* graph: skip the LM head for pooled embeddings
A context that extracts pooled embeddings outputs one vector per
sequence, but every model still expanded its LM head, so the graph
computed n_vocab logits per token and the context kept a host buffer
for them.
The models now only declare t_logits, and set_outputs() expands it
unless the context extracts embeddings with a pooling type other than
NONE. Since the toggle changes the graph shape for a pooled context,
set_embeddings() schedules a re-reserve in that case.
* graph: drop the redundant t_embd expand
Address review from @CISC: with embeddings on, build_pooling() already
expands its output for every pooling type, and a decoder graph without
embeddings does not read t_embd, so set_outputs() only expands
t_logits.
* graph: add build_logits() for the LM head output
Address review from @ggerganov: the LM head is expanded by a dedicated
build_logits(), called from llama_model::build_graph() like
build_pooling(), and set_outputs() only marks the outputs again.
* graph: call build_logits() before build_sampling()
Address review from @ggerganov: build_sampling() reads t_logits, so the
LM head is built first. A pooled context then skips the sampling layers
too, since it has no logits to sample from.
* nit
* llama: read the SWA pattern as a period or a per-layer array
Add llama_model_base::load_swa_pattern(), which reads
sliding_window_pattern either as one flag per layer or as a period
expanded by set_swa_pattern(), and use it in every loader that reads
the key as a period.
These loaders silently ignored an array and applied their default
period, although the converters of olmo2, gemma3n and exaone4 write
arrays. The published GGUFs match the defaults, so their outputs do
not change. The loaders that already accepted both forms lose their
duplicated scalar-then-array block, and use their declared default
period when the key is absent.
* model-saver: write the SWA pattern and the MLA SWA geometry
Write sliding_window_pattern as one flag per layer, nextn layers
included, for every model using SWA. The array is never collapsed to
a scalar, since the loaders read a scalar as a period.
Also write the MLA key/value lengths and KV LoRA rank of the SWA
layers, required by dots3note.
This enables the saver for plamo3, gemma3, cohere2, cohere2moe,
olmo2, exaone-moe, afmoe, mimo2, spark2_5, muse-glimmer, mellum,
laguna, granite_swa, dots3note and maple, all passing the bit-exact
roundtrip of test-llama-archs.
Pascal
Georgi GerganovandSigbjørn Skjæret
ynankani
Xuan-Son Nguyen