build_mlp.py

import torch
import torch.nn as nn
import math
import re

def build_layout_projector():
    projector_type = 'mlp2x_gelu'
    mm_hidden_size = 4
    hidden_size = 4096

    mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
    if mlp_gelu_match:
        mlp_depth = int(mlp_gelu_match.group(1))
        modules = [nn.Linear(mm_hidden_size, hidden_size)]
        for _ in range(1, mlp_depth):
            modules.append(nn.GELU())
            modules.append(nn.Linear(hidden_size, hidden_size))
        return nn.Sequential(*modules)

    if projector_type == 'identity':
        return IdentityMap()

    raise ValueError(f'Unknown projector type: {projector_type}')


class IdentityMap(nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, x, *args, **kwargs):
        return x

    @property
    def config(self):
        return {'mm_projector_type': 'identity'}

class PLoRA(nn.Linear):

    def __init__(self,
                 in_features: int,
                 out_features: int,
                 bias: bool = True,
                 device=None,
                 dtype=None,
                 lora_r=8,
                 lora_alpha=16,
                 lora_dropout=0.05,
                 lora_len=0,
                 **kwargs) -> None:
        super().__init__(in_features, out_features, bias, device, dtype)
        self.lora_r = lora_r
        self.lora_alpha = lora_alpha
        self.lora_len = lora_len
        if lora_dropout > 0.:
            self.lora_dropout = nn.Dropout(p=lora_dropout)
        else:
            self.lora_dropout = lambda x: x
        self.lora_scaling = self.lora_alpha / self.lora_r

        self.Plora_A = nn.Linear(
            in_features, self.lora_r, bias=False, device=device, dtype=dtype)
        self.Plora_B = nn.Linear(
            self.lora_r, out_features, bias=False, device=device, dtype=dtype)

        self.reset_parameters()

    def reset_parameters(self):
        if hasattr(self, 'lora_A'):
            # initialize A the same way as the default for nn.Linear and B to zero
            nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
            nn.init.zeros_(self.lora_B.weight)

    def forward(self, x, im_mask=None):
        res = super().forward(x)
        if im_mask is not None:
            if torch.sum(im_mask) > 0:
                part_x = x[im_mask]
                res[im_mask] += self.Plora_B(
                    self.Plora_A(
                        self.lora_dropout(part_x))) * self.lora_scaling
            else:
                part_x = x[:, :1]
                res[:, :1] += self.Plora_B(
                    self.Plora_A(self.lora_dropout(part_x))) * 0
        return res