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demo of ofa-bert link to demo in paddlenlp (#783) 

* fix

* link demo to paddlenlp
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demo/ofa/bert/README.md
浏览文件 @ 34d44715
[完整代码](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/model_compression/ofa)
# OFA压缩PaddleNLP-BERT模型 # OFA压缩PaddleNLP-BERT模型
BERT-base模型是一个迁移能力很强的通用语义表示模型，但是模型中也有一些参数冗余。本教程将介绍如何使用PaddleSlim对[PaddleNLP](https://paddlenlp.readthedocs.io/zh/latest/)中BERT-base模型进行压缩。 BERT-base模型是一个迁移能力很强的通用语义表示模型，但是模型中也有一些参数冗余。本教程将介绍如何使用PaddleSlim对[PaddleNLP](https://paddlenlp.readthedocs.io/zh/latest/)中BERT-base模型进行压缩。
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demo/ofa/bert/export_model.py 已删除 100644 → 0
浏览文件 @ f43a57b2
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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 logging
import os
import random
import time
import json
from functools import partial
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddlenlp.transformers import BertModel, BertForSequenceClassification, BertTokenizer
from paddlenlp.utils.log import logger
from paddleslim.nas.ofa import OFA, utils
from paddleslim.nas.ofa.convert_super import Convert, supernet
from paddleslim.nas.ofa.layers import BaseBlock
MODEL_CLASSES = {"bert": (BertForSequenceClassification, BertTokenizer), }
def parse_args():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()), )
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(
sum([
list(classes[-1].pretrained_init_configuration.keys())
for classes in MODEL_CLASSES.values()
], [])), )
parser.add_argument(
"--sub_model_output_dir",
default=None,
type=str,
required=True,
help="The output directory where the sub model predictions and checkpoints will be written.",
)
parser.add_argument(
"--static_sub_model",
default=None,
type=str,
help="The output directory where the sub static model will be written. If set to None, not export static model",
)
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.", )
parser.add_argument(
"--n_gpu",
type=int,
default=1,
help="number of gpus to use, 0 for cpu.")
parser.add_argument(
'--width_mult',
type=float,
default=1.0,
help="width mult you want to export")
args = parser.parse_args()
return args
def export_static_model(model, model_path, max_seq_length):
input_shape = [
paddle.static.InputSpec(
shape=[None, max_seq_length], dtype='int64'),
paddle.static.InputSpec(
shape=[None, max_seq_length], dtype='int64')
]
net = paddle.jit.to_static(model, input_spec=input_shape)
paddle.jit.save(net, model_path)
def do_train(args):
paddle.set_device("gpu" if args.n_gpu else "cpu")
args.model_type = args.model_type.lower()
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config_path = os.path.join(args.model_name_or_path, 'model_config.json')
cfg_dict = dict(json.loads(open(config_path).read()))
num_labels = cfg_dict['num_classes']
model = model_class.from_pretrained(
args.model_name_or_path, num_classes=num_labels)
origin_model = model_class.from_pretrained(
args.model_name_or_path, num_classes=num_labels)
sp_config = supernet(expand_ratio=[1.0, args.width_mult])
model = Convert(sp_config).convert(model)
ofa_model = OFA(model)
sd = paddle.load(
os.path.join(args.model_name_or_path, 'model_state.pdparams'))
ofa_model.model.set_state_dict(sd)
best_config = utils.dynabert_config(ofa_model, args.width_mult)
ofa_model.export(
best_config,
input_shapes=[[1, args.max_seq_length], [1, args.max_seq_length]],
input_dtypes=['int64', 'int64'],
origin_model=origin_model)
for name, sublayer in origin_model.named_sublayers():
if isinstance(sublayer, paddle.nn.MultiHeadAttention):
sublayer.num_heads = int(args.width_mult * sublayer.num_heads)
output_dir = os.path.join(args.sub_model_output_dir,
"model_width_%.5f" % args.width_mult)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = origin_model
model_to_save.save_pretrained(output_dir)
if args.static_sub_model != None:
export_static_model(origin_model, args.static_sub_model,
args.max_seq_length)
def print_arguments(args):
"""print arguments"""
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).items()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
if __name__ == "__main__":
args = parse_args()
print_arguments(args)
do_train(args)
demo/ofa/bert/run_glue_ofa.py 已删除 100644 → 0
浏览文件 @ f43a57b2
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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 logging
import os
import random
import time
from functools import partial
import numpy as np
import paddle
import paddle.nn.functional as F
from paddle.io import DataLoader
from paddle.metric import Accuracy
from paddlenlp.data import Stack, Tuple, Pad
from paddlenlp.transformers import BertModel, BertForSequenceClassification, BertTokenizer
from paddlenlp.metrics import AccuracyAndF1, Mcc, PearsonAndSpearman
import paddlenlp.datasets as datasets
from paddleslim.nas.ofa import OFA, DistillConfig, utils
from paddleslim.nas.ofa.utils import nlp_utils
from paddleslim.nas.ofa.convert_super import Convert, supernet
TASK_CLASSES = {
"cola": (datasets.GlueCoLA, Mcc),
"sst-2": (datasets.GlueSST2, Accuracy),
"mrpc": (datasets.GlueMRPC, AccuracyAndF1),
"sts-b": (datasets.GlueSTSB, PearsonAndSpearman),
"qqp": (datasets.GlueQQP, AccuracyAndF1),
"mnli": (datasets.GlueMNLI, Accuracy),
"qnli": (datasets.GlueQNLI, Accuracy),
"rte": (datasets.GlueRTE, Accuracy),
}
MODEL_CLASSES = {"bert": (BertForSequenceClassification, BertTokenizer), }
def parse_args():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train selected in the list: " +
", ".join(TASK_CLASSES.keys()), )
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()), )
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(
sum([
list(classes[-1].pretrained_init_configuration.keys())
for classes in MODEL_CLASSES.values()
], [])), )
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(
"--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.", )
parser.add_argument(
"--batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.", )
parser.add_argument(
"--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument(
"--weight_decay",
default=0.0,
type=float,
help="Weight decay if we apply some.")
parser.add_argument(
"--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument(
"--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument(
"--lambda_logit",
default=1.0,
type=float,
help="lambda for logit loss.")
parser.add_argument(
"--num_train_epochs",
default=3,
type=int,
help="Total number of training epochs to perform.", )
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
)
parser.add_argument(
"--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument(
"--logging_steps",
type=int,
default=500,
help="Log every X updates steps.")
parser.add_argument(
"--save_steps",
type=int,
default=500,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--seed", type=int, default=42, help="random seed for initialization")
parser.add_argument(
"--n_gpu",
type=int,
default=1,
help="number of gpus to use, 0 for cpu.")
parser.add_argument(
'--width_mult_list',
nargs='+',
type=float,
default=[1.0, 5 / 6, 2 / 3, 0.5],
help="width mult in compress")
args = parser.parse_args()
return args
def set_seed(args):
random.seed(args.seed + paddle.distributed.get_rank())
np.random.seed(args.seed + paddle.distributed.get_rank())
paddle.seed(args.seed + paddle.distributed.get_rank())
def evaluate(model, criterion, metric, data_loader, epoch, step,
width_mult=1.0):
with paddle.no_grad():
model.eval()
metric.reset()
for batch in data_loader:
input_ids, segment_ids, labels = batch
logits = model(input_ids, segment_ids, attention_mask=[None, None])
if isinstance(logits, tuple):
logits = logits[0]
loss = criterion(logits, labels)
correct = metric.compute(logits, labels)
metric.update(correct)
results = metric.accumulate()
print("epoch: %d, batch: %d, width_mult: %s, eval loss: %f, %s: %s\n" %
(epoch, step, 'teacher' if width_mult == 100 else str(width_mult),
loss.numpy(), metric.name(), results))
model.train()
### monkey patch for bert forward to accept [attention_mask, head_mask] as attention_mask
def bert_forward(self,
input_ids,
token_type_ids=None,
position_ids=None,
attention_mask=[None, None]):
wtype = self.pooler.dense.fn.weight.dtype if hasattr(
self.pooler.dense, 'fn') else self.pooler.dense.weight.dtype
if attention_mask[0] is None:
attention_mask[0] = paddle.unsqueeze(
(input_ids == self.pad_token_id).astype(wtype) * -1e9, axis=[1, 2])
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids)
encoder_outputs = self.encoder(embedding_output, attention_mask)
sequence_output = encoder_outputs
pooled_output = self.pooler(sequence_output)
return sequence_output, pooled_output
BertModel.forward = bert_forward
### reorder weights according head importance and neuron importance
def reorder_neuron_head(model, head_importance, neuron_importance):
# reorder heads and ffn neurons
for layer, current_importance in enumerate(neuron_importance):
# reorder heads
idx = paddle.argsort(head_importance[layer], descending=True)
nlp_utils.reorder_head(model.bert.encoder.layers[layer].self_attn, idx)
# reorder neurons
idx = paddle.argsort(
paddle.to_tensor(current_importance), descending=True)
nlp_utils.reorder_neuron(
model.bert.encoder.layers[layer].linear1.fn, idx, dim=1)
nlp_utils.reorder_neuron(
model.bert.encoder.layers[layer].linear2.fn, idx, dim=0)
def soft_cross_entropy(inp, target):
inp_likelihood = F.log_softmax(inp, axis=-1)
target_prob = F.softmax(target, axis=-1)
return -1. * paddle.mean(paddle.sum(inp_likelihood * target_prob, axis=-1))
def convert_example(example,
tokenizer,
label_list,
max_seq_length=512,
is_test=False):
"""convert a glue example into necessary features"""
def _truncate_seqs(seqs, max_seq_length):
if len(seqs) == 1: # single sentence
# Account for [CLS] and [SEP] with "- 2"
seqs[0] = seqs[0][0:(max_seq_length - 2)]
else: # sentence pair
# Account for [CLS], [SEP], [SEP] with "- 3"
tokens_a, tokens_b = seqs
max_seq_length -= 3
while True: # truncate with longest_first strategy
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_seq_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
return seqs
def _concat_seqs(seqs, separators, seq_mask=0, separator_mask=1):
concat = sum((seq + sep for sep, seq in zip(separators, seqs)), [])
segment_ids = sum(([i] * (len(seq) + len(sep)) for i, (sep, seq) in
enumerate(zip(separators, seqs))), [])
if isinstance(seq_mask, int):
seq_mask = [[seq_mask] * len(seq) for seq in seqs]
if isinstance(separator_mask, int):
separator_mask = [[separator_mask] * len(sep) for sep in separators]
p_mask = sum((s_mask + mask for sep, seq, s_mask, mask in
zip(separators, seqs, seq_mask, separator_mask)), [])
return concat, segment_ids, p_mask
if not is_test:
# `label_list == None` is for regression task
label_dtype = "int64" if label_list else "float32"
# get the label
label = example[-1]
example = example[:-1]
#create label maps if classification task
if label_list:
label_map = {}
for (i, l) in enumerate(label_list):
label_map[l] = i
label = label_map[label]
label = np.array([label], dtype=label_dtype)
# tokenize raw text
tokens_raw = [tokenizer(l) for l in example]
# truncate to the truncate_length,
tokens_trun = _truncate_seqs(tokens_raw, max_seq_length)
# concate the sequences with special tokens
tokens_trun[0] = [tokenizer.cls_token] + tokens_trun[0]
tokens, segment_ids, _ = _concat_seqs(tokens_trun, [[tokenizer.sep_token]] *
len(tokens_trun))
# convert the token to ids
input_ids = tokenizer.convert_tokens_to_ids(tokens)
valid_length = len(input_ids)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
# input_mask = [1] * len(input_ids)
if not is_test:
return input_ids, segment_ids, valid_length, label
else:
return input_ids, segment_ids, valid_length
def do_train(args):
paddle.set_device("gpu" if args.n_gpu else "cpu")
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
set_seed(args)
args.task_name = args.task_name.lower()
dataset_class, metric_class = TASK_CLASSES[args.task_name]
args.model_type = args.model_type.lower()
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
train_ds = dataset_class.get_datasets(['train'])
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
trans_func = partial(
convert_example,
tokenizer=tokenizer,
label_list=train_ds.get_labels(),
max_seq_length=args.max_seq_length)
train_ds = train_ds.apply(trans_func, lazy=True)
train_batch_sampler = paddle.io.DistributedBatchSampler(
train_ds, batch_size=args.batch_size, shuffle=True)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # input
Pad(axis=0, pad_val=tokenizer.pad_token_id), # segment
Stack(), # length
Stack(dtype="int64" if train_ds.get_labels() else "float32") # label
): [data for i, data in enumerate(fn(samples)) if i != 2]
train_data_loader = DataLoader(
dataset=train_ds,
batch_sampler=train_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
if args.task_name == "mnli":
dev_dataset_matched, dev_dataset_mismatched = dataset_class.get_datasets(
["dev_matched", "dev_mismatched"])
dev_dataset_matched = dev_dataset_matched.apply(trans_func, lazy=True)
dev_dataset_mismatched = dev_dataset_mismatched.apply(
trans_func, lazy=True)
dev_batch_sampler_matched = paddle.io.BatchSampler(
dev_dataset_matched, batch_size=args.batch_size, shuffle=False)
dev_data_loader_matched = DataLoader(
dataset=dev_dataset_matched,
batch_sampler=dev_batch_sampler_matched,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
dev_batch_sampler_mismatched = paddle.io.BatchSampler(
dev_dataset_mismatched, batch_size=args.batch_size, shuffle=False)
dev_data_loader_mismatched = DataLoader(
dataset=dev_dataset_mismatched,
batch_sampler=dev_batch_sampler_mismatched,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
else:
dev_dataset = dataset_class.get_datasets(["dev"])
dev_dataset = dev_dataset.apply(trans_func, lazy=True)
dev_batch_sampler = paddle.io.BatchSampler(
dev_dataset, batch_size=args.batch_size, shuffle=False)
dev_data_loader = DataLoader(
dataset=dev_dataset,
batch_sampler=dev_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
num_labels = 1 if train_ds.get_labels() == None else len(
train_ds.get_labels())
model = model_class.from_pretrained(
args.model_name_or_path, num_classes=num_labels)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
# Step1: Initialize a dictionary to save the weights from the origin BERT model.
origin_weights = {}
for name, param in model.named_parameters():
origin_weights[name] = param
# Step2: Convert origin model to supernet.
sp_config = supernet(expand_ratio=args.width_mult_list)
model = Convert(sp_config).convert(model)
# Use weights saved in the dictionary to initialize supernet.
utils.set_state_dict(model, origin_weights)
del origin_weights
# Step3: Define teacher model.
teacher_model = model_class.from_pretrained(
args.model_name_or_path, num_classes=num_labels)
# Step4: Config about distillation.
mapping_layers = ['bert.embeddings']
for idx in range(model.bert.config['num_hidden_layers']):
mapping_layers.append('bert.encoder.layers.{}'.format(idx))
default_distill_config = {
'lambda_distill': 0.1,
'teacher_model': teacher_model,
'mapping_layers': mapping_layers,
}
distill_config = DistillConfig(**default_distill_config)
# Step5: Config in supernet training.
ofa_model = OFA(model,
distill_config=distill_config,
elastic_order=['width'])
criterion = paddle.nn.loss.CrossEntropyLoss() if train_ds.get_labels(
) else paddle.nn.loss.MSELoss()
metric = metric_class()
if args.task_name == "mnli":
dev_data_loader = (dev_data_loader_matched, dev_data_loader_mismatched)
# Step6: Calculate the importance of neurons and head,
# and then reorder them according to the importance.
head_importance, neuron_importance = nlp_utils.compute_neuron_head_importance(
args.task_name,
ofa_model.model,
dev_data_loader,
loss_fct=criterion,
num_layers=model.bert.config['num_hidden_layers'],
num_heads=model.bert.config['num_attention_heads'])
reorder_neuron_head(ofa_model.model, head_importance, neuron_importance)
lr_scheduler = paddle.optimizer.lr.LambdaDecay(
args.learning_rate,
lambda current_step, num_warmup_steps=args.warmup_steps,
num_training_steps=args.max_steps if args.max_steps > 0 else
(len(train_data_loader) * args.num_train_epochs): float(
current_step) / float(max(1, num_warmup_steps))
if current_step < num_warmup_steps else max(
0.0,
float(num_training_steps - current_step) / float(
max(1, num_training_steps - num_warmup_steps))))
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
epsilon=args.adam_epsilon,
parameters=ofa_model.model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in [
p.name for n, p in ofa_model.model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
])
global_step = 0
tic_train = time.time()
for epoch in range(args.num_train_epochs):
# Step7: Set current epoch and task.
ofa_model.set_epoch(epoch)
ofa_model.set_task('width')
for step, batch in enumerate(train_data_loader):
global_step += 1
input_ids, segment_ids, labels = batch
for width_mult in args.width_mult_list:
# Step8: Broadcast supernet config from width_mult,
# and use this config in supernet training.
net_config = utils.dynabert_config(ofa_model, width_mult)
ofa_model.set_net_config(net_config)
logits, teacher_logits = ofa_model(
input_ids, segment_ids, attention_mask=[None, None])
rep_loss = ofa_model.calc_distill_loss()
if args.task_name == 'sts-b':
logit_loss = 0.0
else:
logit_loss = soft_cross_entropy(logits,
teacher_logits.detach())
loss = rep_loss + args.lambda_logit * logit_loss
loss.backward()
optimizer.step()
lr_scheduler.step()
ofa_model.model.clear_gradients()
if global_step % args.logging_steps == 0:
if (not args.n_gpu > 1) or paddle.distributed.get_rank() == 0:
print(
"global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
% (global_step, epoch, step, loss,
args.logging_steps / (time.time() - tic_train)))
tic_train = time.time()
if global_step % args.save_steps == 0:
if args.task_name == "mnli":
evaluate(
teacher_model,
criterion,
metric,
dev_data_loader_matched,
epoch,
step,
width_mult=100)
evaluate(
teacher_model,
criterion,
metric,
dev_data_loader_mismatched,
epoch,
step,
width_mult=100)
else:
evaluate(
teacher_model,
criterion,
metric,
dev_data_loader,
epoch,
step,
width_mult=100)
for idx, width_mult in enumerate(args.width_mult_list):
net_config = utils.dynabert_config(ofa_model, width_mult)
ofa_model.set_net_config(net_config)
tic_eval = time.time()
if args.task_name == "mnli":
acc = evaluate(ofa_model, criterion, metric,
dev_data_loader_matched, epoch, step,
width_mult)
evaluate(ofa_model, criterion, metric,
dev_data_loader_mismatched, epoch, step,
width_mult)
print("eval done total : %s s" %
(time.time() - tic_eval))
else:
acc = evaluate(ofa_model, criterion, metric,
dev_data_loader, epoch, step, width_mult)
print("eval done total : %s s" %
(time.time() - tic_eval))
if (not args.n_gpu > 1
) or paddle.distributed.get_rank() == 0:
output_dir = os.path.join(args.output_dir,
"model_%d" % global_step)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
def print_arguments(args):
"""print arguments"""
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).items()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
if __name__ == "__main__":
args = parse_args()
print_arguments(args)
if args.n_gpu > 1:
paddle.distributed.spawn(do_train, args=(args, ), nprocs=args.n_gpu)
else:
do_train(args)
demo/ofa/bert/run_glue_ofa_depth.py 已删除 100644 → 0
浏览文件 @ f43a57b2
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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 logging
import os
import random
import time
import math
from functools import partial
import numpy as np
import paddle
import paddle.nn.functional as F
from paddle.io import DataLoader
from paddle.metric import Metric, Accuracy, Precision, Recall
from paddlenlp.data import Stack, Tuple, Pad
from paddlenlp.data.sampler import SamplerHelper
from paddlenlp.transformers import BertModel, BertForSequenceClassification, BertTokenizer
from paddlenlp.utils.log import logger
from paddlenlp.metrics import AccuracyAndF1, Mcc, PearsonAndSpearman
import paddlenlp.datasets as datasets
from paddleslim.nas.ofa import OFA, RunConfig, DistillConfig, utils
from paddleslim.nas.ofa.utils import nlp_utils
from paddleslim.nas.ofa.convert_super import Convert, supernet
TASK_CLASSES = {
"cola": (datasets.GlueCoLA, Mcc),
"sst-2": (datasets.GlueSST2, Accuracy),
"mrpc": (datasets.GlueMRPC, AccuracyAndF1),
"sts-b": (datasets.GlueSTSB, PearsonAndSpearman),
"qqp": (datasets.GlueQQP, AccuracyAndF1),
"mnli": (datasets.GlueMNLI, Accuracy),
"qnli": (datasets.GlueQNLI, Accuracy),
"rte": (datasets.GlueRTE, Accuracy),
}
MODEL_CLASSES = {"bert": (BertForSequenceClassification, BertTokenizer), }
def parse_args():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train selected in the list: " +
", ".join(TASK_CLASSES.keys()), )
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()), )
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(
sum([
list(classes[-1].pretrained_init_configuration.keys())
for classes in MODEL_CLASSES.values()
], [])), )
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(
"--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.", )
parser.add_argument(
"--batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.", )
parser.add_argument(
"--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument(
"--weight_decay",
default=0.0,
type=float,
help="Weight decay if we apply some.")
parser.add_argument(
"--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument(
"--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument(
"--lambda_logit",
default=1.0,
type=float,
help="lambda for logit loss.")
parser.add_argument(
"--lambda_rep",
default=0.1,
type=float,
help="lambda for hidden state distillation loss.")
parser.add_argument(
"--num_train_epochs",
default=3,
type=int,
help="Total number of training epochs to perform.", )
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
)
parser.add_argument(
"--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument(
"--logging_steps",
type=int,
default=500,
help="Log every X updates steps.")
parser.add_argument(
"--save_steps",
type=int,
default=500,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--seed", type=int, default=42, help="random seed for initialization")
parser.add_argument(
"--n_gpu",
type=int,
default=1,
help="number of gpus to use, 0 for cpu.")
parser.add_argument(
'--width_mult_list',
nargs='+',
type=float,
default=[1.0, 5 / 6, 2 / 3, 0.5],
help="width mult in compress")
parser.add_argument(
'--depth_mult_list',
nargs='+',
type=float,
default=[1.0, 0.75, 0.5],
help="width mult in compress")
args = parser.parse_args()
return args
def set_seed(args):
random.seed(args.seed + paddle.distributed.get_rank())
np.random.seed(args.seed + paddle.distributed.get_rank())
paddle.seed(args.seed + paddle.distributed.get_rank())
def evaluate(model,
criterion,
metric,
data_loader,
width_mult=1.0,
depth_mult=1.0):
with paddle.no_grad():
model.eval()
metric.reset()
for batch in data_loader:
input_ids, segment_ids, labels = batch
logits = model(input_ids, segment_ids, attention_mask=[None, None])
if isinstance(logits, tuple):
logits = logits[0]
loss = criterion(logits, labels)
correct = metric.compute(logits, labels)
metric.update(correct)
results = metric.accumulate()
print(
"depth_mult: %f, width_mult: %f, eval loss: %f, %s: %s\n" %
(depth_mult, width_mult, loss.numpy(), metric.name(), results),
end='')
model.train()
### monkey patch for bert forward to accept [attention_mask, head_mask] as attention_mask
def bert_forward(self,
input_ids,
token_type_ids=None,
position_ids=None,
attention_mask=[None, None],
depth_mult=1.0):
wtype = self.pooler.dense.fn.weight.dtype if hasattr(
self.pooler.dense, 'fn') else self.pooler.dense.weight.dtype
if attention_mask[0] is None:
attention_mask[0] = paddle.unsqueeze(
(input_ids == self.pad_token_id).astype(wtype) * -1e9, axis=[1, 2])
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids)
encoder_outputs = self.encoder(
embedding_output, attention_mask, depth_mult=depth_mult)
sequence_output = encoder_outputs
pooled_output = self.pooler(sequence_output)
return sequence_output, pooled_output
BertModel.forward = bert_forward
def transformer_encoder_forward(self, src, src_mask=None, depth_mult=1.):
output = src
depth = round(self.num_layers * depth_mult)
kept_layers_index = []
for i in range(1, depth + 1):
kept_layers_index.append(math.floor(i / depth_mult) - 1)
for i in kept_layers_index:
output = self.layers[i](output, src_mask=src_mask)
if self.norm is not None:
output = self.norm(output)
return output
paddle.nn.TransformerEncoder.forward = transformer_encoder_forward
def sequence_forward(self,
input_ids,
token_type_ids=None,
position_ids=None,
attention_mask=[None, None],
depth=1.0):
_, pooled_output = self.bert(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
attention_mask=attention_mask,
depth_mult=depth)
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
return logits
BertForSequenceClassification.forward = sequence_forward
def soft_cross_entropy(inp, target):
inp_likelihood = F.log_softmax(inp, axis=-1)
target_prob = F.softmax(target, axis=-1)
return -1. * paddle.mean(paddle.sum(inp_likelihood * target_prob, axis=-1))
def convert_example(example,
tokenizer,
label_list,
max_seq_length=512,
is_test=False):
"""convert a glue example into necessary features"""
def _truncate_seqs(seqs, max_seq_length):
if len(seqs) == 1: # single sentence
# Account for [CLS] and [SEP] with "- 2"
seqs[0] = seqs[0][0:(max_seq_length - 2)]
else: # sentence pair
# Account for [CLS], [SEP], [SEP] with "- 3"
tokens_a, tokens_b = seqs
max_seq_length -= 3
while True: # truncate with longest_first strategy
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_seq_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
return seqs
def _concat_seqs(seqs, separators, seq_mask=0, separator_mask=1):
concat = sum((seq + sep for sep, seq in zip(separators, seqs)), [])
segment_ids = sum(([i] * (len(seq) + len(sep)) for i, (sep, seq) in
enumerate(zip(separators, seqs))), [])
if isinstance(seq_mask, int):
seq_mask = [[seq_mask] * len(seq) for seq in seqs]
if isinstance(separator_mask, int):
separator_mask = [[separator_mask] * len(sep) for sep in separators]
p_mask = sum((s_mask + mask for sep, seq, s_mask, mask in
zip(separators, seqs, seq_mask, separator_mask)), [])
return concat, segment_ids, p_mask
if not is_test:
# `label_list == None` is for regression task
label_dtype = "int64" if label_list else "float32"
# get the label
label = example[-1]
example = example[:-1]
#create label maps if classification task
if label_list:
label_map = {}
for (i, l) in enumerate(label_list):
label_map[l] = i
label = label_map[label]
label = np.array([label], dtype=label_dtype)
# tokenize raw text
tokens_raw = [tokenizer(l) for l in example]
# truncate to the truncate_length,
tokens_trun = _truncate_seqs(tokens_raw, max_seq_length)
# concate the sequences with special tokens
tokens_trun[0] = [tokenizer.cls_token] + tokens_trun[0]
tokens, segment_ids, _ = _concat_seqs(tokens_trun, [[tokenizer.sep_token]] *
len(tokens_trun))
# convert the token to ids
input_ids = tokenizer.convert_tokens_to_ids(tokens)
valid_length = len(input_ids)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
# input_mask = [1] * len(input_ids)
if not is_test:
return input_ids, segment_ids, valid_length, label
else:
return input_ids, segment_ids, valid_length
def do_train(args):
paddle.set_device("gpu" if args.n_gpu else "cpu")
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
set_seed(args)
args.task_name = args.task_name.lower()
dataset_class, metric_class = TASK_CLASSES[args.task_name]
args.model_type = args.model_type.lower()
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
train_ds = dataset_class.get_datasets(['train'])
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
trans_func = partial(
convert_example,
tokenizer=tokenizer,
label_list=train_ds.get_labels(),
max_seq_length=args.max_seq_length)
train_ds = train_ds.apply(trans_func, lazy=True)
train_batch_sampler = paddle.io.DistributedBatchSampler(
train_ds, batch_size=args.batch_size, shuffle=True)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # input
Pad(axis=0, pad_val=tokenizer.pad_token_id), # segment
Stack(), # length
Stack(dtype="int64" if train_ds.get_labels() else "float32") # label
): [data for i, data in enumerate(fn(samples)) if i != 2]
train_data_loader = DataLoader(
dataset=train_ds,
batch_sampler=train_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
if args.task_name == "mnli":
dev_dataset_matched, dev_dataset_mismatched = dataset_class.get_datasets(
["dev_matched", "dev_mismatched"])
dev_dataset_matched = dev_dataset_matched.apply(trans_func, lazy=True)
dev_dataset_mismatched = dev_dataset_mismatched.apply(
trans_func, lazy=True)
dev_batch_sampler_matched = paddle.io.BatchSampler(
dev_dataset_matched, batch_size=args.batch_size, shuffle=False)
dev_data_loader_matched = DataLoader(
dataset=dev_dataset_matched,
batch_sampler=dev_batch_sampler_matched,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
dev_batch_sampler_mismatched = paddle.io.BatchSampler(
dev_dataset_mismatched, batch_size=args.batch_size, shuffle=False)
dev_data_loader_mismatched = DataLoader(
dataset=dev_dataset_mismatched,
batch_sampler=dev_batch_sampler_mismatched,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
else:
dev_dataset = dataset_class.get_datasets(["dev"])
dev_dataset = dev_dataset.apply(trans_func, lazy=True)
dev_batch_sampler = paddle.io.BatchSampler(
dev_dataset, batch_size=args.batch_size, shuffle=False)
dev_data_loader = DataLoader(
dataset=dev_dataset,
batch_sampler=dev_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
num_labels = 1 if train_ds.get_labels() == None else len(
train_ds.get_labels())
# Step1: Initialize the origin BERT model.
model = model_class.from_pretrained(
args.model_name_or_path, num_classes=num_labels)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
# Step2: Convert origin model to supernet.
sp_config = supernet(expand_ratio=args.width_mult_list)
model = Convert(sp_config).convert(model)
# Use weights saved in the dictionary to initialize supernet.
weights_path = os.path.join(args.model_name_or_path, 'model_state.pdparams')
origin_weights = paddle.load(weights_path)
model.set_state_dict(origin_weights)
# Step3: Define teacher model.
teacher_model = model_class.from_pretrained(
args.model_name_or_path, num_classes=num_labels)
new_dict = utils.utils.remove_model_fn(teacher_model, origin_weights)
teacher_model.set_state_dict(new_dict)
del origin_weights, new_dict
default_run_config = {'elastic_depth': args.depth_mult_list}
run_config = RunConfig(**default_run_config)
# Step4: Config about distillation.
mapping_layers = ['bert.embeddings']
for idx in range(model.bert.config['num_hidden_layers']):
mapping_layers.append('bert.encoder.layers.{}'.format(idx))
default_distill_config = {
'lambda_distill': args.lambda_rep,
'teacher_model': teacher_model,
'mapping_layers': mapping_layers,
}
distill_config = DistillConfig(**default_distill_config)
# Step5: Config in supernet training.
ofa_model = OFA(model,
run_config=run_config,
distill_config=distill_config,
elastic_order=['depth'])
#elastic_order=['width'])
criterion = paddle.nn.loss.CrossEntropyLoss() if train_ds.get_labels(
) else paddle.nn.loss.MSELoss()
metric = metric_class()
if args.task_name == "mnli":
dev_data_loader = (dev_data_loader_matched, dev_data_loader_mismatched)
lr_scheduler = paddle.optimizer.lr.LambdaDecay(
args.learning_rate,
lambda current_step, num_warmup_steps=args.warmup_steps,
num_training_steps=args.max_steps if args.max_steps > 0 else
(len(train_data_loader) * args.num_train_epochs): float(
current_step) / float(max(1, num_warmup_steps))
if current_step < num_warmup_steps else max(
0.0,
float(num_training_steps - current_step) / float(
max(1, num_training_steps - num_warmup_steps))))
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
epsilon=args.adam_epsilon,
parameters=ofa_model.model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in [
p.name for n, p in ofa_model.model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
])
global_step = 0
tic_train = time.time()
for epoch in range(args.num_train_epochs):
# Step6: Set current epoch and task.
ofa_model.set_epoch(epoch)
ofa_model.set_task('depth')
for step, batch in enumerate(train_data_loader):
global_step += 1
input_ids, segment_ids, labels = batch
for depth_mult in args.depth_mult_list:
for width_mult in args.width_mult_list:
# Step7: Broadcast supernet config from width_mult,
# and use this config in supernet training.
net_config = utils.dynabert_config(ofa_model, width_mult,
depth_mult)
ofa_model.set_net_config(net_config)
logits, teacher_logits = ofa_model(
input_ids, segment_ids, attention_mask=[None, None])
rep_loss = ofa_model.calc_distill_loss()
if args.task_name == 'sts-b':
logit_loss = 0.0
else:
logit_loss = soft_cross_entropy(logits,
teacher_logits.detach())
loss = rep_loss + args.lambda_logit * logit_loss
loss.backward()
optimizer.step()
lr_scheduler.step()
ofa_model.model.clear_gradients()
if global_step % args.logging_steps == 0:
if (not args.n_gpu > 1) or paddle.distributed.get_rank() == 0:
logger.info(
"global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
% (global_step, epoch, step, loss,
args.logging_steps / (time.time() - tic_train)))
tic_train = time.time()
if global_step % args.save_steps == 0:
if args.task_name == "mnli":
evaluate(
teacher_model,
criterion,
metric,
dev_data_loader_matched,
width_mult=100)
evaluate(
teacher_model,
criterion,
metric,
dev_data_loader_mismatched,
width_mult=100)
else:
evaluate(
teacher_model,
criterion,
metric,
dev_data_loader,
width_mult=100)
for depth_mult in args.depth_mult_list:
for width_mult in args.width_mult_list:
net_config = utils.dynabert_config(
ofa_model, width_mult, depth_mult)
ofa_model.set_net_config(net_config)
tic_eval = time.time()
if args.task_name == "mnli":
acc = evaluate(ofa_model, criterion, metric,
dev_data_loader_matched, width_mult,
depth_mult)
evaluate(ofa_model, criterion, metric,
dev_data_loader_mismatched, width_mult,
depth_mult)
print("eval done total : %s s" %
(time.time() - tic_eval))
else:
acc = evaluate(ofa_model, criterion, metric,
dev_data_loader, width_mult,
depth_mult)
print("eval done total : %s s" %
(time.time() - tic_eval))
if (not args.n_gpu > 1
) or paddle.distributed.get_rank() == 0:
output_dir = os.path.join(args.output_dir,
"model_%d" % global_step)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
def print_arguments(args):
"""print arguments"""
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).items()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
if __name__ == "__main__":
args = parse_args()
print_arguments(args)
if args.n_gpu > 1:
paddle.distributed.spawn(do_train, args=(args, ), nprocs=args.n_gpu)
else:
do_train(args)
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