from functools import partial from typing import List, Optional from argparse import Namespace import torch from torch import nn import torch.nn.functional as F from transformers import PreTrainedModel, PreTrainedTokenizer from .configuration_emu import EmuConfig from .constants import * from .modeling_llama import LlamaForCausalLM from .visual import EVAVisionTransformer class EmuPreTrainedModel(PreTrainedModel): config_class = EmuConfig base_model_prefix = "model" supports_gradient_checkpointing = False _no_split_modules = ["LlamaDecoderLayer", "Block"] _skip_keys_device_placement = "past_key_values" def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, nn.Linear): 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_() class EmuForClsAndRegression(EmuPreTrainedModel): def __init__(self, config): super(EmuForClsAndRegression, self).__init__(config) self.lm = LlamaForCausalLM(config=config) self.lm.model.embed_tokens.padding_idx = config.pad_token_id def get_num_layers(self): return len(self.lm.model.layers) class EmuModel(EmuPreTrainedModel): def __init__(self, config): super().__init__(config) vision_config = Namespace(**config.vision_config) self.visual = EVAVisionTransformer( img_size=vision_config.image_size, patch_size=vision_config.patch_size, embed_dim=vision_config.width, depth=vision_config.layers, num_heads=vision_config.width // vision_config.head_width, mlp_ratio=vision_config.mlp_ratio, qkv_bias=vision_config.qkv_bias, drop_path_rate=vision_config.drop_path_rate, norm_layer=partial(nn.LayerNorm, eps=vision_config.layer_norm_eps), xattn=vision_config.xattn, postnorm=vision_config.postnorm, ) self.decoder = EmuForClsAndRegression(config) self.gradient_checkpointing = False self.n_query = vision_config.n_query self.v_query = vision_config.v_query @property def device(self): return next(iter(self.parameters())).device @property def dtype(self): return next(iter(self.parameters())).dtype @torch.no_grad() def encode_image(self, image: torch.Tensor, *, n_query=None): n_query = n_query if n_query is not None else self.n_query image_embeds = self.visual(image) image_embeds = image_embeds[:, 1:, :] b, n, c = image_embeds.shape sqrt_n = int(n**0.5) image_embeds = image_embeds.permute(0, 2, 1).view(b, c, sqrt_n, sqrt_n) stride = int(sqrt_n // (n_query ** 0.5)) image_embeds = F.avg_pool2d(image_embeds, kernel_size=(stride, stride), stride=stride) image_embeds = image_embeds.view(b, c, -1).permute(0, 2, 1).contiguous() return image_embeds class EmuForCausalLM(EmuPreTrainedModel): _auto_class = "AutoModelForCausalLM" def __init__(self, config): super().__init__(config) self.config = config self.model = EmuModel(config) # LM to EVA self.project_down = nn.Linear(config.hidden_size, config.d_model, bias=False) # EVA to LM self.project_up = nn.Linear(config.d_model, config.hidden_size, bias=False) self.n_query = self.model.n_query self.image_placeholder = DEFAULT_IMG_TOKEN + DEFAULT_IMAGE_TOKEN * self.n_query + DEFAULT_IMG_END_TOKEN def device(self, module=None): if module is None: return next(self.parameters()).device return next(module.parameters()).device def dtype(self, module): if module is None: return next(self.parameters()).dtype return next(module.parameters()).dtype @torch.no_grad() def generate_image( self, text: List[str], tokenizer: PreTrainedTokenizer, image: Optional[torch.Tensor] = None, placeholder: str = DEFAULT_IMG_PLACEHOLDER, ): IMAGE, BOI = tokenizer.convert_tokens_to_ids([DEFAULT_IMAGE_TOKEN, DEFAULT_IMG_TOKEN]) if image is not None: prompt_image_embeds = self.model.encode_image(image) _, _, c = prompt_image_embeds.shape prompt_image_embeds = prompt_image_embeds.view(-1, c) prompt_image_embeds = self.project_up(prompt_image_embeds) text = [t.replace(placeholder, self.image_placeholder) for t in text] target_image_embeds = None for num_img_token in range(self.n_query): if num_img_token == 0: text = [f"{t}{DEFAULT_IMG_TOKEN}" for t in text] else: text = [f"{t}{DEFAULT_IMAGE_TOKEN}" for t in text] inputs = tokenizer(text, padding="longest", return_tensors="pt") device = self.device(self.model.decoder.lm.model.embed_tokens) attention_mask = inputs.attention_mask.to(device) input_ids = inputs.input_ids.to(device) # B x N text_embeds = self.model.decoder.lm.model.embed_tokens(input_ids) image_idx = (input_ids == IMAGE) cumsum_idx = torch.flip(torch.cumsum(torch.flip(image_idx, dims=[1]), dim=1), dims=[1]) if image is not None: prompt_idx = torch.logical_and(image_idx, cumsum_idx > num_img_token) text_embeds[prompt_idx] = prompt_image_embeds.to(text_embeds.device) if target_image_embeds is not None: target_idx = torch.logical_and(image_idx, torch.logical_and(cumsum_idx > 0, cumsum_idx <= num_img_token)) text_embeds[target_idx] = self.project_up(target_image_embeds).to(text_embeds.device) outputs = self.model.decoder.lm.model( inputs_embeds=text_embeds, attention_mask=attention_mask, output_hidden_states=True, return_dict=True, ) image_idx = (input_ids == IMAGE) + (input_ids == BOI) cumsum_idx = torch.flip(torch.cumsum(torch.flip(image_idx, dims=[1]), dim=1), dims=[1]) target_idx = torch.logical_and(image_idx, torch.logical_and(cumsum_idx > 0, cumsum_idx <= num_img_token+1)) hidden_states = outputs.hidden_states[-1] target_image_embeds = hidden_states[target_idx.to(hidden_states.device)] target_image_embeds = target_image_embeds.view(-1, target_image_embeds.shape[-1]) target_image_embeds = self.project_down(target_image_embeds) _, C = target_image_embeds.shape B = hidden_states.shape[0] target_image_embeds = target_image_embeds.view(B, -1, C) return target_image_embeds