Replace legacy distributed protocol with SDK-built workloads
This commit is contained in:
Emil
@@ -0,0 +1,31 @@
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"""SDK-built ``similarity-search`` reference workload.
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See ``core.py`` for the sharding/merge scientific core and ``definition.py``
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for the manifest-backed planner/runner/reducer handlers.
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"""
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from .core import (
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merge_search_partials,
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run_search_shard,
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write_search_partial,
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write_search_shards,
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)
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from .definition import (
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MAP_ENTRY_POINT,
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REDUCE_ENTRY_POINT,
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SimilaritySearchSDKWorkload,
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similarity_search_sdk_definition,
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workload_definition,
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)
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__all__ = [
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"MAP_ENTRY_POINT",
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"REDUCE_ENTRY_POINT",
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"SimilaritySearchSDKWorkload",
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"merge_search_partials",
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"run_search_shard",
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"similarity_search_sdk_definition",
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"workload_definition",
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"write_search_partial",
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"write_search_shards",
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]
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@@ -0,0 +1,261 @@
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"""Scientific core for the SDK-built ``similarity-search`` workload.
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Reuses the local reference implementation (``search_similar``) and the shared
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CTX-08 partial format (full-precision ``repr`` scores) so that shard outputs
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and the merged final CSV are byte-identical to the legacy distributed path and
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to the single-process reference.
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"""
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from __future__ import annotations
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import csv
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import heapq
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from pathlib import Path
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from typing import Any, Iterator, Mapping, Sequence
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from scimesh.chemistry.dataset import MoleculeRecord, parse_smiles
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from scimesh.workloads.similarity_search import (
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SimilarityMatch,
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_HeapEntry,
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search_similar,
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write_search_results,
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)
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SEARCH_COLUMNS = ("rank", "chembl_id", "canonical_smiles", "similarity")
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REQUIRED_COLUMNS = {"chembl_id", "canonical_smiles"}
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def write_search_partial(
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output_path: Path,
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matches: Sequence[SimilarityMatch],
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) -> None:
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"""Write a worker partial with a round-trip score, not display rounding.
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The public final CSV continues to use the local CLI's six-decimal display.
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A reducer needs the full binary float representation to rank candidates
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from separate shards exactly as the single-process reference does.
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"""
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with output_path.open("w", encoding="utf-8", newline="") as destination:
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writer = csv.DictWriter(destination, fieldnames=list(SEARCH_COLUMNS))
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writer.writeheader()
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for rank, match in enumerate(matches, start=1):
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writer.writerow(
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{
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"rank": rank,
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"chembl_id": match.molecule_id,
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"canonical_smiles": match.smiles,
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"similarity": repr(match.similarity),
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}
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)
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def run_search_shard(
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input_path: Path,
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parameters: Mapping[str, object],
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output_path: Path,
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) -> dict[str, int]:
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"""Run one planned shard with the local reference implementation.
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This is the worker entry used by the SDK-built runner. It deliberately
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accepts only resolved ``query_smiles``: resolving an identifier
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independently in each shard would make the distributed search
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scientifically invalid, so identifier resolution happens once in the
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planner (or at the worker bridge for v1-wire tasks that still carry
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``query_id``).
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"""
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allowed = {
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"query_smiles",
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"top_k",
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"threshold",
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"threshold_direction",
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"progress_every",
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}
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unknown = set(parameters) - allowed
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if unknown:
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raise ValueError(
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f"unsupported similarity-search parameters: {', '.join(sorted(unknown))}"
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)
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query_smiles = parameters.get("query_smiles")
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if not isinstance(query_smiles, str) or not query_smiles.strip():
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raise ValueError("query_smiles is required for a distributed shard")
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molecule = parse_smiles(query_smiles)
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if molecule is None:
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raise ValueError("query_smiles is invalid")
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top_k = _positive_int(parameters.get("top_k", 20), "top_k")
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threshold = None
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if "threshold" in parameters:
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threshold = _unit_interval(parameters["threshold"], "threshold")
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direction = parameters.get("threshold_direction", "greater")
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if direction not in {"greater", "less"}:
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raise ValueError("threshold_direction must be 'greater' or 'less'")
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assert isinstance(direction, str)
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progress_every = 0
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if "progress_every" in parameters:
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progress_every = _nonnegative_int(
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parameters["progress_every"], "progress_every"
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)
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result = search_similar(
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input_path,
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MoleculeRecord("query", query_smiles, molecule),
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top_k=top_k,
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progress_every=progress_every,
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threshold=threshold,
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threshold_direction=direction,
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)
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write_search_partial(output_path, result.matches)
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return {
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"scanned_rows": result.stats.scanned,
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"valid_molecules": result.stats.valid,
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"invalid_smiles": result.stats.invalid,
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"matches_emitted": len(result.matches),
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}
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def _positive_int(value: object, name: str) -> int:
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if isinstance(value, bool) or not isinstance(value, int) or value < 1:
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raise ValueError(f"{name} must be a positive integer")
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return value
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def _nonnegative_int(value: object, name: str) -> int:
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if isinstance(value, bool) or not isinstance(value, int) or value < 0:
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raise ValueError(f"{name} must be a non-negative integer")
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return value
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def _unit_interval(value: object, name: str) -> float:
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if isinstance(value, bool) or not isinstance(value, (int, float)):
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raise ValueError(f"{name} must be a number between 0 and 1")
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return float(value)
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def write_search_shards(
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input_path: Path,
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workspace: Path,
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shard_rows: int,
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max_rows: int | None = None,
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) -> list[Path]:
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"""Split the input TSV into deterministic row-bounded shards with headers."""
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if (
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isinstance(shard_rows, bool)
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or not isinstance(shard_rows, int)
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or shard_rows < 1
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):
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raise ValueError("shard_rows must be a positive integer")
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if max_rows is not None and (
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isinstance(max_rows, bool) or not isinstance(max_rows, int) or max_rows < 1
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):
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raise ValueError("max_rows must be a positive integer")
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paths: list[Path] = []
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current: Path | None = None
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destination = None
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writer = None
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rows_in_shard = 0
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seen_rows = 0
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try:
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with input_path.open("r", encoding="utf-8", newline="") as source:
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reader = csv.DictReader(source, delimiter="\t")
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fieldnames = tuple(reader.fieldnames or ())
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if not REQUIRED_COLUMNS.issubset(set(fieldnames)):
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missing = sorted(REQUIRED_COLUMNS - set(fieldnames))
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raise ValueError(
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f"dataset is missing required columns: {', '.join(missing)}"
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)
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for row in reader:
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if max_rows is not None and seen_rows >= max_rows:
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break
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if destination is None or rows_in_shard == shard_rows:
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if destination is not None:
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destination.close()
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current = workspace / f"shard-{len(paths)}.tsv"
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destination = current.open("w", encoding="utf-8", newline="")
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writer = csv.DictWriter(
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destination,
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fieldnames=list(fieldnames),
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delimiter="\t",
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lineterminator="\n",
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)
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writer.writeheader()
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paths.append(current)
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rows_in_shard = 0
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assert writer is not None
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writer.writerow(row)
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rows_in_shard += 1
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seen_rows += 1
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finally:
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if destination is not None:
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destination.close()
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if not paths:
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raise ValueError("dataset has no data rows")
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return paths
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def iter_search_partial(
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path: Path,
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threshold_direction: str,
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) -> Iterator[SimilarityMatch]:
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"""Yield strictly ordered partial matches with full-precision scores."""
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if not path.is_file():
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raise ValueError("materialized partial result is missing")
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if threshold_direction not in {"greater", "less"}:
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raise ValueError("threshold_direction must be 'greater' or 'less'")
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previous_key: tuple[float, str, str] | None = None
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with path.open("r", encoding="utf-8", newline="") as source:
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reader = csv.DictReader(source)
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if tuple(reader.fieldnames or ()) != SEARCH_COLUMNS:
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raise ValueError("partial result has an invalid CSV header")
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for expected_rank, row in enumerate(reader, start=1):
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if set(row) != set(SEARCH_COLUMNS) or row["rank"] != str(expected_rank):
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raise ValueError("partial result has an invalid rank")
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try:
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similarity = float(row["similarity"])
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except (TypeError, ValueError) as error:
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raise ValueError("partial result has an invalid similarity") from error
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if not 0 <= similarity <= 1:
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raise ValueError("partial result has an invalid similarity")
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match = SimilarityMatch(
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similarity, row["chembl_id"], row["canonical_smiles"]
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)
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key = match.sort_key(threshold_direction)
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if previous_key is not None and key < previous_key:
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raise ValueError("partial result is not sorted deterministically")
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previous_key = key
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yield match
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def merge_search_partials(
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partial_paths: Sequence[Path],
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parameters: Mapping[str, Any],
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output_path: Path,
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) -> dict[str, int]:
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"""Merge sorted shard partials into one deterministic final top-k CSV.
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Mirrors the CTX-08/CTX-09 reducer: a bounded heap with the local
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tie-breaker, so the merged file equals the single-process reference
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byte-for-byte for the same input and options.
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"""
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if not partial_paths:
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raise ValueError("at least one partial result is required")
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raw_top_k = parameters.get("top_k", 20)
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if isinstance(raw_top_k, bool) or not isinstance(raw_top_k, int) or raw_top_k < 1:
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raise ValueError("top_k must be a positive integer")
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top_k = raw_top_k
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direction = parameters.get("threshold_direction", "greater")
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if direction not in {"greater", "less"}:
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raise ValueError("threshold_direction must be 'greater' or 'less'")
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heap: list[_HeapEntry] = []
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for path in partial_paths:
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for match in iter_search_partial(path, direction):
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rank_key = match.sort_key(direction)
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entry = _HeapEntry(match, rank_key)
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if len(heap) < top_k:
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heapq.heappush(heap, entry)
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elif rank_key < heap[0].rank_key:
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heapq.heapreplace(heap, entry)
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matches = sorted(
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(entry.match for entry in heap),
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key=lambda match: match.sort_key(direction),
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)
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write_search_results(output_path, matches)
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return {"matches_emitted": len(matches), "partial_count": len(partial_paths)}
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@@ -0,0 +1,565 @@
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"""SDK-built ``similarity-search`` workload definition and handlers.
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A direct ``core-batch-v1`` definition (not the legacy adapter): the planner
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resolves the query once, shards deterministically, each map task computes the
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local top-k with the reference implementation, and the reducer merges the
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sorted partials with the same bounded heap and tie-breakers as the local CLI.
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The manifest declares ``byte_exact`` with the exact-artifact verifier and both
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``trusted`` and ``untrusted_quorum`` trust modes.
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"""
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from __future__ import annotations
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import hashlib
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import shutil
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from pathlib import Path
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from typing import Any, Mapping
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from rdkit import Chem
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from scimesh.chemistry.dataset import find_molecule_by_id, parse_smiles
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from scimesh.chemistry.fingerprints import FP_RADIUS, FP_SIZE
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from ...sdk.artifacts import (
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ArtifactCollection,
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ArtifactItem,
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ArtifactRef,
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ArtifactSchema,
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Cardinality,
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CollectionKind,
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OutputManifest,
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PortSpec,
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)
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from ..environment import current_environment_digest, current_scimesh_package_digest
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from ...sdk.execution import (
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CheckpointPolicy,
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ExecutionProfile,
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NetworkPolicy,
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RetryPolicy,
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)
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from ...sdk.identity import ComponentRef, SchemaRef, VersionRange, WorkloadId
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from ...sdk.manifest import (
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DeterminismProfile,
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EnvironmentSpec,
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PackageSpec,
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TrustMode,
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VerifierSpec,
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WorkloadLimits,
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WorkloadManifest,
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)
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from ...sdk.plans import JobRequest, TaskSpec, ValidatedJob, WorkflowPlan
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from ...sdk.protocols import PlanningContext, ReduceContext, TaskContext
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from ...sdk.registry import WorkloadDefinition
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from ...sdk.resources import ResourceRequirements
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from ...sdk.verification import ExactArtifactVerifier
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from ...sdk.workflow import ArtifactEdge, PortRef, StageKind, StageSpec, WorkflowSpec
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from .core import merge_search_partials, run_search_shard, write_search_shards
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MAP_ENTRY_POINT = "scimesh.workloads.search.definition:map_search@v1"
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REDUCE_ENTRY_POINT = "scimesh.workloads.search.definition:reduce_search@v1"
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_MAP_PARAMETERS = (
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"query_smiles",
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"top_k",
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"threshold",
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"threshold_direction",
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"progress_every",
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)
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_REDUCE_PARAMETERS = _MAP_PARAMETERS + ("query_source", "fingerprint")
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def _sha256_file(path: Path) -> str:
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digest = hashlib.sha256()
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with path.open("rb") as source:
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for block in iter(lambda: source.read(1024 * 1024), b""):
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digest.update(block)
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return digest.hexdigest()
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def _parameters_schema() -> dict[str, Any]:
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return {
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"type": "object",
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"additionalProperties": False,
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"properties": {
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"query_id": {"type": "string", "minLength": 1, "maxLength": 200},
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"query_smiles": {"type": "string", "minLength": 1, "maxLength": 200},
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"top_k": {"type": "integer", "minimum": 1},
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"threshold": {"type": "number", "minimum": 0, "maximum": 1},
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"threshold_direction": {"enum": ["greater", "less"]},
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"max_rows": {"type": "integer", "minimum": 1},
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"progress_every": {"type": "integer", "minimum": 0},
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},
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"oneOf": [
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{"required": ["query_id"], "not": {"required": ["query_smiles"]}},
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{"required": ["query_smiles"], "not": {"required": ["query_id"]}},
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],
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}
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def _dataset_schema() -> ArtifactSchema:
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return ArtifactSchema(
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SchemaRef("molecule-table", 1),
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"text/tab-separated-values",
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"utf-8",
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max_bytes=10 * 1024 * 1024 * 1024,
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validator=ComponentRef("delimited-table", 1),
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validator_configuration={
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"required_columns": ["canonical_smiles", "chembl_id"],
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},
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max_records=100_000_000,
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canonicalizer="scimesh-tsv-v1",
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)
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def _search_table_schema(ref: SchemaRef, canonicalizer: str) -> ArtifactSchema:
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return ArtifactSchema(
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ref,
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"text/csv",
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"utf-8",
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max_bytes=1024 * 1024 * 1024,
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validator=ComponentRef("delimited-table", 1),
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validator_configuration={
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"columns": ["rank", "chembl_id", "canonical_smiles", "similarity"],
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},
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max_records=100_000,
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canonicalizer=canonicalizer,
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)
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class SimilaritySearchSDKWorkload:
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"""Manifest-backed planner, runner, and reducer for similarity-search."""
|
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def __init__(
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self,
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*,
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shard_rows: int,
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package_digest: str,
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||||
environment_digest: str,
|
||||
) -> None:
|
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if (
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isinstance(shard_rows, bool)
|
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or not isinstance(shard_rows, int)
|
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or shard_rows < 1
|
||||
):
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raise ValueError("shard_rows must be a positive integer")
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self.entry_point = MAP_ENTRY_POINT
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self.shard_rows = shard_rows
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self.input_port = PortSpec(_dataset_schema())
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self.partial_port = PortSpec(
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_search_table_schema(
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SchemaRef("similarity-search-partial", 1), "scimesh-search-partial-v1"
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||||
)
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||||
)
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self.output_port = PortSpec(
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_search_table_schema(
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SchemaRef("similarity-search-result", 1), "scimesh-search-result-v1"
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||||
)
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||||
)
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resources = ResourceRequirements(
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profile="search-cpu-v1",
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cpu_cores=1,
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memory_mb=1024,
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||||
scratch_mb=1024,
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||||
max_duration_seconds=3600,
|
||||
)
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||||
execution = ExecutionProfile(
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||||
profile="search-python-process-v1",
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network=NetworkPolicy.TRUSTED,
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timeout_seconds=3600,
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||||
checkpoint=CheckpointPolicy(),
|
||||
)
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||||
limits = WorkloadLimits(
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||||
max_input_bytes=self.input_port.schema.max_bytes,
|
||||
max_tasks=10_000,
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||||
max_output_bytes=self.output_port.schema.max_bytes,
|
||||
)
|
||||
trust_modes = ("trusted", "untrusted_quorum")
|
||||
map_stage = StageSpec(
|
||||
stage_id="map",
|
||||
kind=StageKind.MAP,
|
||||
entry_point=MAP_ENTRY_POINT,
|
||||
needs=(),
|
||||
inputs={"input": self.input_port},
|
||||
outputs={"partial": self.partial_port},
|
||||
parameter_names=_MAP_PARAMETERS,
|
||||
resources=resources,
|
||||
execution=execution,
|
||||
retry=RetryPolicy(),
|
||||
verifier=ComponentRef("exact-artifact", 1),
|
||||
trust_modes=trust_modes,
|
||||
max_fan_out=limits.max_tasks,
|
||||
cacheable=True,
|
||||
)
|
||||
reduce_input = PortSpec(
|
||||
schema=self.partial_port.schema,
|
||||
cardinality=Cardinality.MANY,
|
||||
collection=CollectionKind.KEYED,
|
||||
)
|
||||
reduce_stage = StageSpec(
|
||||
stage_id="reduce",
|
||||
kind=StageKind.REDUCE,
|
||||
entry_point=REDUCE_ENTRY_POINT,
|
||||
needs=("map",),
|
||||
inputs={"partials": reduce_input},
|
||||
outputs={"result": self.output_port},
|
||||
parameter_names=_REDUCE_PARAMETERS,
|
||||
resources=resources,
|
||||
execution=execution,
|
||||
retry=RetryPolicy(),
|
||||
verifier=ComponentRef("exact-artifact", 1),
|
||||
trust_modes=trust_modes,
|
||||
max_fan_out=1,
|
||||
cacheable=True,
|
||||
)
|
||||
workflow = WorkflowSpec(
|
||||
workflow_id="search-map-reduce-v1",
|
||||
inputs={"input": self.input_port},
|
||||
stages=(map_stage, reduce_stage),
|
||||
edges=(
|
||||
ArtifactEdge(PortRef("input"), PortRef("input", "map")),
|
||||
ArtifactEdge(PortRef("partial", "map"), PortRef("partials", "reduce")),
|
||||
),
|
||||
outputs={"result": PortRef("result", "reduce")},
|
||||
max_tasks=limits.max_tasks,
|
||||
max_output_bytes=limits.max_output_bytes,
|
||||
)
|
||||
self.manifest = WorkloadManifest(
|
||||
sdk_api=VersionRange(">=1.0,<2.0"),
|
||||
protocol=VersionRange(">=1,<2"),
|
||||
workload=WorkloadId("similarity-search", "1.0.0"),
|
||||
description=(
|
||||
"Exact top-k Tanimoto molecular similarity search over "
|
||||
"deterministic TSV shards with a bounded merge."
|
||||
),
|
||||
package=PackageSpec("scimesh", package_digest),
|
||||
environment=EnvironmentSpec(
|
||||
"python-process",
|
||||
environment_digest,
|
||||
{"adapter": "sdk-native"},
|
||||
),
|
||||
parameters_schema=_parameters_schema(),
|
||||
workflow=workflow,
|
||||
inputs={"input": self.input_port},
|
||||
outputs={"result": self.output_port},
|
||||
determinism=DeterminismProfile.BYTE_EXACT,
|
||||
trust_modes=(TrustMode.TRUSTED, TrustMode.UNTRUSTED_QUORUM),
|
||||
verifier=VerifierSpec(ComponentRef("exact-artifact", 1), {}),
|
||||
limits=limits,
|
||||
capabilities=("similarity-search",),
|
||||
conformance_profiles=("core-batch-v1",),
|
||||
)
|
||||
self._exact_verifier = ExactArtifactVerifier()
|
||||
|
||||
def definition(self) -> WorkloadDefinition:
|
||||
return WorkloadDefinition(
|
||||
manifest=self.manifest,
|
||||
planner=self,
|
||||
runners={MAP_ENTRY_POINT: self},
|
||||
reducers={REDUCE_ENTRY_POINT: self},
|
||||
verifiers={self._exact_verifier.identity.canonical: self._exact_verifier},
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _string(value: object, name: str) -> str:
|
||||
if not isinstance(value, str) or not value.strip() or len(value) > 200:
|
||||
raise ValueError(f"{name} must be a non-empty string")
|
||||
return value
|
||||
|
||||
@staticmethod
|
||||
def _positive_int(value: object, name: str) -> int:
|
||||
if isinstance(value, bool) or not isinstance(value, int) or value < 1:
|
||||
raise ValueError(f"{name} must be a positive integer")
|
||||
return value
|
||||
|
||||
@staticmethod
|
||||
def _nonnegative_int(value: object, name: str) -> int:
|
||||
if isinstance(value, bool) or not isinstance(value, int) or value < 0:
|
||||
raise ValueError(f"{name} must be a non-negative integer")
|
||||
return value
|
||||
|
||||
@staticmethod
|
||||
def _unit_interval(value: object, name: str) -> float:
|
||||
if isinstance(value, bool) or not isinstance(value, (int, float)):
|
||||
raise ValueError(f"{name} must be a number between 0 and 1")
|
||||
return float(value)
|
||||
|
||||
def validate(self, request: JobRequest) -> ValidatedJob:
|
||||
if request.workload != self.manifest.workload:
|
||||
raise ValueError(
|
||||
"similarity-search received a request for another workload"
|
||||
)
|
||||
parameters = request.parameters
|
||||
unknown = set(parameters) - {
|
||||
"query_id",
|
||||
"query_smiles",
|
||||
"top_k",
|
||||
"threshold",
|
||||
"threshold_direction",
|
||||
"max_rows",
|
||||
"progress_every",
|
||||
}
|
||||
if unknown:
|
||||
raise ValueError(
|
||||
"unsupported similarity-search parameters: "
|
||||
+ ", ".join(sorted(unknown))
|
||||
)
|
||||
query_id = parameters.get("query_id")
|
||||
query_smiles = parameters.get("query_smiles")
|
||||
if (query_id is None) == (query_smiles is None):
|
||||
raise ValueError("exactly one of query_id or query_smiles is required")
|
||||
if query_id is not None:
|
||||
self._string(query_id, "query_id")
|
||||
if query_smiles is not None:
|
||||
self._string(query_smiles, "query_smiles")
|
||||
self._positive_int(parameters.get("top_k", 20), "top_k")
|
||||
if "max_rows" in parameters:
|
||||
self._positive_int(parameters["max_rows"], "max_rows")
|
||||
if "progress_every" in parameters:
|
||||
self._nonnegative_int(parameters["progress_every"], "progress_every")
|
||||
if "threshold" in parameters:
|
||||
self._unit_interval(parameters["threshold"], "threshold")
|
||||
if "threshold_direction" in parameters and parameters[
|
||||
"threshold_direction"
|
||||
] not in {"greater", "less"}:
|
||||
raise ValueError("threshold_direction must be 'greater' or 'less'")
|
||||
return ValidatedJob(request, self._resolved_parameters(request))
|
||||
|
||||
def _resolved_parameters(self, request: JobRequest) -> dict[str, object]:
|
||||
parameters = request.parameters
|
||||
query_id = parameters.get("query_id")
|
||||
if isinstance(query_id, str):
|
||||
query_source: dict[str, str] = {"kind": "chembl_id", "value": query_id}
|
||||
else:
|
||||
query_source = {
|
||||
"kind": "smiles",
|
||||
"value": self._string(parameters.get("query_smiles"), "query_smiles"),
|
||||
}
|
||||
resolved: dict[str, object] = {
|
||||
"query_source": query_source,
|
||||
"top_k": self._positive_int(parameters.get("top_k", 20), "top_k"),
|
||||
"threshold_direction": parameters.get("threshold_direction", "greater"),
|
||||
"fingerprint": {
|
||||
"algorithm": "morgan",
|
||||
"radius": FP_RADIUS,
|
||||
"fp_size": FP_SIZE,
|
||||
},
|
||||
}
|
||||
if "threshold" in parameters:
|
||||
resolved["threshold"] = self._unit_interval(
|
||||
parameters["threshold"], "threshold"
|
||||
)
|
||||
if "max_rows" in parameters:
|
||||
resolved["max_rows"] = self._positive_int(
|
||||
parameters["max_rows"], "max_rows"
|
||||
)
|
||||
if "progress_every" in parameters:
|
||||
resolved["progress_every"] = self._nonnegative_int(
|
||||
parameters["progress_every"], "progress_every"
|
||||
)
|
||||
return resolved
|
||||
|
||||
def plan(self, job: ValidatedJob, context: PlanningContext) -> WorkflowPlan:
|
||||
if not isinstance(job, ValidatedJob):
|
||||
raise ValueError("job must be a ValidatedJob")
|
||||
collection = job.request.inputs.get("input")
|
||||
if collection is None:
|
||||
raise ValueError("similarity-search requires the input port")
|
||||
self.input_port.validate_collection(collection, "job input")
|
||||
input_artifact = collection.items[0].artifact
|
||||
input_path = context.catalog.materialize(input_artifact)
|
||||
workspace = context.workspace
|
||||
workspace.mkdir(parents=True, exist_ok=True)
|
||||
resolved = dict(job.resolved_parameters)
|
||||
query_smiles = self._resolve_query(input_path, job.request.parameters)
|
||||
resolved["query_smiles"] = query_smiles
|
||||
max_rows = resolved.get("max_rows")
|
||||
shard_paths = write_search_shards(
|
||||
input_path,
|
||||
workspace,
|
||||
self.shard_rows,
|
||||
int(max_rows) if isinstance(max_rows, int) else None,
|
||||
)
|
||||
task_parameters = {
|
||||
key: value for key, value in resolved.items() if key in set(_MAP_PARAMETERS)
|
||||
}
|
||||
negotiated = context.negotiated
|
||||
map_stage = self.manifest.workflow.stages[0]
|
||||
assert map_stage.verifier is not None
|
||||
tasks: list[TaskSpec] = []
|
||||
for index, path in enumerate(shard_paths):
|
||||
sealed = context.sink.seal(
|
||||
path,
|
||||
declaration=self.input_port.schema,
|
||||
)
|
||||
tasks.append(
|
||||
TaskSpec(
|
||||
workload=self.manifest.workload,
|
||||
package_digest=self.manifest.package.digest,
|
||||
manifest_digest=self.manifest.digest,
|
||||
trust_mode=job.request.trust_mode,
|
||||
sdk_api_version=negotiated.sdk_api_version,
|
||||
protocol_version=negotiated.protocol_version,
|
||||
manifest_schema_version=self.manifest.manifest_schema_version,
|
||||
workflow_schema_version=self.manifest.workflow.schema_version,
|
||||
environment_digest=self.manifest.environment.digest,
|
||||
verifier=map_stage.verifier,
|
||||
selected_features=negotiated.selected_features,
|
||||
optional_fallbacks=negotiated.optional_fallbacks,
|
||||
task_key=f"map/{index:08d}",
|
||||
stage_id="map",
|
||||
parameters=task_parameters,
|
||||
inputs={"input": ArtifactCollection.single(sealed)},
|
||||
expected_outputs={"partial": self.partial_port},
|
||||
resources=map_stage.resources,
|
||||
execution=map_stage.execution,
|
||||
)
|
||||
)
|
||||
return WorkflowPlan(
|
||||
workload=self.manifest.workload,
|
||||
package_digest=self.manifest.package.digest,
|
||||
manifest_digest=self.manifest.digest,
|
||||
trust_mode=job.request.trust_mode,
|
||||
sdk_api_version=negotiated.sdk_api_version,
|
||||
protocol_version=negotiated.protocol_version,
|
||||
manifest_schema_version=self.manifest.manifest_schema_version,
|
||||
workflow_schema_version=self.manifest.workflow.schema_version,
|
||||
environment_digest=self.manifest.environment.digest,
|
||||
verifier=self.manifest.verifier.verifier,
|
||||
selected_features=negotiated.selected_features,
|
||||
optional_fallbacks=negotiated.optional_fallbacks,
|
||||
workflow_id=self.manifest.workflow.workflow_id,
|
||||
resolved_parameters=resolved,
|
||||
tasks=tuple(tasks),
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _resolve_query(input_path: Path, parameters: Mapping[str, object]) -> str:
|
||||
query_id = parameters.get("query_id")
|
||||
if isinstance(query_id, str):
|
||||
record = find_molecule_by_id(input_path, query_id)
|
||||
return Chem.MolToSmiles(record.molecule, canonical=True)
|
||||
supplied = parameters["query_smiles"]
|
||||
assert isinstance(supplied, str)
|
||||
molecule = parse_smiles(supplied)
|
||||
if molecule is None:
|
||||
raise ValueError("query_smiles is invalid")
|
||||
return Chem.MolToSmiles(molecule, canonical=True)
|
||||
|
||||
def run(self, context: TaskContext) -> OutputManifest:
|
||||
context.cancellation.raise_if_cancelled()
|
||||
collection = context.task.inputs.get("input")
|
||||
if collection is None:
|
||||
raise ValueError("search map task requires one input collection")
|
||||
self.input_port.validate_collection(collection, "search map input")
|
||||
source = context.catalog.materialize(collection.items[0].artifact)
|
||||
workspace = context.workspace
|
||||
workspace.mkdir(parents=True, exist_ok=True)
|
||||
input_path = workspace / "input"
|
||||
output_path = workspace / "result.csv"
|
||||
if source.resolve() != input_path.resolve():
|
||||
shutil.copyfile(source, input_path)
|
||||
metrics = run_search_shard(
|
||||
input_path,
|
||||
context.task.parameters,
|
||||
output_path,
|
||||
)
|
||||
context.cancellation.raise_if_cancelled()
|
||||
sealed = context.sink.seal(
|
||||
output_path,
|
||||
declaration=self.partial_port.schema,
|
||||
)
|
||||
return OutputManifest(
|
||||
context.task.task_key,
|
||||
{"partial": ArtifactCollection.single(sealed)},
|
||||
metrics,
|
||||
context.provenance,
|
||||
).validate_against(
|
||||
context.task.expected_outputs,
|
||||
max_output_bytes=self.manifest.limits.max_output_bytes,
|
||||
)
|
||||
|
||||
def reduce(self, context: ReduceContext) -> OutputManifest:
|
||||
context.cancellation.raise_if_cancelled()
|
||||
collection = context.accepted_inputs.get("partials")
|
||||
if (
|
||||
collection is None
|
||||
or collection.kind is not CollectionKind.KEYED
|
||||
or not collection.items
|
||||
):
|
||||
raise ValueError(
|
||||
"search reducer requires a non-empty keyed partial collection"
|
||||
)
|
||||
self.manifest.workflow.stages[1].inputs["partials"].validate_collection(
|
||||
collection,
|
||||
"search reducer partials",
|
||||
)
|
||||
workspace = context.workspace
|
||||
workspace.mkdir(parents=True, exist_ok=True)
|
||||
indexed_items: list[tuple[int, ArtifactItem]] = []
|
||||
for item in collection.items:
|
||||
key = item.key or ""
|
||||
prefix = "map."
|
||||
raw_index = key[len(prefix) :] if key.startswith(prefix) else ""
|
||||
if len(raw_index) != 8 or not raw_index.isdigit():
|
||||
raise ValueError("search partial key must use map.<eight-digit-index>")
|
||||
indexed_items.append((int(raw_index), item))
|
||||
expected_keys = context.task.expected_input_keys.get("partials")
|
||||
if expected_keys is None or {item.key for item in collection.items} != set(
|
||||
expected_keys
|
||||
):
|
||||
raise ValueError(
|
||||
"search partial keys do not match the coordinator expected set"
|
||||
)
|
||||
if sorted(index for index, _ in indexed_items) != list(
|
||||
range(len(indexed_items))
|
||||
):
|
||||
raise ValueError("search partial keys must be complete and contiguous")
|
||||
partial_paths: list[Path] = []
|
||||
for index, item in sorted(indexed_items):
|
||||
artifact: ArtifactRef = item.artifact
|
||||
source = context.catalog.materialize(artifact)
|
||||
target = workspace / artifact.artifact_id
|
||||
if source.resolve() != target.resolve():
|
||||
shutil.copyfile(source, target)
|
||||
if _sha256_file(target) != artifact.sha256:
|
||||
raise ValueError("materialized partial checksum does not match")
|
||||
partial_paths.append(target)
|
||||
result_path = workspace / "result.csv"
|
||||
metrics = merge_search_partials(
|
||||
partial_paths,
|
||||
context.task.parameters,
|
||||
result_path,
|
||||
)
|
||||
context.cancellation.raise_if_cancelled()
|
||||
sealed = context.sink.seal(
|
||||
result_path,
|
||||
declaration=self.output_port.schema,
|
||||
)
|
||||
return OutputManifest(
|
||||
context.task.task_key,
|
||||
{"result": ArtifactCollection.single(sealed)},
|
||||
metrics,
|
||||
context.provenance,
|
||||
).validate_against(
|
||||
context.task.expected_outputs,
|
||||
max_output_bytes=self.manifest.limits.max_output_bytes,
|
||||
)
|
||||
|
||||
|
||||
def similarity_search_sdk_definition(
|
||||
*,
|
||||
shard_rows: int = 10_000,
|
||||
package_digest: str | None = None,
|
||||
environment_digest: str | None = None,
|
||||
) -> SimilaritySearchSDKWorkload:
|
||||
"""Build the SDK-built similarity-search definition for tests."""
|
||||
return SimilaritySearchSDKWorkload(
|
||||
shard_rows=shard_rows,
|
||||
package_digest=package_digest or current_scimesh_package_digest(),
|
||||
environment_digest=environment_digest or current_environment_digest(),
|
||||
)
|
||||
|
||||
|
||||
def workload_definition() -> WorkloadDefinition:
|
||||
"""Installed entry-point factory for the SDK-built similarity-search."""
|
||||
return similarity_search_sdk_definition().definition()
|
||||
Reference in New Issue
Block a user