# 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 os
import re
import copy
import queue
import threading
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from PIL import Image
import numpy as np
import random
from torch import nn
import torch.nn.functional as F
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers.modeling_outputs import CausalLMOutputWithPast, SequenceClassifierOutputWithPast
from transformers.utils import (add_start_docstrings_to_model_forward,
replace_return_docstrings)
from transformers import StoppingCriteria, StoppingCriteriaList
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
try:
from transformers.generation.streamers import BaseStreamer
except: # noqa # pylint: disable=bare-except
BaseStreamer = None
import torchvision.transforms as transforms
from torchvision.transforms.functional import InterpolationMode
from .build_mlp import build_vision_projector, build_vision_tower
from .ixc_utils import Image_transform, Video_transform, load_video, frame2img, get_font
from .configuration_internlm_xcomposer2 import InternLMXcomposer2Config
from .modeling_internlm2 import (InternLM2_INPUTS_DOCSTRING, InternLM2Model,
InternLM2PreTrainedModel)
_CONFIG_FOR_DOC = 'InternLMXcomposer2Config'
image_extensions = {'.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp'}
video_extensions = {'.mp4', '.avi', '.mkv', '.mov', '.wmv'}
class StoppingCriteriaSub(StoppingCriteria):
def __init__(self, stops=[], encounters=1):
super().__init__()
self.stops = stops
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
for stop in self.stops:
if torch.all((stop == input_ids[0][-len(stop):])).item():
return True
return False
def get_stopping_criteria(stop_words_ids):
stop_words_ids = [torch.tensor([i]).cuda() for i in stop_words_ids]
stopping_criteria = StoppingCriteriaList(
[StoppingCriteriaSub(stops=stop_words_ids)])
return stopping_criteria
def set_random_seed(seed, set_cudnn=False):
"""Set the random seed for reproducibility.
Parameters:
seed (int): The seed to use for generating random numbers.
"""
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed) # For multi-GPU.
np.random.seed(seed)
random.seed(seed)
if set_cudnn and torch.backends.cudnn.is_available():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def find_subarray_indices(tensor, subarray):
tensor_len = len(tensor)
subarray_len = len(subarray)
indices = []
if subarray_len > tensor_len:
return indices # Subarray longer than tensor, can't be a match
for i in range(tensor_len - subarray_len + 1):
if torch.equal(tensor[i:i + subarray_len], subarray):
indices.append((i, i + subarray_len))
return indices
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.score = nn.Linear(config.hidden_size, 1, bias=False)
self.tokenizer = None
self.hd_num = 25
self.font = get_font()
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 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):
_, ext = os.path.splitext(image)
if ext.lower() in image_extensions:
image = Image.open(image).convert('RGB')
image = Image_transform(image, hd_num = hd_num)
elif ext.lower() in video_extensions:
image = load_video(image)
image = frame2img(image, self.font)
image = Video_transform(image, hd_num = hd_num)
else:
print ('Unknow input format', image)
return None
image = self.vis_processor(image).unsqueeze(0).to(self.device)
else:
assert isinstance(image, torch.Tensor)
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_tokens=False):
to_regress_tokens = self.tokenizer(
text,
return_tensors='pt',
padding='longest',
truncation=True,
max_length=self.max_length,
add_special_tokens=add_special_tokens
).to(self.device)
targets = self.mask_human_targets(to_regress_tokens.input_ids)
targets = targets.to(self.device)
return to_regress_tokens, targets
def apply_chat_template(self, conversation, image, max_length: int=16384, hd_num: int=24, apply_template=True):
if apply_template:
prompt = ''
for message in conversation:
role = message['role']
content = message['content']
if role in ['system', 'user', 'assistant']:
prompt += f"""[UNUSED_TOKEN_146]{role}\n{content}[UNUSED_TOKEN_145]\n"""
else:
raise NotImplementedError(f"The role '{role}' is not a valid")
# end
prompt = prompt + '</s>'
# reward token id
prompt = prompt + '[UNUSED_TOKEN_130]'
else:
image_nums = len(image)
prompt = conversation
image_nums = len(image)
if image_nums == 1 and prompt.find('<ImageHere>') == -1:
# print ('auto append image at the begining')
prompt = '<ImageHere>' + prompt
parts = prompt.split('<ImageHere>')
wrap_tokens = []
wrap_embeds, wrap_im_mask = [], []
temp_len = 0
need_bos = True
if len(parts) != image_nums + 1:
#raise ValueError('Invalid <ImageHere> prompt format.')
print ('Waring! The image number != given position!')
if image_nums > 1:
hd_num = 6
else:
hu_num = hd_num
for idx, part in enumerate(parts):
if need_bos or 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_im_mask.append(torch.zeros(part_embeds.shape[:2]))
temp_len += part_embeds.shape[1]
if idx < image_nums:
if isinstance(image[idx], str):
img = self.encode_img(image[idx], hd_num)
else:
# torch.tensor
img, _, _ = self.img2emb(image[idx])
wrap_embeds.append(img)
wrap_token = torch.ones(img.shape[:2], dtype=torch.long).to(self.device) * -100
wrap_tokens.append(wrap_token)
wrap_im_mask.append(torch.ones(img.shape[:2]))
temp_len += img.shape[1]
if temp_len > max_length:
break
wrap_tokens = torch.cat(wrap_tokens, dim=1)
wrap_embeds = torch.cat(wrap_embeds, dim=1)
wrap_im_mask = torch.cat(wrap_im_mask, dim=1)
wrap_embeds = wrap_embeds[:, :max_length].to(self.device)
wrap_im_mask = wrap_im_mask[:, :max_length].to(self.device).bool()
return wrap_embeds, wrap_im_mask, temp_len
def get_score(self, conversation: List[dict], image: List[str], max_length: int=16384, hd_num: int=24, apply_template: bool=True):
inputs_embeds, im_mask, _ = self.apply_chat_template(conversation, image, max_length, hd_num, apply_template)
attention_mask = torch.ones(1, inputs_embeds.shape[1]).to(bool).to(self.device)
outputs = self.forward(inputs_embeds=inputs_embeds, attention_mask=attention_mask, im_mask=im_mask)
score = outputs.logits.cpu().item()
return score
def get_scores(self, conversations: List[List[dict]], images: List[List[str]], max_length: int=16384, hd_num: int=24, apply_template: bool=True):
temp_embeds = []
temp_im_mask = []
for conversation, image in zip(conversations, images):
inputs_embeds, im_mask, _ = self.apply_chat_template(conversation, image, max_length, hd_num, apply_template)
temp_embeds.append(inputs_embeds)
temp_im_mask.append(im_mask)
temp_max_len = np.max([i.shape[1] for i in temp_embeds])
temp_max_len = min(temp_max_len, max_length)
batch_input_embeds, batch_atts, batch_im_mask = [], [], []
pad = torch.ones([1, 1]) * self.tokenizer.pad_token_id
pad = pad.long().to(self.device)
pad_emb = self.model.tok_embeddings(pad)
for idx in range(len(temp_embeds)):
temp_len = temp_embeds[idx].shape[1]
dtype = temp_im_mask[idx].dtype
if temp_len >= temp_max_len:
batch_input_embeds.append(temp_embeds[idx][:, :temp_max_len])
batch_atts.append(torch.ones(1, temp_max_len).to(dtype).to(self.device))
batch_im_mask.append(temp_im_mask[idx][:, :temp_max_len])
else:
batch_input_embeds.append(torch.cat([temp_embeds[idx], pad_emb.repeat(1, temp_max_len-temp_len, 1)], dim=1))
batch_atts.append(torch.cat([torch.ones(1, temp_len), torch.zeros(1, temp_max_len-temp_len)], dim=1).to(dtype).to(self.device))
batch_im_mask.append(torch.cat([temp_im_mask[idx], (torch.zeros(1, temp_max_len-temp_len)).to(dtype).to(self.device)], dim=1))
batch_inputs_embeds = torch.cat(batch_input_embeds, dim=0)
batch_atts = torch.cat(batch_atts, dim=0)
batch_im_mask = torch.cat(batch_im_mask, dim=0)
outputs = self.forward(inputs_embeds=batch_inputs_embeds, attention_mask=batch_atts, im_mask=batch_im_mask)
scores = outputs.logits.squeeze().cpu().tolist()
return scores
@torch.no_grad()
def compare(self, conversation1: List[dict], image1: List[str], conversation2: List[dict], image2: List[str], max_length: int=16384, hd_num: int=24, return_logits: bool=False, apply_template: bool=True):
score1 = self.get_score(conversation1, image1, max_length, hd_num, apply_template)
score2 = self.get_score(conversation2, image2, max_length, hd_num, apply_template)
if return_logits:
return score1 > score2, [score1, score2]
else:
return score1 > score2
@torch.no_grad()
def rank(self, conversations: List[List[dict]], images: List[List[str]], max_length: int=16384, hd_num: int=24, return_logits: bool=False, apply_template: bool=True):
scores = self.get_scores(conversations, images, max_length, hd_num, apply_template)
if return_logits:
return sorted(range(len(scores)), key=lambda i: scores[i], reverse=True), scores
else:
return sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)
def interleav_wrap(self, img_list, text_list, image_nums):
temp_tokens = []
temp_embeds = []
temp_im_mask = []
temp_tars = []
# encode_image
img_embeds, img_split = self.vit(img_list, self.plora_glb_GN, self.plora_sub_GN)
img_embeds = self.vision_proj(img_embeds)
for idx, text in enumerate(text_list):
idx_ = idx // 2
image_num = image_nums[idx_]
im_id = int(np.sum(image_nums[:idx_]))
images = []
for i in range(image_num):
st = int(np.sum(img_split[:im_id + i]))
sp = img_split[im_id + i]
temp_img = img_embeds[:, st:st+sp]
images.append(temp_img)
atts_img = torch.ones((len(images), images[0].shape[1]), dtype=torch.long).to(self.device)
img_target = torch.ones(
(len(images), images[0].shape[1]), dtype=torch.long).to(
self.device) * -100
if image_num == 1 and text.find('<ImageHere>') == -1:
text = '<ImageHere>' + text
parts = text.split('<ImageHere>')
wrap_tokens, wrap_embeds, wrap_im_mask = [], [], []
temp_len = 0
need_bos = True
for idx, part in enumerate(parts):
if need_bos or 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_im_mask.append(torch.zeros(part_embeds.shape[:2]).to(self.device))
temp_len += part_embeds.shape[1]
if idx < image_num:
wrap_embeds.append(images[idx])
wrap_token = torch.ones(images[idx].shape[:2], dtype=torch.long).to(self.device) * -100
wrap_tokens.append(wrap_token)
wrap_im_mask.append(torch.ones(images[idx].shape[:2]).to(self.device))
temp_len += images[idx].shape[1]
if temp_len > self.max_length:
break
wrap_tokens = torch.cat(wrap_tokens, dim=1)
wrap_embeds = torch.cat(wrap_embeds, dim=1)
wrap_im_mask = torch.cat(wrap_im_mask, dim=1)
wrap_target = self.mask_human_targets(wrap_tokens).to(self.device)
temp_tokens.append(wrap_tokens)
temp_embeds.append(wrap_embeds)
temp_im_mask.append(wrap_im_mask)
temp_tars.append(wrap_target)
temp_max_len = np.max([i.shape[1] for i in temp_embeds])
temp_max_len = min(temp_max_len, self.max_length)
final_input_ids, final_input_embeds, final_atts, final_tars, final_mask = [], [], [], [], []
pad = torch.ones([1, 1]) * self.tokenizer.pad_token_id
pad = pad.long().to(self.device)
pad_emb = self.model.tok_embeddings(pad)
for idx in range(len(temp_embeds)):
temp_len = temp_embeds[idx].shape[1]
if temp_len >= temp_max_len:
final_input_ids.append(temp_tokens[idx][:, :temp_max_len])
final_input_embeds.append(temp_embeds[idx][:, :temp_max_len])
final_atts.append(torch.ones(1, temp_max_len).to(wrap_target.dtype).to(self.device))
final_tars.append(temp_tars[idx][:, :temp_max_len])
final_mask.append(temp_im_mask[idx][:, :temp_max_len])
else:
final_input_ids.append(torch.cat([temp_tokens[idx], (torch.ones(1, temp_max_len-temp_len) * self.tokenizer.pad_token_id).to(wrap_target.dtype).to(self.device)], dim=1))
final_input_embeds.append(torch.cat([temp_embeds[idx], pad_emb.repeat(1, temp_max_len-temp_len, 1)], dim=1))
final_atts.append(torch.cat([torch.ones(1, temp_len), torch.zeros(1, temp_max_len-temp_len)], dim=1).to(wrap_target.dtype).to(self.device))
final_tars.append(torch.cat([temp_tars[idx], (torch.ones(1, temp_max_len-temp_len)*-100).to(wrap_target.dtype).to(self.device)], dim=1))
final_mask.append(torch.cat([temp_im_mask[idx], (torch.zeros(1, temp_max_len-temp_len)).to(wrap_target.dtype).to(self.device)], dim=1))
input_ids = torch.cat(final_input_ids, dim=0)
inputs_embeds = torch.cat(final_input_embeds, dim=0)
attention_mask = torch.cat(final_atts, dim=0)
targets = torch.cat(final_tars, dim=0)
im_mask = torch.cat(final_mask, dim=0)
# to avoid error in DPO loss
input_ids[input_ids == -100] = self.tokenizer.pad_token_id
return input_ids, inputs_embeds, attention_mask, targets, im_mask
def mask_human_targets(self, input_ids, pure=False):
target_batch = []
system_tokens = torch.tensor([92543, 9081]).to(self.device)
for bs in range(input_ids.shape[0]):
ids = input_ids[bs]
targets = copy.deepcopy(ids)
end_count = 0
last_eoa = 0
# 92542 -> [UNUSED_TOKEN_145]
# 92543 -> [UNUSED_TOKEN_146]
# 9081 -> system
for i, temp_id in enumerate(ids):
if temp_id == 92542:
search_results = find_subarray_indices(targets[last_eoa:i + 1], system_tokens)
if len(search_results) > 0:
targets[last_eoa:i + 1] = -100
last_eoa = i + 1
else:
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:
infer_mode = samples.get('infer_mode', 'base')
if samples['data_type'][0] == 'text':
has_img = False
elif samples['data_type'][0] == 'multi':
has_img = True
else:
raise NotImplementedError
# encode text
text_chosen = samples['chosen'][0]
text_rejected = samples['rejected'][0]
text = [x for pair in zip(text_chosen, text_rejected) for x in pair]
# encode image
if has_img:
image = samples['image'][0]
bs = len(text)
image_nums = []
temp_image = []
for im in image:
if type(im) is list:
image_nums.append(len(im))
temp_image.extend(im)
else:
image_nums.append(1)
temp_image.append(im)
image = temp_image
assert type(image) is list and len(image_nums) * 2 == bs
input_ids_for_loss, to_regress_embeds, attention_mask, targets, im_mask = self.interleav_wrap(
image, text, image_nums)
else:
to_regress_tokens, targets = self.text2emb(
text, add_special_tokens=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()
input_ids_for_loss = to_regress_tokens.input_ids
input_ids_for_loss = input_ids_for_loss[:, :self.max_length]
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)
infer_mode = kwargs.get('infer_mode', 'base')
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)
transformer_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,
infer_mode=infer_mode,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
logits = logits.float()
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
ends = attention_mask.cumsum(dim=1).argmax(dim=1).view(-1,1)
pooled_logits = torch.gather(logits.squeeze(-1), 1, ends)
loss = None
if self.training:
chosen_idx = torch.arange(0, batch_size, 2)
rejected_idx = chosen_idx + 1
loss = -F.logsigmoid(pooled_logits[chosen_idx] - pooled_logits[rejected_idx]).mean()
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def prepare_inputs_for_generation(self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
im_mask=None,
infer_mode='base',
**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,
'infer_mode': infer_mode,
})
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')