sources=[cpp_source], cuda_sources=[cuda_source], functions=['ln_gelu'], verbose=False, extra_cuda_cflags=['-O3'], extra_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self, channels, seq_len, kernel_size, max_dilation_power):\n super().__init__(); self.norms=nn.ModuleList(); self.convs=nn.ModuleList()\n for i in range(max_dilation_power+1):\n d=2**i; self.norms.append(nn.LayerNorm([channels,seq_len])); self.convs.append(nn.Conv1d(channels,channels,kernel_size,padding=d,dilation=d))\n def forward(self,x):\n residual=x\n for norm,conv in zip(self.norms,self.convs):\n x=_ext.ln_gelu(x.contiguous(),norm.weight,norm.bias)\n x=conv(x); x=x+residual\n return x\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.000415', '0.000420', '0.000437', '0.000406', '0.000386']
avg_difference: ['0.000046', '0.000046', '0.000046', '0.000046', '0.000046']
turn 49.4s turn · 9.4s LLM · 0.0s tools
call → run_correctness
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 55.6s turn · 5.6s LLM · 0.0s tools
reasoning
**Optimizing speed**
I’m considering whether to profile or submit, maybe even both. It seems like I need to focus on optimizing further. Profiling the gelu version could help me identify where to improve speed. I'm curious about what will give me the best results, as I want to make sure I'm getting everything just right for the user. Let's see how I can make this process more efficient!
call → profile_kernel
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv1d 614930.0μs (23%) calls=1
#2: aten::convolution 614894.0μs (23%) calls=1
#3: aten::_convolution 614860.0μs (23%) calls=1
#4: aten::cudnn_convolution 606761.0μs (23%) calls=1
#5: aten::layer_norm 63556.0μs (2%) calls=1
... +202 more kernels
--- Timing ---
GPU time: 25013.41 μs
--- Memory Bandwidth ---
DRAM BW: 115662.4/3350 GB/s (100.0%)
read=67503.1 write=48159.3 GB/s
Hit rates: L2=4126.4%
--- Compute Throughput ---
Dominant pipe: tensor-core util=719.0%
Pipe util: FMA=812.3%, Tensor=719.0%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 4389.5%
Eligible warps/cycle: 68.33
Launch: regs/thread=3699, smem/blk=1420160B, block=13572, grid=2519803
--- Memory Access Quality ---
Global ld coalescing: 552.78 sectors/req (552.8x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 324314.4% ####################
short_scoreboard 29980.7% ####################
mio_throttle 25333.5% ####################
--- Optimization Hints ---
-> 324314% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=552.8 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=552.8x (ideal=1.0) -> 553x wasted DRAM BW. Make per-warp accesses contiguous.
turn 617.7s turn · 17.7s LLM · 0.0s tools
reasoning
**Testing model speed**
I'm wondering if DNN might be faster, but I need to check this with ModelNew using conv2d in the forward pass. I think I might need to use conv.weight and unsqueeze it to dimension 2. It's possible I could call torch.conv2d for this. I'm not planning to create any custom kernels except for gelu. Also, I might want to profile the performance results to see how everything is running. Let's see how this goes!
call → run_correctness
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 710.7s turn · 10.7s LLM · 0.0s tools
call → profile_kernel
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::layer_norm 351634.0μs (19%) calls=1
#2: aten::native_layer_norm 351593.0μs (19%) calls=1
#3: aten::empty 341658.0μs (18%) calls=1
#4: aten::conv2d 174588.0μs (9%) calls=1
#5: aten::convolution 174564.0μs (9%) calls=1
... +202 more kernels
--- Timing ---
GPU time: 24993.79 μs [↓19.6μs ok]
--- Memory Bandwidth ---
DRAM BW: 115670.7/3350 GB/s (100.0%)
read=67479.8 write=48190.9 GB/s
Hit rates: L2=4133.4%
--- Compute Throughput ---
Dominant pipe: tensor-core util=720.5% [↑1.5% ok]
Pipe util: FMA=810.2%, Tensor=720.5%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 4391.6% [↑2.1% ok]
Eligible warps/cycle: 68.14
Launch: regs/thread=3699, smem/blk=1420160B, block=13572, grid=2519803
--- Memory Access Quality ---
Global ld coalescing: 552.96 sectors/req (553.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 320935.3% ####################
short_scoreboard 30348.6% ####################
mio_throttle 26199.2% ####################
--- Optimization Hints ---
-> 320935% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=553.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=553.0x (ideal=1.0) -> 553x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 25013.41→24993.79μs (↓19.62) ok
Compute%: 719.00→720.46% (↑1.46) ok
Occupancy: 4389.50→4391.63% (↑2.13) ok
Eligible warps/cyc: 68.33→68.14 (↓0.19) REGRESSED
Ld sectors/req: 552.78→552.96 (↑0.18) REGRESSED
L2 hit%: 4126.36→4133.37% (↑7.01) ok
turn 812.0s turn · 12.0s LLM · 0.0s tools
reasoning
**Evaluating normalization functions**
I’m thinking about how the native layer normalization compares to the torch norm. It seems they’re the same! I also need to consider if using in-place GELU on the normalized output is appropriate. It’s important for efficiency, so I’m curious if y being contiguous matters here. This might become an extension of a custom function for optimization. I should proceed to compile and make any necessary corrections.
call → run_correctness
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 95.8s turn · 5.8s LLM · 0.0s tools
call → profile_kernel
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv1d 180720.0μs (20%) calls=1
#2: aten::convolution 180680.0μs (20%) calls=1
#3: aten::_convolution 180641.0μs (20%) calls=1
#4: aten::cudnn_convolution 172353.0μs (19%) calls=1
#5: aten::native_layer_norm 96811.0μs (10%) calls=1
... +175 more kernels
--- Timing ---
GPU time: 24975.84 μs [↓18.0μs ok]
--- Memory Bandwidth ---
DRAM BW: 115718.1/3350 GB/s (100.0%)
read=67744.8 write=47973.4 GB/s
Hit rates: L2=4120.7%
--- Compute Throughput ---
Dominant pipe: tensor-core util=720.8% [↑0.4% ok]
Pipe util: FMA=779.1%, Tensor=720.8%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 4391.7% [↑0.1% ok]
Eligible warps/cycle: 66.08
Launch: regs/thread=3681, smem/blk=1420160B, block=13572, grid=2519803
--- Memory Access Quality ---
Global ld coalescing: 552.81 sectors/req (552.8x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 323428.6% ####################
short_scoreboard 30145.3% ####################
mio_throttle 26167.8% ####################
--- Optimization Hints ---
-> 323429% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=552.8 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=552.8x (ideal=1.0) -> 553x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 24993.79→24975.84μs (↓17.95) ok
Compute%: 720.46→720.84% (↑0.38) ok
Occupancy: 4391.63→4391.73% (↑0.10) ok
Eligible warps/cyc: 68.14→66.08 (↓2.07) REGRESSED
Ld sectors/req: 552.96→552.81 (↓0.15) ok
L2 hit%: 4133.37→4120.68% (↓12.70) REGRESSED
turn 106.5s turn · 6.5s LLM · 0.0s tools
call → run_correctness
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 115.4s turn · 5.4s LLM · 0.0s tools
call → profile_kernel
result ← profile_kernel (fail)
failprofile_kernel
profile_kernel FAILED: unexpected error during tool execution.
_WorkItemTimeout: work item exceeded soft timeout budget
Traceback (most recent call last):
File "/scratch/abaner/KernelBench/src/kernelbench/agent/agent.py", line 627, in _execute_tool
return tool.execute(self.ctx, **args)
File "/scratch/abaner/KernelBench/src/kernelbench/agent/tools.py", line 494, in execute
return ctx.eval_client.profile_kernel(ctx, kernel_code)
File "/scratch/abaner/KernelBench/src/kernelbench/agent/eval_client.py", line 78, in profile_kernel
result, new_summary = self._call("profile", args, return_aux=True)
File "/scratch/abaner/KernelBench/src/kernelbench/agent/eval_client.py", line 117, in _call
payload = self._response_q.get(timeout=remaining)
File "<string>", line 2, in get
File "/opt/conda/lib/python3.10/multiprocessing/managers.py", line 818, in _callmethod
kind, result = conn.recv()
File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 250, in recv
buf = self._recv_bytes()
File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 414, in _recv_bytes
buf = self._recv(4)
File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 379, in _recv
chunk = read(handle, remaining)
File "/scratch/abaner/KernelBench/scripts/run_sweep.py", line 161, in _alarm_handler
raise _WorkItemTimeout("work item exceeded soft timeout budget")
__mp_main__._WorkItemTimeout: work item exceeded soft timeout budget
turn 12877.4s turn · 877.4s LLM · 0.0s tools
call → static_check
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 1313.7s turn · 13.7s LLM · 0.0s tools · FINAL
call → submit_kernel
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 25.80 μs
Runtime stats: mean=25.80μs median=?μs std=1.37μs
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
Memory: 832.52 MB peak (ref: 832.52 MB, ratio: 1.0x)
Kernel launches: 180 (ref: 198, fusion ratio: 1.1)
Energy: 3633.42 mJ/run (ref: 3633.76 mJ, ratio: 1.0001x)
SOL score: 0.1008
final submitted kernel (72 lines, 2,194 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
cpp_source = r'''
#include <torch/extension.h>
#include <ATen/ATen.h>
torch::Tensor gelu_inplace_cuda(torch::Tensor y);
torch::Tensor ln_gelu(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {
int64_t C = x.size(1);
int64_t L = x.size(2);
auto tup = at::native_layer_norm(x, {C, L}, weight, bias, 1.0e-5);
auto y = std::get<0>(tup);
return gelu_inplace_cuda(y);
}
'''
cuda_source = r'''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <ATen/cuda/CUDAContext.h>
__global__ void gelu_inplace_kernel(float* __restrict__ y, long long n) {
long long i = (long long)blockIdx.x * blockDim.x + threadIdx.x;
const float k = 0.70710678118654752440f;
for (; i < n; i += (long long)blockDim.x * gridDim.x) {
float z = y[i];
y[i] = 0.5f * z * (1.0f + erff(z * k));
}
}
torch::Tensor gelu_inplace_cuda(torch::Tensor y) {
long long n = y.numel();
int threads = 256;
int blocks = (int)((n + threads - 1) / threads);
if (blocks > 65535) blocks = 65535;
gelu_inplace_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n);
return y;
}
'''
_ext = load_inline(
name='ln_gelu_inplace_ext_final',
cpp_sources=[cpp_source],
cuda_sources=[cuda_source],
functions=['ln_gelu'],
verbose=False,
extra_cuda_cflags=['-O3'],
extra_cflags=['-O3'],
)
class ModelNew(nn.Module):
def __init__(self, channels, seq_len, kernel_size, max_dilation_power):
super(ModelNew, self).__init__()
self.norms = nn.ModuleList()
self.convs = nn.ModuleList()
for i in range(max_dilation_power + 1):
dilation = 2 ** i
self.norms.append(nn.LayerNorm([channels, seq_len]))
self.convs.append(nn.Conv1d(channels, channels, kernel_size, padding=dilation, dilation=dilation))
def forward(self, x):
residual = x
for norm, conv in zip(self.norms, self.convs):
x = _ext.ln_gelu(x, norm.weight, norm.bias)
x = conv(x)
x.add_(residual)
return x