chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,271 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# code by Tae Hwan Jung(Jeff Jung) @graykode\n",
|
||||
"# Reference : https://github.com/jadore801120/attention-is-all-you-need-pytorch\n",
|
||||
"# https://github.com/JayParks/transformer, https://github.com/dhlee347/pytorchic-bert\n",
|
||||
"import math\n",
|
||||
"import re\n",
|
||||
"from random import *\n",
|
||||
"import numpy as np\n",
|
||||
"import torch\n",
|
||||
"import torch.nn as nn\n",
|
||||
"import torch.optim as optim\n",
|
||||
"\n",
|
||||
"# sample IsNext and NotNext to be same in small batch size\n",
|
||||
"def make_batch():\n",
|
||||
" batch = []\n",
|
||||
" positive = negative = 0\n",
|
||||
" while positive != batch_size/2 or negative != batch_size/2:\n",
|
||||
" tokens_a_index, tokens_b_index= randrange(len(sentences)), randrange(len(sentences)) # sample random index in sentences\n",
|
||||
" tokens_a, tokens_b= token_list[tokens_a_index], token_list[tokens_b_index]\n",
|
||||
" input_ids = [word_dict['[CLS]']] + tokens_a + [word_dict['[SEP]']] + tokens_b + [word_dict['[SEP]']]\n",
|
||||
" segment_ids = [0] * (1 + len(tokens_a) + 1) + [1] * (len(tokens_b) + 1)\n",
|
||||
"\n",
|
||||
" # MASK LM\n",
|
||||
" n_pred = min(max_pred, max(1, int(round(len(input_ids) * 0.15)))) # 15 % of tokens in one sentence\n",
|
||||
" cand_maked_pos = [i for i, token in enumerate(input_ids)\n",
|
||||
" if token != word_dict['[CLS]'] and token != word_dict['[SEP]']]\n",
|
||||
" shuffle(cand_maked_pos)\n",
|
||||
" masked_tokens, masked_pos = [], []\n",
|
||||
" for pos in cand_maked_pos[:n_pred]:\n",
|
||||
" masked_pos.append(pos)\n",
|
||||
" masked_tokens.append(input_ids[pos])\n",
|
||||
" if random() < 0.8: # 80%\n",
|
||||
" input_ids[pos] = word_dict['[MASK]'] # make mask\n",
|
||||
" elif random() < 0.5: # 10%\n",
|
||||
" index = randint(0, vocab_size - 1) # random index in vocabulary\n",
|
||||
" input_ids[pos] = word_dict[number_dict[index]] # replace\n",
|
||||
"\n",
|
||||
" # Zero Paddings\n",
|
||||
" n_pad = maxlen - len(input_ids)\n",
|
||||
" input_ids.extend([0] * n_pad)\n",
|
||||
" segment_ids.extend([0] * n_pad)\n",
|
||||
"\n",
|
||||
" # Zero Padding (100% - 15%) tokens\n",
|
||||
" if max_pred > n_pred:\n",
|
||||
" n_pad = max_pred - n_pred\n",
|
||||
" masked_tokens.extend([0] * n_pad)\n",
|
||||
" masked_pos.extend([0] * n_pad)\n",
|
||||
"\n",
|
||||
" if tokens_a_index + 1 == tokens_b_index and positive < batch_size/2:\n",
|
||||
" batch.append([input_ids, segment_ids, masked_tokens, masked_pos, True]) # IsNext\n",
|
||||
" positive += 1\n",
|
||||
" elif tokens_a_index + 1 != tokens_b_index and negative < batch_size/2:\n",
|
||||
" batch.append([input_ids, segment_ids, masked_tokens, masked_pos, False]) # NotNext\n",
|
||||
" negative += 1\n",
|
||||
" return batch\n",
|
||||
"# Proprecessing Finished\n",
|
||||
"\n",
|
||||
"def get_attn_pad_mask(seq_q, seq_k):\n",
|
||||
" batch_size, len_q = seq_q.size()\n",
|
||||
" batch_size, len_k = seq_k.size()\n",
|
||||
" # eq(zero) is PAD token\n",
|
||||
" pad_attn_mask = seq_k.data.eq(0).unsqueeze(1) # batch_size x 1 x len_k(=len_q), one is masking\n",
|
||||
" return pad_attn_mask.expand(batch_size, len_q, len_k) # batch_size x len_q x len_k\n",
|
||||
"\n",
|
||||
"def gelu(x):\n",
|
||||
" \"Implementation of the gelu activation function by Hugging Face\"\n",
|
||||
" return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\n",
|
||||
"\n",
|
||||
"class Embedding(nn.Module):\n",
|
||||
" def __init__(self):\n",
|
||||
" super(Embedding, self).__init__()\n",
|
||||
" self.tok_embed = nn.Embedding(vocab_size, d_model) # token embedding\n",
|
||||
" self.pos_embed = nn.Embedding(maxlen, d_model) # position embedding\n",
|
||||
" self.seg_embed = nn.Embedding(n_segments, d_model) # segment(token type) embedding\n",
|
||||
" self.norm = nn.LayerNorm(d_model)\n",
|
||||
"\n",
|
||||
" def forward(self, x, seg):\n",
|
||||
" seq_len = x.size(1)\n",
|
||||
" pos = torch.arange(seq_len, dtype=torch.long)\n",
|
||||
" pos = pos.unsqueeze(0).expand_as(x) # (seq_len,) -> (batch_size, seq_len)\n",
|
||||
" embedding = self.tok_embed(x) + self.pos_embed(pos) + self.seg_embed(seg)\n",
|
||||
" return self.norm(embedding)\n",
|
||||
"\n",
|
||||
"class ScaledDotProductAttention(nn.Module):\n",
|
||||
" def __init__(self):\n",
|
||||
" super(ScaledDotProductAttention, self).__init__()\n",
|
||||
"\n",
|
||||
" def forward(self, Q, K, V, attn_mask):\n",
|
||||
" scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k) # scores : [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]\n",
|
||||
" scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is one.\n",
|
||||
" attn = nn.Softmax(dim=-1)(scores)\n",
|
||||
" context = torch.matmul(attn, V)\n",
|
||||
" return context, attn\n",
|
||||
"\n",
|
||||
"class MultiHeadAttention(nn.Module):\n",
|
||||
" def __init__(self):\n",
|
||||
" super(MultiHeadAttention, self).__init__()\n",
|
||||
" self.W_Q = nn.Linear(d_model, d_k * n_heads)\n",
|
||||
" self.W_K = nn.Linear(d_model, d_k * n_heads)\n",
|
||||
" self.W_V = nn.Linear(d_model, d_v * n_heads)\n",
|
||||
" def forward(self, Q, K, V, attn_mask):\n",
|
||||
" # q: [batch_size x len_q x d_model], k: [batch_size x len_k x d_model], v: [batch_size x len_k x d_model]\n",
|
||||
" residual, batch_size = Q, Q.size(0)\n",
|
||||
" # (B, S, D) -proj-> (B, S, D) -split-> (B, S, H, W) -trans-> (B, H, S, W)\n",
|
||||
" q_s = self.W_Q(Q).view(batch_size, -1, n_heads, d_k).transpose(1,2) # q_s: [batch_size x n_heads x len_q x d_k]\n",
|
||||
" k_s = self.W_K(K).view(batch_size, -1, n_heads, d_k).transpose(1,2) # k_s: [batch_size x n_heads x len_k x d_k]\n",
|
||||
" v_s = self.W_V(V).view(batch_size, -1, n_heads, d_v).transpose(1,2) # v_s: [batch_size x n_heads x len_k x d_v]\n",
|
||||
"\n",
|
||||
" attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) # attn_mask : [batch_size x n_heads x len_q x len_k]\n",
|
||||
"\n",
|
||||
" # context: [batch_size x n_heads x len_q x d_v], attn: [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]\n",
|
||||
" context, attn = ScaledDotProductAttention()(q_s, k_s, v_s, attn_mask)\n",
|
||||
" context = context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v) # context: [batch_size x len_q x n_heads * d_v]\n",
|
||||
" output = nn.Linear(n_heads * d_v, d_model)(context)\n",
|
||||
" return nn.LayerNorm(d_model)(output + residual), attn # output: [batch_size x len_q x d_model]\n",
|
||||
"\n",
|
||||
"class PoswiseFeedForwardNet(nn.Module):\n",
|
||||
" def __init__(self):\n",
|
||||
" super(PoswiseFeedForwardNet, self).__init__()\n",
|
||||
" self.fc1 = nn.Linear(d_model, d_ff)\n",
|
||||
" self.fc2 = nn.Linear(d_ff, d_model)\n",
|
||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" # (batch_size, len_seq, d_model) -> (batch_size, len_seq, d_ff) -> (batch_size, len_seq, d_model)\n",
|
||||
" return self.fc2(gelu(self.fc1(x)))\n",
|
||||
"\n",
|
||||
"class EncoderLayer(nn.Module):\n",
|
||||
" def __init__(self):\n",
|
||||
" super(EncoderLayer, self).__init__()\n",
|
||||
" self.enc_self_attn = MultiHeadAttention()\n",
|
||||
" self.pos_ffn = PoswiseFeedForwardNet()\n",
|
||||
"\n",
|
||||
" def forward(self, enc_inputs, enc_self_attn_mask):\n",
|
||||
" enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs, enc_self_attn_mask) # enc_inputs to same Q,K,V\n",
|
||||
" enc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size x len_q x d_model]\n",
|
||||
" return enc_outputs, attn\n",
|
||||
"\n",
|
||||
"class BERT(nn.Module):\n",
|
||||
" def __init__(self):\n",
|
||||
" super(BERT, self).__init__()\n",
|
||||
" self.embedding = Embedding()\n",
|
||||
" self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])\n",
|
||||
" self.fc = nn.Linear(d_model, d_model)\n",
|
||||
" self.activ1 = nn.Tanh()\n",
|
||||
" self.linear = nn.Linear(d_model, d_model)\n",
|
||||
" self.activ2 = gelu\n",
|
||||
" self.norm = nn.LayerNorm(d_model)\n",
|
||||
" self.classifier = nn.Linear(d_model, 2)\n",
|
||||
" # decoder is shared with embedding layer\n",
|
||||
" embed_weight = self.embedding.tok_embed.weight\n",
|
||||
" n_vocab, n_dim = embed_weight.size()\n",
|
||||
" self.decoder = nn.Linear(n_dim, n_vocab, bias=False)\n",
|
||||
" self.decoder.weight = embed_weight\n",
|
||||
" self.decoder_bias = nn.Parameter(torch.zeros(n_vocab))\n",
|
||||
"\n",
|
||||
" def forward(self, input_ids, segment_ids, masked_pos):\n",
|
||||
" output = self.embedding(input_ids, segment_ids)\n",
|
||||
" enc_self_attn_mask = get_attn_pad_mask(input_ids, input_ids)\n",
|
||||
" for layer in self.layers:\n",
|
||||
" output, enc_self_attn = layer(output, enc_self_attn_mask)\n",
|
||||
" # output : [batch_size, len, d_model], attn : [batch_size, n_heads, d_mode, d_model]\n",
|
||||
" # it will be decided by first token(CLS)\n",
|
||||
" h_pooled = self.activ1(self.fc(output[:, 0])) # [batch_size, d_model]\n",
|
||||
" logits_clsf = self.classifier(h_pooled) # [batch_size, 2]\n",
|
||||
"\n",
|
||||
" masked_pos = masked_pos[:, :, None].expand(-1, -1, output.size(-1)) # [batch_size, max_pred, d_model]\n",
|
||||
" # get masked position from final output of transformer.\n",
|
||||
" h_masked = torch.gather(output, 1, masked_pos) # masking position [batch_size, max_pred, d_model]\n",
|
||||
" h_masked = self.norm(self.activ2(self.linear(h_masked)))\n",
|
||||
" logits_lm = self.decoder(h_masked) + self.decoder_bias # [batch_size, max_pred, n_vocab]\n",
|
||||
"\n",
|
||||
" return logits_lm, logits_clsf\n",
|
||||
"\n",
|
||||
"if __name__ == '__main__':\n",
|
||||
" # BERT Parameters\n",
|
||||
" maxlen = 30 # maximum of length\n",
|
||||
" batch_size = 6\n",
|
||||
" max_pred = 5 # max tokens of prediction\n",
|
||||
" n_layers = 6 # number of Encoder of Encoder Layer\n",
|
||||
" n_heads = 12 # number of heads in Multi-Head Attention\n",
|
||||
" d_model = 768 # Embedding Size\n",
|
||||
" d_ff = 768 * 4 # 4*d_model, FeedForward dimension\n",
|
||||
" d_k = d_v = 64 # dimension of K(=Q), V\n",
|
||||
" n_segments = 2\n",
|
||||
"\n",
|
||||
" text = (\n",
|
||||
" 'Hello, how are you? I am Romeo.\\n'\n",
|
||||
" 'Hello, Romeo My name is Juliet. Nice to meet you.\\n'\n",
|
||||
" 'Nice meet you too. How are you today?\\n'\n",
|
||||
" 'Great. My baseball team won the competition.\\n'\n",
|
||||
" 'Oh Congratulations, Juliet\\n'\n",
|
||||
" 'Thanks you Romeo'\n",
|
||||
" )\n",
|
||||
" sentences = re.sub(\"[.,!?\\\\-]\", '', text.lower()).split('\\n') # filter '.', ',', '?', '!'\n",
|
||||
" word_list = list(set(\" \".join(sentences).split()))\n",
|
||||
" word_dict = {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[MASK]': 3}\n",
|
||||
" for i, w in enumerate(word_list):\n",
|
||||
" word_dict[w] = i + 4\n",
|
||||
" number_dict = {i: w for i, w in enumerate(word_dict)}\n",
|
||||
" vocab_size = len(word_dict)\n",
|
||||
"\n",
|
||||
" token_list = list()\n",
|
||||
" for sentence in sentences:\n",
|
||||
" arr = [word_dict[s] for s in sentence.split()]\n",
|
||||
" token_list.append(arr)\n",
|
||||
"\n",
|
||||
" model = BERT()\n",
|
||||
" criterion = nn.CrossEntropyLoss()\n",
|
||||
" optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
|
||||
"\n",
|
||||
" batch = make_batch()\n",
|
||||
" input_ids, segment_ids, masked_tokens, masked_pos, isNext = map(torch.LongTensor, zip(*batch))\n",
|
||||
"\n",
|
||||
" for epoch in range(100):\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" logits_lm, logits_clsf = model(input_ids, segment_ids, masked_pos)\n",
|
||||
" loss_lm = criterion(logits_lm.transpose(1, 2), masked_tokens) # for masked LM\n",
|
||||
" loss_lm = (loss_lm.float()).mean()\n",
|
||||
" loss_clsf = criterion(logits_clsf, isNext) # for sentence classification\n",
|
||||
" loss = loss_lm + loss_clsf\n",
|
||||
" if (epoch + 1) % 10 == 0:\n",
|
||||
" print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()\n",
|
||||
"\n",
|
||||
" # Predict mask tokens ans isNext\n",
|
||||
" input_ids, segment_ids, masked_tokens, masked_pos, isNext = map(torch.LongTensor, zip(batch[0]))\n",
|
||||
" print(text)\n",
|
||||
" print([number_dict[w.item()] for w in input_ids[0] if number_dict[w.item()] != '[PAD]'])\n",
|
||||
"\n",
|
||||
" logits_lm, logits_clsf = model(input_ids, segment_ids, masked_pos)\n",
|
||||
" logits_lm = logits_lm.data.max(2)[1][0].data.numpy()\n",
|
||||
" print('masked tokens list : ',[pos.item() for pos in masked_tokens[0] if pos.item() != 0])\n",
|
||||
" print('predict masked tokens list : ',[pos for pos in logits_lm if pos != 0])\n",
|
||||
"\n",
|
||||
" logits_clsf = logits_clsf.data.max(1)[1].data.numpy()[0]\n",
|
||||
" print('isNext : ', True if isNext else False)\n",
|
||||
" print('predict isNext : ',True if logits_clsf else False)\n"
|
||||
],
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -0,0 +1,238 @@
|
||||
# %%
|
||||
# code by Tae Hwan Jung(Jeff Jung) @graykode
|
||||
# Reference : https://github.com/jadore801120/attention-is-all-you-need-pytorch
|
||||
# https://github.com/JayParks/transformer, https://github.com/dhlee347/pytorchic-bert
|
||||
import math
|
||||
import re
|
||||
from random import *
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
|
||||
# sample IsNext and NotNext to be same in small batch size
|
||||
def make_batch():
|
||||
batch = []
|
||||
positive = negative = 0
|
||||
while positive != batch_size/2 or negative != batch_size/2:
|
||||
tokens_a_index, tokens_b_index= randrange(len(sentences)), randrange(len(sentences)) # sample random index in sentences
|
||||
tokens_a, tokens_b= token_list[tokens_a_index], token_list[tokens_b_index]
|
||||
input_ids = [word_dict['[CLS]']] + tokens_a + [word_dict['[SEP]']] + tokens_b + [word_dict['[SEP]']]
|
||||
segment_ids = [0] * (1 + len(tokens_a) + 1) + [1] * (len(tokens_b) + 1)
|
||||
|
||||
# MASK LM
|
||||
n_pred = min(max_pred, max(1, int(round(len(input_ids) * 0.15)))) # 15 % of tokens in one sentence
|
||||
cand_maked_pos = [i for i, token in enumerate(input_ids)
|
||||
if token != word_dict['[CLS]'] and token != word_dict['[SEP]']]
|
||||
shuffle(cand_maked_pos)
|
||||
masked_tokens, masked_pos = [], []
|
||||
for pos in cand_maked_pos[:n_pred]:
|
||||
masked_pos.append(pos)
|
||||
masked_tokens.append(input_ids[pos])
|
||||
if random() < 0.8: # 80%
|
||||
input_ids[pos] = word_dict['[MASK]'] # make mask
|
||||
elif random() < 0.5: # 10%
|
||||
index = randint(0, vocab_size - 1) # random index in vocabulary
|
||||
input_ids[pos] = word_dict[number_dict[index]] # replace
|
||||
|
||||
# Zero Paddings
|
||||
n_pad = maxlen - len(input_ids)
|
||||
input_ids.extend([0] * n_pad)
|
||||
segment_ids.extend([0] * n_pad)
|
||||
|
||||
# Zero Padding (100% - 15%) tokens
|
||||
if max_pred > n_pred:
|
||||
n_pad = max_pred - n_pred
|
||||
masked_tokens.extend([0] * n_pad)
|
||||
masked_pos.extend([0] * n_pad)
|
||||
|
||||
if tokens_a_index + 1 == tokens_b_index and positive < batch_size/2:
|
||||
batch.append([input_ids, segment_ids, masked_tokens, masked_pos, True]) # IsNext
|
||||
positive += 1
|
||||
elif tokens_a_index + 1 != tokens_b_index and negative < batch_size/2:
|
||||
batch.append([input_ids, segment_ids, masked_tokens, masked_pos, False]) # NotNext
|
||||
negative += 1
|
||||
return batch
|
||||
# Proprecessing Finished
|
||||
|
||||
def get_attn_pad_mask(seq_q, seq_k):
|
||||
batch_size, len_q = seq_q.size()
|
||||
batch_size, len_k = seq_k.size()
|
||||
# eq(zero) is PAD token
|
||||
pad_attn_mask = seq_k.data.eq(0).unsqueeze(1) # batch_size x 1 x len_k(=len_q), one is masking
|
||||
return pad_attn_mask.expand(batch_size, len_q, len_k) # batch_size x len_q x len_k
|
||||
|
||||
def gelu(x):
|
||||
"Implementation of the gelu activation function by Hugging Face"
|
||||
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
|
||||
|
||||
class Embedding(nn.Module):
|
||||
def __init__(self):
|
||||
super(Embedding, self).__init__()
|
||||
self.tok_embed = nn.Embedding(vocab_size, d_model) # token embedding
|
||||
self.pos_embed = nn.Embedding(maxlen, d_model) # position embedding
|
||||
self.seg_embed = nn.Embedding(n_segments, d_model) # segment(token type) embedding
|
||||
self.norm = nn.LayerNorm(d_model)
|
||||
|
||||
def forward(self, x, seg):
|
||||
seq_len = x.size(1)
|
||||
pos = torch.arange(seq_len, dtype=torch.long)
|
||||
pos = pos.unsqueeze(0).expand_as(x) # (seq_len,) -> (batch_size, seq_len)
|
||||
embedding = self.tok_embed(x) + self.pos_embed(pos) + self.seg_embed(seg)
|
||||
return self.norm(embedding)
|
||||
|
||||
class ScaledDotProductAttention(nn.Module):
|
||||
def __init__(self):
|
||||
super(ScaledDotProductAttention, self).__init__()
|
||||
|
||||
def forward(self, Q, K, V, attn_mask):
|
||||
scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k) # scores : [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]
|
||||
scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is one.
|
||||
attn = nn.Softmax(dim=-1)(scores)
|
||||
context = torch.matmul(attn, V)
|
||||
return context, attn
|
||||
|
||||
class MultiHeadAttention(nn.Module):
|
||||
def __init__(self):
|
||||
super(MultiHeadAttention, self).__init__()
|
||||
self.W_Q = nn.Linear(d_model, d_k * n_heads)
|
||||
self.W_K = nn.Linear(d_model, d_k * n_heads)
|
||||
self.W_V = nn.Linear(d_model, d_v * n_heads)
|
||||
def forward(self, Q, K, V, attn_mask):
|
||||
# q: [batch_size x len_q x d_model], k: [batch_size x len_k x d_model], v: [batch_size x len_k x d_model]
|
||||
residual, batch_size = Q, Q.size(0)
|
||||
# (B, S, D) -proj-> (B, S, D) -split-> (B, S, H, W) -trans-> (B, H, S, W)
|
||||
q_s = self.W_Q(Q).view(batch_size, -1, n_heads, d_k).transpose(1,2) # q_s: [batch_size x n_heads x len_q x d_k]
|
||||
k_s = self.W_K(K).view(batch_size, -1, n_heads, d_k).transpose(1,2) # k_s: [batch_size x n_heads x len_k x d_k]
|
||||
v_s = self.W_V(V).view(batch_size, -1, n_heads, d_v).transpose(1,2) # v_s: [batch_size x n_heads x len_k x d_v]
|
||||
|
||||
attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) # attn_mask : [batch_size x n_heads x len_q x len_k]
|
||||
|
||||
# context: [batch_size x n_heads x len_q x d_v], attn: [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]
|
||||
context, attn = ScaledDotProductAttention()(q_s, k_s, v_s, attn_mask)
|
||||
context = context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v) # context: [batch_size x len_q x n_heads * d_v]
|
||||
output = nn.Linear(n_heads * d_v, d_model)(context)
|
||||
return nn.LayerNorm(d_model)(output + residual), attn # output: [batch_size x len_q x d_model]
|
||||
|
||||
class PoswiseFeedForwardNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(PoswiseFeedForwardNet, self).__init__()
|
||||
self.fc1 = nn.Linear(d_model, d_ff)
|
||||
self.fc2 = nn.Linear(d_ff, d_model)
|
||||
|
||||
def forward(self, x):
|
||||
# (batch_size, len_seq, d_model) -> (batch_size, len_seq, d_ff) -> (batch_size, len_seq, d_model)
|
||||
return self.fc2(gelu(self.fc1(x)))
|
||||
|
||||
class EncoderLayer(nn.Module):
|
||||
def __init__(self):
|
||||
super(EncoderLayer, self).__init__()
|
||||
self.enc_self_attn = MultiHeadAttention()
|
||||
self.pos_ffn = PoswiseFeedForwardNet()
|
||||
|
||||
def forward(self, enc_inputs, enc_self_attn_mask):
|
||||
enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs, enc_self_attn_mask) # enc_inputs to same Q,K,V
|
||||
enc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size x len_q x d_model]
|
||||
return enc_outputs, attn
|
||||
|
||||
class BERT(nn.Module):
|
||||
def __init__(self):
|
||||
super(BERT, self).__init__()
|
||||
self.embedding = Embedding()
|
||||
self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])
|
||||
self.fc = nn.Linear(d_model, d_model)
|
||||
self.activ1 = nn.Tanh()
|
||||
self.linear = nn.Linear(d_model, d_model)
|
||||
self.activ2 = gelu
|
||||
self.norm = nn.LayerNorm(d_model)
|
||||
self.classifier = nn.Linear(d_model, 2)
|
||||
# decoder is shared with embedding layer
|
||||
embed_weight = self.embedding.tok_embed.weight
|
||||
n_vocab, n_dim = embed_weight.size()
|
||||
self.decoder = nn.Linear(n_dim, n_vocab, bias=False)
|
||||
self.decoder.weight = embed_weight
|
||||
self.decoder_bias = nn.Parameter(torch.zeros(n_vocab))
|
||||
|
||||
def forward(self, input_ids, segment_ids, masked_pos):
|
||||
output = self.embedding(input_ids, segment_ids)
|
||||
enc_self_attn_mask = get_attn_pad_mask(input_ids, input_ids)
|
||||
for layer in self.layers:
|
||||
output, enc_self_attn = layer(output, enc_self_attn_mask)
|
||||
# output : [batch_size, len, d_model], attn : [batch_size, n_heads, d_mode, d_model]
|
||||
# it will be decided by first token(CLS)
|
||||
h_pooled = self.activ1(self.fc(output[:, 0])) # [batch_size, d_model]
|
||||
logits_clsf = self.classifier(h_pooled) # [batch_size, 2]
|
||||
|
||||
masked_pos = masked_pos[:, :, None].expand(-1, -1, output.size(-1)) # [batch_size, max_pred, d_model]
|
||||
# get masked position from final output of transformer.
|
||||
h_masked = torch.gather(output, 1, masked_pos) # masking position [batch_size, max_pred, d_model]
|
||||
h_masked = self.norm(self.activ2(self.linear(h_masked)))
|
||||
logits_lm = self.decoder(h_masked) + self.decoder_bias # [batch_size, max_pred, n_vocab]
|
||||
|
||||
return logits_lm, logits_clsf
|
||||
|
||||
if __name__ == '__main__':
|
||||
# BERT Parameters
|
||||
maxlen = 30 # maximum of length
|
||||
batch_size = 6
|
||||
max_pred = 5 # max tokens of prediction
|
||||
n_layers = 6 # number of Encoder of Encoder Layer
|
||||
n_heads = 12 # number of heads in Multi-Head Attention
|
||||
d_model = 768 # Embedding Size
|
||||
d_ff = 768 * 4 # 4*d_model, FeedForward dimension
|
||||
d_k = d_v = 64 # dimension of K(=Q), V
|
||||
n_segments = 2
|
||||
|
||||
text = (
|
||||
'Hello, how are you? I am Romeo.\n'
|
||||
'Hello, Romeo My name is Juliet. Nice to meet you.\n'
|
||||
'Nice meet you too. How are you today?\n'
|
||||
'Great. My baseball team won the competition.\n'
|
||||
'Oh Congratulations, Juliet\n'
|
||||
'Thanks you Romeo'
|
||||
)
|
||||
sentences = re.sub("[.,!?\\-]", '', text.lower()).split('\n') # filter '.', ',', '?', '!'
|
||||
word_list = list(set(" ".join(sentences).split()))
|
||||
word_dict = {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[MASK]': 3}
|
||||
for i, w in enumerate(word_list):
|
||||
word_dict[w] = i + 4
|
||||
number_dict = {i: w for i, w in enumerate(word_dict)}
|
||||
vocab_size = len(word_dict)
|
||||
|
||||
token_list = list()
|
||||
for sentence in sentences:
|
||||
arr = [word_dict[s] for s in sentence.split()]
|
||||
token_list.append(arr)
|
||||
|
||||
model = BERT()
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = optim.Adam(model.parameters(), lr=0.001)
|
||||
|
||||
batch = make_batch()
|
||||
input_ids, segment_ids, masked_tokens, masked_pos, isNext = map(torch.LongTensor, zip(*batch))
|
||||
|
||||
for epoch in range(100):
|
||||
optimizer.zero_grad()
|
||||
logits_lm, logits_clsf = model(input_ids, segment_ids, masked_pos)
|
||||
loss_lm = criterion(logits_lm.transpose(1, 2), masked_tokens) # for masked LM
|
||||
loss_lm = (loss_lm.float()).mean()
|
||||
loss_clsf = criterion(logits_clsf, isNext) # for sentence classification
|
||||
loss = loss_lm + loss_clsf
|
||||
if (epoch + 1) % 10 == 0:
|
||||
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# Predict mask tokens ans isNext
|
||||
input_ids, segment_ids, masked_tokens, masked_pos, isNext = map(torch.LongTensor, zip(batch[0]))
|
||||
print(text)
|
||||
print([number_dict[w.item()] for w in input_ids[0] if number_dict[w.item()] != '[PAD]'])
|
||||
|
||||
logits_lm, logits_clsf = model(input_ids, segment_ids, masked_pos)
|
||||
logits_lm = logits_lm.data.max(2)[1][0].data.numpy()
|
||||
print('masked tokens list : ',[pos.item() for pos in masked_tokens[0] if pos.item() != 0])
|
||||
print('predict masked tokens list : ',[pos for pos in logits_lm if pos != 0])
|
||||
|
||||
logits_clsf = logits_clsf.data.max(1)[1].data.numpy()[0]
|
||||
print('isNext : ', True if isNext else False)
|
||||
print('predict isNext : ',True if logits_clsf else False)
|
||||
Reference in New Issue
Block a user