mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
synced 2025-10-31 02:39:16 +08:00
148 lines
5.0 KiB
Python
148 lines
5.0 KiB
Python
import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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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 .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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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 __call__(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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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))
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def __call__(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 FeedForward(Module):
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def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1):
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super().__init__()
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self.layer1 = nn.Linear(d_model, d_ff)
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self.layer2 = nn.Linear(d_ff, d_model)
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self.dropout = nn.Dropout(dropout)
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def __call__(self, x: torch.Tensor):
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x = self.layer1(x)
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x = F.relu(x)
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x = self.dropout(x)
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return self.layer2(x)
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class TransformerLayer(Module):
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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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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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def __call__(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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z = self.norm_self_attn(x)
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attn_self = self.self_attn(query=z, key=z, value=z, mask=mask)
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x = x + self.dropout(attn_self)
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if src is not None:
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z = self.norm_src_attn(x)
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attn_src = self.src_attn(query=z, key=src, value=src, mask=src_mask)
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x = x + self.dropout(attn_src)
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z = self.norm_ff(x)
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ff = self.feed_forward(z)
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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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def __init__(self, layer: TransformerLayer, n_layers: int):
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super().__init__()
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self.layers = clone_module_list(layer, n_layers)
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self.norm = nn.LayerNorm([layer.size])
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def __call__(self, x: torch.Tensor, mask: torch.Tensor):
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for layer in self.layers:
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x = layer(x=x, mask=mask)
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return self.norm(x)
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class Decoder(Module):
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def __init__(self, layer: TransformerLayer, n_layers: int):
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super().__init__()
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self.layers = clone_module_list(layer, n_layers)
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self.norm = nn.LayerNorm([layer.size])
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def __call__(self, x, memory, src_mask, tgt_mask):
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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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return self.norm(x)
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class Generator(Module):
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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 __call__(self, x):
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return F.log_softmax(self.projection(x), dim=-1)
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class EncoderDecoder(Module):
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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 __call__(self, src: torch.Tensor, tgt: torch.Tensor, src_mask: torch.Tensor,
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tgt_mask: torch.Tensor):
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return self.decode(self.encode(src, src_mask), src_mask,
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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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