L3 P11 · popcorn

correct 11_pipeline_three_stage_mlp.py

model

gpt-5.5-priority

turns / max

14 / 15

tool calls

tokens (total)

wall clock

—

truncated

backend

cuda fp32

variant

popcorn

runtime

0.01 μs

ref runtime

0.03 μs

speedup

2.28x

started

2026-05-04T15:55:40Z

timingkernelbench.timing

kernel runtime

0.01 μs

ref runtime

0.03 μs

speedup

2.28x

kernel mean / median / std

0.01 / — / 0.00 μs

ref mean / median / std

0.03 / — / 0.03 μs

numerical precisioneval_kernel_against_ref

max abs error

7.45e-08

mean abs error

1.56e-08

max rel error

1.76e-05

mean rel error

6.13e-07

gpu memoryextended_metrics.measure_memory

kernel peak

33.06 MB

ref peak

33.06 MB

kernel / ref

1.00x

kernel launches / fusiontorch.profiler

kernel launches

ref launches

fusion ratio (ref / kernel)

22.00x

total cuda time

23 μs

SOL (speed-of-light)nsight_hardware_counters

SOL score (0–1)

0.002

dram util %

0.1%

compute util %

0.0%

bottleneck

unknown

dominant pipe

None

arithmetic intensity

—

ridge point

20.00

roofline / occupancynsight_hardware_counters

dram bandwidth

4.99 GB/s

dram util %

0.1%

fp32 tflops

—

fp32 util %

—

fp16 tflops

—

occupancy %

2.9%

bottleneck

unknown

dominant pipe %

—

arithmetic intensity

—

ridge point

20.00

L1 hit %

—

L2 hit %

98.0%

ld sectors / req

18.58

st sectors / req

—

regs / thread

smem / block

1536 B

block size

peak bw

3350.00 GB/s

peak fp32 tflops

67.00

peak fp16 tflops

1979.00

warp stalls (top 5)nsight

long_scoreboard

2057.3%

short_scoreboard

81.3%

reference PyTorch source (the prompt the agent sees)

"""
Three-stage MLP on packed activations: h1 = relu(h0 @ W1), h2 = relu(h1 @ W2), out = h2 @ W3.
Dimensions are fixed; weights are trainable parameters with default init.
"""

import torch
import torch.nn as nn

from kernelbench.distributed_collectives import default_device

B = 16
H0 = 48
H1 = 64
H2 = 40
H3 = 24


class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.W1 = nn.Linear(H0, H1, bias=False)
        self.W2 = nn.Linear(H1, H2, bias=False)
        self.W3 = nn.Linear(H2, H3, bias=False)

    def forward(self, h0: torch.Tensor) -> torch.Tensor:
        h1 = torch.relu(self.W1(h0))
        h2 = torch.relu(self.W2(h1))
        return self.W3(h2)


def get_inputs():
    dev = default_device()
    p = popcorn_pri
    return [torch.randn(p.jitter_int(B), H0, device=dev)]


def get_init_inputs():
    return []

turn 057.3s turn · 57.3s LLM · 0.0s tools

reasoning

**Considering block structure** I’m thinking about how to structure the blocks for improved performance. Using one block per two rows might not be the best idea. I wonder if more parallel reduction could really help with latency instead. It seems like there could be benefits to adjusting this, so I’ll keep exploring the options. My goal is to optimize everything for efficiency and speed; a more effective setup might really make a difference!

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) · 55.0s turn · 55.0s LLM · 6,457 chars (click to expand)

turn 155.0s turn · 55.0s 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\nB=16; H0=48; H1=64; H2=40; H3=24\n\n_cpp = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n\n_cuda = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n\n__global__ __launch_bounds__(128) void mlp_kernel(const float* __restrict__ h0,\n const float* __restrict__ W1,\n const float* __restrict__ W2,\n const float* __restrict__ W3,\n float* __restrict__ out) {\n __shared__ float h1[H1];\n __shared__ float h2[H2];\n int b = blockIdx.x;\n int t = threadIdx.x;\n const float* x = h0 + b * H0;\n if (t < H1) {\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H0; ++k) s = fmaf(x[k], W1[t * H0 + k], s);\n h1[t] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n if (t < H2) {\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H1; ++k) s = fmaf(h1[k], W2[t * H1 + k], s);\n h2[t] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n if (t < H3) {\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H2; ++k) s = fmaf(h2[k], W3[t * H2 + k], s);\n out[b * H3 + t] = s;\n }\n}\n\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3) {\n auto out = torch::empty({B, H3}, h0.options());\n mlp_kernel<<<B, 128, 0, at::cuda::getCurrentCUDAStream()>>>(\n h0.data_ptr<float>(), W1.data_ptr<float>(), W2.data_ptr<float>(), W3.data_ptr<float>(), out.data_ptr<float>());\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"mlp_fused_ext_v1\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"],\n extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.W1 = nn.Linear(H0, H1, bias=False)\n self.W2 = nn.Linear(H1, H2, bias=False)\n self.W3 = nn.Linear(H2, H3, bias=False)\n def forward(self, h0: torch.Tensor) -> torch.Tensor:\n return _ext.mlp_forward(h0, self.W1.weight, self.W2.weight, self.W3.weight)\n" }

result ← compile_kernel (fail)

failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'mlp_fused_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=mlp_fused_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/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_7e7369baf74f/mlp_fused_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=mlp_fused_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 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_7e7369baf74f/mlp_fused_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=mlp_fused_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 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_7e7369baf74f/mlp_fused_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/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_7e7369baf74f/mlp_fused_ext_v1/cuda.cu(49): error: namespace "at::cuda" has no member "getCurrentCUDAStream" mlp_kernel<<<16, 128, 0, at::cuda::getCurrentCUDAStream()>>>( ^ 1 error detected in the compilation of "/scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_7e7369baf74f/mlp_fused_ext_v1/cuda.cu". ninja: build stopped: subcommand failed.

turn 28.6s turn · 8.6s 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\nB=16; H0=48; H1=64; H2=40; H3=24\n\n_cpp = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n\n_cuda = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n\n__global__ __launch_bounds__(128) void mlp_kernel(const float* __restrict__ h0,\n const float* __restrict__ W1,\n const float* __restrict__ W2,\n const float* __restrict__ W3,\n float* __restrict__ out) {\n __shared__ float h1[H1];\n __shared__ float h2[H2];\n int b = blockIdx.x;\n int t = threadIdx.x;\n const float* x = h0 + b * H0;\n if (t < H1) {\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H0; ++k) s = fmaf(x[k], W1[t * H0 + k], s);\n h1[t] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n if (t < H2) {\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H1; ++k) s = fmaf(h1[k], W2[t * H1 + k], s);\n h2[t] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n if (t < H3) {\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H2; ++k) s = fmaf(h2[k], W3[t * H2 + k], s);\n out[b * H3 + t] = s;\n }\n}\n\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3) {\n auto out = torch::empty({B, H3}, h0.options());\n mlp_kernel<<<B, 128, 0, at::cuda::getCurrentCUDAStream()>>>(\n h0.data_ptr<float>(), W1.data_ptr<float>(), W2.data_ptr<float>(), W3.data_ptr<float>(), out.data_ptr<float>());\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"mlp_fused_ext_v2\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"],\n extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.W1 = nn.Linear(H0, H1, bias=False)\n self.W2 = nn.Linear(H1, H2, bias=False)\n self.W3 = nn.Linear(H2, H3, bias=False)\n def forward(self, h0: torch.Tensor) -> torch.Tensor:\n return _ext.mlp_forward(h0, self.W1.weight, self.W2.weight, self.W3.weight)\n" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 35.7s turn · 5.7s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nB=16; H0=48; H1=64; H2=40; H3=24\n\n_cpp = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n\n_cuda = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n\n__global__ __launch_bounds__(128) void mlp_kernel(const float* __restrict__ h0,\n const float* __restrict__ W1,\n const float* __restrict__ W2,\n const float* __restrict__ W3,\n float* __restrict__ out) {\n __shared__ float h1[H1];\n __shared__ float h2[H2];\n int b = blockIdx.x;\n int t = threadIdx.x;\n const float* x = h0 + b * H0;\n if (t < H1) {\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H0; ++k) s = fmaf(x[k], W1[t * H0 + k], s);\n h1[t] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n if (t < H2) {\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H1; ++k) s = fmaf(h1[k], W2[t * H1 + k], s);\n h2[t] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n if (t < H3) {\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H2; ++k) s = fmaf(h2[k], W3[t * H2 + k], s);\n out[b * H3 + t] = s;\n }\n}\n\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3) {\n auto out = torch::empty({B, H3}, h0.options());\n mlp_kernel<<<B, 128, 0, at::cuda::getCurrentCUDAStream()>>>(\n h0.data_ptr<float>(), W1.data_ptr<float>(), W2.data_ptr<float>(), W3.data_ptr<float>(), out.data_ptr<float>());\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"mlp_fused_ext_v2\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"],\n extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.W1 = nn.Linear(H0, H1, bias=False)\n self.W2 = nn.Linear(H1, H2, bias=False)\n self.W3 = nn.Linear(H2, H3, bias=False)\n def forward(self, h0: torch.Tensor) -> torch.Tensor:\n return _ext.mlp_forward(h0, self.W1.weight, self.W2.weight, self.W3.weight)\n" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=7.45e-08 mean_abs_err=1.56e-08 max_rel_err=1.76e-05

turn 47.9s turn · 7.9s LLM · 0.0s tools

reasoning

**Optimizing timing** I need to optimize the timing of my process. Should I run a profile to see how it performs, or is submitting what I have enough? I think I could improve it, though. Maybe I can benchmark it by submitting only the final output. I should ensure that profiling the correct kernel will actually reflect the runtime accurately. It’s important to get this right!

call → profile_kernel

callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nB=16; H0=48; H1=64; H2=40; H3=24\n\n_cpp = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n\n_cuda = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n\n__global__ __launch_bounds__(128) void mlp_kernel(const float* __restrict__ h0,\n const float* __restrict__ W1,\n const float* __restrict__ W2,\n const float* __restrict__ W3,\n float* __restrict__ out) {\n __shared__ float h1[H1];\n __shared__ float h2[H2];\n int b = blockIdx.x;\n int t = threadIdx.x;\n const float* x = h0 + b * H0;\n if (t < H1) {\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H0; ++k) s = fmaf(x[k], W1[t * H0 + k], s);\n h1[t] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n if (t < H2) {\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H1; ++k) s = fmaf(h1[k], W2[t * H1 + k], s);\n h2[t] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n if (t < H3) {\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H2; ++k) s = fmaf(h2[k], W3[t * H2 + k], s);\n out[b * H3 + t] = s;\n }\n}\n\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3) {\n auto out = torch::empty({B, H3}, h0.options());\n mlp_kernel<<<B, 128, 0, at::cuda::getCurrentCUDAStream()>>>(\n h0.data_ptr<float>(), W1.data_ptr<float>(), W2.data_ptr<float>(), W3.data_ptr<float>(), out.data_ptr<float>());\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"mlp_fused_ext_v2\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"],\n extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.W1 = nn.Linear(H0, H1, bias=False)\n self.W2 = nn.Linear(H1, H2, bias=False)\n self.W3 = nn.Linear(H2, H3, bias=False)\n def forward(self, h0: torch.Tensor) -> torch.Tensor:\n return _ext.mlp_forward(h0, self.W1.weight, self.W2.weight, self.W3.weight)\n" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty 28.0μs (100%) calls=1 --- Timing --- GPU time: 7.74 μs --- Memory Bandwidth --- DRAM BW: 5.0/3350 GB/s (0.1%) read=5.0 write=0.0 GB/s Hit rates: L2=99.0% --- Compute Throughput --- Pipe util: FMA=0.6%, Tensor=0.0% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: UNKNOWN --- Occupancy --- Achieved: 5.6% Eligible warps/cycle: 0.02 WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism Launch: regs/thread=37, smem/blk=1536B, block=128, grid=16 --- Memory Access Quality --- Global ld coalescing: 18.58 sectors/req (18.6x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 1873.0% #################### short_scoreboard 126.8% #################### --- Optimization Hints --- -> 1873% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=18.6 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=18.6x (ideal=1.0) -> 19x wasted DRAM BW. Make per-warp accesses contiguous. -> Low occupancy (6%). Consider __launch_bounds__ or reducing register pressure. -> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching).

turn 521.4s turn · 21.4s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nB=16; H0=48; H1=64; H2=40; H3=24\n_cpp = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n_cuda = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n\n__inline__ __device__ float warp_sum(float v) {\n v += __shfl_down_sync(0xffffffff, v, 16);\n v += __shfl_down_sync(0xffffffff, v, 8);\n v += __shfl_down_sync(0xffffffff, v, 4);\n v += __shfl_down_sync(0xffffffff, v, 2);\n v += __shfl_down_sync(0xffffffff, v, 1);\n return v;\n}\n\n__global__ __launch_bounds__(128) void mlp_kernel_warp(const float* __restrict__ h0,\n const float* __restrict__ W1,\n const float* __restrict__ W2,\n const float* __restrict__ W3,\n float* __restrict__ out) {\n __shared__ float h1[H1];\n __shared__ float h2[H2];\n int b = blockIdx.x;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n const float* x = h0 + b * H0;\n for (int o = warp; o < H1; o += 4) {\n float s = 0.0f;\n #pragma unroll\n for (int k = lane; k < H0; k += 32) s = fmaf(x[k], W1[o * H0 + k], s);\n s = warp_sum(s);\n if (lane == 0) h1[o] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n for (int o = warp; o < H2; o += 4) {\n float s = 0.0f;\n #pragma unroll\n for (int k = lane; k < H1; k += 32) s = fmaf(h1[k], W2[o * H1 + k], s);\n s = warp_sum(s);\n if (lane == 0) h2[o] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n for (int o = warp; o < H3; o += 4) {\n float s = 0.0f;\n #pragma unroll\n for (int k = lane; k < H2; k += 32) s = fmaf(h2[k], W3[o * H2 + k], s);\n s = warp_sum(s);\n if (lane == 0) out[b * H3 + o] = s;\n }\n}\n\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3) {\n auto out = torch::empty({B, H3}, h0.options());\n mlp_kernel_warp<<<B, 128, 0, at::cuda::getCurrentCUDAStream()>>>(\n h0.data_ptr<float>(), W1.data_ptr<float>(), W2.data_ptr<float>(), W3.data_ptr<float>(), out.data_ptr<float>());\n return out;\n}\n\"\"\"\n_ext = load_inline(name=\"mlp_fused_ext_warp_v1\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.W1=nn.Linear(H0,H1,bias=False); self.W2=nn.Linear(H1,H2,bias=False); self.W3=nn.Linear(H2,H3,bias=False)\n def forward(self,h0:torch.Tensor)->torch.Tensor:\n return _ext.mlp_forward(h0,self.W1.weight,self.W2.weight,self.W3.weight)\n" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=5.22e-08 mean_abs_err=1.25e-08 max_rel_err=1.81e-05

turn 68.7s turn · 8.7s 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\nB=16; H0=48; H1=64; H2=40; H3=24\n_cpp = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n_cuda = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n\n__inline__ __device__ float warp_sum(float v) {\n v += __shfl_down_sync(0xffffffff, v, 16);\n v += __shfl_down_sync(0xffffffff, v, 8);\n v += __shfl_down_sync(0xffffffff, v, 4);\n v += __shfl_down_sync(0xffffffff, v, 2);\n v += __shfl_down_sync(0xffffffff, v, 1);\n return v;\n}\n\n__global__ __launch_bounds__(128) void mlp_kernel_warp(const float* __restrict__ h0,\n const float* __restrict__ W1,\n const float* __restrict__ W2,\n const float* __restrict__ W3,\n float* __restrict__ out) {\n __shared__ float h1[H1];\n __shared__ float h2[H2];\n int b = blockIdx.x;\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n const float* x = h0 + b * H0;\n for (int o = warp; o < H1; o += 4) {\n float s = 0.0f;\n #pragma unroll\n for (int k = lane; k < H0; k += 32) s = fmaf(x[k], W1[o * H0 + k], s);\n s = warp_sum(s);\n if (lane == 0) h1[o] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n for (int o = warp; o < H2; o += 4) {\n float s = 0.0f;\n #pragma unroll\n for (int k = lane; k < H1; k += 32) s = fmaf(h1[k], W2[o * H1 + k], s);\n s = warp_sum(s);\n if (lane == 0) h2[o] = s > 0.0f ? s : 0.0f;\n }\n __syncthreads();\n for (int o = warp; o < H3; o += 4) {\n float s = 0.0f;\n #pragma unroll\n for (int k = lane; k < H2; k += 32) s = fmaf(h2[k], W3[o * H2 + k], s);\n s = warp_sum(s);\n if (lane == 0) out[b * H3 + o] = s;\n }\n}\n\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3) {\n auto out = torch::empty({B, H3}, h0.options());\n mlp_kernel_warp<<<B, 128, 0, at::cuda::getCurrentCUDAStream()>>>(\n h0.data_ptr<float>(), W1.data_ptr<float>(), W2.data_ptr<float>(), W3.data_ptr<float>(), out.data_ptr<float>());\n return out;\n}\n\"\"\"\n_ext = load_inline(name=\"mlp_fused_ext_warp_v1\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.W1=nn.Linear(H0,H1,bias=False); self.W2=nn.Linear(H1,H2,bias=False); self.W3=nn.Linear(H2,H3,bias=False)\n def forward(self,h0:torch.Tensor)->torch.Tensor:\n return _ext.mlp_forward(h0,self.W1.weight,self.W2.weight,self.W3.weight)\n" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty 41.0μs (100%) calls=1 --- Timing --- GPU time: 13.89 μs [↑6.1μs REGRESSED] --- Memory Bandwidth --- DRAM BW: 3.3/3350 GB/s (0.1%) [↓0.1% REGRESSED] read=3.3 write=0.0 GB/s Hit rates: L2=92.0% --- Compute Throughput --- Pipe util: FMA=1.2%, Tensor=0.0% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: UNKNOWN --- Occupancy --- Achieved: 6.2% [↑0.6% ok] Eligible warps/cycle: 0.04 WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism Launch: regs/thread=32, smem/blk=1536B, block=128, grid=16 --- Memory Access Quality --- Global ld coalescing: 3.19 sectors/req (3.2x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 2097.4% #################### short_scoreboard 332.0% #################### --- Optimization Hints --- -> 2097% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=3.2 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=3.2x (ideal=1.0) -> 3x wasted DRAM BW. Make per-warp accesses contiguous. -> Low occupancy (6%). Consider __launch_bounds__ or reducing register pressure. -> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching). --- Delta vs Previous Iteration --- GPU time: 7.74→13.89μs (↑6.14) REGRESSED DRAM BW%: 0.15→0.10% (↓0.05) REGRESSED Occupancy: 5.62→6.19% (↑0.57) ok Eligible warps/cyc: 0.02→0.04 (↑0.02) ok Ld sectors/req: 18.58→3.19 (↓15.38) ok L2 hit%: 98.96→91.96% (↓7.00) REGRESSED

turn 7 · 1 tool call(s) · 12.6s turn · 12.6s LLM · 6,689 chars (click to expand)

turn 712.6s turn · 12.6s 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\nB=16; H0=48; H1=64; H2=40; H3=24\n_cpp=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n_cuda=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n__global__ __launch_bounds__(64) void mlp_kernel64(const float* __restrict__ h0,const float* __restrict__ W1,const float* __restrict__ W2,const float* __restrict__ W3,float* __restrict__ out){\n __shared__ float h1[H1]; __shared__ float h2[H2];\n int b=blockIdx.x; int t=threadIdx.x; const float* x=h0+b*H0;\n float s=0.0f;\n #pragma unroll\n for(int k=0;k<H0;++k) s=fmaf(x[k], W1[t*H0+k], s);\n h1[t]=s>0.0f?s:0.0f;\n __syncthreads();\n if(t<H2){ float s2=0.0f; #pragma unroll\n for(int k=0;k<H1;++k) s2=fmaf(h1[k], W2[t*H1+k], s2);\n h2[t]=s2>0.0f?s2:0.0f; }\n __syncthreads();\n if(t<H3){ float s3=0.0f; #pragma unroll\n for(int k=0;k<H2;++k) s3=fmaf(h2[k], W3[t*H2+k], s3);\n out[b*H3+t]=s3; }\n}\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3){\n auto out=torch::empty({B,H3}, h0.options());\n mlp_kernel64<<<B,64,0,at::cuda::getCurrentCUDAStream()>>>(h0.data_ptr<float>(),W1.data_ptr<float>(),W2.data_ptr<float>(),W3.data_ptr<float>(),out.data_ptr<float>());\n return out;\n}\n\"\"\"\n_ext=load_inline(name=\"mlp_fused_ext_64_v1\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__(); self.W1=nn.Linear(H0,H1,bias=False); self.W2=nn.Linear(H1,H2,bias=False); self.W3=nn.Linear(H2,H3,bias=False)\n def forward(self,h0:torch.Tensor)->torch.Tensor:\n return _ext.mlp_forward(h0,self.W1.weight,self.W2.weight,self.W3.weight)\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: kernel did not compile. Error building extension 'mlp_fused_ext_64_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=mlp_fused_ext_64_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/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_a9d8a2ce6995/mlp_fused_ext_64_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=mlp_fused_ext_64_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 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_a9d8a2ce6995/mlp_fused_ext_64_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=mlp_fused_ext_64_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 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_a9d8a2ce6995/mlp_fused_ext_64_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/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_a9d8a2ce6995/mlp_fused_ext_64_v1/cuda.cu(22): error: "#" not expected here if(t<40){ float s2=0.0f; #pragma unroll ^ /scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_a9d8a2ce6995/mlp_fused_ext_64_v1/cuda.cu(22): error: expected an expression if(t<40){ float s2=0.0f; #pragma unroll ^ /scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_a9d8a2ce6995/mlp_fused_ext_64_v1/cuda.cu(26): error: "#" not expected here if(t<24){ float s3=0.0f; #pragma unroll ^ /scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_a9d8a2ce6995/mlp_fused_ext_64_v1/cuda.cu(26): error: expected an expression if(t<24){ float s3=0.0f; #pragma unroll ^ 4 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_a9d8a2ce6995/mlp_fused_ext_64_v1/cuda.cu". ninja: build stopped: subcommand failed.

turn 86.7s turn · 6.7s 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\nB=16; H0=48; H1=64; H2=40; H3=24\n_cpp=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n_cuda=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n__global__ __launch_bounds__(64) void mlp_kernel64(const float* __restrict__ h0,const float* __restrict__ W1,const float* __restrict__ W2,const float* __restrict__ W3,float* __restrict__ out){\n __shared__ float h1[H1]; __shared__ float h2[H2];\n int b=blockIdx.x; int t=threadIdx.x; const float* x=h0+b*H0;\n float s=0.0f;\n #pragma unroll\n for(int k=0;k<H0;++k) s=fmaf(x[k], W1[t*H0+k], s);\n h1[t]=s>0.0f?s:0.0f;\n __syncthreads();\n if(t<H2){\n float s2=0.0f;\n #pragma unroll\n for(int k=0;k<H1;++k) s2=fmaf(h1[k], W2[t*H1+k], s2);\n h2[t]=s2>0.0f?s2:0.0f;\n }\n __syncthreads();\n if(t<H3){\n float s3=0.0f;\n #pragma unroll\n for(int k=0;k<H2;++k) s3=fmaf(h2[k], W3[t*H2+k], s3);\n out[b*H3+t]=s3;\n }\n}\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3){\n auto out=torch::empty({B,H3}, h0.options());\n mlp_kernel64<<<B,64,0,at::cuda::getCurrentCUDAStream()>>>(h0.data_ptr<float>(),W1.data_ptr<float>(),W2.data_ptr<float>(),W3.data_ptr<float>(),out.data_ptr<float>());\n return out;\n}\n\"\"\"\n_ext=load_inline(name=\"mlp_fused_ext_64_v2\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__(); self.W1=nn.Linear(H0,H1,bias=False); self.W2=nn.Linear(H1,H2,bias=False); self.W3=nn.Linear(H2,H3,bias=False)\n def forward(self,h0:torch.Tensor)->torch.Tensor:\n return _ext.mlp_forward(h0,self.W1.weight,self.W2.weight,self.W3.weight)\n" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=7.45e-08 mean_abs_err=1.56e-08 max_rel_err=1.76e-05

turn 95.3s turn · 5.3s 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\nB=16; H0=48; H1=64; H2=40; H3=24\n_cpp=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n_cuda=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n__global__ __launch_bounds__(64) void mlp_kernel64(const float* __restrict__ h0,const float* __restrict__ W1,const float* __restrict__ W2,const float* __restrict__ W3,float* __restrict__ out){\n __shared__ float h1[H1]; __shared__ float h2[H2];\n int b=blockIdx.x; int t=threadIdx.x; const float* x=h0+b*H0;\n float s=0.0f;\n #pragma unroll\n for(int k=0;k<H0;++k) s=fmaf(x[k], W1[t*H0+k], s);\n h1[t]=s>0.0f?s:0.0f;\n __syncthreads();\n if(t<H2){\n float s2=0.0f;\n #pragma unroll\n for(int k=0;k<H1;++k) s2=fmaf(h1[k], W2[t*H1+k], s2);\n h2[t]=s2>0.0f?s2:0.0f;\n }\n __syncthreads();\n if(t<H3){\n float s3=0.0f;\n #pragma unroll\n for(int k=0;k<H2;++k) s3=fmaf(h2[k], W3[t*H2+k], s3);\n out[b*H3+t]=s3;\n }\n}\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3){\n auto out=torch::empty({B,H3}, h0.options());\n mlp_kernel64<<<B,64,0,at::cuda::getCurrentCUDAStream()>>>(h0.data_ptr<float>(),W1.data_ptr<float>(),W2.data_ptr<float>(),W3.data_ptr<float>(),out.data_ptr<float>());\n return out;\n}\n\"\"\"\n_ext=load_inline(name=\"mlp_fused_ext_64_v2\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__(); self.W1=nn.Linear(H0,H1,bias=False); self.W2=nn.Linear(H1,H2,bias=False); self.W3=nn.Linear(H2,H3,bias=False)\n def forward(self,h0:torch.Tensor)->torch.Tensor:\n return _ext.mlp_forward(h0,self.W1.weight,self.W2.weight,self.W3.weight)\n" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty 49.0μs (100%) calls=1 --- Timing --- GPU time: 7.42 μs [↓6.5μs ok] --- Memory Bandwidth --- DRAM BW: 5.2/3350 GB/s (0.2%) [↑0.1% ok] read=5.2 write=0.0 GB/s Hit rates: L2=97.6% --- Compute Throughput --- Pipe util: FMA=0.7%, Tensor=0.0% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: UNKNOWN --- Occupancy --- Achieved: 2.9% [↓3.2% REGRESSED] Eligible warps/cycle: 0.04 WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism Launch: regs/thread=37, smem/blk=1536B, block=64, grid=16 --- Memory Access Quality --- Global ld coalescing: 18.58 sectors/req (18.6x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- long_scoreboard 1939.3% #################### short_scoreboard 93.2% ################## --- Optimization Hints --- -> 1939% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=18.6 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set. -> Global load coalescing=18.6x (ideal=1.0) -> 19x wasted DRAM BW. Make per-warp accesses contiguous. -> Low occupancy (3%) limited by small block (64). Consider __launch_bounds__ or reducing register pressure. -> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching). --- Delta vs Previous Iteration --- GPU time: 13.89→7.42μs (↓6.46) ok DRAM BW%: 0.10→0.15% (↑0.05) ok Occupancy: 6.19→2.94% (↓3.25) REGRESSED Ld sectors/req: 3.19→18.58 (↑15.38) REGRESSED L2 hit%: 91.96→97.58% (↑5.62) ok

turn 10 · 1 tool call(s) · 11.7s turn · 11.7s LLM · 6,849 chars (click to expand)

turn 1011.7s 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\nB=16; H0=48; H1=64; H2=40; H3=24\n_cpp=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n_cuda=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n__device__ __forceinline__ float dot48(const float* x, const float* w){float s=0.f; #pragma unroll\nfor(int k=0;k<H0;++k) s=fmaf(x[k],w[k],s); return s;}\n__global__ __launch_bounds__(32) void mlp_kernel32(const float* __restrict__ h0,const float* __restrict__ W1,const float* __restrict__ W2,const float* __restrict__ W3,float* __restrict__ out){\n __shared__ float h1[H1]; __shared__ float h2[H2];\n int b=blockIdx.x; int t=threadIdx.x; const float* x=h0+b*H0;\n float s=0.0f;\n #pragma unroll\n for(int k=0;k<H0;++k) s=fmaf(x[k], W1[t*H0+k], s);\n h1[t]=s>0.0f?s:0.0f;\n s=0.0f;\n #pragma unroll\n for(int k=0;k<H0;++k) s=fmaf(x[k], W1[(t+32)*H0+k], s);\n h1[t+32]=s>0.0f?s:0.0f;\n __syncthreads();\n float s2=0.0f;\n #pragma unroll\n for(int k=0;k<H1;++k) s2=fmaf(h1[k], W2[t*H1+k], s2);\n h2[t]=s2>0.0f?s2:0.0f;\n if(t<8){\n float s22=0.0f;\n #pragma unroll\n for(int k=0;k<H1;++k) s22=fmaf(h1[k], W2[(t+32)*H1+k], s22);\n h2[t+32]=s22>0.0f?s22:0.0f;\n }\n __syncthreads();\n if(t<H3){\n float s3=0.0f;\n #pragma unroll\n for(int k=0;k<H2;++k) s3=fmaf(h2[k], W3[t*H2+k], s3);\n out[b*H3+t]=s3;\n }\n}\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3){auto out=torch::empty({B,H3},h0.options()); mlp_kernel32<<<B,32,0,at::cuda::getCurrentCUDAStream()>>>(h0.data_ptr<float>(),W1.data_ptr<float>(),W2.data_ptr<float>(),W3.data_ptr<float>(),out.data_ptr<float>()); return out;}\n\"\"\"\n_ext=load_inline(name=\"mlp_fused_ext_32_v2\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__(); self.W1=nn.Linear(H0,H1,bias=False); self.W2=nn.Linear(H1,H2,bias=False); self.W3=nn.Linear(H2,H3,bias=False)\n def forward(self,h0:torch.Tensor)->torch.Tensor:\n return _ext.mlp_forward(h0,self.W1.weight,self.W2.weight,self.W3.weight)\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: kernel did not compile. Error building extension 'mlp_fused_ext_32_v2': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=mlp_fused_ext_32_v2 -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/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_6c425fa20be8/mlp_fused_ext_32_v2/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=mlp_fused_ext_32_v2 -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 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_6c425fa20be8/mlp_fused_ext_32_v2/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=mlp_fused_ext_32_v2 -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 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_6c425fa20be8/mlp_fused_ext_32_v2/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/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_6c425fa20be8/mlp_fused_ext_32_v2/cuda.cu(14): error: "#" not expected here float dot48(const float* x, const float* w){float s=0.f; #pragma unroll ^ /scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_6c425fa20be8/mlp_fused_ext_32_v2/cuda.cu(14): error: expected an expression float dot48(const float* x, const float* w){float s=0.f; #pragma unroll ^ 2 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/l3_all_gpt/popcorn/gpt-5.5-priority/level_3_problem_11_cache/k_6c425fa20be8/mlp_fused_ext_32_v2/cuda.cu". ninja: build stopped: subcommand failed.

turn 1110.2s turn · 10.2s 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\nB=16; H0=48; H1=64; H2=40; H3=24\n_cpp=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n_cuda=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n__global__ __launch_bounds__(32) void mlp_kernel32(const float* __restrict__ h0,const float* __restrict__ W1,const float* __restrict__ W2,const float* __restrict__ W3,float* __restrict__ out){\n __shared__ float h1[H1]; __shared__ float h2[H2];\n int b=blockIdx.x; int t=threadIdx.x; const float* x=h0+b*H0;\n float s=0.0f;\n #pragma unroll\n for(int k=0;k<H0;++k) s=fmaf(x[k], W1[t*H0+k], s);\n h1[t]=s>0.0f?s:0.0f;\n s=0.0f;\n #pragma unroll\n for(int k=0;k<H0;++k) s=fmaf(x[k], W1[(t+32)*H0+k], s);\n h1[t+32]=s>0.0f?s:0.0f;\n __syncthreads();\n float s2=0.0f;\n #pragma unroll\n for(int k=0;k<H1;++k) s2=fmaf(h1[k], W2[t*H1+k], s2);\n h2[t]=s2>0.0f?s2:0.0f;\n if(t<8){\n float s22=0.0f;\n #pragma unroll\n for(int k=0;k<H1;++k) s22=fmaf(h1[k], W2[(t+32)*H1+k], s22);\n h2[t+32]=s22>0.0f?s22:0.0f;\n }\n __syncthreads();\n if(t<H3){\n float s3=0.0f;\n #pragma unroll\n for(int k=0;k<H2;++k) s3=fmaf(h2[k], W3[t*H2+k], s3);\n out[b*H3+t]=s3;\n }\n}\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3){auto out=torch::empty({B,H3},h0.options()); mlp_kernel32<<<B,32,0,at::cuda::getCurrentCUDAStream()>>>(h0.data_ptr<float>(),W1.data_ptr<float>(),W2.data_ptr<float>(),W3.data_ptr<float>(),out.data_ptr<float>()); return out;}\n\"\"\"\n_ext=load_inline(name=\"mlp_fused_ext_32_v3\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\",\"--use_fast_math\"], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__(); self.W1=nn.Linear(H0,H1,bias=False); self.W2=nn.Linear(H1,H2,bias=False); self.W3=nn.Linear(H2,H3,bias=False)\n def forward(self,h0:torch.Tensor)->torch.Tensor:\n return _ext.mlp_forward(h0,self.W1.weight,self.W2.weight,self.W3.weight)\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: unexpected error during tool execution. _WorkItemTimeout: work item exceeded soft timeout budget Traceback (most recent call last): File "/scratch/abaner/KernelBench/src/kernelbench/agent/agent.py", line 627, in _execute_tool return tool.execute(self.ctx, **args) File "/scratch/abaner/KernelBench/src/kernelbench/agent/tools.py", line 365, in execute return ctx.eval_client.run_correctness(ctx, kernel_code) File "/scratch/abaner/KernelBench/src/kernelbench/agent/eval_client.py", line 58, in run_correctness return self._call("correctness", args) File "/scratch/abaner/KernelBench/src/kernelbench/agent/eval_client.py", line 117, in _call payload = self._response_q.get(timeout=remaining) File "<string>", line 2, in get File "/opt/conda/lib/python3.10/multiprocessing/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 250, in recv buf = self._recv_bytes() File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 414, in _recv_bytes buf = self._recv(4) File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 379, in _recv chunk = read(handle, remaining) File "/scratch/abaner/KernelBench/scripts/run_sweep.py", line 161, in _alarm_handler raise _WorkItemTimeout("work item exceeded soft timeout budget") __mp_main__._WorkItemTimeout: work item exceeded soft timeout budget

turn 12955.1s turn · 955.1s 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\nB=16; H0=48; H1=64; H2=40; H3=24\n\n_cpp = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n\n_cuda = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n\n__global__ __launch_bounds__(64) void mlp_kernel64(const float* __restrict__ h0,\n const float* __restrict__ W1,\n const float* __restrict__ W2,\n const float* __restrict__ W3,\n float* __restrict__ out) {\n __shared__ float h1[H1];\n __shared__ float h2[H2];\n int b = blockIdx.x;\n int t = threadIdx.x;\n const float* x = h0 + b * H0;\n\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H0; ++k) s = fmaf(x[k], W1[t * H0 + k], s);\n h1[t] = s > 0.0f ? s : 0.0f;\n\n __syncthreads();\n\n if (t < H2) {\n float s2 = 0.0f;\n #pragma unroll\n for (int k = 0; k < H1; ++k) s2 = fmaf(h1[k], W2[t * H1 + k], s2);\n h2[t] = s2 > 0.0f ? s2 : 0.0f;\n }\n\n __syncthreads();\n\n if (t < H3) {\n float s3 = 0.0f;\n #pragma unroll\n for (int k = 0; k < H2; ++k) s3 = fmaf(h2[k], W3[t * H2 + k], s3);\n out[b * H3 + t] = s3;\n }\n}\n\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3) {\n auto out = torch::empty({B, H3}, h0.options());\n mlp_kernel64<<<B, 64, 0, at::cuda::getCurrentCUDAStream()>>>(\n h0.data_ptr<float>(), W1.data_ptr<float>(), W2.data_ptr<float>(), W3.data_ptr<float>(), out.data_ptr<float>());\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"mlp_fused_ext_final64\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"],\n extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.W1 = nn.Linear(H0, H1, bias=False)\n self.W2 = nn.Linear(H1, H2, bias=False)\n self.W3 = nn.Linear(H2, H3, bias=False)\n def forward(self, h0: torch.Tensor) -> torch.Tensor:\n return _ext.mlp_forward(h0, self.W1.weight, self.W2.weight, self.W3.weight)\n" }

result ← static_check (ok)

okstatic_check static_check PASSED: no violations or warnings detected.

turn 137.7s turn · 7.7s 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\nB=16; H0=48; H1=64; H2=40; H3=24\n\n_cpp = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);\n\"\"\"\n\n_cuda = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define B 16\n#define H0 48\n#define H1 64\n#define H2 40\n#define H3 24\n\n__global__ __launch_bounds__(64) void mlp_kernel64(const float* __restrict__ h0,\n const float* __restrict__ W1,\n const float* __restrict__ W2,\n const float* __restrict__ W3,\n float* __restrict__ out) {\n __shared__ float h1[H1];\n __shared__ float h2[H2];\n int b = blockIdx.x;\n int t = threadIdx.x;\n const float* x = h0 + b * H0;\n\n float s = 0.0f;\n #pragma unroll\n for (int k = 0; k < H0; ++k) s = fmaf(x[k], W1[t * H0 + k], s);\n h1[t] = s > 0.0f ? s : 0.0f;\n\n __syncthreads();\n\n if (t < H2) {\n float s2 = 0.0f;\n #pragma unroll\n for (int k = 0; k < H1; ++k) s2 = fmaf(h1[k], W2[t * H1 + k], s2);\n h2[t] = s2 > 0.0f ? s2 : 0.0f;\n }\n\n __syncthreads();\n\n if (t < H3) {\n float s3 = 0.0f;\n #pragma unroll\n for (int k = 0; k < H2; ++k) s3 = fmaf(h2[k], W3[t * H2 + k], s3);\n out[b * H3 + t] = s3;\n }\n}\n\ntorch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3) {\n auto out = torch::empty({B, H3}, h0.options());\n mlp_kernel64<<<B, 64, 0, at::cuda::getCurrentCUDAStream()>>>(\n h0.data_ptr<float>(), W1.data_ptr<float>(), W2.data_ptr<float>(), W3.data_ptr<float>(), out.data_ptr<float>());\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"mlp_fused_ext_final64\", cpp_sources=_cpp, cuda_sources=_cuda, functions=[\"mlp_forward\"],\n extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.W1 = nn.Linear(H0, H1, bias=False)\n self.W2 = nn.Linear(H1, H2, bias=False)\n self.W3 = nn.Linear(H2, H3, bias=False)\n def forward(self, h0: torch.Tensor) -> torch.Tensor:\n return _ext.mlp_forward(h0, self.W1.weight, self.W2.weight, self.W3.weight)\n" }

result ← submit_kernel (ok)

oksubmit_kernel submit_kernel PASSED: (5 / 5) correctness trials all passed. Kernel runtime: 0.01 μs Runtime stats: mean=0.01μs median=?μs std=0.00μs Numerical precision: max_abs_err=7.45e-08 mean_abs_err=1.56e-08 max_rel_err=1.76e-05 Memory: 33.06 MB peak (ref: 33.06 MB, ratio: 0.9998x) Kernel launches: 1 (ref: 22, fusion ratio: 22.0) SOL score: 0.0015

final submitted kernel (78 lines, 2,494 chars) — full source, untruncated

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

B=16; H0=48; H1=64; H2=40; H3=24

_cpp = r"""
#include <torch/extension.h>
torch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3);
"""

_cuda = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>

#define B 16
#define H0 48
#define H1 64
#define H2 40
#define H3 24

__global__ __launch_bounds__(64) void mlp_kernel64(const float* __restrict__ h0,
                                                   const float* __restrict__ W1,
                                                   const float* __restrict__ W2,
                                                   const float* __restrict__ W3,
                                                   float* __restrict__ out) {
    __shared__ float h1[H1];
    __shared__ float h2[H2];
    int b = blockIdx.x;
    int t = threadIdx.x;
    const float* x = h0 + b * H0;

    float s = 0.0f;
    #pragma unroll
    for (int k = 0; k < H0; ++k) s = fmaf(x[k], W1[t * H0 + k], s);
    h1[t] = s > 0.0f ? s : 0.0f;

    __syncthreads();

    if (t < H2) {
        float s2 = 0.0f;
        #pragma unroll
        for (int k = 0; k < H1; ++k) s2 = fmaf(h1[k], W2[t * H1 + k], s2);
        h2[t] = s2 > 0.0f ? s2 : 0.0f;
    }

    __syncthreads();

    if (t < H3) {
        float s3 = 0.0f;
        #pragma unroll
        for (int k = 0; k < H2; ++k) s3 = fmaf(h2[k], W3[t * H2 + k], s3);
        out[b * H3 + t] = s3;
    }
}

torch::Tensor mlp_forward(torch::Tensor h0, torch::Tensor W1, torch::Tensor W2, torch::Tensor W3) {
    auto out = torch::empty({B, H3}, h0.options());
    mlp_kernel64<<<B, 64, 0, at::cuda::getCurrentCUDAStream()>>>(
        h0.data_ptr<float>(), W1.data_ptr<float>(), W2.data_ptr<float>(), W3.data_ptr<float>(), out.data_ptr<float>());
    return out;
}
"""

_ext = load_inline(name="mlp_fused_ext_final64", cpp_sources=_cpp, cuda_sources=_cuda, functions=["mlp_forward"],
                   extra_cflags=["-O3"], extra_cuda_cflags=["-O3", "--use_fast_math"], verbose=False)

class ModelNew(nn.Module):
    def __init__(self):
        super().__init__()
        self.W1 = nn.Linear(H0, H1, bias=False)
        self.W2 = nn.Linear(H1, H2, bias=False)
        self.W3 = nn.Linear(H2, H3, bias=False)
    def forward(self, h0: torch.Tensor) -> torch.Tensor:
        return _ext.mlp_forward(h0, self.W1.weight, self.W2.weight, self.W3.weight)