Use std::partial_sort to order only the requested top-n tokens instead
of the full vocabulary
logprobs sort: vocab=128000 n_top=0 iters=100
full sort: 8555.6 us/op
partial sort: 704.3 us/op
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
Absorb get_slot_by_id logic into get_available_slot so slot selection
is handled by a single function call. When a specific slot id is
requested, the LCP similarity check still runs to enable proper
prompt cache updates.
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* spec: add spec metrics mean acceptance length and acceptance per pos
* fix as suggestion
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* fix as suggestion
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* fix as suggestion
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* fix as suggestions
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* server: log prompts to directory
Add `--log-prompts-dir` to write each prompt to a separate text file in
the specified directory.
* Apply suggestion from @ngxson
---------
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
* Always export idle slots to RAM
Without this, a slot's VRAM cache may not be written to RAM. If this
slot happens to be busy then later on, this triggers needless
preprocessing in another slot.
* cont : clean-up
---------
Co-authored-by: Christoph Weiss <weiss@wsoptics.de>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* server: avoid unnecessary checkpoint restore when new tokens are present
The pos_min_thold calculation unconditionally subtracts 1 to ensure at
least one token is evaluated for logits when no new tokens exist.
However, when the request contains new tokens beyond the cached prefix,
this -1 is overly conservative and may trigger an unnecessary checkpoint
restore.
Conditionally apply the -1 only when n_past >= task.n_tokens() (no new
tokens), avoiding redundant KV state restoration when there is actual
work to do.
* cont : add ref
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* server: real-time reasoning interruption via control endpoint
Builds on the manual reasoning budget trigger from #23949. Adds a
CONTROL task that mirrors the CANCEL path on the live slot and calls
common_sampler_reasoning_budget_force to end thinking mid-generation.
POST /v1/chat/completions/control with { id_slot, action }, opt-in
reasoning_control arms the budget sampler on demand. Router and single
model. Minimal WebUI button as a skeleton for further UI work.
* ui: track reasoning phase via explicit streaming state
Add isReasoning to the chat store, mirroring the isLoading pattern:
per conversation map, private setter, public accessor and reactive
export. Set from the stream callbacks, true on reasoning chunks, false
on the first content chunk, reset on stream end and resynced on
conversation switch. The skip button now keys off isReasoning so it
shows only during the thinking phase, not the whole generation.
* ui: extract control endpoint and action into constants
Move the chat completion routes, the slots route and the reasoning
control action out of chat.service into api-endpoints and a dedicated
control-actions module. No behavior change, drops the magic strings so
the control protocol has a single source of truth.
* server: target reasoning control by completion id
Address @ngxson review on the control endpoint.
Switch from id_slot to the chat completion id to avoid a TOCTOU: the
slot can be reassigned between the lookup and the control request, so
matching the live completion (oaicompat_cmpl_id) is safe and a finished
one simply matches nothing. Rename the action to reasoning_end, guard
it on the reasoning_control flag of the target slot, and reduce the
response to {success} with an optional message.
* ui: target reasoning control by completion id
Keep the streamed completion id on the message and post it back to the
control endpoint instead of probing /slots. Drops the slot discovery
and the TOCTOU that came with it. Action renamed to reasoning_end,
response read as {success}.
* server: address review from @ngxson
Move the control fields into task_params and drop the redundant
comments on the control path.
* server: document the reasoning control endpoint
* Update tools/ui/src/lib/types/database.d.ts
Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
* ui: rename cmplId to completionId
Per @allozaur review, clearer name for the streamed completion id.
* ui: wire completion id capture through the agentic flow
The webui streams through the agentic flow, which relayed onModel but
not onCompletionId, so the completion id never reached the message and
the control request was never sent. Relay it through the flow and its
callbacks type, declare id on the chunk type, and log an explicit error
when the button fires without a usable id.
* ui: target reasoning control model from the message
The model is a property of the completion, so read it from the streaming
message like the id, not from the model dropdown which is unrelated UI
state. Makes the request self-consistent by construction instead of just
unlikely to drift.
---------
Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
* speculative : add common_speculative_n_max helper function
Extract the speculative max-draft-size logic from server_n_outputs_max
into a reusable common_speculative_n_max() function in common/speculative.
Assisted-by: llama.cpp:local pi
* cont : draft context always has n_parallel outputs
* llama : log n_outputs_max
* speculative : remove draft-simple auto-enable
* ci : enable server tests on PRs
* llama: save more VRAM by reserving n_outputs == n_seqs when possible
* add n_outputs_per_seq
* move n_outputs_max to server-context
* change ubatch to batch everywhere
* common : add common_chat_split_by_role
* cont : fix spans to reach end of message
* server: fix checkpoints creation
- extract message_spans from chat templates
- find the prompt token position before the latest user message
- split prompt batching at that position
- create a context checkpoint before the latest user input
- avoid periodic mid-prompt checkpoints when that position is known
- handle multimodal prompts when mapping text/template positions to server prompt tokens
- add --checkpoint-min-step to control minimum spacing between checkpoints
* cont : clean-up
* Support autoparser detection for message barriers
* server: fix message span delimiter and update docs
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
Add n_prompt_tokens, n_prompt_tokens_processed, and n_prompt_tokens_cache
to the /slots JSON response. These fields are already tracked internally
but were not exposed, making it impossible for clients to monitor prompt
evaluation progress during processing.
The destroy() function in server_context_impl only cleaned up the main
model and context (via llama_init.reset()) but did not free the speculative
decoder (spec), draft context (ctx_dft), or draft model (model_dft).
For MTP (Multi-Token Prediction) models, ctx_dft holds GPU-allocated
resources (KV cache, compute buffers) that are not freed when entering
the sleeping state. On each sleep/resume cycle, new resources are
allocated without the old ones being freed, leading to a VRAM leak
that eventually crashes the server with out-of-memory errors.
Fix by explicitly resetting spec, ctx_dft, and model_dft in destroy()
before resetting llama_init, ensuring proper cleanup order to avoid
use-after-free.
ref: https://github.com/ggml-org/llama.cpp/issues/23395
Assisted-by: llama.cpp:local pi
* mtmd: fit_params now take into account mmproj
* rename alloc_compute_meta to reserve_compute_meta
* rm unused functions
* add ggml_backend_dev_t support
* add debug log
Add graphs reused counter to the per-slot timing output, printed via
llama_perf_context().
Assisted-by: llama.cpp:local pi
Co-authored-by: ggerganov <ggerganov@users.noreply.github.com>
* refactor: Scope console logs to `DEV` + `VITE_DEBUG` env vars
* refactor: skip MCP proxy probe when no server requires it
* refactor: suppress expected disconnect errors during MCP client shutdown
* refactor: Deduplicate requests
* refactor: deduplicate model fetching across ROUTER and MODEL modes
* refactor: Clean up models logic
* chore: Add `.env.example` file
* refactor: replace client-side CORS proxy probe with server status flag
* refactor: Post-review fixes
* test: add vitest client setup with API fetch mocks
Xuan Son Nguyen