Upload modeling_dots_ocr.py

modeling_dots_ocr.py

@@ -0,0 +1,131 @@
+from typing import List, Optional, Tuple, Union
+
+import torch
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.qwen2 import Qwen2ForCausalLM
+
+from .configuration_dots import DotsVisionConfig, DotsOCRConfig
+from .modeling_dots_vision import DotsVisionTransformer
+
+
+DOTS_VLM_MAX_IMAGES = 200
+
+
+class DotsOCRForCausalLM(Qwen2ForCausalLM):
+    config_class = DotsOCRConfig
+
+    def __init__(self, config: DotsOCRConfig):
+        super().__init__(config)
+
+        if isinstance(self.config.vision_config, dict):
+            vision_config = DotsVisionConfig(**self.config.vision_config)
+            self.config.vision_config = vision_config
+        else:
+            vision_config = self.config.vision_config
+
+        self.vision_tower = DotsVisionTransformer(vision_config)
+
+    def prepare_inputs_embeds(
+        self,
+        input_ids: torch.LongTensor,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        grid_thw: Optional[torch.FloatTensor] = None,
+        img_mask: Optional[torch.BoolTensor] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        if pixel_values is not None:
+            assert img_mask is not None
+            if grid_thw.shape[0] > DOTS_VLM_MAX_IMAGES:
+                print(
+                    f"Num image exceeded: {grid_thw.shape[0]} > {DOTS_VLM_MAX_IMAGES}, which may cause FSDP hang"
+                )
+
+            vision_embeddings = self.vision_tower(pixel_values, grid_thw)
+
+            true_indices = torch.nonzero(img_mask).squeeze()
+            if len(true_indices) > vision_embeddings.size(0):
+                print(
+                    f"img_mask sum > VE and will be truncated, mask.sum()={len(true_indices)} {vision_embeddings.size(0)=}"
+                )
+                true_indices = true_indices[: vision_embeddings.size(0)]
+                new_img_mask = torch.zeros_like(img_mask, device=img_mask.device)
+                new_img_mask[true_indices[:, 0], true_indices[:, 1]] = True
+            else:
+                new_img_mask = img_mask
+
+            assert (
+                vision_embeddings.size(0) == new_img_mask.sum()
+            ), f"{vision_embeddings.size(0)=}, {new_img_mask.sum()=}"
+
+            inputs_embeds = inputs_embeds.masked_scatter(
+                new_img_mask.to(inputs_embeds.device).unsqueeze(-1).expand_as(inputs_embeds),
+                vision_embeddings.to(inputs_embeds.device).type(inputs_embeds.dtype),
+            )
+
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        image_grid_thw: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        use_cache: Optional[bool] = None,
+        logits_to_keep: int = 0,
+        **loss_kwargs,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        assert len(input_ids) >= 1, f"empty input_ids {input_ids.shape=} will cause gradnorm nan"
+        if inputs_embeds is None:
+            img_mask = input_ids == self.config.image_token_id
+            inputs_embeds = self.prepare_inputs_embeds(input_ids, pixel_values, image_grid_thw, img_mask)
+
+        outputs = super().forward(
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            labels=labels,
+            use_cache=use_cache if use_cache is not None else self.config.use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            # return_dict=return_dict,
+            logits_to_keep=logits_to_keep,
+            **loss_kwargs,
+        )
+
+        return outputs
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        attention_mask=None,
+        cache_position=None,
+        num_logits_to_keep=None,
+        **kwargs,
+    ):
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            num_logits_to_keep=num_logits_to_keep,
+            **kwargs,
+        )
+
+        if cache_position[0] == 0:
+            model_inputs["pixel_values"] = pixel_values
+
+        return model_inputs