torch.compile (inductor)

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A model that computes Kullback-Leibler Divergence for comparing two distributions.

    Parameters:
        None
    """
    def __init__(self):
        super(Model, self).__init__()

    def forward(self, predictions, targets):
        return torch.nn.functional.kl_div(torch.log(predictions), targets, reduction='batchmean')

batch_size = 8192 * 2
input_shape = (8192 * 2,)
dim = 1

def get_inputs():
    scale = torch.rand(())
    return [(torch.rand(batch_size, *input_shape)*scale).softmax(dim=-1), torch.rand(batch_size, *input_shape).softmax(dim=-1)]

def get_init_inputs():
    return []