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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 VanillaCollator, TestCollator
from prompt import all_prompt
from evaluate import get_topk_results, get_metrics_results
from rq_llama import *
parser = argparse.ArgumentParser(description="RQ-Llama Evaluation")
parser = parse_global_args(parser)
parser = parse_dataset_args(parser)
parser = parse_test_args(parser)
args = parser.parse_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)
rqllama = LlamaWithRQ.from_pretrained(args.ckpt_path, torch_dtype = torch.float16, low_cpu_mem_usage = True, device_map = device_map)
rqllama = DistributedDataParallel(rqllama, 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)
if local_rank == 0:
print("data num:", len(test_data))
ddp_sampler = DistributedSampler(test_data, num_replicas = world_size, rank = local_rank, drop_last = True)
collator = TestCollator(args, rqllama.module.tokenizer)
all_items = test_data.get_all_items()
# print('num_items:', len(all_items))
prefix_allowed_tokens = test_data.get_prefix_allowed_tokens_fn(rqllama.module.tokenizer)
test_loader = DataLoader(
test_data,
batch_size = args.test_batch_size,
collate_fn = collator,
sampler = ddp_sampler,
num_workers = 2,
pin_memory = True
)
rqllama.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 = rqllama.module.model.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 = rqllama.module.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) |