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# System
import torch
from torch import nn
from utils.utils import *
import torch.utils.checkpoint
from typing import List, Optional, Tuple, Union
from .build_module import build_vision_projector, build_vision_tower
from .modeling_internlm2 import InternLM2Model, InternLM2PreTrainedModel
# Dataclass & ModelOutput
from dataclasses import dataclass
from transformers.modeling_outputs import ModelOutput
@dataclass
class TroLCausalLMOutputWithPast(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
class TroLForCausalLM(InternLM2PreTrainedModel):
_auto_class = 'AutoModelForCausalLM'
_tied_weights_keys = ['output.weight']
def __init__(self, config):
super().__init__(config)
# Model
self.model = InternLM2Model(config)
self.vocab_size = config.vocab_size
self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.max_length = config.max_length
# Initialize weights and apply final processing
self.post_init()
# Vision Encoder
self.vit = build_vision_tower()
# Vision Projection
self.vision_proj = build_vision_projector(self.config.hidden_size)
# image processing variable
self.mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).view(1,-1,1,1) * 255
self.std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).view(1,-1,1,1) * 255
# prompt rule
self.prompt_rule = {"system_start": "<s>[UNUSED_TOKEN_146]system\n",
"system_end": "[UNUSED_TOKEN_145]",
"user_start": "[UNUSED_TOKEN_146]user\n",
"user_end": "[UNUSED_TOKEN_145]",
"assistant_start": "[UNUSED_TOKEN_146]assistant\n",
"assistant_end": "[UNUSED_TOKEN_145]\n</s>",
"test_start": "assistant\n",
"test_end": "[UNUSED_TOKEN_145]",
"split": "\n",
}
def image_processor(self, images):
norm_images = (images - self.mean.to(images.device)) / self.std.to(images.device)
return norm_images
def eval_process(
self,
inputs,
data,
tokenizer,
device,
img_token_number,
):
batched_image = []
batched_qa_prompt=[]
for _input in inputs:
# Visualization
# imim = _input['image'].cpu().permute(1, 2, 0)
# adding <image> to question if not included despite being an image, and adding system prompt and <tor> prompt
if 'image' in _input.keys() and not '<image>' in _input['question']: _input['question'] = '<image>\n' + _input['question']
# make question and answer
question = make_instruction(_input['question'], data, self.prompt_rule)
# add bundle image tokens if it has <image> token
question = add_bundle_tokens(question, '<image>', img_token_number)
batched_qa_prompt.append(question)
# making batched image prompt
if 'image' in _input.keys() and _input['image'] != None: batched_image.append(_input['image'].to(device))
'''For Final Outputs'''
qa_prompts = tokenizer(batched_qa_prompt, padding='longest', return_tensors="pt", add_special_tokens=False)
# [1] input_ids
input_ids = qa_prompts.input_ids.to(device)
# [2] attention_mask
attention_mask = qa_prompts.attention_mask.to(device)
# [3] im_mask
im_mask = torch.zeros_like(input_ids).bool()
im_mask[torch.where(input_ids==self.config.image_token_index)] = True
if len(batched_image):
return {"input_ids": input_ids,
"attention_mask": attention_mask,
"im_mask": im_mask,
"image_features": self.clip_features(self.image_processor(torch.stack(batched_image)).to(device))
}
else:
return {"input_ids": input_ids,
"attention_mask": attention_mask,
"im_mask": im_mask,
}
def clip_features(self, image):
self.vit.eval()
return self.vit(image)
def _merge_input_embeds_with_image_features(self, image_features, inputs_embeds, input_ids):
# batch index for image feature
batch_ind_image_feature = 0
# shape of image_features
_, C, D = image_features.shape
for ind, input_id in enumerate(input_ids):
matching = torch.where(input_id==self.config.image_token_index)
num_image_tokens_per_one_sample = len(matching[0]) // C
inputs_embeds[ind][matching] = image_features[batch_ind_image_feature: batch_ind_image_feature+num_image_tokens_per_one_sample].view(-1, D)
batch_ind_image_feature += num_image_tokens_per_one_sample
def forward(
self,
input_ids: torch.LongTensor = None,
image_features: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
im_mask: torch.BoolTensor = 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,
) -> Union[Tuple, TroLCausalLMOutputWithPast]:
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
if inputs_embeds is None:
# 1. Extra the input embeddings
inputs_embeds = self.get_input_embeddings()(input_ids)
# 2. Merge text and images
if image_features is not None and input_ids.shape[1] != 1:
image_features = self.vision_proj(image_features.to(inputs_embeds.dtype))
self._merge_input_embeds_with_image_features(image_features, inputs_embeds, input_ids)
# In case input_ids.shape[1] == 1 & image_features==None & past_key_values != None, we are in the case of
# generation with cache
elif past_key_values is not None and image_features is not None and input_ids.shape[1] == 1:
# Retrieve the first layer to inspect the logits and mask out the hidden states
# that are set to 0
first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
# Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
# Get the target length
target_length = input_ids.shape[1]
past_length = first_layer_past_key_value.shape[-1]
extended_attention_mask = torch.ones(
(attention_mask.shape[0], past_length),
dtype=attention_mask.dtype,
device=attention_mask.device,
)
# Filter out only the tokens that can be un-attended, this can happen
# if one uses Llava + Fused modules where the cache on the
# first iteration is already big enough, or if one passes custom cache
valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
new_batch_index = batch_index[valid_indices]
new_non_attended_tokens = non_attended_tokens[valid_indices]
# Zero-out the places where we don't need to attend
extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
im_mask = torch.zeros(inputs_embeds.shape[:2]).bool().to(inputs_embeds.device)
outputs = self.model(
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)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
if attention_mask is not None:
shift_attention_mask = attention_mask[..., 1:]
shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
else:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(
shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return TroLCausalLMOutputWithPast(
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,
image_features=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}
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
"image_features": image_features,
"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 |