from typing import List, Optional, Tuple, Union import torch import torch.nn as nn import math import pdb from typing import Dict, Any from PIL import Image from transformers import AutoConfig, AutoModelForCausalLM, PretrainedConfig, PreTrainedModel from transformers.modeling_outputs import CausalLMOutputWithPast from .llava_arch import LlavaMetaModel, LlavaMetaForCausalLM from transformers.cache_utils import Cache, DynamicCache from transformers.generation.utils import GenerationConfig import sys from .modeling_phi import PhiForCausalLM, PhiModel, PhiConfig from .generation_utils import build_allava_input ################ Phi ############################### class LlavaPhiConfig(PhiConfig): model_type = "llava_phi" class LlavaPhiModel(LlavaMetaModel, PhiModel): config_class = LlavaPhiConfig def __init__(self, config: PhiConfig): super(LlavaPhiModel, self).__init__(config) class LlavaPhiForCausalLM(PhiForCausalLM, LlavaMetaForCausalLM): config_class = LlavaPhiConfig def __init__(self, config, init_vision_encoder_from_ckpt=True): # note that the default value is set to True for this inference version. In training `init_vision_encoder_from_ckpt` is default to be True. config._attn_implementation = "flash_attention_2" super(PhiForCausalLM, self).__init__(config) # self.model is used in LlavaMetaForCausalLM.get_model(); self.transformer is used in PhiForCausalLM.forward() self.model = LlavaPhiModel(config) if hasattr(self.model, '_use_flash_attention_2'): assert self.model._use_flash_attention_2, 'flash attn is not enabled. check it out!' self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) if init_vision_encoder_from_ckpt: vision_tower = self.get_vision_tower() print(f'loading from CLIP first. This should only be used at inference!!!') vision_tower.load_model() # # Initialize weights and apply final processing self.post_init() def get_model(self): return self.model def get_tokenizer(self): return self.tokenizer def get_processor(self): return self.model.vision_tower.image_processor def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, 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]: if inputs_embeds is None: ( input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels # ) = self.prepare_inputs_labels_for_multimodal( ) = self.prepare_inputs_labels_for_multimodal_new( input_ids, position_ids, attention_mask, past_key_values, labels, images ) # pdb.set_trace() return super().forward( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict ) def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs): ''' This function is called for each token at inference ''' # pdb.set_trace() images = kwargs.pop("images", None) #################################################### # lines from modeling_phi.py #################################################### if past_key_values is not None: if isinstance(past_key_values, Cache): cache_length = past_key_values.get_seq_length() past_length = past_key_values.seen_tokens max_cache_length = past_key_values.get_max_length() else: cache_length = past_length = past_key_values[0][0].shape[2] max_cache_length = None # Keep only the unprocessed tokens: # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as # input) if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard # input_ids based on the past_length. elif past_length < input_ids.shape[1]: input_ids = input_ids[:, past_length:] # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. elif past_length >= input_ids.shape[1]: input_ids = input_ids[:, [-1]] # only keep the last one! # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. if ( max_cache_length is not None and attention_mask is not None and cache_length + input_ids.shape[1] > max_cache_length ): attention_mask = attention_mask[:, -max_cache_length:] position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[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( { "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, } ) #################################################### # end of lines from modeling_phi.py #################################################### if images is not None: model_inputs['images'] = images return model_inputs # def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): # ''' # This function is called for each token at inference # ''' # pdb.set_trace() # images = kwargs.pop("images", None) # _inputs = super().prepare_inputs_for_generation( # input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs # ) # if images is not None: # _inputs['images'] = images # return _inputs # def build_chat_input(self, text, images): # return inputs # def chat(self, tokenizer, messages: List[dict], stream=False, # generation_config: Optional[GenerationConfig]=None): # generation_config = generation_config or self.generation_config # input_ids = build_chat_input(self, tokenizer, messages, generation_config.max_new_tokens) # if stream: # streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Thread(target=self.generate, kwargs=dict( # inputs=input_ids, streamer=streamer, # generation_config=generation_config, # )).start() # return streamer # else: # outputs = self.generate(input_ids, generation_config=generation_config) # response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True) # return response # def collate_text_input(self, ): # pass def chat( self, texts: Optional[str | list[list[str, str]]], images: Optional[str | list[str]] = None, history: Optional[list[str]] = None, stream = False, return_history = False, **kwargs ): ''' texts: if `str`, then generate for a single round; if list[dict], images: str (optional), local path to an image. ''' use_cache = kwargs.pop('use_cache', True) ############################ # merge history ############################ input_ids, image_tensors, history = build_allava_input( tokenizer = self.get_tokenizer(), processor = self.get_processor(), texts = texts, images = images, history=history, return_history=return_history, device = self.device ) ############################ # generate response ############################ # with torch.autocast(device_type='cuda'): if 'cuda' in str(self.device): device_type = 'cuda' else: device_type = 'cpu' with torch.autocast(device_type=device_type, dtype=self.dtype): output_ids = self.generate( inputs=input_ids, images=image_tensors, use_cache=use_cache, **kwargs) answer = self.get_tokenizer().decode(output_ids[0, input_ids.shape[1]:], skip_special_tokens=True).strip() if return_history: history[-1][-1] = answer return answer, history return answer AutoConfig.register("llava_phi", LlavaPhiConfig) AutoModelForCausalLM.register(LlavaPhiConfig, LlavaPhiForCausalLM)