wehub-resource-sync
353 lines
12 KiB
Python
353 lines
12 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange
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from swift.utils import get_logger
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logger = get_logger()
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class ResTuner(nn.Module):
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def __init__(self, dim=None, layer_num=-1, depth=-1, zero_init_last=False, stage='', tuner_cfg=None, **kwargs):
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super().__init__()
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if tuner_cfg is None:
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tuner_cfg = {}
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self.dim = dim
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self.layer_num = layer_num
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self.depth = depth
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self.stage = stage
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self.tuner_cfg = tuner_cfg
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if (isinstance(tuner_cfg, str) and tuner_cfg == 'res_adapter') or \
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(isinstance(tuner_cfg, dict) and 'res_adapter' in tuner_cfg):
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tuner_cfg = tuner_cfg['res_adapter'] if isinstance(tuner_cfg, dict) else tuner_cfg
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self.tuner = ResAdapter(
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dim=dim,
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layer_num=layer_num,
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depth=depth,
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zero_init_last=zero_init_last,
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stage=stage,
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tuner_cfg=tuner_cfg,
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**kwargs)
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elif (isinstance(tuner_cfg, str) and tuner_cfg == 'res_group_adapter') or \
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(isinstance(tuner_cfg, dict) and 'res_group_adapter' in tuner_cfg):
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tuner_cfg = tuner_cfg['res_group_adapter'] if isinstance(tuner_cfg, dict) else tuner_cfg
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self.tuner = ResGroupAdapter(
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dim=dim,
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layer_num=layer_num,
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depth=depth,
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zero_init_last=zero_init_last,
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stage=stage,
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tuner_cfg=tuner_cfg,
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**kwargs)
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elif (isinstance(tuner_cfg, str) and tuner_cfg == 'upsample') or \
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(isinstance(tuner_cfg, dict) and 'upsample' in tuner_cfg):
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tuner_cfg = tuner_cfg['upsample'] if isinstance(tuner_cfg, dict) else tuner_cfg
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if 'upsample_out_channels' in kwargs:
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out_channels = kwargs['upsample_out_channels']
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use_conv = True if out_channels else False
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else:
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out_channels = dim
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use_conv = False
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self.tuner = Upsample(
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channels=dim, use_conv=use_conv, out_channels=out_channels, tuner_cfg=tuner_cfg, **kwargs)
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else:
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self.tuner = Identity()
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def forward(self, x, *args, **kwargs):
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if self.tuner_cfg == 'zero' or 'zero' in self.tuner_cfg:
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x_out = 0.0
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else:
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x_out = self.tuner(x, *args, **kwargs)
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return x_out
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class ResAdapter(nn.Module):
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def __init__(self,
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dim,
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layer_num=-1,
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depth=-1,
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zero_init_last=False,
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stage='',
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tuner_cfg=None,
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act_layer=nn.GELU,
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**kwargs):
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super(ResAdapter, self).__init__()
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self.dim = dim
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self.layer_num = layer_num
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self.depth = depth
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self.adapter_length = tuner_cfg['adapter_length'] if 'adapter_length' in tuner_cfg else 32
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self.adapter_type = tuner_cfg['adapter_type'] if 'adapter_type' in tuner_cfg else None
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self.adapter_weight = tuner_cfg['adapter_weight'] if 'adapter_weight' in tuner_cfg else None
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self.adapter_length = self.adapter_length[self.layer_num] if isinstance(self.adapter_length,
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list) else self.adapter_length
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assert isinstance(self.adapter_length, int) or (isinstance(self.adapter_length, tuple)
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and len(self.adapter_length) == 3)
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if isinstance(self.adapter_length, int):
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self.ln1 = nn.Linear(dim, self.adapter_length)
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else:
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self.ln1 = nn.Linear(self.adapter_length[0], self.adapter_length[1])
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self.activate = act_layer()
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if isinstance(self.adapter_length, int):
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self.ln2 = nn.Linear(self.adapter_length, dim)
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else:
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self.ln2 = nn.Linear(self.adapter_length[1], self.adapter_length[2])
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dim = self.adapter_length[2]
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self._xavier_init_weights(self.ln1)
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if zero_init_last and layer_num == depth - 1:
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self._zero_init_weights(self.ln2)
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else:
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self._xavier_init_weights(self.ln2)
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self.scaling = init_weight_type(dim, self.adapter_weight)
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self._prepared = False
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def _zero_init_weights(self, m):
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if isinstance(m, nn.Linear):
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nn.init.zeros_(m.weight)
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nn.init.zeros_(m.bias)
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def _kaiming_init_weights(self, m):
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if isinstance(m, nn.Linear):
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nn.init.kaiming_uniform_(m.weight, a=math.sqrt(5))
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nn.init.normal_(m.bias)
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def _xavier_init_weights(self, m):
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if isinstance(m, nn.Linear):
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nn.init.xavier_uniform_(m.weight)
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nn.init.normal_(m.bias, std=1e-6)
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def forward(self, x):
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if not self._prepared:
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self.ln1.to(x.device)
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self.activate.to(x.device)
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self.ln2.to(x.device)
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self._prepared = True
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x_dtype = x.dtype
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x = x.to(self.ln1.weight.dtype)
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x_shortcut = x
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if len(x_shortcut.size()) == 4:
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B, C, N1, N2 = x.size()
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x = x.view(x_shortcut.size()[0], x_shortcut.size()[1], -1).permute(0, 2, 1)
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x_adapter = self.ln2(self.activate(self.ln1(x)))
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if self.adapter_weight:
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x_adapter = apply_data_weight(x_adapter, self.scaling, self.adapter_weight)
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if len(x_shortcut.size()) == 4:
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x_adapter = x_adapter.permute(0, 2, 1).view(x_shortcut.size()[0],
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x_adapter.size()[-1],
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x_shortcut.size()[2],
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x_shortcut.size()[3])
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x_out = x_shortcut + x_adapter
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return x_out.to(x_dtype)
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class ResGroupAdapter(nn.Module):
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def __init__(self,
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dim,
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layer_num=-1,
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depth=-1,
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zero_init_last=False,
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stage='',
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tuner_cfg=None,
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act_layer=nn.GELU,
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**kwargs):
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super(ResGroupAdapter, self).__init__()
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self.dim = dim
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self.layer_num = layer_num
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self.depth = depth
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self.adapter_type = tuner_cfg['adapter_type'] if 'adapter_type' in tuner_cfg else None
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self.adapter_weight = tuner_cfg['adapter_weight'] if 'adapter_weight' in tuner_cfg else None
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self.adapter_dim = tuner_cfg['dim'] if 'dim' in tuner_cfg else dim
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self.adapter_head = tuner_cfg['head'] if 'head' in tuner_cfg else 4
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self.adapter_scale_factor = tuner_cfg['scale_factor'] if 'scale_factor' in tuner_cfg else 2
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assert self.adapter_dim % self.adapter_head == 0, 'adapter dim should be divisible by adapter head'
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self.dim_mlp = self.adapter_dim // self.adapter_head
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self.ln1 = nn.Linear(self.dim_mlp, self.dim_mlp * self.adapter_scale_factor)
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self.ln2 = nn.Linear(self.dim_mlp * self.adapter_scale_factor, self.dim_mlp)
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self.activate = act_layer()
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self._kaiming_init_weights(self.ln1)
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if zero_init_last and layer_num == depth - 1:
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self._zero_init_weights(self.ln2)
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else:
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self._kaiming_init_weights(self.ln2)
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self.scaling = init_weight_type(dim, self.adapter_weight)
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self._prepared = False
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def _zero_init_weights(self, m):
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if isinstance(m, nn.Linear):
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nn.init.zeros_(m.weight)
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nn.init.zeros_(m.bias)
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def _kaiming_init_weights(self, m):
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if isinstance(m, nn.Linear):
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nn.init.kaiming_uniform_(m.weight, a=math.sqrt(5))
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nn.init.normal_(m.bias)
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def _xavier_init_weights(self, m):
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if isinstance(m, nn.Linear):
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nn.init.xavier_uniform_(m.weight)
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nn.init.normal_(m.bias, std=1e-6)
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def forward(self, x):
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if not self._prepared:
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self.ln1.to(x.device)
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self.activate.to(x.device)
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self.ln2.to(x.device)
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self._prepared = True
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x_dtype = x.dtype
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x = x.to(self.ln1.weight.dtype)
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x_shortcut = x
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batch, inner_dim, height, width = x.shape
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x_adapter = x.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
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x_adapter = rearrange(x_adapter, 'b n (c h) -> (b h) n c', h=self.adapter_head)
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x_adapter = self.ln2(self.activate(self.ln1(x_adapter)))
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x_adapter = rearrange(x_adapter, '(b h) n c -> b n (c h)', h=self.adapter_head)
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if self.adapter_weight:
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x_adapter = apply_data_weight(x_adapter, self.scaling, self.adapter_weight)
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x_adapter = x_adapter.reshape(batch, height, width, -1).permute(0, 3, 1, 2).contiguous()
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x_out = x_shortcut + x_adapter
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return x_out.to(x_dtype)
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class Identity(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, inputs, *args, **kwargs):
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return inputs
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class Upsample(nn.Module):
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"""
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An upsampling layer with an optional convolution.
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:param channels: channels in the inputs and outputs.
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:param use_conv: a bool determining if a convolution is applied.
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:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
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upsampling occurs in the inner-two dimensions.
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"""
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def __init__(self, channels, use_conv=False, out_channels=None, padding=1, **kwargs):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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if use_conv:
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self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=padding)
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self.init_weights()
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def init_weights(self):
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def _init_weights(m):
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if isinstance(m, nn.Conv2d):
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nn.init.zeros_(m.weight)
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nn.init.zeros_(m.bias)
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self.apply(_init_weights)
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def forward(self, x, target_size=None, *args, **kwargs):
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assert x.shape[1] == self.channels
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if target_size is None:
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x = F.interpolate(x.float(), scale_factor=2, mode='nearest').type_as(x)
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else:
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x = F.interpolate(x.float(), target_size, mode='nearest').type_as(x)
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if self.use_conv:
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x = self.conv(x)
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return x
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def init_weight_type(dim, weight_type):
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if weight_type is None:
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scaling = None
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elif weight_type == 'gate':
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scaling = nn.Linear(dim, 1)
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elif weight_type == 'scale':
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scaling = nn.Parameter(torch.Tensor(1))
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scaling.data.fill_(1)
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elif weight_type == 'scale_kv':
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scaling_k = nn.Parameter(torch.Tensor(1))
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scaling_k.data.fill_(1)
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scaling_v = nn.Parameter(torch.Tensor(1))
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scaling_v.data.fill_(1)
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scaling = (scaling_k, scaling_v)
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elif weight_type == 'scale_channel':
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scaling = nn.Parameter(torch.Tensor(dim))
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scaling.data.fill_(1)
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elif weight_type == 'scale_kv_channel':
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scaling_k = nn.Parameter(torch.Tensor(dim))
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scaling_k.data.fill_(1)
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scaling_v = nn.Parameter(torch.Tensor(dim))
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scaling_v.data.fill_(1)
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scaling = (scaling_k, scaling_v)
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elif weight_type and weight_type.startswith('scalar'):
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scaling = float(weight_type.split('_')[-1])
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else:
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scaling = None
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return scaling
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def apply_data_weight(data, scaling, weight_type):
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if weight_type in ['gate']:
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scaling = torch.mean(torch.sigmoid(scaling(data)), dim=1).view(-1, 1, 1)
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elif weight_type in ['scale', 'scale_channel'] or weight_type.startswith('scalar'):
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scaling = scaling
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else:
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scaling = None
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if scaling is not None:
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data = data * scaling
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return data
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def detach_tensors(feats):
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if type(feats) in [list, tuple]:
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feats = [detach_tensors(feat) if feat is not None else None for feat in feats]
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elif isinstance(feats, dict):
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feats = {key: detach_tensors(val) for key, val in feats.items()}
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elif isinstance(feats, torch.Tensor):
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feats = feats.detach()
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else:
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feats = feats.detach()
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return feats
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def probe_tensors(module, feats, name):
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feats = detach_tensors(feats)
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setattr(module, name, feats)
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def probe_input_pre_hook(self, args):
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input = args[0]
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probe_tensors(self, input, 'probe_input_data')
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return args
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def probe_output_hook(self, args, result):
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output = result
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probe_tensors(self, output, 'probe_output_data')
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return output
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