Spaces:
Running
on
Zero
Running
on
Zero
| # Copyright (C) 2025 AIDC-AI | |
| # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 | |
| # Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. | |
| import torch | |
| from torch import nn, Tensor | |
| from ovis_image.model.ovis.modeling_ovis2_5 import Ovis2_5, Ovis2_5_Config | |
| class OvisEmbedder(nn.Module): | |
| def __init__( | |
| self, | |
| model_path: str, | |
| random_init=False, | |
| **hf_kwargs | |
| ): | |
| super().__init__() | |
| if random_init: | |
| # Initialize Ovis model with random weights for test purpose only | |
| config = Ovis2_5_Config.from_pretrained(model_path) | |
| config.name_or_path = model_path | |
| self.hf_module = Ovis2_5._from_config(config, **hf_kwargs) | |
| else: | |
| self.hf_module = Ovis2_5.from_pretrained( | |
| model_path, **hf_kwargs | |
| ) | |
| self.pad_token_id = self.hf_module.text_tokenizer.pad_token_id | |
| self.user_prompt_begin_id = 28 | |
| # get Qwen3 | |
| self.hf_module = self.hf_module.llm.model | |
| self.hf_module = self.hf_module.eval().requires_grad_(False) | |
| def forward(self, batch_tokens: Tensor, attention_mask = None) -> Tensor: | |
| if attention_mask is None: | |
| attention_mask = torch.ne( | |
| batch_tokens, self.pad_token_id | |
| ).to(device=batch_tokens.device) | |
| outputs = self.hf_module( | |
| input_ids=batch_tokens, | |
| attention_mask=attention_mask, | |
| ) | |
| txt_semantic_embed = outputs.last_hidden_state | |
| txt_semantic_embed = txt_semantic_embed * attention_mask[..., None] | |
| txt_semantic_embed = txt_semantic_embed[:, self.user_prompt_begin_id:, :] | |
| return txt_semantic_embed | |