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distill.py
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distill.py
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# Copyright 2018 Dong-Hyun Lee, Kakao Brain.
#
# Copyright (c) 2020 Intel Corporation
#
# 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.
""" Fine-tuning on A Classification Task with pretrained Transformer """
import json
from typing import NamedTuple
import fire
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import TensorDataset, DataLoader
import checkpoint
import tokenization
import optim
import trainer
import data
import models
from utils import set_seeds, get_device
class Config(NamedTuple):
""" Config for classification """
mode: str = "train"
seed: int = 12345
cfg_data: str = "config/agnews_data.json"
cfg_model: str = "config/bert_base.json"
cfg_optim: str = "config/finetune/agnews/optim.json"
model_file: str = ""
pretrain_file: str = "../uncased_L-12_H-768_A-12/bert_model.ckpt"
save_dir: str = "../exp/bert/finetune/agnews"
comments: str = [] # for comments in json file
def main(config='config/finetune/agnews/train.json'):
cfg = Config(**json.load(open(config, "r")))
cfg_data = data.Config(**json.load(open(cfg.cfg_data, "r")))
cfg_model = models.Config(**json.load(open(cfg.cfg_model, "r")))
cfg_optim = trainer.Config(**json.load(open(cfg.cfg_optim, "r")))
set_seeds(cfg.seed)
### Prepare Dataset and Preprocessing ###
TaskDataset = data.get_class(cfg_data.task) # task dataset class according to the task
tokenizer = tokenization.FullTokenizer(vocab_file=cfg_data.vocab_file, do_lower_case=True)
dataset = TaskDataset(cfg_data.data_file[cfg.mode], pipelines=[
data.RemoveSymbols('\\'),
data.Tokenizing(tokenizer.convert_to_unicode, tokenizer.tokenize),
data.AddSpecialTokensWithTruncation(cfg_data.max_len),
data.TokenIndexing(tokenizer.convert_tokens_to_ids,
TaskDataset.labels,
cfg_data.max_len)
], n_data=None)
tensors = TensorDataset(*dataset.get_tensors()) # To Tensors
data_iter = DataLoader(tensors, batch_size=cfg_optim.batch_size, shuffle=False)
### Fetch Teacher's output and put it into the dataset ###
def fetch_logits(model):
def get_logits(model, batch):
input_ids, segment_ids, input_mask, label_id = batch
logits = model(input_ids, segment_ids, input_mask)
return 0.0, logits
train_loop = trainer.TrainLoop(cfg_optim, model, data_iter, None, None, get_device())
results = torch.cat(train_loop.eval(get_logits, cfg.model_file))
return results
if cfg.mode == "train":
print("Fetching teacher's output...")
teacher = models.Classifier4Transformer(cfg_model, len(TaskDataset.labels))
teacher.load_state_dict(torch.load(cfg.model_file)) # use trained model
with torch.no_grad():
teacher_logits = fetch_logits(teacher)
tensors = TensorDataset(teacher_logits, *dataset.get_tensors()) # To Tensors
data_iter = DataLoader(tensors, batch_size=cfg_optim.batch_size, shuffle=False)
### Models ###
model = models.BlendCNN(cfg_model, len(TaskDataset.labels))
checkpoint.load_embedding(model.embed, cfg.pretrain_file)
optimizer = optim.optim4GPU(cfg_optim, model)
train_loop = trainer.TrainLoop(
cfg_optim, model, data_iter, optimizer, cfg.save_dir, get_device()
)
def get_loss(model, batch, global_step): # make sure loss is a scalar tensor
teacher_logits, input_ids, segment_ids, input_mask, label_id = batch
T = 1.0
logits = model(input_ids, segment_ids, input_mask)
loss = 0.1*nn.CrossEntropyLoss()(logits, label_id)
loss += 0.9*nn.KLDivLoss()(
F.log_softmax(logits/T, dim=1),
F.softmax(teacher_logits/T, dim=1)
)
#loss = 0.9*nn.MSELoss()(logits, teacher_logits)
return loss
def evaluate(model, batch):
input_ids, segment_ids, input_mask, label_id = batch
logits = model(input_ids, segment_ids, input_mask)
_, label_pred = logits.max(1)
result = (label_pred == label_id).float() #.cpu().numpy()
accuracy = result.mean()
return accuracy, result
if cfg.mode == "train":
train_loop.train(get_loss, None, None) # not use pretrain file
print("Training has been done properly.")
elif cfg.mode == "eval":
results = train_loop.eval(evaluate, cfg.model_file)
total_accuracy = torch.cat(results).mean().item()
print(f"Accuracy: {total_accuracy}")
if __name__ == '__main__':
fire.Fire(main)