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run_retrieval.py
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run_retrieval.py
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import argparse
import json
import logging
import os
import pickle
import random
import re
import sys
import faiss
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from transformers import AutoConfig, AutoModel, AutoTokenizer
from transformers.data.processors.utils import (DataProcessor, InputExample,
InputFeatures)
logger = logging.getLogger(__name__)
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
#TODO multi gpu support
# if args.n_gpu > 0:
# torch.cuda.manual_seed_all(args.seed)
def set_dump_path(args, output_dir=None, exp_name=None):
if output_dir is None: output_dir = args.output_dir
if exp_name is None: exp_name = args.exp_name
chars = 'abcdefghijklmnopqrstuvwxyz0123456789'
while True:
exp_id = ''.join(random.choice(chars) for _ in range(10))
if not os.path.isdir(os.path.join(output_dir, exp_name, exp_id)):
break
args.exp_id = exp_id
dump_path = os.path.join(output_dir, exp_name, exp_id)
os.makedirs(dump_path)
args.dump_path = dump_path
def init_exp(args):
# dump parameters
set_dump_path(args)
pickle.dump(args, open(os.path.join(args.dump_path, 'params.pkl'), 'wb'))
# get running command
command = ["python", sys.argv[0]]
for x in sys.argv[1:]:
if x.startswith('--'):
assert '"' not in x and "'" not in x
command.append(x)
else:
assert "'" not in x
if re.match('^[a-zA-Z0-9_]+$', x):
command.append("%s" % x)
else:
command.append("'%s'" % x)
command = ' '.join(command)
args.command = command + ' --exp_id "%s"' % args.exp_id
# check experiment name
assert len(args.exp_name.strip()) > 0
logging.basicConfig(
format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO)
logger = logging.getLogger(__name__)
logger.info("\n".join(
"%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(args)).items())))
logger.info("The experiment will be stored in %s\n" % args.dump_path)
logger.info("Running command: %s" % command)
logger.info("")
def load_and_cache_examples(args, langpair, lang, tokenizer, key="", prefix="tatoeba"):
cache_dir = os.path.join(args.data_dir, "pequod_cache")
os.makedirs(cache_dir, exist_ok=True)
cache_filename = os.path.join(
cache_dir, "cached_%s_%s_%s" % (langpair, lang, key))
if os.path.exists(cache_filename) and not args.overwrite_cache:
logger.info("Loading features from cached file %s" % cache_filename)
features = torch.load(cache_filename)
else:
processer = TatoebaProcesser()
logger.info("Creating features from dataset file at %s" % args.data_dir)
examples = processer.get_examples(args.data_dir, langpair, lang, prefix)
features = TatoebaProcesser.convert_examples_to_features(
examples, tokenizer, args.max_seq_length, 0,
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],)
logger.info("Saving features to cache file %s" % cache_filename)
torch.save(features, cache_filename)
all_input_ids = torch.tensor(
[f.input_ids for f in features], dtype=torch.long)
all_attention_mask = torch.tensor(
[f.attention_mask for f in features], dtype=torch.long)
all_token_type_ids = torch.tensor(
[f.token_type_ids for f in features], dtype=torch.long)
dataset = TensorDataset(
all_input_ids, all_attention_mask, all_token_type_ids)
return dataset
class TatoebaProcesser(DataProcessor):
@classmethod
def convert_examples_to_features(cls, examples, tokenizer, max_length, pad_token_segment_id, pad_token, mask_padding_with_zero=True):
features = []
for ex_index, example in enumerate(examples):
inputs = tokenizer.encode_plus(
example.text_a,
None,
add_special_tokens=True,
max_length=max_length,
)
input_ids, token_type_ids = inputs["input_ids"], inputs["token_type_ids"]
attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
padding_length = max_length - len(input_ids)
input_ids = input_ids + ([pad_token] * padding_length)
attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)
assert len(input_ids) == max_length, "Error with input length {} vs {}".format(len(input_ids), max_length)
assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(len(attention_mask), max_length)
assert len(token_type_ids) == max_length, "Error with input length {} vs {}".format(len(token_type_ids), max_length)
if ex_index < 3:
logger.info("*** Example ***")
logger.info("guid: %s" % (example.guid))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
logger.info("token_type_ids: %s" % " ".join([str(x) for x in token_type_ids]))
features.append(InputFeatures(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
label=None,
))
return features
def get_examples(self, data_dir, langpair, lang, prefix="tatoeba"):
examples = []
fn = os.path.join(data_dir, "%s.%s.%s" % (prefix, langpair, lang))
with open(fn) as fp:
for i, line in enumerate(fp):
line = line.strip()
examples.append(InputExample(
guid="%s-%s-%d" % (langpair, lang, i),
text_a=line,
))
return examples
def to_cuda(tup):
return tuple(t.cuda() for t in tup)
class Evaluator(object):
def __init__(self, args, model, tokenizer, **kwargs):
self.args = args
self.datasets = {}
self.model = model
self.tokenizer = tokenizer
def _parse_batch(self, batch, has_label=True, **kwargs):
_batch = to_cuda(batch)
# _batch = batch
ret = {"input_ids": _batch[0],
"attention_mask": _batch[1],
"token_type_ids": _batch[2] if self.args.model_type == "bert" else None,}
if has_label: ret["labels"] = _batch[3]
ret.update(**kwargs)
return ret
def run(self):
raise NotImplementedError
def get_dataset(self, *args, **kwargs):
if args in self.datasets: return self.datasets[args]
dataset = self.load_and_cache_examples(*args, **kwargs)
self.datasets[args] = dataset
return dataset
def load_and_cache_examples(self, *args, **kwargs):
raise NotImplementedError
def get_dataloader(self, *args, **kwargs):
logger.info("Getting dataloader - args: %s" % str(args))
dataset = kwargs.pop("dataset", self.get_dataset(*args, **kwargs))
dataloader = DataLoader(dataset, batch_size=self.args.eval_batch_size)
return dataloader
def similarity_search(x, y, dim, normalize=False):
num = x.shape[0]
idx = faiss.IndexFlatL2(dim)
if normalize:
faiss.normalize_L2(x)
faiss.normalize_L2(y)
idx.add(x)
scores, prediction = idx.search(y, 1)
return prediction
class TatoebaEvaluator(Evaluator):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.model_langs = ["share_lang", "order"]
self.proj_matrix_fast = kwargs.get("proj_matrix_fast", None)
if self.proj_matrix_fast is not None:
logger.info("proj_matrix_fast:" + str(self.proj_matrix_fast.size()))
self.proj_matrix_fast = self.proj_matrix_fast[0].float().cuda()
self.res = {}
self.cache_key = kwargs.pop('cache_key', None)
def get_mean_emb(self, layer_outputs, pool_mask):
embs = (layer_outputs * pool_mask.unsqueeze(2).float()).sum(dim=1) / \
pool_mask.sum(dim=1).view(-1, 1).float()
return embs.cpu().numpy().astype(np.float32)
def get_cxlm_emb(self, layer_outputs):
if self.proj_matrix_fast is None:
raise ValueError
ret = torch.mm(layer_outputs[:,0,:], self.proj_matrix_fast)
# ret = layer_outputs[:,0,:]
return ret.cpu().numpy().astype(np.float32)
def get_cls_emb(self, layer_outputs):
return layer_outputs[:,0,:].cpu().numpy().astype(np.float32)
def get_embeddings(self, batch, outputs, emb_type=None):
if emb_type is None:
emb_type = self.args.emb_type
last_layer_outputs, first_token_outputs, all_layer_outputs = outputs
if emb_type == "mean":
ret = self.get_mean_emb(all_layer_outputs[self.args.mean_layer_id], batch["attention_mask"])
elif emb_type == "cls":
ret = self.get_cls_emb(all_layer_outputs[-1])
elif emb_type == "cxlm":
ret = self.get_cxlm_emb(all_layer_outputs[8])
else: raise ValueError
# ret = None
del last_layer_outputs, first_token_outputs, all_layer_outputs
torch.cuda.empty_cache()
return ret
def get_langpairs(self):
args = self.args
if args.data_prefix == "tatoeba":
langs = ["ara", "bul", "deu", "ell", "spa", "fra", "hin", "rus", "swh", "tha", "tur", "urd", "vie", "cmn"]
langpairs = ["%s-eng" % lang for lang in langs]
elif args.data_prefix == "cxlm":
langpairs = "ar-en bg-en de-en el-en en-es en-fr en-hi en-ru en-sw en-th en-tr en-ur en-vi en-zh".split()
elif args.data_prefix == "tat15plus":
args.data_prefix = "tatoeba"
l15 = set(["ara", "bul", "deu", "ell", "spa", "fra", "hin", "rus", "swh", "tha", "tur", "urd", "vie", "cmn"])
ld = {'ara':'ar', 'heb':'he', 'vie':'vi', 'ind':'id',
'jav':'jv', 'tgl':'tl', 'eus':'eu', 'mal':'ml',
'tel':'te', 'afr':'af', 'nld':'nl', 'deu':'de',
'ell':'el', 'ben':'bn', 'hin':'hi', 'mar':'mr', 'urd':'ur',
'tam':'ta', 'fra':'fr', 'ita':'it', 'por':'pt', 'spa':'es',
'bul':'bg', 'rus':'ru', 'jpn':'ja', 'kat':'ka', 'kor':'ko',
'tha':'th', 'swh':'sw', 'cmn':'zh', 'kaz':'kk', 'tur':'tr',
'est':'et', 'fin':'fi', 'hun':'hu', 'pes':'fa'}
langs = []
for l in ld:
if l in l15: continue
langs.append(l)
# langs = ["afr", "jpn", "kor", "kaz", "est", "fin", "hun", "pes"]
langpairs = ["%s-eng" % lang for lang in langs]
else: raise ValueError
return langpairs
def run(self):
args = self.args
self.model.eval()
langpairs = self.get_langpairs()
for langpair in langpairs:
lang1, lang2 = langpair.split("-")
logger.info("Eval langpair: %s" % langpair)
dl1 = self.get_dataloader(langpair, lang1)
dl2 = self.get_dataloader(langpair, lang2)
all_emb1 = []
all_emb2 = []
for batch1, batch2 in zip(dl1, dl2):
batch1 = self._parse_batch(batch1, has_label=False)
batch2 = self._parse_batch(batch2, has_label=False)
#forward
with torch.no_grad():
outputs1 = self.model(**batch1)
all_emb1.append(self.get_embeddings(batch1, outputs1))
outputs2 = self.model(**batch2)
all_emb2.append(self.get_embeddings(batch2, outputs2))
all_emb1 = np.concatenate(all_emb1)
all_emb2 = np.concatenate(all_emb2)
emb_sz = all_emb1.shape[-1]
if args.reverse_eval:
all_emb1, all_emb2 = all_emb2, all_emb1
predictions = similarity_search(
all_emb1, all_emb2, emb_sz, normalize=self.args.normalize)
correct = tot = 0
for i, pred in enumerate(predictions):
if i == pred[0]: correct += 1
tot += 1
logger.info("langpair:%s acc:%.2f" % (langpair, 100*correct/tot))
self.res[langpair] = 100*correct/tot
output_fn = os.path.join(args.exp_results_dir, args.exp_name)
if args.reverse_eval: output_fn += "-rev"
with open(output_fn, "w") as fp:
json.dump(self.res, fp)
def load_and_cache_examples(self, langpair, lang, **kwargs):
args = self.args
if self.cache_key is None:
cache_key = "%s-%s" % (args.model_key, args.model_type)
else:
cache_key = self.cache_key
return load_and_cache_examples(
args=args,
langpair=langpair,
lang=lang,
tokenizer=self.tokenizer,
key=cache_key,
prefix=args.data_prefix,
)
class GlueccIrEvaluator(TatoebaEvaluator):
def get_langpairs(self):
return ["zh-classical_zh"]
def get_embeddings(self, batch, outputs, emb_type=None):
if emb_type is None:
emb_type = self.args.emb_type
all_layer_outputs=outputs.hidden_states
if emb_type == "mean":
ret = self.get_mean_emb(all_layer_outputs[self.args.mean_layer_id], batch["attention_mask"])
elif emb_type == "cls":
ret = self.get_cls_emb(all_layer_outputs[-1])
else: raise ValueError
# ret = None
del all_layer_outputs
torch.cuda.empty_cache()
return ret
def _parse_batch(self, batch, has_label=True, **kwargs):
_batch = to_cuda(batch)
# _batch = batch
ret = {"input_ids": _batch[0],
"attention_mask": _batch[1],
"token_type_ids": _batch[2] if self.args.model_type == "bert" else None,
"output_hidden_states": True, }
ret.update(**kwargs)
return ret
def get_params():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--data_dir", default=None, type=str, required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--data_prefix", default="gluecc", type=str)
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--exp_name", default=None, type=str, required=True,
help="Experiment name.")
parser.add_argument("--max_seq_length", default=256, type=int)
parser.add_argument("--mean_layer_id", default=8, type=int)
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
parser.add_argument("--reload", default="", type=str)
parser.add_argument("--do_lower_case", action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument('--overwrite_cache', action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument("--eval_batch_size", default=128, type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument("--emb_type", default="mean", type=str)
parser.add_argument("--normalize", action='store_true')
parser.add_argument("--reverse_eval", action='store_true')
parser.add_argument("--exp_results_dir", default="", type=str, required=True)
parser.add_argument("--model_name", default="hfl/chinese-roberta-wwm-ext", type=str)
return parser.parse_args()
def main():
args = get_params()
init_exp(args)
set_seed(args)
# args.model_name = "hfl/chinese-roberta-wwm-ext"
# args.model_name = "ethanyt/guwenbert-base"
args.device = torch.device('cuda')
model = AutoModel.from_pretrained(args.model_name)
config = AutoConfig.from_pretrained(args.model_name)
args.model_type = config.model_type
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
cache_key = args.model_name.replace("/", "-")
model.to(args.device)
GlueccIrEvaluator(args, model, tokenizer, cache_key=cache_key).run()
if __name__ == "__main__":
main()
"""
python ./examples/retrieval/gluecc.py --data_dir ~/res/gluecc/ir --output_dir ./local-test --exp_name local-test --max_seq_length 512 --exp_results_dir ./local-res
"""