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eval.py
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eval.py
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# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
#
# 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.
import argparse
import time
import math
import os, sys
import torch
from data_utils import get_lm_corpus
from mem_transformer import MemTransformerLM
from utils.exp_utils import get_logger
parser = argparse.ArgumentParser(description='PyTorch Transformer Language Model')
parser.add_argument('--data', type=str, default='data/wikitext-103',
help='location of the data corpus')
parser.add_argument('--dataset', type=str, default='wt103',
choices=['wt103'],
help='dataset name')
parser.add_argument('--split', type=str, default='all',
choices=['all', 'valid', 'test'],
help='which split to evaluate')
parser.add_argument('--batch_size', type=int, default=10,
help='batch size')
parser.add_argument('--tgt_len', type=int, default=5,
help='number of tokens to predict')
parser.add_argument('--ext_len', type=int, default=0,
help='length of the extended context')
parser.add_argument('--mem_len', type=int, default=0,
help='length of the retained previous heads')
parser.add_argument('--clamp_len', type=int, default=-1,
help='max positional embedding index')
parser.add_argument('--cuda', action='store_true',
help='use CUDA')
parser.add_argument('--work_dir', type=str, required=True,
help='path to the work_dir')
parser.add_argument('--no_log', action='store_true',
help='do not log the eval result')
parser.add_argument('--same_length', action='store_true',
help='set same length attention with masking')
parser.add_argument('--fp16', action='store_true',
help='Run in pseudo-fp16 mode (fp16 storage fp32 math).')
args = parser.parse_args()
assert args.ext_len >= 0, 'extended context length must be non-negative'
device = torch.device("cuda" if args.cuda else "cpu")
# Get logger
logging = get_logger(os.path.join(args.work_dir, 'log.txt'),
log_=not args.no_log)
# Load dataset
corpus = get_lm_corpus(args.data, args.dataset)
ntokens = len(corpus.vocab)
va_iter = corpus.get_iterator('valid', args.batch_size, args.tgt_len,
device=device, ext_len=args.ext_len)
te_iter = corpus.get_iterator('test', args.batch_size, args.tgt_len,
device=device, ext_len=args.ext_len)
# Load the best saved model.
with open(os.path.join(args.work_dir, 'model.pt'), 'rb') as f:
model = torch.load(f)
if args.fp16:
model = model.half()
model.backward_compatible()
model = model.to(device)
n_all_param = sum([p.nelement() for p in model.parameters()])
print("total parameters: ", n_all_param)
logging('Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
args.batch_size, args.tgt_len, args.ext_len, args.mem_len, args.clamp_len))
model.reset_length(args.tgt_len, args.ext_len, args.mem_len)
if args.clamp_len > 0:
model.clamp_len = args.clamp_len
if args.same_length:
model.same_length = True
###############################################################################
# Evaluation code
###############################################################################
def evaluate(eval_iter):
# Turn on evaluation mode which disables dropout.
model.eval()
total_len, total_loss = 0, 0.
start_time = time.time()
with torch.no_grad():
mems = tuple()
for idx, (data, target, seq_len) in enumerate(eval_iter):
ret = model(data, target, *mems)
loss, mems = ret[0], ret[1:]
loss = loss.mean()
total_loss += seq_len * loss.item()
total_len += seq_len
total_time = time.time() - start_time
logging('Time : {:.2f}s, {:.2f}ms/segment'.format(
total_time, 1000 * total_time / (idx+1)))
return total_loss / total_len
# Run on test data.
if args.split == 'all':
test_loss = evaluate(te_iter)
valid_loss = evaluate(va_iter)
elif args.split == 'valid':
valid_loss = evaluate(va_iter)
test_loss = None
elif args.split == 'test':
test_loss = evaluate(te_iter)
valid_loss = None
def format_log(loss, split):
if args.dataset in ['enwik8', 'text8']:
log_str = '| {0} loss {1:5.2f} | {0} bpc {2:9.5f} '.format(
split, loss, loss / math.log(2))
else:
log_str = '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(
split, loss, math.exp(loss))
return log_str
log_str = ''
if valid_loss is not None:
log_str += format_log(valid_loss, 'valid')
if test_loss is not None:
log_str += format_log(test_loss, 'test')
logging('=' * 100)
logging(log_str)
logging('=' * 100)