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# SPDX-FileCopyrightText: All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from dataclasses import dataclass
from typing import Any, Optional
import torch
from torch import Tensor
try:
from typing import Self
except ImportError:
# for Python versions < 3.11
from typing_extensions import Self
from modulus.models.gnn_layers.embedder import (
GraphCastDecoderEmbedder,
GraphCastEncoderEmbedder,
)
from modulus.models.gnn_layers.mesh_graph_decoder import MeshGraphDecoder
from modulus.models.gnn_layers.mesh_graph_encoder import MeshGraphEncoder
from modulus.models.gnn_layers.mesh_graph_mlp import MeshGraphMLP
from modulus.models.gnn_layers.utils import CuGraphCSC, set_checkpoint_fn
from modulus.models.layers import get_activation
from modulus.models.meta import ModelMetaData
from modulus.models.module import Module
from modulus.utils.graphcast.graph import Graph
from .graph_cast_processor import GraphCastProcessor
logger = logging.getLogger(__name__)
[docs]def get_lat_lon_partition_separators(partition_size: int):
"""Utility Function to get separation intervals for lat-lon
grid for partition_sizes of interest.
Parameters
----------
partition_size : int
size of graph partition
"""
def _divide(num_lat_chunks: int, num_lon_chunks: int):
# divide lat-lon grid into equally-sizes chunks along both latitude and longitude
if (num_lon_chunks * num_lat_chunks) != partition_size:
raise ValueError(
"Can't divide lat-lon grid into grid {num_lat_chunks} x {num_lon_chunks} chunks for partition_size={partition_size}."
)
# divide latitutude into num_lat_chunks of size 180 / num_lat_chunks
# divide longitude into chunks of size 360 / (partition_size / num_lat_chunks)
lat_bin_width = 180.0 / num_lat_chunks
lon_bin_width = 360.0 / num_lon_chunks
lat_ranges = []
lon_ranges = []
for p_lat in range(num_lat_chunks):
for p_lon in range(num_lon_chunks):
lat_ranges += [
(lat_bin_width * p_lat - 90.0, lat_bin_width * (p_lat + 1) - 90.0)
]
lon_ranges += [
(lon_bin_width * p_lon - 180.0, lon_bin_width * (p_lon + 1) - 180.0)
]
lat_ranges[-1] = (lat_ranges[-1][0], None)
lon_ranges[-1] = (lon_ranges[-1][0], None)
return lat_ranges, lon_ranges
# use two closest factors of partition_size
lat_chunks, lon_chunks, i = 1, partition_size, 0
while lat_chunks < lon_chunks:
i += 1
if partition_size % i == 0:
lat_chunks = i
lon_chunks = partition_size // lat_chunks
lat_ranges, lon_ranges = _divide(lat_chunks, lon_chunks)
# mainly for debugging
if (lat_ranges is None) or (lon_ranges is None):
raise ValueError("unexpected error, abort")
min_seps = []
max_seps = []
for i in range(partition_size):
lat = lat_ranges[i]
lon = lon_ranges[i]
min_seps.append([lat[0], lon[0]])
max_seps.append([lat[1], lon[1]])
return min_seps, max_seps
[docs]class GraphCastNet(Module):
"""GraphCast network architecture
Parameters
----------
multimesh_level: int, optional
Level of the multi-mesh, by default 6
input_res: Tuple[int, int]
Input resolution of the latitude-longitude grid
input_dim_grid_nodes : int, optional
Input dimensionality of the grid node features, by default 474
input_dim_mesh_nodes : int, optional
Input dimensionality of the mesh node features, by default 3
input_dim_edges : int, optional
Input dimensionality of the edge features, by default 4
output_dim_grid_nodes : int, optional
Final output dimensionality of the edge features, by default 227
processor_layers : int, optional
Number of processor layers, by default 16
hidden_layers : int, optional
Number of hiddel layers, by default 1
hidden_dim : int, optional
Number of neurons in each hidden layer, by default 512
aggregation : str, optional
Message passing aggregation method ("sum", "mean"), by default "sum"
activation_fn : str, optional
Type of activation function, by default "silu"
norm_type : str, optional
Normalization type ["TELayerNorm", "LayerNorm"].
Use "TELayerNorm" for optimal performance. By default "LayerNorm".
use_cugraphops_encoder : bool, default=False
Flag to select cugraphops kernels in encoder
use_cugraphops_processor : bool, default=False
Flag to select cugraphops kernels in the processor
use_cugraphops_decoder : bool, default=False
Flag to select cugraphops kernels in the decoder
do_conat_trick: : bool, default=False
Whether to replace concat+MLP with MLP+idx+sum
recompute_activation : bool, optional
Flag for recomputing activation in backward to save memory, by default False.
Currently, only SiLU is supported.
partition_size : int, default=1
Number of process groups across which graphs are distributed. If equal to 1,
the model is run in a normal Single-GPU configuration.
partition_group_name : str, default=None
Name of process group across which graphs are distributed. If partition_size
is set to 1, the model is run in a normal Single-GPU configuration and the
specification of a process group is not necessary. If partitition_size > 1,
passing no process group name leads to a parallelism across the default
process group. Otherwise, the group size of a process group is expected
to match partition_size.
use_lat_lon_partitioning : bool, default=False
flag to specify whether all graphs (grid-to-mesh, mesh, mesh-to-grid)
are partitioned based on lat-lon-coordinates of nodes or based on IDs.
expect_partitioned_input : bool, default=False
Flag indicating whether the model expects the input to be already
partitioned. This can be helpful e.g. in multi-step rollouts to avoid
aggregating the output just to distribute it in the next step again.
global_features_on_rank_0 : bool, default=False
Flag indicating whether the model expects the input to be present
in its "global" form only on group_rank 0. During the input preparation phase,
the model will take care of scattering the input accordingly onto all ranks
of the process group across which the graph is partitioned. Note that only either
this flag or expect_partitioned_input can be set at a time.
produce_aggregated_output : bool, default=True
Flag indicating whether the model produces the aggregated output on each
rank of the procress group across which the graph is distributed or
whether the output is kept distributed. This can be helpful e.g.
in multi-step rollouts to avoid aggregating the output just to distribute
it in the next step again.
produce_aggregated_output_on_all_ranks : bool, default=True
Flag indicating - if produce_aggregated_output is True - whether the model
produces the aggregated output on each rank of the process group across
which the group is distributed or only on group_rank 0. This can be helpful
for computing the loss using global targets only on a single rank which can
avoid either having to distribute the computation of a loss function.
Note
----
Based on these papers:
"GraphCast: Learning skillful medium-range global weather forecasting"
https://arxiv.org/abs/2212.12794
"Forecasting Global Weather with Graph Neural Networks"
https://arxiv.org/abs/2202.07575
"Learning Mesh-Based Simulation with Graph Networks"
https://arxiv.org/abs/2010.03409
"MultiScale MeshGraphNets"
https://arxiv.org/abs/2210.00612
"""
def __init__(
self,
multimesh_level: int = 6,
input_res: tuple = (721, 1440),
input_dim_grid_nodes: int = 474,
input_dim_mesh_nodes: int = 3,
input_dim_edges: int = 4,
output_dim_grid_nodes: int = 227,
processor_layers: int = 16,
hidden_layers: int = 1,
hidden_dim: int = 512,
aggregation: str = "sum",
activation_fn: str = "silu",
norm_type: str = "LayerNorm",
use_cugraphops_encoder: bool = False,
use_cugraphops_processor: bool = False,
use_cugraphops_decoder: bool = False,
do_concat_trick: bool = False,
recompute_activation: bool = False,
partition_size: int = 1,
partition_group_name: Optional[str] = None,
use_lat_lon_partitioning: bool = False,
expect_partitioned_input: bool = False,
global_features_on_rank_0: bool = False,
produce_aggregated_output: bool = True,
produce_aggregated_output_on_all_ranks: bool = True,
):
super().__init__(meta=MetaData())
self.is_distributed = False
if partition_size > 1:
self.is_distributed = True
self.expect_partitioned_input = expect_partitioned_input
self.global_features_on_rank_0 = global_features_on_rank_0
self.produce_aggregated_output = produce_aggregated_output
self.produce_aggregated_output_on_all_ranks = (
produce_aggregated_output_on_all_ranks
)
self.partition_group_name = partition_group_name
# create the lat_lon_grid
self.latitudes = torch.linspace(-90, 90, steps=input_res[0])
self.longitudes = torch.linspace(-180, 180, steps=input_res[1] + 1)[1:]
self.lat_lon_grid = torch.stack(
torch.meshgrid(self.latitudes, self.longitudes, indexing="ij"), dim=-1
)
# Set activation function
activation_fn = get_activation(activation_fn)
# construct the graph
self.graph = Graph(self.lat_lon_grid, multimesh_level)
self.mesh_graph = self.graph.create_mesh_graph(verbose=False)
self.g2m_graph = self.graph.create_g2m_graph(verbose=False)
self.m2g_graph = self.graph.create_m2g_graph(verbose=False)
self.g2m_edata = self.g2m_graph.edata["x"]
self.m2g_edata = self.m2g_graph.edata["x"]
self.mesh_edata = self.mesh_graph.edata["x"]
self.mesh_ndata = self.mesh_graph.ndata["x"]
if use_cugraphops_encoder or self.is_distributed:
kwargs = {}
if use_lat_lon_partitioning:
min_seps, max_seps = get_lat_lon_partition_separators(partition_size)
kwargs = {
"src_coordinates": self.g2m_graph.srcdata["lat_lon"],
"dst_coordinates": self.g2m_graph.dstdata["lat_lon"],
"coordinate_separators_min": min_seps,
"coordinate_separators_max": max_seps,
}
self.g2m_graph, edge_perm = CuGraphCSC.from_dgl(
graph=self.g2m_graph,
partition_size=partition_size,
partition_group_name=partition_group_name,
partition_by_bbox=use_lat_lon_partitioning,
**kwargs,
)
self.g2m_edata = self.g2m_edata[edge_perm]
if self.is_distributed:
self.g2m_edata = self.g2m_graph.get_edge_features_in_partition(
self.g2m_edata
)
if use_cugraphops_decoder or self.is_distributed:
kwargs = {}
if use_lat_lon_partitioning:
min_seps, max_seps = get_lat_lon_partition_separators(partition_size)
kwargs = {
"src_coordinates": self.m2g_graph.srcdata["lat_lon"],
"dst_coordinates": self.m2g_graph.dstdata["lat_lon"],
"coordinate_separators_min": min_seps,
"coordinate_separators_max": max_seps,
}
self.m2g_graph, edge_perm = CuGraphCSC.from_dgl(
graph=self.m2g_graph,
partition_size=partition_size,
partition_group_name=partition_group_name,
partition_by_bbox=use_lat_lon_partitioning,
**kwargs,
)
self.m2g_edata = self.m2g_edata[edge_perm]
if self.is_distributed:
self.m2g_edata = self.m2g_graph.get_edge_features_in_partition(
self.m2g_edata
)
if use_cugraphops_processor or self.is_distributed:
kwargs = {}
if use_lat_lon_partitioning:
min_seps, max_seps = get_lat_lon_partition_separators(partition_size)
kwargs = {
"src_coordinates": self.mesh_graph.ndata["lat_lon"],
"dst_coordinates": self.mesh_graph.ndata["lat_lon"],
"coordinate_separators_min": min_seps,
"coordinate_separators_max": max_seps,
}
self.mesh_graph, edge_perm = CuGraphCSC.from_dgl(
graph=self.mesh_graph,
partition_size=partition_size,
partition_group_name=partition_group_name,
partition_by_bbox=use_lat_lon_partitioning,
**kwargs,
)
self.mesh_edata = self.mesh_edata[edge_perm]
if self.is_distributed:
self.mesh_edata = self.mesh_graph.get_edge_features_in_partition(
self.mesh_edata
)
self.mesh_ndata = self.mesh_graph.get_dst_node_features_in_partition(
self.mesh_ndata
)
self.input_dim_grid_nodes = input_dim_grid_nodes
self.output_dim_grid_nodes = output_dim_grid_nodes
self.input_res = input_res
# by default: don't checkpoint at all
self.model_checkpoint_fn = set_checkpoint_fn(False)
self.encoder_checkpoint_fn = set_checkpoint_fn(False)
self.decoder_checkpoint_fn = set_checkpoint_fn(False)
# initial feature embedder
self.encoder_embedder = GraphCastEncoderEmbedder(
input_dim_grid_nodes=input_dim_grid_nodes,
input_dim_mesh_nodes=input_dim_mesh_nodes,
input_dim_edges=input_dim_edges,
output_dim=hidden_dim,
hidden_dim=hidden_dim,
hidden_layers=hidden_layers,
activation_fn=activation_fn,
norm_type=norm_type,
recompute_activation=recompute_activation,
)
self.decoder_embedder = GraphCastDecoderEmbedder(
input_dim_edges=input_dim_edges,
output_dim=hidden_dim,
hidden_dim=hidden_dim,
hidden_layers=hidden_layers,
activation_fn=activation_fn,
norm_type=norm_type,
recompute_activation=recompute_activation,
)
# grid2mesh encoder
self.encoder = MeshGraphEncoder(
aggregation=aggregation,
input_dim_src_nodes=hidden_dim,
input_dim_dst_nodes=hidden_dim,
input_dim_edges=hidden_dim,
output_dim_src_nodes=hidden_dim,
output_dim_dst_nodes=hidden_dim,
output_dim_edges=hidden_dim,
hidden_dim=hidden_dim,
hidden_layers=hidden_layers,
activation_fn=activation_fn,
norm_type=norm_type,
do_concat_trick=do_concat_trick,
recompute_activation=recompute_activation,
)
# icosahedron processor
if processor_layers <= 2:
raise ValueError("Expected at least 3 processor layers")
self.processor_encoder = GraphCastProcessor(
aggregation=aggregation,
processor_layers=1,
input_dim_nodes=hidden_dim,
input_dim_edges=hidden_dim,
hidden_dim=hidden_dim,
hidden_layers=hidden_layers,
activation_fn=activation_fn,
norm_type=norm_type,
do_concat_trick=do_concat_trick,
recompute_activation=recompute_activation,
)
self.processor = GraphCastProcessor(
aggregation=aggregation,
processor_layers=processor_layers - 2,
input_dim_nodes=hidden_dim,
input_dim_edges=hidden_dim,
hidden_dim=hidden_dim,
hidden_layers=hidden_layers,
activation_fn=activation_fn,
norm_type=norm_type,
do_concat_trick=do_concat_trick,
recompute_activation=recompute_activation,
)
self.processor_decoder = GraphCastProcessor(
aggregation=aggregation,
processor_layers=1,
input_dim_nodes=hidden_dim,
input_dim_edges=hidden_dim,
hidden_dim=hidden_dim,
hidden_layers=hidden_layers,
activation_fn=activation_fn,
norm_type=norm_type,
do_concat_trick=do_concat_trick,
recompute_activation=recompute_activation,
)
# mesh2grid decoder
self.decoder = MeshGraphDecoder(
aggregation=aggregation,
input_dim_src_nodes=hidden_dim,
input_dim_dst_nodes=hidden_dim,
input_dim_edges=hidden_dim,
output_dim_dst_nodes=hidden_dim,
output_dim_edges=hidden_dim,
hidden_dim=hidden_dim,
hidden_layers=hidden_layers,
activation_fn=activation_fn,
norm_type=norm_type,
do_concat_trick=do_concat_trick,
recompute_activation=recompute_activation,
)
# final MLP
self.finale = MeshGraphMLP(
input_dim=hidden_dim,
output_dim=output_dim_grid_nodes,
hidden_dim=hidden_dim,
hidden_layers=hidden_layers,
activation_fn=activation_fn,
norm_type=None,
recompute_activation=recompute_activation,
)
[docs] def set_checkpoint_model(self, checkpoint_flag: bool):
"""Sets checkpoint function for the entire model.
This function returns the appropriate checkpoint function based on the
provided `checkpoint_flag` flag. If `checkpoint_flag` is True, the
function returns the checkpoint function from PyTorch's
`torch.utils.checkpoint`. In this case, all the other gradient checkpoitings
will be disabled. Otherwise, it returns an identity function
that simply passes the inputs through the given layer.
Parameters
----------
checkpoint_flag : bool
Whether to use checkpointing for gradient computation. Checkpointing
can reduce memory usage during backpropagation at the cost of
increased computation time.
Returns
-------
Callable
The selected checkpoint function to use for gradient computation.
"""
# force a single checkpoint for the whole model
self.model_checkpoint_fn = set_checkpoint_fn(checkpoint_flag)
if checkpoint_flag:
self.processor.set_checkpoint_segments(-1)
self.encoder_checkpoint_fn = set_checkpoint_fn(False)
self.decoder_checkpoint_fn = set_checkpoint_fn(False)
[docs] def set_checkpoint_processor(self, checkpoint_segments: int):
"""Sets checkpoint function for the processor excluding the first and last
layers.
This function returns the appropriate checkpoint function based on the
provided `checkpoint_segments` flag. If `checkpoint_segments` is positive,
the function returns the checkpoint function from PyTorch's
`torch.utils.checkpoint`, with number of checkpointing segments equal to
`checkpoint_segments`. Otherwise, it returns an identity function
that simply passes the inputs through the given layer.
Parameters
----------
checkpoint_segments : int
Number of checkpointing segments for gradient computation. Checkpointing
can reduce memory usage during backpropagation at the cost of
increased computation time.
Returns
-------
Callable
The selected checkpoint function to use for gradient computation.
"""
self.processor.set_checkpoint_segments(checkpoint_segments)
[docs] def set_checkpoint_encoder(self, checkpoint_flag: bool):
"""Sets checkpoint function for the embedder, encoder, and the first of
the processor.
This function returns the appropriate checkpoint function based on the
provided `checkpoint_flag` flag. If `checkpoint_flag` is True, the
function returns the checkpoint function from PyTorch's
`torch.utils.checkpoint`. Otherwise, it returns an identity function
that simply passes the inputs through the given layer.
Parameters
----------
checkpoint_flag : bool
Whether to use checkpointing for gradient computation. Checkpointing
can reduce memory usage during backpropagation at the cost of
increased computation time.
Returns
-------
Callable
The selected checkpoint function to use for gradient computation.
"""
self.encoder_checkpoint_fn = set_checkpoint_fn(checkpoint_flag)
[docs] def set_checkpoint_decoder(self, checkpoint_flag: bool):
"""Sets checkpoint function for the last layer of the processor, the decoder,
and the final MLP.
This function returns the appropriate checkpoint function based on the
provided `checkpoint_flag` flag. If `checkpoint_flag` is True, the
function returns the checkpoint function from PyTorch's
`torch.utils.checkpoint`. Otherwise, it returns an identity function
that simply passes the inputs through the given layer.
Parameters
----------
checkpoint_flag : bool
Whether to use checkpointing for gradient computation. Checkpointing
can reduce memory usage during backpropagation at the cost of
increased computation time.
Returns
-------
Callable
The selected checkpoint function to use for gradient computation.
"""
self.decoder_checkpoint_fn = set_checkpoint_fn(checkpoint_flag)
[docs] def encoder_forward(
self,
grid_nfeat: Tensor,
) -> Tensor:
"""Forward method for the embedder, encoder, and the first of the processor.
Parameters
----------
grid_nfeat : Tensor
Node features for the latitude-longitude grid.
Returns
-------
mesh_efeat_processed: Tensor
Processed edge features for the multimesh.
mesh_nfeat_processed: Tensor
Processed node features for the multimesh.
grid_nfeat_encoded: Tensor
Encoded node features for the latitude-longitude grid.
"""
# embedd graph features
(
grid_nfeat_embedded,
mesh_nfeat_embedded,
g2m_efeat_embedded,
mesh_efeat_embedded,
) = self.encoder_embedder(
grid_nfeat,
self.mesh_ndata,
self.g2m_edata,
self.mesh_edata,
)
# encode lat/lon to multimesh
grid_nfeat_encoded, mesh_nfeat_encoded = self.encoder(
g2m_efeat_embedded,
grid_nfeat_embedded,
mesh_nfeat_embedded,
self.g2m_graph,
)
# process multimesh graph
mesh_efeat_processed, mesh_nfeat_processed = self.processor_encoder(
mesh_efeat_embedded,
mesh_nfeat_encoded,
self.mesh_graph,
)
return mesh_efeat_processed, mesh_nfeat_processed, grid_nfeat_encoded
[docs] def decoder_forward(
self,
mesh_efeat_processed: Tensor,
mesh_nfeat_processed: Tensor,
grid_nfeat_encoded: Tensor,
) -> Tensor:
"""Forward method for the last layer of the processor, the decoder,
and the final MLP.
Parameters
----------
mesh_efeat_processed : Tensor
Multimesh edge features processed by the processor.
mesh_nfeat_processed : Tensor
Multi-mesh node features processed by the processor.
grid_nfeat_encoded : Tensor
The encoded node features for the latitude-longitude grid.
Returns
-------
grid_nfeat_finale: Tensor
The final node features for the latitude-longitude grid.
"""
# process multimesh graph
_, mesh_nfeat_processed = self.processor_decoder(
mesh_efeat_processed,
mesh_nfeat_processed,
self.mesh_graph,
)
m2g_efeat_embedded = self.decoder_embedder(self.m2g_edata)
# decode multimesh to lat/lon
grid_nfeat_decoded = self.decoder(
m2g_efeat_embedded, grid_nfeat_encoded, mesh_nfeat_processed, self.m2g_graph
)
# map to the target output dimension
grid_nfeat_finale = self.finale(
grid_nfeat_decoded,
)
return grid_nfeat_finale
[docs] def custom_forward(self, grid_nfeat: Tensor) -> Tensor:
"""GraphCast forward method with support for gradient checkpointing.
Parameters
----------
grid_nfeat : Tensor
Node features of the latitude-longitude graph.
Returns
-------
grid_nfeat_finale: Tensor
Predicted node features of the latitude-longitude graph.
"""
(
mesh_efeat_processed,
mesh_nfeat_processed,
grid_nfeat_encoded,
) = self.encoder_checkpoint_fn(
self.encoder_forward,
grid_nfeat,
use_reentrant=False,
preserve_rng_state=False,
)
# checkpoint of processor done in processor itself
mesh_efeat_processed, mesh_nfeat_processed = self.processor(
mesh_efeat_processed,
mesh_nfeat_processed,
self.mesh_graph,
)
grid_nfeat_finale = self.decoder_checkpoint_fn(
self.decoder_forward,
mesh_efeat_processed,
mesh_nfeat_processed,
grid_nfeat_encoded,
use_reentrant=False,
preserve_rng_state=False,
)
return grid_nfeat_finale
[docs] def forward(
self,
grid_nfeat: Tensor,
) -> Tensor:
invar = self.prepare_input(
grid_nfeat, self.expect_partitioned_input, self.global_features_on_rank_0
)
outvar = self.model_checkpoint_fn(
self.custom_forward,
invar,
use_reentrant=False,
preserve_rng_state=False,
)
outvar = self.prepare_output(
outvar,
self.produce_aggregated_output,
self.produce_aggregated_output_on_all_ranks,
)
return outvar
[docs] def prepare_output(
self,
outvar: Tensor,
produce_aggregated_output: bool,
produce_aggregated_output_on_all_ranks: bool = True,
) -> Tensor:
"""Prepares the output of the model in the shape [N, C, H, W].
Parameters
----------
outvar : Tensor
Output of the final MLP of the model.
produce_aggregated_output : bool
flag indicating whether output is gathered onto each rank
or kept distributed
produce_aggregated_output_on_all_ranks : bool
flag indicating whether output is gatherered on each rank
or only gathered at group_rank 0, True by default and
only valid if produce_aggregated_output is set.
Returns
-------
Tensor
The reshaped output of the model.
"""
if produce_aggregated_output or not self.is_distributed:
# default case: output of shape [N, C, H, W]
if self.is_distributed:
outvar = self.m2g_graph.get_global_dst_node_features(
outvar,
get_on_all_ranks=produce_aggregated_output_on_all_ranks,
)
outvar = outvar.permute(1, 0)
outvar = outvar.view(self.output_dim_grid_nodes, *self.input_res)
outvar = torch.unsqueeze(outvar, dim=0)
return outvar
[docs] def to(self, *args: Any, **kwargs: Any) -> Self:
"""Moves the object to the specified device, dtype, or format.
This method moves the object and its underlying graph and graph features to
the specified device, dtype, or format, and returns the updated object.
Parameters
----------
*args : Any
Positional arguments to be passed to the `torch._C._nn._parse_to` function.
**kwargs : Any
Keyword arguments to be passed to the `torch._C._nn._parse_to` function.
Returns
-------
GraphCastNet
The updated object after moving to the specified device, dtype, or format.
"""
self = super(GraphCastNet, self).to(*args, **kwargs)
self.g2m_edata = self.g2m_edata.to(*args, **kwargs)
self.m2g_edata = self.m2g_edata.to(*args, **kwargs)
self.mesh_ndata = self.mesh_ndata.to(*args, **kwargs)
self.mesh_edata = self.mesh_edata.to(*args, **kwargs)
device, _, _, _ = torch._C._nn._parse_to(*args, **kwargs)
self.g2m_graph = self.g2m_graph.to(device)
self.mesh_graph = self.mesh_graph.to(device)
self.m2g_graph = self.m2g_graph.to(device)
return self
