""" # Copyright 2025 The HuggingFace Inc. team. # # 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. Modeling for ColQwen3 retrieval, aligned with the ColQwen2 reference implementation. """ from dataclasses import dataclass from typing import Optional from torch import nn from transformers import AutoModelForImageTextToText from transformers.configuration_utils import PretrainedConfig from transformers.cache_utils import Cache from transformers.modeling_utils import PreTrainedModel from transformers.utils import ModelOutput, auto_docstring, can_return_tuple, is_torch_available, logging from transformers.models.qwen3_vl.configuration_qwen3_vl import Qwen3VLConfig from .configuration_colqwen3 import ColQwen3Config if is_torch_available(): import torch logger = logging.get_logger(__name__) @auto_docstring class ColQwen3PreTrainedModel(PreTrainedModel): config_class = ColQwen3Config base_model_prefix = "model" _no_split_modules = [] _supports_sdpa = True _supports_flash_attn = True _supports_flex_attn = True def _init_weights(self, module): std = ( self.config.initializer_range if hasattr(self.config, "initializer_range") else getattr(self.config.text_config, "initializer_range", 0.02) ) if isinstance(module, (nn.Linear, nn.Conv2d)): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() @dataclass @auto_docstring( custom_intro=""" Base class for ColQwen3 embeddings output. """ ) class ColQwen3ForRetrievalOutput(ModelOutput): r""" embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): The embeddings of the model. past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): It is a [`~cache_utils.Cache`] instance. """ loss: Optional[torch.FloatTensor] = None embeddings: Optional[torch.Tensor] = None past_key_values: Optional[Cache] = None hidden_states: Optional[tuple[torch.FloatTensor]] = None attentions: Optional[tuple[torch.FloatTensor]] = None @auto_docstring( custom_intro=""" ColQwen3 retrieval model that mirrors the ColQwen2 late-interaction pipeline while using a Qwen3-VL backbone. """ ) class ColQwen3(ColQwen3PreTrainedModel): _checkpoint_conversion_mapping = { # Legacy checkpoints saved from a bare Qwen3VLModel (no `vlm.` nesting). r"^model\.visual": "vlm.model.visual", r"^model\.language_model": "vlm.model.language_model", r"^model\.": "vlm.model.", r"^visual": "vlm.model.visual", r"^language_model": "vlm.model.language_model", r"^custom_text_proj": "embedding_proj_layer", } config_class = ColQwen3Config model_type = ColQwen3Config.model_type def __init__( self, config: ColQwen3Config, attn_impl: Optional[str] = None, mask_non_image_embeddings: bool = False, ): """ Args: config (ColQwen3Config): Configuration carrying nested vision/text configs for the retrieval model. attn_impl (Optional[str], optional): Attention implementation forwarded to the VLM (e.g., "flash_attention_2"). Defaults to None. mask_non_image_embeddings (bool, optional): If True, zero out non-image embeddings after projection. Defaults to False. """ super().__init__(config) self.config = config vision_cfg = ( config.vision_config.to_dict() if isinstance(config.vision_config, PretrainedConfig) else config.vision_config ) text_cfg = config.text_config.to_dict() if isinstance(config.text_config, PretrainedConfig) else config.text_config vlm_config = Qwen3VLConfig( text_config=text_cfg, vision_config=vision_cfg, image_token_id=getattr(config, "image_token_id", 151655), video_token_id=getattr(config, "video_token_id", 151656), vision_start_token_id=getattr(config, "vision_start_token_id", 151652), vision_end_token_id=getattr(config, "vision_end_token_id", 151653), tie_word_embeddings=getattr(config.text_config, "tie_word_embeddings", False), ) self.vlm = AutoModelForImageTextToText.from_config(vlm_config) self.embedding_dim = self.config.embed_dim self.embedding_proj_layer = nn.Linear( self.vlm.config.text_config.hidden_size, self.embedding_dim, ) self.padding_side = getattr(config, "padding_side", "left") self.mask_non_image_embeddings = mask_non_image_embeddings self._tied_weights_keys = [f"vlm.{k}" for k in (self.vlm._tied_weights_keys or [])] self.post_init() if attn_impl is not None and hasattr(self.vlm, "set_attn_implementation"): self.vlm.set_attn_implementation(attn_impl) @classmethod def from_pretrained(cls, *args, config: Optional[ColQwen3Config] = None, **kwargs): key_mapping = kwargs.pop("key_mapping", None) if key_mapping is None: key_mapping = getattr(cls, "_checkpoint_conversion_mapping", None) return super().from_pretrained(*args, config=config, **kwargs, key_mapping=key_mapping) @can_return_tuple @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, pixel_values: Optional[torch.Tensor] = None, image_grid_thw: Optional[torch.LongTensor] = None, cache_position: Optional[torch.LongTensor] = None, pixel_values_videos: Optional[torch.Tensor] = None, video_grid_thw: Optional[torch.LongTensor] = None, ) -> ColQwen3ForRetrievalOutput: r""" image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): The temporal, height and width of feature shape of each image in LLM. video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): The temporal, height and width of feature shape of each video in LLM. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vlm_output = self.vlm.model( input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, pixel_values_videos=pixel_values_videos, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, pixel_values=pixel_values, image_grid_thw=image_grid_thw, video_grid_thw=video_grid_thw, cache_position=cache_position, ) vlm_hidden_states = vlm_output.hidden_states if output_hidden_states else None last_hidden_states = vlm_output[0] proj_dtype = self.embedding_proj_layer.weight.dtype embeddings = self.embedding_proj_layer(last_hidden_states.to(proj_dtype)) denom = embeddings.norm(dim=-1, keepdim=True).clamp_min(torch.finfo(embeddings.dtype).eps) embeddings = embeddings / denom if attention_mask is not None: embeddings = embeddings * attention_mask.unsqueeze(-1) if pixel_values is not None and self.mask_non_image_embeddings: image_mask = (input_ids == self.vlm.config.image_token_id).unsqueeze(-1) embeddings = embeddings * image_mask return ColQwen3ForRetrievalOutput( embeddings=embeddings, past_key_values=vlm_output.past_key_values, hidden_states=vlm_hidden_states, attentions=vlm_output.attentions, ) def get_input_embeddings(self): return self.vlm.get_input_embeddings() def set_input_embeddings(self, value): self.vlm.set_input_embeddings(value) def get_output_embeddings(self): return self.vlm.get_output_embeddings() def set_output_embeddings(self, new_embeddings): self.vlm.set_output_embeddings(new_embeddings) def tie_weights(self): return self.vlm.tie_weights() def resize_token_embeddings( self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True, ) -> nn.Embedding: model_embeds = self.vlm.resize_token_embeddings( new_num_tokens=new_num_tokens, pad_to_multiple_of=pad_to_multiple_of, mean_resizing=mean_resizing, ) self.vlm.config.text_config.vocab_size = model_embeds.num_embeddings self.vlm.config.vocab_size = model_embeds.num_embeddings return model_embeds __all__ = ["ColQwen3", "ColQwen3PreTrainedModel", "ColQwen3ForRetrievalOutput"]