models.py

"""
---
title: Transformer Encoder and Decoder Models
summary: >
  These are PyTorch implementations of Transformer based encoder and decoder models,
  as well as other related modules.
---

# Transformer Encoder and Decoder Models
"""
import math

import torch
import torch.nn as nn
from labml_helpers.module import Module

from labml_nn.utils import clone_module_list
from .feed_forward import FeedForward
from .mha import MultiHeadAttention
from .positional_encoding import get_positional_encoding


class EmbeddingsWithPositionalEncoding(Module):
    """
    <a id="EmbeddingsWithPositionalEncoding">
    ## Embed tokens and add [fixed positional encoding](positional_encoding.html)
    </a>
    """

    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 EmbeddingsWithLearnedPositionalEncoding(Module):
    """
    <a id="EmbeddingsWithLearnedPositionalEncoding">
    ## Embed tokens and add parameterized positional encodings
    </a>
    """

    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.positional_encodings = nn.Parameter(torch.zeros(max_len, 1, d_model), requires_grad=True)

    def __call__(self, x: torch.Tensor):
        pe = self.positional_encodings[:x.shape[0]]
        return self.linear(x) * math.sqrt(self.d_model) + pe


class TransformerLayer(Module):
    """
    <a id="TransformerLayer">
    ## Transformer Layer
    </a>

    This can act as a encoder layer or a decoder layer.

    🗒 Some implementations, including the paper seem to have differences
    in where the layer-normalization is done.
    Here we do a layer normalization before attention and feed-forward networks,
    and add the original residual vectors.
    Alternative is to do a layer normalization after adding the residuals.
    But we found this to be less stable when training.
    We found a detailed discussion about this in paper
     [On Layer Normalization in the Transformer Architecture](https://arxiv.org/abs/2002.04745).
    """

    def __init__(self, *,
                 d_model: int,
                 self_attn: MultiHeadAttention,
                 src_attn: MultiHeadAttention = None,
                 feed_forward: FeedForward,
                 dropout_prob: float):
        """
        * `d_model` is the token embedding size
        * `self_attn` is the self attention module
        * `src_attn` is the source attention module (when this is used in a decoder)
        * `feed_forward` is the feed forward module
        * `dropout_prob` is the probability of dropping out after self attention and FFN
        """
        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])
        # Whether to save input to the feed forward layer
        self.is_save_ff_input = False

    def __call__(self, *,
                 x: torch.Tensor,
                 mask: torch.Tensor,
                 src: torch.Tensor = None,
                 src_mask: torch.Tensor = None):
        # Normalize the vectors before doing self attention
        z = self.norm_self_attn(x)
        # Run through self attention, i.e. keys and values are from self
        self_attn = self.self_attn(query=z, key=z, value=z, mask=mask)
        # Add the self attention results
        x = x + self.dropout(self_attn)

        # If a source is provided, get results from attention to source.
        # This is when you have a decoder layer that pays attention to 
        # encoder outputs
        if src is not None:
            # Normalize vectors
            z = self.norm_src_attn(x)
            # Attention to source. i.e. keys and values are from source
            attn_src = self.src_attn(query=z, key=src, value=src, mask=src_mask)
            # Add the source attention results
            x = x + self.dropout(attn_src)

        # Normalize for feed-forward
        z = self.norm_ff(x)
        # Save the input to the feed forward layer if specified
        if self.is_save_ff_input:
            self.ff_input = z.clone()
        # Pass through the feed-forward network
        ff = self.feed_forward(z)
        # Add the feed-forward results back
        x = x + self.dropout(ff)

        return x


class Encoder(Module):
    """
    <a id="Encoder">
    ## Transformer Encoder
    </a>
    """

    def __init__(self, layer: TransformerLayer, n_layers: int):
        super().__init__()
        # Make copies of the transformer layer
        self.layers = clone_module_list(layer, n_layers)
        # Final normalization layer
        self.norm = nn.LayerNorm([layer.size])

    def __call__(self, x: torch.Tensor, mask: torch.Tensor):
        # Run through each transformer layer
        for layer in self.layers:
            x = layer(x=x, mask=mask)
        # Finally, normalize the vectors
        return self.norm(x)


class Decoder(Module):
    """
    <a id="Decoder">
    ## Transformer Decoder
    </a>
    """

    def __init__(self, layer: TransformerLayer, n_layers: int):
        super().__init__()
        # Make copies of the transformer layer
        self.layers = clone_module_list(layer, n_layers)
        # Final normalization layer
        self.norm = nn.LayerNorm([layer.size])

    def __call__(self, x: torch.Tensor, memory: torch.Tensor, src_mask: torch.Tensor, tgt_mask: torch.Tensor):
        # Run through each transformer layer
        for layer in self.layers:
            x = layer(x=x, mask=tgt_mask, src=memory, src_mask=src_mask)
        # Finally, normalize the vectors
        return self.norm(x)


class Generator(Module):
    """
    <a id="Generator">
    ## Generator
    </a>

    This predicts the tokens and gives the lof softmax of those.
    You don't need this if you are using `nn.CrossEntropyLoss`.
    """

    def __init__(self, n_vocab: int, d_model: int):
        super().__init__()
        self.projection = nn.Linear(d_model, n_vocab)

    def __call__(self, x):
        return self.projection(x)


class EncoderDecoder(Module):
    """
    <a id="EncoderDecoder">
    ## Combined Encoder-Decoder
    </a>
    """

    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):
        # Runs the source through encoder
        enc = self.encode(src, src_mask)
        # Run encodings and targets through decoder
        return self.decode(enc, 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)