* WIP OCP FP8 E4M3 support
* Fix python type-checker
* Materialize input_scales also for FP8 when converting
* Fix conversion of https://huggingface.co/RedHatAI/Qwen3.8-27B-NVFP4
1. Per row FP8 wrongly fell to NVFP4 conversion, as NVFP4 checked on
dims alone. Also check on dtype for FP8
2. Need to reshape FP8 QKV projections scales in the same way that
weights are reshaped
* Allow remaining FP8 tensors to be dequantized during conversion
* Allow tensor-strategy for FP8-only compressed-tensor ckpts
* Fix and test gguf-py behavior for FP8_E4M3
* Disallow QKV-fusion for FP8 temporarily
* Fix conversion of fused qkvz & FP8 for Qwen3-Next
* Avoid fusing FP8 for --fuse-gate-up-exps as we may have different ws
* Emit warnings when not fusing FP8 during conversion
* More FP8 conversion fixes
* Add supported for strided copies to/from FP8 in CPU
* remove unneeded scratch buffer from FP8 cpu cpy/dup path
* add explanatory comments and TODOs
* Add dedicated encoder and decoder tests for FP8
* Whitelist ggml_op_get_rows in metal instead of blacklisting
required to fix failing CI, and white-listing should be more
future-proof than adding FP8 to the black-list
https://github.com/ggml-org/llama.cpp/actions/runs/38433916498/job/115422880633?pr=27512
* Fix more conversion bugs
* Defer weight materialization during conversion
* Blacklist fp8 in metal backend instead of white-list
* Adjust indents
* Fix lint + execute tests
* Add fast CPU kernel for F8_E4M3
* Conversion fixes
* Fix remaining gaps
* Enable tied weights with shared scales
* Make mandatory-F32 rules apply to FP8 during conversion
* Fix python type checks
https://github.com/ggml-org/llama.cpp/actions/runs/39765670052/job/119780379595?pr=27512
* Fix kaleidai unit-test failures
https://github.com/ggml-org/llama.cpp/actions/runs/39765670125/job/119780383150
* Relax numeric precision constraints for pre-check
* Leave fast-FP8 CPU on RISC-V for follow-up
We already exclude arm from the fast-path
* Allow output scale tensors to be located in any gguf shard
* Move test_conversion_fp8.py test execution to behavior-check.yaml
* Apply batched suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Move mixed compressed-tensor logic into dequant_fp8 + add unit-tests for it
* Install all requirements also on Windows for behavior-check
Fixes https://github.com/ggml-org/llama.cpp/actions/runs/40535852590/job/122300758223?pr=27512
* Unmap lazy tensors before clean-up in Windows
Should fix https://github.com/ggml-org/llama.cpp/actions/runs/40539797824/job/122314782459?pr=27512
* LF line endings
https://github.com/ggml-org/llama.cpp/actions/runs/40539798168/job/122314791118?pr=27512
* Extract FP8 tests from llama-arch to its own test-fixture
See https://github.com/ggml-org/llama.cpp/pull/27512#discussion_r4400628221
* Update gguf-py/gguf/scripts/gguf_convert_endian.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* model : add LiquidAI/d1-2B and LiquidAI/d1-omni-2B
Assisted-by: Claude Opus 5.5
* convert : keep the speech task templates out of the d1 GGUFs
Assisted-by: Claude Opus 5.5
* small refactoring: no_batching
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* llama : build the k-pool sparse selection independently of the kq_mask shape
qwen4exp (QSA) and glm5-next pick cache cells by index and no longer read the kq_mask values, but they still shaped the selection like the dense kq_mask. Build the selection as [n_kv, n_tokens] over the cache cells instead, and split it into the streams of the K view right before the attention.
This lets the dense mask change its layout (e.g. per-sequence row lists) without touching the sparse layers.
* improve comment
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : drop the unused build_fused_top_k_mask args, review nits
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* model : add DeepSeek-V4.1-Flash (deepseek41)
* deepseek41 : remove MXFP8, convert the Engram table to BF16
* deepseek41 : compute the compressed-KV read layers once in the cache
* deepseek41 : use get_cells() for the shared-history check, drop llama_kv_cache::seq_pos_tok_le
* deepseek41 : gather the Engram rows in-graph like qwen4exp, drop the host gather
* deepseek41 : keep the Engram hash constants in hparams.ple_*, one module per Engram layer
* deepseek41 : drop the LLAMA_DSV41_KV_MIRROR toggle
* deepseek41 : keep il last in build_hc_mixes
* deepseek41 : sort the top-k rows before the sparse gather
* tests : add deepseek41 to the Metal fusion baseline
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
The amax POOL_1D reduction spans the whole matmul row, so a device cannot
compute it from an axis-0 split. The Meta split-state case reported
MIRRORED, which gives each device a partial max of its own slice. Drop
that case and deny -sm tensor for the arches which build the amax graph,
see [TAG_MOE_AMAX_SCALE].
Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* qwen4exp : build the QSA selection without the kq_mask
* clean
* lang
* qwen4exp : reshape the fused QSA selection to the kq_mask streams
ggml_top_k_mask with a single keep row returns [n_kv, n_tokens], while
build_attn_qsa expects the kq_mask layout [n_kv, n_tokens/n_stream, 1, n_stream].
With several streams the shape assert failed at graph reserve.
* test the QSA optimization
* reword
* update build_fused_top_k_mask instead of handwiring
* simplify
* keep il at the end for consistency
* Add Flint model (FlintForCausalLM). Assisted by Codex
* cleanup
* one more
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* llama: create new fused op for QSA scoring
* enroll glm5.3-next too
* tests : initialize the remaining tensors in test_kpool_mask
The harness sentinels were left uninitialized, so leftover memory could read as NaN and fail the NaN check.
* ggml: generalize the k-pool mask op into ggml_top_k_mask
The k-pool mask op only covered the pooled QSA selection of qwen4exp
and glm5-next, while the DSA models and the DeepSeek V4 CSA build the
same top-k mask by hand with fill, set_rows and add.
ggml_top_k_mask(mask, top_k, score, blocks, extra) keeps the mask
cells of the live picks and the extra cells, and sets the rest to
-inf. Every row tensor is 4D in the layout of top_k, and the mask is
broadcast to those rows, so a selection can also be made per head.
score, blocks and extra are optional: without score every pick is
live, without blocks a pick is a cell, and extra holds the cells a
row keeps without a pick.
llm_graph_context::build_fused_top_k_mask views the row tensors in
the layout of kq_mask and registers the fused node. The k-pool
models, the DSA attention of build_attn and the DeepSeek V4 CSA mask
use it when the probe enables the op, and keep their current path on
backends without it.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* llama : scale moe down projection per expert token
Replace the global amax rescaling for the MoE down projection with a per-expert-token factor computed by a POOL_1D max reduction along the intermediate dimension. Undo the scale on the output with the same exact power-of-two factor. Handle POOL_1D in the Meta split-state logic so a split along axis 0 becomes a replicated per-device partial result, enabling tensor parallelism.
Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB)
* cont : simplify comments
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : refactor amax src1 scaling result
Return the rescaled activation and the optional scale tensor through a small struct instead of an output pointer.
Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB)
* cont : add TODO + clean-up
* model: add Muse Spark Open (text, vision and audio)
Muse Spark Open is a latent space MoE: the residual stream stays at 8192
while the 256 experts live in 4096, with a projection down and up around
them, top-8 sigmoid routing with a correction bias. Attention interleaves
three sliding layers at a 2048 window with rope for one global layer
without rope, with a sigmoid output gate and a weightless QK-norm.
Residuals are norm preserving, their gate is folded at conversion time.
Vision reuses the Muse Glimmer tower. Audio is a causal convolutional
pyramid with no attention and no spectrogram, four stages folding 16, 6,
5 and 4 samples into the channel dim, one embedding per 80 ms of 24 kHz.
The quantizer keeps the latent projections and the attention gate at
Q8_0, a little over 1% of the weights, since the whole residual stream
passes through them and the low-bit types turned the output into a
repeating loop. The model saver writes the post norm epsilon, and the
arch is registered in test-llama-archs.
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Muse Spark: drop the redundant forward expand
* Muse Spark: drop the redundant forward expand and tighten the comments
* Update src/llama-quant.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update src/llama-graph.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
An embedding-only graph has no logits, and build_pooling() returns
early when embeddings are off, so since the models no longer expand
t_embd the graph ends up empty and its inputs are never allocated.
Expand t_embd in that case.
The no-cache attention pads K, V and the mask to 256, but T5 also adds
a relative position bias of [n_tokens, n_tokens], so the add with KQ
fails unless n_tokens divides the padded width. Pad the bias with
zeros up to the KV dimension of the mask, like K and V.
* graph: skip the LM head for pooled embeddings
A context that extracts pooled embeddings outputs one vector per
sequence, but every model still expanded its LM head, so the graph
computed n_vocab logits per token and the context kept a host buffer
for them.
The models now only declare t_logits, and set_outputs() expands it
unless the context extracts embeddings with a pooling type other than
NONE. Since the toggle changes the graph shape for a pooled context,
set_embeddings() schedules a re-reserve in that case.
* graph: drop the redundant t_embd expand
Address review from @CISC: with embeddings on, build_pooling() already
expands its output for every pooling type, and a decoder graph without
embeddings does not read t_embd, so set_outputs() only expands
t_logits.
* graph: add build_logits() for the LM head output
Address review from @ggerganov: the LM head is expanded by a dedicated
build_logits(), called from llama_model::build_graph() like
build_pooling(), and set_outputs() only marks the outputs again.
* graph: call build_logits() before build_sampling()
Address review from @ggerganov: build_sampling() reads t_logits, so the
LM head is built first. A pooled context then skips the sampling layers
too, since it has no logits to sample from.
* nit
The no-cache path builds a [n_tokens, n_tokens] mask and passes K and V
unpadded, so FLASH_ATTN_EXT sees K->ne[1] == n_tokens. The CUDA kernels
for head size 512 require a multiple of FATTN_KQ_STRIDE, so the op falls
back to the CPU for most lengths, and the -fa auto probe (1 token)
disables flash attention for such models.
Pad the mask like llama_kv_cache::get_n_kv and pad K and V with zeros up
to the KV dimension of the mask. clef builds its own causal mask and
pads it the same way.
* model : use exact GELU for ModernBERT encoders
Assisted-by: Codex
* model : keep tanh GELU aliases on ggml_geglu
Assisted-by: Claude Opus 5.5
* model : map gelu_python to ggml_geglu_erf
Assisted-by: Claude Opus 5.5
The reserve builds n_outputs_max_per_seq sampling chains per sampler,
while a decode built one per output row, so the graph changed its
topology after the reserve and GGML_SCHED_NO_REALLOC builds aborted on
the next same sized graph. Every sampler now builds
n_outputs_max_per_seq chains, the ones without a row of the ubatch on
the padding row and not selected, and graph_max_nodes counts them.
* model : support classifier_activation for rerankers
Assisted-by: Claude Opus 5.5
* model : map classifier gelu to gelu_erf and accept tanh
Assisted-by: Claude Opus 5.5
* model : default act_cls to tanh, ModernBERT falls back to gelu_erf
Assisted-by: Claude Opus 5.5
* llama: support both embd + raw tokens in batch
* add to test-llama-archs
* also check case llm_arch_supports_mixed_batch = false
* constant graph topology
* have dedicated input for mixed case
* rm set_tensor_backend
* is_embd --> type
* consolidate m-rope pos handling into one place
* nits
build_rs gathered the extra states (n_rs - n_seqs rows) with their own
get_rows. The worst-case reserve has n_rs == n_seqs, so that node was
sized at zero rows, and any ubatch whose cells are not contiguous forced
a graph reallocation at an unchanged node count, which aborts under
GGML_SCHED_NO_REALLOC.
A single get_rows now gathers the n_rs states: the ubatch states and the
extra states are views of it, and its size only depends on n_rs, which
the reserve already sets to the maximum. A custom getter (mamba ssm_scan)
gathers from the second state, so a single sequence ubatch copies no
state. The views are built once per graph in the input to keep the host
overhead of the graph unchanged.
* CUDA: Handle compute type for NVFP4 on cublass path
Signed-off-by: ynankani <ynankani@nvidia.com>
* Use BF16 compute type for quantized models if HW allows
Signed-off-by: ynankani <ynankani@nvidia.com>
* Set acc prec to bf16 for nvfp4 as it needs atleast bf16 range
Signed-off-by: ynankani <ynankani@nvidia.com>
* Update ggml/src/ggml-cuda/ggml-cuda.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* preserve op_params for per-expert matmul
Signed-off-by: ynankani <ynankani@nvidia.com>
---------
Signed-off-by: ynankani <ynankani@nvidia.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Rebase GLM-Next support onto master, and migrate to llama-memory-hybrid-idx
* Add initial MTP support
* Merge branch optimizations. Reduce allocated compute buffer size, speed up long context decode, fla, and slight MTP improvements.
* Review driven changes, remove env vars, protect tensors
* Strip MTP for initial PR
* Clean up after mtp strip
* Clean up after mtp strip
* Update speculative.cpp
* Update llama-context.h
* Clean up after mtp strip
* Fix tokenizer ignore merges
* Improve quantization protection selection
* Refactor mhc helpers, graph base
* Lint Fixes
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Skip glm5-next in model saver, fix CRLF
* Skip glm5-next in sweep
* Remove T4 fallback
* Review cleanup
* Review suggestions
* Defer separate MTP gguf handling to MTP PR, drop filter
* Repad n_head_kv
* kpool init apply
* Order by descending score
* Drop guard
* read kpool from hparams, clarify kpool cache flags, remove kpool_build_state(nullptr)
* Add glm5-next support to model saver and add arch test fixture
* Review cleanup
* Kpool pooled caching clarify
* Add multi stream support
* Finish Rebase
* Sparse FA fir DSA prefill
* Const
* Update llama-model.cpp to fix rebase error
* gguf-py : merge tensor map entries for HC tensors
* model : use build_gdn_l2_norm in GLM5_NEXT implementation
* chore : remove trailing whitespace
* model : use new OP precision setting API in GLM5_NEXT implementation
* mtmd : use ggml_swiglu_clamp in GLM5V and apply the image token limit
The two clamps around swiglu_split are what ggml_swiglu_clamp already does,
so the clamp bounds collapse back to one value. GLM5V also never called
set_limit_image_tokens(), so --image-max-tokens had no effect.
Assisted-by: Claude Opus 5
(cherry picked from commit 46d18e12d422be4cc04a70e4a9a9e0168bb3d5b7)
* llama : keep the GLM5-Next k-pool layout across ubatches
The layout was rebuilt from a full cell scan on every ubatch. Pools are fixed
by the positions relative to the sequence's first one, so the layout now lives
on the memory and a ubatch only appends to it.
A sequence edit no longer stales every pooled key either, only the ones at or
after the edited position, which makes a tail seq_rm free. The pooling subgraph
is built unconditionally so the graph shape no longer changes every kpool
tokens, and the pool axis is folded into rows before soft_max, which otherwise
exceeds the CUDA gridDim.y limit past n_kv 262144.
Assisted-by: Claude Opus 5
(cherry picked from commit 5d1c40b93e17fddbf73b785efe43e0d02ccb3977)
* model : write the GLM5-Next recurrent rollback checkpoints
The conv state and the delta net state were only written to the live row, so a
rollback restored whatever the checkpoint rows happened to hold. Take the same
route as kimi-k3: build_recurrent_attn for the state, and write all K_rs conv
groups. That also drops a state view that assumed contiguous rows.
Enroll the arch in test-recurrent-state-rollback, which catches this under its
garbage-filled cache pass.
Assisted-by: Claude Opus 5
(cherry picked from commit 5ace37e86d5d448e83ef5dde5632c748185b18cd)
* llama: fix PR #27773 test-save-load-state restore failure
Clear the attention and indexer cache data after a failed hybrid state restore so restored NaNs cannot affect a later sequence.
Assisted-by: Codex
* llama: fix PR #27773 gpu-rocm graph reallocation
Reserve the full GLM5-Next pool capacity and dirty pool count. The gpu-rocm Test step aborts when n_new grows while the graph node count stays fixed; CUDA, Vulkan, Metal, and WebGPU checks report the same error.
Assisted-by: Codex
* llama : fix GLM5-Next k-pool layout staleness after edits and shared teardown
Two defects in the cross-ubatch k-pool layout added by the k-pool commit:
1. Wrong results. An edited sequence only rebuilt its pool layout when its cell
count changed, so if the first ubatch after an edit added back exactly as many
cells as were removed, the stale position-to-cell list survived. With a unified
cache and more than one sequence, where another sequence takes the freed cells,
the reused layout points at the wrong cells (CPU: large logit drift, CUDA: NaN).
Rebuild whenever the sequence is stale, not only on a size mismatch.
2. Slowdown. "shared" mode was assumed to end only with an edit that forces a
rebuild, but sharing also ends when the other sequence is removed. The survivor
kept shared = true, pinning cache_safe off and re-pooling every pool on every
ubatch (server trigger: n>1 completions with -kvu, via the seq_cp in
copy_state_to). In seq_rm, if the layout has shared cells, stale every sequence
so one rebuild re-derives sharing and cache_safe returns to 1.
Assisted-by: Claude Opus 5
* llama : fix build_attn_mha stream stride for non-contiguous q
build_attn_mha split the batch into streams with a stream stride of
q->nb[3]/n_stream. That only equals one stream's span, (ne[2]/n_stream)*nb[2],
when q is contiguous. GLM5-Next is nope-only, so it does not concat a rope part
and passes the permuted q_absorbed straight in, where nb[3] != ne[2]*nb[2]; the
stride was then n_head times too large and every stream s >= 1 read another
head's queries. Split-KV (-np N without --kv-unified) multi-stream prefill was
wrong for every stream past the first. Unified KV and decode were unaffected
(n_stream == 1, and decode takes the gather path). Other MLA models concat rope
so q is contiguous and the computed value is unchanged for them.
Compute the stride from the token dimension, which is identical for a
contiguous q.
Assisted-by: Claude Opus 5
* llama : re-derive GLM5-Next k-pool sharing on state_read/state_drop
The shared-cell teardown added to seq_rm (stale every sequence when the layout
has shared cells, so a survivor does not keep shared = true and pin cache_safe
off) was missing from the other paths that can free shared cells: state_read
and state_drop staled only the one sequence. Apply the same re-derivation there
and correct the comment that claimed sharing ends only via an edit or seq_rm.
Assisted-by: Claude Opus 5
* quant : drop duplicate GLM5-Next hc_ filter
The hc_ name filter was listed twice in the GLM5_NEXT protection block.
Assisted-by: Claude Opus 5
* glm5-next: scope K-pool cache access to indexed operations
* glm5-next: keep K-pool access in hybrid index memory
* glm5-next: keep mHC graph builders model-local
* glm5-next: mark only touched pools per ubatch
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Piotr Wilkin <ilintar@gmail.com>
* model : support classifier_pooling for ModernBERT rerankers
Assisted-by: Claude Opus 5.5
* model : read classifier pooling type in load_hparams
Write classifier.pooling_type from _try_set_pooling_type whenever the
config has classifier_pooling, and read it in
llama_model_base::load_hparams. ModernBERT falls back to mean when it
is unspecified.
Assisted-by: Claude Opus 5.5
* conversion : only accept cls and mean for classifier_pooling
Assisted-by: Claude Opus 5.5
* model : rename classifier_pooling_type to pooling_type_cls
Assisted-by: Claude Opus 5.5
* server : allow splitting RANK pooling for causal LLM rerankers
Rerank models fall into two categories: bidirectional cross-encoders
(BERT, etc.) that require all tokens in a single physical batch, and
causal LLMs repurposed as rerankers (Qwen3, Qwen3-VL) that can use
chunked prefill like any other decoder.
Previously the server rejected all RANK-pooling inputs larger than
n_ubatch, and the graph builder hardcoded QWEN3/QWEN3VL arch checks to
determine last-token pooling. This broke long-document and multimodal
reranking for causal models.
Fix: expose llama_get_causal_attn(ctx) so the server can check the
effective runtime attention type (reflecting any --attention override
or set_causal_attn call). Also expose llama_model_is_causal(model)
for querying the static architectural property from GGUF metadata.
can_split() now permits chunked prefill for RANK pooling when the
context is causal. The graph builder's inline arch check is replaced
with the same cparams.causal_attn predicate, removing the duplication.
Assisted-by: Opencode/Qwen3.8-27B
* remove unused llama_model_is_causal, fix whitespace
Assisted-by: opencode
---------
Co-authored-by: timothywang21 <timothywang21@users.noreply.github.com>
* metal : gate mul_mm_id src1 rescale behind ggml_prec
Assisted-by: Claude Fable 5.1
* ggml-webgpu: reject MUL_MAT_ID when src1 precision is F32
* cuda/vulkan: reject MUL_MAT_ID in supports_op when src1 prec is F32
fix `supports_op` to return false for failing backends when the specified src1 precision is f32
Assisted-by: Claude Fable 5.1
---------
Co-authored-by: yomaytk <yoshimura.masashi.frbs@gmail.com>
* llama-context : report graph inputs and input tensors during sched reserve
- fix the tg (token generation) graph bs label to use n_seqs instead of a hardcoded 1
- report the number of graph inputs from llm_graph_result::inputs for both the pp and tg graphs
- report the number of input tensors (nodes and their src tensors flagged with GGML_TENSOR_FLAG_INPUT)
- log a warning when an input tensor has an op other than GGML_OP_NONE
- log a trace line for each input tensor and the nodes (name and op) that use it
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* cont : count input tensors before reserving the sched
* wip
* llama-graph : name the unnamed graph input tensors
- name the kv-cache idxs input tensors (attn_inp_k_idxs, attn_inp_v_idxs)
- name the recurrent state copy idxs input tensor (rs_s_copy)
- report the input tensor shape in the sched_reserve trace
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* llama-context : rename "graph inputs" to "graph input objects"
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* llama-context : report the sched reserve graph stats on a single line
- print nodes, splits, input objects and input tensors in one line
- when the pp and tg graphs differ, print each value as 'pp / tg'
and annotate the line with the batch sizes used for each graph
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* cont : pad logs
* gguf-py: add Maple tensor constants
Add MODEL_ARCH.MAPLE, its "maple" name, and the tensor list for the
Maple 20B-A1B ternary MoE architecture: token embeddings, output,
attention with Q/K RMS norms, and per-expert FFN tensors.
* convert: add Maple HF->GGUF converter
Register MapleForCausalLM in the HF architecture map and add the
converter for the Maple 20B-A1B ternary MoE model: 24 layers, 256
experts with 8 active, sliding-window attention (SWA-512) interleaved
with global attention at a 3:1 ratio, partial rotary factor 0.5, and
per-expert weight stacking into merged 3D tensors.
* llama: add Maple architecture (20B-A1B ternary MoE)
Add the Maple 20B-A1B ternary MoE architecture: 24 layers, 256
experts with 8 active, sliding-window attention (SWA-512) interleaved
with global attention at a 3:1 ratio, and ternary TQ1_0/TQ2_0
quantization support.
- register LLM_ARCH_MAPLE between MAMBA2 and JAMBA
- implement llama_model_maple: Q/K RMS norms after projection (GEMMA4
style), rope applied only on SWA layers (nope_on_global_attention),
ISWA KV cache, and MoE FFN with swiglu gate clamp at +7 (DEEPSEEK4
style)
- mark MAPLE as unsupported by the model saver (roundtrip skipped)
* tests: mark Maple as MoE-mandatory
Maple is always-MoE: the model throws when n_expert == 0, so the
test harness must only run the MoE config for LLM_ARCH_MAPLE.
* maple: apply review feedback (n_ff_exp_arr, get_arr, rope params)
- load_arch_hparams: use n_ff_exp_arr + n_ff_exp() accessor (upstream
changed these from a scalar member during the rebase)
- sliding_window_pattern: get_arr, the pattern is mandatory for this arch
- partial_rotary_factor: read only from rope_parameters (base.py mirrors
the top-level key automatically)
- document why TOKEN_EMBD/OUTPUT are forced to F16 (they are the two
dense tensors in Maple, and the reference GGUFs ship them as F16)
- add @ModelBase.example("deepgrove/maple-preview")
* tests: add Maple to the SWA pattern array list
get_arr for maple.attention.sliding_window_pattern requires an array, but
the harness only emitted a per-layer array for the arches in its list, so
test-llama-archs -a maple failed to load the model.
Assisted-by: DeepSeek Harness
* maple: move swiglu_clamp_exp to the converter
The loader prefilled 7.0 and read the key optionally. The converter now
writes it and the loader reads it as required, because llama-graph.cpp
skips the clamp when the limit is 0 and an optional read would silently
run unclamped. The test harness provides the key for the same reason.
Also drops tensor_force_quant: base.py already forces FFN_GATE_INP to F32
and TOKEN_EMBD/OUTPUT to F16 for ternary file types.
Assisted-by: DeepSeek Harness
* convert: fix the LazyBase func signature in the Maple converter
ty flagged the stack() closure: it takes no argument, while LazyBase is
annotated with func: Callable[[Any], Any]. Pass the tensor list through
args instead of closing over it, the same way kimi_k3 does, so the
callable shape matches.
Assisted-by: DeepSeek Harness
* model: add Tencent Hy 4 (hy_v4) preview architecture support
Adds support for the Tencent Hy 4 model (Hugging Face architecture
HYV4ForCausalLM, GGUF arch hy_v4):
Add HF -> GGUF conversion script (conversion/hy_v4.py) and wire it into the conversion registry
Register hy_v4 GGUF constants, arch enum, and writer support
Implement the hy-v4 model graph, hparams, vocab and context changes
Register the new arch in llama-arch and models registry
Extend arch tests to cover hy_v4
Assisted by Claude Opus 5
* Update convert_hf_to_gguf_update.py
Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
* Update conversion/base.py
Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
* convert : move hy_v4 entry to the same place as in convert_hf_to_gguf_update.py
* model : apply changes related to n_ff_exp becoming per-layer in Hy4-preview
* n_layer_all
---------
Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* hparams: add per-layer n_ff_exp/n_expert_used arrays with scalar-or-array loading
G1/G2 infrastructure for variable-per-layer expert FFN size and top-k routing
(required for Puzzle-75B which has 5 distinct n_ff_exp values and 7 top-k values
across its 40 MoE layers).
Design: rename scalar members to _impl suffix (following existing convention),
add LLAMA_MAX_LAYERS arrays, add n_ff_exp(il)/n_expert_used(il) accessors with
scalar fallback. No new GGUF keys: reuses existing expert_feed_forward_length and
expert_used_count keys via get_key_or_arr (scalar -> broadcast, array -> per-layer).
- llama-hparams.h: n_ff_exp -> n_ff_exp_impl, n_expert_used -> n_expert_used_impl;
add n_ff_exp_arr / n_expert_used_arr arrays; add per-layer accessor declarations.
- llama-hparams.cpp: implement n_ff_exp(il) and n_expert_used(il); out-of-range
il returns impl safely (shared code, no abort).
- llama-model.cpp: central n_expert_used load changed to get_key_or_arr; derive
impl as max-of-array for validations and backward compat; zero both new arrays;
HunyuanVL override also zeroes n_expert_used_arr.
- llama-graph.cpp: aggregation loop in build_moe_ffn uses hparams.n_expert_used(il)
so per-layer top-k bounds the ggml_view loop correctly.
- All other files: mechanical rename hparams.n_{ff_exp,expert_used} -> *_impl.
Scalar arches are unaffected (broadcast fills all array slots with the single value).
(cherry picked from commit 269a81e03d66e1c353e1a203a0c03a03eb2b1a4e)
* nemotron-h: use per-layer n_ff_exp(il) and n_expert_used(il) at MoE call-sites
Load n_ff_exp via get_key_or_arr into hparams.n_ff_exp_arr in load_arch_hparams;
derive impl as max for existing uniform GGUFs.
In load_arch_tensors, compute n_ff_exp_i = hparams.n_ff_exp(i) with fallback to
n_ff(i)/n_expert_used(i) for GGUFs that omit expert_feed_forward_length.
In build_ffn_layer, pass hparams.n_expert_used(il) to build_moe_ffn so per-layer
top-k is used for expert routing selection.
All other nemotron-h behaviour (mamba2, attention, shared-exp, latent projection,
routed_scaling_factor, expert_weights_norm, sigmoid gating) is unchanged.
(cherry picked from commit b1878a101793cd4e59868ac72635a86ea694987c)
* arch/*.cpp + gguf-py: mechanical rename n_ff_exp->n_ff_exp_impl, n_expert_used->n_expert_used_impl
All non-nemotron arch files continue using the scalar impl member directly.
Behaviour is identical: the impl value is the broadcast value from the GGUF scalar.
gguf_writer: add_expert_feed_forward_length and add_expert_used_count now accept
int | Sequence[int], mirroring add_feed_forward_length, so converters can write
per-layer arrays with the same existing GGUF keys.
(cherry picked from commit 8f009f54bea5ef9a6a354123bd25e9d5ea2d5e03)
* convert: support NemotronHPuzzleForCausalLM (per-block MoE config)
Parse block_configs/mtp_block_configs into per-layer arrays (scalar-or-array
keys), append the MTP [attention, moe] sub-blocks as blk.88/blk.89 with
nextn tensors, accept the backbone.* prefix, and register the arch.
Also fix a pre-existing undeclared _experts attribute on NemotronHModel.
(cherry picked from commit d1a592f278336e78457454eb6c96bca917135f10)
* nemotron-h: distinguish Nemotron 3 Puzzle (75B.A9B) from Super (120B.A12B)
Both have 88 layers; the per-layer expert_used_count array (heterogeneous
for Puzzle, broadcast-uniform for Super) is the discriminator.
(cherry picked from commit f824e09dc812169589cf5662c92d149a4c18c30a)
* convert: accept the official Puzzle BF16 checkpoint's tensor naming
The officially distributed BF16 checkpoint (NVIDIA-Nemotron-Labs-3-Puzzle-
75B-A9B-BF16) names the trunk model.* (model.layers.*, model.embeddings,
model.norm_f) where the original release used the NemotronH-style
backbone.*, and spells the router bias e_score_correction_bias instead of
e_score_correction.bias. Normalize both at the top of
NemotronHPuzzleModel.modify_tensors so either checkpoint converts; every
tensor name in the official index (42683 keys, MTP head included) resolves
through the tensor map after normalization.
(cherry picked from commit 189b67fc2c9d50970416c94b3317a6e7baa49b03)
* laguna: use n_ff_exp_impl for the uniform-MoE FFN size
Laguna landed after this branch was cut and reads hparams.n_ff_exp as a
scalar. This series turns it into a per-layer array with an n_ff_exp(il)
accessor, so the three scalar reads no longer compile. Laguna is a
uniform MoE, so point them at the scalar fallback n_ff_exp_impl, same as
deepseek2/qwen3moe/gemma4 in this series. No behaviour change.
(cherry picked from commit dbedc9e19c50dca0acdfb402362e2707bee424ae)
* arch: extend the n_ff_exp/n_expert_used rename to archs added upstream
kimi-k3, dflash, bailingmoe3, deepseek4, granite-swa and the nemotron-h MTP
block still referenced the scalar fields by their old names. n_ff_exp and
n_expert_used are accessors now, so those reads no longer compile; point the
non-per-layer archs at the _impl scalars and use the indexed form where the
call site is per-layer.
* convert: keep Puzzle opted out of the NemotronH MTP export path
#26725 added MTP export to NemotronHModel, keyed on num_nextn_predict_layers.
Puzzle's config carries that key, but NemotronHPuzzleModel bypasses
NemotronHModel.__init__ (its per-block config needs a different setup), so
_mtp_bid was never assigned and modify_tensors raised AttributeError on any
mtp.* tensor. Puzzle's head is also laid out by mtp_block_configs, not the
mtp.layers.* form the base maps.
Set _mtp_bid to None, drop mtp.* in filter_tensors, and declare
supports_mtp_export = False so --mtp / --no-mtp fail at the CLI.
* llama: replace n_ff_exp/n_expert_used scalars with per-layer accessors
Follow-up to review feedback: the previous revision kept the scalar
hparams fields alongside the new per-layer arrays, which duplicated
state that get_key_or_arr already handles by broadcasting a scalar
value over every layer.
Drop both scalars and expose n_ff_exp(il) / n_expert_used(il) built
exactly like the existing n_head_kv(il) and n_ff(il) accessors: they
index the array and GGML_ABORT out of range, with il defaulting to 0
so genuinely uniform call sites stay a plain n_ff_exp().
Arch loaders now read both keys through get_key_or_arr over
n_layer_all, and the n_expert_used validation checks the maximum
across layers instead of a single field.
* llama: restore per-key required flags on the expert hparam reads
The scalar-to-array conversion passed required=false at every call site,
which silently made mandatory keys optional. Each read now carries the
same required flag it had before the conversion.
Anton IandPascal
Oliver SimonsandSigbjørn Skjæret
Xuan-Son Nguyen
Tarek DakhranandXuan Son Nguyen
Aman GuptaandGeorgi Gerganov
Alexey Kopytko
begcdn
Hoang Vien Duy
alexey-rsiandSigbjørn Skjæret
Mikhail Podvitskii
bri-prismandGeorgi Gerganov
bosh
ynankaniandJohannes Gäßler
Tim Wangandtimothywang21
Michael de Gansandyomaytk
Alex
PikaPikachu
Yaniss Amazouz