graphlemon: auto-commit uncommitted changes for 0123a9cc-01da-4ce6-9622-a210731a…

…de13-main-checkout-drift

graphlemon: auto-commit uncommitted changes for 0123a9cc-01da-4ce6-9622-a210731a…
…de13-main-checkout-drift
graphlemon
1 parent b39f4114
Showing 2 changed files with 191 additions and 54 deletions
src/work_graph_orchestrate.py
src/work_node_run.py
@@ -64,7 +64,9 @@ exception, re-raised unchanged after logging), `quota_limit` (the
 actually happened (N attempts), not a bug.
 """
+import concurrent.futures
 import json
+import threading
 import time
 import uuid
@@ -83,9 +85,61 @@ class ConsistencyCheckFailedError(Exception):
     'blocked' by the time this is raised."""
-def topological_order(graph_manifest):
-    """Kahn's algorithm. Dependencies are read from BOTH `node['depends_on']`
-    and `graph_manifest['edges']` and merged -- `plan_work_graph()`'s own
+class WorkGraphLayerError(Exception):
+    """One or more nodes in a concurrently-executed layer failed --
+    `errors` is the full [(node_id, exception), ...] list (not just the
+    first failure), so a caller can tell whether every failure was the
+    same recoverable kind or something more serious."""
+
+    def __init__(self, errors):
+        self.errors = errors
+        summary = "; ".join("{}: {}".format(node_id, e) for node_id, e in errors)
+        super().__init__("{} node(s) failed in one layer: {}".format(len(errors), summary))
+
+
+class _LockedConn:
+    """sqlite3.Connection-compatible thread-safe proxy: execute/executemany/
+    commit each take a shared lock for the duration of the call. SQLite only
+    ever allows one writer at a time regardless -- this doesn't create new
+    concurrency, it just makes the existing many-scattered-call-sites code
+    (`work_node_run.py`/`work_graph_orchestrate.py`/`execution_log.py`) safe
+    to call from multiple threads without rewriting every call site, since
+    the lock is held only per-statement, not around the (slow) `claude -p`
+    call itself. Mirrors `backend_server.py`'s existing shared-connection +
+    `threading.Lock()` pattern, generalized to per-statement granularity.
+    Attribute access not explicitly wrapped here (cursor()/rollback()/
+    close()) passes through unlocked -- nothing on the concurrent path in
+    this module uses them."""
+
+    def __init__(self, conn, lock=None):
+        self._conn = conn
+        self._lock = lock or threading.Lock()
+
+    def execute(self, *args, **kwargs):
+        with self._lock:
+            return self._conn.execute(*args, **kwargs)
+
+    def executemany(self, *args, **kwargs):
+        with self._lock:
+            return self._conn.executemany(*args, **kwargs)
+
+    def commit(self):
+        with self._lock:
+            return self._conn.commit()
+
+    def __getattr__(self, name):
+        return getattr(self._conn, name)
+
+
+def topological_layers(graph_manifest):
+    """Kahn's algorithm, layered: returns List[List[node]], where each inner
+    list is one round's batch of in-degree-zero nodes -- nodes within a
+    layer are, by construction, mutually independent (neither depends on
+    the other), so they're safe to run concurrently. Layers are ordered
+    (layer N+1 only becomes ready once layer N is fully accounted for);
+    each layer's nodes are sorted by node_id for deterministic test
+    assertions. Dependencies are read from BOTH `node['depends_on']` and
+    `graph_manifest['edges']` and merged -- `plan_work_graph()`'s own
     output isn't schema-validated (it can't be, `--json-schema` doesn't
     work with `--agent`, see work_graph_plan.py), so a real LLM response
     might populate one and not the other; trusting only one would silently
@@ -106,21 +160,30 @@ def topological_order(graph_manifest):
             if dep in dependents:
                 dependents[dep].append(node_id)
-    ready = sorted(node_id for node_id, degree in in_degree.items() if degree == 0)
-    ordered = []
-    while ready:
-        node_id = ready.pop(0)
-        ordered.append(nodes[node_id])
-        for dependent in sorted(dependents[node_id]):
-            in_degree[dependent] -= 1
-            if in_degree[dependent] == 0:
-                ready.append(dependent)
-        ready.sort()
-    if len(ordered) != len(nodes):
+    layers = []
+    current = sorted(node_id for node_id, degree in in_degree.items() if degree == 0)
+    seen_count = 0
+    while current:
+        layers.append([nodes[node_id] for node_id in current])
+        seen_count += len(current)
+        next_layer = set()
+        for node_id in current:
+            for dependent in dependents[node_id]:
+                in_degree[dependent] -= 1
+                if in_degree[dependent] == 0:
+                    next_layer.add(dependent)
+        current = sorted(next_layer)
+    if seen_count != len(nodes):
         raise ValueError(
             "graph_manifest has a dependency cycle or a depends_on/edge references an unknown node_id"
         )
-    return ordered
+    return layers
+
+
+def topological_order(graph_manifest):
+    """Flat view of topological_layers() -- kept for callers (e.g.
+    e2e_pipeline.py) that only need a total order, not the layer grouping."""
+    return [node for layer in topological_layers(graph_manifest) for node in layer]
 def ensure_placeholder_skill(conn, skill_id, owner_org_node):
@@ -193,25 +256,37 @@ def _make_run_fn(conn, target_folder_path, node_exec_id, org_node, prompt, outpu
     return run_fn
+_DEFAULT_MAX_WORKERS = 4  # see this module's own docstring section on G for rationale
+
+
 def run_graph(
     conn, run_id, org_node, skill_id, target_folder_path, graph_manifest,
     build_prompt, input_schema, output_schema, timeout=600, needs_worktree=False,
-    verify_fn=None, sleep=time.sleep, now=time.time,
+    verify_fn=None, sleep=time.sleep, now=time.time, max_workers=_DEFAULT_MAX_WORKERS,
 ):
-    """Runs every node in `graph_manifest` (topologically ordered) through
-    `run_work_node()`, each backed by a real one-node dynamic-workflow
-    `agent()` call using `org_node`'s migrated persona.
+    """Runs every node in `graph_manifest` through `run_work_node()`, each
+    backed by a real one-node dynamic-workflow `agent()` call using
+    `org_node`'s migrated persona. Nodes are grouped into topological
+    layers (`topological_layers()`) and run layer-by-layer with a barrier
+    in between; within a layer -- nodes that are mutually independent by
+    construction -- a layer with 2+ nodes runs them concurrently via a
+    `ThreadPoolExecutor` (bounded by `max_workers`), a layer with exactly 1
+    node runs it synchronously (no thread-pool overhead for the common
+    case: every real graph_manifest observed so far is a single-node-per-
+    layer chain).
     `build_prompt(node, outputs)` is called right before running that
-    node -- `outputs` contains every already-completed node's
-    output_payload so far (topological order guarantees a node's
-    dependencies are always already present), so prompts needing upstream
-    content (e.g. "here is the approved BRD...") can be built just-in-time
-    instead of requiring the whole graph's prompts precomputed before
-    anything runs. This also makes resume-after-restart work correctly:
-    `run_work_node` short-circuits already-`done` nodes and returns their
-    persisted output, which lands in `outputs` the same as a freshly-run
-    one would.
+    node -- `outputs` contains every already-completed EARLIER layer's
+    output_payload (a layer never starts until every node in the previous
+    layer has finished, so this is always safe to read without locking),
+    so prompts needing upstream content (e.g. "here is the approved
+    BRD...") can be built just-in-time instead of requiring the whole
+    graph's prompts precomputed before anything runs. `build_prompt` never
+    sees a same-layer sibling's output -- by definition, same-layer nodes
+    don't depend on each other. This also makes resume-after-restart work
+    correctly: `run_work_node` short-circuits already-`done` nodes and
+    returns their persisted output, which lands in `outputs` the same as a
+    freshly-run one would.
     `needs_worktree=True` (03-C §3.6: "并行写代码的节点必须先申请
     git_worktree_ref") isolates each node's actual file changes in its own
@@ -239,37 +314,89 @@ def run_graph(
     passes: `work_graph_run.status` -> 'verifying' and the regenerated
     output replaces the original in `outputs`.
+    **G (2026-08-06, Work Graph parallel execution)**: a multi-node layer's
+    failures are collected from every node in that layer (a sibling's
+    failure never cancels an already-running sibling -- they're
+    independent work, no reason to throw away completed work), then raised
+    together as `WorkGraphLayerError` once the whole layer has settled.
+    One real limitation, accepted deliberately (see
+    `docs/todo/work-graph-parallel-execution-design.md` §6): a node
+    waiting on a quota-backoff sleep inside `run_work_node` blocks the
+    thread pool worker running it, which in turn blocks the layer barrier
+    even after every sibling has finished -- Python threads have no clean
+    preemptible cancel, and building one is out of scope for this pass.
+
     Returns {node_id: output_payload}.
     """
     ensure_placeholder_skill(conn, skill_id, org_node)
     effective_needs_worktree = needs_worktree or paths.is_self_target(target_folder_path)
-    ordered = topological_order(graph_manifest)
+    layers = topological_layers(graph_manifest)
     outputs = {}
-    for node in ordered:
-        node_id = node["node_id"]
-        node_exec_id = _ensure_node_row(
-            conn, run_id, node_id, org_node, skill_id,
-            "work_graph_node_input.schema.json", "work_graph_node_output.schema.json",
-            {"node_id": node_id},
-        )
-        prompt = build_prompt(node, outputs)
-        run_fn = _make_run_fn(conn, target_folder_path, node_exec_id, org_node, prompt, output_schema, timeout)
-        output = work_node_run.run_work_node(
-            conn, node_exec_id, {"node_id": node_id}, input_schema, output_schema, run_fn,
-            needs_worktree=effective_needs_worktree, repo_path=target_folder_path,
-            sleep=sleep, now=now,
-        )
-
-        if verify_fn is not None:
-            output = _verify_and_maybe_regenerate(
-                conn, run_id, org_node, skill_id, target_folder_path, node, node_id, prompt,
-                output, input_schema, output_schema, timeout, effective_needs_worktree, verify_fn, sleep, now,
+    locked_conn = _LockedConn(conn)
+
+    for layer in layers:
+        if len(layer) == 1:
+            node = layer[0]
+            outputs[node["node_id"]] = _run_one_node(
+                conn, run_id, org_node, skill_id, target_folder_path, node, outputs,
+                build_prompt, input_schema, output_schema, timeout,
+                effective_needs_worktree, verify_fn, sleep, now,
             )
+            continue
+
+        worker_count = min(max_workers, len(layer))
+        errors = []
+        with concurrent.futures.ThreadPoolExecutor(max_workers=worker_count) as pool:
+            futures = {
+                pool.submit(
+                    _run_one_node, locked_conn, run_id, org_node, skill_id, target_folder_path,
+                    node, outputs, build_prompt, input_schema, output_schema, timeout,
+                    effective_needs_worktree, verify_fn, sleep, now,
+                ): node
+                for node in layer
+            }
+            done, _ = concurrent.futures.wait(futures, return_when=concurrent.futures.ALL_COMPLETED)
+            for future in done:
+                node = futures[future]
+                try:
+                    outputs[node["node_id"]] = future.result()
+                except Exception as e:
+                    errors.append((node["node_id"], e))
+
+        if errors:
+            raise WorkGraphLayerError(errors)
-        outputs[node_id] = output
     return outputs
+def _run_one_node(
+    conn, run_id, org_node, skill_id, target_folder_path, node, outputs,
+    build_prompt, input_schema, output_schema, timeout, needs_worktree, verify_fn, sleep, now,
+):
+    """One node's full lifecycle (row creation, prompt, run, optional
+    verify+regenerate) -- shared by both the synchronous single-node-layer
+    path and the ThreadPoolExecutor multi-node-layer path in run_graph(),
+    so there's exactly one implementation of "run a node" to maintain."""
+    node_id = node["node_id"]
+    node_exec_id = _ensure_node_row(
+        conn, run_id, node_id, org_node, skill_id,
+        "work_graph_node_input.schema.json", "work_graph_node_output.schema.json",
+        {"node_id": node_id},
+    )
+    prompt = build_prompt(node, outputs)
+    run_fn = _make_run_fn(conn, target_folder_path, node_exec_id, org_node, prompt, output_schema, timeout)
+    output = work_node_run.run_work_node(
+        conn, node_exec_id, {"node_id": node_id}, input_schema, output_schema, run_fn,
+        needs_worktree=needs_worktree, repo_path=target_folder_path, sleep=sleep, now=now,
+    )
+    if verify_fn is not None:
+        output = _verify_and_maybe_regenerate(
+            conn, run_id, org_node, skill_id, target_folder_path, node, node_id, prompt,
+            output, input_schema, output_schema, timeout, needs_worktree, verify_fn, sleep, now,
+        )
+    return output
+
+
 def _verify_and_maybe_regenerate(
     conn, run_id, org_node, skill_id, target_folder_path, node, node_id, prompt,
     output, input_schema, output_schema, timeout, needs_worktree, verify_fn, sleep, now,
@@ -32,6 +32,7 @@ import json
 import os
 import re
 import subprocess
+import threading
 import time
 import uuid
 from datetime import datetime, timezone
@@ -105,15 +106,24 @@ def _quota_limit_type(text):
     return match.group(1).lower() if match else None
+_worktree_creation_lock = threading.Lock()
+
+
 def create_worktree(repo_path, node_exec_id):
     """`git worktree add` for an isolated code-writing node (01-M
-    work_node_execution.git_worktree_ref)."""
+    work_node_execution.git_worktree_ref). Serialized across threads via a
+    module-level lock -- `git worktree add` mutates shared `.git/worktrees/`
+    metadata, and concurrent Work Graph layers (G) may call this from
+    multiple threads for the same repo_path at once. The lock only wraps
+    this one git invocation (milliseconds), not the node's actual work, so
+    it doesn't meaningfully reduce the parallelism gained elsewhere."""
     worktree_path = os.path.join(repo_path, ".graphlemon-worktrees", node_exec_id)
     branch = "graphlemon/" + node_exec_id
-    subprocess.run(
-        ["git", "worktree", "add", "-b", branch, worktree_path],
-        cwd=repo_path, check=True, capture_output=True, text=True,
-    )
+    with _worktree_creation_lock:
+        subprocess.run(
+            ["git", "worktree", "add", "-b", branch, worktree_path],
+            cwd=repo_path, check=True, capture_output=True, text=True,
+        )
     return worktree_path