feat: stabilize meanflow training and time sampling

as_mamba.py

@@ -40,8 +40,9 @@ class TrainConfig:
     seq_len: int = 5
     lr: float = 2e-4
     weight_decay: float = 1e-2
+    max_grad_norm: float = 1.0
     lambda_flow: float = 1.0
-    lambda_perceptual: float = 0.4
+    lambda_perceptual: float = 2.0
     num_classes: int = 10
     image_size: int = 28
     channels: int = 1
@@ -189,7 +190,7 @@ class ASMamba(nn.Module):
         )
         cond_vec = self.cond_emb(cond)
         feats, h = self.backbone(z_t, cond_vec, h)
-        x_pred = self.clean_head(feats)
+        x_pred = torch.tanh(self.clean_head(feats))
         return x_pred, h
 
     def step(
@@ -326,6 +327,8 @@ def validate_config(cfg: TrainConfig) -> None:
         raise ValueError("time_grid_size must be >= 2.")
     if cfg.lambda_perceptual < 0:
         raise ValueError("lambda_perceptual must be >= 0.")
+    if cfg.max_grad_norm <= 0:
+        raise ValueError("max_grad_norm must be > 0.")
     if cfg.val_sampling_steps != FIXED_VAL_SAMPLING_STEPS:
         raise ValueError(
             f"val_sampling_steps is fixed to {FIXED_VAL_SAMPLING_STEPS} for validation sampling."
@@ -335,19 +338,17 @@ def validate_config(cfg: TrainConfig) -> None:
 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,
-    )
+    num_internal = cfg.seq_len - 1
+    normal = torch.randn(batch_size, num_internal, device=device, dtype=dtype)
+    logit_normal = torch.sigmoid(normal * math.sqrt(0.8))
+    uniform = torch.rand(batch_size, num_internal, device=device, dtype=dtype)
+    use_uniform = torch.rand(batch_size, num_internal, device=device) < 0.1
+    cuts = torch.where(use_uniform, uniform, logit_normal)
     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),
+            cuts,
             torch.ones(batch_size, 1, device=device, dtype=dtype),
         ],
         dim=-1,
@@ -615,6 +616,9 @@ def train(cfg: TrainConfig) -> ASMamba:
 
             optimizer.zero_grad(set_to_none=True)
             losses["total"].backward()
+            grad_norm = torch.nn.utils.clip_grad_norm_(
+                model.parameters(), max_norm=cfg.max_grad_norm
+            )
             optimizer.step()
 
             if global_step % 10 == 0 and rank == 0:
@@ -623,6 +627,7 @@ def train(cfg: TrainConfig) -> ASMamba:
                         "loss/total": float(losses["total"].item()),
                         "loss/flow": float(losses["flow"].item()),
                         "loss/perceptual": float(losses["perceptual"].item()),
+                        "grad/total_norm": float(grad_norm.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()),