[CRCR] Add CRCR results to main HUD grid with monsterization

Integrates CRCR downstream CI results (L2/L3) into the main HUD commit
grid as native columns. CRCR jobs appear under a "CRCR" group alongside
existing groups (Linux, Windows, etc.) and support all existing HUD
features: grouped view, hide-green, hide-always-skipped, job filter,
tooltips, and monsterization.

The approach mirrors the AI advisor verdicts pattern — a parallel
ClickHouse query fetches CRCR results by SHA, maps them into JobData,
and merges them into each row's nameToJobs before grouping runs. This
means zero new UI components; existing JobCell, HudGroupedCell, and
JobConclusion handle rendering and monsterization automatically.

For monsterization, failed CRCR jobs use failed_tests_json (when
available) or a synthetic string (downstream_repo/job_name) as the
failure line hash input, giving each unique failure type its own
monster sprite.

Authored with an AI assistant.

Author: subinz1

torchci/clickhouse_queries/crcr_hud_results/params.json

@@ -0,0 +1,10 @@
+{
+  "params": {
+    "shas": "Array(String)"
+  },
+  "tests": [
+    {
+      "shas": "[\"abc123\"]"
+    }
+  ]
+}

torchci/clickhouse_queries/crcr_hud_results/query.sql

@@ -0,0 +1,20 @@
+SELECT
+    pytorch_head_sha,
+    downstream_repo,
+    downstream_repo_level,
+    workflow_name,
+    job_name,
+    check_run_id,
+    run_id,
+    run_attempt,
+    status,
+    conclusion,
+    workflow_run_url,
+    duration_seconds,
+    failed_tests_json
+FROM
+    default.crcr_workflow_job FINAL
+WHERE
+    pytorch_head_sha IN {shas: Array(String)}
+ORDER BY
+    pytorch_head_sha, downstream_repo, job_name

torchci/lib/JobClassifierUtil.ts

@@ -73,10 +73,15 @@ const GROUP_LIBTORCH = "Libtorch";
 const GROUP_OTHER_VIABLE_STRICT_BLOCKING = "Other viable/strict blocking";
 const GROUP_XPU = "XPU";
 const GROUP_VLLM = "vLLM";
+const GROUP_CRCR = "CRCR";
 const GROUP_OTHER = "other";
 
 // Jobs will be grouped with the first regex they match in this list
 export const groups = [
+  {
+    regex: /^CRCR /,
+    name: GROUP_CRCR,
+  },
   {
     regex: /vllm/i,
     name: GROUP_VLLM,
@@ -235,6 +240,7 @@ const HUD_GROUP_SORTING = [
   GROUP_BINARY_WINDOWS,
   GROUP_MEMORY_LEAK_CHECK,
   GROUP_RERUN_DISABLED_TESTS,
+  GROUP_CRCR,
   // These two groups should always be at the end
   GROUP_OTHER,
   GROUP_UNSTABLE,

torchci/pages/hud/[repoOwner]/[repoName]/[branch]/[[...page]].tsx

@@ -748,6 +748,65 @@ function CopyPermanentLink({
   return <CopyLink textToCopy={url} compressed={false} style={style} />;
 }
 
+interface CrcrHudRow {
+  pytorch_head_sha: string;
+  downstream_repo: string;
+  downstream_repo_level: string;
+  workflow_name: string;
+  job_name: string;
+  check_run_id: string;
+  run_id: string;
+  run_attempt: number;
+  status: string;
+  conclusion: string;
+  workflow_run_url: string;
+  duration_seconds: number;
+  failed_tests_json: string;
+}
+
+function crcrToJobData(row: CrcrHudRow): JobData {
+  let conclusion: string | undefined;
+  if (row.status === "completed") {
+    conclusion = row.conclusion || "failure";
+  } else if (row.status === "in_progress") {
+    conclusion = "pending";
+  } else {
+    conclusion = undefined;
+  }
+
+  const failureLines: string[] = [];
+  if (conclusion === "failure") {
+    if (row.failed_tests_json) {
+      try {
+        const tests = JSON.parse(row.failed_tests_json);
+        if (Array.isArray(tests) && tests.length > 0) {
+          failureLines.push(
+            tests.map((t: any) => t.name || t.classname || "").join("; ")
+          );
+        }
+      } catch {
+        // fall through to synthetic failure line
+      }
+    }
+    if (failureLines.length === 0) {
+      failureLines.push(`${row.downstream_repo}/${row.job_name}`);
+    }
+  }
+
+  return {
+    name: `CRCR ${row.downstream_repo} / ${row.job_name}`,
+    conclusion,
+    htmlUrl: row.workflow_run_url,
+    durationS: row.duration_seconds || undefined,
+    failureLines: failureLines.length > 0 ? failureLines : undefined,
+    id: row.check_run_id,
+    sha: row.pytorch_head_sha,
+    workflowName: `CRCR ${row.downstream_repo}`,
+    jobName: row.job_name,
+    runAttempt: row.run_attempt,
+  };
+}
+
 function GroupedHudTable({ params }: { params: HudParams }) {
   const router = useRouter();
   const { data, isLoading, error } = useHudData(params);
@@ -759,9 +818,6 @@ function GroupedHudTable({ params }: { params: HudParams }) {
       refreshInterval: 300 * 1000, // refresh every 5 minutes
     }
   );
-  const jobNames = new Set(
-    data?.flatMap((row) => Array.from(row.nameToJobs.keys()))
-  );
 
   // Lazy-load AI advisor verdicts for commits on screen
   const shas = useMemo(() => data?.map((row) => row.sha) ?? [], [data]);
@@ -778,6 +834,51 @@ function GroupedHudTable({ params }: { params: HudParams }) {
     return buildVerdictsBySha(deduplicateVerdicts(advisorRows));
   }, [advisorRows]);
 
+  // Lazy-load CRCR results for commits on screen (pytorch/pytorch only)
+  const isPyTorch = isPyTorchPyTorchRepo(params);
+  const { data: crcrRows } = useClickHouseAPIImmutable<CrcrHudRow>(
+    "crcr_hud_results",
+    { shas },
+    isPyTorch && shas.length > 0
+  );
+
+  // Merge CRCR results into each row's nameToJobs so they appear as
+  // regular grid columns (with grouping, filtering, monsterization).
+  const dataWithCrcr = useMemo(() => {
+    if (!data) return data;
+    if (!crcrRows || crcrRows.length === 0) return data;
+
+    const crcrBySha = new Map<string, CrcrHudRow[]>();
+    for (const row of crcrRows) {
+      const list = crcrBySha.get(row.pytorch_head_sha) ?? [];
+      list.push(row);
+      crcrBySha.set(row.pytorch_head_sha, list);
+    }
+
+    return data.map((row) => {
+      const crcrForSha = crcrBySha.get(row.sha);
+      if (!crcrForSha) return row;
+
+      const merged = new Map(row.nameToJobs);
+      for (const cr of crcrForSha) {
+        const jobData = crcrToJobData(cr);
+        const existing = merged.get(jobData.name!);
+        if (
+          !existing ||
+          !existing.id ||
+          cr.run_attempt > (existing.runAttempt ?? 0)
+        ) {
+          merged.set(jobData.name!, jobData);
+        }
+      }
+      return { ...row, nameToJobs: merged };
+    });
+  }, [data, crcrRows]);
+
+  const jobNames = new Set(
+    dataWithCrcr?.flatMap((row) => Array.from(row.nameToJobs.keys()))
+  );
+
   const [hideUnstable] = usePreference("hideUnstable");
   const [hideGreenColumns] = useHideGreenColumnsPreference();
   const [hideNonViableStrict] = useHideNonViableStrictPreference();
@@ -794,7 +895,7 @@ function GroupedHudTable({ params }: { params: HudParams }) {
     jobsViableStrictBlocking,
     groupsViableStrictBlocking,
   } = getGroupingData(
-    data ?? [],
+    dataWithCrcr ?? [],
     jobNames,
     (!useGrouping && hideUnstable) || (useGrouping && !hideUnstable),
     unstableIssuesData ?? [],