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81d2fba 376238e 81d2fba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 | """trainer.py — Training procedures for NSGF and NSGF++.
Implements:
1. Trajectory pool construction (Phase 1: Sinkhorn gradient flow)
2. NSGF velocity field matching training
3. NSF (Neural Straight Flow) training for NSGF++
4. Phase-transition time predictor training
5. End-to-end NSGF++ training pipeline
Reference: arXiv:2401.14069, Section 4.2–4.4, Appendix D, E
"""
import os
import logging
import torch
import torch.nn as nn
import torch.optim as optim
from typing import Optional, Dict, Any, Tuple
from dataset_loader import DatasetLoader
from sinkhorn_flow import (
SinkhornPotentialComputer, SinkhornGradientFlow, TrajectoryPool,
)
from model import VelocityMLP, VelocityUNet, PhaseTransitionPredictor
logger = logging.getLogger(__name__)
class NSGFTrainer:
"""Trainer for the Neural Sinkhorn Gradient Flow model.
Loss (Eq. 14): L(θ) = E_{(x,v,t) ~ pool} ||v_θ(x, t) - v̂(x)||²
"""
def __init__(self, model: nn.Module, data_loader: DatasetLoader,
config: dict, device: str = "cpu"):
self.model = model.to(device)
self.data_loader = data_loader
self.config = config
self.device = device
sink_cfg = config.get("sinkhorn", {})
self.potential_computer = SinkhornPotentialComputer(
blur=sink_cfg.get("blur", 0.5), scaling=sink_cfg.get("scaling", 0.80),
)
self.gradient_flow = SinkhornGradientFlow(
potential_computer=self.potential_computer,
eta=sink_cfg.get("eta", 1.0), num_steps=sink_cfg.get("num_steps", 5),
)
self.pool = TrajectoryPool(max_size=5_000_000)
train_cfg = config.get("training", config.get("nsgf_training", {}))
self.num_iterations = train_cfg.get("num_iterations", 20000)
self.train_batch_size = train_cfg.get("batch_size", 256)
self.lr = train_cfg.get("learning_rate", 1e-3)
self.optimizer = optim.Adam(
self.model.parameters(), lr=self.lr,
betas=(train_cfg.get("beta1", 0.9), train_cfg.get("beta2", 0.999)),
weight_decay=train_cfg.get("weight_decay", 0.0),
)
def build_trajectory_pool(self, num_batches: Optional[int] = None):
if num_batches is None:
num_batches = self.config.get("pool", {}).get("num_batches", 200)
sink_batch_size = self.config.get("sinkhorn", {}).get("batch_size", 256)
logger.info(
f"Building trajectory pool: {num_batches} batches × "
f"{sink_batch_size} samples × {self.gradient_flow.num_steps} steps"
)
for batch_idx in range(num_batches):
X0 = self.data_loader.sample_source(sink_batch_size, self.device)
Y = self.data_loader.sample_target(sink_batch_size, self.device)
_, trajectory = self.gradient_flow.run_flow(X0, Y, store_trajectory=True)
self.pool.add_trajectory(trajectory)
if (batch_idx + 1) % max(1, num_batches // 10) == 0:
logger.info(f" Pool building: {batch_idx + 1}/{num_batches}, pool size: {len(self.pool)}")
logger.info(f"Trajectory pool built. Total entries: {len(self.pool)}")
# Pre-concatenate for O(1) sampling during training
self.pool.finalize()
logger.info("Trajectory pool finalized (pre-concatenated for fast sampling).")
def train(self) -> Dict[str, list]:
self.model.train()
history = {"loss": [], "step": []}
logger.info(f"Starting NSGF velocity field matching: {self.num_iterations} iterations")
for step in range(self.num_iterations):
x_batch, v_batch, t_batch = self.pool.sample(self.train_batch_size, self.device)
t_normalized = t_batch / max(self.gradient_flow.num_steps, 1.0)
v_pred = self.model(x_batch, t_normalized)
loss = ((v_pred - v_batch) ** 2).mean()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if (step + 1) % 500 == 0 or step == 0:
loss_val = loss.item()
history["loss"].append(loss_val)
history["step"].append(step + 1)
logger.info(f" Step {step + 1}/{self.num_iterations}, Loss: {loss_val:.6f}")
logger.info("NSGF training complete.")
return history
class NSFTrainer:
"""Trainer for Neural Straight Flow (Phase 2 of NSGF++).
Straight flow: X_t = (1-t)*P_0 + t*P_1, target velocity = P_1 - P_0
"""
def __init__(self, model: nn.Module, nsgf_model: nn.Module,
data_loader: DatasetLoader, config: dict,
nsgf_num_steps: int = 5, device: str = "cpu"):
self.model = model.to(device)
self.nsgf_model = nsgf_model.to(device)
self.nsgf_model.eval()
self.data_loader = data_loader
self.config = config
self.device = device
self.nsgf_num_steps = nsgf_num_steps
train_cfg = config.get("nsf_training", config.get("training", {}))
self.num_iterations = train_cfg.get("num_iterations", 100000)
self.train_batch_size = train_cfg.get("batch_size", 128)
self.lr = train_cfg.get("learning_rate", 1e-4)
self.optimizer = optim.Adam(
self.model.parameters(), lr=self.lr,
betas=(train_cfg.get("beta1", 0.9), train_cfg.get("beta2", 0.999)),
weight_decay=train_cfg.get("weight_decay", 0.0),
)
@torch.no_grad()
def _generate_nsgf_samples(self, n: int) -> torch.Tensor:
X = self.data_loader.sample_source(n, self.device)
dt = 1.0 / self.nsgf_num_steps
for step in range(self.nsgf_num_steps):
t = torch.full((n,), step * dt, device=self.device)
v = self.nsgf_model(X, t)
X = X + dt * v
return X
def train(self) -> Dict[str, list]:
self.model.train()
history = {"loss": [], "step": []}
logger.info(f"Starting NSF training: {self.num_iterations} iterations")
for step in range(self.num_iterations):
P0 = self._generate_nsgf_samples(self.train_batch_size)
P1 = self.data_loader.sample_target(self.train_batch_size, self.device)
t = torch.rand(self.train_batch_size, device=self.device)
if P0.dim() == 2:
t_expand = t.unsqueeze(-1)
else:
t_expand = t.view(-1, 1, 1, 1)
X_t = (1 - t_expand) * P0 + t_expand * P1
v_target = P1 - P0
v_pred = self.model(X_t, t)
loss = ((v_pred - v_target) ** 2).mean()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if (step + 1) % 500 == 0 or step == 0:
loss_val = loss.item()
history["loss"].append(loss_val)
history["step"].append(step + 1)
logger.info(f" Step {step + 1}/{self.num_iterations}, Loss: {loss_val:.6f}")
logger.info("NSF training complete.")
return history
class PhaseTransitionTrainer:
"""Trainer for the phase-transition time predictor.
Loss: L(ϕ) = E_{t~U(0,1)} ||t - t_ϕ(X_t)||²
"""
def __init__(self, predictor: PhaseTransitionPredictor, nsgf_model: nn.Module,
data_loader: DatasetLoader, config: dict,
nsgf_num_steps: int = 5, device: str = "cpu"):
self.predictor = predictor.to(device)
self.nsgf_model = nsgf_model.to(device)
self.nsgf_model.eval()
self.data_loader = data_loader
self.config = config
self.device = device
self.nsgf_num_steps = nsgf_num_steps
tp_cfg = config.get("time_predictor", {})
self.num_iterations = tp_cfg.get("num_iterations", 40000)
self.batch_size = tp_cfg.get("batch_size", 128)
self.lr = tp_cfg.get("learning_rate", 1e-4)
self.optimizer = optim.Adam(self.predictor.parameters(), lr=self.lr, betas=(0.9, 0.999))
@torch.no_grad()
def _generate_nsgf_samples(self, n: int) -> torch.Tensor:
X = self.data_loader.sample_source(n, self.device)
dt = 1.0 / self.nsgf_num_steps
for step in range(self.nsgf_num_steps):
t = torch.full((n,), step * dt, device=self.device)
v = self.nsgf_model(X, t)
X = X + dt * v
return X
def train(self) -> Dict[str, list]:
self.predictor.train()
history = {"loss": [], "step": []}
logger.info(f"Starting phase predictor training: {self.num_iterations} iterations")
for step in range(self.num_iterations):
P0 = self._generate_nsgf_samples(self.batch_size)
P1 = self.data_loader.sample_target(self.batch_size, self.device)
t = torch.rand(self.batch_size, device=self.device)
if P0.dim() == 4:
t_expand = t.view(-1, 1, 1, 1)
else:
t_expand = t.unsqueeze(-1)
X_t = (1 - t_expand) * P0 + t_expand * P1
t_pred = self.predictor(X_t)
loss = ((t_pred - t) ** 2).mean()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if (step + 1) % 1000 == 0 or step == 0:
loss_val = loss.item()
history["loss"].append(loss_val)
history["step"].append(step + 1)
logger.info(f" Step {step + 1}/{self.num_iterations}, Loss: {loss_val:.6f}")
logger.info("Phase predictor training complete.")
return history
class NSGFPlusPlusTrainer:
"""End-to-end NSGF++ trainer (Algorithm 3 / Appendix D)."""
def __init__(self, nsgf_model: nn.Module, nsf_model: nn.Module,
phase_predictor: PhaseTransitionPredictor,
data_loader: DatasetLoader, config: dict, device: str = "cpu"):
self.nsgf_model = nsgf_model
self.nsf_model = nsf_model
self.phase_predictor = phase_predictor
self.data_loader = data_loader
self.config = config
self.device = device
def train_all(self) -> Dict[str, Any]:
results = {}
logger.info("=" * 60)
logger.info("Phase 1: Training NSGF model")
logger.info("=" * 60)
nsgf_trainer = NSGFTrainer(
model=self.nsgf_model, data_loader=self.data_loader,
config=self.config, device=self.device,
)
nsgf_trainer.build_trajectory_pool()
results["nsgf"] = nsgf_trainer.train()
logger.info("=" * 60)
logger.info("Phase 2: Training NSF (Neural Straight Flow) model")
logger.info("=" * 60)
nsgf_steps = self.config.get("sinkhorn", {}).get("num_steps", 5)
nsf_trainer = NSFTrainer(
model=self.nsf_model, nsgf_model=self.nsgf_model,
data_loader=self.data_loader, config=self.config,
nsgf_num_steps=nsgf_steps, device=self.device,
)
results["nsf"] = nsf_trainer.train()
logger.info("=" * 60)
logger.info("Phase 3: Training phase-transition time predictor")
logger.info("=" * 60)
pt_trainer = PhaseTransitionTrainer(
predictor=self.phase_predictor, nsgf_model=self.nsgf_model,
data_loader=self.data_loader, config=self.config,
nsgf_num_steps=nsgf_steps, device=self.device,
)
results["phase_predictor"] = pt_trainer.train()
logger.info("=" * 60)
logger.info("NSGF++ training complete!")
logger.info("=" * 60)
return results
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