Compile utility & benchmark #443
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younik
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Nov 28, 2025
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| for i in range(batch_size): | ||
| edges = states_tensor[i].edge_index | ||
| adj[i, edges[0], edges[1]] = 1 | ||
| if not self.is_directed: | ||
| adj[i, edges[1], edges[0]] = 1 |
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if we care about efficiency here, I am pretty sure this for loop can be vectorized.
Roughly, something like:
B = torch.arange(batch_size)
edges = states_tensor.edge_index
adj[B, edges[:, 0], edges[:, 1] = 1
if not self.is_directed:
adj[B, edges[:, 1], edges[:, 0]] = 1
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Description
GFlowNet.GFlowNetand theloss(or both) under a large number of environments.