internlm-xcomposer2-4khd-7b / modeling_internlm_xcomposer2.py

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	# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
	#
	# This code is based on transformers/src/transformers/models/llama/modeling_llama.py
	#
	# 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.

	"""PyTorch InternLMXComposer2 model."""
	import copy
	import queue
	import threading
	from typing import List, Optional, Tuple, Union

	import torch
	import torch.utils.checkpoint
	from PIL import Image
	from torch import nn
	from torch.nn import CrossEntropyLoss
	from torchvision import transforms
	from torchvision.transforms.functional import InterpolationMode
	from transformers.modeling_outputs import CausalLMOutputWithPast
	from transformers.utils import (add_start_docstrings_to_model_forward,
	replace_return_docstrings)

	try:
	from transformers.generation.streamers import BaseStreamer
	except: # noqa # pylint: disable=bare-except
	BaseStreamer = None

	from .build_mlp import build_vision_projector, build_vision_tower
	from .ixc_utils import HD_transform
	from .configuration_internlm_xcomposer2 import InternLMXcomposer2Config
	from .modeling_internlm2 import (InternLM2_INPUTS_DOCSTRING, InternLM2Model,
	InternLM2PreTrainedModel)

	_CONFIG_FOR_DOC = 'InternLMXcomposer2Config'


	class InternLMXComposer2ForCausalLM(InternLM2PreTrainedModel):
	_auto_class = 'AutoModelForCausalLM'

	_tied_weights_keys = ['output.weight']

	def __init__(self, config):
	super().__init__(config)
	self.model = InternLM2Model(config)
	self.vocab_size = config.vocab_size
	self.output = nn.Linear(
	config.hidden_size, config.vocab_size, bias=False)
	self.tokenizer = None

	self.max_length = config.max_length
	print(f'Set max length to {self.max_length}')
	# Initialize weights and apply final processing
	self.post_init()
	self.plora_glb_GN = nn.Parameter(torch.zeros([1, 1, 4096]))
	self.plora_sub_GN = nn.Parameter(torch.zeros([1, 1, 1, 4096]))

	self.vit = build_vision_tower()
	self.vision_proj = build_vision_projector()

	self.vis_processor = transforms.Compose([
	transforms.ToTensor(),
	transforms.Normalize((0.48145466, 0.4578275, 0.40821073),
	(0.26862954, 0.26130258, 0.27577711)),
	])

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, InternLM2Model):
	module.gradient_checkpointing = value
	if value:
	self.vit.vision_tower.vision_model.encoder.gradient_checkpointing = value

	def get_input_embeddings(self):
	return self.model.tok_embeddings

	def set_input_embeddings(self, value):
	self.model.tok_embeddings = value

	def get_output_embeddings(self):
	return self.output

	def set_output_embeddings(self, new_embeddings):
	self.output = new_embeddings

	def set_decoder(self, decoder):
	self.model = decoder

	def get_decoder(self):
	return self.model

	def encode_text(self, text, add_special_tokens=False):
	token = self.tokenizer(
	text, return_tensors='pt',
	add_special_tokens=add_special_tokens).input_ids.to(self.device)
	embs = self.model.tok_embeddings(token)
	return embs

	def encode_img(self, image, hd_num=25):
	if image is None:
	return None
	if isinstance(image, str):
	image = Image.open(image).convert('RGB')
	image = HD_transform(image, hd_num = hd_num)
	image = self.vis_processor(image).unsqueeze(0).to(self.device)

	img_embeds, atts_img, img_target = self.img2emb(image)
	return img_embeds

	def img2emb(self, image):
	img_embeds, img_split = self.vit([image],
	self.plora_glb_GN, self.plora_sub_GN)
	if len(img_split) > 1:
	print ('Batch Size >1 is not supported.')
	assert 0
	#print (img_embeds.shape)
	img_embeds = self.vision_proj(img_embeds)
	atts_img = torch.ones(
	img_embeds.size()[:-1], dtype=torch.long).to(img_embeds.device)

	img_target = torch.ones(
	img_embeds.size()[:2], dtype=torch.long).to(
	img_embeds.device) * -100

	return img_embeds, atts_img, img_target

	def prompt_wrap(self, img_embeds, prompt):
	batch_size = img_embeds.shape[0]
	p_before, p_after = prompt.split('<ImageHere>')
	p_before_tokens = self.tokenizer(
	p_before, return_tensors='pt',
	add_special_tokens=True).to(img_embeds.device)

	p_before_embeds = self.model.tok_embeddings(
	p_before_tokens.input_ids).expand(batch_size, -1, -1)
	wrapped_img_embeds = torch.cat([p_before_embeds, img_embeds], dim=1)

	wrapped_atts_img = torch.ones(
	wrapped_img_embeds.size()[:-1],
	dtype=torch.long).to(img_embeds.device)

	wrapped_target = torch.ones(
	batch_size, wrapped_img_embeds.shape[1], dtype=torch.long).to(
	img_embeds.device) * -100

	return wrapped_img_embeds, wrapped_atts_img, wrapped_target

	def text2emb(self, text, add_special=False):
	to_regress_tokens = self.tokenizer(
	text,
	return_tensors='pt',
	padding='longest',
	truncation=True,
	max_length=self.max_length,
	add_special_tokens=add_special).to(self.device)

	targets = self.mask_human_targets(to_regress_tokens.input_ids)
	targets = targets.to(self.device)
	return to_regress_tokens, targets

	def interleav_wrap_chat(self, tokenizer, query, image, history, meta_instruction):
	prompt = ''
	if meta_instruction:
	prompt += f"""[UNUSED_TOKEN_146]system\n{meta_instruction}[UNUSED_TOKEN_145]\n"""
	for record in history:
	prompt += f"""[UNUSED_TOKEN_146]user\n{record[0]}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n{record[1]}[UNUSED_TOKEN_145]\n"""
	prompt += f"""[UNUSED_TOKEN_146]user\n{query}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"""

	im_len = image.shape[1]
	image_nums = len(image)
	parts = prompt.split('<ImageHere>')
	wrap_embeds, wrap_im_mask = [], []
	temp_len = 0

	if len(parts) != image_nums + 1:
	raise ValueError('Invalid <ImageHere> prompt format.')

	for idx, part in enumerate(parts):
	if len(part) > 0:
	part_tokens = tokenizer(part, return_tensors='pt').to(self.device)
	part_embeds = self.model.tok_embeddings(
	part_tokens.input_ids)
	wrap_embeds.append(part_embeds)
	wrap_im_mask.append(torch.zeros(part_embeds.shape[:2]))
	temp_len += part_embeds.shape[1]
	if idx < image_nums:
	wrap_embeds.append(image[idx].unsqueeze(0))
	wrap_im_mask.append(torch.ones(1, image[idx].shape[0]))
	temp_len += im_len

	if temp_len > self.max_length:
	break

	wrap_embeds = torch.cat(wrap_embeds, dim=1)
	wrap_im_mask = torch.cat(wrap_im_mask, dim=1)
	wrap_embeds = wrap_embeds[:, :self.max_length].to(self.device)
	wrap_im_mask = wrap_im_mask[:, :self.max_length].to(self.device).bool()
	inputs = {
	'inputs_embeds': wrap_embeds
	}
	return inputs, wrap_im_mask

	def interleav_wrap(self, img_list, text_list):
	wrap_embeds_list, wrap_atts_list = [], []
	wrap_target_list, wrap_im_mask_list = [], []

	for image, text in zip(img_list, text_list):
	img_embeds, atts_img, img_target = self.img2emb(image)
	text = text[0]
	parts = text.split('<ImageHere>')
	wrap_tokens, wrap_embeds, wrap_atts, wrap_im_mask = [], [], [], []
	temp_len = 0
	image_nums, im_len = img_embeds.shape[:2]
	need_bos = True
	for idx, part in enumerate(parts):
	if len(part) > 0:
	part_tokens = self.tokenizer(
	part,
	return_tensors='pt',
	padding='longest',
	add_special_tokens=need_bos).to(self.device)
	if need_bos:
	need_bos = False
	wrap_tokens.append(part_tokens.input_ids)
	part_embeds = self.model.tok_embeddings(
	part_tokens.input_ids)
	wrap_embeds.append(part_embeds)
	wrap_atts.append(part_tokens.attention_mask)
	wrap_im_mask.append(
	torch.zeros(part_embeds.shape[:2]).to(self.device))

	temp_len += part_embeds.shape[1]
	if idx < image_nums:
	wrap_tokens.append(img_target[idx].unsqueeze(0))
	wrap_embeds.append(img_embeds[idx].unsqueeze(0))
	wrap_atts.append(atts_img[idx].unsqueeze(0))
	wrap_im_mask.append(
	torch.ones_like(atts_img[idx].unsqueeze(0)))

	temp_len += im_len
	if temp_len > self.max_length:
	break

	wrap_tokens = torch.cat(wrap_tokens, dim=1)
	wrap_embeds = torch.cat(wrap_embeds, dim=1)
	wrap_atts = torch.cat(wrap_atts, dim=1)
	wrap_im_mask = torch.cat(wrap_im_mask, dim=1)

	wrap_target = self.mask_human_targets(wrap_tokens).to(self.device)

	wrap_embeds = wrap_embeds[:, :self.max_length].to(self.device)
	wrap_atts = wrap_atts[:, :self.max_length].to(self.device)
	wrap_target = wrap_target[:, :self.max_length].to(self.device)
	wrap_im_mask = wrap_im_mask[:, :self.max_length].to(self.device)

	wrap_embeds_list.append(wrap_embeds)
	wrap_atts_list.append(wrap_atts)
	wrap_target_list.append(wrap_target)
	wrap_im_mask_list.append(wrap_im_mask)

	wrap_embeds = torch.cat(wrap_embeds_list)
	wrap_atts = torch.cat(wrap_atts_list)
	wrap_target = torch.cat(wrap_target_list)
	wrap_im_mask = torch.cat(wrap_im_mask_list)
	return wrap_embeds, wrap_atts, wrap_target, wrap_im_mask

	def mask_human_targets(self, input_ids, pure=False):
	target_batch = []
	for bs in range(input_ids.shape[0]):
	ids = input_ids[bs]
	targets = copy.deepcopy(ids)
	end_count = 0
	last_eoa = 0
	for i, temp_id in enumerate(ids):
	if temp_id == 92542:
	if end_count % 2 == 0:
	targets[last_eoa:i + 6] = -100
	else:
	last_eoa = i + 1
	end_count += 1
	# # eos and following pad
	elif temp_id == 2:
	# loss on eos, but not on pad
	targets[i + 1:] = -100
	break
	# trunction, end at last question
	if temp_id != 2 and end_count % 2 == 0:
	# mask all after the last answer
	targets[last_eoa + 1:] = -100
	target_batch.append(targets.unsqueeze(0))
	target_batch = torch.cat(target_batch, dim=0)
	return target_batch

	@add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
	@replace_return_docstrings(
	output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
	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,
	return_dict: Optional[bool] = None,
	**kwargs) -> Union[Tuple, CausalLMOutputWithPast]:
	r"""
	Args:
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
	config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
	(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
	Returns:
	"""

	samples = kwargs.get('samples', None)
	if samples:
	if samples['data_type'][0] == 'text':
	has_img = False
	elif samples['data_type'][0] == 'multi':
	has_img = True
	else:
	raise NotImplementedError

	# encode text
	text = samples['text_input']
	# encode image
	if has_img:
	image = samples['image']
	to_regress_embeds, attention_mask, targets, im_mask = self.interleav_wrap(
	image, text)
	else:
	to_regress_tokens, targets = self.text2emb(
	text, add_special=True)
	to_regress_embeds = self.model.tok_embeddings(
	to_regress_tokens.input_ids)
	attention_mask = to_regress_tokens.attention_mask
	im_mask = torch.zeros(to_regress_embeds.shape[:2]).cuda()

	inputs_embeds = to_regress_embeds[:, :self.max_length]
	attention_mask = attention_mask[:, :self.max_length]
	targets = targets[:, :self.max_length]
	im_mask = im_mask[:, :self.max_length].bool()
	labels = targets
	else:
	im_mask = kwargs.get('im_mask', None)
	if im_mask is None and inputs_embeds is not None:
	im_mask = torch.zeros(inputs_embeds.shape[:2]).to(
	inputs_embeds.device)
	im_mask = im_mask.bool()

	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

	# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
	outputs = self.model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	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,
	im_mask=im_mask,
	)

	hidden_states = outputs[0]
	logits = self.output(hidden_states)
	logits = logits.float()

	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 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,
	im_mask=None,
	**kwargs):
	if past_key_values is not None:
	past_length = past_key_values[0][0].shape[2]

	# Some generation methods already pass only the last input ID
	if input_ids.shape[1] > past_length:
	remove_prefix_length = past_length
	else:
	# Default to old behavior: keep only final ID
	remove_prefix_length = input_ids.shape[1] - 1

	input_ids = input_ids[:, remove_prefix_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}

	im_mask = im_mask

	model_inputs.update({
	'position_ids': position_ids,
	'past_key_values': past_key_values,
	'use_cache': kwargs.get('use_cache'),
	'attention_mask': attention_mask,
	'im_mask': im_mask,
	})
	return model_inputs

	@staticmethod
	def _reorder_cache(past_key_values, beam_idx):
	reordered_past = ()
	for layer_past in past_key_values:
	reordered_past += (tuple(
	past_state.index_select(0, beam_idx.to(past_state.device))
	for past_state in layer_past), )
	return reordered_past

	def build_inputs(self,
	tokenizer,
	query: str,
	history: List[Tuple[str, str]] = [],
	meta_instruction=''):
	prompt = ''
	if meta_instruction:
	prompt += f"""<s>[UNUSED_TOKEN_146]system\n{meta_instruction}[UNUSED_TOKEN_145]\n"""
	else:
	prompt += '<s>'
	for record in history:
	prompt += f"""[UNUSED_TOKEN_146]user\n{record[0]}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n{record[1]}[UNUSED_TOKEN_145]\n"""
	prompt += f"""[UNUSED_TOKEN_146]user\n{query}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"""
	return tokenizer([prompt], return_tensors='pt')

	@torch.no_grad()
	def chat(
	self,
	tokenizer,
	query: str,
	image: torch.Tensor = None,
	hd_num: int = 25,
	history: List[Tuple[str, str]] = [],
	streamer: Optional[BaseStreamer] = None,
	max_new_tokens: int = 1024,
	do_sample: bool = True,
	num_beams: int = 1,
	temperature: float = 1.0,
	top_p: float = 0.8,
	repetition_penalty: float=1.005,
	meta_instruction:
	str = 'You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔).\n'
	'- InternLM-XComposer (浦语·灵笔) is a multi-modality conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n'
	'- InternLM-XComposer (浦语·灵笔) can understand and communicate fluently in the language chosen by the user such as English and 中文.\n'
	'- InternLM-XComposer (浦语·灵笔) is capable of comprehending and articulating responses effectively based on the provided image.',
	**kwargs,
	):
	if image is None:
	inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
	im_mask = torch.zeros(inputs['input_ids'].shape[:2]).cuda().bool()
	else:
	image = self.encode_img(image, hd_num=hd_num)
	inputs, im_mask = self.interleav_wrap_chat(tokenizer, query, image, history, meta_instruction)
	inputs = {
	k: v.to(self.device)
	for k, v in inputs.items() if torch.is_tensor(v)
	}
	# also add end-of-assistant token in eos token id to avoid unnecessary generation
	eos_token_id = [
	tokenizer.eos_token_id,
	tokenizer.convert_tokens_to_ids(['[UNUSED_TOKEN_145]'])[0]
	]
	outputs = self.generate(
	**inputs,
	streamer=streamer,
	max_new_tokens=max_new_tokens,
	num_beams=num_beams,
	do_sample=do_sample,
	temperature=temperature,
	top_p=top_p,
	eos_token_id=eos_token_id,
	repetition_penalty=repetition_penalty,
	im_mask=im_mask,
	**kwargs,
	)
	if image is None:
	outputs = outputs[0].cpu().tolist()[len(inputs['input_ids'][0]):]
	else:
	outputs = outputs[0].cpu().tolist()
	response = tokenizer.decode(outputs, skip_special_tokens=True)
	response = response.split('[UNUSED_TOKEN_145]')[0]
	history = history + [(query, response)]
	return response, history