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import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
def find_multiple(n: int, k: int) -> int: | |
if k == 0 or n % k == 0: | |
return n | |
return n + k - (n % k) | |
def pad_weight_(w: nn.Embedding | nn.Linear, multiple: int): | |
"""Pad the weight of an embedding or linear layer to a multiple of `multiple`.""" | |
if isinstance(w, nn.Embedding): | |
# Pad input dim | |
if w.weight.shape[1] % multiple == 0: | |
return | |
w.weight.data = F.pad(w.weight.data, (0, 0, 0, w.weight.shape[1] % multiple)) | |
w.num_embeddings, w.embedding_dim = w.weight.shape | |
elif isinstance(w, nn.Linear): | |
# Pad output dim | |
if w.weight.shape[0] % multiple == 0: | |
return | |
w.weight.data = F.pad(w.weight.data, (0, 0, 0, w.weight.shape[0] % multiple)) | |
w.out_features, w.in_features = w.weight.shape | |
else: | |
raise ValueError(f"Unsupported weight type: {type(w)}") | |
def get_device() -> torch.device: | |
if torch.cuda.is_available(): | |
return torch.device(torch.cuda.current_device()) | |
# MPS breaks for whatever reason. Uncomment when it's working. | |
# if torch.mps.is_available(): | |
# return torch.device("mps") | |
return torch.device("cpu") | |
DEFAULT_DEVICE = get_device() | |