espnet/nets/pytorch_backend/transformer/initializer.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Copyright 2019 Shigeki Karita
#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

"""Parameter initialization."""

import torch

from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm


def initialize(model, init_type="pytorch"):
    """Initialize Transformer module.

    :param torch.nn.Module model: transformer instance
    :param str init_type: initialization type
    """
    if init_type == "pytorch":
        return

    # weight init
    for p in model.parameters():
        if p.dim() > 1:
            if init_type == "xavier_uniform":
                torch.nn.init.xavier_uniform_(p.data)
            elif init_type == "xavier_normal":
                torch.nn.init.xavier_normal_(p.data)
            elif init_type == "kaiming_uniform":
                torch.nn.init.kaiming_uniform_(p.data, nonlinearity="relu")
            elif init_type == "kaiming_normal":
                torch.nn.init.kaiming_normal_(p.data, nonlinearity="relu")
            else:
                raise ValueError("Unknown initialization: " + init_type)
    # bias init
    for p in model.parameters():
        if p.dim() == 1:
            p.data.zero_()

    # reset some modules with default init
    for m in model.modules():
        if isinstance(m, (torch.nn.Embedding, LayerNorm)):
            m.reset_parameters()