InfMLLM_7B / modeling_infmllm.py
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import logging
import torch
import torch.nn as nn
from contextlib import suppress
from einops import rearrange
from transformers import LlamaForCausalLM, LlamaTokenizer, PreTrainedModel
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from .eva_vit import create_eva_vit_g
from .pooler import Pooler
def get_autocast(precision, cache_enabled=True):
if precision == "amp":
return lambda: torch.cuda.amp.autocast(cache_enabled=cache_enabled)
elif precision == "amp_bfloat16" or precision == "amp_bf16" or precision == 'bf16':
return lambda: torch.cuda.amp.autocast(dtype=torch.bfloat16, cache_enabled=cache_enabled)
elif precision == 'fp16':
return lambda: torch.cuda.amp.autocast(dtype=torch.float16, cache_enabled=cache_enabled)
elif precision == 'fp32':
return suppress
else:
raise ValueError('not supported precision: {}'.format(precision))
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 init_vision_encoder(model_name,
img_size,
drop_path_rate,
use_grad_checkpoint):
if model_name == "eva_clip_g":
visual_encoder = create_eva_vit_g(
img_size, drop_path_rate, use_grad_checkpoint)
else:
raise ValueError()
ln_vision = LayerNorm(visual_encoder.num_features)
return visual_encoder, ln_vision
class ImageProcessor:
def __init__(self, image_size=364, mean=None, std=None):
if mean is None:
self.mean = mean = (0.48145466, 0.4578275, 0.40821073)
if std is None:
self.std = std = (0.26862954, 0.26130258, 0.27577711)
self.normalize = transforms.Normalize(mean, std)
self.transform = transforms.Compose(
[
transforms.Resize(
(image_size, image_size), interpolation=InterpolationMode.BICUBIC
),
transforms.ToTensor(),
self.normalize,
]
)
def __call__(self, item):
return self.transform(item)
class InfMLLM(PreTrainedModel):
def __init__(self, config):
super().__init__(config)
vit_model = config.vit_model
img_size = config.image_size
lm_model = config.lm_model
lm_tokenizer = config.lm_tokenizer
precision = config.precision
pool_out_size = config.pool_out_size
self.img_processor = ImageProcessor(image_size=img_size)
self.visual_encoder, self.ln_vision = init_vision_encoder(
vit_model, img_size, drop_path_rate=0.0, use_grad_checkpoint=False)
self.lm_tokenizer = LlamaTokenizer.from_pretrained(lm_tokenizer, use_fast=False, trust_remote_code=True)
self.lm_tokenizer.pad_token = self.lm_tokenizer.unk_token
self.lm_model = LlamaForCausalLM.from_pretrained(lm_model, trust_remote_code=True, torch_dtype='auto')
self.pooler = Pooler(dim_in=self.visual_encoder.num_features,
dim_out=self.lm_model.config.hidden_size,
pool_out_size=pool_out_size)
self.llama_proj = nn.Identity()
self.precision = precision
self._apply_lemmatizer = config.apply_lemmatizer if hasattr(config, 'apply_lemmatizer') else False
self._lemmatizer = None
def prompt_wrap(self, img_embeds, atts_img, prompts):
assert len(img_embeds) == len(atts_img) == len(prompts)
bos = torch.ones([1, 1], dtype=torch.long, device=img_embeds.device) * self.lm_tokenizer.bos_token_id
bos_embeds = self.lm_model.get_input_embeddings()(bos)
emb_lists = []
image_mask = []
for each_img_embed, each_prompt in zip(img_embeds, prompts):
assert '<ImageHere>' in each_prompt
p_before, p_after = each_prompt.split('<ImageHere>')
p_before_tokens = self.lm_tokenizer(
p_before, return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
p_after_tokens = self.lm_tokenizer(
p_after, return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
p_before_embed = self.lm_model.get_input_embeddings()(p_before_tokens.input_ids.long()) # [1, 6, 4096]
p_after_embed = self.lm_model.get_input_embeddings()(p_after_tokens.input_ids.long()) # [1, 17, 4096]
# add 1 bos
wrapped_emb = torch.cat([bos_embeds, p_before_embed, each_img_embed[None], p_after_embed], dim=1) # [1, 87, 4096]
emb_lists.append(wrapped_emb)
image_mask.append( torch.tensor([0] * wrapped_emb.size(1)) )
image_mask[-1][range(bos_embeds.size(1) + p_before_embed.size(1),
bos_embeds.size(1) + p_before_embed.size(1) + len(each_img_embed))] = 1
assert image_mask[-1].sum() == each_img_embed.size(0)
emb_lens = [emb.shape[1] for emb in emb_lists]
pad_emb = self.lm_model.get_input_embeddings()(torch.tensor(self.lm_tokenizer.pad_token_id, device=img_embeds.device)) # [4096]
assert not self.training
# during inference mode, padding on the left
wrapped_embs = pad_emb.expand(len(emb_lens), max(emb_lens), -1).clone() # [12, 87, 4096]
wrapped_atts = torch.zeros([len(emb_lens), max(emb_lens)], dtype=torch.int, device=img_embeds.device) # [12, 87]
wrapped_image_masks = torch.zeros([len(emb_lens), max(emb_lens)], dtype=torch.int, device=img_embeds.device) # [12, 87]
for i, emb in enumerate(emb_lists):
wrapped_embs[i, -emb_lens[i]:] = emb
wrapped_atts[i, -emb_lens[i]:] = 1
wrapped_image_masks[i, -emb_lens[i]:] = image_mask[i]
return wrapped_embs, wrapped_atts, wrapped_image_masks
@torch.no_grad()
def forward_image_feature(self, image):
autocast = get_autocast(self.precision, cache_enabled=True)
with autocast():
if image.ndim == 4:
image = image.unsqueeze(1).unsqueeze(1)
assert image.ndim == 6
b, t, f = image.shape[:3]
assert t == 1 and f == 1
image = rearrange(image, "b t f c h w -> (b t f) c h w")
image_embeds = self.ln_vision(self.visual_encoder(image))
image_embeds = rearrange(image_embeds, "(b t f) L D -> b t f L D", t=t, f=f)
query_output= self.pooler(image_embeds)
query_output = query_output.squeeze(1)
embeds_img = self.llama_proj(query_output)
return embeds_img
@torch.no_grad()
def generate(
self,
samples,
use_nucleus_sampling=False,
num_beams=5,
max_length=30,
min_length=1,
top_p=0.9,
repetition_penalty=1.0,
length_penalty=1.0,
num_captions=1,
temperature=1,
):
autocast = get_autocast(self.precision, cache_enabled=True)
with autocast():
image = samples["image"]
embeds_img = self.forward_image_feature(image)
atts_img = torch.ones(embeds_img.size()[:-1], dtype=torch.long).to(image.device)
prompts = samples["prompts"]
assert isinstance(prompts, (tuple, list))
# Convert prompts to embeds and, repalce "<ImageHere>" with img_embeds
inputs_embeds, attention_mask, masks_img = self.prompt_wrap(embeds_img, atts_img, prompts)
model_args = dict(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
do_sample=use_nucleus_sampling,
top_p=top_p,
temperature=temperature,
num_beams=num_beams,
max_length=max_length,
min_length=min_length,
eos_token_id=self.lm_tokenizer.eos_token_id,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
num_return_sequences=num_captions,
)
outputs = self.lm_model.generate(**model_args)
output_text = self.lm_tokenizer.batch_decode(
outputs, skip_special_tokens=True
)
output_text = [text.strip() for text in output_text]
return output_text
@torch.no_grad()
def predict_answers(
self,
samples,
num_beams=5,
max_len=10,
min_len=1,
length_penalty=0,
):
# VQA tasks
autocast = get_autocast(self.precision, cache_enabled=True)
with autocast():
image = samples["image"]
embeds_img = self.forward_image_feature(image)
atts_img = torch.ones(embeds_img.size()[:-1], dtype=torch.long).to(image.device)
prompts = samples["prompts"]
assert isinstance(prompts, (tuple, list))
inputs_embeds, attention_mask, masks_img = self.prompt_wrap(embeds_img, atts_img, prompts)
model_args = dict(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
do_sample=False,
num_beams=num_beams,
max_new_tokens=max_len,
min_length=min_len,
eos_token_id=self.lm_tokenizer.eos_token_id,
length_penalty=length_penalty
)
outputs = self.lm_model.generate(**model_args)
output_text = self.lm_tokenizer.batch_decode(
outputs, skip_special_tokens=True
)
output_text = [text.strip() for text in output_text]
if self._apply_lemmatizer or ("apply_lemmatizer" in samples.keys() and samples["apply_lemmatizer"]):
output_text = self._lemmatize(output_text)
return output_text
def _lemmatize(self, answers):
def apply(answer):
doc = self.lemmatizer(answer)
words = []
for token in doc:
if token.pos_ in ["NOUN", "VERB"]:
words.append(token.lemma_)
else:
words.append(token.text)
answer = " ".join(words)
return answer
return [apply(answer) for answer in answers]
@property
def lemmatizer(self):
if self._lemmatizer is None:
try:
import spacy
self._lemmatizer = spacy.load("en_core_web_sm")
except ImportError:
logging.error(
"""
Please install spacy and en_core_web_sm model to apply lemmatization.
python -m spacy download en_core_web_sm
OR
import spacy.cli
spacy.cli.download("en_core_web_sm")
"""
)
exit(1)
return self._lemmatizer