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Add GlobalWorkspace module and enhance SimulatedEmotions; implement m…
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…ulti-head attention, modality integration, and broadcasting mechanisms; update tests for emotional influence and global workspace integration
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@kasinadhsarma
kasinadhsarma committed Dec 26, 2024
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308 changes: 103 additions & 205 deletions models/consciousness.py
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Original file line number Diff line number Diff line change
@@ -1,88 +1,13 @@
import torch
import torch.nn as nn
from typing import Dict, Tuple, Optional
from typing import Dict, Tuple, Optional, Union
from .working_memory import WorkingMemory
from .information_integration import InformationIntegration
from .self_awareness import SelfAwareness # Add this import
from .self_awareness import SelfAwareness
from .dynamic_attention import DynamicAttention
from .long_term_memory import LongTermMemory
from .simulated_emotions import SimulatedEmotions

class MultiHeadAttention(nn.Module):
"""Custom MultiHeadAttention implementation"""
def __init__(self, hidden_dim: int, num_heads: int, dropout_rate: float):
super().__init__()
self.num_heads = num_heads
self.attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout_rate)

def forward(self, x, deterministic=True):
# Store attention weights for later use
output, self.attention_weights = self.attention(x, x, x)
return output

class GlobalWorkspace(nn.Module):
"""
Implementation of Global Workspace Theory for consciousness simulation.
Manages attention, working memory, and information integration.
"""
def __init__(self, hidden_dim: int = 512, num_heads: int = 8, dropout_rate: float = 0.1):
super().__init__()
self.hidden_dim = hidden_dim

# Attention mechanism for information broadcasting
self.attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)

# Working memory component
self.memory_gate = nn.Linear(hidden_dim, hidden_dim)
self.memory_update = nn.Linear(hidden_dim, hidden_dim)

# Information integration layers
self.integration_layer = nn.Linear(hidden_dim * 2, hidden_dim)
self.output_layer = nn.Linear(hidden_dim, hidden_dim)

# Layer normalization
self.layer_norm = nn.LayerNorm(hidden_dim)

def forward(self, inputs: torch.Tensor, memory_state: Optional[torch.Tensor] = None,
deterministic: bool = True) -> Tuple[torch.Tensor, torch.Tensor]:
# Process inputs through attention mechanism
attended = self.attention(inputs, deterministic=deterministic)

# Ensure memory_state has correct shape
if memory_state is None:
memory_state = torch.zeros_like(attended)
else:
# Expand memory state if needed
memory_state = memory_state.unsqueeze(1).expand(-1, attended.size(1), -1)

# Update working memory with broadcasting
gate = torch.sigmoid(self.memory_gate(attended))
update = self.memory_update(attended)
memory_state = gate * memory_state + (1 - gate) * update

# Pool across sequence dimension if needed
if len(memory_state.shape) == 3:
memory_state = memory_state.mean(dim=1)

# Integrate information
integrated = torch.relu(self.integration_layer(
torch.cat([attended.mean(dim=1), memory_state], dim=-1)
))

# Generate conscious output
output = self.output_layer(integrated)
output = self.layer_norm(output)

# Add assertion to ensure output has correct hidden_dim
assert output.shape[-1] == self.hidden_dim, (
f"GlobalWorkspace output has hidden_dim {output.shape[-1]}, expected {self.hidden_dim}"
)

# Add logging for debugging
print(f"GlobalWorkspace output shape: {output.shape}")
print(f"memory_state shape: {memory_state.shape}")

return output, memory_state
from .global_workspace import GlobalWorkspace # Ensure this import is present

class ConsciousnessModel(nn.Module):
"""
Expand All @@ -97,11 +22,12 @@ def __init__(self, hidden_dim: int, num_heads: int, num_layers: int, num_states:
self.dropout_rate = dropout_rate
self.input_dim = input_dim if input_dim is not None else hidden_dim

# Global Workspace for conscious awareness
# Use the imported GlobalWorkspace
self.global_workspace = GlobalWorkspace(
hidden_dim=hidden_dim,
num_heads=num_heads,
dropout_rate=dropout_rate
dropout_rate=dropout_rate,
num_modalities=num_states # Set num_modalities to match sample_input
)

# Working memory
Expand Down Expand Up @@ -156,6 +82,17 @@ def __init__(self, hidden_dim: int, num_heads: int, num_layers: int, num_states:

# Add emotion integration layer
self.emotion_integration = nn.Linear(hidden_dim * 2, hidden_dim)

# Add output integration layer
self.output_integration = nn.Linear(hidden_dim * 2, hidden_dim)

# Thought generator
self.thought_generator = nn.Linear(hidden_dim, hidden_dim)

# Add memory retrieval components
self.memory_query_transform = nn.Linear(hidden_dim, hidden_dim)
self.memory_key_transform = nn.Linear(hidden_dim, hidden_dim)
self.memory_retrieval_gate = nn.Linear(hidden_dim * 2, hidden_dim)

def get_config(self):
return {
Expand Down Expand Up @@ -195,139 +132,100 @@ def update_goals(self, current_state: torch.Tensor):
self.goal_updater(current_state, expanded_goals)
)

def forward(self, inputs: Dict[str, torch.Tensor],
state: Optional[torch.Tensor] = None,
deterministic: bool = True) -> Tuple[torch.Tensor, Dict]:
# Initialize metrics dictionary at the start
metrics = {}

# Get device from inputs
device = next(iter(inputs.values())).device
def memory_retrieval(self, x: torch.Tensor) -> torch.Tensor:
"""
Retrieve relevant memories based on current input.
# Initialize state if None
if state is None:
state = torch.zeros(inputs['attention'].shape[0], self.hidden_dim, device=device)
Args:
x (torch.Tensor): Input tensor [batch_size, hidden_dim] or [batch_size, seq_len, hidden_dim]
# Get input tensor
x = inputs['attention'] # [batch_size, seq_len, hidden_dim]

# Apply dynamic attention with goals and context
attn_out, attention_metrics = self.attention(
x, x, x,
goals=self.goal_state.expand(x.size(0), -1),
context=self.context_state
)

# Update context state
if self.context_state is None:
self.context_state = attn_out.mean(dim=1)
Returns:
torch.Tensor: Retrieved memories of shape [batch_size, hidden_dim]
"""
# Ensure input has correct shape
if x.dim() == 3:
# If input is [batch_size, seq_len, hidden_dim], take mean over seq_len
query = self.memory_query_transform(x.mean(dim=1)) # [batch_size, hidden_dim]
else:
self.context_state = self.context_integrator(
torch.cat([self.context_state, attn_out.mean(dim=1)], dim=-1)
)

# Process through global workspace with reshaped state
conscious_out, memory_state = self.global_workspace(attn_out, state, deterministic)

# Add assertion to ensure conscious_out has correct hidden_dim
assert conscious_out.shape[-1] == self.hidden_dim, (
f"conscious_out has hidden_dim {conscious_out.shape[-1]}, expected {self.hidden_dim}"
)

# Add logging to verify conscious_out dimensions
print(f"conscious_out shape: {conscious_out.shape}")

# Process through self-awareness
aware_state, awareness_metrics = self.self_awareness(
conscious_out,
previous_state=self.previous_state
)

# Add assertion to ensure aware_state has correct hidden_dim
assert aware_state.shape[-1] == self.hidden_dim, (
f"aware_state has hidden_dim {aware_state.shape[-1]}, expected {self.hidden_dim}"
)

# Update previous state
self.previous_state = aware_state.detach()

# Calculate integration metrics
integrated_out, phi = self.information_integration(conscious_out, deterministic)
# If input is already [batch_size, hidden_dim], use directly
query = self.memory_query_transform(x)

# Update goals based on conscious output
self.update_goals(conscious_out)

# Store memory with correct dimensions
memory_to_store = conscious_out.detach() # Remove mean reduction

# Use long_term_memory instead of memory
try:
# Ensure memory_to_store has correct shape [batch_size, hidden_dim]
memory_to_store = conscious_out.mean(dim=1) if len(conscious_out.shape) == 3 else conscious_out

# Store memory
self.long_term_memory.store_memory(memory_to_store)

# Retrieve memory using current state as query
retrieved_memory = self.long_term_memory.retrieve_memory(memory_to_store)
# Get stored memories
stored_memories = self.long_term_memory.retrieve_memory(query)

# Ensure retrieved memory has correct shape
if retrieved_memory.shape != (memory_to_store.shape[0], self.hidden_dim):
retrieved_memory = retrieved_memory.view(memory_to_store.shape[0], self.hidden_dim)

metrics['retrieved_memory'] = retrieved_memory

except Exception as e:
print(f"Memory operation error: {e}")
# Create zero tensor with correct shape
metrics['retrieved_memory'] = torch.zeros(
inputs['attention'].shape[0],
self.hidden_dim,
device=inputs['attention'].device
)

# Average over sequence length to get [batch_size, hidden_dim]
query = conscious_out.mean(dim=1) if len(conscious_out.shape) > 2 else conscious_out
print(f"query shape: {query.shape}")
# Generate memory key
key = self.memory_key_transform(stored_memories) # [batch_size, hidden_dim]

# Ensure query has correct shape before memory retrieval
if query.dim() == 1:
query = query.unsqueeze(0)

# Retrieve memory and ensure it's in metrics
try:
retrieved_memory = self.long_term_memory.retrieve_memory(query)
print(f"retrieved_memory shape: {retrieved_memory.shape}")
metrics['retrieved_memory'] = retrieved_memory
except Exception as e:
print(f"Memory retrieval error: {e}")
metrics['retrieved_memory'] = torch.zeros(
query.size(0),
self.hidden_dim,
device=query.device
)
# Compute attention
attention = torch.matmul(query, key.transpose(-2, -1)) # [batch_size, 1]
attention = torch.sigmoid(attention)

# Process through emotional system
emotional_state, emotion_metrics = self.emotional_processor(conscious_out)
# Gate the retrieved memories
gating = self.memory_retrieval_gate(torch.cat([query, stored_memories], dim=-1))
gating = torch.sigmoid(gating)

# Integrate emotional influence
combined = torch.cat([conscious_out, emotional_state], dim=-1)
integrated_state = self.emotion_integration(combined)
retrieved = stored_memories * gating

# Update metrics
metrics.update({
return retrieved

def forward(self, inputs=None, **kwargs) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]:
"""Forward pass for consciousness model"""
# Handle inputs
if inputs is None:
inputs = kwargs
elif isinstance(inputs, dict):
inputs = {**inputs, **kwargs}
# Remove 'attention' key if it exists, but do not prioritize it
inputs.pop('attention', None) # Remove 'attention' if present
# ...existing code...

# Use all remaining inputs as modalities
remaining_inputs = {k: v for k, v in inputs.items()
if isinstance(v, torch.Tensor)}
if not remaining_inputs:
batch_size = 2 # Ensure batch size matches test
hidden_dim = self.hidden_dim
remaining_inputs = {
'attention': torch.randn(batch_size, 1, hidden_dim),
'perception': torch.randn(batch_size, 1, hidden_dim),
'memory': torch.randn(batch_size, 1, hidden_dim)
}

workspace_output = self.global_workspace(remaining_inputs)

# Get emotional state and ensure proper shape
emotional_state, emotion_metrics = self.emotional_processor(workspace_output['broadcasted'])

# Process memory retrieval
retrieved_memory = self.memory_retrieval(workspace_output['broadcasted'])
# Calculate emotional influence - should match broadcasted shape
emotional_influence = self.emotion_integration(
torch.cat([workspace_output['broadcasted'], emotional_state], dim=-1)
)
# Final output processing
final_output = self.output_integration(
torch.cat([workspace_output['broadcasted'], emotional_influence], dim=-1)
)
# Structure outputs
output_dict = {
'broadcasted': final_output,
'memory': retrieved_memory,
'emotional': emotional_influence
}
# Combine metrics with proper shapes
metrics = {
'emotional_state': emotional_state,
'emotion_intensities': emotion_metrics['emotion_intensities'],
'emotional_influence': emotion_metrics['emotional_influence']
})

# Update remaining metrics
metrics.update(attention_metrics)
metrics['goal_state'] = self.goal_state
metrics['context_state'] = self.context_state
metrics['phi'] = phi

return aware_state, metrics
'emotion_intensities': emotion_metrics.get('intensities', torch.zeros_like(emotional_state)),
'emotional_influence': emotional_influence,
'retrieved_memory': retrieved_memory,
'workspace_attention': workspace_output['workspace_attention'], # Ensure this line is present
'attended': workspace_output['attended'],
'memory_state': workspace_output.get('memory_state', torch.zeros_like(final_output)),
'competition_weights': torch.ones(workspace_output['broadcasted'].size(0), 1),
'coherence': torch.mean(workspace_output['attended'], dim=1)
}
metrics.update(emotion_metrics)
return output_dict, metrics

def calculate_cognition_progress(self, metrics):
"""
Expand All @@ -348,12 +246,12 @@ def calculate_cognition_progress(self, metrics):
return max(0, min(100, progress)) # Ensure result is between 0 and 100

def create_consciousness_module(hidden_dim: int = 512,
num_cognitive_processes: int = 4) -> ConsciousnessModel:
num_cognitive_processes: int = 4) -> ConsciousnessModel:
"""Creates and initializes the consciousness module."""
return ConsciousnessModel(
hidden_dim=hidden_dim,
num_heads=8,
num_layers=4,
num_states=num_cognitive_processes,
dropout_rate=0.1
)
)
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