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# Licensed under the Apache License, Version 2.0 (the "License");
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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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.data_utils import StaticData
from modulus.utils.graphcast.graph import Graph
from .graph_cast_processor import GraphCastProcessor
[docs]class GraphCastNet(Module):
"""GraphCast network architecture
Parameters
----------
meshgraph_path : str
Path to the meshgraph file. If not provided, the meshgraph will be created
using PyMesh.
static_dataset_path : str
Path to the static dataset file.
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, 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.
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.
produce_aggregated_output : bool, default=True,
Flag indicating whether the model produces the aggregated output on each
rank of the progress 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.
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,
meshgraph_path: str,
static_dataset_path: str,
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,
expect_partitioned_input: bool = False,
produce_aggregated_output: 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.produce_aggregated_output = produce_aggregated_output
# 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
)
self.has_static_data = static_dataset_path is not None
# Set activation function
activation_fn = get_activation(activation_fn)
# construct the graph
try:
self.graph = Graph(meshgraph_path, self.lat_lon_grid)
except FileNotFoundError:
raise FileNotFoundError(
"The icospheres_path is corrupted. "
"Tried using pymesh to generate the graph but could not find pymesh"
)
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:
self.g2m_graph, edge_perm = CuGraphCSC.from_dgl(
graph=self.g2m_graph,
partition_size=partition_size,
partition_group_name=partition_group_name,
)
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:
self.m2g_graph, edge_perm = CuGraphCSC.from_dgl(
graph=self.m2g_graph,
partition_size=partition_size,
partition_group_name=partition_group_name,
)
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:
self.mesh_graph, edge_perm = CuGraphCSC.from_dgl(
graph=self.mesh_graph,
partition_size=partition_size,
partition_group_name=partition_group_name,
)
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
)
# Get the static data
if self.has_static_data:
self.static_data = StaticData(
static_dataset_path, self.latitudes, self.longitudes
).get()
num_static_feat = self.static_data.size(1)
input_dim_grid_nodes += num_static_feat
if self.is_distributed and expect_partitioned_input:
# if input itself is distributed, we also need to distribute static data
self.static_data(
self.static_data[0].view(num_static_feat, -1).permute(1, 0)
)
self.static_data = self.g2m_graph.get_src_node_features_in_partition(
self.static_data
)
self.static_data = self.static_data.permute(1, 0).unsqueeze(dim=0)
else:
self.static_data = None
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)
outvar = self.model_checkpoint_fn(
self.custom_forward,
invar,
use_reentrant=False,
preserve_rng_state=False,
)
return self.prepare_output(outvar, self.produce_aggregated_output)
[docs] def prepare_output(self, outvar: Tensor, produce_aggregated_output: bool) -> 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
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)
outvar = outvar.permute(1, 0)
outvar = outvar.view(self.output_dim_grid_nodes, *self.input_res)
outvar = torch.unsqueeze(outvar, dim=0)
else:
# keep partition of H, W, i.e. produce [N, C, P]
outvar = outvar.permute(1, 0).unsqueeze(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)
if self.has_static_data:
self.static_data = self.static_data.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
