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run_offline_hgt_tokenizer_single.py
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run_offline_hgt_tokenizer_single.py
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# 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.
from typing import List, Optional, Tuple, Union
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModelForCausalLM, \
LlamaConfig, LlamaModel, LlamaForCausalLM, \
CLIPVisionModel, CLIPImageProcessor
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from higpt.model.graph_layers import MPNN, GNN, CLIP, graph_transformer
from higpt.model.meta_hgt import MetaHGTConvCfg, MetaHGTConv
from higpt.model.heteclip_models import Transformer, LayerNorm, CLIPTextCfg
from torch_geometric.data import Data
import json
import os.path as osp
import glob
from tqdm import tqdm
from lightning.pytorch import LightningModule, Trainer, seed_everything
import re
import os
import argparse
from pathlib import Path
DEFAULT_GRAPH_TOKEN = "<graph>"
DEFAULT_GRAPH_PATCH_TOKEN = "<g_patch>"
DEFAULT_G_START_TOKEN = "<g_start>"
DEFAULT_G_END_TOKEN = "<g_end>"
class HeteroLlamaConfig(LlamaConfig):
model_type = "HeteroLlama"
device = 'cuda:0'
def load_metahgt_pretrained(model_name, pretrain_model_path):
# load conig json
assert osp.exists(osp.join(pretrain_model_path, 'graph_config.json')), 'graph_config.json missing'
with open(osp.join(pretrain_model_path, 'graph_config.json'), 'r') as f:
graph_config_dict = json.load(f)
graph_cfg = MetaHGTConvCfg(**graph_config_dict)
assert osp.exists(osp.join(pretrain_model_path, 'text_config.json')), 'text_config.json missing'
with open(osp.join(pretrain_model_path, 'text_config.json'), 'r') as f:
text_config_dict = json.load(f)
text_cfg = CLIPTextCfg(**text_config_dict)
assert model_name == MetaHGTConv
model = model_name(in_channels = graph_cfg.in_channels,
out_channels = graph_cfg.out_channels,
heads = graph_cfg.heads,
dynamic = graph_cfg.dynamic,
text_cfg = text_cfg,)
pkl_files = glob.glob(osp.join(pretrain_model_path, '*.ckpt'))
state_dict = torch.load(pkl_files[0], map_location = 'cpu')['state_dict']
print('loading graph pre train model ...')
gnn_state_dict = {}
for key, value in state_dict.items():
if key.startswith('model.graph_encoder'):
new_key = key.split('model.graph_encoder.')[1]
gnn_state_dict[new_key] = value
model.load_state_dict(gnn_state_dict, strict=False)
return model
def check_offline_hgt():
parser = argparse.ArgumentParser(description='check data')
parser.add_argument('--pretrained_gnn_path', default='/root/paddlejob/workspace/env_run/output/HeteGPT/MetaHGT_imdb_dblp_epoch5', type=str)
parser.add_argument('--ann_path', default='/root/paddlejob/workspace/env_run/output/HetBaseline/data/DBLP/instruct_ds/ann/DBLP_test_std_0_1000_seed_0.json', type=str)
parser.add_argument('--graph_path', default='/root/paddlejob/workspace/env_run/output/HetBaseline/data/DBLP/instruct_ds/graph_data/test', type=str)
parser.add_argument('--data_type', default='dblp', type=str)
args = parser.parse_args()
hgnn_name = args.pretrained_gnn_path.split('/')[-1]
graph_root = Path(args.ann_path).parent.parent.parent
hgt = load_metahgt_pretrained(MetaHGTConv, args.pretrained_gnn_path)
hgt = hgt.to(device)
node_feas_dict = torch.load(f'/root/paddlejob/workspace/env_run/output/HeteGPT/hetegpt/model/meta_hgt/meta_dict/{args.data_type}/node_type.pt')
for k, v in node_feas_dict.items():
node_feas_dict[k] = v.to(device)
edge_feas_dict = torch.load(f'/root/paddlejob/workspace/env_run/output/HeteGPT/hetegpt/model/meta_hgt/meta_dict/{args.data_type}/edge_type.pt')
for k, v in edge_feas_dict.items():
edge_feas_dict[k] = v.to(device)
with open(args.ann_path, 'r', encoding='utf-8') as f:
data_list = json.load(f)
processed_data_list = []
count_cnt = 0
for data_item in tqdm(data_list):
# if count_cnt > 0:
# break
graph_path = osp.join(args.graph_path, data_item['graph']['graph'].split('/')[-1])
graph_dict = torch.load(graph_path)
graph_dict = graph_dict.to(device)
if args.data_type == 'dblp':
new_conf_feas = torch.ones([graph_dict['conference'].num_nodes, 768])
new_conf_feas = new_conf_feas.to(device)
graph_dict['conference'].x = new_conf_feas
with torch.no_grad():
res = hgt(x_dict = graph_dict.x_dict,
edge_index_dict = graph_dict.edge_index_dict, # Support both.
node_type_feas_dict = node_feas_dict,
edge_type_feas_dict = edge_feas_dict)
for k, v in res.items():
res[k] = v.cpu()
if torch.any(torch.isnan(res[k])):
print(k, res[k])
raise ValueError
processed_graph_path = re.sub('graph_data', f'graph_data_processed_{hgnn_name}', data_item['graph']['graph'])
processed_data_item = data_item.copy()
processed_data_item['graph']['graph'] = processed_graph_path
processed_data_list.append(processed_data_item.copy())
save_path = osp.join(graph_root, processed_graph_path)
if osp.exists(osp.dirname(save_path)) is False:
os.makedirs(osp.dirname(save_path), exist_ok=True)
# print(graph_dict.x_dict['author'])
# for k, v in graph_dict.items():
# graph_dict.x_dict[k] = res[k]
graph_dict = graph_dict.to('cpu')
graph_dict.x_dict = res
# print(graph_dict.x_dict['author'])
# print(res['author'])
torch.save(graph_dict, save_path)
count_cnt += 1
# time.sleep(1)
processed_json_file = re.sub('ann', f'ann_processed_{hgnn_name}', args.ann_path)
if osp.exists(osp.dirname(processed_json_file)) is False:
os.makedirs(osp.dirname(processed_json_file), exist_ok=True)
with open(processed_json_file, 'w', encoding='utf-8') as f:
json.dump(processed_data_list, f, ensure_ascii=False, indent=4)
if __name__ == '__main__':
seed_everything(42)
check_offline_hgt()