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sofie_gnn.py
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sofie_gnn.py
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import unittest
import ROOT
import numpy as np
from numpy.testing import assert_almost_equal
if np.__version__ >= "1.20" or np.__version__ < "1.24":
raise RuntimeError(f"This test requires NumPy version <=1.19 or >=1.24")
import graph_nets as gn
from graph_nets import utils_tf
import sonnet as snt
# for generating input data for graph nets,
# from https://github.com/deepmind/graph_nets/blob/master/graph_nets/demos/graph_nets_basics.ipynb
def get_graph_data_dict(num_nodes, num_edges, GLOBAL_FEATURE_SIZE=2, NODE_FEATURE_SIZE=2, EDGE_FEATURE_SIZE=2):
return {
"globals": np.random.rand(GLOBAL_FEATURE_SIZE).astype(np.float32),
"nodes": np.random.rand(num_nodes, NODE_FEATURE_SIZE).astype(np.float32),
"edges": np.random.rand(num_edges, EDGE_FEATURE_SIZE).astype(np.float32),
"senders": np.random.randint(num_nodes, size=num_edges, dtype=np.int32),
"receivers": np.random.randint(num_nodes, size=num_edges, dtype=np.int32),
}
def make_mlp_model():
"""Instantiates a new MLP, followed by LayerNorm.
The parameters of each new MLP are not shared with others generated by
this function.
Returns:
A Sonnet module which contains the MLP and LayerNorm.
"""
return snt.Sequential([
snt.nets.MLP([2,2], activate_final=True),
snt.LayerNorm(axis=-1, create_offset=True, create_scale=True)
])
def CopyData(input_data) :
output_data = ROOT.TMVA.Experimental.SOFIE.Copy(input_data)
return output_data
class MLPGraphIndependent(snt.Module):
"""GraphIndependent with MLP edge, node, and global models."""
def __init__(self, name="MLPGraphIndependent"):
super(MLPGraphIndependent, self).__init__(name=name)
self._network = gn.modules.GraphIndependent(
edge_model_fn = lambda: snt.nets.MLP([2,2], activate_final=True),
node_model_fn = lambda: snt.nets.MLP([2,2], activate_final=True),
global_model_fn = lambda: snt.nets.MLP([2,2], activate_final=True))
def __call__(self, inputs):
return self._network(inputs)
class MLPGraphNetwork(snt.Module):
"""GraphNetwork with MLP edge, node, and global models."""
def __init__(self, name="MLPGraphNetwork"):
super(MLPGraphNetwork, self).__init__(name=name)
self._network = gn.modules.GraphNetwork(
edge_model_fn=make_mlp_model,
node_model_fn=make_mlp_model,
global_model_fn=make_mlp_model)
def __call__(self, inputs):
return self._network(inputs)
class EncodeProcessDecode(snt.Module):
def __init__(self,
edge_output_size=None,
node_output_size=None,
global_output_size=None,
name="EncodeProcessDecode"):
super(EncodeProcessDecode, self).__init__(name=name)
self._encoder = MLPGraphIndependent()
self._core = MLPGraphNetwork()
self._decoder = MLPGraphIndependent()
self._output_transform = MLPGraphIndependent()
def __call__(self, input_op, num_processing_steps):
latent = self._encoder(input_op)
latent0 = latent
output_ops = []
for _ in range(num_processing_steps):
core_input = utils_tf.concat([latent0, latent], axis=1)
latent = self._core(core_input)
decoded_op = self._decoder(latent)
output_ops.append(self._output_transform(decoded_op))
return output_ops
class SOFIE_GNN(unittest.TestCase):
"""
Tests for the pythonizations of ParseFromMemory method of SOFIE GNN.
"""
def test_parse_gnn(self):
'''
Test that parsed GNN model from a graphnets model generates correct
inference code
'''
GraphModule = gn.modules.GraphNetwork(
edge_model_fn=lambda: snt.nets.MLP([2,2], activate_final=True),
node_model_fn=lambda: snt.nets.MLP([2,2], activate_final=True),
global_model_fn=lambda: snt.nets.MLP([2,2], activate_final=True))
GraphData = get_graph_data_dict(2,1)
input_graphs = utils_tf.data_dicts_to_graphs_tuple([GraphData])
output = GraphModule(input_graphs)
# Parsing model to RModel_GNN
model = ROOT.TMVA.Experimental.SOFIE.RModel_GNN.ParseFromMemory(GraphModule, GraphData)
model.Generate()
model.OutputGenerated()
ROOT.gInterpreter.Declare('#include "gnn_network.hxx"')
input_data = ROOT.TMVA.Experimental.SOFIE.GNN_Data()
input_data.node_data = ROOT.TMVA.Experimental.AsRTensor(GraphData['nodes'])
input_data.edge_data = ROOT.TMVA.Experimental.AsRTensor(GraphData['edges'])
input_data.global_data = ROOT.TMVA.Experimental.AsRTensor(GraphData['globals'])
session = ROOT.TMVA_SOFIE_gnn_network.Session()
session.infer(input_data)
output_node_data = output.nodes.numpy()
output_edge_data = output.edges.numpy()
output_global_data = output.globals.numpy().flatten()
assert_almost_equal(output_node_data, np.asarray(input_data.node_data))
assert_almost_equal(output_edge_data, np.asarray(input_data.edge_data))
assert_almost_equal(output_global_data, np.asarray(input_data.global_data))
def test_parse_graph_independent(self):
'''
Test that parsed GraphIndependent model from a graphnets model generates correct
inference code
'''
GraphModule = gn.modules.GraphIndependent(
edge_model_fn=lambda: snt.nets.MLP([2,2], activate_final=True),
node_model_fn=lambda: snt.nets.MLP([2,2], activate_final=True),
global_model_fn=lambda: snt.nets.MLP([2,2], activate_final=True))
GraphData = get_graph_data_dict(2,1)
input_graphs = utils_tf.data_dicts_to_graphs_tuple([GraphData])
output = GraphModule(input_graphs)
# Parsing model to RModel_GraphIndependent
model = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(GraphModule, GraphData)
model.Generate()
model.OutputGenerated()
ROOT.gInterpreter.Declare('#include "graph_independent_network.hxx"')
input_data = ROOT.TMVA.Experimental.SOFIE.GNN_Data()
input_data.node_data = ROOT.TMVA.Experimental.AsRTensor(GraphData['nodes'])
input_data.edge_data = ROOT.TMVA.Experimental.AsRTensor(GraphData['edges'])
input_data.global_data = ROOT.TMVA.Experimental.AsRTensor(GraphData['globals'])
session = ROOT.TMVA_SOFIE_graph_independent_network.Session()
session.infer(input_data)
output_node_data = output.nodes.numpy()
output_edge_data = output.edges.numpy()
output_global_data = output.globals.numpy().flatten()
assert_almost_equal(output_node_data, np.asarray(input_data.node_data))
assert_almost_equal(output_edge_data, np.asarray(input_data.edge_data))
assert_almost_equal(output_global_data, np.asarray(input_data.global_data))
def test_lhcb_toy_inference(self):
'''
Test that parsed stack of SOFIE GNN and GraphIndependent modules generate the correct
inference code
'''
# Instantiating EncodeProcessDecode Model
ep_model = EncodeProcessDecode(2,2,2)
# Initializing randomized input data
GraphData = get_graph_data_dict(2,1)
input_graphs = utils_tf.data_dicts_to_graphs_tuple([GraphData])
# Initializing randomized input data for core
CoreGraphData = get_graph_data_dict(2, 1, 4, 4, 4)
input_graphs_2 = utils_tf.data_dicts_to_graphs_tuple([CoreGraphData])
# Collecting output from GraphNets model stack
output_gn = ep_model(input_graphs, 2)
# Declaring sofie models
encoder = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(ep_model._encoder._network, GraphData, filename = "encoder")
encoder.Generate()
encoder.OutputGenerated()
core = ROOT.TMVA.Experimental.SOFIE.RModel_GNN.ParseFromMemory(ep_model._core._network, CoreGraphData, filename = "core")
core.Generate()
core.OutputGenerated()
decoder = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(ep_model._decoder._network, GraphData, filename = "decoder")
decoder.Generate()
decoder.OutputGenerated()
output_transform = ROOT.TMVA.Experimental.SOFIE.RModel_GraphIndependent.ParseFromMemory(ep_model._output_transform._network, GraphData, filename = "output_transform")
output_transform.Generate()
output_transform.OutputGenerated()
# Including the sofie generated models
ROOT.gInterpreter.Declare('#include "encoder.hxx"')
ROOT.gInterpreter.Declare('#include "core.hxx"')
ROOT.gInterpreter.Declare('#include "decoder.hxx"')
ROOT.gInterpreter.Declare('#include "output_transform.hxx"')
encoder_session = ROOT.TMVA_SOFIE_encoder.Session()
core_session = ROOT.TMVA_SOFIE_core.Session()
decoder_session = ROOT.TMVA_SOFIE_decoder.Session()
output_transform_session = ROOT.TMVA_SOFIE_output_transform.Session()
# Preparing the input data for running inference on sofie
input_data = ROOT.TMVA.Experimental.SOFIE.GNN_Data()
input_data.node_data = ROOT.TMVA.Experimental.AsRTensor(GraphData['nodes'])
input_data.edge_data = ROOT.TMVA.Experimental.AsRTensor(GraphData['edges'])
input_data.global_data = ROOT.TMVA.Experimental.AsRTensor(GraphData['globals'])
# running inference on sofie
encoder_session.infer(input_data)
latent0 = CopyData(input_data)
latent = input_data
output_ops = []
for _ in range(2):
core_input = ROOT.TMVA.Experimental.SOFIE.Concatenate(latent0, latent, axis=1)
core_session.infer(core_input)
latent = CopyData(core_input)
decoder_session.infer(core_input)
output_transform_session.infer(core_input)
output = CopyData(core_input)
output_ops.append(output)
for i in range(0, len(output_ops)):
output_node_data = output_gn[i].nodes.numpy()
output_edge_data = output_gn[i].edges.numpy()
output_global_data = output_gn[i].globals.numpy().flatten()
assert_almost_equal(output_node_data, np.asarray(output_ops[i].node_data))
assert_almost_equal(output_edge_data, np.asarray(output_ops[i].edge_data))
assert_almost_equal(output_global_data, np.asarray(output_ops[i].global_data))
if __name__ == '__main__':
unittest.main()