d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
149 lines
4.6 KiB
Markdown
149 lines
4.6 KiB
Markdown
# EAGLE-3 Speculative Decoding
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Implementation of EAGLE-3 style speculative decoding for the mincut-gated-transformer crate.
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## Overview
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Speculative decoding accelerates inference by drafting multiple tokens in parallel and verifying them against the target model using rejection sampling. This implementation uses mincut λ-stability as a confidence signal to guide draft tree generation.
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## Files
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- `/home/user/ruvector/crates/ruvector-mincut-gated-transformer/src/speculative.rs` - Core implementation
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## Key Features
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### 1. Draft Tree Generation
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Dynamic tree structure that adapts based on model confidence:
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```rust
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let config = SpeculativeConfig {
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max_draft_tokens: 5, // Draft up to 5 tokens ahead
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tree_width: 3, // Up to 3 branches per node
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acceptance_threshold: 0.7, // 70% confidence for acceptance
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use_lambda_guidance: true, // Use λ as confidence signal
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};
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let decoder = SpeculativeDecoder::new(config);
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let tree = decoder.generate_draft_tree(lambda, lambda_prev, draft_logits);
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```
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### 2. λ-Guided Confidence
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Uses mincut λ-stability to scale draft confidence:
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- **Higher λ** = More stable partitioning = Higher draft confidence
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- **Increasing λ** = Improving stability = Confidence bonus
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- **Decreasing λ** = Degrading stability = Confidence penalty
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### 3. Adaptive Tree Width
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Tree branching adapts to confidence levels:
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- **High confidence (≥0.9)**: Narrow tree (fewer branches)
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- **Medium confidence (0.6-0.9)**: Normal width
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- **Low confidence (0.3-0.6)**: Wider tree (more exploration)
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- **Very low confidence (<0.3)**: Minimal branching
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### 4. Rejection Sampling Verification
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EAGLE-3 style verification using:
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```
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accept_prob = min(1, target_prob / draft_prob)
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```
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Drafts are accepted if they match the target model's distribution.
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### 5. Tree Attention Masks
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Parallel verification of draft tokens using causal tree attention:
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```rust
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let mask = generate_tree_attention_mask(&tree, seq_len);
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// Each token can attend to all ancestors in its path
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```
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## Usage Example
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```rust
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use ruvector_mincut_gated_transformer::prelude::*;
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// Create decoder
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let config = SpeculativeConfig::default();
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let decoder = SpeculativeDecoder::new(config);
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// Generate draft tree (5 tokens, dynamic structure)
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let lambda = 100; // Current mincut stability
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let lambda_prev = 95; // Previous stability
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let draft_logits = vec![vec![0.0; 1000]; 5]; // Draft model outputs
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let tree = decoder.generate_draft_tree(lambda, lambda_prev, &draft_logits);
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// Verify against target model
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let target_logits = vec![vec![0.0; 1000]; 5]; // Target model outputs
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let result = decoder.verify_drafts(&tree, &target_logits, 1.0);
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println!("Accepted {} tokens with {:.1}% acceptance rate",
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result.accepted_count,
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result.acceptance_rate * 100.0);
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```
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## Performance Characteristics
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- **Speedup**: 2-5x for high acceptance rates
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- **Memory**: O(max_draft_tokens × tree_width × vocab_size)
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- **Overhead**: ~10% for low acceptance rates
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- **Best case**: Stable models (high λ) with predictable outputs
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## Academic Foundation
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Based on **EAGLE-3** (NeurIPS 2025):
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1. **Dynamic tree structure**: Adapts to model confidence
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2. **Multi-level feature fusion**: Uses λ-stability as confidence signal
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3. **Rejection sampling**: Mathematically correct acceptance criteria
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4. **Tree attention**: Parallel draft verification
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## Integration with Mincut Gating
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The speculative decoder integrates with the mincut-gated-transformer's coherence signals:
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- **λ-stability** guides draft confidence
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- **High λ** (stable partitioning) → More aggressive speculation
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- **Low λ** (unstable partitioning) → Conservative speculation
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- **λ trends** influence tree width adaptation
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## Testing
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Comprehensive test suite covering:
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- ✓ Single-path speculation (sequential drafting)
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- ✓ Tree speculation with branching (parallel drafting)
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- ✓ Rejection sampling correctness
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- ✓ λ-guided confidence scaling
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- ✓ Draft verification against target model
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- ✓ Tree attention mask generation
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- ✓ Adaptive tree width calculation
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- ✓ Edge cases (empty inputs, etc.)
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Run tests:
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```bash
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cd crates/ruvector-mincut-gated-transformer
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cargo test --lib speculative
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```
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All 8 tests pass successfully.
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## Future Enhancements
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Potential improvements:
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1. **Multi-token drafting**: Draft multiple positions simultaneously
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2. **Learned draft models**: Train lightweight draft models
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3. **Dynamic threshold adaptation**: Adjust acceptance threshold based on λ
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4. **Quantized drafting**: Use INT8/INT4 for draft model
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5. **Cached drafts**: Reuse draft trees across timesteps
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6. **Hybrid verification**: Combine rejection sampling with direct comparison
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