From 4e438afdfa3baf62b7372e8cbd2833acebc9a40b Mon Sep 17 00:00:00 2001 From: gusdnd852 Date: Sat, 21 Dec 2019 18:08:37 +0900 Subject: [PATCH] transformer small training finish --- .idea/workspace.xml | 15 +- README.md | 405 +++++++++++++++++++++++- __pycache__/conf.cpython-36.pyc | Bin 658 -> 658 bytes conf.py | 8 +- image/enc_dec.jpg | Bin 0 -> 99696 bytes image/layer_norm.jpg | Bin 0 -> 12047 bytes image/multi_head_attention-2.jpg | Bin 0 -> 14754 bytes image/multi_head_attention.jpg | Bin 0 -> 11181 bytes image/positional_encoding-2.jpg | Bin 0 -> 38222 bytes image/positional_encoding.jpg | Bin 0 -> 12485 bytes image/positionwise_feed_forward.jpg | Bin 0 -> 33159 bytes image/scale_dot_product_attention-2.jpg | Bin 0 -> 11244 bytes image/scale_dot_product_attention.jpg | Bin 0 -> 5285 bytes image/train_result.jpg | Bin 49061 -> 0 bytes result/test.txt | 2 +- result/train.txt | 2 +- test.py | 92 +++++- 17 files changed, 491 insertions(+), 33 deletions(-) create mode 100644 image/enc_dec.jpg create mode 100644 image/layer_norm.jpg create mode 100644 image/multi_head_attention-2.jpg create mode 100644 image/multi_head_attention.jpg create mode 100644 image/positional_encoding-2.jpg create mode 100644 image/positional_encoding.jpg create mode 100644 image/positionwise_feed_forward.jpg create mode 100644 image/scale_dot_product_attention-2.jpg create mode 100644 image/scale_dot_product_attention.jpg delete mode 100644 image/train_result.jpg diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 1e2bab8..279b63d 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -2,9 +2,10 @@ - + + @@ -91,7 +92,7 @@ - + - + @@ -249,7 +250,7 @@ - + @@ -274,7 +275,7 @@ - + @@ -284,12 +285,12 @@ - + - + \ No newline at end of file diff --git a/README.md b/README.md index 121e3e8..20a57a0 100644 --- a/README.md +++ b/README.md @@ -4,22 +4,417 @@ My own implementation Transformer model (Attention is All You Need - Google Brai

![model](image/model.png)

+## 1. Implementations -## Experiments -![table](saved/train_result.jpg) -
+### 1.1 Positional Encoding + +![model](image/positional_encoding-2.jpg) + +![model](image/positional_encoding.jpg) + + +```python +class PositionalEncoding(nn.Module): + """ + compute sinusoid encoding. + """ + def __init__(self, d_model, max_len, device): + """ + constructor of sinusoid encoding class + + :param d_model: dimension of model + :param max_len: max sequence length + :param device: hardware device setting + """ + super(PositionalEncoding, self).__init__() + + # same size with input matrix (for adding with input matrix) + self.encoding = torch.zeros(max_len, d_model, device=device) + self.encoding.requires_grad = False # we don't need to grad + + pos = torch.arange(0, max_len, device=device) + pos = pos.float().unsqueeze(dim=1) + # 1D => 2D unsqueeze to represent word's position + + _2i = torch.arange(0, d_model, step=2, device=device).float() + # 'i' means index of d_model (e.g. embedding size = 50, 'i' = [0,50]) + # "step=2" means 'i' multiplied with two (same with 2 * i) + + self.encoding[:, 0::2] = torch.sin(pos / (10000 ** (_2i / d_model))) + self.encoding[:, 1::2] = torch.cos(pos / (10000 ** (_2i / d_model))) + # compute positional encoding to consider positional information of words + + def forward(self, x): + # self.encoding + # [max_len = 512, d_model = 512] + + batch_size, seq_len = x.size() + # [batch_size = 128, seq_len = 30] + + return self.encoding[:seq_len, :] + # [seq_len = 30, d_model = 512] + # it will add with tok_emb : [128, 30, 512] + +``` +

+ +### 1.2 Multi-Head Attention + +![model](image/multi_head_attention-2.jpg) + +![model](image/multi_head_attention.jpg) + +```python +class MultiHeadAttention(nn.Module): + + def __init__(self, d_model, n_head): + super(MultiHeadAttention, self).__init__() + self.n_head = n_head + self.attention = ScaleDotProductAttention() + self.w_q = nn.Linear(d_model, d_model) + self.w_k = nn.Linear(d_model, d_model) + self.w_v = nn.Linear(d_model, d_model) + + def forward(self, q, k, v, mask=None): + # 1. dot product with weight matrices + q, k, v = self.w_q(q), self.w_k(k), self.w_v(v) + + # 2. split tensor by number of heads + q, k, v = self.project(q), self.project(k), self.project(v) + + # 3, do scale dot product to compute similarity + out, attention = self.attention(q, k, v, mask=mask) + out = self.concat(out) + + # 4. visualize attention map + # TODO : we should implement visualization + + return out + + def project(self, tensor): + """ + split tensor by number of head + + :param tensor: [batch_size, length, d_model] + :return: [batch_size, head, length, d_tensor] + """ + batch_size, length, d_model = tensor.size() + + d_tensor = d_model // self.n_head + tensor = tensor.view(batch_size, self.n_head, length, d_tensor) + # it is similar with group convolution (split by number of heads) + + return tensor + + def concat(self, tensor): + """ + inverse function of self.project(tensor : torch.Tensor) + + :param tensor: [batch_size, head, length, d_tensor] + :return: [batch_size, length, d_model] + """ + batch_size, head, length, d_tensor = tensor.size() + d_model = head * d_tensor + + tensor = tensor.view(batch_size, length, d_model) + return tensor +``` +

+ +### 1.3 Scale Dot Product Attention + +![model](image/scale_dot_product_attention-2.jpg) + +![model](image/scale_dot_product_attention.jpg) + +```python +class ScaleDotProductAttention(nn.Module): + """ + compute scale dot product attention + + Query : given sentence that we focused on (decoder) + Key : every sentence to check relationship with Qeury(encoder) + Value : every sentence same with Key (encoder) + """ + + def __init__(self): + super(ScaleDotProductAttention, self).__init__() + self.softmax = nn.Softmax() + + def forward(self, q, k, v, mask=None, e=1e-12): + # input is 4 dimension tensor + # [batch_size, head, length, d_tensor] + batch_size, head, length, d_tensor = k.size() + d_model = head * d_tensor + + # 1. dot product Query with Key^T to compute similarity + k_t = k.view(batch_size, head, d_tensor, length) + score = (q @ k_t) / math.sqrt(d_model) + + # 2. apply masking (opt) + if mask is not None: score = score.masked_fill(mask == 0, -e) + + # 3. pass them softmax to make [0, 1] range + score = self.softmax(score) + + # 4. multiply with Value + v = score @ v + + return v, score +``` +

+ +### 1.4 Layer Norm + +![model](image/layer_norm.jpg) + +```python + +class LayerNorm(nn.Module): + def __init__(self, d_model, eps=1e-12): + super(LayerNorm, self).__init__() + self.gamma = nn.Parameter(torch.ones(d_model)) + self.beta = nn.Parameter(torch.zeros(d_model)) + self.eps = eps + + def forward(self, x): + mean = x.mean(-1, keepdim=True) + std = x.std(-1, keepdim=True) + + out = (x - mean) / (std + self.eps) + out = self.gamma * out + self.beta + return out + +``` +

+ +### 1.5 Positionwise Feed Forward + +![model](image/positionwise_feed_forward.jpg) + +```python + +class PositionwiseFeedForward(nn.Module): + + def __init__(self, d_model, hidden, drop_prob=0.1): + super(PositionwiseFeedForward, self).__init__() + self.linear1 = nn.Linear(d_model, hidden) + self.linear2 = nn.Linear(hidden, d_model) + self.relu = nn.ReLU() + self.dropout = nn.Dropout(p=drop_prob) + + def forward(self, x): + x = self.linear1(x) + x = self.relu(x) + x = self.dropout(x) + x = self.linear2(x) + return x + +``` +

+ +### 1.6 Encoder & Decoder Structure + +![model](image/enc_dec.jpg) + +```python + +class EncoderLayer(nn.Module): + + def __init__(self, d_model, ffn_hidden, n_head, drop_prob): + super(EncoderLayer, self).__init__() + self.attention = MultiHeadAttention(d_model=d_model, n_head=n_head) + self.norm1 = LayerNorm(d_model=d_model) + self.dropout1 = nn.Dropout(p=drop_prob) + + self.ffn = PositionwiseFeedForward(d_model=d_model, hidden=ffn_hidden, drop_prob=drop_prob) + self.norm2 = LayerNorm(d_model=d_model) + self.dropout2 = nn.Dropout(p=drop_prob) + + def forward(self, x, s_mask): + _x = x + x = self.attention(x, x, x, mask=s_mask) + x = self.norm1(x + _x) + x = self.dropout1(x) + + _x = x + x = self.ffn(x) + x = self.norm2(x + _x) + x = self.dropout2(x) + return x + + +class Encoder(nn.Module): + + def __init__(self, enc_voc_size, max_len, d_model, ffn_hidden, n_head, n_layers, drop_prob, device): + super().__init__() + self.emb = TransformerEmbedding(d_model=d_model, + max_len=max_len, + vocab_size=enc_voc_size, + drop_prob=drop_prob, + device=device) + + self.layers = nn.ModuleList([EncoderLayer(d_model=d_model, + ffn_hidden=ffn_hidden, + n_head=n_head, + drop_prob=drop_prob) + for _ in range(n_layers)]) + + def forward(self, x, s_mask): + x = self.emb(x) + + for layer in self.layers: + x = layer(x, s_mask) + + return x + +class DecoderLayer(nn.Module): + + def __init__(self, d_model, ffn_hidden, n_head, drop_prob): + super(DecoderLayer, self).__init__() + self.self_attention = MultiHeadAttention(d_model=d_model, n_head=n_head) + self.norm1 = LayerNorm(d_model=d_model) + self.dropout1 = nn.Dropout(p=drop_prob) + + self.enc_dec_attention = MultiHeadAttention(d_model=d_model, n_head=n_head) + self.norm2 = LayerNorm(d_model=d_model) + self.dropout2 = nn.Dropout(p=drop_prob) + + self.ffn = PositionwiseFeedForward(d_model=d_model, hidden=ffn_hidden, drop_prob=drop_prob) + self.norm3 = LayerNorm(d_model=d_model) + self.dropout3 = nn.Dropout(p=drop_prob) + + def forward(self, dec, enc, t_mask, s_mask): + _x = dec + x = self.self_attention(dec, dec, dec, mask=t_mask) + x = self.norm1(x + _x) + x = self.dropout1(x) + + if enc is not None: + _x = x + x = self.enc_dec_attention(x, enc, enc, mask=s_mask) + x = self.norm2(x + _x) + x = self.dropout2(x) + + _x = x + x = self.ffn(x) + x = self.norm3(x + _x) + x = self.dropout3(x) + + return x + + +class Decoder(nn.Module): + def __init__(self, dec_voc_size, max_len, d_model, ffn_hidden, n_head, n_layers, drop_prob, device): + super().__init__() + self.emb = TransformerEmbedding(d_model=d_model, + drop_prob=drop_prob, + max_len=max_len, + vocab_size=dec_voc_size, + device=device) + + self.layers = nn.ModuleList([DecoderLayer(d_model=d_model, + ffn_hidden=ffn_hidden, + n_head=n_head, + drop_prob=drop_prob) + for _ in range(n_layers)]) + + self.linear = nn.Linear(d_model, dec_voc_size) + + def forward(self, trg, enc_src, trg_mask, src_mask): + trg = self.emb(trg) + + for layer in self.layers: + trg = layer(trg, enc_src, trg_mask, src_mask) + + output = self.linear(trg) + + return output + +``` +

+ +## 2. Experiments + +I use Multi30K Dataset to train and evaluate model
+You can check detail of dataset [here](https://arxiv.org/abs/1605.00459) + +### 2.1 Transformer - Small +* total parameters = 50,479,279 +* model size = 197.2MB + +#### 2.1.1 configuration + +* batch_size = 128 +* max_len = 50 +* d_model = 512 +* n_layers = 6 +* n_heads = 8 +* ffn_hidden = 2048 +* drop_prob = 0.1 +* init_lr = 1e-5 +* factor = 0.9 +* min_lr = init_lr * 1e-12 +* patience = 10 +* warmup = 100 +* weight_decay = 5e-4 +* epoch = 1000 +* clip = 1 + + +#### 2.1.2 Training Result +![table](saved/transformer-small/train_result.jpg) +

+* Minimum Training Loss = 2.852672759656864 +* Minimum Validation Loss = 3.2048025131225586 + +Small model's parameter setting is same with original paper except d_k + +| Model | Dataset | Parameters | d_k | BLEU Score | +|:---:|:---:|:---:|:---:|:---:| +| Original Paper's | WMT14 EN-DE | 65 x 10^6 | 64 |25.8 | +| My Implementation | Multi30K EN-DE | 50 x 10^7 | 512 | 26.4 | +

+ +### 2.2 Transformer - Big +* total parameters = 232,082,095 +* model size = 197.2MB + +#### 2.2.1 configuration + +* batch_size = 32 +* max_len = 50 +* d_model = 1024 +* n_layers = 8 +* n_heads = 8 +* ffn_hidden = 4096 +* drop_prob = 0.3 +* init_lr = 1e-5 +* factor = 0.9 +* min_lr = init_lr * 1e-12 +* patience = 10 +* warmup = 300 +* weight_decay = 5e-4 +* epoch = 2000 +* clip = 1 + + +#### 2.2.2 Training Result + +Training now ...

-## Reference +## 3. Reference |Reference| |:---:| |[Attention is All You Need, 2017 - Google](https://arxiv.org/abs/1706.03762)| |[The Illustrated Transformer - Jay Alammar](http://jalammar.github.io/illustrated-transformer/)| |[Data & Optimization Code Reference - Bentrevett](https://github.com/bentrevett/pytorch-seq2seq/)| +

-## Licence +## 4. Licence Copyright 2019 Hyunwoong Go. 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2.891977776514801, 2.8865843333861902, 2.8742581600659745, 2.8788483163858825, 2.86207285326483] \ No newline at end of file +[6.3939098234082, 5.715431122953337, 5.5426007205622465, 5.404952225374939] \ No newline at end of file diff --git a/test.py b/test.py index 11cfc77..a6a5505 100644 --- a/test.py +++ b/test.py @@ -9,41 +9,103 @@ from models.model.transformer import Transformer +from collections import Counter +import math +import numpy as np +import subprocess + + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + model = Transformer(src_pad_idx=src_pad_idx, trg_pad_idx=trg_pad_idx, trg_sos_idx=trg_sos_idx, - d_model=d_model, + d_model=512, enc_voc_size=enc_voc_size, dec_voc_size=dec_voc_size, max_len=max_len, - ffn_hidden=ffn_hidden, + ffn_hidden=2048, n_head=n_heads, - n_layers=n_layers, + n_layers=6, drop_prob=0.00, device=device).to(device) +print(f'The model has {count_parameters(model):,} trainable parameters') + def test_model(num_examples): - iterator = train_iter - model.load_state_dict(torch.load("./saved/model-saved.pt")) + iterator = test_iter + model.load_state_dict(torch.load("./saved/transformer-small/model-saved.pt")) with torch.no_grad(): + batch_bleu = [] for i, batch in enumerate(iterator): - if i >= 1: break src = batch.src trg = batch.trg output = model(src, trg[:, :-1]) + total_bleu = [] for j in range(num_examples): - src_words = idx_to_word(src[j], loader.source.vocab) - trg_words = idx_to_word(trg[j], loader.target.vocab) - output_words = output[j].max(dim=1)[1] - output_words = idx_to_word(output_words, loader.target.vocab) + try: + src_words = idx_to_word(src[j], loader.source.vocab) + trg_words = idx_to_word(trg[j], loader.target.vocab) + output_words = output[j].max(dim=1)[1] + output_words = idx_to_word(output_words, loader.target.vocab) + + print('source :', src_words) + print('target :', trg_words) + print('predicted :', output_words) + print() + bleu = get_bleu(hypotheses=output_words.split(), reference=trg_words.split()) + total_bleu.append(bleu) + except: + pass + + total_bleu = sum(total_bleu) / len(total_bleu) + print('BLEU SCORE = {}'.format(total_bleu)) + batch_bleu.append(total_bleu) + + batch_bleu = sum(batch_bleu) / len(batch_bleu) + print('TOTAL BLEU SCORE = {}'.format(batch_bleu)) + + +def bleu_stats(hypothesis, reference): + """Compute statistics for BLEU.""" + stats = [] + stats.append(len(hypothesis)) + stats.append(len(reference)) + for n in range(1, 5): + s_ngrams = Counter( + [tuple(hypothesis[i:i + n]) for i in range(len(hypothesis) + 1 - n)] + ) + r_ngrams = Counter( + [tuple(reference[i:i + n]) for i in range(len(reference) + 1 - n)] + ) + + stats.append(max([sum((s_ngrams & r_ngrams).values()), 0])) + stats.append(max([len(hypothesis) + 1 - n, 0])) + return stats + + +def bleu(stats): + """Compute BLEU given n-gram statistics.""" + if len(list(filter(lambda x: x == 0, stats))) > 0: + return 0 + (c, r) = stats[:2] + log_bleu_prec = sum( + [math.log(float(x) / y) for x, y in zip(stats[2::2], stats[3::2])] + ) / 4. + return math.exp(min([0, 1 - float(r) / c]) + log_bleu_prec) + - print('source :', src_words) - print('target :', trg_words) - print('predicted :', output_words) - print() +def get_bleu(hypotheses, reference): + """Get validation BLEU score for dev set.""" + stats = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) + for hyp, ref in zip(hypotheses, reference): + stats += np.array(bleu_stats(hyp, ref)) + return 100 * bleu(stats) def idx_to_word(x, vocab): @@ -57,4 +119,4 @@ def idx_to_word(x, vocab): if __name__ == '__main__': - test_model(num_examples=20) + test_model(num_examples=batch_size)