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on
Zero
Running
on
Zero
import torch | |
import torch.nn as nn | |
import re | |
from .pooler_projector import PoolerProjector | |
class IdentityMap(nn.Module): | |
def __init__(self): | |
super().__init__() | |
def forward(self, x, *args, **kwargs): | |
return x | |
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) | |
if projector_type == "pooler": | |
return PoolerProjector(config, kwargs["vision_cfg"]) | |
mlp_gelu_match = re.match(r"^mlp(\d+)x_gelu$", projector_type) | |
if mlp_gelu_match: | |
mlp_depth = int(mlp_gelu_match.group(1)) | |
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)] | |
for _ in range(1, mlp_depth): | |
modules.append(nn.GELU()) | |
modules.append(nn.Linear(config.hidden_size, config.hidden_size)) | |
return nn.Sequential(*modules) | |
mlp_gelu_resnet_match = re.match(r"^mlp(\d+)x_res(\d+)x_gelu$", projector_type) | |
if mlp_gelu_resnet_match: | |
mlp_depth = int(mlp_gelu_resnet_match.group(1)) | |
res_depth = int(mlp_gelu_resnet_match.group(2)) | |
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)) | |
for _ in range(res_depth): | |
modules.append(SimpleResBlock(config.hidden_size)) | |
return nn.Sequential(*modules) | |
if projector_type == "identity": | |
return IdentityMap() | |
raise ValueError(f"Unknown projector type: {projector_type}") | |