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import torch
import torch.nn as nn
import re
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 SimpleResBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.pre_norm = nn.LayerNorm(channels)
self.proj = nn.Sequential(
nn.Linear(channels, channels),
nn.GELU(),
nn.Linear(channels, channels)
)
def forward(self, x):
x = self.pre_norm(x)
return x + self.proj(x)
def build_vision_projector(config, delay_load=False, **kwargs):
projector_type = getattr(config, 'mm_projector_type', 'linear')
if projector_type == 'linear':
return nn.Linear(config.mm_hidden_size, config.hidden_size)
mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
if mlp_gelu_match:
mlp_depth = int(mlp_gelu_match.group(1))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
# modules = []
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
return nn.Sequential(*modules)
if projector_type == 'identity':
return IdentityMap()
raise ValueError(f'Unknown projector type: {projector_type}')
class DenseConnector(nn.Module):
def __init__(self, config, mlp_gelu_match):
super().__init__()
self.dense_connector_type = dense_connector_type
if self.dense_connector_type == 'token_cat': # else channel cat
self.avg_pooling_k8 = nn.AvgPool1d(kernel_size=8, stride=8)
elif self.dense_connector_type == 'channel_cat':
pass
elif self.dense_connector_type == 'channel_sum_cat':
self.dc_ln = nn.LayerNorm(mm_hidden_size)
self.dc_linear = nn.Linear(mm_hidden_size, mm_hidden_size),
else:
raise ValueError(f'Unknown dense connector type: {dense_connector_type}')
def forward(self, image_forward_outs, selected_features):
return x
def build_dense_connector(config, delay_load=False, **kwargs):
projector_type = getattr(config, 'mm_projector_type', 'linear')
if projector_type == 'linear':
return nn.Linear(config.mm_hidden_size, config.hidden_size)
mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
if mlp_gelu_match:
DenseConnector(config, mlp_gelu_match)
mlp_depth = int(mlp_gelu_match.group(1))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
# modules = []
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
return nn.Sequential(*modules)
if projector_type == 'identity':
return IdentityMap()
raise ValueError(f'Unknown projector type: {projector_type}')
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