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# Copyright (C) 2021-2024, Mindee.
# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
# This module 'transformer.py' is inspired by https://github.com/wenwenyu/MASTER-pytorch and Decoder is borrowed
import math
from typing import Any, Callable, Optional, Tuple
import torch
from torch import nn
__all__ = ["Decoder", "PositionalEncoding", "EncoderBlock", "MultiHeadAttention", "PositionwiseFeedForward"]
class PositionalEncoding(nn.Module):
"""Compute positional encoding"""
def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000) -> None:
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
# Compute the positional encodings once in log space.
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len).unsqueeze(1).float()
div_term = torch.exp(torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
self.register_buffer("pe", pe.unsqueeze(0))
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward pass
Args:
----
x: embeddings (batch, max_len, d_model)
Returns
-------
positional embeddings (batch, max_len, d_model)
"""
x = x + self.pe[:, : x.size(1)]
return self.dropout(x)
def scaled_dot_product_attention(
query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Scaled Dot-Product Attention"""
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(query.size(-1))
if mask is not None:
# NOTE: to ensure the ONNX compatibility, masked_fill works only with int equal condition
scores = scores.masked_fill(mask == 0, float("-inf"))
p_attn = torch.softmax(scores, dim=-1)
return torch.matmul(p_attn, value), p_attn
class PositionwiseFeedForward(nn.Sequential):
"""Position-wise Feed-Forward Network"""
def __init__(
self, d_model: int, ffd: int, dropout: float = 0.1, activation_fct: Callable[[Any], Any] = nn.ReLU()
) -> None:
super().__init__( # type: ignore[call-overload]
nn.Linear(d_model, ffd),
activation_fct,
nn.Dropout(p=dropout),
nn.Linear(ffd, d_model),
nn.Dropout(p=dropout),
)
class MultiHeadAttention(nn.Module):
"""Multi-Head Attention"""
def __init__(self, num_heads: int, d_model: int, dropout: float = 0.1) -> None:
super().__init__()
assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
self.d_k = d_model // num_heads
self.num_heads = num_heads
self.linear_layers = nn.ModuleList([nn.Linear(d_model, d_model) for _ in range(3)])
self.output_linear = nn.Linear(d_model, d_model)
def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask=None) -> torch.Tensor:
batch_size = query.size(0)
# linear projections of Q, K, V
query, key, value = [
linear(x).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
for linear, x in zip(self.linear_layers, (query, key, value))
]
# apply attention on all the projected vectors in batch
x, attn = scaled_dot_product_attention(query, key, value, mask=mask)
# Concat attention heads
x = x.transpose(1, 2).contiguous().view(batch_size, -1, self.num_heads * self.d_k)
return self.output_linear(x)
class EncoderBlock(nn.Module):
"""Transformer Encoder Block"""
def __init__(
self,
num_layers: int,
num_heads: int,
d_model: int,
dff: int, # hidden dimension of the feedforward network
dropout: float,
activation_fct: Callable[[Any], Any] = nn.ReLU(),
) -> None:
super().__init__()
self.num_layers = num_layers
self.layer_norm_input = nn.LayerNorm(d_model, eps=1e-5)
self.layer_norm_attention = nn.LayerNorm(d_model, eps=1e-5)
self.layer_norm_output = nn.LayerNorm(d_model, eps=1e-5)
self.dropout = nn.Dropout(dropout)
self.attention = nn.ModuleList([
MultiHeadAttention(num_heads, d_model, dropout) for _ in range(self.num_layers)
])
self.position_feed_forward = nn.ModuleList([
PositionwiseFeedForward(d_model, dff, dropout, activation_fct) for _ in range(self.num_layers)
])
def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
output = x
for i in range(self.num_layers):
normed_output = self.layer_norm_input(output)
output = output + self.dropout(self.attention[i](normed_output, normed_output, normed_output, mask))
normed_output = self.layer_norm_attention(output)
output = output + self.dropout(self.position_feed_forward[i](normed_output))
# (batch_size, seq_len, d_model)
return self.layer_norm_output(output)
class Decoder(nn.Module):
"""Transformer Decoder"""
def __init__(
self,
num_layers: int,
num_heads: int,
d_model: int,
vocab_size: int,
dropout: float = 0.2,
dff: int = 2048, # hidden dimension of the feedforward network
maximum_position_encoding: int = 50,
) -> None:
super(Decoder, self).__init__()
self.num_layers = num_layers
self.d_model = d_model
self.layer_norm_input = nn.LayerNorm(d_model, eps=1e-5)
self.layer_norm_masked_attention = nn.LayerNorm(d_model, eps=1e-5)
self.layer_norm_attention = nn.LayerNorm(d_model, eps=1e-5)
self.layer_norm_output = nn.LayerNorm(d_model, eps=1e-5)
self.dropout = nn.Dropout(dropout)
self.embed = nn.Embedding(vocab_size, d_model)
self.positional_encoding = PositionalEncoding(d_model, dropout, maximum_position_encoding)
self.attention = nn.ModuleList([
MultiHeadAttention(num_heads, d_model, dropout) for _ in range(self.num_layers)
])
self.source_attention = nn.ModuleList([
MultiHeadAttention(num_heads, d_model, dropout) for _ in range(self.num_layers)
])
self.position_feed_forward = nn.ModuleList([
PositionwiseFeedForward(d_model, dff, dropout) for _ in range(self.num_layers)
])
def forward(
self,
tgt: torch.Tensor,
memory: torch.Tensor,
source_mask: Optional[torch.Tensor] = None,
target_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
tgt = self.embed(tgt) * math.sqrt(self.d_model)
pos_enc_tgt = self.positional_encoding(tgt)
output = pos_enc_tgt
for i in range(self.num_layers):
normed_output = self.layer_norm_input(output)
output = output + self.dropout(self.attention[i](normed_output, normed_output, normed_output, target_mask))
normed_output = self.layer_norm_masked_attention(output)
output = output + self.dropout(self.source_attention[i](normed_output, memory, memory, source_mask))
normed_output = self.layer_norm_attention(output)
output = output + self.dropout(self.position_feed_forward[i](normed_output))
# (batch_size, seq_len, d_model)
return self.layer_norm_output(output)
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