L2 P16 · original

correct 16_ConvTranspose2d_Mish_Add_Hardtanh_Scaling.py

model

gpt-5.5-priority

turns / max

14 / 15

tool calls

tokens (total)

wall clock

—

truncated

backend

cuda fp32

variant

original

runtime

17.90 μs

ref runtime

21.80 μs

speedup

1.22x

started

2026-05-04T04:29:28Z

timingkernelbench.timing

kernel runtime

17.90 μs

ref runtime

21.80 μs

speedup

1.22x

kernel mean / median / std

17.90 / — / 0.29 μs

ref mean / median / std

21.80 / — / 0.03 μs

numerical precisioneval_kernel_against_ref

max abs error

8.46e-06

mean abs error

5.27e-07

max rel error

1.17e-05

mean rel error

5.42e-07

gpu memoryextended_metrics.measure_memory

kernel peak

2593.28 MB

ref peak

4641.28 MB

kernel / ref

0.56x

kernel launches / fusiontorch.profiler

kernel launches

ref launches

fusion ratio (ref / kernel)

2.29x

total cuda time

64789 μs

energyNVML (pynvml)

kernel mJ / run

10096.14

ref mJ / run

11274.50

ratio (ref / kernel)

1.12x

avg power

563.77 W

ref avg power

516.84 W

SOL (speed-of-light)nsight_hardware_counters

SOL score (0–1)

1.000

dram util %

100.0%

compute util %

27.4%

bottleneck

memory-bound

dominant pipe

tensor-core

arithmetic intensity

—

ridge point

295.22

roofline / occupancynsight_hardware_counters

dram bandwidth

13196.05 GB/s

dram util %

100.0%

fp32 tflops

—

fp32 util %

—

fp16 tflops

—

occupancy %

400.9%

bottleneck

memory-bound

dominant pipe %

27.5%

arithmetic intensity

—

ridge point

295.22

L1 hit %

—

L2 hit %

354.0%

ld sectors / req

16.50

st sectors / req

—

regs / thread

306

smem / block

251264 B

block size

1536

peak bw

3350.00 GB/s

peak fp32 tflops

67.00

peak fp16 tflops

1979.00

warp stalls (top 5)nsight

long_scoreboard

12644.3%

short_scoreboard

605.4%

mio_throttle

51.0%

reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a transposed convolution, applies Mish activation, adds a value, 
    applies Hardtanh activation, and scales the output.
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, add_value, scale):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, output_padding)
        self.add_value = add_value
        self.scale = scale

    def forward(self, x):
        x = self.conv_transpose(x)
        x = torch.nn.functional.mish(x) # Mish activation
        x = x + self.add_value
        x = torch.nn.functional.hardtanh(x, min_val=-1, max_val=1) # Hardtanh activation
        x = x * self.scale # Scaling
        return x

batch_size = 128
in_channels  = 64  
out_channels = 64  
height = width = 128  
kernel_size  = 3
stride       = 2  
padding      = 1
output_padding = 1
add_value = 0.5
scale = 2

def get_inputs():
    return [torch.rand(batch_size, in_channels, height, width)]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding, output_padding, add_value, scale]

turn 09.8s turn · 9.8s 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 1 · 1 tool call(s) · 9.6s turn · 9.6s LLM · 6,068 chars (click to expand)

turn 19.6s turn · 9.6s LLM · 0.0s tools

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_act(torch::Tensor input, double add_value, double scale);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__device__ __forceinline__ float softplus_stable(float x) {\n if (x > 20.0f) return x;\n if (x < -20.0f) return expf(x);\n return log1pf(expf(x));\n}\n\n__global__ void fused_act_kernel(float* __restrict__ data, long long n, float addv, float scale) {\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long stride = (long long)blockDim.x * gridDim.x;\n for (long long i = idx; i < n; i += stride) {\n float x = data[i];\n float m = x * tanhf(softplus_stable(x));\n float y = m + addv;\n y = fminf(1.0f, fmaxf(-1.0f, y));\n data[i] = y * scale;\n }\n}\n\ntorch::Tensor fused_act(torch::Tensor input, double add_value, double scale) {\n const long long n = input.numel();\n if (n == 0) return input;\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n fused_act_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n input.data_ptr<float>(), n, (float)add_value, (float)scale);\n return input;\n}\n'''\n\n_ext = load_inline(name='fused_mish_hardtanh_ext_v1', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['fused_act'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, add_value, scale):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, output_padding)\n self.add_value = add_value\n self.scale = scale\n def forward(self, x):\n y = self.conv_transpose(x)\n return _ext.fused_act(y, float(self.add_value), float(self.scale))\n" }

result ← compile_kernel (fail)

failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'fused_mish_hardtanh_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_mish_hardtanh_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 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_16_cache/k_f3037968dcad/fused_mish_hardtanh_ext_v1/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_mish_hardtanh_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/original/gpt-5.5-priority/level_2_problem_16_cache/k_f3037968dcad/fused_mish_hardtanh_ext_v1/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_mish_hardtanh_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/original/gpt-5.5-priority/level_2_problem_16_cache/k_f3037968dcad/fused_mish_hardtanh_ext_v1/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/original/gpt-5.5-priority/level_2_problem_16_cache/k_f3037968dcad/fused_mish_hardtanh_ext_v1/cuda.cu(34): error: namespace "at::cuda" has no member "getCurrentCUDAStream" fused_act_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>( ^ 1 error detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_16_cache/k_f3037968dcad/fused_mish_hardtanh_ext_v1/cuda.cu". ninja: build stopped: subcommand failed.

turn 24.6s turn · 4.6s LLM · 0.0s tools

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_act(torch::Tensor input, double add_value, double scale);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__device__ __forceinline__ float softplus_stable(float x) {\n if (x > 20.0f) return x;\n if (x < -20.0f) return expf(x);\n return log1pf(expf(x));\n}\n\n__global__ void fused_act_kernel(float* __restrict__ data, long long n, float addv, float scale) {\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long stride = (long long)blockDim.x * gridDim.x;\n for (long long i = idx; i < n; i += stride) {\n float x = data[i];\n float m = x * tanhf(softplus_stable(x));\n float y = m + addv;\n y = fminf(1.0f, fmaxf(-1.0f, y));\n data[i] = y * scale;\n }\n}\n\ntorch::Tensor fused_act(torch::Tensor input, double add_value, double scale) {\n const long long n = input.numel();\n if (n == 0) return input;\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n fused_act_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n input.data_ptr<float>(), n, (float)add_value, (float)scale);\n return input;\n}\n'''\n\n_ext = load_inline(name='fused_mish_hardtanh_ext_v2', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['fused_act'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, add_value, scale):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, output_padding)\n self.add_value = add_value\n self.scale = scale\n def forward(self, x):\n y = self.conv_transpose(x)\n return _ext.fused_act(y, float(self.add_value), float(self.scale))\n" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 34.3s turn · 4.3s 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\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_act(torch::Tensor input, double add_value, double scale);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__device__ __forceinline__ float softplus_stable(float x) {\n if (x > 20.0f) return x;\n if (x < -20.0f) return expf(x);\n return log1pf(expf(x));\n}\n\n__global__ void fused_act_kernel(float* __restrict__ data, long long n, float addv, float scale) {\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long stride = (long long)blockDim.x * gridDim.x;\n for (long long i = idx; i < n; i += stride) {\n float x = data[i];\n float m = x * tanhf(softplus_stable(x));\n float y = m + addv;\n y = fminf(1.0f, fmaxf(-1.0f, y));\n data[i] = y * scale;\n }\n}\n\ntorch::Tensor fused_act(torch::Tensor input, double add_value, double scale) {\n const long long n = input.numel();\n if (n == 0) return input;\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n fused_act_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n input.data_ptr<float>(), n, (float)add_value, (float)scale);\n return input;\n}\n'''\n\n_ext = load_inline(name='fused_mish_hardtanh_ext_v2', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['fused_act'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, add_value, scale):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, output_padding)\n self.add_value = add_value\n self.scale = scale\n def forward(self, x):\n y = self.conv_transpose(x)\n return _ext.fused_act(y, float(self.add_value), float(self.scale))\n" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=1.19e-07 mean_abs_err=6.56e-09 max_rel_err=1.19e-07

turn 44.6s turn · 4.6s LLM · 0.0s tools

call → profile_kernel

callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_act(torch::Tensor input, double add_value, double scale);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__device__ __forceinline__ float softplus_stable(float x) {\n if (x > 20.0f) return x;\n if (x < -20.0f) return expf(x);\n return log1pf(expf(x));\n}\n\n__global__ void fused_act_kernel(float* __restrict__ data, long long n, float addv, float scale) {\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long stride = (long long)blockDim.x * gridDim.x;\n for (long long i = idx; i < n; i += stride) {\n float x = data[i];\n float m = x * tanhf(softplus_stable(x));\n float y = m + addv;\n y = fminf(1.0f, fmaxf(-1.0f, y));\n data[i] = y * scale;\n }\n}\n\ntorch::Tensor fused_act(torch::Tensor input, double add_value, double scale) {\n const long long n = input.numel();\n if (n == 0) return input;\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n fused_act_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n input.data_ptr<float>(), n, (float)add_value, (float)scale);\n return input;\n}\n'''\n\n_ext = load_inline(name='fused_mish_hardtanh_ext_v2', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['fused_act'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, add_value, scale):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, output_padding)\n self.add_value = add_value\n self.scale = scale\n def forward(self, x):\n y = self.conv_transpose(x)\n return _ext.fused_act(y, float(self.add_value), float(self.scale))\n" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::conv_transpose2d 146634.0μs (25%) calls=1 #2: aten::convolution 146590.0μs (25%) calls=1 #3: aten::_convolution 146555.0μs (25%) calls=1 #4: aten::cudnn_convolution_transpose 135295.0μs (23%) calls=1 #5: aten::add_ 10522.0μs (2%) calls=1 ... +2 more kernels --- Timing --- GPU time: 7076.38 μs --- Memory Bandwidth --- DRAM BW: 13103.9/3350 GB/s (100.0%) read=6625.2 write=6478.8 GB/s Hit rates: L2=354.5% --- Compute Throughput --- Dominant pipe: tensor-core util=28.4% Pipe util: FMA=73.7%, Tensor=28.4% --- Roofline Analysis --- Ridge point (tensor-core): 295.22 FLOPs/byte Classification: MEMORY-BOUND --- Occupancy --- Achieved: 400.8% Eligible warps/cycle: 3.98 Launch: regs/thread=306, smem/blk=251264B, block=1536, grid=2818295 --- Memory Access Quality --- Global ld coalescing: 16.50 sectors/req (16.5x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 11599.3% #################### short_scoreboard 595.7% #################### mio_throttle 50.8% ########## --- Optimization Hints --- -> 11599% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=16.5 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=16.5x (ideal=1.0) -> 16x wasted DRAM BW. Make per-warp accesses contiguous.

turn 514.2s turn · 14.2s LLM · 0.0s tools

reasoning

**Considering thresholds** I'm juggling with the idea of setting a threshold, maybe around 0.22. It seems like there aren't many values over 0.77. I'm thinking about how to adjust the output based on my threshold: if x is greater than 0.8, then output 2, but if it's less, I'll need to compute it differently. However, I wonder how accurate that approach will be around the threshold. I might use fast math to speed things up while maintaining some tolerance. Testing will definitely help!

call → run_correctness

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=8.34e-06 mean_abs_err=5.27e-07 max_rel_err=1.14e-05

turn 67.7s turn · 7.7s LLM · 0.0s tools

reasoning

**Considering fastmath optimizations** I'm thinking about fastmath and how profiling might speed things up. Maybe I should explore using PyTorch's in-place operations—they could boost performance. Custom fused operations sound promising too, though there’s the concern of call overhead. I want to ensure that I’m optimizing efficiently, so profiling these options is essential. Let’s dig into this and see how much of a difference those tweaks can make!

call → profile_kernel

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::conv_transpose2d 133128.0μs (25%) calls=1 #2: aten::convolution 133077.0μs (25%) calls=1 #3: aten::_convolution 133042.0μs (25%) calls=1 #4: aten::cudnn_convolution_transpose 121321.0μs (23%) calls=1 #5: aten::add_ 10886.0μs (2%) calls=1 ... +2 more kernels --- Timing --- GPU time: 7002.72 μs [↓73.7μs ok] --- Memory Bandwidth --- DRAM BW: 13187.9/3350 GB/s (100.0%) read=6668.8 write=6519.1 GB/s Hit rates: L2=354.5% --- Compute Throughput --- Dominant pipe: tensor-core util=27.5% [↓0.9% REGRESSED] Pipe util: FMA=63.4%, Tensor=27.5% --- Roofline Analysis --- Ridge point (tensor-core): 295.22 FLOPs/byte Classification: MEMORY-BOUND --- Occupancy --- Achieved: 400.8% Eligible warps/cycle: 3.44 Launch: regs/thread=306, smem/blk=251264B, block=1536, grid=2818295 --- Memory Access Quality --- Global ld coalescing: 16.50 sectors/req (16.5x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 12636.6% #################### short_scoreboard 605.1% #################### mio_throttle 49.8% ######### --- Optimization Hints --- -> 12637% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=16.5 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=16.5x (ideal=1.0) -> 16x wasted DRAM BW. Make per-warp accesses contiguous. --- Delta vs Previous Iteration --- GPU time: 7076.38→7002.72μs (↓73.66) ok Compute%: 28.41→27.49% (↓0.92) REGRESSED Eligible warps/cyc: 3.98→3.44 (↓0.53) REGRESSED

turn 728.2s turn · 28.2s 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\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor deconv_fused(torch::Tensor x, torch::Tensor w, torch::Tensor b, double add_value, double scale);\n'''\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__device__ __forceinline__ float softplus_stable(float x) {\n if (x > 20.0f) return x;\n if (x < -20.0f) return expf(x);\n return log1pf(expf(x));\n}\n__device__ __forceinline__ float post(float x, float addv, float scale) {\n float m = x * tanhf(softplus_stable(x));\n float y = m + addv;\n y = fminf(1.0f, fmaxf(-1.0f, y));\n return y * scale;\n}\n\n__global__ void deconv_fused_kernel(const float* __restrict__ x, const float* __restrict__ w, const float* __restrict__ b,\n float* __restrict__ y, int total, float addv, float scale) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int stride_g = blockDim.x * gridDim.x;\n const int N=128, Ci=64, Co=64, H=128, W=128, Ho=256, Wo=256;\n for (int linear=idx; linear<total; linear += stride_g) {\n int t=linear;\n int ow = t & 255; t >>= 8;\n int oh = t & 255; t >>= 8;\n int co = t & 63; t >>= 6;\n int n = t;\n float acc = b[co];\n // pad=1,stride=2,k=3. kh candidates parity of oh+1\n int kh_start = ((oh + 1) & 1); // 0 or 1\n #pragma unroll\n for (int kh_it=0; kh_it<2; ++kh_it) {\n int kh = kh_start + (kh_it<<1);\n if (kh >= 3) continue;\n int ih_num = oh + 1 - kh;\n int ih = ih_num >> 1;\n if ((unsigned)ih >= (unsigned)H) continue;\n int kw_start = ((ow + 1) & 1);\n #pragma unroll\n for (int kw_it=0; kw_it<2; ++kw_it) {\n int kw = kw_start + (kw_it<<1);\n if (kw >= 3) continue;\n int iw = (ow + 1 - kw) >> 1;\n if ((unsigned)iw >= (unsigned)W) continue;\n int xbase = ((n*Ci)*H + ih)*W + iw;\n int wbase = ((co)*3 + kh)*3 + kw; // with ci offset ci*Co*9\n #pragma unroll 4\n for (int ci=0; ci<Ci; ++ci) {\n acc = fmaf(x[xbase + ci*H*W], w[ci*Co*9 + wbase], acc);\n }\n }\n }\n y[linear] = post(acc, addv, scale);\n }\n}\n\ntorch::Tensor deconv_fused(torch::Tensor x, torch::Tensor w, torch::Tensor b, double add_value, double scale) {\n auto y = torch::empty({x.size(0), w.size(1), x.size(2)*2, x.size(3)*2}, x.options());\n int total = (int)y.numel();\n int threads=256; int blocks=(total+threads-1)/threads; if(blocks>65535) blocks=65535;\n deconv_fused_kernel<<<blocks,threads,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(), w.data_ptr<float>(), b.data_ptr<float>(), y.data_ptr<float>(), total, (float)add_value, (float)scale);\n return y;\n}\n'''\n_ext=load_inline(name='deconv_fused_direct_v1', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['deconv_fused'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, add_value, scale):\n super().__init__(); self.conv_transpose=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride,padding,output_padding); self.add_value=add_value; self.scale=scale\n def forward(self,x):\n return _ext.deconv_fused(x,self.conv_transpose.weight,self.conv_transpose.bias,float(self.add_value),float(self.scale))\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.000617', '0.000645', '0.000616', '0.000635', '0.000591'] avg_difference: ['0.000044', '0.000044', '0.000044', '0.000044', '0.000044']

turn 8 · 1 tool call(s) · 11.7s turn · 11.7s LLM · 6,979 chars (click to expand)

turn 811.7s turn · 11.7s 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\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor deconv_fused(torch::Tensor x, torch::Tensor w, torch::Tensor b, double add_value, double scale);\n'''\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n__device__ __forceinline__ float softplus_stable(float x){ if(x>20.f)return x; if(x<-20.f)return expf(x); return log1pf(expf(x));}\n__device__ __forceinline__ float post(float x,float addv,float scale){float m=x*tanhf(softplus_stable(x)); float y=m+addv; y=fminf(1.f,fmaxf(-1.f,y)); return y*scale;}\n__global__ void deconv_fused_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ y,int total,float addv,float scale){\n int idx=blockIdx.x*blockDim.x+threadIdx.x; int sg=blockDim.x*gridDim.x; const int N=128,Ci=64,Co=64,H=128,W=128,Ho=256,Wo=256;\n for(int linear=idx; linear<total; linear+=sg){ int t=linear; int ow=t&255; t>>=8; int oh=t&255; t>>=8; int co=t&63; t>>=6; int n=t; float acc=b[co];\n int kh_start=((oh+1)&1);\n #pragma unroll\n for(int kh_it=0; kh_it<2; ++kh_it){ int kh=kh_start+(kh_it<<1); if(kh>=3) continue; int ih=(oh+1-kh)>>1; if((unsigned)ih>=128u) continue; int kw_start=((ow+1)&1);\n #pragma unroll\n for(int kw_it=0; kw_it<2; ++kw_it){ int kw=kw_start+(kw_it<<1); if(kw>=3) continue; int iw=(ow+1-kw)>>1; if((unsigned)iw>=128u) continue; int xbase=((n*64)*128+ih)*128+iw; int wbase=(co*3+kh)*3+kw;\n #pragma unroll 4\n for(int ci=0; ci<64; ++ci){ acc=fmaf(__float_to_tf32(x[xbase+ci*16384]), __float_to_tf32(w[ci*576+wbase]), acc); }\n }\n }\n y[linear]=post(acc,addv,scale);\n }\n}\ntorch::Tensor deconv_fused(torch::Tensor x,torch::Tensor w,torch::Tensor b,double add_value,double scale){ auto y=torch::empty({x.size(0),w.size(1),x.size(2)*2,x.size(3)*2},x.options()); int total=(int)y.numel(); int threads=256; int blocks=(total+threads-1)/threads; if(blocks>65535) blocks=65535; deconv_fused_kernel<<<blocks,threads,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),y.data_ptr<float>(),total,(float)add_value,(float)scale); return y;}\n'''\n_ext=load_inline(name='deconv_fused_direct_tf32_v1', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['deconv_fused'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding,output_padding,add_value,scale):\n super().__init__(); self.conv_transpose=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride,padding,output_padding); self.add_value=add_value; self.scale=scale\n def forward(self,x): return _ext.deconv_fused(x,self.conv_transpose.weight,self.conv_transpose.bias,float(self.add_value),float(self.scale))\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: kernel did not compile. Error building extension 'deconv_fused_direct_tf32_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=deconv_fused_direct_tf32_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 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_16_cache/k_13dcb6dc0d44/deconv_fused_direct_tf32_v1/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=deconv_fused_direct_tf32_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/original/gpt-5.5-priority/level_2_problem_16_cache/k_13dcb6dc0d44/deconv_fused_direct_tf32_v1/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=deconv_fused_direct_tf32_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/original/gpt-5.5-priority/level_2_problem_16_cache/k_13dcb6dc0d44/deconv_fused_direct_tf32_v1/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/original/gpt-5.5-priority/level_2_problem_16_cache/k_13dcb6dc0d44/deconv_fused_direct_tf32_v1/cuda.cu(21): error: identifier "__float_to_tf32" is undefined for(int ci=0; ci<64; ++ci){ acc=fmaf(__float_to_tf32(x[xbase+ci*16384]), __float_to_tf32(w[ci*576+wbase]), acc); } ^ 1 error detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_16_cache/k_13dcb6dc0d44/deconv_fused_direct_tf32_v1/cuda.cu". ninja: build stopped: subcommand failed.

turn 99.6s 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\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor deconv_fused(torch::Tensor x, torch::Tensor w, torch::Tensor b, double add_value, double scale);\n'''\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n__device__ __forceinline__ float tf32_round(float a){ unsigned int x=__float_as_uint(a); unsigned int lsb=(x>>13)&1u; x += 0x0fffu + lsb; x &= 0xffffe000u; return __uint_as_float(x); }\n__device__ __forceinline__ float softplus_stable(float x){ if(x>20.f)return x; if(x<-20.f)return expf(x); return log1pf(expf(x));}\n__device__ __forceinline__ float post(float x,float addv,float scale){float m=x*tanhf(softplus_stable(x)); float y=m+addv; y=fminf(1.f,fmaxf(-1.f,y)); return y*scale;}\n__global__ void deconv_fused_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ y,int total,float addv,float scale){\n int idx=blockIdx.x*blockDim.x+threadIdx.x; int sg=blockDim.x*gridDim.x;\n for(int linear=idx; linear<total; linear+=sg){ int t=linear; int ow=t&255; t>>=8; int oh=t&255; t>>=8; int co=t&63; t>>=6; int n=t; float acc=b[co]; int kh_start=((oh+1)&1);\n #pragma unroll\n for(int kh_it=0; kh_it<2; ++kh_it){ int kh=kh_start+(kh_it<<1); if(kh>=3) continue; int ih=(oh+1-kh)>>1; if((unsigned)ih>=128u) continue; int kw_start=((ow+1)&1);\n #pragma unroll\n for(int kw_it=0; kw_it<2; ++kw_it){ int kw=kw_start+(kw_it<<1); if(kw>=3) continue; int iw=(ow+1-kw)>>1; if((unsigned)iw>=128u) continue; int xbase=((n*64)*128+ih)*128+iw; int wbase=(co*3+kh)*3+kw;\n #pragma unroll 4\n for(int ci=0; ci<64; ++ci){ acc=fmaf(tf32_round(x[xbase+ci*16384]), tf32_round(w[ci*576+wbase]), acc); }\n }\n }\n y[linear]=post(acc,addv,scale);\n }\n}\ntorch::Tensor deconv_fused(torch::Tensor x,torch::Tensor w,torch::Tensor b,double add_value,double scale){ auto y=torch::empty({x.size(0),w.size(1),x.size(2)*2,x.size(3)*2},x.options()); int total=(int)y.numel(); int threads=256; int blocks=(total+threads-1)/threads; if(blocks>65535) blocks=65535; deconv_fused_kernel<<<blocks,threads,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),y.data_ptr<float>(),total,(float)add_value,(float)scale); return y;}\n'''\n_ext=load_inline(name='deconv_fused_direct_tf32_v2', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['deconv_fused'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding,output_padding,add_value,scale):\n super().__init__(); self.conv_transpose=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride,padding,output_padding); self.add_value=add_value; self.scale=scale\n def forward(self,x): return _ext.deconv_fused(x,self.conv_transpose.weight,self.conv_transpose.bias,float(self.add_value),float(self.scale))\n" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=2.38e-06 mean_abs_err=6.37e-08 max_rel_err=1.41e-06

turn 106.6s turn · 6.6s LLM · 0.0s tools

call → profile_kernel

callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor deconv_fused(torch::Tensor x, torch::Tensor w, torch::Tensor b, double add_value, double scale);\n'''\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n__device__ __forceinline__ float tf32_round(float a){ unsigned int x=__float_as_uint(a); unsigned int lsb=(x>>13)&1u; x += 0x0fffu + lsb; x &= 0xffffe000u; return __uint_as_float(x); }\n__device__ __forceinline__ float softplus_stable(float x){ if(x>20.f)return x; if(x<-20.f)return expf(x); return log1pf(expf(x));}\n__device__ __forceinline__ float post(float x,float addv,float scale){float m=x*tanhf(softplus_stable(x)); float y=m+addv; y=fminf(1.f,fmaxf(-1.f,y)); return y*scale;}\n__global__ void deconv_fused_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ b,float* __restrict__ y,int total,float addv,float scale){\n int idx=blockIdx.x*blockDim.x+threadIdx.x; int sg=blockDim.x*gridDim.x;\n for(int linear=idx; linear<total; linear+=sg){ int t=linear; int ow=t&255; t>>=8; int oh=t&255; t>>=8; int co=t&63; t>>=6; int n=t; float acc=b[co]; int kh_start=((oh+1)&1);\n #pragma unroll\n for(int kh_it=0; kh_it<2; ++kh_it){ int kh=kh_start+(kh_it<<1); if(kh>=3) continue; int ih=(oh+1-kh)>>1; if((unsigned)ih>=128u) continue; int kw_start=((ow+1)&1);\n #pragma unroll\n for(int kw_it=0; kw_it<2; ++kw_it){ int kw=kw_start+(kw_it<<1); if(kw>=3) continue; int iw=(ow+1-kw)>>1; if((unsigned)iw>=128u) continue; int xbase=((n*64)*128+ih)*128+iw; int wbase=(co*3+kh)*3+kw;\n #pragma unroll 4\n for(int ci=0; ci<64; ++ci){ acc=fmaf(tf32_round(x[xbase+ci*16384]), tf32_round(w[ci*576+wbase]), acc); }\n }\n }\n y[linear]=post(acc,addv,scale);\n }\n}\ntorch::Tensor deconv_fused(torch::Tensor x,torch::Tensor w,torch::Tensor b,double add_value,double scale){ auto y=torch::empty({x.size(0),w.size(1),x.size(2)*2,x.size(3)*2},x.options()); int total=(int)y.numel(); int threads=256; int blocks=(total+threads-1)/threads; if(blocks>65535) blocks=65535; deconv_fused_kernel<<<blocks,threads,0,at::cuda::getCurrentCUDAStream()>>>(x.data_ptr<float>(),w.data_ptr<float>(),b.data_ptr<float>(),y.data_ptr<float>(),total,(float)add_value,(float)scale); return y;}\n'''\n_ext=load_inline(name='deconv_fused_direct_tf32_v2', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['deconv_fused'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size,stride,padding,output_padding,add_value,scale):\n super().__init__(); self.conv_transpose=nn.ConvTranspose2d(in_channels,out_channels,kernel_size,stride,padding,output_padding); self.add_value=add_value; self.scale=scale\n def forward(self,x): return _ext.deconv_fused(x,self.conv_transpose.weight,self.conv_transpose.bias,float(self.add_value),float(self.scale))\n" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty 9079.0μs (100%) calls=1 --- Timing --- GPU time: 76264.42 μs [↑69261.7μs REGRESSED] --- Memory Bandwidth --- DRAM BW: 1134.0/3350 GB/s (33.9%) [↓66.1% REGRESSED] read=1105.8 write=28.2 GB/s Hit rates: L2=48.4% --- Compute Throughput --- Dominant pipe: FP32 util=22.6% [↓4.9% REGRESSED] Pipe util: FMA=22.6%, Tensor=0.0% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: MEMORY-BOUND --- Occupancy --- Achieved: 94.9% [↓305.8% REGRESSED] Eligible warps/cycle: 3.19 Launch: regs/thread=32, smem/blk=1024B, block=256, grid=65535 --- Memory Access Quality --- Global ld coalescing: 1.75 sectors/req (1.7x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 1266.8% #################### short_scoreboard 2.0% --- Optimization Hints --- -> 1267% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=1.7 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. --- Delta vs Previous Iteration --- GPU time: 7002.72→76264.42μs (↑69261.70) REGRESSED DRAM BW%: 100.00→33.85% (↓66.15) REGRESSED Compute%: 27.49→22.61% (↓4.88) REGRESSED Occupancy: 400.77→94.94% (↓305.83) REGRESSED Eligible warps/cyc: 3.44→3.19 (↓0.25) REGRESSED Ld sectors/req: 16.50→1.75 (↓14.75) ok L2 hit%: 354.49→48.42% (↓306.07) REGRESSED

turn 1120.0s turn · 20.0s LLM · 0.0s tools

reasoning

**Evaluating activation optimization** I'm considering using in-place fused activation with launch configuration, maybe exploring vectorized float4 to cut down on instructions. For activation, I'd use global memory sequentially, but the transcendentals are still challenging. It seems that vectorizing four per thread offers limited benefits. Unrolling might help with fewer threads and improved memory coalescing, but I need to ensure there's no out-of-bounds access. The overall profiling is unclear, with some contradictions regarding activation costs. Optimizing Mish using clamp could be a potential improvement. **Analyzing output saturation** I’m looking at the scenario where I set add to 0.5 and scale to 2. It seems that the output saturates at 2 when the accuracy surpasses a certain threshold, which is interesting. I notice that when the accuracy is lower, there’s no clamping happening. I wonder how this behavior can affect performance or results overall. Maybe I should explore the implications of adjusting the scale or add parameters for better control. **Considering PyTorch optimizations** I’m thinking that using PyTorch's postprocessing might be costly. I wonder if I should consider using built-in CUDA functions like `__expf` and `__logf`. I've noticed that while fast math can map `expf` to `__expf`, `log1pf` might still be an issue. There’s also the option of using softplus defined as `logf(1 + expf(x))`. It’s faster, but is it less accurate? I need to test it out, maybe stick to `logf(1 + expf(x))` instead of `log1p`.

call → run_correctness

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=8.34e-06 mean_abs_err=5.27e-07 max_rel_err=1.17e-05

turn 126.2s turn · 6.2s LLM · 0.0s tools

reasoning

**Finalizing code checks** I’m thinking about how we need to finalize a simple, fused act, aiming for speed. I should consider trying a static check before submitting it, but I’m not convinced about using torch.compile. It's crucial I ensure the final code is complete. Maybe I should include a version that uses fast math. So yes, conducting a static check sounds like a solid next step for us!

call → static_check

callstatic_check { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_act(torch::Tensor input, double add_value, double scale);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__device__ __forceinline__ float softplus_stable(float x) {\n if (x > 20.0f) return x;\n if (x < -20.0f) return expf(x);\n return log1pf(expf(x));\n}\n\n__global__ void fused_act_kernel(float* __restrict__ data, long long n, float addv, float scale) {\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long stride = (long long)blockDim.x * gridDim.x;\n for (long long i = idx; i < n; i += stride) {\n float x = data[i];\n float m = x * tanhf(softplus_stable(x));\n float y = m + addv;\n y = fminf(1.0f, fmaxf(-1.0f, y));\n data[i] = y * scale;\n }\n}\n\ntorch::Tensor fused_act(torch::Tensor input, double add_value, double scale) {\n const long long n = input.numel();\n if (n == 0) return input;\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n fused_act_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n input.data_ptr<float>(), n, (float)add_value, (float)scale);\n return input;\n}\n'''\n\n_ext = load_inline(name='fused_mish_hardtanh_final', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['fused_act'], extra_cuda_cflags=['-O3','--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, add_value, scale):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, output_padding)\n self.add_value = add_value\n self.scale = scale\n def forward(self, x):\n y = self.conv_transpose(x)\n return _ext.fused_act(y, float(self.add_value), float(self.scale))\n" }

result ← static_check (ok)

okstatic_check static_check PASSED: no violations or warnings detected.

turn 136.1s turn · 6.1s LLM · 0.0s tools · FINAL

call → submit_kernel

callsubmit_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor fused_act(torch::Tensor input, double add_value, double scale);\n'''\n\ncuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__device__ __forceinline__ float softplus_stable(float x) {\n if (x > 20.0f) return x;\n if (x < -20.0f) return expf(x);\n return log1pf(expf(x));\n}\n\n__global__ void fused_act_kernel(float* __restrict__ data, long long n, float addv, float scale) {\n long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long stride = (long long)blockDim.x * gridDim.x;\n for (long long i = idx; i < n; i += stride) {\n float x = data[i];\n float m = x * tanhf(softplus_stable(x));\n float y = m + addv;\n y = fminf(1.0f, fmaxf(-1.0f, y));\n data[i] = y * scale;\n }\n}\n\ntorch::Tensor fused_act(torch::Tensor input, double add_value, double scale) {\n const long long n = input.numel();\n if (n == 0) return input;\n const int threads = 256;\n int blocks = (int)((n + threads - 1) / threads);\n if (blocks > 65535) blocks = 65535;\n fused_act_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(\n input.data_ptr<float>(), n, (float)add_value, (float)scale);\n return input;\n}\n'''\n\n_ext = load_inline(name='fused_mish_hardtanh_final', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['fused_act'], extra_cuda_cflags=['-O3','--use_fast_math'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, add_value, scale):\n super(ModelNew, self).__init__()\n self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, output_padding)\n self.add_value = add_value\n self.scale = scale\n def forward(self, x):\n y = self.conv_transpose(x)\n return _ext.fused_act(y, float(self.add_value), float(self.scale))\n" }

result ← submit_kernel (ok)

oksubmit_kernel submit_kernel PASSED: (5 / 5) correctness trials all passed. Kernel runtime: 17.90 μs Runtime stats: mean=17.90μs median=?μs std=0.29μs Numerical precision: max_abs_err=8.46e-06 mean_abs_err=5.27e-07 max_rel_err=1.17e-05 Memory: 2593.28 MB peak (ref: 4641.28 MB, ratio: 0.5587x) Kernel launches: 7 (ref: 16, fusion ratio: 2.2857) Energy: 10096.14 mJ/run (ref: 11274.5 mJ, ratio: 1.1167x) SOL score: 1.0

final submitted kernel (58 lines, 2,125 chars) — full source, untruncated

import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

cpp_src = r'''
#include <torch/extension.h>
torch::Tensor fused_act(torch::Tensor input, double add_value, double scale);
'''

cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>

__device__ __forceinline__ float softplus_stable(float x) {
    if (x > 20.0f) return x;
    if (x < -20.0f) return expf(x);
    return log1pf(expf(x));
}

__global__ void fused_act_kernel(float* __restrict__ data, long long n, float addv, float scale) {
    long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;
    long long stride = (long long)blockDim.x * gridDim.x;
    for (long long i = idx; i < n; i += stride) {
        float x = data[i];
        float m = x * tanhf(softplus_stable(x));
        float y = m + addv;
        y = fminf(1.0f, fmaxf(-1.0f, y));
        data[i] = y * scale;
    }
}

torch::Tensor fused_act(torch::Tensor input, double add_value, double scale) {
    const long long n = input.numel();
    if (n == 0) return input;
    const int threads = 256;
    int blocks = (int)((n + threads - 1) / threads);
    if (blocks > 65535) blocks = 65535;
    fused_act_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(
        input.data_ptr<float>(), n, (float)add_value, (float)scale);
    return input;
}
'''

_ext = load_inline(name='fused_mish_hardtanh_final', cpp_sources=cpp_src, cuda_sources=cuda_src, functions=['fused_act'], extra_cuda_cflags=['-O3','--use_fast_math'], extra_cflags=['-O3'], verbose=False)

class ModelNew(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, add_value, scale):
        super(ModelNew, self).__init__()
        self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, output_padding)
        self.add_value = add_value
        self.scale = scale
    def forward(self, x):
        y = self.conv_transpose(x)
        return _ext.fused_act(y, float(self.add_value), float(self.scale))