File size: 1,907 Bytes
0e45eba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | import re
import torch.nn as nn
import torch
from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel
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 MultimodalProjectorConfig(PretrainedConfig):
model_type = "v2l_projector"
def __init__(self, mm_projector_type: str=None, **kwargs):
super().__init__()
self.mm_projector_type = mm_projector_type
class MultimodalProjector(PreTrainedModel):
config_class = MultimodalProjectorConfig
def __init__(
self, mm_projector_cfg: MultimodalProjectorConfig, config: PretrainedConfig
):
super().__init__(mm_projector_cfg)
mm_projector_type = mm_projector_cfg.mm_projector_type
if mm_projector_type == "identity":
self.layers = IdentityMap()
elif mm_projector_type == "linear":
self.layers = nn.Linear(config.mm_hidden_size, config.hidden_size)
else:
mlp_gelu_match = re.match(r"^mlp(\d+)x_gelu$", mm_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))
self.layers = nn.Sequential(*modules)
else:
raise ValueError(f"Unknown projector type: {mm_projector_type}")
def forward(self, x, *args, **kwargs):
return self.layers(x)
AutoConfig.register("v2l_projector", MultimodalProjectorConfig)
AutoModel.register(MultimodalProjectorConfig, MultimodalProjector) |