L1 P18 · popcorn

error 18_CSRMultiHeadSpMM.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

2026-05-02T21:38:56Z

reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Multi-head sparse aggregation with per-edge attention weights.

    Each edge carries one weight per head, and source node features are arranged
    as `(num_nodes, num_heads, head_dim)`. The output aggregates weighted
    source features into `(num_nodes, num_heads, head_dim)`.
    """

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

    def forward(
        self,
        row_ptr: torch.Tensor,
        col_idx: torch.Tensor,
        edge_weight: torch.Tensor,
        node_feat: torch.Tensor,
    ) -> torch.Tensor:
        num_nodes = row_ptr.numel() - 1
        num_heads = node_feat.shape[1]
        head_dim = node_feat.shape[2]
        out = torch.zeros(num_nodes, num_heads, head_dim, dtype=node_feat.dtype, device=node_feat.device)

        for dst in range(num_nodes):
            start = int(row_ptr[dst].item())
            end = int(row_ptr[dst + 1].item())
            if end > start:
                src = col_idx[start:end].long()
                weights = edge_weight[start:end].unsqueeze(-1)
                out[dst] = (node_feat[src] * weights).sum(dim=0)

        return out


num_nodes = 320
avg_degree = 14
num_heads = 8
head_dim = 32


def get_inputs():
    return list(
        popcorn_pri.make_csr_multihead_spmm_graph(
            num_nodes, avg_degree, num_heads, head_dim
        )
    )



def get_init_inputs():
    return []

turn 03.7s turn · 3.7s LLM · 0.0s tools

reasoning

**Planning CUDA extension** I need to craft a CUDA extension for a small operation. We're working with parameters like N=320, H=8, D=32, and E around 4480, which outputs 81920 floats. For each destination, I'll sum degrees around 12-16 over the source for each weight per edge. This leads to about 1.1 million multiplications and additions, which could be slow in PyTorch. I need to work with complete Python and understand GPU utilization better.

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 13.7s turn · 3.7s LLM · 0.0s tools

feedbackLLM call failed: Empty: