Addresses the #192 review: server usage documented in docs/grammar-draft.md
(incl. back-compat statement for the additive SUBMIT field and the #100-class
near-tie caveat); gateway pre-checks grammar payloads at 1 MiB (matching the
engine's gbytes bound); negative tests for non-dict response_format, empty and
oversized grammars, plus an explicit test that malformed GBNF passes the
gateway by design (engine fail-soft, draft-source semantics). Measured compile
overhead: 7.8 us/request typical schema, 17.9 us at the 32-level nesting cap.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The OpenAI gateway previously 400'd every response_format; the mux engine path
ran with speculation disabled entirely. Now:
- openai_server.py: response_format {"type":"json_object"} (generic ws-tolerant
JSON grammar), {"type":"json_schema"} (schema forwarded as-is, compiled
engine-side by schema_gbnf.h), and a raw-GBNF {"type":"gbnf"} extension.
Draft-source semantics throughout: a schema the engine cannot compile costs
the speedup, never the request and never the output.
- SUBMIT protocol: optional 7th field gbytes; grammar text appended to the
payload after the prompt. 6-field headers unchanged (back-compatible).
- Engine: per-slot GrDraft (grammar_setup_text/grammar_teardown split out of
the env-driven setup); walkers fed on every emitted token. Grammar-forced
drafting in run_serve_mux for greedy requests: a drafting slot leaves the
shared batch for one forward and runs the proven single-sequence verify path
(kv_bind + step_all) — the same primitives prefill already uses per
submission — then rejoins; rejected drafts' KV entries are overwritten by the
next forward exactly like the existing prefix-truncation path. Sampling
requests never draft (verification under sampling needs rejection resampling;
out of scope).
Tests: 7-field SUBMIT parse cases; response_format->grammar plumbing incl.
fail cases; test doubles updated; generic JSON grammar parse+walk validated
against grammar.h. make test-c green; python suite green except the known
environmental memory_available failure (#150 fixes it).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The generation() method was re-extracting tools from the raw request body,
bypassing the filtering that render_chat() applies when tool_choice is a
function object (forced function call). This caused parse_tool_calls() to
receive the unfiltered tool list, producing incorrect or missing tool_calls
in the response.
- Add tools and tool_choice parameters to generation() signature
- Pass them from chat_completion() after render_chat() processing
- Add early structural validation of tools in generation_options()
for clear HTTP 400 errors on malformed input
The gateway's tool-calling path had unit coverage (parse_tool_calls,
render_chat) but nothing exercised the real subprocess wire protocol or the
HTTP surface a coding client actually hits. #401 reports plain-text replies
where tool_calls were expected; every documented path checks out, so pin the
whole path down with a mock engine speaking SUBMIT/DATA/DONE and assert:
- non-stream: tool_calls populated, finish_reason tool_calls, no raw markers
- stream: markers suppressed across 20-way chunk splits, tool_calls delta
- tool-result round trip: <|observation|><tool_response> rendering, text reply
- no tools: plain text untouched
Also emit a stderr diagnosis when tools are declared and tool-call markers
are present in the reply but the strict parse matches nothing (typically
quantization-mangled output) pointing at COLI_TOOL_SALVAGE=1 -- the likely
field condition behind #401.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Two operator-surprise fixes:
openai_server.py (#382, LordMZTE): a request without max_tokens defaulted to
min(256, limit) — so `coli serve --ngen 32768` still cut every answer at 256
tokens for clients that omit the field. The operator's configured budget IS the
default now; generation still ends at EOS, so it's a cap, not a target.
convert_fp8_to_int4.py (#383, bokiko): the resolved conversion plan (mode,
source, ebits/io/x bits, grouped-vs-per-row) prints as a [PLAN] line before any
work. The two traps in #383 — --mtp defaulting ebits to 8 with the grouped
branch silently gated off, and --mtp appearing to ignore --indir — are already
defused by the #355 fix (the --mtp pass emits ONLY head tensors on the local
path), but a 3.5-hour job must show its plan at second 1, not in a post-hoc
size sanity check. bokiko's exact invocation now prints:
[PLAN] mode: MTP head only | source: local ./fp8 | experts 8-bit, ... |
PER-ROW (grouped branch needs bits<=4; ebits=8 disables it)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The engine already tracks where each turn's wall time goes (expert-disk
service, I/O wait, expert matmul, attention, lm_head) — it just only spoke
at exit or under PROF=1. Stream it instead:
- glm.c: mux serve emits a per-turn "PROF" protocol line next to TIERS/HITS
(window deltas per request, same convention as the STAT hit%); the phase
window base is now always captured (a few loads per request).
- openai_server.py: parses PROF into a 120-turn rolling window and serves it
at /profile (read-only, same trust level as /health).
- web: new Profiling tab — stat tiles (tok/s, wall, tokens/forward, disk
service), wall-time composition bars for the last turn and the window,
per-turn throughput and stacked phase columns with hover readouts, and a
table of recent turns. Disk service is shown apart from the stack: it
overlaps with compute, so only the I/O wait the compute thread felt counts
inside wall time. Phase colours are a CVD-validated set with gaps + legend
+ table so identity never rides on colour alone.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WhTmF8yvEBgSkUKSVfZF7P
opencode / the ai-sdk OpenAI-compatible client sends a large default max_tokens
(> the server's --max-tokens cap, 1024 by default), and generation_options
returned 400 "must be an integer between 1 and 1024" — even for a trivial
"hello". OpenAI-compatible servers clamp to their own ceiling rather than
reject. Now max_tokens > limit is clamped to limit; only non-int / non-positive
values are a hard error. Test updated to assert the clamp + keep the <1 reject.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Web dashboard: live expert-tier panel (VRAM/RAM/disk), static hosting, coli web command
- Engine: TIERS protocol line (counts + GB for VRAM/RAM/disk experts)
emitted at READY and refreshed after every served turn, computed from
the live pin/LRU/CUDA state.
- Server: dispatcher tracks the latest snapshot, /health exposes it as
"tiers"; the built web UI (web/dist) is served from the same port
(read-only, traversal-safe, SPA fallback) so the dashboard needs no
second process.
- Web: tier bar + legend in the Runtime panel showing where the 19k
experts live right now, with GB per tier; updates with the existing
health polling.
- CLI: `coli web` = serve + auto-open the browser once /health answers
(the 744B load takes minutes; a poller waits for it), --no-browser to
disable, friendly hint when web/dist isn't built.
* coli web: HERE is a path string, not a Path
* web: same-origin default + auto-connect when served by the engine
The page hosted by coli web talked to http://127.0.0.1:8000/v1 by
default while being loaded from http://localhost:8000 - a different
origin, so the very first probe died on CORS ('Failed to fetch').
When the page is engine-served the default (and a stored stale factory
default) now resolve to window.location.origin + /v1, and the console
auto-probes on load; the Vite dev server keeps the classic default.
The server's own port is also whitelisted in DEFAULT_CORS_ORIGINS for
the localhost/127.0.0.1 cross-name case.
* web: fix auto-connect effect returning Promise (React 19 StrictMode crash)
The async connect() call inside useEffect returned a Promise that React
19 StrictMode stored as the effect cleanup. On re-render, React called
destroy_() on that Promise — 'not a function'. Block body with explicit
return undefined prevents any non-function from leaking into the cleanup
slot.
* web: rich streaming metrics — live token counter, tok/s gauge, TTFT, session totals
During generation: a flashing Zap badge counts tokens in real time.
After: Gauge shows tok/s, Timer shows TTFT (time to first token),
Layers shows prompt→completion token counts, Clock shows queue wait.
Session totals (cumulative prompt + completion) appear in the Runtime
panel. All with lucide icons and tabular-nums for stable layout.
Tier bar gains a smoother cubic-bezier transition and slightly taller
height for visual weight.
* web: hardware environment panel — CPU model, GPU count/VRAM, RAM total/free, cores
Engine emits HWINFO protocol line at startup (CPU name from /proc/cpuinfo,
core count, RAM total/available from /proc/meminfo, GPU count + VRAM from
CUDA). Server parses and caches it, /health exposes as 'hwinfo'. Dashboard
renders it with icons at the top of the Runtime section so the user sees
immediately what the engine is running on.
* web: downgrade to React 18 — React 19 effect cleanup bug crashes the dashboard
React 19 treats the return value of every useEffect callback as a
cleanup function. In practice, any async interaction (streamChat's
rapid onDelta re-renders, the health poller, auto-connect) caused
React 19 to store a Promise as inst.destroy and crash with
'destroy_ is not a function' on the next commit cycle. The bug
reproduced in incognito, without StrictMode, and with every effect
converted to explicit block bodies returning undefined — it is a
React 19 regression in the passive-effect unmount path.
React 18 does not exhibit this behavior. Pin to React 18 until
React 19 is fixed or the codebase migrates to a pattern React 19
handles correctly. All 17 vitest pass, ErrorBoundary retained.
* web: Brain page — the expert cortex, live
A 76x256 canvas grid, one cell per expert (19,456 total): colour =
tier (green VRAM / blue RAM / grey disk), brightness = routing heat
(log-bucketed .coli_usage counts), and a white pulse that flashes on
every expert routed in the current turn and decays — you watch the
model think.
- Engine: EMAP protocol line (1 byte/expert: 2bit tier + 6bit heat,
hex) at READY and after each turn; HITS bitmap of this turn's routed
experts (set where eusage increments, cleared on emit).
- Server: parses both, GET /experts serves {rows, cols, map, hits, seq}.
- Web: Brain tab next to Chat; canvas renderer with rAF pulse decay;
hover tooltip shows layer/expert/tier/heat plus an honest depth-role
heuristic (early = surface features ... late = output shaping, MTP
row labelled as the speculative draft head).
* web: Brain page responsive — cells sized from container via ResizeObserver
Cell size derives from the wrapper's actual client box instead of fixed
1400x900, re-rendering on resize; mobile media query tightens padding,
legend and tooltip. The cortex now fills whatever screen it gets.
P6: discover_gpus() used line.split(',', 3) to parse nvidia-smi CSV output.
If the GPU name contains a comma (e.g. 'Tesla, Inc. V100'), split produces
more than 4 fields, the int() parse fails on the wrong field, and the GPU
is silently dropped — doctor reports 'no NVIDIA device detected' with no
clue why. Fixed by using the csv module (handles quoted fields correctly).
P7: require_auth() used plain string != comparison for the API key
('Authorization' header vs expected 'Bearer <key>'). This enables a
timing side-channel that could leak the key byte-by-byte. Low impact when
bound to localhost (default), but serve() only warns when host is
non-localhost without a key, and users do expose on 0.0.0.0. Fixed by
using hmac.compare_digest (constant-time comparison).
Co-authored-by: woolcoxm <13604288+woolcoxm@users.noreply.github.com>
Two OpenAI-compat tool-calling bugs found against the real GLM-5.2 (dnnspaul): (1) string-typed args coerced to numbers — declared schema type now decides, string kept verbatim, bool rejected as number, schema-less params keep permissive decoding; (2) tool_choice was ignored — none/auto/required/{function} now honored, invalid returns 400. Python-only (openai_server.py + tests), engine untouched. 36/36 tests pass (verified independently in a clean worktree).
* openai_server: parse GLM-5.2 tool calls into OpenAI tool_calls
Fills the 'tools not supported yet' stub. Upstream declared nothing and
rejected tools/functions; this makes them real end to end:
render_chat: emit a tools-declaration <|system|> block; replay assistant
tool_calls as <tool_call>NAME<arg_key>K</arg_key><arg_value>V
</arg_value></tool_call>; render tool-result messages as
<|observation|><tool_response>...</tool_response> (one
observation per consecutive tool run).
parse_tool_calls: turn the model's <tool_call> markers back into OpenAI
tool_calls; strip <think> from surfaced content.
generation: non-stream returns message.tool_calls + finish_reason
'tool_calls'; stream suppresses the markers from content
deltas (marker-length hold-back so a split <tool_call> is
caught) and emits tool_calls deltas after generation.
Default-safe: with no tools in the request, render and streaming take the
exact upstream path (byte-identical); tool handling is gated on tools present.
Marker format is authoritative (GLM-5.2 chat_template.jinja).
Optional de-mangler (COLI_TOOL_SALVAGE=1, default OFF) recovers malformed
calls from heavily-quantized models by mapping a lone payload onto the tool's
primary parameter; never rewrites well-formed output, never on by default.
A [api] tool-calls telemetry line reports strict vs de-mangled.
Pure stdlib; no new deps.
* openai_server: COLI_THINK global thinking default (opt-in)
COLI_THINK=1 makes thinking the default when a request sends neither
reasoning_effort nor enable_thinking (a launch-time global switch equivalent
to the old server's --think, useful because reasoningEffort propagation from
openai-compatible front-ends is unreliable). An explicit client value always
wins. Default off => exact OpenAI-standard behavior, so this is inert unless
enabled.
* openai_server: keepalive during long prefill (SSE reasoning pings)
engine.generate() blocks silently through the cold prefill (minutes for a
large prompt), and OpenCode/undici drop the socket after their idle timeout
-> the stream is 'terminated' before the first token. Upstream's SSE path
emits nothing until generation produces text, so it has no heartbeat.
A background pump emits a reasoning_content '.' delta whenever no event has
been written for KA_GAP (10s): the channel that reliably resets the client
timer and lands in the thinking panel, so answer content stays clean. All
wfile writes share a lock so the pump and event() never interleave; a
last-write timestamp gates the pump so it's silent while real tokens flow
(decode). The pump stops as soon as generation returns.
Inert for fast responses (nothing sent if events flow within 10s). No new
deps.
* openai_server: COLI_DEBUG echoes decoded tokens to stderr
Upstream sends decoded tokens only to the client (stdout->SSE), so the
console shows no generation text -- painful when the client has disconnected
or for local debugging. COLI_DEBUG=1 tees each decoded chunk to stderr at the
engine-callback boundary (both the plain and tool-call streaming paths).
Default off; no effect unless enabled.
* openai_server: use GLM-5.2 authoritative tool-declaration block
The tools were declared with a hand-written preamble; GLM-5.2 was trained on
the '# Tools' + <tools></tools> XML structure from chat_template.jinja. With
the wrong preamble the model doesn't recognize the tools as native and
hallucinates other frameworks' syntax (observed: repetitive 'end_action' and
key+value concatenated into one arg). Switch render_chat to the byte-exact
trained format so the model emits well-formed <tool_call> blocks.
FABIOTESS