wehub-resource-sync
127 lines
3.8 KiB
Python
127 lines
3.8 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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from swift.utils import get_logger
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logger = get_logger()
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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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def choose_weight_type(weight_type, dim):
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if 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_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 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 get_weight_value(weight_type, scaling, x):
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if weight_type in ['gate']:
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scaling = torch.mean(torch.sigmoid(scaling(x)), 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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return scaling
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class SCEAdapter(nn.Module):
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def __init__(self,
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dim,
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adapter_length,
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adapter_type=None,
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adapter_weight=None,
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act_layer=nn.GELU,
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zero_init_last=True,
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use_bias=True):
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super(SCEAdapter, self).__init__()
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self.dim = dim
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self.adapter_length = adapter_length
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self.adapter_type = adapter_type
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self.adapter_weight = adapter_weight
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self.zero_init_last = zero_init_last
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self.ln1 = nn.Linear(dim, adapter_length, bias=use_bias)
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self.activate = act_layer()
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self.ln2 = nn.Linear(adapter_length, dim, bias=use_bias)
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self.init_weights()
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self.init_scaling()
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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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def init_weights(self):
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self._kaiming_init_weights(self.ln1)
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if self.zero_init_last:
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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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def init_scaling(self):
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if self.adapter_weight:
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self.scaling = choose_weight_type(self.adapter_weight, self.dim)
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else:
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self.scaling = None
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def forward(self, x, x_shortcut=None, use_shortcut=True, **kwargs):
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if x_shortcut is None:
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x_shortcut = x
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x_shape = x.shape
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if len(x_shape) == 4:
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b, d, h, w = x_shape
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x = x.permute(0, 2, 3, 1).reshape(b, h * w, d)
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out = self.ln2(self.activate(self.ln1(x)))
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if self.adapter_weight:
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scaling = get_weight_value(self.adapter_weight, self.scaling, out)
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out = out * scaling if scaling is not None else out
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if len(x_shape) == 4:
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b, d, h, w = x_shape
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out = out.reshape(b, h, w, -1).permute(0, 3, 1, 2).contiguous()
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if use_shortcut:
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out = x_shortcut + out
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return out
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