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import time
from datasets import load_dataset
import numpy as np
import transformers
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
class custom_RNNCell(nn.Module):
def __init__(self, input_size: int, hidden_size: int, device='cpu'):
# Initialize a basic RNN cell with Xavier-initialized weights.
# :param input_size: Number of input features.
# :param hidden_size: Number of units in the hidden layer.
#:param device: Device to place the tensors on.
super(custom_RNNCell, self).__init__()
self.hidden_size = hidden_size
self.device = device
# Xavier initialization limits
fan_in_Wx = input_size
fan_out_Wx = hidden_size
limit_Wx = np.sqrt(6 / (fan_in_Wx + fan_out_Wx))
fan_in_Wh = hidden_size
fan_out_Wh = hidden_size
limit_Wh = np.sqrt(6 / (fan_in_Wh + fan_out_Wh))
# Convert weights to PyTorch Parameters
self.Wx = nn.Parameter(torch.empty(input_size, hidden_size, device=device))
self.Wh = nn.Parameter(torch.empty(hidden_size, hidden_size, device=device))
self.bh = nn.Parameter(torch.zeros(hidden_size, device=device))
# Initialize using Xavier uniform
nn.init.uniform_(self.Wx, -limit_Wx, limit_Wx)
nn.init.uniform_(self.Wh, -limit_Wh, limit_Wh)
def forward(self, input_t: torch.Tensor, h_prev: torch.Tensor) -> torch.Tensor:
# Forward pass for a basic RNN cell.
# :param input_t: Input at time step t (batch_size x input_size).
# :param h_prev: Hidden state from previous time step (batch_size x hidden_size).
# :return: Updated hidden state.
h_t = torch.tanh(torch.mm(input_t, self.Wx) + torch.mm(h_prev, self.Wh) + self.bh)
return h_t
class custom_GRUCell(nn.Module):
def __init__(self, input_size: int, hidden_size: int, device='cpu'):
# Initialize a GRU cell with Xavier-initialized weights.
# :param input_size: Number of input features.
# :param hidden_size: Number of units in the hidden layer.
# :param device: The device to run the computations on.
super(custom_GRUCell, self).__init__()
self.hidden_size = hidden_size
self.device = device
# Xavier initialization limits
fan_in = input_size + hidden_size
fan_out = hidden_size
limit = (6 / (fan_in + fan_out)) ** 0.5
# Weight matrices for update gate, reset gate, and candidate hidden state
self.Wz = nn.Parameter(torch.empty(input_size + hidden_size, hidden_size, device=device)) # Update gate
self.Wr = nn.Parameter(torch.empty(input_size + hidden_size, hidden_size, device=device)) # Reset gate
self.Wh = nn.Parameter(torch.empty(input_size + hidden_size, hidden_size, device=device)) # Candidate hidden state
# Apply Xavier initialization
nn.init.uniform_(self.Wz, -limit, limit)
nn.init.uniform_(self.Wr, -limit, limit)
nn.init.uniform_(self.Wh, -limit, limit)
# Biases for each gate, initialized to zeros
self.bz = nn.Parameter(torch.zeros(hidden_size, device=device))
self.br = nn.Parameter(torch.zeros(hidden_size, device=device))
self.bh = nn.Parameter(torch.zeros(hidden_size, device=device))
def forward(self, input_t: torch.Tensor, h_prev: torch.Tensor) -> torch.Tensor:
# Forward pass for a single GRU cell.
# :param input_t: Input at time step t (batch_size x input_size).
# :param h_prev: Hidden state from the previous time step (batch_size x hidden_size).
# :return: Updated hidden state.
# Concatenate input and previous hidden state
concat = torch.cat((input_t, h_prev), dim=1)
# Update gate
z_t = torch.sigmoid(torch.matmul(concat, self.Wz) + self.bz)
# Reset gate
r_t = torch.sigmoid(torch.matmul(concat, self.Wr) + self.br)
# Candidate hidden state
concat_reset = torch.cat((input_t, r_t * h_prev), dim=1)
h_hat_t = torch.tanh(torch.matmul(concat_reset, self.Wh) + self.bh)
# Compute final hidden state
h_t = (1 - z_t) * h_prev + z_t * h_hat_t
return h_t
class custom_LSTMCell(nn.Module):
def __init__(self, input_size: int, hidden_size: int, device='cpu'):
super(custom_LSTMCell, self).__init__()
self.hidden_size = hidden_size
self.device = device
# Initialize LSTM weights and biases using Xavier initialization
self.Wf = nn.Parameter(torch.empty(input_size + hidden_size, hidden_size, device=device)) # Forget gate (W_f)
self.Wi = nn.Parameter(torch.empty(input_size + hidden_size, hidden_size, device=device)) # Input gate (W_i)
self.Wc = nn.Parameter(torch.empty(input_size + hidden_size, hidden_size, device=device)) # Candidate cell state (W_c~)
self.Wo = nn.Parameter(torch.empty(input_size + hidden_size, hidden_size, device=device)) # Output gate (W_o)
# Apply Xavier initialization
nn.init.xavier_uniform_(self.Wf)
nn.init.xavier_uniform_(self.Wi)
nn.init.xavier_uniform_(self.Wc)
nn.init.xavier_uniform_(self.Wo)
# Initialize biases
self.bf = nn.Parameter(torch.zeros(hidden_size, device=device)) # Forget gate bias (b_f)
self.bi = nn.Parameter(torch.zeros(hidden_size, device=device)) # Input gate bias (b_i)
self.bc = nn.Parameter(torch.zeros(hidden_size, device=device)) # Candidate state bias (b_c~)
self.bo = nn.Parameter(torch.zeros(hidden_size, device=device)) # Output gate bias (b_o)
# Initialize forget gate bias to positive value to help with training
nn.init.constant_(self.bf, 1.0)
def forward(self, x_t: torch.Tensor, h_prev: torch.Tensor, c_prev: torch.Tensor) -> tuple:
# Forward pass for a single LSTM cell.
# :param x_t: Input at the current time step (batch_size x input_size).
# :param h_prev: Previous hidden state (batch_size x hidden_size).
# :param c_prev: Previous cell state (batch_size x hidden_size).
# :return: Tuple of new hidden state (h_t) and new cell state (c_t).
# Concatenate input and previous hidden state (x_t and h_{t-1})
concat = torch.cat((x_t, h_prev), dim=1)
# Forget gate: decides what to remove from the cell state
f_t = torch.sigmoid(torch.matmul(concat, self.Wf) + self.bf) # Forget gate (σ) -> f_t
# Input gate: decides what to add to the cell state
i_t = torch.sigmoid(torch.matmul(concat, self.Wi) + self.bi) # Input gate (σ) -> i_t
# Candidate cell state: new information to potentially add to the cell state
c_hat_t = torch.tanh(torch.matmul(concat, self.Wc) + self.bc) # Candidate cell state (tanh) -> c~_t
# Update cell state: new cell state (c_t) based on previous state and gates
c_t = f_t * c_prev + i_t * c_hat_t # Cell state update -> c_t
# Output gate: decides what the next hidden state should be
o_t = torch.sigmoid(torch.matmul(concat, self.Wo) + self.bo) # Output gate (σ) -> o_t
# New hidden state (h_t): based on cell state and output gate
h_t = o_t * torch.tanh(c_t) # Hidden state update -> h_t
# Return the new hidden state (h_t) and cell state (c_t)
return h_t, c_t
class RecurrentLayer(nn.Module):
def __init__(self, input_size: int, hidden_size: int, cell_type: str = 'RNN', device='cpu'):
super(RecurrentLayer, self).__init__()
self.hidden_size = hidden_size
self.device = device
self.cell_type = cell_type
# Initialize the appropriate cell type
if cell_type == 'RNN':
self.cell = nn.RNN(input_size, hidden_size, batch_first=True, bidirectional=True)
elif cell_type == 'custom_RNN':
self.cell = custom_RNNCell(input_size, hidden_size)
elif cell_type == 'GRU':
self.cell = nn.GRU(input_size, hidden_size, batch_first=True, bidirectional=True)
elif cell_type == 'custom_GRU':
self.cell = custom_GRUCell(input_size, hidden_size, device)
elif cell_type == 'LSTM':
self.cell = nn.LSTMCell(input_size, hidden_size)
elif cell_type == 'custom_LSTM':
self.cell = custom_LSTMCell(input_size, hidden_size, device)
else:
raise ValueError("Unsupported cell type")
def forward(self, inputs: torch.Tensor) -> tuple:
# Forward pass through the recurrent layer for a sequence of inputs.
# Returns a tuple of (output, last_hidden_state) to match PyTorch's interface.
batch_size, seq_len, _ = inputs.shape
# Initialize hidden states
h_forward = torch.zeros(batch_size, self.hidden_size, device=self.device)
h_backward = torch.zeros(batch_size, self.hidden_size, device=self.device)
if self.cell_type == 'custom_LSTM':
c_forward = torch.zeros(batch_size, self.hidden_size, device=self.device)
c_backward = torch.zeros(batch_size, self.hidden_size, device=self.device)
# Lists to store outputs for both directions
forward_outputs = []
backward_outputs = []
# Forward pass
h = h_forward
c = c_forward if self.cell_type == 'custom_LSTM' else None
for t in range(seq_len):
if self.cell_type == 'custom_LSTM':
h, c = self.cell(inputs[:, t], h, c)
else:
h = self.cell(inputs[:, t], h)
forward_outputs.append(h)
# Backward pass
h = h_backward
c = c_backward if self.cell_type == 'custom_LSTM' else None
for t in range(seq_len - 1, -1, -1):
if self.cell_type == 'custom_LSTM':
h, c = self.cell(inputs[:, t], h, c)
else:
h = self.cell(inputs[:, t], h)
backward_outputs.insert(0, h)
# Stack and concatenate outputs
forward_output = torch.stack(forward_outputs, dim=1) # [batch_size, seq_len, hidden_size]
backward_output = torch.stack(backward_outputs, dim=1) # [batch_size, seq_len, hidden_size]
output = torch.cat((forward_output, backward_output), dim=2) # [batch_size, seq_len, 2*hidden_size]
# Create final hidden state (concatenated forward and backward)
final_hidden = torch.stack([forward_outputs[-1], backward_outputs[-1]], dim=0) # [2, batch_size, hidden_size]
return output, final_hidden
class Attention(nn.Module):
def __init__(self, hidden_size):
super(Attention, self).__init__()
self.W1 = nn.Linear(hidden_size, hidden_size)
self.W2 = nn.Linear(hidden_size, hidden_size)
self.v = nn.Linear(hidden_size, 1, bias=False)
def forward(self, hidden, encoder_outputs):
# hidden: [batch_size, hidden_size]
# encoder_outputs: [batch_size, sequence_len, hidden_size]
sequence_len = encoder_outputs.shape[1]
hidden = hidden.unsqueeze(1).repeat(1, sequence_len, 1)
energy = torch.tanh(self.W1(encoder_outputs) + self.W2(hidden))
attention = self.v(energy).squeeze(2) # [batch_size, sequence_len]
attention_weights = torch.softmax(attention, dim=1)
# Apply attention weights to encoder outputs to get context vector
context = torch.bmm(attention_weights.unsqueeze(1), encoder_outputs).squeeze(1)
return context, attention_weights
class SimpleRecurrentNetworkWithAttention(nn.Module):
def __init__(self, input_size, hidden_size, output_size, cell_type='RNN'):
super(SimpleRecurrentNetworkWithAttention, self).__init__()
self.embedding = nn.Embedding(input_size, hidden_size)
self.attention = Attention(hidden_size * 2) # Use hidden_size * 2 for bidirectional LSTM
self.cell_type = cell_type
if cell_type == 'RNN':
self.cell = nn.RNN(hidden_size, hidden_size, batch_first=True, bidirectional=True)
elif cell_type == 'custom_RNN':
self.cell = RecurrentLayer(hidden_size, hidden_size, cell_type="custom_RNN") #custom_RNNCell(input_size, hidden_size)
elif cell_type == 'GRU':
self.cell = nn.GRU(hidden_size, hidden_size, batch_first=True, bidirectional=True)
elif cell_type == 'custom_GRU':
self.cell = RecurrentLayer(hidden_size, hidden_size, cell_type="custom_GRU")
elif cell_type == 'LSTM':
self.cell = nn.LSTM(hidden_size, hidden_size, batch_first=True, bidirectional=True)
elif cell_type == 'custom_LSTM':
self.cell = RecurrentLayer(hidden_size, hidden_size, cell_type="custom_LSTM")
else:
raise ValueError("Unsupported cell type. Choose from 'RNN', 'custom_RNN', 'GRU', 'custom_GRU', 'LSTM' or 'custom_LSTM'.")
self.fc = nn.Linear(hidden_size * 2, output_size) # hidden_size * 2 for bidirectional
def forward(self, inputs):
embedded = self.embedding(inputs)
rnn_output, hidden = self.cell(embedded)
if isinstance(hidden, tuple): # LSTM returns (hidden, cell_state)
hidden = hidden[0]
# Since it's bidirectional, get the last layer's forward and backward hidden states
hidden = torch.cat((hidden[-2], hidden[-1]), dim=1) # Concatenate forward and backward hidden states
# Apply attention to the concatenated hidden state
context, attention_weights = self.attention(hidden, rnn_output)
# Pass the context vector to the fully connected layer
output = self.fc(context)
return output, attention_weights
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