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# Copyright (c) OpenMMLab. All rights reserved.
from xtuner.model.utils import *
from typing import List, Optional
import torch
from transformers import PreTrainedModel
from xtuner.utils import IGNORE_INDEX, IMAGE_TOKEN_INDEX
def prepare_inputs_labels_for_multimodal_with_visual_prompts(
llm: PreTrainedModel,
input_ids: torch.LongTensor = None,
position_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
labels: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
region_id=None,
regions_feats=None,
mark_id=None,
mark_feats=None,
**kwargs,
):
if pixel_values is None:
return {
'input_ids': input_ids,
'position_ids': position_ids,
'attention_mask': attention_mask,
'past_key_values': past_key_values,
'inputs_embeds': None,
'labels': labels
}
_labels = labels
_position_ids = position_ids
_attention_mask = attention_mask
if attention_mask is None:
attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
else:
attention_mask = attention_mask.bool()
if position_ids is None:
position_ids = torch.arange(
0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
if labels is None:
labels = torch.full_like(input_ids, IGNORE_INDEX)
# remove the padding using attention_mask -- TODO: double check
input_ids = [
cur_input_ids[cur_attention_mask]
for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)
]
labels = [
cur_labels[cur_attention_mask]
for cur_labels, cur_attention_mask in zip(labels, attention_mask)
]
new_inputs_embeds = []
new_labels = []
cur_image_idx = 0
cur_region_idx = 0
cur_mark_id = 0
for batch_idx, cur_input_ids in enumerate(input_ids):
num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
if num_images == 0:
cur_pixel_values = pixel_values[cur_image_idx]
cur_inputs_embeds_1 = llm.get_input_embeddings()(cur_input_ids)
cur_inputs_embeds = torch.cat(
[cur_inputs_embeds_1, cur_pixel_values[0:0]], dim=0)
new_inputs_embeds.append(cur_inputs_embeds)
new_labels.append(labels[batch_idx])
cur_image_idx += 1
continue
need_replace = cur_input_ids == IMAGE_TOKEN_INDEX
need_replace = torch.logical_or(need_replace, cur_input_ids == region_id)
need_replace = torch.logical_or(need_replace, cur_input_ids == mark_id)
num_replace = need_replace.sum()
replace_type = cur_input_ids[need_replace]
image_token_indices = [-1] + torch.where(
need_replace)[0].tolist() + [
cur_input_ids.shape[0]
]
cur_input_ids_noim = []
cur_labels = labels[batch_idx]
cur_labels_noim = []
for i in range(len(image_token_indices) - 1):
cur_input_ids_noim.append(cur_input_ids[image_token_indices[i] +
1:image_token_indices[i +
1]])
cur_labels_noim.append(cur_labels[image_token_indices[i] +
1:image_token_indices[i + 1]])
split_sizes = [x.shape[0] for x in cur_labels_noim]
cur_inputs_embeds = llm.get_input_embeddings()(
torch.cat(cur_input_ids_noim))
cur_inputs_embeds_no_im = torch.split(
cur_inputs_embeds, split_sizes, dim=0)
cur_new_inputs_embeds = []
cur_new_labels = []
for i in range(num_replace + 1):
cur_new_inputs_embeds.append(cur_inputs_embeds_no_im[i])
cur_new_labels.append(cur_labels_noim[i])
if i < num_replace:
# image
if replace_type[i] == IMAGE_TOKEN_INDEX:
cur_pixel_values = pixel_values[cur_image_idx]
cur_image_idx += 1
cur_new_inputs_embeds.append(cur_pixel_values)
cur_new_labels.append(
torch.full((cur_pixel_values.shape[0], ),
IGNORE_INDEX,
device=cur_labels.device,
dtype=cur_labels.dtype))
elif replace_type[i] == region_id:
cur_pixel_values = regions_feats[cur_region_idx:cur_region_idx+1]
cur_region_idx += 1
cur_new_inputs_embeds.append(cur_pixel_values)
cur_new_labels.append(
torch.full((cur_pixel_values.shape[0],),
IGNORE_INDEX,
device=cur_labels.device,
dtype=cur_labels.dtype))
elif replace_type[i] == mark_id:
cur_pixel_values = mark_feats[cur_mark_id:cur_mark_id + 1]
cur_mark_id += 1
cur_new_inputs_embeds.append(cur_pixel_values)
cur_new_labels.append(
torch.full((cur_pixel_values.shape[0],),
IGNORE_INDEX,
device=cur_labels.device,
dtype=cur_labels.dtype))
cur_new_inputs_embeds = torch.cat(cur_new_inputs_embeds)
cur_new_labels = torch.cat(cur_new_labels)
new_inputs_embeds.append(cur_new_inputs_embeds)
new_labels.append(cur_new_labels)
# Combine them
max_len = max(x.shape[0] for x in new_inputs_embeds)
batch_size = len(new_inputs_embeds)
new_inputs_embeds_padded = []
new_labels_padded = torch.full((batch_size, max_len),
IGNORE_INDEX,
dtype=new_labels[0].dtype,
device=new_labels[0].device)
attention_mask = torch.zeros((batch_size, max_len),
dtype=attention_mask.dtype,
device=attention_mask.device)
position_ids = torch.zeros((batch_size, max_len),
dtype=position_ids.dtype,
device=position_ids.device)
for i, (cur_new_embed,
cur_new_labels) in enumerate(zip(new_inputs_embeds, new_labels)):
cur_len = cur_new_embed.shape[0]
new_inputs_embeds_padded.append(
torch.cat((cur_new_embed,
torch.zeros((max_len - cur_len, cur_new_embed.shape[1]),
dtype=cur_new_embed.dtype,
device=cur_new_embed.device)),
dim=0))
if cur_len > 0:
new_labels_padded[i, :cur_len] = cur_new_labels
attention_mask[i, :cur_len] = True
position_ids[i, :cur_len] = torch.arange(
0,
cur_len,
dtype=position_ids.dtype,
device=position_ids.device)
new_inputs_embeds = torch.stack(new_inputs_embeds_padded, dim=0)
if _labels is None:
new_labels = None
else:
new_labels = new_labels_padded
if _attention_mask is None:
attention_mask = None
else:
attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
if _position_ids is None:
position_ids = None
return {
'input_ids': None,
'position_ids': position_ids,
'attention_mask': attention_mask,
'past_key_values': past_key_values,
'inputs_embeds': new_inputs_embeds,
'labels': new_labels,
}
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