feat: map DeepSeek Chat sampling robustness
This commit is contained in:
wuyang
@@ -0,0 +1,334 @@
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import { createHash } from "node:crypto";
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import { readFileSync, statSync, writeFileSync } from "node:fs";
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import { resolve } from "node:path";
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const root = resolve(import.meta.dirname, "..");
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const paths = {
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sampling: resolve(
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root,
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"src/data/deepseek-v2-lite-chat-sampling.json",
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),
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evaluation: resolve(
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root,
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"src/data/deepseek-v2-lite-chat-sampling-eval.json",
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),
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rerun: resolve(
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root,
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"src/data/deepseek-v2-lite-chat-sampling-repro-r0r1.json",
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),
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reproduction: resolve(
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root,
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"src/data/deepseek-v2-lite-chat-sampling-reproduction.json",
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),
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output: resolve(
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root,
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"src/data/deepseek-v2-lite-chat-sampling-compact.json",
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),
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};
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const readJson = (path) => JSON.parse(readFileSync(path, "utf8"));
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const sha256 = (path) => createHash("sha256")
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.update(readFileSync(path))
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.digest("hex");
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const artifact = (path) => ({
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bytes: statSync(path).size,
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sha256: sha256(path),
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});
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const mean = (values) => (
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values.length
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? values.reduce((total, value) => total + value, 0) / values.length
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: null
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);
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const sampling = readJson(paths.sampling);
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const evaluation = readJson(paths.evaluation);
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const reproduction = readJson(paths.reproduction);
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const samplingArtifact = artifact(paths.sampling);
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const evaluationArtifact = artifact(paths.evaluation);
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const rerunArtifact = artifact(paths.rerun);
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if (evaluation.input.sampling_sha256 !== samplingArtifact.sha256) {
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throw new Error("sampling evaluator input hash contract failed");
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}
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if (reproduction.formal.sha256 !== samplingArtifact.sha256) {
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throw new Error("reproduction formal hash contract failed");
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}
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if (reproduction.rerun.sha256 !== rerunArtifact.sha256) {
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throw new Error("reproduction rerun hash contract failed");
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}
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if (
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reproduction.summary.all_preregistered_fields_exact
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!== reproduction.summary.cells
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) {
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throw new Error("sampling reproduction is not exact");
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}
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const conditions = sampling.seed_contract.condition_row_order;
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const edges = Object.keys(
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sampling.summary.by_source_edge[sampling.sources[0].id],
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);
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const edgeLabels = {
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system_eos: "System on − off · EOS",
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system_bos: "System on − off · BOS",
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system_x: "System on − off · x",
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system_period: "System on − off · 句点",
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bos_at_s0: "BOS − EOS · system off",
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bos_at_s1: "BOS − EOS · system on",
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x_at_s0: "x − EOS · system off",
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x_at_s1: "x − EOS · system on",
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period_at_s0: "句点 − EOS · system off",
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period_at_s1: "句点 − EOS · system on",
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};
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const evalKey = (sourceId, baseSeed, condition) => (
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`${sourceId}\0${baseSeed}\0${condition}`
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);
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const evaluationByKey = new Map(
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evaluation.rows.map((row) => [
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evalKey(row.source_id, row.base_seed, row.condition),
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row,
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]),
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);
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const sourceRows = sampling.sources.map((source) => {
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const conditionRows = Object.fromEntries(conditions.map((condition) => {
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const rawSummary = sampling.summary.by_source_condition[
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source.id
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][condition];
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const evalSummary = evaluation.summary.by_source_condition[
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source.id
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][condition];
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const samples = source.runs.map((run) => {
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const output = run.outputs.find(
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(candidate) => candidate.condition === condition,
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);
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const assessed = evaluationByKey.get(
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evalKey(source.id, run.base_seed, condition),
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);
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if (!output || !assessed) {
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throw new Error(
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`sample/evaluation row missing: ${source.id}/${run.base_seed}/${condition}`,
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);
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}
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return {
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replicate: run.replicate_label,
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baseSeed: run.base_seed,
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runSeed: run.run_seed,
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generatedTokens: output.generated_tokens,
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hitEos: output.hit_eos,
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truncated: output.stopped_at_max_new_tokens,
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trajectoryHash: output.generated_token_ids_sha256,
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textHash: output.text_sha256,
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preview: output.text.replace(/\s+/g, " ").trim().slice(0, 220),
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completionClass: assessed.completion_class,
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math: assessed.task_evaluation && source.domain === "math"
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? {
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predicted: assessed.task_evaluation.predicted_final,
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gold: assessed.task_evaluation.gold_final,
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exact: assessed.task_evaluation.fixed_budget_numeric_exact,
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method: assessed.task_evaluation.extraction_method,
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}
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: null,
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code: assessed.task_evaluation && source.domain === "code"
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? {
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ast: assessed.task_evaluation.python_ast_parse,
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status: assessed.task_evaluation.execution.status,
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passed: assessed.task_evaluation.fixed_budget_tests_pass,
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cacheHit: assessed.task_evaluation.execution_cache_hit,
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}
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: null,
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};
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});
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return [condition, {
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samples: rawSummary.samples,
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naturalEos: rawSummary.natural_eos,
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truncated: rawSummary.budget_truncated,
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uniqueTrajectories: rawSummary.unique_generated_token_hashes,
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greedyInSamples: rawSummary.greedy_full_trajectory_in_sample_set,
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pairwiseSimilarity: rawSummary.pairwise_token_similarity,
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generatedTokens: rawSummary.generated_tokens,
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evaluation: evalSummary,
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trajectories: samples,
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}];
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}));
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return {
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id: source.id,
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domain: source.domain,
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label: source.label,
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sourceCharacters: source.source_characters,
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sourceTokens: source.source_tokens,
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conditions: conditionRows,
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edges: Object.fromEntries(edges.map((edge) => [
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edge,
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{
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...sampling.summary.by_source_edge[source.id][edge],
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evaluation: evaluation.summary.by_source_edge[source.id][edge],
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},
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])),
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};
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});
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const conditionSummary = Object.fromEntries(conditions.map((condition) => {
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const sourceConditions = sourceRows.map(
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(source) => source.conditions[condition],
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);
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const assessed = evaluation.summary.by_condition[condition];
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return [condition, {
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outputs: assessed.outputs,
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naturalEos: assessed.natural_eos,
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truncated: assessed.budget_truncated,
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meanGeneratedTokens: assessed.mean_generated_tokens,
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uniqueTrajectoriesAcrossSourceSets: sourceConditions.reduce(
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(total, row) => total + row.uniqueTrajectories,
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0,
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),
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fullEightWayDiversitySets: sourceConditions.filter(
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(row) => row.uniqueTrajectories === 8,
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).length,
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greedyIncludedSourceSets: sourceConditions.filter(
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(row) => row.greedyInSamples,
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).length,
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math: assessed.math,
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code: assessed.code,
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}];
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}));
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const edgeSummary = Object.fromEntries(edges.map((edge) => {
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const rows = sourceRows.map((source) => source.edges[edge]);
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return [edge, {
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label: edgeLabels[edge],
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sources: rows.length,
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meanAlignedSimilarity: mean(rows.map(
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(row) => row.batch_seed_aligned_similarity.mean,
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)),
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meanSymmetricNearestSimilarity: mean(rows.map(
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(row) => row.symmetric_mean_nearest_neighbor_similarity,
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)),
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exactHashIntersections: rows.reduce(
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(total, row) => total + row.generated_hash_set_intersection,
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0,
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),
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exactHashUnion: rows.reduce(
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(total, row) => total + row.generated_hash_set_union,
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0,
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),
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naturalEosDeltaRightMinusLeft: rows.reduce(
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(total, row) => (
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total + row.natural_eos_count_difference_right_minus_left
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),
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0,
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),
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}];
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}));
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const result = {
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schemaVersion: 1,
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generatedAt: new Date().toISOString(),
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contract: {
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protocolId: sampling.protocol_id,
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model: sampling.model.repo,
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revision: sampling.model.revision,
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checkpointIdentity: sampling.model.checkpoint_identity,
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conditions,
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conditionFactors: sampling.generation_contract.conditions,
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edges,
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edgeLabels,
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sources: sampling.sources.length,
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replicates: sampling.seed_contract.executed_base_seeds.length,
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baseSeeds: sampling.seed_contract.executed_base_seeds,
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decode: {
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doSample: sampling.generation_contract.do_sample,
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temperature: sampling.generation_contract.temperature,
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topP: sampling.generation_contract.top_p,
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topK: sampling.generation_contract.top_k,
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maxNewTokens: sampling.generation_contract.max_new_tokens,
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},
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batchSeedAlignedNotCommonRandomNumbers: (
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sampling.seed_contract.batch_seed_aligned_not_common_random_numbers
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),
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counterfactualBoundary: (
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sampling.generation_contract.counterfactual_boundary
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),
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},
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headline: {
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outputs: sampling.summary.outputs,
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naturalEos: sampling.summary.natural_eos,
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truncated: sampling.summary.budget_truncated,
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uniqueTrajectoryHashes: (
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sampling.summary.unique_generated_token_hashes
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),
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firstTwoSeedComparableCells: (
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sampling.summary.first_two_seeds.comparable_cells
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),
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firstTwoSeedDifferentTrajectories: (
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sampling.summary.first_two_seeds.different_trajectories
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),
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promptHashExact: (
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sampling.source_contract.prompt_hash_audit.exact
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),
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promptHashCells: (
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sampling.source_contract.prompt_hash_audit.cells
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),
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greedyIncludedSourceConditionSets: sourceRows.reduce(
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(total, source) => total + conditions.filter(
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(condition) => source.conditions[condition].greedyInSamples,
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).length,
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0,
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),
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sourceConditionSets: sourceRows.length * conditions.length,
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mathExact: evaluation.summary.math.fixed_budget_exact,
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mathOutputs: evaluation.summary.math.outputs,
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mathMajority: evaluation.summary.math.unique_absolute_majority,
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mathGold: evaluation.summary.math.gold,
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codePassed: evaluation.summary.code.tests_pass,
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codeOutputs: evaluation.summary.code.outputs,
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codeUniqueExecutionKeys: (
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evaluation.sandbox.unique_code_cache_entries
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),
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reproducedCells: (
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reproduction.summary.all_preregistered_fields_exact
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),
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reproductionCells: reproduction.summary.cells,
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},
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conditions: conditionSummary,
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edges: edgeSummary,
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sources: sourceRows,
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reproduction: reproduction.summary,
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artifacts: {
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sampling: samplingArtifact,
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evaluation: evaluationArtifact,
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rerun: rerunArtifact,
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reproduction: artifact(paths.reproduction),
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},
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execution: {
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generationSeconds: sampling.sources.reduce(
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(total, source) => total + source.runs.reduce(
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(sourceTotal, run) => sourceTotal + run.generation_seconds,
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0,
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),
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0,
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),
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peakCudaMemoryAllocatedBytes: (
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sampling.execution.peak_cuda_memory_allocated_bytes
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),
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deviceMap: sampling.execution.device_map,
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transformers: sampling.execution.transformers,
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torch: sampling.execution.torch,
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sandbox: evaluation.sandbox,
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},
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claimBoundary: [
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...sampling.claim_boundary,
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...evaluation.claim_boundary,
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...reproduction.claim_boundary,
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],
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};
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writeFileSync(
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paths.output,
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`${JSON.stringify(result, null, 2)}\n`,
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);
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console.log(JSON.stringify({
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output: paths.output,
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...artifact(paths.output),
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headline: result.headline,
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}, null, 2));
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