from transformers import PretrainedConfig, PreTrainedModel import torch, transformers from typing import List, Optional, Tuple, Union from transformers.modeling_outputs import CausalLMOutputWithPast from .VisualTransformer import VisionTransformer, LayerNorm from functools import partial from transformers import TextIteratorStreamer from transformers import StoppingCriteria, GenerationConfig from threading import Thread from dataclasses import dataclass import numpy as np from PIL import Image # Model Constants IGNORE_INDEX = -100 IMAGE_TOKEN_INDEX = -200 DEFAULT_IMAGE_TOKEN = "" DEFAULT_IMAGE_PATCH_TOKEN = "" DEFAULT_IM_START_TOKEN = "" DEFAULT_IM_END_TOKEN = "" class AttrDict(dict): def __init__(self, *args, **kwargs): super(AttrDict, self).__init__(*args, **kwargs) self.__dict__ = self def __getattr__(self, key): if key in self: return self[key] raise AttributeError(f"'AttrDict' object has no attribute '{key}'") class CXRLLAVAConfig(PretrainedConfig): model_type = "CXR-LLAVA" def __init__(self, **kwargs,): if 'llama' in kwargs: self.llama = AttrDict(kwargs['llama']) del kwargs['llama'] self.__dict__.update(kwargs) super().__init__(**kwargs) class CXRLLAVAModel(PreTrainedModel): config_class = CXRLLAVAConfig def __init__(self, config): super().__init__(config) self.tokenizer = transformers.LlamaTokenizer.from_pretrained(config._name_or_path, add_special_tokens=False) self.tokenizer.pad_token = self.tokenizer.unk_token self.tokenizer.sep_token = self.tokenizer.unk_token self.tokenizer.cls_token = self.tokenizer.unk_token self.tokenizer.mask_token = self.tokenizer.unk_token vision_cfg = CLIPVisionCfg(**config.clip_vision_cfg) self.generation_config = GenerationConfig.from_pretrained(config._name_or_path) vision_heads = vision_cfg.width // vision_cfg.head_width norm_layer = LayerNorm act_layer = torch.nn.GELU if vision_cfg.norm_kwargs: norm_layer = partial(norm_layer, **vision_cfg.norm_kwargs) if vision_cfg.act_kwargs is not None: act_layer = partial(act_layer, **vision_cfg.act_kwargs) self.vision_tower = VisionTransformer( in_channels=1, image_size=vision_cfg.image_size, patch_size=vision_cfg.patch_size, width=vision_cfg.width, layers=vision_cfg.layers, heads=vision_heads, mlp_ratio=vision_cfg.mlp_ratio, ls_init_value=vision_cfg.ls_init_value, patch_dropout=vision_cfg.patch_dropout, attentional_pool=vision_cfg.attentional_pool, attn_pooler_queries=vision_cfg.attn_pooler_queries, attn_pooler_heads=vision_cfg.attn_pooler_heads, pos_embed_type=vision_cfg.pos_embed_type, no_ln_pre=vision_cfg.no_ln_pre, final_ln_after_pool=vision_cfg.final_ln_after_pool, pool_type=vision_cfg.pool_type, output_tokens=vision_cfg.output_tokens, output_dim=config.clip_embed_dim, act_layer=act_layer, norm_layer=norm_layer, ) self.vision_tower.image_processor = transformers.CLIPImageProcessor( do_resize=True, size={'shortest_edge': config.clip_vision_cfg['image_size']}, resample=True, do_center_crop=True, crop_size=config.clip_vision_cfg['image_size'], do_rescale=True, rescale_factor=1 / 255, do_normalize=True, image_mean=config.image_preprocess_cfg['mean'], image_std=config.image_preprocess_cfg['std'], do_convert_rgb=False ) def convert_dtype(dtype): if dtype == 'fp32': dtype = torch.float32 elif dtype == 'fp16': dtype = torch.float16 elif dtype == 'bf16': dtype = torch.bfloat16 else: raise Exception("Unsupported dtype") return dtype self.clip_cast_dtype = convert_dtype(config.clip_vision_tower_dtype) self.mm_projector = torch.nn.Linear(config.mm_projector_dim, config.llama['hidden_size']) self.lm_head = torch.nn.Linear(config.llama.hidden_size, config.llama.vocab_size, bias=False) self.llama = transformers.LlamaModel(transformers.LlamaConfig(**config.llama)) self.llama = self.llama.to(torch.bfloat16) self.lm_head = self.lm_head.to(torch.bfloat16) self.vision_tower = self.vision_tower.to(torch.bfloat16) self.mm_projector = self.mm_projector.to(torch.bfloat16) def get_input_embeddings(self): return self.llama.get_input_embeddings() def get_vision_tower(self): return self.vision_tower def gradient_checkpointing_enable(self): return self.llama.gradient_checkpointing_enable() def encode_images(self, images): images = images.to(torch.bfloat16) def _expand_token(token, batch_size: int): return token.view(1, 1, -1).expand(batch_size, -1, -1) # open_clip ViT # https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/transformer.py x = images x = self.vision_tower.conv1(x) # shape = [*, width, grid, grid] x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] # class embeddings and positional embeddings x = torch.cat([_expand_token(self.vision_tower.class_embedding, x.shape[0]).to(x.dtype), x], dim=1) # shape = [*, grid ** 2 + 1, width] x = x + self.vision_tower.positional_embedding.to(x.dtype) x = self.vision_tower.patch_dropout(x) x = self.vision_tower.ln_pre(x) x = x.permute(1, 0, 2) # NLD -> LND x = self.vision_tower.transformer(x) x = x.permute(1, 0, 2) # LND -> NLD if self.vision_tower.attn_pool is not None: if self.vision_tower.attn_pool_contrastive is not None: # This is untested, WIP pooling that should match paper x = self.vision_tower.ln_post(x) # TBD LN first or separate one after each pool? tokens = self.vision_tower.attn_pool(x) if self.vision_tower.attn_pool_type == 'parallel': pooled = self.vision_tower.attn_pool_contrastive(x) else: assert self.vision_tower.attn_pool_type == 'cascade' pooled = self.vision_tower.attn_pool_contrastive(tokens) else: # this is the original OpenCLIP CoCa setup, does not match paper x = self.vision_tower.attn_pool(x) x = self.vision_tower.ln_post(x) pooled, tokens = self.vision_tower._global_pool(x) elif self.vision_tower.final_ln_after_pool: pooled, tokens = self.vision_tower._global_pool(x) pooled = self.vision_tower.ln_post(pooled) else: x = self.vision_tower.ln_post(x) pooled, tokens = self.vision_tower._global_pool(x) if self.vision_tower.proj is not None: pooled = pooled @ self.vision_tower.proj image_features = tokens image_features = image_features.to(torch.bfloat16) image_features = self.mm_projector(image_features) image_features = image_features.to(torch.bfloat16) return image_features def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, # (1,4317) use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, images: Optional[torch.FloatTensor] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal( input_ids, attention_mask, past_key_values, labels, images) outputs = self.llama( input_ids=input_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=return_dict ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) loss = None return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # original multimodal code def prepare_inputs_labels_for_multimodal( self, input_ids, attention_mask, past_key_values, labels, images ): vision_tower = self.vision_tower if vision_tower is None or images is None or input_ids.shape[1] == 1: if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[ 1] == 1: attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device) return input_ids, attention_mask, past_key_values, None, labels if type(images) is list or images.ndim == 5: concat_images = torch.cat([image for image in images], dim=0) image_features = self.encode_images(concat_images) split_sizes = [image.shape[0] for image in images] image_features = torch.split(image_features, split_sizes, dim=0) image_features = [x.flatten(0, 1) for x in image_features] else: image_features = self.encode_images(images) new_input_embeds = [] new_labels = [] if labels is not None else None cur_image_idx = 0 for batch_idx, cur_input_ids in enumerate(input_ids): if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0: # multimodal LLM, but the current sample is not multimodal cur_input_embeds = self.llama.embed_tokens(cur_input_ids) cur_input_embeds = cur_input_embeds + (0. * self.mm_projector(vision_tower.dummy_feature)).sum() new_input_embeds.append(cur_input_embeds) if labels is not None: new_labels.append(labels[batch_idx]) cur_image_idx += 1 continue image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] cur_new_input_embeds = [] if labels is not None: cur_labels = labels[batch_idx] cur_new_labels = [] assert cur_labels.shape == cur_input_ids.shape while image_token_indices.numel() > 0: cur_image_features = image_features[cur_image_idx] image_token_start = image_token_indices[0] if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids[:image_token_start - 1]).detach()) cur_new_input_embeds.append( self.llama.embed_tokens(cur_input_ids[image_token_start - 1:image_token_start])) cur_new_input_embeds.append(cur_image_features) cur_new_input_embeds.append( self.llama.embed_tokens(cur_input_ids[image_token_start + 1:image_token_start + 2])) if labels is not None: cur_new_labels.append(cur_labels[:image_token_start]) cur_new_labels.append( torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) cur_new_labels.append(cur_labels[image_token_start:image_token_start + 1]) cur_labels = cur_labels[image_token_start + 2:] else: cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids[:image_token_start])) cur_new_input_embeds.append(cur_image_features) if labels is not None: cur_new_labels.append(cur_labels[:image_token_start]) cur_new_labels.append( torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) cur_labels = cur_labels[image_token_start + 1:] cur_image_idx += 1 if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): cur_input_ids = cur_input_ids[image_token_start + 2:] else: cur_input_ids = cur_input_ids[image_token_start + 1:] image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] if cur_input_ids.numel() > 0: if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids).detach()) else: cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids)) if labels is not None: cur_new_labels.append(cur_labels) cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds] cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0) new_input_embeds.append(cur_new_input_embeds) if labels is not None: cur_new_labels = torch.cat(cur_new_labels, dim=0) new_labels.append(cur_new_labels) if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds): max_len = max(x.shape[0] for x in new_input_embeds) new_input_embeds_align = [] for cur_new_embed in new_input_embeds: cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0) new_input_embeds_align.append(cur_new_embed) new_input_embeds = torch.stack(new_input_embeds_align, dim=0) if labels is not None: new_labels_align = [] _new_labels = new_labels for cur_new_label in new_labels: cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0) new_labels_align.append(cur_new_label) new_labels = torch.stack(new_labels_align, dim=0) if attention_mask is not None: new_attention_mask = [] for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels): new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device) new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device) cur_new_attention_mask = torch.cat( (new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0) new_attention_mask.append(cur_new_attention_mask) attention_mask = torch.stack(new_attention_mask, dim=0) assert attention_mask.shape == new_labels.shape else: new_input_embeds = torch.stack(new_input_embeds, dim=0) if labels is not None: new_labels = torch.stack(new_labels, dim=0) if attention_mask is not None: new_attn_mask_pad_left = torch.full( (attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device) attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1) assert attention_mask.shape == new_input_embeds.shape[:2] return None, attention_mask, past_key_values, new_input_embeds, new_labels # sw-modified code def prepare_inputs_labels_for_multimodal_use_final_vector( self, input_ids, attention_mask, past_key_values, labels, images ): vision_tower = self.vision_tower if vision_tower is None or images is None or input_ids.shape[1] == 1: if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[ 1] == 1: attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device) return input_ids, attention_mask, past_key_values, None, labels if type(images) is list or images.ndim == 5: concat_images = torch.cat([image for image in images], dim=0) image_features = self.encode_images(concat_images) split_sizes = [image.shape[0] for image in images] image_features = torch.split(image_features, split_sizes, dim=0) image_features = [x.flatten(0, 1) for x in image_features] else: image_features = self.encode_images(images) new_input_embeds = [] new_labels = [] if labels is not None else None cur_image_idx = 0 for batch_idx, cur_input_ids in enumerate(input_ids): if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0: # multimodal LLM, but the current sample is not multimodal cur_input_embeds = self.llama.embed_tokens(cur_input_ids) cur_input_embeds = cur_input_embeds + (0. * self.mm_projector(vision_tower.dummy_feature)).sum() new_input_embeds.append(cur_input_embeds) if labels is not None: new_labels.append(labels[batch_idx]) cur_image_idx += 1 continue image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] cur_new_input_embeds = [] if labels is not None: cur_labels = labels[batch_idx] cur_new_labels = [] assert cur_labels.shape == cur_input_ids.shape while image_token_indices.numel() > 0: cur_image_features = image_features[cur_image_idx] image_token_start = image_token_indices[0] if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids[:image_token_start - 1]).detach()) cur_new_input_embeds.append( self.llama.embed_tokens(cur_input_ids[image_token_start - 1:image_token_start])) cur_new_input_embeds.append(cur_image_features) cur_new_input_embeds.append( self.llama.embed_tokens(cur_input_ids[image_token_start + 1:image_token_start + 2])) if labels is not None: cur_new_labels.append(cur_labels[:image_token_start]) cur_new_labels.append( torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) cur_new_labels.append(cur_labels[image_token_start:image_token_start + 1]) cur_labels = cur_labels[image_token_start + 2:] else: cur_new_input_embeds.append( self.llama.embed_tokens(cur_input_ids[:image_token_start].to(self.device))) cur_new_input_embeds.append(cur_image_features) if labels is not None: cur_new_labels.append(cur_labels[:image_token_start]) cur_new_labels.append( torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) cur_labels = cur_labels[image_token_start + 1:] cur_image_idx += 1 if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): cur_input_ids = cur_input_ids[image_token_start + 2:] else: cur_input_ids = cur_input_ids[image_token_start + 1:] image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] if cur_input_ids.numel() > 0: if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids).detach()) else: cur_new_input_embeds.append(self.llama.embed_tokens(cur_input_ids.to(self.device))) if labels is not None: # seowoo-edit cur_labels = labels[batch_idx] cur_new_labels.append(cur_labels) # [5120] -> [1, 5120] cur_new_input_embeds[1] = torch.unsqueeze(cur_new_input_embeds[1], dim=0) cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds] cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0) new_input_embeds.append(cur_new_input_embeds) if labels is not None: cur_new_labels = torch.cat(cur_new_labels, dim=0) new_labels.append(cur_new_labels) if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds): # print("if 204") max_len = max(x.shape[0] for x in new_input_embeds) new_input_embeds_align = [] for cur_new_embed in new_input_embeds: cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0) new_input_embeds_align.append(cur_new_embed) new_input_embeds = torch.stack(new_input_embeds_align, dim=0) if labels is not None: new_labels_align = [] _new_labels = new_labels for cur_new_label in new_labels: cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0) new_labels_align.append(cur_new_label) new_labels = torch.stack(new_labels_align, dim=0) if attention_mask is not None: new_attention_mask = [] for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels): new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device) new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device) cur_new_attention_mask = torch.cat( (new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0) new_attention_mask.append(cur_new_attention_mask) attention_mask = torch.stack(new_attention_mask, dim=0) assert attention_mask.shape == new_labels.shape else: new_input_embeds = torch.stack(new_input_embeds, dim=0) if labels is not None: new_labels = torch.stack(new_labels, dim=0) if attention_mask is not None: new_attn_mask_pad_left = torch.full( (attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device) attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1) assert attention_mask.shape == new_input_embeds.shape[:2] return None, attention_mask, past_key_values, new_input_embeds, labels def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs ): if past_key_values: input_ids = input_ids[:, -1:] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, "images": kwargs.get("images", None), } ) return model_inputs def apply_chat_template(self, chat): return self.tokenizer.apply_chat_template(chat, tokenize=False) def tokenizer_image_token(self, prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None): prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('')] def insert_separator(X, sep): return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1] input_ids = [] offset = 0 if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id: offset = 1 input_ids.append(prompt_chunks[0][0]) for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)): input_ids.extend(x[offset:]) if return_tensors is not None: if return_tensors == 'pt': return torch.tensor(input_ids, dtype=torch.long) raise ValueError(f'Unsupported tensor type: {return_tensors}') return input_ids def write_radiologic_report(self, image, temperature=0.2, top_p=0.8): chat = [ {"role": "system", "content": "You are a helpful radiologist. Try to interpret chest x ray image and answer to the question that user provides."}, {"role": "user", "content": "\nWrite a radiologic report on the given chest radiograph, including information about atelectasis, cardiomegaly, consolidation, pulmonary edema, pleural effusion, and pneumothorax.\n"} ] response = self.generate_cxr_repsonse(chat=chat,image=image, temperature=temperature, top_p=top_p) return response def write_differential_diagnosis(self, image, temperature=0.2, top_p=0.8): chat = [ {"role": "system", "content": "You are a helpful radiologist. Try to interpret chest x ray image and answer to the question that user provides."}, {"role": "user", "content": "\nWhat are the possible differential diagnoses for this patient?\n"} ] response = self.generate_cxr_repsonse(chat=chat, image=image, temperature=temperature, top_p=top_p) return response def ask_question(self, question, image, temperature=0.2, top_p=0.8): chat = [ {"role": "system", "content": "You are a helpful radiologist. Try to interpret chest x ray image and answer to the question that user provides."}, {"role": "user", "content": "\n"+question} ] response = self.generate_cxr_repsonse(chat=chat, image=image, temperature=temperature, top_p=top_p) return response def generate_cxr_repsonse(self, chat, image, temperature=0.2, top_p=0.8): with torch.no_grad(): streamer = TextIteratorStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15) if np.array(image).max()>255: raise Exception("16-bit image is not supported.") image = image.convert('L') # convert to grayscale image = np.array(image) if len(image.shape) == 2: image = np.expand_dims(image,axis=-1) # (width, height) --> (width, height, 1) prompt = self.apply_chat_template(chat) images = self.vision_tower.image_processor(image, return_tensors='pt')['pixel_values'] images = images.to(self.device) input_ids = self.tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() stopping_criteria = KeywordsStoppingCriteria([""], self.tokenizer, input_ids) image_args = {"images": images} do_sample = True if temperature > 0.001 else False num_image_tokens = 1 max_context_length = getattr(self.config, 'max_position_embeddings', 2048) max_new_tokens = min(512, max_context_length - input_ids.shape[-1] - num_image_tokens) thread = Thread(target=self.generate, kwargs=dict( inputs=input_ids, do_sample=do_sample, temperature=temperature, top_p=top_p, max_new_tokens=max_new_tokens, streamer=streamer, stopping_criteria=[stopping_criteria], use_cache=True, generation_config=self.generation_config, **image_args )) thread.start() generated_text = "" for new_text in streamer: generated_text += new_text return generated_text def tokenizer_image_token(self, prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None): prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('')] def insert_separator(X, sep): return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1] input_ids = [] offset = 0 if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id: offset = 1 input_ids.append(prompt_chunks[0][0]) for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)): input_ids.extend(x[offset:]) if return_tensors is not None: if return_tensors == 'pt': return torch.tensor(input_ids, dtype=torch.long) raise ValueError(f'Unsupported tensor type: {return_tensors}') return input_ids class KeywordsStoppingCriteria(StoppingCriteria): def __init__(self, keywords, tokenizer, input_ids): self.keywords = keywords self.keyword_ids = [] for keyword in keywords: cur_keyword_ids = tokenizer(keyword).input_ids if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id: cur_keyword_ids = cur_keyword_ids[1:] self.keyword_ids.append(torch.tensor(cur_keyword_ids)) self.tokenizer = tokenizer self.start_len = input_ids.shape[1] def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)" # TODO offset = min(output_ids.shape[1] - self.start_len, 3) self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids] for keyword_id in self.keyword_ids: if output_ids[0, -keyword_id.shape[0]:] == keyword_id: return True outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0] for keyword in self.keywords: if keyword in outputs: return True return False @dataclass class CLIPVisionCfg: layers: Union[Tuple[int, int, int, int], int] = 12 width: int = 768 head_width: int = 64 mlp_ratio: float = 4.0 patch_size: int = 16 image_size: Union[Tuple[int, int], int] = 224 ls_init_value: Optional[float] = None # layer scale initial value patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer (overrides pool_type) attn_pooler_queries: int = 256 # n_queries for attentional pooler attn_pooler_heads: int = 8 # n heads for attentional_pooling no_ln_pre: bool = False # disable pre transformer LayerNorm pos_embed_type: str = 'learnable' final_ln_after_pool: bool = False # apply final LayerNorm after pooling pool_type: str = 'tok' output_tokens: bool = False act_kwargs: Optional[dict] = None norm_kwargs: Optional[dict] = None timm_model_name: Optional[str] = None # a valid model name overrides layers, width, patch_size timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '') timm_proj_bias: bool = False # enable bias final projection timm_drop: float = 0. # head dropout timm_drop_path: Optional[float] = None # backbone stochastic depth