mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
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233 lines
7.8 KiB
Python
233 lines
7.8 KiB
Python
"""
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---
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title: Transformer Encoder and Decoder Models
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summary: >
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These are PyTorch implementations of Transformer based encoder and decoder models,
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as well as other related modules.
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---
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# Transformer Encoder and Decoder Models
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"""
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import math
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import torch
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import torch.nn as nn
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from labml_helpers.module import Module
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from labml_nn.utils import clone_module_list
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from .feed_forward import FeedForward
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from .mha import MultiHeadAttention
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from .positional_encoding import get_positional_encoding
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class EmbeddingsWithPositionalEncoding(Module):
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"""
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<a id="EmbeddingsWithPositionalEncoding">
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## Embed tokens and add [fixed positional encoding](positional_encoding.html)
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</a>
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"""
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def __init__(self, d_model: int, n_vocab: int, max_len: int = 5000):
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super().__init__()
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self.linear = nn.Embedding(n_vocab, d_model)
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self.d_model = d_model
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self.register_buffer('positional_encodings', get_positional_encoding(d_model, max_len))
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def forward(self, x: torch.Tensor):
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pe = self.positional_encodings[:x.shape[0]].requires_grad_(False)
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return self.linear(x) * math.sqrt(self.d_model) + pe
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class EmbeddingsWithLearnedPositionalEncoding(Module):
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"""
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<a id="EmbeddingsWithLearnedPositionalEncoding">
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## Embed tokens and add parameterized positional encodings
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</a>
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"""
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def __init__(self, d_model: int, n_vocab: int, max_len: int = 5000):
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super().__init__()
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self.linear = nn.Embedding(n_vocab, d_model)
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self.d_model = d_model
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self.positional_encodings = nn.Parameter(torch.zeros(max_len, 1, d_model), requires_grad=True)
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def forward(self, x: torch.Tensor):
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pe = self.positional_encodings[:x.shape[0]]
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return self.linear(x) * math.sqrt(self.d_model) + pe
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class TransformerLayer(Module):
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"""
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<a id="TransformerLayer">
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## Transformer Layer
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</a>
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This can act as an encoder layer or a decoder layer.
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🗒 Some implementations, including the paper seem to have differences
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in where the layer-normalization is done.
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Here we do a layer normalization before attention and feed-forward networks,
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and add the original residual vectors.
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Alternative is to do a layer normalization after adding the residuals.
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But we found this to be less stable when training.
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We found a detailed discussion about this in the paper
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[On Layer Normalization in the Transformer Architecture](https://arxiv.org/abs/2002.04745).
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"""
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def __init__(self, *,
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d_model: int,
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self_attn: MultiHeadAttention,
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src_attn: MultiHeadAttention = None,
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feed_forward: FeedForward,
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dropout_prob: float):
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"""
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* `d_model` is the token embedding size
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* `self_attn` is the self attention module
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* `src_attn` is the source attention module (when this is used in a decoder)
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* `feed_forward` is the feed forward module
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* `dropout_prob` is the probability of dropping out after self attention and FFN
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"""
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super().__init__()
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self.size = d_model
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self.self_attn = self_attn
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self.src_attn = src_attn
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self.feed_forward = feed_forward
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self.dropout = nn.Dropout(dropout_prob)
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self.norm_self_attn = nn.LayerNorm([d_model])
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if self.src_attn is not None:
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self.norm_src_attn = nn.LayerNorm([d_model])
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self.norm_ff = nn.LayerNorm([d_model])
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# Whether to save input to the feed forward layer
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self.is_save_ff_input = False
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def forward(self, *,
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x: torch.Tensor,
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mask: torch.Tensor,
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src: torch.Tensor = None,
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src_mask: torch.Tensor = None):
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# Normalize the vectors before doing self attention
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z = self.norm_self_attn(x)
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# Run through self attention, i.e. keys and values are from self
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self_attn = self.self_attn(query=z, key=z, value=z, mask=mask)
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# Add the self attention results
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x = x + self.dropout(self_attn)
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# If a source is provided, get results from attention to source.
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# This is when you have a decoder layer that pays attention to
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# encoder outputs
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if src is not None:
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# Normalize vectors
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z = self.norm_src_attn(x)
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# Attention to source. i.e. keys and values are from source
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attn_src = self.src_attn(query=z, key=src, value=src, mask=src_mask)
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# Add the source attention results
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x = x + self.dropout(attn_src)
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# Normalize for feed-forward
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z = self.norm_ff(x)
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# Save the input to the feed forward layer if specified
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if self.is_save_ff_input:
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self.ff_input = z.clone()
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# Pass through the feed-forward network
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ff = self.feed_forward(z)
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# Add the feed-forward results back
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x = x + self.dropout(ff)
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return x
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class Encoder(Module):
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"""
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<a id="Encoder">
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## Transformer Encoder
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</a>
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"""
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def __init__(self, layer: TransformerLayer, n_layers: int):
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super().__init__()
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# Make copies of the transformer layer
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self.layers = clone_module_list(layer, n_layers)
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# Final normalization layer
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self.norm = nn.LayerNorm([layer.size])
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def forward(self, x: torch.Tensor, mask: torch.Tensor):
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# Run through each transformer layer
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for layer in self.layers:
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x = layer(x=x, mask=mask)
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# Finally, normalize the vectors
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return self.norm(x)
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class Decoder(Module):
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"""
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<a id="Decoder">
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## Transformer Decoder
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</a>
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"""
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def __init__(self, layer: TransformerLayer, n_layers: int):
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super().__init__()
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# Make copies of the transformer layer
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self.layers = clone_module_list(layer, n_layers)
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# Final normalization layer
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self.norm = nn.LayerNorm([layer.size])
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def forward(self, x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor):
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# Run through each transformer layer
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for layer in self.layers:
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x = layer(x=x, mask=tgt_mask, src=memory, src_mask=src_mask)
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# Finally, normalize the vectors
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return self.norm(x)
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class Generator(Module):
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"""
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<a id="Generator">
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## Generator
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</a>
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This predicts the tokens and gives the lof softmax of those.
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You don't need this if you are using `nn.CrossEntropyLoss`.
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"""
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def __init__(self, n_vocab: int, d_model: int):
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super().__init__()
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self.projection = nn.Linear(d_model, n_vocab)
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def forward(self, x):
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return self.projection(x)
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class EncoderDecoder(Module):
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"""
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<a id="EncoderDecoder">
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## Combined Encoder-Decoder
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</a>
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"""
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def __init__(self, encoder: Encoder, decoder: Decoder, src_embed: Module, tgt_embed: Module, generator: Module):
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super().__init__()
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self.encoder = encoder
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self.decoder = decoder
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self.src_embed = src_embed
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self.tgt_embed = tgt_embed
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self.generator = generator
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# This was important from their code.
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# Initialize parameters with Glorot / fan_avg.
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for p in self.parameters():
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if p.dim() > 1:
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nn.init.xavier_uniform_(p)
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def forward(self, src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor):
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# Run the source through encoder
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enc = self.encode(src, src_mask)
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# Run encodings and targets through decoder
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return self.decode(enc, src_mask, tgt, tgt_mask)
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def encode(self, src: torch.Tensor, src_mask: torch.Tensor):
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return self.encoder(self.src_embed(src), src_mask)
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def decode(self, memory: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor):
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return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)
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