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improve traceabaility to JAX #163

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improve traceabaility to JAX #163

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10 changes: 6 additions & 4 deletions orb_models/common/atoms/batch/graph_batch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,7 @@ def __init__(
# repeat_interleave, e.g. tensor[node_batch_index] instead of tensor.repeat_interleave(n_node)
self.node_batch_index = torch.arange(
self.n_node.shape[0], dtype=torch.int64, device=self.n_node.device
).repeat_interleave(self.n_node)
).repeat_interleave(self.n_node, output_size=self.positions.shape[0])

def split(self: _T, clone=True) -> list["AtomGraphs"]:
"""Splits batched AtomGraphs into constituent system AtomGraphs.
Expand Down Expand Up @@ -218,11 +218,12 @@ def _get_per_node_graph_indices(self):
TODO: we could cache these indices.
"""
positions = self.node_features["positions"]
graph_indices = torch.zeros(len(positions), device=positions.device, dtype=torch.int)
n_atoms = positions.shape[0]
graph_indices = torch.zeros(n_atoms, device=positions.device, dtype=torch.int)
cumsums = torch.cumsum(self.n_node, dim=0)
graph_indices[:] = torch.searchsorted(
cumsums,
torch.arange(len(positions), device=positions.device),
torch.arange(n_atoms, device=positions.device),
right=True,
)
return graph_indices # (natoms,)
Expand All @@ -234,9 +235,10 @@ def _get_per_edge_graph_indices(self):
"""
graph_indices = torch.zeros_like(self.senders) # (nedges,)
cumsums = torch.cumsum(self.n_edge, dim=0) # (ngraphs,)
n_edges = self.senders.shape[0]
graph_indices[:] = torch.searchsorted(
cumsums,
torch.arange(len(self.senders), device=self.senders.device),
torch.arange(n_edges, device=self.senders.device),
right=True,
)
return graph_indices # (nedges,)
Expand Down
4 changes: 2 additions & 2 deletions orb_models/common/models/segment_ops.py
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Expand Up @@ -31,7 +31,7 @@ def aggregate_nodes(
# assert n_node.sum() == tensor.shape[0]

device = tensor.device
count = len(n_node)
count = n_node.shape[0]
if deterministic:
import os

Expand All @@ -40,7 +40,7 @@ def aggregate_nodes(
https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility"""
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
torch.use_deterministic_algorithms(True)
segments = torch.arange(count, device=device).repeat_interleave(n_node)
segments = torch.arange(count, device=device).repeat_interleave(n_node, output_size=tensor.shape[0])
if reduction == "sum":
return scatter_sum(tensor, segments, dim=0, dim_size=count)
elif reduction == "mean":
Expand Down