Delete model_vlm.py

model_vlm.py

@@ -1,130 +0,0 @@
-from .VLMConfig import VLMConfig
-from .model import *
-from typing import Optional, Tuple, List
-from torch import nn
-import warnings
-from transformers import CLIPProcessor, CLIPModel
-import torch
-
-warnings.filterwarnings('ignore')
-
-
-class VisionProj(nn.Module):
-    def __init__(self, ve_dim=768, lm_dim=512):
-        super().__init__()
-        self.ve_dim = ve_dim
-        self.lm_dim = lm_dim
-        self.vision_proj = nn.Sequential(
-            nn.Linear(self.ve_dim, self.lm_dim)
-        )
-
-    def forward(self, image_encoders):
-        vision_proj = self.vision_proj(image_encoders)
-        return vision_proj
-
-
-# 继承自语言模型
-class MiniMindVLM(MiniMindLM):
-    config_class = VLMConfig
-
-    def __init__(self, params: VLMConfig = None):
-        super().__init__(params)
-        if not params: params = VLMConfig()
-        self.params = params
-        self.vision_encoder, self.processor = self.__class__.get_vision_model()
-        self.vision_proj = VisionProj(lm_dim=params.dim)
-
-    @staticmethod
-    def get_vision_model(model_path="./model/vision_model/clip-vit-base-patch16"):
-        model = CLIPModel.from_pretrained(model_path)
-        processor = CLIPProcessor.from_pretrained(model_path)
-        # 冻结 vision_encoder 的所有参数
-        for param in model.parameters():
-            param.requires_grad = False
-        return model.eval(), processor
-
-    @staticmethod
-    def image2tensor(image, processor):
-        if image.mode in ['RGBA', 'LA']: image = image.convert('RGB')
-        inputs = processor(images=image, return_tensors="pt")['pixel_values']
-        return inputs
-
-    @staticmethod
-    def get_image_embeddings(image_tensors, vision_model):
-        with torch.no_grad():
-            outputs = vision_model.vision_model(pixel_values=image_tensors)
-        img_embedding = outputs.last_hidden_state[:, 1:, :].squeeze()
-        return img_embedding
-
-    def count_vision_proj(self, tokens, h, vision_tensors=None, seqlen=512):
-        def find_indices(tokens, image_ids):
-            image_ids_tensor = torch.tensor(image_ids).to(tokens.device)
-            len_image_ids = len(image_ids)
-            if len_image_ids > tokens.size(1):
-                return None
-            tokens_view = tokens.unfold(1, len_image_ids, 1)
-            matches = (tokens_view == image_ids_tensor).all(dim=2)
-            return {
-                batch_idx: [(idx.item(), idx.item() + len_image_ids - 1) for idx in
-                            matches[batch_idx].nonzero(as_tuple=True)[0]]
-                for batch_idx in range(tokens.size(0)) if matches[batch_idx].any()
-            } or None
-
-        image_indices = find_indices(tokens, self.params.image_ids)
-        if vision_tensors is not None and image_indices:
-            vision_proj = self.vision_proj(vision_tensors)
-            if len(vision_proj.shape) == 3:
-                vision_proj = vision_proj.unsqueeze(0)
-            new_h = []
-            for i in range(h.size(0)):
-                if i in image_indices:
-                    h_i = h[i]
-                    img_idx = 0
-                    for start_idx, end_idx in image_indices[i]:
-                        if img_idx < vision_proj.size(1):
-                            h_i = torch.cat((h_i[:start_idx], vision_proj[i][img_idx], h_i[end_idx + 1:]), dim=0)[
-                                  :seqlen]
-                            img_idx += 1
-                    new_h.append(h_i)
-                else:
-                    new_h.append(h[i])
-            return torch.stack(new_h, dim=0)
-        return h
-
-    def forward(self,
-                input_ids: Optional[torch.Tensor] = None,
-                past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None,
-                use_cache: bool = False,
-                **args):
-        start_pos = args.get('start_pos', 0)
-        pixel_tensors = args.get('pixel_tensors', None)
-        h = self.tok_embeddings(input_ids)
-
-        if pixel_tensors is not None and start_pos == 0:
-            if len(pixel_tensors.shape) == 6:
-                pixel_tensors = pixel_tensors.squeeze(2)
-            bs, num, c, im_h, im_w = pixel_tensors.shape
-            stack_dim = 1 if bs > 1 else 0
-            vision_tensors = torch.stack([
-                MiniMindVLM.get_image_embeddings(pixel_tensors[:, i, :, :, :], self.vision_encoder)
-                for i in range(num)
-            ], dim=stack_dim)
-            h = self.count_vision_proj(tokens=input_ids, h=h, vision_tensors=vision_tensors, seqlen=input_ids.shape[1])
-
-        pos_cis = self.pos_cis[start_pos:start_pos + input_ids.shape[1]]
-        past_kvs = []
-        for l, layer in enumerate(self.layers):
-            h, past_kv = layer(
-                h, pos_cis,
-                past_key_value=past_key_values[l] if past_key_values else None,
-                use_cache=use_cache
-            )
-            past_kvs.append(past_kv)
-
-        logits = self.output(self.norm(h))
-        aux_loss = sum(l.feed_forward.aux_loss for l in self.layers if isinstance(l.feed_forward, MOEFeedForward))
-
-        self.OUT.__setitem__('logits', logits)
-        self.OUT.__setitem__('aux_loss', aux_loss)
-        self.OUT.__setitem__('past_key_values', past_kvs)
-        return self.OUT