deeplearning/modulus/modulus-core/_modules/modulus/models/meshgraphnet/meshgraphnet.html

Source code for modulus.models.meshgraphnet.meshgraphnet

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import torch
import torch.nn as nn
from torch import Tensor

try:
    import dgl  # noqa: F401 for docs
    from dgl import DGLGraph
except ImportError:
    raise ImportError(
        "Mesh Graph Net requires the DGL library. Install the "
        + "desired CUDA version at: \n https://www.dgl.ai/pages/start.html"
    )
from dataclasses import dataclass
from itertools import chain
from typing import Callable, List, Tuple, Union

import modulus  # noqa: F401 for docs
from modulus.models.gnn_layers.mesh_edge_block import MeshEdgeBlock
from modulus.models.gnn_layers.mesh_graph_mlp import MeshGraphMLP
from modulus.models.gnn_layers.mesh_node_block import MeshNodeBlock
from modulus.models.gnn_layers.utils import CuGraphCSC, set_checkpoint_fn
from modulus.models.meta import ModelMetaData
from modulus.models.module import Module


[docs]@dataclass class MetaData(ModelMetaData): name: str = "MeshGraphNet" # Optimization, no JIT as DGLGraph causes trouble jit: bool = False cuda_graphs: bool = False amp_cpu: bool = False amp_gpu: bool = True torch_fx: bool = False # Inference onnx: bool = False # Physics informed func_torch: bool = True auto_grad: bool = True
[docs]class MeshGraphNet(Module): """MeshGraphNet network architecture Parameters ---------- input_dim_nodes : int Number of node features input_dim_edges : int Number of edge features output_dim : int Number of outputs processor_size : int, optional Number of message passing blocks, by default 15 num_layers_node_processor : int, optional Number of MLP layers for processing nodes in each message passing block, by default 2 num_layers_edge_processor : int, optional Number of MLP layers for processing edge features in each message passing block, by default 2 hidden_dim_processor : int, optional Hidden layer size for the message passing blocks, by default 128 hidden_dim_node_encoder : int, optional Hidden layer size for the node feature encoder, by default 128 num_layers_node_encoder : int, optional Number of MLP layers for the node feature encoder, by default 2 hidden_dim_edge_encoder : int, optional Hidden layer size for the edge feature encoder, by default 128 num_layers_edge_encoder : int, optional Number of MLP layers for the edge feature encoder, by default 2 hidden_dim_node_decoder : int, optional Hidden layer size for the node feature decoder, by default 128 num_layers_node_decoder : int, optional Number of MLP layers for the node feature decoder, by default 2 aggregation: str, optional Message aggregation type, by default "sum" do_conat_trick: : bool, default=False Whether to replace concat+MLP with MLP+idx+sum num_processor_checkpoint_segments: int, optional Number of processor segments for gradient checkpointing, by default 0 (checkpointing disabled) Example ------- >>> model = modulus.models.meshgraphnet.MeshGraphNet( ... input_dim_nodes=4, ... input_dim_edges=3, ... output_dim=2, ... ) >>> graph = dgl.rand_graph(10, 5) >>> node_features = torch.randn(10, 4) >>> edge_features = torch.randn(5, 3) >>> output = model(node_features, edge_features, graph) >>> output.size() torch.Size([10, 2]) Note ---- Reference: Pfaff, Tobias, et al. "Learning mesh-based simulation with graph networks." arXiv preprint arXiv:2010.03409 (2020). """ def __init__( self, input_dim_nodes: int, input_dim_edges: int, output_dim: int, processor_size: int = 15, num_layers_node_processor: int = 2, num_layers_edge_processor: int = 2, hidden_dim_processor: int = 128, hidden_dim_node_encoder: int = 128, num_layers_node_encoder: int = 2, hidden_dim_edge_encoder: int = 128, num_layers_edge_encoder: int = 2, hidden_dim_node_decoder: int = 128, num_layers_node_decoder: int = 2, aggregation: str = "sum", do_concat_trick: bool = False, num_processor_checkpoint_segments: int = 0, ): super().__init__(meta=MetaData()) self.edge_encoder = MeshGraphMLP( input_dim_edges, output_dim=hidden_dim_processor, hidden_dim=hidden_dim_edge_encoder, hidden_layers=num_layers_edge_encoder, activation_fn=nn.ReLU(), norm_type="LayerNorm", recompute_activation=False, ) self.node_encoder = MeshGraphMLP( input_dim_nodes, output_dim=hidden_dim_processor, hidden_dim=hidden_dim_node_encoder, hidden_layers=num_layers_node_encoder, activation_fn=nn.ReLU(), norm_type="LayerNorm", recompute_activation=False, ) self.node_decoder = MeshGraphMLP( hidden_dim_processor, output_dim=output_dim, hidden_dim=hidden_dim_node_decoder, hidden_layers=num_layers_node_decoder, activation_fn=nn.ReLU(), norm_type=None, recompute_activation=False, ) self.processor = MeshGraphNetProcessor( processor_size=processor_size, input_dim_node=hidden_dim_processor, input_dim_edge=hidden_dim_processor, num_layers_node=num_layers_node_processor, num_layers_edge=num_layers_edge_processor, aggregation=aggregation, norm_type="LayerNorm", activation_fn=nn.ReLU(), do_concat_trick=do_concat_trick, num_processor_checkpoint_segments=num_processor_checkpoint_segments, )
[docs] def forward( self, node_features: Tensor, edge_features: Tensor, graph: Union[DGLGraph, List[DGLGraph], CuGraphCSC], ) -> Tensor: edge_features = self.edge_encoder(edge_features) node_features = self.node_encoder(node_features) x = self.processor(node_features, edge_features, graph) x = self.node_decoder(x) return x
[docs]class MeshGraphNetProcessor(nn.Module): """MeshGraphNet processor block""" def __init__( self, processor_size: int = 15, input_dim_node: int = 128, input_dim_edge: int = 128, num_layers_node: int = 2, num_layers_edge: int = 2, aggregation: str = "sum", norm_type: str = "LayerNorm", activation_fn: nn.Module = nn.ReLU(), do_concat_trick: bool = False, num_processor_checkpoint_segments: int = 0, ): super().__init__() self.processor_size = processor_size self.num_processor_checkpoint_segments = num_processor_checkpoint_segments edge_block_invars = ( input_dim_node, input_dim_edge, input_dim_edge, input_dim_edge, num_layers_edge, activation_fn, norm_type, do_concat_trick, False, ) node_block_invars = ( aggregation, input_dim_node, input_dim_edge, input_dim_edge, input_dim_edge, num_layers_node, activation_fn, norm_type, False, ) edge_blocks = [ MeshEdgeBlock(*edge_block_invars) for _ in range(self.processor_size) ] node_blocks = [ MeshNodeBlock(*node_block_invars) for _ in range(self.processor_size) ] layers = list(chain(*zip(edge_blocks, node_blocks))) self.processor_layers = nn.ModuleList(layers) self.num_processor_layers = len(self.processor_layers) self.set_checkpoint_segments(self.num_processor_checkpoint_segments)
[docs] def set_checkpoint_segments(self, checkpoint_segments: int): """ Set the number of checkpoint segments Parameters ---------- checkpoint_segments : int number of checkpoint segments Raises ------ ValueError if the number of processor layers is not a multiple of the number of checkpoint segments """ if checkpoint_segments > 0: if self.num_processor_layers % checkpoint_segments != 0: raise ValueError( "Processor layers must be a multiple of checkpoint_segments" ) segment_size = self.num_processor_layers // checkpoint_segments self.checkpoint_segments = [] for i in range(0, self.num_processor_layers, segment_size): self.checkpoint_segments.append((i, i + segment_size)) self.checkpoint_fn = set_checkpoint_fn(True) else: self.checkpoint_fn = set_checkpoint_fn(False) self.checkpoint_segments = [(0, self.num_processor_layers)]
[docs] def run_function( self, segment_start: int, segment_end: int ) -> Callable[ [Tensor, Tensor, Union[DGLGraph, List[DGLGraph]]], Tuple[Tensor, Tensor] ]: """Custom forward for gradient checkpointing Parameters ---------- segment_start : int Layer index as start of the segment segment_end : int Layer index as end of the segment Returns ------- Callable Custom forward function """ segment = self.processor_layers[segment_start:segment_end] def custom_forward( node_features: Tensor, edge_features: Tensor, graph: Union[DGLGraph, List[DGLGraph]], ) -> Tuple[Tensor, Tensor]: """Custom forward function""" for module in segment: edge_features, node_features = module( edge_features, node_features, graph ) return edge_features, node_features return custom_forward
[docs] @torch.jit.unused def forward( self, node_features: Tensor, edge_features: Tensor, graph: Union[DGLGraph, List[DGLGraph], CuGraphCSC], ) -> Tensor: for segment_start, segment_end in self.checkpoint_segments: edge_features, node_features = self.checkpoint_fn( self.run_function(segment_start, segment_end), node_features, edge_features, graph, use_reentrant=False, preserve_rng_state=False, ) return node_features
© Copyright 2023, NVIDIA Modulus Team. Last updated on Nov 21, 2023.