L1 P19 · popcorn

error 19_PairwiseDistanceMatrix.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

2026-05-02T21:00:29Z

reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Compute all-pairs Euclidean distance matrix from a 3-D point cloud.
    A core primitive in molecular dynamics simulations, docking, and
    distance-geometry-based structure prediction.
    """

    def __init__(self):
        super().__init__()

    def forward(self, coords: torch.Tensor) -> torch.Tensor:
        """
        Args:
            coords: (B, N, 3) – 3-D coordinates of N atoms / residues
        Returns:
            dist: (B, N, N) – pairwise Euclidean distance matrix
        """
        diff = coords.unsqueeze(2) - coords.unsqueeze(1)  # (B, N, N, 3)
        dist = torch.sqrt((diff ** 2).sum(dim=-1) + 1e-8)
        return dist


num_atoms = 512
batch_size = 8


def get_inputs():
    p = popcorn_pri
    return [torch.randn(p.jitter_int(batch_size), p.jitter_int(num_atoms), 3)]


def get_init_inputs():
    return []

turn 03.1s turn · 3.1s LLM · 0.0s tools

reasoning

**Optimizing distance calculations** I need to produce code that optimizes pairwise distances with parameters B=8 and N=512, while using fp32. The output will be B*N*N, which is about 2 million. I'll compute the square root, but I need to reference this without it getting too slow. A straightforward custom method sounds ideal, and it's crucial to use load_inline. I might also need to check the GPU specifications to ensure everything runs smoothly.

call → get_gpu_specs

callget_gpu_specs {}

result ← get_gpu_specs (fail)

failget_gpu_specs get_gpu_specs FAILED: unexpected error during tool execution. _WorkItemTimeout: work item exceeded soft timeout budget Traceback (most recent call last): File "/scratch/tejas/PopcornBench/src/kernelbench/agent/agent.py", line 627, in _execute_tool return tool.execute(self.ctx, **args) File "/scratch/tejas/PopcornBench/src/kernelbench/agent/tools.py", line 656, in execute return ctx.eval_client.get_gpu_specs(ctx) File "/scratch/tejas/PopcornBench/src/kernelbench/agent/eval_client.py", line 62, in get_gpu_specs return self._call("specs", args) File "/scratch/tejas/PopcornBench/src/kernelbench/agent/eval_client.py", line 117, in _call payload = self._response_q.get(timeout=remaining) File "<string>", line 2, in get File "/home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/lib/python3.10/multiprocessing/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/lib/python3.10/multiprocessing/connection.py", line 250, in recv buf = self._recv_bytes() File "/home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/lib/python3.10/multiprocessing/connection.py", line 414, in _recv_bytes buf = self._recv(4) File "/home/prabhune/.local/share/uv/python/cpython-3.10-linux-x86_64-gnu/lib/python3.10/multiprocessing/connection.py", line 379, in _recv chunk = read(handle, remaining) File "/scratch/tejas/PopcornBench/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 13.1s turn · 3.1s LLM · 0.0s tools

feedbackLLM call failed: Empty: