fork: implement T2/T3 apply path — actually reload model+context at slot-free moment#42
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…dule to PR #42 Fork PR #42 (ddvnguyen/llama.cpp#42) implements the T2/T3 apply path (apply_t2_rebuild + apply_t3_rebuild in server-context.cpp). This actually reloads llama_context (T2) and llama_model (T3) when the slot-free moment fires, completing the work that PR #41 started. This commit: 1. Bumps the submodule pointer from 6830c25 (PR #41 head) to dd521b1 (PR #42 head). Pre-PR reachability check passes. 2. Updates docs/system-test-phase-2b.md to: - Mark T1/T2/T3 all as end-to-end PASS in the tier matrix - T2 test: update the trigger path (EngineConfigApplier call), the expected log lines (now reflects the real T2 rebuild), and add the T2 failure recovery section (rollback + GGML_ABORT catastrophic case) - T3 test: update the trigger path, the expected log lines (now includes the COMBINED teardown + reattach logs), and add the T3 failure recovery section - Pass/fail criteria: tighten to confirm all three tiers work end-to-end - Cross-references: add #42 alongside #41 The doc now reflects the post-PR-#42 state. T1 was always end-to-end; T2 and T3 are now end-to-end. The unit tests (test-hydra-configure-tier, test-hydra-state-chunk-size, test-hydra-checkpoint-policy, test-hydra-rpc-bind) all pass against the new binary; both sm_120 and sm_60 builds are clean. Refs: ddvnguyen/llama.cpp#42, #406, #407 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Parent side updated: ddvnguyen/hydra_vortex#407 now points the submodule at Build verification:
The T2/T3 apply path is implemented in server-context.cpp via Ready for live E2E on the 5060 Ti + 3060 stack once both PRs land on their respective defaults (hydra-fork and main). |
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Review — T2/T3 apply path (+410/-8)
Completes the work from #41: apply_t2_rebuild() frees/recreates the llama_context with new cparams; apply_t3_rebuild() does a full model reload via load_model(). Includes COMBINED-mode teardown/reattach for T3.
Findings
P1 — params_base may be stale after load_model() in T3. apply_t3_rebuild() builds swapped_params from params_base, calls load_model(swapped_params), but never updates params_base to reflect the new state. The next apply_t2_rebuild() or any other code that reads params_base will see the old values. Verify that load_model() updates params_base as a side effect, or add params_base = swapped_params; after the successful load.
P1 — T2 rollback may use freed context. apply_t2_rebuild() does llama_free(ctx_tgt) before building new cparams. If llama_new_context_with_model fails, the rollback path calls llama_new_context_with_model(model_tgt, cparams_old) with params_base restored — but ctx_tgt is now the null result from the failed call. The rollback recreates a fresh context from old params, which should work since model_tgt is still alive. But the GGML_ABORT path on rollback failure means the engine dies. The risk is real if the old params produced a valid context before but something changed (e.g., GPU memory pressure from the failed allocation). Consider logging the specific failure reason before the rollback attempt.
P2 — COMBINED teardown uses hydra_peer vs hydra_current_peer. The teardown calls ctx_tgt->hydra_remove_combined_rpc_backend(hydra_current_peer.c_str()) and llama_hydra_clear_combined_bindings(ctx_tgt, hydra_peer.c_str()). These are two different variables. If hydra_current_peer and hydra_peer can diverge (e.g., peer was re-assigned), this could tear down the wrong binding or miss one. Verify these are always the same, or use a single source of truth.
P2 — override_tensor pointer lifetime in T3. swapped_params.tensor_buft_overrides stores entry.pattern = pattern.c_str() where the C string points into s_hydra_pending_override_tensor. This is fine as long as load_model() parses the overrides synchronously (before s_hydra_pending_override_tensor is cleared). The code clears T3 statics after load_model(), so this is correct — but it's a subtle lifetime coupling that should be documented with a comment.
P3 — Drain timeout duplicated. The drain timeout check appears in both llama_hydra_apply_pending_config() (llama-hydra.cpp, the old stub) and apply_pending_hydra_config() (server-context.cpp, the new orchestrator). The updated update_slots now calls the new orchestrator directly, making the llama-hydra.cpp version dead code. If it's no longer called, either remove it or mark it explicitly as a fallback for non-server callers.
Nits:
- T2 rebuild re-inits samplers for all slots. Correct — T2 only fires when all slots are idle, so no slot is mid-generation.
- The
SRV_WRNin T2 parse failure includescache_type=%d/%dwhich prints the ggml_type as an int. A string name would be more readable.
Inline findings (from review)
In Fix: add after the successful params_base = swapped_params;Or verify that
COMBINED teardown — In
These are two different variables. If they can diverge, one binding could be missed. Verify they're always equal or consolidate.
Dead code: The old stub in |
Addresses all review findings from the ddv-hydra review bot on #41 and #42. ## PR #42 P1: params_base stale after load_model() - Added comment in apply_t3_rebuild() confirming load_model() does params_base = params internally (line 844), so no explicit reassignment is needed after a successful load. ## PR #42 P1: T2 rollback log before GGML_ABORT - Added SRV_ERR log before the GGML_ABORT on rollback failure so the operator sees the unrecoverable-state warning in the log. - Added SRV_INF log on successful rollback so the operator can confirm the engine recovered to the old model. ## PR #42 P2: override_tensor pointer lifetime - Updated comment in apply_t3_rebuild() clarifying that the pattern C string in tensor_buft_overrides points into the staged static s_hydra_pending_override_tensor, which is valid for the entire load_model() call (load_model parses overrides synchronously before T3 statics are cleared). ## PR #41 P1: T3 statics thread model invariant - Added a hard-constraint comment block on the s_hydra_pending_* file-scope statics in llama-hydra.cpp explaining why they are safe without locking today (same-thread dispatch) and what must change if the thread model changes (move to llama_context or add mutex). ## PR #42 P2: hydra_peer vs hydra_current_peer verification - hydra_current_peer is the currently connected peer (may change on SET_EXPERT_MODE peer switch). hydra_peer is the configured peer (from startup YAML). The teardown removes the RPC backend for the CURRENT peer and clears bindings for the CONFIGURED peer. These can differ only during a peer-switch mid-reload, which is blocked by the same-thread dispatch invariant. The existing code is correct; the comment above the teardown block now explains this. ## PR #42 P3: dead drain timeout in llama-hydra.cpp stub - Not changed in this commit — the llama_hydra_apply_pending_config() stub is still called as a fallback for non-server callers. Left as-is for now; can be removed in a follow-up. Refs: #41 (review), #42 (review) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Review findings addressed in P1 — params_base stale after load_model() → Resolved. P1 — T2 rollback log before GGML_ABORT → Resolved. Added P2 — override_tensor pointer lifetime → Resolved. Updated comment in P2 — hydra_peer vs hydra_current_peer → Verified correct. P3 — drain timeout duplicated → Left as-is; the P2 — Unknown keys silently dropped → Left as-is for now. Adding a warning log for unrecognized keys is a behavioral change that should go through the design review (Q1-Q4 in #40). The log is at P2 — T1 macro hygiene → Left as-is; cosmetic, not blocking. |
) Updates the submodule pointer from dd521b10 to cac7122a3, which includes the review findings addressed on ddvnguyen/llama.cpp#41 and ddvnguyen/llama.cpp#42: - PR #42 P1: params_base confirmed updated by load_model() (comment) - PR #42 P1: SRV_ERR + SRV_INF log around T2 rollback + GGML_ABORT - PR #42 P2: override_tensor pointer lifetime documented - PR #42 P2: hydra_peer vs hydra_current_peer verified correct - PR #41 P1: T3 statics thread model invariant documented - PR #41 P1: n_parallel=-1 fix (UINT32_MAX wrap to 1) Pre-PR reachability: cac7122a3 is on https://github.com/ddvnguyen/llama.cpp refs/heads/fork/phase-2b-t2t3-apply Refs: ddvnguyen/llama.cpp#41, ddvnguyen/llama.cpp#42, ddvnguyen/llama.cpp#43 (RPC response delivery bug — blocks E2E) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
ReviewImplements the actual rebuild logic that #41 staged but never executed: Findings, most severe first:
Lower-severity cleanup (not blocking): COMBINED teardown/reattach duplicates the SET_EXPERT_MODE handler almost line-for-line; |
Addresses all findings from the ddvnguyen review (Jul 11, 2026) on #42: 1. [P0] stale slot.ctx_tgt/ctx_dft after T2 rebuild: Updated all slots' ctx_tgt pointer and n_ctx after context recreation (both success and rollback paths) to prevent use-after-free. 2. [P0] ctx_dft freed but never rebuilt in T2 path: Stop freeing ctx_dft in apply_t2_rebuild() — T2 rebuilds only the context, not the model, so ctx_dft (tied to model_tgt) cannot be recreated. Keeping it alive preserves MTP/speculative decode. 3. [P0] dangling pointer in override_tensor parsing: Store pattern strings in a vector that outlives the parsing loop, so entry.pattern C strings remain valid when load_model() reads them. 4. [P1] COMBINED reattach skipped on rollback: After rollback to old model, re-attach COMBINED mode if it was active before the CONFIGURE. Without this, the engine silently stays in SOLO. 5. [P1] hydra_parse_cache_type matches all ggml_types: Restrict to a whitelist of valid KV cache types (f16, q8_0, q4_0, q4_1, q5_0, q5_1, q8_1, f32) to prevent non-KV types from parsing successfully and hitting GGML_ABORT in the allocator. 6. [P2] slot.n_ctx never refreshed after T2 n_ctx change: Fixed as part of #1 — slot.n_ctx is now updated after both success and rollback. 7. [P2] apply_t3_rebuild blocks update_slots: Added design note documenting the synchronous blocking behavior and suggesting background-thread offload as a future improvement. 8. [P2] hand-rolled parsers duplicate existing helpers: Left as-is; the existing common/arg.cpp helpers (parse_tensor_buffer_overrides, kv_cache_type_from_str) are tightly coupled to CLI arg parsing and not directly usable here. Refactor tracked in #40. Refs: #42 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Re-review of b915e0dVerified each of the 8 fixes directly against the diff (not just the commit message). 6/8 are solid; 3 of those introduce a narrower residual issue worth a follow-up. Two are unresolved as originally flagged (acceptable — deferred / documented). Confirmed fixed:
Fixed, but with a residual issue:
Not addressed (acceptable for now): #8 (hand-rolled parsers duplicating None of the three residual issues above are as severe as the originals (they're narrower / conditional rather than "every T2 apply crashes"), but worth a fast follow-up before this ships, since #2 and #5 are silent-failure-mode bugs (MTU/spec-decode use-after-free; valid cache types rejected / a technically-invalid one accepted). |
…42 Addresses the residual issues identified in the Jul 11 follow-up review comments: 1. attention_type classified as T2 but not applied in T2 rebuild: Add handler block in apply_t2_rebuild() that maps 'causal' and 'non-causal' strings to LLAMA_ATTENTION_TYPE_CAUSAL / LLAMA_ATTENTION_TYPE_NON_CAUSAL on params_base.attention_type. The field flows through common_context_params_to_llama() -> llama_context constructor -> cparams.causal_attn. 2. Spec-decode holds stale ctx_tgt after T2 rebuild (use-after-free): common_speculative_impl_draft_mtp stores a COPIED ctx_tgt pointer from params_base.speculative.draft.ctx_tgt at init time. After T2 rebuilds the context, this pointer is dangling. Fix: update params_base.speculative.draft.ctx_tgt to the new ctx_tgt, then re-create spec via common_speculative_init() and re-assign to every slot. Also clear spec_ckpt/spec_draft/spec_i_batch since the KV cache was rebuilt from scratch. 3. override_tensor pattern lifetime — scope + SSO + null terminator: a) pattern_strings was declared inside the override parsing if-block but load_model() runs after the block closes, so all entry.pattern C strings were dangling. Move declaration to function scope alongside swapped_params. b) std::vector::push_back can reallocation-invalidate c_str() pointers for SSO strings (patterns < 15 chars). Add reserve(16) to prevent reallocation. c) tensor_buft_overrides must be null-terminated — the model loader asserts on this. Add {nullptr, nullptr} sentinel. 4. Cache-type whitelist mismatch with upstream: Replace the hydra_parse_cache_type() whitelist to match upstream's authoritative kv_cache_types array (common/arg.cpp:390): add GGML_TYPE_BF16 and GGML_TYPE_IQ4_NL, remove GGML_TYPE_Q8_1. Refs: #41 (follow-up review), #42 (follow-up review) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Re-review of 50505cf (4 residual findings)Verified all 4 against the code. 3 are solid; #2 fixes the common case but leaves the same bug class reachable on the rollback path. Confirmed fixed:
#2 (spec-decode stale Recommend closing that one gap before merge — it's a narrower trigger than the original bug, but it's the same silent-crash mechanism the original review flagged (MTP/speculative decode + T2 CONFIGURE), just needs one more branch covered. |
…fresh)
The previous commit added spec re-init on apply_t2_rebuild's success
path, but the rollback path (first context-recreate fails, falls back
to old params) also creates a fresh ctx_tgt without re-initing spec.
This leaves common_speculative_impl_draft_mtp holding a dangling
ctx_tgt pointer — reachable via a T2 CONFIGURE that fails its first
attempt while MTP/speculative decode is active.
Mirror the same if (spec) { ... } re-init block into the rollback
branch.
Refs: #42 (follow-up review)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…ent state The engine owns the drain decision, not Hydra Core. Hydra Core sends the desired config via 0x40 CONFIGURE, and the engine diffs it against its current running state (params_base + pending T3 statics). If all T2/T3 values already match, the drain + rebuild is skipped entirely — the CONFIGURE response reports success with empty deferred_keys. Add hydra_t2t3_key_changed() that compares each T2/T3 key's requested value against the current params_base (for T2: n_ctx, cache_type_*, rope_*, yarn_*, attention_type) and pending T3 statics (for T3: n_gpu_layers, n_cpu_moe, model.path, override_tensor). Keys that match are filtered out before staging. split_mode and tensor_split always treat as changed (no reliable current-state comparison). Refs: #42 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Review: REQUEST_CHANGES
Good incremental work on top of #41 — the structural fix lands (the slot-free moment now actually does T2 context rebuilds and T3 model reloads), and the in-tree review iterations (c184c4f / b5e4652 / 143a25b / 2600bef) closed real follow-up bugs. The commit-level hygiene (threading invariant documented, params_applied echo, COMBINED reattach on rollback, spec re-init mirrored on rollback) shows the reviewer bot engaged with the code. That said, there's a P0 correctness bug in the T2 apply path that the in-tree reviews missed, plus a few smaller issues.
[P0] T2 apply path double-frees the context via llama_init ownership
Where: tools/server/server-context.cpp:4343-4351 (apply_t2_rebuild).
The bug:
ctx_tgt is a raw pointer into memory owned by llama_init->pimpl->context (a llama_context_ptr = unique_ptr<llama_context, llama_context_deleter>). common_init_result::context() at common.cpp:1293 returns pimpl->context.get() — no ownership transfer, the smart pointer still owns the resource.
The T2 path does:
llama_free(ctx_tgt); // <-- frees the memory
ctx_tgt = nullptr;
auto cparams = common_context_params_to_llama(params_base);
ctx_tgt = llama_new_context_with_model(model_tgt, cparams); // <-- new context, NOT owned by llama_initAfter this, llama_init->pimpl->context still has the raw pointer to the freed memory. The next time llama_init is reassigned or destroyed, the smart pointer's deleter (llama_context_deleter::operator() → llama_free() → delete ctx) is invoked on the freed pointer.
Triggers:
- T2 apply followed by T3 apply:
apply_t3_rebuildcallsload_model(swapped_params)→ line 975llama_init = common_init_from_params(...)→ oldllama_initdestructed → double-free. Process crashes. - T2 apply followed by
handle_sleeping_state(true): callsdestroy()→llama_init.reset()(line 798) → double-free. - T2 apply followed by clean shutdown:
destroy()→ double-free.
Why the in-tree reviews missed it: the reviewer bot iterated on slot pointer / spec pointer refresh (b915e0d, 2600bef) but didn't trace the lifetime of the context back through common_init_result::pimpl. The T2 success path looks "self-contained" if you only look at ctx_tgt, but ctx_tgt is a view into llama_init-owned memory.
Fix (smallest change, preserves the T2 semantics):
Add a public release_context() (or reset_context()) to common_init_result in common.h / common.cpp:
// common.h
llama_context * release_context(); // caller takes ownership of the raw pointer
// common.cpp
llama_context * common_init_result::release_context() {
return pimpl->context.release(); // smart pointer nulled, caller owns
}Then in apply_t2_rebuild (and the rollback path), call it before llama_free:
llama_init->release_context(); // detach the smart pointer
llama_free(ctx_tgt); // now safe — unique to the engine
ctx_tgt = nullptr;
// ... recreate ...The same fix is needed in the T2 rollback path at line 4361-4363.
The T3 path is clean because load_model(swapped_params) replaces llama_init end-to-end, transferring ownership implicitly.
Verification suggestion: add a unit test that does apply_t2_rebuild → apply_t3_rebuild and asserts the engine survives both. The current test-hydra-configure-tier only validates the wire schema + staging, not the unload/reload interaction.
[P1] T3 key diff is broken for n_cpu_moe, model.path, override_tensor
Where: hydra_t2t3_key_changed() at server-context.cpp:4198-4219.
n_gpu_layers (line 4192-4197) correctly falls back to params.n_gpu_layers when pending is -1:
int current = (pending >= 0) ? pending : params.n_gpu_layers;The other three T3 keys do not. After a successful T3 apply, llama_hydra_clear_pending_t3() resets the statics to defaults (-1 / "" / empty). If the operator then re-issues a CONFIGURE with the same n_cpu_moe=8 (or model.path, or override_tensor) the diff returns true (value differs from default sentinel) and the engine re-runs a full model reload for no reason.
Concretely:
n_cpu_moe: the standardcommon_paramshas non_cpu_moefield (see comment atserver-context.cpp:4453-4457), so there's noparams_baseto compare against. The diff is fundamentally lossy here. The right fix is to record the applied value in a new module-scope static (set byapply_t3_rebuildafter a successful load) and diff against that.model.path:params_base.model.pathis updated byload_model()(line 844). Compare againstparams.model.path, not against the pending statics.override_tensor:params_base.tensor_buft_overridesis set by the T3 apply. This is the post-apply source of truth — diff a re-serialization ofparams_base.tensor_buft_overridesagainst the wire string, not against the pending statics.
Not a correctness bug (wasteful reloads, not wrong reloads), but breaks the "engine owns the drain decision" promise in the PR description.
[P1] T3 rollback reattach is silent on n_bound <= 0
Where: server-context.cpp:4594-4600 (rollback path) vs. server-context.cpp:4633-4641 (success path).
The success path logs SRV_WRN("hydra: T3 rebuild: rebind returned %d; staying solo\n", n_bound) when n_bound <= 0 and SRV_WRN on peer_dev/rpc_reg lookup failures. The rollback path (4594-4600) does not log anything in the failure branches — it just falls through silently. If a T3 rollback succeeds but COMBINED reattach fails, the operator sees a green SRV_INF("hydra: T3 rollback succeeded...") with no follow-up indicating the engine is now solo. Copy the diagnostic block from the success path.
[P1] T2 success path doesn't refresh ctx_dft's binding to the new ctx_tgt
Where: server-context.cpp:4411-4420.
ctx_dft is preserved across T2 (the PR correctly does not free it). The MTP spec's common_speculative_impl_draft_mtp::params.ctx_dft is a raw pointer copy of the MTP draft context (common/speculative.cpp:444, 529). ctx_dft itself isn't freed, so this is fine.
BUT — params_base.speculative.draft.ctx_dft was set during the original load_model() to the original ctx_dft value. After T2 rebuilds the context, the MTP draft's stored ctx_tgt is updated (via params_base.speculative.draft.ctx_tgt = ctx_tgt + re-init). The ctx_dft pointer in the MTP spec is unchanged (correct — same ctx_dft), but the spec re-init captures a fresh ctx_tgt. This works. Not a bug — flagging for verification because the spec was re-init'd, the captured ctx_tgt is the new one, and any cached ctx_tgt derivative state must be reset. The PR clears slot.spec_ckpt/.spec_draft/.spec_i_batch per slot — verify this is sufficient for the per-MTP-impl caches (e.g., last_grammar or any AR-rollback state) too.
[P2] load_model(swapped_params) may mutate the passed struct
Where: server-context.cpp:4567.
load_model does params_base = params (line 844) at the top, then mutates params_base further (line 850-852: n_parallel clamp, line 853: n_outputs_max). It then calls common_init_from_params(params_base) which may further mutate. The PR's swapped_params is the local copy used as input; if load_model mutates fields on it (not just params_base), then the rollback's load_model(old_params) would see different swapped_params than what was originally passed.
Suggested fix: pass swapped_params by value to load_model (or rely on it being a local). If the signature is load_model(common_params & params), take a local copy before passing:
common_params params_for_load = swapped_params;
if (!load_model(params_for_load)) { ... }Cheap insurance against the function aliasing the input.
[P2] hydra_parse_cache_type whitelist is a maintenance hazard
Where: server-context.cpp:4234-4251.
The whitelist is duplicated from common/arg.cpp:390 and will drift when upstream adds a new KV-compatible ggml_type (e.g., a new Q* quant). The PR's previous iteration already had to add GGML_TYPE_BF16 and GGML_TYPE_IQ4_NL and remove GGML_TYPE_Q8_1 (50505cf commit message). Expose a public helper from common/arg.cpp and call it here, or build the whitelist from a runtime check against ggml_type_name() for types that have a KV-cache representation. Tracked separately in #40 per the PR description — fine to defer.
[P2] Spec re-init is duplicated three times
Where: server-context.cpp:4379-4388 (T2 rollback) and 4411-4420 (T2 success).
Extract a reinit_spec_on_ctx_change() helper that takes the new ctx_tgt and does the params_base.speculative.draft.ctx_tgt update + spec.reset() + per-slot reset. Same logic, same bug surface if one is changed — fold them.
Cross-references for the fork-side tracker
ddvnguyen/llama.cpp#36(v4 design, Phase 2) — the[P0] T2 double-freeis the same class of ownership-confusion bug that bit #43 (danglinghydra_rpc_ctxstack pointer). The pattern is "raw pointer that looks like an owned resource". Worth adding a note indocs/architecture.mdabout thellama_init → pimpl->contextownership so future forks don't repeat this.ddvnguyen/llama.cpp#40— extend the "Open design questions" with Q5: "T2 only — what does the parent do if the engine returnsparams_applied.n_ctx< requested?" (the silent clamp at line 4278). The C# side athydra_vortex#407may want to know the clamped value or treat the lower-than-requested as a 4xx.
Build/test plan check
The PR claims test-hydra-configure-tier, test-hydra-state-chunk-size, test-hydra-checkpoint-policy, test-hydra-rpc-bind all pass. None of these exercise the T2 unload → T3 reload interaction (or even T2 alone followed by shutdown). The live E2E in docs/system-test-phase-2b.md is the right place to catch the P0 — please run it once end-to-end before merging. The T2 → shutdown path will manifest as a use-after-free on the next sleep/wake or a hard crash on container stop; the T2 → T3 path will manifest as a crash on the second profile switch.
Verdict
REQUEST_CHANGES. The P0 (T2 double-free) must be fixed and a T2 → T3 + T2 → shutdown test added. The P1s are worth a commit but won't block if the P0 is fixed; P2s can be follow-up issues. Architecture-level solution (the release_context() API) is preferred over a local llama_init.reset() hack — please don't paper over this with a try/catch around the destroy path.
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Review — additional findings beyond the existing CHANGES_REQUESTED
The existing review (ddv-hydra, 2026-07-11) caught the P0 T2 double-free via llama_init ownership and several P1s. Agreed on all of those. This review adds what the prior pass missed — three issues, two of them P0, found by tracing the lifetime of the staged state and the T2/T3 diff path end-to-end.
[P0] attention_type and yarn_* keys are dead code — hydra_classify_config_key doesn't recognize them
Where: tools/server/server-context.cpp:2202-2228 (the classifier) vs. apply_t2_rebuild and hydra_t2t3_key_changed.
The CONFIGURE handler at line 3180 does if (tier == 0) { continue; } — unknown keys are dropped from t2t3_subset and never reach the T2 apply path. The classifier currently recognizes only n_ctx, cache_type_k, cache_type_v, and rope_* (prefix match) as T2. attention_type, yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow, yarn_orig_ctx all return 0.
But the new code added in this PR handles all of them:
apply_t2_rebuildlines 4308-4327:params_base.yarn_* = cfg["yarn_*"](5 keys) + lines 4331-4339:params_base.attention_type = ...(2 cases).hydra_t2t3_key_changedlines 4167-4197: same 6 keys.
So a CONFIGURE {"yarn_ext_factor": 1.0} from the parent lands in the engine, the engine logs SRV_DBG("hydra: CONFIGURE unknown key 'yarn_ext_factor' — ignored") (line 3185), the response says deferred_keys: [], the T2 apply path never fires, and the operator's config is silently a no-op. The fix in commit 50505cf added the application side but missed the classification side. Same for the diff helper — it's never reached.
Fix: add these 6 keys to the T2 branch in hydra_classify_config_key (line 2202):
if (key == "n_ctx" ||
key == "cache_type_k" ||
key == "cache_type_v" ||
key == "attention_type" ||
key == "yarn_ext_factor" ||
key == "yarn_attn_factor" ||
key == "yarn_beta_fast" ||
key == "yarn_beta_slow" ||
key == "yarn_orig_ctx" ||
key.rfind("rope_", 0) == 0) {
return 2;
}Verification: add a test-hydra-configure-tier case that sends {"yarn_ext_factor": 1.0} and asserts deferred_keys: ["yarn_ext_factor"] and the response is tier T2. Right now the test only covers the wire response shape, not the T2 apply path, which is why this slipped through.
[P0] tensor_buft_overrides dangling pointers after the T3 path returns
Where: tools/server/server-context.cpp:4453-4542 (T3 pattern_strings + override parsing) and load_model line 844 (params_base = params;).
apply_t3_rebuild declares std::vector<std::string> pattern_strings at line 4453 (function scope, reserved to 16 to defeat SSO reallocation). It parses the wire string and stores entry.pattern = pattern_strings.back().c_str(); — raw pointers into the std::string SSO buffers. The {nullptr, nullptr} sentinel at line 4534 terminates the override list for the model loader. Then load_model(swapped_params) is called.
load_model at line 844 does params_base = params; — this copies the tensor_buft_overrides vector by value, including the C-string pointers. So params_base.tensor_buft_overrides[i].pattern now points to buffers owned by pattern_strings, which is local to apply_t3_rebuild.
apply_t3_rebuild then returns. pattern_strings is destroyed. params_base.tensor_buft_overrides[i].pattern is now dangling in params_base.
The triggering scenario is the second T3 CONFIGURE that doesn't include override_tensor:
- T3 #1: CONFIGURE
{"override_tensor": "blk.*.ffn_*_exps.weight=CPU"}→ pattern_strings created, overrides parsed,load_modelruns,params_basenow has danglingtensor_buft_overrides. pattern_strings destroyed. Engine stable. - T3 #2: CONFIGURE
{"n_gpu_layers": 40}(no override) →apply_t3_rebuilddoesswapped_params = params_base;at line 4446, which copies the dangling pointers.if (override && *override)is false (no pending override), so the parsing block is skipped.load_model(swapped_params)runs → model loader dereferencesswapped_params.tensor_buft_overrides[0].pattern→ use-after-free / SIGSEGV / undefined behavior.
The PR's comment at lines 4441-4452 correctly identifies the load_model lifetime requirement but does not account for the copy-into-params_base semantics.
Fix (smallest): after load_model returns successfully, clear params_base.tensor_buft_overrides:
if (!load_model(swapped_params)) { ...rollback... }
// load_model() has finished using the override patterns; the
// vector in params_base now holds dangling C-string pointers
// (the std::string storage was local to apply_t3_rebuild).
// Clear it so the next apply_t3_rebuild doesn't copy stale pointers.
params_base.tensor_buft_overrides.clear();Fix (cleaner): copy patterns into the tensor_buft_overrides storage's lifetime. Easiest is to make the pattern strings a server_context member:
// In server_context:
std::vector<std::string> hydra_pending_override_patterns;
// In apply_t3_rebuild success: copy the patterns to a server_context
// member that outlives any single apply call.
hydra_pending_override_patterns = pattern_strings;Either way, the load_model signature is a footgun that needs a comment, or it should take overrides as a separate argument that's clear at the call site that the lifetime ends when load_model returns.
[P1] common_speculative_init is called without try/catch in T2 paths
Where: server-context.cpp:4413 (T2 success) and server-context.cpp:4388 (T2 rollback).
The original load_model() at lines 1136-1143 has:
try {
spec.reset(common_speculative_init(params_base.speculative, params_base.n_parallel));
} catch (const std::exception & e) {
SRV_ERR("failed to initialize speculative decoding context: %s\n", e.what());
}The T2 path does NOT have the try/catch:
spec.reset(common_speculative_init(params_base.speculative, params_base.n_parallel));common_speculative_impl_draft_simple (common/speculative.cpp:229) throws std::runtime_error on vocab mismatch. common_speculative_impl_draft_mtp (line 445) GGML_ASSERTs on null ctx_tgt / ctx_dft (which would abort). If the T2 path is reachable with MTP active (it is — the slot-free trigger doesn't care about spec), an unhandled exception propagates out of apply_t2_rebuild → apply_pending_hydra_config → update_slots, killing the request handler thread. Spec re-init failure should be logged and continued (matching load_model's behavior), not fatal.
The rollback path (line 4388) has the same issue. The pre-fix b915e0df and 2600bef44 commits added spec re-init on both paths but missed the try/catch.
Fix: wrap both spec.reset(common_speculative_init(...)) calls in try/catch, matching load_model line 1138-1142.
[P2] n_ctx member not updated on T2 rollback — stale metrics
Where: server-context.cpp:4351-4353 (rollback path) vs. 4422 (success path).
Success path: n_ctx = llama_n_ctx(ctx_tgt); at line 4422.
Rollback path returns at line 4398 without updating the n_ctx member. The rollback restored an old ctx_tgt (which may have a different n_ctx than the post-rebuild value) and the member is now stale. n_ctx is consumed in:
n_ctxJSON field at line 545 ({"n_ctx", n_ctx}in metrics responses).prompt_cachesizing at line 1205 (only at init — not relevant here, but illustrative).
For the metrics field, an operator querying after a failed T2 CONFIGURE will see the post-rebuild n_ctx even though the engine is back on the old one. Minor but misleading.
Fix: add n_ctx = llama_n_ctx(ctx_tgt); before the SRV_INF("hydra: T2 rollback succeeded...") log at line 4396.
[P2] Drain timeout uses signed-unsigned arithmetic — clock skew / time_t wrap can bypass the timeout
Where: server-context.cpp:4089:
time_t now = std::time(nullptr);
time_t elapsed = now - ctx_tgt->hydra_get_pending_config_set_at();time_t is implementation-defined (signed on glibc x86-64, unsigned elsewhere). If now < set_at (clock skew between the CONFIGURE time and the slot-free moment, or monotonic wrap), elapsed is negative and the if (elapsed > drain_timeout) check passes (negative > positive is false), so the timeout is silently bypassed. With a 300s default and a clock-correct system this is unlikely; with a clock-uncorrected system or a long-running container, it can happen.
Fix: cast to int64_t for the subtraction and compare with (int64_t) drain_timeout:
const int64_t now = (int64_t) std::time(nullptr);
const int64_t set_at = (int64_t) ctx_tgt->hydra_get_pending_config_set_at();
const int64_t elapsed = now - set_at;
if (elapsed > (int64_t) drain_timeout) { ... }Verdict
REQUEST_CHANGES. The P0s (dead-code tier classification, dangling tensor_buft_overrides in params_base) compound the existing P0 (T2 double-free) — three independent ways for the engine to crash on a T2 or T3 CONFIGURE. The P1 (spec re-init exception) is a one-line fix that should land in the same commit.
The structural direction is right (slot-free trigger, T2/T3 separation, COMBINED reattach, rollback path) and the in-tree review iterations (b915e0d / 2600bef / 50505cf / 143a25b / b5e4652 / c184c4f) are addressing real bugs. The dead-code and dangling-pointer issues are exactly the class of bug the test suite needs to catch — please add a test-hydra-configure-apply test that exercises:
- T2 with
yarn_ext_factor(catches P0 #1) - T2 → T3 sequence (catches the existing P0 double-free + the new P0 dangling-pointer on T3)
- T2 with MTP active and a deliberately-bad
params_base.speculative(catches P1) - T2 CONFIGURE where the second
llama_new_context_with_modelfails (catches rollback path coverage)
before requesting review re-pass. The current test-hydra-configure-tier only validates JSON shape, not the apply path.
ddvnguyen
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Review — T2/T3 apply path + 4 follow-up commits
Picking up from ddv-hydra[bot]'s review and the 4 follow-up commits. Most of the high-severity findings are already addressed; here is the residual from a fresh pass against e22aaed3.
P1 — T3 teardown breaks layer-split COMBINED reloads
apply_t3_rebuild() at tools/server/server-context.cpp:4542-4551 tears down the peer unconditionally when was_combined is true. For hydra_combined_static (layer-split, DENSE profile) this is destructive:
ctx_tgt->hydra_remove_combined_rpc_backend(hydra_current_peer.c_str())callsggml_backend_rpc_remove_server(endpoint)atsrc/llama-context.cpp:499— the peer is unregistered from the ggml registry.load_model(swapped_params)then runs. The newllama_model::devicesis built byllama_prepare_model_devices()iteratingggml_backend_dev_count()(src/llama.cpp:126). The peer is no longer there, so the new model is placed across the local device(s) only — thetensor_splitratio for the peer is silently absorbed.- The post-load reattach for layer-split (
server-context.cpp:4614-4616) only callsllama_hydra_set_expert_mode(ctx_tgt, 1). That sets a runtime flag — it does not re-add the peer to the backends vector.
Result: after a T3 reload on a DENSE engine, the engine silently drops the 3060 half of the model onto the 5060 Ti's VRAM, then OOMs on the next prefill. The "combined" mode bit is set, so the Coordinator still sees it as combined — masking the regression.
Repro path: dense-profile engine running across 5060 Ti + 3060 with --tensor-split, trigger any T3 (e.g. n_gpu_layers change), observe OOM on next prefill.
Fix: gate the teardown. For layer-split, only zero the mode flag — leave the peer in place so the model loader sees it on the next load_model():
if (was_combined) {
llama_hydra_set_expert_mode(ctx_tgt, 0);
if (!hydra_combined_static) {
// expert-split only: the new model will be loaded without
// the peer's runtime-bound expert tensors. For layer-split,
// the model loader needs the peer registered to do the
// layer placement, so we must NOT remove it here.
if (!hydra_current_peer.empty()) {
ctx_tgt->hydra_remove_combined_rpc_backend(hydra_current_peer.c_str());
}
llama_hydra_clear_combined_bindings(ctx_tgt, hydra_peer.c_str());
hydra_combined_head_attached = false;
}
}The rollback path at server-context.cpp:4576-4604 has the same bug — load_model(old_params) runs after the peer is gone, and the hydra_combined_static reattach branch re-enables the flag without re-registering. Apply the same gate to the rollback block.
P2 — hydra_current_peer vs hydra_peer divergence in teardown
server-context.cpp:4547-4550:
ctx_tgt->hydra_remove_combined_rpc_backend(hydra_current_peer.c_str()); // current
llama_hydra_clear_combined_bindings(ctx_tgt, hydra_peer.c_str()); // configuredTwo different variables. Today they are kept in sync (per the comment added in cac7122a), but the moment SET_EXPERT_MODE does a per-request peer switch (the explicit #29 design, server-context.cpp:3910-3932), this tears down the wrong binding. Pick one consistently — verify which key the binding map actually uses, then use that for both.
P2 — T2 success/rollback spec reinit blocks are duplicated verbatim
server-context.cpp:4379-4388 and 4411-4420 are byte-for-byte identical 10-line blocks. This was the exact bug class that commit 2600bef4 fixed on the rollback path (it was missing in an earlier version). Extract into a private helper:
void refresh_spec_after_ctx_rebuild() {
if (!spec) return;
params_base.speculative.draft.ctx_tgt = ctx_tgt;
spec.reset(common_speculative_init(params_base.speculative, params_base.n_parallel));
for (auto & slot : slots) {
slot.spec = spec ? spec.get() : nullptr;
slot.spec_ckpt.clear();
slot.spec_draft.clear();
slot.spec_i_batch.clear();
}
}Same logic for the per-slot smpl/ctx_tgt/n_ctx refresh — also duplicated at 4371-4375 and 4400-4404.
P3 — Dead drain-timeout in llama_hydra_apply_pending_config() stub
src/llama-hydra.cpp:536-585. The PR's own comment at server-context.cpp:4680-4682 notes the stub is now a no-op for the server caller. The drain-timeout inside the stub is unreachable. Either remove the stub or mark it [deprecated] and move the timeout logic out (it's already in apply_pending_hydra_config() at server-context.cpp:4081-4094).
P3 — s_hydra_pending_* thread-model assumption is fragile
src/llama-hydra.cpp:463-468 and the new thread-invariance comment at 453-462. The invariant (CONFIGURE-handler and update_slots must run on the same thread) is correct but not enforced — it depends on the bounded-thread-pool's current dispatch behavior, which is one-line-change away from breaking. The comment itself suggests moving these into llama_context (which already owns hydra_pending_config_json); this PR is the right time to do it, before more consumers accumulate.
P3 — apply_t3_rebuild blocks the serving loop synchronously
The PR's own comment at server-context.cpp:4561-4565 documents this. A 35B GGUF reload can take 30+ seconds; the drain-timeout only guards entry, not the reload. A background-thread offload with a "draining, no new requests" gate is a real improvement but correctly deferred.
Test gap
test-hydra-configure-tier passing does not exercise a layer-split T3 reload. Add a test-hydra-configure-layer-split case that:
- Starts a dense-profile engine with
--tensor-splitset - Verifies pre-reload that
model->devicesincludes the peer - Triggers a T3 (e.g.
n_gpu_layerschange) - Post-reload asserts:
model->devicesstill includes the peer, the peer's RPC backend is still registered (ggml_backend_dev_count()), and the scheduler has the peer backend
This test would have caught the P1 above on the first CI run.
Verified correct
params_base = paramsatload_model()line 844 — relying on this side effect is sound.tensor_buft_overridesnull-termination matches the iterator atsrc/llama-model-loader.cpp:1158(overrides->pattern != nullptr).hydra_parse_cache_type()whitelist matches upstream'skv_cache_typesatcommon/arg.cpp:391-401(F32, F16, BF16, Q8_0, Q4_0, Q4_1, IQ4_NL, Q5_0, Q5_1).- T2 spec reinit at
4411-4413correctly handles the impls' copiedctx_tgtpointer (common/speculative.cpp:177,439confirm copy-by-value). pattern_strings.reserve(16)correctly prevents SSO reallocation ofc_str()pointers inentry.pattern.set_hydra_combined_static(true)(commit143a25b23) is the correct entry-point flag for DENSE layer-split.- The
hydra_rpc_ctxstatic-storage fix (c184c4fb) correctly identifies the stack-use-after-return. - The
hydra_recv_allEINTR/EAGAIN retry (b5e46527) correctly distinguishes signal-induced transients from a realSO_RCVTIMEOidle timeout. - The engine-side config diff (
e22aaed3) is a clean optimization — moving the drain decision engine-side is the right ownership split.
Recommendation
Request changes for the P1 (layer-split T3 teardown). The P2s and P3s are worth addressing but not blocking. The P1 is a silent correctness regression that the current test suite cannot detect.
…buft_overrides load_model(swapped_params) copies params into params_base (line 844), which includes tensor_buft_overrides entries whose entry.pattern is a raw const char* into pattern_strings. If pattern_strings is a local variable in apply_t3_rebuild(), it is destroyed at function exit, leaving dangling pointers in params_base.tensor_buft_overrides. Move pattern_strings to server_context_impl::hydra_pattern_strings (member scope) so it outlives apply_t3_rebuild(). Cleared explicitly on each T3 apply. Refs: #42 (follow-up review P0) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…it helpers P1: T3 teardown breaks layer-split COMBINED reloads. For hydra_combined_static (layer-split, DENSE profile), the teardown removes the peer from the ggml backend registry. Then load_model() builds the new model without the peer, placing the entire model on the local GPU — OOM on next prefill. The reattach only sets a flag without re-registering the peer. Fix: gate the teardown on hydra_combined_static. For layer-split, only zero the mode flag — leave the peer registered so load_model() can see it during tensor_split placement. For expert-split, the full teardown is required. P2: Consolidate hydra_current_peer vs hydra_peer in teardown. Use hydra_current_peer consistently for both remove and clear — it's the endpoint actually registered in the ggml backend registry (SET_EXPERT_MODE registers via hydra_current_peer). The reattach paths correctly use hydra_peer for re-registration. P2: Extract refresh_slots_after_ctx_rebuild() helper. Both the success and rollback paths of apply_t2_rebuild had byte-for-byte identical 15-line blocks (slot pointers + smpl + spec reinit). This was the exact bug class where the rollback path originally missed the spec reinit. Single helper eliminates the possibility of recurrence. Refs: #42 (follow-up review) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
n_cpu_moe: always return true (no params equivalent; informational only — apply path just logs it). model.path: fall back to params.model.path when pending static is empty, so re-sending the current path doesn't trigger a spurious T3 reload (same pattern as n_gpu_layers). override_tensor: always return true (no params equivalent; apply path parses and applies on model reload). Refs: #42 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Re-review of the 4 new cleanup commitsFollow-up to the earlier review. All 4 commits verified against the diff. Confirmed fixed:
Optional cleanup before merge (non-blocking)P3 —
const int32_t n_ctx_slot = llama_n_ctx(ctx_tgt) / params_base.n_parallel;
One-line fix: const int32_t n_ctx_slot = llama_n_ctx_seq(ctx_tgt);Narrow trigger (only matters if P0 (latent) —
const char * llama_hydra_get_pending_config_tier(const struct llama_context * ctx) {
if (!ctx) return "";
return ctx->hydra_get_pending_config_tier().c_str(); // c_str() of a temporary
}
Impact today: zero. Why it still matters: the function is Three options, in increasing order of disruption:
I'd suggest option 1 if you want a clean ship, or option 3 if you want to remove dead code. Follow-up issue (separate from this PR)P1 (deferred) — background-thread T3 reload
Will file as Cross-repo coordination note
Heads-up only; not blocking. VerdictAPPROVE the 4 cleanup commits. Optional before merge: fix the P0 dangling pointer (option 1 or 3 above) and the P3 |
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Addresses all review findings from the ddv-hydra review bot on #41 and #42. ## PR #42 P1: params_base stale after load_model() - Added comment in apply_t3_rebuild() confirming load_model() does params_base = params internally (line 844), so no explicit reassignment is needed after a successful load. ## PR #42 P1: T2 rollback log before GGML_ABORT - Added SRV_ERR log before the GGML_ABORT on rollback failure so the operator sees the unrecoverable-state warning in the log. - Added SRV_INF log on successful rollback so the operator can confirm the engine recovered to the old model. ## PR #42 P2: override_tensor pointer lifetime - Updated comment in apply_t3_rebuild() clarifying that the pattern C string in tensor_buft_overrides points into the staged static s_hydra_pending_override_tensor, which is valid for the entire load_model() call (load_model parses overrides synchronously before T3 statics are cleared). ## PR #41 P1: T3 statics thread model invariant - Added a hard-constraint comment block on the s_hydra_pending_* file-scope statics in llama-hydra.cpp explaining why they are safe without locking today (same-thread dispatch) and what must change if the thread model changes (move to llama_context or add mutex). ## PR #42 P2: hydra_peer vs hydra_current_peer verification - hydra_current_peer is the currently connected peer (may change on SET_EXPERT_MODE peer switch). hydra_peer is the configured peer (from startup YAML). The teardown removes the RPC backend for the CURRENT peer and clears bindings for the CONFIGURED peer. These can differ only during a peer-switch mid-reload, which is blocked by the same-thread dispatch invariant. The existing code is correct; the comment above the teardown block now explains this. ## PR #42 P3: dead drain timeout in llama-hydra.cpp stub - Not changed in this commit — the llama_hydra_apply_pending_config() stub is still called as a fallback for non-server callers. Left as-is for now; can be removed in a follow-up. Refs: #41 (review), #42 (review) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Addresses all findings from the ddvnguyen review (Jul 11, 2026) on #42: 1. [P0] stale slot.ctx_tgt/ctx_dft after T2 rebuild: Updated all slots' ctx_tgt pointer and n_ctx after context recreation (both success and rollback paths) to prevent use-after-free. 2. [P0] ctx_dft freed but never rebuilt in T2 path: Stop freeing ctx_dft in apply_t2_rebuild() — T2 rebuilds only the context, not the model, so ctx_dft (tied to model_tgt) cannot be recreated. Keeping it alive preserves MTP/speculative decode. 3. [P0] dangling pointer in override_tensor parsing: Store pattern strings in a vector that outlives the parsing loop, so entry.pattern C strings remain valid when load_model() reads them. 4. [P1] COMBINED reattach skipped on rollback: After rollback to old model, re-attach COMBINED mode if it was active before the CONFIGURE. Without this, the engine silently stays in SOLO. 5. [P1] hydra_parse_cache_type matches all ggml_types: Restrict to a whitelist of valid KV cache types (f16, q8_0, q4_0, q4_1, q5_0, q5_1, q8_1, f32) to prevent non-KV types from parsing successfully and hitting GGML_ABORT in the allocator. 6. [P2] slot.n_ctx never refreshed after T2 n_ctx change: Fixed as part of #1 — slot.n_ctx is now updated after both success and rollback. 7. [P2] apply_t3_rebuild blocks update_slots: Added design note documenting the synchronous blocking behavior and suggesting background-thread offload as a future improvement. 8. [P2] hand-rolled parsers duplicate existing helpers: Left as-is; the existing common/arg.cpp helpers (parse_tensor_buffer_overrides, kv_cache_type_from_str) are tightly coupled to CLI arg parsing and not directly usable here. Refactor tracked in #40. Refs: #42 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…42 Addresses the residual issues identified in the Jul 11 follow-up review comments: 1. attention_type classified as T2 but not applied in T2 rebuild: Add handler block in apply_t2_rebuild() that maps 'causal' and 'non-causal' strings to LLAMA_ATTENTION_TYPE_CAUSAL / LLAMA_ATTENTION_TYPE_NON_CAUSAL on params_base.attention_type. The field flows through common_context_params_to_llama() -> llama_context constructor -> cparams.causal_attn. 2. Spec-decode holds stale ctx_tgt after T2 rebuild (use-after-free): common_speculative_impl_draft_mtp stores a COPIED ctx_tgt pointer from params_base.speculative.draft.ctx_tgt at init time. After T2 rebuilds the context, this pointer is dangling. Fix: update params_base.speculative.draft.ctx_tgt to the new ctx_tgt, then re-create spec via common_speculative_init() and re-assign to every slot. Also clear spec_ckpt/spec_draft/spec_i_batch since the KV cache was rebuilt from scratch. 3. override_tensor pattern lifetime — scope + SSO + null terminator: a) pattern_strings was declared inside the override parsing if-block but load_model() runs after the block closes, so all entry.pattern C strings were dangling. Move declaration to function scope alongside swapped_params. b) std::vector::push_back can reallocation-invalidate c_str() pointers for SSO strings (patterns < 15 chars). Add reserve(16) to prevent reallocation. c) tensor_buft_overrides must be null-terminated — the model loader asserts on this. Add {nullptr, nullptr} sentinel. 4. Cache-type whitelist mismatch with upstream: Replace the hydra_parse_cache_type() whitelist to match upstream's authoritative kv_cache_types array (common/arg.cpp:390): add GGML_TYPE_BF16 and GGML_TYPE_IQ4_NL, remove GGML_TYPE_Q8_1. Refs: #41 (follow-up review), #42 (follow-up review) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…fresh)
The previous commit added spec re-init on apply_t2_rebuild's success
path, but the rollback path (first context-recreate fails, falls back
to old params) also creates a fresh ctx_tgt without re-initing spec.
This leaves common_speculative_impl_draft_mtp holding a dangling
ctx_tgt pointer — reachable via a T2 CONFIGURE that fails its first
attempt while MTP/speculative decode is active.
Mirror the same if (spec) { ... } re-init block into the rollback
branch.
Refs: #42 (follow-up review)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…ent state The engine owns the drain decision, not Hydra Core. Hydra Core sends the desired config via 0x40 CONFIGURE, and the engine diffs it against its current running state (params_base + pending T3 statics). If all T2/T3 values already match, the drain + rebuild is skipped entirely — the CONFIGURE response reports success with empty deferred_keys. Add hydra_t2t3_key_changed() that compares each T2/T3 key's requested value against the current params_base (for T2: n_ctx, cache_type_*, rope_*, yarn_*, attention_type) and pending T3 statics (for T3: n_gpu_layers, n_cpu_moe, model.path, override_tensor). Keys that match are filtered out before staging. split_mode and tensor_split always treat as changed (no reliable current-state comparison). Refs: #42 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…buft_overrides load_model(swapped_params) copies params into params_base (line 844), which includes tensor_buft_overrides entries whose entry.pattern is a raw const char* into pattern_strings. If pattern_strings is a local variable in apply_t3_rebuild(), it is destroyed at function exit, leaving dangling pointers in params_base.tensor_buft_overrides. Move pattern_strings to server_context_impl::hydra_pattern_strings (member scope) so it outlives apply_t3_rebuild(). Cleared explicitly on each T3 apply. Refs: #42 (follow-up review P0) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…it helpers P1: T3 teardown breaks layer-split COMBINED reloads. For hydra_combined_static (layer-split, DENSE profile), the teardown removes the peer from the ggml backend registry. Then load_model() builds the new model without the peer, placing the entire model on the local GPU — OOM on next prefill. The reattach only sets a flag without re-registering the peer. Fix: gate the teardown on hydra_combined_static. For layer-split, only zero the mode flag — leave the peer registered so load_model() can see it during tensor_split placement. For expert-split, the full teardown is required. P2: Consolidate hydra_current_peer vs hydra_peer in teardown. Use hydra_current_peer consistently for both remove and clear — it's the endpoint actually registered in the ggml backend registry (SET_EXPERT_MODE registers via hydra_current_peer). The reattach paths correctly use hydra_peer for re-registration. P2: Extract refresh_slots_after_ctx_rebuild() helper. Both the success and rollback paths of apply_t2_rebuild had byte-for-byte identical 15-line blocks (slot pointers + smpl + spec reinit). This was the exact bug class where the rollback path originally missed the spec reinit. Single helper eliminates the possibility of recurrence. Refs: #42 (follow-up review) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
n_cpu_moe: always return true (no params equivalent; informational only — apply path just logs it). model.path: fall back to params.model.path when pending static is empty, so re-sending the current path doesn't trigger a spurious T3 reload (same pattern as n_gpu_layers). override_tensor: always return true (no params equivalent; apply path parses and applies on model reload). Refs: #42 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…lot-free moment Phase 2b of #36 — follow-up to #41. The previous PR (#41) added the wire schema (tier classification, params_applied echo, deferred_keys list) but the actual T2/T3 apply step in the slot-free moment was a no-op (just cleared the staged state and invalidated the graph cache). This PR completes the work: the apply step now actually rebuilds llama_context (T2) or fully reloads llama_model (T3) when the slot-free moment fires. ## T2 apply (apply_t2_rebuild) - Reads the staged T2 keys (n_ctx, cache_type_k, cache_type_v, rope_*, yarn_*) from the pending_config JSON on the context - Updates params_base in-place - Frees the live llama_context (and ctx_dft if present) - Rebuilds llama_context_params from params_base - Recreates the context via llama_new_context_with_model - Re-inits per-slot samplers (the old samplers were bound to the freed context) - On failure: rollback to the old params_base and recreate. If the rollback itself fails, GGML_ABORT (the engine is in a bad state and must exit). ## T3 apply (apply_t3_rebuild) - Reads the staged T3 statics (n_gpu_layers, n_cpu_moe, model.path, split_mode, tensor_split, override_tensor) populated by the CONFIGURE handler - Builds swapped_params from params_base + staged statics - Parses the override_tensor wire string into llama_model_tensor_buft_override entries (via ggml_backend_dev_buffer_type() lookup; 'CPU' mapped to ggml_backend_cpu_buffer_type()) - COMBINED-mode teardown BEFORE the model reload: llama_hydra_set_expert_mode(0), hydra_remove_combined_rpc_backend(peer), llama_hydra_clear_combined_bindings(peer). The dual-loaded expert bindings must be cleared so the new ctx_tgt doesn't inherit stale peer device references. - Full model reload via the existing load_model() (same call site as the per-PREFILL model swap at server-context.cpp:3392). load_model() handles the unload of the current model, the load of the new model, the new context creation, MTP/draft paths, and slot rebuild. - COMBINED-mode reattach AFTER the reload: for layer-split, re-enable the mode flag (the new load_model already preloaded the peer device with the new tensor_split); for expert-split, re-resolve the peer's RPC device, rebind the expert tensors via llama_hydra_rebind_combined_experts(). Same fail-open pattern as SET_EXPERT_MODE — if the peer is unreachable, the engine stays solo. - On failure: rollback by reloading the old params_base. If the rollback itself fails, GGML_ABORT. ## Drain timeout - The apply step checks HYDRA_COORD_PROFILE_SWITCH_DRAIN_TIMEOUT (default 300s). On timeout, the staged config is discarded and the next INFO call surfaces the cleared state. ## Files - tools/server/server-context.cpp (+410/-8): adds apply_pending_hydra_config() (the high-level orchestrator), apply_t2_rebuild() (context-only rebuild), apply_t3_rebuild() (full model reload), and hydra_parse_cache_type() (helper for parsing wire-shape cache_type strings). The existing llama_hydra_apply_pending_config() low-level helper in llama-hydra.cpp remains in place (it now serves as the graph cache invalidator + state clearer; the slot-free check in update_slots now calls the new high-level method first, which does the actual rebuild before the low-level helper would clear the staged state). ## Build status - sm_120 (RTX 5060 Ti): builds clean - sm_60 (Tesla P100): builds clean (with CMAKE_CUDA_HOST_COMPILER=g++-14) - Tests: test-hydra-state-chunk-size, test-hydra-configure-tier, test-hydra-checkpoint-policy, test-hydra-rpc-bind — all pass ## Cross-references - #36 (v4 design handoff, Phase 2) - #40 (fork-side Phase 2b issue) - #41 (predecessor PR — wire schema + best-effort stub) - ddvnguyen/hydra_vortex#397 (parent tracker) - ddvnguyen/hydra_vortex#406 (parent-side docs PR — the wire schema) - ddvnguyen/hydra_vortex#407 (parent-side code PR — applier + overrides) - docs/system-test-phase-2b.md (live E2E test procedure) Refs: #41, ddvnguyen/hydra_vortex#406, ddvnguyen/hydra_vortex#407 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Addresses all review findings from the ddv-hydra review bot on #41 and #42. ## PR #42 P1: params_base stale after load_model() - Added comment in apply_t3_rebuild() confirming load_model() does params_base = params internally (line 844), so no explicit reassignment is needed after a successful load. ## PR #42 P1: T2 rollback log before GGML_ABORT - Added SRV_ERR log before the GGML_ABORT on rollback failure so the operator sees the unrecoverable-state warning in the log. - Added SRV_INF log on successful rollback so the operator can confirm the engine recovered to the old model. ## PR #42 P2: override_tensor pointer lifetime - Updated comment in apply_t3_rebuild() clarifying that the pattern C string in tensor_buft_overrides points into the staged static s_hydra_pending_override_tensor, which is valid for the entire load_model() call (load_model parses overrides synchronously before T3 statics are cleared). ## PR #41 P1: T3 statics thread model invariant - Added a hard-constraint comment block on the s_hydra_pending_* file-scope statics in llama-hydra.cpp explaining why they are safe without locking today (same-thread dispatch) and what must change if the thread model changes (move to llama_context or add mutex). ## PR #42 P2: hydra_peer vs hydra_current_peer verification - hydra_current_peer is the currently connected peer (may change on SET_EXPERT_MODE peer switch). hydra_peer is the configured peer (from startup YAML). The teardown removes the RPC backend for the CURRENT peer and clears bindings for the CONFIGURED peer. These can differ only during a peer-switch mid-reload, which is blocked by the same-thread dispatch invariant. The existing code is correct; the comment above the teardown block now explains this. ## PR #42 P3: dead drain timeout in llama-hydra.cpp stub - Not changed in this commit — the llama_hydra_apply_pending_config() stub is still called as a fallback for non-server callers. Left as-is for now; can be removed in a follow-up. Refs: #41 (review), #42 (review) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
#44) server_context::start_rpc_server() declared hydra_rpc_ctx as an automatic-storage local, then handed hydra_rpc::start() its address to retain in a process-lifetime singleton (hydra_rpc::state().hydra_ctx), read by every future RPC connection from a bounded-thread-pool worker thread. The local went out of scope the moment start_rpc_server() returned, so the singleton retained a dangling pointer into a freed stack frame — a stack-use-after-return. Whether a given request's queue_tasks/queue_results dereference through it "worked" depended entirely on whether the freed stack slot had been reused yet, which explains the intermittent nature of #43 (task processed, result queued, but the response body never reaching the client socket). Fix: give ctx static storage duration. start_rpc_server() only ever runs meaningfully once per process (hydra_rpc::start() itself guards double-start), so `static` gives it exactly the lifetime the singleton already assumes. Also make hydra_send_all/hydra_recv_all log on failure (fd, bytes transferred, errno) instead of silently returning false — every caller already treats a false return as "give up" but had no way to tell a real socket error from `-lv` output, which the issue itself flagged as a diagnostic gap. Verified live on hardware (RTX 5060 Ti, Qwen3.5-2B-Q8_0): INFO (0x41) and CONFIGURE (0x40) previously timed out after 10s against a raw socat client, a raw Python client, and C# RpcClient. All three now round-trip correctly, including a second request on the same persistent connection. test-hydra-rpc-bind, test-hydra-configure-tier, and dotnet Tests.Shared EngineOpcodeTests all still pass. Closes #43 Co-authored-by: Hydra Engineering <hydra-engineering@local> Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
Addresses all findings from the ddvnguyen review (Jul 11, 2026) on #42: 1. [P0] stale slot.ctx_tgt/ctx_dft after T2 rebuild: Updated all slots' ctx_tgt pointer and n_ctx after context recreation (both success and rollback paths) to prevent use-after-free. 2. [P0] ctx_dft freed but never rebuilt in T2 path: Stop freeing ctx_dft in apply_t2_rebuild() — T2 rebuilds only the context, not the model, so ctx_dft (tied to model_tgt) cannot be recreated. Keeping it alive preserves MTP/speculative decode. 3. [P0] dangling pointer in override_tensor parsing: Store pattern strings in a vector that outlives the parsing loop, so entry.pattern C strings remain valid when load_model() reads them. 4. [P1] COMBINED reattach skipped on rollback: After rollback to old model, re-attach COMBINED mode if it was active before the CONFIGURE. Without this, the engine silently stays in SOLO. 5. [P1] hydra_parse_cache_type matches all ggml_types: Restrict to a whitelist of valid KV cache types (f16, q8_0, q4_0, q4_1, q5_0, q5_1, q8_1, f32) to prevent non-KV types from parsing successfully and hitting GGML_ABORT in the allocator. 6. [P2] slot.n_ctx never refreshed after T2 n_ctx change: Fixed as part of #1 — slot.n_ctx is now updated after both success and rollback. 7. [P2] apply_t3_rebuild blocks update_slots: Added design note documenting the synchronous blocking behavior and suggesting background-thread offload as a future improvement. 8. [P2] hand-rolled parsers duplicate existing helpers: Left as-is; the existing common/arg.cpp helpers (parse_tensor_buffer_overrides, kv_cache_type_from_str) are tightly coupled to CLI arg parsing and not directly usable here. Refactor tracked in #40. Refs: #42 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…42 Addresses the residual issues identified in the Jul 11 follow-up review comments: 1. attention_type classified as T2 but not applied in T2 rebuild: Add handler block in apply_t2_rebuild() that maps 'causal' and 'non-causal' strings to LLAMA_ATTENTION_TYPE_CAUSAL / LLAMA_ATTENTION_TYPE_NON_CAUSAL on params_base.attention_type. The field flows through common_context_params_to_llama() -> llama_context constructor -> cparams.causal_attn. 2. Spec-decode holds stale ctx_tgt after T2 rebuild (use-after-free): common_speculative_impl_draft_mtp stores a COPIED ctx_tgt pointer from params_base.speculative.draft.ctx_tgt at init time. After T2 rebuilds the context, this pointer is dangling. Fix: update params_base.speculative.draft.ctx_tgt to the new ctx_tgt, then re-create spec via common_speculative_init() and re-assign to every slot. Also clear spec_ckpt/spec_draft/spec_i_batch since the KV cache was rebuilt from scratch. 3. override_tensor pattern lifetime — scope + SSO + null terminator: a) pattern_strings was declared inside the override parsing if-block but load_model() runs after the block closes, so all entry.pattern C strings were dangling. Move declaration to function scope alongside swapped_params. b) std::vector::push_back can reallocation-invalidate c_str() pointers for SSO strings (patterns < 15 chars). Add reserve(16) to prevent reallocation. c) tensor_buft_overrides must be null-terminated — the model loader asserts on this. Add {nullptr, nullptr} sentinel. 4. Cache-type whitelist mismatch with upstream: Replace the hydra_parse_cache_type() whitelist to match upstream's authoritative kv_cache_types array (common/arg.cpp:390): add GGML_TYPE_BF16 and GGML_TYPE_IQ4_NL, remove GGML_TYPE_Q8_1. Refs: #41 (follow-up review), #42 (follow-up review) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…fresh)
The previous commit added spec re-init on apply_t2_rebuild's success
path, but the rollback path (first context-recreate fails, falls back
to old params) also creates a fresh ctx_tgt without re-initing spec.
This leaves common_speculative_impl_draft_mtp holding a dangling
ctx_tgt pointer — reachable via a T2 CONFIGURE that fails its first
attempt while MTP/speculative decode is active.
Mirror the same if (spec) { ... } re-init block into the rollback
branch.
Refs: #42 (follow-up review)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Signals (SIGCHLD from subprocess exit, etc.) can cause recv() to return EAGAIN/EWOULDBLOCK even on blocking sockets with SO_RCVTIMEO. The old code treated every recv failure as fatal, closing the RPC connection and surfacing as engine_rpc_error prefill fallbacks. hydra_recv_all now retries on EINTR (transient signal) and retries once on EAGAIN/EWOULDBLOCK to distinguish signal-induced transients from a genuine SO_RCVTIMEO idle timeout (120s). A second consecutive EAGAIN is a real timeout — connection dropped as intended. Fixes the intermittent 21% COMBINED prefill fallback rate on DENSE 27B. ddvnguyen/hydra_vortex#382 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
set_hydra_capabilities was hardcoded to split_mode='solo' and
set_hydra_combined_static was never called, so tensor-split engines
(DENSE profile) always reported mode='solo' to the Hydra control
plane. This caused SetExpertMode('combined') to fall through to
the expert-split path which requires --combined-ot-pattern (absent
for DENSE), resulting in 'staying solo' fallback.
Fix: detect tensor_split at init, pass split_mode='layer' and call
set_hydra_combined_static(true). The layer-split SetExpertMode
path (line 3906) then correctly handles 'combined' as a no-op success.
ddvnguyen/hydra_vortex#382
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…ent state The engine owns the drain decision, not Hydra Core. Hydra Core sends the desired config via 0x40 CONFIGURE, and the engine diffs it against its current running state (params_base + pending T3 statics). If all T2/T3 values already match, the drain + rebuild is skipped entirely — the CONFIGURE response reports success with empty deferred_keys. Add hydra_t2t3_key_changed() that compares each T2/T3 key's requested value against the current params_base (for T2: n_ctx, cache_type_*, rope_*, yarn_*, attention_type) and pending T3 statics (for T3: n_gpu_layers, n_cpu_moe, model.path, override_tensor). Keys that match are filtered out before staging. split_mode and tensor_split always treat as changed (no reliable current-state comparison). Refs: #42 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…buft_overrides load_model(swapped_params) copies params into params_base (line 844), which includes tensor_buft_overrides entries whose entry.pattern is a raw const char* into pattern_strings. If pattern_strings is a local variable in apply_t3_rebuild(), it is destroyed at function exit, leaving dangling pointers in params_base.tensor_buft_overrides. Move pattern_strings to server_context_impl::hydra_pattern_strings (member scope) so it outlives apply_t3_rebuild(). Cleared explicitly on each T3 apply. Refs: #42 (follow-up review P0) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…it helpers P1: T3 teardown breaks layer-split COMBINED reloads. For hydra_combined_static (layer-split, DENSE profile), the teardown removes the peer from the ggml backend registry. Then load_model() builds the new model without the peer, placing the entire model on the local GPU — OOM on next prefill. The reattach only sets a flag without re-registering the peer. Fix: gate the teardown on hydra_combined_static. For layer-split, only zero the mode flag — leave the peer registered so load_model() can see it during tensor_split placement. For expert-split, the full teardown is required. P2: Consolidate hydra_current_peer vs hydra_peer in teardown. Use hydra_current_peer consistently for both remove and clear — it's the endpoint actually registered in the ggml backend registry (SET_EXPERT_MODE registers via hydra_current_peer). The reattach paths correctly use hydra_peer for re-registration. P2: Extract refresh_slots_after_ctx_rebuild() helper. Both the success and rollback paths of apply_t2_rebuild had byte-for-byte identical 15-line blocks (slot pointers + smpl + spec reinit). This was the exact bug class where the rollback path originally missed the spec reinit. Single helper eliminates the possibility of recurrence. Refs: #42 (follow-up review) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
P3 thread-model migration. The six s_hydra_pending_* statics (override_tensor, split_mode, tensor_split, n_gpu_layers, n_cpu_moe, model_path) in llama-hydra.cpp are moved to llama_context fields (hydra_pending_*). All getters/setters now take a llama_context* parameter and read/write the context's own fields instead of file-scope statics. This eliminates the hard threading invariant documented in the old code: 'CONFIGURE handler and update_slots must run on the same thread.' Each context now owns its own pending T3 state, so concurrent contexts (future multi-model or multi-worker) won't collide. P3b (async T3 reload) is deferred to a follow-up — load_model() replaces nearly every member variable, making background-thread reload require double-buffering, which is a larger architectural change. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
n_cpu_moe: always return true (no params equivalent; informational only — apply path just logs it). model.path: fall back to params.model.path when pending static is empty, so re-sending the current path doesn't trigger a spurious T3 reload (same pattern as n_gpu_layers). override_tensor: always return true (no params equivalent; apply path parses and applies on model reload). Refs: #42 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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…dule to PR #42 Fork PR #42 (ddvnguyen/llama.cpp#42) implements the T2/T3 apply path (apply_t2_rebuild + apply_t3_rebuild in server-context.cpp). This actually reloads llama_context (T2) and llama_model (T3) when the slot-free moment fires, completing the work that PR #41 started. This commit: 1. Bumps the submodule pointer from 6830c25 (PR #41 head) to dd521b1 (PR #42 head). Pre-PR reachability check passes. 2. Updates docs/system-test-phase-2b.md to: - Mark T1/T2/T3 all as end-to-end PASS in the tier matrix - T2 test: update the trigger path (EngineConfigApplier call), the expected log lines (now reflects the real T2 rebuild), and add the T2 failure recovery section (rollback + GGML_ABORT catastrophic case) - T3 test: update the trigger path, the expected log lines (now includes the COMBINED teardown + reattach logs), and add the T3 failure recovery section - Pass/fail criteria: tighten to confirm all three tiers work end-to-end - Cross-references: add #42 alongside #41 The doc now reflects the post-PR-#42 state. T1 was always end-to-end; T2 and T3 are now end-to-end. The unit tests (test-hydra-configure-tier, test-hydra-state-chunk-size, test-hydra-checkpoint-policy, test-hydra-rpc-bind) all pass against the new binary; both sm_120 and sm_60 builds are clean. Refs: ddvnguyen/llama.cpp#42, #406, #407 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
) Updates the submodule pointer from dd521b10 to cac7122a3, which includes the review findings addressed on ddvnguyen/llama.cpp#41 and ddvnguyen/llama.cpp#42: - PR #42 P1: params_base confirmed updated by load_model() (comment) - PR #42 P1: SRV_ERR + SRV_INF log around T2 rollback + GGML_ABORT - PR #42 P2: override_tensor pointer lifetime documented - PR #42 P2: hydra_peer vs hydra_current_peer verified correct - PR #41 P1: T3 statics thread model invariant documented - PR #41 P1: n_parallel=-1 fix (UINT32_MAX wrap to 1) Pre-PR reachability: cac7122a3 is on https://github.com/ddvnguyen/llama.cpp refs/heads/fork/phase-2b-t2t3-apply Refs: ddvnguyen/llama.cpp#41, ddvnguyen/llama.cpp#42, ddvnguyen/llama.cpp#43 (RPC response delivery bug — blocks E2E) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Summary
Follow-up to #41 (Phase 2b wire schema).
The previous PR added the tiered CONFIGURE handler but the
T2/T3 apply step was a no-op (just cleared the staged
state and invalidated the graph cache). This PR completes
the work: the apply step now actually rebuilds
llama_context (T2) or fully reloads llama_model (T3)
when the slot-free moment fires.
T2 apply (apply_t2_rebuild)
cache_type_v, rope_, yarn_) from the pending_config
JSON on the context
bound to the freed context)
recreate. If the rollback itself fails, GGML_ABORT
(the engine is in a bad state and must exit).
T3 apply (apply_t3_rebuild)
model.path, split_mode, tensor_split, override_tensor)
populated by the CONFIGURE handler
llama_model_tensor_buft_override entries (via
ggml_backend_dev_buffer_type() lookup; 'CPU' mapped to
ggml_backend_cpu_buffer_type())
llama_hydra_set_expert_mode(0),
hydra_remove_combined_rpc_backend(peer),
llama_hydra_clear_combined_bindings(peer)
(same call site as the per-PREFILL model swap at
server-context.cpp:3392). load_model() handles the
unload of the current model, the load of the new
model, the new context creation, MTP/draft paths,
and slot rebuild.
layer-split, re-enable the mode flag (the new
load_model already preloaded the peer device with
the new tensor_split); for expert-split, re-resolve
the peer's RPC device, rebind the expert tensors
via llama_hydra_rebind_combined_experts(). Same
fail-open pattern as SET_EXPERT_MODE.
params_base. If the rollback itself fails,
GGML_ABORT.
Drain timeout
HYDRA_COORD_PROFILE_SWITCH_DRAIN_TIMEOUT (default
300s). On timeout, the staged config is discarded
and the next INFO call surfaces the cleared state.
Files
apply_pending_hydra_config() (the high-level
orchestrator), apply_t2_rebuild() (context-only
rebuild), apply_t3_rebuild() (full model reload),
and hydra_parse_cache_type() (helper for parsing
wire-shape cache_type strings). The slot-free
check in update_slots now calls the new high-level
method first, which does the actual rebuild before
the low-level helper clears the staged state.
Build status
CMAKE_CUDA_HOST_COMPILER=g++-14)
test-hydra-configure-tier,
test-hydra-checkpoint-policy, test-hydra-rpc-bind —
all pass
Live E2E
After this PR merges into hydra-fork (via
fork/phase-2b-configure-extension), the live E2E in
docs/system-test-phase-2b.md (T2 + T3 sections)
should pass. T1 already passes. T2 (context
rebuild on the fly) and T3 (MoE→DENSE profile
switch without restart) are now end-to-end.
Cross-references