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| import torch | |
| import torch.nn as nn | |
| from typing import Dict, Tuple, List | |
| import numpy as np | |
| class PatternAnalyzer: | |
| def __init__(self): | |
| pass | |
| class SparseActivationManager: | |
| def __init__(self, sparsity_threshold: float = 0.95): | |
| self.sparsity_threshold = sparsity_threshold | |
| self.activation_history = [] | |
| self.pattern_analyzer = PatternAnalyzer() | |
| def compute_pattern(self, input_tensor: torch.Tensor) -> torch.Tensor: | |
| importance_scores = self._compute_importance_scores(input_tensor) | |
| activation_mask = self._generate_activation_mask(importance_scores) | |
| return self._apply_sparse_activation(input_tensor, activation_mask) | |
| def _compute_importance_scores(self, input_tensor: torch.Tensor) -> torch.Tensor: | |
| attention_weights = self._calculate_attention_weights(input_tensor) | |
| gradient_information = self._compute_gradient_information(input_tensor) | |
| return self._combine_importance_metrics(attention_weights, gradient_information) | |
| def _generate_activation_mask(self, importance_scores: torch.Tensor) -> torch.Tensor: | |
| # Create a binary mask based on importance scores and sparsity threshold | |
| return (importance_scores > self.sparsity_threshold).float() | |
| def _apply_sparse_activation(self, input_tensor: torch.Tensor, activation_mask: torch.Tensor) -> torch.Tensor: | |
| # Apply the activation mask to the input tensor | |
| return input_tensor * activation_mask | |
| def _calculate_attention_weights(self, input_tensor: torch.Tensor) -> torch.Tensor: | |
| # Calculate attention weights for the input tensor | |
| return torch.sigmoid(input_tensor) | |
| def _compute_gradient_information(self, input_tensor: torch.Tensor) -> torch.Tensor: | |
| # Compute gradient information for the input tensor | |
| return torch.abs(input_tensor) | |
| def _combine_importance_metrics(self, attention_weights: torch.Tensor, | |
| gradient_information: torch.Tensor) -> torch.Tensor: | |
| # Combine multiple importance metrics into a single score | |
| return attention_weights * gradient_information | |