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