L2 P12 · original

correct 12_Gemm_Multiply_LeakyReLU.py

model

gpt-5.5-priority

turns / max

14 / 15

tool calls

tokens (total)

wall clock

—

truncated

backend

cuda fp32

variant

original

runtime

0.35 μs

ref runtime

0.38 μs

speedup

1.08x

started

2026-05-04T09:06:51Z

timingkernelbench.timing

kernel runtime

0.35 μs

ref runtime

0.38 μs

speedup

1.08x

kernel mean / median / std

0.35 / — / 0.01 μs

ref mean / median / std

0.38 / — / 0.00 μs

numerical precisioneval_kernel_against_ref

max abs error

0.00

mean abs error

0.00

max rel error

0.00

mean rel error

0.00

gpu memoryextended_metrics.measure_memory

kernel peak

609.06 MB

ref peak

641.06 MB

kernel / ref

0.95x

kernel launches / fusiontorch.profiler

kernel launches

ref launches

fusion ratio (ref / kernel)

1.40x

total cuda time

950 μs

SOL (speed-of-light)nsight_hardware_counters

SOL score (0–1)

0.837

dram util %

69.6%

compute util %

83.7%

bottleneck

compute-bound

dominant pipe

FP32

arithmetic intensity

—

ridge point

20.00

roofline / occupancynsight_hardware_counters

dram bandwidth

2331.57 GB/s

dram util %

69.6%

fp32 tflops

—

fp32 util %

—

fp16 tflops

—

occupancy %

89.9%

bottleneck

compute-bound

dominant pipe %

83.7%

arithmetic intensity

—

ridge point

20.00

L1 hit %

—

L2 hit %

141.2%

ld sectors / req

20.00

st sectors / req

—

regs / thread

286

smem / block

35840 B

block size

384

peak bw

3350.00 GB/s

peak fp32 tflops

67.00

peak fp16 tflops

1979.00

warp stalls (top 5)nsight

long_scoreboard

9165.2%

mio_throttle

230.1%

short_scoreboard

181.2%

reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a Gemm, multiplies the result, and applies LeakyReLU.
    """
    def __init__(self, in_features, out_features, multiplier, negative_slope):
        super(Model, self).__init__()
        self.gemm = nn.Linear(in_features, out_features)
        self.multiplier = multiplier
        self.leaky_relu = nn.LeakyReLU(negative_slope)

    def forward(self, x):
        x = self.gemm(x)
        x = x * self.multiplier
        x = self.leaky_relu(x)
        return x

batch_size = 1024
in_features  = 8192  
out_features = 8192
multiplier = 2.0
negative_slope = 0.1

def get_inputs():
    return [torch.rand(batch_size, in_features)]

def get_init_inputs():
    return [in_features, out_features, multiplier, negative_slope]

turn 010.9s turn · 10.9s LLM · 0.0s tools

reasoning

**Crafting ModelNew Operations** I'm thinking about how to craft the ModelNew with the right parameters. Should I initialize linear weights the same way? In the evaluation harness, it seems I may need to copy the state dict from Model to ModelNew. I should define operations like gemm or weight/bias and check references to nn.Linear. Do I need the same state keys as usual? I'll also consider using self.gemm = nn.Linear for the forward calls. It’s quite a complex calculation with large dimensions, but I know PyTorch cublas is optimized for this. **Exploring Optimization Options** I’m considering whether to use cublasLt with a fused epilogue or maybe a custom matmul with tensor cores. I’d have input and weights in fp32 with a tolerance of 1e-4. I think PyTorch’s linear likely defaults to TF32, so I’ll check that. It might be simplest to call torch.mm in C++ and then apply the CUDA kernel for the post-processing. That could reduce overheads but still maintain correctness. I will also consider incorporating custom CUDA kernels as needed for efficiency. **Creating Fused Forward Function** I'm defining the fused_forward host wrapper that takes in several parameters: x, weight, bias, multiplier, and slope. The idea is that I can automatically compute y by calling at::linear with those inputs. Then, I’ll need to launch the kernel for in-place processing. It seems like a streamlined approach, but I want to ensure everything aligns correctly for optimal performance. I’ll keep track of these details as I proceed with the implementation.

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 117.0s turn · 17.0s 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_sources = r'''\n#include <torch/extension.h>\n\nvoid fused_act_launcher(torch::Tensor y, double multiplier, double negative_slope);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope) {\n auto y = at::linear(x, weight, bias);\n fused_act_launcher(y, multiplier, negative_slope);\n return y;\n}\n'''\n\ncuda_sources = 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 fused_act_kernel(float* __restrict__ y, long long n, float mult, float slope) {\n long long idx4 = (long long)(blockIdx.x) * blockDim.x + threadIdx.x;\n long long i = idx4 * 4;\n if (i + 3 < n) {\n float4 v = *reinterpret_cast<float4*>(y + i);\n v.x *= mult; v.y *= mult; v.z *= mult; v.w *= mult;\n v.x = v.x >= 0.0f ? v.x : v.x * slope;\n v.y = v.y >= 0.0f ? v.y : v.y * slope;\n v.z = v.z >= 0.0f ? v.z : v.z * slope;\n v.w = v.w >= 0.0f ? v.w : v.w * slope;\n *reinterpret_cast<float4*>(y + i) = v;\n } else {\n for (; i < n; ++i) {\n float a = y[i] * mult;\n y[i] = a >= 0.0f ? a : a * slope;\n }\n }\n}\n\nvoid fused_act_launcher(torch::Tensor y, double multiplier, double negative_slope) {\n const long long n = y.numel();\n if (n == 0) return;\n const int threads = 256;\n const int blocks = (int)((n + threads * 4LL - 1) / (threads * 4LL));\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n fused_act_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), n, (float)multiplier, (float)negative_slope);\n}\n'''\n\n_ext = load_inline(\n name='gemm_fused_leaky_ext_v1',\n cpp_sources=cpp_sources,\n cuda_sources=cuda_sources,\n functions=['fused_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, multiplier, negative_slope):\n super(ModelNew, self).__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.multiplier = float(multiplier)\n self.negative_slope = float(negative_slope)\n\n def forward(self, x):\n return _ext.fused_forward(x, self.gemm.weight, self.gemm.bias, self.multiplier, self.negative_slope)\n" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 25.6s turn · 5.6s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_sources = r'''\n#include <torch/extension.h>\n\nvoid fused_act_launcher(torch::Tensor y, double multiplier, double negative_slope);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope) {\n auto y = at::linear(x, weight, bias);\n fused_act_launcher(y, multiplier, negative_slope);\n return y;\n}\n'''\n\ncuda_sources = 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 fused_act_kernel(float* __restrict__ y, long long n, float mult, float slope) {\n long long idx4 = (long long)(blockIdx.x) * blockDim.x + threadIdx.x;\n long long i = idx4 * 4;\n if (i + 3 < n) {\n float4 v = *reinterpret_cast<float4*>(y + i);\n v.x *= mult; v.y *= mult; v.z *= mult; v.w *= mult;\n v.x = v.x >= 0.0f ? v.x : v.x * slope;\n v.y = v.y >= 0.0f ? v.y : v.y * slope;\n v.z = v.z >= 0.0f ? v.z : v.z * slope;\n v.w = v.w >= 0.0f ? v.w : v.w * slope;\n *reinterpret_cast<float4*>(y + i) = v;\n } else {\n for (; i < n; ++i) {\n float a = y[i] * mult;\n y[i] = a >= 0.0f ? a : a * slope;\n }\n }\n}\n\nvoid fused_act_launcher(torch::Tensor y, double multiplier, double negative_slope) {\n const long long n = y.numel();\n if (n == 0) return;\n const int threads = 256;\n const int blocks = (int)((n + threads * 4LL - 1) / (threads * 4LL));\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n fused_act_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), n, (float)multiplier, (float)negative_slope);\n}\n'''\n\n_ext = load_inline(\n name='gemm_fused_leaky_ext_v1',\n cpp_sources=cpp_sources,\n cuda_sources=cuda_sources,\n functions=['fused_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, multiplier, negative_slope):\n super(ModelNew, self).__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.multiplier = float(multiplier)\n self.negative_slope = float(negative_slope)\n\n def forward(self, x):\n return _ext.fused_forward(x, self.gemm.weight, self.gemm.bias, self.multiplier, self.negative_slope)\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 35.6s turn · 5.6s 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_sources = r'''\n#include <torch/extension.h>\n\nvoid fused_act_launcher(torch::Tensor y, double multiplier, double negative_slope);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope) {\n auto y = at::linear(x, weight, bias);\n fused_act_launcher(y, multiplier, negative_slope);\n return y;\n}\n'''\n\ncuda_sources = 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 fused_act_kernel(float* __restrict__ y, long long n, float mult, float slope) {\n long long idx4 = (long long)(blockIdx.x) * blockDim.x + threadIdx.x;\n long long i = idx4 * 4;\n if (i + 3 < n) {\n float4 v = *reinterpret_cast<float4*>(y + i);\n v.x *= mult; v.y *= mult; v.z *= mult; v.w *= mult;\n v.x = v.x >= 0.0f ? v.x : v.x * slope;\n v.y = v.y >= 0.0f ? v.y : v.y * slope;\n v.z = v.z >= 0.0f ? v.z : v.z * slope;\n v.w = v.w >= 0.0f ? v.w : v.w * slope;\n *reinterpret_cast<float4*>(y + i) = v;\n } else {\n for (; i < n; ++i) {\n float a = y[i] * mult;\n y[i] = a >= 0.0f ? a : a * slope;\n }\n }\n}\n\nvoid fused_act_launcher(torch::Tensor y, double multiplier, double negative_slope) {\n const long long n = y.numel();\n if (n == 0) return;\n const int threads = 256;\n const int blocks = (int)((n + threads * 4LL - 1) / (threads * 4LL));\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n fused_act_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), n, (float)multiplier, (float)negative_slope);\n}\n'''\n\n_ext = load_inline(\n name='gemm_fused_leaky_ext_v1',\n cpp_sources=cpp_sources,\n cuda_sources=cuda_sources,\n functions=['fused_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, multiplier, negative_slope):\n super(ModelNew, self).__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.multiplier = float(multiplier)\n self.negative_slope = float(negative_slope)\n\n def forward(self, x):\n return _ext.fused_forward(x, self.gemm.weight, self.gemm.bias, self.multiplier, self.negative_slope)\n" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::linear 140505.0μs (50%) calls=1 #2: aten::addmm 140363.0μs (50%) calls=1 #3: aten::t 82.0μs (0%) calls=1 #4: aten::transpose 39.0μs (0%) calls=1 #5: aten::as_strided 14.0μs (0%) calls=1 --- Timing --- GPU time: 2686.91 μs --- Memory Bandwidth --- DRAM BW: 2340.6/3350 GB/s (69.9%) read=1766.4 write=574.2 GB/s Hit rates: L2=142.0% --- Compute Throughput --- Dominant pipe: FP32 util=83.7% Pipe util: FMA=83.7%, Tensor=0.0% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: COMPUTE-BOUND --- Occupancy --- Achieved: 89.6% Eligible warps/cycle: 1.69 Launch: regs/thread=286, smem/blk=35840B, block=384, grid=8704 --- Memory Access Quality --- Global ld coalescing: 20.00 sectors/req (20.0x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 9261.2% #################### mio_throttle 232.6% #################### short_scoreboard 178.0% #################### --- Optimization Hints --- -> 9261% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=20.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=20.0x (ideal=1.0) -> 20x wasted DRAM BW. Make per-warp accesses contiguous. -> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.

turn 433.5s turn · 33.5s LLM · 0.0s tools

reasoning

**Considering cublasSgemm usage** I’m thinking about using cublasSgemm on the H100, and it might utilize TF32 if the math mode is set to TF32_TENSOR_OP. But if PyTorch is set to global false, I’ll have to approach it carefully to ensure everything is handled correctly. Ultimately, verifying correctness will be key here. I may need to write some extension code to sort everything out and make sure it runs smoothly.

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_sources = r'''\n#include <torch/extension.h>\n\nvoid gemm_bias_act_launcher(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor y, double multiplier, double negative_slope);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope) {\n auto y = torch::empty({x.size(0), weight.size(0)}, x.options());\n gemm_bias_act_launcher(x, weight, bias, y, multiplier, negative_slope);\n return y;\n}\n'''\n\ncuda_sources = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cublas_v2.h>\n\n__global__ void bias_act_kernel(float* __restrict__ y, const float* __restrict__ bias, long long n, int cols, float mult, float slope) {\n long long idx4 = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long i = idx4 * 4;\n if (i + 3 < n) {\n float4 v = *reinterpret_cast<float4*>(y + i);\n int c = (int)(i % cols);\n float4 b = *reinterpret_cast<const float4*>(bias + c);\n v.x = (v.x + b.x) * mult;\n v.y = (v.y + b.y) * mult;\n v.z = (v.z + b.z) * mult;\n v.w = (v.w + b.w) * mult;\n v.x = v.x >= 0.0f ? v.x : v.x * slope;\n v.y = v.y >= 0.0f ? v.y : v.y * slope;\n v.z = v.z >= 0.0f ? v.z : v.z * slope;\n v.w = v.w >= 0.0f ? v.w : v.w * slope;\n *reinterpret_cast<float4*>(y + i) = v;\n } else {\n for (; i < n; ++i) {\n float a = (y[i] + bias[i % cols]) * mult;\n y[i] = a >= 0.0f ? a : a * slope;\n }\n }\n}\n\nvoid gemm_bias_act_launcher(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor y, double multiplier, double negative_slope) {\n const int m = (int)x.size(0);\n const int k = (int)x.size(1);\n const int ncol = (int)weight.size(0);\n cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n cublasSetStream(handle, stream);\n const float alpha = 1.0f, beta = 0.0f;\n // Row-major y[m,n] = x[m,k] * weight[n,k]^T, represented as column-major y^T[n,m]\n cublasSgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N,\n ncol, m, k,\n &alpha,\n weight.data_ptr<float>(), k,\n x.data_ptr<float>(), k,\n &beta,\n y.data_ptr<float>(), ncol);\n const long long total = (long long)m * ncol;\n const int threads = 256;\n const int blocks = (int)((total + threads * 4LL - 1) / (threads * 4LL));\n bias_act_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), bias.data_ptr<float>(), total, ncol, (float)multiplier, (float)negative_slope);\n}\n'''\n\n_ext = load_inline(\n name='gemm_fused_leaky_ext_v2',\n cpp_sources=cpp_sources,\n cuda_sources=cuda_sources,\n functions=['fused_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, multiplier, negative_slope):\n super(ModelNew, self).__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.multiplier = float(multiplier)\n self.negative_slope = float(negative_slope)\n\n def forward(self, x):\n return _ext.fused_forward(x, self.gemm.weight, self.gemm.bias, self.multiplier, self.negative_slope)\n" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 57.1s turn · 7.1s LLM · 0.0s tools

call → run_correctness

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00

turn 68.3s turn · 8.3s 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_sources = r'''\n#include <torch/extension.h>\n\nvoid gemm_bias_act_launcher(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor y, double multiplier, double negative_slope);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope) {\n auto y = torch::empty({x.size(0), weight.size(0)}, x.options());\n gemm_bias_act_launcher(x, weight, bias, y, multiplier, negative_slope);\n return y;\n}\n'''\n\ncuda_sources = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cublas_v2.h>\n\n__global__ void bias_act_kernel(float* __restrict__ y, const float* __restrict__ bias, long long n, int cols, float mult, float slope) {\n long long idx4 = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long i = idx4 * 4;\n if (i + 3 < n) {\n float4 v = *reinterpret_cast<float4*>(y + i);\n int c = (int)(i % cols);\n float4 b = *reinterpret_cast<const float4*>(bias + c);\n v.x = (v.x + b.x) * mult;\n v.y = (v.y + b.y) * mult;\n v.z = (v.z + b.z) * mult;\n v.w = (v.w + b.w) * mult;\n v.x = v.x >= 0.0f ? v.x : v.x * slope;\n v.y = v.y >= 0.0f ? v.y : v.y * slope;\n v.z = v.z >= 0.0f ? v.z : v.z * slope;\n v.w = v.w >= 0.0f ? v.w : v.w * slope;\n *reinterpret_cast<float4*>(y + i) = v;\n } else {\n for (; i < n; ++i) {\n float a = (y[i] + bias[i % cols]) * mult;\n y[i] = a >= 0.0f ? a : a * slope;\n }\n }\n}\n\nvoid gemm_bias_act_launcher(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor y, double multiplier, double negative_slope) {\n const int m = (int)x.size(0);\n const int k = (int)x.size(1);\n const int ncol = (int)weight.size(0);\n cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n cublasSetStream(handle, stream);\n const float alpha = 1.0f, beta = 0.0f;\n cublasSgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N,\n ncol, m, k,\n &alpha,\n weight.data_ptr<float>(), k,\n x.data_ptr<float>(), k,\n &beta,\n y.data_ptr<float>(), ncol);\n const long long total = (long long)m * ncol;\n const int threads = 256;\n const int blocks = (int)((total + threads * 4LL - 1) / (threads * 4LL));\n bias_act_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), bias.data_ptr<float>(), total, ncol, (float)multiplier, (float)negative_slope);\n}\n'''\n\n_ext = load_inline(\n name='gemm_fused_leaky_ext_v2',\n cpp_sources=cpp_sources,\n cuda_sources=cuda_sources,\n functions=['fused_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, multiplier, negative_slope):\n super(ModelNew, self).__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.multiplier = float(multiplier)\n self.negative_slope = float(negative_slope)\n\n def forward(self, x):\n return _ext.fused_forward(x, self.gemm.weight, self.gemm.bias, self.multiplier, self.negative_slope)\n" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty 287.0μs (100%) calls=1 --- Timing --- GPU time: 2730.40 μs [↑43.5μs REGRESSED] --- Memory Bandwidth --- DRAM BW: 2343.7/3350 GB/s (70.0%) [↑0.1% ok] read=1775.8 write=567.9 GB/s Hit rates: L2=145.3% --- Compute Throughput --- Dominant pipe: FP32 util=87.0% [↑3.4% ok] Pipe util: FMA=87.0%, Tensor=0.0% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: COMPUTE-BOUND --- Occupancy --- Achieved: 88.5% [↓1.1% REGRESSED] Eligible warps/cycle: 2.31 Launch: regs/thread=283, smem/blk=35328B, block=384, grid=8704 --- Memory Access Quality --- Global ld coalescing: 20.00 sectors/req (20.0x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 3568.4% #################### short_scoreboard 117.9% #################### mio_throttle 48.4% ######### --- Optimization Hints --- -> 3568% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=20.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=20.0x (ideal=1.0) -> 20x wasted DRAM BW. Make per-warp accesses contiguous. -> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics. --- Delta vs Previous Iteration --- GPU time: 2686.91→2730.40μs (↑43.49) REGRESSED DRAM BW%: 69.87→69.96% (↑0.09) ok Compute%: 83.68→87.04% (↑3.36) ok Occupancy: 89.57→88.52% (↓1.05) REGRESSED Eligible warps/cyc: 1.69→2.31 (↑0.62) ok L2 hit%: 141.99→145.34% (↑3.35) ok

turn 7 · 1 tool call(s) · 26.5s turn · 26.5s LLM · 6,274 chars (click to expand)

turn 726.5s turn · 26.5s LLM · 0.0s tools

reasoning

**Evaluating cublasLt implementation** If I copy the state_dict and make changes to the bias, there won’t be a hook. I could compute the scaling in the forward pass, but it doesn’t seem worth it. Instead, I should implement cublasLt with a bias epilogue and then create an action kernel without bias. I’m wondering if cublasLt will produce the same results — correctness is likely preserved, and performance might improve. I need to check the code.

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_sources = r'''\n#include <torch/extension.h>\nvoid lt_bias_act_launcher(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor y, double multiplier, double negative_slope);\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope) {\n auto y = torch::empty({x.size(0), weight.size(0)}, x.options());\n lt_bias_act_launcher(x, weight, bias, y, multiplier, negative_slope);\n return y;\n}\n'''\n\ncuda_sources = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cublasLt.h>\n\n__global__ void act_kernel(float* __restrict__ y, long long n, float mult, float slope) {\n long long idx4 = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long i = idx4 * 4;\n if (i + 3 < n) {\n float4 v = *reinterpret_cast<float4*>(y + i);\n v.x *= mult; v.y *= mult; v.z *= mult; v.w *= mult;\n v.x = v.x >= 0.0f ? v.x : v.x * slope;\n v.y = v.y >= 0.0f ? v.y : v.y * slope;\n v.z = v.z >= 0.0f ? v.z : v.z * slope;\n v.w = v.w >= 0.0f ? v.w : v.w * slope;\n *reinterpret_cast<float4*>(y + i) = v;\n } else {\n for (; i < n; ++i) { float a = y[i] * mult; y[i] = a >= 0.0f ? a : a * slope; }\n }\n}\n\nstatic cublasLtHandle_t get_lt_handle() {\n static cublasLtHandle_t handle = nullptr;\n if (handle == nullptr) cublasLtCreate(&handle);\n return handle;\n}\n\nvoid lt_bias_act_launcher(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor y, double multiplier, double negative_slope) {\n int m = (int)x.size(0), k = (int)x.size(1), n = (int)weight.size(0);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n cublasLtHandle_t ltHandle = get_lt_handle();\n\n cublasLtMatmulDesc_t opDesc;\n cublasLtMatrixLayout_t aDesc, bDesc, cDesc, dDesc;\n cublasLtMatmulDescCreate(&opDesc, CUBLAS_COMPUTE_32F, CUDA_R_32F);\n cublasOperation_t transa = CUBLAS_OP_T, transb = CUBLAS_OP_N;\n cublasLtMatmulDescSetAttribute(opDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(transa));\n cublasLtMatmulDescSetAttribute(opDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(transb));\n cublasLtEpilogue_t epi = CUBLASLT_EPILOGUE_BIAS;\n cublasLtMatmulDescSetAttribute(opDesc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epi, sizeof(epi));\n const void* bias_ptr = bias.data_ptr<float>();\n cublasLtMatmulDescSetAttribute(opDesc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias_ptr, sizeof(bias_ptr));\n\n cublasLtMatrixLayoutCreate(&aDesc, CUDA_R_32F, k, n, k);\n cublasLtMatrixLayoutCreate(&bDesc, CUDA_R_32F, k, m, k);\n cublasLtMatrixLayoutCreate(&cDesc, CUDA_R_32F, n, m, n);\n cublasLtMatrixLayoutCreate(&dDesc, CUDA_R_32F, n, m, n);\n\n cublasLtMatmulPreference_t pref;\n cublasLtMatmulPreferenceCreate(&pref);\n size_t workspaceSize = 0;\n cublasLtMatmulPreferenceSetAttribute(pref, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, &workspaceSize, sizeof(workspaceSize));\n cublasLtMatmulHeuristicResult_t heuristic;\n int returned = 0;\n cublasLtMatmulAlgoGetHeuristic(ltHandle, opDesc, aDesc, bDesc, cDesc, dDesc, pref, 1, &heuristic, &returned);\n const float alpha = 1.0f, beta = 0.0f;\n cublasLtMatmul(ltHandle, opDesc, &alpha,\n weight.data_ptr<float>(), aDesc,\n x.data_ptr<float>(), bDesc,\n &beta,\n y.data_ptr<float>(), cDesc,\n y.data_ptr<float>(), dDesc,\n returned ? &heuristic.algo : nullptr,\n nullptr, 0, stream);\n\n cublasLtMatmulPreferenceDestroy(pref);\n cublasLtMatrixLayoutDestroy(dDesc); cublasLtMatrixLayoutDestroy(cDesc); cublasLtMatrixLayoutDestroy(bDesc); cublasLtMatrixLayoutDestroy(aDesc);\n cublasLtMatmulDescDestroy(opDesc);\n\n long long total = (long long)m * n;\n int threads = 256;\n int blocks = (int)((total + threads * 4LL - 1) / (threads * 4LL));\n act_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), total, (float)multiplier, (float)negative_slope);\n}\n'''\n\n_ext = load_inline(name='gemm_fused_leaky_ext_v3', cpp_sources=cpp_sources, cuda_sources=cuda_sources, functions=['fused_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, multiplier, negative_slope):\n super(ModelNew, self).__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.multiplier = float(multiplier)\n self.negative_slope = float(negative_slope)\n def forward(self, x):\n return _ext.fused_forward(x, self.gemm.weight, self.gemm.bias, self.multiplier, self.negative_slope)\n" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 8 · 1 tool call(s) · 13.6s turn · 13.6s LLM · 6,047 chars (click to expand)

turn 813.6s turn · 13.6s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_sources = r'''\n#include <torch/extension.h>\nvoid lt_bias_act_launcher(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor y, double multiplier, double negative_slope);\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope) {\n auto y = torch::empty({x.size(0), weight.size(0)}, x.options());\n lt_bias_act_launcher(x, weight, bias, y, multiplier, negative_slope);\n return y;\n}\n'''\n\ncuda_sources = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cublasLt.h>\n\n__global__ void act_kernel(float* __restrict__ y, long long n, float mult, float slope) {\n long long idx4 = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long i = idx4 * 4;\n if (i + 3 < n) {\n float4 v = *reinterpret_cast<float4*>(y + i);\n v.x *= mult; v.y *= mult; v.z *= mult; v.w *= mult;\n v.x = v.x >= 0.0f ? v.x : v.x * slope;\n v.y = v.y >= 0.0f ? v.y : v.y * slope;\n v.z = v.z >= 0.0f ? v.z : v.z * slope;\n v.w = v.w >= 0.0f ? v.w : v.w * slope;\n *reinterpret_cast<float4*>(y + i) = v;\n } else {\n for (; i < n; ++i) { float a = y[i] * mult; y[i] = a >= 0.0f ? a : a * slope; }\n }\n}\n\nstatic cublasLtHandle_t get_lt_handle() {\n static cublasLtHandle_t handle = nullptr;\n if (handle == nullptr) cublasLtCreate(&handle);\n return handle;\n}\n\nvoid lt_bias_act_launcher(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor y, double multiplier, double negative_slope) {\n int m = (int)x.size(0), k = (int)x.size(1), n = (int)weight.size(0);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n cublasLtHandle_t ltHandle = get_lt_handle();\n\n cublasLtMatmulDesc_t opDesc;\n cublasLtMatrixLayout_t aDesc, bDesc, cDesc, dDesc;\n cublasLtMatmulDescCreate(&opDesc, CUBLAS_COMPUTE_32F, CUDA_R_32F);\n cublasOperation_t transa = CUBLAS_OP_T, transb = CUBLAS_OP_N;\n cublasLtMatmulDescSetAttribute(opDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(transa));\n cublasLtMatmulDescSetAttribute(opDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(transb));\n cublasLtEpilogue_t epi = CUBLASLT_EPILOGUE_BIAS;\n cublasLtMatmulDescSetAttribute(opDesc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epi, sizeof(epi));\n const void* bias_ptr = bias.data_ptr<float>();\n cublasLtMatmulDescSetAttribute(opDesc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias_ptr, sizeof(bias_ptr));\n\n cublasLtMatrixLayoutCreate(&aDesc, CUDA_R_32F, k, n, k);\n cublasLtMatrixLayoutCreate(&bDesc, CUDA_R_32F, k, m, k);\n cublasLtMatrixLayoutCreate(&cDesc, CUDA_R_32F, n, m, n);\n cublasLtMatrixLayoutCreate(&dDesc, CUDA_R_32F, n, m, n);\n\n cublasLtMatmulPreference_t pref;\n cublasLtMatmulPreferenceCreate(&pref);\n size_t workspaceSize = 0;\n cublasLtMatmulPreferenceSetAttribute(pref, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, &workspaceSize, sizeof(workspaceSize));\n cublasLtMatmulHeuristicResult_t heuristic;\n int returned = 0;\n cublasLtMatmulAlgoGetHeuristic(ltHandle, opDesc, aDesc, bDesc, cDesc, dDesc, pref, 1, &heuristic, &returned);\n const float alpha = 1.0f, beta = 0.0f;\n cublasLtMatmul(ltHandle, opDesc, &alpha,\n weight.data_ptr<float>(), aDesc,\n x.data_ptr<float>(), bDesc,\n &beta,\n y.data_ptr<float>(), cDesc,\n y.data_ptr<float>(), dDesc,\n returned ? &heuristic.algo : nullptr,\n nullptr, 0, stream);\n\n cublasLtMatmulPreferenceDestroy(pref);\n cublasLtMatrixLayoutDestroy(dDesc); cublasLtMatrixLayoutDestroy(cDesc); cublasLtMatrixLayoutDestroy(bDesc); cublasLtMatrixLayoutDestroy(aDesc);\n cublasLtMatmulDescDestroy(opDesc);\n\n long long total = (long long)m * n;\n int threads = 256;\n int blocks = (int)((total + threads * 4LL - 1) / (threads * 4LL));\n act_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), total, (float)multiplier, (float)negative_slope);\n}\n'''\n\n_ext = load_inline(name='gemm_fused_leaky_ext_v3', cpp_sources=cpp_sources, cuda_sources=cuda_sources, functions=['fused_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, multiplier, negative_slope):\n super(ModelNew, self).__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.multiplier = float(multiplier)\n self.negative_slope = float(negative_slope)\n def forward(self, x):\n return _ext.fused_forward(x, self.gemm.weight, self.gemm.bias, self.multiplier, self.negative_slope)\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.000986', '0.000980', '0.001021', '0.000998', '0.000963'] avg_difference: ['0.000084', '0.000084', '0.000084', '0.000084', '0.000084']

turn 910.7s turn · 10.7s LLM · 0.0s tools

reasoning

**Considering compute mode** I'm looking into using TF32 and thinking about whether I need to set the compute or math mode to pedantic. It seems I might need to use `cublasLtMatmulDescCreate` with the `CUBLAS_COMPUTE_32F_PEDANTIC` option. I should give it a try and see how it goes! There’s definitely some detail to work through, but I think I’m on the right track to optimize my computations.

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_sources = r'''\n#include <torch/extension.h>\nvoid lt_bias_act_launcher(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor y, double multiplier, double negative_slope);\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope) {\n auto y = torch::empty({x.size(0), weight.size(0)}, x.options());\n lt_bias_act_launcher(x, weight, bias, y, multiplier, negative_slope);\n return y;\n}\n'''\n\ncuda_sources = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cublasLt.h>\n\n__global__ void act_kernel(float* __restrict__ y, long long n, float mult, float slope) {\n long long idx4 = (long long)blockIdx.x * blockDim.x + threadIdx.x;\n long long i = idx4 * 4;\n if (i + 3 < n) {\n float4 v = *reinterpret_cast<float4*>(y + i);\n v.x *= mult; v.y *= mult; v.z *= mult; v.w *= mult;\n v.x = v.x >= 0.0f ? v.x : v.x * slope;\n v.y = v.y >= 0.0f ? v.y : v.y * slope;\n v.z = v.z >= 0.0f ? v.z : v.z * slope;\n v.w = v.w >= 0.0f ? v.w : v.w * slope;\n *reinterpret_cast<float4*>(y + i) = v;\n } else { for (; i < n; ++i) { float a = y[i] * mult; y[i] = a >= 0.0f ? a : a * slope; } }\n}\nstatic cublasLtHandle_t get_lt_handle(){ static cublasLtHandle_t h=nullptr; if(!h) cublasLtCreate(&h); return h; }\nvoid lt_bias_act_launcher(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, torch::Tensor y, double multiplier, double negative_slope) {\n int m=(int)x.size(0), k=(int)x.size(1), n=(int)weight.size(0); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); cublasLtHandle_t lt=get_lt_handle();\n cublasLtMatmulDesc_t op; cublasLtMatrixLayout_t ad,bd,cd,dd;\n cublasLtMatmulDescCreate(&op, CUBLAS_COMPUTE_32F_PEDANTIC, CUDA_R_32F);\n cublasOperation_t ta=CUBLAS_OP_T,tb=CUBLAS_OP_N; cublasLtMatmulDescSetAttribute(op,CUBLASLT_MATMUL_DESC_TRANSA,&ta,sizeof(ta)); cublasLtMatmulDescSetAttribute(op,CUBLASLT_MATMUL_DESC_TRANSB,&tb,sizeof(tb));\n cublasLtEpilogue_t epi=CUBLASLT_EPILOGUE_BIAS; cublasLtMatmulDescSetAttribute(op,CUBLASLT_MATMUL_DESC_EPILOGUE,&epi,sizeof(epi)); const void* bp=bias.data_ptr<float>(); cublasLtMatmulDescSetAttribute(op,CUBLASLT_MATMUL_DESC_BIAS_POINTER,&bp,sizeof(bp));\n cublasLtMatrixLayoutCreate(&ad,CUDA_R_32F,k,n,k); cublasLtMatrixLayoutCreate(&bd,CUDA_R_32F,k,m,k); cublasLtMatrixLayoutCreate(&cd,CUDA_R_32F,n,m,n); cublasLtMatrixLayoutCreate(&dd,CUDA_R_32F,n,m,n);\n cublasLtMatmulPreference_t pref; cublasLtMatmulPreferenceCreate(&pref); size_t ws=0; cublasLtMatmulPreferenceSetAttribute(pref,CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES,&ws,sizeof(ws)); cublasLtMatmulHeuristicResult_t heur; int ret=0; cublasLtMatmulAlgoGetHeuristic(lt,op,ad,bd,cd,dd,pref,1,&heur,&ret);\n const float alpha=1.0f,beta=0.0f; cublasLtMatmul(lt,op,&alpha,weight.data_ptr<float>(),ad,x.data_ptr<float>(),bd,&beta,y.data_ptr<float>(),cd,y.data_ptr<float>(),dd,ret?&heur.algo:nullptr,nullptr,0,stream);\n cublasLtMatmulPreferenceDestroy(pref); cublasLtMatrixLayoutDestroy(dd); cublasLtMatrixLayoutDestroy(cd); cublasLtMatrixLayoutDestroy(bd); cublasLtMatrixLayoutDestroy(ad); cublasLtMatmulDescDestroy(op);\n long long total=(long long)m*n; int threads=256; int blocks=(int)((total+threads*4LL-1)/(threads*4LL)); act_kernel<<<blocks,threads,0,stream>>>(y.data_ptr<float>(),total,(float)multiplier,(float)negative_slope);\n}\n'''\n_ext = load_inline(name='gemm_fused_leaky_ext_v3p', cpp_sources=cpp_sources, cuda_sources=cuda_sources, functions=['fused_forward'], extra_cflags=['-O3'], extra_cuda_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, multiplier, negative_slope):\n super(ModelNew,self).__init__(); self.gemm=nn.Linear(in_features,out_features); self.multiplier=float(multiplier); self.negative_slope=float(negative_slope)\n def forward(self,x): return _ext.fused_forward(x,self.gemm.weight,self.gemm.bias,self.multiplier,self.negative_slope)\n" }

result ← run_correctness (fail)

turn 1017.0s turn · 17.0s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_sources=r'''\n#include <torch/extension.h>\nvoid launcher(torch::Tensor x, torch::Tensor w, torch::Tensor b, torch::Tensor y, double mult, double slope);\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope){auto y=torch::empty({x.size(0), weight.size(0)}, x.options()); launcher(x,weight,bias,y,multiplier,negative_slope); return y;}\n'''\ncuda_sources=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n#include <cublas_v2.h>\n__global__ void fill_bias(float* __restrict__ y,const float* __restrict__ b,long long total,int cols){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x; long long i=idx*4; if(i+3<total){int c=(int)(i%cols); float4 v=*reinterpret_cast<const float4*>(b+c); *reinterpret_cast<float4*>(y+i)=v;} else {for(;i<total;++i)y[i]=b[i%cols];}}\n__global__ void act(float* __restrict__ y,long long total,float mult,float slope){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x; long long i=idx*4; if(i+3<total){float4 v=*reinterpret_cast<float4*>(y+i); v.x*=mult;v.y*=mult;v.z*=mult;v.w*=mult; v.x=v.x>=0?v.x:v.x*slope; v.y=v.y>=0?v.y:v.y*slope; v.z=v.z>=0?v.z:v.z*slope; v.w=v.w>=0?v.w:v.w*slope; *reinterpret_cast<float4*>(y+i)=v;} else {for(;i<total;++i){float a=y[i]*mult;y[i]=a>=0?a:a*slope;}}}\nvoid launcher(torch::Tensor x, torch::Tensor w, torch::Tensor b, torch::Tensor y, double mult, double slope){int m=(int)x.size(0), k=(int)x.size(1), n=(int)w.size(0); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); long long total=(long long)m*n; int threads=256, blocks=(int)((total+threads*4LL-1)/(threads*4LL)); fill_bias<<<blocks,threads,0,stream>>>(y.data_ptr<float>(),b.data_ptr<float>(),total,n); cublasHandle_t h=at::cuda::getCurrentCUDABlasHandle(); cublasSetStream(h,stream); float alpha=1.0f,beta=1.0f; cublasSgemm(h,CUBLAS_OP_T,CUBLAS_OP_N,n,m,k,&alpha,w.data_ptr<float>(),k,x.data_ptr<float>(),k,&beta,y.data_ptr<float>(),n); act<<<blocks,threads,0,stream>>>(y.data_ptr<float>(),total,(float)mult,(float)slope);}\n'''\n_ext=load_inline(name='gemm_fused_leaky_ext_v4',cpp_sources=cpp_sources,cuda_sources=cuda_sources,functions=['fused_forward'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features,multiplier,negative_slope):\n super(ModelNew,self).__init__(); self.gemm=nn.Linear(in_features,out_features); self.multiplier=float(multiplier); self.negative_slope=float(negative_slope)\n def forward(self,x): return _ext.fused_forward(x,self.gemm.weight,self.gemm.bias,self.multiplier,self.negative_slope)\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 1111.2s turn · 11.2s LLM · 0.0s tools

call → profile_kernel

callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\ncpp_sources=r'''\n#include <torch/extension.h>\nvoid launcher(torch::Tensor x, torch::Tensor w, torch::Tensor b, torch::Tensor y, double mult, double slope);\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope){auto y=torch::empty({x.size(0), weight.size(0)}, x.options()); launcher(x,weight,bias,y,multiplier,negative_slope); return y;}\n'''\ncuda_sources=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n#include <cublas_v2.h>\n__global__ void fill_bias(float* __restrict__ y,const float* __restrict__ b,long long total,int cols){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x; long long i=idx*4; if(i+3<total){int c=(int)(i%cols); float4 v=*reinterpret_cast<const float4*>(b+c); *reinterpret_cast<float4*>(y+i)=v;} else {for(;i<total;++i)y[i]=b[i%cols];}}\n__global__ void act(float* __restrict__ y,long long total,float mult,float slope){long long idx=(long long)blockIdx.x*blockDim.x+threadIdx.x; long long i=idx*4; if(i+3<total){float4 v=*reinterpret_cast<float4*>(y+i); v.x*=mult;v.y*=mult;v.z*=mult;v.w*=mult; v.x=v.x>=0?v.x:v.x*slope; v.y=v.y>=0?v.y:v.y*slope; v.z=v.z>=0?v.z:v.z*slope; v.w=v.w>=0?v.w:v.w*slope; *reinterpret_cast<float4*>(y+i)=v;} else {for(;i<total;++i){float a=y[i]*mult;y[i]=a>=0?a:a*slope;}}}\nvoid launcher(torch::Tensor x, torch::Tensor w, torch::Tensor b, torch::Tensor y, double mult, double slope){int m=(int)x.size(0), k=(int)x.size(1), n=(int)w.size(0); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); long long total=(long long)m*n; int threads=256, blocks=(int)((total+threads*4LL-1)/(threads*4LL)); fill_bias<<<blocks,threads,0,stream>>>(y.data_ptr<float>(),b.data_ptr<float>(),total,n); cublasHandle_t h=at::cuda::getCurrentCUDABlasHandle(); cublasSetStream(h,stream); float alpha=1.0f,beta=1.0f; cublasSgemm(h,CUBLAS_OP_T,CUBLAS_OP_N,n,m,k,&alpha,w.data_ptr<float>(),k,x.data_ptr<float>(),k,&beta,y.data_ptr<float>(),n); act<<<blocks,threads,0,stream>>>(y.data_ptr<float>(),total,(float)mult,(float)slope);}\n'''\n_ext=load_inline(name='gemm_fused_leaky_ext_v4',cpp_sources=cpp_sources,cuda_sources=cuda_sources,functions=['fused_forward'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,in_features,out_features,multiplier,negative_slope):\n super(ModelNew,self).__init__(); self.gemm=nn.Linear(in_features,out_features); self.multiplier=float(multiplier); self.negative_slope=float(negative_slope)\n def forward(self,x): return _ext.fused_forward(x,self.gemm.weight,self.gemm.bias,self.multiplier,self.negative_slope)\n" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty 262.0μs (100%) calls=1 --- Timing --- GPU time: 2770.24 μs [↑39.8μs REGRESSED] --- Memory Bandwidth --- DRAM BW: 3339.4/3350 GB/s (99.7%) [↑29.7% ok] read=1796.2 write=1543.2 GB/s Hit rates: L2=243.5% --- Compute Throughput --- Dominant pipe: FP32 util=92.7% [↑5.7% ok] Pipe util: FMA=92.7%, Tensor=0.0% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: MEMORY-BOUND --- Occupancy --- Achieved: 162.8% [↑74.3% ok] Eligible warps/cycle: 2.82 Launch: regs/thread=315, smem/blk=36352B, block=640, grid=16896 --- Memory Access Quality --- Global ld coalescing: 36.00 sectors/req (36.0x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 10293.9% #################### short_scoreboard 548.5% #################### mio_throttle 326.9% #################### --- Optimization Hints --- -> 10294% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=36.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=36.0x (ideal=1.0) -> 36x wasted DRAM BW. Make per-warp accesses contiguous. --- Delta vs Previous Iteration --- GPU time: 2730.40→2770.24μs (↑39.84) REGRESSED DRAM BW%: 69.96→99.68% (↑29.72) ok Compute%: 87.04→92.72% (↑5.67) ok Occupancy: 88.52→162.84% (↑74.32) ok Eligible warps/cyc: 2.31→2.82 (↑0.51) ok Ld sectors/req: 20.00→36.00 (↑16.00) REGRESSED L2 hit%: 145.34→243.51% (↑98.17) ok

turn 1213.1s turn · 13.1s LLM · 0.0s tools

call → static_check

callstatic_check { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_sources = r'''\n#include <torch/extension.h>\n\nvoid fused_act_launcher(torch::Tensor y, double multiplier, double negative_slope);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope) {\n auto y = at::linear(x, weight, bias);\n fused_act_launcher(y, multiplier, negative_slope);\n return y;\n}\n'''\n\ncuda_sources = 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 fused_act_kernel(float* __restrict__ y, long long n, float mult, float slope) {\n long long idx4 = (long long)(blockIdx.x) * blockDim.x + threadIdx.x;\n long long i = idx4 * 4;\n if (i + 3 < n) {\n float4 v = *reinterpret_cast<float4*>(y + i);\n v.x *= mult; v.y *= mult; v.z *= mult; v.w *= mult;\n v.x = v.x >= 0.0f ? v.x : v.x * slope;\n v.y = v.y >= 0.0f ? v.y : v.y * slope;\n v.z = v.z >= 0.0f ? v.z : v.z * slope;\n v.w = v.w >= 0.0f ? v.w : v.w * slope;\n *reinterpret_cast<float4*>(y + i) = v;\n } else {\n for (; i < n; ++i) {\n float a = y[i] * mult;\n y[i] = a >= 0.0f ? a : a * slope;\n }\n }\n}\n\nvoid fused_act_launcher(torch::Tensor y, double multiplier, double negative_slope) {\n const long long n = y.numel();\n if (n == 0) return;\n const int threads = 256;\n const int blocks = (int)((n + threads * 4LL - 1) / (threads * 4LL));\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n fused_act_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), n, (float)multiplier, (float)negative_slope);\n}\n'''\n\n_ext = load_inline(\n name='gemm_fused_leaky_ext_v1_final',\n cpp_sources=cpp_sources,\n cuda_sources=cuda_sources,\n functions=['fused_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, multiplier, negative_slope):\n super(ModelNew, self).__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.multiplier = float(multiplier)\n self.negative_slope = float(negative_slope)\n\n def forward(self, x):\n return _ext.fused_forward(x, self.gemm.weight, self.gemm.bias, self.multiplier, self.negative_slope)\n" }

result ← static_check (ok)

okstatic_check static_check PASSED: no violations or warnings detected.

turn 1311.5s turn · 11.5s LLM · 0.0s tools · FINAL

call → submit_kernel

callsubmit_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncpp_sources = r'''\n#include <torch/extension.h>\n\nvoid fused_act_launcher(torch::Tensor y, double multiplier, double negative_slope);\n\ntorch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope) {\n auto y = at::linear(x, weight, bias);\n fused_act_launcher(y, multiplier, negative_slope);\n return y;\n}\n'''\n\ncuda_sources = 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 fused_act_kernel(float* __restrict__ y, long long n, float mult, float slope) {\n long long idx4 = (long long)(blockIdx.x) * blockDim.x + threadIdx.x;\n long long i = idx4 * 4;\n if (i + 3 < n) {\n float4 v = *reinterpret_cast<float4*>(y + i);\n v.x *= mult; v.y *= mult; v.z *= mult; v.w *= mult;\n v.x = v.x >= 0.0f ? v.x : v.x * slope;\n v.y = v.y >= 0.0f ? v.y : v.y * slope;\n v.z = v.z >= 0.0f ? v.z : v.z * slope;\n v.w = v.w >= 0.0f ? v.w : v.w * slope;\n *reinterpret_cast<float4*>(y + i) = v;\n } else {\n for (; i < n; ++i) {\n float a = y[i] * mult;\n y[i] = a >= 0.0f ? a : a * slope;\n }\n }\n}\n\nvoid fused_act_launcher(torch::Tensor y, double multiplier, double negative_slope) {\n const long long n = y.numel();\n if (n == 0) return;\n const int threads = 256;\n const int blocks = (int)((n + threads * 4LL - 1) / (threads * 4LL));\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n fused_act_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), n, (float)multiplier, (float)negative_slope);\n}\n'''\n\n_ext = load_inline(\n name='gemm_fused_leaky_ext_v1_final',\n cpp_sources=cpp_sources,\n cuda_sources=cuda_sources,\n functions=['fused_forward'],\n extra_cflags=['-O3'],\n extra_cuda_cflags=['-O3'],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_features, out_features, multiplier, negative_slope):\n super(ModelNew, self).__init__()\n self.gemm = nn.Linear(in_features, out_features)\n self.multiplier = float(multiplier)\n self.negative_slope = float(negative_slope)\n\n def forward(self, x):\n return _ext.fused_forward(x, self.gemm.weight, self.gemm.bias, self.multiplier, self.negative_slope)\n" }

result ← submit_kernel (ok)

oksubmit_kernel submit_kernel PASSED: (5 / 5) correctness trials all passed. Kernel runtime: 0.35 μs Runtime stats: mean=0.35μs median=?μs std=0.01μs Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00 Memory: 609.06 MB peak (ref: 641.06 MB, ratio: 0.9501x) Kernel launches: 5 (ref: 7, fusion ratio: 1.4) SOL score: 0.837

final submitted kernel (71 lines, 2,410 chars) — full source, untruncated

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

cpp_sources = r'''
#include <torch/extension.h>

void fused_act_launcher(torch::Tensor y, double multiplier, double negative_slope);

torch::Tensor fused_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double multiplier, double negative_slope) {
    auto y = at::linear(x, weight, bias);
    fused_act_launcher(y, multiplier, negative_slope);
    return y;
}
'''

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

__global__ void fused_act_kernel(float* __restrict__ y, long long n, float mult, float slope) {
    long long idx4 = (long long)(blockIdx.x) * blockDim.x + threadIdx.x;
    long long i = idx4 * 4;
    if (i + 3 < n) {
        float4 v = *reinterpret_cast<float4*>(y + i);
        v.x *= mult; v.y *= mult; v.z *= mult; v.w *= mult;
        v.x = v.x >= 0.0f ? v.x : v.x * slope;
        v.y = v.y >= 0.0f ? v.y : v.y * slope;
        v.z = v.z >= 0.0f ? v.z : v.z * slope;
        v.w = v.w >= 0.0f ? v.w : v.w * slope;
        *reinterpret_cast<float4*>(y + i) = v;
    } else {
        for (; i < n; ++i) {
            float a = y[i] * mult;
            y[i] = a >= 0.0f ? a : a * slope;
        }
    }
}

void fused_act_launcher(torch::Tensor y, double multiplier, double negative_slope) {
    const long long n = y.numel();
    if (n == 0) return;
    const int threads = 256;
    const int blocks = (int)((n + threads * 4LL - 1) / (threads * 4LL));
    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
    fused_act_kernel<<<blocks, threads, 0, stream>>>(y.data_ptr<float>(), n, (float)multiplier, (float)negative_slope);
}
'''

_ext = load_inline(
    name='gemm_fused_leaky_ext_v1_final',
    cpp_sources=cpp_sources,
    cuda_sources=cuda_sources,
    functions=['fused_forward'],
    extra_cflags=['-O3'],
    extra_cuda_cflags=['-O3'],
    verbose=False,
)

class ModelNew(nn.Module):
    def __init__(self, in_features, out_features, multiplier, negative_slope):
        super(ModelNew, self).__init__()
        self.gemm = nn.Linear(in_features, out_features)
        self.multiplier = float(multiplier)
        self.negative_slope = float(negative_slope)

    def forward(self, x):
        return _ext.fused_forward(x, self.gemm.weight, self.gemm.bias, self.multiplier, self.negative_slope)