| import torch |
| from typing import Optional, Union |
|
|
|
|
| class QwenImageTextEncoder(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| from transformers import Qwen2_5_VLConfig, Qwen2_5_VLModel |
| config = Qwen2_5_VLConfig(**{ |
| "architectures": [ |
| "Qwen2_5_VLForConditionalGeneration" |
| ], |
| "attention_dropout": 0.0, |
| "bos_token_id": 151643, |
| "eos_token_id": 151645, |
| "hidden_act": "silu", |
| "hidden_size": 3584, |
| "image_token_id": 151655, |
| "initializer_range": 0.02, |
| "intermediate_size": 18944, |
| "max_position_embeddings": 128000, |
| "max_window_layers": 28, |
| "model_type": "qwen2_5_vl", |
| "num_attention_heads": 28, |
| "num_hidden_layers": 28, |
| "num_key_value_heads": 4, |
| "rms_norm_eps": 1e-06, |
| "rope_scaling": { |
| "mrope_section": [ |
| 16, |
| 24, |
| 24 |
| ], |
| "rope_type": "default", |
| "type": "default" |
| }, |
| "rope_theta": 1000000.0, |
| "sliding_window": 32768, |
| "text_config": { |
| "architectures": [ |
| "Qwen2_5_VLForConditionalGeneration" |
| ], |
| "attention_dropout": 0.0, |
| "bos_token_id": 151643, |
| "eos_token_id": 151645, |
| "hidden_act": "silu", |
| "hidden_size": 3584, |
| "image_token_id": None, |
| "initializer_range": 0.02, |
| "intermediate_size": 18944, |
| "layer_types": [ |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention", |
| "full_attention" |
| ], |
| "max_position_embeddings": 128000, |
| "max_window_layers": 28, |
| "model_type": "qwen2_5_vl_text", |
| "num_attention_heads": 28, |
| "num_hidden_layers": 28, |
| "num_key_value_heads": 4, |
| "rms_norm_eps": 1e-06, |
| "rope_scaling": { |
| "mrope_section": [ |
| 16, |
| 24, |
| 24 |
| ], |
| "rope_type": "default", |
| "type": "default" |
| }, |
| "rope_theta": 1000000.0, |
| "sliding_window": None, |
| "attn_implementation": "flash_attention_3", |
| "torch_dtype": "bfloat16", |
| "use_cache": True, |
| "use_sliding_window": False, |
| "video_token_id": None, |
| "vision_end_token_id": 151653, |
| "vision_start_token_id": 151652, |
| "vision_token_id": 151654, |
| "vocab_size": 152064 |
| }, |
| "tie_word_embeddings": False, |
| "attn_implementation": "flash_attention_3", |
| "torch_dtype": "bfloat16", |
| "use_cache": True, |
| "use_sliding_window": False, |
| "video_token_id": 151656, |
| "vision_config": { |
| "depth": 32, |
| "fullatt_block_indexes": [ |
| 7, |
| 15, |
| 23, |
| 31 |
| ], |
| "hidden_act": "silu", |
| "hidden_size": 1280, |
| "in_channels": 3, |
| "in_chans": 3, |
| "initializer_range": 0.02, |
| "intermediate_size": 3420, |
| "model_type": "qwen2_5_vl", |
| "num_heads": 16, |
| "out_hidden_size": 3584, |
| "patch_size": 14, |
| "spatial_merge_size": 2, |
| "spatial_patch_size": 14, |
| "temporal_patch_size": 2, |
| "tokens_per_second": 2, |
| "torch_dtype": "bfloat16", |
| "attn_implementation": "flash_attention_3", |
| "window_size": 112 |
| }, |
| "vision_end_token_id": 151653, |
| "vision_start_token_id": 151652, |
| "vision_token_id": 151654, |
| "vocab_size": 152064 |
| }) |
| self.model = Qwen2_5_VLModel(config) |
| self.lm_head = torch.nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) |
| self.config = config |
| |
| def forward( |
| self, |
| input_ids: torch.LongTensor = None, |
| attention_mask: Optional[torch.Tensor] = None, |
| position_ids: Optional[torch.LongTensor] = None, |
| past_key_values = 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, |
| pixel_values: Optional[torch.Tensor] = None, |
| pixel_values_videos: Optional[torch.FloatTensor] = None, |
| image_grid_thw: Optional[torch.LongTensor] = None, |
| video_grid_thw: Optional[torch.LongTensor] = None, |
| rope_deltas: Optional[torch.LongTensor] = None, |
| cache_position: Optional[torch.LongTensor] = None, |
| second_per_grid_ts: Optional[torch.Tensor] = None, |
| logits_to_keep: Union[int, torch.Tensor] = 0, |
| **kwargs, |
| ): |
| output_attentions = False |
| output_hidden_states = True |
|
|
| outputs = self.model( |
| input_ids=input_ids, |
| pixel_values=pixel_values, |
| pixel_values_videos=pixel_values_videos, |
| image_grid_thw=image_grid_thw, |
| video_grid_thw=video_grid_thw, |
| second_per_grid_ts=second_per_grid_ts, |
| position_ids=position_ids, |
| attention_mask=attention_mask, |
| past_key_values=past_key_values, |
| inputs_embeds=inputs_embeds, |
| use_cache=use_cache, |
| output_attentions=output_attentions, |
| output_hidden_states=output_hidden_states, |
| return_dict=True, |
| cache_position=cache_position, |
| **kwargs, |
| ) |
| return outputs.hidden_states |
|
|
|
|
| def vision_forward( |
| self, |
| input_ids: torch.LongTensor = None, |
| attention_mask: Optional[torch.Tensor] = None, |
| position_ids: Optional[torch.LongTensor] = None, |
| past_key_values = None, |
| inputs_embeds: Optional[torch.FloatTensor] = None, |
| pixel_values: Optional[torch.Tensor] = None, |
| pixel_values_videos: Optional[torch.FloatTensor] = None, |
| image_grid_thw: Optional[torch.LongTensor] = None, |
| video_grid_thw: Optional[torch.LongTensor] = None, |
| cache_position: Optional[torch.LongTensor] = None, |
| second_per_grid_ts: Optional[torch.Tensor] = None, |
| **kwargs, |
| ): |
| return self.model.vision_forward( |
| input_ids=input_ids, |
| inputs_embeds=inputs_embeds, |
| pixel_values=pixel_values, |
| pixel_values_videos=pixel_values_videos, |
| image_grid_thw=image_grid_thw, |
| video_grid_thw=video_grid_thw, |
| attention_mask=attention_mask, |
| position_ids=position_ids, |
| past_key_values=past_key_values, |
| cache_position=cache_position, |
| second_per_grid_ts=second_per_grid_ts, |
| ) |
|
|
| def llm_forward( |
| self, |
| inputs_embeds: torch.FloatTensor, |
| position_ids: Optional[torch.LongTensor] = None, |
| attention_mask: Optional[torch.Tensor] = None, |
| past_key_values = None, |
| use_cache: Optional[bool] = None, |
| output_attentions: Optional[bool] = None, |
| output_hidden_states: Optional[bool] = None, |
| cache_position: Optional[torch.LongTensor] = None, |
| **kwargs, |
| ): |
| output_attentions = False |
| output_hidden_states = True |
|
|
| outputs = self.model.llm_forward( |
| inputs_embeds=inputs_embeds, |
| position_ids=position_ids, |
| attention_mask=attention_mask, |
| past_key_values=past_key_values, |
| use_cache=use_cache, |
| output_attentions=output_attentions, |
| output_hidden_states=output_hidden_states, |
| return_dict=True, |
| cache_position=cache_position, |
| **kwargs, |
| ) |
| return outputs.hidden_states |
|
|
