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perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance - #2

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@trvachovtrvachov commented Jul 18, 2026•
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Summary

Accelerates the building-block fingerprint lookup in get_reactants — the single largest cost in ReaSyn inference (~60% of wall time in profiling).

Two changes to FingerprintIndex.query_cuda:

  1. Run the fingerprint cdist on the model's GPU. The query fingerprint arrives as a CPU tensor, so despite the query_cuda name the distance search was executing on the CPU over the 211k×2048 matrix (~53 ms/query). Routed to the model's assigned cuda:<id> → ~53 → 1.4 ms.
  2. RapidFuzz for the invalid-SMILES fallback. Invalid generated SMILES fall back to Levenshtein matching over all 211k building-block strings, previously a Python editdistance loop (~335–460 ms/call — the true dominant cost). Replaced with rapidfuzz.process.cdist (batched C++/SIMD) → ~9–15×, identical distances.

Correctness / safety

  • Results preserved exactly. GPU cdist distances are bit-identical to CPU; to also preserve the selected indices at tied distances, the distance vector is copied back to CPU for topk (GPU/CPU topk break ties differently). RapidFuzz distances are identical to editdistance.
  • Multi-GPU safe. Query is routed to the model's explicit cuda:<gpu_id> (not the process-default GPU 0); fp_cuda cache key canonicalized.
  • RapidFuzz pinned in both pyproject.toml and env.yml; workers=1.

Tests

tests/test_fpindex.py — GPU/CPU parity (incl. the real 211k index), device routing, cache behavior, fallback paths, RapidFuzz↔editdistance identity. 9 passed on A100.

Toggles

REASYN_GPU_QUERY (default on), REASYN_RAPIDFUZZ (default on); set =0 to restore legacy behavior.

Independently reviewed and fixed (multi-GPU device routing, topk tie identity, manifest pinning) before opening.

Testing environment ⚠️

All test/benchmark results above were produced on the review container: Python 3.12 / PyTorch 2.11 / CUDA 13.2 — not the repo-pinned stack (Python 3.10 / PyTorch 2.7 / CUDA 11.8 per env.yml / pyproject.toml). Kernel selection, SDPA backends, and tie-breaking are version-sensitive, so these results should be re-confirmed on the pinned stack / CI before merge.

@trvachov
perf: accelerate fingerprint index queries with verified parity
c06a9fd
Run fingerprint cdist on the model-assigned GPU and accelerate invalid-SMILES Levenshtein lookup with RapidFuzz.
Thread explicit multi-GPU device routing, canonicalize CUDA cache keys, restore legacy CPU topk tie identity, pin RapidFuzz in both manifests, and use workers=1. Add tests/test_fpindex.py for GPU/CPU parity, device routing, cache behavior, fallback paths, and the real index.
Reviewed-and-tested: adversarially verified on the 211k fingerprint index with an A100, on CPU-only paths, and against both model checkpoints.
@trvachovtrvachov mentioned this pull request Jul 18, 2026
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perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance by trvachov · Pull Request #2 · NVIDIA-BioNeMo/ReaSyn · GitHub
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perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance - #2
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perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance#2
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Jul 18, 2026•

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Summary
Accelerates the building-block fingerprint lookup in get_reactants — the single largest cost in ReaSyn inference (~60% of wall time in profiling).
Two changes to FingerprintIndex.query_cuda:
  1. Run the fingerprint cdist on the model's GPU. The query fingerprint arrives as a CPU tensor, so despite the query_cuda name the distance search was executing on the CPU over the 211k×2048 matrix (~53 ms/query). Routed to the model's assigned cuda:<id> → ~53 → 1.4 ms.
  2. RapidFuzz for the invalid-SMILES fallback. Invalid generated SMILES fall back to Levenshtein matching over all 211k building-block strings, previously a Python editdistance loop (~335–460 ms/call — the true dominant cost). Replaced with rapidfuzz.process.cdist (batched C++/SIMD) → ~9–15×, identical distances.
Correctness / safety
  • Results preserved exactly. GPU cdist distances are bit-identical to CPU; to also preserve the selected indices at tied distances, the distance vector is copied back to CPU for topk (GPU/CPU topk break ties differently). RapidFuzz distances are identical to editdistance.
  • Multi-GPU safe. Query is routed to the model's explicit cuda:<gpu_id> (not the process-default GPU 0); fp_cuda cache key canonicalized.
  • RapidFuzz pinned in both pyproject.toml and env.yml; workers=1.
Tests
tests/test_fpindex.py — GPU/CPU parity (incl. the real 211k index), device routing, cache behavior, fallback paths, RapidFuzz↔editdistance identity. 9 passed on A100.
Toggles
REASYN_GPU_QUERY (default on), REASYN_RAPIDFUZZ (default on); set =0 to restore legacy behavior.
Independently reviewed and fixed (multi-GPU device routing, topk tie identity, manifest pinning) before opening.
Testing environment ⚠️
All test/benchmark results above were produced on the review container: Python 3.12 / PyTorch 2.11 / CUDA 13.2 — not the repo-pinned stack (Python 3.10 / PyTorch 2.7 / CUDA 11.8 per env.yml / pyproject.toml). Kernel selection, SDPA backends, and tie-breaking are version-sensitive, so these results should be re-confirmed on the pinned stack / CI before merge.
@trvachov
perf: accelerate fingerprint index queries with verified parity
c06a9fd
Run fingerprint cdist on the model-assigned GPU and accelerate invalid-SMILES Levenshtein lookup with RapidFuzz.
Thread explicit multi-GPU device routing, canonicalize CUDA cache keys, restore legacy CPU topk tie identity, pin RapidFuzz in both manifests, and use workers=1. Add tests/test_fpindex.py for GPU/CPU parity, device routing, cache behavior, fallback paths, and the real index.
Reviewed-and-tested: adversarially verified on the 211k fingerprint index with an A100, on CPU-only paths, and against both model checkpoints.
@trvachovtrvachov
mentioned this pull request
Jul 18, 2026
 Open

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perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance by trvachov · Pull Request #2 · NVIDIA-BioNeMo/ReaSyn · GitHub
Skip to content
perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance - #2
Open
trvachov wants to merge 1 commit into
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trvachov/fpindex-gpu-rapidfuzz-tested
Open
perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance#2
trvachov wants to merge 1 commit into
reasyn_v2from 
trvachov/fpindex-gpu-rapidfuzz-tested
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@trvachovtrvachov
commented
Jul 18, 2026•

edited

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Summary
Accelerates the building-block fingerprint lookup in get_reactants — the single largest cost in ReaSyn inference (~60% of wall time in profiling).
Two changes to FingerprintIndex.query_cuda:
  1. Run the fingerprint cdist on the model's GPU. The query fingerprint arrives as a CPU tensor, so despite the query_cuda name the distance search was executing on the CPU over the 211k×2048 matrix (~53 ms/query). Routed to the model's assigned cuda:<id> → ~53 → 1.4 ms.
  2. RapidFuzz for the invalid-SMILES fallback. Invalid generated SMILES fall back to Levenshtein matching over all 211k building-block strings, previously a Python editdistance loop (~335–460 ms/call — the true dominant cost). Replaced with rapidfuzz.process.cdist (batched C++/SIMD) → ~9–15×, identical distances.
Correctness / safety
  • Results preserved exactly. GPU cdist distances are bit-identical to CPU; to also preserve the selected indices at tied distances, the distance vector is copied back to CPU for topk (GPU/CPU topk break ties differently). RapidFuzz distances are identical to editdistance.
  • Multi-GPU safe. Query is routed to the model's explicit cuda:<gpu_id> (not the process-default GPU 0); fp_cuda cache key canonicalized.
  • RapidFuzz pinned in both pyproject.toml and env.yml; workers=1.
Tests
tests/test_fpindex.py — GPU/CPU parity (incl. the real 211k index), device routing, cache behavior, fallback paths, RapidFuzz↔editdistance identity. 9 passed on A100.
Toggles
REASYN_GPU_QUERY (default on), REASYN_RAPIDFUZZ (default on); set =0 to restore legacy behavior.
Independently reviewed and fixed (multi-GPU device routing, topk tie identity, manifest pinning) before opening.
Testing environment ⚠️
All test/benchmark results above were produced on the review container: Python 3.12 / PyTorch 2.11 / CUDA 13.2 — not the repo-pinned stack (Python 3.10 / PyTorch 2.7 / CUDA 11.8 per env.yml / pyproject.toml). Kernel selection, SDPA backends, and tie-breaking are version-sensitive, so these results should be re-confirmed on the pinned stack / CI before merge.
@trvachov
perf: accelerate fingerprint index queries with verified parity
c06a9fd
Run fingerprint cdist on the model-assigned GPU and accelerate invalid-SMILES Levenshtein lookup with RapidFuzz.
Thread explicit multi-GPU device routing, canonicalize CUDA cache keys, restore legacy CPU topk tie identity, pin RapidFuzz in both manifests, and use workers=1. Add tests/test_fpindex.py for GPU/CPU parity, device routing, cache behavior, fallback paths, and the real index.
Reviewed-and-tested: adversarially verified on the 211k fingerprint index with an A100, on CPU-only paths, and against both model checkpoints.
@trvachovtrvachov
mentioned this pull request
Jul 18, 2026
 Open

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perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance by trvachov · Pull Request #2 · NVIDIA-BioNeMo/ReaSyn · GitHub
Skip to content
perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance - #2
Open
trvachov wants to merge 1 commit into
reasyn_v2from 
trvachov/fpindex-gpu-rapidfuzz-tested
Open
perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance#2
trvachov wants to merge 1 commit into
reasyn_v2from 
trvachov/fpindex-gpu-rapidfuzz-tested
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@trvachovtrvachov
commented
Jul 18, 2026•

edited

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Summary
Accelerates the building-block fingerprint lookup in get_reactants — the single largest cost in ReaSyn inference (~60% of wall time in profiling).
Two changes to FingerprintIndex.query_cuda:
  1. Run the fingerprint cdist on the model's GPU. The query fingerprint arrives as a CPU tensor, so despite the query_cuda name the distance search was executing on the CPU over the 211k×2048 matrix (~53 ms/query). Routed to the model's assigned cuda:<id> → ~53 → 1.4 ms.
  2. RapidFuzz for the invalid-SMILES fallback. Invalid generated SMILES fall back to Levenshtein matching over all 211k building-block strings, previously a Python editdistance loop (~335–460 ms/call — the true dominant cost). Replaced with rapidfuzz.process.cdist (batched C++/SIMD) → ~9–15×, identical distances.
Correctness / safety
  • Results preserved exactly. GPU cdist distances are bit-identical to CPU; to also preserve the selected indices at tied distances, the distance vector is copied back to CPU for topk (GPU/CPU topk break ties differently). RapidFuzz distances are identical to editdistance.
  • Multi-GPU safe. Query is routed to the model's explicit cuda:<gpu_id> (not the process-default GPU 0); fp_cuda cache key canonicalized.
  • RapidFuzz pinned in both pyproject.toml and env.yml; workers=1.
Tests
tests/test_fpindex.py — GPU/CPU parity (incl. the real 211k index), device routing, cache behavior, fallback paths, RapidFuzz↔editdistance identity. 9 passed on A100.
Toggles
REASYN_GPU_QUERY (default on), REASYN_RAPIDFUZZ (default on); set =0 to restore legacy behavior.
Independently reviewed and fixed (multi-GPU device routing, topk tie identity, manifest pinning) before opening.
Testing environment ⚠️
All test/benchmark results above were produced on the review container: Python 3.12 / PyTorch 2.11 / CUDA 13.2 — not the repo-pinned stack (Python 3.10 / PyTorch 2.7 / CUDA 11.8 per env.yml / pyproject.toml). Kernel selection, SDPA backends, and tie-breaking are version-sensitive, so these results should be re-confirmed on the pinned stack / CI before merge.
@trvachov
perf: accelerate fingerprint index queries with verified parity
c06a9fd
Run fingerprint cdist on the model-assigned GPU and accelerate invalid-SMILES Levenshtein lookup with RapidFuzz.
Thread explicit multi-GPU device routing, canonicalize CUDA cache keys, restore legacy CPU topk tie identity, pin RapidFuzz in both manifests, and use workers=1. Add tests/test_fpindex.py for GPU/CPU parity, device routing, cache behavior, fallback paths, and the real index.
Reviewed-and-tested: adversarially verified on the 211k fingerprint index with an A100, on CPU-only paths, and against both model checkpoints.
@trvachovtrvachov
mentioned this pull request
Jul 18, 2026
 Open

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perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance by trvachov · Pull Request #2 · NVIDIA-BioNeMo/ReaSyn · GitHub
Skip to content
perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance - #2
Open
trvachov wants to merge 1 commit into
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trvachov/fpindex-gpu-rapidfuzz-tested
Open
perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance#2
trvachov wants to merge 1 commit into
reasyn_v2from 
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@trvachovtrvachov
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Jul 18, 2026•

edited

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Summary
Accelerates the building-block fingerprint lookup in get_reactants — the single largest cost in ReaSyn inference (~60% of wall time in profiling).
Two changes to FingerprintIndex.query_cuda:
  1. Run the fingerprint cdist on the model's GPU. The query fingerprint arrives as a CPU tensor, so despite the query_cuda name the distance search was executing on the CPU over the 211k×2048 matrix (~53 ms/query). Routed to the model's assigned cuda:<id> → ~53 → 1.4 ms.
  2. RapidFuzz for the invalid-SMILES fallback. Invalid generated SMILES fall back to Levenshtein matching over all 211k building-block strings, previously a Python editdistance loop (~335–460 ms/call — the true dominant cost). Replaced with rapidfuzz.process.cdist (batched C++/SIMD) → ~9–15×, identical distances.
Correctness / safety
  • Results preserved exactly. GPU cdist distances are bit-identical to CPU; to also preserve the selected indices at tied distances, the distance vector is copied back to CPU for topk (GPU/CPU topk break ties differently). RapidFuzz distances are identical to editdistance.
  • Multi-GPU safe. Query is routed to the model's explicit cuda:<gpu_id> (not the process-default GPU 0); fp_cuda cache key canonicalized.
  • RapidFuzz pinned in both pyproject.toml and env.yml; workers=1.
Tests
tests/test_fpindex.py — GPU/CPU parity (incl. the real 211k index), device routing, cache behavior, fallback paths, RapidFuzz↔editdistance identity. 9 passed on A100.
Toggles
REASYN_GPU_QUERY (default on), REASYN_RAPIDFUZZ (default on); set =0 to restore legacy behavior.
Independently reviewed and fixed (multi-GPU device routing, topk tie identity, manifest pinning) before opening.
Testing environment ⚠️
All test/benchmark results above were produced on the review container: Python 3.12 / PyTorch 2.11 / CUDA 13.2 — not the repo-pinned stack (Python 3.10 / PyTorch 2.7 / CUDA 11.8 per env.yml / pyproject.toml). Kernel selection, SDPA backends, and tie-breaking are version-sensitive, so these results should be re-confirmed on the pinned stack / CI before merge.
@trvachov
perf: accelerate fingerprint index queries with verified parity
c06a9fd
Run fingerprint cdist on the model-assigned GPU and accelerate invalid-SMILES Levenshtein lookup with RapidFuzz.
Thread explicit multi-GPU device routing, canonicalize CUDA cache keys, restore legacy CPU topk tie identity, pin RapidFuzz in both manifests, and use workers=1. Add tests/test_fpindex.py for GPU/CPU parity, device routing, cache behavior, fallback paths, and the real index.
Reviewed-and-tested: adversarially verified on the 211k fingerprint index with an A100, on CPU-only paths, and against both model checkpoints.
@trvachovtrvachov
mentioned this pull request
Jul 18, 2026
 Open

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perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance by trvachov · Pull Request #2 · NVIDIA-BioNeMo/ReaSyn · GitHub
Skip to content
perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance - #2
Open
trvachov wants to merge 1 commit into
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trvachov/fpindex-gpu-rapidfuzz-tested
Open
perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance#2
trvachov wants to merge 1 commit into
reasyn_v2from 
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Summary
Accelerates the building-block fingerprint lookup in get_reactants — the single largest cost in ReaSyn inference (~60% of wall time in profiling).
Two changes to FingerprintIndex.query_cuda:
  1. Run the fingerprint cdist on the model's GPU. The query fingerprint arrives as a CPU tensor, so despite the query_cuda name the distance search was executing on the CPU over the 211k×2048 matrix (~53 ms/query). Routed to the model's assigned cuda:<id> → ~53 → 1.4 ms.
  2. RapidFuzz for the invalid-SMILES fallback. Invalid generated SMILES fall back to Levenshtein matching over all 211k building-block strings, previously a Python editdistance loop (~335–460 ms/call — the true dominant cost). Replaced with rapidfuzz.process.cdist (batched C++/SIMD) → ~9–15×, identical distances.
Correctness / safety
  • Results preserved exactly. GPU cdist distances are bit-identical to CPU; to also preserve the selected indices at tied distances, the distance vector is copied back to CPU for topk (GPU/CPU topk break ties differently). RapidFuzz distances are identical to editdistance.
  • Multi-GPU safe. Query is routed to the model's explicit cuda:<gpu_id> (not the process-default GPU 0); fp_cuda cache key canonicalized.
  • RapidFuzz pinned in both pyproject.toml and env.yml; workers=1.
Tests
tests/test_fpindex.py — GPU/CPU parity (incl. the real 211k index), device routing, cache behavior, fallback paths, RapidFuzz↔editdistance identity. 9 passed on A100.
Toggles
REASYN_GPU_QUERY (default on), REASYN_RAPIDFUZZ (default on); set =0 to restore legacy behavior.
Independently reviewed and fixed (multi-GPU device routing, topk tie identity, manifest pinning) before opening.
Testing environment ⚠️
All test/benchmark results above were produced on the review container: Python 3.12 / PyTorch 2.11 / CUDA 13.2 — not the repo-pinned stack (Python 3.10 / PyTorch 2.7 / CUDA 11.8 per env.yml / pyproject.toml). Kernel selection, SDPA backends, and tie-breaking are version-sensitive, so these results should be re-confirmed on the pinned stack / CI before merge.
@trvachov
perf: accelerate fingerprint index queries with verified parity
c06a9fd
Run fingerprint cdist on the model-assigned GPU and accelerate invalid-SMILES Levenshtein lookup with RapidFuzz.
Thread explicit multi-GPU device routing, canonicalize CUDA cache keys, restore legacy CPU topk tie identity, pin RapidFuzz in both manifests, and use workers=1. Add tests/test_fpindex.py for GPU/CPU parity, device routing, cache behavior, fallback paths, and the real index.
Reviewed-and-tested: adversarially verified on the 211k fingerprint index with an A100, on CPU-only paths, and against both model checkpoints.
@trvachovtrvachov
mentioned this pull request
Jul 18, 2026
 Open

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perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance by trvachov · Pull Request #2 · NVIDIA-BioNeMo/ReaSyn · GitHub
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perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance - #2
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perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance#2
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Summary
Accelerates the building-block fingerprint lookup in get_reactants — the single largest cost in ReaSyn inference (~60% of wall time in profiling).
Two changes to FingerprintIndex.query_cuda:
  1. Run the fingerprint cdist on the model's GPU. The query fingerprint arrives as a CPU tensor, so despite the query_cuda name the distance search was executing on the CPU over the 211k×2048 matrix (~53 ms/query). Routed to the model's assigned cuda:<id> → ~53 → 1.4 ms.
  2. RapidFuzz for the invalid-SMILES fallback. Invalid generated SMILES fall back to Levenshtein matching over all 211k building-block strings, previously a Python editdistance loop (~335–460 ms/call — the true dominant cost). Replaced with rapidfuzz.process.cdist (batched C++/SIMD) → ~9–15×, identical distances.
Correctness / safety
  • Results preserved exactly. GPU cdist distances are bit-identical to CPU; to also preserve the selected indices at tied distances, the distance vector is copied back to CPU for topk (GPU/CPU topk break ties differently). RapidFuzz distances are identical to editdistance.
  • Multi-GPU safe. Query is routed to the model's explicit cuda:<gpu_id> (not the process-default GPU 0); fp_cuda cache key canonicalized.
  • RapidFuzz pinned in both pyproject.toml and env.yml; workers=1.
Tests
tests/test_fpindex.py — GPU/CPU parity (incl. the real 211k index), device routing, cache behavior, fallback paths, RapidFuzz↔editdistance identity. 9 passed on A100.
Toggles
REASYN_GPU_QUERY (default on), REASYN_RAPIDFUZZ (default on); set =0 to restore legacy behavior.
Independently reviewed and fixed (multi-GPU device routing, topk tie identity, manifest pinning) before opening.
Testing environment ⚠️
All test/benchmark results above were produced on the review container: Python 3.12 / PyTorch 2.11 / CUDA 13.2 — not the repo-pinned stack (Python 3.10 / PyTorch 2.7 / CUDA 11.8 per env.yml / pyproject.toml). Kernel selection, SDPA backends, and tie-breaking are version-sensitive, so these results should be re-confirmed on the pinned stack / CI before merge.
@trvachov
perf: accelerate fingerprint index queries with verified parity
c06a9fd
Run fingerprint cdist on the model-assigned GPU and accelerate invalid-SMILES Levenshtein lookup with RapidFuzz.
Thread explicit multi-GPU device routing, canonicalize CUDA cache keys, restore legacy CPU topk tie identity, pin RapidFuzz in both manifests, and use workers=1. Add tests/test_fpindex.py for GPU/CPU parity, device routing, cache behavior, fallback paths, and the real index.
Reviewed-and-tested: adversarially verified on the 211k fingerprint index with an A100, on CPU-only paths, and against both model checkpoints.
@trvachovtrvachov
mentioned this pull request
Jul 18, 2026
 Open

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perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance by trvachov · Pull Request #2 · NVIDIA-BioNeMo/ReaSyn · GitHub
Skip to content
perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance - #2
Open
trvachov wants to merge 1 commit into
reasyn_v2from 
trvachov/fpindex-gpu-rapidfuzz-tested
Open
perf(fpindex): GPU fingerprint queries + RapidFuzz edit-distance#2
trvachov wants to merge 1 commit into
reasyn_v2from 
trvachov/fpindex-gpu-rapidfuzz-tested
Conversation
@trvachov
@trvachovtrvachov
commented
Jul 18, 2026•

edited

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Summary
Accelerates the building-block fingerprint lookup in get_reactants — the single largest cost in ReaSyn inference (~60% of wall time in profiling).
Two changes to FingerprintIndex.query_cuda:
  1. Run the fingerprint cdist on the model's GPU. The query fingerprint arrives as a CPU tensor, so despite the query_cuda name the distance search was executing on the CPU over the 211k×2048 matrix (~53 ms/query). Routed to the model's assigned cuda:<id> → ~53 → 1.4 ms.
  2. RapidFuzz for the invalid-SMILES fallback. Invalid generated SMILES fall back to Levenshtein matching over all 211k building-block strings, previously a Python editdistance loop (~335–460 ms/call — the true dominant cost). Replaced with rapidfuzz.process.cdist (batched C++/SIMD) → ~9–15×, identical distances.
Correctness / safety
  • Results preserved exactly. GPU cdist distances are bit-identical to CPU; to also preserve the selected indices at tied distances, the distance vector is copied back to CPU for topk (GPU/CPU topk break ties differently). RapidFuzz distances are identical to editdistance.
  • Multi-GPU safe. Query is routed to the model's explicit cuda:<gpu_id> (not the process-default GPU 0); fp_cuda cache key canonicalized.
  • RapidFuzz pinned in both pyproject.toml and env.yml; workers=1.
Tests
tests/test_fpindex.py — GPU/CPU parity (incl. the real 211k index), device routing, cache behavior, fallback paths, RapidFuzz↔editdistance identity. 9 passed on A100.
Toggles
REASYN_GPU_QUERY (default on), REASYN_RAPIDFUZZ (default on); set =0 to restore legacy behavior.
Independently reviewed and fixed (multi-GPU device routing, topk tie identity, manifest pinning) before opening.
Testing environment ⚠️
All test/benchmark results above were produced on the review container: Python 3.12 / PyTorch 2.11 / CUDA 13.2 — not the repo-pinned stack (Python 3.10 / PyTorch 2.7 / CUDA 11.8 per env.yml / pyproject.toml). Kernel selection, SDPA backends, and tie-breaking are version-sensitive, so these results should be re-confirmed on the pinned stack / CI before merge.
@trvachov
perf: accelerate fingerprint index queries with verified parity
c06a9fd
Run fingerprint cdist on the model-assigned GPU and accelerate invalid-SMILES Levenshtein lookup with RapidFuzz.
Thread explicit multi-GPU device routing, canonicalize CUDA cache keys, restore legacy CPU topk tie identity, pin RapidFuzz in both manifests, and use workers=1. Add tests/test_fpindex.py for GPU/CPU parity, device routing, cache behavior, fallback paths, and the real index.
Reviewed-and-tested: adversarially verified on the 211k fingerprint index with an A100, on CPU-only paths, and against both model checkpoints.
@trvachovtrvachov
mentioned this pull request
Jul 18, 2026
 Open

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