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gg/tests-init-opt
1168
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Georgi Gerganov
|
7bb0fc18f6 |
metal : add sparse FA (#28098)
* metal : support n_kv_max sparse mask hint in flash attention vec kernel
- add kernel_flash_attn_ext_vec_idx: compacts finite mask entries into
a per-row index list (Hillis-Steele scan, one threadgroup per row)
- extend vec FA kernel with optional sparse index gathering (FC slot 5)
- add host-side gate: sparse path when n_kv_max > 0, mask present,
supported head sizes / KV types, n_kv_max <= 4096
- new buffer region extra_idx for the index list
- pipeline getter extended with has_sparse param
- add test cases: head sizes, quant types, nb>1, nr23 variants,
sinks, ALiBi, softcap, permute, v_view_of_k, no-mask fallback
Note: multi-row (nb*nr23[1] > 1) cases still failing - rid mapping
in the store phase needs revisiting for the sparse path.
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* metal : fix sparse flash attention row addressing
- kernel_flash_attn_ext_vec_idx: mask param is half* but nb31 is a byte
stride, so the per-row mask offset was scaled by 2x; cast to char*
before applying the byte strides
- kernel_flash_attn_ext_vec: sparse pidx param is char* so the per-row
element offset was under-scaled by sizeof(int); scale it by sizeof(int)
to get the correct byte offset
- fixes the multi-row (nb*nr23[1] > 1) sparse flash attention failures
Assisted-by: pi:llama.cpp/DeepSeek-v4-0731
* cont : use sparse vec FA for prefill
* metal : single-pass flash attention sparse index compaction
The idx kernel previously read the mask row twice: once to count the finite
entries (for the prefix scan) and again to recover their positions. Since the
kernel is memory-bound, this doubled the mask traffic.
Keep the finite positions in a per-thread register array during the count
pass and write them out directly, avoiding the second mask read. A dense
mask with more than NLOCAL finite entries in a slice falls back to re-reading
the mask to write the remaining positions.
Assisted-by: pi:llama.cpp/DeepSeek-v4-0731
* tests : add perf cases for sparse flash attention prefill
Measure the sparse vec FA kernel across KV sizes, n_kv_max hints and batch
sizes. Run with:
./build/bin/test-backend-ops -b MTL0 -o FLASH_ATTN_EXT -p "n_kv_max=[1-9]" perf
Assisted-by: pi:llama.cpp/DeepSeek-v4-0731
* qwen4 : enable sparse attention
* cont : adjust nsg
* cont : sync test-backend-ops
* cont : disable Qwen4 for now
* cont : clean-up + tests
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 Tarek Dakhran
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5ec4eab69e | misc : prevent RAM peaking at model loading stage (#27483) | ||
 Yaniss Amazouz
|
c61b98b875 |
model: add NVIDIA Nemotron-3-Puzzle-75B-A9B (NemotronHPuzzle) support (#25444)
* 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.
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 Xuan-Son Nguyen
|
159b741427 |
finetune: fix no KV cache (#27199)
* training: fix no KV cache * apply @ ggerganov suggestion |
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Xuan-Son Nguyen
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9400c8946e |
model: correctly support input vision for deepseek4 (#28154)
* model: correctly support input vision for deepseek4 * nits |
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 Aman Gupta
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8e93a9773b | CUDA + ggml: add sparse-fa for DSV4/GLM (#27970) | ||
 Pascal
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b356fa2624 |
kv-cells: look up the n-gram history in the sequence position index (#28040)
get_prev_tokens() rebuilt a (seq, pos) -> token hash map on every ubatch by walking all used cells, while llama_kv_cells already keeps an ordered index of the positions of each sequence in seq_pos, updated on every cell mutation to serve seq_pos_min() and seq_pos_max(). The index now stores (pos, cell) pairs in a std::set instead of a position -> count map, so a repeated position (cache reuse via rm + add, vision inputs with shared positions) yields distinct entries and the removal of a cell erases its own pair. The new seq_pos_tok_le() returns the token of the cell at the largest position <= p in logarithmic time, which is exactly what the old window lookup and its M-RoPE gap fallback computed together. get_prev_tokens() shrinks to a direct lookup per (token, offset) and for_each_token_in() goes away with its only caller. The kv-cache keeps no n-gram logic of its own. Measured on Qwen3.8-Flash-Next UD-Q4_K_XL at 71k context, alternating two binaries with the first run discarded: tg 69.3 -> 72.7 t/s (+4.9%), pp unchanged at ~2720 t/s, greedy output identical, needle retrieved. |
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 Sigbjørn Skjæret
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dfc29b64eb | context : autoscale n_ctx_train when yarn scaling specified (#28030) | ||
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Sigbjørn Skjæret
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f28493c783 | models : appropriately flag noscan ssm_a tensors (#28121) | ||
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Sigbjørn Skjæret
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73159c3039 | model : fix gemma4-assistant (#28183) | ||
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Sigbjørn Skjæret
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d11b3cc7ed | model : load relevant arrays with n_layer_all (#28173) | ||
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Sigbjørn Skjæret
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9d817213a0 |
model : load hparams.n_layer_nextn before n_layer() calls (#28159)
* load hparams.n_layer_nextn before n_layer() calls * remove duplicate loads |
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 Daniel Han
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36b1015438 |
qwen4exp: fix seq_cp, block position keying, mtmd input, cuda abort, add tests (#27941)
* qwen4exp: follow up fixes * -kvu NaN collapse fix Assisted-by: Claude * indexer cache ext.x/ext.y restore fix Assisted-by: Claude * kv-cells: rename seq_set to seq_get_all seq_get is already taken by the single-id getter, so the suggested name cannot be overloaded on return type alone. Assisted-by: Claude * memory-hybrid-idx: implement set_input_qsa on the memory class The context held the whole implementation, where the pattern elsewhere is a thin context forwarding to the memory class, as llama_kv_cache_context does for set_input_kq_mask. The body reads no context state, so it moves unchanged and the context keeps a forwarder. Also shortens the seq_get_all comment as suggested. * tests: check that a sequence state survives a save/restore round-trip Saves seq 0, erases it, restores the blob and saves again, requiring the two blobs to match. Compares blobs rather than generated text, which cannot see a field dropped on the way back in. Note this passes on master for qwen4exp, so it does not demonstrate the ext.x/ext.y drop this PR fixes; reaching that needs 2D mrope content. * tests: give the synthetic qwen4exp a PLE so the state test bites has_cell_ext() is n_pos_per_embd() > 1 || ple_n_heads > 0, and the indexer cache sets rope_type = NONE, so without a PLE it serializes no cell ext at all and the round-trip test cannot see a dropped ext.x/ext.y. With one, removing the ext_set restore in state_read_meta fails the test: 198 of 335692 bytes differ, first at offset 282092. Loading such a model needed two fixes: - the row count of per_layer_token_embd came from require_weight(), which a model synthesised from metadata alone has no file to answer. Derive it from the head ranges and prefer the file's padded count where there is one. - the PLE conv history is a row of the recurrent cache, so a PLE on a full attention layer dereferenced a null p_l. Reject it at load time instead. The meta mirror is skipped for qwen4exp. It returned NaN logits before this fixture carried a PLE, which the nmse check passes since a NaN comparison is false, and aborts with one. -sm tensor on real devices works. Assisted-by: Claude * llama: disable -sm tensor for qwen4exp test-llama-archs skipped the tensor split for this arch from inside the test, so the arch still advertised support it does not have. Declare it in llm_arch_supports_sm_tensor instead and drop the test-side exception; the existing llm_arch_supports_sm_tensor branch then does the skipping. Assisted-by: Claude |
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Xuan-Son Nguyen
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1b89a43e38 | quantize: row-slab stream to avoid thread starvation (#27830) | ||
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Pascal
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0eadefebd3 |
qwen4exp: support recurrent state rollback (#28123)
MTP speculative decoding needs the target state to move back by the number of rejected draft tokens. Without rollback support the context is classified as SEQ_RM_TYPE_FULL and the server serializes the whole recurrent state to host memory on every round, which costs more than the drafting saves. The recurrent cache already holds n_rs_seq + 1 snapshot planes and the delta net writes its SSM state into them, but build_conv_state_at wrote a single plane, so a rollback restored a convolution history that was never captured. It now writes one snapshot per slot, each ending one token earlier, for the delta net QKV convolution and for the PLE convolution alike. Measured on Qwen3.8-Flash-Next UD-Q4_K_XL with the standalone MTP draft, n-max 3 and a single slot: decoding reaches 183 tok/s on code and 144 tok/s on prose. The same branch before this change, where the server falls back to checkpointing the state to host memory, reaches 123 and 83 tok/s, for 108 tok/s without a draft. |
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Pascal
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09412af38a |
qwen4exp: sum the indexer heads by slices (#28023)
* qwen4exp: sum the indexer heads by slices The head reduction went through a transpose and a sum_rows over ne[1], which left sum_rows with ne0 = 4, one block per row for a four element reduction, and the transpose copied the whole block by token surface twice on the way in. The heads are adjacent on ne[1], so each one is a strided view and the sum is a short chain of adds. RTX PRO 6000, Qwen3.8-Flash-Next UD-Q4_K_XL, fa on, 55k context, warm runs on top of #28011: prompt processing 2170 -> 2366 t/s Generation is unaffected. The removed work scales with n_blocks by n_tokens, so the gain grows with context and with ubatch size. * qwen4exp: drop the redundant cont on the indexer query rope returns a freshly allocated, contiguous tensor, so the reshape that feeds the matmul does not need a copy. ggml_reshape_3d asserts contiguity, so a layout that would need the cont cannot slip through silently. Greedy output is unchanged token for token. Address review from @ggerganov |
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 itsnotoger
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2d8d612e4c |
kv-cache : optimize restoring non-contiguous cells (#27991)
* kv cache : batch state restore scatter reads per contiguous run When restoring state into non-contiguous destination cells (e.g. a prompt-cache snapshot into a fragmented ring), state_read_data issued one small copy per KV cell - ~1.4M copies of a few KiB each for a 40k+ token restore, taking 25-63 s on the CUDA backend. The snapshot stores cell rows in cell order, so a maximal run of consecutive destination indices maps to one contiguous block and can be restored with a single copy. Precompute the runs once and use them in all three scatter loops (K, V, transposed V). Byte-identical. The on-device reader copies with a byte cursor when the read and write chunking differs, so the batched reads are safe for it as well. Batching makes equal tensor counts with a different split reachable (save ranges [2,1] vs restore runs [1,2]); the next commit teaches the reader's 1:1 path to fall back to the byte cursor in that case. Verified in a production setup: 1,363,616 copies / 25-63 s -> 224 copies / 221-424 ms for the same restores (42,603 cells, 4 runs). Assisted-by: Claude Code (unsloth/qwen3.8-27b) * context : fall back to the byte cursor when read and write chunking differ the on-device reader copies saved state back with a 1:1 copy by tensor index whenever the write and read sides recorded the same number of tensors, guarded by a per-tensor size assert. equal tensor counts do not imply equal chunking: a state restore may batch its reads per contiguous run of destination cells while the save used per-range reads, so both sides can record two tensors that split the same data differently, and the assert aborts in all builds. compare the per-tensor sizes and only take the 1:1 path when the chunking actually matches, otherwise fall through to the existing byte-cursor copy. both sides enumerate the same logical data in the same order, so the cursor copy is well-defined across tensor boundaries. Assisted-by: Claude Code (unsloth/qwen3.8-27b) * tests : cover state restore scatter reads on host and on-device paths decode the same prefix on two sequences, interleaving the seq 0 cells between the seq 1 cells, so the seq 1 cells are isolated from each other in the kv cache (three cells, two saved ranges). save the seq 1 state, free the interleaved seq 0 cells, and restore: the destination is then non-contiguous (two runs), and the restore-side chunking has the same tensor count as the save-side with a different split, so the scatter path is batched per contiguous run and the on-device reader's byte-cursor fallback is exercised. the restored state is saved again on the host and compared byte for byte with the first save: the blob is serialized in sequence cell order, so the two saves are identical if and only if the scatter restore wrote exactly the same KV content. this documents the byte-identical guarantee of the run-batched scatter reads. one test per io backend: the host (CPU) path and the on-device path. Assisted-by: Claude Code (unsloth/qwen3.8-27b) |
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662a0b0121 |
spec : fuse the DFlash encoder into the KV cache injection (#27310)
* dflash : fuse the encoder into the KV injection decode The encoder is a single fc + norm, but running it as a separate llama_encode forced a device-to-host round trip of its output before the injection decode could re-upload it, plus a second graph build per round. Fold the encoder into the decoder's embd branch and feed the target features directly to one llama_decode. Assisted-by: Claude Fable * nit * Apply batched suggestions from code review Co-authored-by: Ruixiang Wang <[email protected]> * Fix missing references from renaming --------- Co-authored-by: Sigbjørn Skjæret <[email protected]> Co-authored-by: Ruixiang Wang <[email protected]> |
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Pascal
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62acc89c26 |
kv-cells: stop the sequence scan once all sequences are seen (#28011)
for_each_token_in tested all LLAMA_MAX_SEQ sequences for every used cell, while a cell almost always belongs to one. The scan now stops once the cell's own sequences have been seen. Same visit order, same callback arguments, so behaviour is unchanged. get_prev_tokens is the only caller, so this affects the n-gram path. RTX PRO 6000, Qwen3.8-Flash-Next UD-Q4_K_XL, fa on, warm runs: 55k context generation 56.3 -> 74.3 t/s 132k context generation 33.6 -> 50.9 t/s Prompt processing is unchanged, the scan is amortised over the ubatch there. The gain follows the number of used cells, so it grows with context and is invisible on short prompts. |
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Aman Gupta
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0190529ec4 |
ggml: add SWIGLU_CLAMP (#27930)
* ggml: add SWIGLU_CLAMP * add vulkan shader |
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Xuan-Son Nguyen
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2578138397 |
llama: improve TENSOR_READ_LAZY handling (#27837)
* force lazy tensor on cpu if lazy is on * llama: improve TENSOR_READ_LAZY handling |
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 JamePeng
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cc231cb0da |
dflash: pass missing NVFP4 scales to attention operations (#28000)
- DFlash2 NVFP4 draft models produced almost no accepted speculative tokens because the Q, K, V, and output projection scales were not passed to the corresponding graph operations. |
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bdf3955159 |
memory : copy Hadamard matrix to k_rot tensor only if it has buffer assigned to prevent crashes during context shift of unquantized K cache (#27967)
Co-authored-by: Stanisław Szymczyk <[email protected]> Co-authored-by: AesSedai <[email protected]> |
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Xuan-Son Nguyen
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50f068ffff |
bench: add --tensor-read-lazy (#27881)
* bench: add --tensor-read-lazy * rm the alias * rename to LLAMA_LAZY_MODE_* |
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Xuan-Son Nguyen
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6fe7498016 | model: qwen4exp: reduce number of graph splits (#27880) | ||
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Georgi Gerganov
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8663224818 | context : disable non-fused GDN and LID ops (#27877) | ||
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Georgi Gerganov
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4e97ac86eb |
tests : run test-save-load-state across all architectures (#27755)
* tests : run test-save-load-state across all architectures test-save-load-state previously only ran in ctest against a single downloaded model (tinyllamas/stories15M), i.e. only the llama arch. Add a --models DIR mode to test-save-load-state that runs the full save/load suite over every *.gguf in a directory, reporting a per-model PASS/FAIL and exiting non-zero if any model fails, and wire a ctest to run it over all architectures using the existing generate-models fixture (test-llama-archs). The single-model -m mode is preserved (still used by ci/run.sh). Also bump the dummy-model training context in test-llama-archs from 128 to 256 so that the per-sequence context (which is padded up to a multiple of 256) no longer exceeds n_ctx_train and emits the "possible training context overflow" warning. The test is expected to fail until the affected arches are fixed: deepseek4 (host seq-copy), gemma2/gpt-oss/lfm2 (device seq-copy), minimax-01 (state load). It aborts at the first arch that crashes. Assisted-by: pi:llama.cpp/Qwen3.8-27B * tests : match dummy DSA indexer to fused Lightning Indexer kernel The dummy DSA indexer (deepseek32, glm-dsa, ...) used key_length=64 and head_count=1, so the fused Lightning Indexer op's q tensor was shaped [64, 1, ...]. The Metal fused kernel is fixed to DK=128, NH=64, so it rejected the op and the scheduler fell back to CPU, emitting a 'layer assigned to MTL but Lightning Indexer on CPU' warning. Bump key_length to 128 and the DSA head_count to 64 so the fused op runs on the GPU. Assisted-by: pi:llama.cpp/Qwen3.8-27B * tests : add --help and document -o in test-llama-archs Add a --help/-h flag to test-llama-archs and list the existing -o/--out option in the usage text, which was previously missing. Assisted-by: pi:llama.cpp/Qwen3.8-27B * tests : use 64 indexer heads for deepseek4 deepseek4's indexer head count was set to n_head (8), which does not match the fused Lightning Indexer kernel's fixed NH=64, so the fused op fell back to the CPU backend and emitted a device-mismatch warning. Give it the same fixed 64 as the other indexer archs by dropping it from the n_head ternary (only minimax-m3 keeps n_head, since it does not use the fused Lightning Indexer op). Assisted-by: pi:llama.cpp/Qwen3.8-27B * tests : fix dsv4 save-load n_stream mismatch The dsv4 KV cache keeps per-sequence KV/state streams even in unified mode, so its n_stream equals n_seq_max. The test saved the state in the baseline with n_seq_max=1 but loaded it in the seq-copy tests with n_seq_max=2, so state_read threw an n_stream mismatch. Use n_seq_max=2 in the baseline and state-load tests so the save and load agree. Assisted-by: pi:llama.cpp/Qwen3.8-27B * context : relax on-device seq-copy chunk alignment The on-device state seq copy (llama_state_seq_set_data with LLAMA_STATE_SEQ_FLAGS_ON_DEVICE) copied the write-side cpy tensors to the read-side targets 1:1 by index, requiring the writer and reader to emit the same number of chunks in the same order with the same per-chunk sizes. state_write_data chunks per cell-range while state_read_data chunks contiguous-or-per-cell, so the counts diverged for non-contiguous sources (dsv4, SWA) and the copy aborted with "memory buffer mismatch". All state writers and readers enumerate the same logical data in the same order, differing only in chunking. Copy the flat write-side data into the read-side targets with a byte cursor that walks both tensor lists across their boundaries, so the chunking no longer needs to match. Keep the total-size guard; drop the n_tensors equality check. Assisted-by: pi:llama.cpp/Qwen3.8-27B * model : fix dangling hparams ref in minimax-01 LA graph input llm_graph_input_la stored const llama_hparams & hparams, bound to the llm_graph_params temporary in llama_context::process_ubatch. The input object outlives that temporary (it is kept in llm_graph_result::inputs for graph reuse), so set_input() read destroyed stack memory on every graph reuse - test-save-load-state crashed for minimax-01 when the stack region was overwritten (n_layer_all read as 0, abort in llama_hparams::n_head). Store a copy like every other graph input class. Assisted-by: pi:llama.cpp/Qwen3.8-27B * context : handle "worst case" graph and add TODO |
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ca3d5a3e10 |
model: add DSpark support for Nemotron3.5 (#27804)
* model: add DSpark support for Nemotron3.5 * Update src/models/dflash.cpp Co-authored-by: Sigbjørn Skjæret <[email protected]> --------- Co-authored-by: Sigbjørn Skjæret <[email protected]> Co-authored-by: Xuan Son Nguyen <[email protected]> |
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6c84c7d5d8 |
model: add Qwen3.8-Flash-Next (qwen4exp) (#27742)
* gguf: add qwen4exp (Qwen3.8-Flash-Next) arch and converter
Adds the GGUF-side plumbing for HF model_type qwen4_exp:
- MODEL_ARCH.QWEN4EXP plus tensors for the low-rank hyper-connection
variant (hc_*_norm/down/up/inject) and the PLE n-gram hash embeddings.
The DeepSeek-V4 hc_*_fn/base/scale tensors are a different
parameterisation, so these are separate entries rather than reuse.
- Reuses the existing indexer, per_layer_token_embd, SSM and
compress_ratios keys unchanged.
- conversion/qwen4exp.py inherits the Qwen3.5 linear-attention V-head
reorder and interleaved mrope, concatenates the 128 PLE embedding
shards, and splits index_qk_proj into separate indexer q/k tensors.
The PLE hash multipliers reach ~2.4e13. prepare_tensors() casts every
non-float dtype to float32 before modify_tensors() runs, and GGUF array
writes infer INT32 from Python ints, so both paths are bypassed: the
constants are read from the pre-cast lazy tensors and written as
explicit UINT64 arrays.
Additive only; no existing arch changes behaviour.
* llama: load qwen4exp (Qwen3.8-Flash-Next) hparams and tensors
Adds LLM_ARCH_QWEN4EXP with its hparams and tensor loading. The graph
comes in the next commit; this makes the model load and report correct
metadata.
- hyper-connections set n_embd_out_impl = hc_count * n_embd, so the
residual stream is 4x wide and there is no output_norm: the final
mixer's hc_norm is the last norm in the model.
- registered as hybrid and given the same recurrent/attention memory
filters as Qwen3-Next and Qwen3.5.
- reuses the existing indexer, per_layer_token_embd, SSM and
compress_ratios keys as-is.
- the PLE table row count is read back from the file rather than
recomputing the vocab padding rule.
llama-model-loader gains UINT64 array support. That branch previously
threw, so no existing caller changes behaviour; it is needed because the
PLE hash multipliers do not fit in int32.
* qwen4exp: shorten comments
* llama: qwen4exp text graph with hyper-connections, GDN and MoE
Implements the decode graph for Qwen3.8-Flash-Next: the hyper-connection
residual stream, gated delta net layers, the MoE block with its gated shared
expert, and dense full attention. The QSA indexer and the PLE n-gram embedding
are not wired up yet and land in later commits.
Hyper-connections are implemented here rather than shared with deepseek4.cpp.
The two formulations agree on the [n_embd, hc, n_tokens] layout and little
else: DeepSeek-V4 mixes with a full-rank projection and Sinkhorn-normalises
it, whereas this model uses a low-rank down/silu/up sigmoid gate and collapses
by a plain mean. Only the ~10 line stream mean is genuinely common, so sharing
would mean touching DSV4's hot path and its three fused CUDA ops to reuse very
little. What is reused is the substantive part: the LLM_KV_HYPER_CONNECTION_*
keys, the n_embd_out_impl wide-residual support already in the loader, and the
layout convention.
Also allows a checkpoint to carry no PLE layers at all, which makes it
possible to bring the graph up and validate it in stages.
Validated against vLLM, the only working reference implementation. On a
scaled-down model with an init scale large enough to give non-uniform logits,
agreement with vLLM sits at the numerical noise floor: llama.cpp f32 against
its own bf16 gives 84.3% top-1 agreement over 255 positions, and this graph
against vLLM gives 85.1%. The comparison was calibrated by seeding three
deliberate bugs (silu instead of sigmoid on the delta net gate, dropping the
1/hc scale in the mix, dropping the 2x in the combine); each drops top-1 to
between 0% and 11%, an order of magnitude below the floor.
* llama: qwen4exp PLE n-gram hash embedding
Adds the per-layer embedding: a custom I32 graph input hashes each token with
its ngram_size-1 predecessors host-side and the result is a plain row gather
over the shared table, the same shape gemma3n's per-layer embedding uses. The
hash has to run on the host because the splitmix64-derived multipliers reach
2^45, so the products need 64-bit integers and an xor, neither of which ggml
has.
Predecessors that fall outside the ubatch come from a small per-sequence
history on the model, mirroring the per-request ngram_context the reference
carries. It is only trusted when contiguous with the incoming position, so a
fresh prompt or a rewound cache falls back to EOS padding rather than hashing
against stale tokens.
The depthwise conv is written out as a sum of shifted, per-channel-scaled
copies rather than through ggml_conv_1d_dw, which carries a correctness
warning upstream.
Verified two ways. The row indices match a transcription of the reference's
tensor formulation exactly, 1024 of 1024 rows, including sequences with EOS
tokens sprinkled through them to exercise the segment reset. Separately, with
PLE placed on layer 0 so its input is just the token embedding, ple_embd and
ple_gated_value match a PyTorch computation from the same checkpoint to every
printed digit.
End to end over 1023 scored positions the port sits the same distance from
vLLM with PLE as without it, 6.3 points of top-1 against 6.0, so PLE costs no
accuracy relative to the rest of the model. That common offset is vLLM's bf16
activations, which cannot be removed: its QSA kernel refuses float32.
Two bugs found along the way, both caught by the row-index check. The history
was read and updated in the same pass, so a token early in a ubatch could pick
up an earlier token of that same ubatch as prior context; it is now snapshotted
first. And an EOS token was cutting its own context, where the reference takes
the last EOS strictly before the position, so a boundary only hides tokens from
the positions after it.
Known gap: the conv carries no state across ubatches, so it is exact only for a
prefill that starts at position 0. Chunked prefill and decode need the conv
state wired into the recurrent memory, and the conv branch itself is still
numerically unverified because the fixture zeroes its weights.
* llama: carry the qwen4exp PLE conv state across ubatches
The PLE depthwise conv was zero-padding on the left, which is only right for a
prefill that starts at position 0. Decode and chunked prefill saw a truncated
history for the first (kernel-1)*ngram_size positions of every ubatch.
The PLE module sits on a layer that is also a delta-net layer, so both need a
conv history in the same recurrent row. Rather than plumb a per-layer state
size through build_rs and build_conv_state, the row is widened once and each
convolution addresses its own slice through a local helper. n_embd_r() gains
the extra span, which is zero for every other architecture because it is
derived from ple_n_heads.
Verified by feeding the same 1024 token sequence in chunks instead of one
shot: at 64 tokens per decode the logits are bit-identical to the single-shot
run, 1023 of 1023 top-1 and a maximum logprob deviation of exactly zero. At
one token per decode they differ slightly, but the no-PLE model differs more
under the same test (94.6% against 97.1%), so that is the usual gemv-versus-
gemm accumulation difference and not the state.
The conv branch is also no longer unverified. With non-zero conv weights the
port sits 6.3 points of top-1 below the numerical floor, the same distance as
with the weights zeroed and as the model with no PLE at all, so the branch
adds no error of its own.
test-llama-archs passes every existing architecture at 0.00e+00, including the
delta-net models that share this code path.
* llama: fix the qwen4exp PLE conv state and unblock test-llama-archs
build_rs writes into the state tensor in place, zeroing one row and copying the
carried-over states, so calling it twice for the same layer let the second call
clobber the first write-back. The PLE layer is also a delta-net layer, so that
is exactly what happened: both convolutions gathered the same row. They now
share a single gather per layer.
The earlier claim that the conv state was carried correctly was tested on a
fixture whose conv weights are zero, where the branch contributes nothing and
chunking matches trivially. Re-running with non-zero conv weights showed the
divergence, growing with the number of ubatch boundaries: 97.1% top-1 at one
boundary down to 90.2% at seven. With the shared gather it is bit-identical to
the single-shot run at every chunk size tried, 512, 128 and 64, with a maximum
logprob deviation of exactly zero over 1023 positions. The delta-net-only model
stays bit-identical too, so nothing regressed there.
Also derive the delta-net conv channel count the way load_arch_tensors sizes
wqkv instead of from ssm_d_inner. The two agree for this model, but n_embd_r()
only bounds the row and the convolution has to match the tensor feeding it.
test-llama-archs previously aborted on this architecture and took every later
architecture with it. qwen4exp is marked MoE-only, given the hyper-connection
keys and an ssm_d_inner consistent with its tensor derivation, and skipped for
now: the hyper-connection keys written by get_gguf_ctx are not reaching the
synthesised file, which needs a separate look. The suite completes again, 124
architectures at 0.00e+00.
* llama: optional indexer key cache in llama_memory_hybrid
Groundwork for qwen4exp's QSA sparse attention. Its indexer needs a per-token
key history for the full-attention layers, but a hybrid model cannot use
llama_kv_cache_dsa: that class derives from llama_memory_i rather than
llama_kv_cache, and llama_memory_hybrid constructs its attention cache
directly. No existing architecture pairs recurrent state with a sparse
indexer, so there was nothing to reuse wholesale.
llama_memory_hybrid therefore gains a third, optional cache, shaped the same
way llama_kv_cache_dsa shapes its lightning-indexer cache: a copy of hparams
with n_head_kv forced to 1 and n_embd_head_k_full set to indexer_head_size.
It is built only when a filter_idx callback is passed, which defaults to
nullptr, so every existing architecture gets exactly what it got before. The
per-sequence operations and the batch preparation forward to it under a null
check, matching how the DSA cache prepares its two caches over the same
ubatches.
test-llama-archs passes all 124 architectures at 0.00e+00, including the 12 in
the hybrid family that share this code. The qwen4exp fixtures are unchanged:
same logits against vLLM, and chunked evaluation still bit-identical to
single-shot.
* llama: QSA sparse attention for qwen4exp
The full-attention layers of this model do not attend to everything. An
indexer scores one mean-pooled key per block of compress_ratio tokens and
keeps a budget of the best blocks, plus the tail of tokens that do not yet
form a complete block. Below indexer_top_k + compress_ratio - 1 cached
tokens every block fits in the budget, so the result is exactly dense.
What is reused rather than rebuilt:
- the mask machinery. build_attn's DSA overload already turns a list of
token indices into a KQ mask via ggml_set_rows, so that block is lifted
out verbatim into build_attn_mask_top_k and shared with a new overload
on llm_graph_input_attn_kv. DSA's node sequence is unchanged; the new
overload exists because llama_kv_cache_dsa assumes MLA and cannot be
dropped into a hybrid model.
- the indexer key cache, which is the optional third cache added to
llama_memory_hybrid in the previous commit. It holds raw keys, because
pooling happens before the norm and the rotation.
The graph expands block scores rather than block indices: giving every
token of a block its block's score needs only a gather, where expanding
indices would need an integer multiply-add that ggml has no op for. Since
the budget is a whole number of blocks and a block's members tie exactly,
the cut still lands on a block boundary.
Everything that depends on cache layout is computed host-side in
set_input_qsa. Blocks are cuts of the position line rather than of the cell
array, so nothing assumes the cache is contiguous.
Measured on the tiny fixture against vLLM, comparing the selected token
indices directly rather than the logits:
below the budget selection identical, and 1024-token logits are
bit-identical to the pre-QSA dense path
above the budget mean jaccard 0.975
The direct index comparison is what made this correct. The reference
rectifies each head's dot product before summing over heads, which an
earlier reading of it had missed; on logits alone the resulting port looked
fine, because on a randomly initialised fixture the known-correct dense
path already disagrees with vLLM by more than the bug did. Comparing the
indices showed 0.794, and fixing the ReLU moved it to 0.975.
* llama: give the qwen4exp indexer cache the attention cache's slots
The indexer cache found its own slots, independently of the attention
cache. Both are the same size and see the same ubatches, so in a
straight-through prefill they agree, which is why every fixture and every
single-shot parity run passed. They drift once the context is being
rewritten between turns, and then the QSA top-k indices, which are applied
against the attention mask, point at the wrong cells.
The seven-turn chat test caught it on the third turn: llama-server aborted
on the assertion that the two caches report the same n_kv.
The cache is a side buffer addressed by the attention cache's cells, so it
now takes that cache's slot layout instead of computing one. Applying that
layout also marks its cells identically, so the two agree cell for cell by
construction rather than by coincidence, and the assertion can no longer
fire.
Inert where the caches already agreed: test-llama-archs green at 126 archs
and 0.00e+00, and the 4096-token tiny fixture is unchanged at max logit
delta 0.0.
* tests: record what the qwen4exp arch-test skip actually observes
The old note guessed that the hyper-connection keys never reach the file.
They do: dumping the gguf_context handed to llama_model_init_from_user
shows both among its 67 KVs, and the loader still reports one missing.
* tests: cover qwen4exp in test-llama-archs
The arch was skipped with a note guessing that the hyper-connection keys
never reached the synthesised file. They did. The suite builds a model, then
saves and reloads it, and llama_model_saver did not re-emit those keys, so
the failure was in the roundtrip leg rather than the first load. Three gaps,
all in shared code and all additive:
- add_kv_from_model wrote no hyper-connection, compress-ratio or PLE keys.
The PLE group only means anything whole, so it is written or omitted
together; the rest follow the file's existing style of writing every key
unconditionally, since an architecture that does not read one is
unaffected by a zero.
- the saver had no uint64 path at all, which the PLE hash constants need.
- add_tensors_from_model enumerates model-level tensors by hand and was
missing per_layer_tok_embd and the three final-mixer tensors.
Two smaller fixes on the qwen4exp side, both found by running the test:
- build_qsa_top_k divided by the compression ratio before asserting it was
non-zero, so a file without the key crashed instead of reporting.
- a layer with no compression ratio now falls back to dense attention,
which is what the model computes below the budget anyway. The test then
has to write a ratio to reach QSA at all, and an indexer key length no
narrower than n_rot, since the indexer ropes with the main attention's
rotary width.
Full suite: 126 archs, qwen4exp at 0.00e+00 with roundtrip OK. The tiny
fixture is unchanged, max logit delta 0.0 against the pre-QSA dense run.
* convert: stream the qwen4exp PLE table instead of concatenating it
The n-gram table arrives as 128 shards that were held in a dict and then
torch.cat-ed, so the peak was the shards plus the concatenation: around
300 GB of RSS on the real checkpoint, which rules out machines that could
otherwise convert this model.
Each shard is now written straight into a memory-mapped file at its final
row offset and dropped, so the resident set is one shard and the rest is
the page cache's problem. The temporary file sits beside the output and is
removed once the write finishes, including on failure.
Shards other than the last must be uniform for direct placement, which is
asserted rather than assumed, and a shard arriving before the stride is
known is held instead of misplaced.
Verified on the tiny fixture: the resulting GGUF is byte-identical to the
one the concatenating path produced (md5 2d274efac91ad1e9a6007efb0687e597).
* quantize: fall back to F16 for 32-block types with an odd ncols
tensor_type_fallback demotes a tensor whose ncols is not a multiple of the
target's block size, but its switch only enumerates the 256-block types. A
target that is already a 32-block type (iq4_nl, q4_0, q5_0, q8_0, ...) falls
into default: and throws, even though the function already knows how to answer
that case: the ncols check right below the switch resolves an unrepresentable
shape to F16.
Route those types into that check instead of throwing. Only paths that abort
today change, so no quantization that currently succeeds is affected.
Found on a 4-wide depthwise conv kernel. llama-quantize reported nothing but
"failed to quantize model from ...", with no tensor name and no exception text,
which made a quant recipe that had simply not pinned the tensor look like a
corrupt model. It now names the tensor and continues.
* quantize: let --tensor-type name per_layer_token_embd
per_layer_token_embd shares the TOKEN_EMBD category with token_embd.weight, so
--token-embedding-type is returned for it before any --tensor-type pattern is
consulted, and there is no way to give it a tier of its own.
That grouping is fine as a default and stays the default. It is a poor fit for
the size, though: on qwen4exp the table is 97.7 GiB of a 337.6 GiB BF16 file and
about 46% of a 4-bit one, roughly eighty times token_embd.weight, and it is
read by ggml_get_rows rather than a matmul so no imatrix ever covers it.
Allow an explicit --tensor-type pattern to name it, and only it. Nothing
changes unless such a pattern is passed, and token_embd.weight keeps the old
precedence in either case.
Measured on Qwen3.8-Flash-Next, Q4_K_M with an imatrix: the table lands at q8_0
(51.9 GiB, 113.5 GiB total) by following --token-embedding-type, and pinning it
q4_1 gives 30.5 GiB for 92.1 GiB total, 19% off the file.
* quantize: size the output buffer exactly instead of nelements * 4
The per-tensor output buffer was sized `nelements * 4`, described as an upper
bound. It is a very loose one: the output is at most 2 bytes per element
(f16/bf16) and usually well under 1.1 (q8_0 and below), so between 2x and 4x of
it is never touched. The exact size is already known here, since it is what the
quantization loop writes, what new_size sums to, and what the GGUF metadata is
asserted against a few lines later.
On a model whose largest tensor is a few GB none of this matters. On
Qwen3.8-Flash-Next it does: per_layer_token_embd is 51.2 G elements, so the
buffer was 205 GB where 54 GB is needed at q8_0 and 32 GB at q4_1.
Measured on that model, VmHWM of a live llama-quantize was 485 GB per process.
Three of them fit in 2 TB and five did not, which is what an OOM-killed quant
ladder looks like. This removes about 150 GB of that.
Byte-identical output, verified against the same binary built at the parent
commit: q4_K, q8_0, q5_K, q6_K and IQ4_XS, over BF16 and F32 sources, with and
without a PLE table present. Six cases, six matching md5s.
* qwen4exp: hash the image placeholder for multimodal batches
The PLE row indices are computed host-side from ubatch->token, and set_input
returned early when that was null. A multimodal ubatch is exactly that case:
the mtmd layer consumes the image placeholder ids and hands llama_decode
embeddings instead. The early return left the I32 index tensor uninitialised,
so ggml_get_rows indexed a 320 M row table with whatever the buffer happened to
contain, and aborted:
GGML_ASSERT(i01 >= 0 && i01 < ne01) failed
ggml_compute_forward_get_rows
mtmd_helper_decode_image_chunk -> llama_decode
Every image request crashed. Nothing caught it because the vision work had only
ever been verified by converting an mmproj, never by running one.
The reference computes the hash over input_ids, where those positions still
hold the image placeholder, so carry that id through as qwen4exp.ple.image_token_id
and hash it. The key is optional: a file converted before it existed falls back
to the PLE EOS token, which is defined and treats the image as a segment
boundary rather than crashing.
Verified end to end with llama-mtmd-cli, a Q4_K_M base and the F16 mmproj, on a
generated image with known content. The model names the red circle, the blue
square, the inverted green triangle and reads "UNSLOTH 42", each with the right
position.
* qwen4exp: support a non-unified KV cache in QSA
set_input_qsa asserted n_stream == 1, so llama-server could not serve this
model with more than one slot unless -kvu was passed. With a non-unified
cache each sequence owns its own cells, and a cell index means a different
token in each stream, so a single shared mapping is wrong.
- cell_blk, blk_cells and bias gain a stream dimension. At n_stream == 1
these collapse to the shapes they had, so the unified path is unchanged.
- Scoring is now batched over streams. ggml_mul_mat matches ne[2] on both
operands, so stream s's queries only ever meet stream s's blocks; without
this sequences would score against each other's context.
- set_input_qsa loops per stream and resolves cells through
v_cells[seq_to_stream[seq_id]], following set_input_kq_mask_impl, instead
of hardcoding v_cells[0].
- llama_kv_cache_context::get_n_stream() is added, mirroring the ns that
get_k and get_v already derive from the slot info.
build_attn_mask_top_k needed no change: it already expects
[n_top_k, n_batch, 1, n_stream], so the top-k result is reshaped to meet it.
set_input_qsa has exactly one caller, so the blast radius is qwen4exp only.
Validation, UD-Q4_K_XL on one B200:
- unified cache unchanged within noise: 1802.9/68.85 -> 1807.2/69.11 t/s at
batch 1, 2262.5/192.43 -> 2270.1/193.75 at batch 4.
- non-unified now runs at npl 1, 4, 16 where it previously aborted, and is
22% faster than the -kvu workaround at batch 16 (1205 vs 984 t/s total),
since per-stream cells avoid the cross-sequence masking a unified cache
pays for.
- no cross-stream contamination: four concurrent sequences each carrying a
distinct secret all recall their own and no other, on both cache modes.
- test-llama-archs green on qwen4exp, deepseek2, gemma3n, qwen3next, llama.
Note on testing: comparing concurrent output against solo output exactly is
not a valid check. It failed 0/4 with no bug present, and the unified-cache
control failed the same way, because batch composition changes the
floating-point reduction order and near-tied tokens flip. The contamination
test above is what the exit code gates on.
* llama: keep the qwen4exp top-k attention mask arch-local
The QSA graph needed a build_attn that attends only to the cells named by a
top_k tensor, and the first version got it by adding a llm_graph_input_attn_kv
overload to llm_graph_context and factoring the mask construction out of the
existing MLA sparse path into a shared build_attn_mask_top_k.
That put a new arch on the shared attention path and made the deepseek32 and
glm-dsa attention build depend on a helper introduced for qwen4exp. Build the
mask in src/models/qwen4exp.cpp instead and leave llama-graph.{h,cpp} exactly as
they were: the MLA path keeps its own copy of the same node sequence.
The nodes emitted are unchanged, so this is bit-identical.
* llama: hold the qwen4exp indexer cache in a new llama_memory_hybrid_idx
The indexer key cache was added by extending llama_memory_hybrid with an
optional third cache, and the host-side cell/block mapping that drives QSA was
added as set_input_qsa on llama_kv_cache. Both are shared classes that every
hybrid and every attention model goes through.
Move both into a new memory type, llama_memory_hybrid_idx, following
llama_kv_cache_msa: the indexer cache and the pos<->cell translation live with
the sparse-attention memory rather than in the classes that serve every other
architecture. llama-kv-cache.{h,cpp} and llama-memory-hybrid.{h,cpp} are
restored to their unmodified state.
init_batch is repeated from llama_memory_hybrid because the indexer cache has to
be handed the attention cache's slot infos, and those are not reachable through
the context the base returns. Allocating them separately lets the two caches
drift, which is what pointed QSA's top-k at the wrong cells before.
The context derives from llama_memory_hybrid_context so build_inp_mem_hybrid
keeps working unchanged, and get_n_stream is computed from the slot infos
exactly as llama_kv_cache_context did.
Behaviour is unchanged: logits over an 8192-token sequence are bit-identical to
the previous implementation, sparse and dense alike.
* llama: save and restore the qwen4exp indexer KV cache
llama_memory_hybrid_idx forwarded clear, seq_rm, seq_cp, seq_keep, seq_add and
seq_div to the indexer cache but not state_write / state_read, so a saved
session dropped the indexer keys and a restored one selected QSA top-k against
an empty cache. The effect is invisible until the context passes
indexer_top_k + compress_ratio - 1 cells, because QSA is exactly dense below
that and the indexer contents cannot change the result.
The indexer section is written last rather than next to the attention cache it
mirrors. As a suffix, a reader that does not expect it stops early and the
trailing bytes are caught by the size check in state_load_file; placed between
the attention and recurrent sections it would instead be parsed as recurrent
state, which can succeed and restore silent garbage. It follows the same
LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY gate as the attention cache, since a partial
checkpoint deliberately skips the token-level attention caches.
The indexer restores its own cells instead of taking the attention cache's
restored slots. The two caches share size, padding and every sequence
operation, and init_batch hands the indexer the attention cache's slot infos,
so both state_read_meta calls run find_slot over identical occupancy and land
on identical cells.
The overrides live on llama_memory_hybrid_idx, the only memory type that owns
an indexer cache, so llama_memory_hybrid and every architecture that uses it
write and read exactly the bytes they did before.
The session and sequence state versions are bumped because the qwen4exp state
layout changed. The session path already rejects a short read via its size
check, but llama_state_seq_load_file accepts one silently, so only the version
check stops a pre-fix blob from being half-restored by a fixed build.
(cherry picked from commit 2721542354f8e158c3217625f4e2e7b83e51e3fe)
* llama: make the qwen4exp PLE n-gram history per context and serialise it
The PLE hash of a token mixes in the ple_ngram_size - 1 tokens before it, which
a decode ubatch does not carry, so they were remembered in a map on
llama_model_qwen4exp. That is the wrong owner twice over.
A llama_model is shared by every context that loads it, and the map was keyed
only by llama_seq_id, so two contexts running the same sequence id - two server
instances on one model, or a draft/target pair - overwrote each other's window.
The next_pos guard turned that into EOS padding instead of a crash, so it
degraded quality silently.
The map was also in no state blob: grep found ple_hist in neither
llama-kv-cache.cpp nor llama-memory-*.cpp nor llama-context.cpp. A restored
context therefore failed the next_pos check on its first ubatch and hashed the
first tokens after the restore against EOS padding. This is why a session blob
round-tripped byte for byte while the restored context computed different
logits: the state was never in the bytes.
It moves to llama_memory_hybrid_idx, which is per context, is the memory type
qwen4exp always builds, and already does the per-sequence bookkeeping this
needs. Every sequence operation now carries the window with it:
seq_rm a rewind (p1 < 0) truncates the window to the surviving prefix and
moves next_pos to p0, so a rollback keeps exact context; a hole
punched in the middle leaves the window non-contiguous, so it is
dropped
seq_cp the destination inherits the source's window, truncated to the
copied position range - a copied sequence continues with the same
n-grams the source would have used
seq_keep every other sequence's window is dropped, like its cells
seq_add a shift that moves the whole window keeps it and moves next_pos with
it, which is the context-shift case; one that cuts through it drops
it
seq_div positions stop being consecutive, so an overlapping window is
dropped
clear everything is dropped
Dropping means next_pos = -1, which set_input turns into full EOS padding: the
same thing a fresh sequence gets, and the same thing this code did before it
followed the sequence operations at all, so no case is worse than before.
The state payload is a self-delimiting list, u32 count then per entry
{ i32 seq_id, i32 next_pos, u32 n_toks, i32 toks[n_toks] }, so a whole-context
save and a single-sequence save share one format and a single-sequence restore
can retarget the window at its destination seq_id. It is written after the
indexer section, last, for the same reason that one is: as a pure suffix an
older reader stops early instead of parsing these bytes as something else.
Unlike the indexer section it is not under LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY.
The window is recurrent state - it is the input the PLE convolution's own
recurrent state is derived from - and the recurrent cache beside it is written
for partial checkpoints too. Gating it would leave the server's speculative
decoding checkpoints restoring the conv state without the window that produced
it.
No further version bump: LLAMA_SESSION_VERSION 10 and LLAMA_STATE_SEQ_VERSION 3
were introduced for the indexer section in the same unreleased series, and both
changes are qwen4exp-only additions to the same blob layout.
Also fixes the padding of a short window. set_input pads a window shorter than
ngram_size - 1 up to that length, but prev() indexes the snapshot with the most
recent token last, and resize() pads at the back, so the filler EOS landed where
the immediately preceding token belongs. It now pads at the front. A window is
short at a sequence start after a one-token prefill, and after a seq_rm rewind,
which the new bookkeeping makes common.
Every architecture other than qwen4exp builds llama_memory_hybrid rather than
llama_memory_hybrid_idx, has no PLE table and never asks for a history, so
nothing about its graph, its sequence operations or its state bytes changes.
(cherry picked from commit de170364c052c68fcf63285cc0028095edb9f23c)
* qwen4exp: tidy comments and simplify image token read
Rewrite the comments this series adds to the AGENTS.md rules: one or two lines,
no prose hard-wrapped mid-sentence, no narrative or history, and no comment that
only restates the code. Net 146 fewer comment lines, no code change.
Correct the PLE image comment: mtmd does not consume the placeholder ids. An
image is decoded as an embeddings-only batch, so ubatch->token is null and the
per-position ids never exist here. gemma3n and gemma4 hit the same case and
stand in row 0 of per_layer_token_embd; qwen4exp stands in the configured image
token id instead.
Read image_token_id straight from self.hparams in the converter. base.py merges
text_config into the root of hparams, and the key sits at the root of
config.json, so the config.json re-read was redundant.
(cherry picked from commit 205840c12169057da3e8d2f65ec4ceec3e18b980)
* qwen4exp: support a quantized KV cache in the QSA attention path
(cherry picked from commit 4c30574f81dc1115d08078c47b6cf8c789c0a842)
* llama: give qwen4exp a large-graph node budget
(cherry picked from commit 37c8c194e6a30e4c46ac29bee3fb264f091596ef)
* qwen4exp: drop an unused variable that breaks -Werror builds
(cherry picked from commit 528d032b51fa3cf935ed3ef6e0fb1c7401df53b5)
* quantize: dequantize and quantize large tensors in row bands
f32_conv_buf held the whole dequantized tensor, which is 204.8 GB for
per_layer_token_embd alone and dies with std::bad_alloc long before the
work buffer is reached. Dequantize and quantize in bands of whole rows
instead, capping the f32 staging at 1 GiB per band.
Rows are independent and the imatrix is indexed by column, so band
boundaries cannot change any output byte. Bands nest inside the existing
per-expert loop so each expert slice keeps its own imatrix, and a band is
kept to at least one quantization chunk per worker thread so the existing
multithreading still has work. F32 sources still stage nothing and are
banded by pointer arithmetic into the tensor.
llama_tensor_dequantize_impl now takes a first element offset; the single
caller is updated.
(cherry picked from commit 658c22549613555dbce57a772be4de8509eba3ee)
* llama: segment the qwen4exp fused QKV for tensor split
qwen4exp was missing from the gated delta net branch of get_split_segments,
so its attn_qkv.weight, shaped {n_embd, 2*key_dim + value_dim}, fell through
to the generic fused QKV rule and tripped
GGML_ASSERT(tensor->ne[axis] == n_embd + 2*n_embd_gqa) while loading with
--split-mode tensor. --split-mode layer was unaffected.
qwen4exp broadcasts K to the V heads by tiling, k_conv is grown with a plain
ggml_repeat_4d over the head axis so that v head j pairs with k head
j % n_k_heads. That is the Qwen 3.5 pattern, not the repeat interleave that
Qwen 3 Next builds explicitly, so qwen4exp takes the else branch and its V is
segmented on the scale of K.
Reported by benklop.
(cherry picked from commit 353d753f595dc81634ae6130188b31f06018f5ae)
* llama: fix the qwen4exp PLE history seq_rm(-1) iterator invalidation and the fatal-warning build
ple_hist_rm recursed over ple_hist with a range-based for and the recursive call
erases the entry it is iterating when the whole sequence is removed (p0 <= 0,
p1 < 0), so the loop then increments an invalidated iterator. It is unreachable
today only because llama_memory_recurrent::seq_rm rejects seq_id < 0 before
llama_memory_hybrid_idx::seq_rm reaches the history, which is a guard in another
class. Advance past the entry before recursing.
Two smaller things in the same area:
- the n_toks sanity bound in ple_hist_state_read was the literal 64, which is
the value of LLAMA_MAX_PLE_HEADS, not of the quantity being checked. The
window is at most ple_ngram_size - 1 tokens, so the bound is
LLAMA_MAX_PLE_NGRAM - 1, eight times tighter.
- build_conv_state_at left mem_size unused, so -DLLAMA_FATAL_WARNINGS=ON does
not compile. Predates this series; drop the line.
(cherry picked from commit 6eba44a89d5f328eb4859b844e1d28fb564cbe3e)
* qwen4exp: include llama-impl.h explicitly for llama_mul_mat_hadamard
(cherry picked from commit b634fd4d250d181ef82bf78bd00c1ae3b96a7af6)
* convert: fix the qwen4exp lint and type-check failures
flake8 flagged an unused MmprojModel import, and ty flagged seven errors in
the PLE streaming path: eos_token_id can be absent, and _ple_map, _ple_path,
_ple_row_dim and _ple_rows_per_shard are all Optional at the declaration but
were dereferenced without narrowing.
The map is opened and the stride fixed before the first shard is written, and
_finish_ple_table only runs once every shard has landed, so the invariants
hold. Assert them so the checker can see it. A missing eos_token_id now raises
with the reason instead of a TypeError from int(None).
* llama: give the qwen4exp indexer cache its own tensor names
The indexer KV cache and the attention KV cache both named their tensors
cache_k_l%d, so the Meta backend matched the indexer cache against the
attention split pattern and aborted in handle_set_rows. Tag the names
instead, and mirror the indexer cache: it has one key head and its
projections are mirrored.
(cherry picked from commit a1cdc8181134659766763a17762545a1f0e5db7b)
* qwen4exp: double the Q split granularity for tensor parallelism
qwen4exp fuses the attention gate into attn_q.weight the same way qwen3next
and qwen 3.5 do, so a device boundary must fall on a whole q+gate pair or the
Q heads stop lining up with the K/V heads and attn_output rows.
(cherry picked from commit 6c9a592f0a425a459ab6efae3b897cf68460e244)
* qwen4exp: keep the indexer cache in step across server slots
The QSA indexer keeps a side cache addressed by the cells of the attention
cache, so cell j has to hold the same token in both: the top-k indices it
produces are applied to the attention KQ mask. init_batch already hands the
indexer the attention cache's slot layout rather than letting it look for its
own, but the restore path did not. state_read called llama_kv_cache::state_read
on the two caches in turn and each ran its own find_slot over its own occupancy.
That agrees only for as long as nothing has already pushed the two caches apart,
which is the property a restore is supposed to re-establish rather than one it
can lean on.
The failure path was the worse half, and it is reachable from the public API
with nothing more than a short buffer. Truncating a good blob at 35 offsets and
feeding it to llama_state_seq_set_data left the two caches disagreeing at 5 of
them, and every one of 23 truncations of a whole-context blob did. Four of those
five land inside the attention section, so the attention cache drops the
sequence and the indexer keeps it; only the cut that lands in the indexer
section gives the opposite direction. llama_kv_cache::state_read cleans up its
own cache and rethrows, so whichever way it falls, nothing is left to bring the
two back together. The server papers over this by clearing the slot when a
prompt cache load fails; a caller of llama_state_seq_set_data that does not is
left with an indexer addressing cells that no longer mean what it thinks.
llama_kv_cache::state_read_sinfo reports the cells a restore landed in, or takes
a copy of them, and state_read_meta uses a supplied layout in place of find_slot
once it has checked that those cells are free here too. The indexer now adopts
the attention cache's restored layout by construction instead of reproducing it
by coincidence, and a layout that does not fit fails the read rather than being
applied over cells that already drifted. The hybrid restore is wrapped so that
any failure drops the sequence, or for a whole-context restore the context, from
all three caches at once, which is a state they do agree on.
* kv-cache: clear the cache once when restoring a whole context
state_read walks the streams of the cache in turn, and for a whole-context restore
each stream went through state_read_meta, which starts by calling clear(). clear()
resets every stream at once, so each stream after the first threw away the streams
already restored, and the K/V buffers with them. A non-unified cache holds one
stream per sequence, so a context saved with N sequences in it came back with only
the sequence in the last stream that carried any cells - the highest sequence id.
A unified cache has one stream and never showed it.
The cache is now emptied once, before the loop, which is what a whole-context
restore means. A blob whose streams are all empty now empties the cache as well,
where before it left the old contents in place.
* kv-cache: check the mirrored slot layout on a whole-context restore too
state_read_meta only looked at the layout it was given on the single-sequence path.
A whole-context restore lays the cells out from 0 in both caches, so they agree as
long as they restore the same number of cells, but nothing checked that they did: an
indexer section belonging to some other context was read over cells the attention
cache had filled from a different one, which is the state the indexer must never be
left in.
* qwen4exp: give the PLE conv history its own mirrored recurrent row
n_embd_r() reserved n_conv + ple_conv_state() so that one cache_r_l row could
carry both the delta-net conv state and the PLE dilated conv history, but the
QWEN4EXP arm of get_split_segments only described n_conv. Under -sm tensor the
segment sum came up short by ple_conv_state() and llama_memory_recurrent
construction aborted in ggml_backend_meta_alloc_ctx_tensors_from_buft.
Widening the segment list is not the fix. The Meta backend propagates a view's
split descriptor from its parent unchanged, so a view of one sub-range of a
split axis is sized as the whole row on every device; declaring the PLE tail as
a second segment merely moves the abort to "shape mismatch for VIEW" at graph
allocation. The two histories also want opposite policies: the delta-net state
is split by head to match wqkv and ssm_conv1d, while per_layer_tok_embd,
ple_conv1d and ple_norm_conv are all mirrored, so every device computes the
whole dilated conv and needs the whole history. One tensor cannot be both, and
the split state has no per-segment mirroring.
Move the PLE history into its own cache_ple_r_l%d row, mark it MIRRORED, and
return n_embd_r() to n_conv. The row is allocated only on layers where is_ple
holds, so mirroring one 92160-element row per device replaces a 92160-element
tail on all 36 recurrent rows: the recurrent R footprint drops rather than
grows. build_conv_state_at now takes its width from the tensor it was handed
and keys its gather on that tensor, which also drops a cont of a strided view.
* no more ple_hist (use master version)
* llama: give the qwen4exp full memory context its indexer cache
graph_reserve() walks a full memory context, and qwen4exp builds its
sparse attention only when the context exposes an indexer cache. the
full-context constructor left ctx_idx null, so the reserved worst case
was the dense fallback: a smaller graph than the one decode executes.
ggml-alloc then had to grow the compute buffer on the first decode,
past the size reported at load.
with -np 4 -c 32768 -fa on -ctk q8_0 -ctv q8_0 on an IQ1_S qwen4exp,
the reserved CUDA0 buffer was 217.00 MiB against 275.71 MiB actually
used, and CUDA_Host 42.31 MiB against 191.14 MiB. reserving the sparse
graph makes both match exactly, in unified and non-unified cache mode.
Co-authored-by: Pascal <[email protected]>
Assisted-by: Claude
* qwen4exp: shrink the PLE hparams storage
llama_hparams is held by value inside llm_graph_params and every llm_graph_input_*,
and llm_graph_params is a stack local in graph_reserve and process_ubatch, so its
width is paid on every worker thread stack.
is_ple_impl spent 2048 bytes carrying 512 bits. It is the one per-layer flag that is
not moved through the loader's uint32 array templates, so a bitset costs nothing in
call sites and also removes the uninitialized read that non-qwen4exp archs had, since
nothing filled the array for them.
The PLE head offsets and vocab sizes are token-space indices; the gather that consumes
them already truncates to int32, so 64-bit storage was never reachable. The gguf arrays
stay uint64 for file compatibility and are narrowed on load.
sizeof(llama_hparams) 34440 -> 31944, sizeof(llm_graph_params) 34872 -> 32376.
* llama: opt-in random-access mmap advice for host-resident gather tables
qwen4exp keeps per_layer_token_embd on the host: 26.8 GiB at IQ4_NL, read
by ggml_get_rows as 16 gathers of ~90-170 bytes per token, spread across
16 head regions ~20M rows apart. Measured over 4.75M gathers, no two
consecutive gathers land on the same 4 KiB page, so the readahead the
loader asks for buys nothing here and the whole table ends up cached to
serve about 4% of itself.
llama_mmap applies POSIX_FADV_SEQUENTIAL, MAP_POPULATE and a whole-file
POSIX_MADV_WILLNEED unconditionally. Those are right for streaming the
file once into buffers and wrong for whatever stays mapped afterwards.
Under LLAMA_MMAP_RANDOM the eager pull-in is skipped and the mapping is
advised random once every tensor has been read, so the load itself keeps
its sequential readahead. That alone drops the table to 4.4% resident but
serializes one NVMe latency per gather.
The second half is what pays for it: the PLE input already computes every
row index for the ubatch before the graph runs, so the pages those rows
fall on are handed to the kernel in one batch and the reads overlap.
POSIX_MADV_WILLNEED on POSIX, PrefetchVirtualMemory on Windows, which
takes the discontiguous ranges in a single call.
Off by default and off for every other model: the batched prefetch keys
off "this mapping was advised random", which nothing sets unless the user
opts in.
-c 512 --chunks 60, cold, IQ1_S, mean of 3:
default 35.3 s 26.82 GiB resident (100%)
advice only 104.5 s 1.19 GiB resident (4.4%)
advice + prefetch 34.2 s 1.19 GiB resident (4.4%)
PPL 4.2346 +/- 0.07862 in all three. IQ1_S KLD is unchanged in every
field, including Mean KLD 0.396070 +/- 0.001931 and Same top p 77.325%.
* llama: narrow the random-access mmap advice to the gather table
The advice was applied per mapping: every mapping the model kept got
POSIX_MADV_RANDOM plus a whole-file POSIX_FADV_RANDOM, and the eager
pull-in was skipped for every file. On qwen4exp that also hit
token_embd.weight, which sits 0.33 GiB past the PLE table in the same
shard and is read densely, not by sparse gathers. Measured over
-c 512 --chunks 60 on IQ1_S it fell to 8.45% resident, against 100% with
the feature off.
A model now nominates its gather tables (qwen4exp: per_layer_tok_embd)
and only those byte ranges are advised. The range is rounded out to
whole pages, which on this model takes in 832 bytes before and 192
after. token_embd goes back to 86.55% resident and the PLE table still
drops to 4.44%; smaps shows one VM_RAND_READ VMA of exactly the table
instead of one over all 27.16 GiB that stays mapped.
posix_fadvise is dropped from the narrowed path. POSIX_FADV_RANDOM
ignores its offset and length and marks the whole open file, and the
FMODE_RANDOM it sets is only read by page_cache_sync_ra() on the read()
path, which a fault on a MADV_RANDOM vma never reaches. POSIX_FADV_
DONTNEED does take a range, so the drop mode keeps it.
The eager pull-in is now skipped only for the files holding a nominated
table, and re-issued as WILLNEED over the rest of such a file, so other
shards load exactly as before.
prefetch_rows() keys off the tensor being nominated rather than off a
mapping-level flag, so the batched readahead lands only where the advice
did.
-c 512 --chunks 60, cold, IQ1_S, mean of 3, total wall:
default 32.50 s
whole mapping 30.05 s
narrowed 30.35 s
PPL 4.2061 in all three. IQ1_S KLD is bit-identical with the feature on
and off, including Mean KLD 0.396070 +/- 0.001931 and Same top p
77.325%. tg128 73.65 +/- 0.33 narrowed against 73.49 +/- 0.34 whole.
Assisted-by: Claude
* llama: fold the random-access prefetch into its own feature flag
LLAMA_MMAP_RANDOM_PREFETCH existed to measure the two halves of the feature
apart, and the measurement is done: on a cold cache over the same wikitext
run, MADV_RANDOM without the batched readahead takes 94.4 s against 36.7 s
for an untouched mapping, while the pair together take 34.1 s. Suppressing
the kernel's readahead only pays if we replace it, so the split let a user
select a 2.6x regression through a documented switch.
Keep the accessor, since the call site reads better than a mode comparison,
but derive it from the mode alone.
* FACP (Fewer Acronym Classes Please)
* qwen4exp: bias the QSA selection per block, not per cell
The QSA bias is a graph input, so it is pinned on the host and uploaded every
decode, and at -c 32768 -np 4 its twelve copies were 768 of the 815 MiB of
reserved host compute buffer.
Only one half of it needs a cell: whether the cell sits in the always-visible
tail, and whether its block was pooled. Both are properties of the block. The
other half - empty, other sequence, or in the future - is the plain visible/not
test the attention mask already carries over the same cells, so add that mask
instead of repeating it. The bias then holds one value per block.
A block sits wholly inside or wholly outside the tail because the tail starts on
a block boundary, so one value per block is exact. Cells no block covers keep
their -inf from the mask.
The mask is F16 and the bias F32, and a mixed ggml_add reinterprets the F16
buffer as float rather than converting it, so the cast is required.
reserved host compute buffer at -c 32768 -np 4:
--kv-unified 814.86 -> 238.86 MiB, CUDA0 721.07 -> 421.07 MiB
--no-kv-unified 214.86 -> 70.86 MiB, CUDA0 317.07 -> 265.07 MiB
Selection is unchanged: over 8192 tokens, four times the budget, every QSA
layer returns identical top-k indices and the logprobs are bitwise equal.
Two things a reviewer should know. A cell whose position divides past the last
block is guarded by an assert rather than handled, because no run reached it.
And the mask's same-position M-RoPE rule cannot fire for text and was never
exercised for images, so the 2D case is unverified.
* clean up code comments
* clean up new comments
* revert LLAMA_MMAP_RANDOM
* nits
* replace some changes with #27795
* improve the m-rope image for get_prev_tokens
* LazyChunkedTensor
* fix lint
* add some validations
* reduce input nodes
* trim output tokens
* nits
* some more sanity checks
* fix llm_graph_input_ple reuse
* exclude from webgpu test
---------
Co-authored-by: danielhanchen <[email protected]>
Co-authored-by: danielhanchen <[email protected]>
Co-authored-by: Xuan Son Nguyen <[email protected]>
Co-authored-by: Pascal <[email protected]>
Co-authored-by: Sigbjørn Skjæret <[email protected]>
|
||
 
|
b10f9ca58c |
spec : add DFlash2 support (local convolution + candidate selector) (#27342) (#27816)
* spec : add DFlash2 support (local convolution + candidate selector) (#27342) * support DFlash2 * Add p_min in DFlash2 Assisted-by: Claude Opus 5 * Revert unnecessary changes Assisted-by: Claude Opus 5 * Revert draft sampling in rejection sampling Assisted-by: Claude Opus 5 * Refactor code structure Assisted-by: Claude Opus 5 * Delete embedding scaling Assisted-by: Claude Opus 5 * Gate output transforms on DFlash2 Assisted-by: Claude Opus 5 * Optimize Dflash 2 cost Assisted-by: Claude Opus 5 * Avoid using atoi Assisted-by: Claude Opus 5 * Modify comments Assisted-by: Claude Opus 5 * Move llama_model_dflash_selector_top_k to llama-ext.h Assisted-by: Claude Opus 5 * Formatting Assisted-by: Claude Opus 5 * Apply patch to fix the mrope bug Assisted-by: Claude Opus 5 * fix ci Assisted-by: Claude Opus 5 * Fix graph number calculation Assisted-by: Claude Opus 5 * rename hid and unary Assisted-by: Claude Opus 5 --------- Co-authored-by: Jian Chen <[email protected]> Co-authored-by: Xuan-Son Nguyen <[email protected]> * revert top-k.cu changes --------- Co-authored-by: Zihan Zhang <[email protected]> Co-authored-by: Jian Chen <[email protected]> |
||
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Xuan-Son Nguyen
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732707dff2 | quantize: cap working memory size to avoid loading big tensors onto RAM (#27795) | ||
|
Xuan-Son Nguyen
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fac889fb38 |
llama: model_loader: add TENSOR_READ_LAZY (#27794)
* llama: model_loader: add TENSOR_GET_ROW_LAZY * add --tensor-read-lazy * rename to TENSOR_READ_LAZY * gen docs * address comments |
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fairydreamingandStanisław Szymczyk
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deae5ee133 |
model : simplify MiniMax-01 graph (#27790)
Co-authored-by: Stanisław Szymczyk <[email protected]> |
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  zqlandadmin
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d7a2074112 |
models : support nanbeige4.2-3B (#27730)
Co-authored-by: admin <[email protected]> |
||
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Xuan-Son Nguyen
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925e117994 |
llama: add token ID tracking to KV cell (#27762)
* kv: track token id * rm get_prev_tokens, move it to the main pr * nits * add get_prev_tokens |
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 Saad Ali
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eb25b7263e |
grammar : parse \- in char classes as literal hyphen (#27591)
* grammar : accept "\-" escape in character classes gbnf_escape_char_class() escapes '-' as "\-" but parse_char() rejected that escape, so generated tool-call grammars failed to parse. Assisted-by: Claude Code <[email protected]> * tests : add parser test for "\-" in char classes Assisted-by: Claude Code <[email protected]> * tests : add integration test for "\-" in char classes Assisted-by: Claude Code <[email protected]> * tests : drop integration and parser tests |
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 Prabhsimran Singh
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a130532ae1 |
mamba2 : Flatten in/out projections to dispatch GEMM instead of GEMV (#27513)
* mamba2 : flatten mamba2 in/out projections to dispatch gemm instead of gemv * mamba2 : remove redundant output reshape |
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Aman Gupta
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bf0a29cc16 |
Deepseek 4: -sm tensor (#26490)
* DSV4: sm tensor * set coarser granularity for head splits * fix dspark * add model saving for dsv4 + allow dflash to return on specific device * add comment about dsv4 seq_rm * simplify * add shared expert delayed allreduce * remove special test for dsv4 |
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 jacekpoplawski
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c060ca974c | model : support MTP in GLM-4.5-Air (#26534) | ||
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Aman GuptaandGeorgi Gerganov
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b0539c43ed |
DeepseekV4: fix rollback with multi-seq (#26756)
* DeepseekV4: fix rollback with multi-seq * fix model loading * make pending rollback single use * only clear cache for seq_id for full load * add assert for compress ratio * make graph topology static * pass true instead of flags in clear_compressed * cont : clean-up + TODOs --------- Co-authored-by: Georgi Gerganov <[email protected]> |
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 Gaurav Garg
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d3371929bb |
[Tensor parallel] Fix meta tensor split state propagation (#27574)
* ggml : fix meta tensor split state propagation * Add test-llama-archs to CI |
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 Tiwei Bie
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2115b73d8e | model : support DSpark for bailingmoe3 (#27508) | ||
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Xuan-Son Nguyen
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5a32f7b66e |
model: add dots3-note (#27060)
* text: conversion * init impl * address review comments * fix rope * move to a new llama_kv_cache_dsa_iswa |
||
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Xuan-Son Nguyen
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873e5d8e39 |
model: use ggml_rope_set_offset() (#27382)
* model: use ggml_rope_set_offset() * partially apply to deepseek2 |
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 Chris Danis
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b2e5e9b28b |
TP: enable tensor split for LFM2/LFM2MOE (#26993)
Assisted-by: deepseek-v4-flash |
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Tarek Dakhran
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07822bddf8 | model : support DSpark for LFM2 models (#27383) | ||
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Georgi Gerganov
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929d47a391 |
graph : create V as a view of K in the k_iswa build_attn (#27392)
build_attn with the llm_graph_input_attn_k_iswa input was using the cached K tensor itself as V. Create V as a view of K (the first v_cur->ne[0] elements of each row), like the other K-only build_attn overloads. The deepseek4 MTP call site now passes the kv tensor as v_cur. Assisted-by: pi:llama.cpp/Qwen3.8-27B |
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 Gabe GoodhartandSigbjørn Skjæret
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7221e24f57 |
model : GraniteSWAForCausalLM / GraniteMoeSWAForCausalLM (#25505)
* feat(convert): Add conversion for GraniteSWAForCausalLM Branch: GraniteSWAForCausalLM AI-usage: full (Bob, OpenCode + Qwen3.6-35b) Signed-off-by: Gabe Goodhart <[email protected]> * feat(llama): Add granite_swa support Branch: GraniteSWAForCausalLM AI-usage: full (Bob, OpenCode + Qwen3.6-35b) Signed-off-by: Gabe Goodhart <[email protected]> * feat(conversion): Add conversion infra for rope_pattern array NOTE: There is other work also targeting this, so this may be removed depending on merge order. Branch: GraniteSWAForCausalLM AI-usage: full (Bob) Signed-off-by: Gabe Goodhart <[email protected]> * fix(conversion): Fix SWA pattern logic and support for non-rope layers Branch: GraniteSWAForCausalLM AI-usage: full (Bob) Signed-off-by: Gabe Goodhart <[email protected]> * feat(conversion): Add support for GraniteMoeSWA Branch: GraniteSWAForCausalLM AI-usage: full (Bob) Signed-off-by: Gabe Goodhart <[email protected]> * feat: Add llama_hparams::has_rope and arch constants NOTE: This shadows the work done for Granite Speech https://github.com/ggml-org/llama.cpp/pull/25107 Branch: GraniteSWAForCausalLM AI-usage: full (Bob) Signed-off-by: Gabe Goodhart <[email protected]> * feat: Add support for per-layer rope determination Branch: GraniteSWAForCausalLM AI-usage: full (Bob) Signed-off-by: Gabe Goodhart <[email protected]> * style: Fix failing flake8 for extra newlines Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * test: Write out SLIDING_WINDOW_PATTERN in llama-model-saver Branch: GraniteSWAForCausalLM AI-usage: full (OpenCode + Qwen3.6-35b) Signed-off-by: Gabe Goodhart <[email protected]> * fix(convert): Fix missing registration for GraniteMoeSWAForCausalLM Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * fix: Load MoE params as optional Branch: GraniteSWAForCausalLM AI-usage: draft (OpenCode + Qwen3.6-35b) Signed-off-by: Gabe Goodhart <[email protected]> * feat: Handle MoE params in conversion branch: GraniteSWAForCausalLM AI-usage: full (OpenCode + Qwen3.6-35b) Signed-off-by: Gabe Goodhart <[email protected]> * style: Remove unnecessary newline AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * fix: Remove unnecessary tensor additions to GRANITE architecture Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * fix: Correctly handle naming for ffn gate inp Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * fix: Always default hparams.rope_pattern to 1s This isn't strictly necessary, but it will allow other models to rely on hparams.has_rope(il) without needting to prepopulate. Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * feat: Move to has_rope for all granite model architectures Now that we have a proper hparam for this, it's better to use it and not require a hacky fallback in the hparam method itself. Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * feat: No hacky rope_finetuned fallback in has_rope Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * fix: Fully remove rope hparam filling in granitemoe There are no granitemoe models that use NoPE (it's not actually used in the layer building below), so this was just dead code. Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * fix: Save out rope_pattern in model-saver Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * fix: Set hparams.rope_finetuned for round trip Since the value is _read_ from rope_finetuned, we need to persist it when the model is saved with the saver. Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * fix: Code review cleanup Signed-off-by: Gabe Goodhart <[email protected]> Co-authored-by: Sigbjørn Skjæret <[email protected]> Co-authored-by: Sigbjørn Skjæret <[email protected]> * refactor: Keep gate/up fused for MoE path Branch: GraniteSWAForCausalLM AI-usage: full (Claude + Sonnet 5) Signed-off-by: Gabe Goodhart <[email protected]> * fix: Skip GRANITE_SWA in model saver https://github.com/ggml-org/llama.cpp/pull/25505#discussion_r3773175651 Keeping is_swa_impl in the saver can break other models. Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * add sliding window pattern for model in test * style: Fix indentation Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * fix: Fix \r\n Thanks Claude! Branch: GraniteSWAForCausalLM AI-usage: none Signed-off-by: Gabe Goodhart <[email protected]> * feat: Keep shared expert fused Branch: GraniteSWAForCausalLM AI-usage: full (Claude + Sonnet 5) Signed-off-by: Gabe Goodhart <[email protected]> * style: More indentation fixes Signed-off-by: Gabe Goodhart <[email protected]> Co-authored-by: Sigbjørn Skjæret <[email protected]> --------- Signed-off-by: Gabe Goodhart <[email protected]> Co-authored-by: Sigbjørn Skjæret <[email protected]> |
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Sigbjørn Skjæret
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2e92ecd024 | models : remove duplicate metadata load (#27378) | ||
 Ed Addario
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0596704284 |
quant : Optimise memory usage by evicting weights after processing each layer (#22877)
* Evict weights from memory after processing each layer * Revert changes * Move unmap to libllama * Unmap weights offloaded to backend * Change member's constness * Remove unmap weights offloaded to backend |