L1 P53 · original

error 53_Min_reduction_over_a_dimension.py

model

gpt-5.5-priority

turns / max

2 / 10

tool calls

tokens (total)

wall clock

—

truncated

backend

cuda fp32

variant

original

runtime

—

ref runtime

0.00 μs

speedup

—

started

2026-05-02T22:02:51Z

reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs min reduction over a specific dimension.
    """
    def __init__(self, dim: int):
        """
        Initializes the model with the dimension to reduce over.

        Args:
            dim (int): The dimension to reduce over.
        """
        super(Model, self).__init__()
        self.dim = dim

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies min reduction over the specified dimension to the input tensor.

        Args:
            x (torch.Tensor): Input tensor.

        Returns:
            torch.Tensor: Output tensor after min reduction over the specified dimension.
        """
        return torch.min(x, dim=self.dim)[0]

batch_size = 128
dim1 = 4096
dim2 = 4095

def get_inputs():
    x = torch.rand(batch_size, dim1, dim2)
    return [x]

def get_init_inputs():
    return [1] # Example, change to desired dimension

turn 077.8s turn · 77.8s LLM · 0.0s tools

reasoning

**Creating a CUDA implementation** I need to craft a CUDA function for the minimum operation over dimension 1, initializing with only dimension 1. The input shape is 128x4096x4095 in contiguous fp32 format, and I want the output to be [128, 4095] after reducing 4096 for each batch and column. It's essential that this runs faster than the torch min function. I'll need to create a Python file and also gather information about the GPU specifications to proceed.

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 177.8s turn · 77.8s LLM · 0.0s tools

feedbackLLM call failed: Empty: