fix(mps): gradient exploding and nan loss issues with ACT

src/lerobot/policies/act/modeling_act.py

@@ -485,12 +485,10 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, tuple[Tensor, Tenso
                 self.encoder_env_state_input_proj(batch["observation.environment_state"])
             )
 
-        # Camera observation features and positional embeddings.
         if self.config.image_features:
-            all_cam_features = []
-            all_cam_pos_embeds = []
-
             # For a list of images, the H and W may vary but H*W is constant.
+            # NOTE: If modifying this section, verify on MPS devices that
+            # gradients remain stable (no explosions or NaNs).
             for img in batch["observation.images"]:
                 cam_features = self.backbone(img)["feature_map"]
                 cam_pos_embed = self.encoder_cam_feat_pos_embed(cam_features).to(dtype=cam_features.dtype)
@@ -500,11 +498,10 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, tuple[Tensor, Tenso
                 cam_features = einops.rearrange(cam_features, "b c h w -> (h w) b c")
                 cam_pos_embed = einops.rearrange(cam_pos_embed, "b c h w -> (h w) b c")
 
-                all_cam_features.append(cam_features)
-                all_cam_pos_embeds.append(cam_pos_embed)
-
-            encoder_in_tokens.extend(torch.cat(all_cam_features, axis=0))
-            encoder_in_pos_embed.extend(torch.cat(all_cam_pos_embeds, axis=0))
+                # Extend immediately instead of accumulating and concatenating
+                # Convert to list to extend properly
+                encoder_in_tokens.extend(list(cam_features))
+                encoder_in_pos_embed.extend(list(cam_pos_embed))
 
         # Stack all tokens along the sequence dimension.
         encoder_in_tokens = torch.stack(encoder_in_tokens, axis=0)

src/lerobot/scripts/train.py

@@ -180,7 +180,7 @@ def train(cfg: TrainPipelineConfig):
         batch_size=cfg.batch_size,
         shuffle=shuffle,
         sampler=sampler,
-        pin_memory=device.type != "cpu",
+        pin_memory=device.type == "cuda",
         drop_last=False,
     )
     dl_iter = cycle(dataloader)
@@ -207,7 +207,7 @@ def train(cfg: TrainPipelineConfig):
 
         for key in batch:
             if isinstance(batch[key], torch.Tensor):
-                batch[key] = batch[key].to(device, non_blocking=True)
+                batch[key] = batch[key].to(device, non_blocking=device.type == "cuda")
 
         train_tracker, output_dict = update_policy(
             train_tracker,