diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 5d18a6d..0b0c2af 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -42,3 +42,17 @@ jobs: PY - name: Regression suite (locked recipes can't silently drift) run: pytest tests/ -v + + routing-cpu: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + - name: Install CPU-only routing test dependencies + run: | + pip install -e . pytest + pip install torch --index-url https://download.pytorch.org/whl/cpu + - name: Core and routing tensor fixtures without vLLM or a GPU + run: pytest tests/ --ignore=tests/studio -v diff --git a/experiments/vllm_decode_routing_hook.py b/experiments/vllm_decode_routing_hook.py index a3dee80..63a0da8 100644 --- a/experiments/vllm_decode_routing_hook.py +++ b/experiments/vllm_decode_routing_hook.py @@ -3,15 +3,18 @@ from a running vLLM server. Answers the e10 question ("does decode concentrate more than prefill?") on models that only run on vLLM — the fused MoE path exports nothing per token, so this wraps the router's expert selection. -STATUS: written against vLLM 0.28 (`FusedMoERouter.select_experts` in -`vllm/model_executor/layers/fused_moe/router/fused_moe_router.py`); import-tested in the 0.28 image (installs and patches the -method); NOT yet exercised on a live server at the time of this commit — -verification on a GLM-5.3 TP8 deployment is scheduled; results will be appended to the GLM-5.3 note. +STATUS: CPU fixtures and a single-GB10 generation check on a Nemotron-H MoE +using vLLM 0.29.1rc1.dev513+g0961bbae2 with FlashInfer attention. This is not +validation of GLM-5.3 TP8, expert parallelism, or arbitrary vLLM versions. -How it works: wraps `select_experts` to record the returned (topk_weights, topk_ids); classifies each token as decode or -prefill from the forward context's attention metadata (`max_query_len == 1` → pure decode batch; otherwise per-request -`query_start_loc` gives the query length of each request and single-token requests are decode); accumulates per-layer -counts and mass; dumps JSON every VLLM_ROUTING_DUMP_EVERY calls (default 500) to VLLM_ROUTING_DUMP_DIR (one file per rank). +How it works: wraps `select_experts` to record the returned (topk_weights, topk_ids). +Scheduler flags distinguish actual prefill from decode where available. The +FlashInfer bridge preserves these before they become kernel-dispatch counts. +Other metadata falls back to query boundaries and sequence lengths; missing +metadata/padding stays unknown. Disable speculation, +and validate controlled request schedules before drawing conclusions. Accumulates +counts and mass; dumps JSON every VLLM_ROUTING_DUMP_EVERY calls (default 500) to +VLLM_ROUTING_DUMP_DIR (one file per process). See notes/vllm-routing-capture.md. IMPORTANT: Python-level hooks do not run inside CUDA-graph replay. Capture with graphs off (`--enforce-eager`, or a compilation config with cudagraph_mode NONE) — slower, but this is a measurement run, not serving. @@ -20,9 +23,261 @@ directory on PYTHONPATH of the vLLM workers, e.g. for docker: -v /path/hook:/hook -e PYTHONPATH=/hook -e VLLM_ROUTING_DUMP_DIR=/capture -e VLLM_ROUTING_DUMP_EVERY=500 Then drive the server with real prompts (prefill) that generate a few hundred tokens each (decode). Analyse with: - python3 vllm_decode_routing_hook.py --analyse /capture/*.json # n_eff, top-K coverage, decode/prefill ratio per layer + python3 vllm_decode_routing_hook.py --analyse /capture/*.json --rank-mode replicated """ -import json, math, os, sys, time +import atexit, json, math, os, sys, time, uuid + + +def phase_mask(metadata, n_tokens, device): + """0=prefill, 1=single-token decode, -1=unknown/padding. + + Query length and backend kernel splits are heuristics, not scheduler phase + flags. Preserve explicit is_prefilling flags where available. Disable speculative + decoding for this capture: multi-token verification is not a prefill. + Missing or inconsistent metadata must not inflate the prefill histogram. + """ + import torch + unknown = torch.full((n_tokens,), -1, dtype=torch.int8, device=device) + if isinstance(metadata, dict): + # Hybrid models also carry state-space metadata, whose request ordering + # is backend-specific. Use attention groups with full query boundaries. + groups = [m for m in metadata.values() if getattr(m, "query_start_loc", None) is not None + or type(m).__name__ == "FlashInferMetadata"] + masks = [phase_mask(m, n_tokens, device) for m in groups] + if not masks or any(not torch.equal(masks[0], m) for m in masks[1:]): + return unknown + return masks[0] + try: + scheduler_mask = getattr(metadata, "_pollard_phase_mask", None) + if scheduler_mask is not None: + actual = int(metadata.num_actual_tokens) + if scheduler_mask.ndim != 1 or scheduler_mask.numel() != actual or actual > n_tokens: + return unknown + if not bool(((scheduler_mask == -1) | (scheduler_mask == 0) | (scheduler_mask == 1)).all()): + return unknown + mask = unknown.clone() + mask[:actual] = scheduler_mask.to(device) + return mask + if type(metadata).__name__ == "FlashInferMetadata": + # Legacy fallback without the metadata bridge: this is a KERNEL + # dispatch split. A one-token prefill continuation can appear here + # as decode. Do not treat this as validated scheduler accounting. + decode = int(metadata.num_decode_tokens) + prefill = int(metadata.num_prefill_tokens) + actual = int(metadata.num_actual_tokens) + if min(decode, prefill) < 0 or decode + prefill != actual or actual > n_tokens: + return unknown + mask = unknown.clone() + mask[:decode] = 1 + mask[decode:actual] = 0 + return mask + starts = metadata.query_start_loc.to("cpu").tolist() + actual = int(getattr(metadata, "num_actual_tokens", starts[-1])) + if not starts or starts[0] != 0 or starts[-1] != actual or actual > n_tokens: + return unknown + seq_lens = getattr(metadata, "seq_lens", None) + seq_lens = seq_lens.to("cpu").tolist() if seq_lens is not None else None + prefilling = getattr(metadata, "is_prefilling", None) + if prefilling is not None: + if prefilling.dtype != torch.bool or prefilling.ndim != 1 or prefilling.numel() != len(starts) - 1: + return unknown + prefilling = prefilling.to("cpu").tolist() + if seq_lens is not None and len(seq_lens) < len(starts) - 1: + return unknown + mask = unknown.to("cpu").clone() + for r, (start, stop) in enumerate(zip(starts, starts[1:])): + if stop <= start: + return unknown + size = stop - start + # A one-token NEW prompt is not a decode. Without sequence lengths + # a one-token query cannot be classified safely. + if seq_lens is not None and seq_lens[r] < size: + return unknown + phase = (int(not prefilling[r]) if prefilling is not None else + (0 if size > 1 else (-1 if seq_lens is None else int(seq_lens[r] > size)))) + mask[start:stop] = phase + return mask.to(device) + except (AttributeError, IndexError, TypeError, ValueError, RuntimeError): + return unknown + + +class Capture: + """Per-process router observations; no generated text or request IDs.""" + def __init__(self, dump_dir, every=500): + if every <= 0: + raise ValueError("VLLM_ROUTING_DUMP_EVERY must be positive") + self.dump_dir, self.every = dump_dir, every + self.layers, self.order = {}, {} + self.calls, self.errors = 0, 0 + self.mixed_router_calls = 0 + self.started = time.time() + self._set_identity() + + def _set_identity(self): + pid = os.getpid() + if getattr(self, "pid", None) != pid: + self.instance = uuid.uuid4().hex + self.pid = pid + self.rank = os.environ.get("RANK") or os.environ.get("LOCAL_RANK") + # RANK can be absent or identical on several workers. Never overwrite + # another worker's capture, even when processes share a dump directory. + self.path = os.path.join(self.dump_dir, f"routing_rank{self.rank or 'unknown'}_pid{self.pid}_{self.instance}.json") + + def reset(self): + """Start a measured interval after model initialization/warmup.""" + self._set_identity() + self.layers.clear() + self.order.clear() + self.calls = self.errors = 0 + self.mixed_router_calls = 0 + self.started = time.time() + if os.path.exists(self.path): + os.unlink(self.path) # only this capture's old cumulative snapshot + + def record(self, router, weights, ids, metadata): + import torch + if os.getpid() != self.pid: + # sitecustomize may run before vLLM forks. Inherited state/path must + # not cause several workers to overwrite the parent's file. + self.reset() + if ids.ndim != 2 or weights.shape != ids.shape: + raise ValueError("routing ids and weights must have the same 2D shape") + if not ids.numel(): + return + if int(ids.min()) < 0 or not bool(torch.isfinite(weights).all()) or bool((weights < 0).any()): + raise ValueError("invalid expert ids or routing weights") + key = getattr(router, "layer_name", None) or getattr(router, "prefix", None) + if key is None: + key = self.order.setdefault(id(router), f"router{len(self.order)}") + experts = max(int(getattr(router, "global_num_experts", 0) or + getattr(router, "num_experts", 0) or 0), int(ids.max()) + 1) + layer = self.layers.setdefault(key, {"experts": experts, **{ + ph: {"count": [0.0] * experts, "mass": [0.0] * experts, "tokens": 0} + for ph in ("decode", "prefill", "unknown")}}) + # Use the stored width, not this call's inferred maximum. A later batch + # can visit previously unseen experts when the router has no E attribute. + experts = max(experts, layer["experts"]) + for ph in ("decode", "prefill", "unknown"): + for field in ("count", "mass"): + layer[ph][field].extend([0.0] * (experts - len(layer[ph][field]))) + layer["experts"] = experts + phases = phase_mask(metadata, ids.shape[0], ids.device) + observed = set() + for ph, value in (("decode", 1), ("prefill", 0), ("unknown", -1)): + select = phases == value + if not bool(select.any()): + continue + observed.add(ph) + chosen = ids[select].reshape(-1).to(torch.int64) + w = weights[select].reshape(-1).to(torch.float32) + count = torch.bincount(chosen, minlength=experts).to("cpu").tolist() + mass = torch.zeros(experts, dtype=torch.float32, device=chosen.device).index_add_(0, chosen, w).to("cpu").tolist() + for e in range(experts): + layer[ph]["count"][e] += count[e] + layer[ph]["mass"][e] += mass[e] + layer[ph]["tokens"] += int(select.sum()) + if {"decode", "prefill"} <= observed: + self.mixed_router_calls += 1 + self.calls += 1 + if self.calls % self.every == 0: + self.flush() + + def flush(self): + if os.getpid() != self.pid: + return # an unused fork must not write inherited observations + if not self.calls and not self.errors: + return + os.makedirs(self.dump_dir, exist_ok=True) + data = {"meta": {"rank": self.rank, "pid": os.getpid(), "calls": self.calls, + "mixed_router_calls": self.mixed_router_calls, + "errors": self.errors, "elapsed_s": time.time() - self.started, + "vllm": _vllm_version(), "phase_method": "scheduler-flag-or-backend-query-heuristic"}, + "layers": self.layers} + with open(self.path + ".tmp", "w") as f: + json.dump(data, f, allow_nan=False) + os.replace(self.path + ".tmp", self.path) + + +def assign_layer_names(model): + """Call on each worker's model before capture, so rank keys are stable.""" + from vllm.model_executor.layers.fused_moe.router.fused_moe_router import FusedMoERouter + count = 0 + for name, module in model.named_modules(): + router = getattr(module, "router", None) + if isinstance(router, FusedMoERouter): + router.layer_name = name + count += 1 + return count + + +class RoutingWorkerExtension: + """Named collective RPC methods; no insecure callable serialization needed. + + Pass worker_extension_cls='vllm_decode_routing_hook.RoutingWorkerExtension' + to LLM, or the corresponding --worker-extension-cls option to vllm serve. + """ + def pollard_start_capture(self): + from vllm.model_executor.layers.fused_moe.router.fused_moe_router import FusedMoERouter + count = assign_layer_names(self.model_runner.get_model()) + capture = getattr(FusedMoERouter.select_experts, "_pollard_capture", None) + if capture is None: + raise RuntimeError("routing hook is not installed in this worker") + capture.reset() + return {"named_routers": count} + + def pollard_flush_capture(self): + from vllm.model_executor.layers.fused_moe.router.fused_moe_router import FusedMoERouter + capture = getattr(FusedMoERouter.select_experts, "_pollard_capture", None) + if capture is None: + raise RuntimeError("routing hook is not installed in this worker") + capture.flush() + return {"calls": capture.calls, "errors": capture.errors, "layers": len(capture.layers), + "file": os.path.basename(capture.path)} + + def pollard_remove_capture(self): + """Flush and restore the original router for a same-engine control.""" + from vllm.model_executor.layers.fused_moe.router.fused_moe_router import FusedMoERouter + wrapped = FusedMoERouter.select_experts + if not hasattr(wrapped, "_pollard_original"): + raise RuntimeError("routing hook is not installed in this worker") + wrapped._pollard_capture.flush() + FusedMoERouter.select_experts = wrapped._pollard_original + bridge = getattr(wrapped, "_pollard_builder_bridge", None) + if bridge is not None: + cls, original, installed = bridge + if cls.build is installed: + cls.build = original + return {"removed": True} + + +def preserve_flashinfer_scheduler_phase(builder_cls, on_error=None): + """Preserve scheduler phase before FlashInfer reduces it to a kernel split. + + A one-token prefill continuation can use a decode kernel. Backend token + counts therefore do not establish request phase. This bridge requires the + common metadata's explicit is_prefilling flags; otherwise it marks unknown. + """ + import torch + original = builder_cls.build + + def build(self, *args, **kwargs): + result = original(self, *args, **kwargs) + common = kwargs.get("common_attn_metadata") + if common is None and len(args) > 1: + common = args[1] # build(common_prefix_len, common_attn_metadata, ...) + try: + actual = int(result.num_actual_tokens) + mask = torch.full((actual,), -1, dtype=torch.int8) + if getattr(common, "is_prefilling", None) is not None: + mask = phase_mask(common, actual, "cpu") + result._pollard_phase_mask = mask + except Exception: # Recording must not alter the backend's return path. + if on_error is not None: + on_error() + return result + + builder_cls.build = build + return builder_cls, original, build def _install(): @@ -31,67 +286,44 @@ def _install(): from vllm.model_executor.layers.fused_moe.router import fused_moe_router as fmr except Exception as e: # noqa: BLE001 print(f"[routing-hook] not installed: {e}", file=sys.stderr); return - dump_dir = os.environ.get("VLLM_ROUTING_DUMP_DIR", "/tmp/vllm_routing"); os.makedirs(dump_dir, exist_ok=True) - every = int(os.environ.get("VLLM_ROUTING_DUMP_EVERY", "500")) - state = {"layers": {}, "order": {}, "calls": 0, "started": time.time()} - rank = os.environ.get("RANK") or os.environ.get("LOCAL_RANK") or str(os.getpid()) - - def decode_mask(n_tokens, device): - """bool[n_tokens]: True for decode tokens. Falls back to 'all prefill' if metadata is unavailable.""" - try: - from vllm.forward_context import get_forward_context - md = get_forward_context().attn_metadata - if isinstance(md, dict): md = next(iter(md.values())) - mql = getattr(md, "max_query_len", None) - if mql is not None and int(mql) == 1: return torch.ones(n_tokens, dtype=torch.bool, device=device) - qsl = getattr(md, "query_start_loc", None) - if qsl is not None: - qsl = qsl.to("cpu"); lens = (qsl[1:] - qsl[:-1]); m = torch.zeros(n_tokens, dtype=torch.bool) - for r in range(len(lens)): - if int(lens[r]) == 1 and int(qsl[r]) < n_tokens: m[int(qsl[r])] = True - return m.to(device) - except Exception: # noqa: BLE001 - pass - return torch.zeros(n_tokens, dtype=torch.bool, device=device) - - def record(router, topk_weights, topk_ids): - key = getattr(router, "layer_name", None) or getattr(router, "prefix", None) - if key is None: - key = state["order"].setdefault(id(router), f"router{len(state['order'])}") - E = int(getattr(router, "global_num_experts", 0) or getattr(router, "num_experts", 0) or int(topk_ids.max()) + 1) - L = state["layers"].setdefault(key, {"experts": E, "decode": {"count": [0.0] * E, "mass": [0.0] * E, "tokens": 0}, - "prefill": {"count": [0.0] * E, "mass": [0.0] * E, "tokens": 0}}) - if topk_ids.shape[1] and topk_ids.max() >= E: # grow if the expert count guess was low - for ph in ("decode", "prefill"): - L[ph]["count"] += [0.0] * (int(topk_ids.max()) + 1 - E); L[ph]["mass"] += [0.0] * (int(topk_ids.max()) + 1 - E) - L["experts"] = E = int(topk_ids.max()) + 1 - dm = decode_mask(topk_ids.shape[0], topk_ids.device) - for ph, sel in (("decode", dm), ("prefill", ~dm)): - if not bool(sel.any()): continue - ids = topk_ids[sel].reshape(-1).to(torch.int64); w = topk_weights[sel].reshape(-1).to(torch.float32) - cnt = torch.bincount(ids, minlength=E).to("cpu"); mass = torch.zeros(E, dtype=torch.float32, device=ids.device).index_add_(0, ids, w).to("cpu") - for e in range(E): - L[ph]["count"][e] += float(cnt[e]); L[ph]["mass"][e] += float(mass[e]) - L[ph]["tokens"] += int(sel.sum()) - state["calls"] += 1 - if state["calls"] % every == 0: - tmp = os.path.join(dump_dir, f"routing_rank{rank}.json.tmp") - json.dump({"meta": {"rank": rank, "calls": state["calls"], "elapsed_s": time.time() - state["started"], "vllm": _vllm_version()}, "layers": state["layers"]}, open(tmp, "w")) - os.replace(tmp, os.path.join(dump_dir, f"routing_rank{rank}.json")) + capture = Capture(os.environ.get("VLLM_ROUTING_DUMP_DIR", "/tmp/vllm_routing"), + int(os.environ.get("VLLM_ROUTING_DUMP_EVERY", "500"))) + if getattr(fmr.FusedMoERouter.select_experts, "_pollard_capture", False): + return + # Save short runs below the periodic interval on normal process exit. SIGKILL + # cannot flush; use a small dump interval for interrupted experiments. + atexit.register(capture.flush) orig = fmr.FusedMoERouter.select_experts + bridge = None + try: + from vllm.v1.attention.backends.flashinfer import FlashInferMetadataBuilder + def bridge_error(): + capture.errors += 1 + bridge = preserve_flashinfer_scheduler_phase(FlashInferMetadataBuilder, bridge_error) + except ImportError: + pass # Other backends still use their own query metadata. def wrapped(self, *args, **kwargs): out = orig(self, *args, **kwargs) try: tw, ti = out[0], out[1] - if hasattr(ti, "shape") and ti.ndim == 2: record(self, tw, ti) + from vllm.forward_context import get_forward_context + try: + md = get_forward_context().attn_metadata + except Exception: + md = None + capture.record(self, tw, ti, md) except Exception as e: # noqa: BLE001 — never break serving - if state["calls"] == 0: print(f"[routing-hook] record failed: {e}", file=sys.stderr) + capture.errors += 1 + if capture.errors == 1: print(f"[routing-hook] record failed: {e}", file=sys.stderr) return out + wrapped._pollard_capture = capture + wrapped._pollard_original = orig + wrapped._pollard_builder_bridge = bridge fmr.FusedMoERouter.select_experts = wrapped - print(f"[routing-hook] installed; dumping every {every} calls to {dump_dir}", file=sys.stderr) + print(f"[routing-hook] installed; dumping every {capture.every} calls to {capture.dump_dir}", file=sys.stderr) def _vllm_version(): @@ -101,15 +333,51 @@ def _vllm_version(): return "?" -def analyse(paths, Ks=(8, 16, 32, 64, 128, 192)): - """Merge rank dumps (same layer keys → sum) and print per-layer n_eff and top-K coverage for decode vs prefill.""" - layers = {} +def analyse(paths, Ks=(8, 16, 32, 64, 128, 192), rank_mode=None): + """Merge disjoint rank observations, or select one verified TP replica.""" + if len(paths) > 1 and rank_mode not in ("replicated", "disjoint"): + raise ValueError("multiple captures: choose --rank-mode replicated (TP replicas) or disjoint (different tokens)") + captures = [] for p in paths: - d = json.load(open(p)) + with open(p) as f: + data = json.load(f) + if data.get("meta", {}).get("errors", 0): + raise ValueError("capture has recording errors; inspect the private worker log before analysis") + if not data.get("layers"): + raise ValueError("capture has no routing observations") + captures.append(data) + if rank_mode == "replicated" and len(captures) > 1: + # Count replicated TP router observations once. Do not guess a rank + # layout from identical-looking distribution shapes alone. + first = captures[0]["layers"] + for other in captures[1:]: + if set(first) != set(other["layers"]): + raise ValueError("replicated captures have different layer keys") + for key, layer in first.items(): + candidate = other["layers"][key] + if layer["experts"] != candidate["experts"]: + raise ValueError("replicated captures have different expert counts") + for ph in ("decode", "prefill", "unknown"): + left, right = layer.get(ph), candidate.get(ph) + if left is None and right is None: + continue + if left is None or right is None or left["tokens"] != right["tokens"] or left["count"] != right["count"]: + raise ValueError("replicated captures differ; check capture boundaries and rank layout") + if len(left["mass"]) != len(right["mass"]) or any(not math.isclose(a, b, rel_tol=1e-5, abs_tol=1e-6) + for a, b in zip(left["mass"], right["mass"])): + raise ValueError("replicated captures have different routing mass") + captures = captures[:1] + layers = {} + for d in captures: for k, L in d["layers"].items(): A = layers.setdefault(k, {"experts": L["experts"], "decode": {"count": [0.0] * L["experts"], "mass": [0.0] * L["experts"], "tokens": 0}, - "prefill": {"count": [0.0] * L["experts"], "mass": [0.0] * L["experts"], "tokens": 0}}) - for ph in ("decode", "prefill"): + "prefill": {"count": [0.0] * L["experts"], "mass": [0.0] * L["experts"], "tokens": 0}, + "unknown": {"count": [0.0] * L["experts"], "mass": [0.0] * L["experts"], "tokens": 0}}) + if A["experts"] != L["experts"]: + raise ValueError(f"expert count differs for {k}; do not merge incompatible captures") + for ph in ("decode", "prefill", "unknown"): + if ph not in L: + continue for e in range(L["experts"]): A[ph]["count"][e] += L[ph]["count"][e]; A[ph]["mass"][e] += L[ph]["mass"][e] A[ph]["tokens"] += L[ph]["tokens"] @@ -117,20 +385,28 @@ def stats(c): tot = sum(c) if tot <= 0: return None p = sorted((x / tot for x in c), reverse=True); H = -sum(x * math.log(x) for x in p if x > 0) - return {"n_eff": math.exp(H), "cov": {K: sum(p[:K]) for K in Ks if K <= len(p)}} + return {"n_eff": math.exp(H), "cov": {K: sum(p[:K]) for K in Ks}} print("layer | decode tokens n_eff top64 top128 | prefill tokens n_eff top64 top128 | n_eff ratio decode/prefill") + print(f"rank mode: {rank_mode or 'single'}; token totals are router observations, not request totals") ratios = [] for k in sorted(layers, key=lambda s: (len(s), s)): L = layers[k]; sd, sp_ = stats(L["decode"]["count"]), stats(L["prefill"]["count"]) f = lambda s, ph: (f"{L[ph]['tokens']:>8} {s['n_eff']:6.1f} {s['cov'].get(64, 0)*100:5.1f}% {s['cov'].get(128, 0)*100:5.1f}%" if s else " —") r = (sd["n_eff"] / sp_["n_eff"]) if sd and sp_ else float("nan"); ratios.append(r) if sd and sp_ else None print(f"{k:>28} | {f(sd, 'decode')} | {f(sp_, 'prefill')} | {r:.2f}") + if L["unknown"]["tokens"]: + print(f" {k}: {L['unknown']['tokens']} unclassified/padded observations excluded from phase comparison") if ratios: print(f"median n_eff ratio decode/prefill over {len(ratios)} layers: {sorted(ratios)[len(ratios)//2]:.2f} (<1 = decode concentrates more)") if __name__ == "__main__": if len(sys.argv) > 2 and sys.argv[1] == "--analyse": - analyse(sys.argv[2:]) + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("--analyse", nargs="+", required=True) + parser.add_argument("--rank-mode", choices=("replicated", "disjoint")) + args = parser.parse_args() + analyse(args.analyse, rank_mode=args.rank_mode) else: print(__doc__) else: diff --git a/experiments/vllm_routing_check.py b/experiments/vllm_routing_check.py new file mode 100644 index 0000000..741e684 --- /dev/null +++ b/experiments/vllm_routing_check.py @@ -0,0 +1,115 @@ +#!/usr/bin/env python3 +"""Check routing capture on fixed synthetic requests, not model quality or speed. + +Run once with the hook installed on worker PYTHONPATH and --capture-dir pointing +to the workers' shared dump directory. Run again without the hook and without +--capture-dir; compare output_sha256 for both batches. No request text, generated +text, model paths, hostnames or worker filenames are included in the summary. +""" +import argparse +import hashlib +import json +from pathlib import Path + +PROMPTS = [ + "Explain why a database transaction needs isolation. Give a concrete example.", + "Write a Python function that merges two sorted lists without changing its inputs.", + "A gardener has a rectangular plot measuring 12 by 8 metres. Explain how to calculate the perimeter and area.", + "Tell a short story about a lighthouse keeper repairing a broken radio.", +] + + +def summarize_outputs(outputs): + sequences = [list(x.outputs[0].token_ids) for x in outputs] + if any(not ids for ids in sequences): + raise ValueError("expected at least one generated token per request") + return {"requests": len(outputs), + "prompt_tokens": sum(len(x.prompt_token_ids) for x in outputs), + "output_tokens": sum(map(len, sequences)), + # The last generated token is returned, not fed back to the router. + "expected_decode_tokens": sum(len(ids) - 1 for ids in sequences), + "output_sha256": hashlib.sha256(json.dumps(sequences, separators=(",", ":")).encode()).hexdigest()} + + +def check_capture(directory, workers, expected): + if not workers: + raise ValueError("no worker reports") + reports = [] + for worker in workers: + name = worker["file"] + if Path(name).name != name or not name.startswith("routing_") or not name.endswith(".json"): + raise ValueError("expected a routing capture basename") + data = json.loads((Path(directory) / name).read_text()) + if worker["errors"] or data["meta"]["errors"]: + raise ValueError("worker reported recording errors") + if not data["layers"] or len(data["layers"]) != worker["layers"]: + raise ValueError("worker has missing routing layers") + phases = {"prefill": expected["prompt_tokens"], + "decode": expected["expected_decode_tokens"], "unknown": 0} + for layer in data["layers"].values(): + if any(layer[phase]["tokens"] != count for phase, count in phases.items()): + raise ValueError("per-layer token counts disagree with the requests") + reports.append({"layers": len(data["layers"]), "calls": worker["calls"], + "errors": 0, "tokens_per_layer": phases}) + return reports + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--model", required=True) + parser.add_argument("--capture-dir", help="shared worker dump directory; omit for an unhooked control") + parser.add_argument("--output", type=Path, required=True, help="aggregate JSON summary") + parser.add_argument("--moe-backend", help="optional runtime-specific backend, e.g. marlin") + parser.add_argument("--serial", action="store_true", help="one request at a time to control batch shape") + parser.add_argument("--same-engine-control", action="store_true", help="remove the hook and repeat twice in the same engine") + args = parser.parse_args() + if args.same_engine_control and not args.capture_dir: + parser.error("--same-engine-control requires --capture-dir") + # Keep vLLM optional for CPU fixtures and --help. + import vllm + from vllm import LLM, SamplingParams + options = dict(model=args.model, trust_remote_code=False, tensor_parallel_size=1, + enforce_eager=True, max_model_len=2048, max_num_seqs=1 if args.serial else 4, + gpu_memory_utilization=0.55, enable_prefix_caching=False, + enable_chunked_prefill=True, compilation_config={"mode": 0}, seed=0, + kernel_config={"enable_flashinfer_autotune": False}) + if args.moe_backend: + options["moe_backend"] = args.moe_backend + if args.capture_dir: + options["worker_extension_cls"] = "vllm_decode_routing_hook.RoutingWorkerExtension" + llm = LLM(**options) + def generate(max_tokens): + params = SamplingParams(temperature=0, max_tokens=max_tokens, ignore_eos=True) + if args.serial: + return [llm.generate([prompt], params)[0] for prompt in PROMPTS] + return llm.generate(PROMPTS, params) + batches = [] + for max_tokens in (1, 48): + if args.capture_dir: + named = llm.collective_rpc("pollard_start_capture") + if any(x["named_routers"] == 0 for x in named): + raise ValueError("no named MoE routers in this model") + outputs = generate(max_tokens) + result = summarize_outputs(outputs) + result["max_tokens"] = max_tokens + if args.capture_dir: + result["workers"] = check_capture(args.capture_dir, llm.collective_rpc("pollard_flush_capture"), result) + batches.append(result) + summary = {"schema_version": 1, "vllm": vllm.__version__, "capture": bool(args.capture_dir), + "flashinfer_autotune": False, + "serial": args.serial, + "quality_benchmark": False, "performance_benchmark": False, "batches": batches} + if args.same_engine_control: + summary["hook_removal"] = llm.collective_rpc("pollard_remove_capture") + controls = [[summarize_outputs(generate(n)) for n in (1, 48)] for _ in range(2)] + summary["same_engine_controls"] = controls + summary["control_repeats_match"] = all(x["output_sha256"] == y["output_sha256"] + for x, y in zip(*controls)) + summary["capture_matches_controls"] = all(x["output_sha256"] == y["output_sha256"] + for control in controls for x, y in zip(batches, control)) + args.output.write_text(json.dumps(summary, indent=2) + "\n") + print(json.dumps(summary)) + + +if __name__ == "__main__": + main() diff --git a/experiments/vllm_routing_profiles.py b/experiments/vllm_routing_profiles.py new file mode 100644 index 0000000..397a86e --- /dev/null +++ b/experiments/vllm_routing_profiles.py @@ -0,0 +1,201 @@ +#!/usr/bin/env python3 +"""Compare routing on original synthetic workloads, not quality or throughput. + +Uses the routing-check worker extension. Raw snapshots are kept separately from +the aggregate report, without request text or generated text. Synthetic domain +differences do not establish behavior on real traffic or a quantization benefit. +""" +import argparse +import hashlib +import itertools +import json +import math +from pathlib import Path + +from vllm_routing_check import check_capture, summarize_outputs + +DOMAINS = ("code", "reasoning", "general") +CONTEXTS = {"short": 256, "long": 1536} +KS = (8, 16, 32, 64, 128) + + +def workload_text(domain, variant, paragraphs): + """Original, deterministic public fixtures. No copied or private corpus.""" + if domain not in DOMAINS or variant not in range(4) or paragraphs < 1: + raise ValueError("invalid workload settings") + sections = [] + for i in range(paragraphs): + n = i + variant * 101 + if domain == "code": + sections.append(f"def update_{n}(records, limit={n % 19 + 2}):\n" + " kept = [r for r in records if r['active']]\n" + " return sorted(kept, key=lambda r: r['score'])[:limit]\n" + f"# Review function {n} for empty inputs, ties, and mutation.\n") + elif domain == "reasoning": + sections.append(f"Warehouse {n} starts with {n % 29 + 14} crates. " + f"It receives {n % 11 + 3} deliveries of {n % 7 + 2} crates " + f"and sends out {n % 13 + 5} crates. Each crate contains " + "six sealed boxes. Track the remaining crates and boxes, " + "showing each step and stating any assumptions.\n") + else: + sections.append(f"At station {n}, the volunteer archivists catalogued " + "letters and photographs from the old coastal railway. " + "A visitor asked how the weather affected daily journeys. " + "The curator compared passenger diaries with maintenance " + "notes and explained why accounts sometimes disagreed.\n") + instruction = {"code": "Review the code and suggest a safe improvement.", + "reasoning": "Explain the arithmetic for the final warehouse.", + "general": "Summarize the archive account in plain language."}[domain] + return "\n".join(sections) + "\n" + instruction + + +def workload_tokens(tokenizer, domain, target, variant): + """Build a token-length fixture without depending on a chat template. + + Truncation deliberately makes this a controlled token-stream experiment, + not a scored instruction-following task. Actual encoded IDs are hashed. + """ + if not isinstance(target, int) or isinstance(target, bool) or target < 1: + raise ValueError("target must be a positive token count") + sections = 1 + while True: + ids = tokenizer.encode(workload_text(domain, variant, sections), add_special_tokens=False) + if len(ids) >= target: + return ids[:target] + sections *= 2 + + +def normalized(values): + if not values or any(not math.isfinite(v) or v < 0 for v in values): + raise ValueError("expected finite nonnegative routing values") + total = sum(values) + if total <= 0: + raise ValueError("routing histogram is empty") + return [v / total for v in values] + + +def coverage(values, indices): + p = normalized(values) + return sum(p[i] for i in indices) + + +def top_indices(values, k): + return sorted(range(len(values)), key=lambda i: (-values[i], i))[:k] + + +def js_bits(left, right): + if len(left) != len(right): + raise ValueError("expert widths disagree") + p, q = normalized(left), normalized(right) + middle = [(a + b) / 2 for a, b in zip(p, q)] + return sum(0.5 * v * math.log2(v / m) for dist in (p, q) + for v, m in zip(dist, middle) if v > 0) + + +def describe_layers(data): + results = [] + # Router module names are model identifiers, not host filesystem paths. + for ordinal, (name, layer) in enumerate(sorted(data["layers"].items())): + row = {"layer": ordinal, "router_name": name, "experts": layer["experts"]} + for phase in ("prefill", "decode"): + h = layer[phase] + row[phase] = {"tokens": h["tokens"], + "count": list(h["count"]), "mass": list(h["mass"]), + "selection_coverage": {str(k): coverage(h["count"], top_indices(h["count"], k)) for k in KS}, + "weight_coverage": {str(k): coverage(h["mass"], top_indices(h["mass"], k)) for k in KS}} + results.append(row) + return results + + +def compare_layers(source, target): + if source["layers"].keys() != target["layers"].keys(): + raise ValueError("capture layer names disagree") + comparisons = [] + for ordinal, name in enumerate(sorted(source["layers"])): + a, b = source["layers"][name], target["layers"][name] + if a["experts"] != b["experts"]: + raise ValueError("capture expert widths disagree") + row = {"layer": ordinal} + for phase in ("prefill", "decode"): + row[phase] = {"selection_js_bits": js_bits(a[phase]["count"], b[phase]["count"]), + "source_cache_target_selection_coverage": { + str(k): coverage(b[phase]["count"], top_indices(a[phase]["count"], k)) for k in KS}} + comparisons.append(row) + return comparisons + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--model", required=True) + parser.add_argument("--capture-dir", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--moe-backend") + parser.add_argument("--batched", action="store_true", help="four concurrent requests instead of serial requests") + parser.add_argument("--max-tokens", type=int, default=32) + args = parser.parse_args() + if not 2 <= args.max_tokens <= 128: + parser.error("--max-tokens must be between 2 and 128") + import vllm + from vllm import LLM, SamplingParams + options = dict(model=args.model, trust_remote_code=False, tensor_parallel_size=1, + enforce_eager=True, max_model_len=2048, max_num_seqs=4 if args.batched else 1, + max_num_batched_tokens=1024, gpu_memory_utilization=0.55, + enable_prefix_caching=False, enable_chunked_prefill=True, + compilation_config={"mode": 0}, seed=0, + kernel_config={"enable_flashinfer_autotune": False}, + worker_extension_cls="vllm_decode_routing_hook.RoutingWorkerExtension") + if args.moe_backend: + options["moe_backend"] = args.moe_backend + llm = LLM(**options) + tokenizer = llm.get_tokenizer() + params = SamplingParams(temperature=0, max_tokens=args.max_tokens, ignore_eos=True) + profiles, captured = [], {} + requests = {} + for domain, (context, length) in itertools.product(DOMAINS, CONTEXTS.items()): + key = f"{domain}-{context}" + ids = [workload_tokens(tokenizer, domain, length, variant) for variant in range(4)] + requests[key] = [{"prompt_token_ids": x} for x in ids] + def generate(key): + if args.batched: + return llm.generate(requests[key], params) + return [llm.generate([x], params)[0] for x in requests[key]] + raw_dir = args.capture_dir / "snapshots" + raw_dir.mkdir(exist_ok=True) + for key in requests: + workers = llm.collective_rpc("pollard_start_capture") + if any(w["named_routers"] == 0 for w in workers): + raise ValueError("no named routers") + result = summarize_outputs(generate(key)) + reports = llm.collective_rpc("pollard_flush_capture") + result["workers"] = check_capture(args.capture_dir, reports, result) + if len(reports) != 1: + raise ValueError("this harness supports one worker; do not merge ranks implicitly") + data = json.loads((args.capture_dir / reports[0]["file"]).read_text()) + # reset() deletes the worker's previous snapshot, so preserve each profile first. + (raw_dir / f"{key}.json").write_text(json.dumps(data) + "\n") + captured[key] = data + result.update(profile=key, prompt_ids_sha256=hashlib.sha256( + json.dumps(requests[key], separators=(",", ":")).encode()).hexdigest(), + mixed_router_calls=data["meta"].get("mixed_router_calls"), + layers=describe_layers(data)) + profiles.append(result) + print(json.dumps({"completed": key, "prompt_tokens": result["prompt_tokens"], + "decode_tokens": result["expected_decode_tokens"]}), flush=True) + removal = llm.collective_rpc("pollard_remove_capture") + controls = [[summarize_outputs(generate(key)) for key in requests] for _ in range(2)] + summary = {"schema_version": 1, "vllm": vllm.__version__, "synthetic_workloads": True, + "quality_benchmark": False, "performance_benchmark": False, + "serial": not args.batched, "tensor_parallel_size": 1, + "max_num_batched_tokens": 1024, "max_tokens": args.max_tokens, + "flashinfer_autotune": False, "prefix_caching": False, + "profiles": profiles, "hook_removal": removal, + "control_repeats_match": all(a["output_sha256"] == b["output_sha256"] for a, b in zip(*controls)), + "capture_matches_controls": all(a["output_sha256"] == b["output_sha256"] for run in controls for a, b in zip(profiles, run)), + "same_engine_controls": controls, + "comparisons": [{"source": a, "target": b, "layers": compare_layers(captured[a], captured[b])} + for a, b in itertools.permutations(requests, 2)]} + args.output.write_text(json.dumps(summary, indent=2, allow_nan=False) + "\n") + + +if __name__ == "__main__": + main() diff --git a/notes/vllm-routing-capture.md b/notes/vllm-routing-capture.md new file mode 100644 index 0000000..cb07ba9 --- /dev/null +++ b/notes/vllm-routing-capture.md @@ -0,0 +1,117 @@ +# Checking the vLLM routing recorder + +A [GB10 accounting and control result](vllm-routing-check-gb10.json) +is included for a Nemotron-H MoE on a local vLLM 0.29 development image. All 23 +layers matched 68 prompt and 188 decode-feedback tokens; serial same-engine +greedy outputs matched two repeats after removing the hook. Fresh-process +four-request outputs did not match, so this is not batch-invariance or model +quality evidence. The checkpoint revision is unknown and the image is not a +stock release. Use the supplied check to validate your own pinned runtime. + +Use an isolated measurement job with CUDA graphs and speculative decoding off. +The Python hook does not observe CUDA-graph replay, and multi-token speculative +verification cannot be classified by query length alone. Disable prefix caching +for the initial comparison; retain the runtime's supported chunked-prefill +setting and record it. + +For FlashInfer attention, the recorder preserves the common attention metadata's +`is_prefilling` flags while building the backend metadata. Its decode-kernel +token count is not a request-phase count: a one-token chunked-prefill +continuation can use a decode kernel. If the bridge has no scheduler flags, +observations stay unknown. Removing the recorder also restores the original +metadata builder. + +Other supported attention metadata uses scheduler flags where exposed, otherwise +query boundaries and sequence lengths. The recorder keeps +unavailable/inconsistent metadata and padding in an +`unknown` bucket. Unknown observations are excluded from the decode/prefill +comparison, not silently counted as prefill. One-token queries are classified +using sequence lengths where available. That fallback remains a query-length heuristic: +one-token chunked-prefill continuations can still look like decode. Confirm the +split against controlled request schedules before drawing a routing conclusion. + +Pass `worker_extension_cls="vllm_decode_routing_hook.RoutingWorkerExtension"` +to `LLM` (or `--worker-extension-cls` to the server). After initialization, call +`llm.collective_rpc("pollard_start_capture")`. This labels routers from model +module paths and resets warmup observations. Call +`llm.collective_rpc("pollard_flush_capture")` at the end. These are named worker +methods, so enabling insecure callable serialization is unnecessary. Server +users need their controller to invoke these worker methods at the capture +boundaries; importing sitecustomize alone does not reset warmup. + +Without layer-name assignment, fallback `router0` keys are observation-order +labels, not established architectural layer numbers. + +The installation path is unchanged: put the hook and a `sitecustomize.py` that +imports it on the workers' PYTHONPATH. Each process writes its own PID-qualified +file with a random instance suffix, including across forks and containers. +Short runs flush at normal interpreter exit; SIGKILL cannot flush. For a +controlled offline job, use `collective_rpc` to call each worker capture's +`flush()` before shutting down. A small dump interval limits losses on failures. + +For several dumps, choose a rank interpretation explicitly: + +```sh +python experiments/vllm_decode_routing_hook.py --analyse captures/*.json --rank-mode replicated +``` + +`replicated` verifies matching layer keys, counts and routing mass, then counts +one TP replica. `disjoint` sums observations from different token sets. Do not +merge old periodic snapshots of the same cumulative capture. Expert parallelism, +pipeline parallelism, load-balanced expert IDs and changing rank layouts need +separate validation; the hook does not infer them. + +For the first live check, match per-layer totals against the request token +counts. Check a prompt-only batch, a decode-only batch and a mixed batch. Check +that router outputs are unchanged with and without recording. Capture throughput +with instrumentation is not a serving benchmark: use an uninstrumented run for +speed comparisons. + +## Reproduce the accounting check + +Use a fresh capture directory shared by the driver and workers. Put the hook +and the importing `sitecustomize.py` in a separate directory on worker PYTHONPATH. +Set `VLLM_ROUTING_DUMP_DIR` to that shared capture directory, then run: + +```sh +python experiments/vllm_routing_check.py --model /path/to/moe-model \ + --capture-dir /path/to/capture --output capture-summary.json +``` + +The check disables CUDA graphs, speculation, prefix caching and FlashInfer +kernel autotuning, uses TP1, and +resets recording after warmup and before each batch. Four fixed synthetic +requests first generate one token each (no decode feedback), then 48 tokens each. +Every recorded layer must match the prompt-token count and the generated-token +count minus one final token per request. Unknown tokens or recording errors fail +the check. Backend-specific overrides can be passed with `--moe-backend`. + +Start a separate process with the hook directory removed from PYTHONPATH (and +without the importing sitecustomize), omit `--capture-dir`, and run again: + +```sh +python experiments/vllm_routing_check.py --model /path/to/moe-model \ + --output control-summary.json +``` + +Compare the `output_sha256` values in corresponding batches. A match verifies +greedy output on these requests only. It is not a general quality test, does not +prove a mixed prefill/decode scheduler batch occurred, and does not validate TP +replication or expert parallelism. Run those separately before publishing a +multi-node routing conclusion. Only these fixed synthetic outputs are hashed; +do not use output fingerprints to publish private evaluation data. + +If separate processes disagree, do not attribute that to the recorder without +checking repeatability of the unhooked runtime. For a more controlled comparison, +add `--serial --same-engine-control` to the capture command. The check processes +one request at a time, flushes and restores the original router through +`pollard_remove_capture`, then repeats both batches twice without the hook in +the same engine. `control_repeats_match` checks baseline repeatability and +`capture_matches_controls` checks the captured results against both controls. +Removal is one-way for that worker; start a new engine to install the hook again. +Neither flag should be read as proof that a four-request or multi-node batch is +unchanged, or that capture overhead is negligible. + +Only aggregate expert counts and routing mass are written, not prompts or +generated text. Review model names, layer names, timestamps and metadata before +publication. Keep raw server logs private. diff --git a/notes/vllm-routing-check-gb10.json b/notes/vllm-routing-check-gb10.json new file mode 100644 index 0000000..e2c469c --- /dev/null +++ b/notes/vllm-routing-check-gb10.json @@ -0,0 +1,126 @@ +{ + "tested_on": "2026-10-04", + "scope": "routing accounting and greedy-output controls, not quality or performance", + "hardware": { + "device": "NVIDIA GB10", + "devices_used": 1, + "architecture": "aarch64", + "os": "NixOS" + }, + "model": { + "name": "NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4", + "checkpoint_revision": null, + "config_sha256": "f1d98b530846087dc08b574a219713a94f945bf6583dc7230a19ebf1e8c50933" + }, + "runtime": { + "version": "0.29.1rc1.dev513+g0961bbae2", + "build": "local image, not a stock upstream release; also contains support patches for another model family", + "tensor_parallel_size": 1, + "moe_backend": "marlin", + "attention_backend": "FlashInfer", + "cuda_graphs": false, + "speculation": false, + "prefix_caching": false, + "chunked_prefill": true, + "flashinfer_autotune": false, + "seed": 0 + }, + "limitations": [ + "The cached checkpoint revision was not recorded; this is not a pinned quantization benchmark.", + "The earlier four-request capture passed token accounting, but fresh-process output hashes differed even with autotuning off for longer generations.", + "Serial same-engine control matches do not establish batch invariance, TP/EP correctness, or negligible overhead.", + "GLM TP8, longer-domain routing profiles and measured-vs-uniform quantization remain untested." + ], + "check": { + "schema_version": 1, + "vllm": "0.29.1rc1.dev513+g0961bbae2", + "capture": true, + "flashinfer_autotune": false, + "serial": true, + "quality_benchmark": false, + "performance_benchmark": false, + "batches": [ + { + "requests": 4, + "prompt_tokens": 68, + "output_tokens": 4, + "expected_decode_tokens": 0, + "output_sha256": "f7b3ab81626a2ffa862be5939d91c0e119cfc1e315ce47abbec44ab790045601", + "max_tokens": 1, + "workers": [ + { + "layers": 23, + "calls": 92, + "errors": 0, + "tokens_per_layer": { + "prefill": 68, + "decode": 0, + "unknown": 0 + } + } + ] + }, + { + "requests": 4, + "prompt_tokens": 68, + "output_tokens": 192, + "expected_decode_tokens": 188, + "output_sha256": "92a9e2812bc06edc76c5cf7fc528cda22497c53e558342c530c06e2f9ec4e082", + "max_tokens": 48, + "workers": [ + { + "layers": 23, + "calls": 4416, + "errors": 0, + "tokens_per_layer": { + "prefill": 68, + "decode": 188, + "unknown": 0 + } + } + ] + } + ], + "hook_removal": [ + { + "removed": true + } + ], + "same_engine_controls": [ + [ + { + "requests": 4, + "prompt_tokens": 68, + "output_tokens": 4, + "expected_decode_tokens": 0, + "output_sha256": "f7b3ab81626a2ffa862be5939d91c0e119cfc1e315ce47abbec44ab790045601" + }, + { + "requests": 4, + "prompt_tokens": 68, + "output_tokens": 192, + "expected_decode_tokens": 188, + "output_sha256": "92a9e2812bc06edc76c5cf7fc528cda22497c53e558342c530c06e2f9ec4e082" + } + ], + [ + { + "requests": 4, + "prompt_tokens": 68, + "output_tokens": 4, + "expected_decode_tokens": 0, + "output_sha256": "f7b3ab81626a2ffa862be5939d91c0e119cfc1e315ce47abbec44ab790045601" + }, + { + "requests": 4, + "prompt_tokens": 68, + "output_tokens": 192, + "expected_decode_tokens": 188, + "output_sha256": "92a9e2812bc06edc76c5cf7fc528cda22497c53e558342c530c06e2f9ec4e082" + } + ] + ], + "control_repeats_match": true, + "capture_matches_controls": true + } +} diff --git a/notes/vllm-routing-profiles-gb10-batched.json b/notes/vllm-routing-profiles-gb10-batched.json new file mode 100644 index 0000000..8c4ce80 --- /dev/null +++ b/notes/vllm-routing-profiles-gb10-batched.json @@ -0,0 +1 @@ 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diff --git a/notes/vllm-routing-profiles.md b/notes/vllm-routing-profiles.md new file mode 100644 index 0000000..07a388c --- /dev/null +++ b/notes/vllm-routing-profiles.md @@ -0,0 +1,104 @@ +# Controlled routing workloads + +`experiments/vllm_routing_profiles.py` measures expert-selection coverage and +routing-weight coverage separately for code, arithmetic reasoning and general +text. Each domain has four original synthetic prompts at 256 and 1536 tokens. +These are controlled fixtures, not a representative sample of production +traffic. They are deliberately truncated token streams, not scored tasks. + +The harness uses the same worker extension as `vllm_routing_check.py`. Run with +the hook and importing `sitecustomize.py` on the worker PYTHONPATH: + +```sh +python experiments/vllm_routing_profiles.py \ + --model /model --capture-dir /capture --output /capture/profiles.json +``` + +Add `--moe-backend marlin` only if that is the supported backend for your model +and runtime. Capture runs use eager execution, disabled prefix caching, +disabled FlashInfer autotuning, chunked prefill and a 1024-token scheduler +budget. The default is one request at a time. `--batched` allows four concurrent +requests; it does not by itself prove a mixed prefill/decode step occurred. +`mixed_router_calls` counts individual router calls containing both phases, +not scheduler steps. Null means the recorder did not provide this counter. +This harness supports a single worker, not multi-node TP or EP. + +Each interval excludes initialization and preceding profiles. The harness +checks every layer's prompt and decode-feedback totals and rejects errors or +unknown observations. The last generated token is not a routing observation. +It saves each raw snapshot before the next reset can replace it. + +The public summary contains per-expert histograms, coverage curves and hashes, +not text or token IDs. Router names identify model modules, not host paths. +Layer numbers are ordinals in sorted router-name order, not the architecture's +layer numbers. Keep the raw snapshot directory and runtime logs +private unless separately reviewed. Review the aggregate file before sharing. + +After capture, the original router is restored and all profiles are repeated +twice in the same engine. The report distinguishes control repeatability from +capture/control agreement. A failed comparison remains a failed comparison; +it should not be described as recorder transparency. + +Cross-profile comparisons report Jensen-Shannon divergence in bits and the +target demand covered by the source profile's top-K experts. Source-selected +coverage is not the target's own top-K coverage. This distinction can show how +an expert cache fitted to one workload loses coverage on another. + +These measurements do not establish task accuracy, expert-pruning safety, +quantization quality, full expert-combination reuse, or throughput. The Python +recorder synchronizes tensors and can affect scheduling. Measure speed +separately without instrumentation. NVFP4 checkpoint routing is not an original +BF16 reference for a matched-size quantization comparison. + +## GB10 results + +The [serial result](vllm-routing-profiles-gb10-serial.json) and +[batched result](vllm-routing-profiles-gb10-batched.json) use a cached Nemotron-H +NVFP4 checkpoint and a local vLLM 0.29 development image, not a stock release. +The 52 weight shards, tokenizer and other inference files were content-hashed +and checked against public revision +`e8f3c7c4de75ad84fe1bcef95d38eca76214480b`; the report includes config and +tokenizer hashes. This is not an original BF16 reference. +MoE execution uses Marlin weight-only FP4, not a native +FP4 speed comparison. + +Each run captures 21,504 prompt tokens and 744 decode-feedback tokens across +the six profiles. All 23 router layers match the request totals, with no +unknown tokens or recording errors. Published routing mass is rounded to eight +decimal places; the private raw captures retain the original precision. + +The batched run records 414 router calls containing both prefill and decode. +An earlier run counted a one-token prompt continuation as decode in all 23 +layers: 6,143 prefill and 125 decode tokens instead of 6,144 and 124. Preserving +the scheduler phase flags corrects that failure without changing the backend's +kernel dispatch. + +Five batched profiles match both unhooked controls. The short-code profile +matches the first control but the two controls differ. The aggregate control +flags therefore remain false. Correct accounting is not proof of unchanged +batched output or a resolved cause for that output difference. + +These are small synthetic samples. The per-expert vectors support checking +cache coverage across workloads, not concluding that experts can be deleted +or that a quantized model has preserved quality. No multi-node run is included. + +All six serial profiles match both unhooked repeats with the final recorder. +The following values are median selection coverage across the 23 routers, +choosing each router's top 32 experts from the same profile and phase: + +| Profile | Prefill | Decode | +|---|---:|---:| +| Code, short | 57.5% | 61.6% | +| Code, long | 59.0% | 70.2% | +| Reasoning, short | 71.0% | 73.1% | +| Reasoning, long | 66.7% | 73.7% | +| General text, short | 66.4% | 76.6% | +| General text, long | 70.3% | 75.3% | + +Using long-general-text decode to choose the top-32 cache, then applying it to +short-code decode, covers only 26.6% of selections at the median router. Choosing +the set from short code itself covers 61.6%. This demonstrates workload shift +in these fixtures, not expected coverage on real traffic. These are single +captures with four prompt variants per profile; uncertainty has not been +estimated. The short and long fixtures share text and are not disjoint +calibration and held-out evaluation sets for a quantization study. diff --git a/tests/test_recipes.py b/tests/test_recipes.py index 84faeb0..14544e8 100644 --- a/tests/test_recipes.py +++ b/tests/test_recipes.py @@ -1762,7 +1762,7 @@ def test_layer_access_goes_through_text_layers(): -def test_probe_places_a_model_too_big_for_the_accelerator(): +def test_probe_places_a_model_too_big_for_the_accelerator(monkeypatch): """The probe pinned the WHOLE model to one device, so the first model bigger than the box OOM'd (Gemma4 12B: 22GB onto a 16GB Mac). It must shard+offload instead of dying.""" try: @@ -1771,6 +1771,10 @@ def test_probe_places_a_model_too_big_for_the_accelerator(): print(" (skipped: torch not installed -- `pip install pollard-weights[flybrain]`)") return import pollard_probe as P + # A placement contract, not a query of the test runner's hardware. Linux + # builds of Torch expose torch.mps but cannot call its memory-query API. + monkeypatch.setattr(P, "_accel_bytes", lambda _dev: 16 * (1 << 30)) + monkeypatch.setattr(P, "_host_bytes", lambda: 64 * (1 << 30)) d = tempfile.mkdtemp() # State the size; do not write it. Truncating 400GB here is free on a filesystem with sparse # files and writes 400 REAL GB on one without -- running this suite on NTFS filled a disk. @@ -2709,4 +2713,3 @@ def test_probe_stability_report_separates_stable_from_corpus_dependent_rankings( st2 = stability_report(unstable, slices) assert st2["verdict"] == "UNSTABLE" and st2["groups"]["ffn"]["spearman_min"] < 0.5, st2 assert "multi-domain" in st2["advice"] - diff --git a/tests/test_vllm_routing_capture.py b/tests/test_vllm_routing_capture.py new file mode 100644 index 0000000..d5ffdcb --- /dev/null +++ b/tests/test_vllm_routing_capture.py @@ -0,0 +1,279 @@ +"""Routing fixtures need no vLLM server; tensor cases use optional CPU torch.""" +import importlib.util +import builtins +import json +import sys +from pathlib import Path +from types import ModuleType, SimpleNamespace +from unittest.mock import patch + +import pytest + +spec = importlib.util.spec_from_file_location( + "pollard_routing_fixture", Path(__file__).parents[1] / "experiments/vllm_decode_routing_hook.py") +H = importlib.util.module_from_spec(spec) +# Import the helpers without installing hooks into a real vLLM runtime. Some +# test environments have vLLM installed but intentionally expose no GPU. +_import = builtins.__import__ + + +def _without_vllm(name, *args, **kwargs): + if name == "vllm" or name.startswith("vllm."): + raise ModuleNotFoundError("vLLM installation is disabled for unit fixtures") + return _import(name, *args, **kwargs) + + +with patch("builtins.__import__", side_effect=_without_vllm): + spec.loader.exec_module(H) + + +@pytest.fixture +def torch(): + return pytest.importorskip("torch") + + +def metadata(torch, starts, seq_lens=None, actual=None): + result = SimpleNamespace(query_start_loc=torch.tensor(starts)) + if seq_lens is not None: + result.seq_lens = torch.tensor(seq_lens) + if actual is not None: + result.num_actual_tokens = actual + return result + + +def test_mixed_batch_new_prompt_decode_and_padding(torch): + md = metadata(torch, [0, 3, 4, 5], [3, 20, 1], actual=5) + assert H.phase_mask(md, 7, "cpu").tolist() == [0, 0, 0, 1, 0, -1, -1] + + +@pytest.mark.parametrize("md", [None, {}, SimpleNamespace(max_query_len=1)]) +def test_missing_metadata_is_not_prefill(torch, md): + assert H.phase_mask(md, 2, "cpu").tolist() == [-1, -1] + + +def test_single_queries_without_sequence_lengths_are_unknown(torch): + assert H.phase_mask(metadata(torch, [0, 1, 3]), 3, "cpu").tolist() == [-1, 0, 0] + + +@pytest.mark.parametrize("starts,seq_lens,actual", [([1, 2], [2], 2), ([0, 3], [2], 3), + ([0, 2, 1], [2, 2], 1), ([0, 3], [3], 2)]) +def test_invalid_metadata_is_unknown(torch, starts, seq_lens, actual): + assert H.phase_mask(metadata(torch, starts, seq_lens, actual), 4, "cpu").tolist() == [-1] * 4 + + +def test_attention_groups_must_agree(torch): + a, b = metadata(torch, [0, 1], [1]), metadata(torch, [0, 1], [5]) + assert H.phase_mask({"a": a, "b": b}, 1, "cpu").tolist() == [-1] + + +def test_hybrid_state_space_metadata_is_not_used_as_attention(torch): + md = {"attention": metadata(torch, [0, 2, 3], [2, 8]), + "mamba": SimpleNamespace(num_prefills=1, num_decodes=1)} + assert H.phase_mask(md, 3, "cpu").tolist() == [0, 0, 1] + + +def test_flashinfer_uses_its_explicit_decode_first_split(torch): + md = type("FlashInferMetadata", (), {})() + md.num_decode_tokens, md.num_prefill_tokens, md.num_actual_tokens = 2, 3, 5 + assert H.phase_mask({"attn": md, "mamba": SimpleNamespace()}, 7, "cpu").tolist() == [1, 1, 0, 0, 0, -1, -1] + md.num_prefill_tokens = 4 + assert H.phase_mask(md, 7, "cpu").tolist() == [-1] * 7 + + +def test_scheduler_phase_corrects_single_token_prefill_continuation(torch): + md = metadata(torch, [0, 1, 2, 4], [30, 20, 42], actual=4) + md.is_prefilling = torch.tensor([True, False, True]) + assert H.phase_mask(md, 5, "cpu").tolist() == [0, 1, 0, 0, -1] + md.is_prefilling = torch.tensor([True]) + assert H.phase_mask(md, 5, "cpu").tolist() == [-1] * 5 + + +def test_flashinfer_bridge_preserves_request_phase_not_kernel_split(torch): + class Builder: + def build(self, common_prefix_len, common_attn_metadata, fast_build=False): + result = type("FlashInferMetadata", (), {})() + result.num_actual_tokens = 2 + result.num_decode_tokens = 2 # two one-token queries, both decode kernels + result.num_prefill_tokens = 0 + return result + cls, original, installed = H.preserve_flashinfer_scheduler_phase(Builder) + md = metadata(torch, [0, 1, 2], [30, 20], actual=2) + md.is_prefilling = torch.tensor([True, False]) + result = Builder().build(0, md) + assert H.phase_mask(result, 3, "cpu").tolist() == [0, 1, -1] + assert result.num_decode_tokens == 2 # the serving backend's split is unchanged + md.is_prefilling = None + result = Builder().build(common_prefix_len=0, common_attn_metadata=md) + assert H.phase_mask(result, 2, "cpu").tolist() == [-1, -1] + assert cls.build is installed + cls.build = original + + +def test_expert_width_grows_and_never_shrinks(torch, tmp_path): + capture = H.Capture(str(tmp_path), every=100) + router = SimpleNamespace(prefix="model.layers.0.mlp") + md = metadata(torch, [0, 1], [5]) + for expert in (1, 3, 0): + capture.record(router, torch.tensor([[1.0]]), torch.tensor([[expert]]), md) + layer = capture.layers[router.prefix] + assert layer["experts"] == 4 + assert layer["decode"]["count"] == [1, 1, 0, 1] + assert layer["decode"]["tokens"] == 3 + assert not list(tmp_path.glob("*.json")) + capture.flush() + data = json.loads(Path(capture.path).read_text()) + assert data["meta"]["calls"] == 3 + assert data["layers"] == capture.layers + assert not list(tmp_path.glob("*.tmp")) + + +def test_empty_routing_batch_is_harmless(torch, tmp_path): + capture = H.Capture(str(tmp_path)) + capture.record(SimpleNamespace(), torch.empty((0, 2)), torch.empty((0, 2), dtype=torch.int64), None) + assert capture.calls == 0 and not capture.layers + + +def test_counts_only_router_calls_with_both_phases(torch, tmp_path): + capture = H.Capture(str(tmp_path), every=100) + router = SimpleNamespace(prefix="layer0", global_num_experts=2) + mixed = metadata(torch, [0, 2, 3], [2, 8]) + capture.record(router, torch.ones((3, 1)), torch.zeros((3, 1), dtype=torch.int64), mixed) + capture.record(router, torch.ones((1, 1)), torch.zeros((1, 1), dtype=torch.int64), None) + assert capture.mixed_router_calls == 1 + capture.flush() + assert json.loads(Path(capture.path).read_text())["meta"]["mixed_router_calls"] == 1 + capture.reset() + assert capture.mixed_router_calls == 0 + + +def test_unknown_tokens_are_kept_separate(torch, tmp_path): + capture = H.Capture(str(tmp_path), every=1) + capture.record(SimpleNamespace(prefix="layer0", global_num_experts=4), + torch.tensor([[0.25, 0.75]]), torch.tensor([[1, 3]]), None) + layer = capture.layers["layer0"] + assert layer["prefill"]["tokens"] == layer["decode"]["tokens"] == 0 + assert layer["unknown"]["count"] == [0, 1, 0, 1] + assert sum(layer["unknown"]["mass"]) == 1.0 + + +def dump(tmp_path, name, count=2): + data = {"layers": {"layer0": {"experts": 2, "decode": { + "count": [count, 0], "mass": [float(count), 0], "tokens": count}, + "prefill": {"count": [0, 2], "mass": [0, 2.0], "tokens": 2}}}} + path = tmp_path / name + path.write_text(json.dumps(data)) + return str(path) + + +def test_rank_mode_is_explicit(tmp_path): + paths = [dump(tmp_path, "a.json"), dump(tmp_path, "b.json")] + with pytest.raises(ValueError, match="rank-mode"): + H.analyse(paths) + + +def test_replicated_ranks_count_once(tmp_path, capsys): + H.analyse([dump(tmp_path, "a.json"), dump(tmp_path, "b.json")], rank_mode="replicated") + output = capsys.readouterr().out + assert "replicated" in output + assert "100.0%" in output # top64 covers all experts in this two-expert fixture + + +def test_replicated_ranks_with_different_boundaries_fail(tmp_path): + with pytest.raises(ValueError, match="captures differ"): + H.analyse([dump(tmp_path, "a.json"), dump(tmp_path, "b.json", 3)], rank_mode="replicated") + + +def test_capture_names_do_not_collide_without_rank(tmp_path, monkeypatch): + monkeypatch.delenv("RANK", raising=False) + monkeypatch.delenv("LOCAL_RANK", raising=False) + monkeypatch.setattr(H.os, "getpid", lambda: 10) + a = H.Capture(str(tmp_path)) + monkeypatch.setattr(H.os, "getpid", lambda: 11) + assert a.path != H.Capture(str(tmp_path)).path + + +def test_fork_adopts_child_identity_and_discards_inherited_counts(torch, tmp_path, monkeypatch): + monkeypatch.setattr(H.os, "getpid", lambda: 10) + capture = H.Capture(str(tmp_path), every=1) + router = SimpleNamespace(prefix="layer0", num_experts=4) + capture.record(router, torch.tensor([[1.0]]), torch.tensor([[2]]), None) + parent = capture.path + monkeypatch.setattr(H.os, "getpid", lambda: 11) + capture.record(router, torch.tensor([[1.0]]), torch.tensor([[3]]), None) + assert capture.path != parent and capture.calls == 1 + assert capture.layers["layer0"]["unknown"]["count"] == [0, 0, 0, 1] + assert json.loads(Path(parent).read_text())["meta"]["pid"] == 10 + assert json.loads(Path(capture.path).read_text())["meta"]["pid"] == 11 + + +def test_captures_with_recording_errors_are_not_analyzed(tmp_path): + path = Path(dump(tmp_path, "error.json")) + data = json.loads(path.read_text()) + data["meta"] = {"errors": 1} + path.write_text(json.dumps(data)) + with pytest.raises(ValueError, match="recording errors"): + H.analyse([str(path)]) + + +def test_invalid_dump_interval(tmp_path): + with pytest.raises(ValueError, match="positive"): + H.Capture(str(tmp_path), every=0) + + +def test_installed_wrapper_returns_unchanged_output_and_flushes(torch, tmp_path, monkeypatch): + class Router: + prefix = "layer0" + num_experts = 4 + def select_experts(self, weights, ids): + return weights, ids + # Exercise the installation path with real CPU tensors and a fake vLLM + # boundary. Syntax checks alone cannot catch undefined names in _install. + names = ["vllm", "vllm.model_executor", "vllm.model_executor.layers", + "vllm.model_executor.layers.fused_moe", "vllm.model_executor.layers.fused_moe.router", + "vllm.model_executor.layers.fused_moe.router.fused_moe_router", "vllm.forward_context"] + modules = {name: ModuleType(name) for name in names} + modules[names[-2]].FusedMoERouter = Router + modules[names[-3]].fused_moe_router = modules[names[-2]] + modules[names[-1]].get_forward_context = lambda: SimpleNamespace(attn_metadata=metadata(torch, [0, 1], [8])) + for name, module in modules.items(): + monkeypatch.setitem(sys.modules, name, module) + backend = ModuleType("vllm.v1.attention.backends.flashinfer") + class Builder: + def build(self, common_prefix_len, common_attn_metadata): + return SimpleNamespace(num_actual_tokens=1) + backend.FlashInferMetadataBuilder = Builder + original_build = Builder.build + monkeypatch.setitem(sys.modules, backend.__name__, backend) + monkeypatch.setenv("VLLM_ROUTING_DUMP_DIR", str(tmp_path)) + monkeypatch.setenv("VLLM_ROUTING_DUMP_EVERY", "1") + H._install() + assert Builder.build is not original_build + wrapped = Router.select_experts + router = Router() + model = SimpleNamespace(named_modules=lambda: [("model.layers.3.mlp", SimpleNamespace(router=router))]) + assert H.assign_layer_names(model) == 1 + assert router.layer_name == "model.layers.3.mlp" + H._install() + assert Router.select_experts is wrapped + weights, ids = torch.tensor([[1.0]]), torch.tensor([[2]]) + result = Router().select_experts(weights, ids) + assert result[0] is weights and result[1] is ids + data = json.loads(next(tmp_path.glob("*.json")).read_text()) + assert data["layers"]["layer0"]["decode"]["tokens"] == 1 + extension = H.RoutingWorkerExtension() + extension.model_runner = SimpleNamespace(get_model=lambda: model) + assert extension.pollard_start_capture() == {"named_routers": 1} + assert wrapped._pollard_capture.calls == 0 + assert not list(tmp_path.glob("*.json")) + router.select_experts(weights, ids) + assert extension.pollard_flush_capture()["layers"] == 1 + # A failed observation must not fail the original router call. + Router().select_experts(torch.tensor([[1.0]]), torch.tensor([[-1]])) + assert wrapped._pollard_capture.errors == 1 + wrapped._pollard_capture.flush() + H.atexit.unregister(wrapped._pollard_capture.flush) + assert extension.pollard_remove_capture() == {"removed": True} + assert Router.select_experts is wrapped._pollard_original + assert Builder.build is original_build + with pytest.raises(RuntimeError, match="not installed"): + extension.pollard_start_capture() diff --git a/tests/test_vllm_routing_check.py b/tests/test_vllm_routing_check.py new file mode 100644 index 0000000..e627224 --- /dev/null +++ b/tests/test_vllm_routing_check.py @@ -0,0 +1,40 @@ +"""The live-check accounting and output must be testable without vLLM.""" +import importlib.util +import json +from pathlib import Path +from types import SimpleNamespace + +import pytest + +spec = importlib.util.spec_from_file_location("routing_check", Path(__file__).parents[1] / "experiments/vllm_routing_check.py") +C = importlib.util.module_from_spec(spec) +spec.loader.exec_module(C) + + +def test_last_generated_token_is_not_a_decode_observation(): + outputs = [SimpleNamespace(prompt_token_ids=[1, 2], outputs=[SimpleNamespace(token_ids=ids)]) + for ids in ([3], [4, 5, 6])] + result = C.summarize_outputs(outputs) + assert result["prompt_tokens"] == 4 and result["output_tokens"] == 4 + assert result["expected_decode_tokens"] == 2 + assert len(result["output_sha256"]) == 64 + assert "token_ids" not in json.dumps(result) + + +@pytest.mark.parametrize("failure", [None, "unknown", "errors", "empty", "path"]) +def test_capture_checks_counts_and_rejects_bad_reports(tmp_path, failure): + worker = {"file": "routing_fixture.json", "errors": 0, "calls": 3, "layers": 1} + data = {"meta": {"errors": 0}, "layers": {"layer": { + "prefill": {"tokens": 4}, "decode": {"tokens": 2}, "unknown": {"tokens": 0}}}} + if failure == "unknown": data["layers"]["layer"]["unknown"]["tokens"] = 1 + if failure == "errors": data["meta"]["errors"] = 1 + if failure == "empty": data["layers"] = {} + if failure == "path": worker["file"] = "../routing_fixture.json" + (tmp_path / "routing_fixture.json").write_text(json.dumps(data)) + if failure: + with pytest.raises(ValueError): + C.check_capture(tmp_path, [worker], {"prompt_tokens": 4, "expected_decode_tokens": 2}) + else: + report = C.check_capture(tmp_path, [worker], {"prompt_tokens": 4, "expected_decode_tokens": 2}) + assert report[0]["tokens_per_layer"] == {"prefill": 4, "decode": 2, "unknown": 0} + assert "routing_fixture" not in json.dumps(report) diff --git a/tests/test_vllm_routing_profiles.py b/tests/test_vllm_routing_profiles.py new file mode 100644 index 0000000..5f933c7 --- /dev/null +++ b/tests/test_vllm_routing_profiles.py @@ -0,0 +1,75 @@ +"""Workload provenance and routing comparisons do not need a GPU.""" +import importlib.util +import json +from pathlib import Path +import sys + +import pytest + +EXPERIMENTS = Path(__file__).parents[1] / "experiments" +spec = importlib.util.spec_from_file_location("routing_profiles", EXPERIMENTS / "vllm_routing_profiles.py") +P = importlib.util.module_from_spec(spec) +sys.path.insert(0, str(EXPERIMENTS)) +try: + spec.loader.exec_module(P) +finally: + sys.path.pop(0) + + +class Tokenizer: + def encode(self, text, add_special_tokens): + assert add_special_tokens is False + return list(text.encode()) + + +def test_workloads_are_deterministic_distinct_and_bounded(): + streams = [P.workload_tokens(Tokenizer(), d, n, v) + for d in P.DOMAINS for n in P.CONTEXTS.values() for v in range(4)] + assert len(streams) == 24 + assert len({tuple(x) for x in streams}) == 24 + assert all(len(x) in P.CONTEXTS.values() for x in streams) + assert streams[0] == P.workload_tokens(Tokenizer(), "code", 256, 0) + + +@pytest.mark.parametrize("target", [0, -1, True, 1.5]) +def test_rejects_invalid_token_targets(target): + with pytest.raises(ValueError): + P.workload_tokens(Tokenizer(), "code", target, 0) + + +@pytest.mark.parametrize("values", [[], [0, 0], [-1, 2], [float("nan"), 1], [float("inf")]]) +def test_rejects_invalid_histograms(values): + with pytest.raises(ValueError): + P.normalized(values) + + +def test_js_has_known_values_and_width_check(): + assert P.js_bits([1, 0], [1, 0]) == 0 + assert P.js_bits([1, 0], [0, 1]) == 1 + assert P.js_bits([1, 2], [2, 1]) == pytest.approx(P.js_bits([2, 1], [1, 2])) + with pytest.raises(ValueError): + P.js_bits([1], [1, 2]) + + +def capture(counts): + return {"layers": {"model.public.layer": {"experts": len(counts), **{ + phase: {"tokens": 4, "count": counts, "mass": counts} for phase in ("prefill", "decode")}}}} + + +def test_curves_and_transfer_use_source_not_target_top_k(): + # Source puts all demand in expert 0. Target puts it in expert 8. + source = capture([1] + [0] * 8) + target = capture([0] * 8 + [1]) + row = P.compare_layers(source, target)[0] + assert row["decode"]["selection_js_bits"] == 1 + assert row["decode"]["source_cache_target_selection_coverage"]["8"] == 0 + assert row["decode"]["source_cache_target_selection_coverage"]["16"] == 1 + assert P.describe_layers(target)[0]["decode"]["selection_coverage"]["8"] == 1 + assert "model.public" not in json.dumps(row) + + +def test_rejects_incompatible_layers(): + with pytest.raises(ValueError): + P.compare_layers(capture([1, 2]), capture([1, 2, 3])) + with pytest.raises(ValueError): + P.compare_layers(capture([1]), {"layers": {}})