ALLaVA-3B / modeling_llava_phi.py

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	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 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)