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[tmva][sofie] explicit cast of senders and receivers to int64_t in GNN tutorial for safe run in mac #23575

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Merged
guitargeek merged 2 commits into from
Oct 1, 2026
Merged

[tmva][sofie] explicit cast of senders and receivers to int64_t in GNN tutorial for safe run in mac #23575

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f151b89
[tmva][sofie] explicit cast of senders and receivers to int64_t in TM…
sanjibansg Oct 1, 2026
eeec63c
[tmva][sofie] use casted pointers in infer method of core model
sanjibansg Oct 1, 2026
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8 changes: 6 additions & 2 deletions tutorials/machine_learning/TMVA_SOFIE_GNN.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,12 +13,13 @@
##
## \author

import cppyy
import time

import numpy as np
import ROOT
import torch
import torch.nn as nn

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tutorials/machine_learning/TMVA_SOFIE_GNN.py:16:1: I001 Import block is un-sorted or un-formatted
  help: Organize imports

# defining graph properties
num_nodes = 5
Expand Down Expand Up @@ -200,6 +201,9 @@

receivers = np.ascontiguousarray(graphData["receivers"], dtype=np.int64)
senders = np.ascontiguousarray(graphData["senders"], dtype=np.int64)
i64p = cppyy.ll.cast["const int64_t*"]
receivers_ptr = i64p(receivers.ctypes.data)
senders_ptr = i64p(senders.ctypes.data)

latent = self._as_arrays(
self.encoder_session.infer(c(graphData["nodes"]), c(graphData["edges"]), c(graphData["globals"])),
Expand All @@ -210,8 +214,8 @@
for _ in range(processing_steps):
core_input = tuple(np.concatenate([a, b], axis=1) for a, b in zip(latent0, latent))
latent = self._as_arrays(
self.core_session.infer(c(core_input[0]), c(core_input[1]), c(core_input[2]), receivers, senders),
n_nodes, n_edges,
self.core_session.infer(c(core_input[0]), c(core_input[1]), c(core_input[2]), receivers_ptr, senders_ptr),
n_nodes, n_edges,
)
decoded = self._as_arrays(
self.decoder_session.infer(c(latent[0]), c(latent[1]), c(latent[2])), n_nodes, n_edges
Expand Down
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