DeepEP HT: full decode CUDA graphs via worst-token dispatch (whole-step + graphed TBO) - #27
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DeepEP HT: full decode CUDA graphs via worst-token dispatch (whole-step + graphed TBO)#27fergusfinn wants to merge 30 commits into
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Pull request overview
This PR enables DeepEP high-throughput (HT) decode serving to run as full-step CUDA graphs by introducing a “worst-token” dispatch mode (UCCL worst-token kernels) and extending the DBO (ubatching) CUDA-graph stack to support breakable capture/replay around eager DeepEP/MoE segments.
Changes:
- Add DeepEP-HT worst-token dispatch plumbing and compilation-mode selection to allow full decode CUDA graphs (decode-only capture, prefill eager).
- Implement DBO “breakable CUDA graph” capture/replay path for DeepEP-HT (including metadata refresh/copy, SM control factoring, and alignment fixes).
- Add deterministic workspace reservation utilities and multiple safety/debug hooks (DP wave coalesce, KV-insert debug, DBO decision logging, dummy-run capture shape tweaks).
Reviewed changes
Copilot reviewed 26 out of 26 changed files in this pull request and generated 1 comment.
Show a summary per file
| File | Description |
|---|---|
| vllm/v1/worker/workspace.py | Add deterministic workspace reservation APIs for multi-tensor allocations. |
| vllm/v1/worker/ubatching.py | Allow certain DBO yield/switch operations to become eager breaks during breakable CUDA-graph capture. |
| vllm/v1/worker/ubatch_utils.py | Add tensor alignment helper, improve ubatch-slice robustness, and enforce DeepEP-HT minimum decode threshold. |
| vllm/v1/worker/sm_control.py | New helper module to compute comm/compute SM splits and locate all2all manager. |
| vllm/v1/worker/gpu_worker.py | Plumb allow_microbatching into dummy warmups and kernel warmup to avoid problematic startup microbatching. |
| vllm/v1/worker/gpu_ubatch_wrapper.py | Add breakable DBO CUDA-graph capture/replay and metadata refresh logic for DeepEP-HT DP>1. |
| vllm/v1/worker/gpu_model_runner.py | Add breakable-DBO capture logic, padded decode capture shaping, exact graph-memory profiling path, and MoE workspace reservation. |
| vllm/v1/worker/dp_utils.py | Improve DBO abort logging and replace last-ubatch check with generalized empty-ubatch detection. |
| vllm/v1/engine/coordinator.py | Add optional DP START wave coalescing delay controlled by env var. |
| vllm/v1/attention/backends/utils.py | Normalize metadata scalar types to Python int to avoid dtype surprises. |
| vllm/v1/attention/backends/mla/sparse_swa.py | Ensure deterministic token-to-request index buffer contents for graph safety. |
| vllm/v1/attention/backends/mla/indexer.py | Respect SM control for compute kernels; skip zero-query chunks from padded requests; refine extend-vs-decode behavior. |
| vllm/utils/multi_stream_utils.py | Add enable switch and disable multi-stream overlap during breakable CUDA-graph capture. |
| vllm/models/deepseek_v4/nvidia/model.py | Introduce full-capture eager-break custom op for FFN to support breakable capture, and register layers in static forward context. |
| vllm/models/deepseek_v4/compressor.py | Make token-to-request indices deterministic and assert bounds for graph compatibility. |
| vllm/models/deepseek_v4/attention.py | Gate post-GEMM overlap by token threshold and add optional KV-insert metadata debug checks outside capture. |
| vllm/model_executor/warmup/kernel_warmup.py | Thread allow_microbatching through warmups/autotune dummy runs. |
| vllm/model_executor/layers/sparse_attn_indexer.py | Mark sparse attention indexer as an eager break during full capture. |
| vllm/model_executor/layers/fused_moe/prepare_finalize/deepep_ht.py | Add worst-token dispatch sizing and enforce synchronous dispatch/combine under worst-token mode; accept device-side per-expert counts. |
| vllm/model_executor/layers/fused_moe/modular_kernel.py | Add MoE max-workspace reservation hooks and improve workspace allocation error context. |
| vllm/model_executor/layers/fused_moe/all2all_utils.py | Pass max_tokens_per_rank into DeepEP-HT prepare/finalize for capacity-aware behavior. |
| vllm/envs.py | Add new env flags for DBO breakable graphs, worst-token dispatch, debug logging, DP wave coalesce, and KV-insert debug. |
| vllm/config/vllm.py | Pass use_ubatching into compilation splitting-op selection. |
| vllm/config/compilation.py | Add DBO breakable and worst-token mode logic to adjust cudagraph mode/splitting ops for v1. |
| vllm/compilation/breakable_cudagraph.py | Add DBO-specific breakable enablement and full-capture eager-break decorator support. |
| vllm/_custom_ops.py | Ensure DeepSeek hash-routing tensors match topk_indices.dtype before invoking the fused op. |
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Summary
This PR introduces CUDA graph support for DeepEP high-throughput decode via worst-token dispatch, along with several DBO improvements, metadata padding fixes, and a hash routing bugfix. The core feature (worst-token dispatch) is well-designed with appropriate fallbacks, but there's a Blocking issue with the async_finish gating logic that causes unnecessary serialization for prefill/eager batches.
Verdict: Changes need revision before merging due to the Blocking finding below.
Research Notes
- CUDAGraphMode.FULL_DECODE_ONLY: Dual-mode
(FULL, NONE)that resolves at runtime based on batch type (uniform decode → FULL, mixed/prefill → NONE) - ExpertTokensMetadata: Primary consumer
compute_aligned_M()has safe fallback whenexpert_num_tokens_cpu=None - UCCL worst-token kernels: External dependency (fergusfinn/uccl#7) provides host-count-sync-free dispatch
- DBO barrier abort:
threading.BrokenBarrierErrorhandling afterbarrier.abort()is correct Python pattern
Suggested Next Steps
- Fix async_finish gating to use
num_worst_tokens > 0instead of global env var (Blocking) - Document how VLLM_MULTI_STREAM_GEMM_TOKEN_THRESHOLD default (1024) was determined
- Add test coverage for DBO error propagation path
- Verify cudagraph_runtime_mode resolution during capture vs runtime for FULL_DECODE_ONLY
General Findings
- Worst-token dispatch feature properly falls back to eager path when conditions aren't met
- Metadata padding fixes correctly zero-fill padded regions for CUDA graph capture
- SM control centralization in sm_control.py improves code organization
- Hash topk dtype alignment prevents potential crashes from int64/int32 mismatches
There was a problem hiding this comment.
Summary
This PR implements DeepEP high-throughput full decode CUDA graphs via worst-token dispatch, enabling stream-capturable dispatch+combine without host count synchronization. The implementation introduces two complementary mechanisms:
- Worst-token dispatch (
VLLM_DEEPEP_HT_WORST_TOKEN_DISPATCH=1): Skips host count sync, uses static worst-case recv shapes, and returns device-side per-expert counts for CUDA-graph-safe operation - DBO breakable cudagraph (
VLLM_DBO_BREAKABLE_CUDAGRAPH=1): Captures decode compute segments while leaving DeepEP MoE work eager via@eager_break_during_full_capturedecorator onvllm::deepseek_v4_ffn
These modes are correctly made mutually exclusive in compilation.py. The code properly handles stream synchronization, ForwardContext state management, and workspace reservation.
Verdict: Approved with minor documentation suggestion.
Research notes
- Fetched DeepEP HT dispatch/combine API usage patterns from existing codebase
- Verified
ExpertTokensMetadatasemantics: device tensor only whenexpert_num_tokens_cpu=None - Confirmed
moe_layer_indexlifecycle: created at 0 increate_forward_context(), incremented per MOE layer inget_layer_from_name(), reset before replay via_refresh_breakable_metadata() - Validated worst-token calculation:
num_tokens * num_dispatchersmatches DeepEP's capacity contract for EP worst-case routing
Suggested next steps
- Non-blocking: Add inline comment or TODO noting the <64 token capture crash limitation near
_num_worst_tokens()or compilation gating logic - Consider adding an assertion in
_num_worst_tokens()that validatesnum_dispatchers_is inavailable_rank_configswhen worst-token mode is active
General findings
- Stream synchronization: Correctly forces
async_finish=Falsefor both dispatch and combine when worst-token mode is active, preventing race between graph replay and pending comm-stream work - Mutual exclusivity: Compilation config correctly gates worst-token dispatch (
not dbo_breakable_cudagraph) vs DBO breakable (dbo_breakable_cudagraph) paths - Metadata refresh:
_refresh_breakable_metadata()correctly copies freshruntime_context.moe_layer_index(always 0 from new ForwardContext) to captured context before replay - Workspace reservation:
_reserve_deepep_ht_moe_workspace()uses matching worst-case formulamax_tokens_per_rank * num_dispatchers()
General findings (auto-demoted from inline due to pre-validation)
- Nit
vllm/config/compilation.py:1224— Consider extracting the mutual exclusivity invariant into a named helper or assertion for clarity.- (demoted: line 1224 (side=RIGHT) is not part of any diff hunk in vllm/config/compilation.py)
fergusfinn
changed the title
Align hash routing metadata to the topk index dtype before invoking the sqrt-softplus topk custom op. DeepEP may request int64 topk indices while DeepSeek V4 hash MoE metadata remains int32, but the CUDA op dispatches hash metadata from the topk index dtype. Co-authored-by: Trae AI <trae-ai@users.noreply.github.com> Signed-off-by: wangyicong <wangyicong@bytedance.com> (cherry picked from commit 01e7beb)
Dummy runs for CUDA graph capture left query_start_loc and token_to_req_indices stale in the padded region between num_reqs and num_reqs_padded, and built metadata at the padded request count instead of the real one. Zero-fill the padded token->request mappings, fill the padded query_start_loc tail with the final cumsum, and pass num_reqs / num_reqs_padded to the builders explicitly so captured decode graphs see the same padded-metadata layout runtime replay provides.
An exception in a ubatch thread previously left the other thread and the launcher waiting on the ready barrier forever. Abort the barrier on error, join both threads, and re-raise with the original cause; also fail loudly when a thread exits without producing output.
With the UCCL ht-cudagraph-worst-tokens kernels (fergusfinn/uccl#7), DeepEP high-throughput dispatch+combine are stream-capturable: no host count sync, worst-case static recv shapes, per-expert counts in a device tensor. Behind VLLM_DEEPEP_HT_WORST_TOKEN_DISPATCH (default off): - allow FULL_DECODE_ONLY for deepep_high_throughput instead of forcing cudagraph_mode NONE - size num_worst_tokens per captured shape (padded step tokens x num_dispatchers) from the forward context; eager steps (prefill, mixed, uncaptured) keep the host-synced path with zero padding cost - build ExpertTokensMetadata directly from the device counts tensor (expert_num_tokens_cpu=None); fused_marlin_moe never reads counts - join dispatch AND combine back to the compute stream (async off) whenever worst-token mode is active: async comm-stream work in flight races subsequently replayed graphs on the UCCL ring buffers Validated 2-node EP8 DSv4-Flash (capture sizes 64..2048): conc 1024 ISL/OSL 1024/128 -> 1642 tok/s (+45% vs the thread-DBO baseline); conc 8192 1024/1024 -> 9243 tok/s (+13%), 8192/8192 requests, 0 failures. Known: shapes <64 tokens crash capture (suspected lazy NCCL/EPLB init mid-capture; floor the capture list via compilation config), and the blanket sync fence taxes prefill (event fence is follow-up).
Temporary instrumentation for the conc-8192 warm-server wedge: on RuntimeError from dispatch/combine, append token count, worst-token size, cudagraph runtime mode, and batch descriptor so a CPU-timeout localizes the step shape without debug logging on the hot path.
Under DBO ubatching each microbatch thread's forward context carries the whole step's batch descriptor, so descriptor-based sizing would allocate 2x worst-case recv buffers per ubatch (and pad expert compute to match). The dispatched tensor's row count is DP-padding-uniform across ranks and equals exactly what this dispatch can send: the padded step size for whole-batch decode graphs, the padded slice under ubatching. Size from it. This is the only deepep_ht change needed to run worst-token HT dispatch inside UBatchWrapper's captured two-microbatch interleave: the dbo yield and stream-switch hooks are already in the dispatch path, async_finish is already forced off under both dbo and worst-token mode, and handles are already stored per ubatch id. Co-authored-by: Claude
UBatchWrapper passes cudagraph_runtime_mode=NONE into the per-ubatch forward contexts while capturing a full ubatched graph, so gating worst-token mode on the context mode records the host-synced dispatch protocol into the graph: its CPU poll then waits on a kernel that never executes under stream capture, and every rank dies with 'DeepEP error: timeout (dispatch CPU)' at the first capture shape. Check the stream's capture status directly in addition to the context mode. Co-authored-by: Claude
Three soundness fixes from a line-by-line audit of the DBO/ubatch stack, all turning silent-divergence or silent-wedge failure modes into loud errors: 1. maybe_create_ubatch_slices: the mid-loop empty-slice guard silently fell back to an un-ubatched run on the local rank. All DP ranks have already agreed to ubatch via the coordinate_batch_across_dp all-reduce by this point, so a local fallback issues half as many EP dispatches as peer ranks and deadlocks the DP group inside DeepEP (100s dispatch-CPU timeout). The condition is provably unreachable while the DP-wide has_empty_ubatch agreement holds, so raise on the broken invariant instead of diverging. Also drop the split_point list-of-points branch: no caller passes it, and per-rank split points would bypass the DP-wide agreement entirely. 2. ensure_tensor_alignment: a clone made while preparing a CUDA-graph capture is baked into the graph at its capture-time address and read stale on every replay (silent corruption). The current step-32 capture grids keep every slice 64B-aligned so no clone happens today, but that is an accident of the grid, not an invariant. Thread allow_clone=False through the capture paths (_make_ubatch_metadata for model inputs, split_attn_metadata for attention metadata) and raise with a pointer at cudagraph_capture_sizes if a misaligned slice ever reaches capture. Eager ubatched runs keep the clone (the CuteDSL FFI alignment fix). 3. _run_ubatches: error propagation aborted the ready barrier, but a thread that dies mid-model leaves its peer parked on a CPU handoff event that will never be set, and the unbounded join wedged the worker silently. Poll the join while healthy, and once an error is recorded wake all handoff events best-effort, bound the remaining join, and raise naming any thread that failed to unwind. Assisted-by: Claude Co-authored-by: Claude <noreply@anthropic.com>
max_num_tokens_per_rank() consumers all gate on the BatchedExperts activation format (oracle experts construction, humming, wna16 marlin) or the EEP reconfigure path; DeepEP HT declares Standard, so the value added here was never read. Restore the upstream None and drop the constructor threading. Assisted-by: Claude Co-authored-by: Claude <noreply@anthropic.com>
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On a dispatch/combine CPU timeout the existing annotation shows what each rank was sending but not WHICH collective it was stuck in, so a wedge with ragged per-rank token counts (legitimate on eager steps) cannot distinguish 'all ranks in the same stuck collective' (transport fault) from 'ranks in different collectives' (scheduling desync). Count host-issued dispatches and combines per rank and report seq + dbo ubatch id in the timeout annotations. Healthy EP ranks advance these in lockstep (graph replays bump no rank's counter; runtime mode is DP-synced per step), so comparing seq across ranks' logs answers the question directly, and seq mod num-moe-layers localizes the layer. An int increment on the hot path, read only when raising. Assisted-by: Claude Co-authored-by: Claude <noreply@anthropic.com>
Aux-stream overlap of indexer/compressor work with the default stream only pays below VLLM_MULTI_STREAM_GEMM_TOKEN_THRESHOLD tokens; above it the streams serialize anyway and the event traffic is pure overhead. Adds an enable switch to maybe_execute_in_parallel and applies the threshold at the DSv4 attention call sites. [restored after being pruned as a perf rider 2026-06-11] Two reasons this is load-bearing, not cosmetic: 1. Upstream's own policy: vllm-project#41526 (which set the 1024-token default) states multi-stream should be disabled for prefill-sized batches; upstream applies that only at the pre-GEMM site. This commit extends the same gate to the post-GEMM 3-way overlap (wq_b+kv_insert || indexer || compressor), which upstream ships ungated. 2. The only IMA observed on this branch (2026-06-11, conc-4096 ramp, heavy mixed prefill on GH200) faulted inside the post-GEMM trio the first time 8192-token chunks ran it ungated. Every certified run (gsm8k, 30-min conc-8192 soak) had this gate active. n=1 and async-attributed, so not proof of a race -- compute-sanitizer racecheck at 8192 tokens is the follow-up that would settle it -- but the gate restores the certified configuration at zero cost. Assisted-by: Claude Co-authored-by: Claude <noreply@anthropic.com>
EngineCore.__init__ calls freeze_gc_heap() and maybe_attach_gc_debug_callback(), but the multiproc worker processes never did -- their post-init heap (model tree, captured CUDA graphs, compiled artifacts; millions of tracked objects) stayed unfrozen, so every gen-2 collection walked all of it. Observed on GH200 EP8 serving as occasional ~1s stop-the-world worker pauses: a profiler trace shows the main thread parked mid-_make_metadata_with_slice with zero instrumented ops for 1043ms, the GPU idle for 976ms after queued work drains, and the other 7 ranks stalled in notify_dispatch waiting for the frozen rank's next launch (the same global-rendezvous signature as any rank-local pause under EP). Mirror EngineCore: freeze the heap at the end of compile_or_warm_up_model (all weights/graphs/warmup state allocated by then) and attach the gc_utils debug callback so VLLM_GC_DEBUG=1 logs remaining collections for before/after verification. Assisted-by: Claude Co-authored-by: Claude <noreply@anthropic.com>
…on, wave trace + TBO mode counters Findings from a jammed closed-loop bench (4096 reqs, conc-1024, 2-node EP8): load concentrated onto two engines until capacity-pinned (169 waiting, reason=capacity) while six peers drained; after client aborts the system settled with all engines asleep and reruns crawled at ~7x slower with two engines taking no traffic. Four changes: 1. Fix (shadow ban): requests handled while an engine is paused (e.g. aborts draining it) were processed inside _process_input_queue's blocking loop, which re-blocks on queue.get() without ever reaching run_busy_loop's _maybe_publish_request_counts() -- so the engine never publishes its zeroed counts, the LB avoids it forever, and no request can ever wake it. Add a _publish_idle_state() hook called after each idle-handled request; DP engines publish counts there. 2. Safety net (wake watchdog): the wave protocol has message races (front-end new-request notifications are silently dropped when the coordinator believes engines are running; nothing retries) that can strand work on paused engines indefinitely. The coordinator now re-broadcasts START_DP_WAVE (idempotent for running engines) when its stats show unfinished work while engines are believed paused, or while no stats update has arrived for 30s -- converting any lost wake-up into a bounded ~10s hiccup regardless of cause. This is deliberately load-independent: arbitrary imbalance or message loss must degrade, not wedge. 3. Mitigation (VLLM_DP_LB_P2C): power-of-two-choices engine selection. The global min-score scan converges every API server onto the same engine between coordinator refreshes (requests queued in an engine's input socket are invisible to its counts), which under slow steps pins one engine at capacity while peers idle. Random pairing bounds the skew. Off by default. 4. Observability: VLLM_DP_TRACE=1 logs every finish-sync vote (step, wave, local/global votes, counts), engine sleep/wake transitions, wave starts, and coordinator wave decisions; UBatchWrapper logs execution-mode counters every 30s (ubatched_replay / eager / capture) as direct evidence of graphs+TBO health in production. Assisted-by: Claude Co-authored-by: Claude <noreply@anthropic.com>
…lence The silence heuristic misfired at steady state: a saturated decode cluster has constant request counts, publishes nothing (publishes are change-gated), and 30s of stats silence is its healthy condition -- observed as a benign rebroadcast every 10s under load (which also demonstrated the rebroadcast is harmless to running engines). Replace it with an explicit signal: engines force-publish a SchedulerStats with engines_paused=True when entering the paused state (bypassing the changed-counts gate). The coordinator tracks the flag per engine and re-wakes only when some engine is paused while work exists anywhere in the group -- exactly the invariant the wave protocol must maintain, robust to either side's running-state belief being stale, and structurally free of the steady-state false positive. Flags clear optimistically on each wave broadcast and self-correct via re-published sleep markers. Assisted-by: Claude Co-authored-by: Claude <noreply@anthropic.com>
…nstead of asserting A uniform decode batch above the largest captured CUDA graph falls to the eager MoE path, whose workspace needs exceed anything warmup sized. The locked workspace must not grow (captured graphs hold device pointers into it), so the assertion turned one over-cap step into a dead worker -> EP barrier starvation -> full-server wedge (observed at conc-8192 with cap-1024: 65/8192 requests, then death). Locked+oversized requests now get a transient allocator-pooled buffer, leaving graph memory untouched; rate-limited warning names the caller. Also warn at lock time when max_num_seqs makes over-cap decode batches reachable.
…buffer instead of asserting" This reverts commit a6a5ee8.
TILELANG_CACHE_DIR points at node-local storage shared by every worker process on the node. On a cold node, workers concurrently JIT-compile the same kernel into the same cache path; the loser loads a torn module and dies with tvm_ffi AttributeError: Module has no function 'main' (observed on a fresh 2-node DSv4-Flash boot, all four workers of one node at once). Suffix the cache and tmp dirs with the worker's local rank before anything imports tilelang. Co-authored-by: Claude
… buffer instead of asserting" This reverts commit 0e0743c.
The cold-boot race fixed for tilelang exists identically for Triton, DeepGEMM, CUTE-DSL, inductor and torch-extensions caches (all pointed at node-shared $LOCALDIR by the launcher). Observed: a worker died with CUDA invalid argument immediately after Triton JIT of _pack_seq_kernel on a cold node while its tilelang compiles (already per-rank) succeeded.
…t tilelang" This reverts commit 548045d.
This reverts commit fafa885.
With api_server_count=N all workers accept from one shared listen socket; under a connection burst epoll wakeup order concentrates streams on a few workers (measured 47%/26%/15% of 7936 streams on 3 of 8 workers). The busiest worker event loop then bounds output streaming: engines finished a conc-7936 wave in 251s (~32k tok/s generated) while clients drained for 692s (11.7k tok/s) behind the most-loaded ApiServer. VLLM_API_SOCKET_PER_WORKER=1: each worker swaps its inherited dup for its own SO_REUSEPORT socket bound to the same address; the kernel then hash- balances connections across workers. Parent keeps the original bound (not listening) socket as the port reservation.
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Serves DSv4-Flash decode steps as single CUDA graphs on DeepEP high-throughput, in two modes from one branch — requires the UCCL worst-token kernels (fergusfinn/uccl#7):
VLLM_DEEPEP_HT_WORST_TOKEN_DISPATCH=1,ENABLE_DBO=0): whole decode step — attention, dispatch, experts, combine — as one FULL graph per shape. Prefill eager.ENABLE_DBO=1): the DBO two-microbatch interleave captured inside the full decode graphs (UBatchWrapper capture-time threads; replay is thread-free). Needs a dense capture grid (e.g. step-32): with a sparse grid, DP padding doubles the batch, the second microbatch ends up empty, and the DP-wide ubatch agreement silently aborts every step back to eager.Results (2-node EP8, GH200/CXI):
Certification runs on stage A (fixed UCCL kernels, full-warmup boot): conc-8192 sustained 30 min at 14,425 tok/s (16384/16384 requests, two overlapped waves keeping the plateau saturated — not comparable to the single-wave 9242 above); gsm8k 0.950/0.945 strict-match across two boots through the fully-graphed path. The kernel-level positive control (hammer wedges pre-fix kernels, passes fixed ones) lives in fergusfinn/uccl#7.
Stage B serves conc-1024 (1024/1024) at 6.6–7.0k tok/s, 99% GPU busy, ubatched-graph replay on every uniform decode step.
Structure (9 commits, base 891e215):
v1: pad attention metadata correctly for FULL decode captureDBO: propagate ubatch thread errors instead of hanging the barrierDP coordinator: optional start-wave coalescing(rider, default off)DSv4: gate post-GEMM multi-stream overlap by token threshold(rider)debug: annotate DeepEP HT dispatch/combine failures with step contextmode=NONEinto per-ubatch contexts during capture; checkis_current_stream_capturing()so the host-synced protocol is never recorded into a graphdebug: env-gated UBatchWrapper dispatch-decision logging(VLLM_UBATCH_DEBUG_LOGGING=1)The earlier breakable-cudagraph DBO stack (~1,400 lines) is dropped; preserved at
wip-backup-20260610.Known limitations / open items:
tail: 0exactly), then cascading dispatch CPU timeouts. Not the host-counter clobber (fixed in uccl#7, all stores verified gated), not KV pressure. Does not reproduce on fresh boots or in the standalone hammer so far; treat sustained ragged traffic as uncertified.Module has no function 'main'at the dummy run). The per-rank-cache workaround was deliberately dropped (no upstream path); relaunch on failure — second boot finds the cache warm.Assisted by Claude. Benchmarks, certification harness, and the UCCL capture/replay + ragged-hammer tests: fergusfinn/uccl#7 and the isambard deployment repo.