co-instruct / modeling_mplug_owl2.py

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	# Copyright 2023 Haotian Liu & Qinghao Ye (Modified from LLaVA)
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	from abc import ABC, abstractmethod
	from typing import List, Optional, Tuple, Union

	import torch
	import torch.nn as nn
	from torch.nn import CrossEntropyLoss

	import copy
	import os
	import sys
	from transformers import TextStreamer

	dir_path = os.path.dirname(os.path.realpath(__file__))
	sys.path.insert(0, dir_path)

	from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, CLIPImageProcessor, LlamaConfig, LlamaModel, LlamaForCausalLM
	from transformers.modeling_outputs import CausalLMOutputWithPast

	from .configuration_mplug_owl2 import MPLUGOwl2Config, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig
	from .visual_encoder import MplugOwlVisionModel, MplugOwlVisualAbstractorModel
	from .modeling_llama2 import replace_llama_modality_adaptive
	IGNORE_INDEX = -100
	IMAGE_TOKEN_INDEX = -200
	DEFAULT_IMAGE_TOKEN = "<\|image\|>"
	from icecream import ic

	def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
	prompt_chunks = [tokenizer(chunk).input_ids if len(chunk) > 0 else [] for chunk in prompt.split(DEFAULT_IMAGE_TOKEN)]

	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 expand2square(pil_img, background_color):
	from PIL import Image
	width, height = pil_img.size
	if width == height:
	return pil_img
	elif width > height:
	result = Image.new(pil_img.mode, (width, width), background_color)
	result.paste(pil_img, (0, (width - height) // 2))
	return result
	else:
	result = Image.new(pil_img.mode, (height, height), background_color)
	result.paste(pil_img, ((height - width) // 2, 0))
	return result

	class MPLUGOwl2MetaModel:
	def __init__(self, config):
	super(MPLUGOwl2MetaModel, self).__init__(config)
	self.vision_model = MplugOwlVisionModel(
	MplugOwlVisionConfig(**config.visual_config["visual_model"])
	)
	self.visual_abstractor = MplugOwlVisualAbstractorModel(
	MplugOwlVisualAbstractorConfig(**config.visual_config["visual_abstractor"]), config.hidden_size
	)

	def get_vision_tower(self):
	vision_model = getattr(self, 'vision_model', None)
	if type(vision_model) is list:
	vision_model = vision_model[0]
	return vision_model

	def get_visual_abstractor(self):
	visual_abstractor = getattr(self, 'visual_abstractor', None)
	if type(visual_abstractor) is list:
	visual_abstractor = visual_abstractor[0]
	return visual_abstractor


	class MPLUGOwl2MetaForCausalLM(ABC):
	@abstractmethod
	def get_model(self):
	pass

	def encode_images(self, images):
	image_features = self.get_model().vision_model(images).last_hidden_state
	image_features = self.get_model().visual_abstractor(encoder_hidden_states=image_features).last_hidden_state
	return image_features

	def prepare_inputs_labels_for_multimodal(
	self, input_ids, attention_mask, past_key_values, labels, images
	):
	if images is None or input_ids.shape[1] == 1:
	if past_key_values 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)
	multiway_indices = torch.zeros_like(input_ids).long().to(self.device)
	return input_ids, multiway_indices, 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_modality_indicators = []
	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
	# FIXME: this is a hacky fix, for deepspeed zero3 to work
	half_len = cur_input_ids.shape[0] // 2
	cur_image_features = image_features[cur_image_idx]
	cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len])
	cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:])
	cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0)
	new_input_embeds.append(cur_input_embeds)

	cur_modality_indicators = torch.zeros(len(cur_input_embeds)).long().to(self.device)
	new_modality_indicators.append(cur_modality_indicators)
	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 = []
	cur_modality_indicators = []
	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]
	cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start]))
	cur_new_input_embeds.append(cur_image_features)

	# Add modality indicator
	assert image_token_start == len(cur_input_ids[:image_token_start])
	cur_modality_indicators.append(torch.zeros(len(cur_input_ids[:image_token_start])).long())
	cur_modality_indicators.append(torch.ones(len(cur_image_features)).long())

	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
	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:
	cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids))
	cur_modality_indicators.append(torch.zeros(len(cur_input_ids)).long())
	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)

	# Modality
	cur_modality_indicators = [x.to(device=self.device) for x in cur_modality_indicators]
	cur_modality_indicators = torch.cat(cur_modality_indicators, dim=0)
	new_modality_indicators.append(cur_modality_indicators)


	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)

	# Embedding
	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)

	# Modality
	new_modality_indicators_align = []
	for cur_modality_indicator in new_modality_indicators:
	cur_new_embed = torch.cat((cur_modality_indicator, torch.zeros(max_len - cur_modality_indicator.shape[0], dtype=cur_modality_indicator.dtype, device=cur_modality_indicator.device)), dim=0)
	new_modality_indicators_align.append(cur_new_embed)
	new_modality_indicators = torch.stack(new_modality_indicators_align, dim=0)

	# Label
	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)

	# Attention Mask
	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)
	new_modality_indicators = torch.stack(new_modality_indicators, 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, new_modality_indicators, attention_mask, past_key_values, new_input_embeds, new_labels



	class MPLUGOwl2LlamaModel(MPLUGOwl2MetaModel, LlamaModel):
	config_class = MPLUGOwl2Config

	def __init__(self, config: MPLUGOwl2Config):
	super(MPLUGOwl2LlamaModel, self).__init__(config)


	class MPLUGOwl2LlamaForCausalLM(LlamaForCausalLM, MPLUGOwl2MetaForCausalLM):
	config_class = MPLUGOwl2Config

	def __init__(self, config):
	super(LlamaForCausalLM, self).__init__(config)
	self.model = MPLUGOwl2LlamaModel(config)

	self.tokenizer = AutoTokenizer.from_pretrained("q-future/co-instruct-preview")
	self.image_processor = CLIPImageProcessor.from_pretrained("q-future/co-instruct-preview")
	self.streamer = TextStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True)

	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
	self.preferential_ids_ = [id_[1] for id_ in self.tokenizer(["excellent","good","fair","poor","bad"])["input_ids"]]

	# Initialize weights and apply final processing
	self.post_init()


	def get_model(self):
	return self.model

	def chat(self, prompt: str, images, **generate_kwargs):
	input_ids = tokenizer_image_token(prompt, self.tokenizer, -200, return_tensors='pt').unsqueeze(0).to(self.device)
	images = [expand2square(img, tuple(int(x*255) for x in self.image_processor.image_mean)) for img in images]
	image_tensor = self.image_processor.preprocess(images, return_tensors="pt")["pixel_values"].half().to(self.device)

	return self.generate(input_ids, images=image_tensor, streamer=self.streamer, **generate_kwargs)
	def score(self, images,
	task_: str = "quality",
	input_: str = "image",
	):
	if not hasattr(self, "weight_tensor"):
	self.weight_tensor = torch.Tensor([5.,4.,3.,2.,1.]).half().to(self.device)
	prompt = "USER: How would you rate the {} of this {}?\n<\|image\|>\nASSISTANT: The {} of the {} is".format(task_, input_, input_, task_)
	if input_ == "image":
	images = [expand2square(img, tuple(int(x*255) for x in self.image_processor.image_mean)) for img in images]
	input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device)
	with torch.inference_mode():
	image_tensor = self.image_processor.preprocess(images, return_tensors="pt")["pixel_values"].half().to(self.device)
	output_logits = self(input_ids.repeat(image_tensor.shape[0], 1),
	images=image_tensor)["logits"][:,-1, self.preferential_ids_]
	return torch.softmax(output_logits, -1) @ self.weight_tensor
	else:
	video = [[expand2square(frame, tuple(int(x*255) for x in self.image_processor.image_mean)) for frame in vid] for vid in images]
	input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device)
	with torch.inference_mode():
	video_tensors = [self.image_processor.preprocess(vid, return_tensors="pt")["pixel_values"].half().to(self.model.device) for vid in video]
	output_logits = self(input_ids.repeat(len(video_tensors), 1),
	images=video_tensors)["logits"][:,-1, self.preferential_ids_]
	return torch.softmax(output_logits, -1) @ self.weight_tensor

	def forward(
	self,
	input_ids: torch.LongTensor = None,
	# modality_indicators: 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,
	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, modality_indicators, attention_mask, past_key_values, inputs_embeds, labels = \
	self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)

	# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
	outputs = self.model(
	input_ids=input_ids,
	modality_indicators=modality_indicators,
	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
	if labels is not None:
	# Shift so that tokens < n predict n
	shift_logits = logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	# Flatten the tokens
	loss_fct = CrossEntropyLoss()
	shift_logits = shift_logits.view(-1, self.config.vocab_size)
	shift_labels = shift_labels.view(-1)
	# Enable model/pipeline parallelism
	shift_labels = shift_labels.to(shift_logits.device)
	loss = loss_fct(shift_logits, shift_labels)

	if not return_dict:
	output = (logits,) + outputs[1:]
	return (loss,) + output if loss is not None else output

	return CausalLMOutputWithPast(
	loss=loss,
	logits=logits,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)

	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

	AutoConfig.register("mplug_owl2", MPLUGOwl2Config)
	AutoModelForCausalLM.register(MPLUGOwl2Config, MPLUGOwl2LlamaForCausalLM)

	replace_llama_modality_adaptive()

	if __name__ == "__main__":
	config = MPLUGOwl2Config.from_pretrained('q-future/one-align')
	from icecream import ic
	# config = MPLUGOwl2Config()
	model = AutoModelForCausalLM(config)

	images = torch.randn(2, 3, 448, 448)
	input_ids = torch.cat([
	torch.ones(8).long(), torch.tensor([-1]1).long(), torch.ones(8).long(), torch.tensor([-1]1).long(), torch.ones(8).long()
	], dim=0).unsqueeze(0)
	labels = input_ids.clone()
	labels[labels < 0] = -100

	# image_feature = model.encode_images(images)
	# ic(image_feature.shape)

	output = model(images=images, input_ids=input_ids, labels=labels)
	ic(output.loss)
	ic(output.logits.shape)

	model.save_pretrained('/cpfs01/shared/public/test/tmp_owl')