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on
Zero
Running
on
Zero
| import torch | |
| import torch.nn as nn | |
| import re | |
| from ola_vlm.model.multimodal_projector.resampler import Resampler | |
| 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_resampler(config, num_queries=None): | |
| return Resampler( | |
| dim=config["probe_output_dim"], | |
| depth=config["probe_depth"], | |
| dim_head=config["probe_dim_head"], | |
| heads=config["probe_num_heads"], | |
| num_queries=config["num_queries"] if num_queries is None else num_queries, | |
| embedding_dim=config.hidden_size, | |
| output_dim=config["probe_output_dim"], | |
| ff_mult=config["probe_ff_mult"], | |
| ) | |
| 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) | |
| if projector_type == 'identity': | |
| return IdentityMap() | |
| raise ValueError(f'Unknown projector type: {projector_type}') | |