84 lines
2.3 KiB
Python
84 lines
2.3 KiB
Python
# WanSeamlessFlow/utils/optimization.py
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import numpy as np
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import torch
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from typing import List, Tuple
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def calc_embedding_distance(embed1, embed2):
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"""
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Calculate distance between two embeddings.
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Args:
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embed1: First embedding tensor
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embed2: Second embedding tensor
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Returns:
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Distance measure (lower means more similar)
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"""
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# Move to CPU for numpy compatibility
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e1 = embed1.cpu().numpy()
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e2 = embed2.cpu().numpy()
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# Calculate L2 distance
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return np.sum((e1 - e2)**2)
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def optimize_embedding_order(embeddings: List[torch.Tensor]) -> List[int]:
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"""
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Optimize the order of embeddings to minimize semantic distance between consecutive prompts.
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Args:
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embeddings: List of embedding tensors
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Returns:
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Optimized ordering indices
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"""
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if len(embeddings) <= 1:
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return list(range(len(embeddings)))
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# Calculate embedding centroids
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means = [torch.mean(embed, dim=0).cpu().numpy() for embed in embeddings]
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# Nearest neighbor ordering
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order = [0] # Start with first prompt
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remaining = set(range(1, len(means)))
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while remaining:
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curr, best_dist = order[-1], float('inf')
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best_next = None
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for i in remaining:
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# Calculate distance between current and candidate
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dist = np.sum((means[curr] - means[i])**2)
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if dist < best_dist:
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best_dist, best_next = dist, i
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order.append(best_next)
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remaining.remove(best_next)
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return order
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def compute_transition_cost_matrix(embeddings: List[torch.Tensor]) -> np.ndarray:
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"""
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Compute full transition cost matrix between all embeddings.
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Args:
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embeddings: List of embedding tensors
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Returns:
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Matrix where cost_matrix[i][j] is transition cost from i to j
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"""
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n = len(embeddings)
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cost_matrix = np.zeros((n, n), dtype=np.float32)
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# Calculate centroids for efficiency
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means = [torch.mean(embed, dim=0).cpu().numpy() for embed in embeddings]
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# Compute all pairwise distances
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for i in range(n):
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for j in range(n):
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if i != j:
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cost_matrix[i, j] = np.sum((means[i] - means[j])**2)
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return cost_matrix |