add model. prefix to all keys in state dict

src/lerobot/policies/pi0_openpi/modeling_pi0openpi.py

@@ -871,14 +871,109 @@ class PI0OpenPIPolicy(PreTrainedPolicy):
         self.reset()
 
     @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        """Override the from_pretrained method to display important disclaimer."""
+    def from_pretrained(
+        cls, *args, **kwargs
+    ):  # TODO(pepijn): modify this back so we do not have to add model. prefix to all keys in the state dict
+        """Override the from_pretrained method to handle key remapping and display important disclaimer."""
         print(
             "⚠️  DISCLAIMER: The PI0OpenPI model is a direct PyTorch port of the OpenPI implementation. \n"
             "   This implementation follows the original OpenPI structure for compatibility. \n"
             "   Original implementation: https://github.com/Physical-Intelligence/openpi"
         )
-        return super().from_pretrained(*args, **kwargs)
+
+        # Store original strict mode
+        original_strict = kwargs.get("strict", True)
+        # Temporarily set strict=False to avoid loading issues, we'll handle it manually
+        kwargs["strict"] = False
+
+        # Call parent from_pretrained with strict=False
+        model = super().from_pretrained(*args, **kwargs)
+
+        # Extract the pretrained_model_name_or_path from args or kwargs for remapping
+        if len(args) > 0:
+            pretrained_model_name_or_path = args[0]
+        elif "pretrained_model_name_or_path" in kwargs:
+            pretrained_model_name_or_path = kwargs["pretrained_model_name_or_path"]
+        else:
+            return model
+
+        # Now manually load and remap the state dict
+        try:
+            from transformers.utils import cached_file
+
+            # Try to load the pytorch_model.bin or model.safetensors file
+            print(f"Loading model from: {pretrained_model_name_or_path}")
+            try:
+                # Try safetensors first
+                resolved_file = cached_file(
+                    pretrained_model_name_or_path,
+                    "model.safetensors",
+                    cache_dir=kwargs.get("cache_dir"),
+                    force_download=kwargs.get("force_download", False),
+                    resume_download=kwargs.get("resume_download"),
+                    proxies=kwargs.get("proxies"),
+                    use_auth_token=kwargs.get("use_auth_token"),
+                    revision=kwargs.get("revision"),
+                    local_files_only=kwargs.get("local_files_only", False),
+                )
+                from safetensors.torch import load_file
+
+                original_state_dict = load_file(resolved_file)
+                print("✓ Loaded state dict from model.safetensors")
+            except Exception as e:
+                print(f"Could not load state dict from remote files: {e}")
+                return model
+
+            # Create a new state dict with "model." prefix for all keys that don't already have it
+            remapped_state_dict = {}
+            remap_count = 0
+
+            for key, value in original_state_dict.items():
+                if not key.startswith("model."):
+                    new_key = f"model.{key}"
+                    remapped_state_dict[new_key] = value
+                    remap_count += 1
+                    if remap_count <= 10:  # Only print first 10 to avoid spam
+                        print(f"Remapped: {key} -> {new_key}")
+                else:
+                    remapped_state_dict[key] = value
+
+            if remap_count > 10:
+                print(f"... and {remap_count - 10} more keys remapped")
+
+            print(f"Total keys remapped: {remap_count}")
+
+            # Load the remapped state dict into the model
+            missing_keys, unexpected_keys = model.load_state_dict(remapped_state_dict, strict=original_strict)
+
+            if missing_keys:
+                print(f"⚠️  Missing keys when loading state dict: {len(missing_keys)} keys")
+                if len(missing_keys) <= 5:
+                    for key in missing_keys:
+                        print(f"  - {key}")
+                else:
+                    for key in missing_keys[:5]:
+                        print(f"  - {key}")
+                    print(f"  ... and {len(missing_keys) - 5} more")
+
+            if unexpected_keys:
+                print(f"⚠️  Unexpected keys when loading state dict: {len(unexpected_keys)} keys")
+                if len(unexpected_keys) <= 5:
+                    for key in unexpected_keys:
+                        print(f"  - {key}")
+                else:
+                    for key in unexpected_keys[:5]:
+                        print(f"  - {key}")
+                    print(f"  ... and {len(unexpected_keys) - 5} more")
+
+            if not missing_keys and not unexpected_keys:
+                print("✅ All keys loaded successfully!")
+
+        except Exception as e:
+            print(f"⚠️  Warning: Could not remap state dict keys: {e}")
+            print("Using default loading behavior")
+
+        return model
 
     def get_optim_params(self) -> dict:  # see lerobot pi0 `get_optim_params`
         return self.parameters()