File size: 8,384 Bytes
8a506a6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 |
import argparse
import json
import os
import sys
import torch
import transformers
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel
from peft import PeftModel
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import LlamaForCausalLM, LlamaTokenizer, LlamaConfig
from utils import *
from collator import TestCollator
from prompt import all_prompt
from evaluate import get_topk_results, get_metrics_results
def test_ddp(args):
set_seed(args.seed)
world_size = int(os.environ.get("WORLD_SIZE", 1))
local_rank = int(os.environ.get("LOCAL_RANK") or 0)
torch.cuda.set_device(local_rank)
if local_rank == 0:
print(vars(args))
dist.init_process_group(backend="nccl", world_size=world_size, rank=local_rank)
device_map = {"": local_rank}
device = torch.device("cuda",local_rank)
tokenizer = LlamaTokenizer.from_pretrained(args.ckpt_path)
args.lora=True
if args.lora:
model = LlamaForCausalLM.from_pretrained(
args.base_model,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map=device_map,
)
model.resize_token_embeddings(len(tokenizer))
model = PeftModel.from_pretrained(
model,
args.ckpt_path,
torch_dtype=torch.float16,
device_map=device_map,
)
else:
model = LlamaForCausalLM.from_pretrained(
args.ckpt_path,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map=device_map,
)
# assert model.config.vocab_size == len(tokenizer)
model = DistributedDataParallel(model, device_ids=[local_rank])
if args.test_prompt_ids == "all":
if args.test_task.lower() == "seqrec":
prompt_ids = range(len(all_prompt["seqrec"]))
elif args.test_task.lower() == "itemsearch":
prompt_ids = range(len(all_prompt["itemsearch"]))
elif args.test_task.lower() == "fusionseqrec":
prompt_ids = range(len(all_prompt["fusionseqrec"]))
else:
prompt_ids = [int(_) for _ in args.test_prompt_ids.split(",")]
test_data = load_test_dataset(args)
ddp_sampler = DistributedSampler(test_data, num_replicas=world_size, rank=local_rank, drop_last=True)
test_data = load_test_dataset(args)
collator = TestCollator(args, tokenizer)
all_items = test_data.get_all_items()
prefix_allowed_tokens = test_data.get_prefix_allowed_tokens_fn(tokenizer)
test_loader = DataLoader(test_data, batch_size=args.test_batch_size, collate_fn=collator,
sampler=ddp_sampler, num_workers=2, pin_memory=True)
if local_rank == 0:
print("data num:", len(test_data))
model.eval()
metrics = args.metrics.split(",")
all_prompt_results = []
with torch.no_grad():
for prompt_id in prompt_ids:
if local_rank == 0:
print("Start prompt: ",prompt_id)
test_loader.dataset.set_prompt(prompt_id)
metrics_results = {}
total = 0
for step, batch in enumerate(tqdm(test_loader)):
inputs = batch[0].to(device)
targets = batch[1]
bs = len(targets)
num_beams = args.num_beams
while True:
try:
output = model.module.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_new_tokens=10,
prefix_allowed_tokens_fn=prefix_allowed_tokens,
num_beams=num_beams,
num_return_sequences=num_beams,
output_scores=True,
return_dict_in_generate=True,
early_stopping=True,
)
break
except torch.cuda.OutOfMemoryError as e:
print("Out of memory!")
num_beams = num_beams -1
print("Beam:", num_beams)
except Exception:
raise RuntimeError
output_ids = output["sequences"]
scores = output["sequences_scores"]
output = tokenizer.batch_decode(
output_ids, skip_special_tokens=True
)
topk_res = get_topk_results(output, scores, targets, num_beams,
all_items=all_items if args.filter_items else None)
bs_gather_list = [None for _ in range(world_size)]
dist.all_gather_object(obj=bs, object_list=bs_gather_list)
total += sum(bs_gather_list)
res_gather_list = [None for _ in range(world_size)]
dist.all_gather_object(obj=topk_res, object_list=res_gather_list)
if local_rank == 0:
all_device_topk_res = []
for ga_res in res_gather_list:
all_device_topk_res += ga_res
batch_metrics_res = get_metrics_results(all_device_topk_res, metrics)
for m, res in batch_metrics_res.items():
if m not in metrics_results:
metrics_results[m] = res
else:
metrics_results[m] += res
if (step + 1) % 50 == 0:
temp = {}
for m in metrics_results:
temp[m] = metrics_results[m] / total
print(temp)
dist.barrier()
if local_rank == 0:
for m in metrics_results:
metrics_results[m] = metrics_results[m] / total
all_prompt_results.append(metrics_results)
print("======================================================")
print("Prompt {} results: ".format(prompt_id), metrics_results)
print("======================================================")
print("")
dist.barrier()
dist.barrier()
if local_rank == 0:
mean_results = {}
min_results = {}
max_results = {}
for m in metrics:
all_res = [_[m] for _ in all_prompt_results]
mean_results[m] = sum(all_res)/len(all_res)
min_results[m] = min(all_res)
max_results[m] = max(all_res)
print("======================================================")
print("Mean results: ", mean_results)
print("Min results: ", min_results)
print("Max results: ", max_results)
print("======================================================")
save_data={}
save_data["test_prompt_ids"] = args.test_prompt_ids
save_data["mean_results"] = mean_results
save_data["min_results"] = min_results
save_data["max_results"] = max_results
save_data["all_prompt_results"] = all_prompt_results
with open(args.results_file, "w") as f:
json.dump(save_data, f, indent=4)
print("Save file: ", args.results_file)
import smtplib
from email.mime.text import MIMEText
mail_host = 'smtp.qq.com'
mail_code = 'ouzplpngooqndjcb'
sender = '1849334588@qq.com'
receiver = 'esperanto1949@foxmail.com'
task = '[v67: evaluate lcrec]'
message = MIMEText('Task {task} Finished'.format(task = task), 'plain', 'utf-8')
message['Subject'] = 'Auto Email'
message['From'] = sender
message['To'] = receiver
server = smtplib.SMTP_SSL("smtp.qq.com", 465)
server.login(sender, mail_code)
server.sendmail(sender, receiver, message.as_string())
server.quit()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="LLMRec_test")
parser = parse_global_args(parser)
parser = parse_dataset_args(parser)
parser = parse_test_args(parser)
args = parser.parse_args()
test_ddp(args)
|