config.yaml

## config.yaml
## Neural Sinkhorn Gradient Flow (NSGF++) Configuration
## Based on arXiv:2401.14069

# ============================================================
# 2D Synthetic Experiments (Section 5.1, Appendix E.1)
# ============================================================
experiment_2d:
  # Datasets: 8gaussians, moons, scurve, checkerboard, 8gaussians_moons
  dataset: "8gaussians"
  source: "gaussian"  # source distribution: standard Gaussian N(0, I)

  # MLP Architecture (Appendix E.1: 3 hidden layers, 256 hidden units)
  model:
    input_dim: 2
    hidden_dim: 256
    num_hidden_layers: 3
    time_emb_dim: 64
    activation: "silu"

  # Sinkhorn gradient flow parameters
  sinkhorn:
    epsilon: 0.1           # regularization coefficient ε
    blur: 0.5              # GeomLoss blur parameter (blur^p ~ ε)
    scaling: 0.80          # GeomLoss multiscale scaling
    eta: 1.0               # gradient flow step size η
    num_steps: 10          # T: number of gradient flow time steps
    batch_size: 256        # n: minibatch size for Sinkhorn flow

  # Trajectory pool
  pool:
    num_batches: 200       # number of batches to build pool
    experience_replay: true

  # Velocity field matching training
  training:
    num_iterations: 20000
    batch_size: 256
    learning_rate: 0.001
    optimizer: "adam"
    beta1: 0.9
    beta2: 0.999
    weight_decay: 0.0

  # Inference / Sampling
  inference:
    num_euler_steps: 10    # 10 or 100 Euler steps (uniform schedule)
    num_samples: 1024      # samples for evaluation

  # Evaluation
  evaluation:
    num_test_samples: 1024  # W2 computed against 1024 test samples
    metric: "w2"           # 2-Wasserstein distance

# ============================================================
# Image Benchmark Experiments (Section 5.2, Appendix E.2)
# ============================================================
experiment_mnist:
  dataset: "mnist"
  image_size: 28
  in_channels: 1

  # UNet Architecture (Appendix E.2, Dhariwal & Nichol 2021)
  unet:
    model_channels: 32          # base channels
    num_res_blocks: 1           # depth = 1
    channel_mult: [1, 2, 2]
    num_heads: 1
    num_head_channels: -1       # use num_heads instead
    attention_resolutions: [16]
    dropout: 0.0
    use_scale_shift_norm: true  # AdaGN

  # Sinkhorn gradient flow (Phase 1)
  sinkhorn:
    blur: 0.5
    scaling: 0.80
    eta: 1.0
    num_steps: 5               # T <= 5 for NSGF phase
    batch_size: 256

  # Trajectory pool (Appendix E.2: 256 batch * 1500 batches * 5 steps < 20GB)
  pool:
    num_batches: 1500
    storage_limit_gb: 20

  # Velocity field matching training (NSGF model)
  nsgf_training:
    num_iterations: 100000
    batch_size: 128
    learning_rate: 0.0001
    optimizer: "adam"
    beta1: 0.9
    beta2: 0.999
    weight_decay: 0.0

  # Neural Straight Flow (Phase 2)
  nsf_training:
    num_iterations: 100000
    batch_size: 128
    learning_rate: 0.0001
    optimizer: "adam"
    beta1: 0.9
    beta2: 0.999
    weight_decay: 0.0

  # Phase-transition time predictor (CNN)
  time_predictor:
    conv_channels: [32, 64, 128, 256]
    kernel_size: 3
    stride: 1
    padding: 1
    pool_size: 2
    num_iterations: 40000
    learning_rate: 0.0001
    batch_size: 128

  # Inference
  inference:
    nsgf_steps: 5              # 5-step Euler in NSGF phase
    nsf_steps: 55              # remaining steps for straight flow
    total_nfe: 60              # total NFE = nsgf_steps + nsf_steps

  # Evaluation (Appendix E.2: FID between 10K gen and test)
  evaluation:
    num_generated: 10000
    metrics: ["fid"]

experiment_cifar10:
  dataset: "cifar10"
  image_size: 32
  in_channels: 3

  # UNet Architecture (Appendix E.2)
  unet:
    model_channels: 128         # base channels
    num_res_blocks: 2           # depth = 2
    channel_mult: [1, 2, 2, 2]
    num_heads: 4
    num_head_channels: 64
    attention_resolutions: [16]
    dropout: 0.0
    use_scale_shift_norm: true

  # Sinkhorn gradient flow (Phase 1)
  # NOTE: batch_size reduced from paper's 128 to 32 for T4 16GB VRAM.
  # Sinkhorn on 3072-dim flattened vectors (3x32x32) with tensorized backend
  # uses O(N^2 * D) memory. 128 samples OOMs on T4; 32 fits comfortably.
  # Compensate by increasing pool batches (32 * 10000 = 320K ≈ 128 * 2500).
  sinkhorn:
    blur: 1.0
    scaling: 0.85
    eta: 1.0
    num_steps: 5
    batch_size: 32

  # Trajectory pool — adjusted for smaller Sinkhorn batch
  # 32 batch * 10000 batches * 5 steps = 1.6M entries (same order as paper)
  pool:
    num_batches: 10000
    storage_limit_gb: 45

  # Velocity field matching training (NSGF model)
  nsgf_training:
    num_iterations: 200000
    batch_size: 128
    learning_rate: 0.0001
    optimizer: "adam"
    beta1: 0.9
    beta2: 0.999
    weight_decay: 0.0

  # Neural Straight Flow (Phase 2)
  nsf_training:
    num_iterations: 200000
    batch_size: 128
    learning_rate: 0.0001
    optimizer: "adam"
    beta1: 0.9
    beta2: 0.999
    weight_decay: 0.0

  # Phase-transition time predictor (same CNN architecture)
  time_predictor:
    conv_channels: [32, 64, 128, 256]
    kernel_size: 3
    stride: 1
    padding: 1
    pool_size: 2
    num_iterations: 40000
    learning_rate: 0.0001
    batch_size: 128

  # Inference
  inference:
    nsgf_steps: 5
    nsf_steps: 54
    total_nfe: 59              # paper reports NFE=59 for CIFAR-10

  # Evaluation
  evaluation:
    num_generated: 10000
    metrics: ["fid", "is"]
    # Paper target: FID=5.55, IS=8.86