FunASR/funasr/torch_utils/initialize.py

#!/usr/bin/env python3

"""Initialize modules for espnet2 neural networks."""

import math
import torch
from typeguard import check_argument_types


def initialize(model: torch.nn.Module, init: str):
    """Initialize weights of a neural network module.

    Parameters are initialized using the given method or distribution.

    Custom initialization routines can be implemented into submodules
    as function `espnet_initialization_fn` within the custom module.

    Args:
        model: Target.
        init: Method of initialization.
    """
    assert check_argument_types()

    if init == "chainer":
        # 1. lecun_normal_init_parameters
        for p in model.parameters():
            data = p.data
            if data.dim() == 1:
                # bias
                data.zero_()
            elif data.dim() == 2:
                # linear weight
                n = data.size(1)
                stdv = 1.0 / math.sqrt(n)
                data.normal_(0, stdv)
            elif data.dim() in (3, 4):
                # conv weight
                n = data.size(1)
                for k in data.size()[2:]:
                    n *= k
                stdv = 1.0 / math.sqrt(n)
                data.normal_(0, stdv)
            else:
                raise NotImplementedError

        for mod in model.modules():
            # 2. embed weight ~ Normal(0, 1)
            if isinstance(mod, torch.nn.Embedding):
                mod.weight.data.normal_(0, 1)
            # 3. forget-bias = 1.0
            elif isinstance(mod, torch.nn.RNNCellBase):
                n = mod.bias_ih.size(0)
                mod.bias_ih.data[n // 4 : n // 2].fill_(1.0)
            elif isinstance(mod, torch.nn.RNNBase):
                for name, param in mod.named_parameters():
                    if "bias" in name:
                        n = param.size(0)
                        param.data[n // 4 : n // 2].fill_(1.0)
            if hasattr(mod, "espnet_initialization_fn"):
                mod.espnet_initialization_fn()

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

        # TODO(xkc): Hacking s3prl_frontend and wav2vec2encoder initialization
        if getattr(model, "encoder", None) and getattr(
            model.encoder, "reload_pretrained_parameters", None
        ):
            model.encoder.reload_pretrained_parameters()
        if getattr(model, "frontend", None) and getattr(
            model.frontend, "reload_pretrained_parameters", None
        ):
            model.frontend.reload_pretrained_parameters()
        if getattr(model, "postencoder", None) and getattr(
            model.postencoder, "reload_pretrained_parameters", None
        ):
            model.postencoder.reload_pretrained_parameters()