Sure! Here’s a high-level implementation of the Neural MorphGear in Python using PyTorch. This model dynamically switches between different architectures (RNN, Transformer, SSM) based on input sequence length or task-specific requirements. We’ll implement a control module that selects the appropriate model based on the sequence characteristics.
Code Implementation:
import torch
import torch.nn as nn
import torch.nn.functional as F
Define minimal RNN (LSTM/GRU)
class MinimalRNN(nn.Module):
def init(self, input_size, hidden_size, rnn_type='LSTM'):
super(MinimalRNN, self).init()
if rnn_type == 'LSTM':
self.rnn = nn.LSTM(input_size, hidden_size, batch_first=True)
elif rnn_type == 'GRU':
self.rnn = nn.GRU(input_size, hidden_size, batch_first=True)
self.hidden_size = hidden_size
def forward(self, x):
# Initialize hidden and cell states (if LSTM)
h0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
c0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
# Forward propagate
if isinstance(self.rnn, nn.LSTM):
out, _ = self.rnn(x, (h0, c0))
else:
out, _ = self.rnn(x, h0)
return out
Define Transformer block
class TransformerModel(nn.Module):
def init(self, input_size, n_heads=8, num_layers=4, hidden_dim=256):
super(TransformerModel, self).init()
self.transformer = nn.Transformer(d_model=input_size, nhead=n_heads, num_encoder_layers=num_layers)
self.fc = nn.Linear(input_size, hidden_dim)
def forward(self, src):
# Assuming src is (batch_size, seq_len, input_size)
src = src.permute(1, 0, 2) # (seq_len, batch_size, input_size) for transformer input
transformer_out = self.transformer(src)
return transformer_out.permute(1, 0, 2) # return to (batch_size, seq_len, input_size)
Define State-Space Model (simplified)
class StateSpaceModel(nn.Module):
def init(self, input_size, hidden_size):
super(StateSpaceModel, self).init()
self.ss_layer = nn.Linear(input_size, hidden_size)
def forward(self, x):
return F.relu(self.ss_layer(x))
Define the Neural MorphGear control module
class NeuralMorphGear(nn.Module):
def init(self, input_size, hidden_size, task_threshold=100):
super(NeuralMorphGear, self).init()
self.hidden_size = hidden_size
self.task_threshold = task_threshold
# Define three different architectures
self.minimal_rnn = MinimalRNN(input_size, hidden_size, rnn_type='LSTM')
self.transformer = TransformerModel(input_size)
self.ssm = StateSpaceModel(input_size, hidden_size)
# A linear layer for final output processing
self.output_layer = nn.Linear(hidden_size, input_size)
def forward(self, x, task_type='simple'):
# Dynamically choose architecture based on task type or sequence length
if task_type == 'simple' or x.size(1) < self.task_threshold: # Short sequence or simple task
out = self.minimal_rnn(x)
elif task_type == 'complex' or x.size(1) >= self.task_threshold: # Long sequence or complex task
out = self.transformer(x)
else: # Fall back to SSM for efficiency
out = self.ssm(x)
# Process the output through a final layer (common)
out = self.output_layer(out)
return out
Example Usage:
if name == "main":
# Define some inputs
batch_size = 32
seq_len = 150 # This will determine the architecture selected
input_size = 64
hidden_size = 128
# Create an instance of the Neural MorphGear
morph_gear = NeuralMorphGear(input_size=input_size, hidden_size=hidden_size, task_threshold=100)
# Input tensor (batch_size, seq_len, input_size)
x = torch.randn(batch_size, seq_len, input_size)
# Forward pass through the MorphGear
output = morph_gear(x, task_type='complex')
print("Output shape:", output.shape)
Explanation:
1. Minimal RNN: This can either be an LSTM or a GRU. It handles shorter sequences or tasks where recurrence is efficient and sufficient.
2. Transformer: This block is used for tasks that require handling long sequences or complex dependencies across the input. The transformer is configured with attention heads and encoder layers, making it ideal for capturing long-range dependencies.
3. State-Space Model (SSM): A simplified state-space model for tasks where capturing patterns over long sequences efficiently is needed. It avoids the quadratic complexity of the transformer but offers efficiency over long sequences.
4. Control Module: This module dynamically selects which architecture to use based on the input sequence length or a task-specific parameter (task_type). It acts like the gear mechanism of the MorphGear, shifting between RNN, Transformer, and SSM configurations.
5. Task Threshold: A configurable threshold (e.g., 100 sequence length) determines when to switch between the minimal RNN and Transformer, simulating the “gearing” mechanism where the model adapts based on input complexity.
How It Works:
• For shorter sequences (seq_len < task_threshold) or simple tasks, the Minimal RNN is chosen for efficiency.
• For longer sequences or more complex tasks, the Transformer is chosen for handling long-range dependencies.
• If energy efficiency is prioritized (e.g., if the task is time-series or less complex), the State-Space Model (SSM) is activated.
This hybrid approach ensures that each task or input is handled optimally, balancing between computation efficiency and model complexity.
Feel free to modify the architecture or add more task-specific parameters to refine the switching mechanism based on the specific use case!
import torch
import torch.nn as nn
import torch.nn.functional as F
Define minimal RNN (LSTM/GRU)
class MinimalRNN(nn.Module):
def init(self, input_size, hidden_size, rnn_type='LSTM'):
super(MinimalRNN, self).init()
if rnn_type == 'LSTM':
self.rnn = nn.LSTM(input_size, hidden_size, batch_first=True)
elif rnn_type == 'GRU':
self.rnn = nn.GRU(input_size, hidden_size, batch_first=True)
self.hidden_size = hidden_size
def forward(self, x):
# Initialize hidden and cell states (if LSTM)
h0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
c0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
# Forward propagate
if isinstance(self.rnn, nn.LSTM):
out, _ = self.rnn(x, (h0, c0))
else:
out, _ = self.rnn(x, h0)
return out
Define Transformer block
class TransformerModel(nn.Module):
def init(self, input_size, n_heads=8, num_layers=4, hidden_dim=256):
super(TransformerModel, self).init()
self.transformer = nn.Transformer(d_model=input_size, nhead=n_heads, num_encoder_layers=num_layers)
self.fc = nn.Linear(input_size, hidden_dim)
def forward(self, src):
# Assuming src is (batch_size, seq_len, input_size)
src = src.permute(1, 0, 2) # (seq_len, batch_size, input_size) for transformer input
transformer_out = self.transformer(src)
return transformer_out.permute(1, 0, 2) # return to (batch_size, seq_len, input_size)
Define State-Space Model (simplified)
class StateSpaceModel(nn.Module):
def init(self, input_size, hidden_size):
super(StateSpaceModel, self).init()
self.ss_layer = nn.Linear(input_size, hidden_size)
def forward(self, x):
return F.relu(self.ss_layer(x))
Define the Neural MorphGear control module
class NeuralMorphGear(nn.Module):
def init(self, input_size, hidden_size, task_threshold=100):
super(NeuralMorphGear, self).init()
self.hidden_size = hidden_size
self.task_threshold = task_threshold
# Define three different architectures
self.minimal_rnn = MinimalRNN(input_size, hidden_size, rnn_type='LSTM')
self.transformer = TransformerModel(input_size)
self.ssm = StateSpaceModel(input_size, hidden_size)
# A linear layer for final output processing
self.output_layer = nn.Linear(hidden_size, input_size)
def forward(self, x, task_type='simple'):
# Dynamically choose architecture based on task type or sequence length
if task_type == 'simple' or x.size(1) < self.task_threshold: # Short sequence or simple task
out = self.minimal_rnn(x)
elif task_type == 'complex' or x.size(1) >= self.task_threshold: # Long sequence or complex task
out = self.transformer(x)
else: # Fall back to SSM for efficiency
out = self.ssm(x)
# Process the output through a final layer (common)
out = self.output_layer(out)
return out
Example Usage:
if name == "main":
# Define some inputs
batch_size = 32
seq_len = 150 # This will determine the architecture selected
input_size = 64
hidden_size = 128
# Create an instance of the Neural MorphGear
morph_gear = NeuralMorphGear(input_size=input_size, hidden_size=hidden_size, task_threshold=100)
# Input tensor (batch_size, seq_len, input_size)
x = torch.randn(batch_size, seq_len, input_size)
# Forward pass through the MorphGear
output = morph_gear(x, task_type='complex')
print("Output shape:", output.shape)
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