refactor(as_mamba): Remove dt prediction and use fixed dt

Removes the `dt_head` network and associated configuration parameters
(dt_min, dt_max, lambda_nfe, warmup_epochs). Replaces predicted time
steps with a fixed value derived from sequence length. Eliminates
the warmup phase and NFE loss calculation.

as_mamba.py

@@ -25,15 +25,11 @@ class TrainConfig:
     batch_size: int = 128
     steps_per_epoch: int = 50
     epochs: int = 60
-    warmup_epochs: int = 15
     seq_len: int = 20
     lr: float = 1e-3
     weight_decay: float = 1e-2
-    dt_min: float = 1e-3
-    dt_max: float = 0.06
     lambda_flow: float = 1.0
     lambda_pos: float = 1.0
-    lambda_nfe: float = 0.05
     radius_min: float = 0.6
     radius_max: float = 1.4
     center_min: float = -6.0
@@ -53,7 +49,7 @@ class TrainConfig:
     val_every: int = 200
     val_samples: int = 256
     val_plot_samples: int = 16
-    val_max_steps: int = 100
+    val_max_steps: int = 0
 
 
 class Mamba2Backbone(nn.Module):
@@ -92,8 +88,6 @@ class ASMamba(nn.Module):
     def __init__(self, cfg: TrainConfig) -> None:
         super().__init__()
         self.cfg = cfg
-        self.dt_min = float(cfg.dt_min)
-        self.dt_max = float(cfg.dt_max)
 
         args = Mamba2Config(
             d_model=cfg.d_model,
@@ -107,27 +101,20 @@ class ASMamba(nn.Module):
         self.backbone = Mamba2Backbone(args, use_residual=cfg.use_residual)
         self.input_proj = nn.Linear(3, cfg.d_model)
         self.delta_head = nn.Linear(cfg.d_model, 3)
-        self.dt_head = nn.Sequential(
-            nn.Linear(cfg.d_model, cfg.d_model),
-            nn.SiLU(),
-            nn.Linear(cfg.d_model, 1),
-        )
 
     def forward(
         self, x: Tensor, h: Optional[list[InferenceCache]] = None
-    ) -> tuple[Tensor, Tensor, list[InferenceCache]]:
+    ) -> tuple[Tensor, list[InferenceCache]]:
         x_proj = self.input_proj(x)
         feats, h = self.backbone(x_proj, h)
         delta = self.delta_head(feats)
-        dt_raw = self.dt_head(feats).squeeze(-1)
+        return delta, h
-        dt = torch.clamp(F.softplus(dt_raw), min=self.dt_min, max=self.dt_max)
-        return delta, dt, h
 
     def step(
         self, x: Tensor, h: list[InferenceCache]
-    ) -> tuple[Tensor, Tensor, list[InferenceCache]]:
+    ) -> tuple[Tensor, list[InferenceCache]]:
-        delta, dt, h = self.forward(x.unsqueeze(1), h)
+        delta, h = self.forward(x.unsqueeze(1), h)
-        return delta[:, 0, :], dt[:, 0], h
+        return delta[:, 0, :], h
 
     def init_cache(self, batch_size: int, device: torch.device) -> list[InferenceCache]:
         return [
@@ -240,24 +227,23 @@ def sample_batch(
 
 def compute_losses(
     delta: Tensor,
-    dt: Tensor,
     x_seq: Tensor,
     x0: Tensor,
     v_gt: Tensor,
     t_seq: Tensor,
     cfg: TrainConfig,
-) -> tuple[Tensor, Tensor, Tensor]:
+) -> tuple[Tensor, Tensor]:
-    target_disp = v_gt[:, None, :] * dt.unsqueeze(-1)
+    dt_fixed = 1.0 / cfg.seq_len
+    target_disp = v_gt[:, None, :] * dt_fixed
     flow_loss = F.mse_loss(delta, target_disp)
 
-    t_next = t_seq[None, :, None] + dt.unsqueeze(-1)
+    t_next = t_seq[None, :, None] + dt_fixed
     t_next = torch.clamp(t_next, 0.0, 1.0)
     x_target = x0[:, None, :] + t_next * v_gt[:, None, :]
     x_next_pred = x_seq + delta
     pos_loss = F.mse_loss(x_next_pred, x_target)
 
-    nfe_loss = (-torch.log(dt)).mean()
+    return flow_loss, pos_loss
-    return flow_loss, pos_loss, nfe_loss
 
 
 def validate(
@@ -271,12 +257,13 @@ def validate(
 ) -> None:
     model.eval()
     center_b, radius_b = sphere_b
+    steps = cfg.seq_len if cfg.val_max_steps <= 0 else cfg.val_max_steps
 
     with torch.no_grad():
         x0 = sample_points_in_sphere(
             sphere_a[0], float(sphere_a[1].item()), cfg.val_samples, device
         )
-        traj = rollout_trajectory(model, x0, max_steps=cfg.val_max_steps)
+        traj = rollout_trajectory(model, x0, steps=steps)
 
     x_final = traj[:, -1, :]
     center_b_cpu = center_b.detach().cpu()
@@ -291,6 +278,7 @@ def validate(
             "val/final_dist_mean": float(dist.mean().item()),
             "val/final_dist_min": float(dist.min().item()),
             "val/final_dist_max": float(dist.max().item()),
+            "val/steps": float(steps),
         },
         step=step,
     )
@@ -351,22 +339,15 @@ def train(cfg: TrainConfig) -> tuple[ASMamba, tuple[Tensor, Tensor], tuple[Tenso
 
     global_step = 0
     for epoch in range(cfg.epochs):
-        warmup = epoch < cfg.warmup_epochs
         model.train()
-        for p in model.dt_head.parameters():
-            p.requires_grad = not warmup
 
         for _ in range(cfg.steps_per_epoch):
             x0, x1, x_seq, t_seq = sample_batch(cfg, sphere_a, sphere_b, device)
             v_gt = x1 - x0
 
-            delta, dt, _ = model(x_seq)
+            delta, _ = model(x_seq)
-            if warmup:
+            flow_loss, pos_loss = compute_losses(
-                dt = torch.full_like(dt, 1.0 / cfg.seq_len)
-
-            flow_loss, pos_loss, nfe_loss = compute_losses(
                 delta=delta,
-                dt=dt,
                 x_seq=x_seq,
                 x0=x0,
                 v_gt=v_gt,
@@ -375,8 +356,6 @@ def train(cfg: TrainConfig) -> tuple[ASMamba, tuple[Tensor, Tensor], tuple[Tenso
             )
 
             loss = cfg.lambda_flow * flow_loss + cfg.lambda_pos * pos_loss
-            if not warmup:
-                loss = loss + cfg.lambda_nfe * nfe_loss
 
             optimizer.zero_grad(set_to_none=True)
             loss.backward()
@@ -388,11 +367,6 @@ def train(cfg: TrainConfig) -> tuple[ASMamba, tuple[Tensor, Tensor], tuple[Tenso
                         "loss/total": float(loss.item()),
                         "loss/flow": float(flow_loss.item()),
                         "loss/pos": float(pos_loss.item()),
-                        "loss/nfe": float(nfe_loss.item()),
-                        "dt/mean": float(dt.mean().item()),
-                        "dt/min": float(dt.min().item()),
-                        "dt/max": float(dt.max().item()),
-                        "stage": 0 if warmup else 1,
                     },
                     step=global_step,
                 )
@@ -408,33 +382,20 @@ def train(cfg: TrainConfig) -> tuple[ASMamba, tuple[Tensor, Tensor], tuple[Tenso
 def rollout_trajectory(
     model: ASMamba,
     x0: Tensor,
-    max_steps: int = 100,
+    steps: int,
 ) -> Tensor:
     device = x0.device
     model.eval()
     h = model.init_cache(batch_size=x0.shape[0], device=device)
     x = x0
-    total_time = torch.zeros(x0.shape[0], device=device)
     traj = [x0.detach().cpu()]
 
     with torch.no_grad():
-        for _ in range(max_steps):
+        for _ in range(steps):
-            delta, dt, h = model.step(x, h)
+            delta, h = model.step(x, h)
-            dt = torch.clamp(dt, min=model.dt_min, max=model.dt_max)
-            remaining = 1.0 - total_time
-            overshoot = dt > remaining
-            if overshoot.any():
-                scale = (remaining / dt).unsqueeze(-1)
-                delta = torch.where(overshoot.unsqueeze(-1), delta * scale, delta)
-                dt = torch.where(overshoot, remaining, dt)
-
             x = x + delta
-            total_time = total_time + dt
             traj.append(x.detach().cpu())
 
-            if torch.all(total_time >= 1.0 - 1e-6):
-                break
-
     return torch.stack(traj, dim=1)
 
 
@@ -504,7 +465,8 @@ def run_training_and_plot(cfg: TrainConfig) -> Path:
     x0 = sample_points_in_sphere(
         sphere_a[0], float(sphere_a[1].item()), plot_samples, device
     )
-    traj = rollout_trajectory(model, x0, max_steps=cfg.val_max_steps)
+    steps = cfg.seq_len if cfg.val_max_steps <= 0 else cfg.val_max_steps
+    traj = rollout_trajectory(model, x0, steps=steps)
     output_dir = Path(cfg.output_dir)
     save_path = output_dir / "as_mamba_trajectory.png"
     plot_trajectories(traj, sphere_a, sphere_b, save_path)

main.py

@@ -6,7 +6,6 @@ from as_mamba import TrainConfig, run_training_and_plot
 def build_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser(description="Train AS-Mamba on sphere-to-sphere flow.")
     parser.add_argument("--epochs", type=int, default=None)
-    parser.add_argument("--warmup-epochs", type=int, default=None)
     parser.add_argument("--batch-size", type=int, default=None)
     parser.add_argument("--steps-per-epoch", type=int, default=None)
     parser.add_argument("--seq-len", type=int, default=None)