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[perf] Vectorize generate_batch_meta's force_fetch readiness check #152

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Merged
0oshowero0 merged 1 commit into from
Aug 12, 2026
Merged

[perf] Vectorize generate_batch_meta's force_fetch readiness check #152

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21 changes: 15 additions & 6 deletions transfer_queue/controller.py
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Original file line number Diff line number Diff line change
Expand Up @@ -781,8 +781,10 @@ def get_field_custom_backend_meta(
>>> partition.get_field_custom_backend_meta([0, 1], ["field_a", "field_b"])
{0: {'field_a': {'meta1': 'xxx'}, 'field_b': {'meta1': 'xxx'}}, 1: {...}}
"""

wanted = set(field_names)
return {
idx: {f: v for f, v in self.field_custom_backend_meta[idx].items() if f in field_names}
idx: {f: v for f, v in self.field_custom_backend_meta[idx].items() if f in wanted}
for idx in global_indices
if idx in self.field_custom_backend_meta
}
Expand Down Expand Up @@ -1434,11 +1436,18 @@ def generate_batch_meta(
if field_name in partition.field_name_mapping
]
if field_indices:
for i, global_idx in enumerate(batch_global_indexes):
if global_idx < partition.production_status.shape[0]:
sample_status = partition.production_status[global_idx, field_indices]
if torch.all(sample_status == 1):
production_status[i] = 1
rows = np.fromiter(batch_global_indexes, dtype=np.int64, count=batch_size)
in_range = rows < partition.production_status.shape[0]
if in_range.any():
row_idx = torch.from_numpy(rows[in_range])
col_idx = torch.as_tensor(field_indices, dtype=torch.long)
ready = (
(partition.production_status[row_idx[:, None], col_idx] == 1)
.all(dim=1)
.to(torch.int8)
.numpy()
)
production_status[in_range] = ready
Comment on lines +1439 to +1450

custom_meta_dict = partition.get_custom_meta(batch_global_indexes)
custom_backend_meta = partition.get_field_custom_backend_meta(batch_global_indexes, data_fields)
Expand Down
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