L1 P29 · popcorn

error 29_logsumexp_across_virtual_ranks.py

model

gpt-5.5-priority

turns / max

2 / 10

tool calls

tokens (total)

wall clock

—

truncated

backend

cuda fp32

variant

popcorn

runtime

—

ref runtime

0.00 μs

speedup

—

started

2026-05-02T21:56:21Z

reference PyTorch source (the prompt the agent sees)

"""
Stable log-sum-exp over virtual ranks: out = logsumexp(x, dim=0), shape (B, D).
"""

import torch
import torch.nn as nn

from kernelbench.distributed_collectives import default_device

R = 4
B = 10
D = 32


class Model(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return torch.logsumexp(x, dim=0)


def get_inputs():
    dev = default_device()
    p = popcorn_pri
    return [torch.randn(p.jitter_int(R), p.jitter_int(B), p.jitter_int(D, align=8), device=dev)]


def get_init_inputs():
    return []

turn 03.8s turn · 3.8s LLM · 0.0s tools

reasoning

**Exploring CUDA kernel for logsumexp** I need to craft a complete Python program with a CUDA extension to perform the logsumexp operation efficiently over R=4 dims for a fixed shape of 4x10x32 in fp32 format. My goal is to make it faster than PyTorch. I'm considering a simple kernel with 320 elements that compute 4 values each and possibly using one thread per output. I'll need to manage the compilation and define a class while keeping track of overhead and performance. Let’s figure out the specs!

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 17117.0s turn · 7117.0s LLM · 0.0s tools

feedbackLLM call failed: _WorkItemTimeout: work item exceeded soft timeout budget