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MTPLX_BATCH_INVARIANT_PREFILL=1 (default off) makes every prefill row independent of how many rows share its forward. On MLX 0.32 three kernel choices follow the row count and round differently: split-K quantized matmul for narrow outputs (router N=256, shared expert, GDN a/b, k/v), gather_qmv against gather_qmm_rhs for the routed experts, and the unfused against the fused SDPA below 1024 query rows. On Qwen3.6-35B-A3B the 8th and 9th expert often tie within a bf16 step, so those differences change the routing and move scores between block layouts. In the prefill phase only: QuantizedLinear runs as a two-batch matmul of at least 33 rows per batch (no split-K, one NAX tile shape), SwitchGLU pads to 4 token rows per expert (always the sorted tiled kernel), and mlx-lm's scaled_dot_product_attention (hooked in qwen3_next and in base, where attention_split imports it per call) always runs the fused causal kernel, with zero query rows in front below 9 rows. The lone final prompt token runs on the stock kernels (stock_prefill_kernels): padded to the lane's minimums it cost TTFT, and no caller compares that row with a wider forward. Class swaps and one function hook; decode and verify keep the stock kernels. The lane is installed only where it covers every quantized projection (affine QuantizedLinear, stock SwitchGLU with at least 16 experts) and only on MoE models; a dense model is refused with "dense_model" unless MTPLX_BATCH_INVARIANT_PREFILL_DENSE=1. On a refusal nothing is swapped or hooked. /health reports the install report or the refusal reason under degradation.batch_invariant_prefill. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Prompt scoring (/v1/completions with echo and max_tokens 0) ran its trunk in forwards of 256 rows, the width of its logits slices. With the batch-invariant prefill lane installed the result no longer depends on the width, so the trunk now runs at the prefill chunk a generation would use (the server's live setting, else the profile) and the target lm_head is applied per 256-row slice of the post-norm rows, inside the prefill phase: logits residency stays at 256 rows. mtp_patch exposes the head as logits_from_post_norm, the same call __call__ uses. Without the lane the default stays 256 rows, because on MoE models a wider trunk changes the routing and the scores. MTPLX_PROMPT_SCORE_TRUNK_CHUNK names a width explicitly. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
…the invariant lane With the batch-invariant prefill lane installed, a cold prompt shorter than the block-restore floor (MTPLX_SESSION_BLOCK_PREFIX_MIN_MATCH_TOKENS, 512) is prefilled without the tail boundary grid. On Qwen3.6-35B-A3B the grid cuts one extra forward off the last chunk (~0.1 s, all experts read again) whenever a session bank is attached, e.g. [0,331],[331,395] for a 396-token prompt. With the lane both layouts give bitwise the same states, so only the cost changes; without it the cut stays because removing it would change the routing and the scores. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
…iant lane A restore boundary inside the last prefill chunk ends a forward on families without in-forward hooks (the tail ladder). On Qwen3.6-35B-A3B that extra forward reads the routed experts again: ~0.1 s per warm turn and per first-token request with a session bank from 512 tokens. New mtplx/gdn_inforward_boundaries.py publishes the two hooks the prefill loops already look for (boundary_capture_scope, take_boundary_captures). While a capture is armed, each mlx-lm GDN layer (qwen3_5, qwen3_next) runs its stock __call__ once per segment on its own cache, which is exactly the ladder's arithmetic for that layer, and records its state between segments. Attention and MoE see the wide forward, so the hooks are installed only together with the batch-invariant prefill lane (MTPLX_BATCH_INVARIANT_PREFILL=1 and the lane admitted for the model); MTPLX_GDN_BOUNDARY_INFORWARD=0 restores the ladder. The install report gains gdn_inforward_layers. Tests (tiny quantized Qwen3.5-MoE, real cold and warm prefill loops, Metal): boundary positions, every record leaf, logits, hidden and cache bit-identical to the ladder with 3 forwards instead of 6-7; no hooks without the lane or on a refused model. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
…ad (opt-in) New agent sessions share a fixed head (tool schemas and system text) and diverge at the first user turn, but restored only from the oldest GDN boundary: thinning to 8 boundaries per entry keeps the oldest and records near the tail, and the donor is usually a long conversation. With MTPLX_SESSION_HEAD_ANCHOR=1 (default off): - the head end is read from the prompt tokens: the first <|im_start|> turn that is not a system turn (mtplx/session_head_anchor.py), so it is token-exact for every ChatML template, harness and endpoint; - the prefill makes it a mandatory span end (recorded in-forward where the model publishes boundary hooks) and the sink, inheritance and thinning never drop that record; - the SSD block-prefix lane restores to the exact match instead of the block-aligned one, so the unaligned anchor is reachable after a restart. Switch off: no anchors, identical planning and retention. Tests: tests/test_session_head_anchor.py (43): head detection on fake and cached real ChatML tokenizers, planning, thinning, sink, inheritance and SSD; a bank simulation of a long chat; on a tiny Qwen3.5-MoE with the invariant lane and in-forward boundaries, the anchor leaves the prefill bit-identical and a new session restored at the anchor (RAM or SSD) gives the cold prefill's logits bit for bit. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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Depends on #550 (and through it on #549). This branch is #550 plus one commit; only the last commit (
perf(session-bank): anchor a GDN boundary at the end of the prompt head (opt-in)) is new here. The feature itself does not need the batch-invariant lane or the in-forward boundaries: without them the anchor is an ordinary mandatory span end of the tail ladder. The dependency is in the tests: the bit-for-bit restore tests run a tiny Qwen3.5-MoE on the lane with in-forward boundaries and reuse the fixtures oftests/test_gdn_inforward_boundaries.pyfrom #550. Once #550 is merged, the diff reduces to that commit.Summary
Opt-in
MTPLX_SESSION_HEAD_ANCHOR=1: on hybrid (GDN) models, every prefill that goes to the session bank records a GDN restore boundary exactly where the prompt's fixed head (tool schemas and system turn) ends, and boundary thinning never drops it. A new session with the same head then restores the whole head from any banked entry of an earlier session, instead of only up to the oldest kept boundary. Default off; off keeps planning and retention unchanged.Motivation
Agent harnesses start every session with the same head: tool schemas and the system text, often several thousand tokens. Sessions diverge at the first user turn. On a hybrid model a restore needs a recurrent (GDN) state at or below the shared prefix, and the bank only holds states at recorded boundaries. Thinning keeps at most 8 boundaries per entry: the oldest record and a geometric ladder near the tail. The donor for a new session is usually a long conversation, whose kept boundaries sit near its tail, far past the head, so a new session restores only from the oldest boundary and prefills the rest of the head again.
A boundary exactly at the end of the head, kept through thinning and inheritance, makes the whole head reusable.
Change
mtplx/session_head_anchor.py:turn_markers(tokenizer)finds the ChatML turn-open token (atomic<|im_start|>) and the system role token;session_head_length(prompt_ids, markers)returns the number of tokens before the first turn that is not a system turn. Read from the prompt tokens, so it is token-exact for every ChatML template, harness and endpoint (chat completions and/v1/messages). No anchor for tokenizers without ChatML turns (for example Gemma 4), for image prompts, or for a head shorter thanMTPLX_SESSION_BLOCK_PREFIX_MIN_MATCH_TOKENS(no restore could use it).mtplx/generation.py:GdnBoundarySink: the boundary list a prefill appends to, plus its anchors. A plain list stays a sink without anchors._mandatory_prefill_edgesmerges the existing stable-prefix edge with the anchors; the cold loops, the warm suffix loop and_predicted_first_prefill_spanplan the anchor as a span end (inside the forward where the model publishes in-forward boundary hooks)._thin_gdn_boundary_records(..., keep=...), the sink's thinning and_inherited_gdn_boundariesnever drop a record at an anchor.mtplx/cache_bank/cold_tier.py: with the switch on, the SSD block-prefix lane restores to the exact match instead of the block-aligned one (the RAM lane already does), so an unaligned anchor is reachable after a restart. The prefix decode takes any length, and a boundary restore picks the newest persisted boundary at or below the match.mtplx/runtime_options.py:session_head_anchor_enabled()(MTPLX_SESSION_HEAD_ANCHOR, default off).How to enable:
MTPLX_SESSION_HEAD_ANCHOR=1, with a session bank on a hybrid model.Tests
New
tests/test_session_head_anchor.py(43 tests):~/.mtplx/models),/v1/messages, Gemma without ChatML turns.SessionBank: a new session restores at the anchor with the switch on, at the oldest boundary with it off; the anchor survives the SSD tier.tests/test_qwen4_ple_first_gather_early.py(updated for the planner helper),tests/test_gdn_inforward_boundaries.py,tests/test_batch_invariant_prefill.pyandtests/test_session_bank.pypass.MTPLX_CONFIG=/nonexistent: 9,535 passed, 67 skipped, 1 failed. The failure istest_laguna_model.py::test_laguna_s_2_1_ar_route_skips_qwen_performance_hooks, which fails the same way onmainon a Mac with less than 85.3 GiB (addressed in Tests no longer read the user's ~/.mtplx/config.toml or depend on the Mac's memory size #535). Ruff: new files clean, no new findings in the changed files.🤖 Generated with Claude Code