basic transformer

.gitignore

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+__pycache__

transformers/__init__.py

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+import copy
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from common.models import clone_module_list
+from labml.configs import BaseConfigs, option
+from labml.helpers.pytorch.module import Module
+from transformer.models.multi_headed_attention import MultiHeadedAttention
+from transformers.positional_encoding import PositionalEncoding, get_positional_encoding
+
+
+class EmbeddingsWithPositionalEncoding(Module):
+    def __init__(self, d_model: int, n_vocab: int, max_len: int = 5000):
+        super().__init__()
+        self.linear = nn.Embedding(n_vocab, d_model)
+        self.d_model = d_model
+        self.register_buffer('positional_encodings', get_positional_encoding(d_model, max_len))
+
+    def __call__(self, x: torch.Tensor):
+        pe = self.positional_encodings[:x.shape[0]].requires_grad_(False)
+        return self.linear(x) * math.sqrt(self.d_model) + pe
+
+
+class FeedForward(Module):
+    def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1):
+        super().__init__()
+        self.layer1 = nn.Linear(d_model, d_ff)
+        self.layer2 = nn.Linear(d_ff, d_model)
+        self.dropout = nn.Dropout(dropout)
+
+    def __call__(self, x: torch.Tensor):
+        x = self.layer1(x)
+        x = F.relu(x)
+        x = self.dropout(x)
+        return self.layer2(x)
+
+
+class TransformerLayer(Module):
+    def __init__(self, *,
+                 d_model: int,
+                 self_attn: MultiHeadedAttention,
+                 src_attn: MultiHeadedAttention = None,
+                 feed_forward: FeedForward,
+                 dropout_prob: float):
+        super().__init__()
+        self.size = d_model
+        self.self_attn = self_attn
+        self.src_attn = src_attn
+        self.feed_forward = feed_forward
+        self.dropout = nn.Dropout(dropout_prob)
+        self.norm_self_attn = nn.LayerNorm([d_model])
+        if self.src_attn is not None:
+            self.norm_src_attn = nn.LayerNorm([d_model])
+        self.norm_ff = nn.LayerNorm([d_model])
+
+    def __call__(self, *,
+                 x: torch.Tensor,
+                 mask: torch.Tensor,
+                 src: torch.Tensor = None,
+                 src_mask: torch.Tensor = None):
+        z = self.norm_self_attn(x)
+        attn_self = self.self_attn(query=z, key=z, value=z, mask=mask)
+        x = x + self.dropout(attn_self)
+
+        if src is not None:
+            z = self.norm_src_attn(x)
+            attn_src = self.src_attn(query=z, key=src, value=src, mask=src_mask)
+            x = x + self.dropout(attn_src)
+
+        z = self.norm_ff(x)
+        ff = self.feed_forward(z)
+        x = x + self.dropout(ff)
+
+        # guard(x.shape, attn_self.shape, attn_src.shape, ff.shape,
+        #       '_batch_size', '_seq_len', 'd_model')
+
+        return x
+
+
+class Encoder(Module):
+    def __init__(self, layer: TransformerLayer, n_layers: int):
+        super().__init__()
+        self.layers = clone_module_list(layer, n_layers)
+        self.norm = nn.LayerNorm([layer.size])
+
+    def __call__(self, x: torch.Tensor, mask: torch.Tensor):
+        for layer in self.layers:
+            x = layer(x=x, mask=mask)
+        return self.norm(x)
+
+
+class Decoder(Module):
+    def __init__(self, layer: TransformerLayer, n_layers: int):
+        super().__init__()
+        self.layers = clone_module_list(layer, n_layers)
+        self.norm = nn.LayerNorm([layer.size])
+
+    def __call__(self, x, memory, src_mask, tgt_mask):
+        for layer in self.layers:
+            x = layer(x=x, mask=tgt_mask, src=memory, src_mask=src_mask)
+        return self.norm(x)
+
+
+class Generator(Module):
+    def __init__(self, n_vocab: int, d_model: int):
+        super().__init__()
+        self.projection = nn.Linear(d_model, n_vocab)
+
+    def __call__(self, x):
+        return F.log_softmax(self.projection(x), dim=-1)
+
+
+class EncoderDecoder(Module):
+    def __init__(self, encoder: Encoder, decoder: Decoder, src_embed: Module, tgt_embed: Module, generator: Module):
+        super().__init__()
+        self.encoder = encoder
+        self.decoder = decoder
+        self.src_embed = src_embed
+        self.tgt_embed = tgt_embed
+        self.generator = generator
+
+        # This was important from their code.
+        # Initialize parameters with Glorot / fan_avg.
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+
+    def __call__(self, src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor,
+                 tgt_mask: torch.Tensor):
+        return self.decode(self.encode(src, src_mask), src_mask,
+                           tgt, tgt_mask)
+
+    def encode(self, src: torch.Tensor, src_mask: torch.Tensor):
+        return self.encoder(self.src_embed(src), src_mask)
+
+    def decode(self, memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor):
+        return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)
+
+
+class TransformerConfigs(BaseConfigs):
+    n_heads: int = 8
+    d_model: int = 512
+    n_layers: int = 6
+    d_ff: int = 2048
+    dropout: float = 0.1
+    n_src_vocab: int
+    n_tgt_vocab: int
+
+    encoder_attn: MultiHeadedAttention = 'mha'
+    decoder_attn: MultiHeadedAttention = 'mha'
+    decoder_mem_attn: MultiHeadedAttention = 'mha'
+    feed_forward: FeedForward
+
+    encoder_layer: TransformerLayer = 'normal'
+    decoder_layer: TransformerLayer = 'normal'
+
+    encoder: Encoder = 'normal'
+    decoder: Decoder = 'normal'
+
+    src_embed: Module = 'fixed_pos'
+    tgt_embed: Module = 'fixed_pos'
+
+    generator: Generator = 'default'
+
+    encoder_decoder: EncoderDecoder
+
+
+@option(TransformerConfigs.feed_forward, 'default')
+def _feed_forward(c: TransformerConfigs):
+    return FeedForward(c.d_model, c.d_ff, c.dropout)
+
+
+@option(TransformerConfigs.encoder_attn, 'mha')
+def _encoder_mha(c: TransformerConfigs):
+    return MultiHeadedAttention(c.n_heads, c.d_model)
+
+
+@option(TransformerConfigs.decoder_attn, 'mha')
+def _decoder_mha(c: TransformerConfigs):
+    return MultiHeadedAttention(c.n_heads, c.d_model)
+
+
+@option(TransformerConfigs.decoder_mem_attn, 'mha')
+def _decoder_mem_mha(c: TransformerConfigs):
+    return MultiHeadedAttention(c.n_heads, c.d_model)
+
+
+@option(TransformerConfigs.encoder_layer, 'normal')
+def _encoder_layer(c: TransformerConfigs):
+    return TransformerLayer(d_model=c.d_model, self_attn=c.encoder_attn,
+                            src_attn=None, feed_forward=copy.deepcopy(c.feed_forward),
+                            dropout_prob=c.dropout)
+
+
+@option(TransformerConfigs.decoder_layer, 'normal')
+def _decoder_layer(c: TransformerConfigs):
+    return TransformerLayer(d_model=c.d_model, self_attn=c.decoder_attn,
+                            src_attn=c.decoder_mem_attn, feed_forward=copy.deepcopy(c.feed_forward),
+                            dropout_prob=c.dropout)
+
+
+@option(TransformerConfigs.encoder, 'normal')
+def _encoder(c: TransformerConfigs):
+    return Encoder(c.encoder_layer, c.n_layers)
+
+
+@option(TransformerConfigs.decoder, 'normal')
+def _decoder(c: TransformerConfigs):
+    return Decoder(c.decoder_layer, c.n_layers)
+
+
+@option(TransformerConfigs.generator, 'default')
+def _generator(c: TransformerConfigs):
+    return Generator(c.n_tgt_vocab, c.d_model)
+
+
+@option(TransformerConfigs.src_embed, 'fixed_pos')
+def _src_embed_with_positional(c: TransformerConfigs):
+    return EmbeddingsWithPositionalEncoding(c.d_model, c.n_src_vocab)
+
+
+@option(TransformerConfigs.tgt_embed, 'fixed_pos')
+def _tgt_embed_with_positional(c: TransformerConfigs):
+    return EmbeddingsWithPositionalEncoding(c.d_model, c.n_tgt_vocab)
+
+
+@option(TransformerConfigs.encoder_decoder, 'normal')
+def _encoder_decoder(c: TransformerConfigs):
+    return EncoderDecoder(c.encoder, c.decoder, c.src_embed, c.tgt_embed, c.generator)

transformers/label_smoothing_loss.py

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+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import torch.nn as nn
+
+from labml.helpers.pytorch.module import Module
+
+
+class LabelSmoothingLoss(Module):
+    def __init__(self, size: int, padding_idx: int, smoothing: float = 0.0):
+        super().__init__()
+        self.loss = nn.KLDivLoss(reduction='sum')
+        self.padding_idx = padding_idx
+        self.confidence = 1.0 - smoothing
+        self.smoothing = smoothing
+        self.size = size
+        self.true_dist = None
+
+    def __call__(self, x: torch.Tensor, target: torch.Tensor):
+        assert x.size(1) == self.size
+        true_dist = x.clone()
+        true_dist.fill_(self.smoothing / (self.size - 2))
+        true_dist.scatter_(1, target.unsqueeze(1), self.confidence)
+        true_dist[:, self.padding_idx] = 0
+        mask = torch.nonzero(target == self.padding_idx, as_tuple=False)
+        if mask.dim() > 0:
+            true_dist.index_fill_(0, mask.squeeze(), 0.0)
+        self.true_dist = true_dist
+        return self.loss(x, true_dist.detach())
+
+
+def _test_label_smoothing():
+    smooth_loss = LabelSmoothingLoss(5, 0, 0.4)
+    predict = torch.tensor([[0, 0.2, 0.7, 0.1, 0],
+                            [0, 0.2, 0.7, 0.1, 0],
+                            [0, 0.2, 0.7, 0.1, 0]], dtype=torch.float)
+    _ = smooth_loss(predict.log(),
+                    torch.tensor([2, 1, 0], dtype=torch.long))
+
+    # Show the target distributions expected by the system.
+    plt.imshow(smooth_loss.true_dist)
+    plt.show()
+
+    smooth_loss = LabelSmoothingLoss(5, 0, 0.1)
+
+    def loss_sample(x):
+        d = x + 3 * 1
+        predict2 = torch.tensor([[0, x / d, 1 / d, 1 / d, 1 / d],
+                                 ], dtype=torch.float)
+        # print(predict)
+        return smooth_loss(predict2.log(),
+                           torch.tensor([1], dtype=torch.long)).item()
+
+    plt.plot(np.arange(1, 100), [loss_sample(x) for x in range(1, 100)])
+    plt.show()
+
+
+if __name__ == '__main__':
+    _test_label_smoothing()

transformers/positional_encoding.py

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+import math
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import torch.nn as nn
+
+from labml.helpers.pytorch.module import Module
+
+
+class PositionalEncoding(Module):
+    def __init__(self, d_model: int, dropout_prob: float, max_len: int = 5000):
+        super().__init__()
+        self.dropout = nn.Dropout(dropout_prob)
+
+        self.register_buffer('positional_encodings', get_positional_encoding(d_model, max_len))
+
+    def __call__(self, x: torch.Tensor):
+        pe = self.positional_encodings[:x.shape[0]].detach().requires_grad_(False)
+        x = x + pe
+        x = self.dropout(x)
+        return x
+
+
+def get_positional_encoding(d_model: int, max_len: int = 5000):
+    encodings = torch.zeros(max_len, d_model)
+    position = torch.arange(0, max_len, dtype=torch.float32).unsqueeze(1)
+    two_i = torch.arange(0, d_model, 2, dtype=torch.float32)
+    div_term = torch.exp(two_i * -(math.log(10000.0) / d_model))
+    encodings[:, 0::2] = torch.sin(position * div_term)
+    encodings[:, 1::2] = torch.cos(position * div_term)
+    encodings = encodings.unsqueeze(1).requires_grad_(False)
+
+    return encodings
+
+
+def _test_positional_encoding():
+    plt.figure(figsize=(15, 5))
+    pe = get_positional_encoding(20, 100)
+    plt.plot(np.arange(100), pe[:, 0, 4:8].numpy())
+    plt.legend(["dim %d" % p for p in [4, 5, 6, 7]])
+    plt.title("Positional encoding")
+    plt.show()
+
+
+if __name__ == '__main__':
+    _test_positional_encoding()