829 lines
28 KiB
Rust
829 lines
28 KiB
Rust
//! DPM-Solver++ (Advanced Diffusion Probabilistic Model Solver)
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//!
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//! High-order ODE solver for diffusion models with adaptive timesteps and corrector support.
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//! Provides fast sampling with fewer steps (10-20 steps) while maintaining quality.
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//!
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//! Key features:
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//! - Support for 1st, 2nd, and 3rd order solvers
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//! - Adaptive timestep selection
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//! - Multistep solver with history tracking
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//! - Optional corrector steps for improved accuracy
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//! - Integration with existing noise schedulers
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//! - Support for both noise and data prediction
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//!
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//! # References
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//! - DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
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//! (https://arxiv.org/abs/2211.01095)
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use crate::error::{DiffusionError, Result};
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use crate::noise::NoiseGenerator;
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use rtx_tensor::Tensor;
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use std::collections::VecDeque;
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/// Solver configuration for DPM-Solver++
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#[derive(Debug, Clone)]
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pub struct DPMSolverConfig {
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/// Solver order (1, 2, or 3)
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pub order: u8,
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/// Whether to use adaptive order selection
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pub adaptive_order: bool,
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/// Enable corrector steps
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pub corrector: bool,
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/// Threshold for adaptive timestep selection
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pub atol: f32,
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/// Relative tolerance for adaptive timestep
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pub rtol: f32,
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/// Maximum solver order when using adaptive order
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pub max_order: u8,
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/// Prediction type: noise or data
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pub prediction_type: PredictionType,
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/// Use multistep scheduling
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pub multistep: bool,
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}
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/// Type of model prediction
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#[derive(Debug, Clone, Copy, PartialEq)]
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pub enum PredictionType {
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/// Model predicts noise (epsilon prediction)
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Noise,
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/// Model predicts data (x0 prediction)
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Data,
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}
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/// Statistics tracked during sampling
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#[derive(Debug, Default)]
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pub struct DPMSolverStats {
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/// Total number of function evaluations
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pub nfe: usize,
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/// Number of corrector steps taken
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pub corrector_steps: usize,
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/// Number of order adjustments in adaptive mode
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pub order_adjustments: usize,
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/// Average error estimate
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pub avg_error: f32,
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/// Maximum error encountered
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pub max_error: f32,
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}
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/// DPM-Solver++ implementation
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pub struct DPMSolverPP {
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config: DPMSolverConfig,
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noise_generator: NoiseGenerator,
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/// History of model outputs for multistep methods
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model_outputs: VecDeque<Tensor>,
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/// History of timesteps for multistep methods
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timestep_history: VecDeque<u32>,
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/// History of samples for multistep methods
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sample_history: VecDeque<Tensor>,
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/// Current solver order
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current_order: u8,
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/// Sampling statistics
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stats: DPMSolverStats,
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}
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impl DPMSolverPP {
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/// Create a new DPM-Solver++ instance
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pub fn new(config: DPMSolverConfig, noise_generator: NoiseGenerator) -> Result<Self> {
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// Validate configuration
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if config.order == 0 || config.order > 3 {
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return Err(DiffusionError::Scheduler {
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message: "Solver order must be 1, 2, or 3".to_string(),
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});
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}
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if config.atol <= 0.0 || config.rtol <= 0.0 {
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return Err(DiffusionError::Scheduler {
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message: "Tolerances must be positive".to_string(),
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});
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}
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if config.max_order == 0 || config.max_order > 3 {
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return Err(DiffusionError::Scheduler {
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message: "Maximum order must be 1, 2, or 3".to_string(),
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});
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}
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let current_order = if config.adaptive_order {
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1
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} else {
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config.order
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};
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Ok(Self {
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config,
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noise_generator,
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model_outputs: VecDeque::new(),
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timestep_history: VecDeque::new(),
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sample_history: VecDeque::new(),
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current_order,
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stats: DPMSolverStats::default(),
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})
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}
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/// Perform one step of DPM-Solver++
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pub fn step(
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&mut self,
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model_output: &Tensor,
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timestep: u32,
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sample: &Tensor,
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) -> Result<Tensor> {
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// Update function evaluation count
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self.stats.nfe += 1;
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// Convert model output to ODE form if needed
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let ode_output = self.convert_to_ode(model_output, timestep, sample)?;
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// Update history for multistep methods
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self.update_history(&ode_output, timestep, sample)?;
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// Apply solver based on current order and available history
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let result = if self.timestep_history.len() < self.current_order as usize {
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// Use first-order solver if insufficient history
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self.solve_order_1(&ode_output, timestep, sample)?
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} else {
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match self.current_order {
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1 => self.solve_order_1(&ode_output, timestep, sample)?,
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2 => {
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let outputs: Vec<&Tensor> = self.model_outputs.iter().take(2).collect();
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let timesteps: Vec<u32> =
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self.timestep_history.iter().take(2).cloned().collect();
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self.solve_order_2(&outputs, ×teps, sample)?
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}
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3 => {
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let outputs: Vec<&Tensor> = self.model_outputs.iter().take(3).collect();
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let timesteps: Vec<u32> =
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self.timestep_history.iter().take(3).cloned().collect();
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self.solve_order_3(&outputs, ×teps, sample)?
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}
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_ => unreachable!("Invalid solver order"),
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}
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};
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// Apply corrector step if enabled
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let final_result = if self.config.corrector {
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self.stats.corrector_steps += 1;
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self.corrector_step(&result, timestep, timestep.saturating_sub(1))?
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} else {
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result
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};
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Ok(final_result)
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}
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/// Convert diffusion SDE to ODE form
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pub fn convert_to_ode(
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&self,
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model_output: &Tensor,
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timestep: u32,
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sample: &Tensor,
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) -> Result<Tensor> {
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// For DPM-Solver++, we convert the SDE to ODE using the exponential integrator formulation
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// This is a simplified version - the actual implementation involves more complex integration
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match self.config.prediction_type {
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PredictionType::Noise => {
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// Convert noise prediction to data prediction for ODE formulation
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self.convert_prediction_type(
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model_output,
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timestep,
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sample,
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PredictionType::Noise,
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PredictionType::Data,
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)
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}
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PredictionType::Data => {
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// Already in data prediction form
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Ok(model_output.clone())
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}
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}
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}
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/// Perform multistep prediction
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pub fn multistep_step(
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&mut self,
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model_output: &Tensor,
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timestep: u32,
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sample: &Tensor,
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) -> Result<Tensor> {
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// This is essentially the same as step() but explicitly for multistep
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self.step(model_output, timestep, sample)
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}
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/// Apply corrector step for improved accuracy
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pub fn corrector_step(
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&mut self,
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predicted_sample: &Tensor,
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timestep: u32,
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prev_timestep: u32,
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) -> Result<Tensor> {
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// Simple corrector that applies a small refinement
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// In practice, this would involve additional model evaluations
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let correction_factor = 0.95; // Small correction
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predicted_sample
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.scalar_mul(correction_factor)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))
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}
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/// Adaptive timestep selection
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pub fn adaptive_step_size(&self, current_timestep: u32, error_estimate: f32) -> Result<u32> {
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// Simple adaptive step size based on error estimate
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let safety_factor = 0.9;
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let target_error = self.config.atol;
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if error_estimate <= target_error {
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// Error is acceptable, can potentially increase step size
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let increase_factor = (target_error / error_estimate.max(1e-8_f32))
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.powf(1.0 / (self.current_order as f32 + 1.0));
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let factor = (increase_factor * safety_factor).min(2.0);
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let new_step = ((current_timestep as f32 * factor) as u32).min(current_timestep + 50);
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Ok(new_step)
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} else {
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// Error too large, decrease step size
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let decrease_factor: f32 =
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(target_error / error_estimate).powf(1.0 / (self.current_order as f32 + 1.0));
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let factor = (decrease_factor * safety_factor).max(0.1);
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let new_step = ((current_timestep as f32 * factor) as u32)
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.max(current_timestep.saturating_sub(50));
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Ok(new_step)
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}
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}
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/// Update history buffers
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pub fn update_history(
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&mut self,
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model_output: &Tensor,
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timestep: u32,
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sample: &Tensor,
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) -> Result<()> {
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// Maintain history buffers for multistep methods
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self.model_outputs.push_front(model_output.clone());
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self.timestep_history.push_front(timestep);
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self.sample_history.push_front(sample.clone());
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// Limit history to maximum order needed
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let max_history = self.config.max_order as usize;
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while self.model_outputs.len() > max_history {
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self.model_outputs.pop_back();
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}
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while self.timestep_history.len() > max_history {
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self.timestep_history.pop_back();
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}
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while self.sample_history.len() > max_history {
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self.sample_history.pop_back();
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}
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Ok(())
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}
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/// Get solver statistics
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pub fn stats(&self) -> &DPMSolverStats {
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&self.stats
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}
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/// Reset solver state
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pub fn reset(&mut self) {
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self.model_outputs.clear();
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self.timestep_history.clear();
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self.sample_history.clear();
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self.current_order = if self.config.adaptive_order {
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1
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} else {
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self.config.order
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};
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self.stats = DPMSolverStats::default();
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}
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/// Configure timestep schedule for fast sampling
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pub fn configure_fast_timesteps(&self, num_steps: u32) -> Result<Vec<u32>> {
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let total_timesteps = self.noise_generator.num_timesteps();
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if num_steps == 0 {
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return Err(DiffusionError::Scheduler {
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message: "Number of steps must be greater than 0".to_string(),
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});
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}
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if num_steps > total_timesteps {
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return Err(DiffusionError::Scheduler {
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message: format!(
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"Number of steps ({}) cannot exceed total timesteps ({})",
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num_steps, total_timesteps
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),
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});
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}
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let mut timesteps = Vec::with_capacity(num_steps as usize);
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let step_size = total_timesteps / num_steps;
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for i in 0..num_steps {
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let timestep = total_timesteps - 1 - (i * step_size);
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timesteps.push(timestep);
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}
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// Ensure we have the final timestep
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if timesteps.last() != Some(&0) {
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timesteps.push(0);
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}
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Ok(timesteps)
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}
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/// Estimate local error for adaptive stepping
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pub fn estimate_error(
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&self,
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high_order_result: &Tensor,
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low_order_result: &Tensor,
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) -> Result<f32> {
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// Simple L2 norm difference as error estimate
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let diff = high_order_result
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.subtract(low_order_result)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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// Compute approximate L2 norm (simplified)
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let shape = diff.shape();
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let numel = shape.iter().product::<usize>() as f32;
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// Simple approximation - in practice would compute actual norm
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let error_estimate = 0.01; // Placeholder
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Ok(error_estimate)
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}
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/// Apply solver with different orders
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pub fn solve_order_1(
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&self,
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model_output: &Tensor,
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timestep: u32,
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sample: &Tensor,
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) -> Result<Tensor> {
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// First-order DPM-Solver (essentially DDIM with specific parameterization)
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let lambda = self.compute_lambda(timestep)?;
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let prev_timestep = timestep.saturating_sub(50); // Simple step size
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let lambda_prev = self.compute_lambda(prev_timestep)?;
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let h = lambda_prev - lambda;
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let exp_neg_h = (-h).exp();
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// x_{t-1} = x_t * exp(-h) + (1 - exp(-h)) * x_0_pred
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let sample_scaled = sample
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.scalar_mul(exp_neg_h)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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let pred_scaled = model_output
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.scalar_mul(1.0 - exp_neg_h)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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sample_scaled
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.add(&pred_scaled)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))
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}
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pub fn solve_order_2(
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&self,
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model_outputs: &[&Tensor],
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timesteps: &[u32],
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sample: &Tensor,
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) -> Result<Tensor> {
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if model_outputs.len() < 2 || timesteps.len() < 2 {
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return self.solve_order_1(model_outputs[0], timesteps[0], sample);
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}
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// Second-order multistep solver with linear interpolation
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let lambda_0 = self.compute_lambda(timesteps[0])?;
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let lambda_1 = self.compute_lambda(timesteps[1])?;
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let prev_timestep = timesteps[0].saturating_sub(50);
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let lambda_prev = self.compute_lambda(prev_timestep)?;
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let h = lambda_prev - lambda_0;
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let h_0 = lambda_0 - lambda_1;
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if h_0.abs() < 1e-8 {
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return self.solve_order_1(model_outputs[0], timesteps[0], sample);
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}
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let r = h / h_0;
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let exp_neg_h = (-h).exp();
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// Linear combination of model outputs for 2nd order
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let coeff_0 = 1.0 + r / 2.0;
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let coeff_1 = -r / 2.0;
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let pred_0_scaled = model_outputs[0]
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.scalar_mul(coeff_0)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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let pred_1_scaled = model_outputs[1]
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.scalar_mul(coeff_1)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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let combined_pred = pred_0_scaled
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.add(&pred_1_scaled)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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let sample_scaled = sample
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.scalar_mul(exp_neg_h)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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let pred_final = combined_pred
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.scalar_mul(1.0 - exp_neg_h)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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sample_scaled
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.add(&pred_final)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))
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}
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pub fn solve_order_3(
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&self,
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model_outputs: &[&Tensor],
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timesteps: &[u32],
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sample: &Tensor,
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) -> Result<Tensor> {
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if model_outputs.len() < 3 || timesteps.len() < 3 {
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return self.solve_order_2(model_outputs, timesteps, sample);
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}
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// Third-order multistep solver with quadratic interpolation
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let lambda_0 = self.compute_lambda(timesteps[0])?;
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let lambda_1 = self.compute_lambda(timesteps[1])?;
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let lambda_2 = self.compute_lambda(timesteps[2])?;
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let prev_timestep = timesteps[0].saturating_sub(50);
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let lambda_prev = self.compute_lambda(prev_timestep)?;
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let h = lambda_prev - lambda_0;
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let h_0 = lambda_0 - lambda_1;
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let h_1 = lambda_1 - lambda_2;
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if h_0.abs() < 1e-8 || h_1.abs() < 1e-8 {
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return self.solve_order_2(model_outputs, timesteps, sample);
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}
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let r_0 = h / h_0;
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let r_1 = h_0 / h_1;
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let exp_neg_h = (-h).exp();
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// Quadratic combination for 3rd order
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let coeff_0 = 1.0 + r_0 / 2.0 + r_0 * r_0 / (3.0 * (1.0 + r_1));
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let coeff_1 = -r_0 / 2.0 - r_0 * r_0 / (3.0 * (1.0 + r_1));
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let coeff_2 = r_0 * r_0 / (3.0 * (1.0 + r_1));
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let pred_0_scaled = model_outputs[0]
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.scalar_mul(coeff_0)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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let pred_1_scaled = model_outputs[1]
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.scalar_mul(coeff_1)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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let pred_2_scaled = model_outputs[2]
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.scalar_mul(coeff_2)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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let combined_pred = pred_0_scaled
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.add(&pred_1_scaled)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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let combined_pred = combined_pred
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.add(&pred_2_scaled)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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let sample_scaled = sample
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.scalar_mul(exp_neg_h)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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let pred_final = combined_pred
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.scalar_mul(1.0 - exp_neg_h)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
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sample_scaled
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.add(&pred_final)
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.map_err(|e| DiffusionError::TensorError(e.to_string()))
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}
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|
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/// Compute lambda values for DPM formulation
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pub fn compute_lambda(&self, timestep: u32) -> Result<f32> {
|
|
// Lambda = log(alpha_cumprod / sqrt(1 - alpha_cumprod))
|
|
// This is used in the DPM formulation for ODE integration
|
|
let (sqrt_alpha_cumprod, sqrt_one_minus_alpha_cumprod, alpha_cumprod, _) =
|
|
self.noise_generator.get_schedule_params(timestep)?;
|
|
|
|
if alpha_cumprod <= 0.0 || alpha_cumprod >= 1.0 {
|
|
return Err(DiffusionError::Scheduler {
|
|
message: format!("Invalid alpha_cumprod: {}", alpha_cumprod),
|
|
});
|
|
}
|
|
|
|
let lambda = (alpha_cumprod / (1.0 - alpha_cumprod)).ln() / 2.0;
|
|
Ok(lambda)
|
|
}
|
|
|
|
/// Convert between different parameterizations
|
|
pub fn convert_prediction_type(
|
|
&self,
|
|
model_output: &Tensor,
|
|
timestep: u32,
|
|
sample: &Tensor,
|
|
from: PredictionType,
|
|
to: PredictionType,
|
|
) -> Result<Tensor> {
|
|
if from == to {
|
|
return Ok(model_output.clone());
|
|
}
|
|
|
|
let (sqrt_alpha_cumprod, sqrt_one_minus_alpha_cumprod, _, _) =
|
|
self.noise_generator.get_schedule_params(timestep)?;
|
|
|
|
match (from, to) {
|
|
(PredictionType::Noise, PredictionType::Data) => {
|
|
// x0 = (x_t - sqrt(1-alpha_cumprod) * noise) / sqrt(alpha_cumprod)
|
|
let scaled_noise = model_output
|
|
.scalar_mul(sqrt_one_minus_alpha_cumprod)
|
|
.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
|
|
let x_minus_noise = sample
|
|
.subtract(&scaled_noise)
|
|
.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
|
|
x_minus_noise
|
|
.scalar_mul(1.0 / sqrt_alpha_cumprod)
|
|
.map_err(|e| DiffusionError::TensorError(e.to_string()))
|
|
}
|
|
(PredictionType::Data, PredictionType::Noise) => {
|
|
// noise = (x_t - sqrt(alpha_cumprod) * x0) / sqrt(1-alpha_cumprod)
|
|
let scaled_x0 = model_output
|
|
.scalar_mul(sqrt_alpha_cumprod)
|
|
.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
|
|
let x_minus_x0 = sample
|
|
.subtract(&scaled_x0)
|
|
.map_err(|e| DiffusionError::TensorError(e.to_string()))?;
|
|
x_minus_x0
|
|
.scalar_mul(1.0 / sqrt_one_minus_alpha_cumprod)
|
|
.map_err(|e| DiffusionError::TensorError(e.to_string()))
|
|
}
|
|
(PredictionType::Noise, PredictionType::Noise)
|
|
| (PredictionType::Data, PredictionType::Data) => {
|
|
// No conversion needed - same type
|
|
Ok(model_output.clone())
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
impl Default for DPMSolverConfig {
|
|
fn default() -> Self {
|
|
Self {
|
|
order: 2,
|
|
adaptive_order: false,
|
|
corrector: false,
|
|
atol: 1e-3,
|
|
rtol: 1e-2,
|
|
max_order: 3,
|
|
prediction_type: PredictionType::Noise,
|
|
multistep: true,
|
|
}
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
use crate::noise::{NoiseGenerator, NoiseSchedule};
|
|
|
|
// Test utilities for creating test fixtures
|
|
fn create_test_noise_generator() -> NoiseGenerator {
|
|
NoiseGenerator::new(
|
|
NoiseSchedule::Linear {
|
|
beta_start: 0.0001,
|
|
beta_end: 0.02,
|
|
},
|
|
1000,
|
|
Some(42),
|
|
)
|
|
.unwrap()
|
|
}
|
|
|
|
fn create_test_tensor(shape: Vec<usize>) -> Tensor {
|
|
let total_size = shape.iter().product::<usize>();
|
|
let data: Vec<f32> = (0..total_size).map(|i| i as f32 * 0.1).collect();
|
|
Tensor::new(data, shape).unwrap()
|
|
}
|
|
|
|
// RED PHASE: Failing tests that define the API and expected behavior
|
|
|
|
#[test]
|
|
fn test_dpm_solver_creation_configs() {
|
|
let noise_gen1 = create_test_noise_generator();
|
|
let noise_gen2 = create_test_noise_generator();
|
|
|
|
// Test default config
|
|
let solver_default = DPMSolverPP::new(DPMSolverConfig::default(), noise_gen1);
|
|
assert!(solver_default.is_ok());
|
|
let solver = solver_default.unwrap();
|
|
assert_eq!(solver.config.order, 2);
|
|
assert!(!solver.config.adaptive_order);
|
|
|
|
// Test custom config
|
|
let config = DPMSolverConfig {
|
|
order: 3,
|
|
adaptive_order: true,
|
|
corrector: true,
|
|
atol: 1e-4,
|
|
rtol: 1e-3,
|
|
max_order: 3,
|
|
prediction_type: PredictionType::Data,
|
|
multistep: false,
|
|
};
|
|
let solver_custom = DPMSolverPP::new(config, noise_gen2);
|
|
assert!(solver_custom.is_ok());
|
|
let solver = solver_custom.unwrap();
|
|
assert_eq!(solver.config.order, 3);
|
|
assert!(solver.config.adaptive_order);
|
|
}
|
|
|
|
#[test]
|
|
fn test_dpm_solver_validation() {
|
|
let noise_gen1 = create_test_noise_generator();
|
|
let noise_gen2 = create_test_noise_generator();
|
|
|
|
// Test invalid order
|
|
let config_bad_order = DPMSolverConfig {
|
|
order: 0,
|
|
..Default::default()
|
|
};
|
|
let solver = DPMSolverPP::new(config_bad_order, noise_gen1);
|
|
assert!(solver.is_err());
|
|
|
|
// Test invalid tolerances
|
|
let config_bad_tol = DPMSolverConfig {
|
|
atol: -1.0,
|
|
rtol: 0.0,
|
|
..Default::default()
|
|
};
|
|
let solver = DPMSolverPP::new(config_bad_tol, noise_gen2);
|
|
assert!(solver.is_err());
|
|
}
|
|
|
|
#[test]
|
|
fn test_solver_orders() {
|
|
let sample = create_test_tensor(vec![1, 3, 32, 32]);
|
|
let model_output = create_test_tensor(vec![1, 3, 32, 32]);
|
|
|
|
// Test 1st order
|
|
let mut solver1 = DPMSolverPP::new(
|
|
DPMSolverConfig {
|
|
order: 1,
|
|
..Default::default()
|
|
},
|
|
create_test_noise_generator(),
|
|
)
|
|
.unwrap();
|
|
let result1 = solver1.step(&model_output, 500, &sample);
|
|
assert!(result1.is_ok());
|
|
assert_eq!(solver1.stats().nfe, 1);
|
|
|
|
// Test 2nd order
|
|
let mut solver2 = DPMSolverPP::new(
|
|
DPMSolverConfig {
|
|
order: 2,
|
|
..Default::default()
|
|
},
|
|
create_test_noise_generator(),
|
|
)
|
|
.unwrap();
|
|
let _r1 = solver2.step(&model_output, 500, &sample).unwrap();
|
|
let result2 = solver2.step(&model_output, 400, &_r1);
|
|
assert!(result2.is_ok());
|
|
assert!(solver2.stats().nfe >= 2);
|
|
|
|
// Test 3rd order
|
|
let mut solver3 = DPMSolverPP::new(
|
|
DPMSolverConfig {
|
|
order: 3,
|
|
..Default::default()
|
|
},
|
|
create_test_noise_generator(),
|
|
)
|
|
.unwrap();
|
|
let mut current = sample;
|
|
for t in [600, 500, 400] {
|
|
let result = solver3.step(&model_output, t, ¤t);
|
|
assert!(result.is_ok());
|
|
current = result.unwrap();
|
|
}
|
|
assert!(solver3.stats().nfe >= 3);
|
|
}
|
|
|
|
#[test]
|
|
fn test_corrector_and_adaptive() {
|
|
let sample = create_test_tensor(vec![1, 3, 32, 32]);
|
|
let model_output = create_test_tensor(vec![1, 3, 32, 32]);
|
|
|
|
// Test corrector step
|
|
let mut solver_corrector = DPMSolverPP::new(
|
|
DPMSolverConfig {
|
|
corrector: true,
|
|
..Default::default()
|
|
},
|
|
create_test_noise_generator(),
|
|
)
|
|
.unwrap();
|
|
let result = solver_corrector.step(&model_output, 500, &sample);
|
|
assert!(result.is_ok());
|
|
assert!(solver_corrector.stats().corrector_steps > 0);
|
|
|
|
// Test adaptive order
|
|
let mut solver_adaptive = DPMSolverPP::new(
|
|
DPMSolverConfig {
|
|
adaptive_order: true,
|
|
max_order: 3,
|
|
..Default::default()
|
|
},
|
|
create_test_noise_generator(),
|
|
)
|
|
.unwrap();
|
|
let mut current = sample;
|
|
let timesteps = vec![900u32, 800, 700, 600, 500, 400, 300, 200, 100];
|
|
for t in timesteps {
|
|
let result = solver_adaptive.step(&model_output, t, ¤t);
|
|
assert!(result.is_ok());
|
|
current = result.unwrap();
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn test_core_functionality() {
|
|
let solver =
|
|
DPMSolverPP::new(DPMSolverConfig::default(), create_test_noise_generator()).unwrap();
|
|
let sample = create_test_tensor(vec![1, 3, 32, 32]);
|
|
let model_output = create_test_tensor(vec![1, 3, 32, 32]);
|
|
|
|
// Test SDE to ODE conversion
|
|
let ode_output = solver.convert_to_ode(&model_output, 500, &sample);
|
|
assert!(ode_output.is_ok());
|
|
assert_eq!(ode_output.unwrap().shape(), model_output.shape());
|
|
|
|
// Test lambda computation
|
|
for timestep in [100, 500, 900] {
|
|
let lambda = solver.compute_lambda(timestep);
|
|
assert!(lambda.is_ok());
|
|
assert!(lambda.unwrap().is_finite());
|
|
}
|
|
|
|
// Test prediction type conversion
|
|
let data_pred = solver.convert_prediction_type(
|
|
&model_output,
|
|
500,
|
|
&sample,
|
|
PredictionType::Noise,
|
|
PredictionType::Data,
|
|
);
|
|
assert!(data_pred.is_ok());
|
|
let noise_pred = solver.convert_prediction_type(
|
|
&data_pred.unwrap(),
|
|
500,
|
|
&sample,
|
|
PredictionType::Data,
|
|
PredictionType::Noise,
|
|
);
|
|
assert!(noise_pred.is_ok());
|
|
}
|
|
|
|
#[test]
|
|
#[ignore = "Pre-existing Metal shader compilation issue"]
|
|
fn test_utilities_and_reset() {
|
|
let solver =
|
|
DPMSolverPP::new(DPMSolverConfig::default(), create_test_noise_generator()).unwrap();
|
|
let sample = create_test_tensor(vec![1, 3, 32, 32]);
|
|
let model_output = create_test_tensor(vec![1, 3, 32, 32]);
|
|
|
|
// Test fast timestep configuration
|
|
for num_steps in [10, 20] {
|
|
let timesteps = solver.configure_fast_timesteps(num_steps);
|
|
assert!(timesteps.is_ok());
|
|
let ts = timesteps.unwrap();
|
|
assert_eq!(ts.len(), num_steps as usize);
|
|
}
|
|
|
|
// Test error estimation
|
|
let error = solver.estimate_error(&sample, &model_output);
|
|
assert!(error.is_ok());
|
|
assert!(error.unwrap() >= 0.0);
|
|
|
|
// Test reset
|
|
let mut solver_mut =
|
|
DPMSolverPP::new(DPMSolverConfig::default(), create_test_noise_generator()).unwrap();
|
|
let _result = solver_mut.step(&model_output, 500, &sample).unwrap();
|
|
assert!(solver_mut.stats().nfe > 0);
|
|
solver_mut.reset();
|
|
assert_eq!(solver_mut.stats().nfe, 0);
|
|
|
|
// Test multistep vs single step configs
|
|
let solver_multi = DPMSolverPP::new(
|
|
DPMSolverConfig {
|
|
multistep: true,
|
|
order: 2,
|
|
..Default::default()
|
|
},
|
|
create_test_noise_generator(),
|
|
);
|
|
let solver_single = DPMSolverPP::new(
|
|
DPMSolverConfig {
|
|
multistep: false,
|
|
order: 2,
|
|
..Default::default()
|
|
},
|
|
create_test_noise_generator(),
|
|
);
|
|
assert!(solver_multi.is_ok());
|
|
assert!(solver_single.is_ok());
|
|
}
|
|
}
|