* 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
* 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)
* 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
* tests : refactor test-save-load-state to accept token input
- Default prompt is now empty; when not provided, generate n_batch
random tokens (useful for models without a tokenizer)
- Tokenization happens once upfront; pass token vector to test functions
- generate_tokens prints token IDs instead of decoded pieces
- Use llama_model_get_vocab / llama_vocab_n_tokens API
- Upgrade log level from LOG_TRC to LOG_INF for visibility
Assisted-by: llama.cpp:local pi
* cont : use llama_tokens alias
* common : fix state save in common_prompt_batch_decode
This commit addresses a bug in common_prompt_batch_decode that affects
the session state store/restore in completion.cpp and
save-load-state.cpp.
The motivation for this is that currently the code is saving n-1 tokens
in both the session_tokens and in the KV cache. Then when loading the
session tokens, and if the prompt matches, it would replay the last
saved token (n-1) into the next position, effectively replaying the
same token in the wrong position.
The fix is to store all n tokens in session_tokens, while the memory
state only reflects n-1 processed tokens as the saving happens before
the last token is decoded in common_prompt_batch_decode.
I ran both completion.cpp and save-load-state.cpp with a transformer, a
recurrent, and a hybrid model.
Resolves: https://github.com/ggml-org/llama.cpp/issues/23400
Co-authored-by: fairydreaming <[email protected]>
* tests : move save-load-state from examples to tests
- Move examples/save-load-state/ to tests/test-save-load-state.cpp
- Remove subdirectory reference from examples/CMakeLists.txt
- Add test to tests/CMakeLists.txt as a model test
- Remove CODEOWNERS entry for removed example directory
Assisted-by: llama.cpp:local pi
* cont : update ci
Daniel Han
itsnotoger
Georgi Gerganov
helanfxz
Aman Gupta
Daniel Beveniusandfairydreaming