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config.py
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import numpy as np
import random
import torch
import argparse
RAW_DATASET_ROOT_FOLDER = 'data'
EXPERIMENT_ROOT = 'experiments'
STATE_DICT_KEY = 'model_state_dict'
OPTIMIZER_STATE_DICT_KEY = 'optimizer_state_dict'
PROJECT_NAME = 'llmrec'
def set_template(args):
if args.dataset_code == None:
print('******************** Dataset Selection ********************')
dataset_code = {'1': 'ml-100k', 'b': 'beauty', 'g': 'games'}
args.dataset_code = dataset_code[input('Input 1 for ml-100k, b for beauty and g for games: ')]
if args.dataset_code == 'ml-100k':
args.bert_max_len = 200
else:
args.bert_max_len = 50
if 'llm' in args.model_code:
batch = 16 if args.dataset_code == 'ml-100k' else 12
args.lora_micro_batch_size = batch
else:
batch = 16 if args.dataset_code == 'ml-100k' else 64
args.train_batch_size = batch
args.val_batch_size = batch
args.test_batch_size = batch
if torch.cuda.is_available(): args.device = 'cuda'
else: args.device = 'cpu'
args.optimizer = 'AdamW'
args.lr = 0.001
args.weight_decay = 0.01
args.enable_lr_schedule = False
args.decay_step = 10000
args.gamma = 1.
args.enable_lr_warmup = False
args.warmup_steps = 100
args.metric_ks = [1, 5, 10, 20, 50]
args.rerank_metric_ks = [1, 5, 10]
args.best_metric = 'Recall@10'
args.rerank_best_metric = 'NDCG@10'
args.bert_num_blocks = 2
args.bert_num_heads = 2
args.bert_head_size = None
parser = argparse.ArgumentParser()
################
# Dataset
################
parser.add_argument('--dataset_code', type=str, default=None)
parser.add_argument('--min_rating', type=int, default=0)
parser.add_argument('--min_uc', type=int, default=5)
parser.add_argument('--min_sc', type=int, default=5)
parser.add_argument('--seed', type=int, default=42)
################
# Dataloader
################
parser.add_argument('--train_batch_size', type=int, default=64)
parser.add_argument('--val_batch_size', type=int, default=64)
parser.add_argument('--test_batch_size', type=int, default=64)
parser.add_argument('--num_workers', type=int, default=8)
parser.add_argument('--sliding_window_size', type=float, default=1.0)
parser.add_argument('--negative_sample_size', type=int, default=10)
################
# Trainer
################
# optimization #
parser.add_argument('--device', type=str, default='cuda', choices=['cpu', 'cuda'])
parser.add_argument('--num_epochs', type=int, default=500)
parser.add_argument('--optimizer', type=str, default='AdamW', choices=['AdamW', 'Adam'])
parser.add_argument('--weight_decay', type=float, default=None)
parser.add_argument('--adam_epsilon', type=float, default=1e-9)
parser.add_argument('--momentum', type=float, default=None)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--max_grad_norm', type=float, default=5.0)
parser.add_argument('--enable_lr_schedule', type=bool, default=True)
parser.add_argument('--decay_step', type=int, default=10000)
parser.add_argument('--gamma', type=float, default=1)
parser.add_argument('--enable_lr_warmup', type=bool, default=True)
parser.add_argument('--warmup_steps', type=int, default=100)
# evaluation #
parser.add_argument('--val_strategy', type=str, default='iteration', choices=['epoch', 'iteration'])
parser.add_argument('--val_iterations', type=int, default=500) # only for iteration val_strategy
parser.add_argument('--early_stopping', type=bool, default=True)
parser.add_argument('--early_stopping_patience', type=int, default=20)
parser.add_argument('--metric_ks', nargs='+', type=int, default=[1, 5, 10, 20, 50])
parser.add_argument('--rerank_metric_ks', nargs='+', type=int, default=[1, 5, 10])
parser.add_argument('--best_metric', type=str, default='Recall@10')
parser.add_argument('--rerank_best_metric', type=str, default='NDCG@10')
parser.add_argument('--use_wandb', type=bool, default=False)
################
# Retriever Model
################
parser.add_argument('--model_code', type=str, default=None)
parser.add_argument('--bert_max_len', type=int, default=50)
parser.add_argument('--bert_hidden_units', type=int, default=64)
parser.add_argument('--bert_num_blocks', type=int, default=2)
parser.add_argument('--bert_num_heads', type=int, default=2)
parser.add_argument('--bert_head_size', type=int, default=32)
parser.add_argument('--bert_dropout', type=float, default=0.2)
parser.add_argument('--bert_attn_dropout', type=float, default=0.2)
parser.add_argument('--bert_mask_prob', type=float, default=0.25)
################
# LLM Model
################
parser.add_argument('--llm_base_model', type=str, default='meta-llama/Llama-2-7b-hf')
parser.add_argument('--llm_base_tokenizer', type=str, default='meta-llama/Llama-2-7b-hf')
parser.add_argument('--llm_max_title_len', type=int, default=32)
parser.add_argument('--llm_max_text_len', type=int, default=1536)
parser.add_argument('--llm_max_history', type=int, default=20)
parser.add_argument('--llm_train_on_inputs', type=bool, default=False)
parser.add_argument('--llm_negative_sample_size', type=int, default=19) # 19 negative & 1 positive
parser.add_argument('--llm_system_template', type=str, # instruction
default="Given user history in chronological order, recommend an item from the candidate pool with its index letter.")
parser.add_argument('--llm_input_template', type=str, \
default='User history: {}; \n Candidate pool: {}')
parser.add_argument('--llm_load_in_4bit', type=bool, default=True)
parser.add_argument('--llm_retrieved_path', type=str, default=None)
parser.add_argument('--llm_cache_dir', type=str, default=None)
################
# Lora
################
parser.add_argument('--lora_r', type=int, default=8)
parser.add_argument('--lora_alpha', type=int, default=32)
parser.add_argument('--lora_dropout', type=float, default=0.05)
parser.add_argument('--lora_target_modules', type=list, default=['q_proj', 'v_proj'])
parser.add_argument('--lora_num_epochs', type=int, default=1)
parser.add_argument('--lora_val_iterations', type=int, default=100)
parser.add_argument('--lora_early_stopping_patience', type=int, default=20)
parser.add_argument('--lora_lr', type=float, default=1e-4)
parser.add_argument('--lora_micro_batch_size', type=int, default=16)
################
args = parser.parse_args()