Source code for lightwood.helpers.torch

import functools
import torch
from torch.nn.functional import pad
from lightwood.helpers.device import get_devices

def concat_vectors_and_pad(vec_list, max_):
    assert len(vec_list) > 0
    assert len(vec_list) <= max_
    assert max_ > 0

    cat_vec =, dim=0)

    pad_size = max_ - len(vec_list)
    padding = (0, pad_size * vec_list[0].size(0))
    padded = pad(cat_vec[None], padding, 'constant', 0)[0]

    return padded

def average_vectors(vec_list):
    assert len(vec_list) > 0
    return[emb[None] for emb in vec_list], dim=0).mean(0)

[docs]class LightwoodAutocast: """ Equivalent to torch.cuda.amp.autocast, but checks device compute capability to activate the feature only when the GPU has tensor cores to leverage AMP. """ active = False def __init__(self, enabled=True): self.major = 0 # GPU major version torch_version = [int(i) for i in torch.__version__.split('.')[:-1]] if not enabled or not torch.cuda.is_available() or torch_version[0] < 1 or torch_version[1] < 6: self._enabled = False else: device, _ = get_devices() if device.type == 'cuda': # tensor cores only exist from 7 onwards # if this is not the case, then AMP is unnecessary overhead self.major, _ = torch.cuda.get_device_capability(device) self._enabled = enabled if self.major > 6 else False else: self._enabled = False # gpu is available but cpu is forced self.prev = self._enabled # necessary reference to exit = self._enabled def __enter__(self): if self._enabled: self.prev = torch.is_autocast_enabled() torch.set_autocast_enabled(self._enabled) torch.autocast_increment_nesting() def __exit__(self, *args): if self._enabled: # Drop the cache when we exit to a nesting level that's outside any instance of autocast if torch.autocast_decrement_nesting() == 0: torch.clear_autocast_cache() torch.set_autocast_enabled(self.prev) return False def __call__(self, func): @functools.wraps(func) def decorate_autocast(*args, **kwargs): with self: return func(*args, **kwargs) return decorate_autocast