feat: add GLM5Next MTP, optimize (#29928)

• llama : add GLM5-Next NextN (MTP) graph

Build the GLM5-Next multi-token-prediction head as graph_mtp: the NextN block
embeds enorm(tok)+hnorm(h) through eh_proj, runs one plain DSA layer and the
shared lm_head, reusing the trunk's builders through the no_build tag ctor.
llama_memory_recurrent also tolerates a partial seq_rm when the context holds
no recurrent layers, which is what the MTP draft context needs.

Assisted-by: Claude

• llama : glm5-next: skip dead compute in headless NextN forwards

A NextN forward with no output rows (the MTP catch-up and the draft-context
prefill) persists only through its cache writes, so the headless graph keeps
the MLA latent, indexer key|gate and pooled-key writes and drops the query
path, the indexer selection, the attention body, the FFN and the LM head. The
4-token catch-up falls from 6.9 ms to 0.33 ms of kernels; the greedy output
hashes and the draft acceptance are unchanged.

Assisted-by: Claude

• llama : glm5-next: fix NextN extraction contracts and shared-tail rollback

Three fixes from the architectural review. The headless graph prune now also
requires that no unmasked nextn extraction is live, because that mode reads
n_tokens hidden rows regardless of the logits flags. Masked extraction
publishes the hidden rows gathered by the output ids, so a batch whose output
flags are not a prefix exports the right rows. A partial recurrent rollback
whose tail cell is shared with another sequence is now rejected instead of
silently moving that sequence's tail.

Assisted-by: Claude

• llama : glm5-next: tidy comments in the MTP changes

Assisted-by: Claude

• llama : glm5-next: crop the MTP graph to the output rows instead of pruning it

Replace the headless NextN prune with the crop pattern the other MTP
graphs use: gather the attention output and the block input at the
output ids before the position-wise FFN and the shared head. A NextN
forward with no output rows (the MTP catch-up and the draft-context
prefill) then runs the FFN and the head over zero rows. The 4-token
catch-up falls from 6.9 ms to 2.9 ms of kernels; greedy output hashes
are unchanged.

Assisted-by: Claude

• glm5-next: use the nextn crop helpers in the MTP graph

Replace the local crop condition and the masked select of t_h_nextn
with crop_before_nextn and crop_after_nextn, so the MTP graph narrows
its rows the same way as the main graph and the other models.

Describe the shared cell and empty filter branches of the recurrent
partial rollback.

• glm5-next: load MTP-only and trunk-only GGUF files

Make the trunk tensors optional when the file only holds the NextN
layer, and the NextN tensors optional when the file only holds the
trunk, so the split MTP GGUF loads as a draft model.

Co-authored-by: Georgi Gerganov ggerganov@gmail.com

Co-authored-by: Pascal admin@serveurperso.com
Co-authored-by: Georgi Gerganov ggerganov@gmail.com

sampling : use greedy selection for eligible temperature-zero chains (#29797)

• sampling : use greedy selection for eligible temperature-zero chains

Assisted-by: OpenAI Codex

• Apply suggestion from @ggerganov

Co-authored-by: Georgi Gerganov ggerganov@gmail.com

• sampling: simplify zero-temperature greedy eligibility

Allow the same greedy selection on CPU and grammar/reasoning-budget paths.
Keep distribution sampling for dynamic temperature and requested probabilities.
Cover the common sampler selection and probability behavior in the existing sampler tests.

Assisted-by: OpenAI Codex

• sampling : use greedy selection after final top-k with k=1

Assisted-by: OpenAI Codex

• cont : clean-up

Co-authored-by: Georgi Gerganov ggerganov@gmail.com

vocab : add plamo fim tokens (#30090)

musa: use the tile lightning indexer kernel (#30080)

Signed-off-by: Xiaodong Ye xiaodong.ye@mthreads.com

vendor : update cpp-httplib to 0.60.0 (#30081)

imatrix : include clocale for std::setlocale (#30079)

Co-authored-by: yarik dazzywi@github.com

ggml-webgpu: fix flash_attn supports_op check for overlapping KV (#28205)

tests : retain the anchor when testing recurrent rollback (#29923)

[SYCL] fix the issue in mixed different model GPUs in FA (#29071)

• fix mixed different model GPUs issue

• Update ggml/src/ggml-sycl/ggml-sycl.cpp

Co-authored-by: Georgi Gerganov ggerganov@gmail.com

Co-authored-by: Georgi Gerganov ggerganov@gmail.com

sycl: accelerate GLM MLA prefill with MKL flash attention (#29171)

• sycl: accelerate GLM MLA prefill with MKL flash attention

GLM-4.7 Flash uses an MLA shape with 576-wide Q/K heads, a 512-wide
V head, GQA 20, and F16 KV. The SYCL dispatcher rejects this shape
because the normal MKL flash-attention gate requires matching K/V
widths and caps the head dimension at 512, so prompt processing falls
back to the substantially slower TILE kernel.

Admit only the validated 576/576/512, GQA-20 F16 shape to the existing
MKL pipeline. Keep all other mismatched K/V shapes on their current
fallback paths.

Handle GLM's V cache as a narrower strided view of K rows. Select the
strided F16 descriptor when row stride is padded, and alias K/V
dequantization buffers only when their logical widths match. Restrict
the stride exception to a real V view sharing K's row stride.

Add the exact 576/512, GQA-20 prompt-path backend test.

On an Intel Arc Pro B70 at master e613ef2, pp8192 improves from
432.80 to 1292.29 tok/s (2.99x, +198.6%). tg256 remains unchanged
within noise at 45.67 versus 45.65 tok/s. The exact MLA test passes
and debug output confirms MKL dispatch.

• sycl: store MKL flash attention scores in F16

Keep the QK GEMM output in F16 instead of F32. The online softmax still
converts each score to F32 for its max, exponent, and sum, so the
per-element math is unchanged apart from score rounding, and the F32
matrix was being written only to be consumed as F16 probabilities.

The F32 score matrix is the largest flash-attention intermediate on this
path; storing it as F16 halves its size and traffic. This builds on the
coalesced softmax loads from 1aa2954, which read each score row
cooperatively, so the smaller dtype pays off.

Measured on an Intel Arc Pro B70 with the dispatch from the previous
commit, -ngl 999 -b 4096 -ub 1024 -ctk f16 -ctv f16 -fa on:

pp8192 1583.9 -> 1657.1 tok/s (+4.6%)
pp64000 610.0 -> 684.5 tok/s (+12.2%)
pp131072 ~354 -> 402.1 tok/s (+13.5%)

tg256 at 8k context is unchanged (32.64), and the FLASH_ATTN_EXT suite
shows no new failures. The exact GLM MLA backend cases pass against CPU.
Adjust the ~354 baseline figure if you prefer citing only measured pairs (the 131k dispatch-only point came from the equivalent maintained build). Optionally add Assisted-by: per the contribution guidelines since AI contributed to the change.

• Revert "sycl: store MKL flash attention scores in F16"

This reverts commit 265f974.