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(测试版) LSTM 词汇语言模型上的动态量化¶
作者: James Reed
编辑: Seth Weidman
简介¶
量化涉及将模型的权重和激活从浮点数转换为整数，这可以使模型尺寸更小，推理速度更快，并且仅对准确性造成轻微的影响。
在本教程中，我们将应用最简单的量化形式 - 动态量化 - 到基于 LSTM 的下一个词预测模型，紧密遵循 PyTorch 示例中的 词汇语言模型。
# imports
import os
from io import open
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
1. 定义模型¶
在这里，我们定义 LSTM 模型架构，遵循词汇语言模型示例中的 模型。
class LSTMModel(nn.Module):
"""Container module with an encoder, a recurrent module, and a decoder."""
def __init__(self, ntoken, ninp, nhid, nlayers, dropout=0.5):
super(LSTMModel, self).__init__()
self.drop = nn.Dropout(dropout)
self.encoder = nn.Embedding(ntoken, ninp)
self.rnn = nn.LSTM(ninp, nhid, nlayers, dropout=dropout)
self.decoder = nn.Linear(nhid, ntoken)
self.init_weights()
self.nhid = nhid
self.nlayers = nlayers
def init_weights(self):
initrange = 0.1
self.encoder.weight.data.uniform_(-initrange, initrange)
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-initrange, initrange)
def forward(self, input, hidden):
emb = self.drop(self.encoder(input))
output, hidden = self.rnn(emb, hidden)
output = self.drop(output)
decoded = self.decoder(output)
return decoded, hidden
def init_hidden(self, bsz):
weight = next(self.parameters())
return (weight.new_zeros(self.nlayers, bsz, self.nhid),
weight.new_zeros(self.nlayers, bsz, self.nhid))
2. 加载文本数据¶
接下来，我们将Wikitext-2 数据集加载到一个Corpus中，同样遵循词语语言模型示例中的预处理步骤。
class Dictionary(object):
def __init__(self):
self.word2idx = {}
self.idx2word = []
def add_word(self, word):
if word not in self.word2idx:
self.idx2word.append(word)
self.word2idx[word] = len(self.idx2word) - 1
return self.word2idx[word]
def __len__(self):
return len(self.idx2word)
class Corpus(object):
def __init__(self, path):
self.dictionary = Dictionary()
self.train = self.tokenize(os.path.join(path, 'train.txt'))
self.valid = self.tokenize(os.path.join(path, 'valid.txt'))
self.test = self.tokenize(os.path.join(path, 'test.txt'))
def tokenize(self, path):
"""Tokenizes a text file."""
assert os.path.exists(path)
# Add words to the dictionary
with open(path, 'r', encoding="utf8") as f:
for line in f:
words = line.split() + ['<eos>']
for word in words:
self.dictionary.add_word(word)
# Tokenize file content
with open(path, 'r', encoding="utf8") as f:
idss = []
for line in f:
words = line.split() + ['<eos>']
ids = []
for word in words:
ids.append(self.dictionary.word2idx[word])
idss.append(torch.tensor(ids).type(torch.int64))
ids = torch.cat(idss)
return ids
model_data_filepath = 'data/'
corpus = Corpus(model_data_filepath + 'wikitext-2')
3. 加载预训练模型¶
本教程介绍动态量化，这是一种在模型训练完成后应用的量化技术。因此，我们将简单地将一些预训练权重加载到此模型架构中；这些权重是通过使用词语语言模型示例中的默认设置训练五个时期获得的。
ntokens = len(corpus.dictionary)
model = LSTMModel(
ntoken = ntokens,
ninp = 512,
nhid = 256,
nlayers = 5,
)
model.load_state_dict(
torch.load(
model_data_filepath + 'word_language_model_quantize.pth',
map_location=torch.device('cpu'),
weights_only=True
)
)
model.eval()
print(model)
LSTMModel(
(drop): Dropout(p=0.5, inplace=False)
(encoder): Embedding(33278, 512)
(rnn): LSTM(512, 256, num_layers=5, dropout=0.5)
(decoder): Linear(in_features=256, out_features=33278, bias=True)
)
现在，让我们生成一些文本以确保预训练模型正常工作 - 与之前类似，我们遵循此处的步骤。
input_ = torch.randint(ntokens, (1, 1), dtype=torch.long)
hidden = model.init_hidden(1)
temperature = 1.0
num_words = 1000
with open(model_data_filepath + 'out.txt', 'w') as outf:
with torch.no_grad(): # no tracking history
for i in range(num_words):
output, hidden = model(input_, hidden)
word_weights = output.squeeze().div(temperature).exp().cpu()
word_idx = torch.multinomial(word_weights, 1)[0]
input_.fill_(word_idx)
word = corpus.dictionary.idx2word[word_idx]
outf.write(str(word.encode('utf-8')) + ('\n' if i % 20 == 19 else ' '))
if i % 100 == 0:
print('| Generated {}/{} words'.format(i, 1000))
with open(model_data_filepath + 'out.txt', 'r') as outf:
all_output = outf.read()
print(all_output)
| Generated 0/1000 words
| Generated 100/1000 words
| Generated 200/1000 words
| Generated 300/1000 words
| Generated 400/1000 words
| Generated 500/1000 words
| Generated 600/1000 words
| Generated 700/1000 words
| Generated 800/1000 words
| Generated 900/1000 words
b'.' b'Ross' b"'" b'final' b'focus' b'respects' b'with' b'rice' b'Rajeev' b'implements' b'.' b'<unk>' b'Darwin' b',' b'a' b'comfortably' b',' b'called' b'that' b'it'
b'is' b'"' b'significant' b'alive' b'"' b'from' b'perform' b'@-@' b'hearted' b',' b'can' b'be' b'among' b'what' b'he' b'is' b'a' b'Sixth' b'minister' b'as'
b'a' b'analysis' b',' b'bathtub' b'for' b'1798' b'and' b'an' b'Nourrit' b'who' b'left' b'the' b'same' b'name' b',' b'which' b'they' b'saw' b'to' b'"'
b'let' b'most' b'or' b'me' b'of' b'its' b'all' b'time' b'that' b'might' b'have' b'done' b'on' b'back' b'on' b'their' b'character' b'position' b'.' b'"'
b'<eos>' b'The' b'2010' b'Peach' b'Bird' b"'" b'Union' b'(' b'1888' b')' b',' b'which' b'could' b'be' b'actively' b'composed' b'in' b'London' b'and' b'in'
b'1609' b'.' b'The' b'work' b'have' b'October' b',' b'but' b',' b'since' b'the' b'parish' b'of' b'times' b'is' b'hard' b'and' b'severely' b'ignored' b'the'
b'plums' b',' b'they' b'<unk>' b'or' b'Giuseppe' b'Leo' b'Rodman' b'for' b'the' b'game' b'<unk>' b',' b'and' b'were' b'released' b'and' b'because' b'it' b'apparently'
b'spent' b'before' b'with' b'those' b'arena' b'to' b'deciding' b'.' b'"' b'strumming' b'on' b'You' b'then' b'heard' b'enough' b'that' b'we' b'have' b'rhythm' b'channels'
b'in' b'a' b'video' b'off' b'his' b'complete' b'novel' b'"' b'.' b'The' b'population' b'of' b'Ceres' b'will' b'be' b'negative' b'for' b'strictly' b'@-@' b'hawk'
b'to' b'come' b'into' b'Year' b'1' b'.' b'There' b'is' b'a' b'pair' b'of' b'using' b'526' b',' b'O2' b',' b'nose' b',' b'<unk>' b'and'
b'coalitions' b'with' b'promyelocytic' b'officials' b'were' b'somewhat' b'developing' b'.' b'The' b'work' b'would' b'be' b'tested' b'as' b'a' b'hunt' b'to' b'Castle' b'network' b'including'
b'possible' b'gear' b'.' b'<eos>' b'<eos>' b'=' b'=' b'Behavior' b'=' b'=' b'<eos>' b'<eos>' b'<unk>' b'Michael' b'David' b'J.' b'M.' b'hilarious' b'(' b'died'
b'port' b'6' b':' b'12' b'<eos>' b'Ffordd' b'admirable' b'reality' b')' b'<eos>' b'trade' b'classifications' b',' b'without' b'a' b'creator' b';' b'of' b'even' b'@-@'
b'narial' b'earth' b',' b'building' b'rare' b'sounds' b',' b'Ridgway' b'contents' b',' b'any' b'GAA' b'in' b'air' b',' b'bleeding' b'.' b'<eos>' b'John' b'Leonard'
b'Rick' b'Smith' b'(' b'Evangeline' b'J.' b'Male' b')' b',' b'who' b'are' b'also' b'known' b'to' b'be' b'generally' b'portrayed' b'as' b'director' b'of' b'the'
b'Roman' b'origin' b'of' b'Sport' b'@-@' b'class' b'consent' b',' b'a' b'new' b'example' b'of' b'high' b'non' b'@-@' b'Crusader' b'forces' b'could' b'be' b'found'
b'by' b'<unk>' b'the' b'death' b'of' b'fish' b'highways' b'.' b'<eos>' b'<eos>' b'=' b'=' b'Background' b'=' b'=' b'<eos>' b'<eos>' b'The' b'majority' b'of'
b'year' b',' b'Superman' b',' b'was' b'also' b'built' b'into' b'alphabet' b'.' b'The' b'NW' b'were' b'written' b'by' b'other' b'astronomers' b'such' b'as' b'<unk>'
b'Jermaine' b'Farr' b',' b'with' b'respond' b'to' b'power' b'(' b'reorganize' b')' b'.' b'These' b'birds' b'have' b'had' b'hosted' b'North' b'AIDS' b'since' b'vocalization'
b'.' b'It' b'depicting' b'an' b'Normal' b'female' b'extended' b'after' b',' b'leaving' b'Petrie' b'resembled' b'Taylor' b'issues' b'has' b'significant' b'governmental' b'features' b',' b'called'
b'it' b',' b'"' b'Parts' b'as' b'well' b'to' b'kill' b'us' b'from' b'Haifa' b'is' b'an' b'gift' b'off' b'them' b'.' b'"' b'In' b'a'
b'review' b'that' b'Downs' b',' b'"' b'Every' b'blames' b'recent' b'human' b'parallels' b'you' b'is' b'Zeller' b'envisioned' b',' b'you' b'The' b'last' b'an' b'middle'
b'adult' b'person' b'in' b'ratio' b'of' b'male' b'throwing' b'lists' b'daily' b'letters' b'even' b',' b'attack' b',' b'and' b'inflict' b'you' b'into' b'Lost' b','
b'but' b'you' b'Rock' b'have' b'access' b'to' b'the' b'Mendip' b'conception' b'who' b"'re" b'overthrow' b'what' b'everything' b'in' b'than' b'store' b'particles' b'.' b'"'
b'The' b'face' b'recognized' b'Innis' b'was' b'of' b'unrepentant' b'Ulaid' b'.' b'glider' b'rent' b'for' b'Sister' b'Weber' b'are' b'exposing' b'to' b'seek' b'during' b'the'
b'hear' b'film' b'dislike' b"'s" b'staged' b'alignment' b'.' b'Another' b'cloth' b'was' b'only' b'impressed' b'by' b'Lab' b',' b'they' b'also' b'occasionally' b'learnt' b'a'
b'listener' b'.' b'<eos>' b'As' b'Plunkett' b"'s" b'death' b',' b'many' b'images' b'entrusted' b'to' b'join' b'items' b'display' b'models' b'than' b'foot' b'in' b'British'
b'countries' b'.' b'<unk>' b'indicated' b'is' b'also' b'safe' b'to' b'decide' b'down' b'McFarland' b',' b'even' b'that' b'searching' b'approaches' b'a' b'winds' b'for' b'two'
b'years' b'of' b'established' b'.' b'It' b'is' b'safe' b'that' b'<unk>' b'responded' b'in' b'(' b'the' b'19th' b'century' b',' b'including' b'A.' b"'\xc3\xa9tat" b';'
b'it' b'will' b'be' b'in' b'their' b'longer' b',' b'propel' b'"' b'<unk>' b'"' b',' b'which' b'aiding' b'God' b'@-@' b'black' b'overly' b',' b'astronomical'
b',' b'business' b',' b'<unk>' b',' b'<unk>' b',' b'or' b'grey' b'timeline' b'by' b'dismissal' b'before' b'mutualistic' b',' b'and' b'substrate' b'attention' b'given' b'as'
b'a' b'certain' b'species' b'of' b'153' b'stages' b'.' b'<unk>' b'in' b'toilet' b'can' b'be' b'found' b'to' b'signs' b'of' b'450' b',' b'compared' b'to'
b'50' b'%' b'closer' b',' b'while' b'manuscripts' b'may' b'be' b'"' b'distinguished' b'it' b'"' b'.' b'Incubation' b'resemble' b'Jordan' b'a' b'extremes' b',' b'Illinois'
b'concluding' b'much' b'of' b'the' b'player' b"'s" b'earlier' b'the' b'<unk>' b'broods' b'policies' b'.' b'<eos>' b'As' b'a' b'year' b',' b'he' b'is' b'found'
b'to' b'scare' b'taking' b'place' b'upon' b'behind' b'other' b'device' b',' b'including' b'its' b'further' b'sequence' b',' b'which' b'saw' b'him' b'a' b'painting' b'of'
b'conspiracy' b'that' b'enters' b'<unk>' b'to' b'cook' b'.' b'By' b'this' b'attacks' b',' b'they' b'are' b'shown' b'that' b'<unk>' b'(' b'an' b'one' b'@-@'
b'year' b')' b',' b'"' b'vision' b'(' b'still' b'most' b'equivalent' b'mourning' b')' b',' b'a' b'high' b'man' b'or' b'sings' b'large' b'Bruins' b'and'
b'rifles' b'all' b'by' b'night' b'<unk>' b',' b'not' b'nursing' b'.' b'"' b'Some' b'authors' b'like' b'H.' b'<unk>' b'<unk>' b'is' b'a' b'pure' b'character'
b'.' b'The' b'Admiralty' b'covers' b'Bob' b'cottonwood' b',' b'a' b'reflection' b'that' b'God' b'heard' b'parallel' b'.' b'reporters' b'went' b'forward' b'with' b'his' b'unusually'
b'controversial' b'Fern\xc3\xa1ndez' b',' b'back' b'"' b'that' b'many' b'authors' b"'re" b'forbidden' b'between' b'Black' b'Island' b'worker' b'!' b"'" b'learns' b'"' b'(' b'2006'
b')' b',' b'whose' b'<unk>' b'will' b'be' b'seen' b'as' b'a' b'child' b'.' b'Scully' b'is' b'trouble' b'apart' b'in' b'the' b'nominally' b',' b'and'
b'only' b'they' b'can' b'not' b'specifically' b'specify' b'after' b'they' b'could' b'be' b'rapidly' b'known' b'.' b'However' b',' b'it' b'may' b'assassinate' b'double' b'in'
b'other' b'ways' b',' b'even' b'because' b'he' b'provide' b'11' b'shock' b',' b'<unk>' b'the' b'Canary' b'Sun' b'breaker' b'.' b'<unk>' b'even' b'<unk>' b'by'
b'a' b'variety' b'of' b'other' b'factors' b',' b'which' b'Canterbury' b'doesn' b"'t" b'be' b'named' b'as' b'they' b'have' b'the' b'127th' b'mention' b'.' b'flocks'
b'fail' b'to' b'be' b'Allah' b',' b'depressed' b'peninsula' b',' b'<unk>' b',' b'and' b'@-@' b'head' b'ice' b'<unk>' b',' b'which' b'may' b'be' b'applied'
b'to' b'both' b'New' b'Zealand' b'.' b'The' b'food' b'and' b'so' b'they' b'can' b'react' b'into' b'Blue' b'or' b'eye' b'itself' b'.' b'They' b'may'
b'improve' b'their' b'position' b'complimented' b'up' b'or' b'place' b'resulted' b'on' b'all' b'Alfa' b'to' b'keep' b'care' b'of' b'Ceres' b',' b'orbiting' b'or' b'wide'
b',' b'then' b'by' b'its' b'space' b'.' b'<unk>' b',' b'they' b'were' b'will' b'try' b'the' b'kakapo' b'of' b'unusual' b',' b'<unk>' b'<unk>' b'or'
b'synthesize' b'Dead' b'(' b'860' b'<unk>' b'<unk>' b')' b'on' b'Activision' b'rather' b'@-@' b'thirds' b'of' b'spotlight' b'its' b'spectrum' b':' b'dying' b',' b'when'
b'British' b'behaviour' b'was' b'a' b'calculate' b'compound' b'to' b'merge' b',' b'with' b'some' b'chicks' b'to' b'use' b'their' b'bestow' b'.' b'It' b'may' b'indicate'
虽然它不是GPT-2，但看起来模型已经开始学习语言的结构了！
我们几乎准备好演示动态量化了。我们只需要定义几个辅助函数。
bptt = 25
criterion = nn.CrossEntropyLoss()
eval_batch_size = 1
# create test data set
def batchify(data, bsz):
# Work out how cleanly we can divide the dataset into ``bsz`` parts.
nbatch = data.size(0) // bsz
# Trim off any extra elements that wouldn't cleanly fit (remainders).
data = data.narrow(0, 0, nbatch * bsz)
# Evenly divide the data across the ``bsz`` batches.
return data.view(bsz, -1).t().contiguous()
test_data = batchify(corpus.test, eval_batch_size)
# Evaluation functions
def get_batch(source, i):
seq_len = min(bptt, len(source) - 1 - i)
data = source[i:i+seq_len]
target = source[i+1:i+1+seq_len].reshape(-1)
return data, target
def repackage_hidden(h):
"""Wraps hidden states in new Tensors, to detach them from their history."""
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
def evaluate(model_, data_source):
# Turn on evaluation mode which disables dropout.
model_.eval()
total_loss = 0.
hidden = model_.init_hidden(eval_batch_size)
with torch.no_grad():
for i in range(0, data_source.size(0) - 1, bptt):
data, targets = get_batch(data_source, i)
output, hidden = model_(data, hidden)
hidden = repackage_hidden(hidden)
output_flat = output.view(-1, ntokens)
total_loss += len(data) * criterion(output_flat, targets).item()
return total_loss / (len(data_source) - 1)
4. 测试动态量化¶
最后，我们可以对模型调用torch.quantization.quantize_dynamic！具体来说，
我们指定希望模型中的
nn.LSTM和nn.Linear模块被量化。我们指定希望将权重转换为
int8值。
import torch.quantization
quantized_model = torch.quantization.quantize_dynamic(
model, {nn.LSTM, nn.Linear}, dtype=torch.qint8
)
print(quantized_model)
LSTMModel(
(drop): Dropout(p=0.5, inplace=False)
(encoder): Embedding(33278, 512)
(rnn): DynamicQuantizedLSTM(512, 256, num_layers=5, dropout=0.5)
(decoder): DynamicQuantizedLinear(in_features=256, out_features=33278, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
)
模型看起来相同；这给我们带来了什么好处？首先，我们看到模型大小显著减小。
def print_size_of_model(model):
torch.save(model.state_dict(), "temp.p")
print('Size (MB):', os.path.getsize("temp.p")/1e6)
os.remove('temp.p')
print_size_of_model(model)
print_size_of_model(quantized_model)
Size (MB): 113.944064
Size (MB): 79.738484
其次，我们看到推理时间更快，而评估损失没有变化。
注意：我们为单线程比较将线程数设置为1，因为量化模型以单线程运行。
torch.set_num_threads(1)
def time_model_evaluation(model, test_data):
s = time.time()
loss = evaluate(model, test_data)
elapsed = time.time() - s
print('''loss: {0:.3f}\nelapsed time (seconds): {1:.1f}'''.format(loss, elapsed))
time_model_evaluation(model, test_data)
time_model_evaluation(quantized_model, test_data)
loss: 5.167
elapsed time (seconds): 201.9
loss: 5.168
elapsed time (seconds): 117.3
在MacBook Pro上本地运行，不使用量化，推理大约需要200秒，而使用量化则只需大约100秒。
