Support models with unconnected nodes removed from input (LAM)

CHANGELOG.md

@@ -11,6 +11,9 @@ Keep it human-readable, your future self will thank you!
 ## [Unreleased]
 
 ### Added
+- Add support for models with unconnected nodes dropped from input [#95](https://github.com/ecmwf/anemoi-inference/pull/95).
+- Change trigger for boundary forcings [#95](https://github.com/ecmwf/anemoi-inference/pull/95).
+- Add support for automatic loading of anemoi-datasets of more general type [#95](https://github.com/ecmwf/anemoi-inference/pull/95).
 - Add initial state output in netcdf format
 - Fix: Enable inference when no constant forcings are used
 - Add anemoi-transform link to documentation

src/anemoi/inference/checkpoint.py

@@ -312,7 +312,7 @@ def mars_requests(self, *, variables, dates, use_grib_paramid=False, **kwargs):
 
     @cached_property
     def _supporting_arrays(self):
-        return self._metadata.supporting_arrays
+        return self._metadata._supporting_arrays
 
     @property
     def name(self):

src/anemoi/inference/forcings.py

@@ -136,8 +136,10 @@ def __init__(self, context, input, variables, variables_mask):
         self.variables_mask = variables_mask
         assert isinstance(input, DatasetInput), "Currently only boundary forcings from dataset supported."
         self.input = input
-        num_lam, num_other = input.ds.grids
-        self.spatial_mask = np.array([False] * num_lam + [True] * num_other, dtype=bool)
+        if "output_mask" in context.checkpoint._supporting_arrays:
+            self.spatial_mask = ~context.checkpoint.load_supporting_array("output_mask")
+        else:
+            self.spatial_mask = np.array([False] * len(input["latitudes"]), dtype=bool)
         self.kinds = dict(retrieved=True)  # Used for debugging
 
     def __repr__(self):

src/anemoi/inference/inputs/dataset.py

@@ -28,12 +28,25 @@ class DatasetInput(Input):
 
     def __init__(self, context, args, kwargs):
         super().__init__(context)
+
+        grid_indices = kwargs.pop("grid_indices", None)
+
         self.args, self.kwargs = args, kwargs
         if context.verbosity > 0:
             LOG.info(
                 "Opening dataset with\nargs=%s\nkwargs=%s", json.dumps(args, indent=4), json.dumps(kwargs, indent=4)
             )
 
+        if grid_indices is None and "grid_indices" in context.checkpoint._supporting_arrays:
+            grid_indices = context.checkpoint.load_supporting_array("grid_indices")
+            if context.verbosity > 0:
+                LOG.info(
+                    "Loading supporting array `grid_indices` from checkpoint, \
+                    the input grid will be reduced accordingly."
+                )
+
+        self.grid_indices = slice(None) if grid_indices is None else grid_indices
+
     @cached_property
     def ds(self):
         from anemoi.datasets import open_dataset
@@ -48,11 +61,13 @@ def create_input_state(self, *, date=None):
             raise ValueError("`date` must be provided")
 
         date = to_datetime(date)
+        latitudes = self.ds.latitudes
+        longitudes = self.ds.longitudes
 
         input_state = dict(
             date=date,
-            latitudes=self.ds.latitudes,
-            longitudes=self.ds.longitudes,
+            latitudes=latitudes[self.grid_indices],
+            longitudes=longitudes[self.grid_indices],
             fields=dict(),
         )
 
@@ -69,7 +84,8 @@ def create_input_state(self, *, date=None):
             if variable not in requested_variables:
                 continue
             # Squeeze the data to remove the ensemble dimension
-            fields[variable] = np.squeeze(data[:, i], axis=1)
+            values = np.squeeze(data[:, i], axis=1)
+            fields[variable] = values[:, self.grid_indices]
 
         return input_state
 
@@ -82,6 +98,8 @@ def load_forcings(self, *, variables, dates):
         data = np.squeeze(data, axis=2)
         # Reorder the dimensions to (variable, date, values)
         data = np.swapaxes(data, 0, 1)
+        # apply reduction to `grid_indices`
+        data = data[..., self.grid_indices]
         return data
 
     def _load_dates(self, dates):

src/anemoi/inference/metadata.py

@@ -139,6 +139,8 @@ def output_tensor_index_to_variable(self):
     @cached_property
     def number_of_grid_points(self):
         """Return the number of grid points per fields"""
+        if "grid_indices" in self._supporting_arrays:
+            return len(self.load_supporting_array("grid_indices"))
         try:
             return self._metadata.dataset.shape[-1]
         except AttributeError:
@@ -510,14 +512,13 @@ def _find(x):
                         _find(y)
 
             if isinstance(x, dict):
-                if "dataset" in x:
+                if "dataset" in x and isinstance(x["dataset"], str):
                     result.append(x["dataset"])
 
                 for k, v in x.items():
                     _find(v)
 
         _find(self._config.dataloader.training.dataset)
-
         return result
 
     def open_dataset_args_kwargs(self, *, use_original_paths, from_dataloader=None):
@@ -717,9 +718,7 @@ def boundary_forcings_inputs(self, context, input_state):
 
         result = []
 
-        output_mask = self._config_model.get("output_mask", None)
-        if output_mask is not None:
-            assert output_mask == "cutout", "Currently only cutout as output mask supported."
+        if "output_mask" in self._supporting_arrays:
             result.append(
                 context.create_boundary_forcings(
                     self.prognostic_variables,