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import os
import sys
import mmengine
import torch
import torch.nn as nn
from mmengine.device import get_device
from transformers import StoppingCriteriaList
from opencompass.registry import MM_MODELS
from .utils import StoppingCriteriaSub
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
def load_package():
"""Load required packages from MiniGPT-4."""
current_file_path = os.path.abspath(__file__)
current_folder_path = os.path.dirname(current_file_path)
sys.path.append(os.path.join(current_folder_path, 'MiniGPT-4')) # noqa
try:
# the latest version of MiniGPT4
from minigpt4.models.minigpt4 import MiniGPT4
except ImportError:
# the old version of MiniGPT4
from minigpt4.models.mini_gpt4 import MiniGPT4
sys.path.pop(-1)
return MiniGPT4
MiniGPT4 = load_package()
@MM_MODELS.register_module('minigpt-4')
class MiniGPT4Inferencer(MiniGPT4):
"""Inference code of MiniGPT-4.
Args:
llama_model (str): The path of vicuna path.
prompt_constructor (dict): The config of prompt constructor.
post_processor (dict): The config of post processor.
do_sample (bool): Whether use sampling. Defaults to False.
max_length (int): The max length of output. Defaults to 30.
img_size (int): The size of image. Defaults to 224.
low_resource (bool): Whether loaded in low precision.
Defaults to False.
is_caption_task (bool): Whether the task is caption task.
Defaults to False.
"""
def __init__(self,
llama_model: str,
prompt_constructor: dict,
post_processor: dict,
do_sample: bool = False,
max_length: int = 30,
img_size: int = 224,
low_resource: bool = False,
is_caption_task: bool = False,
mode: str = 'generation',
n_segments: int = 1) -> None:
super().__init__(llama_model=llama_model,
low_resource=low_resource,
img_size=img_size)
self.mode = mode
self.n_segments = n_segments
cur_device = get_device()
stop_words_ids = [
torch.tensor([835]).to(cur_device),
torch.tensor([2277, 29937]).to(cur_device),
]
self.stopping_criteria = StoppingCriteriaList(
[StoppingCriteriaSub(stops=stop_words_ids)])
self.prompt_constructor = mmengine.registry.build_from_cfg(
prompt_constructor, MM_MODELS)
if post_processor is not None:
self.post_processor = mmengine.registry.build_from_cfg(
post_processor, MM_MODELS)
self.do_sample = do_sample
self.max_length = max_length
self.is_caption_task = is_caption_task
def forward(self, batch):
if self.mode == 'generation':
return self.generate(batch)
elif self.mode == 'loss':
return self.loss(batch)
else:
raise RuntimeError(f'Invalid mode "{self.mode}".')
def encode_img(self, image):
device = image.device
with self.maybe_autocast():
if image.dim() == 5:
inputs_llama, atts_llama = [], []
for j in range(image.size(2)):
this_frame = image[:, :, j, :, :]
frame_embeds = self.ln_vision(
self.visual_encoder(this_frame))
frame_atts = torch.ones(frame_embeds.size()[:-1],
dtype=torch.long).to(image.device)
query_tokens = self.query_tokens.expand(
frame_embeds.shape[0], -1, -1)
frame_query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=frame_embeds,
encoder_attention_mask=frame_atts,
return_dict=True,
)
frame_inputs_llama = self.llama_proj(
frame_query_output.last_hidden_state[:, :query_tokens.
size(1), :])
frame_atts_llama = torch.ones(
frame_inputs_llama.size()[:-1],
dtype=torch.long).to(image.device)
inputs_llama.append(frame_inputs_llama)
atts_llama.append(frame_atts_llama)
inputs_llama = torch.cat(inputs_llama, dim=1)
atts_llama = torch.cat(atts_llama, dim=1)
else:
image_embeds = self.ln_vision(
self.visual_encoder(image)).to(device)
image_atts = torch.ones(image_embeds.size()[:-1],
dtype=torch.long).to(device)
query_tokens = self.query_tokens.expand(
image_embeds.shape[0], -1, -1)
query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
inputs_llama = self.llama_proj(query_output.last_hidden_state)
atts_llama = torch.ones(inputs_llama.size()[:-1],
dtype=torch.long).to(image.device)
return inputs_llama, atts_llama
def pack_inputs(self, batch):
images = [image.unsqueeze(0) for image in batch['inputs']]
data_samples = [data_sample for data_sample in batch['data_samples']]
images = torch.cat(images, dim=0).to(get_device())
inputs = {'image': images, 'data_samples': data_samples}
return inputs
def generate(self, batch):
inputs = self.pack_inputs(batch)
inputs = self.prompt_constructor(inputs)
image = inputs['image']
prompt = inputs['prompt']
data_samples = inputs['data_samples']
# The main process of generation
img_embeds, _ = self.encode_img(image)
prompt_segs = prompt.split('<ImageHere>')
prompt_seg_tokens = [
self.llama_tokenizer(seg,
return_tensors='pt',
add_special_tokens=i == 0).
to(self.llama_model.model.embed_tokens.weight.device).input_ids
for i, seg in enumerate(prompt_segs)
]
prompt_seg_embs = [
self.llama_model.model.embed_tokens(seg)
for seg in prompt_seg_tokens
]
prompt_seg_embs = [prompt_seg_embs[0], img_embeds, prompt_seg_embs[1]]
prompt_embs = torch.cat(prompt_seg_embs, dim=1)
# generate output
outputs = self.llama_model.generate(
inputs_embeds=prompt_embs,
max_length=self.max_length,
num_beams=5,
do_sample=self.do_sample,
min_length=1,
top_p=0.9,
repetition_penalty=1.0,
length_penalty=-1.0,
temperature=1.0,
stopping_criteria=self.stopping_criteria,
num_return_sequences=1)
for i, data_sample in enumerate(data_samples):
output_token = outputs[i]
output_text = self.post_processor(output_token,
self.llama_tokenizer)
if self.is_caption_task:
data_sample.pred_caption = output_text
else:
data_sample.pred_answer = output_text
data_samples[i] = data_sample
return data_samples
def loss(self, batch):
inputs = self.pack_inputs(batch)
inputs = self.prompt_constructor(inputs)
image = inputs['image']
batch_size = image.size(0)
prompt = inputs['prompt']
data_samples = inputs['data_samples']
choices = data_samples[0].choices
with torch.no_grad():
img_embeds, atts_img = self.encode_img(image)
img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img,
prompt)
self.llama_tokenizer.padding_side = 'right'
n_cands = len(choices)
losses = []
for n in range(self.n_segments):
seg_len = n_cands // self.n_segments
if n == (self.n_segments - 1):
seg_len = n_cands - seg_len * (self.n_segments - 1)
to_regress_tokens = self.llama_tokenizer(
choices,
return_tensors='pt',
padding='longest',
truncation=True,
max_length=self.max_txt_len,
add_special_tokens=False).to(image.device)
targets = to_regress_tokens.input_ids.masked_fill(
to_regress_tokens.input_ids ==
self.llama_tokenizer.pad_token_id, -100)
empty_targets = (
torch.ones([atts_img.shape[0], atts_img.shape[1] + 1],
dtype=torch.long).to(image.device).fill_(
-100) # plus one for bos
)
empty_targets = empty_targets.repeat_interleave(seg_len, dim=0)
targets = torch.cat([empty_targets, targets], dim=1)
bos = torch.ones([batch_size, 1],
dtype=to_regress_tokens.input_ids.dtype,
device=to_regress_tokens.input_ids.device
) * self.llama_tokenizer.bos_token_id
bos_embeds = self.llama_model.model.embed_tokens(bos)
bos_embeds = bos_embeds.repeat_interleave(seg_len, dim=0)
img_embeds = img_embeds.repeat_interleave(seg_len, dim=0)
atts_bos = atts_img[:, :1]
atts_bos = atts_bos.repeat_interleave(seg_len, dim=0)
atts_img = atts_img.repeat_interleave(seg_len, dim=0)
to_regress_embeds = self.llama_model.model.embed_tokens(
to_regress_tokens.input_ids)
inputs_embeds = torch.cat(
[bos_embeds, img_embeds, to_regress_embeds], dim=1)
attention_mask = torch.cat(
[atts_bos, atts_img, to_regress_tokens.attention_mask],
dim=1)
with self.maybe_autocast():
outputs = self.llama_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
return_dict=True,
labels=targets,
reduction='none',
)
loss = outputs.loss
loss = loss.view(targets.size(0), -1).sum(1)
loss = loss.reshape(batch_size, seg_len)
losses.append(loss)
# losses of 4 choices
losses = torch.cat(losses, dim=-1)[0]
for i, data_sample in enumerate(data_samples):
data_sample.losses = losses
data_samples[i] = data_sample
return data_samples
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