L2 P37 · original

error 37_Matmul_Swish_Sum_GroupNorm.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

2026-05-02T21:44:19Z

reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A model that performs a matrix multiplication, applies Swish activation, sums with a bias term, and normalizes with GroupNorm.
    """
    def __init__(self, in_features, out_features, num_groups, bias_shape):
        super(Model, self).__init__()
        self.matmul = nn.Linear(in_features, out_features)
        self.bias = nn.Parameter(torch.randn(bias_shape))
        self.group_norm = nn.GroupNorm(num_groups, out_features)

    def forward(self, x):
        """
        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_features).
        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_features).
        """
        x = self.matmul(x)
        x = torch.sigmoid(x) * x  # Swish activation
        x = x + self.bias
        x = self.group_norm(x)
        return x

batch_size = 32768
in_features = 1024
out_features = 4096
num_groups = 64
bias_shape = (out_features,)

def get_inputs():
    return [torch.rand(batch_size, in_features)]

def get_init_inputs():
    return [in_features, out_features, num_groups, bias_shape]

turn 010.9s turn · 10.9s LLM · 0.0s tools

reasoning

**Optimizing Python File for Model** I need to produce a Python file and optimize it for performance. The operation involves significant computations with PyTorch, potentially utilizing cuBLAS for speed. I’m thinking of replacing the forward method, possibly using torch for matrix multiplication while ensuring that the ModelNew has the same parameters and weights. Constructor arguments must be defined properly for compatibility. I should also organize modules and ensure similarity in parameter naming for effective comparisons between the models. Finally, implementing the forward logic will be crucial for activation. **Calculating GroupNorm Efficiently** I'm adding a bias parameter that will be broadcasted during the GroupNorm computation over (N, C) with a group size of 64. For each sample and group, I need to compute the mean and variance after applying swish and the second bias. Each CUDA kernel will ideally operate on one block per (N, G), but with 2 million blocks, the overhead may be too high. I’m thinking of using one block per row with 256 threads to process all channels more efficiently while reducing elements through warps. This should help with performance! **Optimizing Group Processing** For the group, I’m thinking of utilizing 32 lanes to process two values each. By reducing the sums and sum of squares through warp shuffles, I can improve efficiency. Once that's done, I can write out 64 outputs. This approach seems to maximize the processing power and should keep things streamlined. It's all about finding the best ways to make the computations efficient while ensuring accuracy!

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 110.9s turn · 10.9s LLM · 0.0s tools

feedbackLLM call failed: Empty: