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on
Zero
Running
on
Zero
File size: 4,726 Bytes
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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)
class MlpGeLUPostCrossAttProjector(nn.Module):
def __init__(self, mlp_gelu_cross_att_match, config):
super(MlpGeLUPostCrossAttProjector, self).__init__()
mlp_depth = int(mlp_gelu_cross_att_match.group(1))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
self.projector = nn.Sequential(*modules)
query_num = int(mlp_gelu_cross_att_match.group(3))
self.query = nn.Parameter(torch.zeros(1, query_num, config.hidden_size))
self.query.data.normal_(mean=0.0, std=0.02)
att_layer_num = int(mlp_gelu_cross_att_match.group(2))
decoder_layer = nn.TransformerDecoderLayer(
config.hidden_size,
config.num_attention_heads,
dim_feedforward=config.hidden_size * 4,
dropout=0,
activation="gelu",
batch_first=True,
)
self.cross_att_layers = nn.TransformerDecoder(
decoder_layer, att_layer_num, norm=None
)
def forward(self, vision_embedding):
projected_embedding = self.projector(vision_embedding)
batch_size = vision_embedding.shape[0]
query = self.query.expand(batch_size, -1, -1)
output = self.cross_att_layers(query, projected_embedding)
return output
class MlpGeLUPreCrossAttProjector(nn.Module):
def __init__(self, mlp_gelu_cross_att_match, config):
super(MlpGeLUPreCrossAttProjector, self).__init__()
mlp_depth = int(mlp_gelu_cross_att_match.group(1))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
self.projector = nn.Sequential(*modules)
query_num = int(mlp_gelu_cross_att_match.group(3))
self.query = nn.Parameter(torch.zeros(1, query_num, config.mm_hidden_size))
self.query.data.normal_(mean=0.0, std=0.02)
att_layer_num = int(mlp_gelu_cross_att_match.group(2))
decoder_layer = nn.TransformerDecoderLayer(
config.mm_hidden_size,
8,
dim_feedforward=config.mm_hidden_size * 4,
dropout=0,
activation="gelu",
batch_first=True,
)
self.cross_att_layers = nn.TransformerDecoder(
decoder_layer, att_layer_num, norm=None
)
def forward(self, vision_embedding):
batch_size = vision_embedding.shape[0]
query = self.query.expand(batch_size, -1, -1)
output = self.cross_att_layers(query, vision_embedding)
output = self.projector(output)
return output
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$", projector_type)
if mlp_gelu_match:
mlp_depth = int(mlp_gelu_match.group(1))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
return nn.Sequential(*modules)
mlp_gelu_pre_cross_att_match = re.match(
r"^mlp(\d+)x_gelu_pre_(\d+)att_(\d+)q$", projector_type
)
if mlp_gelu_pre_cross_att_match:
return MlpGeLUPreCrossAttProjector(mlp_gelu_pre_cross_att_match, config)
mlp_gelu_post_cross_att_match = re.match(
r"^mlp(\d+)x_gelu_post_(\d+)att_(\d+)q$", projector_type
)
if mlp_gelu_post_cross_att_match:
return MlpGeLUPostCrossAttProjector(mlp_gelu_post_cross_att_match, config)
if projector_type == "identity":
return IdentityMap()
raise ValueError(f"Unknown projector type: {projector_type}")
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