From 9b2968997cf416af2b3fd7c3c6b447782b415a6d Mon Sep 17 00:00:00 2001 From: Logic Date: Wed, 11 Mar 2026 16:33:40 +0800 Subject: [PATCH] Implement Mamba MeanFlow x-prediction training --- as_mamba.py | 335 +++++++++++++++++++++++++++++++--------------- main.py | 8 +- pyproject.toml | 2 + train_as_mamba.sh | 16 +-- uv.lock | 113 ++++++++++++++++ 5 files changed, 353 insertions(+), 121 deletions(-) diff --git a/as_mamba.py b/as_mamba.py index f93f2aa..b0d5d27 100644 --- a/as_mamba.py +++ b/as_mamba.py @@ -1,11 +1,14 @@ from __future__ import annotations import math -from dataclasses import asdict, dataclass import os +from dataclasses import asdict, dataclass from pathlib import Path from typing import Iterator, Optional +import lpips + +os.environ.setdefault("MPLCONFIGDIR", "/tmp/mamba_diffusion_mplconfig") import matplotlib matplotlib.use("Agg") @@ -16,12 +19,17 @@ import torch.nn.functional as F from datasets import load_dataset from matplotlib import pyplot as plt from torch import Tensor, nn +from torch.func import jvp from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from mamba2_minimal import InferenceCache, Mamba2, Mamba2Config, RMSNorm +FIXED_VAL_SAMPLING_STEPS = 5 +FIXED_VAL_TIME_GRID = (1.0, 0.8, 0.6, 0.4, 0.2, 0.0) + + @dataclass class TrainConfig: seed: int = 42 @@ -29,13 +37,11 @@ class TrainConfig: epochs: int = 50 steps_per_epoch: int = 200 batch_size: int = 128 - seq_len: int = 20 + seq_len: int = 5 lr: float = 2e-4 weight_decay: float = 1e-2 - dt_min: float = 1e-3 - dt_max: float = 0.06 - dt_alpha: float = 8.0 lambda_flow: float = 1.0 + lambda_perceptual: float = 0.4 num_classes: int = 10 image_size: int = 28 channels: int = 1 @@ -52,10 +58,12 @@ class TrainConfig: use_residual: bool = False output_dir: str = "outputs" project: str = "as-mamba-mnist" - run_name: str = "mnist-flow" + run_name: str = "mnist-meanflow" val_every: int = 200 val_samples_per_class: int = 8 val_grid_rows: int = 4 + val_sampling_steps: int = FIXED_VAL_SAMPLING_STEPS + time_grid_size: int = 256 use_ddp: bool = False @@ -127,6 +135,11 @@ def sinusoidal_embedding(t: Tensor, dim: int) -> Tensor: return emb.to(t.dtype) +def safe_time_divisor(t: Tensor) -> Tensor: + eps = torch.finfo(t.dtype).eps + return torch.where(t > 0, t, torch.full_like(t, eps)) + + class ASMamba(nn.Module): def __init__(self, cfg: TrainConfig) -> None: super().__init__() @@ -150,32 +163,42 @@ class ASMamba(nn.Module): ) self.backbone = Mamba2Backbone(args, use_residual=cfg.use_residual) self.cond_emb = nn.Embedding(cfg.num_classes, cfg.d_model) - self.delta_head = nn.Linear(cfg.d_model, input_dim) + self.clean_head = nn.Linear(cfg.d_model, input_dim) def forward( self, - x: Tensor, - dt: Tensor, + z_t: Tensor, + r: Tensor, + t: Tensor, cond: Tensor, h: Optional[list[InferenceCache]] = None, ) -> tuple[Tensor, list[InferenceCache]]: - if dt.dim() == 1: - dt = dt.unsqueeze(1) - elif dt.dim() == 3 and dt.shape[-1] == 1: - dt = dt.squeeze(-1) - dt = dt.to(dtype=x.dtype) - dt_emb = sinusoidal_embedding(dt, x.shape[-1]) - x = x + dt_emb + if r.dim() == 1: + r = r.unsqueeze(1) + elif r.dim() == 3 and r.shape[-1] == 1: + r = r.squeeze(-1) + if t.dim() == 1: + t = t.unsqueeze(1) + elif t.dim() == 3 and t.shape[-1] == 1: + t = t.squeeze(-1) + + r = r.to(dtype=z_t.dtype) + t = t.to(dtype=z_t.dtype) + z_t = z_t + sinusoidal_embedding(r, z_t.shape[-1]) + sinusoidal_embedding( + t, z_t.shape[-1] + ) cond_vec = self.cond_emb(cond) - feats, h = self.backbone(x, cond_vec, h) - delta = self.delta_head(feats) - return delta, h + feats, h = self.backbone(z_t, cond_vec, h) + x_pred = self.clean_head(feats) + return x_pred, h def step( - self, x: Tensor, dt: Tensor, cond: Tensor, h: list[InferenceCache] + self, z_t: Tensor, r: Tensor, t: Tensor, cond: Tensor, h: list[InferenceCache] ) -> tuple[Tensor, list[InferenceCache]]: - delta, h = self.forward(x.unsqueeze(1), dt.unsqueeze(1), cond, h) - return delta[:, 0, :], h + x_pred, h = self.forward( + z_t.unsqueeze(1), r.unsqueeze(1), t.unsqueeze(1), cond, h + ) + return x_pred[:, 0, :], h def init_cache(self, batch_size: int, device: torch.device) -> list[InferenceCache]: return [ @@ -242,6 +265,34 @@ class SwanLogger: finish() +class LPIPSPerceptualLoss(nn.Module): + def __init__(self, cfg: TrainConfig) -> None: + super().__init__() + torch_home = Path(cfg.output_dir) / ".torch" + torch_home.mkdir(parents=True, exist_ok=True) + os.environ["TORCH_HOME"] = str(torch_home) + self.channels = cfg.channels + self.loss_fn = lpips.LPIPS(net="vgg", verbose=False) + self.loss_fn.eval() + for param in self.loss_fn.parameters(): + param.requires_grad_(False) + + def _prepare_images(self, images: Tensor) -> Tensor: + if images.shape[1] == 1: + return images.repeat(1, 3, 1, 1) + if images.shape[1] != 3: + raise ValueError( + "LPIPS perceptual loss expects 1-channel or 3-channel images. " + f"Got {images.shape[1]} channels." + ) + return images + + def forward(self, pred: Tensor, target: Tensor) -> Tensor: + pred_rgb = self._prepare_images(pred) + target_rgb = self._prepare_images(target) + return self.loss_fn(pred_rgb, target_rgb).mean() + + def set_seed(seed: int) -> None: torch.manual_seed(seed) if torch.cuda.is_available(): @@ -266,40 +317,42 @@ def unwrap_model(model: nn.Module) -> nn.Module: return model.module if hasattr(model, "module") else model -def validate_time_config(cfg: TrainConfig) -> None: - if cfg.seq_len <= 0: - raise ValueError("seq_len must be > 0") - base = 1.0 / cfg.seq_len - if cfg.dt_max <= base: +def validate_config(cfg: TrainConfig) -> None: + if cfg.seq_len != 5: raise ValueError( - "dt_max must be > 1/seq_len to allow non-uniform dt_seq. " - f"Got dt_max={cfg.dt_max}, seq_len={cfg.seq_len}, 1/seq_len={base}." + f"seq_len must be 5 for the required 5-block training setup (got {cfg.seq_len})." ) - if cfg.dt_min >= cfg.dt_max: + if cfg.time_grid_size < 2: + raise ValueError("time_grid_size must be >= 2.") + if cfg.lambda_perceptual < 0: + raise ValueError("lambda_perceptual must be >= 0.") + if cfg.val_sampling_steps != FIXED_VAL_SAMPLING_STEPS: raise ValueError( - f"dt_min must be < dt_max (got dt_min={cfg.dt_min}, dt_max={cfg.dt_max})." + f"val_sampling_steps is fixed to {FIXED_VAL_SAMPLING_STEPS} for validation sampling." ) -def sample_time_sequence( - cfg: TrainConfig, batch_size: int, device: torch.device -) -> Tensor: - alpha = float(cfg.dt_alpha) - if alpha <= 0: - raise ValueError("dt_alpha must be > 0") - dist = torch.distributions.Gamma(alpha, 1.0) - raw = dist.sample((batch_size, cfg.seq_len)).to(device) - dt_seq = raw / raw.sum(dim=-1, keepdim=True) - base = 1.0 / cfg.seq_len - max_dt = float(cfg.dt_max) - if max_dt <= base: - return torch.full_like(dt_seq, base) - max_current = dt_seq.max(dim=-1, keepdim=True).values - if (max_current > max_dt).any(): - gamma = (max_dt - base) / (max_current - base) - gamma = gamma.clamp(0.0, 1.0) - dt_seq = gamma * dt_seq + (1.0 - gamma) * base - return dt_seq +def sample_block_times( + cfg: TrainConfig, batch_size: int, device: torch.device, dtype: torch.dtype +) -> tuple[Tensor, Tensor]: + # Sampling sorted discrete cut points allows repeated boundaries, so zero-length + # interior blocks occur with non-zero probability while keeping t > 0. + cuts = torch.randint( + 1, + cfg.time_grid_size, + (batch_size, cfg.seq_len - 1), + device=device, + ) + cuts, _ = torch.sort(cuts, dim=-1) + boundaries = torch.cat( + [ + torch.zeros(batch_size, 1, device=device, dtype=dtype), + cuts.to(dtype=dtype) / float(cfg.time_grid_size), + torch.ones(batch_size, 1, device=device, dtype=dtype), + ], + dim=-1, + ) + return boundaries[:, :-1], boundaries[:, 1:] def build_dataloader( @@ -346,15 +399,66 @@ def infinite_loader(loader: DataLoader) -> Iterator[dict]: yield batch +def build_noisy_sequence( + x0: Tensor, + eps: Tensor, + t_seq: Tensor, +) -> tuple[Tensor, Tensor]: + z_t = (1.0 - t_seq.unsqueeze(-1)) * x0[:, None, :] + t_seq.unsqueeze(-1) * eps[:, None, :] + v_gt = eps - x0 + return z_t, v_gt + + def compute_losses( - delta: Tensor, + model: nn.Module, + perceptual_loss_fn: LPIPSPerceptualLoss, + x0: Tensor, + z_t: Tensor, v_gt: Tensor, - dt_seq: Tensor, -) -> dict[str, Tensor]: - losses: dict[str, Tensor] = {} - target_disp = v_gt[:, None, :] * dt_seq.unsqueeze(-1) - losses["flow"] = F.mse_loss(delta, target_disp) - return losses + r_seq: Tensor, + t_seq: Tensor, + cond: Tensor, + cfg: TrainConfig, +) -> tuple[dict[str, Tensor], Tensor]: + seq_len = z_t.shape[1] + safe_t = safe_time_divisor(t_seq).unsqueeze(-1) + + x_pred, _ = model(z_t, r_seq, t_seq, cond) + u = (z_t - x_pred) / safe_t + + x_pred_inst, _ = model(z_t, t_seq, t_seq, cond) + v_inst = ((z_t - x_pred_inst) / safe_t).detach() + + def u_fn(z_in: Tensor, r_in: Tensor, t_in: Tensor) -> Tensor: + x_pred_local, _ = model(z_in, r_in, t_in, cond) + return (z_in - x_pred_local) / safe_time_divisor(t_in).unsqueeze(-1) + + _, dudt = jvp( + u_fn, + (z_t, r_seq, t_seq), + (v_inst, torch.zeros_like(r_seq), torch.ones_like(t_seq)), + ) + corrected_velocity = u + (t_seq - r_seq).unsqueeze(-1) * dudt.detach() + target_velocity = v_gt[:, None, :].expand(-1, seq_len, -1) + + pred_images = x_pred.reshape( + x0.shape[0] * seq_len, cfg.channels, cfg.image_size, cfg.image_size + ) + target_images = ( + x0.reshape(x0.shape[0], cfg.channels, cfg.image_size, cfg.image_size) + .unsqueeze(1) + .expand(-1, seq_len, -1, -1, -1) + .reshape(x0.shape[0] * seq_len, cfg.channels, cfg.image_size, cfg.image_size) + ) + + losses = { + "flow": F.mse_loss(corrected_velocity, target_velocity), + "perceptual": perceptual_loss_fn(pred_images, target_images), + } + losses["total"] = cfg.lambda_flow * losses["flow"] + cfg.lambda_perceptual * losses[ + "perceptual" + ] + return losses, x_pred def make_grid(images: Tensor, nrow: int) -> np.ndarray: @@ -389,30 +493,36 @@ def save_image_grid( plt.close(fig) -def rollout_trajectory( +def sample_class_images( model: ASMamba, - x0: Tensor, + cfg: TrainConfig, + device: torch.device, cond: Tensor, - dt_seq: Tensor, ) -> Tensor: - device = x0.device model.eval() - h = model.init_cache(batch_size=x0.shape[0], device=device) - x = x0 - if dt_seq.dim() == 1: - dt_seq = dt_seq.unsqueeze(0).expand(x0.shape[0], -1) - elif dt_seq.shape[0] == 1 and x0.shape[0] > 1: - dt_seq = dt_seq.expand(x0.shape[0], -1) - traj = [x0.detach().cpu()] + input_dim = cfg.channels * cfg.image_size * cfg.image_size + z_t = torch.randn(cond.shape[0], input_dim, device=device) + time_grid = torch.tensor(FIXED_VAL_TIME_GRID, device=device) with torch.no_grad(): - for step_idx in range(dt_seq.shape[1]): - dt = dt_seq[:, step_idx] - delta, h = model.step(x, dt, cond, h) - x = x + delta - traj.append(x.detach().cpu()) + for step_idx in range(FIXED_VAL_SAMPLING_STEPS): + t_cur = torch.full( + (cond.shape[0],), + float(time_grid[step_idx].item()), + device=device, + ) + t_next = time_grid[step_idx + 1] + x_pred, _ = model( + z_t.unsqueeze(1), + t_cur.unsqueeze(1), + t_cur.unsqueeze(1), + cond, + ) + x_pred = x_pred[:, 0, :] + u_inst = (z_t - x_pred) / safe_time_divisor(t_cur).unsqueeze(-1) + z_t = z_t + (t_next - t_cur).unsqueeze(-1) * u_inst - return torch.stack(traj, dim=1) + return z_t.view(cond.shape[0], cfg.channels, cfg.image_size, cfg.image_size) def log_class_samples( @@ -424,24 +534,13 @@ def log_class_samples( ) -> None: if cfg.val_samples_per_class <= 0: return + training_mode = model.training model.eval() - max_steps = cfg.seq_len - input_dim = cfg.channels * cfg.image_size * cfg.image_size - dt_seq = torch.full( - (cfg.val_samples_per_class, max_steps), - 1.0 / max_steps, - device=device, - ) - for cls in range(cfg.num_classes): cond = torch.full( (cfg.val_samples_per_class,), cls, device=device, dtype=torch.long ) - x0 = torch.randn(cfg.val_samples_per_class, input_dim, device=device) - traj = rollout_trajectory(model, x0, cond, dt_seq=dt_seq) - x_final = traj[:, -1, :].view( - cfg.val_samples_per_class, cfg.channels, cfg.image_size, cfg.image_size - ) + x_final = sample_class_images(model, cfg, device, cond) save_path = Path(cfg.output_dir) / f"val_class_{cls}_step_{step:06d}.png" save_image_grid(x_final, save_path, nrow=cfg.val_grid_rows) logger.log_image( @@ -450,12 +549,25 @@ def log_class_samples( caption=f"class {cls} step {step}", step=step, ) - model.train() + if training_mode: + model.train() + + +def build_perceptual_loss( + cfg: TrainConfig, device: torch.device, rank: int, use_ddp: bool +) -> LPIPSPerceptualLoss: + if use_ddp and rank != 0: + torch.distributed.barrier() + perceptual_loss_fn = LPIPSPerceptualLoss(cfg).to(device) + if use_ddp and rank == 0: + torch.distributed.barrier() + return perceptual_loss_fn def train(cfg: TrainConfig) -> ASMamba: - validate_time_config(cfg) + validate_config(cfg) use_ddp, rank, world_size, device = setup_distributed(cfg) + del world_size set_seed(cfg.seed + rank) output_dir = Path(cfg.output_dir) if rank == 0: @@ -467,48 +579,53 @@ def train(cfg: TrainConfig) -> ASMamba: optimizer = torch.optim.AdamW( model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay ) + perceptual_loss_fn = build_perceptual_loss(cfg, device, rank, use_ddp) logger = SwanLogger(cfg, enabled=(rank == 0)) loader, sampler = build_dataloader(cfg, distributed=use_ddp) loader_iter = infinite_loader(loader) global_step = 0 - for _ in range(cfg.epochs): + for epoch_idx in range(cfg.epochs): if sampler is not None: - sampler.set_epoch(global_step) + sampler.set_epoch(epoch_idx) model.train() for _ in range(cfg.steps_per_epoch): batch = next(loader_iter) - x1 = batch["pixel_values"].to(device) + x0 = batch["pixel_values"].to(device) cond = batch["labels"].to(device) - b = x1.shape[0] - x1 = x1.view(b, -1) - x0 = torch.randn_like(x1) - v_gt = x1 - x0 - dt_seq = sample_time_sequence(cfg, b, device) - t_seq = torch.cumsum(dt_seq, dim=-1) - t_seq = torch.cat([torch.zeros(b, 1, device=device), t_seq[:, :-1]], dim=-1) - x_seq = x0[:, None, :] + t_seq[:, :, None] * v_gt[:, None, :] + b = x0.shape[0] + x0 = x0.view(b, -1) + eps = torch.randn_like(x0) - delta, _ = model(x_seq, dt_seq, cond) + r_seq, t_seq = sample_block_times(cfg, b, device, x0.dtype) + z_t, v_gt = build_noisy_sequence(x0, eps, t_seq) - losses = compute_losses( - delta=delta, + losses, _ = compute_losses( + model=model, + perceptual_loss_fn=perceptual_loss_fn, + x0=x0, + z_t=z_t, v_gt=v_gt, - dt_seq=dt_seq, + r_seq=r_seq, + t_seq=t_seq, + cond=cond, + cfg=cfg, ) - loss = cfg.lambda_flow * losses["flow"] - optimizer.zero_grad(set_to_none=True) - loss.backward() + losses["total"].backward() optimizer.step() - if global_step % 10 == 0: + if global_step % 10 == 0 and rank == 0: logger.log( { - "loss/total": float(loss.item()), + "loss/total": float(losses["total"].item()), "loss/flow": float(losses["flow"].item()), + "loss/perceptual": float(losses["perceptual"].item()), + "time/r_mean": float(r_seq.mean().item()), + "time/t_mean": float(t_seq.mean().item()), + "time/zero_block_frac": float((t_seq == r_seq).float().mean().item()), }, step=global_step, ) diff --git a/main.py b/main.py index 784cbb3..8447b7e 100644 --- a/main.py +++ b/main.py @@ -4,7 +4,7 @@ from as_mamba import TrainConfig, run_training_and_plot def build_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser(description="Train AS-Mamba on MNIST flow matching.") + parser = argparse.ArgumentParser(description="Train AS-Mamba on MNIST MeanFlow x-prediction.") parser.add_argument("--epochs", type=int, default=None) parser.add_argument("--steps-per-epoch", type=int, default=None) parser.add_argument("--batch-size", type=int, default=None) @@ -15,10 +15,8 @@ def build_parser() -> argparse.ArgumentParser: parser.add_argument("--output-dir", type=str, default=None) parser.add_argument("--project", type=str, default=None) parser.add_argument("--run-name", type=str, default=None) - parser.add_argument("--dt-alpha", type=float, default=None) - parser.add_argument("--dt-min", type=float, default=None) - parser.add_argument("--dt-max", type=float, default=None) parser.add_argument("--lambda-flow", type=float, default=None) + parser.add_argument("--lambda-perceptual", type=float, default=None) parser.add_argument("--num-classes", type=int, default=None) parser.add_argument("--image-size", type=int, default=None) parser.add_argument("--channels", type=int, default=None) @@ -36,6 +34,8 @@ def build_parser() -> argparse.ArgumentParser: parser.add_argument("--val-every", type=int, default=None) parser.add_argument("--val-samples-per-class", type=int, default=None) parser.add_argument("--val-grid-rows", type=int, default=None) + parser.add_argument("--val-sampling-steps", type=int, default=None) + parser.add_argument("--time-grid-size", type=int, default=None) parser.add_argument("--use-ddp", action=argparse.BooleanOptionalAction, default=None) return parser diff --git a/pyproject.toml b/pyproject.toml index 5eb84d3..394ed37 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,8 +7,10 @@ requires-python = ">=3.12" dependencies = [ "datasets>=2.19.0", "einops>=0.7.0", + "lpips>=0.1.4", "matplotlib>=3.8.0", "numpy>=1.26.0", "swanlab>=0.5.0", "torch>=2.2.0", + "torchvision>=0.24.1", ] diff --git a/train_as_mamba.sh b/train_as_mamba.sh index c16d48c..83d3a13 100755 --- a/train_as_mamba.sh +++ b/train_as_mamba.sh @@ -5,13 +5,11 @@ DEVICE="cuda" EPOCHS=2000 STEPS_PER_EPOCH=200 BATCH_SIZE=512 -SEQ_LEN=1 +SEQ_LEN=5 LR=1e-3 WEIGHT_DECAY=1e-2 -DT_MIN=5e-4 -DT_MAX=1.1 -DT_ALPHA=9.0 LAMBDA_FLOW=1.0 +LAMBDA_PERCEPTUAL=0.4 NUM_CLASSES=10 IMAGE_SIZE=28 CHANNELS=1 @@ -30,8 +28,10 @@ USE_DDP=true VAL_EVERY=1000 VAL_SAMPLES_PER_CLASS=8 VAL_GRID_ROWS=4 +VAL_SAMPLING_STEPS=5 +TIME_GRID_SIZE=256 PROJECT="as-mamba-mnist" -RUN_NAME="mnist-flow-res-5seq" +RUN_NAME="mnist-meanflow-xpred" OUTPUT_DIR="outputs" USE_RESIDUAL_FLAG="--use-residual" @@ -47,10 +47,8 @@ uv run torchrun --nproc_per_node=2 main.py \ --seq-len "${SEQ_LEN}" \ --lr "${LR}" \ --weight-decay "${WEIGHT_DECAY}" \ - --dt-min "${DT_MIN}" \ - --dt-max "${DT_MAX}" \ - --dt-alpha "${DT_ALPHA}" \ --lambda-flow "${LAMBDA_FLOW}" \ + --lambda-perceptual "${LAMBDA_PERCEPTUAL}" \ --num-classes "${NUM_CLASSES}" \ --image-size "${IMAGE_SIZE}" \ --channels "${CHANNELS}" \ @@ -69,6 +67,8 @@ uv run torchrun --nproc_per_node=2 main.py \ --val-every "${VAL_EVERY}" \ --val-samples-per-class "${VAL_SAMPLES_PER_CLASS}" \ --val-grid-rows "${VAL_GRID_ROWS}" \ + --val-sampling-steps "${VAL_SAMPLING_STEPS}" \ + --time-grid-size "${TIME_GRID_SIZE}" \ --project "${PROJECT}" \ --run-name "${RUN_NAME}" \ --output-dir "${OUTPUT_DIR}" diff --git a/uv.lock b/uv.lock index e20404a..028ae80 100644 --- 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