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import math |
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from typing import List, Optional |
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import json |
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import timm |
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import torch |
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import torchvision |
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from PIL import Image |
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from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD |
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from torchvision import transforms |
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from .configuration_minicpm import MiniCPMVConfig |
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from .modeling_minicpm import MiniCPMForCausalLM, MiniCPMPreTrainedModel |
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from .resampler import Resampler |
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class MiniCPMVPreTrainedModel(MiniCPMPreTrainedModel): |
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config_class = MiniCPMVConfig |
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class MiniCPMV(MiniCPMVPreTrainedModel): |
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def __init__(self, config): |
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super().__init__(config) |
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self.llm = MiniCPMForCausalLM(config) |
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self.vpm = self.init_vision_module() |
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self.vision_dim = self.vpm.embed_dim |
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self.embed_dim = self.llm.config.hidden_size |
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self.resampler = self.init_resampler(self.embed_dim, self.vision_dim) |
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self.transform = self.init_transform() |
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def init_vision_module(self): |
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model = timm.create_model( |
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self.config.vision_encoder, |
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pretrained=False, |
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num_classes=0, |
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dynamic_img_size=True, |
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dynamic_img_pad=True |
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) |
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if isinstance(model, timm.models.VisionTransformer): |
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if model.attn_pool is not None: |
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model.attn_pool = torch.nn.Identity() |
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if self.config.drop_vision_last_layer: |
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model.blocks = model.blocks[:-1] |
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return model |
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def init_resampler(self, embed_dim, vision_dim): |
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return Resampler( |
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grid_size=int(math.sqrt(self.config.query_num)), |
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embed_dim=embed_dim, |
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num_heads=embed_dim // 128, |
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kv_dim=vision_dim, |
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adaptive=True |
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) |
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def init_transform(self): |
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return transforms.Compose( |
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[ |
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transforms.ToTensor(), |
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transforms.Normalize( |
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mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD |
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), |
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] |
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) |
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def get_input_embeddings(self): |
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return self.llm.embed_tokens |
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def set_input_embeddings(self, value): |
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self.llm.embed_tokens = value |
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def get_output_embeddings(self): |
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return self.llm.lm_head |
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def set_output_embeddings(self, new_embeddings): |
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self.llm.lm_head = new_embeddings |
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def set_decoder(self, decoder): |
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self.llm = decoder |
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def get_decoder(self): |
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return self.llm |
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def get_vision_embedding(self, pixel_values): |
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res = [] |
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dtype = self.vpm.pos_embed.data.dtype |
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for pixel_value in pixel_values: |
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H, W = pixel_value.shape[-2:] |
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tgt_size = ( |
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math.ceil(H / self.vpm.patch_embed.patch_size[0]), math.ceil(W / self.vpm.patch_embed.patch_size[0])) |
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vision_embedding = self.vpm.forward_features(pixel_value.unsqueeze(0).type(dtype)) |
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if hasattr(self.vpm, 'num_prefix_tokens') and self.vpm.num_prefix_tokens > 0: |
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vision_embedding = vision_embedding[:, self.vpm.num_prefix_tokens:] |
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res.append(self.resampler(vision_embedding, tgt_size)) |
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return torch.vstack(res) |
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def get_vllm_embedding(self, data): |
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if "vision_hidden_states" not in data: |
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pixel_values_list = data["pixel_values"] |
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vision_hidden_states = [] |
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for pixel_values in pixel_values_list: |
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if len(pixel_values) > 0: |
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vision_hidden_states.append(self.get_vision_embedding(pixel_values)) |
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elif self.training: |
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dtype = self.vpm.pos_embed.data.dtype |
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device = self.vpm.pos_embed.data.device |
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dummy_image = torch.zeros( |
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(1, 3, 224, 224), device=device, dtype=dtype |
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) |
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vision_hidden_states.append(self.get_vision_embedding(dummy_image)) |
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else: |
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vision_hidden_states.append([]) |
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else: |
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vision_hidden_states = data["vision_hidden_states"] |
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vllm_embedding = ( |
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self.llm.model.embed_tokens(data["input_ids"]) * self.llm.config.scale_emb |
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) |
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vision_hidden_states = [ |
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i.type(vllm_embedding.dtype) if isinstance(i, torch.Tensor) else i |
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for i in vision_hidden_states |
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] |
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bs = len(data["input_ids"]) |
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for i in range(bs): |
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cur_vs_hs = vision_hidden_states[i] |
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if len(cur_vs_hs) > 0: |
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cur_vllm_emb = vllm_embedding[i] |
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cur_image_bound = data["image_bounds"][i] |
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if len(cur_image_bound) > 0: |
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image_indices = torch.stack( |
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[ |
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torch.arange(r[0], r[1], dtype=torch.long) |
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for r in cur_image_bound |
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] |
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).to(vllm_embedding.device) |
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cur_vllm_emb.scatter_( |
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0, |
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image_indices.view(-1, 1).repeat(1, cur_vllm_emb.shape[-1]), |
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cur_vs_hs.view(-1, cur_vs_hs.shape[-1]), |
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) |
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elif self.training: |
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cur_vllm_emb += cur_vs_hs[0].mean() * 0 |
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return vllm_embedding, vision_hidden_states |
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def forward(self, data, **kwargs): |
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vllm_embedding, vision_hidden_states = self.get_vllm_embedding(data) |
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position_ids = data["position_ids"] |
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if position_ids.dtype != torch.int64: |
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position_ids = position_ids.long() |
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return self.llm( |
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input_ids=None, |
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position_ids=position_ids, |
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inputs_embeds=vllm_embedding, |
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**kwargs |
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) |
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def _decode_text(self, result_ids, tokenizer): |
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result_text = [] |
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for result in result_ids: |
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result = result[result != 0] |
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if result[0] == tokenizer.bos_id: |
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result = result[1:] |
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if result[-1] == tokenizer.eos_id: |
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result = result[:-1] |
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result_text.append(tokenizer.decode(result).strip()) |
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return result_text |
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def _decode(self, inputs_embeds, tokenizer, **kwargs): |
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output = self.llm.generate( |
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inputs_embeds=inputs_embeds, |
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pad_token_id=0, |
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eos_token_id=tokenizer.eos_token_id if tokenizer is not None else kwargs.pop("eos_token_id", 2), |
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**kwargs |
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) |
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return output |
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def generate( |
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self, |
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input_ids, |
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pixel_values=None, |
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image_sizes=[], |
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image_bounds=[], |
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tgt_sizes=[], |
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tokenizer=None, |
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vision_hidden_states=None, |
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**kwargs |
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): |
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bs = len(input_ids) |
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img_list = pixel_values |
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if img_list == None: |
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img_list = [[] for i in range(bs)] |
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assert bs == len(img_list) |
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if vision_hidden_states is None: |
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pixel_values = [] |
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for i in range(bs): |
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img_inps = [] |
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for img in img_list[i]: |
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img_inps.append(img.to(self.device, self.dtype)) |
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pixel_values.append(img_inps) |
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( |
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input_embeds, |
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vision_hidden_states, |
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) = self.get_vllm_embedding({ |
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"input_ids": input_ids, |
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"pixel_values": pixel_values, |
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"image_sizes": image_sizes, |
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"image_bounds": image_bounds, |
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"tgt_sizes": tgt_sizes |
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}) |
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result = self._decode(input_embeds, tokenizer, **kwargs) |
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return result |
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def chat( |
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self, |
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image, |
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msgs, |
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context, |
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tokenizer, |
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processor, |
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vision_hidden_states=None, |
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max_new_tokens=1024, |
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sampling=True, |
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max_inp_length=2048, |
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**kwargs |
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): |
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if isinstance(msgs, str): |
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msgs = json.loads(msgs) |
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if image is not None and isinstance(msgs[0]['content'], str): |
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msgs[0]['content'] = '(<image>./</image>)\n' + msgs[0]['content'] |
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prompt = processor.tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) |
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inputs = processor(prompt, [image], return_tensors="pt").to(self.device) |
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if sampling: |
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generation_config = { |
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"top_p": 0.8, |
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"top_k": 100, |
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"temperature": 0.7, |
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"do_sample": True, |
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"repetition_penalty": 1.05 |
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} |
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else: |
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generation_config = { |
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"num_beams": 3, |
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"repetition_penalty": 1.2, |
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} |
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generation_config.update( |
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(k, kwargs[k]) for k in generation_config.keys() & kwargs.keys() |
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) |
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with torch.inference_mode(): |
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res = self.generate( |
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**inputs, |
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tokenizer=tokenizer, |
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max_new_tokens=max_new_tokens, |
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vision_hidden_states=vision_hidden_states, |
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**generation_config, |
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) |
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res = self._decode_text(res, tokenizer) |
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answer = res[0] |
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context = msgs.copy() |
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context.append({"role": "assistant", "content": answer}) |
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return answer, context, generation_config |
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