Feature/hierarchical graphs (#37)

* Hard coded implementation of the hierarchical graph model

* Added implementation of Hierarchical Graph networks

* Added instantiate model in interface init

* if-else branching instead of hydra:instantiate, have to fix this in the future.

* Added changes before migration

* WORKING implementation of Hierarchical graph network

* Refactor and cleaning

* Added example config

* Minor refactor

* Refactor

* Refactor and rebase

* Refactor and small changes for merge

* Re-added asserts in mapper

* Added entry in changelog

* Refactor pre-merge

* Refactored the hierarchical model

* Test dimentions completed.

* Fixed dynamo issue

* Refactored using NamedNodesAttributes

* Fixed with git pre-commit

* Update src/anemoi/models/models/hierarchical.py

Co-authored-by: Mario Santa Cruz <[email protected]>

* Update src/anemoi/models/models/hierarchical.py

Co-authored-by: Mario Santa Cruz <[email protected]>

---------

Co-authored-by: Mario Santa Cruz <[email protected]>

CHANGELOG.md

@@ -14,6 +14,7 @@ Keep it human-readable, your future self will thank you!
 
 ### Added
 
+- New AnemoiModelEncProcDecHierarchical class available in models [#37](https://github.com/ecmwf/anemoi-models/pull/37)
 - Add anemoi-transform link to documentation
 - Codeowners file
 - Pygrep precommit hooks

src/anemoi/models/layers/processor.py

@@ -336,6 +336,7 @@ def forward(
         *args,
         **kwargs,
     ) -> Tensor:
+
         shape_nodes = change_channels_in_shape(shard_shapes, self.num_channels)
         edge_attr = self.trainable(self.edge_attr, batch_size)
 

src/anemoi/models/models/__init__.py

@@ -6,3 +6,8 @@
 # In applying this licence, ECMWF does not waive the privileges and immunities
 # granted to it by virtue of its status as an intergovernmental organisation
 # nor does it submit to any jurisdiction.
+
+from .encoder_processor_decoder import AnemoiModelEncProcDec
+from .hierarchical import AnemoiModelEncProcDecHierarchical
+
+__all__ = ["AnemoiModelEncProcDec", "AnemoiModelEncProcDecHierarchical"]

src/anemoi/models/models/hierarchical.py

@@ -0,0 +1,308 @@
+# (C) Copyright 2024 ECMWF.
+#
+# This software is licensed under the terms of the Apache Licence Version 2.0
+# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
+# In applying this licence, ECMWF does not waive the privileges and immunities
+# granted to it by virtue of its status as an intergovernmental organisation
+# nor does it submit to any jurisdiction.
+#
+
+import logging
+from typing import Optional
+
+import einops
+import torch
+from anemoi.utils.config import DotDict
+from hydra.utils import instantiate
+from torch import Tensor
+from torch import nn
+from torch.distributed.distributed_c10d import ProcessGroup
+from torch_geometric.data import HeteroData
+
+from anemoi.models.distributed.shapes import get_shape_shards
+from anemoi.models.layers.graph import NamedNodesAttributes
+from anemoi.models.layers.graph import TrainableTensor
+from anemoi.models.models import AnemoiModelEncProcDec
+
+LOGGER = logging.getLogger(__name__)
+
+
+class AnemoiModelEncProcDecHierarchical(AnemoiModelEncProcDec):
+    """Message passing hierarchical graph neural network."""
+
+    def __init__(
+        self,
+        *,
+        model_config: DotDict,
+        data_indices: dict,
+        graph_data: HeteroData,
+    ) -> None:
+        """Initializes the graph neural network.
+
+        Parameters
+        ----------
+        config : DotDict
+            Job configuration
+        data_indices : dict
+            Data indices
+        graph_data : HeteroData
+            Graph definition
+        """
+        nn.Module.__init__(self)
+
+        self._graph_data = graph_data
+        self._graph_name_data = model_config.graph.data
+        self._graph_hidden_names = model_config.graph.hidden
+        self.num_hidden = len(self._graph_hidden_names)
+
+        # Unpack config for hierarchical graph
+        self.level_process = model_config.model.enable_hierarchical_level_processing
+
+        # hidden_dims is the dimentionality of features at each depth
+        self.hidden_dims = {
+            hidden: model_config.model.num_channels * (2**i) for i, hidden in enumerate(self._graph_hidden_names)
+        }
+
+        self._calculate_shapes_and_indices(data_indices)
+        self._assert_matching_indices(data_indices)
+        self.data_indices = data_indices
+
+        self.multi_step = model_config.training.multistep_input
+
+        # self.node_attributes = {hidden_name: NamedNodesAttributes(model_config.model.trainable_parameters[hidden_name], self._graph_data)
+        #                         for hidden_name in self._graph_hidden_names}
+        self.node_attributes = NamedNodesAttributes(model_config.model.trainable_parameters.hidden, self._graph_data)
+
+        input_dim = self.multi_step * self.num_input_channels + self.node_attributes.attr_ndims[self._graph_name_data]
+
+        # Encoder data -> hidden
+        self.encoder = instantiate(
+            model_config.model.encoder,
+            in_channels_src=input_dim,
+            in_channels_dst=self.node_attributes.attr_ndims[self._graph_hidden_names[0]],
+            hidden_dim=self.hidden_dims[self._graph_hidden_names[0]],
+            sub_graph=self._graph_data[(self._graph_name_data, "to", self._graph_hidden_names[0])],
+            src_grid_size=self.node_attributes.num_nodes[self._graph_name_data],
+            dst_grid_size=self.node_attributes.num_nodes[self._graph_hidden_names[0]],
+        )
+
+        # Level processors
+        if self.level_process:
+            self.down_level_processor = nn.ModuleDict()
+            self.up_level_processor = nn.ModuleDict()
+
+            for i in range(0, self.num_hidden):
+                nodes_names = self._graph_hidden_names[i]
+
+                self.down_level_processor[nodes_names] = instantiate(
+                    model_config.model.processor,
+                    num_channels=self.hidden_dims[nodes_names],
+                    sub_graph=self._graph_data[(nodes_names, "to", nodes_names)],
+                    src_grid_size=self.node_attributes.num_nodes[nodes_names],
+                    dst_grid_size=self.node_attributes.num_nodes[nodes_names],
+                    num_layers=model_config.model.level_process_num_layers,
+                )
+
+                self.up_level_processor[nodes_names] = instantiate(
+                    model_config.model.processor,
+                    num_channels=self.hidden_dims[nodes_names],
+                    sub_graph=self._graph_data[(nodes_names, "to", nodes_names)],
+                    src_grid_size=self.node_attributes.num_nodes[nodes_names],
+                    dst_grid_size=self.node_attributes.num_nodes[nodes_names],
+                    num_layers=model_config.model.level_process_num_layers,
+                )
+
+            # delete final upscale (does not exist): |->|->|<-|<-|
+            del self.up_level_processor[nodes_names]
+
+        # Downscale
+        self.downscale = nn.ModuleDict()
+
+        for i in range(0, self.num_hidden - 1):
+            src_nodes_name = self._graph_hidden_names[i]
+            dst_nodes_name = self._graph_hidden_names[i + 1]
+
+            self.downscale[src_nodes_name] = instantiate(
+                model_config.model.encoder,
+                in_channels_src=self.hidden_dims[src_nodes_name],
+                in_channels_dst=self.node_attributes.attr_ndims[dst_nodes_name],
+                hidden_dim=self.hidden_dims[dst_nodes_name],
+                sub_graph=self._graph_data[(src_nodes_name, "to", dst_nodes_name)],
+                src_grid_size=self.node_attributes.num_nodes[src_nodes_name],
+                dst_grid_size=self.node_attributes.num_nodes[dst_nodes_name],
+            )
+
+        # Upscale
+        self.upscale = nn.ModuleDict()
+
+        for i in range(1, self.num_hidden):
+            src_nodes_name = self._graph_hidden_names[i]
+            dst_nodes_name = self._graph_hidden_names[i - 1]
+
+            self.upscale[src_nodes_name] = instantiate(
+                model_config.model.decoder,
+                in_channels_src=self.hidden_dims[src_nodes_name],
+                in_channels_dst=self.hidden_dims[dst_nodes_name],
+                hidden_dim=self.hidden_dims[src_nodes_name],
+                out_channels_dst=self.hidden_dims[dst_nodes_name],
+                sub_graph=self._graph_data[(src_nodes_name, "to", dst_nodes_name)],
+                src_grid_size=self.node_attributes.num_nodes[src_nodes_name],
+                dst_grid_size=self.node_attributes.num_nodes[dst_nodes_name],
+            )
+
+        # Decoder hidden -> data
+        self.decoder = instantiate(
+            model_config.model.decoder,
+            in_channels_src=self.hidden_dims[self._graph_hidden_names[0]],
+            in_channels_dst=input_dim,
+            hidden_dim=self.hidden_dims[self._graph_hidden_names[0]],
+            out_channels_dst=self.num_output_channels,
+            sub_graph=self._graph_data[(self._graph_hidden_names[0], "to", self._graph_name_data)],
+            src_grid_size=self.node_attributes.num_nodes[self._graph_hidden_names[0]],
+            dst_grid_size=self.node_attributes.num_nodes[self._graph_name_data],
+        )
+
+        # Instantiation of model output bounding functions (e.g., to ensure outputs like TP are positive definite)
+        self.boundings = nn.ModuleList(
+            [
+                instantiate(cfg, name_to_index=self.data_indices.internal_model.output.name_to_index)
+                for cfg in getattr(model_config.model, "bounding", [])
+            ]
+        )
+
+    def _create_trainable_attributes(self) -> None:
+        """Create all trainable attributes."""
+        self.trainable_data = TrainableTensor(trainable_size=self.trainable_data_size, tensor_size=self._data_grid_size)
+        self.trainable_hidden = nn.ModuleDict()
+
+        for hidden in self._graph_hidden_names:
+            self.trainable_hidden[hidden] = TrainableTensor(
+                trainable_size=self.trainable_hidden_size, tensor_size=self._hidden_grid_sizes[hidden]
+            )
+
+    def forward(self, x: Tensor, model_comm_group: Optional[ProcessGroup] = None) -> Tensor:
+        batch_size = x.shape[0]
+        ensemble_size = x.shape[2]
+
+        # add data positional info (lat/lon)
+        x_trainable_data = torch.cat(
+            (
+                einops.rearrange(x, "batch time ensemble grid vars -> (batch ensemble grid) (time vars)"),
+                self.node_attributes(self._graph_name_data, batch_size=batch_size),
+            ),
+            dim=-1,  # feature dimension
+        )
+
+        # Get all trainable parameters for the hidden layers -> initialisation of each hidden, which becomes trainable bias
+        x_trainable_hiddens = {}
+        for hidden in self._graph_hidden_names:
+            x_trainable_hiddens[hidden] = self.node_attributes(hidden, batch_size=batch_size)
+
+        # Get data and hidden shapes for sharding
+        shard_shapes_data = get_shape_shards(x_trainable_data, 0, model_comm_group)
+        shard_shapes_hiddens = {}
+        for hidden, x_latent in x_trainable_hiddens.items():
+            shard_shapes_hiddens[hidden] = get_shape_shards(x_latent, 0, model_comm_group)
+
+        # Run encoder
+        x_data_latent, curr_latent = self._run_mapper(
+            self.encoder,
+            (x_trainable_data, x_trainable_hiddens[self._graph_hidden_names[0]]),
+            batch_size=batch_size,
+            shard_shapes=(shard_shapes_data, shard_shapes_hiddens[self._graph_hidden_names[0]]),
+            model_comm_group=model_comm_group,
+        )
+
+        # Run processor
+        x_encoded_latents = {}
+        x_skip = {}
+
+        ## Downscale
+        for i in range(0, self.num_hidden - 1):
+            src_hidden_name = self._graph_hidden_names[i]
+            dst_hidden_name = self._graph_hidden_names[i + 1]
+
+            # Processing at same level
+            if self.level_process:
+                curr_latent = self.down_level_processor[src_hidden_name](
+                    curr_latent,
+                    batch_size=batch_size,
+                    shard_shapes=shard_shapes_hiddens[src_hidden_name],
+                    model_comm_group=model_comm_group,
+                )
+
+            # store latents for skip connections
+            x_skip[src_hidden_name] = curr_latent
+
+            # Encode to next hidden level
+            x_encoded_latents[src_hidden_name], curr_latent = self._run_mapper(
+                self.downscale[src_hidden_name],
+                (curr_latent, x_trainable_hiddens[dst_hidden_name]),
+                batch_size=batch_size,
+                shard_shapes=(shard_shapes_hiddens[src_hidden_name], shard_shapes_hiddens[dst_hidden_name]),
+                model_comm_group=model_comm_group,
+            )
+
+        # Processing hidden-most level
+        if self.level_process:
+            curr_latent = self.down_level_processor[dst_hidden_name](
+                curr_latent,
+                batch_size=batch_size,
+                shard_shapes=shard_shapes_hiddens[dst_hidden_name],
+                model_comm_group=model_comm_group,
+            )
+
+        ## Upscale
+        for i in range(self.num_hidden - 1, 0, -1):
+            src_hidden_name = self._graph_hidden_names[i]
+            dst_hidden_name = self._graph_hidden_names[i - 1]
+
+            # Process to next level
+            curr_latent = self._run_mapper(
+                self.upscale[src_hidden_name],
+                (curr_latent, x_encoded_latents[dst_hidden_name]),
+                batch_size=batch_size,
+                shard_shapes=(shard_shapes_hiddens[src_hidden_name], shard_shapes_hiddens[dst_hidden_name]),
+                model_comm_group=model_comm_group,
+            )
+
+            # Add skip connections
+            curr_latent = curr_latent + x_skip[dst_hidden_name]
+
+            # Processing at same level
+            if self.level_process:
+                curr_latent = self.up_level_processor[dst_hidden_name](
+                    curr_latent,
+                    batch_size=batch_size,
+                    shard_shapes=shard_shapes_hiddens[dst_hidden_name],
+                    model_comm_group=model_comm_group,
+                )
+
+        # Run decoder
+        x_out = self._run_mapper(
+            self.decoder,
+            (curr_latent, x_data_latent),
+            batch_size=batch_size,
+            shard_shapes=(shard_shapes_hiddens[self._graph_hidden_names[0]], shard_shapes_data),
+            model_comm_group=model_comm_group,
+        )
+
+        x_out = (
+            einops.rearrange(
+                x_out,
+                "(batch ensemble grid) vars -> batch ensemble grid vars",
+                batch=batch_size,
+                ensemble=ensemble_size,
+            )
+            .to(dtype=x.dtype)
+            .clone()
+        )
+
+        # residual connection (just for the prognostic variables)
+        x_out[..., self._internal_output_idx] += x[:, -1, :, :, self._internal_input_idx]
+
+        for bounding in self.boundings:
+            # bounding performed in the order specified in the config file
+            x_out = bounding(x_out)
+
+        return x_out