correct
39_Fused1DTemporalConvolution.py
started
2026-05-04T06:58:48Z
timingkernelbench.timing
kernel mean / median / std
0.04 / — / 5.84e-04 μs
ref mean / median / std
0.08 / — / 0.03 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
1.92x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
2367.96 GB/s
warp stalls (top 5)nsight
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
import torch.nn.functional as F
# Default ModelNew is PyTorch-only. run_and_check: use check_kernel=False with backend=cuda
# until the candidate file contains a real CUDA kernel (see kernel_static_checker).
class Model(nn.Module):
"""
**Fused depthwise–pointwise 1D temporal convolution** on ``(B, C, T)`` sequences.
Fuses a **temporal depthwise** ``k``-tap conv (per-channel along time) with
**SiLU** and a **pointwise** ``1×1`` mix—standard in temporal encoders for
proprio, audio, or token-wise time series in robotics / VLA backbones.
Padding uses dilated effective radius so length is unchanged for odd
``kernel_size``.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
dilation: int = 1,
):
super().__init__()
assert kernel_size % 2 == 1, "odd kernel_size keeps T unchanged with symmetric padding"
assert dilation >= 1
self.padding = ((kernel_size - 1) * dilation) // 2
self.dw = nn.Conv1d(
in_channels,
in_channels,
kernel_size,
padding=self.padding,
dilation=dilation,
groups=in_channels,
bias=False,
)
self.pw = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: (B, C_in, T) temporal features.
Returns:
(B, C_out, T)
"""
x = self.dw(x)
x = F.silu(x)
return self.pw(x)
class ModelNew(nn.Module):
"""KernelBench candidate entry point; default delegates to ``Model``."""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
dilation: int = 1,
):
super().__init__()
self._impl = Model(in_channels, out_channels, kernel_size, dilation=dilation)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self._impl(x)
batch_size = 16
in_channels = 128
out_channels = 128
kernel_size = 5
dilation = 2
seq_len = 2048
def get_inputs():
p = popcorn_pri
x = torch.randn(p.jitter_int(batch_size), in_channels, p.jitter_int(seq_len))
return [x]
def get_init_inputs():
return [in_channels, out_channels, kernel_size, dilation]
turn 027.4s turn · 27.4s LLM · 0.0s tools
call → get_gpu_specs
callget_gpu_specs
{}
result ← get_gpu_specs (ok)
okget_gpu_specs
GPU specs for NVIDIA H100 80GB HBM3:
total memory (runtime): 79.1 GB
spec entry: H100
GPU Architecture: Hopper
GPU Memory: 80GB
Memory Bandwidth: 3.35 TB/s
FP64 TFLOPS: 34
FP64 Tensor Core TFLOPS: 67
FP32 TFLOPS: 67
TF32 Tensor Core TFLOPS: 989 with sparsity
BFLOAT16 Tensore Core TFLOPS: 1979 with sparsity
FP16 Tensor Core TFLOPS: 1979 with sparsity
FP8 Tensor Core TFLOPS: 3958 with sparsity
INT8 Tensor Core TOPS: 3958 with sparsity
Register File Size: 64K 32-bit registers per SM
Maximum number of registers per thread: 255
Maximum number of thread blocks per SM: 32
Shared memory capacity per SM: 228 KB
Maximum shared memory per thread block: 227 KB
turn 1 · 1 tool call(s) · 28.7s turn · 28.7s LLM · 8,841 chars (click to expand)
turn 128.7s turn · 28.7s LLM · 0.0s tools
reasoning
**Evaluating tile configurations**
I'm considering how to optimize my tile configurations. With BN at 32, I want to see about reducing the recompute factor to 4 and using blocks of 16x32. When it comes to threads, 512 seems too many if each output is just linked to one thread. What if each thread had multiple outputs? There’s also the idea of using BM16 with BN32, using 256 threads to compute 2 output columns while sharing dimensions for A and W. It's all a bit complex!
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\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\n#define BM 16\n#define BN 128\n#define BK 32\n\n__global__ void fused_dw_silu_pw_kernel(const float* __restrict__ x,\n const float* __restrict__ dw,\n const float* __restrict__ pw,\n float* __restrict__ out,\n int B, int C, int O, int T, int Ksz, int dilation) {\n int m0 = blockIdx.x * BM;\n int tid = threadIdx.x; // 0..255\n int row = tid >> 4; // 0..15\n int cg = tid & 15; // 0..15 groups of 8 output channels\n int obase = cg * 8;\n\n __shared__ float As[BM][BK];\n __shared__ float Ws[BK][BN];\n\n float acc[8];\n #pragma unroll\n for (int i=0;i<8;i++) acc[i] = 0.0f;\n\n int m = m0 + row;\n int b = m / T;\n int t = m - b * T;\n int half = Ksz >> 1;\n\n for (int k0=0; k0<C; k0 += BK) {\n // load activation tile: BM x BK = 512 elems; first 512/256=2 elems per thread\n int aidx = tid;\n #pragma unroll\n for (int l=0; l<2; ++l) {\n int idx = aidx + l*256;\n int rr = idx / BK;\n int kk = idx - rr*BK;\n int c = k0 + kk;\n int mm = m0 + rr;\n float av = 0.0f;\n if (mm < B*T && c < C) {\n int bb = mm / T;\n int tt0 = mm - bb*T;\n float s = 0.0f;\n for (int j=0; j<Ksz; ++j) {\n int tt = tt0 + (j - half) * dilation;\n if ((unsigned)tt < (unsigned)T) {\n s += x[(bb*C + c)*T + tt] * dw[(c*Ksz) + j];\n }\n }\n av = s / (1.0f + expf(-s));\n }\n As[rr][kk] = av;\n }\n // load weight tile: BK x BN = 4096 elems; 16 elems/thread\n for (int idx = tid; idx < BK*BN; idx += 256) {\n int kk = idx / BN;\n int oo = idx - kk*BN;\n int c = k0 + kk;\n float wv = 0.0f;\n if (c < C && oo < O) wv = pw[oo*C + c];\n Ws[kk][oo] = wv;\n }\n __syncthreads();\n\n if (m < B*T) {\n #pragma unroll\n for (int kk=0; kk<BK; ++kk) {\n float a = As[row][kk];\n #pragma unroll\n for (int i=0;i<8;i++) {\n int o = obase + i;\n if (o < O) acc[i] += a * Ws[kk][o];\n }\n }\n }\n __syncthreads();\n }\n\n if (m < B*T) {\n #pragma unroll\n for (int i=0;i<8;i++) {\n int o = obase + i;\n if (o < O) out[(b*O + o)*T + t] = acc[i];\n }\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor dw_w, torch::Tensor pw_w, int64_t dilation) {\n TORCH_CHECK(x.is_cuda(), \"x must be CUDA\");\n auto B = (int)x.size(0);\n auto C = (int)x.size(1);\n auto T = (int)x.size(2);\n auto O = (int)pw_w.size(0);\n auto Ksz = (int)dw_w.size(2);\n auto out = torch::empty({B, O, T}, x.options());\n int blocks = (B*T + BM - 1) / BM;\n dim3 grid(blocks);\n dim3 block(256);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n fused_dw_silu_pw_kernel<<<grid, block, 0, stream>>>(\n x.data_ptr<float>(), dw_w.data_ptr<float>(), pw_w.data_ptr<float>(), out.data_ptr<float>(),\n B, C, O, T, Ksz, (int)dilation);\n return out;\n}\n'''\n\n_ext = load_inline(name='fused_dw_silu_pw_ext_v1', cpp_sources='', cuda_sources=_cuda_src,\n functions=['fused_forward'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, kernel_size: int, dilation: int = 1):\n super().__init__()\n assert kernel_size % 2 == 1\n self.padding = ((kernel_size - 1) * dilation) // 2\n self.dilation = dilation\n self.dw = nn.Conv1d(in_channels, in_channels, kernel_size, padding=self.padding, dilation=dilation, groups=in_channels, bias=False)\n self.pw = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False)\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.fused_forward(x.contiguous(), self.dw.weight, self.pw.weight, self.dilation)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'fused_dw_silu_pw_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_dw_silu_pw_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_eb16e188dcb3/fused_dw_silu_pw_ext_v1/main.cpp -o main.o
FAILED: [code=1] main.o
/opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_dw_silu_pw_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_eb16e188dcb3/fused_dw_silu_pw_ext_v1/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_eb16e188dcb3/fused_dw_silu_pw_ext_v1/main.cpp: In function 'void pybind11_init_fused_dw_silu_pw_ext_v1(pybind11::module_&)':
/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_eb16e188dcb3/fused_dw_silu_pw_ext_v1/main.cpp:4:52: error: 'fused_forward' was not declared in this scope
4 | m.def("fused_forward", torch::wrap_pybind_function(fused_forward), "fused_forward");
| ^~~~~~~~~~~~~
[2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=fused_dw_silu_pw_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_eb16e188dcb3/fused_dw_silu_pw_ext_v1/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
ninja: build stopped: subcommand failed.
turn 212.8s turn · 12.8s LLM · 0.0s tools
reasoning
**Declaring in C++**
I’m realizing I need to add a declaration in the cpp_sources. Let me think about what prototype I need to include. It’s essential to have that correctly defined so everything compiles without any hiccups. I want to make sure I’ve got the right parameters and return types laid out, too, since those details matter a lot in C++. I guess taking a moment to double-check everything will save me trouble later!
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 = 'torch::Tensor fused_forward(torch::Tensor x, torch::Tensor dw_w, torch::Tensor pw_w, int64_t dilation);'\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\n#define BM 16\n#define BN 128\n#define BK 32\n\n__global__ void fused_dw_silu_pw_kernel(const float* __restrict__ x,\n const float* __restrict__ dw,\n const float* __restrict__ pw,\n float* __restrict__ out,\n int B, int C, int O, int T, int Ksz, int dilation) {\n int m0 = blockIdx.x * BM;\n int tid = threadIdx.x;\n int row = tid >> 4;\n int cg = tid & 15;\n int obase = cg * 8;\n __shared__ float As[BM][BK];\n __shared__ float Ws[BK][BN];\n float acc[8];\n #pragma unroll\n for (int i=0;i<8;i++) acc[i] = 0.0f;\n int m = m0 + row;\n int b = m / T;\n int t = m - b * T;\n int half = Ksz >> 1;\n for (int k0=0; k0<C; k0 += BK) {\n #pragma unroll\n for (int l=0; l<2; ++l) {\n int idx = tid + l*256;\n int rr = idx / BK;\n int kk = idx - rr*BK;\n int c = k0 + kk;\n int mm = m0 + rr;\n float av = 0.0f;\n if (mm < B*T && c < C) {\n int bb = mm / T;\n int tt0 = mm - bb*T;\n float s = 0.0f;\n for (int j=0; j<Ksz; ++j) {\n int tt = tt0 + (j - half) * dilation;\n if ((unsigned)tt < (unsigned)T) s += x[(bb*C + c)*T + tt] * dw[c*Ksz + j];\n }\n av = s / (1.0f + expf(-s));\n }\n As[rr][kk] = av;\n }\n for (int idx = tid; idx < BK*BN; idx += 256) {\n int kk = idx / BN;\n int oo = idx - kk*BN;\n int c = k0 + kk;\n Ws[kk][oo] = (c < C && oo < O) ? pw[oo*C + c] : 0.0f;\n }\n __syncthreads();\n if (m < B*T) {\n #pragma unroll\n for (int kk=0; kk<BK; ++kk) {\n float a = As[row][kk];\n #pragma unroll\n for (int i=0;i<8;i++) {\n int o = obase + i;\n if (o < O) acc[i] += a * Ws[kk][o];\n }\n }\n }\n __syncthreads();\n }\n if (m < B*T) {\n #pragma unroll\n for (int i=0;i<8;i++) {\n int o = obase + i;\n if (o < O) out[(b*O + o)*T + t] = acc[i];\n }\n }\n}\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor dw_w, torch::Tensor pw_w, int64_t dilation) {\n int B = (int)x.size(0), C = (int)x.size(1), T = (int)x.size(2), O = (int)pw_w.size(0), Ksz = (int)dw_w.size(2);\n auto out = torch::empty({B, O, T}, x.options());\n int blocks = (B*T + BM - 1) / BM;\n fused_dw_silu_pw_kernel<<<blocks, 256, 0, at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), dw_w.data_ptr<float>(), pw_w.data_ptr<float>(), out.data_ptr<float>(), B, C, O, T, Ksz, (int)dilation);\n return out;\n}\n'''\n_ext = load_inline(name='fused_dw_silu_pw_ext_v2', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['fused_forward'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, kernel_size: int, dilation: int = 1):\n super().__init__()\n assert kernel_size % 2 == 1\n self.padding = ((kernel_size - 1) * dilation) // 2\n self.dilation = dilation\n self.dw = nn.Conv1d(in_channels, in_channels, kernel_size, padding=self.padding, dilation=dilation, groups=in_channels, bias=False)\n self.pw = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False)\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.fused_forward(x.contiguous(), self.dw.weight, self.pw.weight, self.dilation)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 37.2s turn · 7.2s 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 = 'torch::Tensor fused_forward(torch::Tensor x, torch::Tensor dw_w, torch::Tensor pw_w, int64_t dilation);'\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#define BM 16\n#define BN 128\n#define BK 32\n__global__ void fused_dw_silu_pw_kernel(const float* __restrict__ x,const float* __restrict__ dw,const float* __restrict__ pw,float* __restrict__ out,int B,int C,int O,int T,int Ksz,int dilation){\n int m0=blockIdx.x*BM; int tid=threadIdx.x; int row=tid>>4; int cg=tid&15; int obase=cg*8;\n __shared__ float As[BM][BK]; __shared__ float Ws[BK][BN]; float acc[8];\n #pragma unroll\n for(int i=0;i<8;i++) acc[i]=0.0f;\n int m=m0+row; int b=m/T; int t=m-b*T; int half=Ksz>>1;\n for(int k0=0;k0<C;k0+=BK){\n #pragma unroll\n for(int l=0;l<2;++l){ int idx=tid+l*256; int rr=idx/BK; int kk=idx-rr*BK; int c=k0+kk; int mm=m0+rr; float av=0.0f; if(mm<B*T && c<C){ int bb=mm/T; int tt0=mm-bb*T; float s=0.0f; for(int j=0;j<Ksz;++j){ int tt=tt0+(j-half)*dilation; if((unsigned)tt<(unsigned)T) s+=x[(bb*C+c)*T+tt]*dw[c*Ksz+j]; } av=s/(1.0f+expf(-s)); } As[rr][kk]=av; }\n for(int idx=tid;idx<BK*BN;idx+=256){ int kk=idx/BN; int oo=idx-kk*BN; int c=k0+kk; Ws[kk][oo]=(c<C && oo<O)?pw[oo*C+c]:0.0f; }\n __syncthreads();\n if(m<B*T){\n #pragma unroll\n for(int kk=0;kk<BK;++kk){ float a=As[row][kk];\n #pragma unroll\n for(int i=0;i<8;i++){ int o=obase+i; if(o<O) acc[i]+=a*Ws[kk][o]; }\n }\n }\n __syncthreads();\n }\n if(m<B*T){\n #pragma unroll\n for(int i=0;i<8;i++){ int o=obase+i; if(o<O) out[(b*O+o)*T+t]=acc[i]; }\n }\n}\ntorch::Tensor fused_forward(torch::Tensor x,torch::Tensor dw_w,torch::Tensor pw_w,int64_t dilation){ int B=(int)x.size(0),C=(int)x.size(1),T=(int)x.size(2),O=(int)pw_w.size(0),Ksz=(int)dw_w.size(2); auto out=torch::empty({B,O,T},x.options()); int blocks=(B*T+BM-1)/BM; fused_dw_silu_pw_kernel<<<blocks,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),dw_w.data_ptr<float>(),pw_w.data_ptr<float>(),out.data_ptr<float>(),B,C,O,T,Ksz,(int)dilation); return out; }\n'''\n_ext = load_inline(name='fused_dw_silu_pw_ext_v2c', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['fused_forward'], extra_cuda_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,dilation:int=1):\n super().__init__(); assert kernel_size%2==1; self.padding=((kernel_size-1)*dilation)//2; self.dilation=dilation; self.dw=nn.Conv1d(in_channels,in_channels,kernel_size,padding=self.padding,dilation=dilation,groups=in_channels,bias=False); self.pw=nn.Conv1d(in_channels,out_channels,kernel_size=1,bias=False)\n def forward(self,x:torch.Tensor)->torch.Tensor:\n return _ext.fused_forward(x.contiguous(),self.dw.weight,self.pw.weight,self.dilation)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.000339', '0.000349', '0.000381', '0.000335', '0.000373']
avg_difference: ['0.000044', '0.000044', '0.000044', '0.000044', '0.000044']
turn 4 · 1 tool call(s) · 17.3s turn · 17.3s LLM · 8,921 chars (click to expand)
turn 417.3s turn · 17.3s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src='torch::Tensor fused_forward(torch::Tensor x, torch::Tensor dw_w, torch::Tensor pw_w, int64_t dilation);'\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#define BM 16\n#define BN 128\n#define BK 32\n__global__ void fused_dw_silu_pw_kernel(const float* __restrict__ x,const float* __restrict__ dw,const float* __restrict__ pw,float* __restrict__ out,int B,int C,int O,int T,int Ksz,int dilation){\n int m0=blockIdx.x*BM, tid=threadIdx.x, row=tid>>4, cg=tid&15, obase=cg*8; __shared__ float As[BM][BK]; __shared__ float Ws[BK][BN]; float acc[8];\n #pragma unroll\n for(int i=0;i<8;i++) acc[i]=0.0f; int m=m0+row,b=m/T,t=m-b*T,half=Ksz>>1;\n for(int k0=0;k0<C;k0+=BK){\n #pragma unroll\n for(int l=0;l<2;l++){int idx=tid+l*256,rr=idx/BK,kk=idx-rr*BK,c=k0+kk,mm=m0+rr; float av=0.0f; if(mm<B*T&&c<C){int bb=mm/T,tt0=mm-bb*T; float s=0.0f; for(int j=0;j<Ksz;j++){int tt=tt0+(j-half)*dilation; if((unsigned)tt<(unsigned)T){ float xx=__float_to_tf32(x[(bb*C+c)*T+tt]); float ww=__float_to_tf32(dw[c*Ksz+j]); s=fmaf(xx,ww,s);} } av=s/(1.0f+expf(-s));} As[rr][kk]=av;}\n for(int idx=tid;idx<BK*BN;idx+=256){int kk=idx/BN,oo=idx-kk*BN,c=k0+kk; Ws[kk][oo]=(c<C&&oo<O)?__float_to_tf32(pw[oo*C+c]):0.0f;}\n __syncthreads(); if(m<B*T){\n #pragma unroll\n for(int kk=0;kk<BK;kk++){float a=__float_to_tf32(As[row][kk]);\n #pragma unroll\n for(int i=0;i<8;i++){int o=obase+i; if(o<O) acc[i]=fmaf(a,Ws[kk][o],acc[i]);}\n }} __syncthreads();}\n if(m<B*T){#pragma unroll\n for(int i=0;i<8;i++){int o=obase+i; if(o<O) out[(b*O+o)*T+t]=acc[i];}}\n}\ntorch::Tensor fused_forward(torch::Tensor x,torch::Tensor dw_w,torch::Tensor pw_w,int64_t dilation){int B=x.size(0),C=x.size(1),T=x.size(2),O=pw_w.size(0),Ksz=dw_w.size(2); auto out=torch::empty({B,O,T},x.options()); fused_dw_silu_pw_kernel<<<(B*T+BM-1)/BM,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),dw_w.data_ptr<float>(),pw_w.data_ptr<float>(),out.data_ptr<float>(),B,C,O,T,Ksz,(int)dilation); return out;}\n'''\n_ext=load_inline(name='fused_dw_silu_pw_ext_tf32test',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_forward'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,dilation:int=1):\n super().__init__(); self.padding=((kernel_size-1)*dilation)//2; self.dilation=dilation; self.dw=nn.Conv1d(in_channels,in_channels,kernel_size,padding=self.padding,dilation=dilation,groups=in_channels,bias=False); self.pw=nn.Conv1d(in_channels,out_channels,kernel_size=1,bias=False)\n def forward(self,x): return _ext.fused_forward(x.contiguous(),self.dw.weight,self.pw.weight,self.dilation)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: kernel did not compile.
Error building extension 'fused_dw_silu_pw_ext_tf32test': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_dw_silu_pw_ext_tf32test -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_5fc68eae9919/fused_dw_silu_pw_ext_tf32test/main.cpp -o main.o
[2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=fused_dw_silu_pw_ext_tf32test -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_5fc68eae9919/fused_dw_silu_pw_ext_tf32test/cuda.cu -o cuda.cuda.o
FAILED: [code=2] cuda.cuda.o
/usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=fused_dw_silu_pw_ext_tf32test -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_5fc68eae9919/fused_dw_silu_pw_ext_tf32test/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_5fc68eae9919/fused_dw_silu_pw_ext_tf32test/cuda.cu(18): error: identifier "__float_to_tf32" is undefined
for(int l=0;l<2;l++){int idx=tid+l*256,rr=idx/32,kk=idx-rr*32,c=k0+kk,mm=m0+rr; float av=0.0f; if(mm<B*T&&c<C){int bb=mm/T,tt0=mm-bb*T; float s=0.0f; for(int j=0;j<Ksz;j++){int tt=tt0+(j-half)*dilation; if((unsigned)tt<(unsigned)T){ float xx=__float_to_tf32(x[(bb*C+c)*T+tt]); float ww=__float_to_tf32(dw[c*Ksz+j]); s=fmaf(xx,ww,s);} } av=s/(1.0f+expf(-s));} As[rr][kk]=av;}
^
/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_5fc68eae9919/fused_dw_silu_pw_ext_tf32test/cuda.cu(19): error: identifier "__float_to_tf32" is undefined
for(int idx=tid;idx<32*128;idx+=256){int kk=idx/128,oo=idx-kk*128,c=k0+kk; Ws[kk][oo]=(c<C&&oo<O)?__float_to_tf32(pw[oo*C+c]):0.0f;}
^
/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_5fc68eae9919/fused_dw_silu_pw_ext_tf32test/cuda.cu(22): error: identifier "__float_to_tf32" is undefined
for(int kk=0;kk<32;kk++){float a=__float_to_tf32(As[row][kk]);
^
/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_5fc68eae9919/fused_dw_silu_pw_ext_tf32test/cuda.cu(26): error: "#" not expected here
if(m<B*T){#pragma unroll
^
/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_5fc68eae9919/fused_dw_silu_pw_ext_tf32test/cuda.cu(26): error: expected an expression
if(m<B*T){#pragma unroll
^
/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_5fc68eae9919/fused_dw_silu_pw_ext_tf32test/cuda.cu(27): error: expected a ";"
for(int i=0;i<8;i++){int o=obase+i; if(o<O) out[(b*O+o)*T+t]=acc[i];}}
^
6 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_5fc68eae9919/fused_dw_silu_pw_ext_tf32test/cuda.cu".
ninja: build stopped: subcommand failed.
turn 5 · 1 tool call(s) · 8.6s turn · 8.6s LLM · 14,716 chars (click to expand)
turn 58.6s turn · 8.6s 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_cpp_src='torch::Tensor fused_forward(torch::Tensor x, torch::Tensor dw_w, torch::Tensor pw_w, int64_t dilation);'\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#define BM 16\n#define BN 128\n#define BK 32\n__device__ __forceinline__ float to_tf32(float x){ float y; asm(\"cvt.rna.tf32.f32 %0, %1;\" : \"=f\"(y) : \"f\"(x)); return y; }\n__global__ void fused_dw_silu_pw_kernel(const float* __restrict__ x,const float* __restrict__ dw,const float* __restrict__ pw,float* __restrict__ out,int B,int C,int O,int T,int Ksz,int dilation){\n int m0=blockIdx.x*BM, tid=threadIdx.x, row=tid>>4, cg=tid&15, obase=cg*8; __shared__ float As[BM][BK]; __shared__ float Ws[BK][BN]; float acc[8];\n #pragma unroll\n for(int i=0;i<8;i++) acc[i]=0.0f; int m=m0+row,b=m/T,t=m-b*T,half=Ksz>>1;\n for(int k0=0;k0<C;k0+=BK){\n #pragma unroll\n for(int l=0;l<2;l++){int idx=tid+l*256,rr=idx/BK,kk=idx-rr*BK,c=k0+kk,mm=m0+rr; float av=0.0f; if(mm<B*T&&c<C){int bb=mm/T,tt0=mm-bb*T; float s=0.0f; for(int j=0;j<Ksz;j++){int tt=tt0+(j-half)*dilation; if((unsigned)tt<(unsigned)T){ float xx=to_tf32(x[(bb*C+c)*T+tt]); float ww=to_tf32(dw[c*Ksz+j]); s=fmaf(xx,ww,s);} } av=s/(1.0f+expf(-s));} As[rr][kk]=av;}\n for(int idx=tid;idx<BK*BN;idx+=256){int kk=idx/BN,oo=idx-kk*BN,c=k0+kk; Ws[kk][oo]=(c<C&&oo<O)?to_tf32(pw[oo*C+c]):0.0f;}\n __syncthreads(); if(m<B*T){\n #pragma unroll\n for(int kk=0;kk<BK;kk++){float a=to_tf32(As[row][kk]);\n #pragma unroll\n for(int i=0;i<8;i++){int o=obase+i; if(o<O) acc[i]=fmaf(a,Ws[kk][o],acc[i]);}\n }} __syncthreads();}\n if(m<B*T){\n #pragma unroll\n for(int i=0;i<8;i++){int o=obase+i; if(o<O) out[(b*O+o)*T+t]=acc[i];}\n }\n}\ntorch::Tensor fused_forward(torch::Tensor x,torch::Tensor dw_w,torch::Tensor pw_w,int64_t dilation){int B=x.size(0),C=x.size(1),T=x.size(2),O=pw_w.size(0),Ksz=dw_w.size(2); auto out=torch::empty({B,O,T},x.options()); fused_dw_silu_pw_kernel<<<(B*T+BM-1)/BM,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),dw_w.data_ptr<float>(),pw_w.data_ptr<float>(),out.data_ptr<float>(),B,C,O,T,Ksz,(int)dilation); return out;}\n'''\n_ext=load_inline(name='fused_dw_silu_pw_ext_tf32test2',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_forward'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,dilation:int=1):\n super().__init__(); self.padding=((kernel_size-1)*dilation)//2; self.dilation=dilation; self.dw=nn.Conv1d(in_channels,in_channels,kernel_size,padding=self.padding,dilation=dilation,groups=in_channels,bias=False); self.pw=nn.Conv1d(in_channels,out_channels,kernel_size=1,bias=False)\n def forward(self,x): return _ext.fused_forward(x.contiguous(),self.dw.weight,self.pw.weight,self.dilation)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: kernel did not compile.
Error building extension 'fused_dw_silu_pw_ext_tf32test2': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_dw_silu_pw_ext_tf32test2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_9018713b43fb/fused_dw_silu_pw_ext_tf32test2/main.cpp -o main.o
[2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=fused_dw_silu_pw_ext_tf32test2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_9018713b43fb/fused_dw_silu_pw_ext_tf32test2/cuda.cu -o cuda.cuda.o
FAILED: [code=255] cuda.cuda.o
/usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=fused_dw_silu_pw_ext_tf32test2 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_39_cache/k_9018713b43fb/fused_dw_silu_pw_ext_tf32test2/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 213; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 217; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 233; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 237; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 253; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 257; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 273; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 277; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 304; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 308; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 327; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 331; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 350; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 354; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 418; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 422; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 438; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 442; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 458; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 462; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 478; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 482; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 511; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 515; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 534; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 538; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 557; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 561; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 666; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 687; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 708; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 743; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 766; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 791; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 814; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 834; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 900; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 952; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1004; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1056; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1108; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1160; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1212; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1264; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1316; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1368; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1420; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1472; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1524; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1576; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1628; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1680; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1732; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1784; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1836; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1888; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1940; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 1992; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 2044; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 2096; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 2148; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 2200; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 2252; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 2304; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 2356; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 2408; error : Arguments mismatch for instruction 'cvt'
ptxas /var/tmp/tmpxft_0030f523_00000000-6_cuda.ptx, line 2460; error : Arguments mismatch for instruction 'cvt'
ptxas fatal : Ptx assembly aborted due to errors
ninja: build stopped: subcommand failed.
turn 611.9s turn · 11.9s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src='torch::Tensor fused_forward(torch::Tensor x, torch::Tensor dw_w, torch::Tensor pw_w, int64_t dilation);'\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#define BM 16\n#define BN 128\n#define BK 32\n__device__ __forceinline__ float to_tf32(float x){ unsigned int b=__float_as_uint(x); unsigned int r=0x00000fffU + ((b >> 13) & 1U); b = (b + r) & 0xffffe000U; return __uint_as_float(b); }\n__global__ void fused_dw_silu_pw_kernel(const float* __restrict__ x,const float* __restrict__ dw,const float* __restrict__ pw,float* __restrict__ out,int B,int C,int O,int T,int Ksz,int dilation){\n int m0=blockIdx.x*BM, tid=threadIdx.x, row=tid>>4, cg=tid&15, obase=cg*8; __shared__ float As[BM][BK]; __shared__ float Ws[BK][BN]; float acc[8];\n #pragma unroll\n for(int i=0;i<8;i++) acc[i]=0.0f; int m=m0+row,b=m/T,t=m-b*T,half=Ksz>>1;\n for(int k0=0;k0<C;k0+=BK){\n #pragma unroll\n for(int l=0;l<2;l++){int idx=tid+l*256,rr=idx/BK,kk=idx-rr*BK,c=k0+kk,mm=m0+rr; float av=0.0f; if(mm<B*T&&c<C){int bb=mm/T,tt0=mm-bb*T; float s=0.0f; for(int j=0;j<Ksz;j++){int tt=tt0+(j-half)*dilation; if((unsigned)tt<(unsigned)T){ s=fmaf(x[(bb*C+c)*T+tt],dw[c*Ksz+j],s);} } av=s/(1.0f+expf(-s));} As[rr][kk]=av;}\n for(int idx=tid;idx<BK*BN;idx+=256){int kk=idx/BN,oo=idx-kk*BN,c=k0+kk; Ws[kk][oo]=(c<C&&oo<O)?to_tf32(pw[oo*C+c]):0.0f;}\n __syncthreads(); if(m<B*T){\n #pragma unroll\n for(int kk=0;kk<BK;kk++){float a=to_tf32(As[row][kk]);\n #pragma unroll\n for(int i=0;i<8;i++){int o=obase+i; if(o<O) acc[i]=fmaf(a,Ws[kk][o],acc[i]);}\n }} __syncthreads();}\n if(m<B*T){\n #pragma unroll\n for(int i=0;i<8;i++){int o=obase+i; if(o<O) out[(b*O+o)*T+t]=acc[i];}\n }\n}\ntorch::Tensor fused_forward(torch::Tensor x,torch::Tensor dw_w,torch::Tensor pw_w,int64_t dilation){int B=x.size(0),C=x.size(1),T=x.size(2),O=pw_w.size(0),Ksz=dw_w.size(2); auto out=torch::empty({B,O,T},x.options()); fused_dw_silu_pw_kernel<<<(B*T+BM-1)/BM,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),dw_w.data_ptr<float>(),pw_w.data_ptr<float>(),out.data_ptr<float>(),B,C,O,T,Ksz,(int)dilation); return out;}\n'''\n_ext=load_inline(name='fused_dw_silu_pw_ext_tf32pw',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_forward'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,dilation:int=1):\n super().__init__(); self.padding=((kernel_size-1)*dilation)//2; self.dilation=dilation; self.dw=nn.Conv1d(in_channels,in_channels,kernel_size,padding=self.padding,dilation=dilation,groups=in_channels,bias=False); self.pw=nn.Conv1d(in_channels,out_channels,kernel_size=1,bias=False)\n def forward(self,x): return _ext.fused_forward(x.contiguous(),self.dw.weight,self.pw.weight,self.dilation)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=8.34e-07 mean_abs_err=4.97e-08 max_rel_err=3.67e+00
turn 78.5s turn · 8.5s 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='torch::Tensor fused_forward(torch::Tensor x, torch::Tensor dw_w, torch::Tensor pw_w, int64_t dilation);'\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#define BM 16\n#define BN 128\n#define BK 32\n__device__ __forceinline__ float to_tf32(float x){ unsigned int b=__float_as_uint(x); unsigned int r=0x00000fffU + ((b >> 13) & 1U); b = (b + r) & 0xffffe000U; return __uint_as_float(b); }\n__global__ void fused_dw_silu_pw_kernel(const float* __restrict__ x,const float* __restrict__ dw,const float* __restrict__ pw,float* __restrict__ out,int B,int C,int O,int T,int Ksz,int dilation){\n int m0=blockIdx.x*BM, tid=threadIdx.x, row=tid>>4, cg=tid&15, obase=cg*8; __shared__ float As[BM][BK]; __shared__ float Ws[BK][BN]; float acc[8];\n #pragma unroll\n for(int i=0;i<8;i++) acc[i]=0.0f; int m=m0+row,b=m/T,t=m-b*T,half=Ksz>>1;\n for(int k0=0;k0<C;k0+=BK){\n #pragma unroll\n for(int l=0;l<2;l++){int idx=tid+l*256,rr=idx/BK,kk=idx-rr*BK,c=k0+kk,mm=m0+rr; float av=0.0f; if(mm<B*T&&c<C){int bb=mm/T,tt0=mm-bb*T; float s=0.0f; for(int j=0;j<Ksz;j++){int tt=tt0+(j-half)*dilation; if((unsigned)tt<(unsigned)T){ s=fmaf(x[(bb*C+c)*T+tt],dw[c*Ksz+j],s);} } av=s/(1.0f+expf(-s));} As[rr][kk]=av;}\n for(int idx=tid;idx<BK*BN;idx+=256){int kk=idx/BN,oo=idx-kk*BN,c=k0+kk; Ws[kk][oo]=(c<C&&oo<O)?to_tf32(pw[oo*C+c]):0.0f;}\n __syncthreads(); if(m<B*T){\n #pragma unroll\n for(int kk=0;kk<BK;kk++){float a=to_tf32(As[row][kk]);\n #pragma unroll\n for(int i=0;i<8;i++){int o=obase+i; if(o<O) acc[i]=fmaf(a,Ws[kk][o],acc[i]);}\n }} __syncthreads();}\n if(m<B*T){\n #pragma unroll\n for(int i=0;i<8;i++){int o=obase+i; if(o<O) out[(b*O+o)*T+t]=acc[i];}\n }\n}\ntorch::Tensor fused_forward(torch::Tensor x,torch::Tensor dw_w,torch::Tensor pw_w,int64_t dilation){int B=x.size(0),C=x.size(1),T=x.size(2),O=pw_w.size(0),Ksz=dw_w.size(2); auto out=torch::empty({B,O,T},x.options()); fused_dw_silu_pw_kernel<<<(B*T+BM-1)/BM,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),dw_w.data_ptr<float>(),pw_w.data_ptr<float>(),out.data_ptr<float>(),B,C,O,T,Ksz,(int)dilation); return out;}\n'''\n_ext=load_inline(name='fused_dw_silu_pw_ext_prof1',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_forward'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,dilation:int=1):\n super().__init__(); self.padding=((kernel_size-1)*dilation)//2; self.dilation=dilation; self.dw=nn.Conv1d(in_channels,in_channels,kernel_size,padding=self.padding,dilation=dilation,groups=in_channels,bias=False); self.pw=nn.Conv1d(in_channels,out_channels,kernel_size=1,bias=False)\n def forward(self,x): return _ext.fused_forward(x.contiguous(),self.dw.weight,self.pw.weight,self.dilation)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 4434.0μs (100%) calls=1
--- Timing ---
GPU time: 506.34 μs
--- Memory Bandwidth ---
DRAM BW: 40.1/3350 GB/s (1.2%)
read=33.3 write=6.7 GB/s
Hit rates: L2=89.6%
--- Compute Throughput ---
Dominant pipe: FP32 util=6.1%
Pipe util: FMA=6.1%, Tensor=0.2%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 57.1%
Eligible warps/cycle: 0.44
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=48, smem/blk=19456B, block=256, grid=2048
--- Memory Access Quality ---
Global ld coalescing: 28.67 sectors/req (28.7x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
mio_throttle 2666.2% ####################
short_scoreboard 1041.4% ####################
long_scoreboard 736.1% ####################
--- Optimization Hints ---
-> 2666% stalls: memory-instruction throttle. Too many outstanding memory ops; batch loads, reduce address divergence.
-> Global load coalescing=28.7x (ideal=1.0) -> 29x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching).
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
turn 813.7s turn · 13.7s 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_cpp_src='torch::Tensor fused_forward(torch::Tensor x, torch::Tensor dw_w, torch::Tensor pw_w, int64_t dilation);'\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#define BM 16\n#define BN 128\n#define BK 32\n__device__ __forceinline__ float to_tf32(float x){ unsigned int b=__float_as_uint(x); unsigned int r=0x00000fffU + ((b >> 13) & 1U); b = (b + r) & 0xffffe000U; return __uint_as_float(b); }\n__global__ void fused_dw_silu_pw_kernel(const float* __restrict__ x,const float* __restrict__ dw,const float* __restrict__ pw,float* __restrict__ out,int B,int C,int O,int T,int Ksz,int dilation){\n int m0=blockIdx.x*BM, tid=threadIdx.x, row=tid>>4, cg=tid&15, obase=cg*8; __shared__ float As[BM][BK]; __shared__ float Ws[BK][BN]; float acc[8];\n #pragma unroll\n for(int i=0;i<8;i++) acc[i]=0.0f; int m=m0+row,b=m/T,t=m-b*T,half=Ksz>>1;\n for(int k0=0;k0<C;k0+=BK){\n #pragma unroll\n for(int l=0;l<2;l++){int idx=tid+l*256; int kk=idx/BM; int rr=idx-kk*BM; int c=k0+kk; int mm=m0+rr; float av=0.0f; if(mm<B*T&&c<C){int bb=mm/T,tt0=mm-bb*T; float s=0.0f; for(int j=0;j<Ksz;j++){int tt=tt0+(j-half)*dilation; if((unsigned)tt<(unsigned)T) s=fmaf(x[(bb*C+c)*T+tt],dw[c*Ksz+j],s); } av=s/(1.0f+expf(-s));} As[rr][kk]=av;}\n for(int idx=tid;idx<BK*BN;idx+=256){int oo=idx/BK; int kk=idx-oo*BK; int c=k0+kk; Ws[kk][oo]=(c<C&&oo<O)?to_tf32(pw[oo*C+c]):0.0f;}\n __syncthreads(); if(m<B*T){\n #pragma unroll\n for(int kk=0;kk<BK;kk++){float a=to_tf32(As[row][kk]);\n #pragma unroll\n for(int i=0;i<8;i++){int o=obase+i; if(o<O) acc[i]=fmaf(a,Ws[kk][o],acc[i]);}\n }} __syncthreads();}\n if(m<B*T){\n #pragma unroll\n for(int i=0;i<8;i++){int o=obase+i; if(o<O) out[(b*O+o)*T+t]=acc[i];}\n }\n}\ntorch::Tensor fused_forward(torch::Tensor x,torch::Tensor dw_w,torch::Tensor pw_w,int64_t dilation){int B=x.size(0),C=x.size(1),T=x.size(2),O=pw_w.size(0),Ksz=dw_w.size(2); auto out=torch::empty({B,O,T},x.options()); fused_dw_silu_pw_kernel<<<(B*T+BM-1)/BM,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),dw_w.data_ptr<float>(),pw_w.data_ptr<float>(),out.data_ptr<float>(),B,C,O,T,Ksz,(int)dilation); return out;}\n'''\n_ext=load_inline(name='fused_dw_silu_pw_ext_coal',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_forward'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,dilation:int=1):\n super().__init__(); self.padding=((kernel_size-1)*dilation)//2; self.dilation=dilation; self.dw=nn.Conv1d(in_channels,in_channels,kernel_size,padding=self.padding,dilation=dilation,groups=in_channels,bias=False); self.pw=nn.Conv1d(in_channels,out_channels,kernel_size=1,bias=False)\n def forward(self,x): return _ext.fused_forward(x.contiguous(),self.dw.weight,self.pw.weight,self.dilation)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=8.34e-07 mean_abs_err=4.97e-08 max_rel_err=3.67e+00
turn 98.1s 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_cpp_src='torch::Tensor fused_forward(torch::Tensor x, torch::Tensor dw_w, torch::Tensor pw_w, int64_t dilation);'\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#define BM 16\n#define BN 128\n#define BK 32\n__device__ __forceinline__ float to_tf32(float x){ unsigned int b=__float_as_uint(x); unsigned int r=0x00000fffU + ((b >> 13) & 1U); b = (b + r) & 0xffffe000U; return __uint_as_float(b); }\n__global__ void fused_dw_silu_pw_kernel(const float* __restrict__ x,const float* __restrict__ dw,const float* __restrict__ pw,float* __restrict__ out,int B,int C,int O,int T,int Ksz,int dilation){\n int m0=blockIdx.x*BM, tid=threadIdx.x, row=tid>>4, cg=tid&15, obase=cg*8; __shared__ float As[BM][BK]; __shared__ float Ws[BK][BN]; float acc[8];\n #pragma unroll\n for(int i=0;i<8;i++) acc[i]=0.0f; int m=m0+row,b=m/T,t=m-b*T,half=Ksz>>1;\n for(int k0=0;k0<C;k0+=BK){\n #pragma unroll\n for(int l=0;l<2;l++){int idx=tid+l*256; int kk=idx/BM; int rr=idx-kk*BM; int c=k0+kk; int mm=m0+rr; float av=0.0f; if(mm<B*T&&c<C){int bb=mm/T,tt0=mm-bb*T; float s=0.0f; for(int j=0;j<Ksz;j++){int tt=tt0+(j-half)*dilation; if((unsigned)tt<(unsigned)T) s=fmaf(x[(bb*C+c)*T+tt],dw[c*Ksz+j],s); } av=s/(1.0f+expf(-s));} As[rr][kk]=av;}\n for(int idx=tid;idx<BK*BN;idx+=256){int oo=idx/BK; int kk=idx-oo*BK; int c=k0+kk; Ws[kk][oo]=(c<C&&oo<O)?to_tf32(pw[oo*C+c]):0.0f;}\n __syncthreads(); if(m<B*T){\n #pragma unroll\n for(int kk=0;kk<BK;kk++){float a=to_tf32(As[row][kk]);\n #pragma unroll\n for(int i=0;i<8;i++){int o=obase+i; if(o<O) acc[i]=fmaf(a,Ws[kk][o],acc[i]);}\n }} __syncthreads();}\n if(m<B*T){\n #pragma unroll\n for(int i=0;i<8;i++){int o=obase+i; if(o<O) out[(b*O+o)*T+t]=acc[i];}\n }\n}\ntorch::Tensor fused_forward(torch::Tensor x,torch::Tensor dw_w,torch::Tensor pw_w,int64_t dilation){int B=x.size(0),C=x.size(1),T=x.size(2),O=pw_w.size(0),Ksz=dw_w.size(2); auto out=torch::empty({B,O,T},x.options()); fused_dw_silu_pw_kernel<<<(B*T+BM-1)/BM,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),dw_w.data_ptr<float>(),pw_w.data_ptr<float>(),out.data_ptr<float>(),B,C,O,T,Ksz,(int)dilation); return out;}\n'''\n_ext=load_inline(name='fused_dw_silu_pw_ext_prof2',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_forward'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,dilation:int=1):\n super().__init__(); self.padding=((kernel_size-1)*dilation)//2; self.dilation=dilation; self.dw=nn.Conv1d(in_channels,in_channels,kernel_size,padding=self.padding,dilation=dilation,groups=in_channels,bias=False); self.pw=nn.Conv1d(in_channels,out_channels,kernel_size=1,bias=False)\n def forward(self,x): return _ext.fused_forward(x.contiguous(),self.dw.weight,self.pw.weight,self.dilation)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 238.0μs (100%) calls=1
--- Timing ---
GPU time: 437.18 μs [↓69.2μs ok]
--- Memory Bandwidth ---
DRAM BW: 46.8/3350 GB/s (1.4%) [↑0.2% ok]
read=38.7 write=8.1 GB/s
Hit rates: L2=88.4%
--- Compute Throughput ---
Dominant pipe: FP32 util=6.9% [↑0.8% ok]
Pipe util: FMA=6.9%, Tensor=0.2%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 47.0% [↓10.1% REGRESSED]
Eligible warps/cycle: 0.40
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=64, smem/blk=19456B, block=256, grid=2048
--- Memory Access Quality ---
Global ld coalescing: 3.77 sectors/req (3.8x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
mio_throttle 1512.3% ####################
short_scoreboard 1276.4% ####################
long_scoreboard 367.7% ####################
--- Optimization Hints ---
-> 1512% stalls: memory-instruction throttle. Too many outstanding memory ops; batch loads, reduce address divergence.
-> Global load coalescing=3.8x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching).
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
--- Delta vs Previous Iteration ---
GPU time: 506.34→437.18μs (↓69.15) ok
DRAM BW%: 1.20→1.40% (↑0.20) ok
Compute%: 6.09→6.85% (↑0.76) ok
Occupancy: 57.09→47.03% (↓10.06) REGRESSED
Eligible warps/cyc: 0.44→0.40 (↓0.04) REGRESSED
Ld sectors/req: 28.67→3.77 (↓24.89) ok
L2 hit%: 89.55→88.42% (↓1.13) REGRESSED
turn 1025.4s turn · 25.4s 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_cpp_src='torch::Tensor fused_forward(torch::Tensor x, torch::Tensor dw_w, torch::Tensor pw_w, int64_t dilation);'\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <mma.h>\nusing namespace nvcuda;\n#define BM 16\n#define BN 128\n#define BK 32\n__global__ void fused_dw_silu_pw_wmma_kernel(const float* __restrict__ x,const float* __restrict__ dw,const float* __restrict__ pw,float* __restrict__ out,int B,int C,int O,int T,int Ksz,int dilation){\n int tid=threadIdx.x; int warp=tid>>5; int lane=tid&31; int m0=blockIdx.x*BM;\n __shared__ float As[BM][BK];\n __shared__ float Ws[BK][BN];\n __shared__ float Cs[8][BM][16];\n wmma::fragment<wmma::accumulator,16,16,8,float> cfrag;\n wmma::fill_fragment(cfrag, 0.0f);\n int half=Ksz>>1;\n for(int k0=0;k0<C;k0+=BK){\n #pragma unroll\n for(int l=0;l<2;l++){\n int idx=tid+l*256; int kk=idx/BM; int rr=idx-kk*BM; int c=k0+kk; int mm=m0+rr; float av=0.0f;\n if(mm<B*T && c<C){ int bb=mm/T; int tt0=mm-bb*T; float s=0.0f;\n for(int j=0;j<Ksz;j++){ int tt=tt0+(j-half)*dilation; if((unsigned)tt<(unsigned)T) s=fmaf(x[(bb*C+c)*T+tt], dw[c*Ksz+j], s); }\n av=s/(1.0f+expf(-s));\n }\n As[rr][kk]=av;\n }\n for(int idx=tid; idx<BK*BN; idx+=256){ int oo=idx/BK; int kk=idx-oo*BK; int c=k0+kk; Ws[kk][oo]=(c<C && oo<O)?pw[oo*C+c]:0.0f; }\n __syncthreads();\n int n0=warp*16;\n #pragma unroll\n for(int kk=0; kk<BK; kk+=8){\n wmma::fragment<wmma::matrix_a,16,16,8,wmma::precision::tf32,wmma::row_major> afrag;\n wmma::fragment<wmma::matrix_b,16,16,8,wmma::precision::tf32,wmma::row_major> bfrag;\n wmma::load_matrix_sync(afrag, &As[0][kk], BK);\n wmma::load_matrix_sync(bfrag, &Ws[kk][n0], BN);\n wmma::mma_sync(cfrag, afrag, bfrag, cfrag);\n }\n __syncthreads();\n }\n wmma::store_matrix_sync(&Cs[warp][0][0], cfrag, 16, wmma::mem_row_major);\n __syncthreads();\n int base_m=m0;\n for(int e=tid; e<8*BM*16; e+=256){ int w=e/(BM*16); int rem=e-w*BM*16; int rr=rem/16; int cc=rem-rr*16; int o=w*16+cc; int m=base_m+rr; if(m<B*T && o<O){ int b=m/T; int t=m-b*T; out[(b*O+o)*T+t]=Cs[w][rr][cc]; } }\n}\ntorch::Tensor fused_forward(torch::Tensor x,torch::Tensor dw_w,torch::Tensor pw_w,int64_t dilation){int B=x.size(0),C=x.size(1),T=x.size(2),O=pw_w.size(0),Ksz=dw_w.size(2); auto out=torch::empty({B,O,T},x.options()); fused_dw_silu_pw_wmma_kernel<<<(B*T+BM-1)/BM,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),dw_w.data_ptr<float>(),pw_w.data_ptr<float>(),out.data_ptr<float>(),B,C,O,T,Ksz,(int)dilation); return out;}\n'''\n_ext=load_inline(name='fused_dw_silu_pw_ext_wmma1',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['fused_forward'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,dilation:int=1):\n super().__init__(); self.padding=((kernel_size-1)*dilation)//2; self.dilation=dilation; self.dw=nn.Conv1d(in_channels,in_channels,kernel_size,padding=self.padding,dilation=dilation,groups=in_channels,bias=False); self.pw=nn.Conv1d(in_channels,out_channels,kernel_size=1,bias=False)\n def forward(self,x): return _ext.fused_forward(x.contiguous(),self.dw.weight,self.pw.weight,self.dilation)\n"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: (0 / 5) trials did not all match.
correctness_issue: Output mismatch
max_difference: ['0.000975', '0.000981', '0.000999', '0.000917', '0.001021']
avg_difference: ['0.000131', '0.000131', '0.000131', '0.000131', '0.000131']
turn 1112.9s turn · 12.9s LLM · 0.0s tools
reasoning
**Evaluating performance optimization**
I'm considering whether to use wmma and if that needs correction. I might check the PyTorch convolution again since I think it could struggle with tensor operations. Our current non-wmma solution is correct but slow. I could look into disabling TF32 globally to see if it helps. Should I implement depthwise_silu kernel only for performance testing? I wonder if the execution time improves with cuDNN speed after profiling. I should focus on correctness first. Let's give this a shot!
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src='torch::Tensor dw_silu_forward(torch::Tensor x, torch::Tensor dw_w, int64_t dilation);'\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__global__ void dw_silu_kernel(const float* __restrict__ x,const float* __restrict__ dw,float* __restrict__ a,int B,int C,int T,int Ksz,int dilation){\n int idx=blockIdx.x*blockDim.x+threadIdx.x; int N=B*C*T; if(idx>=N) return; int t=idx%T; int q=idx/T; int c=q%C; int b=q/C; int half=Ksz>>1; float s=0.0f;\n for(int j=0;j<Ksz;j++){ int tt=t+(j-half)*dilation; if((unsigned)tt<(unsigned)T) s=fmaf(x[(b*C+c)*T+tt], dw[c*Ksz+j], s); }\n a[idx]=s/(1.0f+expf(-s));\n}\ntorch::Tensor dw_silu_forward(torch::Tensor x,torch::Tensor dw_w,int64_t dilation){int B=x.size(0),C=x.size(1),T=x.size(2),Ksz=dw_w.size(2); auto a=torch::empty_like(x); int N=B*C*T; dw_silu_kernel<<<(N+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),dw_w.data_ptr<float>(),a.data_ptr<float>(),B,C,T,Ksz,(int)dilation); return a;}\n'''\n_ext=load_inline(name='dw_silu_ext_callconv',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['dw_silu_forward'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,dilation:int=1):\n super().__init__(); self.padding=((kernel_size-1)*dilation)//2; self.dilation=dilation; self.dw=nn.Conv1d(in_channels,in_channels,kernel_size,padding=self.padding,dilation=dilation,groups=in_channels,bias=False); self.pw=nn.Conv1d(in_channels,out_channels,kernel_size=1,bias=False)\n def forward(self,x):\n a=_ext.dw_silu_forward(x.contiguous(), self.dw.weight, self.dilation)\n return F.conv1d(a, self.pw.weight, None, 1, 0, 1, 1)\n"
}
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 126.0s turn · 6.0s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src='torch::Tensor dw_silu_forward(torch::Tensor x, torch::Tensor dw_w, int64_t dilation);'\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__global__ void dw_silu_kernel(const float* __restrict__ x,const float* __restrict__ dw,float* __restrict__ a,int B,int C,int T,int Ksz,int dilation){\n int idx=blockIdx.x*blockDim.x+threadIdx.x; int N=B*C*T; if(idx>=N) return; int t=idx%T; int q=idx/T; int c=q%C; int b=q/C; int half=Ksz>>1; float s=0.0f;\n for(int j=0;j<Ksz;j++){ int tt=t+(j-half)*dilation; if((unsigned)tt<(unsigned)T) s=fmaf(x[(b*C+c)*T+tt], dw[c*Ksz+j], s); }\n a[idx]=s/(1.0f+expf(-s));\n}\ntorch::Tensor dw_silu_forward(torch::Tensor x,torch::Tensor dw_w,int64_t dilation){int B=x.size(0),C=x.size(1),T=x.size(2),Ksz=dw_w.size(2); auto a=torch::empty_like(x); int N=B*C*T; dw_silu_kernel<<<(N+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),dw_w.data_ptr<float>(),a.data_ptr<float>(),B,C,T,Ksz,(int)dilation); return a;}\n'''\n_ext=load_inline(name='dw_silu_ext_callconv_prof',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['dw_silu_forward'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,dilation:int=1):\n super().__init__(); self.padding=((kernel_size-1)*dilation)//2; self.dilation=dilation; self.dw=nn.Conv1d(in_channels,in_channels,kernel_size,padding=self.padding,dilation=dilation,groups=in_channels,bias=False); self.pw=nn.Conv1d(in_channels,out_channels,kernel_size=1,bias=False)\n def forward(self,x):\n a=_ext.dw_silu_forward(x.contiguous(), self.dw.weight, self.dilation)\n return F.conv1d(a, self.pw.weight, None, 1, 0, 1, 1)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::conv1d 128054.0μs (25%) calls=1
#2: aten::convolution 128016.0μs (25%) calls=1
#3: aten::_convolution 127987.0μs (25%) calls=1
#4: aten::cudnn_convolution 127166.0μs (25%) calls=1
#5: aten::empty_like 288.0μs (0%) calls=1
... +7 more kernels
--- Timing ---
GPU time: 47.07 μs [↓390.1μs ok]
--- Memory Bandwidth ---
DRAM BW: 1809.5/3350 GB/s (54.0%) [↑52.6% ok]
read=1677.8 write=131.7 GB/s
Hit rates: L2=115.2%
--- Compute Throughput ---
Dominant pipe: tensor-core util=29.1% [↑22.3% ok]
Pipe util: FMA=41.7%, Tensor=29.1%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 97.3% [↑50.2% ok]
Eligible warps/cycle: 4.93
Launch: regs/thread=248, smem/blk=67584B, block=384, grid=16640
--- Memory Access Quality ---
Global ld coalescing: 18.89 sectors/req (18.9x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 499.1% ####################
short_scoreboard 206.0% ####################
mio_throttle 86.3% #################
--- Optimization Hints ---
-> 499% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=18.9 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=18.9x (ideal=1.0) -> 19x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 437.18→47.07μs (↓390.11) ok
DRAM BW%: 1.40→54.02% (↑52.62) ok
Compute%: 6.85→29.10% (↑22.25) ok
Occupancy: 47.03→97.28% (↑50.24) ok
Eligible warps/cyc: 0.40→4.93 (↑4.54) ok
Ld sectors/req: 3.77→18.89 (↑15.12) REGRESSED
L2 hit%: 88.42→115.23% (↑26.81) ok
turn 1311.1s turn · 11.1s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src='torch::Tensor dw_silu_forward(torch::Tensor x, torch::Tensor dw_w, int64_t dilation);'\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__global__ void dw_silu_kernel5d2(const float* __restrict__ x,const float* __restrict__ dw,float* __restrict__ a,int B,int C,int T){\n int idx=blockIdx.x*blockDim.x+threadIdx.x; int N=B*C*T; if(idx>=N) return;\n int t=idx & 2047; int q=idx >> 11; int c=q & 127; int base=idx;\n float s=0.0f;\n if(t>=4) s=fmaf(x[base-4], dw[c*5+0], s);\n if(t>=2) s=fmaf(x[base-2], dw[c*5+1], s);\n s=fmaf(x[base], dw[c*5+2], s);\n if(t+2<T) s=fmaf(x[base+2], dw[c*5+3], s);\n if(t+4<T) s=fmaf(x[base+4], dw[c*5+4], s);\n a[idx]=s/(1.0f+expf(-s));\n}\n__global__ void dw_silu_kernel_generic(const float* __restrict__ x,const float* __restrict__ dw,float* __restrict__ a,int B,int C,int T,int Ksz,int dilation){\n int idx=blockIdx.x*blockDim.x+threadIdx.x; int N=B*C*T; if(idx>=N) return; int t=idx%T; int q=idx/T; int c=q%C; int b=q/C; int half=Ksz>>1; float s=0.0f;\n for(int j=0;j<Ksz;j++){ int tt=t+(j-half)*dilation; if((unsigned)tt<(unsigned)T) s=fmaf(x[(b*C+c)*T+tt], dw[c*Ksz+j], s); }\n a[idx]=s/(1.0f+expf(-s));\n}\ntorch::Tensor dw_silu_forward(torch::Tensor x,torch::Tensor dw_w,int64_t dilation){int B=x.size(0),C=x.size(1),T=x.size(2),Ksz=dw_w.size(2); auto a=torch::empty_like(x); int N=B*C*T; if(Ksz==5 && dilation==2 && T==2048 && C==128){ dw_silu_kernel5d2<<<(N+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),dw_w.data_ptr<float>(),a.data_ptr<float>(),B,C,T); } else { dw_silu_kernel_generic<<<(N+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),dw_w.data_ptr<float>(),a.data_ptr<float>(),B,C,T,Ksz,(int)dilation); } return a;}\n'''\n_ext=load_inline(name='dw_silu_ext_finaltest',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['dw_silu_forward'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels:int,out_channels:int,kernel_size:int,dilation:int=1):\n super().__init__(); self.padding=((kernel_size-1)*dilation)//2; self.dilation=dilation; self.dw=nn.Conv1d(in_channels,in_channels,kernel_size,padding=self.padding,dilation=dilation,groups=in_channels,bias=False); self.pw=nn.Conv1d(in_channels,out_channels,kernel_size=1,bias=False)\n def forward(self,x):\n a=_ext.dw_silu_forward(x.contiguous(), self.dw.weight, self.dilation)\n return F.conv1d(a, self.pw.weight, None, 1, 0, 1, 1)\n"
}
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 149.9s turn · 9.9s LLM · 0.0s tools · FINAL
call → submit_kernel
callsubmit_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = 'torch::Tensor dw_silu_forward(torch::Tensor x, torch::Tensor dw_w, int64_t dilation);'\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\n__global__ void dw_silu_kernel5d2(const float* __restrict__ x,\n const float* __restrict__ dw,\n float* __restrict__ a,\n int B, int C, int T) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int N = B * C * T;\n if (idx >= N) return;\n\n // Fast path for the benchmark shape: C=128, T=2048, kernel=5, dilation=2.\n int t = idx & 2047;\n int q = idx >> 11;\n int c = q & 127;\n int base = idx;\n\n const float* wc = dw + c * 5;\n float s = 0.0f;\n if (t >= 4) s = fmaf(x[base - 4], wc[0], s);\n if (t >= 2) s = fmaf(x[base - 2], wc[1], s);\n s = fmaf(x[base], wc[2], s);\n if (t + 2 < T) s = fmaf(x[base + 2], wc[3], s);\n if (t + 4 < T) s = fmaf(x[base + 4], wc[4], s);\n\n a[idx] = s / (1.0f + expf(-s));\n}\n\n__global__ void dw_silu_kernel_generic(const float* __restrict__ x,\n const float* __restrict__ dw,\n float* __restrict__ a,\n int B, int C, int T, int Ksz, int dilation) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int N = B * C * T;\n if (idx >= N) return;\n\n int t = idx % T;\n int q = idx / T;\n int c = q % C;\n int b = q / C;\n int half = Ksz >> 1;\n\n float s = 0.0f;\n for (int j = 0; j < Ksz; ++j) {\n int tt = t + (j - half) * dilation;\n if ((unsigned)tt < (unsigned)T) {\n s = fmaf(x[(b * C + c) * T + tt], dw[c * Ksz + j], s);\n }\n }\n a[idx] = s / (1.0f + expf(-s));\n}\n\ntorch::Tensor dw_silu_forward(torch::Tensor x, torch::Tensor dw_w, int64_t dilation) {\n int B = (int)x.size(0);\n int C = (int)x.size(1);\n int T = (int)x.size(2);\n int Ksz = (int)dw_w.size(2);\n auto a = torch::empty_like(x);\n int N = B * C * T;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n\n if (Ksz == 5 && dilation == 2 && T == 2048 && C == 128) {\n dw_silu_kernel5d2<<<(N + 255) / 256, 256, 0, stream>>>(\n x.data_ptr<float>(), dw_w.data_ptr<float>(), a.data_ptr<float>(), B, C, T);\n } else {\n dw_silu_kernel_generic<<<(N + 255) / 256, 256, 0, stream>>>(\n x.data_ptr<float>(), dw_w.data_ptr<float>(), a.data_ptr<float>(), B, C, T, Ksz, (int)dilation);\n }\n return a;\n}\n'''\n\n_ext = load_inline(\n name='dw_silu_pw_candidate_final',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['dw_silu_forward'],\n extra_cuda_cflags=['-O3'],\n verbose=False,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, kernel_size: int, dilation: int = 1):\n super().__init__()\n assert kernel_size % 2 == 1\n assert dilation >= 1\n self.padding = ((kernel_size - 1) * dilation) // 2\n self.dilation = dilation\n self.dw = nn.Conv1d(\n in_channels,\n in_channels,\n kernel_size,\n padding=self.padding,\n dilation=dilation,\n groups=in_channels,\n bias=False,\n )\n self.pw = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n a = _ext.dw_silu_forward(x.contiguous(), self.dw.weight, self.dilation)\n return F.conv1d(a, self.pw.weight, None, 1, 0, 1, 1)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 0.04 μs
Runtime stats: mean=0.04μs median=?μs std=0.00μs
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
Memory: 81.13 MB peak (ref: 81.13 MB, ratio: 1.0x)
Kernel launches: 12 (ref: 23, fusion ratio: 1.9167)
SOL score: 0.7069
final submitted kernel (114 lines, 3,722 chars) — full source, untruncated
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline
_cpp_src = 'torch::Tensor dw_silu_forward(torch::Tensor x, torch::Tensor dw_w, int64_t dilation);'
_cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void dw_silu_kernel5d2(const float* __restrict__ x,
const float* __restrict__ dw,
float* __restrict__ a,
int B, int C, int T) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int N = B * C * T;
if (idx >= N) return;
// Fast path for the benchmark shape: C=128, T=2048, kernel=5, dilation=2.
int t = idx & 2047;
int q = idx >> 11;
int c = q & 127;
int base = idx;
const float* wc = dw + c * 5;
float s = 0.0f;
if (t >= 4) s = fmaf(x[base - 4], wc[0], s);
if (t >= 2) s = fmaf(x[base - 2], wc[1], s);
s = fmaf(x[base], wc[2], s);
if (t + 2 < T) s = fmaf(x[base + 2], wc[3], s);
if (t + 4 < T) s = fmaf(x[base + 4], wc[4], s);
a[idx] = s / (1.0f + expf(-s));
}
__global__ void dw_silu_kernel_generic(const float* __restrict__ x,
const float* __restrict__ dw,
float* __restrict__ a,
int B, int C, int T, int Ksz, int dilation) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int N = B * C * T;
if (idx >= N) return;
int t = idx % T;
int q = idx / T;
int c = q % C;
int b = q / C;
int half = Ksz >> 1;
float s = 0.0f;
for (int j = 0; j < Ksz; ++j) {
int tt = t + (j - half) * dilation;
if ((unsigned)tt < (unsigned)T) {
s = fmaf(x[(b * C + c) * T + tt], dw[c * Ksz + j], s);
}
}
a[idx] = s / (1.0f + expf(-s));
}
torch::Tensor dw_silu_forward(torch::Tensor x, torch::Tensor dw_w, int64_t dilation) {
int B = (int)x.size(0);
int C = (int)x.size(1);
int T = (int)x.size(2);
int Ksz = (int)dw_w.size(2);
auto a = torch::empty_like(x);
int N = B * C * T;
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
if (Ksz == 5 && dilation == 2 && T == 2048 && C == 128) {
dw_silu_kernel5d2<<<(N + 255) / 256, 256, 0, stream>>>(
x.data_ptr<float>(), dw_w.data_ptr<float>(), a.data_ptr<float>(), B, C, T);
} else {
dw_silu_kernel_generic<<<(N + 255) / 256, 256, 0, stream>>>(
x.data_ptr<float>(), dw_w.data_ptr<float>(), a.data_ptr<float>(), B, C, T, Ksz, (int)dilation);
}
return a;
}
'''
_ext = load_inline(
name='dw_silu_pw_candidate_final',
cpp_sources=_cpp_src,
cuda_sources=_cuda_src,
functions=['dw_silu_forward'],
extra_cuda_cflags=['-O3'],
verbose=False,
)
class ModelNew(nn.Module):
def __init__(self, in_channels: int, out_channels: int, kernel_size: int, dilation: int = 1):
super().__init__()
assert kernel_size % 2 == 1
assert dilation >= 1
self.padding = ((kernel_size - 1) * dilation) // 2
self.dilation = dilation
self.dw = nn.Conv1d(
in_channels,
in_channels,
kernel_size,
padding=self.padding,
dilation=dilation,
groups=in_channels,
bias=False,
)
self.pw = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
a = _ext.dw_silu_forward(x.contiguous(), self.dw.weight, self.dilation)
return F.conv1d(a, self.pw.weight, None, 1, 0, 1, 1)