337 lines
14 KiB
Python
337 lines
14 KiB
Python
#
|
|
# Copyright 2016 The BigDL Authors.
|
|
#
|
|
# 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.
|
|
#
|
|
# Some parts of this file is adapted from
|
|
# https://github.com/haotian-liu/LLaVA/blob/v1.1.1/llava/model/builder.py
|
|
# and
|
|
# https://github.com/haotian-liu/LLaVA/blob/v1.1.1/llava/serve/cli.py
|
|
#
|
|
# Copyright 2023 Haotian Liu
|
|
#
|
|
# 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.
|
|
|
|
import argparse
|
|
import torch
|
|
import time
|
|
|
|
from transformers import AutoModelForCausalLM
|
|
from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM
|
|
from transformers import AutoTokenizer
|
|
|
|
from llava.constants import (
|
|
DEFAULT_IMAGE_PATCH_TOKEN,
|
|
IMAGE_TOKEN_INDEX,
|
|
DEFAULT_IMAGE_TOKEN,
|
|
DEFAULT_IM_START_TOKEN,
|
|
DEFAULT_IM_END_TOKEN
|
|
)
|
|
from llava.conversation import conv_templates, SeparatorStyle
|
|
from llava.utils import disable_torch_init
|
|
from llava.mm_utils import (
|
|
process_images,
|
|
tokenizer_image_token,
|
|
get_model_name_from_path,
|
|
KeywordsStoppingCriteria
|
|
)
|
|
|
|
from bigdl.llm import optimize_model
|
|
|
|
# Load the pretrained model.
|
|
# Adapted from llava.model.builder.load_pretrained_model.
|
|
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False,
|
|
device_map="auto", device="cpu"):
|
|
kwargs = {"device_map": device_map}
|
|
kwargs['torch_dtype'] = torch.float32
|
|
|
|
if 'llava' in model_name.lower():
|
|
# Load LLaVA model
|
|
if 'lora' in model_name.lower() and model_base is None:
|
|
warnings.warn('There is `lora` in model name but no `model_base` is provided.'
|
|
'If you are loading a LoRA model, please provide the `model_base` argument'
|
|
'. Detailed instruction:'
|
|
'https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.')
|
|
if 'lora' in model_name.lower() and model_base is not None:
|
|
lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_base, use_fast=False)
|
|
print('Loading LLaVA from base model...')
|
|
model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
|
|
config=lora_cfg_pretrained, **kwargs)
|
|
token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
|
|
if model.lm_head.weight.shape[0] != token_num:
|
|
model.lm_head.weight = torch.nn.Parameter(torch.empty(
|
|
token_num, tokem_dim, device=model.device, dtype=model.dtype))
|
|
model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(
|
|
token_num, tokem_dim, device=model.device, dtype=model.dtype))
|
|
|
|
print('Loading additional LLaVA weights...')
|
|
if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
|
|
non_lora_trainables = torch.load(os.path.join(model_path,
|
|
'non_lora_trainables.bin'),
|
|
map_location='cpu')
|
|
else:
|
|
# this is probably from HF Hub
|
|
from huggingface_hub import hf_hub_download
|
|
|
|
def load_from_hf(repo_id, filename, subfolder=None):
|
|
cache_file = hf_hub_download(
|
|
repo_id=repo_id,
|
|
filename=filename,
|
|
subfolder=subfolder)
|
|
return torch.load(cache_file, map_location='cpu')
|
|
non_lora_trainables = load_from_hf(
|
|
model_path, 'non_lora_trainables.bin')
|
|
non_lora_trainables = {(k[11:] if k.startswith(
|
|
'base_model.') else k): v for k, v in non_lora_trainables.items()}
|
|
if any(k.startswith('model.model.') for k in non_lora_trainables):
|
|
non_lora_trainables = {(k[6:] if k.startswith(
|
|
'model.') else k): v for k, v in non_lora_trainables.items()}
|
|
model.load_state_dict(non_lora_trainables, strict=False)
|
|
|
|
from peft import PeftModel
|
|
print('Loading LoRA weights...')
|
|
model = PeftModel.from_pretrained(model, model_path)
|
|
print('Merging LoRA weights...')
|
|
model = model.merge_and_unload()
|
|
print('Model is loaded...')
|
|
elif model_base is not None:
|
|
# this may be mm projector only
|
|
print('Loading LLaVA from base model...')
|
|
if 'mpt' in model_name.lower():
|
|
if not os.path.isfile(os.path.join(model_path, 'configuration_mpt.py')):
|
|
shutil.copyfile(os.path.join(model_base, 'configuration_mpt.py'), os.path.join(
|
|
model_path, 'configuration_mpt.py'))
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_base, use_fast=True)
|
|
cfg_pretrained = AutoConfig.from_pretrained(
|
|
model_path, trust_remote_code=True)
|
|
model = LlavaMPTForCausalLM.from_pretrained(
|
|
model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
|
|
else:
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_base, use_fast=False)
|
|
cfg_pretrained = AutoConfig.from_pretrained(model_path)
|
|
model = LlavaLlamaForCausalLM.from_pretrained(
|
|
model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
|
|
|
|
mm_projector_weights = torch.load(os.path.join(
|
|
model_path, 'mm_projector.bin'), map_location='cpu')
|
|
mm_projector_weights = {k: v.to(torch.float32)
|
|
for k, v in mm_projector_weights.items()}
|
|
model.load_state_dict(mm_projector_weights, strict=False)
|
|
else:
|
|
if 'mpt' in model_name.lower():
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_path, use_fast=True)
|
|
model = LlavaMPTForCausalLM.from_pretrained(
|
|
model_path, low_cpu_mem_usage=True, **kwargs)
|
|
else:
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_path, use_fast=False)
|
|
model = LlavaLlamaForCausalLM.from_pretrained(
|
|
model_path, low_cpu_mem_usage=True, **kwargs)
|
|
else:
|
|
# Load language model
|
|
if model_base is not None:
|
|
# PEFT model
|
|
from peft import PeftModel
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_base, use_fast=False)
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
model_base, torch_dtype=torch.float32, low_cpu_mem_usage=True, device_map="auto")
|
|
print(f"Loading LoRA weights from {model_path}")
|
|
model = PeftModel.from_pretrained(model, model_path)
|
|
print(f"Merging weights")
|
|
model = model.merge_and_unload()
|
|
print('Convert to FP32...')
|
|
model.to(torch.float32)
|
|
else:
|
|
use_fast = False
|
|
if 'mpt' in model_name.lower():
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_path, use_fast=True)
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs)
|
|
else:
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_path, use_fast=False)
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
model_path, low_cpu_mem_usage=True, **kwargs)
|
|
|
|
image_processor = None
|
|
|
|
if 'llava' in model_name.lower():
|
|
mm_use_im_start_end = getattr(
|
|
model.config, "mm_use_im_start_end", False)
|
|
mm_use_im_patch_token = getattr(
|
|
model.config, "mm_use_im_patch_token", True)
|
|
if mm_use_im_patch_token:
|
|
tokenizer.add_tokens(
|
|
[DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
|
|
if mm_use_im_start_end:
|
|
tokenizer.add_tokens(
|
|
[DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
|
|
model.resize_token_embeddings(len(tokenizer))
|
|
|
|
vision_tower = model.get_vision_tower()
|
|
if not vision_tower.is_loaded:
|
|
vision_tower.load_model()
|
|
vision_tower.to(device=device, dtype=torch.float32)
|
|
image_processor = vision_tower.image_processor
|
|
|
|
if hasattr(model.config, "max_sequence_length"):
|
|
context_len = model.config.max_sequence_length
|
|
else:
|
|
context_len = 2048
|
|
|
|
return tokenizer, model, image_processor, context_len
|
|
|
|
# Initialize conversation from templates and get conversation roles.
|
|
def get_conv_and_role(model_name):
|
|
if 'llama-2' in model_name.lower():
|
|
conv_mode = "llava_llama_2"
|
|
elif "v1" in model_name.lower():
|
|
conv_mode = "llava_v1"
|
|
elif "mpt" in model_name.lower():
|
|
conv_mode = "mpt"
|
|
else:
|
|
conv_mode = "llava_v0"
|
|
|
|
conv = conv_templates[conv_mode].copy()
|
|
if "mpt" in model_name.lower():
|
|
roles = ('user', 'assistant')
|
|
else:
|
|
roles = conv.roles
|
|
|
|
return conv, roles
|
|
|
|
# Load image from a url or path.
|
|
def load_image(image_file):
|
|
import requests
|
|
from PIL import Image
|
|
from io import BytesIO
|
|
|
|
if image_file.startswith('http://') or image_file.startswith('https://'):
|
|
response = requests.get(image_file)
|
|
image = Image.open(BytesIO(response.content)).convert('RGB')
|
|
else:
|
|
image = Image.open(image_file).convert('RGB')
|
|
return image
|
|
|
|
def generate_image_tensor(image_file):
|
|
image = load_image(image_file)
|
|
model_cfg = {"image_aspect_ratio": 'pad'}
|
|
image_tensor = process_images([image], image_processor, model_cfg)
|
|
return image_tensor
|
|
|
|
# Generate input prompt with user input.
|
|
def get_prompt(mm_use_im_start_end, first_round, conv, user_input):
|
|
if first_round:
|
|
# first message
|
|
if mm_use_im_start_end:
|
|
user_input = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + \
|
|
DEFAULT_IM_END_TOKEN + '\n' + user_input
|
|
else:
|
|
user_input = DEFAULT_IMAGE_TOKEN + '\n' + user_input
|
|
conv.append_message(conv.roles[0], user_input)
|
|
else:
|
|
# later messages
|
|
conv.append_message(conv.roles[0], user_input)
|
|
conv.append_message(conv.roles[1], None)
|
|
return conv.get_prompt()
|
|
|
|
def get_stopping_criteria(conv, tokenizer, input_ids):
|
|
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
|
|
keywords = [stop_str]
|
|
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
|
|
return stopping_criteria
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(
|
|
description='Predict Tokens using `generate()` API for LLaVA model')
|
|
parser.add_argument('--repo-id-or-model-path', type=str, default="liuhaotian/llava-v1.5-13b",
|
|
help='The huggingface repo id for the LLaVA model to be downloaded'
|
|
', or the path to the huggingface checkpoint folder')
|
|
parser.add_argument('--image-path-or-url', type=str,
|
|
required=True, help='Image path or url for the input image that the chat will focus on')
|
|
parser.add_argument('--n-predict', type=int, default=512,
|
|
help='Max tokens to predict')
|
|
|
|
args = parser.parse_args()
|
|
model_path = args.repo_id_or_model_path
|
|
model_name = get_model_name_from_path(model_path)
|
|
|
|
# Disable the redundant torch default initialization to accelerate model creation.
|
|
disable_torch_init()
|
|
|
|
# Load model
|
|
tokenizer, model, image_processor, _ = load_pretrained_model(model_path=model_path,
|
|
model_base=None,
|
|
model_name=model_name)
|
|
|
|
# With only one line to enable BigDL-LLM optimization on model
|
|
model = optimize_model(model)
|
|
|
|
# Generate image tensor
|
|
image_tensor = generate_image_tensor(args.image_path_or_url)
|
|
|
|
# Get conversation template and roles
|
|
conv, roles = get_conv_and_role(model_name)
|
|
|
|
first_round = True
|
|
while True:
|
|
try:
|
|
user_input = input(f"{roles[0]}: ")
|
|
except EOFError:
|
|
user_input = ""
|
|
if not user_input:
|
|
print("exit...")
|
|
break
|
|
|
|
prompt = get_prompt(model.config.mm_use_im_start_end, first_round, conv, user_input)
|
|
first_round = False
|
|
input_ids = tokenizer_image_token(
|
|
prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0)
|
|
stopping_criteria = get_stopping_criteria(conv, tokenizer, input_ids)
|
|
|
|
# Generate predicted tokens
|
|
with torch.inference_mode():
|
|
st = time.time()
|
|
output_ids = model.generate(
|
|
input_ids,
|
|
images=image_tensor,
|
|
do_sample=True,
|
|
max_new_tokens=args.n_predict,
|
|
use_cache=True,
|
|
stopping_criteria=[stopping_criteria])
|
|
end = time.time()
|
|
#print(f'Inference time: {end-st} s')
|
|
|
|
outputs = tokenizer.decode(
|
|
output_ids[0, input_ids.shape[1]:], skip_special_tokens=True).strip()
|
|
conv.messages[-1][-1] = outputs
|
|
print(f"{roles[1]}: ", end="")
|
|
print(outputs)
|