Replace legacy distributed protocol with SDK-built workloads
This commit is contained in:
Emil
@@ -0,0 +1,40 @@
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"""SDK-built ``similarity-graph`` workload.
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A user workload script built on the SciMesh Workload SDK. See ``core.py`` for
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the block/pair scientific core and ``definition.py`` for the manifest-backed
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planner/runner/reducer handlers.
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"""
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from .core import (
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block_pair_from_key,
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check_pair_coverage,
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compute_block_edges,
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merge_edge_partials,
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parse_molecule_blocks,
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read_block_rows,
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write_block_tsv,
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write_edge_csv,
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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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SimilarityGraphSDKWorkload,
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similarity_graph_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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"SimilarityGraphSDKWorkload",
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"block_pair_from_key",
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"check_pair_coverage",
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"compute_block_edges",
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"merge_edge_partials",
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"parse_molecule_blocks",
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"read_block_rows",
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"similarity_graph_sdk_definition",
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"workload_definition",
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"write_block_tsv",
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"write_edge_csv",
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]
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@@ -0,0 +1,249 @@
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"""Scientific core for the SDK-built ``similarity-graph`` workload.
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Molecules are parsed once into deterministic row-ordered blocks; every block
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pair ``(i, j)`` with ``i <= j`` becomes one map task (diagonal tasks compare
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pairs ``a < b`` inside a block, off-diagonal tasks compare every molecule
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across two blocks). The reducer enforces the CTX-10 pair-coverage invariant:
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the union of task pair sets must equal all unordered molecule pairs exactly
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once, and the merged edge set must contain no duplicate unordered pair.
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"""
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from __future__ import annotations
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import csv
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from pathlib import Path
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from typing import Iterable, Sequence
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from rdkit import Chem, DataStructs
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from scimesh.chemistry.dataset import iter_valid_molecules
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from scimesh.chemistry.fingerprints import fingerprint
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EDGE_COLUMNS = ("source_id", "target_id", "similarity")
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MoleculeBlock = list[tuple[str, str]] # (chembl_id, smiles), row-ordered
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def parse_molecule_blocks(
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input_path: Path,
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block_size: int,
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max_rows: int | None = None,
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) -> tuple[list[MoleculeBlock], dict[str, int]]:
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"""Parse valid molecules into deterministic row-ordered blocks.
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Mirrors the local reference's strictness: an empty or duplicate
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``chembl_id`` fails the run, because the edge identity is the molecule id.
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Invalid SMILES rows are skipped and counted.
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"""
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if (
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isinstance(block_size, bool)
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or not isinstance(block_size, int)
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or block_size < 1
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):
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raise ValueError("block_size must be a positive integer")
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from scimesh.chemistry.dataset import DatasetStats
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stats = DatasetStats()
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blocks: list[MoleculeBlock] = []
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current: MoleculeBlock = []
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seen_ids: set[str] = set()
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for record in iter_valid_molecules(input_path, stats, max_rows=max_rows):
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if not record.molecule_id:
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raise ValueError("dataset contains an empty chembl_id")
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if record.molecule_id in seen_ids:
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raise ValueError(
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f"dataset contains a duplicate chembl_id: {record.molecule_id}"
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)
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seen_ids.add(record.molecule_id)
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current.append((record.molecule_id, record.smiles))
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if len(current) == block_size:
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blocks.append(current)
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current = []
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if current:
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blocks.append(current)
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if not blocks:
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raise ValueError("dataset has no valid molecules")
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return blocks, {
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"rows_scanned": stats.scanned,
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"valid_molecules": stats.valid,
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"invalid_smiles": stats.invalid,
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"block_count": len(blocks),
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}
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def write_block_tsv(rows: MoleculeBlock, path: Path) -> None:
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"""Write one molecule block as a header TSV with the input column names."""
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path.parent.mkdir(parents=True, exist_ok=True)
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with path.open("w", encoding="utf-8", newline="") as destination:
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writer = csv.DictWriter(
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destination,
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fieldnames=["chembl_id", "canonical_smiles"],
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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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for molecule_id, smiles in rows:
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writer.writerow({"chembl_id": molecule_id, "canonical_smiles": smiles})
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def read_block_rows(path: Path) -> MoleculeBlock:
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rows: MoleculeBlock = []
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with path.open("r", encoding="utf-8", newline="") as source:
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reader = csv.DictReader(source, delimiter="\t")
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for row in reader:
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molecule_id = row.get("chembl_id", "")
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smiles = row.get("canonical_smiles", "")
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if not molecule_id or not smiles:
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raise ValueError("block artifact contains an invalid row")
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rows.append((molecule_id, smiles))
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return rows
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def compute_block_edges(
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left: MoleculeBlock,
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right: MoleculeBlock,
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threshold: float,
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threshold_direction: str,
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) -> list[tuple[str, str, float]]:
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"""Compare one planned block pair and emit only thresholded edges."""
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if not 0.0 <= threshold <= 1.0:
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raise ValueError("threshold must be between 0 and 1")
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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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left_fingerprints = [
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(molecule_id, fingerprint(Chem.MolFromSmiles(smiles)))
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for molecule_id, smiles in left
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]
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right_fingerprints = [
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(molecule_id, fingerprint(Chem.MolFromSmiles(smiles)))
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for molecule_id, smiles in right
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]
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diagonal = left is right or left == right
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edges: list[tuple[str, str, float]] = []
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for left_index, (left_id, left_fp) in enumerate(left_fingerprints):
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right_start = left_index + 1 if diagonal else 0
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for right_index in range(right_start, len(right_fingerprints)):
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right_id, right_fp = right_fingerprints[right_index]
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similarity = DataStructs.TanimotoSimilarity(left_fp, right_fp)
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matches_threshold = (
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similarity >= threshold
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if threshold_direction == "greater"
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else similarity <= threshold
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)
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if matches_threshold:
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edges.append((left_id, right_id, similarity))
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return edges
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def write_edge_csv(output_path: Path, edges: Iterable[tuple[str, str, float]]) -> None:
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"""Write an edge table CSV with six-decimal similarity values.
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Uses the CSV module's default ``\\r\\n`` line terminator so the bytes match
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the local ``write_graph_edges`` reference exactly.
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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(EDGE_COLUMNS))
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writer.writeheader()
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for source_id, target_id, similarity in edges:
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writer.writerow(
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{
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"source_id": source_id,
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"target_id": target_id,
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"similarity": f"{similarity:.6f}",
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}
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)
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def read_edge_csv(path: Path) -> list[tuple[str, str, float]]:
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"""Read a materialized edge CSV with strict row validation."""
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if not path.is_file():
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raise ValueError("materialized edge partial is missing")
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edges: list[tuple[str, str, float]] = []
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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 ()) != EDGE_COLUMNS:
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raise ValueError("edge partial has an invalid CSV header")
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for row in reader:
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if set(row) != set(EDGE_COLUMNS):
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raise ValueError("edge partial has an invalid row")
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source_id = row["source_id"]
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target_id = row["target_id"]
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if not source_id or not target_id:
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raise ValueError("edge partial contains an empty molecule id")
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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("edge partial has an invalid similarity") from error
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if not 0 <= similarity <= 1:
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raise ValueError("edge partial has an invalid similarity")
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edges.append((source_id, target_id, similarity))
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return edges
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def block_pair_from_key(key: str) -> tuple[int, int]:
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"""Parse ``map.<i>x<j>`` partial keys into block indices."""
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prefix = "map."
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if not key.startswith(prefix):
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raise ValueError("graph partial key must use map.<left>x<right>")
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raw = key[len(prefix) :]
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left_raw, separator, right_raw = raw.partition("x")
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if not separator or not left_raw.isdigit() or not right_raw.isdigit():
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raise ValueError("graph partial key must use map.<left>x<right>")
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return int(left_raw), int(right_raw)
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def check_pair_coverage(pairs: Sequence[tuple[int, int]]) -> None:
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"""Enforce the pair-coverage invariant: every block pair exactly once.
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``pairs`` must contain every ``(i, j)`` with ``0 <= i <= j < n`` exactly
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once, where ``n`` is derived from the largest referenced block index.
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"""
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if not pairs:
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raise ValueError("graph partial keys cover no block pairs")
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unique = set(pairs)
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if len(unique) != len(pairs):
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raise ValueError("graph partial keys contain a duplicate block pair")
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if any(left > right or left < 0 or right < 0 for left, right in unique):
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raise ValueError("graph partial keys reference an invalid block pair")
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n = max(right for _, right in unique) + 1
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expected = {(left, right) for left in range(n) for right in range(left, n)}
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missing = sorted(expected - unique)
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if missing:
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raise ValueError(
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"graph partial keys do not cover the full block pair set: "
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+ ", ".join(f"{left}x{right}" for left, right in missing)
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)
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unexpected = sorted(unique - expected)
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if unexpected:
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raise ValueError(
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"graph partial keys cover pairs outside the block pair set: "
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+ ", ".join(f"{left}x{right}" for left, right in unexpected)
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)
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def merge_edge_partials(
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partial_paths: Sequence[Path],
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output_path: Path,
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) -> dict[str, int]:
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"""Merge edge partials with duplicate detection and deterministic sort.
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The merged edge list is sorted by ``(source_id, target_id, -similarity)``,
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matching the local brute-force reference exactly.
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"""
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if not partial_paths:
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raise ValueError("graph reducer requires at least one edge partial")
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edges: list[tuple[str, str, float]] = []
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seen_pairs: set[tuple[str, str]] = set()
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for path in partial_paths:
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for source_id, target_id, similarity in read_edge_csv(path):
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unordered = (min(source_id, target_id), max(source_id, target_id))
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if unordered in seen_pairs:
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raise ValueError(
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f"graph partials contain a duplicate unordered pair: {unordered[0]}, {unordered[1]}"
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)
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seen_pairs.add(unordered)
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edges.append((source_id, target_id, similarity))
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edges.sort(key=lambda edge: (edge[0], edge[1], -edge[2]))
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write_edge_csv(output_path, edges)
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return {"partial_count": len(partial_paths), "edges_emitted": len(edges)}
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@@ -0,0 +1,500 @@
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"""SDK-built ``similarity-graph`` workload definition and handlers.
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Built directly on the ``core-batch-v1`` profile: molecules are parsed once
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into deterministic row-ordered blocks, every block pair ``(i, j)`` with
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``i <= j`` becomes one map task, and the reducer enforces the CTX-10
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pair-coverage invariant (every unordered molecule pair compared exactly once)
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before emitting a deterministically sorted edge list that is byte-identical
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to the local brute-force reference.
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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 ...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 ...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 ..environment import current_environment_digest, current_scimesh_package_digest
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from .core import (
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block_pair_from_key,
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check_pair_coverage,
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compute_block_edges,
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merge_edge_partials,
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parse_molecule_blocks,
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read_block_rows,
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write_block_tsv,
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write_edge_csv,
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)
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MAP_ENTRY_POINT = "scimesh.workloads.graph.definition:map_graph@v1"
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REDUCE_ENTRY_POINT = "scimesh.workloads.graph.definition:reduce_graph@v1"
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_MAP_PARAMETERS = ("left_block", "right_block", "threshold", "threshold_direction")
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_REDUCE_PARAMETERS = ("threshold", "threshold_direction", "block_size", "max_rows")
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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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"threshold": {"type": "number", "minimum": 0, "maximum": 1},
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"threshold_direction": {"enum": ["greater", "less"]},
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"block_size": {"type": "integer", "minimum": 1},
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"max_rows": {"type": "integer", "minimum": 1},
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},
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"required": ["threshold"],
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}
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def _molecule_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 _edge_schema() -> ArtifactSchema:
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return ArtifactSchema(
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SchemaRef("similarity-edge-table", 1),
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"text/csv",
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"utf-8",
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max_bytes=100 * 1024 * 1024 * 1024,
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validator=ComponentRef("delimited-table", 1),
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validator_configuration={
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"columns": ["source_id", "target_id", "similarity"],
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},
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max_records=1_000_000_000,
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canonicalizer="similarity-edge-table-v1",
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)
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||||
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||||
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class SimilarityGraphSDKWorkload:
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"""Manifest-backed planner, runner, and reducer for similarity-graph."""
|
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|
||||
def __init__(
|
||||
self,
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||||
*,
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||||
package_digest: str,
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||||
environment_digest: str,
|
||||
) -> None:
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self.entry_point = MAP_ENTRY_POINT
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self.input_port = PortSpec(_molecule_schema())
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self.block_port = PortSpec(_molecule_schema())
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self.partial_port = PortSpec(_edge_schema())
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self.output_port = PortSpec(_edge_schema())
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resources = ResourceRequirements(
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profile="graph-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(
|
||||
profile="graph-python-process-v1",
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||||
network=NetworkPolicy.TRUSTED,
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||||
timeout_seconds=3600,
|
||||
checkpoint=CheckpointPolicy(),
|
||||
)
|
||||
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={"left": self.block_port, "right": self.block_port},
|
||||
outputs={"partial": self.partial_port},
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||||
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="graph-block-pairs-v1",
|
||||
inputs={"input": self.input_port},
|
||||
stages=(map_stage, reduce_stage),
|
||||
edges=(
|
||||
ArtifactEdge(PortRef("input"), PortRef("left", "map")),
|
||||
ArtifactEdge(PortRef("input"), PortRef("right", "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-graph", "1.0.0"),
|
||||
description=(
|
||||
"Exact sparse Tanimoto similarity graph over deterministic "
|
||||
"block pairs with a duplicate-safe, coverage-checked 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-graph",),
|
||||
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 _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)
|
||||
|
||||
@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
|
||||
|
||||
def validate(self, request: JobRequest) -> ValidatedJob:
|
||||
if request.workload != self.manifest.workload:
|
||||
raise ValueError("similarity-graph received a request for another workload")
|
||||
parameters = request.parameters
|
||||
unknown = set(parameters) - {
|
||||
"threshold",
|
||||
"threshold_direction",
|
||||
"block_size",
|
||||
"max_rows",
|
||||
}
|
||||
if unknown:
|
||||
raise ValueError(
|
||||
"unsupported similarity-graph parameters: " + ", ".join(sorted(unknown))
|
||||
)
|
||||
threshold = parameters.get("threshold")
|
||||
if threshold is None:
|
||||
raise ValueError("threshold is required")
|
||||
self._unit_interval(threshold, "threshold")
|
||||
if "threshold_direction" in parameters and parameters[
|
||||
"threshold_direction"
|
||||
] not in {"greater", "less"}:
|
||||
raise ValueError("threshold_direction must be 'greater' or 'less'")
|
||||
if "block_size" in parameters:
|
||||
self._positive_int(parameters["block_size"], "block_size")
|
||||
if "max_rows" in parameters:
|
||||
self._positive_int(parameters["max_rows"], "max_rows")
|
||||
return ValidatedJob(request, request.parameters)
|
||||
|
||||
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-graph 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)
|
||||
parameters = job.resolved_parameters
|
||||
threshold = self._unit_interval(parameters.get("threshold"), "threshold")
|
||||
direction = parameters.get("threshold_direction", "greater")
|
||||
if direction not in {"greater", "less"}:
|
||||
raise ValueError("threshold_direction must be 'greater' or 'less'")
|
||||
block_size = int(parameters.get("block_size", 1_000))
|
||||
max_rows = parameters.get("max_rows")
|
||||
blocks, stats = parse_molecule_blocks(
|
||||
input_path,
|
||||
block_size,
|
||||
int(max_rows) if isinstance(max_rows, int) else None,
|
||||
)
|
||||
task_parameters = {
|
||||
"threshold": threshold,
|
||||
"threshold_direction": direction,
|
||||
}
|
||||
negotiated = context.negotiated
|
||||
map_stage = self.manifest.workflow.stages[0]
|
||||
assert map_stage.verifier is not None
|
||||
block_refs: list[ArtifactRef] = []
|
||||
for index, block in enumerate(blocks):
|
||||
path = workspace / f"block-{index:04d}.tsv"
|
||||
write_block_tsv(block, path)
|
||||
block_refs.append(
|
||||
context.sink.seal(
|
||||
path,
|
||||
declaration=self.block_port.schema,
|
||||
)
|
||||
)
|
||||
tasks: list[TaskSpec] = []
|
||||
for left in range(len(blocks)):
|
||||
for right in range(left, len(blocks)):
|
||||
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/{left:04d}x{right:04d}",
|
||||
stage_id="map",
|
||||
parameters={
|
||||
**task_parameters,
|
||||
"left_block": left,
|
||||
"right_block": right,
|
||||
},
|
||||
inputs={
|
||||
"left": ArtifactCollection.single(block_refs[left]),
|
||||
"right": ArtifactCollection.single(block_refs[right]),
|
||||
},
|
||||
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=dict(parameters),
|
||||
tasks=tuple(tasks),
|
||||
)
|
||||
|
||||
def run(self, context: TaskContext) -> OutputManifest:
|
||||
context.cancellation.raise_if_cancelled()
|
||||
parameters = context.task.parameters
|
||||
left_block = parameters.get("left_block")
|
||||
right_block = parameters.get("right_block")
|
||||
if (
|
||||
isinstance(left_block, bool)
|
||||
or not isinstance(left_block, int)
|
||||
or isinstance(right_block, bool)
|
||||
or not isinstance(right_block, int)
|
||||
):
|
||||
raise ValueError("graph map task requires block indices")
|
||||
diagonal = left_block == right_block
|
||||
left_collection = context.task.inputs.get("left")
|
||||
right_collection = context.task.inputs.get("right")
|
||||
if left_collection is None or right_collection is None:
|
||||
raise ValueError("graph map task requires left and right block inputs")
|
||||
self.block_port.validate_collection(left_collection, "graph map left input")
|
||||
self.block_port.validate_collection(right_collection, "graph map right input")
|
||||
workspace = context.workspace
|
||||
workspace.mkdir(parents=True, exist_ok=True)
|
||||
left_path = context.catalog.materialize(left_collection.items[0].artifact)
|
||||
right_path = context.catalog.materialize(right_collection.items[0].artifact)
|
||||
left_rows = read_block_rows(left_path)
|
||||
right_rows = (
|
||||
left_rows
|
||||
if diagonal and left_path.resolve() == right_path.resolve()
|
||||
else read_block_rows(right_path)
|
||||
)
|
||||
threshold = self._unit_interval(parameters.get("threshold"), "threshold")
|
||||
direction = parameters.get("threshold_direction", "greater")
|
||||
if direction not in {"greater", "less"}:
|
||||
raise ValueError("threshold_direction must be 'greater' or 'less'")
|
||||
checked_pairs = (
|
||||
len(left_rows) * (len(left_rows) - 1) // 2
|
||||
if diagonal
|
||||
else len(left_rows) * len(right_rows)
|
||||
)
|
||||
edges = compute_block_edges(
|
||||
left_rows,
|
||||
right_rows,
|
||||
threshold,
|
||||
direction,
|
||||
)
|
||||
output_path = workspace / "result.csv"
|
||||
write_edge_csv(output_path, edges)
|
||||
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)},
|
||||
{"checked_pairs": checked_pairs, "edges_emitted": len(edges)},
|
||||
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(
|
||||
"graph reducer requires a non-empty keyed partial collection"
|
||||
)
|
||||
self.manifest.workflow.stages[1].inputs["partials"].validate_collection(
|
||||
collection,
|
||||
"graph reducer partials",
|
||||
)
|
||||
pairs = [block_pair_from_key(item.key or "") for item in collection.items]
|
||||
check_pair_coverage(pairs)
|
||||
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(
|
||||
"graph partial keys do not match the coordinator expected set"
|
||||
)
|
||||
workspace = context.workspace
|
||||
workspace.mkdir(parents=True, exist_ok=True)
|
||||
partial_paths: list[Path] = []
|
||||
for item in sorted(collection.items, key=lambda value: value.key or ""):
|
||||
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_edge_partials(partial_paths, 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_graph_sdk_definition(
|
||||
*,
|
||||
package_digest: str | None = None,
|
||||
environment_digest: str | None = None,
|
||||
) -> SimilarityGraphSDKWorkload:
|
||||
"""Build the SDK-based similarity-graph definition for tests."""
|
||||
return SimilarityGraphSDKWorkload(
|
||||
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-based similarity-graph."""
|
||||
return similarity_graph_sdk_definition().definition()
|
||||
Reference in New Issue
Block a user