Initial commit
This commit is contained in:
redclawsystems
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//! TIES (Task-specific Interference Elimination) algorithm implementation
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//!
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//! TIES resolves parameter interference by:
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//! 1. Eliminating redundant parameters through sign voting
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//! 2. Selecting top-k parameters by magnitude
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//! 3. Rescaling merged parameters appropriately
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//!
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//! Reference: "Resolving Interference When Merging Models" (Yadav et al., 2024)
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use crate::algorithms::MergeUtils;
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use crate::config::{RescaleMethod, TiesConfig};
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use crate::error::{MergeError, Result};
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use crate::types::{
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MergeInfo, MergeStatistics, MergedModel, Model, ModelReference, QualityMetrics,
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};
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use indexmap::IndexMap;
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use std::collections::HashMap;
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use tracing::{debug, info};
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/// TIES merging algorithm implementation
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pub struct TiesMerger {
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config: TiesConfig,
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}
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impl TiesMerger {
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/// Create a new TIES merger with configuration
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pub fn new(config: TiesConfig) -> Self {
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Self { config }
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}
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/// Execute TIES merging on multiple models
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pub async fn merge(&self, models: &[Model]) -> Result<MergedModel> {
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if models.len() < 2 {
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return Err(MergeError::algorithm(
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"TIES",
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"At least 2 models required for merging",
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));
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}
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info!("Starting TIES merge of {} models", models.len());
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let start_time = std::time::Instant::now();
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// Step 1: Validate model compatibility
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self.validate_compatibility(models)?;
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// Step 2: Compute task vectors (differences from base model)
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let task_vectors = self.compute_task_vectors(models)?;
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// Step 3: Apply TIES algorithm
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let merged_parameters = self.apply_ties_algorithm(&task_vectors, models)?;
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// Step 4: Create merged model
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let mut merged_model = models[0].clone();
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merged_model.id = uuid::Uuid::new_v4();
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merged_model.name = format!("TIES_merged_{}", models.len());
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merged_model.parameters = merged_parameters;
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// Step 5: Compute merge statistics and quality metrics
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let statistics = self.compute_statistics(&merged_model, models, start_time.elapsed());
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let quality_metrics = self.compute_quality_metrics(&merged_model, models)?;
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let merge_info = MergeInfo {
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strategy: "TIES".to_string(),
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source_models: models
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.iter()
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.map(|m| ModelReference {
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id: m.id,
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name: m.name.clone(),
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path: std::path::PathBuf::from(&m.name),
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weight: Some(1.0 / models.len() as f32),
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})
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.collect(),
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merged_at: chrono::Utc::now(),
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config: serde_json::to_value(&self.config)
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.map_err(|e| MergeError::internal(format!("Config serialization: {e}")))?,
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statistics,
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};
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info!("TIES merge completed in {:?}", start_time.elapsed());
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Ok(MergedModel {
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model: merged_model,
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merge_info,
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quality_metrics,
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})
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}
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/// Validate that models are compatible for TIES merging
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fn validate_compatibility(&self, models: &[Model]) -> Result<()> {
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let base_model = &models[0];
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for (i, model) in models.iter().enumerate().skip(1) {
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if !base_model.is_compatible_with(model) {
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return Err(MergeError::compatibility(format!(
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"Model {i} incompatible with base model"
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)));
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}
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}
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Ok(())
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}
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/// Compute task vectors (parameter differences from base model)
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fn compute_task_vectors(&self, models: &[Model]) -> Result<Vec<IndexMap<String, Vec<f32>>>> {
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info!("Computing task vectors from {} models", models.len());
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let base_model = &models[0];
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let mut task_vectors = Vec::new();
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for model in models.iter().skip(1) {
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let mut task_vector = IndexMap::new();
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for (param_name, base_param) in &base_model.parameters {
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if let Some(model_param) = model.parameters.get(param_name) {
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let diff =
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MergeUtils::elementwise_op(&model_param.data, &base_param.data, |a, b| {
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a - b
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})?;
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task_vector.insert(param_name.clone(), diff);
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}
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}
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task_vectors.push(task_vector);
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}
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Ok(task_vectors)
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}
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/// Apply the TIES algorithm to merge task vectors
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fn apply_ties_algorithm(
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&self,
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task_vectors: &[IndexMap<String, Vec<f32>>],
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models: &[Model],
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) -> Result<IndexMap<String, crate::types::ParameterTensor>> {
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info!(
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"Applying TIES algorithm with density: {}",
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self.config.density
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);
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let base_model = &models[0];
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let mut merged_parameters = IndexMap::new();
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let mut total_conflicts = 0;
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let mut total_dropped = 0;
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for (param_name, base_param) in &base_model.parameters {
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debug!("Processing parameter: {}", param_name);
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// Collect all task vectors for this parameter
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let param_vectors: Vec<&Vec<f32>> = task_vectors
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.iter()
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.filter_map(|tv| tv.get(param_name))
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.collect();
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if param_vectors.is_empty() {
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// Keep base parameter if no task vectors available
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merged_parameters.insert(param_name.clone(), base_param.clone());
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continue;
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}
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// Step 1: Sign consistency check
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let (consistent_values, conflicts) = if self.config.enable_sign_voting {
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self.resolve_sign_conflicts(¶m_vectors, param_name)?
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} else {
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// Simple average without sign checking
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let param_slices: Vec<&[f32]> =
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param_vectors.iter().map(|v| v.as_slice()).collect();
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let avg = MergeUtils::average_parameters(¶m_slices)?;
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(avg, 0)
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};
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total_conflicts += conflicts;
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// Step 2: Magnitude-based parameter selection
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let importance_scores = MergeUtils::compute_magnitude_importance(&consistent_values);
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let k = (consistent_values.len() as f32 * self.config.density) as usize;
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let selection_mask = MergeUtils::select_top_k_parameters(&importance_scores, k);
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// Count dropped parameters
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let dropped = selection_mask.iter().filter(|&&x| !x).count();
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total_dropped += dropped;
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// Step 3: Apply selection mask and rescaling
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let mut final_values = consistent_values.clone();
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MergeUtils::apply_sparsity_mask(&mut final_values, &selection_mask)?;
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// Rescale if needed
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match self.config.rescale_method {
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RescaleMethod::Magnitude => {
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self.rescale_by_magnitude(&mut final_values, &selection_mask)?;
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}
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RescaleMethod::SignConsistency => {
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self.rescale_by_sign_consistency(&mut final_values, ¶m_vectors)?;
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}
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RescaleMethod::None => {
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// No rescaling
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}
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}
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// Add back to base parameters
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let merged_values =
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MergeUtils::elementwise_op(&base_param.data, &final_values, |base, delta| {
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base + delta
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})?;
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// Create merged parameter tensor
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let mut merged_param = base_param.clone();
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merged_param.data = merged_values;
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merged_parameters.insert(param_name.clone(), merged_param);
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}
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info!(
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"TIES algorithm completed. Conflicts resolved: {}, Parameters dropped: {}",
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total_conflicts, total_dropped
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);
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Ok(merged_parameters)
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}
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/// Resolve sign conflicts using voting mechanism
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fn resolve_sign_conflicts(
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&self,
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param_vectors: &[&Vec<f32>],
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param_name: &str,
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) -> Result<(Vec<f32>, usize)> {
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if param_vectors.is_empty() {
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return Ok((vec![], 0));
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}
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let param_len = param_vectors[0].len();
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let mut resolved_values = vec![0.0; param_len];
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let mut conflicts = 0;
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debug!(
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"Resolving sign conflicts for parameter: {} ({} vectors, {} elements)",
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param_name,
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param_vectors.len(),
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param_len
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);
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// Process each parameter element
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for i in 0..param_len {
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let values: Vec<f32> = param_vectors.iter().map(|v| v[i]).collect();
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// Check sign consistency
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let positive_count = values.iter().filter(|&&x| x > 0.0).count();
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let negative_count = values.iter().filter(|&&x| x < 0.0).count();
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let _zero_count = values.iter().filter(|&&x| x == 0.0).count();
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let total_nonzero = positive_count + negative_count;
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if total_nonzero == 0 {
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resolved_values[i] = 0.0;
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continue;
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}
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// Determine consensus sign
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let consensus_positive =
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positive_count as f32 / total_nonzero as f32 >= self.config.voting_threshold;
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let consensus_negative =
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negative_count as f32 / total_nonzero as f32 >= self.config.voting_threshold;
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if consensus_positive && !consensus_negative {
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// Use only positive values
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let positive_values: Vec<f32> =
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values.iter().filter(|&&x| x > 0.0).copied().collect();
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if !positive_values.is_empty() {
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resolved_values[i] =
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positive_values.iter().sum::<f32>() / positive_values.len() as f32;
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}
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if negative_count > 0 {
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conflicts += negative_count;
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}
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} else if consensus_negative && !consensus_positive {
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// Use only negative values
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let negative_values: Vec<f32> =
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values.iter().filter(|&&x| x < 0.0).copied().collect();
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if !negative_values.is_empty() {
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resolved_values[i] =
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negative_values.iter().sum::<f32>() / negative_values.len() as f32;
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}
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if positive_count > 0 {
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conflicts += positive_count;
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}
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} else {
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// No clear consensus - use magnitude-weighted average
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let total_magnitude: f32 = values.iter().map(|x| x.abs()).sum();
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if total_magnitude > 0.0 {
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resolved_values[i] =
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values.iter().map(|&x| x * x.abs()).sum::<f32>() / total_magnitude;
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}
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conflicts += std::cmp::min(positive_count, negative_count);
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}
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}
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debug!(
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"Sign conflict resolution completed for {}: {} conflicts resolved",
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param_name, conflicts
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);
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Ok((resolved_values, conflicts))
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}
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/// Rescale parameters based on magnitude
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fn rescale_by_magnitude(&self, values: &mut [f32], mask: &[bool]) -> Result<()> {
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let active_count = mask.iter().filter(|&&x| x).count();
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if active_count == 0 {
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return Ok(());
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}
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let total_count = mask.len();
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let rescale_factor = (total_count as f32) / (active_count as f32);
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for (value, &active) in values.iter_mut().zip(mask.iter()) {
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if active {
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*value *= rescale_factor;
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}
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}
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Ok(())
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}
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/// Rescale parameters based on sign consistency
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fn rescale_by_sign_consistency(
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&self,
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values: &mut [f32],
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param_vectors: &[&Vec<f32>],
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) -> Result<()> {
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if param_vectors.is_empty() {
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return Ok(());
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}
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let param_slices: Vec<&[f32]> = param_vectors.iter().map(|v| v.as_slice()).collect();
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let consistency_scores =
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MergeUtils::check_sign_consistency(¶m_slices, self.config.sign_threshold);
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let consistent_count = consistency_scores.iter().filter(|&&x| x).count();
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if consistent_count == 0 {
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return Ok(());
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}
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let rescale_factor = (consistency_scores.len() as f32) / (consistent_count as f32);
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for (value, &consistent) in values.iter_mut().zip(consistency_scores.iter()) {
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if consistent {
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*value *= rescale_factor;
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}
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}
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Ok(())
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}
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/// Compute merge statistics
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fn compute_statistics(
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&self,
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merged_model: &Model,
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_source_models: &[Model],
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duration: std::time::Duration,
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) -> MergeStatistics {
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let parameters_merged = merged_model.parameter_count();
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let memory_usage_mb = merged_model.memory_size() / (1024 * 1024);
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MergeStatistics {
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parameters_merged,
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parameters_conflicted: 0, // Would be tracked during merging
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parameters_dropped: 0, // Would be tracked during merging
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duration_ms: duration.as_millis() as u64,
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memory_usage_mb,
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gpu_memory_mb: None,
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}
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}
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/// Compute quality metrics for the merged model
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fn compute_quality_metrics(
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&self,
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merged_model: &Model,
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source_models: &[Model],
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) -> Result<QualityMetrics> {
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let mut consistency_scores = Vec::new();
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// Compute consistency with each source model
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for source in source_models {
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let similarity = MergeUtils::compute_similarity(merged_model, source)?;
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consistency_scores.push(similarity);
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}
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let consistency_score =
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consistency_scores.iter().sum::<f32>() / consistency_scores.len() as f32;
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// Compute complexity score (normalized parameter variance)
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let all_params: Vec<f32> = merged_model
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.parameters
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.values()
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.flat_map(|p| p.data.iter())
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.copied()
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.collect();
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let (_, std_dev, _, _) = MergeUtils::compute_moments(&all_params);
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let complexity_score = std_dev.min(1.0); // Normalized to [0, 1]
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let mut quality_indicators = HashMap::new();
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quality_indicators.insert("parameter_diversity".to_string(), std_dev);
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quality_indicators.insert("average_similarity".to_string(), consistency_score);
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let mut validation_results = HashMap::new();
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validation_results.insert("sign_consistency".to_string(), consistency_score > 0.5);
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validation_results.insert("magnitude_preservation".to_string(), complexity_score > 0.1);
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let mut performance_predictions = HashMap::new();
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performance_predictions.insert("expected_accuracy".to_string(), consistency_score * 0.9);
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performance_predictions.insert("stability_score".to_string(), 1.0 - complexity_score);
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Ok(QualityMetrics {
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consistency_score,
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complexity_score,
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quality_indicators,
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validation_results,
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performance_predictions,
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})
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}
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}
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/// Convenience function for TIES merging
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pub async fn merge_models(models: &[Model], config: &TiesConfig) -> Result<MergedModel> {
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let merger = TiesMerger::new(config.clone());
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merger.merge(models).await
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::types::{DataType, ModelArchitecture, ParameterTensor};
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fn create_test_model(name: &str, params: Vec<(String, Vec<f32>)>) -> Model {
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let arch = ModelArchitecture {
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arch_type: "test".to_string(),
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num_layers: 1,
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hidden_dim: params.len(),
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params: HashMap::new(),
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};
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let mut model = Model::new(name.to_string(), arch);
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for (param_name, data) in params {
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let param = ParameterTensor::new(param_name, vec![data.len()], DataType::Float32, data);
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model.add_parameter(param);
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}
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model
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}
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#[tokio::test]
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async fn test_ties_merge_basic() -> Result<()> {
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let model1 = create_test_model("model1", vec![("weight".to_string(), vec![1.0, 2.0, 3.0])]);
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let model2 = create_test_model("model2", vec![("weight".to_string(), vec![1.1, 2.1, 3.1])]);
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let config = TiesConfig::default();
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let merger = TiesMerger::new(config);
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let result = merger.merge(&[model1, model2]).await?;
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assert!(result.model.parameters.contains_key("weight"));
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assert_eq!(result.merge_info.strategy, "TIES");
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Ok(())
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}
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#[tokio::test]
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async fn test_ties_sign_conflict_resolution() -> Result<()> {
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let merger = TiesMerger::new(TiesConfig {
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enable_sign_voting: true,
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voting_threshold: 0.6,
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..TiesConfig::default()
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||||
});
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let vec1 = vec![1.0, -2.0, 3.0];
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let vec2 = vec![2.0, -1.0, 4.0];
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let vec3 = vec![-1.0, -3.0, -2.0];
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let param_vectors = vec![&vec1, &vec2, &vec3];
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let (resolved, conflicts) = merger.resolve_sign_conflicts(¶m_vectors, "test")?;
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assert!(conflicts > 0);
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assert_eq!(resolved.len(), 3);
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// Second element should be consistently negative
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assert!(resolved[1] < 0.0);
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Ok(())
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_ties_density_selection() -> Result<()> {
|
||||
let model1 = create_test_model(
|
||||
"model1",
|
||||
vec![("weight".to_string(), vec![0.0, 0.0, 0.0, 0.0, 0.0])],
|
||||
);
|
||||
|
||||
let model2 = create_test_model(
|
||||
"model2",
|
||||
vec![
|
||||
("weight".to_string(), vec![1.0, 0.1, 0.5, 0.05, 0.8]), // Different magnitudes
|
||||
],
|
||||
);
|
||||
|
||||
let config = TiesConfig {
|
||||
density: 0.6, // Keep top 60% = 3 parameters
|
||||
..TiesConfig::default()
|
||||
};
|
||||
|
||||
let merger = TiesMerger::new(config);
|
||||
let result = merger.merge(&[model1, model2]).await?;
|
||||
|
||||
let merged_weight = result.model.get_parameter("weight").unwrap();
|
||||
let non_zero_count = merged_weight.data.iter().filter(|&&x| x != 0.0).count();
|
||||
|
||||
// Should have at most 3 non-zero parameters (top 60%)
|
||||
assert!(non_zero_count <= 3);
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_ties_rescaling() -> Result<()> {
|
||||
let model1 = create_test_model("model1", vec![("weight".to_string(), vec![0.0, 0.0])]);
|
||||
|
||||
let model2 = create_test_model("model2", vec![("weight".to_string(), vec![1.0, 1.0])]);
|
||||
|
||||
let config = TiesConfig {
|
||||
density: 0.5, // Keep only 50% of parameters
|
||||
rescale_method: RescaleMethod::Magnitude,
|
||||
..TiesConfig::default()
|
||||
};
|
||||
|
||||
let merger = TiesMerger::new(config);
|
||||
let result = merger.merge(&[model1, model2]).await?;
|
||||
|
||||
let merged_weight = result.model.get_parameter("weight").unwrap();
|
||||
|
||||
// Check that rescaling was applied
|
||||
let non_zero_values: Vec<f32> = merged_weight
|
||||
.data
|
||||
.iter()
|
||||
.filter(|&&x| x != 0.0)
|
||||
.cloned()
|
||||
.collect();
|
||||
if !non_zero_values.is_empty() {
|
||||
// Rescaled values should be larger than original due to density < 1.0
|
||||
assert!(non_zero_values.iter().any(|&x| x > 1.0));
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_task_vector_computation() -> Result<()> {
|
||||
let model1 = create_test_model("base", vec![("weight".to_string(), vec![1.0, 2.0, 3.0])]);
|
||||
|
||||
let model2 = create_test_model("task", vec![("weight".to_string(), vec![2.0, 3.0, 4.0])]);
|
||||
|
||||
let merger = TiesMerger::new(TiesConfig::default());
|
||||
let task_vectors = merger.compute_task_vectors(&[model1, model2])?;
|
||||
|
||||
assert_eq!(task_vectors.len(), 1);
|
||||
assert!(task_vectors[0].contains_key("weight"));
|
||||
|
||||
let task_vector = &task_vectors[0]["weight"];
|
||||
assert_eq!(task_vector, &vec![1.0, 1.0, 1.0]);
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_quality_metrics_computation() -> Result<()> {
|
||||
let model1 = create_test_model("model1", vec![("weight".to_string(), vec![1.0, 2.0, 3.0])]);
|
||||
|
||||
let model2 = create_test_model("model2", vec![("weight".to_string(), vec![1.1, 2.1, 3.1])]);
|
||||
|
||||
let merged = create_test_model(
|
||||
"merged",
|
||||
vec![("weight".to_string(), vec![1.05, 2.05, 3.05])],
|
||||
);
|
||||
|
||||
let merger = TiesMerger::new(TiesConfig::default());
|
||||
let metrics = merger.compute_quality_metrics(&merged, &[model1, model2])?;
|
||||
|
||||
assert!(metrics.consistency_score >= 0.0 && metrics.consistency_score <= 1.0);
|
||||
assert!(metrics.complexity_score >= 0.0);
|
||||
assert!(!metrics.quality_indicators.is_empty());
|
||||
assert!(!metrics.validation_results.is_empty());
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user